Wearable Health Technology for the Diagnosis and Management of Atrial Fibrillation: A Systematic Review
Bibliographic record
Abstract
Introduction: Atrial Fibrillation (AF), the most commonly sustained arrhythmia, affects approximately 200,000 Canadians and costs an annual $815 million to the Ontario provincial healthcare system. The current model for chronic AF management (favouring infrequent hospital visits) in combination with the challenge of detecting cardiac arrythmias provides opportunity for wearable devices to improve quality of care. Physicians must be aware of available wearable devices, their accuracy, validity, and potential to improve patient care. Methods: PubMed and Google Scholar databases were searched for primary studies involving AF and wearable devices published from database inception to 2020. Due to the heterogeneity of the findings, quantitative synthesis of the information could not be conducted. A narrative synthesis was performed for the obtained results, including a discussion of apparent trends and common study outcome measures: sensitivity, specificity, positive-, and negative-predictive-values. The narrative included comparisons to similar reviews and a discussion on both the current and future research directions. Results: The systematic search produced 214 studies, of which, 7 met the pre-determined eligibility criteria. The studies were published between 2018-2020 in 5 countries. Each eligible study was described and summarized; devices investigated include the Apple Watch with Kardia Band (Atrial fibrillation-sensing watch; AFSW), Huawei wearable devices (Honor 4 wristband, Honor Watch or Watch GT), CardioTracker (CART) + a pulse oximeter, and the RITMA system. Discussion: This systematic review comprehensively examined wearable devices used to deliver care for patients with AF. It was found that currently available wearable technologies for AF detection and management varied greatly in their accuracy, application settings, and accessibility. With technological advancements and further exploration into AF devices’ usability, widespread implementation of wearable technology in AF care is plausible. Successful application of these technologies can improve AF care and contribute to a significant reduction in AF-related healthcare costs. Conclusion: There are many devices available for AF care that show promise for certain applications. Additionally, there remains a demand for large-scale, high-quality research in this field to investigate the advantages and shortcomings of various devices that have shown promise in preliminary research. Future research should focus on randomized controlled trials to generate conclusive findings for wearable technologies in AF.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".