%0 Journal Article %@ 2561-7605 %I JMIR Publications %V 8 %N %P e64074 %T Values of Stakeholders Involved in Applying Surveillance Technology for People With Dementia in Nursing Homes: Scoping Review %A van Gaans-Riteco,Daniëlle %A Stoop,Annerieke %A Wouters,Eveline %+ Academic Collaborative Center Care for Older Adults, Tranzo, Scientific Center for Care and Wellbeing, Tilburg School of Social and Behavioral Sciences, Tilburg University, Professor Cobbenhagenlaan 125, Tilburg, 5037DB, The Netherlands, 31 134662969, d.p.c.vangaans-riteco@tilburguniversity.edu %K surveillance technology %K nursing home %K stakeholders %K values %K dementia %K safety %D 2025 %7 20.3.2025 %9 Review %J JMIR Aging %G English %X Background: Due to the progressive nature of dementia, concerns about the safety of nursing home residents are frequently raised. Surveillance technology, enabling visual and auditory monitoring, is often seen as a solution for ensuring safe and efficient care. However, tailoring surveillance technology to individual needs is challenging due to the complex and dynamic care environment involving multiple formal and informal stakeholders, each with unique perspectives. Objective: This study aims to explore the scientific literature on the perspectives and values of stakeholders involved in applying surveillance technology for people with dementia in nursing homes. Methods: We conducted a scoping review and systematically searched 5 scientific databases. We identified 31 articles published between 2005 and 2024. Stakeholder characteristics were extracted and synthesized according to the theory of basic human values by Schwartz. Results: In total, 12 stakeholder groups were identified, with nursing staff, residents, and informal caregivers being the most frequently mentioned. Among stakeholder groups close to residents, values related to benevolence, security, conformity, and tradition were most commonly addressed. Furthermore, values such as self-direction, power, and achievement seemed important to most stakeholder groups. Conclusions: Several stakeholder groups emphasized the importance of being and feeling involved in the application of surveillance technologies. In addition, they acknowledged the necessity of paying attention to stakeholders’ perspectives and values. Across these stakeholder groups, values related to benevolence, security, and self-direction were represented, although various stakeholders assigned different meanings to these values. Awareness of stakeholders’ perspectives demands a willingness to acknowledge each other’s values and bridge differences. %M 39899267 %R 10.2196/64074 %U https://aging.jmir.org/2025/1/e64074 %U https://doi.org/10.2196/64074 %U http://www.ncbi.nlm.nih.gov/pubmed/39899267 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63572 %T Experiences With an In-Bed Real-Time Motion Monitoring System on a Geriatric Ward: Mixed Methods Study %A Walzer,Stefan %A Schön,Isabel %A Pfeil,Johanna %A Merz,Nicola %A Marx,Helga %A Ziegler,Sven %A Kunze,Christophe %+ , Care and Technology Lab, Furtwangen University, Robert-Gerwig-Platz 1, Furtwangen im Schwarzwald, 78120, Germany, 49 7723920295, stefan.walzer@hs-furtwangen.de %K nurses %K geriatric patients %K cognitive impairment %K technology %K fall prevention %K hospital %K mixed methods %K patient %K learning process %K assessment %K autonomy %K impairment %K real-time motion %K university %K geriatric ward %K survey %K anxiety %K willingness %K patient privacy %K effectiveness %K monitoring system %K health care practice %D 2025 %7 4.3.2025 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 40053780 %R 10.2196/63572 %U https://formative.jmir.org/2025/1/e63572 %U https://doi.org/10.2196/63572 %U http://www.ncbi.nlm.nih.gov/pubmed/40053780 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e60438 %T Collecting Real-World Data via an In-Home Smart Medication Dispenser: Longitudinal Observational Study of Survey Panel Persistency, Response Rates, and Psychometric Properties %A Ogorek,Benjamin %A Rhoads,Thomas %A Smith,Erica %K real-world data %K real-world evidence %K patient-reported outcomes %K longitudinal studies %K survey methods %D 2025 %7 3.2.2025 %9 %J JMIR Hum Factors %G English %X Background: A smart medication dispenser called “spencer” is a novel generator of longitudinal survey data. The patients dispensing medication act as a survey panel and respond to questions about quality of life and patient-reported outcomes. Objectives: Our goal was to evaluate panel persistency, survey response rates, reliability, and validity of surveys administered via spencer to 4138 polychronic patients residing in the United States and Canada. Methods: Patients in a Canadian health care provider’s program were included if they were dispensing via spencer in the June 2021 to February 2024 time frame and consented to have their data used for research. Panel persistency was estimated via discrete survival methods for 2 years and survey response rates were computed for 1 year. Patients were grouped by mean response rates in the 12th month (<90% vs ≥90%) to observe differential response rate trends. For reliability and validity, we used a spencer question about recent falls with ternary responses value-coded −1, 0, and 1. For reliability, we computed Pearson correlation between mean scores over 2 years of survey responses, and transitions between mean score intervals of [0, 0.5), [−0.5, 0.5), and [0.5, 1]. For validity, we measured the association between the falls question and known factors influencing fall risk: age, biological sex, quality of life, physical and emotional health, and use of selective serotonin reuptake inhibitors or serotonin-norepinephrine reuptake inhibitors, using repeated-measures regression for covariates and Kendall τ for concomitant spencer questions. Results: From 4138 patients, dispenser persistency was 68.3% (95% CI 66.8%‐69.8%) at 1 year and 51% (95% CI 49%‐53%) at 2 years. Within the cohort observed beyond 1 year, 82.3% (1508/1832) kept surveys enabled through the 12th month with a mean response rate of 84.1% (SD 26.4%). The large SD was apparent in the subgroup analysis, where a responder versus nonresponder dichotomy was observed. For 234 patients with ≥5 fall risk responses in each of the first 2 years, the Pearson correlation estimate between yearly mean scores was 0.723 (95% CI 0.630‐0.798). For mean score intervals [0, 0.5), [−0.5, 0.5), and [0.5, 1], self-transitions were the most common, with 59.8% (140/234) of patients starting and staying in [0.5, 1]. Fall risk responses were not significantly associated with sex (P=.66) or age (P=.76) but significantly related to selective serotonin reuptake inhibitor or serotonin-norepinephrine reuptake inhibitor usage, quality of life, depressive symptoms, physical health, disability, and trips to the emergency room (P<.001). Conclusions: A smart medication dispenser, spencer, generated years of longitudinal survey data from patients in their homes. Panel attrition was low, and patients continued to respond at high rates. A fall risk measure derived from the survey data showed evidence of reliability and validity. An alternative to web-based panels, spencer is a promising tool for generating patient real-world data. %R 10.2196/60438 %U https://humanfactors.jmir.org/2025/1/e60438 %U https://doi.org/10.2196/60438 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 14 %N %P e60622 %T Interstep Variations of Stairways and Associations of High-Contrast Striping and Fall-Related Events: Observational Study %A Harper,Sara A %A Brown,Chayston %A Poulsen,Shandon L %A Barrett,Tyson S %A Dakin,Christopher J %+ Kinesiology Department, University of Alabama in Huntsville, 301 Sparkman Drive NW, Huntsville, AL, 35899, United States, 1 256 824 2184, sah0075@uah.edu %K stairs %K stairway safety %K riser height %K tread depth %K horizontal-vertical illusion %K fall risk %K fall prevention %K videos %K Monte Carlo simulation %K public health %K vision-based strategy %K health promotion %K adults %K geriatric %D 2025 %7 8.1.2025 %9 Original Paper %J Interact J Med Res %G English %X Background: Interstep variations in step riser height and tread depth within a stairway could negatively impact safe stair negotiation by decreasing step riser height predictability and, consequently, increasing stair users’ fall risk. Unfortunately, interstep variations in riser height and depth are common, particularly in older stairways, but its impact may be lessened by highlighting steps’ edges using a high-contrast stripe on the top front edge of each step. Objective: This study aimed to determine (1) if fall-related events are associated with greater interstep riser height and depth variations and (2) if such fall-related events are reduced in the presence of contrast-enhanced step edges compared with a control stairway. Methods: Stair users were video recorded on 2 public stairways in a university building. One stairway had black vinyl stripes applied to the step’s edges and black-and-white vertical stripes on the last and top steps’ faces. The stairway with striping was counterbalanced, with the striped stairway than a control, and the control with stripes. Each stair user recorded was coded for whether they experienced a fall-related event. A total of 10,000 samples (observations) of 20 fall-related events were drawn with 0.25 probability from each condition to determine the probability of observing a distribution with the constraints outlined by the hypotheses by a computerized Monte Carlo simulation. Results: In total, 11,137 individual stair user observations had 20 fall-related events. The flights that had 14 mm in interstep riser height variation and 38 