Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 29, 2020
Open Peer Review Period: Dec 17, 2020 - Feb 11, 2021
Date Accepted: Jul 5, 2021
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring test retest reliability and longitudinal stability of digital biomarkers for Parkinson’s disease in the m-Power dataset
ABSTRACT
Background:
Digital biomarkers (DB) as captured using sensors embedded in modern smart devices are a promising technology for home-based symptom monitoring in Parkinson’s disease (PD). Despite extensive application in recent studies test-retest reliability and longitudinal stability of DB has not been well addressed in this context.
Objective:
We utilized the large-scale m-Power dataset 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 PD patients and healthy volunteers.
Methods:
Intraclass Correlation Coefficients (ICC) were computed to estimate the test-retest reliability of features that also differentiate between 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 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 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 PD patients. Motivational, learning and other confounds may cause a variation in performance that needs to be considered in DB longitudinal applications. Clinical Trial: Not applicable
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