ArticleBMC neurology2023
Design of the STEPS trial: a phase II randomized controlled trial evaluating eHealth-supported motor-cognitive home training for Parkinson's disease.
Article in BMC neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05510739 (Support for Physical Activity in Everyday Life With Parkinson's Disease Using eHealth Technology), which is not on this map. Cited by 4 papers.
What it found
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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.
Support for Physical Activity in Everyday Life With Parkinson's Disease Using eHealth Technology
Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Relating movement behaviours and non-motor characteristics in people with Parkinson's disease: A compositional data analysis approach.Journal of Parkinson's disease · 2025Trial
- Integrating GPS and Accelerometry to Capture Life-Space Mobility in Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Impact of dual-tasking and balance confidence on turns and transitions: a cross-sectional study in Parkinson's disease.Scientific reports · 2026Article
- Clinical Assessment of Walking Capacity in People With Parkinson Disease: Are 2 Minutes Sufficient?Physical therapy · 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
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundElectronic health (eHealth) technology offers the potential to support and motivate physical activity for symptom management in Parkinson's disease (PD). It is also recommended that motor exercise in PD be complemented with cognitive training aimed at attentional or executive functions. This paper describes the protocol for a double-blind randomized controlled trial to evaluate the effects of motor-cognitive training in the home environment, supported by eHealth. METHODS/
designThe Support for home Training using Ehealth in Parkinsons diseaSe (STEPS) is a double-blind single center randomized controlled trial. Two parallel groups will include in total 120 participants with mild to moderate PD who will receive either (i) the intervention (a progressive 10-week individualized motor-cognitive eHealth training with cognitive behavioral elements to increase physical activity levels) or (ii) an active control group (an individualized 10-week paper-based home exercise program). The active control group will not receive motor-cognitive exercises or cognitive behavioral approaches to increase physical activity level. The primary outcome is walking capacity assessed by the six-minute walk test (6MWT). Secondary outcomes will include gait performance during single and dual task conditions, gait speed, functional mobility and lower limb strength, balance, physical activity behavior and a range of patient reported outcome measures relevant in PD. DISCUSSION: The STEPS trial will answer the question whether 10 weeks of eHealth supported motor-cognitive exercise in the home environment can improve walking capacity in PD when compared to a standard paper exercise program. Findings from this study will also strengthen the evidence concerning the efficacy of PD-specific eHealth interventions with a view meeting future health care demands by addressing issues of inaccessibility to specialized neurological rehabilitation in PD.
trial registrationClinicalTrials.gov August 2022, NCT05510739.
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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.