TY - JOUR
T1 - Digital Biomarkers of Mobility in Parkinson's Disease during Daily Living
AU - Shah, Vrutangkumar V.
AU - McNames, James
AU - Mancini, Martina
AU - Carlson-Kuhta, Patricia
AU - Nutt, John G.
AU - El-Gohary, Mahmoud
AU - Lapidus, Jodi A.
AU - Horak, Fay B.
AU - Curtze, Carolin
N1 - Publisher Copyright:
© 2020 - IOS Press and the authors. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Background: Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). Objective: To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. Methods: We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC. Results: Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC>0.80. Turn angle (AUC=0.89, 95% CI: 0.79-0.97) and swing time variability (AUC=0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Conclusion: Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.
AB - Background: Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). Objective: To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring. Methods: We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC. Results: Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC>0.80. Turn angle (AUC=0.89, 95% CI: 0.79-0.97) and swing time variability (AUC=0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures. Conclusion: Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.
KW - Parkinson's disease
KW - biomarkers
KW - continuous monitoring
KW - digital outcome measures of mobility
KW - inertial sensors
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U2 - 10.3233/JPD-201914
DO - 10.3233/JPD-201914
M3 - Article
C2 - 32417795
AN - SCOPUS:85089125194
SN - 1877-7171
VL - 10
SP - 1099
EP - 1111
JO - Journal of Parkinson's Disease
JF - Journal of Parkinson's Disease
IS - 3
ER -