Validation of the mobgap analytical pipeline for estimation of digital mobility outcomes from a single wearable device across all-cause dementia and discrete dementia disease subtypes
Abstract
Wearable-derived digital mobility outcomes have potential clinical applications in dementia, yet technical validation of analytical pipelines in this population remains limited. This study evaluated the validity of the mobgap pipeline for estimating walking speed, initial contact detection, stride duration, cadence, and stride length across all-cause dementia and discrete subtypes. Eighty-six participants were recruited from two studies (GaitDem: Alzheimer’s Dementia (AD) = 31, Dementia with Lewy-bodies (DLB) = 25, Parkinson’s Dementia (PDD) = 8, Vascular Dementia (VaD) = 6; UCL DMS Atypical: posterior cortical atrophy (PCA) = 16), completing laboratory walking trials validated against an instrumented walkway (GaitDem) or infrared motion capture (DMS Atypical). DMOs were estimated using the mobgap pipeline, optimised for impaired walking. Walking speed was estimated with good agreement across all-cause dementia (Intraclass correlation coefficient (ICC) = 0.83, absolute error = 0.12 m/s, relative error = 11.84%), comparable to previous laboratory validations in gait-impaired populations. Cadence showed the strongest performance (ICC = 0.98, absolute error = 1.34 s/m, relative error = 1.24%), while stride length was the most challenging (ICC = 0.77, relative error = 11.85%). Initial contact detection achieved high sensitivity (0.99) but moderate positive predictive value (PPV) (0.85). By subtype, ICC for walking speed ranged from 0.76 (AD) to 0.89 (VaD), with PDD presenting the greatest challenge across most digital mobility outcomes, likely reflecting motor impairment severity rather than dementia subtype. These findings support the technical validity of mobgap for estimating digital mobility outcomes across common and atypical dementia subtypes, and highlight the need for real-world validation to establish its clinical utility.