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| Home > Publications database > Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study > print |
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| 100 | 1 | _ | |a Sahandi Far, Mehran |0 P:(DE-Juel1)177922 |b 0 |
| 245 | _ | _ | |a Exploring Test-Retest Reliability and Longitudinal Stability of Digital Biomarkers for Parkinson Disease in the m-Power Data Set: Cohort Study |
| 260 | _ | _ | |a Richmond, Va. |c 2021 |b Healthcare World |
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| 520 | _ | _ | |a Background: Digital biomarkers (DB), as captured using sensors embedded in modern smart devices, are a promising technology for home-based sign and symptom monitoring in Parkinson disease (PD).Objective: Despite extensive application in recent studies, test-retest reliability and longitudinal stability of DB have not been well addressed in this context. We utilized the large-scale m-Power data set to establish the test-retest reliability and longitudinal stability of gait, balance, voice, and tapping tasks in an unsupervised and self-administered daily life setting in patients with PD and healthy controls (HC).Methods: Intraclass correlation coefficients were computed to estimate the test-retest reliability of features that also differentiate between patients with PD and healthy volunteers. In addition, we tested for longitudinal stability of DB measures in PD and HC, as well as for their sensitivity to PD medication effects.Results: Among the features differing between PD and HC, only a few tapping and voice features had good to excellent test-retest reliabilities and medium to large effect sizes. All other features performed poorly in this respect. Only a few features were sensitive to medication effects. The longitudinal analyses revealed significant alterations over time across a variety of features and in particular for the tapping task.Conclusions: These results indicate the need for further development of more standardized, sensitive, and reliable DB for application in self-administered remote studies in patients with PD. Motivational, learning, and other confounders may cause variations in performance that need to be considered in DB longitudinal applications. |
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| 700 | 1 | _ | |a Eickhoff, Simon B |0 P:(DE-Juel1)131678 |b 1 |
| 700 | 1 | _ | |a Goni, Maria |0 P:(DE-Juel1)177908 |b 2 |
| 700 | 1 | _ | |a Dukart, Juergen |0 P:(DE-Juel1)177727 |b 3 |e Corresponding author |
| 773 | _ | _ | |a 10.2196/26608 |g Vol. 23, no. 9, p. e26608 - |0 PERI:(DE-600)2028830-X |n 9 |p e26608 - |t Journal of medical internet research |v 23 |y 2021 |x 1438-8871 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/896732/files/Invoice_JMIR-26608-APF-0035714.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/896732/files/M-Power_JMIR_v12_JD_allAccepted.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/896732/files/PDF_Exploring.pdf |y OpenAccess |
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