Quantifying the association between habitual energy expenditure and blood pressure

Nicholas J Warehama, Man-Yu Wongb, Susie Henningsa, Joanne Mitchella, Kirsten Renniea, Kennedy Cruickshankc and Nicholas E Daya,d

a Department of Community Medicine, University of Cambridge, Cambridge CB2 2SR, UK.
b Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong.
c Clinical Epidemiology Unit, University of Manchester Medical School,M13 9PT, UK.
d Medical Research Council Biostatistics Unit, Cambridge CB2 2SR, UK.

Reprint requests to: Dr NJ Wareham, Department of Community Medicine, Institute of Public Health, University of Cambridge, Robinson Way, Cambridge CB2 2SR, UK. E-mail: njw1004{at}medschl.cam.ac.uk


    Abstract
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
Background Previous studies have demonstrated an association between physical inactivity and hypertension, but the methods used to assess activity have been subjective and imprecise. Recently methods have become available allowing measurement of energy expenditure in free-living populations. Our aim was to employ these methods to assess the independent association between energy expenditure, cardio-respiratory fitness and blood pressure.

Methods In a cross-sectional study of 775 people (45–70 years) participating in a continuing population-based cohort study, energy expenditure was assessed by 4 days of heart rate monitoring with individual calibration of the relationship between heart rate and energy expenditure, a method validated against doubly-labelled water and whole body calorimetry. Cardio-respiratory fitness was assessed in a sub-maximal test. To adjust for measurement error in the assessment of usual energy expenditure and fitness, 190 subjects repeated both tests on three further occasions at 4-monthly intervals.

Results A highly significant linear trend in blood pressure was found across quintiles of the physical activity level, the ratio of total energy expenditure to basal metabolic rate. The differences in the mean systolic/diastolic blood pressure between the top and bottom quintile was 6.3/4.4 mmHg in men and 10.7/5.9 mmHg in women. These effects were independent of obesity and cardio-respiratory fitness. Correction for measurement error suggests that the true underlying relationship between usual energy expenditure and blood pressure is stronger still.

Conclusions These findings are compatible with a strong association between usual energy expenditure and blood pressure and support public health strategies aimed at increasing overall energy expenditure.

Keywords Energy expenditure, physical activity, exercise, fitness, blood pressure, hypertension, population study

Accepted 5 January 2000


    Introduction
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
The public health benefits of increasing the frequency of moderately intense physical activity are widely promoted1 and many previous epidemiological studies have shown that individuals who are physically inactive have higher blood pressure and are more likely to develop hypertension.2–5 However, the use of subjective measures of physical activity has made it difficult to accurately quantify the relationship with blood pressure.6 Therefore, it is uncertain whether benefits would result from small changes in the frequency of moderately intense activity leading to raised overall energy expenditure. Quantitating the relationship between physical activity and blood pressure presents many measurement problems, and few studies have been able to employ methods which are both objective and quantitative.6 Physical activity is multi-dimensional and it is rare for the separate effects of its underlying dimensions to be measured,7 a problem compounded by the fact that they are not truly independent. Even when the underlying dimension can be specified and measured precisely, the true exposure is not physical activity at one time, but the usual or habitual level, a problem analogous to that of usual blood pressure measurement.8

The aim of our study was to quantitate the cross-sectional relationship between habitual energy expenditure and blood pressure by using objective and quantitative methods. The gold standard techniques for measuring energy expenditure are doubly labelled water and whole body calorimetry.9 However, the cost of the former and the subject restrictions required of the latter, generally preclude their use in populations.10 More practical methods in free-living individuals include heart rate monitoring with individual calibration of the relationship between heart rate and energy expenditure. This method's validity has been demonstrated by comparison with the gold standard methods.11,12 We used this method in a population-based study to demonstrate that the metabolic syndrome is closely linked to the level of habitual energy expenditure.13,14 In that study, a sub-group underwent repeated measures, so that the regression dilution bias due to measurement error and inherent variability in energy expenditure could be estimated. However, the approach used was univariate. Biostatistical techniques exist for considering the situation where several exposures are measured with error15 and for modelling the sensitivity of the effect size estimates to changes in underlying assumptions.16 These methods are employed here.


