@Article{info:doi/10.2196/68082,
author="Friedman, Ben
and Beamer, A. Brock
and Beans, Jeffrey
and Gray, Vicki
and Alon, Gad
and Ryan, Alice
and Katzel, I. Leslie
and Sorkin, D. John
and Addison, Odessa",
title="Neuromuscular Electrical Stimulation to Maximize Hip Abductor Strength and Reduce Fall Risk in Older Veterans: Protocol for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2025",
month="May",
day="1",
volume="14",
pages="e68082",
keywords="neuromuscular electrical stimulation",
keywords="balance intervention",
keywords="fall prevention",
keywords="hip abductor strengthening",
keywords="dynamic balance",
keywords="veterans",
keywords="muscle function",
keywords="gait variability",
keywords="multimodality balance intervention",
abstract="Background: Nearly half of all veterans are 65 years and older, and they have a higher prevalence of functional disabilities compared to the nonveteran population. Balance impairments resulting in injurious falls are a leading cause of morbidity and mortality in older adults. Instability or fear of falling can significantly reduce physical activity and social participation, even in the absence of falls. Dysmobility is a leading factor in long-care admissions, and therefore, maintenance of mobility throughout aging is crucial. Recent evidence indicates lower extremity muscle weakness as a key risk factor for falls, with lower limb muscle strength and quality being critical for balance recovery. The primary hip abductors, the gluteus maximus, medius, and minimus, are particularly essential for balance recovery. Objective: This study aims to test the hypothesis that adding neuromuscular electrical stimulation (NMES) to a multimodality balance intervention (MMBI) will yield greater reductions in fall risk and improvements in muscle and mobility function compared with MMBI alone. Methods: This randomized controlled trial will enroll 80 veterans aged 55 years and older at risk for falls (defined by a four-square step test [FSST] time >12 seconds, history of falls, or fear of falling). Participants will be randomized to receive either NMES + MMBI or MMBI alone. The 12-week outpatient center--based intervention will include 3 sessions per week, focusing on hip abductor strength, balance, and mobility. Assessments will occur at baseline, postintervention, and at 6- and 12-month follow-ups. Primary outcomes include fall risk and dynamic balance, measured by FSST and hip abductor strength using a Biodex dynamometer. Secondary outcomes will examine muscle composition through computed tomography (CT) scans and assess gait variability parameters. Results: This study was funded on January 1, 2022, with a data collection period from April 1, 2022, to December 31, 2026. As of March 2025, we have screened 100 potential participants and excluded 38. Out of the 61 participants enrolled to date, 21 have completed the 12-month follow-up, 32 have completed the 6-month follow-up, and 41 have completed the posttesting. A total of 4 participants are currently in the intervention phase; 1 has just completed the baseline testing, while 15 have been dropped from the study. Conclusions: This trial will be the first large, randomized controlled trial to evaluate NMES as an adjunct to an MMBI for fall prevention in older veterans. If successful, NMES combined with hip abductor strengthening and balance training could provide a low-cost, scalable solution to reduce falls, improve balance and mobility, and decrease health care costs related to falls in older adults. This study will address a critical gap in knowledge about the effectiveness of NMES in enhancing rehabilitation outcomes for fall prevention. Trial Registration: ClinicalTrials.gov NCT04969094; https://clinicaltrials.gov/study/NCT04969094 International Registered Report Identifier (IRRID): DERR1-10.2196/68082 ",
doi="10.2196/68082",
url="https://www.researchprotocols.org/2025/1/e68082"
}


@Article{info:doi/10.2196/63609,
author="Silva, Malpriya S. Sandun
and Wabe, Nasir
and Nguyen, D. Amy
and Seaman, Karla
and Huang, Guogui
and Dodds, Laura
and Meulenbroeks, Isabelle
and Mercado, Ibarra Crisostomo
and Westbrook, I. Johanna",
title="Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach",
journal="JMIR Aging",
year="2025",
month="Apr",
day="7",
volume="8",
pages="e63609",
keywords="falls prevention",
keywords="dashboard architecture",
keywords="predictive",
keywords="sustainability",
keywords="challenges",
keywords="decision support",
keywords="falls",
keywords="aged care",
keywords="geriatric",
keywords="older adults",
keywords="economic burden",
keywords="prevention",
keywords="electronic health record",
keywords="EHR",
keywords="intervention",
keywords="decision-making",
keywords="patient safety",
keywords="risks",
keywords="older people",
keywords="monitoring",
abstract="Background: Falls are a prevalent and serious health condition among older people in residential aged care facilities, causing significant health and economic burdens. However, the likelihood of future falls can be predicted, and thus, falls can be prevented if appropriate prevention programs are implemented. Current fall prevention programs in residential aged care facilities rely on risk screening tools with suboptimal predictive performance, leading to significant concerns regarding resident safety. Objective: This study aimed to develop a predictive, dynamic dashboard to identify residents at risk of falls with associated decision support. This paper provides an overview of the technical process, including the challenges faced and the strategies used to overcome them during the development of the dashboard. Methods: A predictive dashboard was co-designed with a major residential aged care partner in New South Wales, Australia. Data from resident profiles, daily medications, fall incidents, and fall risk assessments were used. A dynamic fall risk prediction model and personalized rule-based fall prevention recommendations were embedded in the dashboard. The data ingestion process into the dashboard was designed to mitigate the impact of underlying data system changes. This approach aims to ensure resilience against alterations in the data systems. Results: The dashboard was developed using Microsoft Power BI and advanced R programming by linking data silos. It includes dashboard views for those managing facilities and for those caring for residents. Data drill-through functionality was used to navigate through different dashboard views. Resident-level change in daily risk of falling and risk factors and timely evidence-based recommendations were output to prevent falls and enhance prescriptive decision support. Conclusions: This study emphasizes the significance of a sustainable dashboard architecture and how to overcome the challenges faced when developing a dashboard amid underlying data system changes. The development process used an iterative dashboard co-design process, ensuring the successful implementation of knowledge into practice. Future research will focus on the implementation and evaluation of the dashboard's impact on health processes and economic outcomes. International Registered Report Identifier (IRRID): RR2-https://doi.org/10.1136/bmjopen-2021-048657 ",
doi="10.2196/63609",
url="https://aging.jmir.org/2025/1/e63609"
}


@Article{info:doi/10.2196/64449,
author="Gustafson Sr, H. David
and Mares, Marie-Louise
and Johnston, C. Darcie
and Curtin, J. John
and Pe-Romashko, Klaren
and Landucci, Gina",
title="Comparison of Smart Display Versus Laptop Platforms for an eHealth Intervention to Improve Functional Health for Older Adults With Multiple Chronic Conditions: Protocol for a Randomized Clinical Trial",
journal="JMIR Res Protoc",
year="2025",
month="Apr",
day="3",
volume="14",
pages="e64449",
keywords="eHealth",
keywords="aged",
keywords="geriatrics",
keywords="functional health",
keywords="multiple chronic conditions",
keywords="smart display",
keywords="smart speaker",
keywords="primary care",
keywords="quality of life",
abstract="Background: Maintaining functional health, or the ability to live independently, is a primary goal of individuals as they age, but most older adults develop chronic conditions that threaten this goal. Physical activity is a key aspect of self-care that can improve functional health, and digital interventions offering guidance on appropriate exercise can help. However, older adults with multiple morbidities may be unable to use a laptop or smartphone-based eHealth because poor vision, dexterity, mobility, or other physical challenges make typing or touch navigation difficult. A smart display platform---comprising a smart speaker plus a small visual screen---has the potential to remove these barriers because it is voice-activated. Objective: The study aims to compare usage patterns of an eHealth intervention for older adults when delivered via a voice-based smart display versus a typing-based laptop, and assess whether the smart display outperforms the laptop in improving functional health and its specific physical and mental aspects. Methods: A minimum of 356 adults aged 60 years and older with at least 5 chronic health conditions are to be recruited from primary care clinics and community organizations. Participants will be randomized 1:1 to 12 months of access to an evidence-based intervention, ElderTree, delivered on either a smart display or a touchscreen laptop, with a postintervention follow-up at 18 months. The primary outcome is differences between groups on a comprehensive measure of physical and mental functional health. Secondary outcomes are between-group differences in the subscales of functional health (eg, physical function and depression), as well as measures of health distress, loneliness, unscheduled health care, and falls. We will also examine mediators and moderators of the effects of ElderTree on both platforms. Participants will complete surveys at baseline, 6, 12, and 18 months, and ElderTree use data will be collected continuously during the intervention period in system logs. We will use linear mixed-effect models to evaluate outcomes over time, with treatment condition and time point as between-subjects factors. Separate analyses will be conducted for each outcome. Results: Recruitment began in July 2023 and was completed in May 2024, with 387 participants enrolled. The 12-month intervention period will end in May 2025; data collection will end in November 2025. Findings will be disseminated via peer-reviewed publications. Conclusions: Voice-activated digital health interventions have theoretical but untested advantages over typing-based technologies for older adults with physical limitations. As the population ages, and as multiple morbidities threaten the functional health of the majority of older adults, innovations in self-management are a matter of public health as well as individual quality of life. Trial Registration: ClinicalTrials.gov NCT05240534; https://clinicaltrials.gov/study/NCT05240534 International Registered Report Identifier (IRRID): DERR1-10.2196/64449 ",
doi="10.2196/64449",
url="https://www.researchprotocols.org/2025/1/e64449",
url="http://www.ncbi.nlm.nih.gov/pubmed/40080672"
}


@Article{info:doi/10.2196/67539,
author="Pettersson, Beatrice
and Lundin-Olsson, Lillemor
and Skelton, A. Dawn
and Liv, Per
and Zingmark, Magnus
and Rosendahl, Erik
and Sandlund, Marlene",
title="Effectiveness of the Safe Step Digital Exercise Program to Prevent Falls in Older Community-Dwelling Adults: Randomized Controlled Trial",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="31",
volume="27",
pages="e67539",
keywords="geriatric medicine",
keywords="aging",
keywords="accidental falls",
keywords="independent living",
keywords="exercise therapy",
keywords="fall prevention",
keywords="electronic health",
keywords="mobile health",
keywords="preventive medicine",
keywords="self-management",
keywords="effectiveness",
keywords="randomized controlled trial",
keywords="older adults",
keywords="digital technology",
abstract="Background: Falls among older adults are a significant public health issue due to their high incidence, severe consequences, and substantial economic impact. Exercise programs incorporating balance and functional exercises have been shown to reduce fall rates, but adherence and scaling up the interventions remain challenges. Digital technology offers a promising avenue to deliver this type of exercise, potentially improving exercise adherence and enabling self-management of exercise in the aging population. Objective: This study aims to assess the effectiveness of the Safe Step app, a self-managed, unsupervised, home-based digital exercise program, in reducing fall rates or fall risk in community-dwelling older adults. Additional aims were to describe fall-related injuries in both the exercise and control groups, study attrition, and adherence to the Safe Step exercise program. Methods: Community-dwelling individuals, aged 70 years or older, who had experienced falls or a decline in balance in the past year were randomized to either an exercise group using the Safe Step app combined with educational videos, or a control group receiving educational videos alone. Both interventions lasted for 1 year. Information regarding fall events was self-reported monthly through questionnaires. Exercise adherence was monitored through questionnaires every third month. Negative binomial and logistic regression estimated the incidence rate ratio of fall rate and the risk ratio (RR) of experiencing falls, respectively. Fall-related injuries, study attrition, and exercise adherence were reported descriptively. Results: In total, 1628 people were enrolled in the study, 79\% were women, and the mean age was 75.8 (SD 4.4) years (range 70-94 years). The intention-to-treat analysis showed no significant difference in fall rates between the exercise and control groups after 12 months (2.21 falls per person-year in the exercise group and 2.41 in the control group; incidence rate ratio 0.92, 95\% CI 0.76-1.11; P=.37). The risk of experiencing at least 1 fall was significantly lower (11\%) in the exercise group compared to the control group (53\% vs 59.6\%; RR 0.89, 95\% CI 0.80-0.99; P=.03). No differences were observed regarding the risk of 2 or more falls (34.1\% in the exercise group, 37.1\% in the control group; RR 0.92, 95\% CI 0.79-1.06; P=.23). Injurious fall rates were similar between the exercise and control group. During the trial, 161 (20\%) participants from the exercise group and 63 (8\%) from the control group formally withdrew. The proportion of exercise group participants meeting the 90-minute weekly exercise goal was 12.7\%, 13.4\%, 8.6\%, and 9.1\% at 3, 6, 9, and 12 months, respectively. Conclusions: Access to a self-managed unsupervised digital exercise program can be an effective component of a primary fall prevention strategy for community-dwelling older adults. Further research is needed to explore the mediating factors that influence the outcomes and develop strategies that enhance adherence for optimal impact in this population. Trial Registration: ClinicalTrials.gov NCT03963570; https://clinicaltrials.gov/study/NCT03963570 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2019-036194 ",
doi="10.2196/67539",
url="https://www.jmir.org/2025/1/e67539"
}


@Article{info:doi/10.2196/63572,
author="Walzer, Stefan
and Sch{\"o}n, Isabel
and Pfeil, Johanna
and Merz, Nicola
and Marx, Helga
and Ziegler, Sven
and Kunze, Christophe",
title="Experiences With an In-Bed Real-Time Motion Monitoring System on a Geriatric Ward: Mixed Methods Study",
journal="JMIR Form Res",
year="2025",
month="Mar",
day="4",
volume="9",
pages="e63572",
keywords="nurses",
keywords="geriatric patients",
keywords="cognitive impairment",
keywords="technology",
keywords="fall prevention",
keywords="hospital",
keywords="mixed methods",
keywords="patient",
keywords="learning process",
keywords="assessment",
keywords="autonomy",
keywords="impairment",
keywords="real-time motion",
keywords="university",
keywords="geriatric ward",
keywords="survey",
keywords="anxiety",
keywords="willingness",
keywords="patient privacy",
keywords="effectiveness",
keywords="monitoring system",
keywords="health care practice",
abstract="Background: Older adults now make up about two-thirds of hospital admissions, with up to 50\% experiencing cognitive impairments such as dementia. These patients often struggle with adherence to care plans and maintaining regular day or night cycles, presenting challenges for nurses. Hospitals are typically unprepared to manage this patient population, resulting in increased nurse workload and challenges like managing motor agitation, which can lead to falls or accidental removal of medical devices. Objective: This study aimed to (1) assess how an in-bed real-time motion monitoring system (IRMS) impacts nurses' perceptions of physical and mental stress, (2) evaluate the IRMS's effect on the care process, (3) explore ethical implications like patient autonomy and privacy, and (4) understand how nurses acquire knowledge about the technology and how this affects their assessment of the IRMS. Methods: The IRMS, which provides real-time motion monitoring and bed edge or exit information, was implemented in the geriatric ward of a university medical center. The study followed a monocentric, explorative evaluation design using a mixed methods approach. It lasted 24 weeks and had two phases. In Phase 0 (6 weeks), patients received standard care. In Phase 1 (18 weeks), the IRMS was introduced. Initial data were gathered through focus groups and participant observations during manufacturer training sessions. At the end of the intervention, a survey, a second focus group, and an interview were conducted to capture nurses' experiences. The study follows the Good Reporting of a Mixed Method Study (GRAMMS) checklist for reporting. Results: Initial training sessions with 12 participants (10 nurses and 2 physiotherapists) showed varying levels of engagement, with the second session demonstrating more optimism and interprofessional collaboration. A total of 10 questionnaires were completed (10/21, 48\%). Survey results showed that 80\% (8/10) of nurses found the IRMS valuable for assessing the quality of work, and 90\% (9/10) were willing to continue using it. The system was regarded as reliable for monitoring bed edge and exit events. Usability was positively rated, with minimal concerns about documentation burden. Focus group discussions (n=3 per session) indicated that nurses viewed the system as reliable and appreciated its role in reducing anxiety related to fall prevention. However, concerns about patient privacy and monitoring were raised. Nurses expressed a willingness to continue using the IRMS but reaffirmed their ability to care for patients without it. Conclusions: Nurses had a generally positive attitude toward the IRMS, recognizing its benefits, particularly for nighttime monitoring. Although its effectiveness in preventing falls remains inconclusive, the system helps reduce nurses' fear of falls and enhances their responsiveness. The study highlights the broader impact of the IRMS beyond fall prevention and stresses the importance of thoughtful integration into health care practice. ",
doi="10.2196/63572",
url="https://formative.jmir.org/2025/1/e63572",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053780"
}


@Article{info:doi/10.2196/67406,
author="Moran, Ryan
and Wing, David
and Davey, Hope
and Barkai, Hava
and Nichols, Jeanne",
title="Development and Implementation of Strong Foundations, a Digitally Delivered Fall Prevention Program: Usability and Feasibility Pilot Exercise Cohort Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="28",
volume="9",
pages="e67406",
keywords="digital health",
keywords="fall prevention",
keywords="fall risk",
keywords="older adults",
keywords="geriatrics",
keywords="system usability scale",
keywords="Strong Foundations",
keywords="feasibility",
keywords="public health",
keywords="user acceptance",
keywords="exercise",
keywords="usability",
keywords="digital technology",
keywords="mobile phone",
abstract="Background: Falls remain a major public health problem and a significant cause of preventable injury. Maintaining strength and balance by staying active can prevent falls in older adults, and public health advocates support referral to community exercise programs. Given the growth in use and acceptance of technological interfaces, there remains an interest in understanding the role of a synchronous exercise program designed to improve strength, postural alignment, and balance specifically designed to be delivered in a digital environment with respect to usability and feasibility. Objective: This study aims to design and implement a synchronously delivered digital fall prevention program to adults aged 60 years and older, to understand the usability, feasibility, and attendance. Methods: The ``Strong Foundations'' program, a 12-week, live, digitally delivered fall-prevention exercise program was informed from different existing in-person exercises and piloted to older adults who were considered a low fall risk by scores of 4 or less from the Centers for Disease Control and Prevention's (CDC's) Stopping Elderly Accidents and Deaths Initiative (STEADI) Staying Independent questionnaire. The System Usability Scale (SUS) measured usability and feasibility at the completion of this program, and digital measures of age-related function (timed up and go [TUG] and 30-second chair stand [30 CS]) were collected pre- and postintervention. Data were collected in 2021. Results: A total of 39 older adults were recruited and 38 completed the 12-week program with an average age of 72 years. The average SUS was 80.6, with an 85\% attendance rate and an 8.5 (out of 10) self-reported satisfaction score. Digitally collected TUG and 30 CS statistically improved pre- and postintervention by 9\% and 24\%, respectively; by week 12, 64\% (23/36) of participants improved in the timed up and go and 91\% (32/35) improved the chair stands. Conclusion: There was excellent usability and acceptability for Strong Foundations, a novel fall-prevention program designed to be delivered digitally and promising improvement of objective measures of fall risk. ",
doi="10.2196/67406",
url="https://formative.jmir.org/2025/1/e67406"
}


@Article{info:doi/10.2196/68957,
author="Wing, David
and Nichols, F. Jeanne
and Barkai, Shoshana Hava
and Culbert, Olivia
and Moreno, Daniel
and Higgins, Michael
and O'Brien, Anna
and Perez, Mariana
and Davey, Hope
and Moran, Ryan",
title="Building Strong Foundations: Nonrandomized Interventional Study of a Novel,?Digitally Delivered Fall Prevention Program?for Older Adults",
journal="JMIR Aging",
year="2025",
month="Feb",
day="26",
volume="8",
pages="e68957",
keywords="exercise",
keywords="older adults",
keywords="digital intervention",
keywords="Zoom",
keywords="balance",
keywords="posture",
keywords="strength",
keywords="fall prevention",
abstract="Background: Injuries from falls are a major concern among older adults. Targeted exercise has been shown to improve fall risk, and recommendations for identifying and referring older adults for exercise-based interventions exist. However, even when very inexpensive or free, many do not use available fall prevention programs, citing barriers related to convenience and safety. These issues are even greater among older adults residing in rural areas where facilities are less abundant. These realities highlight the need for different approaches to reducing falls in novel ways that increase reach and are safe and effective. Web-based delivery of exercise interventions offers some exciting and enticing prospects. Objective: Our objective was to assess the efficacy of the Strong Foundations exercise program to change markers of physical function, posture, balance, strength, and fall risk. Methods: Strong Foundations is a once weekly (60 minutes), 12-week iterative program with 3 core components: postural alignment and control, balance and mobility, and muscular strength and power. We used a quasi-experimental design to determine changes in physical function specific to balance, postural control, and muscular strength among older adults at low or moderate risk of falling. Results: A total of 55 low-risk and 37 moderate-risk participants were recruited. Participants significantly improved on the 30-second Chair Stand (mean change of 1, SD 3.3 repetitions; P=.006) and Timed Up and Go (mean change of 0.2, SD 0.7 seconds; P=.004), with the moderate-risk group generally improving to a greater degree than the low-risk group. Additionally, Short Physical Performance Battery performance improved significantly in the moderate-risk category (P=.02). The majority of postural measures showed statistically significant improvement for both groups (P<.05). Measures of ``relaxed'' posture showed improvements between 6\% and 27\%. When an ``as tall as possible'' posture was adopted, improvements were {\textasciitilde}36\%. Conclusions: In this 12-week, iterative, web-based program, we found older adults experienced improvement not only in measures used in clinical contexts, such as the 30-second Chair Stand and Timed Up and Go, but also contextualized gains by providing deeper phenotypical measurement related to posture, strength, and balance. Further, many of the physical improvements were attenuated by baseline fall risk level, with those with the highest level of risk having the greater gains, and, thus, the most benefit from such interventions. ",
doi="10.2196/68957",
url="https://aging.jmir.org/2025/1/e68957"
}


@Article{info:doi/10.2196/66692,
author="Steinmetz, Carolin
and Stenzel, Christina
and Sylvester, Maj
and Glage, Denis
and Linke, Anne
and Sadlonova, Monika
and von Arnim, F. Christine A.
and Schnieder, Marlena
and Valentov{\'a}, Miroslava
and Heinemann, Stephanie",
title="Use of a Technology-Based Fall Prevention Program With Visual Feedback in the Setting of Early Geriatric Rehabilitation: Controlled and Nonrandomized Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="11",
volume="9",
pages="e66692",
keywords="fall prevention",
keywords="fall prevention program",
keywords="early geriatric rehabilitation",
keywords="gerontology",
keywords="older adult",
keywords="elder",
keywords="aging",
keywords="digital exercise intervention",
keywords="digital activity",
keywords="physical exercise",
keywords="functional capacity",
keywords="new technology",
keywords="technology-based",
keywords="digital intervention",
keywords="feasibility",
abstract="Background: The Otago program (OP) is evidence-based and focuses on fall prevention in older people. The feasibility and usability of a short-term digital program modeled after the principles of the OP in the setting of early geriatric rehabilitation (EGR) are unclear. Objective: This study investigated the feasibility and usability of an additional technology-based fall prevention program (FPP) in the setting of EGR. Methods: We performed a feasibility study in the setting of EGR. A sample of 30 patients (mobility at least by walker; mini-mental status test score >17) was recruited between March and June 2024 and compared with a retrospective cohort (n=30, former EGR patients). All patients in the intervention group (IG) received a supervised, OP-modified FPP thrice/week for 20 minutes using a technology-based platform called ``Pixformance.'' The device is a digital trainer and enables real-time corrections. The primary end point was the feasibility (given when 80\% of the IG participated in 6 trainings within 2 weeks). Secondary outcomes were usability (patients' and facilitators' perspective; ?75\%), risk of falls (Berg Balance Scale), mobility (Timed Up and Go Test), functional independence (Functional Independence Measure), and activities of daily living (Barthel Index). Several further exploratory end points were analyzed including anxiety and depression (Four-Item Patient Health Questionnaire; PH-Q4). Data were accessed at entry to EGR and after 2 weeks prior to discharge. To analyze the pre-posttest results, the dependent Student t test and the Wilcoxon test were applied. A mixed ANOVA with repeated measurements was used for statistical analyses of time-, group-, and interaction-related changes. Results: A cohort of 60 patients (mean 80.2, SD 6.1 y; 58\% females, 35/60) was analyzed. The main indication for EGR was stroke (9/60, 15\%). Patients were recruited into a prospective IG (n=30) and a retrospective control group (n=30). Of the 30 patients in the prospective IG, 11 patients (37\%) completed 6 training sessions within 2 weeks. Reasons why participants did not complete 6 training sessions were diagnostic appointments (33\%), pain/discomfort (33\%), or fatigue (17\%). EGR patients rated FPP usability at 84\% and facilitators at 65\% out of 100\%. Pre-posttest analysis of the standard assessments showed a significant interaction in Berg Balance Scale (<.01). In both groups, a significant improvement over time was found in the Timed Up and Go Test (<.01), Barthel Index (<.01), and Functional Independence Measure (<.01). Likewise, in the IG, the PH-Q4 score (.02) improved. Conclusions: While the technology-based FPP in the EGR setting was generally well-accepted by patients, with high usability ratings, its feasibility was limited. Only 37\% of participants completed the required additional training sessions. Further studies should test the technology-based FPP as an integrated part of the EGR complex therapy concept. Our findings suggest potential benefits of incorporating technology-based FPPs in EGR, but further refinement is needed to enhance participation and feasibility. ",
doi="10.2196/66692",
url="https://formative.jmir.org/2025/1/e66692"
}


