A brief bout of moderate intensity physical activity improves preadolescent children's behavioral inhibition but does not change their energy intake.

Children in rural communities consume more energy-dense foods relative to their urban peers. Identifying effective interventions for improving energy intake patterns are needed to address these geographic disparities. The primary aim of this study was to harness the benefits of physical activity on children's executive functioning to see if these improvements lead to acute changes in eating behaviors. In a randomized crossover design, 91 preadolescent (8-10y; M age = 9.48 ± 0.85; 50.5% female; 85.7% White, 9.9% Multiracial, 9.9% Hispanic) children (86% rural) completed a 20-minute physical activity condition (moderate intensity walking) and time-matched sedentary condition (reading and/or coloring) ~ 14 days apart. Immediately following each condition, participants completed a behavioral inhibition task and then eating behaviors (total energy intake, relative energy intake, snack intake) were measured during a multi-array buffet test meal. After adjusting for period and order effects, body fat (measured via DXA), and depressive symptoms, participants experienced significant small improvements in their behavioral inhibition following the physical activity versus sedentary condition (p = 0.04, Hedge's g = 0.198). Eating behaviors did not vary by condition, nor did improvements in behavioral inhibition function as a mediator (ps > 0.09). Thus, in preadolescent children, small improvements in behavioral inhibition from physical activity do not produce acute improvements in energy intake. Additional research is needed to clarify whether the duration and/or intensity of physical activity sessions would produce different results in this age group, and whether intervention approaches and corresponding mechanisms of change vary by individual factors, like age and degree of food cue responsivity.

An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research.

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.

A Gradual Effects Model for Single-Case Designs.

Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data.

Four Methods for Analyzing Partial Interval Recording Data, with Application to Single-Case Research.

Partial interval recording (PIR) is a procedure for collecting measurements during direct observation of behavior. It is used in several areas of educational and psychological research, particularly in connection with single-case research. Measurements collected using partial interval recording suffer from construct invalidity because they are not readily interpretable in terms of the underlying characteristics of the behavior. Using an alternating renewal process model for the behavior under observation, we demonstrate that ignoring the construct invalidity of PIR data can produce misleading inferences, such as inferring that an intervention reduces the prevalence of an undesirable behavior when in fact it has the opposite effect. We then propose four different methods for analyzing PIR summary measurements, each of which can be used to draw inferences about interpretable behavioral parameters. We demonstrate the methods by applying them to data from two single-case studies of problem behavior.