Showing papers by "Ulf Ekelund published in 2007"

Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European youth heart study

Abstract: High levels of cardiorespiratory fitness (CRF) and physical activity (PA) are associated with a favourable metabolic risk profile. However, there has been no thorough exploration of the independent contributions of cardiorespiratory fitness and subcomponents of activity (total PA, time spent sedentary, and time spent in light, moderate and vigorous intensity PA) to metabolic risk factors in children and the relative importance of these factors. We performed a population-based, cross-sectional study in 9- to 10- and 15- to 16-year-old boys and girls from three regions of Europe (n = 1709). We examined the independent associations of subcomponents of PA and CRF with metabolic risk factors (waist circumference, BP, fasting glucose, insulin, triacylglycerol and HDL-cholesterol levels). Clustered metabolic risk was expressed as a continuously distributed score calculated as the average of the standardised values of the six subcomponents. CRF (standardised β = −0.09, 95% CI −0.12, −0.06), total PA (standardised β = −0.08, 95% CI −0.10, −0.05) and all other subcomponents of PA were significantly associated with clustered metabolic risk. After excluding waist circumference from the summary score and further adjustment for waist circumference as a confounding factor, the magnitude of the association between CRF and clustered metabolic risk was attenuated (standardised β = −0.05, 95% CI −0.08, −0.02), whereas the association with total PA was unchanged (standardised β = −0.08 95% CI −0.10, −0.05). PA and CRF are separately and independently associated with individual and clustered metabolic risk factors in children. The association between CRF and clustered risk is partly mediated or confounded by adiposity, whereas the association between activity and clustered risk is independent of adiposity. Our results suggest that fitness and activity affect metabolic risk through different pathways.

Accelerometers and pedometers: methodology and clinical application.

Abstract: Purpose of review The relationship between physical activity and health varies considerably, partly due to the difficulty of assessing physical activity accurately. This review examines recent literature on the validation of movement sensors to assess habitual physical activity. Recommendations are given for the use of movement sensors during free-living conditions and methods of data analysis and interpretation are discussed. Recent findings Recent progress in physical-activity research includes detailed comparative studies of different monitor brands. The move away from using linear-regression equations and the use of novel data-analysis strategies is increasing the accuracy with which energy expenditure can be estimated from accelerometry. New technologies, including the combination of accelerometry with the measurement of physiological parameters, have great potential for the increased accuracy of physical-activity assessment. Summary Accelerometry is able to adequately assess physical activity and its association with health outcomes but currently methods have limited accuracy for the estimation of free-living energy expenditure. Pedometers provide an inexpensive overall measure of physical activity but are unable to assess intensity, frequency and duration of activity or to estimate energy expenditure. Interpretation of monitor output is best kept as close to the measurement domain as possible.

Association of Weight Gain in Infancy and Early Childhood with Metabolic Risk in Young Adults

Abstract: Context: Early postnatal life has been suggested as an important window during which risks for long-term health may be influenced. Objective: The aim of this study was to examine the independent associations between weight gain during infancy (0–6 months) and early childhood (3–6 yr) with components of the metabolic syndrome in young adults. Design: This was a prospective cohort study (The Stockholm Weight Development Study). Setting: The study was conducted in a general community. Participants: Subjects included 128 (54 males) singletons, followed from birth to 17 yr. Main Outcome Measure: None of these young adults met the full criteria for the metabolic syndrome. We therefore calculated a continuous clustered metabolic risk score by averaging the standardized values of the following components: waist circumference, blood pressure, fasting triglycerides, high-density lipoprotein cholesterol, glucose, and insulin level. Results: Clustered metabolic risk at age 17 yr was predicted by weight gain during in...

Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity

Abstract: Combining accelerometry with heart rate (HR) monitoring may improve precision of physical activity measurement. Considerable variation exists in the relationships between physical activity intensity (PAI) and HR and accelerometry, which may be reduced by individual calibration. However, individual calibration limits feasibility of these techniques in population studies, and less burdensome, yet valid, methods of calibration are required. We aimed to evaluate the precision of different individual calibration procedures against a reference calibration procedure: a ramped treadmill walking-running test with continuous measurement of PAI by indirect calorimetry in 26 women and 25 men [mean (SD): 35 (9) yr, 1.69 (0.10) m, 70 (14) kg]. Acceleration (along the longitudinal axis of the trunk) and HR were measured simultaneously. Alternative calibration procedures included treadmill testing without calorimetry, submaximal step and walk tests with and without calorimetry, and nonexercise calibration using sleeping HR and gender. Reference accelerometry and HR models explained >95% of the between-individual variance in PAI (P < 0.001). This fraction dropped to 73 and 81%, respectively, for accelerometry and HR models calibrated with treadmill tests without calorimetry. Step-test calibration captured 62-64% (accelerometry) and 68% (HR) of the variance between individuals. Corresponding values were 63-76% and 59-61% for walk-test calibration. There was only little benefit of including calorimetry during step and walk calibration for HR models. Nonexercise calibration procedures explained 54% (accelerometry) and 30% (HR) of the between-individual variance. In conclusion, a substantial proportion of the between-individual variance in relationships between PAI, accelerometry, and HR is captured with simple calibration procedures, feasible for use in epidemiological studies.