@Article{info:doi/10.2196/64444,
author="Walzer, Stefan
and Sch{\"o}n, Isabel
and Pfeil, Johanna
and Klemm, Sam
and Ziegler, Sven
and Schmoor, Claudia
and Kunze, Christophe",
title="Nurses' Perspectives and Experiences of Using a Bed-Exit Information System in an Acute Hospital Setting: Mixed Methods Study",
journal="JMIR Form Res",
year="2025",
month="Feb",
day="5",
volume="9",
pages="e64444",
keywords="cognitive impairment",
keywords="bed-exit",
keywords="technology",
keywords="fall prevention",
keywords="inpatient",
keywords="hospital",
keywords="mixed methods",
keywords="nurse",
keywords="information system",
keywords="acute hospital",
keywords="support",
keywords="online questionnaire",
keywords="cognitively impaired",
keywords="workload",
abstract="Background: Technology that detects early when a patient at risk of falling leaves the bed can support nurses in acute care hospitals. Objective: To develop a better understanding of nurses' perspectives and experiences with a bed-exit information system (BES) in an acute care hospital setting. Methods: BES was implemented on 3 wards of a university medical center. Nurses completed 2 online surveys at each time point (P0 and P1) and participated in focus groups before (P0) and after (P1) implementation. Additional patient data were collected. Descriptive statistics summarized the survey results, while content analysis was applied to focus group data. Patient rates and adverse events in both phases were compared using negative binomial models. Reporting of this study adhered to the GRAMMS checklist. Results: A total of 30 questionnaires were completed at P0 (30/72, 42\%) and 24 at P1 (24/71, 33\%). Of the participants, 15 completed both questionnaires (complete cases). At P1, 64\% (9/14) of participants agreed that their perceived workload and strain in caring for patients with cognitive impairment was reduced by the use of the BES. The adverse event rate per patient per day was reduced by a factor of 0.61 (95\% CI 0.393-0.955; P=.03). In addition, 11 nurses participated in 4 focus groups before and after the intervention. Participants found it challenging to operationalize the use of the BES due to the heterogeneity of care settings, but certain behaviors of patients with cognitive impairment were recognized as indicating a need for intervention. Negative experiences included information overload and alarm fatigue, leading to occasional removal of the system. Conclusions: While BES provides some support in managing patients with cognitive impairment, its impact remains limited to specific scenarios and does not significantly reduce nurses' workload or strain. Our findings highlight the need to manage expectations of BES performance to ensure alignment between expected and actual benefits. To improve BES effectiveness and long-term implementation, future research should consider both objective measures of patient care and subjective factors such as nurse experience, structural conditions, and technical specifications. Improving information mechanisms within call systems could help reduce alarm fatigue and increase perceived usefulness. Overall, successful integration of BES in acute care settings will require close collaboration with nursing staff to drive meaningful healthcare innovation and ensure that the technology meets the needs of both patients and nurses. Trial Registration: German Register for Clinical Studies DRKS00021720; https://drks.de/search/de/trial/DRKS00021720 ",
doi="10.2196/64444",
url="https://formative.jmir.org/2025/1/e64444"
}


@Article{info:doi/10.2196/58073,
author="Saito, Chihiro
and Nakatani, Eiji
and Sasaki, Hatoko
and E Katsuki, Naoko
and Tago, Masaki
and Harada, Kiyoshi",
title="Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study",
journal="JMIR Hum Factors",
year="2025",
month="Jan",
day="13",
volume="12",
pages="e58073",
keywords="falls",
keywords="inpatient falls",
keywords="acute care hospital",
keywords="predictive factor",
keywords="risk factors",
abstract="Background: Falls in hospitalized patients are a serious problem, resulting in physical injury, secondary complications, impaired activities of daily living, prolonged hospital stays, and increased medical costs. Establishing a fall prediction scoring system to identify patients most likely to fall can help prevent falls among hospitalized patients. Objectives: This study aimed to identify predictive factors of falls in acute care hospital patients, develop a scoring system, and evaluate its validity. Methods: This single-center, retrospective cohort study involved patients aged 20 years or older admitted to Shizuoka General Hospital between April 2019 and September 2020. Demographic data, candidate predictors at admission, and fall occurrence reports were collected from medical records. The outcome was the time from admission to a fall requiring medical resources. Two-thirds of cases were randomly selected as the training set for analysis, and univariable and multivariable Cox regression analyses were used to identify factors affecting fall risk. We scored the fall risk based on the estimated hazard ratios (HRs) and constructed a fall prediction scoring system. The remaining one-third of cases was used as the test set to evaluate the predictive performance of the new scoring system. Results: A total of 13,725 individuals were included. During the study period, 2.4\% (326/13,725) of patients experienced a fall. In the training dataset (n=9150), Cox regression analysis identified sex (male: HR 1.60, 95\% CI 1.21?2.13), age (65 to <80 years: HR 2.26, 95\% CI 1.48?3.44; ?80 years: HR 2.50, 95\% CI 1.60?3.92 vs 20-<65 years), BMI (18.5 to <25 kg/m{\texttwosuperior}: HR 1.36, 95\% CI 0.94?1.97; <18.5 kg/m{\texttwosuperior}: HR 1.57, 95\% CI 1.01?2.44 vs ?25 kg/m{\texttwosuperior}), independence degree of daily living for older adults with disabilities (bedriddenness rank A: HR 1.81, 95\% CI 1.26?2.60; rank B: HR 2.03, 95\% CI 1.31?3.14; rank C: HR 1.23, 95\% CI 0.83?1.83 vs rank J), department (internal medicine: HR 1.23, 95\% CI 0.92?1.64; emergency department: HR 1.81, 95\% CI 1.26?2.60 vs department of surgery), and history of falls within 1 year (yes: HR 1.66, 95\% CI 1.21?2.27) as predictors of falls. Using these factors, we developed a fall prediction scoring system categorizing patients into 3 risk groups: low risk (0-4 points), intermediate risk (5-9 points), and high risk (10-15 points). The c-index indicating predictive performance in the test set (n=4575) was 0.733 (95\% CI 0.684?0.782). Conclusions: We developed a new fall prediction scoring system for patients admitted to acute care hospitals by identifying predictors of falls in Japan. This system may be useful for preventive interventions in patient populations with a high likelihood of falling in acute care settings. ",
doi="10.2196/58073",
url="https://humanfactors.jmir.org/2025/1/e58073"
}


@Article{info:doi/10.2196/58895,
author="White-Lewis, Sharon
and Lightner, Joseph
and Crowley, Julia
and Grimes, Amanda
and Spears, Kathleen
and Chesnut, Steven",
title="Disaster Preparedness Intervention for Older Adults (Seniors' Positive Involvement in Community Emergencies): Protocol for a Quasi-Experimental Study",
journal="JMIR Res Protoc",
year="2024",
month="Dec",
day="4",
volume="13",
pages="e58895",
keywords="older adults",
keywords="disaster preparedness",
keywords="emergency preparedness",
keywords="disaster protocol",
keywords="disaster engagement",
keywords="disaster recovery",
keywords="personal preparedness",
keywords="community dwelling older adults",
keywords="elderly",
keywords="aging in place",
keywords="activities in daily life",
keywords="resiliency",
keywords="community health",
abstract="Background: Older adults comprise a substantial proportion of the US population requiring support during disaster events. Previous research demonstrates that older adults are resilient but deficient in disaster preparedness and lacking in community engagement. There is a gap in high-quality research in this area. Objective: This study aims to fill this gap by developing a 4-phase intervention to improve mobility and balance, decrease fall risks (mitigation), increase knowledge of disaster preparedness (preparedness), improve community emergency operation plans (response), and improve self-efficacy in disaster recovery (recovery) for older adults. Methods: This is a community-based, 10-month study in a large Midwestern urban and suburban location targeting community-dwelling older adults. The 4 phases of interventions address mitigation, preparedness, response, and recovery---aspects improving outcomes from disaster events. In total, 4 to 6 one-hour seminars each month are provided to community-dwelling older adults to improve disaster preparedness and recovery planning. A critical incident packet with resources on essential information such as medications, a communication plan, evacuation resources, and supplies was started and is being reviewed. Preintervention surveys are orally given, with research assistants aiding in any difficulties the participants have. After the surveys, 2 individual 20-minute presentations separated by a short break for snacks and initial completion of their disaster plan preserve the older adult's attention. Mitigation efforts to improve mobility and safety are offered with 10 visits to the older adults' residences, adapting physical activity and balance exercises to the individual's needs. To address response needs, the emergency operations plans for 2 of the major cities are being amended for specific functional needs and access guidelines. Measurements include accelerometers to assess improvement in mobility, fall risk assessments, an abbreviated Federal Emergency Management Association Household Survey, an assessment for disaster engagement with partners tool, a brief pain inventory assessment, and the General Self-Efficacy Scale. We analyze data descriptively and compare pre- and postintervention data for each phase with paired-samples t test and other nonparametric techniques (proportion tests and Wilcoxon signed-rank tests). Overarching objectives prioritized during this intervention include underscoring respect for the experience and resilience found in older adults and engaging them in specialized roles to support their communities during disaster events. Results: The intervention was funded in July 2023; enrollment began in November 2023 and is continuing. We will conclude data collection by July 2025. Published study results can be expected in early 2025. Conclusions: With improved disaster preparedness, mobility, recovery planning, and inclusion as a resource in community disasters, older adults are expected to be safer and be able to age in place. If successful, future studies will focus on outreach and sustainability. This study will serve as a model for older adult disaster preparedness and community involvement. International Registered Report Identifier (IRRID): DERR1-10.2196/58895 ",
doi="10.2196/58895",
url="https://www.researchprotocols.org/2024/1/e58895"
}


@Article{info:doi/10.2196/55681,
author="Alves, A. S{\'o}nia
and Temme, Steffen
and Motamedi, Seyedamirhosein
and Kura, Marie
and Weber, Sebastian
and Zeichen, Johannes
and Pommer, Wolfgang
and Baumgart, Andr{\'e}",
title="Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis",
journal="JMIR Aging",
year="2024",
month="Dec",
day="4",
volume="7",
pages="e55681",
keywords="falls",
keywords="older adults",
keywords="mHealth",
keywords="prognostic tool",
keywords="clinical validity",
keywords="AI",
keywords="mobile health",
keywords="artificial intelligence",
abstract="Background: Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ?60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention. Objective: This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences. Methods: This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses. Results: The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 ($\rho$=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds ($\rho$=0.954, P<.001) and those using walking aids ($\rho$=0.955, P<.001). Threshold values revealed that an FRS of 45\%, 32\%, and 24\% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes. Conclusions: The FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability. ",
doi="10.2196/55681",
url="https://aging.jmir.org/2024/1/e55681"
}


@Article{info:doi/10.2196/57050,
author="Matos Queir{\'o}s, Alcina
and von Gunten, Armin
and Rosselet Amoussou, Jo{\"e}lle
and Lima, Maria Andreia
and Martins, Manuela Maria
and Verloo, Henk",
title="Relationship Between Depression and Falls Among Nursing Home Residents: Integrative Review",
journal="Interact J Med Res",
year="2024",
month="Nov",
day="28",
volume="13",
pages="e57050",
keywords="depression",
keywords="falls",
keywords="nursing home",
keywords="nursing home resident",
keywords="cross-sectional study",
keywords="cohort study",
keywords="integrative review",
keywords="fall risk",
keywords="older adults",
abstract="Background: Depression is a highly prevalent psychopathological condition among older adults, particularly those institutionalized in nursing homes (NHs). Unfortunately, it is poorly identified and diagnosed. NH residents are twice as likely to fall as community-dwelling older adults. There is a need for more knowledge about the mechanisms and relationships between depression and falls. Objective: This study aims to identify, analyze, and synthesize research on the relationships between depression and falls among NH residents. Methods: A literature search was conducted in October 2023 in the following bibliographic databases: MEDLINE ALL Ovid, Embase, CINAHL with Full Text EBSCO, APA PsycInfo Ovid, Web of Science Core Collection, the Cochrane Database of Systematic Reviews Wiley, and ProQuest Dissertations \& Theses A\&I. Clinical trials were searched for in the Cochrane Central Register of Controlled Trials Wiley, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform. Additional searches were performed using Google Scholar, the DART-Europe E-theses Portal, and backward citation tracking. The Newcastle-Ottawa Scale and the Appraisal tool for Cross-Sectional Studies were used to evaluate study quality. Results: The review included 7 quantitative studies published in 7 different countries from 3 continents; of these, 6 (86\%) were cross-sectional studies, and 1 (14\%) was a prospective cohort study. Results suggested high frequencies of depressive symptoms and falls among older adults living in NHs, and depressive symptoms were considered a risk factor for falls. The 15-item and 10-item versions of the Geriatric Depression Scale were the most commonly used measurement tools, followed by the Cornell Scale for Depression in Dementia and the Resident Assessment Instrument-Minimum Data Set 2.0. The prevalence of depression was heterogeneous, varying from 21.5\% to 47.7\% of NH residents. The studies used heterogeneous descriptions of a fall, and some considered the risk of falls, recurrent fallers, and near falls in their data. The prevalence of fallers was disparate, varying from 17.2\% to 63.1\%. Of the 7 retained studies, 6 (86\%) reported a relationship between depression and falls or the risk of falls. Among the 19 other risk factors identified in the review as being associated with falls among NH residents were a history of falls in the last 180 days, >1 fall in the past 12 months, and respiratory illnesses. Conclusions: There is a paucity of research examining falls among older adults with depressive symptoms in NHs. These findings should alert nurses to the need to consider depression as a risk factor in their work to prevent falls. More research is needed to gain a comprehensive understanding of fall risk among NH residents with depressive symptoms. International Registered Report Identifier (IRRID): RR2-10.2196/46995 ",
doi="10.2196/57050",
url="https://www.i-jmr.org/2024/1/e57050",
url="http://www.ncbi.nlm.nih.gov/pubmed/39608784"
}


@Article{info:doi/10.2196/59634,
author="Parsons, Rex
and Blythe, Robin
and Cramb, Susanna
and Abdel-Hafez, Ahmad
and McPhail, Steven",
title="An Electronic Medical Record--Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="13",
volume="26",
pages="e59634",
keywords="clinical prediction model",
keywords="falls",
keywords="patient safety",
keywords="prognostic",
keywords="electronic medical record",
keywords="EMR",
keywords="intervention",
keywords="hospital",
keywords="risk assessment",
keywords="clinical decision",
keywords="support system",
keywords="in-hospital fall",
keywords="survival model",
keywords="inpatient falls",
abstract="Background: Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts. Objective: The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data. Methods: We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve. Results: There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95\% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95\% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots. Conclusions: Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance. ",
doi="10.2196/59634",
url="https://www.jmir.org/2024/1/e59634"
}


@Article{info:doi/10.2196/58110,
author="Merchant, Aziz Reshma
and Loke, Bernard
and Chan, Huak Yiong",
title="Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study",
journal="JMIR Form Res",
year="2024",
month="Oct",
day="3",
volume="8",
pages="e58110",
keywords="falls",
keywords="fall prevention",
keywords="wearables",
keywords="older adult",
keywords="community dwelling older adults",
keywords="gait",
keywords="gait kinetics",
keywords="gait analysis",
keywords="biomechanics",
keywords="sensors",
keywords="gerontology",
abstract="Background: Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously. Objective: The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls. Methods: This is a substudy of 156 community-dwelling older adults ?60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness). Results: The participants' mean age was 74.6 years. Of the 22 participants, 9 (41\%) were robust, 10 (46\%) were prefrail, and 3 (14\%) were frail. In addition, 8 of 22 (36\%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83. Conclusions: Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level. ",
doi="10.2196/58110",
url="https://formative.jmir.org/2024/1/e58110",
url="http://www.ncbi.nlm.nih.gov/pubmed/39361400"
}


@Article{info:doi/10.2196/55322,
author="Zhu, Julia Shiyi
and Bennell, L. Kim
and Hinman, S. Rana
and Harrison, Jenny
and Kimp, J. Alexander
and Nelligan, K. Rachel",
title="Development of a 12-Week Unsupervised Online Tai Chi Program for People With Hip and Knee Osteoarthritis: Mixed Methods Study",
journal="JMIR Aging",
year="2024",
month="Sep",
day="30",
volume="7",
pages="e55322",
keywords="intervention development",
keywords="osteoarthritis",
keywords="Tai Chi",
keywords="web-based intervention",
keywords="online",
keywords="telehealth",
keywords="unsupervised exercise",
keywords="exercise",
keywords="physical activity",
keywords="arthritis",
keywords="development",
keywords="web based",
keywords="hip",
keywords="knee",
keywords="gerontology",
keywords="geriatric",
keywords="older adult",
keywords="aging",
keywords="bone",
keywords="workout",
keywords="digital health",
keywords="eHealth",
keywords="literature review",
keywords="telemedicine",
abstract="Background: Osteoarthritis is a leading contributor to global disability. While evidence supports the effectiveness of Tai Chi in improving symptoms for people with hip/knee osteoarthritis, access to in-person Tai Chi classes may be difficult for many people. An unsupervised online Tai Chi intervention for people with osteoarthritis can help overcome accessibility barriers. The Approach to Human-Centered, Evidence-Driven Adaptive Design (AHEAD) framework provides a practical guide for co-designing such an intervention. Objective: This study aims to develop an unsupervised online Tai Chi program for people with hip/knee osteoarthritis. Methods: An iterative process was conducted using the AHEAD framework. Initially, a panel of Tai Chi instructors and people with osteoarthritis was assembled. A literature review was conducted to inform the content of a survey (survey 1), which was completed by the panel and additional Australian Tai Chi instructors to identify Tai Chi movements for potential inclusion. Selection of Tai Chi movements was based on 3 criteria: those that were appropriate (for people with hip/knee osteoarthritis aged 45+ years), safe (to be performed at home unsupervised), and practical (to be delivered online using prerecorded videos). Movements that met these criteria were then ranked in a second survey (survey 2; using conjoint analysis methodology). Survey findings were discussed in a focus group, and the Tai Chi movements for program use were identified. A draft of the online Tai Chi program was developed, and a final survey (survey 3) was conducted with the panel to rate the appropriateness and safety of the proposed program. The final program was developed, and usability testing (think-aloud protocol) was conducted with people with knee osteoarthritis. Results: The panel consisted of 10 Tai Chi instructors and 3 people with osteoarthritis. The literature review identified Yang Style 24 as a common and effective Tai Chi style used in hip/knee osteoarthritis studies. Surveys 1 (n=35) and 2 (n=27) produced a ranked list of 24 Tai Chi movements for potential inclusion. This list was refined and informed by a focus group, with 10 Tai Chi movements being selected for inclusion (known as the Yang Style 10 form). Survey 3 (n=13) found that 92\% (n=12) of the panel members believed that the proposed draft Tai Chi program was appropriate and safe, resulting in its adoption. The final program was produced and hosted on a customized website, ``My Joint Tai Chi,'' which was further refined based on user feedback (n=5). ``My Joint Tai Chi'' is currently being evaluated in a randomized controlled trial. Conclusions: This study demonstrates the use of the AHEAD framework to develop an unsupervised online Tai Chi intervention (``My Joint Tai Chi'') for people with hip/knee osteoarthritis. This intervention is now being tested for effectiveness and safety in a randomized controlled trial. ",
doi="10.2196/55322",
url="https://aging.jmir.org/2024/1/e55322"
}


@Article{info:doi/10.2196/57601,
author="Suffoletto, Brian
and Kim, David
and Toth, Caitlin
and Mayer, Waverly
and Glaister, Sean
and Cinkowski, Chris
and Ashenburg, Nick
and Lin, Michelle
and Losak, Michael",
title="Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study",
journal="JMIR Aging",
year="2024",
month="Sep",
day="4",
volume="7",
pages="e57601",
keywords="older adult",
keywords="older adults",
keywords="elder",
keywords="elderly",
keywords="older person",
keywords="older people",
keywords="ageing",
keywords="aging",
keywords="gait",
keywords="balance",
keywords="fall",
keywords="falls",
keywords="functional decline",
keywords="fall risk",
keywords="fall risks",
keywords="mobility",
keywords="phone",
keywords="sensors",
keywords="patient monitoring",
keywords="monitoring",
keywords="emergency department",
keywords="emergency departments",
keywords="ED",
keywords="emergency room",
keywords="ER",
keywords="discharge",
keywords="mobile application",
keywords="mobile applications",
keywords="app",
keywords="apps",
keywords="application",
keywords="applications",
keywords="digital health",
keywords="digital technology",
keywords="digital intervention",
keywords="digital interventions",
keywords="smartphone",
keywords="smartphones",
keywords="prediction",
keywords="mobile phone",
abstract="Background: Older adults discharged from the emergency department (ED) face elevated risk of falls and functional decline. Smartphones might enable remote monitoring of mobility after ED discharge, yet their application in this context remains underexplored. Objective: This study aimed to assess the feasibility of having older adults provide weekly accelerometer data from an instrumented Timed Up-and-Go (TUG) test over an 11-week period after ED discharge. Methods: This single-center, prospective, observational, cohort study recruited patients aged 60 years and older from an academic ED. Participants downloaded the GaitMate app to their iPhones that recorded accelerometer data during 11 weekly at-home TUG tests. We measured adherence to TUG test completion, quality of transmitted accelerometer data, and participants' perceptions of the app's usability and safety. Results: Of the 617 approached patients, 149 (24.1\%) consented to participate, and of these 149 participants, 9 (6\%) dropped out. Overall, participants completed 55.6\% (912/1639) of TUG tests. Data quality was optimal in 31.1\% (508/1639) of TUG tests. At 3-month follow-up, 83.2\% (99/119) of respondents found the app easy to use, and 95\% (114/120) felt safe performing the tasks at home. Barriers to adherence included the need for assistance, technical issues with the app, and forgetfulness. Conclusions: The study demonstrates moderate adherence yet high usability and safety for the use of smartphone TUG tests to monitor mobility among older adults after ED discharge. Incomplete TUG test data were common, reflecting challenges in the collection of high-quality longitudinal mobility data in older adults. Identified barriers highlight the need for improvements in user engagement and technology design. ",
doi="10.2196/57601",
url="https://aging.jmir.org/2024/1/e57601"
}


@Article{info:doi/10.2196/56750,
author="Zhang, Jinxi
and Li, Zhen
and Liu, Yu
and Li, Jian
and Qiu, Hualong
and Li, Mohan
and Hou, Guohui
and Zhou, Zhixiong",
title="An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design",
journal="J Med Internet Res",
year="2024",
month="Aug",
day="5",
volume="26",
pages="e56750",
keywords="fall detection",
keywords="deep learning",
keywords="self-attention",
keywords="accelerometer",
keywords="gyroscope",
keywords="human health",
keywords="wearable sensors",
keywords="Sisfall",
keywords="MobiFall",
abstract="Background: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors--based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning--based FDSs using manual feature extraction, and deep learning (DL)--based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. Objective: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Methods: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. Results: The fall detection accuracy of the DSCS model was 99.32\% (recall=99.15\%; precision=98.58\%) and 99.65\% (recall=100\%; precision=98.39\%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41\% (recall=95.12\%; specificity=97.55\%). Conclusions: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation. ",
doi="10.2196/56750",
url="https://www.jmir.org/2024/1/e56750"
}


