Descriptive Epidemiology of Cardiorespiratory Fitness in UK Adults: The Fenland Study

Gonzales, Tomas I.; Westgate, Kate; Hollidge, Stefanie; Lindsay, Tim; Wijndaele, Katrien; Forouhi, Nita G.; Griffin, Simon J.; Wareham, Nick; Brage, Sören

doi:10.1249/mss.0000000000003068

Cited by 5 publications

(8 citation statements)

References 54 publications

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“…VO 2 max estimates were expressed in both ml O 2 ⋅min -1 ⋅kg -1 total-body mass and as ml O 2 ⋅min -1 ⋅kg -1 fat-free mass. This VO 2 max estimation procedure has been validated against directly measured VO 2 max in a subsample of Fenland Study participants (43 women; 42 men), demonstrating low bias and high correlation with directly measured VO 2 max (r = 0.70) [ 18 , 19 ]. For longitudinal analyses, fitness was assessed using the same methodology that was used at baseline.…”

Section: Methodsmentioning

confidence: 99%

Resting heart rate is a population-level biomarker of cardiorespiratory fitness: The Fenland Study

et al. 2023

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Introduction Few large studies have evaluated the relationship between resting heart rate (RHR) and cardiorespiratory fitness. Here we examine cross-sectional and longitudinal relationships between RHR and fitness, explore factors that influence these relationships, and demonstrate the utility of RHR for remote population monitoring. Methods In cross-sectional analyses (The UK Fenland Study: 5,722 women, 5,143 men, aged 29-65y), we measured RHR (beats per min, bpm) while seated, supine, and during sleep. Fitness was estimated as maximal oxygen consumption (ml⋅min-1⋅kg-1) from an exercise test. Associations between RHR and fitness were evaluated while adjusting for age, sex, adiposity, and physical activity. In longitudinal analyses (6,589 participant subsample), we re-assessed RHR and fitness after a median of 6 years and evaluated the association between within-person change in RHR and fitness. During the coronavirus disease-2019 pandemic, we used a smartphone application to remotely and serially measure RHR (1,914 participant subsample, August 2020 to April 2021) and examined differences in RHR dynamics by pre-pandemic fitness level. Results Mean RHR while seated, supine, and during sleep was 67, 64, and 57 bpm. Age-adjusted associations (beta coefficients) between RHR and fitness were -0.26, -0.29, and -0.21 ml⋅kg-1⋅beat-1 in women and -0.27, -0.31, and -0.19 ml⋅kg-1⋅beat-1 in men. Adjustment for adiposity and physical activity attenuated the RHR-to-fitness relationship by 10% and 50%, respectively. Longitudinally, a 1-bpm increase in supine RHR was associated with a 0.23 ml⋅min-1⋅kg-1 decrease in fitness. During the pandemic, RHR increased in those with low pre-pandemic fitness but was stable in others. Conclusions RHR is a valid population-level biomarker of cardiorespiratory fitness. Physical activity and adiposity attenuate the relationship between RHR and fitness.

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Section: Methodsmentioning

confidence: 99%

Resting heart rate is a population-level biomarker of cardiorespiratory fitness: The Fenland Study

et al. 2023

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“…We used the described exercise test calibration framework to develop a self-paced walk test calibration model. For each individual in a participant sample, we first derived the intercept and slope coefficients defining the HR-energy expenditure relationship ( β 0 i and β 1 i ) using a standardised treadmill test, 13 with intensity at each time point previously established using indirect calorimetry, 9 as the reference exercise test. We then constructed parameter set P x i consisting of features derived from both the walk test and participant-level characteristics known to influence exercise capacity.…”

Section: Methodsmentioning

confidence: 99%

“…The treadmill test was performed using standardised procedures as described in detail elsewhere. 13 Briefly, the test consisted of several stages of progressively faster walking and increasing incline levels for 15 minutes, followed by 5 minutes of running. For the walk test, two markers were placed 20 meters apart in a flat, indoor corridor.…”

Section: Clinical Exercise Testingmentioning

confidence: 99%

A self-paced walk test for individual calibration of heart rate to energy expenditure

