Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation

Please cite as: Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation JMIR Mhealth Uhealth 2016;4(1):e7 DOI: 10.2196/mhealth.4225 PMID: 26818775 PMCID: 4749845Background: Physical inactivity and sedentary behavior increase the risk of chronic illness and death. The newest generation of “wearable” activity trackers offers potential as a multifaceted intervention to help people become more active. Objective: To examine the usability and usefulness of wearable activity trackers for older adults living with chronic illness. Methods: We recruited a purposive sample of 32 participants over the age of 50, who had been previously diagnosed with a chronic illness, including vascular disease, diabetes, arthritis, and osteoporosis. Participants were between 52 and 84 years of age (mean 64); among the study participants, 23 (72%) were women and the mean body mass index was 31 kg/m2 . Participants tested 5 trackers, including a simple pedometer (Sportline or Mio) followed by 4 wearable activity trackers (Fitbit Zip, Misfit Shine, Jawbone Up 24, and Withings Pulse) in random order. Selected devices represented the range of wearable products and features available on the Canadian market in 2014. Participants wore each device for at least 3 days and evaluated it using a questionnaire developed from the Technology Acceptance Model. We used focus groups to explore participant experiences and a thematic analysis approach to data collection and analysis. Results: Our study resulted in 4 themes: (1) adoption within a comfort zone; (2) self-awareness and goal setting; (3) purposes of data tracking; and (4) future of wearable activity trackers as health care devices. Prior to enrolling, few participants were aware of wearable activity trackers. Most also had been asked by a physician to exercise more and cited this as a motivation for testing the devices. None of the participants planned to purchase the simple pedometer after the study, citing poor accuracy and data loss, whereas 73% (N=32) planned to purchase a wearable activity tracker. Preferences varied but 50% felt they would buy a Fitbit and 42% felt they would buy a Misfit, Jawbone, or Withings. The simple pedometer had a mean acceptance score of 56/95 compared with 63 for the Withings, 65 for the Misfit and Jawbone, and 68 for the Fitbit. To improve usability, older users may benefit from devices that have better compatibility with personal computers or less-expensive Android mobile phones and tablets, and have comprehensive paper-based user manuals and apps that interpret user data. Conclusions: For older adults living with chronic illness, wearable activity trackers are perceived as useful and acceptable. New users may need support to both set up the device and learn how to interpret their data

Factors Influencing Continued Wearable Device Use in Older Adult Populations: Quantitative Study

Background: The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective: This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods: Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results: The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions: This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies

Rethinking Wearable Activity Trackers as Assistive Technologies: A Qualitative Study on Long-Term Use

This study proposes that wearable activity trackers (WATs), such as Fitbit, Apple Watch, can be viewed as assistive technologies to promote older adults’ health and independent living. Qualitative interview data with 20 older adults (65 and older) who had used WATs for six months or longer were analyzed within the framework of the Match Person and Technology (MPT) model. We found that personal and psychosocial factors, environmental factors, and technology-related factors contributed to the participants’ long-term engagement with WATs. Determination and self-discipline, support from one’s family members and friends, and goal setting and feedback of goal accomplishment were among the most mentioned facilitators of using WATs for more than six months. We discussed the design implications of these findings

Older adults’ experiences with using wearable devices:Qualitative systematic review and meta-synthesis

Background: Older adults may use wearable devices for various reasons, ranging from monitoring clinically relevant health metrics or detecting falls to monitoring physical activity. Little is known about how this population engages with wearable devices, and no qualitative synthesis exists to describe their shared experiences with long-term use. Objective: This study aims to synthesize qualitative studies of user experience after a multi-day trial with a wearable device to understand user experience and the factors that contribute to the acceptance and use of wearable devices. Methods: We conducted a systematic search in CINAHL, APA PsycINFO, PubMed, and Embase (2015-2020; English) with fixed search terms relating to older adults and wearable devices. A meta-synthesis methodology was used. We extracted themes from primary studies, identified key concepts, and applied reciprocal and refutational translation techniques; findings were synthesized into third-order interpretations, and finally, a “line-of-argument” was developed. Our overall goal was theory development, higher-level abstraction, and generalizability for making this group of qualitative findings more accessible. Results: In total, we reviewed 20 papers; 2 evaluated fall detection devices, 1 tested an ankle-worn step counter, and the remaining 17 tested activity trackers. The duration of wearing ranged from 3 days to 24 months. The views of 349 participants (age: range 51-94 years) were synthesized. Four key concepts were identified and outlined: motivation for device use, user characteristics (openness to engage and functional ability), integration into daily life, and device features. Motivation for device use is intrinsic and extrinsic, encompassing many aspects of the user experience, and appears to be as, if not more, important than the actual device features. To overcome usability barriers, an older adult must be motivated by the useful purpose of the device. A device that serves its intended purpose adds value to the user’s life. The user’s needs and the support structure around the device—aspects that are often overlooked—seem to play a crucial role in long-term adoption. Our “line-of-argument” model describes how motivation, ease of use, and device purpose determine whether a device is perceived to add value to the user’s life, which subsequently predicts whether the device will be integrated into the user’s life. Conclusions: The added value of a wearable device is the resulting balance of motivators (or lack thereof), device features (and their accuracy), ease of use, device purpose, and user experience. The added value contributes to the successful integration of the device into the daily life of the user. Useful device features alone do not lead to continued use. A support structure should be placed around the user to foster motivation, encourage peer engagement, and adapt to the user’s preferences

