Category: Parkinson’s Disease: Clinical Trials
Objective: We investigated if digital measures of different aspects of gait (walking and turning) from a week of daily activities increase discriminative ability to predict future falls in patients with PD with or without a fall history.
Background: Although much is known about the multifactorial nature of falls in Parkinson’s disease (PD), it remains unclear whether digital measures from different aspects of gait (walking and turning) can improve prediction of future falls over fall history alone.
Method: We recruited 34 subjects with PD (17 fallers 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 fall history in the past six months. Subjects wore three inertial sensors (Clario Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. We derived 55 gait, turning, and activity measures averaged across all strides. An Area Under Curve (AUC) was calculated for each digital measure, and logistic regression employing a ‘best subsets selection strategy’ was used to find combinations of measures that discriminated future fallers from non-fallers. Participants were followed over the year after obtaining gait and turning measures for self-reported falls.
Results: Twenty-five subjects reported falls in the follow-up year. Mobility activity measures 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. 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 foot (9x), pitch angle of the foot during mid-swing (8x), and the maximum average velocity of a turn (7x).
Conclusion: These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.
To cite this abstract in AMA style:V. Shah, J. Mcnames, G. Harker, P. Carlson-Kuhta, J. Nutt, M. El Gohary, K. Sowalsky,, M. Mancini, F. Horak. Gait Characteristics from Daily Life Increase Ability to Predict Future Falls in People with Parkinson’s Disease [abstract]. Mov Disord. 2022; 37 (suppl 2). https://www.mdsabstracts.org/abstract/gait-characteristics-from-daily-life-increase-ability-to-predict-future-falls-in-people-with-parkinsons-disease/. Accessed September 28, 2023.
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MDS Abstracts - https://www.mdsabstracts.org/abstract/gait-characteristics-from-daily-life-increase-ability-to-predict-future-falls-in-people-with-parkinsons-disease/