Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors : Validation Study
Rissanen, A.-P. E., Rottensteiner, M., Kujala, U. M., Kurkela, J. L. O., Wikgren, J., & Laukkanen, J. A. (2022). Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors : Validation Study. JMIR Cardio, 6(2), Article e35796. https://doi.org/10.2196/35796
Julkaistu sarjassa
JMIR CardioTekijät
Päivämäärä
2022Oppiaine
PsykologiaMonitieteinen aivotutkimuskeskusLiikuntalääketiedeHyvinvoinnin tutkimuksen yhteisöPsychologyCentre for Interdisciplinary Brain ResearchSports and Exercise MedicineSchool of WellbeingTekijänoikeudet
©Antti-Pekka E Rissanen, Mirva Rottensteiner, Urho M Kujala, Jari L O Kurkela, Jan Wikgren, Jari A Laukkanen. Originally published in JMIR Cardio (https://cardio.jmir.org), 25.10.2022.
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: In total, 74 adults (age range 35-64 years; n=56, 76% were women; mean BMI 28.7, SD 4.6 kg/m2 ) with frequent cardiovascular risk factors (eg, n=64, 86% hypertension; n=18, 24% prediabetes; n=14, 19% type 2 diabetes; and n=51, 69% metabolic syndrome) performed a 30-minute self-paced walk on an indoor track and a cardiopulmonary exercise test on a treadmill. CRF, quantified as peak O2 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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Julkaisija
JMIR Publications Inc.ISSN Hae Julkaisufoorumista
2561-1011Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/159332558
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
This study was funded by Business Finland (Finnish government organization for innovation funding and trade, travel, and investment promotion, Helsinki, Finland; grant 2697/31/2018) and Firstbeat Technologies Oy (Jyväskylä, Finland).Lisenssi
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