Developments in Digital Wearable in Heart Failure and the Rationale for the Design of TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple CPET Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a highly prevalent condition characterized by exercise intolerance, an important metric for ambulatory prognostication. However, current methods to assess exercise capacity are often limited to tertiary HF centers, lacking scalability or accessibility. Wearable devices can enable near-continuous dynamic biometrics including exercise tolerance. METHODS: Leveraging the capabilities of Apple Watch and a custom application, the TRUE-HF (Ted Rogers Understanding of Exacerbations in Heart Failure) Apple cardiopulmonary exercise testing study aims to investigate whether HealthKit data from Apple Watch can estimate cardiorespiratory fitness, as compared with the gold standard peak oxygen uptake from cardiopulmonary exercise testing. The TRUE-HF study will evaluate the potential impact of wearable technology in the functional assessment of ambulatory patients with HF. The primary end point is to use HealthKit variables to estimate a TRUE-HF peak oxygen uptake. We outline key features of this trial designed to reduce the burden of wearable technology. In addition, we highlight the benefits of various machine learning analyses, with a particular focus on transformer models for the wearable space. CONCLUSIONS: Using cutting-edge wearable technology and machine learning analytics, TRUE-HF may provide state-of-the-art assessment of functional capacity by measuring participant-generated free-world data. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05008692.
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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 | 0.058 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".