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Pervasive computing is changing the monitoring landscape for patients to communicate their healthcare information in real-time to clinicians and researchers. We developed a framework based on a smartwatch application allowing researchers to execute a study that is customized to their needs. The application is configured to collect patient generated data in remote settings from both sensor-based (location and movement) and user-reported health factors through the visual display. For example, data are used to investigate concurrent symptoms and mobility patterns in free-living conditions. To support the collection and analysis of this data in a robust and scalable fashion, we have developed an event driven, serverless computing platform using Amazon cloud services. This system also allows multiple campaigns to run concurrently each under the auspices of a different researcher. The framework is ideal for harnessing and scaling the utilization of smart wearable devices in research and clinical settings.

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 two 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 two-year study (77.4%). 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). Conclusion 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.

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