Quantifying the Relationship Between At-Home Shoulder Physiotherapy Participation and Outcome: What can a Watch Tell Us?

Abstract

Introduction: Exercise-based physiotherapy is an established treatment of rotator cuff injury. Objective assessment of at-home exercise is critical to understand its relationship with clinical outcomes. This study uses the Smart Physiotherapy Activity Recognition System to measure at-home physiotherapy participation in patients with rotator cuff injury based on inertial sensor data captured from smart watches. Relationships between participation and clinical outcomes, long-term durability of outcome improvements, and factors predictive of participation were evaluated.

Methods: Patients participated in a 12-week rotator cuff physiotherapy program in a prospective single-center study. Patients wore smart watches during supervised weekly in-clinic physiotherapy sessions and while performing exercises at home. Demographic information and rotator-cuff diagnosis were collected at baseline and assessed as predictors of physiotherapy participation. Outcome measures (pain, disability [Disabilities of the Arm, Shoulder and Hand], strength, range of motion) were collected over duration of treatment and at 12-month follow-up (pain and disability). Machine learning algorithms identified and classified periods of exercise to evaluate participation and adherence.

Results: One hundred ten patients enrolled and initiated treatment, with 92 patients included in the analysis. All outcomes showed significant improvements from baseline at each time point. Mean total weekly at-home participation decreased from 35.6 ± 28.9 minutes in weeks 0 to 4 to 28.9 ± 25.7 minutes in weeks 8 to 12 (t = 2.23, P = 0.023). For the full cohort, significant relationships were found between physiotherapy participation and disability, manual strength, external rotation, internal rotation, and abduction. Significant predictors of participation included greater age, being unmarried, diagnosed rotator cuff tear, and measures of self-efficacy, social support, and comorbidity. Higher participation rates led to significant improvements in outcomes for partial thickness/no-tear patients but not for full-thickness tears.

Discussion: Machine learning methods applied to data collected from smart watches enabled objective assessment of physiotherapy participation in the home setting. Although most patients improved with physiotherapy, patients with full-thickness rotator cuff tears were not similarly responsive to higher exercise volumes.