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Keywords:

  • Accelerometers;
  • Children;
  • CVD;
  • Daily physical activity

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

Aim:  This study evaluates whether accelerometer-measured physical activity is related to higher composite risk factor scores for cardiovascular disease (CVD) in children.

Methods:  Cross-sectional study that included 223 children aged 7.9–11.1 years (boys n = 123, girls n = 100). Daily physical activity was assessed by accelerometers for 4 days. Body fat was quantified by dual X-ray absorptiometry. Maximal oxygen uptake was measured during a maximal exercise test. Resting heart rate and blood pressure were measured. Z-scores [(value for the individual − mean value for group)/SD] were calculated for each variable, and the sum of different risk factor z-scores used as an index of composite risk factors score for CVD.

Results:  Partial correlations, from General Linear Model, between moderate to vigorous physical activity (MVPA), vigorous physical activity (VPA) and general physical activity versus index of composite risk factor score were in boys 0.29, 0.33 and 0.30 (all p < 0.05), respectively. The corresponding correlations in girls were −0.28, −0.32 (both p < 0.05) and −0.18 (NS), respectively.

Conclusion:  Low amounts of MVPA and VPA were related to higher composite risk factor scores for CVD in children aged 8–11 years.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

It is well known that physical inactivity in adults is associated with a wide range of diseases and all causes of death (1). The arteriosclerotic process begins at an early age and risk factors for cardiovascular disease (CVD) such as physical inactivity, obesity and high blood pressure tend to cluster, and they are directly correlated to asymptomatic arteriosclerosis (2,3). Most studies in children have, however, only explored the relation between physical activity and a single risk factor for CVD (4,5), whereas data with broader analysis are scarce (6,7).

A principal barrier of studying health-related aspects of physical activity in children is the difficulties of obtaining an accurate assessment of a child’s physical activity level. Wide ranges of self-report methods have previously been used in health research because of their low cost and ease of administration. This raises concern because they are known to have limited accuracy in the measurement of daily physical activity in subjects of all ages and are considered inappropriate to use in younger children (8). In contrast, accelerometers record both the intensity and frequency of activity and are therefore an objective method of assessment of physical activity (9,10). This technique could therefore have the ability to give new insights into the relationships between physical activity and risk factors for CVD.

The purpose of the present investigation was to evaluate the relation between objectively measured physical activity and composite risk factor scoring for CVD in young children.

Key notes

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References
  • The present study shows that children with low amounts of daily physical activity have higher composite risk factor score for cardiovascular disease at age 9–11 years.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

Participants and anthropometric measures

Recruitment and methodology of this study cohort have previously been presented in detail (4,11–14).

Four hundred and seventy-seven children (boys = 259, girls = 218) from four different schools in Malmö, Sweden, were invited to participate in the study, whereas 248 (boys = 140 and girls = 108) accepted the invitation. Information regarding height and body mass was obtained of all invited children from the general health data register. The anthropometric data were compared between the participants and non-participants to evaluate whether a selection bias had occurred. Total body height was measured to the nearest centimetre using a fixed stadiometer (Hultafors AB, Hultafors, Sweden), and body mass was measured to the nearest kilogram with a standardized scale (Avery Berkel model HL 120; Avery Weigh-Tronix Inc, Fairmont, MN, USA), while the children were dressed in light clothing. Puberty status was assessed by self-evaluation according to Tanner (15). Written informed consent was obtained from all participating children’s parents. The study was approved by the institutional ethics committee of Lund University in Sweden.

Blood pressure

A Dinamap paediatric vital signs monitor (model XL; Critikron, Inc, Tampa, FL, USA) was used to measure resting heart rate (rHR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) in the seated position after 15 min of rest (6,7). The mean of three measurements was used in all analyses. Pulse pressure (PP) was calculated as SBP–DBP and mean arterial pressure (MAP) as SBP/3 + 2DBP/3 (16). This equipment has been validated in children (17).

Measurement of daily physical activity

Accelerometers were used to assess physical activity. Each child wore an accelerometer (MTI model 7164; Manufacturing Technology Incorporated, Pensacola, FL, USA) strapped by an elastic waist belt around the right hip for four consecutive days. A recording epoch of 10 sec was selected. A minimum recording of three separate days of 8 h of valid recording was required.

Calibration of all accelerometers was obtained by placing them in a standardized vertical movement to minimize inter-instrumental variation. The derived calibration factor was then used for final calculation of the physical activity variables.

