A scoping review of safer mobility behaviour assessment and intervention: implications for people with Parkinson’s disease
Bibliographic record
Abstract
PURPOSE: To provide a summary of assessments and interventions used to target safer mobility behaviour for fall prevention in older adults and people with Parkinson's disease (PwPD). MATERIALS AND METHODS: This scoping review included older adults (aged ≥65 years) and PwPD within home, community, or primary care settings. Assessment tools with >50% items designed to assess mobility behaviour and interventions with at least one component relevant to safer mobility behaviour were included. RESULTS: Out of 27 686 records identified, 75 were included. Ten assessment tools that assessed behavioural strategies to reduce falls or fear of falling avoidance behaviour were included. The reporting of psychometric properties and subsequent quality ratings were mixed. Although no assessment tools were designed specifically for PwPD, four were used with PwPD. Eight out of 19 interventions involving education and movement strategy training were designed for PwPD. However, they varied in content, dosage and proportion of overall intervention. CONCLUSIONS: This review provides an overview of safer mobility behaviour assessments and interventions for fall prevention. Future assessment tools should be designed to assess specific impairments in Parkinson's disease (PD) that influence mobility behaviour. Future interventions should consider individualised behavioural strategies that address the heterogeneity and progression of PD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".