Television, physical activity, diet, and body weight status: the ARIC cohort

Study population

The Atherosclerosis Risk in Communities (ARIC) Study was designed to examine risk factors for cardiovascular disease and its related morbidity and mortality. Participants were selected from four US communities: Washington County, Maryland; northwest suburbs of Minneapolis, Minnesota; Jackson, Mississippi; and Forsyth County, North Carolina. A probability sample of men and women ages 45 to 64 years was recruited from each community, from which 15,792 adults completed the first clinic visit. The first clinic visit occurred between 1987 and 1989 and included a physical examination and an interview by trained personnel regarding medical history, health status, diet, and physical activity. Clinic visits were repeated at approximately three-year intervals. Further details on the ARIC cohort are available elsewhere [24, 25].

Television and physical activity measurement

The Baecke physical activity questionnaire was designed to study habitual physical activity and distinguish between different dimensions of physical activity using semi-continuous indices of sport, leisure, and work activity [26]. The questionnaire was interviewer administered at the baseline (1986–1989) and third clinic visit (1993–1995) of the ARIC study. The questionnaire included an item on television watching as part of the leisure activity index. This subjective question of television exposure asked: "During your leisure time do you watch television?" and allowed five responses: never, seldom, sometimes, often, and very often. The answers were ranked on an ordinal scale from 1 (low) to 5 (high). From this ordinal scale, television watching was collapsed into three exposure levels: low (never/seldom, n = 2878), medium (sometimes, n = 7293), and high (often/very often, n = 5403). Because television watching is a component question of the leisure index, the remaining three questions on leisure activity were analyzed separately. These items included information on walking and biking and were ordinally scaled from five responses (never, seldom, sometimes, often, and very often). The third question asked about minutes spent walking or biking to and from work or shopping and respondents could answer < 5 minutes/week, 5–<15 minutes/week, 15–<30 minutes/week, 30–<45 minutes/week, or ≥ 45 minutes/week.

Sport and work physical activity were assessed with semi-continuous indices created from individual component questions and have been described in detail elsewhere [26–28]. Using the reported activities from the sport index, participants were also dichotomised as sufficiently or insufficiently active. Sufficient activity was defined as regular physical activity, or participation in at least one hour per week of activity for 10 or more months of the year [27, 28]. Insufficient activity was identified by less than one hour per week and/or less than 10 months per year.

The reliability and validity of the Baecke questionnaire have been examined in both European and American populations. Test-retest reliability correlations of total physical activity from the three indices range from 0.65 to 0.93 [26, 29–31]. Validation of the Baecke has been conducted using physical activity diaries, maximum oxygen consumption, accelerometers, and doubly-labeled water [29, 30, 32–39]. In a doubly-labeled water study of three physical activity questionnaires, the Baecke had the highest agreement for total physical activity (Pearson's r = 0.69) [40].

Diet measurement

Usual dietary intake was collected at baseline and at the third clinic visit using a semi-quantitative, interviewer-administered food frequency questionnaire (FFQ). The ARIC FFQ contains 66-items and was based on the original Willett 61-item FFQ [41]. The ARIC FFQ is organized into seven sections; 1) dairy, 2) fruit, 3) vegetables, 4) meats and fish, 5) sweets and baked goods, 6) miscellaneous, carbohydrates, fried foods, and 7) beverages. Interviewers asked participants; "In the last year how often, on average, did you consume ______?" Nine responses were available for each food item ranging from "almost never" to "more than 6 times per day". Each response was assigned a weight to estimate servings per day and daily intake of nutrients [42].

Using the standard serving sizes from the FFQ, daily servings of each food item were calculated and summed to create food groups. Dietary outcomes were categorized into the following food groups: fruit, vegetables, fruit and vegetables combined, salty snacks, sweets, sweetened drinks [43]. Total caloric intake, total fat and percent saturated fat, and estimated nutrient values were calculated at the Channing Laboratory, Harvard Medical School.