Physical Activity and Sedentary Behaviors among Finnish Youth

Abstract: Purpose: There is general concern about the low level of physical activity and the high amount of time devoted to sedentary behavior among adolescents. This study aimed to determine the proportion of young Finns meeting the current guidelines for youth physical activity (= 60 min of moderate- to vigorous-intensity physical activity per day) and TV viewing (< 2 h·d-1) and to examine associations between physical activity and different sedentary behaviors. Methods: The study population consisted of 6928 boys and girls, members of the northern Finland birth cohort 1986 who, in 2001-2002, at age 15-16 yr, responded to a mailed questionnaire inquiring about their time spent in moderate to vigorous (MVPA), light (LPA), and commuting (CPA) physical activity, and different sedentary behaviors. Results: Fifty-nine percent of the boys and 50% of the girls reported 60 min or more of total physical activity per day. Only 23% of boys and 10% of girls reported 60 min of MVPA per day. Forty-eight percent of boys and 44% of girls reported more than 2 h of daily TV viewing. High amounts of TV viewing and computer use were associated with lower levels of physical activity in both genders. Conclusion: Many adolescents exceeded the recommended level of TV viewing and did not meet current recommendations for health-related physical activity. The inverse associations of physical activity with TV viewing and computer use suggest that measures aimed to reduce sedentary behaviors may, at least partly, increase physical activity among youth.

Physical Activity and Metabolic Risk in Individuals With a Family History of Type 2 Diabetes

Abstract: OBJECTIVE —We sought to examine the independent associations between different dimensions of physical activity with intermediary and clustered metabolic risk factors in overweight individuals with an increased risk of type 2 diabetes to inform future preventive action. RESEARCH DESIGN AND METHODS —We measured total body movement and five other subcomponents of physical activity by accelerometry in 258 adults (aged 30–50 years) with a family history of type 2 diabetes. We estimated aerobic fitness from an incremental treadmill exercise test. We measured body composition by bioimpedance and waist circumference, blood pressure, fasting triglycerides, HDL cholesterol, glucose, and insulin with standard methods. We constructed a standardized continuously distributed variable for clustered risk. RESULTS —Total body movement (counts · day−1) was significantly and independently associated with three of six risk factors (fasting triglycerides, insulin, and HDL) and with clustered metabolic risk ( P = 0.004) after adjustment for age, sex, and obesity. Time spent at moderate- and vigorous-intensity physical activity (MPVA) was independently associated with clustered metabolic risk ( P = 0.03). Five- and 10-min bouts of MVPA, time spent sedentary, time spent at light-intensity activity, and aerobic fitness were not significantly related with clustered risk after adjustment for confounding factors. CONCLUSIONS —Total body movement is associated with intermediary phenotypic risk factors for cardiovascular disease and metabolic disease and with clustered metabolic risk independent of aerobic fitness and obesity. Increasing the total amount of physical activity in sedentary and overweight individuals may have beneficial effects on metabolic risk factors.

Is the association between physical activity and body mass index obesity dependent

Abstract: BACKGROUND: Most studies indicate an inverse relationship between physical activity (PA) and body mass index (BMI). However, the impact of obesity on this relationship is unclear. OBJECTIVE: To scr ...

Comparison of two Actigraph models for assessing free-living physical activity in Indian adolescents

Abstract: This aim of this study was to compare the new Actigraph (GT1M) with the widely used Model 7164. Seven days of free-living physical activity were measured simultaneously using both the Model 7164 and GT1M in 30 Indian adolescents (mean age 15.8 years, s = 0.6). The GT1M was on average 9% lower per epoch than model 7164, thus a correction factor of 0.91 is suggested for comparison between the two monitors. The differences between monitors increased in magnitude with intensity of activity (P < 0.001) but remained randomly distributed (r = 0.01, P = 0.96). No significant difference was observed between monitors for time spent in moderate (P = 0.31) and vigorous (P = 0.34) physical activity when using the same epoch length. The Model 7164 classified less time as sedentary (P < 0.001) and more time as light-intensity activity (P < 0.001) than the GT1M. In conclusion, data from the GT1M can be compared with historical data using average counts per minute with a correction factor, and the two models migh...

