Wearable Devices for Physical Activity and Healthcare Monitoring in Elderly People: A Critical Review

The future of healthy ageing: Wearables in public health, disease prevention and healthcare

Wearables have evolved into accessible tools for sports, research, and interventions. Their use has expanded to real-time monitoring of behavioural parameters related to ageing and health. This paper provides an overview of the literature on wearables in disease prevention and healthcare over the life course (not only in the older population), based on insights from the Future of Diagnostics Workshop (Leiden, January 2024). Wearable-generated parameters include blood glucose, heart rate, step count, energy expenditure, and oxygen saturation. Integrating wearables in healthcare is protracted and far from mainstream implementation, but promises better diagnosis, biomonitoring, and assessment of medical interventions. The main lifestyle factors monitored directly with wearables or through smartphone applications for disease prevention include physical activity, energy expenditure, gait, sleep, and sedentary behaviour. Insights on dietary consumption and nutrition have resulted from continuous glucose monitors. These factors are important for healthy ageing due to their effect on underlying disease pathways. Inclusivity and engagement, data quality and ease of interpretation, privacy and ethics, user autonomy in decision making, and efficacy present challenges to but also opportunities for their use, especially by older people. These need to be addressed before wearables can be integrated into mainstream medical and public health strategies. Furthermore, six key considerations need to be tackled: 1) engagement, health literacy, and compliance with personalised feedback, 2) technical and standardisation requirements for scalability, 3) accountability, data safety/security, and ethical concerns, 4) technological considerations, access, and capacity building, 5) clinical relevance and risk of overdiagnosis/overmedicalisation, and 6) the clinician's perspective in implementation.

Adoption barriers and facilitators of wearable health devices with AI integration: a patient-centred perspective

Introduction

Wearable devices that incorporate artificial intelligence (AI) have revolutionised healthcare through continuous monitoring, early detection, and tailored management of chronic diseases.

Methods

This cross-sectional study analysed patients' perceptions, trust, and awareness of AI-driven wearable health technologies, emphasising the identification of primary facilitators and barriers to adoption. A total of 455 participants, comprising individuals with chronic conditions, were recruited through convenience and stratified sampling methods. Data were collected via an online questionnaire that included demographic questions, Likert-scale items, and multiple-choice questions to evaluate awareness of particular AI features and the functionalities of wearable devices.

Results and discussion

The findings indicated predominantly positive perceptions, with most participants concurring that wearable devices improve proactive care, facilitate remote consultations, and deliver precise health insights. Concerns regarding technical failures, data accuracy, and the potential reduction of human interaction were significant. No notable demographic differences were identified; however, participants with chronic conditions expressed more favourable perceptions. The research emphasises the necessity of user education, technical reliability, and professional oversight for the successful integration of AI-powered wearables in the management of chronic diseases.

Ethical and legal implications of health monitoring wearable devices: A scoping review

BACKGROUND

health monitoring wearable devices (HMWDs) are increasingly implemented for personalized and preventive care. This review aims to summarize the existing literature on the ethical and legal implications of HMWDs in healthcare.

METHODS

the study design is a scoping review and narrative synthesis of scientific literature. The literature search was conducted in May 2023, and updated on March 15th, 2024, from the following databases: PubMed, EBSCO (including CINAHL, PsycInfo, Index to Legal Periodicals & Books, Philosopher's Index), HeinOnline, Engineering Village, Nexis Uni and Cochrane Library. Pairs of blinded authors independently screened articles using Rayyan software, and manually checked reference lists of included articles. Peer-reviewed articles in English discussing ethical and/or legal implications of HMWDs in healthcare were included. A thematic synthesis approach was used to identify and summarize ethical and legal issues and recommendations.

PROTOCOL REGISTRATION

https://osf.io/kfuh4/.

FINDINGS

overall, out of 7767 records retrieved, 405 full texts were assessed, and 12 articles, published between 2017 and 2024, were included. We identified 6 main themes: the use of HMWDs may adversely affect and reshape care relationships and the healthcare system; the use of HMWDs raises a variety of justice-related concerns; there are ethical issues related to personal data; HMWDs present several risks but the benefits are still uncertain; there are ethical issues regarding clinical research on HMWDs; and the current regulatory framework is inadequate.

INTERPRETATION

the use of HMWDs in clinical and research settings raises several ethical and legal concerns, ranging from patient safety to autonomy, justice, and data protection. Implementing HMWDs without addressing these concerns may lead to dehumanization and datafication of care relationships and further marginalization of vulnerable populations.

Research progress and visual analysis of smart wearable devices for the elderly in the context of ageing 4.0

BackgroundThe world's population is ageing quickly, making managing older people's health more difficult. Although smart wearable devices (SWDs) have gained attention as possible instruments for enhancing health outcomes in this population, it is still uncertain how useful and long-lasting they will be.Objectives: The purpose of this study is to methodically evaluate the advancement of academic research and development concerning SWDs for the aged, with an emphasis on the effects these diseases have on quality of life, illness prevention, and health management.Method: 649 publications published in the Web of Science Core Collection between 2013 and 2023 underwent bibliometric analysis. VOSviewer and CiteSpace software were used to assess topic changes, publishing trends, and citation patterns. Co-citation analysis was used to pinpoint important literature and research focus clusters.ResultsThe results show a notable rise in publications about SWDs for the aged, emphasising essential topics, including fall detection, technology adoption, and healthcare applications. According to the report, multidisciplinary research integration is expanding, and publications like IEEE SENSORS JOURNAL and SENSORS-BASEL are making significant contributions.ConclusionThe research emphasises the crucial need to conduct long-term monitoring studies to verify the health advantages of SWDs for senior citizens. It highlights the necessity for user-centred design and urges future research to concentrate on improving wearable technology accuracy to maximise its usefulness in managing geriatric health.