mm in interstep depth variation were associated with 80% (16/20) of the fall-related events observed. Furthermore, 2 fall-related events were observed for low interstep variation with no striping, and 2 fall-related events were observed during low interstep variation with striping. A total of 20 fall-related events were observed, with 4 occurring on flights of stairs with low interstep variation. For stairs with high variability in step dimensions, 13 of 16 (81%) fall-related events occurred on the control stairway (no striping) compared with 3 of 16 (19%) on the high-contrast striping stairway. The distribution of fall-related events we observed between conditions likely did not occur by chance, with a probability of 0.04. Conclusions: These data support the premise that a vision-based strategy (ie, striping) may counteract fall risk associated with interstep riser height and tread depth variation. Possibly, perception and action elicited through the horizontal-vertical illusion (striping) may have a positive impact on the incidence of fall-related events in the presence of high interstep riser height and depth variation. The findings of this study suggest that contrast enhancement (ie, striping) may be a simple and effective way to reduce the risk of falls associated with interstep variation, highlighting the potential for this approach to make a significant impact on fall prevention efforts. %M 39773894 %R 10.2196/60622 %U https://www.i-jmr.org/2025/1/e60622 %U https://doi.org/10.2196/60622 %U http://www.ncbi.nlm.nih.gov/pubmed/39773894 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e57622 %T Evaluating a Smart Textile Loneliness Monitoring System for Older People: Co-Design and Qualitative Focus Group Study %A Probst,Freya %A Rees,Jessica %A Aslam,Zayna %A Mexia,Nikitia %A Molteni,Erika %A Matcham,Faith %A Antonelli,Michela %A Tinker,Anthea %A Shi,Yu %A Ourselin,Sebastien %A Liu,Wei %+ Department of Engineering, King's College London, Strand Building, Strand Campus, London, WC2R 2LS, United Kingdom, 44 20 7836 5454, wei.liu@kcl.ac.uk %K loneliness %K smart textiles %K wearable technology %K health monitoring %K older people %K co-design %K design requirement %K mobile phone %D 2024 %7 17.12.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Previous studies have explored how sensor technologies can assist in in the detection, recognition, and prevention of subjective loneliness. These studies have shown a correlation between physiological and behavioral sensor data and the experience of loneliness. However, little research has been conducted on the design requirements from the perspective of older people and stakeholders in technology development. The use of these technologies and infrastructural questions have been insufficiently addressed. Systems generally consist of sensors or software installed in smartphones or homes. However, no studies have attempted to use smart textiles, which are fabrics with integrated electronics. Objective: This study aims to understand the design requirements for a smart textile loneliness monitoring system from the perspectives of older people and stakeholders. Methods: We conducted co-design workshops with 5 users and 6 stakeholders to determine the design requirements for smart textile loneliness monitoring systems. We derived a preliminary product concept of the smart wearable and furniture system. Digital and physical models and a use case were evaluated in a focus group study with older people and stakeholders (n=7). Results: The results provided insights for designing systems that use smart textiles to monitor loneliness in older people and widen their use. The findings informed the general system, wearables and furniture, materials, sensor positioning, washing, sensor synchronization devices, charging, intervention, and installation and maintenance requirements. This study provided the first insight from a human-centered perspective into smart textile loneliness monitoring systems for older people. Conclusions: We recommend more research on the intervention that links to the monitored loneliness in a way that addresses different needs to ensure its usefulness and value to people. Future systems must also reflect on questions of identification of system users and the available infrastructure and life circumstances of people. We further found requirements that included user cooperation, compatibility with other worn medical devices, and long-term durability. %M 39688889 %R 10.2196/57622 %U https://aging.jmir.org/2024/1/e57622 %U https://doi.org/10.2196/57622 %U http://www.ncbi.nlm.nih.gov/pubmed/39688889 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59634 %T An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation %A Parsons,Rex %A Blythe,Robin %A Cramb,Susanna %A Abdel-Hafez,Ahmad %A McPhail,Steven %+ Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, 4059, Australia, 61 31380905, rex.parsons@hdr.qut.edu.au %K clinical prediction model %K falls %K patient safety %K prognostic %K electronic medical record %K EMR %K intervention %K hospital %K risk assessment %K clinical decision %K support system %K in-hospital fall %K survival model %K inpatient falls %D 2024 %7 13.11.2024 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 39536309 %R 10.2196/59634 %U https://www.jmir.org/2024/1/e59634 %U https://doi.org/10.2196/59634 %U http://www.ncbi.nlm.nih.gov/pubmed/39536309 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e53447 %T Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol for a Feasibility Study %A Chung,Jane %A Pretzer-Aboff,Ingrid %A Parsons,Pamela %A Falls,Katherine %A Bulut,Eyuphan %+ Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road NE, Atlanta, GA, 30322, United States, 1 4047277980, jane.chung@emory.edu %K Wi-Fi sensing %K dementia %K mild cognitive impairment %K older adults %K health disparities %K in-home activities %K mobility %K machine learning %D 2024 %7 12.11.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk of developing dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitive impairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks, so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, these technologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to these technologies due to limited resources and technology experience. There is a need to develop new sensing technology that is discreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore, it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations between clinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information–based device-free, low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-home activities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy aging needs of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state information data from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuously over a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotations to test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we explored the acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the same study protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023. Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing ML models for Wi-Fi sensing–based activity and mobility assessment, community-based recruitment and data collection, ground truth, and older adults’ Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitive decline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessing their level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447 %M 39531268 %R 10.2196/53447 %U https://www.researchprotocols.org/2024/1/e53447 %U https://doi.org/10.2196/53447 %U http://www.ncbi.nlm.nih.gov/pubmed/39531268 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e60673 %T Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study %A Scaramozza,Matthew %A Ruet,Aurélie %A Chiesa,Patrizia A %A Ahamada,Laïtissia %A Bartholomé,Emmanuel %A Carment,Loïc %A Charre-Morin,Julie %A Cosne,Gautier %A Diouf,Léa %A Guo,Christine C %A Juraver,Adrien %A Kanzler,Christoph M %A Karatsidis,Angelos %A Mazzà,Claudia %A Penalver-Andres,Joaquin %A Ruiz,Marta %A Saubusse,Aurore %A Simoneau,Gabrielle %A Scotland,Alf %A Sun,Zhaonan %A Tang,Minao %A van Beek,Johan %A Zajac,Lauren %A Belachew,Shibeshih %A Brochet,Bruno %A Campbell,Nolan %+ Biogen, 225 Binney St, Cambridge, MA, 02142, United States, 1 781 464 2000, matt.scaramozza@biogen.com %K multiple sclerosis %K sensor-derived measure %K smartphone %K cognitive function %K motor function %K digital biomarkers %K mobile phone %D 2024 %7 8.