Bjarne M. Nes

Age‐predicted maximal heart rate in healthy subjects: The HUNT Fitness Study

Maximal heart rate (HRmax) declines substantially with age, but the magnitude and possible modifying effect of gender, body composition, and physical activity are not fully established. The present study examined the relationship between HRmax and age in 3320 healthy men and women within a wide age range using data from the HUNT Fitness Study (2007–2008). Subjects were included if a maximal effort could be verified during a maximal exercise test. General linear modeling was used to determine the effect of age on HRmax. Subsequently, the effects of gender, body mass index (BMI), physical activity status, and maximal oxygen uptake were examined. Mean predicted HRmax by three former prediction formulas were compared with measured HRmax within 10‐year age groups. HRmax was univariately explained by the formula 211 − 0.64·age (SEE, 10.8), and we found no evidence of interaction with gender, physical activity, VO2max level, or BMI groups. There were only minor age‐adjusted differences in HRmax between these groups. Previously suggested prediction equations underestimated measured HRmax in subjects older than 30 years. HRmax predicted by age alone may be practically convenient for various groups, although a standard error of 10.8 beats/min must be taken into account. HRmax in healthy, older subjects and women were higher than previously reported.

Aerobic Interval Training Reduces the Burden of Atrial Fibrillation in the Short Term A Randomized Trial

Background— Exercise training is an effective treatment for important atrial fibrillation (AF) comorbidities. However, a high level of endurance exercise is associated with an increased AF prevalence. We assessed the effects of aerobic interval training (AIT) on time in AF, AF symptoms, cardiovascular health, and quality of life in AF patients. Methods and Results— Fifty-one patients with nonpermanent AF were randomized to AIT (n=26) consisting of four 4-minute intervals at 85% to 95% of peak heart rate 3 times a week for 12 weeks or to a control group (n=25) continuing their regular exercise habits. An implanted loop recorder measured time in AF continuously from 4 weeks before to 4 weeks after the intervention period. Cardiac function, peak oxygen uptake ( O2peak), lipid status, quality of life, and AF symptoms were evaluated before and after the 12-week intervention period. Mean time in AF increased from 10.4% to 14.6% in the control group and was reduced from 8.1% to 4.8% in the exercise group (P=0.001 between groups). AF symptom frequency (P=0.006) and AF symptom severity (P=0.009) were reduced after AIT. AIT improved O2peak, left atrial and ventricular ejection fraction, quality-of-life measures of general health and vitality, and lipid values compared with the control group. There was a trend toward fewer cardioversions and hospital admissions after AIT. Conclusions— AIT for 12 weeks reduces the time in AF in patients with nonpermanent AF. This is followed by a significant improvement in AF symptoms, O2peak, left atrial and ventricular function, lipid levels, and QoL. Clinical Trial Registration— URL: http://www.clinicaltrials.gov. Unique identifier: NCT01325675.

Estimating V·O 2peak from a nonexercise prediction model : the HUNT Study, Norway.

PURPOSE Cardiorespiratory fitness is suggested to be an important marker of cardiovascular risk but is rarely evaluated in health care settings. In the present study, directly measured peak oxygen uptake (V·O 2peak) from a diverse population of 4637 healthy participants were used to develop and cross-validate a new nonexercise regression model of cardiorespiratory fitness for men and women. METHODS AND RESULTS Multivariable regression analysis was used to develop a nonexercise model of cardiorespiratory fitness for men and women separately with V·O 2peak as the outcome. In the final models, 2067 men (mean age = 48.8 yr) and 2193 women (mean age = 47.9 yr) were included, respectively. Cross-validation of the models was done by standard data splitting procedures with evaluation of constant error and total error of a model developed on one sample and cross-validated on another sample. Age, waist circumference, leisure time physical activity, and resting HR, successively, were the most potent predictors of V·O 2peak for both men and women. Together, 61% and 56% of variance in V·O 2peak, for men and women, respectively, were explained by the full models. SEE was 5.70 and 5.14 for the models including men and women, respectively. CONCLUSIONS The nonexercise regression model developed in the present study was fairly accurate in predicting V·O 2peak in this healthy population of men and women. The model might be generalized to other healthy populations and might be a valid tool for a rough assessment of cardiorespiratory fitness in an outpatient setting.

A simple nonexercise model of cardiorespiratory fitness predicts long-term mortality.

