Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Monday, March 11, 2019 at 4:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Monday, March 11, 2019 at 4:00 PM.
JMIR Aging
Digital health technologies, apps, and informatics for patient education, medicine and nursing, preventative interventions, and clinical care / home care for elderly populations
JMIR Aging (JA, Founding Editor-in-chief: Jing Wang, Professor and Vice Dean for Research, Hugh Roy Cullen Professor, UT Health San Antonio School of Nursing, San Antonio, TX, USA) is a new sister journal of JMIR (the leading open-access journal in health informatics (Impact Factor 2017: 4.671), focusing on technologies, medical devices, apps, engineering, informatics applications and patient education for medicine and nursing, education, preventative interventions and clinical care / home care for elderly populations. In addition, aging-focused big data analytics using data from electronic health record systems, health insurance databases, federal reimbursement databases (e.g. U.S. Medicare and Medicaid), and other large databases are also welcome.
As open access journal we are read by clinicians, nurses/allied health professionals, informal caregivers and patients alike and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).
During a limited period of time, there are no fees to publish in this journal. Articles are carfully copyedited and XML-tagged, ready for submission in PubMed Central.
Background: It is estimated that the number of individuals living with dementia worldwide will increase from 50 million in 2017 to 152 million by 2050. Assistive technology has been recognized as a promising tool to improve the lives of persons living with memory loss and their caregivers. The use of assistive technology in dementia care is expanding, although it is most often intended to manage care and promote safety. There is a lack of assistive technology designed to aid persons with memory loss in participating in meaningful activities. The Social Support Aid (SSA) is a mobile phone-based app that employs facial recognition software. It was designed to assist persons with memory loss remember the names and relationships of the people they interact with to promote social engagement. Objective: This study uses a pilot randomized controlled trial (RCT) design to evaluate the SSA. The objectives were to ascertain (1) the feasibility and utility of the SSA, (2) whether the outcomes of SSA use suggest potential benefits for persons living with memory loss and their care partners, and (3) how study design components could inform subsequent RCTs. Methods: Persons with memory loss were randomized to the SSA (n=20) or the usual care control group (n=28). Quantitative data were collected at three timepoints (baseline, 3 months, and 6 months). Participants in the intervention group participated in qualitative interviews following completion of their 6-month survey. Results: Participant eligibility, willingness to be randomized, and retention were not barriers to conducting a full-scale RCT; however, recruitment strategies should be addressed before doing so. Feasibility and utility scores indicated that participants felt neutral about the technology. Use of the SSA was not significantly associated with changes in quality of social interactions or quality of life measures over the 6 months of follow-up (P>.05). The qualitative analysis revealed three themes that described how and why the SSA worked or not: (1) outcomes, (2) reasons why it was or was not useful, and (3) recommendations. Conclusions: There is a need to develop effective assistive technology that improves the quality of life of persons with memory loss. Assistive technology that allows persons living with memory loss to maintain some level of autonomy should be a priority for future research. This study suggests reasons why the SSA facial recognition software did not appear to improve the quality of social interaction and quality of life of people with memory loss. Results also provide recommendations for future assistive technology development and evaluation. Trial Registration: ClinicalTrials.gov NCT03645694; https://clinicaltrials.gov/ct2/show/NCT03645694 (Archived by WebCite at http://www.webcitation.org/78dcVZIqq)
Background: Novel and sustainable approaches to optimizing home-based primary care (HBPC) programs are needed to meet the medical needs of a growing number of homebound older adults in the United States. Telehealth may be a viable option for scaling HBPC programs. Objective: The purpose of this qualitative study was to gain insight into the perspectives of HBPC staff regarding adopting telehealth technology to increase the reach of HBPC to more homebound patients. Methods: We collected qualitative data from HBPC staff (ie, physicians, registered nurses, nurse practitioners, care managers, social workers, and medical coordinators) at a practice in the New York metropolitan area through 16 semistructured interviews and three focus groups. Data were analyzed thematically using the template analysis approach with Self-Determination Theory concepts (ie, relatedness, competence, and autonomy) as an analytical lens. Results: Four broad themes—pros and cons of scaling, technology impact on staff autonomy, technology impact on competence in providing care, and technology impact on the patient-caregiver-provider relationship—and multiple second-level themes emerged from the analysis. Staff acknowledged the need to scale the program without diminishing effective patient-centered care. Participants perceived alerts generated from patients and caregivers using telehealth as potentially increasing burden and necessitating a rapid response from an already busy staff while increasing ambiguity. However, they also noted that telehealth could increase efficiency and enable more informed care provision. Telehealth could enhance the patient-provider relationship by enabling caregivers to be an integral part of the patient’s care team. Staff members raised the concern that patients or caregivers might unnecessarily overutilize the technology, and that some home visits are more appropriate in person rather than via telehealth. Conclusions: These findings suggest the importance of considering the perspectives of medical professionals regarding telehealth adoption. A proactive approach exploring the benefits and concerns professionals perceive in the adoption of health technology within the HBPC program will hopefully facilitate the optimal integration of telehealth innovations.