@Article{info:doi/10.2196/52575,
author="Kikkenborg, Julie
and Magelund, Emma
and Riise, Silke Maria
and Kayser, Lars
and Terp, Rikke",
title="Knowledge, Skills, and Experience With Technology in Relation to Nutritional Intake and Physical Activity Among Older Adults at Risk of Falls: Semistructured Interview Study",
journal="JMIR Hum Factors",
year="2024",
month="May",
day="8",
volume="11",
pages="e52575",
keywords="eHealth",
keywords="self-management",
keywords="fall prevention",
keywords="older adults",
keywords="physical activity",
keywords="nutritional intake",
keywords="Readiness and Enablement Index for Health Technology",
keywords="READHY",
keywords="social support",
keywords="support",
keywords="management",
keywords="fall",
keywords="nutrition",
keywords="diet",
keywords="qualitative study",
keywords="malnutrition",
keywords="physical inactivity",
keywords="injury",
keywords="injuries",
keywords="food",
keywords="food intake",
keywords="nutritional needs",
keywords="outpatient clinic",
keywords="social network",
keywords="mobile phone",
abstract="Background: More than one-third of older adults (aged ?65 y) experience falls every year. The prevalent modifiable risk factors for falling are malnutrition and physical inactivity, among others. The involvement of older adults in the prevention of falls can decrease injuries, hospitalizations, and dependency on health care professionals. In this regard, eHealth can support older adults' self-management through more physical activity and adequate food intake. eHealth must be tailored to older adults' needs and preferences so that they can reap its full benefits. Therefore, it is necessary to gain insight into the knowledge, skills, and mindset of older adults living at home who are at risk of falls regarding eHealth. Objective: This qualitative study aims to explore older adults' use of everyday digital services and technology and how they acquire knowledge about and manage their nutritional intake and physical activity in relation to their health. Methods: Semistructured interviews were conducted with 15 older adults (n=9, 60\% women; n=6, 40\% men; age range 71-87 y) who had all experienced falls or were at risk of falling. These individuals were recruited from a geriatric outpatient clinic. The interviews were analyzed using deductive content analysis based on a modification of the Readiness and Enablement Index for Health Technology framework. Results: The qualitative data showed that the informants' social networks had a positive impact on their self-management, use of technology, and mindset toward nutritional intake and physical activity. Although the informants generally lived active lives, they all lacked knowledge about how their food intake influenced their physical health, including their risk of falling. Another finding was the large diversity in the use of technology among the informants, which was related to their mindset toward technology. Conclusions: Older adults can use technology for everyday purposes, but some need additional introduction and support to be able to use it for managing their health. They also need to learn about the importance of proper nutritional intake and physical activity in preventing falls. Older adults need a more personalized introduction to technology, nutrition, and physical activity in their contact with health professionals. ",
doi="10.2196/52575",
url="https://humanfactors.jmir.org/2024/1/e52575",
url="http://www.ncbi.nlm.nih.gov/pubmed/38717810"
}


@Article{info:doi/10.2196/54934,
author="Gonz{\'a}lez-Castro, Ana
and Leir{\'o}s-Rodr{\'i}guez, Raquel
and Prada-Garc{\'i}a, Camino
and Ben{\'i}tez-Andrades, Alberto Jos{\'e}",
title="The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review",
journal="J Med Internet Res",
year="2024",
month="Apr",
day="29",
volume="26",
pages="e54934",
keywords="machine learning",
keywords="accidental falls",
keywords="public health",
keywords="patient care",
keywords="artificial intelligence",
keywords="AI",
keywords="fall risk",
abstract="Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis. Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk. Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices. Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82\% (18/22) of them extracted data through tests or functional assessments, and the remaining 18\% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70\% in the predictive models obtained through AI. Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy. Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv ",
doi="10.2196/54934",
url="https://www.jmir.org/2024/1/e54934",
url="http://www.ncbi.nlm.nih.gov/pubmed/38684088"
}


@Article{info:doi/10.2196/52592,
author="Barton, J. Hanna
and Maru, Apoorva
and Leaf, A. Margaret
and Hekman, J. Daniel
and Wiegmann, A. Douglas
and Shah, N. Manish
and Patterson, W. Brian",
title="Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department--Based Clinical Decision Support Tool to Prevent Future Falls",
journal="JMIR Hum Factors",
year="2024",
month="Apr",
day="18",
volume="11",
pages="e52592",
keywords="emergency medicine",
keywords="clinical decision support",
keywords="health IT",
keywords="human factors",
keywords="work systems",
keywords="SEIPS",
keywords="Systems Engineering Initiative for Patient Safety",
keywords="educational outreach",
keywords="academic detailing",
keywords="implementation method",
keywords="department-based",
keywords="CDS",
keywords="clinical care",
keywords="evidence-based",
keywords="CDS tool",
keywords="gerontology",
keywords="geriatric",
keywords="geriatrics",
keywords="older adult",
keywords="older adults",
keywords="elder",
keywords="elderly",
keywords="older person",
keywords="older people",
keywords="preventative intervention",
keywords="team-based analysis",
keywords="machine learning",
keywords="high-risk patient",
keywords="high-risk patients",
keywords="pharmaceutical",
keywords="pharmaceutical sales",
keywords="United States",
keywords="fall-risk prediction",
keywords="EHR",
keywords="electronic health record",
keywords="interview",
keywords="ED environment",
keywords="emergency department",
abstract="Background: Clinical decision support (CDS) tools that incorporate machine learning--derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing---personal visits to clinicians by an expert in a specific health IT tool---as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. Objective: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department--based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. Methods: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. Results: The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. Conclusions: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians. ",
doi="10.2196/52592",
url="https://humanfactors.jmir.org/2024/1/e52592",
url="http://www.ncbi.nlm.nih.gov/pubmed/38635318"
}


@Article{info:doi/10.2196/54395,
author="Rein, B. David
and Hackney, E. Madeleine
and Haddad, K. Yara
and Sublett, A. Farah
and Moreland, Briana
and Imhof, Laurie
and Peterson, Cora
and Legha, K. Jaswinder
and Mark, Janice
and Vaughan, P. Camille
and Johnson II, M. Theodore
and Bergen, Gwen
and ",
title="Telemedicine-Based Risk Program to Prevent Falls Among Older Adults: Protocol for a Randomized Quality Improvement Trial",
journal="JMIR Res Protoc",
year="2024",
month="Mar",
day="26",
volume="13",
pages="e54395",
keywords="aging",
keywords="cost-effectiveness",
keywords="elderly",
keywords="fall risk screening",
keywords="fall risk",
keywords="falls",
keywords="medication management",
keywords="older adults",
keywords="physical therapy",
keywords="prevention",
keywords="public health",
keywords="telemedicine",
abstract="Background: The Center for Disease Control and Prevention's Stopping Elderly Accidents, Deaths, and Injuries (STEADI) initiative offers health care providers tools and resources to assist with fall risk screening and multifactorial fall risk assessment and interventions. Its effectiveness has never been evaluated in a randomized trial. Objective: This study aims to describe the protocol for the STEADI Options Randomized Quality Improvement Trial (RQIT), which was designed to evaluate the impact on falls and all-cause health expenditures of a telemedicine-based form of STEADI implemented among older adults aged 65 years and older, within a primary care setting. Methods: STEADI Options was a pragmatic RQIT implemented within a health system comparing a telemedicine version of the STEADI fall risk assessment to the standard of care (SOC). Before screening, we randomized all eligible patients in participating clinics into the STEADI arm or SOC arm based on their scheduled provider. All received the Stay Independent screener (SIS) to determine fall risk. Patients were considered at risk for falls if they scored 4 or more on the SIS or answered affirmatively to any 1 of the 3 key questions within the SIS. Patients screened at risk for falls and randomized to the STEADI arm were offered a registered nurse (RN)--led STEADI assessment through telemedicine; the RN provided assessment results and recommendations to the providers, who were advised to discuss fall-prevention strategies with their patients. Patients screened at risk for falls and randomized to the SOC arm were asked to participate in study data collection only. Data on recruitment, STEADI assessments, use of recommended prevention services, medications, and fall occurrences were collected using electronic health records and patient surveys. Using staff time diaries and administrative records, the study prospectively collected data on STEADI implementation costs and all-cause outpatient and inpatient charges incurred over the year following enrollment. Results: The study enrolled 720 patients (n=307, 42.6\% STEADI arm; n=353, 49\% SOC arm; and n=60, 8.3\% discontinued arm) from September 2020 to December 2021. Follow-up data collection was completed in January 2023. As of February 2024, data analysis is complete, and results are expected to be published by the end of 2025. Conclusions: The STEADI RQIT evaluates the impact of a telemedicine-based, STEADI-based fall risk assessment on falls and all-cause health expenditures and can provide information on the intervention's effectiveness and cost-effectiveness. Trial Registration: ClinicalTrials.gov NCT05390736, http://clinicaltrials.gov/ct2/show/NCT05390736 International Registered Report Identifier (IRRID): RR1-10.2196/54395 ",
doi="10.2196/54395",
url="https://www.researchprotocols.org/2024/1/e54395",
url="http://www.ncbi.nlm.nih.gov/pubmed/38346180"
}


@Article{info:doi/10.2196/54854,
author="Haag, Susan
and Kepros, John",
title="Head Protection Device for Individuals at Risk for Head Injury due to Ground-Level Falls: Single Trauma Center User Experience Investigation",
journal="JMIR Hum Factors",
year="2024",
month="Mar",
day="19",
volume="11",
pages="e54854",
keywords="health care interventions and technologies",
keywords="user experience research",
keywords="usability",
keywords="brain injury",
keywords="ground-level fall (GLF)",
keywords="head protection device (HPD)",
keywords="fall risk",
keywords="patient compliance",
abstract="Background: Falls represent a large percentage of hospitalized patients with trauma as they may result in head injuries. Brain injury from ground-level falls (GLFs) in patients is common and has substantial mortality. As fall prevention initiatives have been inconclusive, we changed our strategy to injury prevention. We identified a head protection device (HPD) with impact-resistant technology, which meets head impact criteria sustained in a GLF. HPDs such as helmets are ubiquitous in preventing head injuries in sports and industrial activities; yet, they have not been studied for daily activities. Objective: We investigated the usability of a novel HPD on patients with head injury in acute care and home contexts to predict future compliance. Methods: A total of 26 individuals who sustained head injuries, wore an HPD in the hospital, while ambulatory and were evaluated at baseline and 2 months post discharge. Clinical and demographic data were collected; a usability survey captured HPD domains. This user experience design revealed patient perceptions, satisfaction, and compliance. Nonparametric tests were used for intragroup comparisons (Wilcoxon signed rank test). Differences between categorical variables including sex, race, and age (age group 1: 55-77 years; age group 2: 78+ years) and compliance were tested using the chi-square test. Results: Of the 26 patients enrolled, 12 (46\%) were female, 18 (69\%) were on anticoagulants, and 25 (96\%) were admitted with a head injury due to a GLF. The median age was 77 (IQR 55-92) years. After 2 months, 22 (85\%) wore the device with 0 falls and no GLF hospital readmissions. Usability assessment with 26 patients revealed positive scores for the HPD post discharge regarding satisfaction (mean 4.8, SD 0.89), usability (mean 4.23, SD 0.86), effectiveness (mean 4.69, SD 0.54), and relevance (mean 4.12, SD 1.10). Nonparametric tests showed positive results with no significant differences between 2 observations. One issue emerged in the domain of aesthetics; post discharge, 8 (30\%) patients had a concern about device weight. Analysis showed differences in patient compliance regarding age ($\chi$12=4.27; P=.04) but not sex ($\chi$12=1.58; P=.23) or race ($\chi$12=0.75; P=.60). Age group 1 was more likely to wear the device for normal daily activities. Patients most often wore the device ambulating, and protection was identified as the primary benefit. Conclusions: The HPD intervention is likely to have reasonably high compliance in a population at risk for GLFs as it was considered usable, protective, and relevant. The feasibility and wearability of the device in patients who are at risk for GLFs will inform future directions, which includes a multicenter study to evaluate device compliance and effectiveness. Our work will guide other institutions in pursuing technologies and interventions that are effective in mitigating injury in the event of a fall in this high-risk population. ",
doi="10.2196/54854",
url="https://humanfactors.jmir.org/2024/1/e54854",
url="http://www.ncbi.nlm.nih.gov/pubmed/38502170"
}


@Article{info:doi/10.2196/51899,
author="Thiamwong, Ladda
and Xie, Rui
and Park, Joon-Hyuk
and Lighthall, Nichole
and Loerzel, Victoria
and Stout, Jeffrey",
title="Optimizing a Technology-Based Body and Mind Intervention to Prevent Falls and Reduce Health Disparities in Low-Income Populations: Protocol for a Clustered Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2023",
month="Oct",
day="3",
volume="12",
pages="e51899",
keywords="fall prevention",
keywords="fear of falling",
keywords="low income",
keywords="older adults",
keywords="exercise",
keywords="technology",
abstract="Background: The lack of health care coverage, low education, low motivation, and inconvenience remain barriers to participating in fall prevention programs, especially among low-income older adults. Low-income status also contributes to negative aging self-perceptions and is associated with a high perceived barrier to care. Existing fall prevention intervention technologies do not enable participants and practitioners to interact and collaborate, even with technologies that bring viable strategies to maintain independence, prevent disability, and increase access to quality care. Research is also limited on the use of technology to enhance motivation and help individuals align their perception with physiological fall risk. We developed a novel, 8-week Physio-Feedback Exercise Program (PEER), which includes (1) technology-based physio-feedback using a real-time portable innovative technology---the BTrackS Balance Tracking System, which is reliable and affordable, allows for home testing, and provides feedback and tracks balance progression; (2) cognitive reframing using the fall risk appraisal matrix; and (3) peer-led exercises focusing on balance, strength training, and incorporating exercises into daily activities. Objective: This study consists of 3 aims. Aim 1 is to examine the effects of the technology-based PEER intervention on fall risk, dynamic balance, and accelerometer-based physical activity (PA). Aim 2 is to examine the effects of the PEER intervention on fall risk appraisal shifting and negative self-perceptions of aging. Aim 3 is to explore participants' experiences with the PEER intervention and potential barriers to accessing and adopting the technology-based PEER intervention to inform future research. Methods: This is an intention-to-treat, single-blinded, parallel, 2-arm clustered randomized controlled trial study. We will collect data from 340 low-income older adults at baseline (T1) and measure outcomes after program completion (T2) and follow-up at 3 months (T3) and 6 months (T4). Participants will be enrolled if they meet all the following inclusion criteria: aged ?60 years, cognitively intact, and able to stand without assistance. Exclusion criteria were as follows: a medical condition precluding exercise or PA, currently receiving treatment from a rehabilitation facility, plan to move within 1 year, hospitalized >3 times in the past 12 months, and does not speak English or Spanish. Results: As of August 2023, the enrollment of participants is ongoing. Conclusions: This study addresses the public health problem by optimizing a customized, technology-driven approach that can operate in low-resource environments with unlimited users to prevent falls and reduce health disparities in low-income older adults. The PEER is a novel intervention that combines concepts of physio-feedback, cognitive reframing, and peer-led exercise by motivating a shift in self-estimation of fall risk to align with physiological fall risk to improve balance, PA, and negative aging self-perception. Trial Registration: ClinicalTrials.gov NCT05778604; https://www.clinicaltrials.gov/ct2/show/study/NCT05778604 International Registered Report Identifier (IRRID): DERR1-10.2196/51899 ",
doi="10.2196/51899",
url="https://www.researchprotocols.org/2023/1/e51899",
url="http://www.ncbi.nlm.nih.gov/pubmed/37788049"
}


@Article{info:doi/10.2196/48128,
author="Hekman, J. Daniel
and Cochran, L. Amy
and Maru, P. Apoorva
and Barton, J. Hanna
and Shah, N. Manish
and Wiegmann, Douglas
and Smith, A. Maureen
and Liao, Frank
and Patterson, W. Brian",
title="Effectiveness of an Emergency Department--Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study",
journal="JMIR Res Protoc",
year="2023",
month="Aug",
day="3",
volume="12",
pages="e48128",
keywords="falls",
keywords="emergency medicine",
keywords="machine learning",
keywords="clinical decision support",
keywords="automated screening",
keywords="geriatrics",
abstract="Background: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38\%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. Objective: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. Methods: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95\% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. Results: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15\% (45/339) of patients have scheduled an appointment with the clinic. Conclusions: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. Trial Registration: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064 International Registered Report Identifier (IRRID): DERR1-10.2196/48128 ",
doi="10.2196/48128",
url="https://www.researchprotocols.org/2023/1/e48128",
url="http://www.ncbi.nlm.nih.gov/pubmed/37535416"
}


@Article{info:doi/10.2196/46930,
author="Sczuka, Sarah Kim
and Schneider, Marc
and Schellenbach, Michael
and Kerse, Ngaire
and Becker, Clemens
and Klenk, Jochen",
title="Evaluating the Effect of Activity and Environment on Fall Risk in a Paradigm-Depending Laboratory Setting: Protocol for an Experimental Pilot Study",
journal="JMIR Res Protoc",
year="2023",
month="May",
day="10",
volume="12",
pages="e46930",
keywords="fall risk",
keywords="fall risk factor",
keywords="fall-related activity",
keywords="laboratory setting",
keywords="study protocol",
keywords="fall",
keywords="fall risk model",
keywords="older people",
keywords="elderly",
keywords="analysis of fall",
abstract="Background: Knowledge about the causal factors leading to falls is still limited, and fall prevention interventions urgently need to be more effective to limit the otherwise increasing burden caused by falls in older people. To identify individual fall risk, it is important to understand the complex interplay of fall-related factors. Although fall events are common, they are seldom observed, and fall reports are often biased. Due to the rapid development of wearable inertial sensors, an objective approach to capture fall events and the corresponding circumstances is provided. Objective: The aim of this work is to operationalize a prototypical dynamic fall risk model regarding 4 ecologically valid real-world scenarios (opening a door, slipping, tripping, and usage of public transportation). We hypothesize that individual fall risk is associated with an interplay of intrinsic risk factors, activity, and environmental factors that can be estimated by using data measured within a laboratory simulation setting. Methods: We will recruit 30 community-dwelling people aged 60 years or older. To identify several fall-related intrinsic fall risk factors, appropriate clinical assessments will be selected. The experimental setup is adaptable so that the level of fall risk for each activity and each environmental factor is adjustable. By different levels of difficulty, the effect on the risk of falling will be investigated. An 8-camera motion tracking system will be used to record absolute body motions and limits of stability. All laboratory experiments will also be recorded by inertial sensors (L5, dominant leg) and video camera. Logistic regression analyses will be used to model the association between risk factors and falls. Continuous fall risk will be modeled by generalized linear regression models using margin of stability as outcome parameter. Results: The results of this project will prove the concept and establish methods to further use the dynamic fall risk model. Recruitment and measurement initially began in October 2020 but were halted because of the COVID-19 pandemic. Recruitment and measurements recommenced in October 2022, and by February 2023, a total of 25 of the planned 30 subjects have been measured. Conclusions: In the field of fall prevention, a more precise fall risk model will have a significant impact on research leading to more effective prevention approaches. Given the described burden related to falls and the high prevalence, considerable improvements in fall prevention will have a significant impact on individual quality of life and also on society in general by reducing institutionalization and health care costs. The setup will enable the analysis of fall events and their circumstances ecologically valid in a laboratory setting and thereby will provide important information to estimate the individual instantaneous fall risk. International Registered Report Identifier (IRRID): DERR1-10.2196/46930 ",
doi="10.2196/46930",
url="https://www.researchprotocols.org/2023/1/e46930",
url="http://www.ncbi.nlm.nih.gov/pubmed/37163327"
}


@Article{info:doi/10.2196/42231,
author="Mali, Namrata
and Restrepo, Felipe
and Abrahams, Alan
and Sands, Laura
and Goldberg, M. David
and Gruss, Richard
and Zaman, Nohel
and Shields, Wendy
and Omaki, Elise
and Ehsani, Johnathon
and Ractham, Peter
and Kaewkitipong, Laddawan",
title="Safety Concerns in Mobility-Assistive Products for Older Adults: Content Analysis of Online Reviews",
journal="J Med Internet Res",
year="2023",
month="Mar",
day="2",
volume="25",
pages="e42231",
keywords="injury prevention",
keywords="consumer-reported injuries",
keywords="older adults",
keywords="online reviews",
keywords="mobility-assistive devices",
keywords="product failures",
abstract="Background: Older adults who have difficulty moving around are commonly advised to adopt mobility-assistive devices to prevent injuries. However, limited evidence exists on the safety of these devices. Existing data sources such as the National Electronic Injury Surveillance System tend to focus on injury description rather than the underlying context, thus providing little to no actionable information regarding the safety of these devices. Although online reviews are often used by consumers to assess the safety of products, prior studies have not explored consumer-reported injuries and safety concerns within online reviews of mobility-assistive devices. Objective: This study aimed to investigate injury types and contexts stemming from the use of mobility-assistive devices, as reported by older adults or their caregivers in online reviews. It not only identified injury severities and mobility-assistive device failure pathways but also shed light on the development of safety information and protocols for these products. Methods: Reviews concerning assistive devices were extracted from the ``assistive aid'' categories, which are typically intended for older adult use, on Amazon's US website. The extracted reviews were filtered so that only those pertaining to mobility-assistive devices (canes, gait or transfer belts, ramps, walkers or rollators, and wheelchairs or transport chairs) were retained. We conducted large-scale content analysis of these 48,886 retained reviews by coding them according to injury type (no injury, potential future injury, minor injury, and major injury) and injury pathway (device critical component breakage or decoupling; unintended movement; instability; poor, uneven surface handling; and trip hazards). Coding efforts were carried out across 2 separate phases in which the team manually verified all instances coded as minor injury, major injury, or potential future injury and established interrater reliability to validate coding efforts. Results: The content analysis provided a better understanding of the contexts and conditions leading to user injury, as well as the severity of injuries associated with these mobility-assistive devices. Injury pathways---device critical component failures; unintended device movement; poor, uneven surface handling; instability; and trip hazards---were identified for 5 product types (canes, gait and transfer belts, ramps, walkers and rollators, and wheelchairs and transport chairs). Outcomes were normalized per 10,000 posting counts (online reviews) mentioning minor injury, major injury, or potential future injury by product category. Overall, per 10,000 reviews, 240 (2.4\%) described mobility-assistive equipment--related user injuries, whereas 2318 (23.18\%) revealed potential future injuries. Conclusions: This study highlights mobility-assistive device injury contexts and severities, suggesting that consumers who posted online reviews attribute most serious injuries to a defective item, rather than user misuse. It implies that many mobility-assistive device injuries may be preventable through patient and caregiver education on how to evaluate new and existing equipment for risk of potential future injury. ",
doi="10.2196/42231",
url="https://www.jmir.org/2023/1/e42231",
url="http://www.ncbi.nlm.nih.gov/pubmed/36862459"
}


@Article{info:doi/10.2196/36325,
author="Raffegeau, E. Tiphanie
and Young, R. William
and Fino, C. Peter
and Williams, Mark A.",
title="A Perspective on Using Virtual Reality to Incorporate the Affective Context of Everyday Falls Into Fall Prevention",
journal="JMIR Aging",
year="2023",
month="Jan",
day="11",
volume="6",
pages="e36325",
keywords="aging",
keywords="balance",
keywords="perturbation",
keywords="locomotion",
keywords="cognition",
keywords="exergame",
keywords="anxiety",
doi="10.2196/36325",
url="https://aging.jmir.org/2023/1/e36325",
url="http://www.ncbi.nlm.nih.gov/pubmed/36630173"
}


@Article{info:doi/10.2196/32453,
author="Frechette, Mikaela
and Fanning, Jason
and Hsieh, Katherine
and Rice, Laura
and Sosnoff, Jacob",
title="The Usability of a Smartphone-Based Fall Risk Assessment App for Adult Wheelchair Users: Observational Study",
journal="JMIR Form Res",
year="2022",
month="Sep",
day="16",
volume="6",
number="9",
pages="e32453",
keywords="usability testing",
keywords="mobile health",
keywords="wheeled device user",
keywords="fall risk",
keywords="telehealth",
keywords="mHealth",
keywords="mobile device",
keywords="smartphone",
keywords="health applications",
keywords="older adults",
keywords="elderly population",
keywords="device usability",
abstract="Background: Individuals who use wheelchairs and scooters rarely undergo fall risk screening. Mobile health technology is a possible avenue to provide fall risk assessment. The promise of this approach is dependent upon its usability. Objective: We aimed to determine the usability of a fall risk mobile health app and identify key technology development insights for aging adults who use wheeled devices. Methods: Two rounds (with 5 participants in each round) of usability testing utilizing an iterative design-evaluation process were performed. Participants completed use of the custom-designed fall risk app, Steady-Wheels. To quantify fall risk, the app led participants through 12 demographic questions and 3 progressively more challenging seated balance tasks. Once completed, participants shared insights on the app's usability through semistructured interviews and completion of the Systematic Usability Scale. Testing sessions were recorded and transcribed. Codes were identified within the transcriptions to create themes. Average Systematic Usability Scale scores were calculated for each round. Results: The first round of testing yielded 2 main themes: ease of use and flexibility of design. Systematic Usability Scale scores ranged from 72.5 to 97.5 with a mean score of 84.5 (SD 11.4). After modifications were made, the second round of testing yielded 2 new themes: app layout and clarity of instruction. Systematic Usability Scale scores improved in the second iteration and ranged from 87.5 to 97.5 with a mean score of 91.9 (SD 4.3). Conclusions: The mobile health app, Steady-Wheels, has excellent usability and the potential to provide adult wheeled device users with an easy-to-use, remote fall risk assessment tool. Characteristics that promoted usability were guided navigation, large text and radio buttons, clear and brief instructions accompanied by representative illustrations, and simple error recovery. Intuitive fall risk reporting was achieved through the presentation of a single number located on a color-coordinated continuum that delineated low, medium, and high risk. ",
doi="10.2196/32453",
url="https://formative.jmir.org/2022/9/e32453",
url="http://www.ncbi.nlm.nih.gov/pubmed/36112405"
}


