ArticleBMJ open2026
Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.
Article in BMJ open, 2026. 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
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
1 citing paper in PubMed.
- Improving Balance and Gait in Older People with Parkinson's Disease: A Randomized Controlled Trial of Technology-Assisted Rehabilitation Interventions.Bioengineering (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) is a common neurodegenerative disorder characterised by high prevalence and disability rates, severely impairing patients' quality of life and imposing a substantial societal burden. Rehabilitation interventions are an essential component of PD management; however, conventional face-to-face rehabilitation is constrained by limited resources and poor adherence. In recent years, the integration of artificial intelligence (AI) with wearable technologies has offered new avenues for rehabilitation, enabling continuous monitoring, objective assessment and personalised feedback. Although relevant studies have emerged, most are limited by small sample sizes, short-term designs and a lack of comprehensive synthesis. This study aims to conduct a scoping review to summarise the current applications of AI-driven wearable technologies in PD rehabilitation and functional assessment, and to identify existing research gaps. METHODS AND ANALYSIS: This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and employ the Joanna Briggs Institute three-step search strategy. After an initial pilot search, a comprehensive search strategy will be developed to systematically search 10 databases (CNKI, Wanfang Data Knowledge Service Platform, SinoMed, the Cochrane Library, PubMed, Web of Science, CINAHL, Scopus, Embase and the IEEE Xplore Digital Library). One researcher will independently perform data extraction, and another will independently verify the extracted data. Eligible studies will include original research articles, dissertations and conference papers published after 2020, involving PD patients and using AI-driven wearable devices for rehabilitation or functional assessment. Data will be synthesised narratively and presented using tables and figures. ETHICS AND DISSEMINATION: This study involves only publicly available published data and therefore does not require ethical approval. Findings will be disseminated through peer-reviewed journal publications and presentations at academic conferences.
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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.