Assessing Digital Health Technologies for Outcome Measurement in Parkinson's Disease Drug Trials: A Systematic Review
Bibliographic record
Abstract
Traditional clinical assessments in Parkinson's disease (PD) trials are limited by subjectivity and inter-rater variability. Digital health technologies (DHT) offer an objective continuous assessment of motor symptoms and are increasingly used in clinical research. This review evaluated the role of DHTs as outcome tools in pharmacological trials for PD. A systematic search of MEDLINE and Embase was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, covering studies up to August 31, 2025. Eligible studies included randomized controlled trials, open-label or crossover designs, and observational studies using DHTs to assess motor outcome variables in PD. Studies focusing only on technology development or with fewer than 10 participants were excluded. Data extracted included study design, DHT type, assessment setting, and motor parameters measured. Study quality was appraised using an eight-criterion tool, and level of evidence was rated using the Oxford Centre for Evidence-Based Medicine framework. A total of 42 studies were included, covering 26 distinct DHTs. These comprised 11 wearable sensors and 15 nonwearable systems such as motion capture platforms and force-sensing assessments. DHTs were used to measure bradykinesia, tremor, gait, balance, and nocturnal motor symptoms in both supervised and unsupervised settings. Fifteen studies were rated as high quality, 14 moderate, and 13 low. Among currently available tools, only Opal reached the threshold of Level 1a evidence. Other validated tools included the Parkinson's Kinetigraph, Actiwatch, and Roche PD Mobile Application (Level 1b). DHTs offer valuable tools for objective assessment in PD trials, though broader adoption requires greater standardization and regulatory alignment. © 2025 International Parkinson and Movement Disorder Society.
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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 | 0.037 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".