@Article{info:doi/10.2196/36872,
author="Strutz, Nicole
and Brodowski, Hanna
and Kiselev, Joern
and Heimann-Steinert, Anika
and M{\"u}ller-Werdan, Ursula",
title="App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study",
journal="JMIR Aging",
year="2022",
month="Aug",
day="16",
volume="5",
number="3",
pages="e36872",
keywords="mobility",
keywords="fall risk",
keywords="smartphone",
keywords="app",
keywords="analysis",
keywords="older people",
keywords="accuracy",
keywords="mobility restriction",
abstract="Background: Falls and the risk of falling in older people pose a high risk for losing independence. As the risk of falling progresses over time, it is often not adequately diagnosed due to the long intervals between contacts with health care professionals. This leads to the risk of falling being not properly detected until the first fall. App-based software able to screen fall risks of older adults and to monitor the progress and presence of fall risk factors could detect a developing fall risk at an early stage prior to the first fall. As smartphones become more common in the elderly population, this approach is easily available and feasible. Objective: The aim of the study is to evaluate the app Lindera Mobility Analysis (LIN). The reference standards determined the risk of falling and validated functional assessments of mobility. Methods: The LIN app was utilized in home- and community-dwelling older adults aged 65 years or more. The Berg Balance Scale (BBS), the Tinetti Test (TIN), and the Timed Up \& Go Test (TUG) were used as reference standards. In addition to descriptive statistics, data correlation and the comparison of the mean difference of analog measures (reference standards) and digital measures were tested. Spearman rank correlation analysis was performed and Bland-Altman (B-A) plots drawn. Results: Data of 42 participants could be obtained (n=25, 59.5\%, women). There was a significant correlation between the LIN app and the BBS (r=--0.587, P<.001), TUG (r=0.474, P=.002), and TIN (r=--0.464, P=.002). B-A plots showed only few data points outside the predefined limits of agreement (LOA) when combining functional tests and results of LIN. Conclusions: The digital app LIN has the potential to detect the risk of falling in older people. Further steps in establishing the validity of the LIN app should include its clinical applicability. Trial Registration: German Clinical Trials Register DRKS00025352; https://tinyurl.com/65awrd6a ",
doi="10.2196/36872",
url="https://aging.jmir.org/2022/3/e36872",
url="http://www.ncbi.nlm.nih.gov/pubmed/35972785"
}


@Article{info:doi/10.2196/32288,
author="Noublanche, Fr{\'e}d{\'e}ric
and Simon, Romain
and Ben-Sadoun, Gr{\'e}gory
and Annweiler, C{\'e}dric",
title="Physical Activity and Fall Prevention in Geriatric Inpatients in an Acute Care Unit (AGIR Study): Protocol for a Usability Study",
journal="JMIR Res Protoc",
year="2022",
month="Jul",
day="11",
volume="11",
number="7",
pages="e32288",
keywords="fall prevention",
keywords="physical activity",
keywords="older patients",
keywords="geriatric acute care unit",
abstract="Background: Falls are one of the world's top 10 risks associated with disability in people older than 60 years. They also represent more than two-thirds of adverse events in hospitals, mainly affecting patients older than 65 years. Physical activity is a central intervention in fall prevention for older people. Whatever the details of the prevention strategy that is adopted (ie, how a mono- or multifactorial intervention is evaluated, the category of person the intervention targets, and where it is used), it is important to ensure that the proposed intervention is feasible and usable for the patient and the health care team. Objective: The primary objective is to study the usability of carrying out a physical activity intervention, including 3 types of exercises, in older patients hospitalized in a geriatric acute care unit and categorized according to 3 fall risk levels: low, moderate, and high. The secondary objectives are to determine the difficulty of the physical exercise for patients with different fall risk levels, to study the health care team's perceptions of the intervention's feasibility, and to study the benefits for patients. Methods: This is an open-label, unicenter, nonrandomized, usability prospective clinical trial. The intervention tested is a daily physical activity program. It consists of 3 types of physical exercise: staying out of bed for at least 3 hours, performing balance exercises while standing for 2 minutes, and the Five Times Sit to Stand transfer exercise. These exercises are carried out under the supervision of the health care team. Fall risk in the patients is classified with the Brief Geriatric Assessment tool. The exercise program starts on the second day of hospitalization after inclusion in the study. Patient assessment continues until the last day of hospitalization or the 20th day of hospitalization, whichever is earlier. For each fall-risk group and each type of exercise, the intervention will be defined as usable if at least 80\% of the participants complete 75\% or more of the exercises (ie, the ratio between the number of days when the patient completes a type of exercise and the total number of hospitalization days). The perceived feasibility by the health care team is measured with 2 scales, measuring perceived difficulty and time spent with the patient. The intervention benefit is evaluated using the performance of the Five Times Sit to Stand test before and after the intervention. Results: The first patient was recruited on March 16, 2015. The study enrolled 266 patients, including 75 with low fall risk, 105 with moderate risk, and 85 with high risk. Conclusions: We have not yet analyzed the results, but our observations suggest that the usability of each type of exercise for a given patient will depend on their fall risk level. Trial Registration: ClinicalTrials.gov NCT02393014; https://clinicaltrials.gov/ct2/show/NCT02393014 International Registered Report Identifier (IRRID): DERR1-10.2196/32288 ",
doi="10.2196/32288",
url="https://www.researchprotocols.org/2022/7/e32288",
url="http://www.ncbi.nlm.nih.gov/pubmed/35816381"
}


@Article{info:doi/10.2196/24376,
author="Latulippe, Karine
and Giroux, Dominique
and Guay, Manon
and Kairy, Dahlia
and Vincent, Claude
and Boivin, Katia
and Morales, Ernesto
and Obradovic, Natasa
and Provencher, V{\'e}ronique",
title="Mobile Videoconferencing for Occupational Therapists' Assessments of Patients' Home Environments Prior to Hospital Discharge: Mixed Methods Feasibility and Comparative Study",
journal="JMIR Aging",
year="2022",
month="Jul",
day="5",
volume="5",
number="3",
pages="e24376",
keywords="caregivers",
keywords="feasibility",
keywords="mixed methods",
keywords="mobile videoconferencing",
keywords="mobile phone",
keywords="occupational therapy",
keywords="discharge planning",
keywords="home assessment",
abstract="Background: Occupational therapists who work in hospitals need to assess patients' home environment in preparation for hospital discharge in order to provide recommendations (eg, technical aids) to support their independence and safety. Home visits increase performance in everyday activities and decrease the risk of falls; however, in some countries, home visits are rarely made prior to hospital discharge due to the cost and time involved. In most cases, occupational therapists rely on an interview with the patient or a caregiver to assess the home. The use of videoconferencing to assess patients' home environments could be an innovative solution to allow better and more appropriate recommendations. Objective: The aim of this study was (1) to explore the added value of using mobile videoconferencing compared with standard procedure only and (2) to document the clinical feasibility of using mobile videoconferencing to assess patients' home environments. Methods: Occupational therapists assessed home environments using, first, the standard procedure (interview),  and then, videoconferencing (with the help of a family caregiver located in patients' homes, using an electronic tablet). We used a concurrent mixed methods design. The occupational therapist's responsiveness to telehealth, time spent on assessment, patient's occupational performance and satisfaction, and major events influencing the variables were collected as quantitative data. The perceptions of occupational therapists and family caregivers regarding the added value of using this method and the nature of changes made to recommendations as a result of the videoconference (if any) were collected as qualitative data, using questionnaires and semistructured interviews. Results: Eight triads (6 occupational therapists, 8 patients, and 8 caregivers) participated. The use of mobile videoconferencing generally led occupational therapists to modify the initial intervention plan (produced after the standard interview). Occupational therapists and caregivers perceived benefits in using mobile videoconferencing (eg, the ability to provide real-time comments or feedback), and they also perceived disadvantages (eg, videoconferencing requires additional time and greater availability of caregivers). Some occupational therapists believed that mobile videoconferencing added value to assessments, while others did not. Conclusions: The use of mobile videoconferencing in the context of hospital discharge planning has raised questions of clinical feasibility. Although mobile videoconferencing provides multiple benefits to hospital discharge, including more appropriate occupational therapist recommendations, time constraints made it more difficult to perceive the added value. However, with smartphone use, interdisciplinary team involvement, and patient participation in the videoconference visit, mobile videoconferencing can become an asset to hospital discharge planning. International Registered Report Identifier (IRRID): RR2-10.2196/11674 ",
doi="10.2196/24376",
url="https://aging.jmir.org/2022/3/e24376",
url="http://www.ncbi.nlm.nih.gov/pubmed/35787486"
}


@Article{info:doi/10.2196/34796,
author="Alberto, Napolitano Silsam
and Ansai, Hotta Juliana
and Janducci, Lu{\'i}sa Ana
and Florido, Businaro Jo{\~a}o Vitor
and Novaes, Cachapuz Areta Dames
and Caetano, Duarte Maria Joana
and Rossi, Giusti Paulo
and Tavares, Costa Larissa Riani
and Lord, Ronald Stephen
and Gramani-Say, Karina",
title="A Case Management Program at Home to Reduce Fall Risk in Older Adults (the MAGIC Study): Protocol for a Single-Blind Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2022",
month="Jun",
day="13",
volume="11",
number="6",
pages="e34796",
keywords="accidental falls",
keywords="risk management",
keywords="aged",
keywords="fall prevention",
abstract="Background: Individual case management programs may be particularly effective in reducing fall risk as they can better identify barriers and facilitators to health recommendations. Objective: This paper describes the protocol for a single-blind, parallel-group randomized controlled trial that aims to investigate the effectiveness and cost-effectiveness of a home-based multifactorial program targeting fall risk factors among people aged 60 years and over who have fallen at least twice in the past 12 months (the MAGIC trial). Methods: Older people with a history of at least 2 falls in the last year will be divided into 2 groups. The intervention group will receive case management at home for reducing the risk of falls, including a multidimensional assessment, explanation of fall risk factors, and elaboration and monitoring of an individualized intervention plan based on the identified fall risk factors, personal preferences, and available resources. The control group will be monitored once a month. Assessments (clinical data, fall risk awareness, physical and mental factors, safety at home, feet and shoes, and risk and rate of falls) will be carried out at baseline, after 16 weeks of the intervention, and at the posttrial 6-week and 1-year follow-up. After 16 weeks of the intervention, satisfaction and adherence to the intervention will also be assessed. Economic health will be evaluated for the period up to the posttrial 1-year follow-up. Results: Data collection started in April 2021, and we expected to end recruitment in December 2021. This case management program will address multifactorial assessments using validated tools and the implementation of individualized intervention plans focused on reducing fall risk factors. Conclusions: This trial may provide reliable and valuable information about the effectiveness of case management for increasing fall risk awareness and reducing fall risk in older people. Trial Registration: Brazilian Clinical Trials Registry (ReBec) RBR-3t85fd; https://ensaiosclinicos.gov.br/rg/RBR-3t85fd International Registered Report Identifier (IRRID): DERR1-10.2196/34796 ",
doi="10.2196/34796",
url="https://www.researchprotocols.org/2022/6/e34796",
url="http://www.ncbi.nlm.nih.gov/pubmed/35700005"
}


@Article{info:doi/10.2196/35373,
author="Thapa, Rahul
and Garikipati, Anurag
and Shokouhi, Sepideh
and Hurtado, Myrna
and Barnes, Gina
and Hoffman, Jana
and Calvert, Jacob
and Katzmann, Lynne
and Mao, Qingqing
and Das, Ritankar",
title="Predicting Falls in Long-term Care Facilities: Machine Learning Study",
journal="JMIR Aging",
year="2022",
month="Apr",
day="1",
volume="5",
number="2",
pages="e35373",
keywords="vital signs",
keywords="machine learning",
keywords="blood pressure",
keywords="skilled nursing facilities",
keywords="independent living facilities",
keywords="assisted living facilities",
keywords="fall prediction",
keywords="elderly care",
keywords="elderly population",
keywords="older adult",
keywords="aging",
abstract="Background: Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective: The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods: This retrospective study obtained EHR data (2007-2021) from Juniper Communities' proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities' fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results: The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95\% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident's number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions: This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities. ",
doi="10.2196/35373",
url="https://aging.jmir.org/2022/2/e35373",
url="http://www.ncbi.nlm.nih.gov/pubmed/35363146"
}


@Article{info:doi/10.2196/32724,
author="Kraus, Moritz
and Saller, Michael Maximilian
and Baumbach, Felix Sebastian
and Neuerburg, Carl
and Stumpf, Cordula Ulla
and B{\"o}cker, Wolfgang
and Keppler, Martin Alexander",
title="Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole--Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study",
journal="JMIR Med Inform",
year="2022",
month="Jan",
day="5",
volume="10",
number="1",
pages="e32724",
keywords="wearables",
keywords="insole sensors",
keywords="orthogeriatric",
keywords="artificial intelligence",
keywords="prediction models",
keywords="machine learning",
keywords="gait analysis",
keywords="digital sensors",
keywords="digital health",
keywords="aging",
keywords="prediction algorithms",
keywords="geriatric",
keywords="mobile health",
keywords="mobile insoles",
abstract="Background: Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective: The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods: This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score?8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results: Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions: A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients. ",
doi="10.2196/32724",
url="https://medinform.jmir.org/2022/1/e32724",
url="http://www.ncbi.nlm.nih.gov/pubmed/34989684"
}


@Article{info:doi/10.2196/30558,
author="Jacobson, L. Claire
and Foster, C. Lauren
and Arul, Hari
and Rees, Amanda
and Stafford, S. Randall",
title="A Digital Health Fall Prevention Program for Older Adults: Feasibility Study",
journal="JMIR Form Res",
year="2021",
month="Dec",
day="23",
volume="5",
number="12",
pages="e30558",
keywords="older adults",
keywords="accidental falls",
keywords="fall prevention",
keywords="digital health",
keywords="technology",
keywords="exercise",
keywords="longevity and healthy aging",
keywords="program evaluation",
keywords="aging",
keywords="elderly",
keywords="health strategy",
abstract="Background: About 1 in 3 adults aged 65 and older falls annually. Exercise interventions are effective in reducing the fall risk and fall rate among older adults. In 2020, startup company Age Bold Inc. disseminated the Bold Fall Prevention Program, aiming to reduce falls among older adults through a remotely delivered, digital exercise program. Objective: We conducted a feasibility study to assess the delivery of the Bold Fall Prevention Program remotely and evaluate the program's impact on 2 primary outcomes---annualized fall rate and weekly minutes of physical activity (PA)---over 6 months of follow-up. Methods: Older adults at high risk of falling were screened and recruited for the feasibility study via nationwide digital advertising strategies. Self-reported outcomes were collected via surveys administered at the time of enrollment and after 3 and 6 months. Responses were used to calculate changes in the annualized fall rate and minutes of PA per week. Results: The remote delivery of a progressive digital fall prevention program and associated research study, including remote recruitment, enrollment, and data collection, was deemed feasible. Participants successfully engaged at home with on-demand video exercise classes, self-assessments, and online surveys. We enrolled 65 participants, of whom 48 (74\%) were women, and the average participant age was 72.6 years. Of the 65 participants, 54 (83\%) took at least 1 exercise class, 40 (62\%) responded to at least 1 follow-up survey at either 3 or 6 months, 20 (31\%) responded to both follow-up surveys, and 25 (39\%) were lost to follow-up. Among all participants who completed at least 1 follow-up survey, weekly minutes of PA increased by 182\% (ratio change=2.82, 95\% CI 1.26-6.37, n=35) from baseline and annualized falls per year decreased by 46\% (incidence rate ratio [IRR]=0.54, 95\% CI 0.32-0.90, n=40). Among only 6-month survey responders (n=31, 48\%), weekly minutes of PA increased by 206\% (ratio change=3.06, 95\% CI 1.43-6.55) from baseline to 6 months (n=30, 46\%) and the annualized fall rate decreased by 28\% (IRR=0.72, 95\% CI 0.42-1.23) from baseline to 6 months. Conclusions: The Bold Fall Prevention Program provides a feasible strategy to increase PA and reduce the burden of falls among older adults. ",
doi="10.2196/30558",
url="https://formative.jmir.org/2021/12/e30558",
url="http://www.ncbi.nlm.nih.gov/pubmed/34837492"
}


@Article{info:doi/10.2196/30135,
author="Hsu, Yu-Cheng
and Wang, Hailiang
and Zhao, Yang
and Chen, Frank
and Tsui, Kwok-Leung",
title="Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation",
journal="J Med Internet Res",
year="2021",
month="Dec",
day="20",
volume="23",
number="12",
pages="e30135",
keywords="fall risk",
keywords="balance",
keywords="activity recognition",
keywords="automatic framework",
keywords="community-dwelling elderly",
abstract="Background: Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest. Objective: The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults. Methods: In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals. Results: The framework developed in this study yielded mean accuracies of 87\%, 86\%, and 89\% in detecting sit-to-stand, turning 360{\textdegree}, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90\%, 92\%, and 86\% in classifying abnormal sit-to-stand, turning 360{\textdegree}, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN. Conclusions: The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a ?exible solution to relieve the community's burden of continuous health monitoring. ",
doi="10.2196/30135",
url="https://www.jmir.org/2021/12/e30135",
url="http://www.ncbi.nlm.nih.gov/pubmed/34932008"
}


@Article{info:doi/10.2196/29744,
author="Pech, Marion
and Sauzeon, Helene
and Yebda, Thinhinane
and Benois-Pineau, Jenny
and Amieva, Helene",
title="Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?",
journal="JMIR Aging",
year="2021",
month="Dec",
day="9",
volume="4",
number="4",
pages="e29744",
keywords="elderly people",
keywords="new technologies",
keywords="fall",
keywords="acceptability",
keywords="digital divide",
keywords="aging",
keywords="falls",
keywords="fall prevention",
keywords="detection",
keywords="geriatrics",
keywords="barriers",
keywords="technology acceptance",
keywords="home care",
keywords="seniors",
doi="10.2196/29744",
url="https://aging.jmir.org/2021/4/e29744",
url="http://www.ncbi.nlm.nih.gov/pubmed/34889755"
}


@Article{info:doi/10.2196/26886,
author="Yadav, Lalit
and Gill, K. Tiffany
and Taylor, Anita
and De Young, Jennifer
and Chehade, J. Mellick",
title="Identifying Opportunities, and Motivation to Enhance Capabilities, Influencing the Development of a Personalized Digital Health Hub Model of Care for Hip Fractures: Mixed Methods Exploratory Study",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="28",
volume="23",
number="10",
pages="e26886",
keywords="digital health",
keywords="mixed-methods",
keywords="hip fractures",
keywords="behavior change",
keywords="patient education",
keywords="model of care",
keywords="mobile phone",
keywords="patient networked units",
abstract="Background: Most older people after a hip fracture injury never return to their prefracture status, and some are admitted to residential aged care facilities. Advancement of digital technology has helped in optimizing health care including self-management and telerehabilitation. Objective: This study aims to understand the perspectives of older patients with hip fracture and their family members and residential aged caregivers on the feasibility of developing a model of care using a personalized digital health hub. Methods: We conducted a mixed methods study in South Australia involving patients aged 50 years and older, their family members, and residential aged caregivers. Quantitative data analysis included basic demographic characteristics, and access to digital devices was analyzed using descriptive statistics. Spearman rank-order correlation was used to examine correlations between the perceived role of a personalized digital health hub in improving health and the likelihood of subsequent use. Findings from qualitative analysis were interpreted using constructs of capability, opportunity, and motivation to help understand the factors influencing the likelihood of potential personalized digital health hub use. Results: This study recruited 100 participants---55 patients, 13 family members, and 32 residential aged caregivers. The mean age of the patients was 76.4 (SD 8.4, range 54-88) years, and 60\% (33/55) of the patients were female. Approximately 50\% (34/68) of the patients and their family members had access to digital devices, despite less than one-third using computers as part of their occupation. Approximately 72\% (72/100) of the respondents thought that personalized digital health hub could improve health outcomes in patients. However, a moderate negative correlation existed with increasing age and likelihood of personalized digital health hub use (Spearman $\rho$=--0.50; P<.001), and the perceived role of the personalized digital health hub in improving health had a strong positive correlation with the likelihood of personalized digital health hub use by self (Spearman $\rho$=0.71; P<.001) and by society, including friends and family members (Spearman $\rho$=0.75; P<.001). Most patients (54/55, 98\%) believed they had a family member, friend, or caregiver who would be able to help them use a personalized digital health hub. Qualitative analysis explored capability by understanding aspects of existing knowledge, including willingness to advance digital navigation skills. Access could be improved through supporting opportunities, and factors influencing intrinsic motivation were considered crucial for designing a personalized digital health hub--enabled model of care. Conclusions: This study emphasized the complex relationship between capabilities, motivation, and opportunities for patients, their family members, and formal caregivers as a patient networked unit. The next stage of research will continue to involve a cocreation approach followed by iterative processes and understand the factors influencing the development and successful integration of complex digital health care interventions in real-world scenarios. ",
doi="10.2196/26886",
url="https://www.jmir.org/2021/10/e26886",
url="http://www.ncbi.nlm.nih.gov/pubmed/34709183"
}


@Article{info:doi/10.2196/27848,
author="Kamnardsiri, Teerawat
and Phirom, Kochaphan
and Boripuntakul, Sirinun
and Sungkarat, Somporn",
title="An Interactive Physical-Cognitive Game-Based Training System Using Kinect for Older Adults: Development and Usability Study",
journal="JMIR Serious Games",
year="2021",
month="Oct",
day="27",
volume="9",
number="4",
pages="e27848",
keywords="digital game",
keywords="interactive game-based training",
keywords="physical-cognitive training",
keywords="exergaming",
keywords="Kinect sensors",
keywords="older adults",
keywords="falls",
keywords="PACES",
keywords="user-centered design",
keywords="game-based exercise",
abstract="Background: Declines in physical and cognitive functions are recognized as important risk factors for falls in older adults. Promising evidence suggests that interactive game-based systems that allow simultaneous physical and cognitive exercise are a potential approach to enhance exercise adherence and reduce fall risk in older adults. However, a limited number of studies have reported the development of a combined physical-cognitive game-based training system for fall risk reduction in older adults. Objective: The aim of this study is to develop and evaluate the usability of an interactive physical-cognitive game-based training system (game-based exercise) for older adults. Methods: In the development phase (Part I), a game-based exercise prototype was created by integrating knowledge and a literature review as well as brainstorming with experts on effective fall prevention exercise for older adults. The output was a game-based exercise prototype that covers crucial physical and cognitive components related to falls. In the usability testing (Part II), 5 games (ie, Fruits Hunter, Where Am I?, Whack a Mole, Sky Falls, and Crossing Poison River) with three difficulty levels (ie, beginner, intermediate, and advanced levels) were tested in 5 older adults (mean age 70.40 years, SD 5.41 years). After completing the games, participants rated their enjoyment level while engaging with the games using the Physical Activity Enjoyment Scale (PACES) and commented on the games. Descriptive statistics were used to describe the participants' characteristics and PACES scores. Results: The results showed that the average PACES score was 123 out of 126 points overall and between 6.66 and 7.00 for each item, indicating a high level of enjoyment. Positive feedback, such as praise for the well-designed interactions and user-friendly interfaces, was also provided. Conclusions: These findings suggest that it is promising to implement an interactive, physical-cognitive game-based exercise in older adults. The effectiveness of a game-based exercise program for fall risk reduction has yet to be determined. ",
doi="10.2196/27848",
url="https://games.jmir.org/2021/4/e27848",
url="http://www.ncbi.nlm.nih.gov/pubmed/34704953"
}


