Analysis
REGRESSION, SECONDARY
Baseline sociodemographics and PA level at study enrollment were summarized for each condition, and descriptive statistics are reported. Correlation analysis was used to explore potential confounding effects of the intervention. Although interest was not in strict statistical hypothesis testing, a modest benchmark of As the primary study aim was to determine the feasibility and acceptability of the intervention, retention rates at the end of treatment were compared between conditions using chi-square tests, and participants’ satisfaction was summarized and compared between groups. Hypothesis testing was used in this case to examine between-group differences in these metrics.The secondary outcome of interest was minutes per week of MVPA. Graphical methods suggested that PA data were skewed, and transformations toward normality were not successful (data were still skewed following log transformation). As such, quantile regression was used to estimate the effects of intervention versus control conditions on median minutes per week of MVPA. Models controlled for value of the outcome at study enrollment; employment (which differed significantly between groups and was significantly correlated with MVPA outcomes); and in the case of objectively measured MVPA, accelerometer wear time. As a subsequent step, we explored the potential conditional effects of key demographic and neighborhood-level variables to better understand the potential moderators of the treatment effect using a similar analytic approach. Models included the main effects of the condition and interactions with the potential moderator.Next, using a generalized linear model with logit link function, we examined the effects of treatment condition (intervention vs control condition) on the odds of meeting national guidelines for PA (defined as at least 150 min/wk of MVPA). Interest was in estimating the odds ratios and the corresponding CIs. Models were adjusted for employment (confounder identified a priori). Using a similar analytic approach (quantile regression and generalized linear model), we explored the effects of dose on treatment outcomes (min/wk of MVPA and meeting national guidelines) within the intervention condition. In all cases, interest was not in determining statistical significance but rather in estimating effect sizes for a future fully powered study.Although limited by sample size, to generate hypotheses related to mechanisms of the treatment, a product-of-coefficients approach with bootstrapped SEs (10,000 bootstrapped samples) was used. Given the pilot nature of this study, the focus was on univariate mediation models and estimating path coefficients (effects of treatment condition on changes in the mediators [a path], effects of changes in mediators on PA outcomes [b path], and the indirect effect of the intervention [ab path]).In all cases, models used a likelihood or quasi-likelihood–based approach to estimation and thus used all available data (on the intent to treat sample) without directly imputing missing outcomes. All analysis was performed using SAS 9.4 (SAS Institute Inc), with significance level set at .05 a priori.