Wearables are a viable digital health tool for older Indigenous adults living remotely in Australia (research)
Bibliographic record
Abstract
Objective: Health programs for Indigenous people are most effective, acceptable, and sustainable when Indigenous perspectives are prioritized. Codesign builds on Indigenous people's creativity and propensity to experiment with new technologies and ensures research is designed and implemented in a culturally safe and respectful manner. Limited research has focused on older Indigenous people as partners in digital health. No research has focused on the acceptability and feasibility of older Indigenous people using wearables for heart health monitoring. This study provides insights into the acceptability and feasibility for ≥55-year-old Indigenous people living in remote locations to use wearables (watches and patches) to detect atrial fibrillation (AF) and high blood pressure. Methods: This mixed methods study was codesigned and coimplemented with the local Aboriginal Controlled Health Service in a remote area of New South Wales, Australia. It included active involvement and codesign with the participants. The devices used in this study included a Withings Scan watch and a Biobeat patch. Results: Despite challenging conditions (>36°C) and variable internet connectivity, 11 Indigenous older adults participated in a five-day wearables program in a remote location. Participants indicated that using digital health devices was acceptable and feasible for older Indigenous users. They described high levels of comfort, safety and convenience when using wearables (patches and watches) to detect AF. They were active participants in codesigning the program. Conclusion: Older Indigenous Australians are motivated to use wearable health devices. They are keen to participate in codesign innovative health tech programs to ensure new health technologies are acceptable to Indigenous people and feasible for remote locations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".