@Article{info:doi/10.2196/28923,
author="Reuter, Katja
and Liu, Chang
and Le, NamQuyen
and Angyan, Praveen
and Finley, M. James",
title="General Practice and Digital Methods to Recruit Stroke Survivors to a Clinical Mobility Study: Comparative Analysis",
journal="J Med Internet Res",
year="2021",
month="Oct",
day="13",
volume="23",
number="10",
pages="e28923",
keywords="clinical trial",
keywords="stroke",
keywords="falls",
keywords="digital media",
keywords="social media",
keywords="advertising",
keywords="participant recruitment",
keywords="Facebook",
keywords="Google",
keywords="clinical research",
keywords="research methods",
keywords="recruitment practices",
keywords="enrollment",
abstract="Background: Participant recruitment remains a barrier to conducting clinical research. The disabling nature of a stroke, which often includes functional and cognitive impairments, and the acute stage of illness at which patients are appropriate for many trials make recruiting patients particularly complex and challenging. In addition, people aged 65 years and older, which includes most stroke survivors, have been identified as a group that is difficult to reach and is commonly underrepresented in health research, particularly clinical trials. Digital media may provide effective tools to support enrollment efforts of stroke survivors in clinical trials. Objective: The objective of this study was to compare the effectiveness of general practice (traditional) and digital (online) methods of recruiting stroke survivors to a clinical mobility study. Methods: Recruitment for a clinical mobility study began in July 2018. Eligible study participants included individuals 18 years and older who had a single stroke and were currently ambulatory in the community. General recruiting practice included calling individuals listed in a stroke registry, contacting local physical therapists, and placing study flyers throughout a university campus. Between May 21, 2019, and June 26, 2019, the study was also promoted digitally using the social network Facebook and the search engine marketing tool Google AdWords. The recruitment advertisements (ads) included a link to the study page to which users who clicked were referred. Primary outcomes of interest for both general practice and digital methods included recruitment speed (enrollment rate) and sample characteristics. The data were analyzed using the Lilliefors test, the Welch two-sample t test, and the Mann-Whitney test. Significance was set at P=.05. All statistical analyses were performed in MATLAB 2019b. Results: Our results indicate that digital recruitment methods can address recruitment challenges regarding stroke survivors. Digital recruitment methods allowed us to enroll study participants at a faster rate (1.8 participants/week) compared to using general practice methods (0.57 participants/week). Our findings also demonstrate that digital and general recruitment practices can achieve an equivalent level of sample representativeness. The characteristics of the enrolled stroke survivors did not differ significantly by age (P=.95) or clinical scores (P=.22; P=.82). Comparing the cost-effectiveness of Facebook and Google, we found that the use of Facebook resulted in a lower cost per click and cost per enrollee per ad. Conclusions: Digital recruitment can be used to expedite participant recruitment of stroke survivors compared to more traditional recruitment practices, while also achieving equivalent sample representativeness. Both general practice and digital recruitment methods will be important to the successful recruitment of stroke survivors. Future studies could focus on testing the effectiveness of additional general practice and digital media approaches and include robust cost-effectiveness analyses. Examining the effectiveness of different messaging and visual approaches tailored to culturally diverse and underrepresented target subgroups could provide further data to move toward evidence-based recruitment strategies. ",
doi="10.2196/28923",
url="https://www.jmir.org/2021/10/e28923",
url="http://www.ncbi.nlm.nih.gov/pubmed/34643544"
}


@Article{info:doi/10.2196/23663,
author="Singh, Ajit Devinder Kaur
and Goh, Wen Jing
and Shaharudin, Iqbal Muhammad
and Shahar, Suzana",
title="A Mobile App (FallSA) to Identify Fall Risk Among Malaysian Community-Dwelling Older Persons: Development and Validation Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Oct",
day="12",
volume="9",
number="10",
pages="e23663",
keywords="fall risk",
keywords="self-screening",
keywords="mobile app",
keywords="older person",
abstract="Background: Recent falls prevention guidelines recommend early routine fall risk assessment among older persons. Objective: The purpose of this study was to develop a Falls Screening Mobile App (FallSA), determine its acceptance, concurrent validity, test-retest reliability, discriminative ability, and predictive validity as a self-screening tool to identify fall risk among Malaysian older persons. Methods: FallSA acceptance was tested among 15 participants (mean age 65.93 [SD 7.42] years); its validity and reliability among 91 participants (mean age 67.34 [SD 5.97] years); discriminative ability and predictive validity among 610 participants (mean age 71.78 [SD 4.70] years). Acceptance of FallSA was assessed using a questionnaire, and it was validated against a comprehensive fall risk assessment tool, the Physiological Profile Assessment (PPA). Participants used FallSA to test their fall risk repeatedly twice within an hour. Its discriminative ability and predictive validity were determined by comparing participant fall risk scores between fallers and nonfallers and prospectively through a 6-month follow-up, respectively. Results: The findings of our study showed that FallSA had a high acceptance level with 80\% (12/15) of older persons agreeing on its suitability as a falls self-screening tool. Concurrent validity test demonstrated a significant moderate correlation (r=.518, P<.001) and agreement (k=.516, P<.001) with acceptable sensitivity (80.4\%) and specificity (71.1\%). FallSA also had good reliability (intraclass correlation .948; 95\% CI .921-.966) and an internal consistency ($\alpha$=.948, P<.001). FallSA score demonstrated a moderate to strong discriminative ability in classifying fallers and nonfallers. FallSA had a predictive validity of falls with positive likelihood ratio of 2.27, pooled sensitivity of 82\% and specificity of 64\%, and area under the curve of 0.802. Conclusions: These results suggest that FallSA is a valid and reliable fall risk self-screening tool. Further studies are required to empower and engage older persons or care givers in the use of FallSA to self-screen for falls and thereafter to seek early prevention intervention. ",
doi="10.2196/23663",
url="https://mhealth.jmir.org/2021/10/e23663",
url="http://www.ncbi.nlm.nih.gov/pubmed/34636740"
}


@Article{info:doi/10.2196/32085,
author="McLaren, Ruth
and Smith, F. Paul
and Lord, Sue
and Kaur, Kamal Preet
and Zheng, Yiwen
and Taylor, Denise",
title="Noisy Galvanic Vestibular Stimulation Combined With a Multisensory Balance Program?in Older Adults With Moderate to High Fall Risk: Protocol for a Feasibility Study for a Randomized Controlled Trial",
journal="JMIR Res Protoc",
year="2021",
month="Oct",
day="5",
volume="10",
number="10",
pages="e32085",
keywords="older adult",
keywords="balance",
keywords="rehabilitation",
keywords="noisy galvanic vestibular stimulation",
keywords="nGVS",
keywords="brain stimulation",
abstract="Background: Reduced mobility and falls are common among older adults. Balance retraining programs are effective in reducing falls and in improving balance and mobility. Noisy galvanic vestibular stimulation is a low-level electrical stimulation used to reduce the threshold for the firing of vestibular neurons via a mechanism of stochastic resonance. Objective: This study aims to determine the feasibility of using noisy galvanic vestibular stimulation to augment a balance training program for older adults at risk of falls. We hypothesize that noisy galvanic vestibular stimulation will enhance the effects of balance retraining in older adults at risk of falls Methods: In this 3-armed randomized controlled trial, community dwelling older adults at risk of falling will be randomly assigned to a noisy galvanic vestibular stimulation plus balance program (noisy galvanic vestibular stimulation group), sham plus balance program (sham group), or a no treatment group (control). Participants will attend the exercise group twice a week for 8 weeks with assessment of balance and gait pretreatment, posttreatment, and at 3 months postintervention. Primary outcome measures include postural sway, measured by center of pressure velocity, area and root mean square, and gait parameters such as speed, step width, step variability, and double support time. Spatial memory will also be measured using the triangle completion task and the 4 Mountains Test. Results: Recruitment began in November 2020. Data collection and analysis are expected to be completed by December 2022. Conclusions: This study will evaluate the feasibility of using noisy galvanic vestibular stimulation alongside balance retraining in older adults at risk of falls and will inform the design of a fully powered randomized controlled trial. Trial Registration: New Zealand Clinical Trials Registry (ACTRN12620001172998); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379944 International Registered Report Identifier (IRRID): DERR1-10.2196/32085 ",
doi="10.2196/32085",
url="https://www.researchprotocols.org/2021/10/e32085",
url="http://www.ncbi.nlm.nih.gov/pubmed/34609323"
}


@Article{info:doi/10.2196/24665,
author="Lapierre, Nolwenn
and Um Din, Nathavy
and Igout, Manuella
and Chevrier, Jo{\"e}l
and Belmin, Jo{\"e}l",
title="Effects of a Rehabilitation Program Using a Patient-Personalized Exergame on Fear of Falling and Risk of Falls in Vulnerable Older Adults: Protocol for a Randomized Controlled Group Study",
journal="JMIR Res Protoc",
year="2021",
month="Aug",
day="26",
volume="10",
number="8",
pages="e24665",
keywords="older adult",
keywords="fall",
keywords="fear of falling",
keywords="exergame",
keywords="randomized controlled trial",
keywords="psychomotor therapy",
keywords="rehabilitation",
keywords="fear",
keywords="risk",
keywords="elderly",
keywords="protocol",
keywords="therapy",
abstract="Background: Older adults often experience physical, sensory, and cognitive decline. Therefore, they have a high risk of falls, which leads to severe health and psychological consequences and can induce fear of falling. Rehabilitation programs using exergames to prevent falls are being increasingly studied. Medimoov is a movement-based patient-personalized exergame for rehabilitation in older adults. A preliminary study showed that its use may influence functional ability and motivation. Most existing studies that evaluate the use of exergames do not involve an appropriate control group and do not focus on patient-personalized exergames. Objective: This study aims to evaluate the effects of Medimoov on risk of falls and fear of falling in older adults compared with standard psychomotor rehabilitation. Methods: This is a serial, comparative, randomized controlled group study. Both groups (n=25 in each) will receive psychomotor rehabilitation care. However, the methods of delivery will be different; one group will be exposed to the Medimoov exergame platform, and the other only to traditional means of psychomotor rehabilitation. The selection criteria will be (1) age of 65 years or older, (2) ability to answer a questionnaire, (3) ability to stand in a bipedal position for at least 1 minute, (4) score of 13 or greater on the Short Fall Efficacy Scale, and (5) stable medical condition. An evaluation will be made prior to starting the intervention, after 4 weeks of intervention, and at the end of the intervention (after 8 weeks), and it will focus on (1) risk of falls, (2) fear of falling, and (3) cognitive evaluations. Physical activity outside the session will also be assessed by actimetry. The outcome assessment will be performed according to intention-to-treat analysis. Results: The protocol (2019-11-22) has been approved by the Comit{\'e} de Protection des Personnes Nord-Ouest I--Universit{\'e} de Rouen (2019-A00395-52), which is part of the French national ethical committee. The study received funding in February 2020. As of October 2020 (submission date), and due to the context of the COVID-19 pandemic, a total of 10 participants out of 50 had been enrolled in the study. The projected date for the end of the data collection is December 2021. Data analyses have not been started yet, and publication of the results is expected for Spring 2022. Conclusions: The effects of psychomotor rehabilitation using the Medimoov exergame platform on the risk and fear of falls will be evaluated. This pilot study will be the basis for larger trials. Trial Registration: ClinicalTrials.gov NCT04134988; https://clinicaltrials.gov/ct2/show/NCT04134988 International Registered Report Identifier (IRRID): DERR1-10.2196/24665 ",
doi="10.2196/24665",
url="https://www.researchprotocols.org/2021/8/e24665",
url="http://www.ncbi.nlm.nih.gov/pubmed/34435968"
}


@Article{info:doi/10.2196/25781,
author="Ara{\'u}jo, F{\'a}tima
and Nogueira, Nilza Maria
and Silva, Joana
and Rego, S{\'i}lvia",
title="A Technological-Based Platform for Risk Assessment, Detection, and Prevention of Falls Among Home-Dwelling Older Adults: Protocol for a Quasi-Experimental Study",
journal="JMIR Res Protoc",
year="2021",
month="Aug",
day="12",
volume="10",
number="8",
pages="e25781",
keywords="fall prevention",
keywords="technological platform",
keywords="elderly",
keywords="Otago Exercise Program",
abstract="Background: According to the United Nations, it is estimated that by 2050, the number of people aged 80 years and older will have increased by 3 times. Increased longevity is often accompanied by structural and functional changes that occur throughout an individual's lifespan. These changes are often aggravated by chronic comorbidities, adopted behaviors or lifestyles, and environmental exposure, among other factors. Some of the related outcomes are loss of muscle strength, decreased balance control, and mobility impairments, which are strongly associated with the occurrence of falls in the elderly. Despite the continued undervaluation of the importance of knowledge on fall prevention among the elderly population by primary care health professionals, several evidence-based (single or multifaceted) fall prevention programs such as the Otago Exercise Program (OEP) have demonstrated a significant reduction in the risk of falls and fall-related injuries in the elderly within community settings. Recent studies have strived to integrate technology into physical exercise programs, which is effective for adherence and overcoming barriers to exercise, as well as improving physical functioning. Objective: This study aims to assess the impact of the OEP on the functionality of home-dwelling elderly using a common technological platform. Particularly, the impact on muscle strength, balance, mobility, risk of falling, the perception of fear of falling, and the perception of the elderly regarding the ease of use of technology are being examined in this study. Methods: A quasi-experimental study (before and after; single group) will be conducted with male and female participants aged 65 years or older living at home in the district of Porto. Participants will be recruited through the network COLABORAR, with a minimum of 30 participants meeting the study inclusion and exclusion criteria. All participants will sign informed consent forms. The data collection instrument consists of sociodemographic and clinical variables (self-reported), functional evaluation variables, and environmental risk variables. The data collection tool integrates primary and secondary outcome variables. The primary outcome is gait (timed-up and go test; normal step). The secondary outcome variables are lower limb strength and muscle resistance (30-second chair stand test), balance (4-stage balance test), frequency of falls, functional capacity (Lawton and Brody - Portuguese version), fear of falling (Falls Efficacy Scale International - Portuguese version), usability of the technology (System Usability Scale - Portuguese version), and environmental risk variables (home fall prevention checklist for older adults). Technological solutions, such as the FallSensing Home application and Kallisto wearable device, will be used, which will allow the detection and prevention of falls. The intervention is characterized by conducting the OEP through a common technological platform 3 times a week for 8 weeks. Throughout these weeks, the participants will be followed up in person or by telephone contact by the rehabilitation nurse. Considering the COVID-19 outbreak, all guidelines from the National Health Service will be followed. The project was funded by InnoStars, in collaboration with the Local EIT Health Regional Innovation Scheme Hub of the University of Porto. Results: This study was approved on October 9, 2020 by the Ethics Committee of Escola Superior de Enfermagem do Porto (ESEP). The recruitment process was meant to start in October, but due to the COVID-19 pandemic, it was suspended. We expect to restart the study by the beginning of the third quarter of 2021. Conclusions: The findings of this study protocol will contribute to the design and development of future robust studies for technological tests in a clinical context. Trial Registration: ISRCTN 15895163; https://www.isrctn.com/ISRCTN15895163 International Registered Report Identifier (IRRID): PRR1-10.2196/25781 ",
doi="10.2196/25781",
url="https://www.researchprotocols.org/2021/8/e25781",
url="http://www.ncbi.nlm.nih.gov/pubmed/34387557"
}


@Article{info:doi/10.2196/26235,
author="Pettersson, Beatrice
and Janols, Rebecka
and Wiklund, Maria
and Lundin-Olsson, Lillemor
and Sandlund, Marlene",
title="Older Adults' Experiences of Behavior Change Support in a Digital Fall Prevention Exercise Program: Qualitative Study Framed by the Self-determination Theory",
journal="J Med Internet Res",
year="2021",
month="Jul",
day="30",
volume="23",
number="7",
pages="e26235",
keywords="accidental falls",
keywords="aged",
keywords="exercise",
keywords="qualitative research",
keywords="eHealth",
keywords="self-management",
keywords="fall prevention",
keywords="behavior change",
keywords="self-determination theory",
keywords="classification of motivation and behavior change techniques",
abstract="Background: Exercise is an effective intervention to prevent falls in older adults; however, long-term adherence is often poor. To increase adherence, additional support for behavior change has been advocated. However, consistency in the reporting of interventions using behavior change techniques is lacking. Recently, a classification system has been developed to increase consistency in studies using behavior change techniques within the self-determination theory. Objective: This study aimed to explore expressions of self-determination among community-dwelling older adults using a self-managed digital fall prevention exercise program comprising behavior change support (the Safe Step program), which was developed in co-creation with intended users. Methods: The qualitative study design was based on open-ended responses to questionnaires, and individual and focus group interviews. A deductive qualitative content analysis was applied using the classification system of motivation and behavior change techniques as an analytical matrix, followed by an inductive analysis. Twenty-five participants took part in a feasibility study and exercised in their homes with the Safe Step program for 4 months. The exercise program was available on computers,smartphones, and tablets, and was fully self-managed. Results: In the deductive analysis, expressions of support were demonstrated for all three basic human psychological needs, namely, autonomy, competence, and relatedness. These expressions were related to 11 of the 21 motivation and behavior change techniques in the classification system. The inductive analysis indicated that autonomy (to be in control) was valued and enabled individual adaptations according to different rationales for realizing exercise goals. However, the experience of autonomy was also two-sided and depended on the participants' competence in exercise and the use of technology. The clarity of the program and exercise videos was seen as key for support in performance and competent choices. Although augmented techniques for social support were requested, support through relatedness was found within the program. Conclusions: In this study, the Safe Step program supported the establishment of new exercise routines, as well as the three basic human psychological needs, with autonomy and competence being expressed as central in this context. Based on the participants' experiences, a proposed addition to the classification system used as an analytical matrix has been presented. Trial Registration: ClinicalTrials.gov NCT02916849; https://clinicaltrials.gov/ct2/show/NCT02916849 ",
doi="10.2196/26235",
url="https://www.jmir.org/2021/7/e26235",
url="http://www.ncbi.nlm.nih.gov/pubmed/34328438"
}


@Article{info:doi/10.2196/24974,
author="Shorr, I. Ronald
and Ahrentzen, Sherry
and Luther, L. Stephen
and Radwan, Chad
and Hahm, Bridget
and Kazemzadeh, Mahshad
and Alliance, Slande
and Powell-Cope, Gail
and Fischer, M. Gary",
title="Examining the Relationship Between Environmental Factors and Inpatient Hospital Falls: Protocol for a Mixed Methods Study",
journal="JMIR Res Protoc",
year="2021",
month="Jul",
day="13",
volume="10",
number="7",
pages="e24974",
keywords="falls",
keywords="accidental falls",
keywords="hospital design and construction",
keywords="health facility environment",
keywords="hospital units",
keywords="evidence-based facility design",
keywords="nursing",
keywords="environmental factors",
keywords="well-being",
keywords="accident",
abstract="Background: Patient falls are the most common adverse events reported in hospitals. Although it is well understood that the physical hospital environment contributes to nearly 40\% of severe or fatal hospital falls, there are significant gaps in the knowledge about the relationship between inpatient unit design and fall rates. The few studies that have examined unit design have been conducted in a single hospital (non-Veterans Health Administration [VHA]) or a small number of inpatient units, limiting generalizability. The goal of this study is to identify unit design factors contributing to inpatient falls in the VHA. Objective: The first aim of the study is to investigate frontline and management perceptions of and experiences with veteran falls as they pertain to inpatient environmental factors. An iterative rapid assessment process will be used to analyze the data. Interview findings will directly inform the development of an environmental assessment survey to be conducted as part of aim 2 and to contribute to interpretation of aim 2. The second aim of this study is to quantify unit design factors and compare spatial and environmental factors of units with higher- versus lower-than-expected fall rates. Methods: We will first conduct walk-through interviews with facility personnel in 10 medical/surgical units at 3 VHA medical centers to identify environmental fall risk factors. Data will be used to finalize an environmental assessment survey for nurse managers and facilities managers. We will then use fall data from the VA Inpatient Evaluation Center and patient data from additional sources to identify 50 medical/surgical nursing units with higher- and lower-than-expected fall rates. We will measure spatial factors by analyzing computer-aided design files of unit floorplans and environmental factors from the environmental assessment survey. Statistical tests will be performed to identify design factors that distinguish high and low outliers. Results: The VA Health Services Research and Development Service approved funding for the study. The research protocol was approved by institutional review boards and VA research committees at both sites. Data collection started in February 2018. Results of the data analysis are expected by February 2022. Data collection and analysis was completed for aim 1 with a manuscript of results in progress. For aim 2, the medical/surgical units were categorized into higher- and lower-than-expected fall categories, the environmental assessment surveys were distributed to facility managers and nurse managers. Data to measure spatial characteristics are being compiled. Conclusions: To our knowledge, this study is the first to objectively identify spatial risks for falls in hospitals within in a large multihospital system. Findings can contribute to evidence-based design guidelines for hospitals such as those of the Facility Guidelines Institute and the Department of Veterans Affairs. The metrics for characterizing spatial features are quantitative indices that could be incorporated in larger scale contextual studies examining contributors to falls, which to date often exclude physical environmental factors at the unit level. Space syntax measures could be used as physical environmental factors in future research examining a range of contextual factors---social, personal, organizational, and environmental---that contribute to patient falls. International Registered Report Identifier (IRRID): DERR1-10.2196/24974 ",
doi="10.2196/24974",
url="https://www.researchprotocols.org/2021/7/e24974",
url="http://www.ncbi.nlm.nih.gov/pubmed/34255724"
}


@Article{info:doi/10.2196/15641,
author="Piau, Antoine
and Steinmeyer, Zara
and Charlon, Yoann
and Courbet, Laetitia
and Rialle, Vincent
and Lepage, Benoit
and Campo, Eric
and Nourhashemi, Fati",
title="A Smart Shoe Insole to Monitor Frail Older Adults' Walking Speed: Results of Two Evaluation Phases Completed in a Living Lab and Through a 12-Week Pilot Study",
journal="JMIR Mhealth Uhealth",
year="2021",
month="Jul",
day="5",
volume="9",
number="7",
pages="e15641",
keywords="frail older adults",
keywords="walking speed",
keywords="outpatient monitoring",
keywords="activity tracker",
keywords="shoe insert",
abstract="Background: Recent World Health Organization reports propose wearable devices to collect information on activity and walking speed as innovative health indicators. However, mainstream consumer-grade tracking devices and smartphone apps are often inaccurate and require long-term acceptability assessment. Objective: Our aim is to assess the user acceptability of an instrumented shoe insole in frail older adults. This device monitors participants' walking speed and differentiates active walking from shuffling after step length calibration. Methods: A multiphase evaluation has been designed: 9 older adults were evaluated in a living lab for a day, 3 older adults were evaluated at home for a month, and a prospective randomized trial included 35 older adults at home for 3 months. A qualitative research design using face-to-face and phone semistructured interviews was performed. Our hypothesis was that this shoe insole was acceptable in monitoring long-term outdoor and indoor walking. The primary outcome was participants' acceptability, measured by a qualitative questionnaire and average time of insole wearing per day. The secondary outcome described physical frailty evolution in both groups. Results: Living lab results confirmed the importance of a multiphase design study with participant involvement. Participants proposed insole modifications. Overall acceptability had mixed results: low scores for reliability (2.1 out of 6) and high scores for usability (4.3 out of 6) outcomes. The calibration phase raised no particular concern. During the field test, a majority of participants (mean age 79 years) were very (10/16) or quite satisfied (3/16) with the insole's comfort at the end of the follow-up. Participant insole acceptability evolved as follows: 63\% (12/19) at 1 month, 50\% (9/18) at 2 months, and 75\% (12/16) at 3 months. A total of 9 participants in the intervention group discontinued the intervention because of technical issues. All participants equipped for more than a week reported wearing the insole every day at 1 month, 83\% (15/18) at 2 months, and 94\% (15/16) at 3 months for 5.8, 6.3, and 5.1 hours per day, respectively. Insole data confirmed that participants effectively wore the insole without significant decline during follow-up for an average of 13.5 days per 4 months and 5.6 hours per day. For secondary end points, the change in frailty parameters or quality of life did not differ for those randomly assigned to the intervention group compared to usual care. Conclusions: Our study reports acceptability data on an instrumented insole in indoor and outdoor walking with remote monitoring in frail older adults under real-life conditions. To date, there is limited data in this population set. This thin instrumentation, including a flexible battery, was a technical challenge and seems to provide an acceptable solution over time that is valued by participants. However, users still raised certain acceptability issues. Given the growing interest in wearable health care devices, these results will be useful for future developments. Trial Registration: ClinicalTrials.gov NCT02316600; https://clinicaltrials.gov/ct2/show/NCT02316600 ",
doi="10.2196/15641",
url="https://mhealth.jmir.org/2021/7/e15641",
url="http://www.ncbi.nlm.nih.gov/pubmed/36260404"
}


