Size and structure of motor variability in young and old adults performing a rhythmic, repetitive tapping task

Clinically relevant estimation of minimal number of trials for the uncontrolled manifold analysis

Movement biomarkers are crucial for assessing sensorimotor impairments and tracking the effects of interventions over time. The Uncontrolled Manifold (UCM) analysis has been proposed as a novel biomarker for evaluating movement stability and coordination in various motor tasks across neurological and musculoskeletal disorders. Through inter-trial analysis, the UCM partitions the variance of elemental variables (e.g., finger forces) into components that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force). A third index, ΔV, is computed as the normalized difference between VORT and VUCM. However, the minimum number of trials required to achieve stable UCM estimates, considering its clinimetric properties, is unknown. This study aimed to determine the minimal number (N) of trials for UCM estimates by computing bootstrap estimates of standard errors (SE) at different N trials using thresholds based on the minimal detectable change (MDC, i.e., the minimum change in an outcome measure beyond measurement error). Thirteen adults (24.6 ± 1.1 years old) performed a finger-pressing coordination task. We computed the 95 % confidence intervals (CI) of bootstrap SE distributions for each UCM estimate and detected the lowest number of trials with the 95 % CI of SE below each MDC threshold. We found the minimal N of trials required was VUCM = 14, VORT = 4 and ΔV = 18. Our findings highlight that a relatively low number of trials (i.e., N = 18) are sufficient to compute all UCM estimates beyond the MDC, supporting the use of the UCM framework in clinical settings where many repetitions of a motor task are not practical.

Test-Retest reliability and measurement error of the uncontrolled manifold analysis: A step towards the clinical translation

The uncontrolled manifold (UCM) analysis has gained broad application in biomechanics and neuroscience for investigating the structure of motor variability in functional tasks. The UCM utilizes inter-trial analysis to partition the variance of elemental variables (e.g., finger forces, joint angles) that affect (VORT) and do not affect (VUCM) a performance variable (e.g., total force, end-effector position). However, to facilitate the translation of UCM into clinical settings, it is crucial to demonstrate the reliability of UCM estimates: VORT, VUCM, and their normalized difference, ΔV. This study aimed to determine the test-retest reliability using the intraclass correlation coefficient (ICC3,K), Bland-Altman plots, the standard error of measurement (SEM), and the minimal detectable change (MDC) of UCM estimate. Fifteen healthy individuals (24.8 ± 1.2 yrs old) performed a finger coordination task, with sessions separated by one hour, one day, and one week. Excellent reliability was found for VORT (ICC3,K = 0.97) and VUCM (ICC3,K = 0.92), whereas good reliability was observed for ΔV (ICC3,K = 0.84). Bland-Altman plots reveled no systematic differences. SEM% values were 24.57 %, 26.80 % and 12.49 % for VORT, VUCM and ΔV respectively, while the normalized MDC% values were 68.12 %, 74.30 % and 34.61 % for VORT, VUCM and ΔV respectively. Our results support the use of UCM as a reliable method for investigating the structure of movement variability. The excellent measurement properties make the UCM a promising tool for tracking changes in motor behavior over time (i.e., effects of interventions in prospective studies).