Associations Between Daily-living Physical Activity and Laboratory-based Assessments of Motor Severity in Patients with Falls and Parkinson's Disease
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Introduction: Recent work suggests that wearables can augment conventional measures of Parkinson's disease (PD). We evaluated the relationship between conventional measures of disease and motor severity (e.g., MDS-UPDRS part III), laboratory-based measures of gait and balance, and daily-living physical activity measures in patients with PD.
Methods: Data from 125 patients (age: 71.7 ± 6.5 years, Hoehn and Yahr: 1-3, 60.5% men) were analyzed. The MDS-UPDRS-part III was used as the gold standard of motor symptom severity. Gait and balance were quantified in the laboratory. Daily-living gait and physical activity metrics were extracted from an accelerometer worn on the lower back for 7 days.
Results: In multivariate analyses, daily-living physical activity and gait metrics, laboratory-based balance, demographics and subject characteristics together explained 46% of the variance in MDS-UPDRS-part III scores. Daily-living measures accounted for 62% of the explained variance, laboratory measures 30%, and demographics and subject characteristics 7% of the explained variance. Conversely, demographics and subject characteristics, laboratory-based measures of gait symmetry, and motor symptom severity together explained less than 30% of the variance in total daily-living physical activity. MDS-UPDRS-part III scores accounted for 13% of the explained variance, i.e., <4% of all the variance in total daily-living activity.
Conclusions: Our findings suggest that conventional measures of motor symptom severity do not strongly reflect daily-living activity and that daily-living measures apparently provide important information that is not captured in a conventional one-time, laboratory assessment of gait, balance or the MDS-UPDRS. To provide a more complete evaluation, wearable devices should be considered.
Lang C, van Dieen J, Brodie M, Welzel J, Maetzler W, Singh N Front Neurol. 2024; 15:1455692.
PMID: 39445193 PMC: 11496290. DOI: 10.3389/fneur.2024.1455692.
Digital outcome measures from smartwatch data relate to non-motor features of Parkinson's disease.
Schalkamp A, Harrison N, Peall K, Sandor C NPJ Parkinsons Dis. 2024; 10(1):110.
PMID: 38811633 PMC: 11137004. DOI: 10.1038/s41531-024-00719-w.
Cai W, Young C, Yuan R, Lee B, Ryman S, Kim J Proc Natl Acad Sci U S A. 2024; 121(22):e2316149121.
PMID: 38768342 PMC: 11145286. DOI: 10.1073/pnas.2316149121.
Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.
Boborzi L, Decker J, Rezaei R, Schniepp R, Wuehr M Sensors (Basel). 2024; 24(9).
PMID: 38732771 PMC: 11085719. DOI: 10.3390/s24092665.
Tam W, Alajlani M, Abd-Alrazaq A J Med Internet Res. 2023; 25:e42950.
PMID: 37594791 PMC: 10474516. DOI: 10.2196/42950.