Obesity is an important source of bias in the assessment of cardiorespiratory fitness

Does heart rate variability mediate the association between chronic stress, cardiorespiratory fitness, and working memory in young adults?

Young adulthood is a demanding development phase rendering individuals at risk for high levels of stress. While chronic stress may impair working memory maintenance, cardiorespiratory fitness is suggested to have a protective effect. Heart rate variability (HRV) contributes to this cognitive domain, but also retaliates to stress and aerobic exercise. Therefore, the present study investigated the mediating role of resting HRV on the association between chronic stress, cardiorespiratory fitness, and working memory maintenance in young healthy adults. Healthy participants (N = 115, 48% female) aged 18-35 years (M = 24.1, SD = 3.8) completed the Åstrand test on a bicycle ergometer to estimate maximal oxygen consumption [ V ̇ O 2 max $$ \dot{\mathrm{V}}{\mathrm{O}}_{2\max } $$ (ml/min/kg)]. In addition, working memory maintenance was assessed using the modified Sternberg task with low (three items) and high cognitive load (six items). Using electrocardiography, HRV was recorded and the LF/HF ratio was extracted for mediation analyses. Path analysis revealed that cardiorespiratory fitness was significantly associated with accuracy on high cognitive load trials (β = 0.19, p = 0.035), but not on trials with low cognitive load. Perceived levels of chronic stress failed to show a significant association with working memory maintenance, independently of cognitive load. The pattern of results remained unchanged after introduction of HRV as a mediator (β = 0.18, p = 0.045). In conclusion, higher cardiorespiratory fitness is associated with better maintenance of verbal information in working memory. However, this association cannot be explained by vagal influences on memory processing driven by the autonomic nervous system.

Modeling Physiological Predictors of Running Velocity for Endurance Athletes

Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (V_AT), respiratory compensation point (V_RCP), and maximal velocity (V_max) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of V_AT, V_RCP, V_max, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (n_males = 3300, n_females = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m^-2; VO_2max = 53.20 ± 7.17 mL·min^-1·kg^-1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For V_AT R² = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For V_RCP R² = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For V_max R² = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Associations of physical activity, sedentary time, and cardiorespiratory fitness with heart rate variability in 6- to 9-year-old children: the PANIC study

Purpose

To study the associations of physical activity (PA), sedentary time (ST), and cardiorespiratory fitness (CRF) with heart rate variability (HRV) in children.

Methods

The participants were a population sample of 377 children aged 6–9 years (49% boys). ST, light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and moderate-to-vigorous PA (MVPA), and PA energy expenditure (PAEE) were assessed using a combined heart rate and movement sensor, maximal power output per kilograms of lean body mass as a measure of CRF by maximal cycle ergometer exercise test, and HRV variables (SDNN, RMSSD, LF, and HF) using 5 min resting electrocardiography. Data were analysed by linear regression adjusted for years from peak height velocity.

Results

In boys, ST was inversely associated (β = − 0.185 to − 0.146, p ≤ 0.049) and MVPA, VPA, PAEE, and CRF were directly associated (β = 0.147 to 0.320, p ≤ 0.048) with HRV variables. CRF was directly associated with all HRV variables and PAEE was directly associated with RMSSD after mutual adjustment for ST, PAEE, and CRF (β = 0.169 to 0.270, p ≤ 0.046). In girls, ST was inversely associated (β = − 0.382 to − 0.294, p < 0.001) and LPA, MPA, VPA, MVPA, and PAEE were directly associated with HRV variables (β = 0.144 to 0.348, p ≤ 0.049). After mutual adjustment for ST, PAEE, and CRF, only the inverse associations of ST with HRV variables remained statistically significant.

Conclusions

Higher ST and lower PA and CRF were associated with poorer cardiac autonomic nervous system function in children. Lower CRF in boys and higher ST in girls were the strongest correlates of poorer cardiac autonomic function.

Electronic supplementary material

The online version of this article (10.1007/s00421-019-04231-5) contains supplementary material, which is available to authorized users.

Effect of Body Mass Index on Exercise Capacity in Patients With Hypertrophic Cardiomyopathy

The objective of this study was to evaluate the relation between body mass index (BMI), exercise capacity, and symptoms in patients with hypertrophic cardiomyopathy (HC) and to utilize results of cardiopulmonary exercise tests (CPX) and transthoracic echocardiograms to understand the mechanism(s) of reduced exercise capacity across body mass index groups. Over a 6-year period, 510 consecutive patients with HC seen at a tertiary referral center underwent (CPX) and a transthoracic echocardiogram. Increasing BMI was associated with decreased exercise capacity as assessed by peak VO₂ (ml/kg/min). However, the prevalence of cardiac impairment did not vary by BMI group. In conclusion, these findings suggest that in some patients with hypertrophic cardiomyopathy, cardiac impairment is not the primary cause of exercise limitation and weight loss may result in improved exercise capacity.

Cardiorespiratory Fitness and Adiposity as Determinants of Metabolic Health-Pooled Analysis of Two Twin Cohorts

Context

The joint effects of cardiorespiratory fitness (CRF) and body composition on metabolic health are not well known.

Objective

To examine the associations of CRF, fat-free mass index (FFMI), and fat mass index (FMI) with metabolic health in individual twins and controlling for genetic and shared environmental effects by studying monozygotic intrapair differences.

Design, Setting, and Participants

Two cross-sectional samples of healthy adult monozygotic and dizygotic twins were drawn from population-based Danish and Finnish national twin registries (n = 996 and n = 309).

Main Measures

CRF was defined as VO2max divided by fat-free mass. Insulin sensitivity and acute insulin response indices were derived from an oral glucose tolerance test. A continuous metabolic syndrome score was calculated. Visceral and liver fat were measured in the Finnish sample. Associations were analyzed separately in both cohorts with multivariate linear regression and aggregated with meta-analytic methods.

Results

Insulin sensitivity, acute insulin response, metabolic syndrome score, visceral, and liver fat amount had strong and statistically significant associations with FMI (|β| 0.53 to 0.79), whereas their associations with CRF and FFMI were at most weak (|β| 0.02 to 0.15). The results of the monozygotic intrapair differences analysis showed the same pattern.

Conclusions

Although FMI is strongly associated with worsening of metabolic health traits, even after controlling for genetic and shared environmental factors, there was little evidence for the effects of CRF or FFMI on metabolic health. This suggests that changing FMI rather than CRF or FFMI may affect metabolic health irrespective of genetic or early environmental determinants.