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Smartphones and wearables are revolutionizing the assessment of cognitive and motor function in neurological disorders, allowing for objective, frequent, and remote data collection. However, these assessments typically provide a plethora of sensor-derived measures (SDMs), and selecting the most suitable measure for a given context of use is a challenging, often overlooked problem. Objective: This analysis aims to develop and apply an SDM selection framework, including automated data quality checks and the evaluation of statistical properties, to identify robust SDMs that describe the cognitive and motor function of people with multiple sclerosis (MS). Methods: The proposed framework was applied to data from a cross-sectional study involving 85 people with MS and 68 healthy participants who underwent in-clinic supervised and remote unsupervised smartphone-based assessments. The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. Selected SDMs were then analyzed to establish their reliability, using an intraclass correlation coefficient and minimal detectable change at 95% CI. The convergence of selected SDMs with in-clinic MS functional measures and patient-reported outcomes was also evaluated. Results: A total of 16 (34%) of the 47 SDMs passed the selection framework. All selected SDMs demonstrated moderate-to-good reliability in remote settings (intraclass correlation coefficient 0.5-0.85; minimal detectable change at 95% CI 19%-35%). Selected SDMs extracted from the smartphone-based cognitive test demonstrated good-to-excellent correlation (Spearman correlation coefficient, |ρ|>0.75) with the in-clinic Symbol Digit Modalities Test and fair correlation with Expanded Disability Status Scale (EDSS) scores (0.25≤|ρ|<0.5). SDMs extracted from the manual dexterity tests showed either fair correlation (0.25≤|ρ|<0.5) or were not correlated (|ρ|<0.25) with the in-clinic 9-hole peg test and EDSS scores. Most selected SDMs from mobility tests showed fair correlation with the in-clinic timed 25-foot walk test and fair to moderate-to-good correlation (0.5<|ρ|≤0.75) with EDSS scores. SDM correlations with relevant patient-reported outcomes varied by functional domain, ranging from not correlated (cognitive test SDMs) to good-to-excellent correlation (|ρ|>0.75) for mobility test SDMs. Overall, correlations were similar when smartphone-based tests were performed in a clinic or remotely. Conclusions: Reported results highlight that smartphone-based assessments are suitable tools to remotely obtain high-quality SDMs of cognitive and motor function in people with MS. The presented SDM selection framework promises to increase the interpretability and standardization of smartphone-based SDMs in people with MS, paving the way for their future use in interventional trials. %M 39515815 %R 10.2196/60673 %U https://formative.jmir.org/2024/1/e60673 %U https://doi.org/10.2196/60673 %U http://www.ncbi.nlm.nih.gov/pubmed/39515815 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58380 %T Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention %A Wen,Ming-Huan %A Chen,Po-Yin %A Lin,Shirling %A Lien,Ching-Wen %A Tu,Sheng-Hsiang %A Chueh,Ching-Yi %A Wu,Ying-Fang %A Tan Cheng Kian,Kelvin %A Hsu,Yeh-Liang %A Bai,Dorothy %+ School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, No.250, Wuxing Street, Xinyi District, Taipei, 110, Taiwan, 886 227361661 ext 6332, dbai@tmu.edu.tw %K patient safety %K falls %K fall prevention %K fall risk %K sensors %K Internet of Things %K bed-exit alert %K motion-sensing mattress system %K care quality %K quality improvement %K ubiquitous health %K mHealth %D 2024 %7 3.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. Objective: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. Methods: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. Results: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). Conclusions: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety. %M 39361417 %R 10.2196/58380 %U https://www.jmir.org/2024/1/e58380 %U https://doi.org/10.2196/58380 %U http://www.ncbi.nlm.nih.gov/pubmed/39361417 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e58110 %T Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study %A Merchant,Reshma Aziz %A Loke,Bernard %A Chan,Yiong Huak %+ Division of Geriatric Medicine, Department of Medicine, National University Hospital, 1E Kent Ridge Road, Singapore, 119228, Singapore, 65 +6567795555, mdcram@nus.edu.sg %K falls %K fall prevention %K wearables %K older adult %K community dwelling older adults %K gait %K gait kinetics %K gait analysis %K biomechanics %K sensors %K gerontology %D 2024 %7 3.10.2024 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 39361400 %R 10.2196/58110 %U https://formative.jmir.org/2024/1/e58110 %U https://doi.org/10.2196/58110 %U http://www.ncbi.nlm.nih.gov/pubmed/39361400 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e57601 %T Feasibility of Measuring Smartphone Accelerometry Data During a Weekly Instrumented Timed Up-and-Go Test After Emergency Department Discharge: Prospective Observational Cohort Study %A Suffoletto,Brian %A Kim,David %A Toth,Caitlin %A Mayer,Waverly %A Glaister,Sean %A Cinkowski,Chris %A Ashenburg,Nick %A Lin,Michelle %A Losak,Michael %K older adult %K older adults %K elder %K elderly %K older person %K older people %K ageing %K aging %K gait %K balance %K fall %K falls %K functional decline %K fall risk %K fall risks %K mobility %K phone %K sensors %K patient monitoring %K monitoring %K emergency department %K emergency departments %K ED %K emergency room %K ER %K discharge %K mobile application %K mobile applications %K app %K apps %K application %K applications %K digital health %K digital technology %K digital intervention %K digital interventions %K smartphone %K smartphones %K prediction %K mobile phone %D 2024 %7 4.9.2024 %9 %J JMIR Aging %G English %X 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. %R 10.2196/57601 %U https://aging.jmir.org/2024/1/e57601 %U https://doi.org/10.2196/57601 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e49403 %T Exploring the Experiences of Community-Dwelling Older Adults on Using Wearable Monitoring Devices With Regular Support From Community Health Workers, Nurses, and Social Workers: Qualitative Descriptive Study %A Wong,Arkers Kwan Ching %A Bayuo,Jonathan %A Su,Jing Jing %A Chow,Karen Kit Sum %A Wong,Siu Man %A Wong,Bonnie Po %A Lee,Athena Yin Lam %A Wong,Frances Kam Yuet %+ School of Nursing, The Hong Kong Polytechnic University, GH 502, Hung Hom, Kowloon, China (Hong Kong), 852 34003805, arkers.wong@polyu.edu.hk %K community-dwelling older adults %K focus group %K wearable monitoring devices %K mobile phone %D 2024 %7 7.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of wearable monitoring devices (WMDs), such as smartwatches, is advancing support and care for community-dwelling older adults across the globe. Despite existing evidence of the importance of WMDs in preventing problems and promoting health, significant concerns remain about the decline in use after a period of time, which warrant an understanding of how older adults experience the devices. Objective: This study aims to explore and describe the experiences of community-dwelling older adults after receiving our interventional program, which included the use of a smartwatch with support from a community health workers, nurses, and social workers, including the challenges that they experienced while using the device, the perceived benefits, and strategies to promote their sustained use of the device. Methods: We used a qualitative descriptive approach in this study. Older adults who had taken part in an interventional study involving the use of smartwatches and who were receiving regular health and social support were invited to participate in focus group discussions at the end of the trial. Purposive sampling was used to recruit potential participants. Older adults who agreed to participate were assigned to focus groups based on their community. The focus group discussions were facilitated and moderated by 2 members of the research team. All discussions were recorded and transcribed verbatim. We used the constant comparison analytical approach to analyze the focus group data. Results: A total of 22 participants assigned to 6 focus groups participated in the study. The experiences of community-dwelling older adults emerged as (1) challenges associated with the use of WMDs, (2) the perceived benefits of using the WMDs, and (3) strategies to promote the use of WMDs. In addition, the findings also demonstrate a hierarchical pattern of health-seeking behaviors by older adults: seeking assistance first from older adult volunteers, then from social workers, and finally from nurses. Conclusions: Ongoing use of the WMDs is potentially possible, but it is important to ensure the availability of technical support, maintain active professional follow-ups by nurses and social workers, and include older adult volunteers to support other older adults in such programs. %M 39110493 %R 10.2196/49403 %U https://www.jmir.org/2024/1/e49403 %U https://doi.org/10.2196/49403 %U http://www.ncbi.nlm.nih.gov/pubmed/39110493 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e56750 %T An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design %A Zhang,Jinxi %A Li,Zhen %A Liu,Yu %A Li,Jian %A Qiu,Hualong %A Li,Mohan %A Hou,Guohui %A Zhou,Zhixiong %+ Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, 11 North Third Ring West Road, Haidian District, Beijing, 100191, China, 86 13552505679, zhouzhixiong@cupes.edu.cn %K fall detection %K deep learning %K self-attention %K accelerometer %K gyroscope %K human health %K wearable sensors %K Sisfall %K MobiFall %D 2024 %7 5.8.2024 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 39102676 %R 10.2196/56750 %U https://www.jmir.org/2024/1/e56750 %U https://doi.org/10.2196/56750 %U http://www.ncbi.nlm.nih.gov/pubmed/39102676 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55638 %T Assessing the Efficacy of the ARMOR Tool–Based Deprescribing Intervention for Fall Risk Reduction in Older Patients Taking Fall Risk–Increasing Drugs (DeFRID Trial): Protocol for a Randomized Controlled Trial %A Priyadarshini,Rekha %A Eerike,Madhavi %A Varatharajan,Sakthivadivel %A Ramaswamy,Gomathi %A Raj,Gerard Marshall %A Cherian,Jerin Jose %A Rajendran,Priyadharsini %A Gunasekaran,Venugopalan %A Rao,Shailaja V %A Konda,Venu Gopala Rao %+ Department of Pharmacology, All India Institute of Medical Sciences Bibinagar, Bibinagar, Hyderabad, 508126, India, 91 9941476332, dr.madhavieerike@gmail.com %K deprescribing %K geriatric %K fall risk–increasing drugs %K FRIDs %K ARMOR tool %K Assess, Review, Minimize, Optimize, and Reassess %K falls %K older patients %K fall risk %D 2024 %7 11.