Objective This analysis assessed the acceptability and recruitment implications of a high-throughput, community-based biomechanics protocol among individuals with knee osteoarthritis (OA).

Design During the Shared Strides Study, high-throughput markerless biomechanics assessment was conducted at community sites to help facilitate research engagement in the OA population. In this cross-sectional study, biomechanics data during a set of activities of daily living (ADLs) and questionnaire data were collected. Adults aged 40 years or older with knee OA participated at one of four sites across Gainesville, FL—two on-campus and two community-based. Eligible individuals were either screened over the phone and scheduled for a specific date and time or screened on site for potential same-day participation. Participant acceptability of the community-based biomechanics data collection approach was assessed using a 15-item custom questionnaire. Recruitment characteristics and participant preferences were compared across sites.

Results The high-throughput community-based data collection approach was well received. Compared with on-campus sites, community-based sites had higher engagement from walk-in participants and new research participants (40% of the sample). Familiarity with, and distance to, a data collection site were important factors in research engagement in this population. No differences in demographic characteristics existed between sites (p > 0.05), but recruitment resulted in a large sample size (n = 85) likely representative of the communities surrounding the selected sites.

Conclusions Integrating markerless motion capture with a community-based research approach may enhance the participant experience and facilitate larger, more heterogeneous sample sizes, ultimately reducing bias and homogeneity in current OA biomechanics research.

The authors have declared no competing interest.

This research was supported by a grant from the National Institute on Aging, Claude D. Pepper Older Americans Independence Center at the University of Florida (P30 AG028740). This project utilized REDCap, which is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR001427). KEC was supported by an Investigator Award from the Rheumatology Research Foundation.

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