WEARABLES IN LONG-TERM DEMENTIA RESEARCH: A MIXED METHOD STUDY OF USER EXPERIENCES AND SUPPORT NEEDS
Bibliographic record
Abstract
Abstract Passive wearables data collection may be specifically beneficial to aging research featuring dementia populations, who have caregiving and cognitive burdens that can make study participation and reliable data collection more difficult, especially as dementia progresses. This three-phase project aims to inform best practice recommendations to enhance recruitment and adherence in long-term wearables research featuring this population. Based on our systematic review and preliminary in-house data testing, we selected three wearables offering different capabilities and form (from Garmin, Pulse HR, and AngelSense) to test real world usability, data quality, and support needs. This is the first study to recruit persons living with dementia and their caregivers to evaluate multiple devices outside of a laboratory or focus group setting (N=12 dyads). The person living with dementia assented to wearing each wearable for two weeks. Their caregiver rated many facets of each device following its use with the Quebec User Evaluation of Satisfaction with Assistive Technology measure. Open-ended questions and a cumulative semi-structured interview provided context and in-depth comparative perspectives of their experiences in the study. Wearable durability, simplicity, and data availability/monitoring capacity were important to participant favorability and adherence. Technical help and check-ins were also deemed highly valuable. Data indicate how the devices suited the dyads’ needs or caused issues, as well as how study staff could better support ongoing use. Collectively, our findings suggest ideal criteria to guide wearable selection and key protocol factors that can enhance participant recruitment and adherence in long-term dementia research.
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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 | 0.063 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".