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American Journal of Clinical Nutrition, Vol. 88, No. 3, 612-617, September 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality?1,2,3

Ulf Ekelund1, Søren Brage1, Herve Besson1, Stephen Sharp1 and Nicholas J Wareham1

1 From the Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom

2 Supported by The Medical Research Council.

3 Reprints not available. Address correspondence to U Ekelund, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Box 285, CB2 0QQ, Cambridge, United Kingdom. E-mail: ulf.ekelund{at}mrc-epid.cam.ac.uk


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background:Whether obesity is a cause or a consequence of a sedentary lifestyle has not yet been fully elucidated, which leaves uncertainty about the direction of causality.

Objective:We aimed to assess the longitudinal associations between objectively measured time spent being sedentary (sedentary time) and obesity indicators.

Design:The study was a prospective, population-based cohort study in 393 middle-aged healthy whites (n = 176 M, 217 F). Sedentary time (% of daytime hours) was measured by individually calibrated monitoring of the heart rate. Body weight (BW), body mass index (BMI), and waist circumference (WC) were assessed by standard clinical procedures. Fat mass (FM) was assessed with bioimpedance. All measurements were collected at baseline and at 5.6-y follow-up.

Results:At baseline, sedentary time was significantly correlated with FM (partial r = 0.10, P = 0.043) and WC (partial r = 0.11, P = 0.027) after adjustment for sex and age. At follow-up, sedentary time was significantly correlated with BW (partial r = 0.19, P < 0.0001), BMI (partial r = 0.20, P < 0.0001), WC (partial r = 0.15, P = 0.003), and FM (partial r = 0.19, P < 0.0001). Sedentary time did not predict any of the obesity indicators at follow-up. In contrast, BW (β = 0.33; 95% CI: 0.15, 0.50), BMI (1.10; 0.58, 1.63), FM (0.59; 0.11, 0.40), and WC (0.44; 0.23, 0.66) predicted sedentary time at follow-up after adjustment for sex, baseline age, baseline sedentary time, baseline physical activity energy expenditure, and follow-up time.

Conclusion:BMI, FM, and WC may predict sedentary time, but our results do not suggest that sedentary time predicts future obesity.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Obesity, which is increasing in prevalence in most parts of the world, is considered as one of the greatest health challenges of the 21st century (1–3). Although obesity is multifactorial, including genetic, biological, cultural, and socioeconomic components, it is likely that an imbalance between energy intake and energy expenditure (EE) is the main underlying cause of the current epidemic (4, 5).

In prospective population-based cohort studies, subjects who report higher levels of leisure-time physical activity or regular participation in exercise are less likely to gain in weight or body mass index (BMI), although the results have been somewhat inconsistent (6, 7). More recently, a review of the literature on the prospective associations between baseline physical activity and gain in body weight or BMI suggested that low levels of baseline physical activity were only weakly related to such gain (8). Others have suggested that sedentary behavior predicts gain in body weight or BMI (9, 10), an association that appeared to be independent of exercise levels and other confounding factors (9).

However, because persons who are overweight or obese may be less likely to stay active, it is not fully clear whether obesity is a cause or a consequence of sedentary behavior. Three longitudinal studies suggested that a high baseline BMI predicts sedentary behavior or physical inactivity, whereas those studies provided no compelling evidence for a long-term influence of physical inactivity on gains in BMI or the development of obesity (11–13).

Previous studies examining the prospective and longitudinal associations between sedentary behavior and BMI have relied on self-reported data (9, 10, 13). In those studies, sedentary behavior has been defined either by the amount of time spent watching television (9, 10) or by the absence of regular participation in sports and strenuous physical activity (13). Objective measurements of sedentary behavior are less associated with recall bias (which may differ by obesity status), and they reduce measurement error and improve the ability to determine the strength and direction of association.

