Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease

To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. We recruited 34 individuals with PD (17 with...

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Published in:Frontiers in neurology Vol. 14; p. 1096401
Main Authors: Shah, Vrutangkumar V., Jagodinsky, Adam, McNames, James, Carlson-Kuhta, Patricia, Nutt, John G., El-Gohary, Mahmoud, Sowalsky, Kristen, Harker, Graham, Mancini, Martina, Horak, Fay B.
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 28.02.2023
Subjects:
daily life
future falls
gait
inertial sensors
Neurology
Parkinson's disease
turning
inertial sensors
daily life
Parkinson's disease
gait
future falls
turning
ISSN:1664-2295, 1664-2295
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Summary:To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed email every 2 weeks over the year after the study for self-reported falls. Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). These findings highlight the importance of considering precise digital measures, captured sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.
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Reviewed by: Elisabetta Dell'Anna, Consultant, Milan, Italy; Andrea Cereatti, Polytechnic University of Turin, Italy
This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology
Edited by: Maurizio Ferrarin, Fondazione Don Carlo Gnocchi Onlus (IRCCS), Italy
ISSN:1664-2295
1664-2295
DOI:10.3389/fneur.2023.1096401