Background Movement Disorder Society–Unified Parkinson's Rating Scale Part III (MDS‐UPDRS III) is the gold standard for assessing medication effects in patients with Parkinson's disease (PD). However, short and rater‐independent measurements would be ideal for future trials. Objectives To assess the ability of 3 different finger tapping tasks to detect levodopa/carbidopa‐induced changes over time and to determine their correlation and compare their discriminatory power with MDS‐UPDRS III. Methods This was a randomized, double‐blind, crossover study in 20 patients with PD receiving levodopa/carbidopa and placebo capsules after overnight medication withdrawal. Pre‐ and up to 3.5 hours postdose, MDS‐UPDRS III and tapping tasks were performed. Tasks included 2 touchscreen‐based alternate finger tapping tasks (index finger versus index–middle finger tapping) and a thumb–index finger task using a goniometer. Results In the alternate index finger tapping task, levodopa/carbidopa compared with placebo resulted in significantly faster (total taps: 12.5 [95% confidence interval, CI, 6.7–18.2]) and less accurate tapping (total spatial error: 240 mm [95% CI, 123–357 mm]) with improved rhythm (intertap interval standard deviation [SD], −16.3% [95% CI, −29.9% to 0.0%]). In the thumb–index finger task, tapping was significantly faster (mean opening velocity, 151 degree/s [64–237 degree/s]), with a higher mean amplitude (8.4 degrees [3.7–13.0 degrees]) and improved rhythm (intertap interval SD, −46.4% [95% CI, −63.7% to −20.9%]). The speed‐related endpoints showed a moderate‐to‐strong correlation with the MDS‐UPDRS III (r = 0.45–0.70). The effect sizes of total taps and spatial error in the alternate index finger tapping task and opening velocity in the thumb–index finger task were comparable with the MDS‐UPDRS III. In contrast, the MDS‐UPDRS III performed better than the alternate index–middle finger task. Conclusion The alternate index finger and the thumb–index finger tapping tasks provide short, rater‐independent measurements that are sensitive to levodopa/carbidopa effects with a similar effect size as the MDS‐UPDRS III.

Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects almost 2% of the population above the age of 65. To better quantify the effects of new medications, fast and objective methods are needed. Touchscreen-based tapping tasks are simple yet effective tools for quantifying drug effects on PD-related motor symptoms, especially bradykinesia. However, there is no consensus on the optimal task set-up. The present study compares four tapping tasks in 14 healthy participants. In alternate finger tapping (AFT), tapping occurred with the index and middle finger with 2.5 cm between targets, whereas in alternate side tapping (AST) the index finger with 20 cm between targets was used. Both configurations were tested with or without the presence of a visual cue. Moreover, for each tapping task, within- and between-day repeatability and (potential) sensitivity of the calculated parameters were assessed. Visual cueing reduced tapping speed and rhythm, and improved accuracy. This effect was most pronounced for AST. On average, AST had a lower tapping speed with impaired accuracy and improved rhythm compared to AFT. Of all parameters, the total number of taps and mean spatial error had the highest repeatability and sensitivity. The findings suggest against the use of visual cueing because it is crucial that parameters can vary freely to accurately capture medication effects. The choice for AFT or AST depends on the research question, as these tasks assess different aspects of movement. These results encourage further validation of non-cued AFT and AST in PD patients.

The validation of objective and easy‐to‐implement biomarkers that can monitor the effects of fast‐acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo‐controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb–index finger tapping (TIFT) tasks and the Movement Disorder Society‐Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS‐UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS‐UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS‐UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS‐UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS‐UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.