Summary: Current outcome measures, including strength/range of motion testing, patient-reported outcomes (PROs), and motor skill testing, may provide inadequate granularity in reflecting functional upper extremity (UE) use after distal radius fracture (DRF) repair. Accelerometry analysis also has shortcomings, namely, an inability to differentiate functional versus nonfunctional movements. The objective of this study was to evaluate the accuracy of machine learning (ML) analyses in capturing UE functional movements based on accelerometry data for patients after DRF repair. In this prospective study, six patients were enrolled 2–6 weeks after DRF open reduction and internal fixation (ORIF). They all performed standardized activities while wearing a wrist accelerometer, and the data were analyzed by an ML algorithm. These activities were also videotaped and evaluated by visual inspection. Our novel ML algorithm was able to predict from accelerometry data whether the limb was performing a movement rated as functional, with accuracy of 90.4% ± 3.6% for within-subject modeling and 79.8% ± 8.9% accuracy for between-subject modeling. The application of ML algorithms to accelerometry data allowed for capture of functional UE activity in patients after DRF open reduction and internal fixation and accurately predicts functional UE use. Such analyses could improve our understanding of recovery and enhance routine postoperative rehabilitation in DRF patients.

Objective: This study aims to investigate the validity of machine learning-derived amount of real-world functional upper extremity (UE) use in individuals with stroke. We hypothesized that machine learning classification of wrist-worn accelerometry will be as accurate as frame-by-frame video labeling (ground truth). A second objective was to validate the machine learning classification against measures of impairment, function, dexterity, and self-reported UE use.Design: Cross-sectional and convenience sampling.Setting: Outpatient rehabilitation.Participants: Individuals (>18 years) with neuroimaging-confirmed ischemic or hemorrhagic stroke >6-months prior (n = 31) with persistent impairment of the hemiparetic arm and upper extremity Fugl-Meyer (UEFM) score = 12–57.Methods: Participants wore an accelerometer on each arm and were video recorded while completing an “activity script” comprising activities and instrumental activities of daily living in a simulated apartment in outpatient rehabilitation. The video was annotated to determine the ground-truth amount of functional UE use.Main outcome measures: The amount of real-world UE use was estimated using a random forest classifier trained on the accelerometry data. UE motor function was measured with the Action Research Arm Test (ARAT), UEFM, and nine-hole peg test (9HPT). The amount of real-world UE use was measured using the Motor Activity Log (MAL).Results: The machine learning estimated use ratio was significantly correlated with the use ratio derived from video annotation, ARAT, UEFM, 9HPT, and to a lesser extent, MAL. Bland–Altman plots showed excellent agreement between use ratios calculated from video-annotated and machine-learning classification. Factor analysis showed that machine learning use ratios capture the same construct as ARAT, UEFM, 9HPT, and MAL and explain 83% of the variance in UE motor performance.Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.