    Patients and Methods
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
Selection of the subjects and metabolic tests
The volunteers were participants in the Isle of Ely Study, a continuing population-based cohort study in Ely, Cambridgeshire, the design of which has been described previously.17,18 The original sample of 1122 individuals without known diabetes were recruited between 1990–1992 at random from a population-based sampling frame consisting of all people in Ely, Cambridgeshire aged 40–65 years in 1990.17 The initial response rate was 74%. Between 1994–1997 a 4.5-year follow-up study was undertaken of all those individuals who did not have diabetes by WHO criteria at baseline (n = 1071). Twenty subjects had died in the interim and 937 of the remaining volunteers attended for follow-up (89% restudy rate).18 These individuals constituted the sample for this particular study and 83% of the group agreed to participate in this study. Sixty-four of the 162 individuals who did not undertake all the tests were excluded for medical reasons including angina or dysrhythmia, treatment with beta-blocking agents or the presence of a pacemaker. A total of 775 people aged 45–70 years attended the clinic at 8:30 a.m. having fasted since 10 p.m. the previous evening. At this visit, height and weight were measured in light clothing and body circumferences were measured in duplicate using a metal tape. Body fat percentage was obtained using a standard impedance technique (Bodystat, Isle of Man). Blood pressure was measured with the subject seated using an Accutorr automatic syphgmomanometer. Three measurements of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were taken at minute intervals and are presented in the data as the mean of these three measurements. Ethical permission for the study was granted by the Cambridge Local Research Ethics Committee.

Assessment of resting and exercise oxygen consumption—heart rate relationship
Following the measurement of anthropometry and blood pressure, a standard protocol for individually calibrating heart rate and energy expenditure was used.13,14 This method relies on the computation for each individual of resting energy expenditure (REE), and the slope and intercept of the regression line describing the linear relationship between heart rate and energy expenditure during exercise. The final parameter that is measured is the Flex heart rate, which is the level used in the method to distinguish between resting and activity. When heart rate data is collected over the following 4 days, energy expenditure when the heart rate is lower than Flex is assumed to equal REE and when it is above Flex, then it is predicted by the simple linear regression computed during the calibration. In this study, oxygen consumption, energy expenditure and heart rate were measured at rest with the subject lying prone and then seated. Subjects bicycled on a cycle ergometer at several different workloads to provide the slope and the intercept of the line relating energy expenditure to heart rate. Each subject cycled at 50 revolutions per minute and the workload was progressively increased from 0 Watts (W), through 37.5 W, 75 W and 125 W in stages each lasting 5 minutes. At each workload three separate readings were made of heart rate, minute volume and expired air oxygen concentration. Mean resting energy expenditure was taken as the average of the lying and sitting values. Flex heart rate was calculated as the mean of the highest resting pulse rate and the lowest on exercise. Finally the slope and intercept of the least squares regression line of the exercise points were calculated. The VO2max was measured from the linear regression as predicted oxygen consumption at maximal heart rate (220-age) and is expressed in the results per unit body weight. The volunteers wore the heart rate monitor (Polar Electro, Finland) continuously during the waking hours over the 4 days following their visit to the clinic. No account was taken of whether these days included only weekdays, or a combination of weekdays and weekend days. In a previous study, we demonstrated that the mean paired difference between individual recordings of week day and weekend day energy expenditure was 0.0008 (P = 0.98).13 Thus, there was no evidence that the 4-day sampling strategy would introduce bias.

Heart rate readings were directly downloaded into a computer via a serial interface and the individual calibration data were used to predict minute energy expenditure for each person. Sleeping energy expenditure was calculated as 95% of basal metabolic rate (BMR) where this was derived from published prediction equations.19,20 A physical activity level (PAL), which is the ratio of total energy expenditure to BMR, was computed for each day and averaged over the 4-day period.

Repeated measures sub-study
A random group of 190 subjects in the cohort reattended for measurements on a further three occasions at 4-monthly intervals for one year, when height, weight and impedance were also remeasured. The calibration between heart rate and resting and exercise energy expenditure was repeated and the volunteers then underwent 4-day heart rate monitoring.

Statistical analysis
The within- and between-subject mean square and the reliability coefficients for PAL, height, weight, Body mass index (BMI), percentage body fat and VO2max per kg were estimated using the formulae described by Armstrong et al.21 By this method, each of n subjects is measured k times with Xij being the jth measure of subject i.i is the mean of k measurements in subject i, and = overall mean. The reliability coefficient is given as

= (BMS – WMS)/(BMS + (k – 1)WMS) where BMS = between subject mean square = Between subject sum of squares/degrees of freedom= k {Sigma}i (i)2/(n – 1) and WMS = within subject mean square = within subject sum of squares/degrees of freedom = {Sigma}i {Sigma}j (xiji)2/n(k – 1). Simple linear regression and multiple regression were undertaken using SAS and the regression coefficients are presented per standard deviation for each variable. The univariate correction was undertaken using the univariate reliability coefficients. The multivariate correction factors for PAL and VO2max per kg were estimated from the variance-covariance matrices using the method described by Wong et al.16 Both the univariate and bivariate correction factors were calculated under the assumption that the errors associated with repeated measures on the same individual were independent. In the absence of other methods for measuring PAL or VO2max per kg, the validity of this assumption cannot be assessed. We investigated the effect of departures from this independence assumption by assuming a range of values from 0.0 to 0.2 for the correlation between the errors in measuring PAL and VO2max per kg.


