Reliability of Wearable Sensors for Assessing Gait and Chair Stand Function at Home in People With Knee Osteoarthritis.

OBJECTIVE: To assess the reliability of wearable sensors for at-home assessment of walking and chair stand activities in people with knee osteoarthritis (OA). METHODS: Baseline data from participants with knee OA (n = 20) enrolled in a clinical trial of an exercise intervention were used. Participants completed an in-person laboratory visit and a video conference-enabled at-home visit. In both visits, participants performed walking and chair stand tasks while fitted with 3 inertial sensors. During the at-home visit, participants self-donned the sensors and completed 2 sets of acquisitions separated by a 15-minute break, when they removed and redonned the sensors. Participants completed a survey on their experience with the at-home visit. During the laboratory visit, researchers placed the sensors on the participants. Spatiotemporal metrics of walking gait and chair stand duration were extracted from the sensor data. We used intraclass correlation coefficients (ICCs) and the Bland-Altman plot for statistical analyses. RESULTS: For test-retest reliability during the at-home visit, all ICCs were good to excellent (0.85-0.95). For agreement between at-home and laboratory visits, ICCs were moderate to good (0.59-0.87). Systematic differences were noted between at-home and laboratory data due to faster task speed during the laboratory visits. Participants reported a favorable experience during the at-home visit. CONCLUSION: Our method of estimating spatiotemporal gait measures and chair stand duration function remotely was reliable, feasible, and acceptable in people with knee OA. Wearable sensors could be used to remotely assess walking and chair stand in participant's natural environments in future studies.

Considerations for Conducting Bring Your Own "Device" (BYOD) Clinical Studies.

Background: Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own "device" (BYOD) manner where participants use their technologies to generate study data. Summary: The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages: We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.