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. 2023 Feb 28:14:1096401.
doi: 10.3389/fneur.2023.1096401. eCollection 2023.

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

Vrutangkumar V Shah  1   2 , Adam Jagodinsky  2 , James McNames  2   3 , Patricia Carlson-Kuhta  1 , John G Nutt  1 , Mahmoud El-Gohary  2 , Kristen Sowalsky  2 , Graham Harker  1 , Martina Mancini  1 , Fay B Horak  1   2
Affiliations

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

Vrutangkumar V Shah et al. Front Neurol. .

Abstract

Objectives: 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.

Methods: 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 via email every 2 weeks over the year after the study for self-reported falls.

Results: 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).

Conclusions: These findings highlight the importance of considering precise digital measures, captured via 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.

Keywords: Parkinson's disease; daily life; future falls; gait; inertial sensors; turning.

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Conflict of interest statement

OHSU and VS, JM, ME-G, and FH have a significant financial interest in APDM Wearable Technologies, a Clario company, that may have a commercial interest in the results of this research and technology. This potential conflict of interest has been reviewed and managed by OHSU. KS and AJ are employees of APDM Wearable Technologies, a Clario company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
ROC plots to predict future falls based on falls history alone (blue line) and various combinations of gait measures (top 4 from Table 2).
Figure 2
Figure 2
AUC values to predict future falls based on falls history alone and various combinations of gait measures (top 4 from Table 2).
See this image and copyright information in PMC

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