Stanford researchers introduce a home-based osteoarthritis risk tool using the sit-stand test
Researchers from Stanford University have created a mobile-friendly app designed to estimate the risk of osteoarthritis at home by analyzing a simple sit-stand test. This work was described in detail in npj Digital Medicine.
Musculoskeletal disorders affect more than 1.5 billion people worldwide, and early detection can help prevent progression. Yet there are relatively few quantitative, objective tests available to gauge musculoskeletal health, especially for early screening and self-monitoring in daily life.
To address this gap, the team developed an online tool that processes user-recorded home videos. The technology proved sufficiently sensitive to predict overall health status and the likelihood of knee or hip osteoarthritis when applied in population-level analyses.
The core assessment mirrors the sit-stand test, where a user sits with arms folded, then stands up and returns to a seated position. This sequence is performed five times while being videotaped. Slower transitions between positions may signal reduced lower-body strength. Beyond timing, the app evaluates posture and movement by examining joint angles and the velocity of body segments during the motion.
In validation testing, the instrument was evaluated with a diverse group of 405 participants, spanning ages 18 to 96 and averaging about 37.5 years. Consistent with prior studies in lab and clinical settings, the analysis found that greater forward lean during the rise from seated to standing correlated with higher osteoarthritis risk. This association held even after adjusting for age, gender, and body mass index, suggesting the measure captures meaningful information about musculoskeletal health beyond basic demographics.
Overall, the Stanford study demonstrates that a simple home-based video task can provide actionable indicators of osteoarthritis risk, offering a potential pathway for early screening and ongoing monitoring without visiting a clinic. The authors note that while further research is needed to refine accuracy across broader populations, the approach represents a promising step toward accessible, objective musculoskeletal health assessment in everyday settings as reported by Stanford University researchers in npj Digital Medicine.