Measurement Instruments to Assess Functional Mobility in Parkinson's Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Functional mobility (FM) is a person's ability to move to accomplish activities of daily living; it bridges the concepts of mobility and functional ability. There is frequently a loss of FM in Parkinson's disease (PD). Several instruments have been used to assess this concept in PD; however, there is no consensus on which are the most appropriate. OBJECTIVE: We aimed to identify and critically appraise which measurement instruments have been used to assess FM. METHODS: A systematic review was conducted using the databases CENTRAL, MEDLINE, Embase, and PEDro from their inception to January 2019 to identify all observational and experimental studies conducted in PD or atypical parkinsonism that included an FM assessment. Two reviewers independently screened citations, extracted data, and assessed clinimetric properties. RESULTS: We included 95 studies that assessed FM in PD. Fifty-five (57.9%) studies mentioned FM in the article, and 39 (41.1%) specified the measurement tools used to evaluate FM. FM was the primary outcome in 12 (12.6%) studies. The Timed Up and Go test was the most frequently used measurement tool. Only one study presented a definition of FM. Several overlapping terms were used, the most common being mobility. CONCLUSION: Several studies reported the use of FM measurement tools in PD, though with frequent misconceptions, an inadequate context of use, or suboptimal assessment. We propose the establishment of the concept of FM applied to PD, followed by the adequate clinimetric validation of existing measurement tools to provide a comprehensive and reliable evaluation of FM in 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 | 0.030 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".