Three different activity variables were estimated: general physical activity (GPA), moderate to vigorous physical activity (MVPA) and vigorous physical activity (VPA). GPA was considered to be the total accelerometer counts per valid minute of monitoring (mean counts/min). A previous validation study has shown that this variable correlates with GPA measured by doubly labelled water in children (18). The other two variables represented the time that the child was engaged in activities of different intensities. Previous validation studies have generated cut-off points for accelerometer counts corresponding to activities of varying intensities (19,20). These cut-off points made it possible to estimate roughly the number of minutes the child was engaged in an activity above a specific intensity threshold. Daily accumulation of minutes of MVPA (corresponding to a minimum of a brisk walk) and VPA (corresponding to running at different speeds) were measured. Cut-off points used for all children were >3500 counts/min for MVPA and >6000 counts/min for VPA (19,20).

Dual-energy X-ray absorptiometry (DXA)

Whole-body composition was measured by DXA (DPX-L version 1.3z; Lunar, Madison, WI, USA). Total body fat mass (TBF) and abdominal fat mass (AFM) were quantified. TBF was also expressed as percentage of total body mass times 100 (BF%). Body fat distribution was calculated as AFM/TBF. DXA is an accurate and precise method for the quantification of fat mass (21), including abdominal fat (22).

Measurement of aerobic fitness

Aerobic fitness was determined by a maximal exercise test performed on an electrically braked cycle ergometer (Rodby rhc, model RE 990; Rodby Innovation AB, Karlskoga, Sweden). Heart rate (HR), maximum heart rate (max HR), respiratory exchange ratio (RER) and maximum RER (max RER) were recorded and calculated. Maximal oxygen uptake (VO2PEAK) was determined as the highest value during the last minute of exercise. VO2PEAK was scaled to body mass. The exercise test was considered acceptable if it met one of the following criteria: RER ≥1.0, max HR >90% of predicted value (196 beats/min) or signs of intense effort [e.g. hyperpnoea, facial flushing or inability in keeping adequate revolutions per min (53–64)], (23).

Statistical analyses

All analyses were made in Statistica 7.1 and 9.1 (StatSoft Inc, Tulsa, OK, USA). The descriptive data are presented as means ± SD. Z-scores (value for the individual − mean value for group)/SD were calculated. Logarithmic transformations were performed on BF% and AFM before z-scores were constructed because of skewness. Sum of z-scores for BF%, AFM, AFM/TBF, SBP, DBP, rHR, MAP, PP and −VO2PEAK were calculated in boys and girls, separately, and used as an index of composite risk factor score for CVD. Group differences between mean values were tested using unpaired Student’s t-test. Partial Correlation, from General Linear Model (GLM), was used to measure the degree of association between different physical activity measurements and z-score for a separate risk factor and the sum of z-scores (composite risk factor score), controlling for school location and gender. Furthermore, GLM analyses were also used to estimate the total variance in composite risk factor score that could be explained by the different physical activity measurements. A statistical expert performed multilevel method statistics, Mixed Model with random effects of school location, and concluded that the overall findings were identical with those derived from GLM in Statistica 9.1. The statistical experts advised us that GLM could be used instead of Mixed Model statistics. A value of p < 0.05 was regarded as a statistically significant difference.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

A complete data set was available in 223 children (girls n = 100, boys n = 123). A total of 20 children were excluded because of lack of sufficient accelerometer data. Two children had no blood pressure measurements, one child did not have an adequate exercise test and two did not have DXA measurements. Five girls were Tanner stage 2 and the rest of the participants were Tanner stage 1.

The anthropometric data are displayed in Table 1. There were no significant differences between boys and girls with regard to SBP, DBP, MAP and PP. Significant differences were found for rHR between boys and girls.

Table 1.   Anthropometrics, age, cardiovascular variables, dual-energy X-ray absorptiometry (DXA), physical activity and fitness
 Girls n = 100Boys n = 123p-Value
  1. Values are presented as mean ± SD.

