Analysis Plan Aim 1

The association of television watching with physical activity or diet (aim 1a) was

answered using a cross-sectional design. The second part of this aim (Aim 1b) addressed the prospective associations between television watching with physical activity or diet by using a longitudinal design. The central difference between the cross-sectional and longitudinal analyses is that the outcome measures were taken from the third clinic visit. The main exposure and other covariate information were taken from baseline values.

Data reduction

Any individual who did not answer the question on television watching at the baseline visit was excluded from all analyses. Individuals who were missing information on

television watching at the third clinic visit were included for the baseline cross-sectional analysis but excluded from longitudinal analyses. For both cross-sectional and longitudinal analyses, any participant who selected a racial group other than white or African American was excluded because of small sample size. Additionally, African Americans from the Washington County, MD and Minneapolis, MN sites were also excluded because the small samples make the control of race by site difficult.

Covariates

A number of variables were considered as potential confounders of the relationship between television watching and diet or physical activity. These variables were decided on a

priori and included both socio-demographic and health related variables. The conceptual

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Figure 4.1. Aim 1 Conceptual Model

Television Watching Physical activity Diet Potential Confounders • Marital Status • Occupational Status • Race/Ethnicity • Education • Age • Smoking • General Health • Weight Status Sociodemographic: _Health:

All sociodemographic variables were self-reported at baseline. Because racial groups other than African American and white were excluded, race was dichotomized as white or African American. Race was also combined with the study center from which the participant was recruited. This gave us a race by site variable that is organized into five categories (Forsyth County African American; Forsyth County white; Washington County white; Minneapolis white; and Jackson African American). Education was also self-reported and organized into categories (<high school, high school, >high school). Occupational

information was reported at each visit and was estimated with two variables. The first occupational variable describes whether or not the participant is currently employed; the second describes the type of employment which was categorized.

A number of health measures were also collected. At each visit smoking status was obtained during interviews and defined categorically as current, former, or never smoker. A subjective general health question was also asked at each visit and ranked the participants health as excellent, good, fair, or poor. Anthropometric measurements were obtained at each clinic visit and included weight and height body mass index. Body mass index, weight in kilograms by height in meters squared, can be used as an indication of body habitus. It was categorized into underweight (<18.5 kg/m2), normal (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2) and obese (≥30 kg/m2) (WHO 1995).

i. Univariate Analysis Aim 1a: Cross-sectional

Initial analysis described the frequency and distribution of television watching with each outcome. Television watching was described first, as a categorical variable using the original

5- level Likert scale and second, categorized into three levels: low, medium, and high exposure. Participants who watched television “never” or “seldom” were identified as low exposure. Participants who reported “sometimes” watching were categorized into the medium exposure group, and participants watching “often” or “very often” were assigned to the high exposure category. The choice to categorize television watching into these groups was guided by information from the average hours reported by Nielsen Media Research and the ancillary data (Television At A Glance 2005) (Appendix B). Nielsen Media Research, the television ratings company collects data on television viewing audiences, reported that the average television exposure for men and women over 18 years of age was between four and six hours per day (Nielsen Media Research 2006; Television At A Glance 2005). The ancillary data from Belgium also indicated that the mean television hours in the “often” and “very often” categories of the Baecke (high exposure) were around four and five respectively (Appendix B). For the purposes of this study, individuals assigned to “low” exposure represented people who were exposed to television at a level less than what is considered normal or average.

ii. Bivariate Analysis Aim 1a: Cross-sectional

The crude associations between television watching at baseline and each of the outcomes at baseline were examined using contingency tables and the appropriate measures of

association (e.g., Mantel Haenszel, etc). Covariates of interest were also examined

separately with the exposure and each outcome of interest. This information helped identify meaningful cut-points for categorical variables.