@Article{info:doi/10.2196/27972,
author="Nishchyk, Anna
and Chen, Weiqin
and Pripp, Hugo Are
and Bergland, Astrid",
title="The Effect of Mixed Reality Technologies for Falls Prevention Among Older Adults: Systematic Review and Meta-analysis",
journal="JMIR Aging",
year="2021",
month="Jun",
day="30",
volume="4",
number="2",
pages="e27972",
keywords="falls",
keywords="fall prevention",
keywords="mixed reality",
keywords="augmented reality",
keywords="virtual reality",
keywords="physical exercise",
abstract="Background: Falling is one of the most common and serious age-related issues, and falls can significantly impair the quality of life of older adults. Approximately one-third of people over 65 experience a fall annually. Previous research has shown that physical exercise could help reduce falls among older adults and improve their health. However, older adults often find it challenging to follow and adhere to physical exercise programs. Interventions using mixed reality (MR) technology could help address these issues. MR combines artificial augmented computer-generated elements with the real world. It has frequently been used for training and rehabilitation purposes. Objective: The aim of this systematic literature review and meta-analysis was to investigate the use of the full spectrum of MR technologies for fall prevention intervention and summarize evidence of the effectiveness of this approach. Methods: In our qualitative synthesis, we analyzed a number of features of the selected studies, including aim, type of exercise, technology used for intervention, study sample size, participant demographics and history of falls, study design, involvement of health professionals or caregivers, duration and frequency of the intervention, study outcome measures, and results of the study. To systematically assess the results of the selected studies and identify the common effect of MR interventions, a meta-analysis was performed. Results: Seven databases were searched, and the initial search yielded 5838 results. With the considered inclusion and exclusion criteria, 21 studies were included in the qualitative synthesis and 12 were included in meta-analysis. The majority of studies demonstrated a positive effect of an MR intervention on fall risk factors among older participants. The meta-analysis demonstrated a statistically significant difference in Berg Balance Scale score between the intervention and control groups (ES: 0.564; 95\% CI 0.246-0.882; P<.001) with heterogeneity statistics of I2=54.9\% and Q=17.74 (P=.02), and a statistical difference in Timed Up and Go test scores between the intervention and control groups (ES: 0.318; 95\% CI 0.025-0.662; P<.001) with heterogeneity statistics of I2=77.6\% and Q=44.63 (P<.001). The corresponding funnel plot and the Egger test for small-study effects (P=.76 and P=.11 for Berg Balance Scale and Timed Up and Go, respectively) indicate that a minor publication bias in the studies might be present in the Berg Balance Scale results. Conclusions: The literature review and meta-analysis demonstrate that the use of MR interventions can have a positive effect on physical functions in the elderly. MR has the potential to help older users perform physical exercises that could improve their health conditions. However, more research on the effect of MR fall prevention interventions should be conducted with special focus given to MR usability issues. ",
doi="10.2196/27972",
url="https://aging.jmir.org/2021/2/e27972",
url="http://www.ncbi.nlm.nih.gov/pubmed/34255643"
}


@Article{info:doi/10.2196/17551,
author="Bayen, Eleonore
and Nickels, Shirley
and Xiong, Glen
and Jacquemot, Julien
and Subramaniam, Raghav
and Agrawal, Pulkit
and Hemraj, Raheema
and Bayen, Alexandre
and Miller, L. Bruce
and Netscher, George",
title="Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="17",
volume="23",
number="6",
pages="e17551",
keywords="artificial intelligence",
keywords="video monitoring",
keywords="real-time video detection",
keywords="fall",
keywords="time on the ground",
keywords="Alzheimer disease",
keywords="dementia",
keywords="memory care facilities",
abstract="Background: Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fall-related injuries due to the trauma itself, prolonged immobilization on the floor results in a wide range of comorbidities and may double the risk of death in elderly. Thus, reducing the length of Time On the Ground (TOG) in fallers seems crucial in vulnerable individuals with cognitive disorders who cannot get up independently. Objective: This study aimed to examine the effect of a new technology called SafelyYou Guardian (SYG) on early post-fall care including reduction of Time Until staff Assistance (TUA) and TOG. Methods: SYG uses continuous video monitoring, artificial intelligence, secure networks, and customized computer applications to detect and notify caregivers about falls in real time while providing immediate access to video footage of falls. The present observational study was conducted in 6 California memory care facilities where SYG was installed in bedrooms of consenting residents and families. Fall events were video recorded over 10 months. During the baseline installation period (November 2017 to December 2017), SYG video captures of falls were not provided on a regular basis to facility staff review. During a second period (January 2018 to April 2018), video captures were delivered to facility staff on a regular weekly basis. During the third period (May 2018 to August 2018), real-time notification (RTN) of any fall was provided to facility staff. Two digital markers (TUA, TOG) were automatically measured and compared between the baseline period (first 2 months) and the RTN period (last 4 months). The total number of falls including those happening outside of the bedroom (such as common areas and bathrooms) was separately reported by facility staff. Results: A total of 436 falls were recorded in 66 participants suffering from Alzheimer disease or related dementias (mean age 87 years; minimum 65, maximum 104 years). Over 80\% of the falls happened in bedrooms, with two-thirds occurring overnight (8 PM to 8 AM). While only 8.1\% (22/272) of falls were scored as moderate or severe, fallers were not able to stand up alone in 97.6\% (247/253) of the cases. Reductions of 28.3 (CI 19.6-37.1) minutes in TUA and 29.6 (CI 20.3-38.9) minutes in TOG were observed between the baseline and RTN periods. The proportion of fallers with TOG >1 hour fell from 31\% (8/26; baseline) to zero events (RTN period). During the RTN period, 76.6\% (108/141) of fallers received human staff assistance in less than 10 minutes, and 55.3\% (78/141) of them spent less than 10 minutes on the ground. Conclusions: SYG technology is capable of reducing TOG and TUA while efficiently covering the area (bedroom) and time zone (nighttime) that are at highest risk. After 6 months of SYG monitoring, TOG was reduced by a factor of 3. The drastic reduction of TOG is likely to decrease secondary comorbid complications, improve post-fall prognosis, and reduce health care costs. ",
doi="10.2196/17551",
url="https://www.jmir.org/2021/6/e17551",
url="http://www.ncbi.nlm.nih.gov/pubmed/34137723"
}


@Article{info:doi/10.2196/22215,
author="Gaspar, Martins Andr{\'e}a G.
and Lap{\~a}o, Velez Lu{\'i}s",
title="eHealth for Addressing Balance Disorders in the Elderly: Systematic Review",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e22215",
keywords="balance disorders",
keywords="falls",
keywords="elderly",
keywords="eHealth",
keywords="telemedicine",
abstract="Background: The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective: The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods: A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results: Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions: eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed. ",
doi="10.2196/22215",
url="https://www.jmir.org/2021/4/e22215",
url="http://www.ncbi.nlm.nih.gov/pubmed/33908890"
}


@Article{info:doi/10.2196/24728,
author="Daniels, Helen
and Hollinghurst, Joe
and Fry, Richard
and Clegg, Andrew
and Hillcoat-Nall{\'e}tamby, Sarah
and Nikolova, Silviya
and Rodgers, E. Sarah
and Williams, Neil
and Akbari, Ashley",
title="The Value of Routinely Collected Data in Evaluating Home Assessment and Modification Interventions to Prevent Falls in Older People: Systematic Literature Review",
journal="JMIR Aging",
year="2021",
month="Apr",
day="23",
volume="4",
number="2",
pages="e24728",
keywords="falls",
keywords="aged",
keywords="routinely collected data",
keywords="evaluation research",
keywords="systematic review",
abstract="Background: Falls in older people commonly occur at home. Home assessment and modification (HAM) interventions can be effective in reducing falls; however, there are some concerns over the validity of evaluation findings. Routinely collected data could improve the quality of HAM evaluations and strengthen their evidence base. Objective: The aim of this study is to conduct a systematic review of the evidence of the use of routinely collected data in the evaluations of HAM interventions. Methods: We searched the following databases from inception until January 31, 2020: PubMed, Ovid, CINAHL, OpenGrey, CENTRAL, LILACS, and Web of Knowledge. Eligible studies were those evaluating HAMs designed to reduce falls involving participants aged 60 years or more. We included study protocols and full reports. Bias was assessed using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) tool. Results: A total of 7 eligible studies were identified in 8 papers. Government organizations provided the majority of data across studies, with health care providers and third-sector organizations also providing data. Studies used a range of demographic, clinical and health, and administrative data. The purpose of using routinely collected data spanned recruiting and creating a sample, stratification, generating independent variables or covariates, and measuring key study-related outcomes. Nonhome-based modification interventions (eg, in nursing homes) using routinely collected data were not included in this study. We included two protocols, which meant that the results of those studies were not available. MeSH headings were excluded from the PubMed search because of a reduction in specificity. This means that some studies that met the inclusion criteria may not have been identified. Conclusions: Routine data can be used successfully in many aspects of HAM evaluations and can reduce biases and improve other important design considerations. However, the use of these data in these studies is currently not widespread. There are a number of governance barriers to be overcome to allow these types of linkage and to ensure that the use of routinely collected data in evaluations of HAM interventions is exploited to its full potential. ",
doi="10.2196/24728",
url="https://aging.jmir.org/2021/2/e24728",
url="http://www.ncbi.nlm.nih.gov/pubmed/33890864"
}


@Article{info:doi/10.2196/27381,
author="Thiamwong, Ladda
and Stout, R. Jeffrey
and Park, Joon-Hyuk
and Yan, Xin",
title="Technology-Based Fall Risk Assessments for Older Adults in Low-Income Settings: Protocol for a Cross-sectional Study",
journal="JMIR Res Protoc",
year="2021",
month="Apr",
day="7",
volume="10",
number="4",
pages="e27381",
keywords="body composition",
keywords="falls",
keywords="risk assessment",
keywords="technology",
keywords="wearable devices",
keywords="accidental falls",
keywords="fear",
abstract="Background: One-third of older adults have maladaptive fall risk appraisal (FRA), a condition in which there is a discrepancy between the level of fear of falling (FOF) and physiological fall risk (balance performance). Older adults who overestimate their physiological fall risk and report a high FOF are less likely to participate in physical activity. Limited data suggest that the association among FOF, body composition, and physical activity intensity differs by fear severity. Objective: This study aims to examine the associations among FRA, body composition, and physical activity using assistive health technology, including the BTrackS balance system, bioelectrical impedance analysis, and activity monitoring devices. This study also aims to examine the feasibility of recruitment and acceptability of technologies and procedures for use among older adults in low-income settings. Methods: This cross-sectional study will be conducted in older adults' homes or apartments in low-income settings in Central Florida, United States. Following consent, participants will be contacted, and our team will visit them twice. The first visit includes questionnaire completion (eg, sociodemographic or FOF) and balance performance test using the BTrackS balance system. The participants will be stratified by the FRA matrix. In addition, they will perform hand grip strength and dynamic balance performance tests. Participants will then be asked to wear the ActiGraph GT9X Link wireless activity monitor on the nondominant wrist for 7 consecutive days. The second visit includes body composition testing and a structured interview about the acceptability of the technologies and procedures. Results: Ethical approval was obtained from the institutional review board of the University of Central Florida (protocol number 2189; September 10, 2020). As of December 2020, participation enrollment is ongoing and the results are expected to be published in Summer 2022. Conclusions: Accurate FRA is essential for implementing physical activity programs, especially in older adults with low income. This study will provide data for developing technology-based fall risk assessments to improve participation in physical activity, thus enhancing healthy longevity among older adults in low-income settings. International Registered Report Identifier (IRRID): PRR1-10.2196/27381 ",
doi="10.2196/27381",
url="https://www.researchprotocols.org/2021/4/e27381",
url="http://www.ncbi.nlm.nih.gov/pubmed/33825688"
}


@Article{info:doi/10.2196/24455,
author="Strauss, H. Daniel
and Davoodi, M. Natalie
and Healy, Margaret
and Metts, L. Christopher
and Merchant, C. Roland
and Banskota, Swechya
and Goldberg, M. Elizabeth",
title="The Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II to Assess the Use of the Apple Watch in Older Emergency Department Patients With Falls: Protocol for a Mixed Methods Study",
journal="JMIR Res Protoc",
year="2021",
month="Apr",
day="1",
volume="10",
number="4",
pages="e24455",
keywords="fall intervention",
keywords="geriatric care",
keywords="Apple Watch",
keywords="wearable technology",
abstract="Background: Falls are a common problem among older adults that lead to injury, emergency department (ED) visits, and institutionalization. The Apple Watch can detect falls and alert caregivers and clinicians that help is needed; the device could also be used to objectively collect data on gait, fitness, and falls as part of clinical trials. However, little is known about the ease of use of this technology among older adult ED patients, a population at high risk of recurrent falls. Objective: The goal of this study---the Geriatric Acute and Post-Acute Fall Prevention Intervention (GAPcare) II---is to examine the feasibility, acceptability, and usability of the Apple Watch Series 4 paired with the iPhone and our research app Rhode Island FitTest (RIFitTest) among older adult ED patients seeking care for falls. Methods: We will conduct field-testing with older adult ED patients (n=25) who sustained a fall and their caregivers (n=5) to determine whether they can use the Apple Watch, iPhone, and app either (1) continuously or (2) periodically, with or without telephone assistance from the research staff, to assess gait, fitness, and/or falls over time. During the initial encounter, participants will receive training in the Apple Watch, iPhone, and our research app. They will receive an illustrated training manual and a number to call if they have questions about the research protocol or device usage. Participants will complete surveys and cognitive and motor assessments on the app during the study period. At the conclusion of the study, we will solicit participant feedback through semistructured interviews. Qualitative data will be summarized using framework matrix analyses. Sensor and survey response data will be analyzed using descriptive statistics. Results: Recruitment began in December 2019 and was on pause from April 2020 until September 2020 due to the COVID-19 pandemic. Study recruitment will continue until 30 participants are enrolled. This study has been approved by the Rhode Island Hospital Institutional Review Board (approval 1400781-16). Conclusions: GAPcare II will provide insights into the feasibility, acceptability, and usability of the Apple Watch, iPhone, and the RIFitTest app in the population most likely to benefit from the technology: older adults at high risk of recurrent falls. In the future, wearables could be used as part of fall prevention interventions to prevent injury before it occurs. Trial Registration: ClinicalTrials.gov NCT04304495; https://clinicaltrials.gov/ct2/show/NCT04304495 International Registered Report Identifier (IRRID): DERR1-10.2196/24455 ",
doi="10.2196/24455",
url="https://www.researchprotocols.org/2021/4/e24455",
url="http://www.ncbi.nlm.nih.gov/pubmed/33792553"
}


@Article{info:doi/10.2196/23381,
author="Vollmer Dahlke, Deborah
and Lee, Shinduk
and Smith, Lee Matthew
and Shubert, Tiffany
and Popovich, Stephen
and Ory, G. Marcia",
title="Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study",
journal="JMIR Aging",
year="2021",
month="Jan",
day="27",
volume="4",
number="1",
pages="e23381",
keywords="wearables",
keywords="falls alert technology",
keywords="falls",
keywords="caregivers",
keywords="care recipients",
abstract="Background: Wearable technology for fall alerts among older adult care recipients is one of the more frequently studied areas of technology, given the concerning consequences of falls among this population. Falls are quite prevalent in later life. While there is a growing amount of literature on older adults' acceptance of technology, less is known about how caregivers' attitudes toward technology can impact care recipients' use of such technology. Objective: The objective of our study was to examine associations between caregivers' attitudes toward technology for caregiving and care recipients' use of fall alert wearables. Methods: This study examined data collected with an online survey from 626 caregivers for adults 50 years and older. Adapted from the technology acceptance model, a structural equation model tested the following prespecified hypotheses: (1) higher perceived usefulness of technologies for caregiving would predict higher perceived value of and greater interest in technologies for caregiving; (2) higher perceived value of technologies for caregiving would predict greater interest in technologies for caregiving; and (3) greater interest in technologies for caregiving would predict greater use of fall alert wearables among care recipients. Additionally, we included demographic factors (eg, caregivers' and care recipients' ages) and caregiving context (eg, caregiver type and caregiving situation) as important predictors of care recipients' use of fall alert wearables. Results: Of 626 total respondents, 548 (87.5\%) with all valid responses were included in this study. Among care recipients, 28\% used fall alert wearables. The final model had a good to fair model fit: a confirmatory factor index of 0.93, a standardized root mean square residual of 0.049, and root mean square error of approximation of 0.066. Caregivers' perceived usefulness of technology was positively associated with their attitudes toward using technology in caregiving (b=.70, P<.001) and interest in using technology for caregiving (b=.22, P=.003). Greater perceived value of using technology in caregiving predicted greater interest in using technology for caregiving (b=.65, P<.001). Greater interest in using technology for caregiving was associated with greater likelihood of care recipients using fall alert wearables (b=.27, P<.001). The caregiver type had the strongest inverse relationship with care recipients' use of fall alert wearables (unpaid vs paid caregiver) (b=--.33, P<.001). Conclusions: This study underscores the importance of caregivers' attitudes in care recipients' technology use for falls management. Raising awareness and improving perception about technologies for caregiving may help caregivers and care recipients adopt and better utilize technologies that can promote independence and enhance safety. ",
doi="10.2196/23381",
url="http://aging.jmir.org/2021/1/e23381/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33502320"
}


@Article{info:doi/10.2196/19690,
author="Hawley-Hague, Helen
and Tacconi, Carlo
and Mellone, Sabato
and Martinez, Ellen
and Chiari, Lorenzo
and Helbostad, Jorunn
and Todd, Chris",
title="One-to-One and Group-Based Teleconferencing for Falls Rehabilitation: Usability, Acceptability, and Feasibility Study",
journal="JMIR Rehabil Assist Technol",
year="2021",
month="Jan",
day="12",
volume="8",
number="1",
pages="e19690",
keywords="aged",
keywords="postural balance",
keywords="telerehabilitation",
keywords="patient compliance",
keywords="accidental falls",
keywords="mobile phone",
abstract="Background: Falls have implications for the health of older adults. Strength and balance interventions significantly reduce the risk of falls; however, patients seldom perform the dose of exercise that is required based on evidence. Health professionals play an important role in supporting older adults as they perform and progress in their exercises. Teleconferencing could enable health professionals to support patients more frequently, which is important in exercise behavior. Objective: This study aims to examine the overall concept and acceptability of teleconferencing for the delivery of falls rehabilitation with health care professionals and older adults and to examine the usability, acceptability, and feasibility of teleconferencing delivery with health care professionals and patients. Methods: There were 2 stages to the research: patient and public involvement workshops and usability and feasibility testing. A total of 2 workshops were conducted, one with 5 health care professionals and the other with 8 older adults from a community strength and balance exercise group. For usability and feasibility testing, we tested teleconferencing both one-to-one and in small groups on a smartphone with one falls service and their patients for 3 weeks. Semistructured interviews and focus groups were used to explore acceptability, usability, and feasibility. Focus groups were conducted with the service that used teleconferencing with patients and 2 other services that received only a demonstration of how teleconferencing works. Qualitative data were analyzed using the framework approach. Results: In the workshops, the health care professionals thought that teleconferencing provided an opportunity to save travel time. Older adults thought that it could enable increased support. Safety is of key importance, and delivery needs to be carefully considered. Both older adults and health care professionals felt that it was important that technology did not eliminate face-to-face contact. There were concerns from older adults about the intrusiveness of technology. For the usability and feasibility testing, 7 patients and 3 health care professionals participated, with interviews conducted with 6 patients and a focus group with the health care team. Two additional teams (8 health professionals) took part in a demonstration and focus group. Barriers and facilitators were identified, with 5 barriers around reliability due to poor connectivity, cost of connectivity, safety concerns linked to positioning of equipment and connectivity, intrusiveness of technology, and resistance to group teleconferencing. Two facilitators focused on the positive benefits of increased support and monitoring and positive solutions for future improvements. Conclusions: Teleconferencing as a way of delivering fall prevention interventions can be acceptable to older adults, patients, and health care professionals if it works effectively. Connectivity, where there is no Wi-Fi provision, is one of the largest issues. Therefore, local infrastructure needs to be improved. A larger usability study is required to establish whether better equipment for delivery improves usability. ",
doi="10.2196/19690",
url="http://rehab.jmir.org/2021/1/e19690/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33433398"
}


@Article{info:doi/10.2196/20061,
author="Reven{\"a}s, {\AA}sa
and Johansson, Ann-Christin
and Ehn, Maria",
title="Integrating Key User Characteristics in User-Centered Design of Digital Support Systems for Seniors' Physical Activity Interventions to Prevent Falls: Protocol for a Usability Study",
journal="JMIR Res Protoc",
year="2020",
month="Dec",
day="21",
volume="9",
number="12",
pages="e20061",
keywords="eHealth",
keywords="mobile health",
keywords="internet-based interventions",
keywords="physical activity",
keywords="exercise",
keywords="older adults",
keywords="gender",
keywords="user feedback",
keywords="user involvement",
keywords="user-centered design",
abstract="Background: The goal of user-centered design (UCD) is to understand the users' perspective and to use that knowledge to shape more effective solutions. The UCD approach provides insight into users' needs and requirements and thereby improves the design of the developed services. However, involving users in the development process does not guarantee that feedback from different subgroups of users will shape the development in ways that will make the solutions more useful for the entire target user population. Objective: The aim of this study was to describe a protocol for systematic analysis and prioritization of feedback from user subgroups in the usability testing of a digital motivation support for fall-preventive physical activity (PA) interventions in seniors (aged 65 years and older). This protocol can help researchers and developers to systematically exploit feedback from relevant user subgroups in UCD. Methods: Gender, PA level, and level of technology experience have been identified in the literature to influence users' experience and use of digital support systems for fall-preventive PA interventions in seniors. These 3 key user characteristics were dichotomized and used to define 8 (ie, 23) possible user subgroups. The presented method enables systematic tracking of the user subgroups' contributions in iterative development. The method comprises (1) compilation of difficulties and deficiencies in the digital applications identified in usability testing, (2) clustering of the identified difficulties and deficiencies, and (3) prioritization of deficiencies to be rectified. Tracking user subgroup representation in the user feedback ensures that the development process is prioritized according to the needs of different subgroups. Mainly qualitative data collection methods are used. Results: A protocol was developed to ensure that feedback from users representing all possible variants of 3 selected key user characteristics (gender, PA level, and level of technology experience) is considered in the iterative usability testing of a digital support for seniors' PA. The method was applied in iterative usability testing of two digital applications during spring/summer 2018. Results from the study on the users' experiences and the iterative modification of the digital applications are expected to be published during 2021. Conclusions: Methods for systematic collection, analysis, and prioritization of feedback from user subgroups might be particularly important in heterogenous user groups (eg, seniors). This study can contribute to identifying and improving the understanding of potential differences between user subgroups of seniors in their use and experiences of digital support for fall-preventive PA interventions. This knowledge may be relevant for developing digital support systems that are appropriate, useful, and attractive to users and for enabling the design of digital support systems that target specific user subgroups (ie, tailoring of the support). The protocol needs to be further used and investigated in order to validate its potential value. International Registered Report Identifier (IRRID): RR1-10.2196/20061 ",
doi="10.2196/20061",
url="http://www.researchprotocols.org/2020/12/e20061/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33346732"
}