Increase in physical activity energy expenditure is associated with reduced metabolic risk independent of change in fatness and fitness

Abstract: Objective: We sought to examine whether change in physical activity energy expenditure (PAEE) is associated with change in metabolic risk factors and whether this association is independent of chan ...

Comparison of two methods to assess PAEE during six activities in children.

Abstract: Purpose: The purpose of this study was to compare the accuracy of physical activity energy expenditure (PAEE)-prediction models using accelerometry alone (ACC) and accelerometry combined with heart rate monitoring (HR+ACC) to estimate PAEE during six common activities in children (lying, sitting, slow and brisk walking, hop-scotch, running). Three PAEE-prediction models derived using the current data, and five previously published prediction models were cross-validated to estimate PAEE in this sample. Methods: PAEE was assessed using ACC, HR+ACC, and indirect calorimetry during six activities in 145 children (12.4 +/- 0.2 yr). One ACC and two HR+ACC PAEE-prediction models were derived using linear regression on data from the current study. These three new models were cross-validated using a jackknife approach, and a modified Bland-Altman method was used to assess the validity of all eight models. Results: PAEE predictions using the one ACC and two HR+ACC models derived in the current study correlated strongly with measured values (RMSE = 97.3-118.0 J[middle dot]min-1[middle dot]kg-1). All five previously published models agreed well overall (RMSE = 115.6-245.3 J[middle dot]min-1[middle dot]kg-1), but systematic error was present for most of these, to a greater extent for ACC. Conclusions: ACC and HR+ACC can both be used to predict overall PAEE during these six activities in children; however, systematic error was present in all predictions. Although both ACC and HR+ACC provide accurate predictions of overall PAEE, according to the activities in this study, PAEE-prediction models using HR+ACC may be more accurate and widely applicable than those based on accelerometry alone

Differential effects of fatness, fitness and physical activity energy expenditure on whole-body, liver and fat insulin sensitivity

Abstract: The relative contributions of fitness (maximal oxygen uptake), physical activity energy expenditure (PAEE) and fatness to whole-body, liver and fat insulin sensitivity is uncertain. The aim of this study was to determine whether fitness and PAEE are associated with whole-body, liver and fat insulin sensitivity independently of body fat. We recruited 25 men (mean [SD] age 53 [6] years). Whole-body (M value) and liver (percentage suppression of endogenous glucose output) insulin sensitivity were estimated using a hyperinsulinaemic–euglycaemic clamp. Insulin sensitivity in fat (insulin sensitivity index for NEFA) was estimated during an OGTT. Total and truncal fat were measured by dual-energy X-ray absorptiometry, fitness by treadmill, and PAEE (n = 21) by 3 day heart rate monitoring and Baecke questionnaire. In univariate analyses, fatness was strongly associated with insulin sensitivity (whole-body, liver and fat). Fitness was associated with whole-body (r = 0.53, p < 0.007) and liver (0.42, p = 0.04) insulin sensitivity, while PAEE was associated with liver insulin sensitivity (r = 0.55, p = 0.01). Regression models were established to describe associations between fatness, fitness and physical activity and measures of insulin sensitivity (whole-body, fat and liver) as outcomes. Only fatness was independently associated with whole-body insulin sensitivity (B coefficient −0.01, p = 0.001). Fitness was not associated with any outcome. Only PAEE was independently associated with liver insulin sensitivity (B coefficient 13.5, p = 0.02). Fatness explains most of the variance in whole-body insulin sensitivity. In contrast, PAEE explains most of the variance in liver insulin sensitivity.

