![]() 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. Additional work with a larger and more diverse sample is necessary to confirm associations between smartwatch-measured features and traditional measures. ROAMM was usable, acceptable, and effective at measuring mobility and risk factors for mobility decline in our pilot sample. Some smartwatch features were correlated with their respective traditional measurements (e.g., certain GPS-derived life-space mobility features (r=0.50-0.51, p<0.05) and ecologically-measured pain (r=0.72, p=0.01)), but others were not (e.g., ecologically-measured fatigue). 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 two-year study (77.4%). 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. 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.ĭata come from a sample of 31 participants (Mage=74.7, 51.6% female) who used ROAMM for approximately two weeks. However, traditional approaches to mobility assessment are limited in their ability to capture daily fluctuations that align with sporadic health events. Early detection of mobility decline is critical to prevent subsequent reductions in quality of life, disability, and mortality. ![]()
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