Wearable Accelerometer-Derived Measures of Physical Activity in Heart Failure: Insights From the DETERMINE trials
Bibliographic record
Abstract
INTRODUCTION: Wearable accelerometers allow continuous assessment of physical activity during normal living conditions and may be useful in evaluating the effects of treatment for heart failure. We explored the relationships between accelerometer measures of physical activity and 6-minute walk distance and patient-reported measures of functional limitation in participants across the entire spectrum of left ventricular ejection fraction in the DETERMINE (Dapagliflozin EffecT on ExeRcise capacity using a 6-MINutE walk test in patients with heart failure) trials. METHODS: A subgroup of patients in the DETERMINE trials wore a waist-based accelerometer during 7-day periods at 3 points during the trial: between screening and randomization and during weeks 8 and 14. Patients completed the Kansas City Cardiomyopathy Questionnaire (KCCQ) and 6-minute walk distance (6MWD) at baseline and at weeks 8 and 16. RESULTS: Of the 817 patients randomized, 319 (39%) had adequate baseline accelerometer data. Patients with lower levels of physical activity had lower (ie, worse) KCCQ scores and 6MWD, higher NT-proBNP levels and BMIs, worse kidney function, and a greater likelihood of diabetes and atrial fibrillation. Baseline accelerometer values had weak correlations with KCCQ summary scores (Pearson r = 0.06-0.21) and weak to moderate correlations with 6MWD (Pearson r = 0.20-0.31). The change from baseline to 16 weeks in accelerometer-measured physical activity correlated weakly with the change in KCCQ summary scores (Pearson r = 0-0.18) and 6MWD (r = 0.01-0.10). CONCLUSIONS: In the DETERMINE trials, accelerometer-based measures of physical activity correlated modestly with KCCQ summary scores and 6MWD. Accelerometer-based assessments of physical activity may provide additional information complementing that obtained from standard measures of functional limitation in patients with heart failure.
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 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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 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".