Background: In Quebec, Canada, many public, community, and private organizations provide resources to caregivers of functionally impaired older adults. Nevertheless, these resources may be difficult for caregivers to find. A co-design study was conducted to address the gap between caregivers and access to resources. The purpose of this study was to support the process of help seeking by caregivers of functionally impaired older adults through electronic health (eHealth). Objective: The purpose of this study was to focus on the identification of functional and content requirements for an eHealth tool to support the help-seeking process of caregivers of functionally impaired older adults. Methods: This study uses a co-design process based on qualitative action research approach to develop an eHealth tool with health and social service professionals (HSSPs), community workers, and caregivers. The participants acted as co-designers in identifying requirements for the tool. A total of 4 design workshops and 1 advisory committee session were held in different locations in Quebec, Canada. Activities were videotaped and analyzed with a conceptual framework of user experience. Results: A total of 11 caregivers, 16 community workers, and 11 HSSPs participated in identifying the requirements for the eHealth tool. Several functional and content requirements were identified for each user need (19). Content requirements differed depending on the category of participant, corresponding to the concept of user segmentation in the design of information and communication technology. Nevertheless, there were disagreements among co-designers about specific functionalities, which included (1) functionalities related to the social Web, (2) functionalities related to the evaluation of resources for caregivers, and (3) functionalities related to the emerging technologies. Several co-design sessions were required to resolve disagreements. Conclusions: Co-designers (participants) were able to identify functional and content requirements for each of the previously identified needs; however, several discussions were required to achieve consensus. Decision making was influenced by identity, social context, and participants’ knowledge, and it is a challenge to reconcile the different perspectives. The findings stressed the importance of allowing more time to deal with the iterative aspect of the design activity, especially during the identification of requirements of an eHealth tool. International Registered Report Identifier (IRRID): RR2-10.2196/11634
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.
Background: Although family technical support seems intuitive, there is very little research exploring this topic. Objective: The objective of this study was to conduct a subanalysis of data collected from a large-scale qualitative project regarding older adults’ experiences in using health information technology. Specifically, the subanalysis explored older adults’ experiences with technology support from family members to inform strategies for promoting older adults’ engagement with new health technologies. Although the primary analysis of the original study was theoretically driven, this paper reports results from an inductive, open-coding analysis. Methods: This is a subanalysis of a major code identified unexpectedly from a qualitative study investigating older adults’ use experience of a widespread health technology, the patient portal. A total of 24 older patients (≥65 years) with multiple chronic conditions (Charlson Comorbidity Index >2) participated in focus groups conducted at the patients’ primary clinic. While conducting the primary theoretically driven analysis, coders utilized an open-coding approach to ensure important ideas not reflected in the theoretical code book were captured. Open coding resulted in 1 code: family support. This subanalysis further categorized family support by who provided tech support, how tech support was offered, and the opinions of older participants about receiving family tech support. Results: The participants were not specifically asked about family support, yet themes around family assistance and encouragement for technology emerged from every focus group. Participants repeatedly mentioned that they called their grandchildren and adult children if they needed help with technology. Participants also reported that family members experienced difficulty when teaching technology use. Family members struggled to explain simple technology tasks and were frustrated by the slow teaching process. Conclusions: The results suggest that older adults ask their family members, particularly grandchildren, to support them in the use of new technologies. However, family may experience difficulties in providing this support. Older adults will be increasingly expected to use health technologies, and family members may help with tech support. Providers and health systems should consider potential family support and engagement strategies to foster adoption and use among older patients.
