Recruitment Challenges and Strategies in a Technology-Based Intervention for Dementia Caregivers: Descriptive Study
Bibliographic record
Abstract
Background: Researchers have encountered challenges in recruiting unpaid caregivers of people living with Alzheimer disease and related dementias for intervention studies. However, little is known about the reasons for nonparticipation in in-home smart health interventions in community-based settings. Objective: This study aimed to (1) assess recruitment rates in a smart health technology intervention for caregivers of people living with Alzheimer disease and related dementias and reasons for nonparticipation among them and (2) discuss lessons learned from recruitment challenges and strategies to improve recruitment. Methods: The smart health intervention was a 4-month, single-arm trial designed to evaluate an in-home, technology-based intervention that monitors stressful moments for caregiving dyads through acoustic signals and to provide the caregivers with real-time stress management strategies. The recruitment involved two main methods: on-site engagement by a recruiter from a memory clinic and social media advertising. Caregivers were screened for eligibility by phone between January 2021 and September 2023. The recruitment rates, reasons for nonparticipation, and participant demographics were analyzed using descriptive statistics. Results: Of 201 caregivers contacted, 11 were enrolled in this study. Eighty-two caregivers did not return the screening call, and others did not participate due to privacy concerns (n=30), lack of interest (n=29), and burdensome study procedures (n=26). Our recruitment strategies included addressing privacy concerns, visualizing collected data through a dashboard, boosting social media presence, increasing the recruitment budget, updating advertisements, and preparing and deploying additional study devices. Conclusions: This study highlighted barriers to participation in the smart health intervention. Despite several recruitment strategies, enrollment rates remained below expectations. These findings underscore the need for future research to explore alternative methods for increasing the recruitment of informal dementia caregivers in technology-based intervention studies.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".