Therefore, the aim of the present study was to examine the prospective associations between the objectively measured amount of time spent being sedentary (sedentary time) and markers of obesity in a population-based cohort of otherwise healthy middle-aged whites. The objective measurements of the amount of sedentary time were made by using individually calibrated monitoring of the heart rate (HR); measures of body composition were available at 2 time-points.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study population
Participants were selected from the Medical Research Council Ely Study (14, 15), a prospective, population-based cohort study of the etiology and pathogenesis of type 2 diabetes and related metabolic disorders. The participants were examined between 1994 and 1996 (baseline) and again between 2001 and 2003 (follow-up). The median time at follow-up was 5.6 y after baseline. A cohort of 739 subjects (311 M and 428 F) provided complete data on anthropometric measurements, body-composition variables, the amount of sedentary time (min/d), and physical activity EE (PAEE) at baseline. Of these 739 subjects, 240 were >65 y old at follow-up and, for safety reasons, they were excluded from undertaking exercise testing. Thus, no measure of sedentary time or of PAEE was available for these persons at follow-up. Of the remaining 499 participants, 48 were being treated with anti-hypertensive medication equivalent to 50 mg atenolol at follow-up, and they were excluded from the exercise test. The amount of sedentary time and PAEE were successfully assessed at baseline and follow-up in 393 participants (176 M and 217 F; 87% of those eligible), who constitute the sample for this report.

At baseline, data on socioeconomic status (SES) and smoking were collected by self-report. Our measure of SES is based on job title, formal qualifications for the job, and the specific type of work performed. Smoking status was recorded as nonsmoker, former smoker, and current smoker.

All participants provided written informed consent. Ethical approval of the study was granted by the Cambridge Local Research Ethics Committee.

Measurements
Body weight and body composition
At baseline and at follow-up, participants attended the laboratory after a 10-h overnight fast. Height (cm) and body weight (kg) were measured by using a rigid stadiometer and a calibrated scale while the subjects were wearing light clothing. Waist circumference (WC; in cm) was measured with a metal anthropometric tape placed midway between the lower rib cage and the iliac crest at the end of a gentle expiration. Three measurements were taken, and the 2 closest were averaged. Resistance ({Omega}) was assessed by using a standard bioimpedance technique (Bodystat, Douglas, United Kingdom), which was previously shown to be a valid (16) and reliable (17) measure of percentage body fat. Total body water and fat-free mass (FFM) were calculated by using the impedance index (height2/resistance), and body weight and resistance were calculated according to the equations published by Sun et al (18). Fat mass (FM) was calculated as body weight minus FFM. Exactly the same procedures and equipment were used at the baseline and follow-up visits.

Assessment of physical activity energy expenditure and sedentary time
At both baseline and follow-up, resting EE (REE) was measured by indirect calorimetry in the fasted state, and resting HR was measured by using an HR monitor (Polar Electro Ltd, Kemple, Finland) after {approx}10 min of rest in a supine position. At baseline, the individual relation of oxygen uptake to HR was assessed during a graded exercise test on a cycle ergometer. At follow-up, this relation was assessed during a submaximal walking treadmill test as previously described (19). Flex HR was calculated as the mean of the highest resting HR and the lowest HR while exercising. Participants wore HR monitors (Polar Electro Ltd) continuously during the waking hours over a 4-d period, and minute-by-minute EE was calculated from the monitor results and averaged over the span of time when the monitor was worn. EE was calculated according the individual relation between HR and EE for HRs above the flex HR, whereas measured REE was used for HRs below the flex HR. Sedentary time was calculated as all minutes below flex HR and expressed as a percentage of the total HR recording. PAEE was calculated as average EE minus REE. All subjects provided ≥35 h of HR data at both time-points.

Statistical analysis
Descriptive characteristics are summarized as means and SDs at baseline and follow-up. Cross-sectional associations between variables were examined by using Pearson correlation coefficients and partial correlation coefficients.

To examine whether the amount of sedentary time predicted body weight, BMI, FM, and WC at follow-up, we fitted multiple linear regression models with each obesity indicator in turn as the outcome and with baseline sedentary behavior as the exposure variable. In this model, we also adjusted for age, sex, PAEE, duration of follow-up, and baseline phenotype (ie, body weight, BMI, FM, and WC). In preliminary analyses, further adjustments were made for smoking status and SES, but these variables were not associated with either obesity or sedentary time, and their inclusion in the models did not change the direction or magnitude of the associations. We therefore excluded these variables from the final model.

