Commercially available products for the digital tracking of biomarkers in Parkinson's Disease
Bibliographic record
Abstract
Background Parkinson’s Disease (PD) is a debilitating neurological disorder which affects 8.5 million people globally. Diagnosis of PD is made upon presentation of motor symptoms. However, there is a well-recognised prodromal phase of PD, when patients experience non-motor symptoms, and subtle motor symptoms, before the onset of the cardinal motor symptoms. Biomarkers of this prodromal phase can provide a diagnostic window into early disease processes, assisting with differential diagnosis of PD and enabling earlier treatment. Due to increased availability of commercially-available products, both wearable devices and smartphone applications are being explored for potential to identify PD biomarkers. Such products can provide clinicians with early warning of disease progression, and supply researchers with tools for monitoring PD outside of laboratory settings. Methods This systematic review critically examined the academic literature published in the English language to identify currently-available products designed to track biomarkers of PD across 6 databases between January 2000 and March 2025. Results 27 papers were identified which captured physiological biomarkers in PD patients using commercially-available products. Current products emphasize the capture of early motor dysfunction through both upper limb and eye movements. There is a lack of literature on the validation of commercially-available products for the detection of PD, despite an increase in advanced data analysis algorithms. Conclusion There is a critical need for validation of devices for the tracking of biomarkers of PD, which may be utilised for detection during the prodromal phase.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".