Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living
Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD). 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. We recruited 29 peopl...
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| Veröffentlicht in: | Journal of Parkinson's disease Jg. 10; H. 3; S. 1099 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Netherlands
01.01.2020
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| ISSN: | 1877-718X, 1877-718X |
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| Abstract | Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).
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.
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.
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.
Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures. |
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| AbstractList | Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).
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.
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.
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.
Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures. Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).BACKGROUNDIdentifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).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.OBJECTIVETo determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring.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.METHODSWe 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.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.RESULTSDuration 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.Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.CONCLUSIONClinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures. |
| Author | Mancini, Martina Curtze, Carolin Carlson-Kuhta, Patricia Horak, Fay B Shah, Vrutangkumar V Nutt, John G El-Gohary, Mahmoud McNames, James Lapidus, Jodi A |
| Author_xml | – sequence: 1 givenname: Vrutangkumar V surname: Shah fullname: Shah, Vrutangkumar V organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 2 givenname: James surname: McNames fullname: McNames, James organization: APDM, Inc., Portland, OR, USA – sequence: 3 givenname: Martina surname: Mancini fullname: Mancini, Martina organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 4 givenname: Patricia surname: Carlson-Kuhta fullname: Carlson-Kuhta, Patricia organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 5 givenname: John G surname: Nutt fullname: Nutt, John G organization: Department of Neurology, Oregon Health & Science University, Portland, OR, USA – sequence: 6 givenname: Mahmoud surname: El-Gohary fullname: El-Gohary, Mahmoud organization: APDM, Inc., Portland, OR, USA – sequence: 7 givenname: Jodi A surname: Lapidus fullname: Lapidus, Jodi A organization: School of Public Health, Oregon Health & Science University-Portland State University, Portland, OR, USA – sequence: 8 givenname: Fay B surname: Horak fullname: Horak, Fay B organization: APDM, Inc., Portland, OR, USA – sequence: 9 givenname: Carolin surname: Curtze fullname: Curtze, Carolin organization: Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA |
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| Snippet | Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).
To determine which digital outcome measures of... Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).BACKGROUNDIdentifying digital biomarkers of mobility is... |
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| Title | Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living |
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