A smartphone-based tapping task as a marker of medication response in Parkinson’s disease: a proof of concept study

Abstract

Tapping tasks have the potential to distinguish between ON–OFF fluctuations in Parkinson’s disease (PD) possibly aiding assessment of medication status in e-diaries and research. This proof of concept study aims to assess the feasibility and accuracy of a smartphone-based tapping task (developed as part of the cloudUPDRS-project) to discriminate between ON–OFF used in the home setting without supervision. 32 PD patients performed the task before their first medication intake, followed by two test sessions after 1 and 3 h. Testing was repeated for 7 days. Index finger tapping between two targets was performed as fast as possible with each hand. Self-reported ON–OFF status was also indicated. Reminders were sent for testing and medication intake. We studied task compliance, objective performance (frequency and inter-tap distance), classification accuracy and repeatability of tapping. Average compliance was 97.0% (± 3.3%), but 16 patients (50%) needed remote assistance. Self-reported ON–OFF scores and objective tapping were worse pre versus post medication intake (p < 0.0005). Repeated tests showed good to excellent test–retest reliability in ON (0.707 ≤ ICC ≤ 0.975). Although 7 days learning effects were apparent, ON–OFF differences remained. Discriminative accuracy for ON–OFF was particularly good for right-hand tapping (0.72 ≤ AUC ≤ 0.80). Medication dose was associated with ON–OFF tapping changes. Unsupervised tapping tests performed on a smartphone have the potential to classify ON–OFF fluctuations in the home setting, despite some learning and time effects. Replication of these results are needed in a wider sample of patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00702-023-02659-w.

Improving naturalistic neuroscience with patient engagement strategies

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Enhancing public health in developing nations through smartphone-based motor assessment

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Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson’s Disease Using Machine Learning

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