Wearable Devices for Hemodynamic Assessment in Cardiovascular Disease: A Short Literature Review
Bibliographic record
Abstract
Hemodynamic parameters are frequently used in patients with cardiovascular disease to assess cardiac function, monitor disease progression, propose interventions, and determine prognosis. However, they require extensive resources, including specialized equipment and trained personnel, to measure with accuracy and precision. Wearable devices such as wristwatches have been shown to assess heart function, such as heart rate and detection of irregular heart rhythms. These wearable devices have also evolved to measure hemodynamic variables in a noninvasive, dynamic, and rapid manner. However, there is limited research on the accuracy of these wearables for hemodynamic function. This review assesses wearable devices and their utility compared with a clinical reference standard for hemodynamic assessment and highlights the strengths and weaknesses of such devices. Limited studies have found that wearable devices can demonstrate strong correlations when assessing cardiac output, stroke volume, systolic blood pressure and timing intervals, and pre-ejection period. Reproducibility studies in similar clinical conditions are needed, and many of the wearable devices have not received FDA/Health Canada approval, restricting their clinical use. Our review summarizes the current research landscape of wearable devices and hemodynamic assessment and proposes a framework for future research applications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".