Anthropometrics and age
Age (years)9.8 ± 0.69.8 ± 0.60.36 NS
Height (cm)140 ± 8140 ± 70.97 NS
Body mass (kg)35 ± 834 ± 80.85 NS
BMI (kg/m2)17.5 ± 2.917.4 ± 2.80.77 NS
Cardiovascular variables
Systolic blood pressure (mmHg)105 ± 9104 ± 80.55 NS
Diastolic blood pressure (mmHg)61 ± 760 ± 60.17 NS
Resting heart rate (beats/min)85 ± 1080 ± 11<0.01
Mean arterial pressure (mmHg)76 ± 775 ± 50.22 NS
Pulse pressure (mmHg)44 ± 745 ± 50.66 NS
DXA
Total body fat mass (kg)8.3 ± 5.26.3 ± 4.9<0.01
Percent body fat (%)22.6 ± 9.016.2 ± 8.7<0.001
Abdominal fat mass (kg)3.3 ± 2.43.4 ± 2.2<0.01
Body fat distribution0.38 ± 0.050.36 ± 0.04<0.05
Physical fitness
VO2PEAK (mL/min/kg)35.7 ± 6.341.7 ± 7.2<0.001
Physical activity
General physical activity (mean counts/min)620 ± 154746 ± 240<0.001
Moderate–vigorous physical activity (min)35 ± 1345 ± 20<0.001
Vigorous physical activity (min)11 ± 715 ± 10<0.001

Boys had lower TBF, percentage body fat and AFM. In addition, boys had higher VO2PEAK and were more physically active.

Partial correlations, from GLM, between different physical activity levels and individual z-scores are shown in Table 2, where boys and girls were analysed together with adjustment for gender and school location. Significant differences were observed between genders in all physical activity levels (VPA, MVPA and GPA) and individual z-scores for all DXA measurements and for VO2PEAK. Additionally, VPA was significantly correlated to individual z-scores of SBP and rHR but reached only borderline significance for MAP (p = 0.07). MVPA partial correlations with DBP, MAP and rHR were in the hypothesized direction but reached only borderline significance (p = 0.09, p = 0.06 and p = 0.09).

Table 2.   Partial correlations between different physical activity levels and individual z-scores for boys and girls (n = 223), with adjustment for gender and school location
 SBPDBPMAPPPrHRBF%AFMAFM/TBFVO2PEAK
  1. SBP = systolic blood pressure; TBF = total body fat mass; rHR = resting heart rate; PP = pulse pressure; MAP = mean arterial pressure; DBP = diastolic blood pressure; AFM = abdominal fat mass.

General physical activity−0.11 p = 0.13−0.08 p = 0.25−0.11 p = 0.11−0.05 p = 0.48−0.09 p = 0.18−0.31 p < 0.001−0.28 p < 0.001−0.20 p < 0.010.23 p < 0.001
Moderate–vigorous physical activity−0.10 p = 0.16−0.12 p = 0.09−0.13 p = 0.06−0.01 p = 0.87−0.12 p = 0.09−0.32 p < 0.001−0.29 p < 0.001−0.21 p < 0.010.32 p < 0.001
Vigorous physical activity−0.14 p < 0.05−0.08 p = 0.26−0.12 p = 0.07−0.09 p = 0.19−0.15 p < 0.05−0.38 p < 0.001−0.34 p < 0.001−0.23 p < 0.0010.28 p < 0.001

GLM-derived partial correlations for physical activity and the sum of z-scores (composite risk factor score for CVD) are shown in Table 3, where boys and girls were analysed separately. There were significant correlations between VPA and MVPA and the sum of z-scores in both boys and girls, while GPA only was significantly correlated with the sum of z-scores in boys.

Table 3.   Partial correlations between different physical activity levels and sum of z-scores (composite risk factor score for cardiovascular disease), with adjustment for school location
 Sum of z-scores for boys (n = 123)Sum of z-scores for girls (n = 100)
Vigorous physical activity−0.33 p < 0.001−0.32 p < 0.01
Moderate–vigorous physical activity−0.29 p < 0.01−0.28 p < 0.01
General physical activity−0.30 p < 0.001−0.18 NS

General Linear Model analysis showed that 10% of the variance in the sum of z-scores could be explained by VPA and 8% by MVPA. The analyses were also performed for boys and girls separately. A total of 11% of the variance in composite risk factor score could be explained by VPA, 8% by MVPA and 11% by GPA in boys. The corresponding findings in girls were VPA (10%) and MVPA (8%). GPA variance was not analyses in girls as GLM partial correlations did not show significant correlations between sum of z-score and GPA.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

The salient finding of the present investigation was the inverse relationship between physical activity and composite risk factor score for CVD. Children that were physically more active tended to have lower composite risk factor scores for CVD compared with children with a lower amount of MVPA and VPA. This was also observed for children with higher amounts of GPA in boys. Between 8% and 11% of the variance in composite risk factor score could be explained by the different physical activity measurements. This may not only represent statistically significant findings but also represent a clinically relevant association.

