Enhancing Enrollment and Adherence in Long-Term Wearable Research on Dementia: Qualitative Systematic Review and Meta-Synthesis
Bibliographic record
Abstract
BACKGROUND: With the rapid expansion of wearable technologies, there is increased interest in their utility for passive data collection applications in research on aging. Wearables can be beneficial for research with people with dementia and their families, who have burdens that can make both study participation and reliable data collection more difficult, especially as dementia progresses, but their use also has challenges. Population-specific issues affecting the success of wearables for data collection can include remembering to wear a device, fluctuating acceptance of the device or study participation, and reliance on already strained caregivers. OBJECTIVE: This study aimed to systematically evaluate contemporary wearables research to describe persons with dementia's experiences with wearables, their desired qualities, and protocol needs to enhance participant buy-in and sustained wearing for better quality dementia research. METHODS: We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist for systematic reviews and searched 3 scholarly databases using Medical Subject Headings (MeSH) terms for papers published since 2018 featuring the use or discussion of wearable devices for persons with dementia. We screened 1757 abstracts and retained 58 for full-text review. RESULTS: We present synthesized preferences, barriers, and facilitators to buy-in and adherence to wearables in dementia research. A total of 29 factors were categorized into 4 overarching categories aligned with study development: device selection, protocol considerations, enhancing recruitment, and promoting adherence. CONCLUSIONS: These findings inform researcher guidelines for wearable device selection and protocol design to enhance the utility of wearables in future longitudinal research featuring persons with dementia and their caregivers.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".