The original Willett FFQ has been validated in a number of populations and is a well-recognized dietary tool with validity correlations between 0.35 and 0. 74 [41, 44, 45]. The reliability of the ARIC FFQ has been estimated among both white and African Americans participants in the ARIC study [46]. Median reliability coefficients were 0.48 and 0.63 for White women and men, respectively, and 0.45 and 0.50 in African American women and men, respectively.

Measurement of body weight status

Anthropometric measurements of weight and height were obtained during both clinic visits and were used to calculate body mass index (BMI) (weight in kilograms divided by height in meters squared). Four weight status groups were created using BMI: underweight (<18.5 kg/m²), normal weight (≥ 18.5–<25 kg/m²), overweight (≥ 25–<30 kg/m²), and obese (≥ 30 kg/m²) [47].

Measurement of other study variables

At baseline, participants reported cigarette smoking (current, former, never) and years of education (<high school, high school, some college, or higher). Also at baseline, participants were asked to rank their general health as excellent, good, fair, or poor. Because one of the study centers was entirely African American (Jackson, MS) and others predominately white, a race-center variable to control for effects of race and center was created by combining each participant's race with their respective study center (e.g., Forsyth County African American; Forsyth County white).

Statistical analysis

In order to examine the associations between television, physical activity, diet, and body weight status participants were excluded who did not answer the television question at baseline (n = 32) or were missing information on age, race, gender, body mass index, general health status, or smoking (n = 83). Additionally, in order to control for the effect of race and center, individuals other than white or African American and all non-white participants from Minneapolis and Washington were excluded (n = 103). Excluded from the prospective analysis were those individuals who died (n = 722) or did not return to the third clinic visit (n = 2,125), as well as those missing information on both physical activity and diet outcomes at the third clinic visit (n = 49). Final sample sizes were 15,574 for the cross-sectional analysis and 12,678 for the prospective analysis, the latter referred to as "the cohort".

The associations of baseline television watching with baseline and six-year physical activity, diet, and body weight status were estimated from multivariable linear and logistic models. Analysis of variance (ANOVA) was used to estimate the mean of each outcome variable at each of the three levels of television exposure (low, medium, high). For the linear models, analyses were also performed using log transformations of all non-normally distributed outcomes. The results were unchanged using log-transformed variables and therefore, for ease of interpretation, they were left in their original state. For logistic models, outcomes were dichotomized as: having a BMI >= 25, being below the median for positive outcomes (e.g., physical activity), or above the median for negative outcomes (e.g., total energy intake). Low television exposure was defined as the referent and the odds of each outcome were estimated for medium and high television exposure.

To assess the temporal association of baseline television watching with physical activity and diet over the six years of follow-up, the baseline value of each outcome was included in all prospective models for a further "baseline-adjusted" model. In a longitudinal analysis, adding the baseline outcome variable to the model will adjust for the cross-sectional associations at baseline between television and each outcome of interest.

The analysis for this study was approved by the Public Health-Nursing IRB at the University of North Carolina at Chapel Hill, and participants provided written, informed consent at each ARIC center.

Cross-sectional association with television watching

In a linear model, differences were examined between the adjusted means of each outcome at the three levels of exposure. These models indicated that individuals with higher television exposure participated in less sport physical activity than people who were exposed to medium or low amounts of television. There was no apparent relationship between television exposure and physical activity from the work index (Figure 1). Examining diet, we found that participants who reported high television exposure ate more servings per day of salty snacks (0.07, 95% CI 0.04, 0.09), sweets (0.16, 95% CI 0.10, 0.22), sweetened drinks (0.11, 95% CI 0.07, 0.15), percent of calories from fat (0.78, 95% CI 1.09, 0.47), and percent of calories from saturated fat (0.27, 95% CI 0.47, 0.13) than people who reported low television exposure (Figures 2, 3, 4). Those with high exposure also consumed almost one-half serving fewer fruits and vegetables per day (-0.41, 95% CI -0.52, -0.30) (Figure 2).

