Occupational Physical Activity Opposes Obesity: A Cross-Sectional Modern Replication of the Morris 1953 London Busmen Study.

OBJECTIVE: Examine the importance of occupational light-intensity physical activity (PA) and short bouts of moderate-vigorous PA (LSBPA), to opposing obesity, including an approximate replication of the London busmen study comparing waist circumference of workers with high versus low levels of occupational activity. METHODS: Working adults wore an accelerometer, completed anthropometric measurements, and provided work schedules. Participants' (n = 435) activity was classified as either occupational or non-occupational minutes, and by intensity. RESULTS: Body fat percentage was inversely associated with occupational-LSBPA in participants who did not meet PA guidelines, but not in those who met guidelines. In the London busmen replication, more active workers had smaller waist circumferences than sedentary workers, controlling for non-occupational activity. CONCLUSIONS: Work-related LSBPA may be an important and under-recognized source of PA that opposes adiposity for people who do not meet PA guidelines.

An examination of compensation effects in accelerometer-measured occupational and non-occupational physical activity.

Self-report data suggests a large proportion of total physical activity (PA) occurs at work. However, adults with higher levels of occupational PA may compensate by engaging in less non-occupational PA. The study aims were to 1) estimate the intensity, volume, and duration of PA in American adults that occurs at work, and 2) determine if those more active at work are less active outside of work. A cross-sectional sample of full-time employed adults (N = 510) was recruited from Georgia city and county governments in 2013-2015. Participants wore an Actigraph GT3X + accelerometer for two weeks. In 2016, for 442 participants with complete data including work schedules and self-reported job titles, accelerometer wear minutes were classified as either occupational or non-occupational, and as sedentary, LPA (light-intensity PA), or MVPA (moderate-to-vigorous intensity PA). The proportion of daily PA that occurred during work was 41.2% for total PA, 41.0% for LPA, and 39.5% for MVPA. Higher levels of occupational LPA were associated with lower levels of non-occupational LPA (r = - 0.38, P < 0.0001). However, higher levels of occupational MVPA were associated with higher levels of non-occupational MVPA (r = 0.17, P < 0.0001). These associations remained significant in a MANOVA adjusting for labor sector and other covariates. On average, employed adults get more LPA and MVPA outside of work. Adults who do more occupational MVPA do not compensate by doing less non-occupational MVPA. In contrast, adults who do more occupational LPA do compensate by doing less non-occupational LPA. Evaluations of interventions to reduce sedentary behavior should be designed to detect compensation effects.

Using regression mixture models with non-normal data: Examining an ordered polytomous approach.

Mild to moderate skew in errors can substantially impact regression mixture model results; one approach for overcoming this includes transforming the outcome into an ordered categorical variable and using a polytomous regression mixture model. This is effective for retaining differential effects in the population; however, bias in parameter estimates and model fit warrant further examination of this approach at higher levels of skew. The current study used Monte Carlo simulations; three thousand observations were drawn from each of two subpopulations differing in the effect of X on Y. Five hundred simulations were performed in each of the ten scenarios varying in levels of skew in one or both classes. Model comparison criteria supported the accurate two class model, preserving the differential effects, while parameter estimates were notably biased. The appropriate number of effects can be captured with this approach but we suggest caution when interpreting the magnitude of the effects.

Not quite normal: Consequences of violating the assumption of normality in regression mixture models.

Regression mixture models are a new approach for finding differential effects which have only recently begun to be used in applied research. This approach comes at the cost of the assumption that error terms are normally distributed within classes. The current study uses Monte Carlo simulations to explore the effects of relatively minor violations of this assumption, the use of an ordered polytomous outcome is then examined as an alternative which makes somewhat weaker assumptions, and finally both approaches are demonstrated with an applied example looking at differences in the effects of family management on the highly skewed outcome of drug use. Results show that violating the assumption of normal errors results in systematic bias in both latent class enumeration and parameter estimates. Additional classes which reflect violations of distributional assumptions are found. Under some conditions it is possible to come to conclusions that are consistent with the effects in the population, but when errors are skewed in both classes the results typically no longer reflect even the pattern of effects in the population. The polytomous regression model performs better under all scenarios examined and comes to reasonable results with the highly skewed outcome in the applied example. We recommend that careful evaluation of model sensitivity to distributional assumptions be the norm when conducting regression mixture models.

Developing measures on the perceptions of the built environment for physical activity: a confirmatory analysis.

BACKGROUND: Minimal validity evidence exists for scales assessing the built environment for physical activity. The purpose of this study was to assess the test-retest reliability and invariance of a three-factor model (Neighborhood Characteristics, Safety/Crime, and Access to Physical Activity Facilities) across gender, race, geographic location, and level of physical activity. METHODS: To assess measurement invariance, a random sample of 1,534 adults living in North Carolina or Mississippi completed a computer assisted telephone interview that included items examining perceptions of the neighborhood for physical activity. Construct level test-retest reliability data were collected from a purposeful sample of 106 participants who were administered the questionnaire twice, approximately two weeks apart. Fit indices, Cronbach's alpha, Mokken H and Spearman correlation coefficients (SCC) were used to evaluate configural and co/variance invarianc,e and intraclass correlation coefficients (ICC) were used to assess reliability. RESULTS: Construct test-retest reliability was strong (ICC 0.90 to 0.93). SCC for Neighborhood Characteristics and Crime/Safety were weak with Access (0.21 and 0.25), but strong between Crime/Safety and Neighborhood Characteristics (0.62). Acceptable fit and evidence of measurement invariance was found for gender, race (African American and White), geographic location, and level of physical activity. Fit indices consistently approached or were greater than 0.90 for goodness of fit index, normed fit index, and comparative fit index which is evidence of configural invariance. There was weak support of variance and covariance invariance for all groups that was indicative of factorial validity. CONCLUSIONS: Support of the validity and reliability of the three-factor model across groups expands the possibilities for analysis to include latent variable modeling, and suggests these built environment constructs may be used in other settings and populations.

Validity of a scale assessing the built environment for physical activity.

OBJECTIVES: To examine the construct validity and reliability of existing self-report items measuring perceptions of the built environment for physical activity. METHODS: Exploratory factor analysis, internal consistency, and nonparametric scaling were assessed using data from a cross-sectional sample of adults (n = 479). RESULTS: A new 5-factor model was identified. Internal consistency and scaling reliabilities were moderate to strong, providing support for the new structure. CONCLUSIONS: Further studies are needed to confirm these results. Subscales should be treated as indices and use sum scores in analysis to detect subtle variations and provide more precise power calculations.

Assessing differential effects: applying regression mixture models to identify variations in the influence of family resources on academic achievement.

Developmental scientists frequently seek to understand effects of environmental contexts on development. Traditional analytic strategies assume similar environmental effects for all children, sometimes exploring possible moderating influences or exceptions (e.g., outliers) as a secondary step. These strategies are poorly matched to ecological models of human development that posit complex individual by environment interactions. An alternative conceptual framework is proposed that tests the hypothesis that the environment has differential (nonuniform) effects on children. A demonstration of the utility of this framework is provided by examining the effects of family resources on children's academic outcomes in a multisite study (N = 6,305). Three distinctive groups of children were identified, including 1 group particularly resilient to influence of low levels of family resources. Predictors of group differences including parenting and child demographics are tested, the replicability of the results are examined, and findings are contrasted with those obtained with traditional regression interaction effects. This approach is proposed as a partial solution to advance theories of the environment, social ecological systems research, and behavioral genetics to create well-tailored environments for children.