    Results
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
The characteristics of the 775 people who completed this study are shown in Table 1Go. The mean blood pressure in men was 130.5 mmHg SBP and 79.9 mmHg DBP, whereas for women the corresponding means were 124.4 and 74.4 respectively. Forty-four men and 60 women were receiving an anti-hypertensive agent, and in these subjects the mean blood pressure was higher at 137.9/84.4 mmHg in men and 130.6/77.1 mmHg in women. Subjects were grouped into sex-specific quintiles for PAL, the ratio of total energy expenditure to basal metabolic rate. In both men and women, there was a strong negative linear trend between decreasing energy expenditure and increasing blood pressure, after adjustment for age and blood pressure treatment. Not only was this strongly statistically significant (P < 0.001 in men and women), it was also clinically relevant as the effect size was large. In men the difference between subjects in the top and bottom quintiles for PAL was 6.3 mmHg for the age-adjusted SBP and 4.4 mmHg for DBP. In women, the effect size was even greater, such that the SBP in women in the lowest quintile of PAL was 10.7 mmHg higher than for women in the highest quintile. The DBP difference between these two groups was 5.9 mmHg (Table 2Go).


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Table 1 Characteristics of the subjects: The Isle of Ely Study 1994–1997 (n = 775)
 

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Table 2 Mean age and treatment adjusted blood pressure by sex-specific physical activity level (PAL) quintile: The Isle of Ely Study 1994–1997 (n = 775)
 
These associations are with a single measure of physical activity level, which like blood pressure itself, is an inherently variable phenomenon. Therefore, the relationship between usual physical activity and blood pressure is likely to be stronger still. In Figure 1Go, we show the relationship between DBP and PAL in the subjects who underwent repeat measurement. The open squares and the dotted regression line show the relationship between quintiles of PAL at the first visit and DBP. The filled squares and the solid regression line show the relationship with DBP for the mean of the other three measures of PAL, showing that the slope of the relationship is steeper.



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Figure 1 Relationship between single measure of physical activity level (PAL) ({square}) and diastolic blood pressure (DBP) (----), and between mean of three independent repeat measures of PAL ({blacksquare}) and DBP (—): The Ely repeat measures sub-study, 1994–1997 (n = 190)

 
Physical activity level is also related to indices of obesity including BMI, waist-hip ratio and percentage body fat as measured by impedance, although these relationships are stronger in men than in women (Table 3Go). As these factors are also associated with both SBP and DBP, the possibility of confounding exists. In men and women, both SBP and DBP show higher order correlations with the measure of cardio-respiratory fitness (VO2max per kg) than they do with PAL. This might be misinterpreted as being evidence of a closer association between fitness and blood pressure, than with habitual energy expenditure. However, this interpretation would ignore the differences in underlying variability and measurement error in VO2max per kg and PAL, the significant correlation that exists between them (r = 0.29 in men and 0.38 in women) and their differing patterns of confounding with other important covariates (age and obesity).


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Table 3 Partial correlation coefficient between systolic and diastolic blood pressure, physical activity level (PAL), VO2max per kg and other anthropometric variables, adjusted for blood pressure therapy: The Isle of Ely Study 1994–1997 (n = 775)
 
In order to separate out the aetiological effects of two correlated variables which are estimated with differing degrees of precision from a single measurement, we employed a repeated measures design sub-study and used multivariate correction factors to estimate the effect size. Table 4Go shows the within and between subject mean square, the computed true between subject mean square and reliability coefficient for the 190 subjects with repeat measures. Weight and height are both highly stable phenomena and are measured precisely, thus a single estimate provides an extremely good estimate of the usual value. The VO2max per kg is measured less precisely and there is also a greater degree of inherent biological variability. There is an even greater degree of variability with PAL, and therefore any observed association shown between PAL and an outcome is likely to be an underestimate of the true association.


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Table 4 Reliability coefficients for measurements stratified by sex: The Isle of Ely Study, 1994–1997 (n = 190)
 
Table 5Go shows a series of models, increasing in complexity, each of which shows the effect size for either PAL or VO2max per kg predicting SBP or DBP. In each case, the effect size is shown per standard deviation to allow comparison between variables with different distributions. In the model without correction for measurement error but adjusting for important confounding factors, the effect sizes for PAL are slightly greater than for VO2max, but the difference is small especially in women. When the effect of measurement error is accounted for using the univariate correction method, the stronger association between blood pressure and PAL becomes clearer. The results of the multiply-adjusted model with multivariate correction are likely to be closer to the true value than the simple univariate correction, as they include consideration of confounding, correlation between the two variables of interest and also inherent variability. The results in the most complex model suggest that effect sizes seen for PAL in the simple univariate model are not very different from the true value, but that the estimates for VO2max are exaggerated. The model predicts that a standard deviation change in PAL would be associated with a fall in SBP in men of 3.17 mmHg and 5.07 mmHg in women, whilst the corresponding changes in DBP would be 2.24 mmHg (men) and 2.88 mmHg (women). These analyses were repeated with the data restricted to subjects who were not receiving drug treatments and were unchanged. Sensitivity analyses demonstrated that the results were unaffected by correlated error ranging from 0.0 to 0.2 between PAL and VO2max per kg.


