Safety and Sufficient Cardiac Rehabilitation With a Wearable Activity Tracker in a Patient With Acute Myocardial Infarction and Residual Stenosis
Bibliographic record
Abstract
Guidelines recommend exercise-based cardiac rehabilitation (CR) 2 - 3 times per week. However, this high number of visits per week to outpatient CR can be a burden that lowers patient compliance. Home-based exercise is a key for patients to perform a sufficient volume of exercise. But we sometimes need to be careful in patients who has coronary artery stenosis. Wearable activity trackers would be useful for maintaining an appropriate intensity and sufficient volume of home-based exercise. A 65-year-old male patient who did not have unremarkable past medical history had chest pain and visited our hospital. The primary diagnosis was acute myocardial infarction and the culprit lesion which was 99% stenosis in the posterior descending artery of the left circumflex artery was successfully treated. He was also diagnosed with obesity, hypertension, diabetes mellitus, and dyslipidemia and had residual 75% stenosis in the left anterior descending artery. He was started pharmacotherapy and planned elective percutaneous coronary intervention after 5 months. He was required an exercise-based CR after discharge. Outpatient CR was scheduled for once a week and he needed additional home-based exercise. We used a wearable activity tracker (iAide2-W, TOKAI Corp, Gifu, Japan) to check appropriate intensity of exercise and maintain a sufficient volume for home-based exercise. This device was able to monitor the metabolic equivalent by an acceleration sensor by telemetry. We could check the intensity of exercise at a specialized online site. Thanks to this device, he was able to reduce the body weight and increase the exercise tolerance without any chest pain. The percent predicted oxygen intake per body weight increased from 84% to 95% at the anaerobic threshold and from 68% to 83% at the peak. After 5 months, he treated the residual stenosis successfully. Wearable activity trackers can be used to evaluate biological information in daily life and are expected to be useful for CR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".