We then modeled the amount of sedentary time at follow-up as the outcome variable and baseline body weight, BMI, FM, and WC as exposure variables in separate models with adjustment for baseline sedentary time and the same confounding factors as described above. Finally, we examined whether a change in body weight, BMI, FM, and WC predicted the amount of sedentary time, independent of baseline obesity indicators and baseline sedentary time, in multiple regression analysis with adjustment for the same confounding variables as above. All analyses were conducted with SPSS for WINDOWS software (version 15; SPSS Inc, Chicago, IL).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
The descriptive characteristics of men and women at baseline and follow-up are shown in Table 1Go. Body weight, BMI, FM, WC, flex HR, and sedentary time increased significantly, and FFM decreased significantly between baseline and follow-up in both men and women.


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  		TABLE 1 Descriptive characteristics of participants at baseline and follow-up: the MRC Ely Study, 1994–20031

 
Participants recorded 52.5 ± 10.0 and 50.1 ± 9.4 h of HR data over a 4-d period at baseline and follow-up, respectively. At baseline, the amount of sedentary time was significantly correlated with FM (partial r = 0.10, P = 0.043) and WC (partial r = 0.11, P = 0.027) after adjustment for sex and age. Similarly, at follow-up, the amount of sedentary time was significantly correlated with body weight (partial r = 0.19, P < 0.0001), BMI (partial r = 0.20, P < 0.0001), WC (partial r = 0.15, P = 0.003), and FM (partial r = 0.19, P < 0.0001) after adjustment for age and sex.

We then analyzed whether the amount of sedentary time predicted change in body weight, BMI, FM, or WC at follow-up in multiple linear regression models. Baseline sedentary time was not significantly related to any of those outcomes at follow-up after adjustment for sex, age, follow-up time, and baseline phenotype. Further adjustments for smoking status and SES did not change these observations (data not shown). We did not observe any significant interactions between the amount of sedentary time and age or sex.

We next modeled body weight, BMI, FM, and WC as predictor variables with the amount of sedentary time at follow-up as the dependent variable in separate models after adjustment for sex, baseline age, baseline PAEE, baseline sedentary time, and follow-up time (Table 2Go). Body weight (β = 0.33; 95% CI: 0.15, 0.50), BMI (β = 1.10; 95% CI: 0.58, 1.63), FM (β = 0.59; 95% CI: 0.11, 0.40), and WC (β = 0.44; 95% CI: 0.23, 0.66) significantly and independently predicted the amount of sedentary time at follow-up. The amount of sedentary time at follow-up, stratified by quartiles of baseline FM and BMI, is shown in Figure 1Go. Higher FM (P for trend = 0.004) and BMI (P for trend = 0.003) at baseline were associated with a greater amount of sedentary time at follow-up after adjustment for sex, baseline age, baseline PAEE, baseline sedentary time, and follow-up time. The substitution of percentage body fat for FM did not change the results (data not shown).


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  		TABLE 2 Associations between obesity indicators at baseline and between change in obesity indicators at baseline and at follow-up with the amount of time spent being sedentary (sedentary time) at follow-up in middle-aged British whites1

 

Figure 1
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  		FIGURE 1. Mean (±SE) amount of time spent being sedentary (% of daytime hours) at follow-up stratified by quartiles of fat mass (A; P for trend = 0.004) and BMI (B; P for trend = 0.003) at baseline in middle-aged British whites (n = 393). Data are adjusted for baseline age, sex, the amount of time spent being sedentary, physical activity energy expenditure (PAEE; kJ/min), and follow-up time.

 
We also examined whether change in body weight and obesity indicators between baseline and follow-up predicted the amount of sedentary time at follow-up (Table 2Go). Change in body weight was positively associated with the amount of sedentary sedentary (β = 0.52; 95% CI: 0.17, 0.88; P = 0.004) after adjustment for sex, baseline age, baseline PAEE, baseline sedentary time, and follow-up time. Similarly, changes in BMI (β = 1.49; 95% CI: 0.48, 2.50; P = 0.004) and FM (β = 0.57; 95% CI: 0.10, 1.05; P = 0.018) between baseline and follow-up also were significantly and independently associated with the amount of sedentary time at follow-up, after adjustment for the same confounders as above. Change in WC was not significantly associated with the amount of sedentary time at follow-up (β = 0.14; 95% CI: –0.20, 0.49; P = 0.41). Further adjustment for baseline phenotype slightly attenuated these associations, although they remained statistically significant (Table 2Go). As in our previous model, replacing FM with percentage body fat did not change the results (data not shown).

