Recruitment for Remote Decentralized Studies in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Traditional in-person Parkinson's disease (PD) research studies are often slow to recruit and place unnecessary burden on participants. The ongoing COVID-19 pandemic has added new impetus to the development of new research models. OBJECTIVE: To compare recruitment processes and outcomes of three remote decentralized observational PD studies with video visits. METHODS: We examined the number of participants recruited, speed of recruitment, geographic distribution of participants, and strategies used to enhance recruitment in FIVE, a cross-sectional study of Fox Insight participants with and without PD (n = 203); VALOR-PD, a longitudinal study of 23andMe, Inc. research participants carrying the LRRK2 G2019S variant with and without PD (n = 277); and AT-HOME PD, a longitudinal study of former phase III clinical trial participants with PD (n = 226). RESULTS: Across the three studies, 706 participants from 45 U.S. states and Canada enrolled at a mean per study rate of 4.9 participants per week over an average of 51 weeks. The cohorts were demographically homogenous with regard to race (over 95%white) and level of education (over 90%with more than a high school education). The number of participants living in primary care Health Professional Shortage Areas in each study ranged from 30.3-42.9%. Participants reported interest in future observational (98.5-99.6%) and interventional (76.1-87.6%) research studies with remote video visits. CONCLUSION: Recruitment of large, geographically dispersed remote cohorts from a single location is feasible. Interest in participation in future remote decentralized PD studies is high. More work is needed to identify best practices for recruitment, particularly of diverse participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".