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Falls in older patients can lead to serious health complications and increased health care costs. Fall risk–increasing drugs (FRIDs) are a group of drugs that may induce falls or increase the tendency to fall (ie, fall risk). Deprescribing is the process of withdrawal from an inappropriate medication, supervised by a health care professional, with the goal of managing polypharmacy and improving outcomes. Objective: This study aims to assess the effectiveness of a deprescribing intervention based on the Assess, Review, Minimize, Optimize, and Reassess (ARMOR) tool in reducing the risk of falls in older patients and evaluate the cost-effectiveness of deprescribing FRIDs. Methods: This is an open-label, parallel-group randomized controlled academic trial. Individuals aged 60-80 years who are currently taking 5 or more prescribed drugs, including at least 1 FRID, will be recruited. Demographic data, medical conditions, medication lists, orthostatic hypotension, and fall history details will be collected. Fall concern will be assessed using the Fall Efficacy Scale, and fall risk will be assessed by the Timed Up and Go test and Tinetti Performance-Oriented Mobility Assessment tool. In this study, all treating physicians will be randomized using a stratified randomization method based on seniority. Randomized physicians will do deprescribing with the ARMOR tool for patients on FRIDs. Participants will maintain diaries, and monthly phone follow-ups will be undertaken to monitor falls and adverse events. Physical assessments will be performed to evaluate fall risk every 3 months for a year. The rationality of prescription drugs will be evaluated using the World Health Organization’s core indicators. Results: The study received a grant from the Indian Council of Medical Research–Safe and Rational Use of Medicine in October 2023. The study is scheduled to commence in April 2024 and conclude by 2026. Efficacy will be measured by fall frequency and changes in fall risk scores. Cost-effectiveness analysis will also include the incremental cost-effectiveness ratio calculation. Adverse events related to deprescription will be recorded. Conclusions: This trial will provide essential insights into the efficacy of the ARMOR tool in reducing falls among the geriatric population who are taking FRIDs. Additionally, it will provide valuable information on the cost-effectiveness of deprescribing practices, offering significant implications for improving the well-being of older patients and optimizing health care resource allocation. The findings from this study will be pertinent for health care professionals, policy makers, and researchers focused on geriatric care and fall prevention strategies. Trial Registration: Clinical Trials Registry – India CTRI/2023/12/060516; https://ctri.nic.in/Clinicaltrials/pubview2.php International Registered Report Identifier (IRRID): PRR1-10.2196/55638 %M 38861709 %R 10.2196/55638 %U https://www.researchprotocols.org/2024/1/e55638 %U https://doi.org/10.2196/55638 %U http://www.ncbi.nlm.nih.gov/pubmed/38861709 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e52592 %T 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 %A Barton,Hanna J %A Maru,Apoorva %A Leaf,Margaret A %A Hekman,Daniel J %A Wiegmann,Douglas A %A Shah,Manish N %A Patterson,Brian W %+ BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison, 800 University Bay Dr., Madison, WI, 53705, United States, 1 (608) 890 8682, hbarton@wisc.edu %K emergency medicine %K clinical decision support %K health IT %K human factors %K work systems %K SEIPS %K Systems Engineering Initiative for Patient Safety %K educational outreach %K academic detailing %K implementation method %K department-based %K CDS %K clinical care %K evidence-based %K CDS tool %K gerontology %K geriatric %K geriatrics %K older adult %K older adults %K elder %K elderly %K older person %K older people %K preventative intervention %K team-based analysis %K machine learning %K high-risk patient %K high-risk patients %K pharmaceutical %K pharmaceutical sales %K United States %K fall-risk prediction %K EHR %K electronic health record %K interview %K ED environment %K emergency department %D 2024 %7 18.4.2024 %9 Original Paper %J JMIR Hum Factors %G English %X 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. %M 38635318 %R 10.2196/52592 %U https://humanfactors.jmir.org/2024/1/e52592 %U https://doi.org/10.2196/52592 %U http://www.ncbi.nlm.nih.gov/pubmed/38635318 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e54854 %T Head Protection Device for Individuals at Risk for Head Injury due to Ground-Level Falls: Single Trauma Center User Experience Investigation %A Haag,Susan %A Kepros,John %+ Scottsdale Osborn Medical Center, 7400 E Osborn Rd, Scottsdale, AZ, 85251, United States, 1 480 323 4018, susan.haag@ymail.com %K health care interventions and technologies %K user experience research %K usability %K brain injury %K ground-level fall (GLF) %K head protection device (HPD) %K fall risk %K patient compliance %D 2024 %7 19.3.2024 %9 Original Paper %J JMIR Hum Factors %G English %X 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 (χ12=4.27; P=.04) but not sex (χ12=1.58; P=.23) or race (χ12=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. %M 38502170 %R 10.2196/54854 %U https://humanfactors.jmir.org/2024/1/e54854 %U https://doi.org/10.2196/54854 %U http://www.ncbi.nlm.nih.gov/pubmed/38502170 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e48995 %T BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study %A Cheligeer,Cheligeer %A Wu,Guosong %A Lee,Seungwon %A Pan,Jie %A Southern,Danielle A %A Martin,Elliot A %A Sapiro,Natalie %A Eastwood,Cathy A %A Quan,Hude %A Xu,Yuan %+ Centre for Health Informatics, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada, 1 (403) 210 9554, yuxu@ucalgary.ca %K accidental falls %K electronic medical records %K data mining %K machine learning %K patient safety %K natural language processing %K adverse event %D 2024 %7 30.1.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls. Objective: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model. Methods: A cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture. Results: To address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F1-score model (F1=64.4). Our proposed method outperformed 3 classical ML algorithms and an International Classification of Diseases code–based algorithm for fall detection, showing its potential for improved performance in diverse clinical settings. Conclusions: The developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals. %M 38289643 %R 10.2196/48995 %U https://medinform.jmir.org/2024/1/e48995 %U https://doi.org/10.2196/48995 %U http://www.ncbi.nlm.nih.gov/pubmed/38289643 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e49331 %T A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack %A Block,Valerie J %A Koshal,Kanishka %A Wijangco,Jaeleene %A Miller,Nicolette %A Sara,Narender %A Henderson,Kyra %A Reihm,Jennifer %A Gopal,Arpita %A Mohan,Sonam D %A Gelfand,Jeffrey M %A Guo,Chu-Yueh %A Oommen,Lauren %A Nylander,Alyssa %A Rowson,James A %A Brown,Ethan %A Sanders,Stephen %A Rankin,Katherine %A Lyles,Courtney R %A Sim,Ida %A Bove,Riley %+ Department of Neurology, University of California San Francisco Weill Institute, University of California San Francisco, Box 3126 1651 4th St, Room 612A, San Francisco, CA, 94143, United States, 1 (415) 353 2069, riley.bove@ucsf.edu %K digital health %K mobile tools %K falls %K prevention %K behavioral medicine %K implementation science %K closed-loop monitoring %K multiple sclerosis %K mobile phone %D 2024 %7 11.1.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Falls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient’s specific context. Objective: This study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts. Methods: Stakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users’ needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits. Results: A sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility—with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient’s community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring). Conclusions: To our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record–embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences. %M 38206662 %R 10.2196/49331 %U https://humanfactors.jmir.org/2024/1/e49331 %U https://doi.org/10.2196/49331 %U http://www.ncbi.nlm.nih.gov/pubmed/38206662 %0 Journal Article %@ 2561-7605 %I %V 6 %N %P e49587 %T Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis %A Al Abiad,Nahime %A van Schooten,Kimberley S %A Renaudin,Valerie %A Delbaere,Kim %A Robert,Thomas %K fall risk biomarkers %K prospective falls %K sensor placement %K inertial measurement units %K fall prediction %K older adults %K older adult %K geriatric %K geriatrics %K elderly %K fall %K sensor %K sensors %K inertial measurement %K model %K predict %K prediction %K predictive %D 2023 %7 24.11.2023 %9 %J JMIR Aging %G English %X Background: In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices. Objective: Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls. Methods: A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric. Results: The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent). Conclusions: These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12615000138583; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367746 %R 10.2196/49587 %U https://aging.jmir.org/2023/1/e49587 %U https://doi.org/10.2196/49587 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e41220 %T Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study %A Hellec,Justine %A Hayotte,Meggy %A Chorin,Frédéric %A Colson,Serge S %A d'Arripe-Longueville,Fabienne %+ Université Côte d'Azur, LAMHESS, Campus STAPS, Sciences du Sport, 261, Boulevard du Mercantour, Nice, 06205, France, 33 489153905, justine.hellec@univ-cotedazur.fr %K Unified Theory of Acceptance and Use of Technology 2 %K fall prevention %K fall detection %K older people %K older adults %K facilitating conditions %K effort expectancy %K smart eyeglasses %D 2023 %7 12.5.