@Article{info:doi/10.2196/20691,
author="Mascret, Nicolas
and Delbes, Lisa
and Voron, Am{\'e}lie
and Temprado, Jean-Jacques
and Montagne, Gilles",
title="Acceptance of a Virtual Reality Headset Designed for Fall Prevention in Older Adults: Questionnaire Study",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="14",
volume="22",
number="12",
pages="e20691",
keywords="technology acceptance model",
keywords="acceptability",
keywords="acceptance",
keywords="virtual reality",
keywords="elderly",
keywords="fall",
keywords="eHealth",
keywords="self-efficacy",
keywords="achievement goals",
abstract="Background: Falls are a common phenomenon among people aged 65 and older and affect older adults' health, quality of life, and autonomy. Technology-based intervention programs are designed to prevent the occurrence of falls and their effectiveness often surpasses that of more conventional programs. However, to be effective, these programs must first be accepted by seniors. Objective: Based on the technology acceptance model, this study aimed to examine the acceptance among older adults before a first use of a virtual reality headset (VRH) used in an intervention program designed to prevent falls. Methods: A sample of 271 French older adults (mean age 73.69 years, SD 6.37 years) voluntarily and anonymously filled out a questionnaire containing the focal constructs (perceived usefulness, perceived enjoyment, perceived ease of use, intention to use, fall-related self-efficacy, and self-avoidance goals) adapted to the VRH, which was designed to prevent falls. Results: The results of the structural equation modeling analysis showed that intention to use the VRH was positively predicted by perceived usefulness, perceived enjoyment, and perceived ease of use. Perceived usefulness of the VRH was also negatively predicted by fall-related self-efficacy (ie, the perceived level of confidence of an individual when performing daily activities without falling) and positively predicted by self-avoidance goals (ie, participating in a physical activity to avoid physical regression). Conclusions: A better understanding of the initial acceptance among older adults of this VRH is the first step to involving older adults in intervention programs designed to prevent falls using this kind of device. ",
doi="10.2196/20691",
url="http://www.jmir.org/2020/12/e20691/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33315019"
}


@Article{info:doi/10.2196/15460,
author="Hawley-Hague, Helen
and Tacconi, Carlo
and Mellone, Sabato
and Martinez, Ellen
and Ford, Claire
and Chiari, Lorenzo
and Helbostad, Jorunn
and Todd, Chris",
title="Smartphone Apps to Support Falls Rehabilitation Exercise: App Development and Usability and Acceptability Study",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Sep",
day="28",
volume="8",
number="9",
pages="e15460",
keywords="aged",
keywords="postural balance",
keywords="telerehabilitation",
keywords="patient compliance",
keywords="accidental falls",
abstract="Background: Falls have implications for older adults' health and well-being. Strength and balance interventions significantly reduce the risk of falls. However, patients do not always perform the unsupervised home exercise needed for fall reduction. Objective: This study aims to develop motivational smartphone apps co-designed with health professionals and older adults to support patients to perform exercise proven to aid fall reduction and to explore the apps' usability and acceptability with both health professionals and patients. Methods: There were 3 phases of app development that included analysis, design, and implementation. For analysis, we examined the literature to establish key app components and had a consultation with 12 older adults attending a strength and balance class, exercise instructors, and 3 fall services. For design, we created prototype apps and conducted 2 patient and public involvement workshops, one with 5 health professionals and the second with 8 older adults from an exercise group. The apps were revised based on the feedback. For implementation, we tested them with one fall service and their patients for 3 weeks. Participatory evaluation was used through testing, semistructured interviews, and focus groups to explore acceptability and usability. Focus groups were conducted with the service that tested the apps and two other services. Qualitative data were analyzed using the framework approach. Results: On the basis of findings from the literature and consultations in the analysis phase, we selected Behavior Change Techniques, such as goal setting, action planning, and feedback on behavior, to be key parts of the app. We developed goals using familiar icons for patients to select and add while self-reporting exercise and decided to develop 2 apps, one for patients (My Activity Programme) and one for health professionals (Motivate Me). This enabled health professionals to guide patients through the goal-setting process, making it more accessible to nontechnology users. Storyboards were created during the design phase, leading to prototypes of ``Motivate Me'' and ``My Activity Programme.'' Key changes from the workshops included being able to add more details about the patients' exercise program and a wider selection of goals within ``Motivate Me.'' The overall app design was acceptable to health professionals and older adults. In total, 7 patients and 3 health professionals participated in testing in the implementation phase, with interviews conducted with 6 patients and focus groups, with 3 teams (11 health professionals). Barriers, facilitators, and further functionality were identified for both apps, with 2 cross-cutting themes around phone usability and confidence. Conclusions: The motivational apps were found to be acceptable for older adults taking part in the design stage and patients and health professionals testing the apps in a clinical setting. User-led design is important to ensure that the apps are usable and acceptable. ",
doi="10.2196/15460",
url="http://mhealth.jmir.org/2020/9/e15460/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32985992"
}


@Article{info:doi/10.2196/19516,
author="Dolci, Elisa
and Sch{\"a}rer, Barbara
and Grossmann, Nicole
and Musy, Naima Sarah
and Z{\'u}{\~n}iga, Franziska
and Bachnick, Stefanie
and Simon, Michael",
title="Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="21",
volume="22",
number="9",
pages="e19516",
keywords="falls",
keywords="adverse event",
keywords="harm",
keywords="algorithm",
keywords="natural language processing",
abstract="Background: Falls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods---voluntary incident reports and manual chart reviews---are error-prone and time consuming, respectively. Using a search algorithm to examine patients' electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative. Objective: This study's purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls. Methods: Conducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm's in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm's in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values. Results: Twenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95\%), the Global Trigger Tool detected 18 (90\%), and incident reports detected 14 (67\%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100\%), the manual chart review identified 14 (93\%), and the patient-reported fall measure identified 5 (33\%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm's positive predictive values were 50\% (development sample) and 47\% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11\% of the full case number) require review. Conclusions: The newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures. ",
doi="10.2196/19516",
url="http://www.jmir.org/2020/9/e19516/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32955445"
}


@Article{info:doi/10.2196/19732,
author="Kim, Ben
and McKay, M. Sandra
and Lee, Joon",
title="Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="3",
volume="22",
number="9",
pages="e19732",
keywords="frailty",
keywords="mobile health",
keywords="wearables",
keywords="physical activity",
keywords="home care",
keywords="prediction",
keywords="predictive modeling, older adults",
keywords="activities of daily living, sleep",
abstract="Background: Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0\% to 59.1\%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population. Objective: The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods: From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum of 8 days using a wearable device. Detailed sociodemographic information and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device were used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection to predict frailty. Results: A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76\% (28/37) were female; 13 participants were frail, significantly older (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions: We proved that a wearable device could be used to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services. ",
doi="10.2196/19732",
url="https://www.jmir.org/2020/9/e19732",
url="http://www.ncbi.nlm.nih.gov/pubmed/32880582"
}


@Article{info:doi/10.2196/20321,
author="Shu, Sara
and Woo, P. Benjamin K.",
title="Digital Media as a Proponent for Healthy Aging in the Older Chinese American Population: Longitudinal Analysis",
journal="JMIR Aging",
year="2020",
month="Jun",
day="16",
volume="3",
number="1",
pages="e20321",
keywords="geriatrics",
keywords="health promotion",
keywords="health education",
keywords="social media",
keywords="Parkinson disease",
keywords="fall prevention",
keywords="oral health",
keywords="pulmonary disease",
keywords="gastrointestinal health",
abstract="Background: Ensuring health literacy among underserved populations is essential amid an aging population. Accessible and appropriate (both culturally and linguistically) information is important when considering digital media education for older Chinese Americans. Objective: This study aims to investigate how social media fare over time in disseminating health information and how we may most effectively educate this population. Methods: For this study, 5 geriatric-themed educational videos about Parkinson disease, fall prevention, gastrointestinal health, oral health, and pulmonary disease were uploaded to YouTube. Data were collected over a 40-month period. Descriptive statistics and chi-square analysis were used to compare results from the first and second 20-month periods. Results: In 40 months, the 5 videos in aggregate accrued 1171.1 hours of watch time, 7299 views, and an average view duration of 9.6 minutes. Comparing the first and second 20-month periods, there was a significant increase in mobile device usage, from 79.4\% (3541/4458) to 83.3\% (2367/2841). There was no significant difference in the usage of various external traffic sources and methods of sharing, with WhatsApp accounting for the majority of sharing in both 20-month periods. Conclusions: Our study provides insight into where to focus future strategies to optimize digital media content, and how to best recruit, direct, and disseminate health education to an older adult Chinese American population. Combining the success of YouTube, social media, and messaging platforms such as WhatsApp can help to transcend cultural and linguistic barriers to promote healthy aging. ",
doi="10.2196/20321",
url="http://aging.jmir.org/2020/1/e20321/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32543447"
}


@Article{info:doi/10.2196/16213,
author="Peng, Li-Ning
and Hsiao, Fei-Yuan
and Lee, Wei-Ju
and Huang, Shih-Tsung
and Chen, Liang-Kung",
title="Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="11",
volume="22",
number="6",
pages="e16213",
keywords="multimorbidity frailty index",
keywords="machine learning",
keywords="random forest",
keywords="unplanned hospitalizations",
keywords="intensive care unit admissions",
keywords="mortality",
abstract="Background: Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. Objective: This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. Methods: In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. Results: The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions: The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society. ",
doi="10.2196/16213",
url="http://www.jmir.org/2020/6/e16213/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32525481"
}


@Article{info:doi/10.2196/16678,
author="Tarekegn, Adane
and Ricceri, Fulvio
and Costa, Giuseppe
and Ferracin, Elisa
and Giacobini, Mario",
title="Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches",
journal="JMIR Med Inform",
year="2020",
month="Jun",
day="4",
volume="8",
number="6",
pages="e16678",
keywords="predictive modeling",
keywords="frailty",
keywords="machine learning",
keywords="genetic programming",
keywords="imbalanced dataset",
keywords="elderly people",
keywords="classification",
abstract="Background: Frailty is one of the most critical age-related conditions in older adults. It is often recognized as a syndrome of physiological decline in late life, characterized by a marked vulnerability to adverse health outcomes. A clear operational definition of frailty, however, has not been agreed so far. There is a wide range of studies on the detection of frailty and their association with mortality. Several of these studies have focused on the possible risk factors associated with frailty in the elderly population while predicting who will be at increased risk of frailty is still overlooked in clinical settings. Objective: The objective of our study was to develop predictive models for frailty conditions in older people using different machine learning methods based on a database of clinical characteristics and socioeconomic factors. Methods: An administrative health database containing 1,095,612 elderly people aged 65 or older with 58 input variables and 6 output variables was used. We first identify and define six problems/outputs as surrogates of frailty. We then resolve the imbalanced nature of the data through resampling process and a comparative study between the different machine learning (ML) algorithms -- Artificial neural network (ANN), Genetic programming (GP), Support vector machines (SVM), Random Forest (RF), Logistic regression (LR) and Decision tree (DT) -- was carried out. The performance of each model was evaluated using a separate unseen dataset. Results: Predicting mortality outcome has shown higher performance with ANN (TPR 0.81, TNR 0.76, accuracy 0.78, F1-score 0.79) and SVM (TPR 0.77, TNR 0.80, accuracy 0.79, F1-score 0.78) than predicting the other outcomes. On average, over the six problems, the DT classifier has shown the lowest accuracy, while other models (GP, LR, RF, ANN, and SVM) performed better. All models have shown lower accuracy in predicting an event of an emergency admission with red code than predicting fracture and disability. In predicting urgent hospitalization, only SVM achieved better performance (TPR 0.75, TNR 0.77, accuracy 0.73, F1-score 0.76) with the 10-fold cross validation compared with other models in all evaluation metrics. Conclusions: We developed machine learning models for predicting frailty conditions (mortality, urgent hospitalization, disability, fracture, and emergency admission). The results show that the prediction performance of machine learning models significantly varies from problem to problem in terms of different evaluation metrics. Through further improvement, the model that performs better can be used as a base for developing decision-support tools to improve early identification and prediction of frail older adults. ",
doi="10.2196/16678",
url="http://medinform.jmir.org/2020/6/e16678/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32442149"
}


@Article{info:doi/10.2196/14453,
author="Zhong, Runting
and Rau, Patrick Pei-Luen",
title="A Mobile Phone--Based Gait Assessment App for the Elderly: Development and Evaluation",
journal="JMIR Mhealth Uhealth",
year="2020",
month="May",
day="26",
volume="8",
number="5",
pages="e14453",
keywords="aged",
keywords="gait",
keywords="mHealth",
keywords="telemedicine",
keywords="falls prevention",
abstract="Background: Gait disorders are common among older adults. With an increase in the use of technology among older adults, a mobile phone app provides a solution for older adults to self-monitor their gait quality in daily life. Objective: This study aimed to develop a gait-monitoring mobile phone app (Pocket Gait) and evaluate its acceptability and usability among potential older users. Methods: The app was developed to allow older adults to track their gait quality, including step frequency, acceleration root mean square (RMS), step regularity, step symmetry, and step variability. We recruited a total of 148 community-dwelling older adults aged 60 years and older from two cities in China: Beijing and Chongqing. They walked in three ways (single task, dual task, and fast walking) using a smartphone with the gait-monitoring app installed and completed an acceptability and usability survey after the walk test. User acceptability was measured by a questionnaire including four quantitative measures: perceived ease of use, perceived usefulness, ease of learning, and intention to use. Usability was measured using the System Usability Scale (SUS). Interviews were conducted with participants to collect open-ended feedback questions. Results: Task type had a significant effect on all gait parameters, namely, step frequency, RMS, step variability, step regularity, and step symmetry (all P values <.001). Age had a significant effect on step frequency (P=.01), and region had a significant effect on step regularity (P=.04). The acceptability of the gait-monitoring app was positive among older adults. Participants identified the usability of the system with an overall score of 59.7 (SD 10.7) out of 100. Older adults from Beijing scored significantly higher SUS compared with older adults from Chongqing (P<.001). The age of older adults was significantly associated with their SUS score (P=.048). Older adults identified improvements such as a larger font size, inclusion of reference values for gait parameters, and inclusion of heart rate and blood pressure monitoring. Conclusions: This mobile phone app is a health management tool for older adults to self-manage their gait quality and prevent adverse outcomes. In the future, it will be important to take factors such as age and region into consideration while designing a mobile phone--based gait assessment app. The feedback of the participants would help to design more elderly-friendly products. ",
doi="10.2196/14453",
url="https://mhealth.jmir.org/2020/5/e14453",
url="http://www.ncbi.nlm.nih.gov/pubmed/32473005"
}


@Article{info:doi/10.2196/16970,
author="Nakatani, Hayao
and Nakao, Masatoshi
and Uchiyama, Hidefumi
and Toyoshiba, Hiroyoshi
and Ochiai, Chikayuki",
title="Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study",
journal="JMIR Med Inform",
year="2020",
month="Apr",
day="22",
volume="8",
number="4",
pages="e16970",
keywords="fall",
keywords="risk factor",
keywords="prediction",
keywords="nursing record",
keywords="natural language processing",
keywords="machine learning",
abstract="Background: Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective: The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input---unstructured nursing records obtained from Japanese electronic medical records (EMRs)---using a natural language processing (NLP) algorithm and machine learning. Methods: The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results: The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions: We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased. ",
doi="10.2196/16970",
url="http://medinform.jmir.org/2020/4/e16970/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32319959"
}


@Article{info:doi/10.2196/13961,
author="Sczuka, Sarah Kim
and Schwickert, Lars
and Becker, Clemens
and Klenk, Jochen",
title="Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study",
journal="J Med Internet Res",
year="2020",
month="Apr",
day="3",
volume="22",
number="4",
pages="e13961",
keywords="falls",
keywords="simulation",
keywords="inertial sensor",
keywords="method",
abstract="Background: Falls are a common health problem, which in the worst cases can lead to death. To develop reliable fall detection algorithms as well as suitable prevention interventions, it is important to understand circumstances and characteristics of real-world fall events. Although falls are common, they are seldom observed, and reports are often biased. Wearable inertial sensors provide an objective approach to capture real-world fall signals. However, it is difficult to directly derive visualization and interpretation of body movements from the fall signals, and corresponding video data is rarely available. Objective: The re-enactment method uses available information from inertial sensors to simulate fall events, replicate the data, validate the simulation, and thereby enable a more precise description of the fall event. The aim of this paper is to describe this method and demonstrate the validity of the re-enactment approach. Methods: Real-world fall data, measured by inertial sensors attached to the lower back, were selected from the Fall Repository for the Design of Smart and Self-Adaptive Environments Prolonging Independent Living (FARSEEING) database. We focused on well-described fall events such as stumbling to be re-enacted under safe conditions in a laboratory setting. For the purposes of exemplification, we selected the acceleration signal of one fall event to establish a detailed simulation protocol based on identified postures and trunk movement sequences. The subsequent re-enactment experiments were recorded with comparable inertial sensor configurations as well as synchronized video cameras to analyze the movement behavior in detail. The re-enacted sensor signals were then compared with the real-world signals to adapt the protocol and repeat the re-enactment method if necessary. The similarity between the simulated and the real-world fall signals was analyzed with a dynamic time warping algorithm, which enables the comparison of two temporal sequences varying in speed and timing. Results: A fall example from the FARSEEING database was used to show the feasibility of producing a similar sensor signal with the re-enactment method. Although fall events were heterogeneous concerning chronological sequence and curve progression, it was possible to reproduce a good approximation of the motion of a person's center of mass during fall events based on the available sensor information. Conclusions: Re-enactment is a promising method to understand and visualize the biomechanics of inertial sensor-recorded real-world falls when performed in a suitable setup, especially if video data is not available. ",
doi="10.2196/13961",
url="https://www.jmir.org/2020/4/e13961",
url="http://www.ncbi.nlm.nih.gov/pubmed/32242825"
}


@Article{info:doi/10.2196/16131,
author="Rabe, Sophie
and Azhand, Arash
and Pommer, Wolfgang
and M{\"u}ller, Swantje
and Steinert, Anika",
title="Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study",
journal="JMIR Aging",
year="2020",
month="Feb",
day="14",
volume="3",
number="1",
pages="e16131",
keywords="falls",
keywords="seniors",
keywords="fall risk assessment",
keywords="app",
keywords="mHealth",
keywords="retrospective cohort study",
keywords="discriminative ability",
abstract="Background: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants. Objective: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app's discriminative ability based on real-world data. Methods: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model. Results: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4\%) reported no previous falls (nonfaller), while 103 (42.5\%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100\%, specificity=52\%, and accuracy=73\%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100\%, specificity=52\%, and accuracy=73\%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88\%, specificity=62\%, and accuracy=73\%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88\%, specificity=67\%, and accuracy=76\%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88\%, specificity=57\%, and accuracy=70\%. Conclusions: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93\%, and an average specificity of 58\%. Average overall accuracy was 73\%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score's prospective accuracy will be further validated in a prospective trial. ",
doi="10.2196/16131",
url="http://aging.jmir.org/2020/1/e16131/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32130111"
}


@Article{info:doi/10.2196/12114,
author="Rasche, Peter
and Nitsch, Verena
and Rentemeister, Lars
and Coburn, Mark
and Buecking, Benjamin
and Bliemel, Christopher
and Bollheimer, Cornelius Leo
and Pape, Hans-Christoph
and Knobe, Matthias",
title="The Aachen Falls Prevention Scale: Multi-Study Evaluation and Comparison",
journal="JMIR Aging",
year="2019",
month="May",
day="16",
volume="2",
number="1",
pages="e12114",
keywords="meta-analysis",
keywords="elderly",
keywords="self-assessment",
keywords="hip injuries",
keywords="leg injuries",
keywords="sensitivity",
keywords="specificity",
abstract="Background: Fall risk assessment is a time-consuming and resource-intensive activity. Patient-driven self-assessment as a preventive measure might be a solution to reduce the number of patients undergoing a full clinical fall risk assessment. Objective: The aim of this study was (1) to analyze test accuracy of the Aachen Falls Prevention Scale (AFPS) and (2) to compare these results with established fall risk assessment measures identified by a review of systematic reviews. Methods: Sensitivity, specificity, and receiver operating curves (ROC) of the AFPS were calculated based on data retrieved from 2 independent studies using the AFPS. Comparison with established fall risk assessment measures was made by conducting a review of systematic reviews and corresponding meta-analysis. Electronic databases PubMed, Web of Science, and EMBASE were searched for systematic reviews and meta-analyses that reviewed fall risk assessment measures between the years 2000 and 2018. The review of systematic reviews was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. The Revised Assessment of Multiple SysTemAtic Reviews (R-AMSTAR) was used to assess the methodological quality of reviews. Sensitivity, specificity, and ROC were extracted from each review and compared with the calculated values of the AFPS. Results: Sensitivity, specificity, and ROC of the AFPS were evaluated based on 2 studies including a total of 259 older adults. Regarding the primary outcome of the AFPS subjective risk of falling, pooled sensitivity is 57.0\% (95\% CI 0.467-0.669) and specificity is 76.7\% (95\% CI 0.694-0.831). If 1 out of the 3 subscales of the AFPS is used to predict a fall risk, pooled sensitivity could be increased up to 90.0\% (95\% CI 0.824-0.951), whereas mean specificity thereby decreases to 50.0\% (95\% CI 0.42-0.58). A systematic review for fall risk assessment measures produced 1478 articles during the study period, with 771 coming from PubMed, 530 from Web of Science, and 177 from EMBASE. After eliminating doublets and assessing full text, 8 reviews met the inclusion criteria. All were of sufficient methodological quality (R-AMSTAR score ?22). A total number of 9 functional or multifactorial fall risk assessment measures were extracted from identified reviews, including Timed Up and Go test, Berg Balance Scale, Performance-Oriented Mobility Assessment, St Thomas's Risk Assessment Tool in Falling Elderly, and Hendrich II Fall Risk Model. Comparison of these measures with pooled sensitivity and specificity of the AFPS revealed a sufficient quality of the AFPS in terms of a patient-driven self-assessment tool. Conclusions: It could be shown that the AFPS reaches a test accuracy comparable with that of the established methods in this initial investigation. However, it offers the advantage that the users can perform the self-assessment independently at home without involving trained health care professionals. ",
doi="10.2196/12114",
url="http://aging.jmir.org/2019/1/e12114/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518273"
}


@Article{info:doi/10.2196/11505,
author="Cho, Insook
and Boo, Eun-Hee
and Chung, Eunja
and Bates, W. David
and Dykes, Patricia",
title="Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data",
journal="J Med Internet Res",
year="2019",
month="Feb",
day="19",
volume="21",
number="2",
pages="e11505",
keywords="across sites validation",
keywords="electronic medical records",
keywords="inpatient falls",
keywords="nursing dataset",
keywords="predictive model",
abstract="Background: Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. Objective: In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. Methods: We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation. Results: The initial model showed an error rate of 11.7\% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87\% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients. Conclusions: A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall. ",
doi="10.2196/11505",
url="https://www.jmir.org/2019/2/e11505/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30777849"
}


@Article{info:doi/10.2196/10008,
author="Duckworth, Megan
and Adelman, Jason
and Belategui, Katherine
and Feliciano, Zinnia
and Jackson, Emily
and Khasnabish, Srijesa
and Lehman, Sun I-Fong
and Lindros, Ellen Mary
and Mortimer, Heather
and Ryan, Kasey
and Scanlan, Maureen
and Berger Spivack, Linda
and Yu, Ping Shao
and Bates, Westfall David
and Dykes, C. Patricia",
title="Assessing the Effectiveness of Engaging Patients and Their Families in the Three-Step Fall Prevention Process Across Modalities of an Evidence-Based Fall Prevention Toolkit: An Implementation Science Study",
journal="J Med Internet Res",
year="2019",
month="Jan",
day="21",
volume="21",
number="1",
pages="e10008",
keywords="clinical decision support",
keywords="fall prevention",
keywords="fall prevention toolkit",
keywords="health information technology",
keywords="implementation science",
keywords="patient safety",
abstract="Background: Patient falls are a major problem in hospitals. The development of a Patient-Centered Fall Prevention Toolkit, Fall TIPS (Tailoring Interventions for Patient Safety), reduced falls by 25\% in acute care hospitals by leveraging health information technology to complete the 3-step fall prevention process---(1) conduct fall risk assessments; (2) develop tailored fall prevention plans with the evidence-based interventions; and (3) consistently implement the plan. We learned that Fall TIPS was most effective when patients and family were engaged in all 3 steps of the fall prevention process. Over the past decade, our team developed 3 Fall TIPS modalities---the original electronic health record (EHR) version, a laminated paper version that uses color to provide clinical decision support linking patient-specific risk factors to the interventions, and a bedside display version that automatically populates the bedside monitor with the patients' fall prevention plan based on the clinical documentation in the EHR. However, the relative effectiveness of each Fall TIPS modality for engaging patients and family in the 3-step fall prevention process remains unknown. Objective: This study aims to examine if the Fall TIPS modality impacts patient engagement in the 3-step fall prevention process and thus Fall TIPS efficacy. Methods: To assess patient engagement in the 3-step fall prevention process, we conducted random audits with the question, ``Does the patient/family member know their fall prevention plan?'' In addition, audits were conducted to measure adherence, defined by the presence of the Fall TIPS poster at the bedside. Champions from 3 hospitals reported data from April to June 2017 on 6 neurology and 7 medical units. Peer-to-peer feedback to reiterate the best practice for patient engagement was central to data collection. Results: Overall, 1209 audits were submitted for the patient engagement measure and 1401 for the presence of the Fall TIPS poster at the bedside. All units reached 80\% adherence for both measures. While some units maintained high levels of patient engagement and adherence with the poster protocol, others showed improvement over time, reaching clinically significant adherence (>80\%) by the final month of data collection. Conclusions: Each Fall TIPS modality effectively facilitates patient engagement in the 3-step fall prevention process, suggesting all 3 can be used to integrate evidence-based fall prevention practices into the clinical workflow. The 3 Fall TIPS modalities may prove an effective strategy for the spread, allowing diverse institutions to choose the modality that fits with the organizational culture and health information technology infrastructure. ",
doi="10.2196/10008",
url="http://www.jmir.org/2019/1/e10008/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30664454"
}