Background: Sundown syndrome (ie, agitation later in the day) is common in older adults with dementia. The underlying etiology for these behaviors is unclear. Possibilities include increased caregiver fatigue at the end of the day and disruption of circadian rhythms by both age and neurodegenerative illness. Objective: This study sought to examine circumseptan (weekly) patterns in search volumes related to sundown syndrome, in order to determine if such searches peaked at the end of the weekend, a time when caregiver supports are least available. We also sought to examine both seasonal differences and associations of state-by-state search activity with both state latitude and yearly sun exposure. Methods: Daily Internet search query data was obtained from Google Trends (2005-2017 inclusive). Circumseptan patterns were determined by wavelet analysis, and seasonality was determined by the difference in search volumes between winter (December, January, and February) and summer (June, July, and August) months. Geographic associations between percent sunny days and latitude were done on a state-by-state basis. Results: “Sundowning” searches showed a significant increase at the end of the weekend with activity being 10.9% (SD 4.0) higher on Sunday as compared to the rest of the week. Search activity showed a seasonal pattern with search activity significantly highest in the winter months (36.6 [SD 0.6] vs 13.7 [SD 0.2], P<.001). State-by-state variations in “sundowning” searches showed a significant negative association with increasing mean daily sunlight (R2=.16, β=-.429 [SD .149], P=.006) and showed a positive association with increasing latitude (R2=.38, β=.648 [SD .122], P<.001). Conclusions: Interest in “sundowning” is highest after a weekend, which is a time when external caregiver support is reduced. Searches related to sundown syndrome also were highest in winter, in states with less sun, and in states at more northerly latitudes, supporting disrupted circadian rhythms as another contributing factor to these behaviors.
Background: Caregivers of functionally dependent older persons sometimes seek formal services to support their relatives. However, this process of help-seeking is complex. Objective: The overall aim of the study was to use a co-design approach to develop an electronic health (eHealth) tool to support caregivers in their process of help-seeking. This study presents the first step of the design phase, which aimed to prioritize the user needs to be considered during the development of an eHealth tool. Methods: A total of 3 groups of caregivers, community workers, and health and social service professionals participated in either a co-design session (1 or 2) or an advisory committee in 2 rural areas and 1 urban area. The needs identified in the academic literature and during a previous study were sorted (Technique for Research of Information by Animation of a Group of Experts [TRIAGE] method) by the participants (referred to in this study as co-designers) to obtain a consensus on those to be prioritized. Needs identified, grouped, and removed were ranked and compared. Results: Of the initial list of 32 needs, 12 were modified or merged, 3 added, and 7 deleted as the co-designers felt that the needs were poorly formulated, redundant, irrelevant, or impossible to meet. In the end, 19 needs were identified for the design of the eHealth tool. Conclusions: Many of the identified needs are informational (eg, having access to up-to-date information) and are probably met by existing tools. However, many others are emotional (eg, being encouraged to use the services) and offer an interesting challenge to eHealth tool development. International Registered Report Identifier (IRRID): RR2-10.2196/11634
Background: Evaluation of digital health applications to support older adults’ independence and family caregiving is needed. Digital health is increasingly providing opportunities for older adults and their family caregivers to educate, engage, and share health information across digital platforms. Few apps have documented evidence of usability by older adults and their caregivers. Objective: The objective of this study was to determine the usability of a mobile app in a community-based older adult population aged ≥65 years. The app was designed to improve engagement of the patient-informal caregiver team. Methods: This observational usability study was conducted in participants’ homes and independent living facilities in