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted. Objective: On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities. Methods: A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group. Results: The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51). Conclusions: To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances. %M 37171835 %R 10.2196/41220 %U https://www.jmir.org/2023/1/e41220 %U https://doi.org/10.2196/41220 %U http://www.ncbi.nlm.nih.gov/pubmed/37171835 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 4 %P e38865 %T Electronic Tracking Devices for People With Dementia: Content Analysis of Company Websites %A Howes,Jared %A Denier,Yvonne %A Gastmans,Chris %+ Centre for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35, Box 7001, Leuven, 3000, Belgium, 32 16372182, jaredmichael.howes@kuleuven.be %K dementia %K wandering %K electronic tracking devices %K bioethics %K locators %K monitors %K surveillance devices %K management %K technology %K care tool %K caregiver %K device %K vulnerable %K elderly %D 2022 %7 11.11.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Electronic tracking devices, also known as locators, monitors, or surveillance devices, are increasingly being used to manage dementia-related wandering and, subsequently, raising various ethical questions. Despite the known importance technology design has on the ethics of technologies, little research has focused on the companies responsible for the design and development of electronic tracking devices. This paper is the first to perform a qualitative analysis of the ethically related content of the websites of companies that design and develop electronic tracking devices. Objective: The aim of this study was to understand how companies that design, develop, and market electronic tracking devices for dementia care frame, through textual marketing content, the vulnerabilities and needs of persons with dementia and caregivers, the way in which electronic tracking devices respond to these vulnerabilities and needs, and the ethical issues and values at stake. Methods: Electronic tracking device company websites were identified via a Google search, 2 device recommendation lists (Alzheimer’s Los Angeles and the Canadian Agency for Drugs and Technologies in Health), and the 2 recent reviews of wander management technology by Neubauer et al and Ray et al. To be included, websites must be official representations of companies (not market or third-party websites) developing and selling electronic tracking devices for use in dementia care. The search was conducted on December 22, 2020, returning 199 websites excluding duplicates. Data synthesis and analysis were conducted on the textual content of the included websites using a modified form of the Qualitative Analysis Guide of Leuven. Results: In total, 29 websites met the inclusion criteria. Most (15/29, 52%) companies were in the United States. The target audience of the websites was largely caregivers. A range of intertwined vulnerabilities facing persons with dementia and their caregivers were identified, and the companies addressed these via care tools that centered on certain values such as providing information while preserving privacy. Life after device implementation was characterized as a world aspired to that sees increased safety for persons with dementia and peace of mind for caregivers. Conclusions: The way electronic tracking device content is currently conveyed excludes persons with dementia as a target audience. In presenting their products as a response to vulnerabilities, particular values are linked to design elements. A limitation of the results is the opaque nature of website content origins. How or when values arise in the process of design, development, and marketing is unknown. Therefore, further research should explore the process companies use to identify vulnerabilities, how values are decided upon and integrated into the design of products, and the perceptions of developers regarding the ethics of electronic tracking devices. %M 36367765 %R 10.2196/38865 %U https://aging.jmir.org/2022/4/e38865 %U https://doi.org/10.2196/38865 %U http://www.ncbi.nlm.nih.gov/pubmed/36367765 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 3 %P e38211 %T An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study %A Bijlani,Nivedita %A Nilforooshan,Ramin %A Kouchaki,Samaneh %+ Centre for Vision, Speech and Signal Processing, University of Surrey, 388 Stag Hill, Guildford, GU2 7XH, United Kingdom, 44 1483 300 800, n.bijlani@surrey.ac.uk %K contextual matrix profile %K multidimensional anomaly detection %K outlier detection %K sensor-based remote health monitoring %K dementia %K unsupervised learning %D 2022 %7 19.9.2022 %9 Original Paper %J JMIR Aging %G English %X Background: Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability. Objective: This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations. Methods: We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia. Results: The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns. Conclusions: To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios. %M 36121687 %R 10.2196/38211 %U https://aging.jmir.org/2022/3/e38211 %U https://doi.org/10.2196/38211 %U http://www.ncbi.nlm.nih.gov/pubmed/36121687 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 9 %P e32453 %T The Usability of a Smartphone-Based Fall Risk Assessment App for Adult Wheelchair Users: Observational Study %A Frechette,Mikaela %A Fanning,Jason %A Hsieh,Katherine %A Rice,Laura %A Sosnoff,Jacob %+ Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS, 66103, United States, 1 913 588 5235, jsosnoff@kumc.edu %K usability testing %K mobile health %K wheeled device user %K fall risk %K telehealth %K mHealth %K mobile device %K smartphone %K health applications %K older adults %K elderly population %K device usability %D 2022 %7 16.9.2022 %9 Original Paper %J JMIR Form Res %G English %X 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. %M 36112405 %R 10.2196/32453 %U https://formative.jmir.org/2022/9/e32453 %U https://doi.org/10.2196/32453 %U http://www.ncbi.nlm.nih.gov/pubmed/36112405 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 5 %N 2 %P e28260 %T Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study %A Shahid,Zahraa Khais %A Saguna,Saguna %A Åhlund,Christer %+ Division of Computer Science, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Forskargatan 1, Skellefteå, 931 77, Sweden, 46 704741624, zahraa.shahid@ltu.se %K Activities of daily living %K smart homes %K elderly care %K anomaly detection %K IoT devices %K smart device %K elderly %K sensors %K digital sensors %K Internet of things %D 2022 %7 11.4.2022 %9 Original Paper %J JMIR Aging %G English %X Background: One of the main challenges of health monitoring systems is the support of older persons in living independently in their homes and with relatives. Smart homes equipped with internet of things devices can allow older persons to live longer in their homes. Previous surveys used to identify sensor-based data sets in human activity recognition systems have been limited by the use of public data set characteristics, data collected in a controlled environment, and a limited number of older participants. Objective: The objective of our study is to build a model that can learn the daily routines of older persons, detect deviations in daily living behavior, and notify these anomalies in near real-time to relatives. Methods: We extracted features from large-scale sensor data by calculating the time duration and frequency of visits. Anomalies were detected using a parametric statistical approach, unusually short or long durations being detected by estimating the mean (μ) and standard deviation (σ) over hourly time windows (80 to 355 days) for different apartments. The confidence level is at least 75% of the tested values within two (σ) from the mean. An anomaly was triggered where the actual duration was outside the limits of 2 standard deviations (μ−2σ, μ+2σ), activity nonoccurrence, or absence of activity. Results: The patterns detected from sensor data matched the routines self-reported by users. Our system observed approximately 1000 meals and bathroom activities and notifications sent to 9 apartments between July and August 2020. A service evaluation of received notifications showed a positive user experience, an average score of 4 being received on a 1 to 5 Likert-like scale. One was poor, two fair, three good, four very good, and five excellent. Our approach considered more than 75% of the observed meal activities were normal. This figure, in reality, was 93%, normal observed meal activities of all participants falling within 2 standard deviations of the mean. Conclusions: In this research, we developed, implemented, and evaluated a real-time monitoring system of older participants in an uncontrolled environment, with off-the-shelf sensors and internet of things devices being used in the homes of older persons. We also developed an SMS-based notification service and conducted user evaluations. This service acts as an extension of the health/social care services operated by the municipality of Skellefteå provided to older persons and relatives. %M 35404260 %R 10.2196/28260 %U https://aging.jmir.org/2022/2/e28260 %U https://doi.org/10.2196/28260 %U http://www.ncbi.nlm.nih.gov/pubmed/35404260 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e32724 %T Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study %A Kraus,Moritz %A Saller,Maximilian Michael %A Baumbach,Sebastian Felix %A Neuerburg,Carl %A Stumpf,Ulla Cordula %A Böcker,Wolfgang %A Keppler,Alexander Martin %+ Department of Orthopaedics and Trauma Surgery, Musculoskeletal University Center Munich, Ludwig-Maximilians Universität Munich, Marchioninistr. 