@Article{info:doi/10.2196/11975,
author="Valenzuela, Trinidad
and Razee, Husna
and Schoene, Daniel
and Lord, Ronald Stephen
and Delbaere, Kim",
title="An Interactive Home-Based Cognitive-Motor Step Training Program to Reduce Fall Risk in Older Adults: Qualitative Descriptive Study of Older Adults' Experiences and Requirements",
journal="JMIR Aging",
year="2018",
month="Nov",
day="30",
volume="1",
number="2",
pages="e11975",
keywords="aged",
keywords="community-dwelling",
keywords="exercise",
keywords="home-based training",
keywords="adherence",
keywords="motivation",
keywords="exergame",
keywords="active video games",
keywords="falls",
keywords="qualitative research",
abstract="Background: Falls are a major contributor to the burden of disease in older adults. Home-based exercise programs are effective in reducing the rate and risk of falls in older adults. However, adherence to home-based exercise programs is low, limiting the efficacy of interventions. The implementation of technology-based exercise programs for older adults to use at home may increase exercise adherence and, thus, the effectiveness of fall prevention interventions. More information about older adults' experiences when using technologies at home is needed to enable the design of programs that are tailored to older adults' needs. Objective: This study aimed to (1) explore older adults' experiences using SureStep, an interactive cognitive-motor step training program to reduce fall risk unsupervised at home; (2) explore program features that older adults found encouraged program uptake and adherence; (3) identify usability issues encountered by older adults when using the program; and (4) provide guidance for the design of a future technology-based exercise program tailored to older adults to use at home as a fall prevention strategy. Methods: This study was part of a larger randomized controlled trial. The qualitative portion of the study and the focus of this paper used a qualitative descriptive design. Data collectors conducted structured, open-ended in-person interviews with study participants who were randomly allocated to use SureStep at home for 4 months. All interviews were audiotaped and ranged from 45 to 60 min. Thematic analysis was used to analyze collected data. This study was guided by Pender's Health Promotion Model. Results: Overall, 24 older adults aged 70 to 97 years were interviewed. Findings suggest older adults are open to use technology-based exercise programs at home, and in the context of optimizing adherence to home-based exercise programs for the prevention of falls, findings suggest that program developers should develop exercise programs in ways that provide older adults with a fun and enjoyable experience (thus increasing intrinsic motivation to exercise), focus on improving outcomes that are significant to older adults (thus increasing self-determined extrinsic motivation), offer challenging yet attainable exercises (thus increasing perceived self-competence), provide positive feedback on performance (thus increasing self-efficacy), and are easy to use (thus reducing perceived barriers to technology use). Conclusions: This study provides important considerations when designing technology-based programs so they are tailored to the needs of older adults, increasing both usability and acceptability of programs and potentially enhancing exercise participation and long-term adherence to fall prevention interventions. Program uptake and adherence seem to be influenced by (1) older adults' perceived benefits of undertaking the program, (2) whether the program is stimulating, and (3) the perceived barriers to exercise and technology use. Older adults shared important recommendations for future development of technologies for older adults to use at home. ",
doi="10.2196/11975",
url="http://aging.jmir.org/2018/2/e11975/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518251"
}


@Article{info:doi/10.2196/11569,
author="Hsieh, L. Katherine
and Fanning, T. Jason
and Rogers, A. Wendy
and Wood, A. Tyler
and Sosnoff, J. Jacob",
title="A Fall Risk mHealth App for Older Adults: Development and Usability Study",
journal="JMIR Aging",
year="2018",
month="Nov",
day="20",
volume="1",
number="2",
pages="e11569",
keywords="usability",
keywords="fall risk",
keywords="mHealth app",
keywords="mobile phone",
abstract="Background: Falls are the leading cause of injury-related death in older adults. Due to various constraints, objective fall risk screening is seldom performed in clinical settings. Smartphones offer a high potential to provide fall risk screening for older adults in home settings. However, there is limited understanding of whether smartphone technology for falls screening is usable by older adults who present age-related changes in perceptual, cognitive, and motor capabilities. Objective: The aims of this study were to develop a fall risk mobile health (mHealth) app and to determine the usability of the fall risk app in healthy, older adults. Methods: A fall risk app was developed that consists of a health history questionnaire and 5 progressively challenging mobility tasks to measure individual fall risk. An iterative design-evaluation process of semistructured interviews was performed to determine the usability of the app on a smartphone and tablet. Participants also completed a Systematic Usability Scale (SUS). In the first round of interviews, 6 older adults participated, and in the second round, 5 older adults participated. Interviews were videotaped and transcribed, and the data were coded to create themes. Average SUS scores were calculated for the smartphone and tablet. Results: There were 2 themes identified from the first round of interviews, related to perceived ease of use and perceived usefulness. While instructions for the balance tasks were difficult to understand, participants found it beneficial to learn about their risk for falls, found the app easy to follow, and reported confidence in using the app on their own. Modifications were made to the app, and following the second round of interviews, participants reported high ease of use and usefulness in learning about their risk of falling. Few differences were reported between using a smartphone or tablet. Average SUS scores ranged from 79 to 84. Conclusions: Our fall risk app was found to be highly usable by older adults as reported from interviews and high scores on the SUS. When designing a mHealth app for older adults, developers should include clear and simple instructions and preventative strategies to improve health. Furthermore, if the design accommodates for age-related sensory changes, smartphones can be as effective as tablets. A mobile app to assess fall risk has the potential to be used in home settings by older adults. ",
doi="10.2196/11569",
url="http://aging.jmir.org/2018/2/e11569/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518234"
}


@Article{info:doi/10.2196/11772,
author="Lam, HT Nikki
and Woo, KP Benjamin",
title="Digital Media Recruitment for Fall Prevention Among Older Chinese-American Individuals: Observational, Cross-Sectional Study",
journal="JMIR Aging",
year="2018",
month="Nov",
day="01",
volume="1",
number="2",
pages="e11772",
keywords="ethnic groups",
keywords="falls",
keywords="geriatrics",
keywords="health education",
keywords="social media",
keywords="mobile phone",
abstract="Background: Research in fall prevention programs has increased in recent years in response to the aging demographics of the United States. To date, limited research and outreach programs have focused on ethnic minorities due to increased cost, language barriers, and cultural differences. Digital media platforms may be a cost-effective avenue to initiate fall prevention programs for minority populations. Objective: The objective of this study was to determine whether Facebook advertisements are a practical recruitment method for health education to the Chinese-speaking population. Methods: This was an observational, cross-sectional study. We uploaded a video on fall education on YouTube and initiated an advertisement campaign on Facebook that was linked to the video. The target population was older adults aged >45 years who used Facebook and were presented with the advertisement (N=1039). We recorded metrics such as the number of unique individuals reached, the number of views of the advertisement, the number of clicks, user gender and age, and traffic sources to the advertisement. Data were analyzed with descriptive statistics. Results: Our Facebook advertisement had 1087 views (1039 unique viewers). There were 121 link clicks with a click-through rate of 11.13\% (121/1087). The cost per link click was approximately US \$0.06. Among the viewers, 91.41\% (936/1024) were females and 8.59\% (88/1024) were males. In the 45-54 age group, the ad reached 50 people, with 1 link click (2.00\%). In the 55-64 age group, the ad reached 572 people, with 57 link clicks (9.97\%). In the ?65 age group, the ad reached 417 people, with 63 link clicks (15.11\%). Conclusions: Facebook was able to directly target the advertisement to the desired older ethnic population at a low cost. Engagement was highest among females and among those aged ?65 years. Hence, our results suggest that Facebook can serve as an alternative platform for dissemination of health information to geriatric patients in addition to print-based and face-to-face communication. ",
doi="10.2196/11772",
url="http://aging.jmir.org/2018/2/e11772/",
url="http://www.ncbi.nlm.nih.gov/pubmed/31518249"
}


@Article{info:doi/10.2196/10304,
author="Martins, Correia Anabela
and Moreira, Juliana
and Silva, Catarina
and Silva, Joana
and Tonelo, Cl{\'a}udia
and Baltazar, Daniela
and Rocha, Clara
and Pereira, Telmo
and Sousa, In{\^e}s",
title="Multifactorial Screening Tool for Determining Fall Risk in Community-Dwelling Adults Aged 50 Years or Over (FallSensing): Protocol for a Prospective Study",
journal="JMIR Res Protoc",
year="2018",
month="Aug",
day="02",
volume="7",
number="8",
pages="e10304",
keywords="accidental falls",
keywords="primary prevention",
keywords="adults",
keywords="clinical protocol",
keywords="pressure platform",
keywords="inertial sensors",
abstract="Background: Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors. Objective: The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling. Methods: This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior's universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test ``modified,'' and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise. Results: The enrollment began in June 2016 and we anticipate study completion by the end of 2018. Conclusions: The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. Registered Report Identifier: RR1-10.2196/10304 ",
doi="10.2196/10304",
url="http://www.researchprotocols.org/2018/8/e10304/",
url="http://www.ncbi.nlm.nih.gov/pubmed/30072360"
}


@Article{info:doi/10.2196/mhealth.9563,
author="Hong, Jeeyoung
and Kong, Hyoun-Joong
and Yoon, Hyung-Jin",
title="Web-Based Telepresence Exercise Program for Community-Dwelling Elderly Women With a High Risk of Falling: Randomized Controlled Trial",
journal="JMIR Mhealth Uhealth",
year="2018",
month="May",
day="28",
volume="6",
number="5",
pages="e132",
keywords="telegeriatrics",
keywords="resistance exercise",
keywords="supervised exercise",
keywords="home exercise",
keywords="WebRTC",
keywords="telepresence",
abstract="Background: While physical exercise is known to help prevent falls in the elderly, bad weather and long distance between the home and place of exercise represent substantial deterrents for the elderly to join or continue attending exercise programs outside their residence. Conventional modalities for home exercise can be helpful but do not offer direct and prompt feedback to the participant, which minimizes the benefit. Objective: We aimed to develop an elderly-friendly telepresence exercise platform and to evaluate the effects of a 12-week telepresence exercise program on fall-related risk factors in community-dwelling elderly women with a high risk of falling. Methods: In total, 34 women aged 68-91 years with Fall Risk Assessment scores >14 and no medical contraindication to physical training-based therapy were recruited in person from a senior citizen center. The telepresence exercise platform included a 15-inch tablet computer, custom-made peer-to-peer video conferencing server system, and broadband Internet connectivity. The Web-based program included supervised resistance exercises performed using elastic resistance bands and balance exercise for 20-40 minutes a day, three times a week, for 12 weeks. During the telepresence exercise session, each participant in the intervention group was supervised remotely by a specialized instructor who provided feedback in real time. The women in the control group maintained their lifestyle without any intervention. Fall-related physical factors (body composition and physical function parameters) and psychological factors (Korean Falls Efficacy Scale score, Fear of Falling Questionnaire score) before and after the 12-week interventional period were examined in person by an exercise specialist blinded to the group allocation scheme. Results: Of the 30 women enrolled, 23 completed the study. Compared to women in the control group (n=13), those in the intervention group (n=10) showed significant improvements on the scores for the chair stand test (95\% confidence interval -10.45 to -5.94, P<.001), Berg Balance Scale (95\% confidence interval -2.31 to -0.28, P=.02), and Fear of Falling Questionnaire (95\% confidence interval 0.69-3.5, P=.01). Conclusions: The telepresence exercise program had positive effects on fall-related risk factors in community-dwelling elderly women with a high risk of falling. Elderly-friendly telepresence technology for home-based exercises can serve as an effective intervention to improve fall-related physical and psychological factors. Trial Registration: Clinical Research Information Service KCT0002710; https://cris.nih.go.kr/cris/en/search/ search\_result\_st01.jsp?seq=11246 (Archived by WebCite at http://www.webcitation.org/6zdSUEsmb) ",
doi="10.2196/mhealth.9563",
url="http://mhealth.jmir.org/2018/5/e132/"
}


@Article{info:doi/10.2196/humanfactors.7718,
author="Harte, Richard
and Hall, Tony
and Glynn, Liam
and Rodr{\'i}guez-Molinero, Alejandro
and Scharf, Thomas
and Quinlan, R. Leo
and {\'O}Laighin, Gear{\'o}id",
title="Enhancing Home Health Mobile Phone App Usability Through General Smartphone Training: Usability and Learnability Case Study",
journal="JMIR Hum Factors",
year="2018",
month="Apr",
day="26",
volume="5",
number="2",
pages="e18",
keywords="smartphone",
keywords="aged",
keywords="elderly",
keywords="wearable electronic devices",
keywords="telemedicine",
keywords="user-computer interface",
keywords="education",
keywords="user centered-design",
keywords="usability",
keywords="connected health",
keywords="human factors",
keywords="falls detection",
abstract="Background: Each year, millions of older adults fall, with more than 1 out of 4 older people experiencing a fall annually, thereby causing a major social and economic impact. Falling once doubles one's chances of falling again, making fall prediction an important aspect of preventative strategies. In this study, 22 older adults aged between 65 and 85 years were trained in the use of a smartphone-based fall prediction system. The system is designed to continuously assess fall risk by measuring various gait and balance parameters using a smart insole and smartphone, and is also designed to detect falls. The use case of the fall prediction system in question required the users to interact with the smartphone via an app for device syncing, data uploads, and checking system status. Objective: The objective of this study was to observe the effect that basic smartphone training could have on the user experience of a group that is not technically proficient with smartphones when using a new connected health system. It was expected that even short rudimentary training could have a large effect on user experience and therefore increase the chances of the group accepting the new technology. Methods: All participants received training on how to use the system smartphone app; half of the participants (training group) also received extra training on how to use basic functions of the smartphone, such as making calls and sending text messages, whereas the other half did not receive this extra training (no extra training group). Comparison of training group and no extra training group was carried out using metrics such as satisfaction rating, time taken to complete tasks, cues required to complete tasks, and errors made during tasks. Results: The training group fared better in the first 3 days of using the system. There were significant recorded differences in number of cues required and errors committed between the two groups. By the fourth and fifth day of use, both groups were performing at the same level when using the system. Conclusions: Supplementary basic smartphone training may be critical in trials where a smartphone app--based system for health intervention purposes is being introduced to a population that is not proficient with technology. This training could prevent early technology rejection and increase the engagement of older participants and their overall user experience with the system. ",
doi="10.2196/humanfactors.7718",
url="http://humanfactors.jmir.org/2018/2/e18/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29699969"
}


@Article{info:doi/10.2196/mhealth.9467,
author="Rasche, Peter
and Mertens, Alexander
and Brandl, Christopher
and Liu, Shan
and Buecking, Benjamin
and Bliemel, Christopher
and Horst, Klemens
and Weber, David Christian
and Lichte, Philipp
and Knobe, Matthias",
title="Satisfying Product Features of a Fall Prevention Smartphone App and Potential Users' Willingness to Pay: Web-Based Survey Among Older Adults",
journal="JMIR Mhealth Uhealth",
year="2018",
month="Mar",
day="27",
volume="6",
number="3",
pages="e75",
keywords="prevention",
keywords="cell phone",
keywords="accidents",
abstract="Background: Prohibiting falls and fall-related injuries is a major challenge for health care systems worldwide, as a substantial proportion of falls occur in older adults who are previously known to be either frail or at high risk for falls. Hence, preventive measures are needed to educate and minimize the risk for falls rather than just minimize older adults' fall risk. Health apps have the potential to address this problem, as they enable users to self-assess their individual fall risk. Objective: The objective of this study was to identify product features of a fall prevention smartphone app, which increase or decrease users' satisfaction. In addition, willingness to pay (WTP) was assessed to explore how much revenue such an app could generate. Methods: A total of 96 participants completed an open self-selected Web-based survey. Participants answered various questions regarding health status, subjective and objective fall risk, and technical readiness. Seventeen predefined product features of a fall prevention smartphone app were evaluated twice: first, according to a functional (product feature is implemented in the app), and subsequently by a dysfunctional (product feature is not implemented in the app) question. On the basis of the combination of answers from these 2 questions, the product feature was assigned to a certain category (must-be, attractive, one-dimensional, indifferent, or questionable product feature). This method is widely used in user-oriented product development and captures users' expectations of a product and how their satisfaction is influenced by the availability of individual product features. Results: Five product features were identified to increase users' acceptance, including (1) a checklist of typical tripping hazards, (2) an emergency guideline in case of a fall, (3) description of exercises and integrated workout plans that decrease the risk of falling, (4) inclusion of a continuous workout program, and (5) cost coverage by health insurer. Participants' WTP was assessed after all 17 product features were rated and revealed a median monthly payment WTP rate of {\texteuro}5.00 (interquartile range 10.00). Conclusions: The results show various motivating product features that should be incorporated into a fall prevention smartphone app. Results reveal aspects that fall prevention and intervention designers should keep in mind to encourage individuals to start joining their program and facilitate long-term user engagement, resulting in a greater interest in fall risk prevention. ",
doi="10.2196/mhealth.9467",
url="http://mhealth.jmir.org/2018/3/e75/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29588268"
}


@Article{info:doi/10.2196/resprot.8854,
author="Rasmussen, Rune
and Midttun, Mette
and Kolenda, Tine
and Ragle, Anne-Mette
and S{\o}rensen, Winther Thea
and Vinther, Anders
and Zerahn, Bo
and Pedersen, Maria
and Overgaard, Karsten",
title="Therapist-Assisted Progressive Resistance Training, Protein Supplements, and Testosterone Injections in Frail Older Men with Testosterone Deficiency: Protocol for a Randomized Placebo-Controlled Trial",
journal="JMIR Res Protoc",
year="2018",
month="Mar",
day="02",
volume="7",
number="3",
pages="e71",
keywords="accidental falls",
keywords="aged",
keywords="exercise",
keywords="testosterone",
keywords="therapeutics",
keywords="men",
abstract="Background: Fall accidents are a major cause of mortality among the elderly and the leading cause of traumatic brain injury. After a fall, many elderly people never completely recover and need help in coping with everyday life. Due to the increasing older population in the world, injuries, disabilities, and deaths caused by falls are a growing worldwide problem. Muscle weakness leads to greatly increased risk of falling, decreased quality of life, and decline in functional capacity. Muscle mass and muscle power decrease about 40\% from age 20 to 80 years, and the level of testosterone decreases with age and leads to impaired muscle mass. In addition, 20\% of men older than 60 years---and 50\% older than 80 years---have low levels of testosterone. Treatments after a fall are significant financial burdens on health and social care, and it is important to find treatments that can enhance function in the elderly people. Objective: The purpose of this study is to investigate whether testosterone and progressive resistance training alone or combined can improve muscle strength and reduce the risk of falls in older men. Additionally, we will examine whether such treatments can improve quality of life, functional capacity, including sexual function, and counteract depression. Methods: This is a randomized placebo-controlled, double-blind trial in which frail older men with testosterone deficiency are treated with testosterone supplemental therapy and therapist-assisted progressive resistance training for 20 weeks, with the possibility to continue treatment for 1 year. Four study arms of 48 participants each are provided based on factorial assignment to testosterone supplemental therapy and progressive resistance training. The 4 groups are as follows: controls given placebo injections without physical exercise for 20 weeks, testosterone-alone group given testosterone injections without physical exercise for 20 weeks, training-alone group given placebo injections for 20 weeks combined with 16 weeks of progressive strength training, and combination group given testosterone injections for 20 weeks combined with 16 weeks of progressive strength training. Performance in the 30-second chair stand test to measure improvement of general strength, balance, and power in lower extremities is the primary endpoint. Secondary endpoints comprising tests of cognition, muscle strength, and quality of life are applied before and after the training. Results: Funding was provided in October 2016. Results are expected to be available in 2020. Sample size was calculated to 152 participants divided into 4 equal-sized groups. Due to age, difficulty in transport, and the time-consuming intervention, up to 25\% dropouts are expected; thus, we aim to include at least 192 participants. Conclusions: This investigation will evaluate the efficacy of testosterone supplemental therapy alone or combined with progressive resistance training. Additionally, improvements in quality of life and cognition are explored. Trial Registration: Clinicaltrials.gov NCT02873559; https://clinicaltrials.gov/ct2/show/NCT02873559 (Archived by WebCite at?http://www.webcitation.org/6x0BhU2p3) ",
doi="10.2196/resprot.8854",
url="https://www.researchprotocols.org/2018/3/e71/",
url="http://www.ncbi.nlm.nih.gov/pubmed/29500160"
}


@Article{info:doi/10.2196/mhealth.7046,
author="Harte, Richard
and Quinlan, R. Leo
and Glynn, Liam
and Rodr{\'i}guez-Molinero, Alejandro
and Baker, MA Paul
and Scharf, Thomas
and {\'O}Laighin, Gear{\'o}id",
title="Human-Centered Design Study: Enhancing the Usability of a Mobile Phone App in an Integrated Falls Risk Detection System for Use by Older Adult Users",
journal="JMIR Mhealth Uhealth",
year="2017",
month="May",
day="30",
volume="5",
number="5",
pages="e71",
keywords="human-centered design",
keywords="user-centered design",
keywords="human-computer interface",
keywords="human factors engineering",
keywords="eHealth",
keywords="engineering psychology",
keywords="mHealth",
abstract="Background: Design processes such as human-centered design (HCD), which involve the end user throughout the product development and testing process, can be crucial in ensuring that the product meets the needs and capabilities of the user, particularly in terms of safety and user experience. The structured and iterative nature of HCD can often conflict with the necessary rapid product development life-cycles associated with the competitive connected health industry. Objective: The aim of this study was to apply a structured HCD methodology to the development of a smartphone app that was to be used within a connected health fall risk detection system. Our methodology utilizes so called discount usability engineering techniques to minimize the burden on resources during development and maintain a rapid pace of development. This study will provide prospective designers a detailed description of the application of a HCD methodology. Methods: A 3-phase methodology was applied. In the first phase, a descriptive ``use case'' was developed by the system designers and analyzed by both expert stakeholders and end users. The use case described the use of the app and how various actors would interact with it and in what context. A working app prototype and a user manual were then developed based on this feedback and were subjected to a rigorous usability inspection. Further changes were made both to the interface and support documentation. The now advanced prototype was exposed to user testing by end users where further design recommendations were made. Results: With combined expert and end-user analysis of a comprehensive use case having originally identified 21 problems with the system interface, we have only seen and observed 3 of these problems in user testing, implying that 18 problems were eliminated between phase 1 and 3. Satisfactory ratings were obtained during validation testing by both experts and end users, and final testing by users shows the system requires low mental, physical, and temporal demands according to the NASA Task Load Index (NASA-TLX). Conclusions: From our observation of older adults' interactions with smartphone interfaces, there were some recurring themes. Clear and relevant feedback as the user attempts to complete a task is critical. Feedback should include pop-ups, sound tones, color or texture changes, or icon changes to indicate that a function has been completed successfully, such as for the connection sequence. For text feedback, clear and unambiguous language should be used so as not to create anxiety, particularly when it comes to saving data. Warning tones or symbols, such as caution symbols or shrill tones, should only be used if absolutely necessary. Our HCD methodology, designed and implemented based on the principles of the International Standard Organizaton (ISO) 9241-210 standard, produced a functional app interface within a short production cycle, which is now suitable for use by older adults in long term clinical trials. ",
doi="10.2196/mhealth.7046",
url="http://mhealth.jmir.org/2017/5/e71/",
url="http://www.ncbi.nlm.nih.gov/pubmed/28559227"
}