Enhancing Emotional Well-Being With IoT Data Solutions for Depression: A Systematic Review

Effectively caring for adults with depression is challenging. While technology offers potential improvements in emotional well-being through better monitoring, standardised methods to gather and analyse relevant data are highly fragmented. This Systematic Literature Review (SLR) explores using Internet of Things (IoT) based data collection and analysis to enhance emotional well-being and manage depression effectively. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we report in-depth findings from 42 studies, which were selected from an initial set of 559 published works. We find that current literature extensively covers important topics like IoT for detecting, analysing, and monitoring emotions, therapeutic interventions for emotional well-being, and predicting, detecting, and managing depression. IoT-based data collection and analysis solutions predominantly employ sensors and AI, respectively. The literature review identifies a gap in prioritising active systems that engage users, highlighting the need to address key aspects such as privacy and security.

Effects of Twelve Weeks of Square Stepping Exercises on Physical and Cognitive Function and Plasma Content of SMP30: A Randomised Control Trial

BACKGROUND

Ageing and sedentary lifestyles affect physical and cognitive function and markers of frailty, increasing the risk of falls in older adults and affecting their quality of life. The aim of this study was to evaluate the effects of a Square Step Exercise programme on physical and cognitive function and plasma SMP30 levels for the prevention of falls in older adults.

METHODS

A randomised controlled trial was designed with 44 participants assigned to an experimental group (SSE group) and a control group. The SSE group performed SSE sessions twice a week for three months, with a follow-up in the fourth month. The assessments of physical function included tests such as the Four-Square Step Test, Brisk Walking and its dual-task variant, Time Up and Go and its imagined and dual-task variants, 30 s Sit-to-Stand and its dual-task and imagined variants and a 6 min walking test. Questionnaires were also used to assess the risk and fear of falling. Resting EEG activity was also recorded to assess electrocortical brain activity. SMP30 levels were measured by Western blotting.

RESULTS

The SSE group showed significant improvements compared to the control group in the Four-Square Step Test (p < 0.001), Brisk Walking (p < 0.05) and reduction in the fear of falling (p < 0.001) after the training programme, but these adaptations were not maintained one month after the programme ended (p < 0.05). No significant changes were observed in the remaining variables of physical function, cognitive function, fall risk questionnaire, EEG activity or plasma levels of SMP30 compared to the control group (p > 0.05).

CONCLUSIONS

The SSE programme showed efficacy in improving balance, gait speed and reducing fear of falling in older adults but did not show improvement over the control group in other areas of physical or cognitive function or plasma SMP30 levels for fall prevention.

Navigating Through Innovation in Elderly's Health: A Scoping Review of Digital Health Interventions

Objectives

Comprehensively map and summarize digital health initiatives for the elderly and caregivers.

Methods

Scoping review between April and May 2022 based on Joanna Briggs methodology. Databases used included PubMed, Cochrane Library, CINAHL Plus, and Web of Science, along with grey literature and hand searches. Two reviewers independently conducted screening and eligibility phases, with a third resolving disagreements. Data were thematically analyzed.

Results

The review included 421 documents. Most documents were published between 2013 and 2022, with a recent increase. Most studies, originating from high-income countries, focused on home applications and were mainly in the testing and validation stages. Telephones and computers were the predominant devices. Health objectives included monitoring, prevention, and treatment, with interventions utilizing directed communication and personal health monitoring for individuals, and telemedicine and decision support for healthcare providers.

Conclusion

Increasing integration of technology in older adults' lives, along with their increasing proficiency, is driving a significant rise in digital health interventions. Despite this growth, further research in middle- and low-income countries, for caregivers and evaluating effectiveness and feasibility of these technological interventions is needed.

The Role of Smartwatch Technology in the Provision of Care for Type 1 or 2 Diabetes Mellitus or Gestational Diabetes: Systematic Review

Background

The use of smart technology in the management of all forms of diabetes mellitus has grown significantly in the past 10 years. Technologies such as the smartwatch have been proposed as a method of assisting in the monitoring of blood glucose levels as well as other alert prompts such as medication adherence and daily physical activity targets. These important outcomes reach across all forms of diabetes and have the potential to increase compliance of self-monitoring with the aim of improving long-term outcomes such as hemoglobin A1c (HbA1c).

Objective

This systematic review aims to explore the literature for evidence of smartwatch technology in type 1, 2, and gestational diabetes.

Methods

A systematic review was undertaken by searching Ovid MEDLINE and CINAHL databases. A second search using all identified keywords and index terms was performed on Ovid MEDLINE (January 1966 to August 2023), Embase (January 1980 to August 2023), Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, latest issue), CINAHL (from 1982), IEEE Xplore, ACM Digital Libraries, and Web of Science databases. Type 1, type 2, and gestational diabetes were eligible for inclusion. Quantitative studies such as prospective cohort or randomized clinical trials that explored the feasibility, usability, or effect of smartwatch technology in people with diabetes were eligible. Outcomes of interest were changes in blood glucose or HbA1c, physical activity levels, medication adherence, and feasibility or usability scores.