We then stratified our cohort into 2 groups on the basis of change in FM between baseline and follow-up. FM losers (n = 284) lost 1.9 ± 2.0 kg FM between baseline and follow-up, and FM gainers gained 3.9 ± 3.9 kg (n = 109). The amount of sedentary time was stratified into those who gained and those who lost FM between baseline and follow-up (Figure 2Go). Those who gained FM between baseline and follow-up spent significantly more time being sedentary than did those who lost FM (P for group difference = 0.025) after adjustment for sex, baseline age, baseline FM, baseline PAEE, baseline sedentary time, and follow-up time.


Figure 2
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  		FIGURE 2. Mean (±SE) amount of time spent being sedentary (% of daytime hours) at follow-up stratified by fat mass losers and gainers between baseline and follow-up in middle-aged British whites (n = 393). Data are adjusted for baseline age, sex, the amount of time spent being sedentary, physical activity energy expenditure (kJ/min), fat mass, and follow-up time (P for group differences = 0.025).

 
Finally, we examined whether age or sex modified the observed associations by including the interactions of age and sex with obesity indicators. We did not observe any significant interactions between body weight or obesity indicators and age or sex in any of our models.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our results suggest that body weight, BMI, FM, and WC predicted an increase in the objectively measured amount of sedentary time over a period of 5.6 y in healthy middle-aged whites. These associations were independent of sex, baseline age, baseline PAEE, baseline sedentary time, smoking, SES, and follow-up time. Subjects who gained FM between baseline and follow-up spent significantly more time being sedentary at follow-up than did those who lost FM between the 2 measurements. In contrast, baseline sedentary time was unrelated to gain in body weight, BMI, FM, or WC.

Few previous studies used objective physical activity assessment methods to examine the prospective associations between physical activity and obesity outcomes (20, 21). Other investigators, using the doubly labeled water method in Pima Indians, were unable to show an inverse association between baseline PAEE and gain in body weight (21).

Using data from the present cohort, we previously showed that PAEE predicts gain in FM in younger (<53 y old) but not older (>53 y old) persons (20). We also previously suggested that PAEE predicts progression toward the metabolic syndrome (22) and that an increase in PAEE is associated with lower metabolic risk independent of change in fitness and fatness (19). However, neither of those earlier studies considered the objectively measured amount of sedentary time. Together with previous observations by our group (20), the results of the present study suggest that objectively measured sedentary time and PAEE are differently associated with gain in body weight, BMI, and FM and that they may be considered as separate entities in relation to health outcomes.

Others have suggested that the amount of self-reported amount of time spent watching television predicts a gain in self-reported BMI in women (9) and that the baseline and the change in television viewing predict a gain in self-reported body weight over 1 y in previously successful weigh maintainers (10), independent of confounding factors. However, the data on the associations between television viewing and gain in body weight are inconsistent, and some investigators were unable to show such an association (23, 24). This is perhaps not surprising, because television viewing is only an indicator of sedentary behavior, and it has been suggested to act as a surrogate measure of other behaviors affecting energy balance, such as increased food consumption and snacking (23, 25). Clearly, future studies are needed to examine the relation of television viewing, dietary intake, the amount of sedentary time, and overall levels of physical activity with future weight gain.

Physical inactivity or sedentary behavior also may be a consequence rather than a cause of body-weight gain (11–13). Two of these studies (12, 13) were well placed to examine the temporal sequence of body weight and sedentary behavior because of their longitudinal design. However, limitations include the self-reporting assessment methods that took into account only leisure-time and occupational activity (11, 12) and the categorization as sedentary persons those who do not participate regularly in structured recreational sport or in strenuous physical activity (13). Therefore, the results of the present study, obtained by using an objective measure of the amount of sedentary time, extend these previous observations and suggest that higher body weight, BMI, and FM may predict future sedentary behavior independent of baseline PAEE and other confounding factors; however, our results do not fully support the hypothesis that sedentary behavior is associated with a gain in body weight and FM. The results of the present study are further supported by recent observations suggesting that objectively measured walking distance decreased significantly in both lean and obese persons after experimentally induced weight gain (26).