Baltimore, Maryland. Community-dwelling older adults aged ≥65 years and their caregivers enrolled as a dyad (n=24, 12 dyads). The usability evaluation was a mobile and Web-based app that allowed older adult users to record social and health information and share this information with their caregivers. The older adult-caregiver dyad downloaded the app to a smart phone or accessed the Web version, participated in training and onboarding, and used the app for a 1-month period. Participants responded to weekly surveys sent by app push notifications and to the usability and satisfaction surveys at the end of the study. Participant satisfaction and usability were assessed using the Modified Mobile Application Rating Scale (M-MARS) and the System Usability Scale (SUS). Results: The final sample comprised 16 people (8 dyads). Responses to the M-MARS were comparable between older adults and caregiver respondents in terms of engagement and functionality. Caregivers rated aesthetics slightly higher (mean 3.7) than older adult participants did (mean 3.3). Although most responses to the SUS were around the mean (2.3-3.4), older adults and their caregivers differed with regard to integration of app features (mean 3.7 vs 2.8) and the need to learn more before using the app (mean 2.3 vs 3.1). Conclusions: Technology ownership and use among older adults and caregivers was high. Usability and engagement of the mobile app was average. Additional training is recommended for older adults and their caregivers, including that on targeted behaviors for digital health record keeping.
Background: Fall risk assessment is a time-consuming and resource-intensive activity. Patient-driven self-assessment as a preventive measure might be a solution to reduce the number of patients undergoing a full clinical fall risk assessment. Objective: The aim of this study was (1) to analyze test accuracy of the Aachen Falls Prevention Scale (AFPS) and (2) to compare these results with established fall risk assessment measures identified by a review of systematic reviews. Methods: Sensitivity, specificity, and receiver operating curves (ROC) of the AFPS were calculated based on data retrieved from 2 independent studies using the AFPS. Comparison with established fall risk assessment measures was made by conducting a review of systematic reviews and corresponding meta-analysis. Electronic databases PubMed, Web of Science, and EMBASE were searched for systematic reviews and meta-analyses that reviewed fall risk assessment measures between the years 2000 and 2018. The review of systematic reviews was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. The Revised Assessment of Multiple SysTemAtic Reviews (R-AMSTAR) was used to assess the methodological quality of reviews. Sensitivity, specificity, and ROC were extracted from each review and compared with the calculated values of the AFPS. Results: Sensitivity, specificity, and ROC of the AFPS were evaluated based on 2 studies including a total of 259 older adults. Regarding the primary outcome of the AFPS subjective risk of falling, pooled sensitivity is 57.0% (95% CI 0.467-0.669) and specificity is 76.7% (95% CI 0.694-0.831). If 1 out of the 3 subscales of the AFPS is used to predict a fall risk, pooled sensitivity could be increased up to 90.0% (95% CI 0.824-0.951), whereas mean specificity thereby decreases to 50.0% (95% CI 0.42-0.58). A systematic review for fall risk assessment measures produced 1478 articles during the study period, with 771 coming from PubMed, 530 from Web of Science, and 177 from EMBASE. After eliminating doublets and assessing full text, 8 reviews met the inclusion criteria. All were of sufficient methodological quality (R-AMSTAR score ≥22). A total number of 9 functional or multifactorial fall risk assessment measures were extracted from identified reviews, including Timed Up and Go test, Berg Balance Scale, Performance-Oriented Mobility Assessment, St Thomas’s Risk Assessment Tool in Falling Elderly, and Hendrich II Fall Risk Model. Comparison of these measures with pooled sensitivity and specificity of the AFPS revealed a sufficient quality of the AFPS in terms of a patient-driven self-assessment tool. Conclusions: It could be shown that the AFPS reaches a test accuracy comparable with that of the established methods in this initial investigation. However, it offers the advantage that the users can perform the self-assessment independently at home without involving trained health care professionals.
