Innovative Recruitment Strategies to Increase Diversity of Participation in Parkinson’s Disease Research: The Fox Insight Cohort Experience
Bibliographic record
Abstract
BACKGROUND: Clinical research in Parkinson's disease (PD) faces practical and ethical challenges due to two interrelated problems: participant under-recruitment and lack of diversity. Fox Insight (FI) is a web-based longitudinal study collecting patient-reported outcomes and genetic data worldwide to inform therapeutic studies. FI's online platform provides an opportunity to evaluate online strategies for recruiting large, diverse research cohorts. OBJECTIVE: This project aimed to determine 1) whether FI's digital marketing was associated with increased enrollment overall and from under-represented patient groups, compared to traditional recruitment methods; 2) the clinical and demographic characteristics of samples recruited online, and 3) the cost of this online recruitment. METHOD: FI recruitment during a 6-week baseline period without digital promotion was compared to recruitment during several periods of digital outreach. Separate online recruiting intervals included general online study promotion and unique Facebook and Google ad campaigns targeting under-represented subgroups: early PD, late/advanced PD, and residents of underrepresented/rural geographic areas. RESULTS: Early PD, late PD, and geotargeting campaigns enrolled more individuals in their respective cohorts compared to baseline. All online campaigns also yielded greater total FI enrollment, attracting more participants who were non-White, Hispanic, older, female, and had lower educational attainment and income, and more medical comorbidities. Cost per new participant ranged from $21 (Facebook) to $108 (Google). CONCLUSION: Digital marketing may allow researchers to increase, accelerate, and diversify recruitment for PD clinical studies, by tailoring digital ads to target PD cohort characteristics.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".