Consumer-led screening for atrial fibrillation using consumer-facing wearables, devices and apps: A survey of health care professionals by AF-SCREEN international collaboration
Bibliographic record
Abstract
AIM: A variety of consumer-facing wearables, devices and apps are marketed directly to consumers to detect atrial fibrillation (AF). However, their management is not defined. Our aim was to explore their role for AF screening via a survey. METHODS AND RESULTS: An anonymous web-based survey was undertaken by 588 health care professionals (HCPs) (response rate 23.7%). Overall, 57% HCPs currently advise wearables/apps for AF detection in their patients: this was much higher for electrophysiologists and nurses/allied health professionals (74-75%) than cardiologists (57%) or other physicians (34-38%). Approximately 46% recommended handheld (portable) single-lead dedicated ECG devices, or, less frequently, wristband ECG monitors with similar differentials between HCPs . Only 10-15% HCPs advised photoplethysmographic wristband monitors or smartphone apps. In over half of the HCP consultations for AF detected by wearables/apps, the decision to screen was entirely the patient's. About 45% of HCPs perceive a potential role for AF screening in people aged >65 years or in those with risk factors. Almost 70% of HCPs believed we are not yet ready for mass consumer-initiated screening for AF using wearable devices/apps, with patient anxiety, risk of false positives and negatives, and risk of anticoagulant-related bleeding perceived as potential disadvantages, and perceived need for appropriate management pathways. CONCLUSIONS: There is a great potential for appropriate use of consumer-facing wearables/apps for AF screening. However, it appears that there is a need to better define suitable individuals for screening and an appropriate mechanism for managing positive results before they can be recommended by HCPs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".