15, Munich, 81377, Germany, 49 89 4400 0, alexander.keppler@med.uni-muenchen.de %K wearables %K insole sensors %K orthogeriatric %K artificial intelligence %K prediction models %K machine learning %K gait analysis %K digital sensors %K digital health %K aging %K prediction algorithms %K geriatric %K mobile health %K mobile insoles %D 2022 %7 5.1.2022 %9 Original Paper %J JMIR Med Inform %G English %X 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. %M 34989684 %R 10.2196/32724 %U https://medinform.jmir.org/2022/1/e32724 %U https://doi.org/10.2196/32724 %U http://www.ncbi.nlm.nih.gov/pubmed/34989684 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e30135 %T Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation %A Hsu,Yu-Cheng %A Wang,Hailiang %A Zhao,Yang %A Chen,Frank %A Tsui,Kwok-Leung %+ School of Public Health (Shenzhen), Sun Yat-sen University, Room 111, Unit 1, Gezhi Garden 3#, No. 132, East Outer Ring Road, Guangzhou Higher Education Mega Center, Guangzhou, 510000, China, 86 020 83226383, zhaoy393@mail.sysu.edu.cn %K fall risk %K balance %K activity recognition %K automatic framework %K community-dwelling elderly %D 2021 %7 20.12.2021 %9 Original Paper %J J Med Internet Res %G English %X 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°, 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°, 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 flexible solution to relieve the community’s burden of continuous health monitoring. %M 34932008 %R 10.2196/30135 %U https://www.jmir.org/2021/12/e30135 %U https://doi.org/10.2196/30135 %U http://www.ncbi.nlm.nih.gov/pubmed/34932008 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e17411 %T Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review %A Patel,Vikas %A Orchanian-Cheff,Ani %A Wu,Robert %+ Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, ON, M5S 1A8, Canada, 1 4169756585, vik.patel@mail.utoronto.ca %K wearable %K inpatient %K continuous monitoring %D 2021 %7 18.8.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients. Objective: This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients. Methods: This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. Results: Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%. Conclusions: The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients’ digital biomarker readings and eventually determine whether these wearable devices improve health outcomes. %M 34406121 %R 10.2196/17411 %U https://mhealth.jmir.org/2021/8/e17411 %U https://doi.org/10.2196/17411 %U http://www.ncbi.nlm.nih.gov/pubmed/34406121 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 8 %P e25781 %T A Technological-Based Platform for Risk Assessment, Detection, and Prevention of Falls Among Home-Dwelling Older Adults: Protocol for a Quasi-Experimental Study %A Araújo,Fátima %A Nogueira,Maria Nilza %A Silva,Joana %A Rego,Sílvia %+ Escola Superior de Enfermagem do Porto (ESEP), Inovação e Desenvolvimento em Enfermagem, Centro de Investigação em Tecnologias e Serviços de Saúde, Rua Dr. António Bernardino, 830, 844, 856, Porto, 4200-072, Portugal, 351 00351 225 073 5, araujo@esenf.pt %K fall prevention %K technological platform %K elderly %K Otago Exercise Program %D 2021 %7 12.8.2021 %9 Protocol %J JMIR Res Protoc %G English %X 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 %M 34387557 %R 10.2196/25781 %U https://www.researchprotocols.org/2021/8/e25781 %U https://doi.org/10.2196/25781 %U http://www.ncbi.nlm.nih.gov/pubmed/34387557 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e17551 %T Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study %A Bayen,Eleonore %A Nickels,Shirley %A Xiong,Glen %A Jacquemot,Julien %A Subramaniam,Raghav %A Agrawal,Pulkit %A Hemraj,Raheema %A Bayen,Alexandre %A Miller,Bruce L %A Netscher,George %+ Department of Neuro-rehabilitation, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris, Sorbonne Université, 47 Bd de l'Hôpital, Paris, 75013, France, 33 142161101, eleonore.bayen@gbhi.org %K artificial intelligence %K video monitoring %K real-time video detection %K fall %K time on the ground %K Alzheimer disease %K dementia %K memory care facilities %D 2021 %7 17.6.2021 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 34137723 %R 10.2196/17551 %U https://www.jmir.org/2021/6/e17551 %U https://doi.org/10.2196/17551 %U http://www.ncbi.nlm.nih.gov/pubmed/34137723 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e22215 %T eHealth for Addressing Balance Disorders in the Elderly: Systematic Review %A Gaspar,Andréa G Martins %A Lapão,Luís Velez %+ Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade NOVA de Lisboa, Rua da Junqueira, 100, Lisbon, 1349-008, Portugal, 351 213 652 600, andreamartinsbr@hotmail.com %K balance disorders %K falls %K elderly %K eHealth %K telemedicine %D 2021 %7 28.4.2021 %9 Review %J J Med Internet Res %G English %X 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. %M 33908890 %R 10.2196/22215 %U https://www.jmir.org/2021/4/e22215 %U https://doi.org/10.2196/22215 %U http://www.ncbi.nlm.nih.gov/pubmed/33908890 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 2 %P e26875 %T Requirements for Unobtrusive Monitoring to Support Home-Based Dementia Care: Qualitative Study Among Formal and Informal Caregivers %A Wrede,Christian %A Braakman-Jansen,Annemarie %A van Gemert-Pijnen,Lisette %+ Centre for eHealth and Wellbeing Research, Department of Psychology, Health & Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands, 31 (0)53 489 7537, c.wrede@utwente.nl %K in-home monitoring %K ambient assisted living %K assistive technologies %K dementia %K home care %K informal care %K aging in place %D 2021 %7 12.4.2021 %9 Original Paper %J JMIR Aging %G English %X Background: Due to a growing shortage in residential care, people with dementia will increasingly be encouraged to live at home for longer. Although people with dementia prefer extended independent living, this also puts more pressure on both their informal and formal care networks. To support (in)formal caregivers of people with dementia, there is growing interest in unobtrusive contactless in-home monitoring technologies that allow caregivers to remotely monitor the lifestyle, health, and safety of their care recipients. Despite their potential, these solutions will only be viable if they meet the expectations and needs of formal and informal caregivers of people with dementia. Objective: The objective of this study was to explore the expected benefits, barriers, needs, and requirements toward unobtrusive in-home monitoring from the perspective of formal and informal caregivers of community-dwelling people with dementia. Methods: A combination of semistructured interviews and focus groups was used to collect data among informal (n=19) and formal (n=16) caregivers of people with dementia. Both sets of participants were presented with examples of unobtrusive in-home monitoring followed by questions addressing expected benefits, barriers, and needs. Relevant in-home monitoring goals were identified using a previously developed topic list. Interviews and focus groups were transcribed and inductively analyzed. Requirements for unobtrusive in-home monitoring were elicited based on the procedure of van Velsen and Bergvall-Kåreborn. Results: Formal and informal caregivers saw unobtrusive in-home monitoring as a support tool that should particularly be used to monitor (the risk of) falls, day and night rhythm, personal hygiene, nocturnal restlessness, and eating and drinking behavior. Generally, (in)formal caregivers reported cross-checking self-care information, extended independent living, objective communication, prevention and proactive measures, emotional reassurance, and personalized and optimized care as the key benefits of unobtrusive in-home monitoring. Main concerns centered around privacy, information overload, and ethical concerns related to dehumanizing care. Furthermore, 16 requirements for unobtrusive in-home monitoring were generated that specified desired functions, how the technology should communicate with the user, which services surrounding the technology were seen as needed, and how the technology should be integrated into the existing work context. Conclusions: Despite the presence of barriers, formal and informal caregivers of people with dementia generally saw value in unobtrusive in-home monitoring, and felt that these systems could contribute to a shift from reactive to more proactive and less obtrusive care. However, the full potential of unobtrusive in-home monitoring can only unfold if relevant concerns are considered. Our requirements can inform the development of more acceptable and goal-directed in-home monitoring technologies to support home-based dementia care. %M 33843596 %R 10.2196/26875 %U https://aging.jmir.org/2021/2/e26875 %U https://doi.org/10.2196/26875 %U http://www.ncbi.nlm.nih.gov/pubmed/33843596 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 3 %P e25121 %T Predictive Modeling of 30-Day Emergency Hospital Transport of German Patients Using a Personal Emergency Response: Retrospective Study and Comparison with the United States %A op den Buijs,Jorn %A Pijl,Marten %A Landgraf,Andreas %+ Philips Research, High Tech Campus 34, Eindhoven, 5656 AE, Netherlands, 31 631926890, jorn.op.den.buijs@philips.com %K emergency hospital transport %K predictive modeling %K personal emergency response system %K population health management %K emergency transport %K emergency response system %K emergency response %K health management %D 2021 %7 8.3.