Health Wearable Tools and Health Promotion

The application of wearable technology for health purposes is a multidisciplinary research topic. To summarize key contributions and simultaneously identify outstanding gaps in research, the input-mechanism-output (I-M-O) framework was applied to synthesize findings from 275 relevant papers in the period 2010–2021. Eighteen distinct cross-disciplinary themes were identified and organized under the I-M-O framework. Studies that covered input factors have largely been technocentric, exploring the design of various health wearables, with less emphasis on usability. While studies on user acceptance and engagement are increasing, there remains room for growth in user- centric aspects such as engagement. While measurement of physiological health indictors has grown more sophisticated due to sensitivity of sensors and the advancements in predictive algorithms, a rapidly growing area of research is that of measuring and tracking mental states and emotional health.Relatively few studies explore theoretically backed explanations of the role of health wearables, with technocentric theories predicting adoption favored. These mainly focused on mechanisms of adoption, while postadoption use and health behavior change were less explored. As a consequence, compared to adoption mechanisms, there is an opportunity to increase our understanding of the continued use of wearables and their effects on sustained health behavior change. While a range of incentives such as social, feedback, financial, and gamification are being tested, it is worth noting that negative attitudes, such as privacy concerns, are being paid much more attention as well. Output factors were studied in both individual and organizational settings, with the former receiving considerably more attention than the latter. The progress of research on health wearables was discussed from an interdisciplinary angle, and the role of social scientists was highlighted for the advancement of research on wearable health

Role Of Digital Health Wearables In The Wellbeing And Quality Of Life Of Older People And Carers

The number of adults aged 65 and over has increased by 2% across Europe in the past 15 years, and in Northern Ireland by 22% between 2003-2013. The proportion of the population in this age group is projected to increase by 63% to just under 0.5 million by 2033 – which will be a quarter of the population in Northern Ireland. Given Northern Ireland’s Active Ageing Strategy (2015-2021), there is an increasing focus on encouraging physical activity as we get older to preserve mobility and motor skills, and to enjoy the benefits of living longer and to minimise health problems associated with ageing. Over the last two years, we have been investigating the role of wearable activity tracking technologies in self-monitoring of activity by people aged over 55. Example technologies include activity trackers from Fitbit, Garmin and Samsung, and smart watches. Typically, these devices record steps walked, sleep patterns, calories expended and heart rate. Based on empirical investigations, this policy paper describes the benefits of activity monitors for people aged over 55 for self-monitoring of physical activity, for adopting healthy lifestyles, and for increasing or maintaining physical activity as a way to avoid high blood pressure, obesity, diabetes, and other medical conditions associated with weight or lower physical activity. It outlines the role of activity trackers in post-operative monitoring of mobility during rehabilitation, in caring, and for possible use of the data for diagnosis and medical interventions. It then discusses the challenges for adoption of these technologies, given currently, off-the-shelf devices are designed and calibrated for use by physically fit (typically young active people) with unrealistic fitness targets for the older generation

The use of mHealth solutions in active and healthy ageing promotion: an explorative scoping review

The global population aged 60 years and over is expected to almost double between 2015 and 2050 from 12.0% to 22.0%, which will directly impact countries' labor market composition and increase the economic pressure on their healthcare systems. One way to address these challenges is to promote Active and Healthy Ageing (AHA) using mobile Health (mHealth). This research aims to provide an initial overview of the width and the depth of contemporary preventive mHealth solutions that promote AHA among healthy, independent older adults (individuals aged 60 years and over). To do so, an explorative scoping review was applied to search online databases for recent studies (March 2015 - March 2020) addressing the promotion of mHealth solutions targeting healthy and independent older adults. We identified 31 publications that met the inclusion criteria. Most of them utilized either mobile (n=25) and/or wearable (n=11) devices. mHealth solutions mostly promoted AHA by targeting older adults’ active lifestyles or independence. Most of the studies (n=27) did not apply a theoretical framework on which the mHealth promotion was based. User-experience was positive (n=12) when the solution was easy to use but negative (n=11) when the participants were resistant or faced challenges using the device and/or technology. The review concludes that mHealth offers the opportunity to combat the issues faced by an unhealthy and dependent aging population by promoting AHA through focusing on older adults’ Lifestyle, Daily functioning, and Participation. Future research should use multidisciplinary integrated approaches and strong theoretical and methodological foundations to investigate mHealth solutions' impact on AHA behavioral change

Tying mobile health tools to the users’ needs – Motivational drivers

Objective: The primary aim of this thesis is to contribute novel insights into the distinctive attributes of ICT systems, with a particular emphasis on features preferred by users in the realm of mobile health (mHealth) applications and devices. The study aimed at identifying motivational factors that enhance and sustain the usage and adaption of mHealth applications, wearables, and trackers among both healthy individuals and those affected by chronic diseases (sickle cell and diabetes). Methods: In total, 584 participants completed the survey and answered the specific questions important for this thesis. A descriptive analysis of the demographics as well as regular use of tracking technologies and of the most motivating features of wearable sensors was performed. Further, the approach of binary logistic regression was applied to investigate the association between the importance of specific features and age, gender and health status. Results: The descriptive analysis revealed that relevant personalized feedback and the ease of use of mobile health apps, wearables and trackers represent the most motivating features for a prolonged use. The logistic regression analysis revealed a statistically significant and positive association between having a chronic disease, age, gender, and the importance of notifications of mobile phones and managing a condition. The point estimates for several features like sensor accuracy and range of values as well as ergonomic and design and personalized/tailored features indicated a positive association between people with chronic diseases, age and gender. But these results were inconclusive. Conclusion: This study provided valuable insight into the motivational drivers and adoption patterns of mobile Health applications and wearable devices among young and elderly individuals with and without chronic diseases. However, external validity and generalizability of the results was not given due to study limitation and low statistical power. Further research is therefore needed

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future