Key result
Wearable HRV features improve VO2max prediction R² to 0.47 compared to baseline models.
Why the study?
Wearable sensors offer a scalable alternative to cardiopulmonary exercise testing, but whether wearable ECG-derived heart rate variability features improve remote VO2 max estimation in preoperative patients remained unclear.
Does the inclusion of HRV features improve remote VO2 max estimation in a preoperative cohort, and do open-source SQIs accurately assess synthetic ECG signal quality compared to expert clinical assessment?
Observational (n=163)
No
Does the inclusion of HRV features improve remote VO2 max estimation in a preoperative cohort, and do open-source SQIs accurately assess synthetic ECG signal quality compared to expert clinical assessment?
Effect estimate: R² 0.05 (95% CI 0.00-0.11)
Absolute Event Rate: 0.47% vs 0.42%
p-value: p=0.042
Long-term HRV features can modestly improve remote VO2 max predictions in clinical cohorts, but current open-source ECG signal quality indices show limited reliability and consistency compared to expert clinical assessment.
May modestly improve preoperative VO2max estimates with wearables; extends sensor research but leaves clinical adoption open.
Background Wearable sensors offer a scalable alternative to cardiopulmonary exercise testing for assessing cardiorespiratory fitness, and there is growing evidence to support their use for remote VO 2 max estimation. This study investigated whether heart rate variability (HRV) measures derived from wearable ECG sensors improve VO 2 max estimations in a preoperative cohort and compared the relative contributions of short- and long-term HRV features. ECG and accelerometer data from 198 participants scheduled for major abdominal surgery (REMOTES study, ClinicalTrials.gov: ID NCT06042023) were collected over 72 h. Measures including physical activity, steps, heart rate, and HRV were extracted. Short-term (5-minutes) and long-term (24-hour) heart rate variability features were extracted from free-living ECG data. Two LASSO regression models with five-fold cross-validation were developed: a baseline model (excluding HRV) and a HRV model. Results After exclusions, 163 participants were included in analyses. The HRV model outperformed the baseline across all metrics, achieving a higher R 2 (0.47 ± 0.12 vs. 0.42 ± 0.13) and lower mean absolute error (2.63 ± 0.34 vs. 2.77 ± 0.38 ml/kg/min), root mean square error (3.38 ± 0.53 vs. 3.54 ± 0.57 ml/kg/min) and absolute percentage error (15.55 ± 2.19% vs. 16.22 ± 2.45%). Analysis of feature contributions identified long-term HRV (SDANN HR 24), age, gender, and step-counts as key contributors to model performance. Conclusion HRV features from wearable data, especially long-term measures, can improve remote VO 2 max predictions in a clinical cohort. While performance gains were small, these findings support the integration of HRV features into remote monitoring systems in real-world settings. Long-term HRV measures derived from heart rate signals offer a practical option for cardiorespiratory fitness assessment, requiring minimal additional processing. Trail registration This study was registered at ClinicalTrials.gov (Clinical trial number: NCT06042023) and was registered retrospectively on 11/09/2023.
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Syversen et al. (2026) conducted an observational in Cardiorespiratory fitness assessment in preoperative cohort (n=163). Wearable ECG sensor vs. Standard baseline model excluding HRV features was evaluated on VO2max prediction accuracy (R² 0.05, 95% CI 0.00-0.11, p=0.042). Including heart rate variability features from wearable sensors improved VO2max prediction R² by 0.05, achieving a maximum R² of 0.47 compared to 0.42 for the baseline model.
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