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Table 5 Effect of correcting for measurement error on the standardized regression co-efficients for physical activity level (PAL) and VO2max predicting diastolic and systolic blood pressure (BP): The Isle of Ely Study 1994–1997 (n = 775)
 

    Discussion
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
The data in this study suggest that low habitual energy expenditure is closely related to increased blood pressure, and that this association is stronger than that seen with cardio-respiratory fitness. This relationship is not only statistically significant, but also clinically relevant as the effect size is large. The difference in the mean age-adjusted SBP between top and bottom quintiles was 10 mmHg in women and 7 mmHg in men. When the regression dilution effect of measurement error and inherent variability is taken into consideration, a one standard deviation change in the physical activity level is associated with a 5 mmHg fall in SBP in women and a 3 mmHg fall in men. A population shift of a quarter of one standard deviation in PAL is a potentially feasible change as this increase in energy expenditure is equivalent to walking for an extra 30 minutes over the course of a day. The data from this study suggest that the degree of change in energy expenditure would be associated with a population fall in SBP of 1.25 mmHg in women and 0.75 mmHg in men.

Even though there is a large literature of the relationship between physical activity and increased blood pressure, it is difficult to make this type of quantitative estimate from existing studies. The longitudinal studies from the University of Pennsylvania Alumni Study clearly demonstrate that self-reported participation in sports and exercise predict the incidence of self-reported hypertension over the subsequent 22–31 years of follow-up.2,3 Similar results were obtained in Harvard male alumni in whom the risk of developing hypertension was 1.30 in those with a physical activity index <2000 kcal/week compared to those whose index was >2000.4 In that study, the physical activity index was assessed using a composite of the energy cost of walking, stair climbing and sporting activity.22 Although indicative of an underlying association, the subjective nature of the questionnaire, its limited focus and the lack of validation study against objective measures of energy expenditure, make it unlikely that quantitative estimates based on this data would be accurate.23 Similar criticisms could be made of other epidemiological studies using physical activity questionnaires focusing on leisure time activity.5,24,25

The fact that in some of these studies the effect of physical activity is restricted to those who participate in vigorous activity26 could be interpreted as evidence that exercise needs to be conditioning for it to have a benefit on blood pressure. Indeed in the follow-up study from the Cooper Clinic, those individuals who were in the lowest fitness group had a relative risk for future hypertension of 1.52 (95% CI : 1.08–2.15) compared to those in the highest fitness group.27 From these data one might conclude that a public health recommendation aimed at reducing blood pressure through physical activity should advocate moderately intense cardio-respiratory fitness-enhancing exercise.28 However, such a conclusion would ignore the fact that an alternative explanation to the failure to find an association between blood pressure and activity at lower levels is the difficulty of accurately assessing overall energy expenditure. These issues can only be resolved in epidemiological studies by employing measurement instruments which can objectively quantitate the different dimensions of activity. Although the current study is cross-sectional, further follow-up will confirm whether the observations seen in this study are reinforced over time.

An alternative to attempting to estimate the likely public health benefit through observational studies is to rely on estimates from intervention studies. However, recent reviews of experimental studies of habitual physical activity and hypertension29–31 demonstrate that although physical activity appears to lower blood pressure, the findings need to be interpreted carefully as few studies have avoided all the major pitfalls that cause bias in experimental studies. Thus, although it is intuitively appealing to look to intervention studies to provide an answer to estimating the benefit of increasing physical activity at a population level, the methodological difficulties that these studies present make this more challenging than would at first seem apparent. Therefore, it is likely that observational studies will continue to provide much of the support ive information needed to inform the process of measuring the public health benefit of population changes. Improving the quality of the estimates that are derived from such epidemiological studies requires advances in the assessment and quantitation of exposures such as physical activity and diet, combined with the development of statistical modelling to separate their different aetiological effects.


    Acknowledgments
 
The Isle of Ely Study was funded by the British Diabetic Association, the Anglia and Oxford Regional Health Authority and the Medical Research Council. NJW is a Medical Research Council Clinician Scientist Fellow. The work of Dr Wong was supported by the British Council. We are grateful to the staff of the St Mary's Street Surgery, Ely and to H Shannasy, S Curran, P Murgatroyd, and Drs M Hennings and AM Prentice for their help with the fieldwork for this study.


    References
 Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Discussion
 References
 
1 US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 1996.

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