Trial reportJournal of neurology2025
The effects of task-specific home-based touchscreen training in people with Parkinson's disease: a pilot randomized controlled trial.
Trial report in Journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Effects of Digital Health Interventions on Motor Symptoms, Nonmotor Symptoms, and Quality of Life in Patients With Parkinson Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2026Pooled it
- Construct validity, intrarater and interrater reliability of the Italian version of the systematic screening of handwriting difficulties (SOS) test in people with Parkinson's disease.Journal of neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundManual dexterity deficits impair the ability to effectively use touchscreen devices in people with Parkinson's disease (PD).
objectiveTo examine the effects and feasibility of a home-based, unsupervised tablet-task training on task-specific performance in a randomized controlled trial and to determine which individuals are likely to benefit.
methodsThirty-four PD patients were randomized and included into an experimental training (EXP, N = 16) and passive control group (CTL, N = 18). The EXP practiced a Swipe-Slide Pattern (SSP) task on a tablet (5x/week for 2 weeks) as fast and accurately as possible in single and dual task conditions. Performance on the SSP and an untrained mobile phone task (MPT) were tested before and after two weeks of training and after four weeks follow-up. SSP-Time (primary outcome), SSP-Accuracy (% correct) and MPT-Time were recorded. Linear mixed models were used to assess training effects.
resultsThe home-based task-specific training program significantly improved the SSP-Time immediately after training (p < 0.001, d = 0.917) and at follow-up (p = 0.006, d = 0.663), and showed excellent compliance rates (average 98%). No transfer occurred to the untrained MPT. Worse baseline SSP-performance and older age were significantly associated with short- and long-term gains (p < 0.010).
conclusionHome-based, unsupervised touchscreen training is feasible and effective to improve movement time of the trained task, albeit without transfer to an untrained task. The heterogeneity and variability of the effects underscore the importance of personalizing rehabilitation programs in PD, according to baseline performance. Future studies should investigate a wider range of transfer tasks and clinical determinants that could impact the training response. CLINICAL
trial registrationhttps://clinicaltrials.gov/ : NTC05696197, retrospectively registered on January 13, 2023.
Indexed as
Identifiers
40204991What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.