Results

Of the 8558 titles and abstracts screened, 5 studies were included for qualitative synthesis in this review. A total of 322 participants with either type 1 or type 2 diabetes mellitus were included in the review. A total of 4 studies focused on the feasibility and usability of smartwatch technology in diabetes management. One study conducted a proof-of-concept randomized clinical trial including smartwatch technology for exercise time prescriptions for participants with type 2 diabetes mellitus. Adherence of participants to smartwatch technology varied between included studies, with one reporting input submissions of 58% and another reporting that participants logged 50% more entries than they were required to. One study reported significantly improved glycemic control with integrated smartwatch technology, with increased exercise prescriptions; however, this study was not powered and required a longer observational period.

Conclusions

This systematic review has highlighted the lack of robust randomized clinical trials that explore the efficacy of smartwatch technology in the management of patients with type 1, type 2, and gestational diabetes. Further research is required to establish the role of integrated smartwatch technology in important outcomes such as glycemic control, exercise participation, drug adherence, and diet monitoring in people with all forms of diabetes mellitus.

Assessment of Wearable Device Adherence for Monitoring Physical Activity in Older Adults: Pilot Cohort Study

Background

Physical activity has emerged as a modifiable behavioral factor to improve cognitive function. However, research on adherence to remote monitoring of physical activity in older adults is limited.

Objective

This study aimed to assess adherence to remote monitoring of physical activity in older adults within a pilot cohort from objective user data, providing insights for the scalability of such monitoring approaches in larger, more comprehensive future studies.

Methods

This study included 22 participants from the Boston University Alzheimer's Disease Research Center Clinical Core. These participants opted into wearing the Verisense watch as part of their everyday routine during 14-day intervals every 3 months. Eighteen continuous physical activity measures were assessed. Adherence was quantified daily and cumulatively across the follow-up period. The coefficient of variation was used as a key metric to assess data consistency across participants over multiple days. Day-to-day variability was estimated by calculating intraclass correlation coefficients using a 2-way random-effects model for the baseline, second, and third days.

Results

Adherence to the study on a daily basis outperformed cumulative adherence levels. The median proportion of adherence days (wearing time surpassed 90% of the day) stood at 92.1%, with an IQR spanning from 86.9% to 98.4%. However, at the cumulative level, 32% (7/22) of participants in this study exhibited lower adherence, with the device worn on fewer than 4 days within the requested initial 14-day period. Five physical activity measures have high variability for some participants. Consistent activity data for 4 physical activity measures might be attainable with just a 3-day period of device use.

Conclusions

This study revealed that while older adults generally showed high daily adherence to the wearable device, consistent usage across consecutive days proved difficult. These findings underline the effectiveness of wearables in monitoring physical activity in older populations and emphasize the ongoing necessity to simplify usage protocols and enhance user engagement to guarantee the collection of precise and comprehensive data.

Anticipation, agency and aging-conditions for making movement irresistible

This article describes an approach to developing and maintaining interpersonal agency through guided movement and responsive technologies. Making Movement Irresistible (MMI), considered conditions for developing a digital, online and wearable intervention that could make the act of movement irresistible for older residents in care, and encourage improvisational and social interactions. Working within a co-design framework, we combined making material objects and moving together as a method of examining the efficacy of human to human, and human to technology relationships to cultivate agency. Given that movement as performance is frequently not practiced or uncomfortable, we invited a variety of experts as our co-designers to notice the nuances of movement that interested them and to document these using drawing, writing and visuals. This documentation was gathered regularly in journals as the workshops progressed, leading to a coherent capture of data as it emerged. This data allowed us to attribute value to how simple actions could become a conduit for more ambitious, exploratory interactions. Our playful methods afforded the participation of co-designers, enabling us to situate our proposed intervention within a relational and social, rather than medical model, of ageing. Making movement do-able and relational, so that it can be shared and extended with a partner or carer, informed the idea to design a wearable device that could detect movement variability, resulting in a prototype, named emitts®. The device makes use of the hand as way in to accessing whole body interaction. Our work with responsiveness of visual feedback avoided deterministic targets, as with no two movements being identical, the reported problem of compliance with repetitive tasks could be reduced. The technology foregrounded movement that was capricious and improvisational, offering new modes of artistic practice and engagement through play and performance. The case we describe highlights the importance of understanding the conditions that augment social interaction, rather than specifying design criteria for determining interaction. The longer-term health benefits of our intervention have yet to be measured, however, our collaboration has revealed how interpersonal agency emerges when we socially, aesthetically, and physiologically stimulate movement, making it irresistible where there may otherwise be resistance.

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

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.

Unveiling wearables: exploring the global landscape of biometric applications and vital signs and behavioral impact

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users' trust. As technology continues to advance, the refinement of IoT devices' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals' health outcomes and the role of wearables in promoting a healthier lifestyle.