Similar relationships were observed between increasing television watching and body weight status. At baseline, participants with medium and high television exposure had a 22 and 43 percent greater odds of being overweight or obese than individuals with low exposure (Table 4). However the absolute magnitude of effect was less dramatic when comparing adjusted means in a linear model, and the only significant comparison was between low and high television exposure (Figure 5). Further adjustment for sport physical activity and total kilocalories had no appreciable affect on these results.

Prospective association with television watching

The associations between television watching and body weight status were almost the same for both medium and high television exposures in the prospective model (Table 4). However, both exposure levels were still associated with greater odds of being overweight or obese at six-years follow-up relative to low exposure. Additionally, when the baseline term for body weight status was added, the prospective association attenuated to the null.

Post-hoc exploratory analyses were conducted to examine the effect of different cut-points and model choices. Regardless of the model choice (linear versus logistic model), consistent associations were observed between television and each outcome using different exposure and outcome categorizations (continuous, tertile, quartile, different dichotomization). Even when outliers were removed, the conclusions were the same (data not shown). Lastly, we also restricted the sample to only participants with complete data however the conclusions were again the same (data not shown).

Limitations

This study is the first large cohort analysis to examine the associations of reported television exposure with physical activity, diet, and body weight status. However, it is important to recognize that the question measuring television exposure was subjective and semi-quantitative, and could result in misclassification. Additionally, although the Baecke questionnaire has shown validity and reliability in other populations [29, 39, 40] the television question used in this analysis has not been validated as a single item, to our knowledge. Although better measurement tools have been developed and great strides have been made in physical activity epidemiology, [52] no tool has been designed to adequately measure sedentary behaviors [53]. The television question in the ARIC study is also not an absolute measure of time, and is a subjective measure of exposure. To minimize this limitation, ancillary data was obtained (M. Hulens, personal communication, 2004 [54]) which compared the Baecke television question with a second, concurrent report of the continuous number of hours of television exposure in a Belgian population. Agreement between continuous hours of television and the single item Baecke was acceptable (chi square 92.3, p < 0.0001).

Food frequency questionnaires like the ARIC-FFQ collect information on a limited number of food items and do not assess total energy intake; therefore, these dietary outcomes contain measurement error. Yet until better methods are developed, FFQs are a practical method for gathering information on dietary behaviors in large cohort studies [44]. Although validation of the ARIC FFQ is not available, the Willet FFQ, on which the ARIC questionnaire is based, has been validated and used in other well-recognized cohort studies [41, 55, 56]. Reliability of this tool has been shown to be between 0.45 – 0.63 across visits (3 years) in this study population [46]. Future studies could consider exploring measures of diet quality, in addition to the components that we assessed.

Another limitation of this study is the loss of participants between visits. These individuals (n = 2,847) were less healthy, reported higher television exposure, and had more negative diet and physical activity patterns than those who remained in the study. Because these individuals fall into both the high exposure and more unhealthy outcome categories at baseline, it is unlikely that the results would be different had these participants remained in the study.

This study is observational and relies on recall of both exposure and outcomes. Therefore, in a study of this nature it is impossible to untangle any effect of previous television, activity, and dietary behaviors from our observations. Additionally, baseline data collection began in the mid-eighties and societal television, physical activity, and diet behaviors will not be the same as present day. It is important to recognize that the generalizability of this study to current sedentary behaviors and other populations is limited.

Additionally, physical activity and diet are arguably mediators on the pathway between television exposure and overweight or obesity. For example, physical activity has been previously found to be an effect modifier of the association between television and obesity [19]. A fundamental principle of adjusting for a confounder is that the covariate is not an effect modifier or mediator of the relationship between exposure and outcome. Therefore, in our example adjusting for a mediator (e.g., physical activity) like a confounder may not result in the best estimate of the association [57]. Lastly, although an attempt was made to establish temporality, true cause and effect cannot be ascertained from this study design.