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Predictive analytics based on data from remote monitoring of elderly via a personal emergency response system (PERS) in the United States can identify subscribers at high risk for emergency hospital transport. These risk predictions can subsequently be used to proactively target interventions and prevent avoidable, costly health care use. It is, however, unknown if PERS-based risk prediction with targeted interventions could also be applied in the German health care setting. Objective: The objectives were to develop and validate a predictive model of 30-day emergency hospital transport based on data from a German PERS provider and compare the model with our previously published predictive model developed on data from a US PERS provider. Methods: Retrospective data of 5805 subscribers to a German PERS service were used to develop and validate an extreme gradient boosting predictive model of 30-day hospital transport, including predictors derived from subscriber demographics, self-reported medical conditions, and a 2-year history of case data. Models were trained on 80% (4644/5805) of the data, and performance was evaluated on an independent test set of 20% (1161/5805). Results were compared with our previously published prediction model developed on a data set of PERS users in the United States. Results: German PERS subscribers were on average aged 83.6 years, with 64.0% (743/1161) females, with 65.4% (759/1161) reported 3 or more chronic conditions. A total of 1.4% (350/24,847) of subscribers had one or more emergency transports in 30 days in the test set, which was significantly lower compared with the US data set (2455/109,966, 2.2%). Performance of the predictive model of emergency hospital transport, as evaluated by area under the receiver operator characteristic curve (AUC), was 0.749 (95% CI 0.721-0.777), which was similar to the US prediction model (AUC=0.778 [95% CI 0.769-0.788]). The top 1% (12/1161) of predicted high-risk patients were 10.7 times more likely to experience an emergency hospital transport in 30 days than the overall German PERS population. This lift was comparable to a model lift of 11.9 obtained by the US predictive model. Conclusions: Despite differences in emergency care use, PERS-based collected subscriber data can be used to predict use outcomes in different international settings. These predictive analytic tools can be used by health care organizations to extend population health management into the home by identifying and delivering timelier targeted interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource use. %M 33682679 %R 10.2196/25121 %U https://medinform.jmir.org/2021/3/e25121 %U https://doi.org/10.2196/25121 %U http://www.ncbi.nlm.nih.gov/pubmed/33682679 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 4 %N 1 %P e23381 %T Attitudes Toward Technology and Use of Fall Alert Wearables in Caregiving: Survey Study %A Vollmer Dahlke,Deborah %A Lee,Shinduk %A Smith,Matthew Lee %A Shubert,Tiffany %A Popovich,Stephen %A Ory,Marcia G %+ DVD Associates, LLC, 8402 Silver Mountain CV, Austin, TX, 78737, United States, 1 512 699 4493, deborahvd@gmail.com %K wearables %K falls alert technology %K falls %K caregivers %K care recipients %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Aging %G English %X 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. %M 33502320 %R 10.2196/23381 %U http://aging.jmir.org/2021/1/e23381/ %U https://doi.org/10.2196/23381 %U http://www.ncbi.nlm.nih.gov/pubmed/33502320 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19223 %T A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study %A Wang,Hailiang %A Zhao,Yang %A Yu,Lisha %A Liu,Jiaxing %A Zwetsloot,Inez Maria %A Cabrera,Javier %A Tsui,Kwok-Leung %+ School of Data Science, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China, 86 34422177, kltsui@cityu.edu.hk %K telehealth monitoring %K personalized health %K technology acceptance %K digital biomarkers %K digital phenotyping %K wearables %K falls detection %K fitness tracker %K sensors %K elderly population %D 2020 %7 30.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective: Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods: The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results: A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions: The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system. %M 32996887 %R 10.2196/19223 %U http://www.jmir.org/2020/9/e19223/ %U https://doi.org/10.2196/19223 %U http://www.ncbi.nlm.nih.gov/pubmed/32996887 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 4 %P e13961 %T Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study %A Sczuka,Kim Sarah %A Schwickert,Lars %A Becker,Clemens %A Klenk,Jochen %+ Department of Clinical Gerontology, Robert-Bosch-Hospital, Auerbachstraße 110, Stuttgart, 70376, Germany, 49 711 8101 6078, kim.sczuka@rbk.de %K falls %K simulation %K inertial sensor %K method %D 2020 %7 3.4.2020 %9 Original Paper %J J Med Internet Res %G English %X 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. %M 32242825 %R 10.2196/13961 %U https://www.jmir.org/2020/4/e13961 %U https://doi.org/10.2196/13961 %U http://www.ncbi.nlm.nih.gov/pubmed/32242825 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 2 %P e14583 %T A Communication Infrastructure for the Health and Social Care Internet of Things: Proof-of-Concept Study %A Della Mea,Vincenzo %A Popescu,Mihai Horia %A Gonano,Dario %A Petaros,Tomaž %A Emili,Ivo %A Fattori,Maria Grazia %+ Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, Udine, 33100, Italy, 39 0432 558461, vincenzo.dellamea@uniud.it %K health services for the aged %K remote sensing technology %K sensors and actuators %K embedded systems %K Internet of Things %K LoRaWAN %D 2020 %7 25.2.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Increasing life expectancy and reducing birth rates indicate that the world population is becoming older, with many challenges related to quality of life for old and fragile people, as well as their informal caregivers. In the last few years, novel information and communication technology techniques generally known as the Internet of Things (IoT) have been developed, and they are centered around the provision of computation and communication capabilities to objects. The IoT may provide older people with devices that enable their functional independence in daily life by either extending their own capacity or facilitating the efforts of their caregivers. LoRa is a proprietary wireless transmission protocol optimized for long-range, low-power, low–data-rate applications. LoRaWAN is an open stack built upon LoRa. Objective: This paper describes an infrastructure designed and experimentally developed to support IoT deployment in a health care setup, and the management of patients with Alzheimer’s disease and dementia has been chosen for a proof-of-concept study. The peculiarity of the proposed approach is that it is based on the LoRaWAN protocol stack, which exploits unlicensed frequencies and allows for the use of very low-power radio devices, making it a rational choice for IoT communication. Methods: A complete LoRaWAN-based infrastructure was designed, with features partly decided in agreement with caregivers, including outdoor patient tracking to control wandering; fall recognition; and capability of collecting data for further clinical studies. Further features suggested by caregivers were night motion surveillance and indoor tracking for large residential structures. Implementation involved a prototype node with tracking and fall recognition capabilities, a middle layer based on an existing network server, and a Web application for overall management of patients and caregivers. Tests were performed to investigate indoor and outdoor capabilities in a real-world setting and study the applicability of LoRaWAN in health and social care scenarios. Results: Three experiments were carried out. One aimed to test the technical functionality of the infrastructure, another assessed indoor features, and the last assessed outdoor features. The only critical issue was fall recognition, because a slip was not always easy to recognize. Conclusions: The project allowed the identification of some advantages and restrictions of the LoRaWAN technology when applied to the health and social care sectors. Free installation allows the development of services that reach ranges comparable to those available with cellular telephony, but without running costs like telephony fees. However, there are technological limitations, which restrict the scenarios in which LoRaWAN is applicable, although there is room for many applications. We believe that setting up low-weight infrastructure and carefully determining whether applications can be concretely implemented within LoRaWAN limits might help in optimizing community care activities while not adding much burden and cost in information technology management. %M 32130158 %R 10.2196/14583 %U http://medinform.jmir.org/2020/2/e14583/ %U https://doi.org/10.2196/14583 %U http://www.ncbi.nlm.nih.gov/pubmed/32130158 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 2 %N 1 %P e12153 %T Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study %A Yang,Yang %A Hirdes,John P %A Dubin,Joel A %A Lee,Joon %+ Faculty of Applied