Endurance exercise preserves physical function in adult and older male C57BL/6 mice: high intensity interval training (HIIT) versus voluntary wheel running (VWR)

Exercise has been shown to improve physical function, mitigate aspects of chronic disease and to potentially alter the trajectory of age-related onset of frailty and sarcopenia. Reliable and valid preclinical models are necessary to elucidate the underlying mechanisms at the intersection of age, exercise, and functional decline. The purpose of this study was to compare, head to head, the effects of two common pre-clinical models of endurance exercise: high intensity interval training (HIIT) and voluntary wheel running (VWR). The hypothesis was that a prescribed and regimented exercise program, HIIT, would prove to be a superior training method to unregulated voluntary exercise, VWR. To investigate this hypothesis, we evaluated adult (n = 24, designated 10 m, aged 6 months at the beginning of the study, 10 months at its completion) and older adult (n = 18, designated 26 m, aging from 22 months to 26 months over the course of the study) C57BL/6 male mice. These mice were randomly assigned (with selection criteria) to a 13-week program of voluntary wheel running (VWR), high intensity interval training (HIIT), or sedentary control (SED). The functional aptitude of each mouse was determined pre- and post-training using our composite CFAB (comprehensive functional assessment battery) scoring system consisting of voluntary wheel running (volitional exercise and activity rate), treadmill (endurance), rotarod (overall motor function), grip meter (forelimb strength), and inverted cling (whole body strength/endurance). To measure sarcopenia, we tracked body mass, body composition (with EchoMRI), plantar flexor torque (in 10 m), and measured muscle wet mass post-training. Overall, adult CFAB scores decreased while body mass and percent body fat increased as they matured; however, exercise significantly mitigated the changes (p < 0.05) compared to SED. Older adults demonstrated preservation of function (CFAB) and reduced body fat (p < 0.05) compared to SED. To conclude, both types of exercise maintained physical function equally in older mice.

Exploring Disparities in Healthcare Wearable Use among Cardiovascular Patients: Findings from a National Survey

Background

Use of healthcare wearable devices holds significant potential for improving the prevention and management of cardiovascular diseases (CVD). However, we have limited knowledge on the actual use of wearable devices by CVD patients and the key factors associated with their use. This study aims to assess wearable device use and willingness to share health data among CVD patients, while identifying socio-demographic, health, and technology-related factors associated with wearable technology use.

Methods

Using a national survey of 933 CVD patients, we assess use of wearable healthcare devices (use, frequency of use and willingness to share health data from wearable with a provider), and a set of socio-demographic factors (age, gender, race, education and household income), health-related variables (general health, presence of comorbid conditions: diabetes and high blood pressure, attitude towards exercise) and technology self-efficacy using logistic regression.

Results

Of the 933 CVD patients, 18.34% reported using a healthcare wearable device in the prior 12 months. Of those, 41.92% indicated using it every day and another 19.76% indicated using it 'almost every day'. 83.54% of wearable users indicated their willingness to share health data with their healthcare providers. Female CVD patients are more likely to use wearables compared to men (odds ratio (OR) = 1.65, 95% confidence interval (CI) = 1.04-2.63). The odds decrease with age, and are significantly high in patients with higher income levels. In comparison with non-Hispanic White, Hispanic (OR = 0.14, 95% CI = 0.03-0.70) and African Americans (OR = 0.17, 95% CI = 0.04-0.86) are less likely to use healthcare wearables. CVD patients who perceive their general health to be better (OR = 1.45, 95% CI = 1.11-1.89) and those who enjoy exercising (OR = 1.76, 95% CI = 1.22-2.55) are more likely to use wearables. CVD patients who use the internet for searching for medical information (OR = 2.10, 95% CI = 1.17-3.77) and those who use electronic means to make appointments with their providers (OR = 2.35, 95% CI = 1.48-3.74) are more inclined to use wearables.

Conclusions

Addressing low wearable device usage among CVD patients requires targeted policy interventions to ensure equitable access. Variations in gender, age, race/ethnicity, and income levels emphasize the need for tailored strategies. Technological self-efficacy, positive health perceptions, and exercise enjoyment play significant roles in promoting wearable use. These insights should guide healthcare leaders in designing effective strategies for integrating wearables into cardiovascular care.

A novel smart somatosensory wearable assistive device for older adults' home rehabilitation during the COVID-19 pandemic

Background

Due to the Coronavirus disease 19 (COVID-19) related social distancing measures and health service suspension, physical activity has declined, leading to increased falling risk and disability, and consequently, compromising the older adult health. How to improve the quality of older adult life has become a crucial social issue.

Objective

In traditional rehabilitation, manual and repetitive muscle training cannot identify the patient's rehabilitation effect, and increasing the willingness to use it is not easy. Therefore, based on the usability perspective, this study aims to develop a novel smart somatosensory wearable assistive device (called SSWAD) combined with wireless surface electromyography (sEMG) and exergame software and hardware technology. The older adult can do knee extension, ankle dorsiflexion, and ankle plantar flexion rehabilitation exercises at home. Meanwhile, sEMG values can be digitally recorded to assist physicians (or professionals) in judgment, treatment, or diagnosis.

Methods

To explore whether the novel SSWAD could improve the older adult willingness to use and motivation for home rehabilitation, 25 frail older adult (12 males and 13 females with an average age of 69.3) perform the rehabilitation program with the SSWAD, followed by completing the system usability scale (SUS) questionnaire and the semi-structured interview for the quantitative and qualitative analyses. In addition, we further investigate whether the factor of gender or prior rehabilitation experience would affect the home rehabilitation willingness or not.

Results

According to the overall SUS score, the novel SSWAD has good overall usability performance (77.70), meaning that the SSWAD makes older adult feel interested and improves their willingness for continuous rehabilitation at home. In addition, the individual item scores of SUS are shown that female older adult with prior rehabilitation experience perform better in "Learnability" (t = 2.35, p = 0.03) and "Confidence" (t = -3.24, p = 0.01). On the contrary, male older adult without rehabilitation experience are more willing to adopt new technologies (t = -2.73, p = 0.02), and perform better in "Learnability" (t = 2.18, p = 0.04) and "Confidence" (t = -3.75, p < 0.001) with the SSWAD. In addition, the result of the semi-structured interview shows that the operation of the SSWAD is highly flexible, thus reducing older adult burden during the rehabilitation exercise and using them long-term.

