Abstract

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances. Trial registration ISRCTN – 12246987.

Original languageEnglish
Article number78
JournalJournal of NeuroEngineering and Rehabilitation
Volume20
Issue number1
DOIs
StatePublished - Dec 2023

Funding

FundersFunder number
CNTW
IMI2 JU853981
NHS Foundation Trust
Northumberland and Tyne and Wear
Wellcome Trust Clinical Research Facility
Horizon 2020 Framework Programme
European Federation of Pharmaceutical Industries and Associations
National Institute for Health and Care Research
Newcastle University
Generalitat de Catalunya
Newcastle upon Tyne Hospitals NHS Foundation Trust
Ministerio de Ciencia e InnovaciónCEX2018-000806-S
Innovative Medicines Initiative820820
NIHR Newcastle Biomedical Research Centre
NIHR Leicester Clinical Research FacilityIS-BRC-1215–20017

    Keywords

    • Accelerometer
    • Algorithms
    • Cadence
    • DMOs
    • Digital health
    • Real-world gait
    • SL
    • Validation
    • Walking
    • Wearable sensor

    Fingerprint

    Dive into the research topics of 'Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium'. Together they form a unique fingerprint.

    Cite this