Identifying barriers and facilitators for using a smartwatch to monitor health among older adults.

Smartwatches are a type of wearable device that enable continuous monitoring of an individual's activities and critical health metrics. As the number of older adults age 65+ continues to grow in the U.S., so does their usage of smartwatches, making it necessary to understand the real-world uptake and use of these devices to monitor health. In this study, older adults with a relatively high level of education and digital skills were provided with a smartwatch equipped with a mobile application (ROAMM) that was worn for a median of 14 days. Usability surveys were distributed, and a qualitative analysis was performed about participants' experience using the smartwatch and ROAMM application. Constructs from the Technology Acceptance Model and Consolidated Framework for Implementation Research were incorporated into in-depth interviews, which were recorded and transcribed. Data were analyzed using the constant comparative method. Interviews among 30 older adults revealed the following main themes: 1) familiarization with the device and adoption and acceptance, 2) factors encouraging usage, such as a doctor's endorsement or the appeal of tracking one's health, and 3) barriers to usage, such as insufficient education and training and the desire for additional functionality. Overall, participants found the smartwatch easy to use and were likely to continue using the device in a long-term study. Data generated from smartwatches have the potential to engage individuals about their health and could inspire them to participate more actively during clinical encounters.

Feasibility of a Smartwatch Platform to Assess Ecological Mobility: Real-Time Online Assessment and Mobility Monitor.

BACKGROUND: Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. We aim to describe findings from a pilot study of our Real-time Online Assessment and Mobility Monitor (ROAMM) smartwatch application, which uniquely captures multiple streams of data in real time in ecological settings. METHODS: Data come from a sample of 31 participants (Mage = 74.7, 51.6% female) who used ROAMM for approximately 2 weeks. We describe the usability and feasibility of ROAMM, summarize prompt data using descriptive metrics, and compare prompt data with traditional survey-based questionnaires or other established measures. RESULTS: Participants were satisfied with ROAMM's function (87.1%) and ranked the usability as "above average." Most were highly engaged (average adjusted compliance = 70.7%) and the majority reported being "likely" to enroll in a 2-year study (77.4%). Some smartwatch features were correlated with their respective traditional measurements (eg, certain GPS-derived life-space mobility features (r = 0.50-0.51, p < .05) and ecologically measured pain (r = 0.72, p = .01), but others were not (eg, ecologically measured fatigue). CONCLUSIONS: ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. By monitoring multiple data streams simultaneously in ecological settings, this technology could uniquely contribute to the evolution of mobility measurement and risk factors for mobility loss.

Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults.

Assessments of Life-space Mobility (LSM) evaluate the locations of movement and their frequency over a period of time to understand mobility patterns. Advancements in and miniaturization of GPS sensors in mobile devices like smartwatches could facilitate objective and high-resolution assessment of life-space mobility. The purpose of this study was to compare self-reported measures to GPS-based LSM extracted from 27 participants (44.4% female, aged 65+ years) who wore a smartwatch for 1-2 weeks at two different site locations (Connecticut and Florida). GPS features (e.g., excursion size/span) were compared to self-reported LSM with and without an indicator for needing assistance. Although correlations between self-reported measures and GPS-based LSM were positive, none were statistically significant. The correlations improved slightly when needing assistance was included, but statistical significance was achieved only for excursion size (r=0.40, P=0.04). The poor correlations between GPS-based and self-reported indicators suggest that they capture different dimensions of life-space mobility.