Validity of Heart Rate Measurement Using Wearable Devices During Cardiopulmonary Exercise Testing in Patients With Cardiovascular Disease: Prospective Pilot Validation Study
Bibliographic record
Abstract
Background: Wearable devices offer a promising solution for remotely monitoring heart rate (HR) during home-based cardiac rehabilitation. However, evidence regarding their accuracy across varying exercise intensities and patient profiles remains limited, particularly in populations with cardiovascular disease (CVD) such as those with heart failure (HF). Objective: The objective of this study was to evaluate the accuracy of HR measurements obtained using the Fitbit Inspire 3 during cardiopulmonary exercise testing (CPX) in patients with CVD, including those with HF. Methods: In this single-center, prospective pilot study, we enrolled 30 patients with CVD undergoing CPX. HR was simultaneously recorded using electrocardiography and the Fitbit Inspire 3 at 1-minute intervals across various CPX phases: rest, exercise below and above the anaerobic threshold (AT), and recovery. The correlation between the two methods was assessed using the Pearson correlation coefficient. Measurement error was quantified by mean absolute error and mean absolute percentage error (MAPE), with a MAPE of ≤10% defined as the threshold for acceptable agreement. Results: All data points were 630 points per minute. The Fitbit Inspire 3 device demonstrated a strong overall correlation with electrocardiography-derived HR (r=0.90; IQR 0.88-0.91) and an acceptable MAPE of 5.40% (SD 8.33%). The total error was 14.9% (94/630), with overestimation and underestimation of 37 (5.8%) points and 57 (9%) points, respectively. The rate of HR underestimation reached 19 (16%) points during exercise above the AT, compared to 1 (3%) point at rest. When stratified by HF stage (B vs C), underestimation was more pronounced in patients with HF (14/275, 5% points vs 40/355, 11.2% points). Conclusions: The Fitbit Inspire 3 provides acceptable validity for HR monitoring during CPX in patients with CVD. However, clinicians should interpret HR data with caution during high-intensity exercise, especially in patients with HF.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".