The index used in the present investigation was composed of a number of separate risk factors for CVD, such as SBP, DBP, MAP and PP (16,24), rHR (25), VO2PEAK (1) and body fat measurements (26). Numerous studies in children have studied relationships between physical activity and single risk factors for CVD, such as physical activity and body fat (5,27–29), physical activity and blood pressure (30), and physical activity and aerobic fitness (5,31). Data on more comprehensive analyses of the relationship between objectively measured physical activity and risk factors for CVD are scarce. We are aware of only two population-based studies in younger subjects that have evaluated the relationship between objectively measured physical activity and risk factor for CVD in a broader analysis. Hurtig-Wennlof et al. (7) investigated a population-based sample of 1125 subjects aged 9–10 and 15–16 years. Physical activity (GPA and MVPA) was assessed by accelerometry for four consecutive days and related to sum of skin folds, SBP, DBP, aerobic fitness and serum levels of triglycerides, cholesterol and insulin resistance. Univariate analyses were used, and this study indicated that the amount of physical activity correlates weakly to single risk factors for CVD. There is, to our knowledge, only one study that has related objectively measured physical activity to composite risk factor score for CVD in a population-based sample in children (6). Andersen et al. investigated both single risk factors and also performed a comprehensive analysis of composite risk factor score for CVD related to GPA and daily accumulation of 5 or 10 min bouts of physical activity above 2000 counts/min (6). Their finding was a clear graded relationship where the quintile with highest activity displayed lowest accumulation of risk factors and a steady increase in accumulation of risk factors could be observed with decreasing activity quintiles. This study confirmed the results from the above-mentioned studies, but differs in some aspects. MAP, rHR and PP were included in this investigation. Furthermore, DXA was used, which is a more accurate measurement for the quantification of body fat, instead of sum of four skin folds and BMI. In the current study, blood samples were not included; hence, it is not possible to include risk factors such as serum levels of triglycerides, cholesterol or insulin.

Much of the correlation between physical activity variables and composite risk factor score for CVD in the current study was driven by body fat measurements and VO2PEAK. Daily accumulation of VPA was, however, associated with single z-scores for SBP, rHR, BF%, AFM and VO2PEAK in GLM analysis. In addition, we present correlations in the hypothesized direction for all physical activity variables versus all other investigated risk factors. The nonsignificance for some of these relationships could be attributed to limited sample size. The principal purpose, however, with the current analyses was to investigate the relationship between physical activity and composite risk factor score for CVD. The accumulation of these risk factors, if started in early childhood and sustained during a long time is believed to have greater impact on CVD and mortality than one single risk factor (6), and aggregation of CVD risk factors ‘increase the severity of asymptomatic coronary and aortic atherosclerosis in young children’ (2). The advantage of a composite risk factor score analysis is that it yields a comprehensive perspective. The disadvantage is that each factor is given equal importance as the sum of z-scores is added together, and this may be assumptive.

The main strengths of this study include the use of direct measurements of physiological variables, including measurement of physical activity with accelerometers, quantifying body fat with DXA and aerobic fitness assessed by direct measurement of VO2PEAK. We are aware of the limitations of a cross-sectional study, which cannot differentiate cause and effect. Moreover, it is not possible at present to say whether children who spend less amount of their time physically active will develop CVD as adults. It is, though, of great interest that these changes can be observed even at this young age and questions arise whether it is the low amount of physical activity that leads to these effects or that children that have higher composite risk factor score tend to be less physically active. One of the weaknesses of this study was the somewhat low inclusion frequency (52%), although no differences in height, body mass and BMI were found between the children who participated and did not participate in the study (4). In addition, there is no ‘gold standard’ method for measuring daily physical activity. Accelerometers have in recent years gained popularity as an objective measurement device for daily physical activity and represent a substantial improvement to self-reporting methods (8). Accelerometers may, however, have limitations. A critical issue is how to select cut-off points to define different activity intensities. There is no consensus regarding which cut-off points to use to define MVPA and VPA (5). There are several proposed cut-off points, and the range for defining the lower limit for MVPA or VPA is significant. Our selections of cut-off points were based on a combination of two large validation studies (19,20), which appears a reasonable approach because a conclusive and comprehensive validation study is absent. One should however be cautious about the exact number of minutes of MVPA and VPA performed per day because this is heavily influenced by the selection of cut-off points. Our practice to stratify the population according to quartiles partly eliminates this problem.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

We conclude that children with low daily amounts of physical activity have a tendency to show higher composite risk factor score for CVD already at a young age. A longitudinal study is in progress to evaluate the causality of physical activity and composite risk factor scoring for CVD.

Acknowledgement

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References

Financial support for this study was received from The Swedish Heart and Lung Association and Lund University Grants.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Key notes
  5. Methods
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgement
  10. References
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