Quantifying motor pattern composition in late-life depression with wearable accelerometry and standardized fine-motor...

This study aims to characterize differences during fine-motor execution between late-life depression (LLD) and healthy control (HC) older adults, and to evaluate the feasibility and robustness of classification models based on cluster-derived features.

We enrolled 95 individuals in 2024 at The First People's Hospital of Yunnan Province (LLD, n = 58; HC, n = 37). Participants wore AX6 triaxial accelerometers on both wrists (100 Hz) and completed four Purdue Pegboard subtests and the Clock Drawing Test. Preprocessing included three-axis standardization, computation of signal vector magnitude (SVM), sliding-window segmentation, principal component analysis, and Gaussian mixture model clustering.

On scales, HAMD-17 was significantly higher in LLD than HC, and MMSE also differed. LLD performed worse than HC on all four Purdue Pegboard subtests with significant differences, while the raw Clock Drawing score was not significant. Task-level comparisons of "pattern composition" were significant across all five tasks (Purdue subtests p = 0.001 each; Clock Drawing test p = 0.010). Of 50 clusterwise comparisons, 31 showed significant between-group differences with replication across tasks. Classification performance was as follows: Lasso-logistic, accuracy 0.89 (95% CI 0.81-0.95), sensitivity 0.81, specificity 0.95, AUC 0.95; XGBoost, accuracy 0.89 (95% CI 0.81-0.95), sensitivity 0.91, specificity 0.89, AUC 0.94. Overall, LLD more often exhibited intermittent, higher-peak, rhythm-unstable kinematic patterns during task execution.

Wrist-worn accelerometry combined with standardized fine-motor tasks can distill interpretable, reproducible "motor pattern fingerprints," objectively delineating differences between LLD and HC in rhythmic organization and motor stability.