Conclusion

This novel SSWAD receives consistently positive feedback regardless of the gender or prior rehabilitation experience of elders. The SSWAD could be used as a novel way of home rehabilitation for elders, especially during the COVID-19 pandemic. Older adult can do rehabilitation exercises at home, and physicians could make proper judgments or adjust suitable treatments online according to the sEMG data, which older adult can know their rehabilitation progress at the same time. Most importantly, older adult do not have to go to the hospital every time for rehabilitation, which significantly reduces time and the risk of infection.

Adherence to cognitive and physical exercise engagement: a challenge to successful dementia risk reduction and prevention efforts

With human life expectancy and proportion of older adults increasing, global use of evidence-supported preventative methods to minimize risk of brain-related disabilities such as Alzheimer's disease and other dementias-as well as interventions to slow rate of disease progression-is important. Sustained engagement in cognitive and physical exercise programs may prevent or delay dementia onset as well as maximize health and function of those with dementia. Despite awareness of the importance of cognitive and physical exercise to brain health, exercise program adherence by older adults is extremely challenging. In this Perspective article, we summarize what is known about contributors to exercise program adherence and strategies to promote it. We discuss our viewpoint on knowledge gaps regarding exercise adherence and research that needs to be conducted. We conclude by proposing a multi-dimensional exercise adherence assessment framework that includes portable neurophysiologic technologies to inform initial design and updating of individualized exercise programs that optimize sustained exercise program engagement and, ultimately, maximize brain health in older adults with and without mild cognitive impairment and dementia.

Wearable device use in older adults associated with physical activity guideline recommendations: Empirical research quantitative

AIMS AND OBJECTIVES

To (1) describe the socio-demographic and behavioural characteristics of older adults who use wearable devices for physical activity monitoring and (2) explore whether wearable device use increases the possibilities of meeting physical activity guideline recommendations among older adults and older adults with known cardiovascular disease or risk.

BACKGROUND

Finding ways to increase physical activity and reduce cardiovascular disease risk among older adults is a public health priority. Wearable technology has great potential for promoting physical activity among older adults.

DESIGN

A secondary analysis of the national data.

METHODS

A nationally representative sample of older adults aged 65 years and older (mean age = 73.79 years, N = 1484) and a subsample of older adults with known cardiovascular disease or cardiovascular disease risk (mean = 74.32 years, N = 1098) was used in the analysis. All analyses were weighted to account for the complex survey design. This study was reported according to the STROBE checklist.

RESULTS

The overall prevalence of wearable device use among older adults and older adults with cardiovascular disease risk was 16% and 14%, respectively. Older adults with higher household incomes, better self-rated health, and greater exercise enjoyment were more likely to use wearable devices. Compared with non-users, older adult users were more likely to meet the recommended levels of moderate (55% vs. 31%) and strengthening activity guidelines (46% vs. 25%), but not of the sedentary behaviour guideline (69% vs. 62%). Similar findings were also seen in older adults with known cardiovascular disease or risk.

CONCLUSION

The uptake of wearable devices in older adults, particularly those with known cardiovascular disease or risk is still low. The use of wearable devices is an important facilitator of physical activity. It is critical to provide individualised support for their engagement.

RELEVANCE TO CLINICAL PRACTICE

Age-friendly design and individualised support are recommended to increase older adults' adoption of wearable devices to improve their physical health.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution was involved in this study since we used publicly available data.

Understanding physical activity from a cultural-contextual lens

This paper aims to emphasize the need to acknowledge unique cultural and contextual meanings of physical activity to improve health outcomes in different communities. Leininger's Sunrise Model was used as the theoretical base to understand the complex cultural and contextual factors that influence physical activity. Beliefs and practices surrounding physical activity are influenced by a variety of cultural and contextual factors. Providing culturally relevant contexts to the meaning of physical activity allows opportunities for improving policies or programs that would engage individuals and communities in physical activity in culturally meaningful ways. Incorporating cultural and contextual factors is critical to promote physical activity, especially in minority and vulnerable communities.

Association Between Wearable Device Use and Levels of Physical Activity Among Older Adults in the US: Evidence From the 2019-2020 Health Information National Trends Survey

Objective To examine the relationship between electronic wearable device (WD) use and physical activity (PA) levels among older adults in the US. Methods Data were pooled from 3310 older adults from the 2019 and 2020 Health Information National Trends Survey. The explanatory variable was WD use, and the outcomes were weekly PA levels, resistance training, and sedentary time. Logistic regression was conducted to investigate the association between WD use and the reported outcome variables. Separate logistic models were also fitted to explore the relationship between WD use and physical activity outcomes among a subgroup of older adults with chronic conditions. Results A total of 14.4% of older adults reported WD use. Older adults who use WD were more likely to meet national guidelines for weekly levels of PA (odds ratio (OR) 1.60, 95% confidence intervals (CI) (1.10, 2.32); p = 0.015) and resistance strength training (OR 1.54, 95% CI (1.14, 2.09); p = 0.005) when compared with their counterparts not using WD. After restricting the analysis to those with chronic conditions only, WD use was only associated with a higher level of weekly strength training (OR 1.68, 95% CI 1.19, 2.38; p = 0.004). Conclusion  WD use may be associated with increased physical activity among older adults, including those with chronic health conditions. Further studies are needed to examine the factors influencing the adoption and sustained use of WD in older adults.