Background: An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear. Objective: This study aimed to assess older adults’ perceptions of using mobile and connected health technologies. Methods: Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone. Results: The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device (P<.001) and joining an online health community (P=.05). Conclusions: Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.
Background: The application of technology is an important and growing aspect in the field of long-term care. Growing evidence shows the positive impact of technology aids in helping the lives of the elderly. However, it is not known which aspects of information and communications technology (ICT) are preferred by older adults living in long-term care facilities. Objective: The goal of the research was to compare the impact of ICT-communication, ICT-entertainment, and conventional care on older adults’ health and psychological change after interventions among older adults in assisted-living facilities. Methods: A three-arm group-randomized trial design was used to evaluate participants who resided in three different but comparable assisted-living facilities and received different aspects of the ICT interventions. A total of 54 older adults with disabilities received one of the three interventions over 12 weeks and completed pre- and postevaluations on quality of life, social support, and psychological well-being. Results: Participants completing this study had a mean age of 73 (SD 11.4) years, and 50% (27/54) were male. Both the ICT-communication and ICT-entertainment groups showed significant improvement in the mental component of quality of life (4.11, P=.012 and 37.32, P<.001, respectively), family/friend–related social support (0.05, P=.001 and 0.04, P<.001, respectively), happiness (0.79, P=.038 and 3.72, P=.001, respectively), and depressive symptoms (–2.74, P=.001 and –7.33, P<.001, respectively). Importantly, participants in the ICT-entertainment group improved significantly more than the other two groups. The ICT-entertainment group also showed improvement in the physical component of quality of life (20.49, P<.001) and health care worker–related social support (0.1, P=.008). Conclusions: Results suggest that the entertainment but not the communication part of ICT is the most effective health promotion method for improving the health and psychological well-being of older adults in assisted-living facilities.
Background: Smart Health technologies (s-Health technologies) are being developed to support people with dementia (PwD) and their informal caregivers at home, to improve care and reduce the levels of burden and stress they experience. However, although s-Health technologies have the potential to facilitate this, the factors influencing a successful implementation in this population are still unknown. Objective: The aim of this study was to review existing literature to explore the factors influencing PwD and their informal caregivers’ adoption of s-Health technologies for home care. Methods: Following the Arksey and O’Malley methodology, this study is a scoping review providing a narrative description of the scientific literature on factors influencing s-Health technology adoption for PwD and their informal caregivers. A search was conducted using PubMed, the Cochrane library, the IEEE library, and Scopus. Publications screening was conducted by 2 researchers based on inclusion criteria, and full-text analysis was then conducted by 1 researcher. The included articles were thematically analyzed by 2 researchers to gain an insight into factors influencing adoption that PwD and their informal caregivers have to encounter when using s-Health technologies. Relevant information was identified and coded. Codes were later discussed between the researchers for developing and modifying them and for achieving a consensus, and the researchers organized the codes into broader themes. Results: Emerging themes were built in a way that said something specific and meaningful about the research question, creating a list of factors influencing the adoption of s-Health technologies for PwD and their informal caregivers, including attitudinal aspects, ethical issues, technology-related challenges, condition-related challenges, and identified gaps. A design framework was created as a guide for future research and innovation in the area of s-Health technologies for PwD and their informal caregivers: DemDesCon for s-Health Technologies. DemDesCon for s-Health Technologies addresses 4 domains to consider for the design and development of s-Health technologies for this population: cognitive decline domain, physical decline domain, social domain, and development domain. Conclusions: Although s-Health technologies have been used in health care scenarios, more work is needed for them to fully achieve their potential for use in dementia care. Researchers, businesses, and public governments need to collaborate to design and implement effective technology solutions for PwD and their informal caregivers, but the lack of clear design guidelines seems to be slowing the process. We believe that the DemDesCon framework will provide them with the guidance and assistance needed for creating meaningful devices for PwD home care and informal caregivers, filling a much-needed space in the present knowledge gap.