Westgate,

Gonzales,

Hollidge

et al. 2023

Preprint

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Introduction: Estimating free-living physical activity (PA) with continuous heart rate (HR) monitoring is challenging due to individual variation in the relationship between HR and energy expenditure. This variation can be captured through individual calibration with graded exercise tests, but structured tests with prescribed load requires medical screening and are not always feasible in population settings. We present and evaluate an individual calibration method using HR response to a less demanding self-paced walk test. Methods: 643 participants from the Fenland Study (Cambridgeshire, UK) completed a 200-meter self-paced walk test, a treadmill test, and one week of continuous HR and accelerometry monitoring. Mixed effects regression was used to derive a walk test calibration model from HR response to the walk using treadmill-based parameters as criterion. Free-living PA estimates from the calibration model were compared with treadmill-calibrated as well as non-exercise calibrated estimates. Results: The walk calibration model captured 57% of the variance in the HR-energy expenditure relationship determined by the treadmill test. Applying the walk calibration method to data from free-living yielded similar PA estimates to those using treadmill calibration (52.7 vs 52.0 kJ · kg-1· day-1; mean difference: 0.7 kJ · kg-1· day-1, 95% CI: -0.0, 1.5) and high correlation (r=0.89). Individual differences were observed (RMSE: 10.0 kJ · kg-1· day-1; 95% limits of agreement: -20.6, 19.1 kJ · kg-1· day-1). Compared to using a non-exercise group calibration model (RMSE: 14.0 kJ · kg-1· day-1; 95% limits of agreement: -30.4, 24.5 kJ · kg-1· day-1), the walk calibration improved precision by 29%. Conclusions: A 200-meter self-paced walk test captures a significant proportion of the between-individual variation in the HR-energy expenditure relationship and facilitates estimation of free-living PA in population settings.

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“…We used the described exercise test calibration framework to develop a self-paced walk test calibration model. For each individual in a participant sample, we first derived the intercept and slope coefficients defining the HR-energy expenditure relationship (β 0 i and β 1 i ) using a standardized treadmill test (Gonzales et al, 2023), with intensity at each time point previously established using indirect calorimetry (Brage et al, 2007), as the reference exercise test. We then constructed parameter set P x i consisting of features derived from both the walk test and participant-level characteristics known to influence exercise capacity.…”

Section: Application Of Calibration Model For Self-paced Walk Testmentioning

confidence: 99%

“…The treadmill test was performed using standardized procedures as described in detail elsewhere (Gonzales et al, 2023). Briefly, the test consisted of several stages of progressively faster walking and increasing incline levels for 15 min, followed by 5 min of running.…”

Section: Clinical Exercise Testingmentioning

confidence: 99%

A Self-Paced Walk Test for Individual Calibration of Heart Rate to Energy Expenditure

Westgate,

Gonzales,

Hollidge

et al. 2024

Journal for the Measurement of Physical Behaviour

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Introduction: Estimating free-living physical activity (PA) with continuous heart rate (HR) monitoring is challenging due to individual variation in the relationship between HR and energy expenditure. This variation can be captured through individual calibration with graded exercise tests, but structured tests with prescribed load require medical screening and are not always feasible in population settings. We present and evaluate an individual calibration method using HR response to a less demanding self-paced walk test. Methods: Six hundred and forty-three participants from the Fenland Study (Cambridgeshire, the United Kingdom) completed a 200-m self-paced walk test, a treadmill test, and 1 week of continuous HR and accelerometry monitoring. Mixed-effects regression was used to derive a walk test calibration model from HR response to the walk using treadmill-based parameters as criterion. Free-living PA estimates from the calibration model were compared with treadmill-calibrated and non-exercise-calibrated estimates. Results: Walk calibration captured 57% of the variance in the HR–energy expenditure relationship determined by the treadmill test. Applying walk calibration to data from free-living yielded similar PA estimates to those using treadmill calibration (52.7 vs. 52.0 kJ·kg−1·day−1; mean difference: 0.7 kJ·kg−1·day−1, 95% confidence interval [−0.0, 1.5]) and high correlation (r = .89). Individual differences were observed (root mean square error: 10.0 kJ·kg−1·day−1; 95% limits of agreement: −20.6, 19.1 kJ·kg−1·day−1). Walk calibration improved precision by 29% compared with nonexercise group calibration (root mean square error: 14.0 kJ·kg−1·day−1; 95% limits of agreement: −30.4, 24.5 kJ·kg−1·day−1). Conclusions: A 200-m self-paced walk test captures between-individual variation in the HR–energy expenditure relationship and facilitates estimation of free-living PA in population settings.

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Descriptive Epidemiology of Cardiorespiratory Fitness in UK Adults: The Fenland Study

Cited by 5 publications

References 54 publications

Resting heart rate is a population-level biomarker of cardiorespiratory fitness: The Fenland Study

Resting heart rate is a population-level biomarker of cardiorespiratory fitness: The Fenland Study

A self-paced walk test for individual calibration of heart rate to energy expenditure

A Self-Paced Walk Test for Individual Calibration of Heart Rate to Energy Expenditure

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