Use of Actigraphy (Wearable Digital Sensors to Monitor Activity) in Heart Failure Randomized Clinical Trials: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Actigraphy-based measurements of physiologic parameters may enable design of patient-centric heart failure (HF) clinical trials. Recently, the Heart Failure Collaboratory focused on recommendations for meaningful change and use of actigraphy as an end point in HF clinical trials. We aimed to evaluate randomized controlled trials (RCTs) that have quantified the impact of HF interventions using actigraphy. METHODS: Using a scoping review strategy, we evaluated the use of actigraphy in HF RCTs. Studies were identified through electronic searches of Embase, OVID Medline, PubMed, and Cochrane Review. Data on trial characteristics and results were collected. RESULTS: We identified 11 RCTs with a total of 1,455 participants. The risk of bias across the included trials was high overall. All trials had the primary outcomes reflecting measures of either physical activity (n = 8), sleep (n = 2), or both (n = 1). Five trials evaluated response to pharmacologic therapies compared with placebo, 3 evaluated physical activity interventions, 2 evaluated group or cognitive therapy, and 1 evaluated sleep-ventilation strategy. Sample sizes ranged from 30 to 619 participants. There was significant heterogeneity relating to device type, body placement site, and handling of missing actigraphy data. Duration of monitoring ranged from 48 hours to 12 weeks. None of the studies evaluating pharmacologic therapies (n = 5) demonstrated a significant improvement of actigraphy-based primary end point measurements. CONCLUSIONS: There is significant heterogeneity in the use, methodology, and results of actigraphy-based HF RCTs. Our results highlight the need to develop, standardize, and validate actigraphy-specific outcomes for use in HF clinical trials.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".