PURPOSE Cardiorespiratory fitness (CRF) is a strong predictor of future health, but measurements of CRF are time consuming and involve costly test procedures. We assessed whether a simple, non-exercise-based test of CRF predicted long-term all-cause and cardiovascular disease (CVD) mortality. METHODS In this prospective cohort study, we used a previously published nonexercise test to estimate CRF in healthy men (n = 18,348) and women (n = 18,764) from the first HUNT study (1984-1986) in Norway. We used Cox regression to obtain HR for mortality during a mean follow-up of 24 yr. Assessment of model validity was performed by standard procedures of discrimination and calibration. RESULTS CRF was inversely associated with all-cause and CVD mortality in men and women below 60 yr of age at baseline, after adjustment for confounders. For each MET-higher CRF (MET, approximately 3.5 mL·kg·min), HR for CVD mortality was 21% lower in both men (95% confidence interval (CI), 17%-26%) and women (95% CI, 12%-29%). HR for all-cause mortality was 15% (95% CI, 12%-17%) lower in men and 8% (95% CI, 3%-3%) lower in women for each MET-higher CRF. The ability of the model to discriminate mortality risk among participants below 60 yr was better for CRF (area under the curve (AUC), 0.70-0.77) compared with that for each variable that constituted the model (AUC, 0.55-0.63) and an aggregated sum of z-scores for each variable (AUC, 0.61-0.65). Comparison of observed and predicted risk indicated good model calibration. CONCLUSIONS This method of assessing CRF is feasible and practically useful in primary care for identification of apparently healthy individuals at increased risk of premature CVD disease and all-cause mortality.

Prediction of Cardiovascular Mortality by Estimated Cardiorespiratory Fitness Independent of Traditional Risk Factors: The HUNT Study

Objective To assess the predictive value of estimated cardiorespiratory fitness (eCRF) and evaluate the additional contribution of traditional risk factors in cardiovascular disease (CVD) mortality prediction. Participants and Methods The study included healthy men (n=18,721) and women (n=19,759) aged 30 to 74 years. A nonexercise algorithm estimated cardiorespiratory fitness. Cox proportional hazards models evaluated the primary (CVD mortality) and secondary (all‐cause, ischemic heart disease, and stroke mortality) end points. The added predictive value of traditional CVD risk factors was evaluated using the Harrell C statistic and net reclassification improvement. Results After a median follow‐up of 16.3 years (range, 0.04–17.4 years), there were 3863 deaths, including 1133 deaths from CVD (734 men and 399 women). Low eCRF was a strong predictor of CVD and all‐cause mortality after adjusting for established risk factors. The C statistics for eCRF and CVD mortality were 0.848 (95% CI, 0.836–0.861) and 0.878 (95% CI, 0.862–0.894) for men and women, respectively, increasing to 0.851 (95% CI, 0.839–0.863) and 0.881 (95% CI, 0.865–0.897), respectively, when adding clinical variables. By adding clinical variables to eCRF, the net reclassification improvement of CVD mortality was 0.014 (95% CI, −0.023 to 0.051) and 0.052 (95% CI, −0.023 to 0.127) in men and women, respectively. Conclusion Low eCRF is independently associated with CVD and all‐cause mortality. The inclusion of traditional clinical CVD risk factors added little to risk discrimination and did not improve the classification of risk beyond this simple eCRF measurement, which may be proposed as a practical and cost‐effective first‐line approach in primary prevention settings.

Personal Activity Intelligence (PAI), Sedentary Behavior and Cardiovascular Risk Factor Clustering – the HUNT Study

Prolonged sedentary behavior (SB) positively associates with clustering of risk factors for cardiovascular disease (CVD). The recently developed metric for physical activity (PA) tracking called Personal Activity Intelligence (PAI) takes into account age, sex, resting and maximum heart rate, and a score of ≥100 weekly PAI has been shown to reduce the risk of premature CVD death in healthy as well as individuals with known CVD risk factors, regardless of whether or not the current PA recommendations were met. The aim of the present study was to examine if PAI modifies the associations between SB and CVD risk factor (CV-RF) clustering in a large apparently healthy general population cohort (n=29,950, aged ≥20 years). Logistic regression revealed that in those with ≥100 weekly PAI, the likelihood of CV-RF clustering prevalence associated with prolonged SB was attenuated across age groups. Monitoring weekly PAI-level could be useful to ensure that people perform enough PA to combat SBs deleterious association with CV-RF.

Global Fitness Levels: Findings From a Web-Based Surveillance Report

IMPORTANCE Cardiorespiratory fitness (CRF) has shown to improve the classification beyond traditional risk factors and cumulative lifetime risk of death, however, there is no formal multicenter database that provides representative sample on a global scale to accurately interpret CRF measures. OBJECTIVE The objective of the study was to describe worldwide web-surveillance data of CRF. DESIGN Cross-sectional population based study. SETTING Data was collected through a web-based questionnaire, including questions on country and city of residence, ethnicity, level of education, age, gender and anthropometric data such as height, weight, waistline, and maximal and resting pulse rate, on a freely available webpage (www.worldfitnesslevel.org). We used a previously validated non-exercise algorithm to estimate CRF (eCRF). PARTICIPANTS Population based volunteer sample of 730,432 healthy participants, predominantly white race (82.1%), and 50.8% women. MAIN OUTCOME AND MEASURES Mean estimated cardiorespiratory fitness across regions and countries worldwide. RESULTS The mean eCRF values were 50.4mL.kg-1.min-1 (95% confidence interval [CI], 50.3-50.4) for men, and 40.6mL.kg-1.min-1 (95% CI, 40.6-40.7) for women, and with advancing age, eCRF values decreased in all regions and countries with a substantial heterogeneity across the regions. On average, men and women spent 7.2h/day, and 6.8h/day being sedentary, and mean eCRF values were higher among those who were meeting the current recommendations of physical activity, regardless of time spent sedentary. CONCLUSION AND RELEVANCE The global eCRF surveillance data will help to identify target populations at greater risk, to understand which intervention strategies work for which populations, and to better inform health professionals and policy makers to devise strategies to improve physical activity, CRF, and public health.

