Introduction: The use of dual-task model such as dual-task gait has been extensively studied to assess cognitive-motor performance among older adults. However, space restriction and safety factor limit its applications in remote assessment. To address the gap, we propose a video processing-based approach to remotely quantify cognitive-motor performance using a 20-s repetitive elbow flexion-extension test with dual-task condition, called video-based motoric-cognitive meter (MCM). Methods: Eighteen older participants (age: 78.6 ± 6.5 years) who were clinically diagnosed as having either mild cognitive impairment or dementia were included in this study. Participants were asked to perform 20-s repetitive elbow flexion-extension exercise with a memory exercise by counting backward from a two-digit number. During the test, all movements of the forearm were recorded by a video camera. As a comparator, a validated wrist-worn sensor was used, which allowed quantifying upper extremity kinematics. Results: The results showed a good agreement (r ≥ 0.530 and ICC2,1 ≥ 0.681) between the derived dual-task upper extremity motor performance from the proposed video-based MCM and a clinically validated sensor-based MCM. We also observed moderate correlations (r ≥ 0.496) between some measures of video-based MCM (flexion time, extension time, and flexion-extension time) and clinical cognitive scale (Mini-Mental State Examination [MMSE]). Additionally, some measures of dual-task upper extremity motor performance (speed, flexion time, extension time, and flexion-extension time) were associated with dual-task gait speed (r ≥ 0.557), which has been found to be correlated with cognitive impairment. Lastly, the selected dual-task motor performance metric (flexion time) was sensitive to predict MMSE scores in linear regression analyses with statistical significance (adjusted R2 = 0.306, p = 0.025). Conclusion: This study proposes a video processing-based approach to analyze dual-task upper extremity motor performance from a simple and convenient upper extremity function test. The results indicate concurrent validity of the proposed video-based MCM compared with the sensor-based MCM, and associations between dual-task upper extremity motor performance and clinically validated cognitive markers (MMSE scores and dual-task gait). Future studies are warranted to explore sensitivity of this solution to promote remote assessment of cognitive-motor performance among older adults in telehealth applications.

Keywords:
Dementia, Cognitive-motor performance, Dual-task condition, Telehealth, Remote patient monitoring
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