The ability to detect an association between obesity indicators and the amount of sedentary time, which we report in the present study, depends on several factors. These include the precision of the measurement of the exposure and the outcome, the sample size, and the magnitude of the association between the exposure and the outcome. The present study was undertaken in a randomly selected, population-based cohort of middle-aged adults for whom objective measurements of the amount of sedentary time were available at 2 time-points. We validated our measure of the amount of sedentary time by using simultaneous measurement by HR monitoring and accelerometry in a separate group of middle-aged men and women (n = 278) who were participating in the ProActive trial (27). The correlation between accelerometer-measured sedentary time (defined as <500 counts/min) and the amount of time spent below flex HR was r = 0.45 (P < 0.001), which indicates moderate associations between the 2 measures (U Ekelund, unpublished observations, 2007).

Inferring causality from prospective studies with measurements of exposures and outcomes with different degree of measurement error is problematic. One limitation of previous observational studies is their reliance on self-reported measurement of the behavior (9–13), which makes it difficult to determine the direction of causality. There is a marked difference in measurement precision between the measure of obesity and that of PAEE or sedentary behavior. When the more imprecise variable is used as the outcome, the magnitude of effect is estimated accurately, but with error. When the more imprecise variable is used as the exposure, the measure of effect is attenuated. Because physical activity and sedentary behavior usually are measured much less precisely than is body weight, it is not surprising that baseline body weight predicts follow-up physical activity, whereas, because of measurement error, the reverse may not be true. The objective measure of the amount of sedentary time that we used in the present study is likely to be more precise than self-reported sedentary time, and it may reduce random error when modeled as the outcome and also may reduce the attenuation of the effect size when modeled as the exposure. However, we cannot rule out the possibility of a bidirectional association, because sedentary behavior may predict future obesity in other populations who differ in age, baseline degree of obesity, and activity levels.

Statistical models, such as those reported in Table 2Go, in which change in the exposure variable is modeled against change in the dependent variable, may be less prone to confounding than are conventional cross-sectional association models, and thus, their use would strengthen the evidence of causality (28). The reason for this is that, for confounding to persist in a change model, it is necessary that the confounder(s) change in a way similar to that of the exposure over time. However, the statistical model does not provide stronger inference of the direction of the association than does a cross-sectional model.

The association between sedentary behavior and obesity is of public health importance. Therefore, future longitudinal studies including multiple, repeated, precise measurements of the exposure and outcome from early age (before the amount of fat accumulated may hinder physical activity) are more likely to be able to address the issue of bidirectional or reverse causality. Unfortunately, it is unlikely that randomized controlled trials can address the issue of the direction of causality because of the long duration of follow-up required and the potential ethical issues surrounding the conduct of such a trial. The strengths of the present study include the objective measurement of PAEE and of the amount of sedentary time by using individually calibrated HR monitoring, the randomly selected population-based cohort, the multiple indicators of obesity including assessment of FM by bioimpedance, and the consistent results observed in continuous and categorical analyses.

In conclusion, our results suggest that body weight, BMI, FM, and WC predict a greater amount of sedentary time in healthy, middle-aged whites, but the amount of sedentary time at baseline was unrelated to any of the obesity indicators at follow-up.


   ACKNOWLEDGMENTS
 
We are grateful to all of the volunteers who devoted their time, to Susie Hennings, who was the lead field epidemiologist, and to Sue Emms, who was the lead study nurse on the project; both of them were responsible for the data collection.

The authors' responsibilities were as follows—UE: conceived the hypothesis for this study, performed the data analyses, and wrote the draft of the manuscript; SB, SS, HB, and NJW: provided critical input for the conception of this particular study and for the data analyses and assisted with editing of the manuscript; NJW, the principal investigator of the Medical Research Council Ely Study: was responsible for the overall study design; and all authors: took part in the discussion of the results and approved the final version of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication March 28, 2008. Accepted for publication June 9, 2008.





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