Health Sciences, School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 (226) 317 3726, y24yang@uwaterloo.ca %K falls %K elderly %K wearable devices %K machine learning %K interRAI %D 2019 %7 07.06.2019 %9 Original Paper %J JMIR Aging %G English %X Background:  Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC). Objective:  This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data. Methods:  A prospective, observational study was conducted among 3 faller groups (G0, G1, G2+) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF). Results:  Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G0+G1} and G2+, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G0 and {G1+G2+} did not result in better classification performance than that between {G0+G1} and G2+. Conclusions:  Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments. %M 31518278 %R 10.2196/12153 %U http://aging.jmir.org/2019/1/e12153/ %U https://doi.org/10.2196/12153 %U http://www.ncbi.nlm.nih.gov/pubmed/31518278 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 4 %P e12447 %T A Smart Home System for Information Sharing, Health Assessments, and Medication Self-Management for Older People: Protocol for a Mixed-Methods Study %A Norell Pejner,Margaretha %A Ourique de Morais,Wagner %A Lundström,Jens %A Laurell,Hélène %A Skärsäter,Ingela %+ Department of Health and Care, School of Health and Welfare, Halmstad University, Kristian IV:s väg 3, Halmstad, 301 18, Sweden, 46 721 561709, ingela.skarsater@hh.se %K assessments %K medication %K mixed methods %K older people %K self-management %K smart homes %D 2019 %7 30.04.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Older adults often want to stay in a familiar place, such as their home, as they get older. This so-called aging in place, which may involve support from relatives or care professionals, can promote older people’s independence and well-being. The combination of aging and disease, however, can lead to complex medication regimes and difficulties for care providers in correctly assessing the older person's health. In addition, the organization of health care is fragmented, which makes it difficult for health professionals to encourage older people to participate in their own care. It is also a challenge to perform adequate health assessments and to engage in appropriate communication between health care professionals. Objective: The purpose of this paper is to describe the design for an integrated home-based system that can acquire and compile health-related evidence for guidance and information-sharing among care providers and care receivers in order to support and promote medication self-management among older people. Methods: The authors used a participatory design approach for this mixed-methods project, which was divided into four phases. Phase I, Conceptualization, consists of the conceptualization of a system to support medication self-management, objective health assessments, and communication between health care professionals. Phase II, Development of a System, consists of building and bringing together the conceptualized systems from Phase I. Phase III, Pilot Study, and Phase IV, Full-Scale Intervention, are described briefly. Results: Participants in Phase I were people who were involved in some way in the care of older adults and included older adults themselves, relatives of older adults, care professionals, and industrial partners. With input from Phase I participants, we identified two relevant concepts for promoting medication self-management, both of which related to systems that participants believed could provide guidance for the older adults themselves, relatives of older adults, and care professionals. The systems will also encourage information-sharing between care providers and care receivers. The first is the concept of the Intelligent Age-Friendly Home (IAFH), defined as an integrated residential system that evolves to sense, reason, and act in response to individuals’ needs, preferences, and behaviors as these change over time. The second concept is the Medication safety, Objective assessments of health-related behaviors, and Personalized medication reminders (MedOP) system, a system that would be supported by the IAFH, and which consists of three related components: one that assesses health behaviors, another that communicates health data, and a third that promotes medication self-management. Conclusions: The participants in this project were older adults, relatives of older adults, care professionals, and our industrial partners. With input from the participants, we identified two main concepts that could comprise a system for health assessment, communication, and medication self-management: the IAFH and the MedOP system. These concepts will be tested in this study to determine whether they can facilitate and promote medication self-management among older people. International Registered Report Identifier (IRRID): DERR1-10.2196/12447 %M 31038459 %R 10.2196/12447 %U http://www.researchprotocols.org/2019/4/e12447/ %U https://doi.org/10.2196/12447 %U http://www.ncbi.nlm.nih.gov/pubmed/31038459 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11516 %T Sensor-Based Passive Remote Monitoring and Discordant Values: Qualitative Study of the Experiences of Low-Income Immigrant Elders in the United States %A Berridge,Clara %A Chan,Keith T %A Choi,Youngjun %+ University of Washington, 4101 15th Ave NE, Seattle, WA, 98105, United States, 1 206 685 2180, clarawb@uw.edu %K immigrants %K ubiquitous sensing %K acculturation %K passive monitoring %K independent living %K family caregiving %K culturally appropriate technology %D 2019 %7 25.03.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Remote monitoring technologies are positioned to mitigate the problem of a dwindling care workforce and disparities in access to care for the growing older immigrant population in the United States. To achieve these ends, designers and providers need to understand how these supports can be best provided in the context of various sociocultural environments that shape older adults’ expectations and care relationships, yet few studies have examined how the same remote monitoring technologies may produce different effects and uses depending on what population is using them in a particular context. Objective: This study aimed to examine the experiences and insights of low-income, immigrant senior residents, family contacts, and staff of housing that offered a sensor-based passive monitoring system designed to track changes in movement around the home and trigger alerts for caregivers. The senior housing organization had been offering the QuietCare sensor system to its residents for 6 years at the time of the study. We are interested in adoption and discontinuation decisions and use over time, rather than projected acceptance. Our research question is how do cultural differences influence use and experiences with this remote monitoring technology? The study does not draw generalizable conclusions about how cultural groups interact with a given technology, but rather, it examines how values are made visible in elder care technology interactions. Methods: A total of 41 participants (residents, family, and staff) from 6 large senior housing independent living apartment buildings were interviewed. Interviews were conducted in English and Korean with these participants who collectively had immigrated to the United States from 10 countries. Results: The reactions of immigrant older adults to the passive monitoring system reveal that this tool offered to them was often mismatched with their values, needs, and expectations. Asian elders accepted the intervention social workers offered largely to appease them, but unlike their US-born counterparts, they adopted reluctantly without hope that it would ameliorate their situation. Asian immigrants discontinued use at the highest rate of all residents, and intergenerational family cultural conflict contributed to this termination. Social workers reported that none of the large population of Russian-speaking residents agreed to use QuietCare. Bilingual and bicultural social workers played significant roles as cultural navigators in the promotion of QuietCare to residents. Conclusions: This research into the interactions of culturally diverse people with the same monitoring technology reveals the significant role that social values and context play in shaping how people and families interact with and experience elder care interventions. If technology-based care services are to reach their full potential, it will be important to identify the ways in which cultural values produce different uses and responses to technologies intended to help older adults live independently. %M 30907741 %R 10.2196/11516 %U http://mhealth.jmir.org/2019/3/e11516/ %U https://doi.org/10.2196/11516 %U http://www.ncbi.nlm.nih.gov/pubmed/30907741 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e71 %T 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 %A Harte,Richard %A Quinlan,Leo R %A Glynn,Liam %A Rodríguez-Molinero,Alejandro %A Baker,Paul MA %A Scharf,Thomas %A ÓLaighin,Gearóid %+ NUI Galway, Physiology, School of Medicine, University Road, Galway,, Ireland, 353 91 493710, leo.quinlan@nuigalway.ie %K human-centered design %K user-centered design %K human-computer interface %K human factors engineering %K eHealth %K engineering psychology %K mHealth %D 2017 %7 30.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X 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. %M 28559227 %R 10.2196/mhealth.7046 %U http://mhealth.jmir.org/2017/5/e71/ %U https://doi.org/10.2196/mhealth.7046 %U http://www.ncbi.nlm.nih.gov/pubmed/28559227