Clinician Perspectives on the Design and Application of Wearable Cardiac Technologies for Older Adults: Qualitative Study
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Bibliographic record
Abstract
BACKGROUND: New wearable devices (for example, AliveCor or Zio patch) offer promise in detecting arrhythmia and monitoring cardiac health status, among other clinically useful parameters in older adults. However, the clinical utility and usability from the perspectives of clinicians is largely unexplored. OBJECTIVE: This study aimed to explore clinician perspectives on the use of wearable cardiac monitoring technology for older adults. METHODS: A descriptive qualitative study was conducted using semistructured focus group interviews. Clinicians were recruited through purposive sampling of physicians, nurses, and allied health staff working in 3 tertiary-level hospitals. Verbatim transcripts were analyzed using thematic content analysis to identify themes. RESULTS: Clinicians representing physicians, nurses, and allied health staff working in 3 tertiary-level hospitals completed 4 focus group interviews between May 2019 and July 2019. There were 50 participants (28 men and 22 women), including cardiologists, geriatricians, nurses, and allied health staff. The focus groups generated the following 3 overarching, interrelated themes: (1) the current state of play, understanding the perceived challenges of patient cardiac monitoring in hospitals, (2) priorities in cardiac monitoring, what parameters new technologies should measure, and (3) cardiac monitoring of the future, "the ideal device." CONCLUSIONS: There remain pitfalls related to the design of wearable cardiac technology for older adults that present clinical challenges. These pitfalls and challenges likely negatively impact the uptake of wearable cardiac monitoring in routine clinical care. Partnering with clinicians and patients in the co-design of new wearable cardiac monitoring technologies is critical to optimize the use of these devices and their uptake in clinical care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it