Changed Digital Technology Perceptions and Influencing Factors among Older Adults during the COVID-19 Pandemic

This correlational study aimed to identify factors that contribute to changes in perceptions of digital technology among older adults during the COVID-19 pandemic. This study utilized raw data from "The 2021 Report on the Digital Divide," a nationwide survey conducted in South Korea. Data were collected from 1171 older adults (aged ≥ 65 years) from September to December 2021. Multiple regression analyses were performed to examine the factors influencing changes in the perception of digital technology. Over one-third of the participants reported positive changes in their perceptions of digital technology during the pandemic. Key factors included self-efficacy for digital devices (β = 0.35, p < 0.001), digital networking (β = 0.11, p < 0.001), accessibility to digital devices (β = 0.10, p = 0.002), and perceived health (β = 0.08, p = 0.003). The expansion of digital technology owing to the pandemic has served as a catalyst for changes in older adults' perceptions. Healthcare providers and caregivers should consider digital technology perceptions and influencing factors when providing digital healthcare services. The results can be utilized to identify vulnerable older adults with negative perceptions of digital technology, thus minimizing disparities in access to digital healthcare services.

Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review

The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly

It is essential to estimate the stress state of the elderly to improve their QoL. Stress states change every day and hour, depending on the activities performed and the duration/intensity. However, most existing studies estimate stress states using only biometric information or specific activities (e.g., sleep duration, exercise duration/amount, etc.) as explanatory variables and do not consider all daily living activities. It is necessary to link various daily living activities and biometric information in order to estimate the stress state more accurately. Specifically, we construct a stress estimation model using machine learning with the answers to a stress status questionnaire obtained every morning and evening as the ground truth and the biometric data during each of the performed activities and the new proposed indicator including biological and activity perspectives as the features. We used the following methods: Baseline Method 1, in which the RRI variance and Lorenz plot area for 4 h after waking and 24 h before the questionnaire were used as features; Baseline Method 2, in which sleep time was added as a feature to Baseline Method 1; the proposed method, in which the Lorenz plot area per activity and total time per activity were added. We compared the results with the proposed method, which added the new indicators as the features. The results of the evaluation experiments using the one-month data collected from five elderly households showed that the proposed method had an average estimation accuracy of 59%, 7% better than Baseline Method 1 (52%) and 4% better than Baseline Method 2 (55%).

A new approach to digital health? Virtual COVID-19 care: A scoping review

Aims

The use of virtual care enabled by digital technologies has increased, prompted by public health restrictions in response to COVID-19. Non-hospitalized persons in the acute phase of COVID-19 illness may have unique health needs while self-isolating in the community. This scoping review aimed to explore the nature of care, the use of digital technologies, and patient outcomes arising from virtual care among community-based self-isolating COVID-19 patients.

Methods

Literature searches for peer-reviewed articles were conducted in four bibliographic databases: CINAHL, Medline, Embase and Cochrane Database of Systematic Reviews between January and February 2022, followed by hand-searching reference lists of included articles. Two levels of screening using defined eligibility criteria among two independent reviewers were completed.

Results

Of the 773 articles retrieved, 19 were included. Results indicate that virtual care can be safe while enabling timely detection of clinical deterioration to improve the illness trajectory. COVID-19 virtual care was delivered by single health professionals or by multidisciplinary teams using a range of low-technology methods such as telephone to higher technology methods like wearable technology that transmitted physiological data to the care teams for real-time or asynchronous monitoring.

Conclusion

The review described the varied nature of virtual care including its design, implementation, and evaluation. Further research is needed for continued exploration on how to leverage digital health assets for the delivery of appropriate and safe virtual COVID-19 community care, which can support patient recovery, control transmission, and prevent intensifying the burden on the health care system, especially during surges.

Wearable Activity Tracker Use and Physical Activity Among Informal Caregivers in the United States: Quantitative Study

BACKGROUND

With an increase in aging population and chronic medical conditions in the United States, the role of informal caregivers has become paramount as they engage in the care of their loved ones. Mounting evidence suggests that such responsibilities place substantial burden on informal caregivers and can negatively impact their health. New wearable health and activity trackers (wearables) are increasingly being used to facilitate and monitor healthy behaviors and to improve health outcomes. Although prior studies have examined the efficacy of wearables in improving health and well-being in the general population, little is known about their benefits among informal caregivers.

OBJECTIVE

This study aimed to examine the association between use of wearables and levels of physical activity (PA) among informal caregivers in the United States.

METHODS

We used data from the National Cancer Institute's Health Information National Trends Survey 5 (cycle 3, 2019 and cycle 4, 2020) for a nationally representative sample of 1273 community-dwelling informal caregivers-aged ≥18 years, 60% (757/1273) female, 75.7% (990/1273) had some college or more in education, and 67.3% (885/1273) had ≥1 chronic medical condition-in the United States. Using jackknife replicate weights, a multivariable logistic regression was fit to assess an independent association between the use of wearables and a binary outcome: meeting or not meeting the current World Health Organization's recommendation of PA for adults (≥150 minutes of at least moderate-intensity PA per week).

RESULTS

More than one-third (466/1273, 37.8%) of the informal caregivers met the recommendations for adult PA. However, those who reported using wearables (390/1273, 31.7%) had slightly higher odds of meeting PA recommendations (adjusted odds ratios 1.1, 95% CI 1.04-1.77; P=.04) compared with those who did not use wearables.

