Abstract
Patients with knee osteoarthritis walk with reduced speed and knee flexion excursion in the early stance phase. A slow walking speed is also associated with falls in older adults. A novel vision-based smartphone application could potentially facilitate the early detection of knee osteoarthritis and fall prevention. This study aimed to test the validity and reliability of the app-captured gait speed and peak knee flexion during the initial stance phase of gait. Twenty adults (aged 23-68 years) walked at self-selected comfortable walking speeds while the gait speed and knee flexion were simultaneously measured using retroreflective sensors and Xsens motion trackers and the app in two separate sessions for validity and reliability tests. Pearson's r correlation and Bland-Altman plots were used to examine the correlations and agreements between the sensor- and app-measured outcomes. One-sample t-tests were performed to examine whether systematic bias existed. The intraclass correlation coefficient (ICC) was calculated to assess the test-retest reliability of the app. Very high correlations were found between the sensor and app measurements for gait speed (r = 0.98, p < 0.001) and knee flexion (r = 0.91-0.92, all p < 0.001). No significant bias was detected for the final app version. The app also showed a good to excellent test-retest reliability for measuring the gait speed and peak knee flexion (ICC = 0.86-0.94). This vision-based smartphone application is valid and reliable for capturing the walking speed and knee flexion during the initial stance of gait, potentially aiding in the early detection of knee osteoarthritis and fall prevention in community living locations.