ArticleMovement disorders clinical practice2021
A Self-Reported Clinical Tool Predicts Falls in People with Parkinson's Disease.
Article in Movement disorders clinical practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Determining Falls Risk in People with Parkinson's Disease Using Wearable Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- American Geriatrics Society response to the World Falls Guidelines.Journal of the American Geriatrics Society · 2024Article
- Prevention of Falls in Parkinson's Disease: Guidelines and Gaps.Movement disorders clinical practice · 2023Review
- Primary care for people with Parkinson's disease in Brazil: A referral flowchart based on risk of falls.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA 3-step clinical prediction tool including falling in the previous year, freezing of gait in the past month and self-selected gait speed <1.1 m/s has shown high accuracy in predicting falls in people with Parkinson's disease (PD). The accuracy of this tool when including only self-report measures is yet to be determined.
objectivesTo validate the 3-step prediction tool using only self-report measures (3-step self-reported prediction tool), and to externally validate the 3-step clinical prediction tool.
methodsThe clinical tool was used with 137 individuals with PD. Participants also answered a question about self-reported gait speed, enabling scoring of the self-reported tool, and were followed-up for 6 months. An intraclass correlation coefficient (ICC
resultsForty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC
conclusionsThe 3-step self-reported prediction tool showed excellent test-retest reliability and was validated with acceptable accuracy in predicting falls in the next 6 months. The 3-step clinical prediction tool was externally validated with similar accuracy. The 3-step self-reported prediction tool may be useful to identify people with PD at risk of falls in e/tele-health settings.
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Registered trials
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