Total daily energy expenditure and patterns of physical activity in adolescents assessed by two different methods

Abstract: Minute-by-minute heart rate monitoring and an activity diary were used simultaneously during three days in 30 randomly selected adolescents (16 boys, 14 girls; mean age 150+/-10) Total daily energy expenditure (TDEE) and its components (energy expenditure during sleep, during rest and in physical activity) and times spent at different intensity levels (sedentary, light, moderate physical activity and vigorous physical activity) were compared TDEE from heart rate monitoring averaged 109+/-27 MJ x d(-1) compared to 113+/-23 MJ x d(-1) from the activity diary (NS) The limits of agreement (mean+/-2 SD) were -354 MJ x d(-1) and 274 MJ x d(-1) There was no significant difference for any of the TDEE components between the methods (MANOVA) A significant method effect (P<0001) was observed for time spent in sedentary and light physical activity (MANOVA) No significant difference was observed for time spent in moderate and vigorous physical activity According to this, heart rate monitoring and activity diary are comparable for group assessment of TDEE and its components, and for estimating time spent in moderate and vigorous physical activity The activity diary underestimated time spent in moderate and vigorous physical activity for inactive subjects and consequently overestimated highly active subjects

Does physical activity equally predict gain in fat mass among obese and nonobese young adults

Abstract: Differences in energy metabolism and physical activity (PA) may contribute to the long-term regulation of body weight (BW). To examine the associations between metabolic determinants, energy expenditure and objectively measured components of PA with change in BW and fat mass (FM). Prospective (4 years.), case–control study in obese (n=13) and normal weight (n=15) young adults. At baseline, we measured resting metabolic rate, substrate oxidation, movement economy (ml O2 kg−1 min−1), aerobic fitness (VO2max), total and PA energy expenditure by doubly labelled water, and PA by accelerometry. Fat mass was measured by DXA. At follow-up we repeated our measurements of PA and FM. Fat mass increased significantly (P<0.001) in both groups. Physical activity did not change between baseline and ‘follow up’. Change in overall PA (counts per minute) was inversely associated with change in BW and (β=−0.0124, P=0.054) and FM (β=−0.008, P=0.04). Post hoc analyses suggested that this association was explained by changes in the normal weight group only (β=−0.01; P=0.008; and β=−0.0097; P=0.009, for BW and FM, respectively). Metabolic determinants, energy expenditure estimates and subcomponents of PA (i.e. time spent at different intensity levels) were not significantly associated with change in BW or FM. Our results suggest an independent association between PA and FM. However, this association may differ depending on obesity status. The gain in FM, without any change in PA, may suggest that dietary intake is the major contributor to the positive energy balance.

Physical activity energy expenditure may mediate the relationship between plasma leptin levels and worsening insulin resistance independently of adiposity.

Abstract: Leptin regulates a constellation of neuroendocrine processes that control energy homeostasis. The infusion of leptin in rodents lacking endogenous leptin promotes physical activity energy expenditu...

Physical Activity and Mammographic Breast Density in the EPIC-Norfolk Cohort Study

Abstract: Physical inactivity and high mammographic breast density have both been associated with increased breast cancer risk. However, the association between physical activity and mammographic breast density remains inconsistent. In the European Prospective Investigation of Cancer (EPIC)-Norfolk population-based cohort study (United Kingdom), the authors investigated the cross-sectional association between physical activity level at baseline during 1993-1997 and breast density among 1,394 postmenopausal, cancer-free women. Usual physical activity was assessed by a brief, validated questionnaire. Percentage breast density was determined visually from mammograms by three trained radiologists using the Boyd six-category scale. The association between physical activity level and breast density risk category was examined. No statistically significant association between physical activity and percentage breast density was observed in the unadjusted or adjusted regression models. A suggested increase in breast density for the most active women in the unadjusted regression analysis (odds ratio = 1.13, 95% confidence interval: 0.71, 1.80) was reversed after inclusion of body mass index and reproductive and lifestyle variables (odds ratio = 0.78, 95% confidence interval: 0.45, 1.34). The lack of an association between physical activity and percentage breast density suggests that an association between physical activity and breast cancer risk is unlikely to be mediated through an effect on mammographic breast density.

New methods for improved evaluation of patients with suspected acute coronary syndrome in the emergency department

Abstract: This paper aims to identify and review new and unproven emergency department (ED) methods for improved evaluation in cases of suspected acute coronary syndrome (ACS). Systematic news coverage through PubMed from 2000 to 2006 identified papers on new methods for ED assessment of patients with suspected ACS. Articles found described decision support models, new ECG methods, new biomarkers and point-of-care testing, cardiac imaging, immediate exercise tests and the chest pain unit concept. None of these new methods is likely to be the perfect solution, and the best strategy today is therefore a combination of modern methods, where the optimal protocol depends on local resources and expertise. With a suitable combination of new methods, it is likely that more patients can be managed as outpatients, that length of stay can be shortened for those admitted, and that some patients with ACS can get earlier treatment.