Evaluating the Utility of mHealth ECG Heart Monitoring for the Detection and Management of Atrial Fibrillation in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Little attention has focused on the integration of mobile health (mHealth) technology with self-management approaches to improve the detection and management of atrial fibrillation (AF) in clinical practice. OBJECTIVE: The objective of this study was to investigate the differences between mHealth and usual care over a 6-month follow-up period among patients with a known history of atrial fibrillation. METHODS: QoL assessments were administered at baseline and 6 months to a subset of the patients undergoing daily ECG monitoring. Differences between groups were assessed by t-test, Fisher's exact test, and Cox proportional hazard models. RESULTS: Among the 23 patients with smartphone ECG monitors (16 males and 7 females, mean age 55 ± 10), 14 (61%) had detection of recurrent AF/AFL versus 30% of controls. During the follow-up period, patients given smartphone ECG monitors were more than twice as likely to have an episode of recurrent AF/AFL detected (hazard ratio: 2.55; 95% CI: 1.06 - 6.11; p = 0.04). Among the 13 patients with baseline and 6 month QoL assessments, significant improvements were observed in the physical functioning (p = 0.009), role physical (p = 0.007), vitality (p = 0.03), and mental health domains (p = 0.02). CONCLUSIONS: Cardiac mHealth self-monitoring is a feasible and effective mechanism for enhancing AF/AFL detection that improves quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".