TY - JOUR
T1 - Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
AU - for the Mobilise-D consortium
AU - Micó-Amigo, M. Encarna
AU - Bonci, Tecla
AU - Paraschiv-Ionescu, Anisoara
AU - Ullrich, Martin
AU - Kirk, Cameron
AU - Soltani, Abolfazl
AU - Küderle, Arne
AU - Gazit, Eran
AU - Salis, Francesca
AU - Alcock, Lisa
AU - Aminian, Kamiar
AU - Becker, Clemens
AU - Bertuletti, Stefano
AU - Brown, Philip
AU - Buckley, Ellen
AU - Cantu, Alma
AU - Carsin, Anne Elie
AU - Caruso, Marco
AU - Caulfield, Brian
AU - Cereatti, Andrea
AU - Chiari, Lorenzo
AU - D’Ascanio, Ilaria
AU - Eskofier, Bjoern
AU - Fernstad, Sara
AU - Froehlich, Marcel
AU - Garcia-Aymerich, Judith
AU - Hansen, Clint
AU - Hausdorff, Jeffrey M.
AU - Hiden, Hugo
AU - Hume, Emily
AU - Keogh, Alison
AU - Kluge, Felix
AU - Koch, Sarah
AU - Maetzler, Walter
AU - Megaritis, Dimitrios
AU - Mueller, Arne
AU - Niessen, Martijn
AU - Palmerini, Luca
AU - Schwickert, Lars
AU - Scott, Kirsty
AU - Sharrack, Basil
AU - Sillén, Henrik
AU - Singleton, David
AU - Vereijken, Beatrix
AU - Vogiatzis, Ioannis
AU - Yarnall, Alison J.
AU - Rochester, Lynn
AU - Mazzà, Claudia
AU - Del Din, Silvia
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Algorithms
KW - Cadence
KW - DMOs
KW - Digital health
KW - Real-world gait
KW - SL
KW - Validation
KW - Walking
KW - Wearable sensor
UR - http://www.scopus.com/inward/record.url?scp=85161825196&partnerID=8YFLogxK
U2 - 10.1186/s12984-023-01198-5
DO - 10.1186/s12984-023-01198-5
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C2 - 37316858
AN - SCOPUS:85161825196
SN - 1743-0003
VL - 20
JO - Journal of NeuroEngineering and Rehabilitation
JF - Journal of NeuroEngineering and Rehabilitation
IS - 1
M1 - 78
ER -