Key result
A wearable device estimating cardiorespiratory fitness based on heart rate and body acceleration showed high accuracy compared to measured fitness, with a mean difference of -0.1 mL/kg/min.
Why the study?
Wearable technologies estimating CRF based on heart rate and body acceleration exist, but their validity in clinically relevant populations is poorly known.
Does a wearable technology estimating cardiorespiratory fitness based on heart rate and body acceleration accurately reflect measured cardiorespiratory fitness in working-aged adults with cardiovascular risk factors?
Cross-Sectional (n=74)
No
Does a wearable technology estimating cardiorespiratory fitness based on heart rate and body acceleration accurately reflect measured cardiorespiratory fitness in working-aged adults with cardiovascular risk factors?
Wearable technology using heart rate and body acceleration can estimate cardiorespiratory fitness with acceptable accuracy in adults with cardiovascular risk factors, though accuracy is reduced in patients with type 2 diabetes.
Answers are grounded in this paper and cite their sources.
May support wearable CRF estimation in at-risk adults; leaves open prospective validation before clinical use.
BACKGROUND: Cardiorespiratory fitness (CRF) is an independent risk factor for cardiovascular morbidity and mortality. Adding CRF to conventional risk factors (eg, smoking, hypertension, impaired glucose metabolism, and dyslipidemia) improves the prediction of an individual's risk for adverse health outcomes such as those related to cardiovascular disease. Consequently, it is recommended to determine CRF as part of individualized risk prediction. However, CRF is not determined routinely in everyday clinical practice. Wearable technologies provide a potential strategy to estimate CRF on a daily basis, and such technologies, which provide CRF estimates based on heart rate and body acceleration, have been developed. However, the validity of such technologies in estimating individual CRF in clinically relevant populations is poorly known. OBJECTIVE: The objective of this study is to evaluate the validity of a wearable technology, which provides estimated CRF based on heart rate and body acceleration, in working-aged adults with cardiovascular risk factors. METHODS: uptake, was both estimated (self-paced walk: a wearable single-lead electrocardiogram device worn to record continuous beat-to-beat R-R intervals and triaxial body acceleration) and measured (cardiopulmonary exercise test: ventilatory gas analysis). The accuracy of the estimated CRF was evaluated against that of the measured CRF. RESULTS: Measured CRF averaged 30.6 (SD 6.3; range 20.1-49.6) mL/kg/min. In all participants (74/74, 100%), mean difference between estimated and measured CRF was -0.1 mL/kg/min (P=.90), mean absolute error was 3.1 mL/kg/min (95% CI 2.6-3.7), mean absolute percentage error was 10.4% (95% CI 8.5-12.5), and intraclass correlation coefficient was 0.88 (95% CI 0.80-0.92). Similar accuracy was observed in various subgroups (sexes, age, BMI categories, hypertension, prediabetes, and metabolic syndrome). However, mean absolute error was 4.2 mL/kg/min (95% CI 2.6-6.1) and mean absolute percentage error was 16.5% (95% CI 8.6-24.4) in the subgroup of patients with type 2 diabetes (14/74, 19%). CONCLUSIONS: The error of the CRF estimate, provided by the wearable technology, was likely below or at least very close to the clinically significant level of 3.5 mL/kg/min in working-aged adults with cardiovascular risk factors, but not in the relatively small subgroup of patients with type 2 diabetes. From a large-scale clinical perspective, the findings suggest that wearable technologies have the potential to estimate individual CRF with acceptable accuracy in clinically relevant populations.
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Rissanen et al. (2022) conducted a cross-sectional in Cardiovascular risk factors (n=74). Wearable single-lead ECG device (Bodyguard 2) vs. Cardiopulmonary exercise test (CPET) was evaluated on Mean difference between estimated and measured cardiorespiratory fitness (peak O2 uptake) (MD -0.1, 95% CI -1 to 0.9, p=0.90). A wearable device estimating cardiorespiratory fitness based on heart rate and body acceleration showed high accuracy compared to measured fitness, with a mean difference of -0.1 mL/kg/min.
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