CONCLUSIONS

The results demonstrated a positive association between the use of wearables and levels of PA among informal caregivers in the United States. Therefore, efforts to incorporate wearable technology into the development of health-promoting programs or interventions for informal caregivers could potentially improve their health and well-being. However, any such effort should address the disparities in access to innovative digital technologies, including wearables, to promote health equity. Future longitudinal studies are required to further support the current findings of this study.

Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus

Currently, wearable technology is present in different fields that aim to satisfy our needs in daily life, including the improvement of our health in general, the monitoring of patient health, ensuring the safety of people in the workplace or supporting athlete training. The objective of this bibliometric analysis is to examine and map the scientific advances in wearable technologies in healthcare, as well as to identify future challenges within this field and put forward some proposals to address them. In order to achieve this objective, a search of the most recent related literature was carried out in the Scopus database. Our results show that the research can be divided into two periods: before 2013, it focused on design and development of sensors and wearable systems from an engineering perspective and, since 2013, it has focused on the application of this technology to monitoring health and well-being in general, and in alignment with the Sustainable Development Goals wherever feasible. Our results reveal that the United States has been the country with the highest publication rates, with 208 articles (34.7%). The University of California, Los Angeles, is the institution with the most studies on this topic, 19 (3.1%). Sensors journal (Switzerland) is the platform with the most studies on the subject, 51 (8.5%), and has one of the highest citation rates, 1461. We put forward an analysis of keywords and, more specifically, a pennant chart to illustrate the trends in this field of research, prioritizing the area of data collection through wearable sensors, smart clothing and other forms of discrete collection of physiological data.

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Anti-Reflective Zeolite Coating for Implantable Bioelectronic Devices

Since sunlight is one of the most easily available and clean energy supplies, solar cell development and the improvement of its conversion efficiency represent a highly interesting topic. Superficial light reflection is one of the limiting factors of the photovoltaic cells (PV) efficiency. To this end, interfacial layer with anti-reflective properties reduces this phenomenon, improving the energy potentially available for transduction. Nanoporous materials, because of the correlation between the refractive index and the porosity, allow low reflection, improving light transmission through the coating. In this work, anti-reflective coatings (ARCs) deposited on commercial PV cells, which were fabricated using two different Linde Type A (LTA) zeolites (type 3A and 4A), have been investigated. The proposed technique allows an easier deposition of a zeolite-based mixture, avoiding the use of chemicals and elevated temperature calcination processes. Results using radiation in the range 470–610 nm evidenced substantial enhancement of the fill factor, with maximum achieved values of over 40%. At 590 and 610 nm, which are the most interesting bands for implantable devices, FF is improved, with a maximum of 22% and 10%, respectively. ARCs differences are mostly related to the morphology of the zeolite powder used, which resulted in thicker and rougher coatings using zeolite 3A. The proposed approach allows a simple and reliable deposition technique, which can be of interest for implantable medical devices.

A fabric-based multifunctional sensor for the early detection of skin decubitus ulcers

Monitoring biosignals at the skin interface is necessary to suppress the potential for decubitus ulcers in immobile patients confined to bed. We develop conformally contacted, disposable, and breathable fabric-based electronic devices to detect skin impedance, applied pressure, and temperature, simultaneously. Based on the experimental evaluation of the multifunctional sensors, a combination of robust AgNW electrodes, soft ionogel capacitive pressure sensor, and resistive temperature sensor on fabric provides alarmed the initiation of early-stage decubitus ulcers without signal distortion under the external stimulus. For clinical verification, an animal model is established with a pair of magnets to mimic a human decubitus ulcers model in murine in vivo. The evidence of pressure-induced ischemic injury is confirmed with the naked eye and histological and molecular biomarker analyses. Our multifunctional integrated sensor detects the critical time for early-stage decubitus ulcer, establishing a robust correlation with the biophysical parameters of skin ischemia and integrity, including temperature and impedance.

Accuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes

Nowadays, the use of wearable devices is spreading in different fields of application, such as healthcare, digital health, and sports monitoring. In sport applications, the present trend is to continuously monitor the athletes’ physiological parameters during training or competitions to maximize performance and support coaches. This paper aims to evaluate the performances in heart rate assessment, in terms of accuracy and precision, of both wrist-worn and chest-strap commercial devices used during swimming activity, considering a test population of 10 expert swimmers. Three devices were employed: Polar H10 cardiac belt, Polar Vantage V2, and Garmin Venu Sq smartwatches. The former was used as a reference device to validate the data measured by the two smartwatches. Tests were performed both in dry and wet conditions, considering walking/running on a treadmill and different swimming styles in water, respectively. The measurement accuracy and precision were evaluated through standard methods, i.e., Bland–Altman plot, analysis of deviations, and Pearson’s correlation coefficient. Results show that both precision and accuracy worsen during swimming activity (with an absolute increase of the measurement deviation in the range of 13–56 bpm for mean value and 49–52 bpm for standard deviation), proving how water and arms movement act as relevant interference inputs. Moreover, it was found that wearable performance decreases when activity intensity increases, highlighting the need for specific research for wearable applications in water, with a particular focus on swimming-related sports activities.

Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers

Physiological signals (e.g., heart rate, skin conductance) that were traditionally studied in neuroscientific laboratory research are currently being used in numerous real-life studies using wearable technology. Physiological signals obtained with wearables seem to offer great potential for continuous monitoring and providing biofeedback in clinical practice and healthcare research. The physiological data obtained from these signals has utility for both clinicians and researchers. Clinicians are typically interested in the day-to-day and moment-to-moment physiological reactivity of patients to real-life stressors, events, and situations or interested in the physiological reactivity to stimuli in therapy. Researchers typically apply signal analysis methods to the data by pre-processing the physiological signals, detecting artifacts, and extracting features, which can be a challenge considering the amount of data that needs to be processed. This paper describes the creation of a “Wearables” R package and a Shiny “E4 dashboard” application for an often-studied wearable, the Empatica E4. The package and Shiny application can be used to visualize the relationship between physiological signals and real-life stressors or stimuli, but can also be used to pre-process physiological data, detect artifacts, and extract relevant features for further analysis. In addition, the application has a batch process option to analyze large amounts of physiological data into ready-to-use data files. The software accommodates users with a downloadable report that provides opportunities for a careful investigation of physiological reactions in daily life. The application is freely available, thought to be easy to use, and thought to be easily extendible to other wearable devices. Future research should focus on the usability of the application and the validation of the algorithms.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers’ physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers’ physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers’ PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO₂. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers’ physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys’ physical fitness prediction, and 99.26% for girls’ physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers’ physical fitness levels by their running PPG recordings.

Remote Healthcare for Elderly People Using Wearables: A Review

The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.

Pilots for Healthy and Active Ageing (PHArA-ON) Project: Definition of New Technological Solutions for Older People in Italian Pilot Sites Based on Elicited User Needs

BACKGROUND

The Pilots for Healthy and Active Ageing (PHArA-ON) project aimsto ensure reality smart and active living for Europe's ageing population by creating a set of integrated and highly customizable interoperable open platforms with advanced services, devices, and technologies and tools. The aim of the present study was to determine the needs and preferences of older people and their caregivers for improving healthy and active aging and guiding the technological development of thePHArA-ON system.

METHODS

A pre-structured interview was administered to older adults, informal caregivers and professional caregivers (including social operators) taking part in the piloting sessions.

RESULTS

Interviews were carried out in Umana Persone Social Enterprise R&D Network (UP) in Tuscany, and Ospedale Casa SollievodellaSofferenza (CSS) in Apulia. A total of 22 older adults, 22 informal caregivers, 13 professional caregivers and 4 social operators were recruited. A prioritization analysis of services, according to the stakeholder's needs, has determined two fundamental need categories: Heath Management (i.e., stimulation and monitoring), and Socialisation (i.e., promoting social inclusion).

CONCLUSIONS

The main scientific contributions to this study are the following: to design and evaluate technology in the context of healthy and active ageing, to acquire relevant knowledge on user needs to develop technologies that can handle the real life situations of older people, obtain useful insights about the attitude and availability of end-users in using technologies in clinical practice, and to provide important guidelines to improve the PHArA-ON system. Specific experimentation stages were also carried out to understand which kind of technology is more acceptable, and to obtain feedback regarding the development priority related to the impact of the proposed services. Research through fruitful and continuous interaction with the different subjects involved in the development process of the system, as well as with stakeholders, enabled the implementation of a platform which could be further and easily integrated and improved.

Too old for technology? Use of wearable healthcare devices by older adults and their willingness to share health data with providers

Wearable healthcare devices offer tremendous promise to effectively track and improve the well-being of older adults. Yet, little is known about the use of wearable devices by older adults. Drawing upon a national survey in US with 1481 older adults, we examine the use of wearable healthcare devices and the key predictors of use viz. sociodemographic factors, health conditions, and technology self-efficacy. We also examine if the predictors are associated with elders' willingness to share health data from wearable devices with healthcare providers. We find low level of wearable use (17.49%) among US older adults. We find significant positive associations between technology self-efficacy, health conditions, and demographic factors (gender, race, education, and annual household income) and use of wearable devices. Men were less likely (OR = 0.62, 95% CI 0.36-1.04) and Asians were more likely (OR = 2.60, 95% CI 0.89-7.64) to use wearables, as did healthy adults (OR = 1.98, 95% CI 1.37-2.87). Those who electronically communicated with their doctors (OR = 1.86, 95% CI 1.16-2.97), and those who searched online for health information (OR = 1.79, 95% CI 1.03-3.10) were more likely to use wearables. Though 80.15% of wearable users are willing to share health data with providers, those with greater technology self-efficacy and favorable attitudes toward exercise are more willing.

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

Frailty syndrome is an independent risk factor for serious health episodes, disability, hospitalization, falls, loss of mobility, and cardiovascular disease. Its high reversibility demands personalized interventions among which exercise programs are highly efficient to contribute to its delay. Information technology-based solutions to support frailty have been recently approached, but most of them are focused on assessment and not on intervention. This paper describes a sensor-based mHealth platform integrated in a service-based architecture inside the FRAIL project towards the remote monitoring and intervention of pre-frail and frail patients at home. The aim of this platform is constituting an efficient and scalable system for reducing both the impact of aging and the advance of frailty syndrome. Among the results of this work are: (1) the development of elderly-focused sensors and platform; (2) a technical validation process of the sensor devices and the mHealth platform with young adults; and (3) an assessment of usability and acceptability of the devices with a set of pre-frail and frail patients. After the promising results obtained, future steps of this work involve performing a clinical validation in order to quantify the impact of the platform on health outcomes of frail patients.