ArticleSystematic reviews2022
The efficacy of imagery in the rehabilitation of people with Parkinson's disease: protocol for a systematic review and meta-analysis.
Article in Systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Convolutional neural networks-based early Parkinson's disease classification using cycling data from a steerable indoor bicycle.Scientific reports · 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) is a neurodegenerative disorder of the nervous system that affects movement. Individuals with PD commonly experience difficulty initiating movements, slowness of movements, decreased balance, and decreased standing ability. It has been shown that these motor symptoms adversely affect the independence of individuals with PD. Imagery is the cognitive process whereby a motor action is internally reproduced and repeated without overt physical movement. Recent studies support the use of imagery in improving rehabilitation outcomes in the PD population. However, these data have inconsistencies and have not yet been synthesised. The study will review the evidence on the use of imagery in individuals with PD and to determine its efficacy in improving rehabilitation outcomes.
methodsRandomised controlled clinical trials comparing the effects of imagery and control on activities, body structure and function, and participation outcomes for people with PD will be included. A detailed computer-aided search of the literature will be performed from inception to June 2021 in the following databases: MEDLINE, EMBASE, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Scopus. Two independent reviewers will screen articles for relevance and methodological validity. The Physiotherapy Evidence Database (PEDro) scale will be utilised to evaluate the risk of bias of selected studies. Data from included studies will be extracted by two independent reviewers through a customised, pre-set data extraction sheet. Studies using imagery with comparable outcome measures will be pooled for meta-analysis using the random effect model with 95% CI. If individual studies are heterogeneous, a descriptive review will analyse variance in interventions and outcomes. A narrative data analysis will be considered where there is insufficient data to perform a meta-analysis. DISCUSSION: Several studies investigating imagery in the PD population have drawn dissimilar conclusions regarding its effectiveness in rehabilitation outcomes and clinical applicability. Therefore, this systematic review will gather and critically appraise all relevant data, to generate a conclusion and recommendations to guide both clinical practice and future research on using imagery in the rehabilitation of people with PD.
fundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42021230556.
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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.