Aim 1b: Longitudinal

Because we had already examined the associations between television watching and the covariates, only crude associations between covariates and values of the outcome at the third clinic visit were explored for this analysis. There were no significant differences between the cross-sectional and longitudinal bivariate analyses.

iii. Multivariable Analysis Aim 1a: Cross-sectional

Linear (continuous outcomes) and logistic (categorical) regression analyses were used to estimate the cross-sectional associations between our main exposure and each outcome. Linear models provided an estimate of the means for each outcome by level of television exposure. Models were also explored using television watching exposure as a five-level Likert variable, as well as a three level categorical variable (low, medium, high).

Multivariable analysis included variables for the different covariates and confounders of interest. Categorical variables were included as indicator variables. Additional models included controlling for the ‘opposite’ outcomes. For example, dietary outcomes were examined with and without controlling for physical activity. Because the Willett FFQ has been shown to be more valid with energy adjustment, dietary outcomes were explored in models simultaneously adjusted for total calories (Subar et al. 2001b).

Aim 1b: Longitudinal

For the second objective, linear and logistic regression were also used to estimate the measure of six-year association, or risk between television watching and each outcome at the third clinic visit. Models were run with the same television exposure variables as the cross- sectional analysis.

The only significant difference between the cross-sectional and longitudinal analyses was the outcome variable and interpretation of the beta parameter. In the longitudinal analyses each parameter estimate represents increasing risk (estimated by odds ratio in logistic model) of the outcome at the third clinic visit (rather than prevalence). Additionally, the outcomes at baseline were included in the longitudinal model which was advantageous in helping to address temporality of the exposure and outcome relationship.

Multivariable Linear Regression

Multivariable linear regression was performed for both cross-sectional and longitudinal analysis. The continuous outcomes in each model followed the same general formula:

y = α + β1X1 + γkVk + e

where the parameter β1 indicates the coefficient of the main exposure, the parameter γk

indicates the coefficient of all covariates or potential confounders. A number of models were run and each separate model had a unique intercept and regression parameters.

For example, a model of the association between television watching and the number of servings of fruits and vegetables was:

Prevalence (servings fruits and vegetables) = α + β1(television)+ γkVk + e

where :

α = servings per day of fruits and vegetables in participants with all referent levels of the independent variables

β1 = the estimated average increase in servings per day of fruits and vegetables with an

increase in television exposure, while all other covariates are at referent levels

γk = represents the coefficients of all potential covariates or confounders

Multivariable Logistic Regression

For dichotomous outcomes, each model followed the same general formula: ln[p/(1-p)] = α + β1E1 + γkVk

where the parameter β1 indicates the coefficient of the main exposure, the parameter γk

indicates the coefficient of potential confounders. A number of models were explored and each separate model had a unique intercept and regression parameters. Outcomes were dichotomized whenever possible. For example, a model of the association between television and the physical activity outcome (dichotomized) was:

ln[P(inactivity l television) ] = α+ β1(television)+ γkVk

where:

α = intercept, background log odds of being inactive for all referent levels of the covariates

β1 = expected increase in the log odds of inactivity associated with an increase in television

exposure when all other covariates are fixed

Assessment of Confounding

The bivariate analyses helped guide assessment for confounding with an a priori list of important covariates. In order for a variable to be a confounder it must be associated with both the exposure of interest (television) and the outcome of interest (diet or physical activity). This variable may not be causal intermediate and lie on the pathway between exposure and outcome.

A covariate was considered a potential confounder if there was 1) evidence from the literature, 2) relationship in the conceptual model, or 3) indicated by the bivariate analysis. To test for the impact of the potential confounder between exposure and outcome, each model was analyzed with and without the potential confounder. When comparing the beta coefficients of the main exposure between the crude and adjusted models, meaningful differences of 10% or more indicated that the additional covariate was a confounder. Additional Models

Because retirement was previously associated with change in leisure time activities, additional modeling explored these relationships while controlling for retirement status. The participants were stratified by retirement status (e.g., fully-retired, partially-retired, retired during follow-up).