Digital gait biomarkers in Parkinson’s disease: susceptibility/risk, progression, response to exercise, and prognosis
Bibliographic record
Abstract
This narrative review examines the utility of gait digital biomarkers in Parkinson's disease (PD) research and clinical trials across four contexts: disease susceptibility/risk, disease progression, response to exercise, and fall prediction. The review of the literature to date suggests that upper body characteristics of gait (e.g., arm swing, trunk motion) may indicate susceptibility/risk of PD, while pace aspects (e.g., gait speed, stride length) are informative for tracking disease progression, exercise response, and fall likelihood. Dynamic stability aspects (e.g., trunk regularity, double-support time) worsen with disease progression but can improve with exercise. Gait variability emerges as a sensitive biomarker across all 4 contexts but with low specificity. The lack of standardized gait testing protocols and the lack of a minimum set of quantified digital gait biomarkers limit data harmonization across studies. Future studies, using a commonly agreed upon protocol, could be used to demonstrate the utility of specific gait biomarkers for clinical practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".