Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease - 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. (Frontiers in Neurology, 2023)
Objectives: To investigate if digital measures of different aspects of gait (walking and turning) collected passively over a week of daily activities increase discriminative ability to predict future falls compared to fall history. Methods: 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 falls in past six months. Subjects wore 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring, and derived 80 digital measures incorporating various aspects of gait. 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 two 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) 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 (9x), pitch angle of the foot during mid-swing (8x), and peak turn velocity (7x). 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.