Validation of an Algorithmic Pipeline for Wrist-Worn Devices to Estimate Walking Speed in People with Multiple Long-Term Conditions.

Wrist-worn devices offer a practical means of monitoring gait, yet no validated end-to-end pipeline exists for deriving digital mobility outcomes (DMOs), including cadence, stride length (SL), and walking speed (WS), in people with multiple long-term conditions (MLTC, the coexistence of two or more long-term conditions). This study presents the first modular pipeline for wrist-worn devices for DMO estimation, validated in 45 older adults with MLTC (65-90 years), whose conditions spanned four multimorbidity clusters (cardiometabolic, painful conditions, pulmonary, and cancer), across laboratory tasks, using stereophotogrammetry as the reference. Algorithms were selected independently for each block (gait sequence detection (GSD), initial contact detection (ICD), SL) using novel, fine-tuned, and adaptive versions of established methods developed on an independent cohort. Blocks were first validated independently before being integrated into a pipeline capturing cumulative error propagation. GSD achieved a recall of 0.90. ICD was robust across algorithms, with the best-performing algorithm achieving a recall of 0.76 and precision of 0.82. At the pipeline level, the best-performing pipeline achieved an SL error of 0.14 m with near-zero bias, cadence absolute error of 7.71 steps/min, and WS absolute error of 0.13 m/s. These findings support wrist-worn devices for objective gait assessment in multimorbid populations, establishing a validated open-source pipeline for real-world deployment.