Predicting VO2peak from Submaximal- and Peak Exercise Models: The HUNT 3 Fitness Study, Norway.

Purpose Peak oxygen uptake (VO2peak) is seldom assessed in health care settings although being inversely linked to cardiovascular risk and all-cause mortality. The aim of this study was to develop VO2peak prediction models for men and women based on directly measured VO2peak from a large healthy population Methods VO2peak prediction models based on submaximal- and peak performance treadmill work were derived from multiple regression analysis. 4637 healthy men and women aged 20–90 years were included. Data splitting was used to generate validation and cross-validation samples. Results The accuracy for the peak performance models were 10.5% (SEE = 4.63 mL⋅kg-1⋅min-1) and 11.5% (SEE = 4.11 mL⋅kg-1⋅min-1) for men and women, respectively, with 75% and 72% of the variance explained. For the submaximal performance models accuracy were 14.1% (SEE = 6.24 mL⋅kg-1⋅min-1) and 14.4% (SEE = 5.17 mL⋅kg-1⋅min-1) for men and women, respectively, with 55% and 56% of the variance explained. The validation and cross-validation samples displayed SEE and variance explained in agreement with the total sample. Cross-classification between measured and predicted VO2peak accurately classified 91% of the participants within the correct or nearest quintile of measured VO2peak. Conclusion Judicious use of the exercise prediction models presented in this study offers valuable information in providing a fairly accurate assessment of VO2peak, which may be beneficial for risk stratification in health care settings.

Physical activity modifies the risk of atrial fibrillation in obese individuals: The HUNT3 study:

Background Atrial fibrillation is the most common heart rhythm disorder, and high body mass index is a well-established risk factor for atrial fibrillation. The objective of this study was to examine the associations of physical activity and body mass index and risk of atrial fibrillation, and the modifying role of physical activity on the association between body mass index and atrial fibrillation. Design The design was a prospective cohort study. Methods This study followed 43,602 men and women from the HUNT3 study in 2006–2008 until first atrial fibrillation diagnosis or end of follow-up in 2015. Atrial fibrillation diagnoses were collected from hospital registers and validated by medical doctors. Cox proportional hazard regression analysis was performed to assess the association between physical activity, body mass index and atrial fibrillation. Results During a mean follow-up of 8.1 years (352,770 person-years), 1459 cases of atrial fibrillation were detected (4.1 events per 1000 person-years). Increasing levels of physical activity were associated with gradually lower risk of atrial fibrillation (p trend 0.069). Overweight and obesity were associated with an 18% (hazard ratio 1.18, 95% confidence interval 1.03–1.35) and 59% (hazard ratio 1.59, 95% confidence interval 1.37–1.84) increased risk of atrial fibrillation, respectively. High levels of physical activity attenuated some of the higher atrial fibrillation risk in obese individuals (hazard ratio 1.53, 95% confidence interval 1.03–2.28 in active and 1.96, 95% confidence interval 1.44–2.67 in inactive) compared to normal weight active individuals. Conclusion Overweight and obesity were associated with increased risk of atrial fibrillation. Physical activity offsets some, but not all, atrial fibrillation risk associated with obesity.

High-intensity interval training improves obstructive sleep apnoea

Background Three hours per week of vigorous physical activity is found to be associated with reduced odds of sleep-disordered breathing. Aim To investigate whether 12 weeks of high-intensity interval training (HIIT) reduced the apnoea–hypopnea index (AHI) in obese subjects with moderate-to-severe obstructive sleep apnoea. Methods In a prospective randomised controlled exercise study, 30 (body mass index 37±6 kg/m2, age 51±9 years) patients with sleep apnoea (AHI 41.5±25.3 events/hour) were randomised 1:1 to control or 12 weeks of supervised HIIT (4×4 min of treadmill running or walking at 90%–95% of maximal heart rate two times per week). Results In the HIIT group, the AHI was reduced by 7.5±11.6 events/hour (within-group p<0.05), self-reported sleepiness (Epworth scale) improved from 10.0±3.6 to 7.3±3.7 (between-group p<0.05) and maximal oxygen uptake improved from 28.2±7.4 to 30.2±7.7 mL/kg/min (between-group p<0.05) from baseline to 12 weeks. The AHI, self-reported sleepiness and VO2maxwere unchanged from baseline to 12 weeks in controls (baseline AHI 50.3±25.5 events/hour, Epworth score 5.9±4.3, maximal oxygen uptake 27.0±6.8 mL/kg/min). Body weight remained unchanged in both groups. Conclusion Twelve weeks of HIIT improved the AHI and self-reported daytime sleepiness in subjects with obese sleep apnoea without any change in the desaturation index and body weight.