Julie M. Rutledge

The science of deimplementation—reducing harmful or ineffective practices—has focused almost exclusively on clinical prescribing, with no studi

This pilot evaluated strategies to decrease detrimental feeding practices in early care and education, which are hypothesized to compete with evidence-based feeding and obesity prevention practices. This study made two key comparisons: (1) a between-site comparison of sites receiving (a) no implementation or de-implementation strategies (i.e., Basic Support; B), (b) implementation strategies only (i.e., Enhanced Support; E), and (c) implementation and de-implementation strategies (i.e., De-implementation + Enhanced Support; D + E) and (2) a within-site pre-post comparison among sites with D + E. At nutrition lessons, the D + E group had more Positive Comments (Hedege’s g = 0.60) and higher Role Model fidelity (Hedege’s g = 1.34) compared to the E group. At meals, assistant teachers in the D + E group had higher Positive Comments than in the B group (g = 0.72). For within-group comparisons, the D + E group decreased Negative Comments (t(19) = 2.842, p = 0.01), increased Positive Comments (t(20) = 2.314, p = 0.031), and improved use of the program mascot at nutrition lessons (t(21) = 3.899, p = 0.001). At meals, lead teachers’ Negative Comments decreased (t(22) = 2.73, p = 0.01). Qualitative data identified strengths and opportunities for iteration. Despite a COVID interruption, mid-point comparisons and qualitative feedback suggest promise of the de-implementation strategy package.

Background: The current study sought to (1) describe a new classification approach for types of implementer behavior and (2) explore the implementer behavior change in response to tailored implementation facilitation based on the classifications. Methods: A small-scale, cluster-randomized hybrid type III implementation trial was conducted in 38 early care and education classrooms that were part of the Together, We Inspire Smart Eating (WISE) program. WISE focuses on 4 evidence-based practices (EBPs), which are implemented by teachers to promote nutrition. External facilitators (N = 3) used a modified Rapid Assessment Procedure Informed Clinical Ethnography (RAPICE) to complete immersion (i.e., observations) and thematic content analyses of interviews to identify the characteristics of teachers’ behavior at varying levels of implementation fidelity. Three key factors—attitudes toward the innovation, fidelity/adaptations, and influence—were identified that the research team used to classify teachers’ implementation behavior. This process resulted in a novel classification approach. To assess the reliability of applying the classification approach, we assessed the percent agreement between the facilitators. Based on the teachers’ classification, the research team developed a tailored facilitation response. To explore behavior change related to the tailored facilitation, change in fidelity and classification across the school year were evaluated. Results: The classifications include (1) enthusiastic adopters (positive attitude, meeting fidelity targets, active influence), (2) over-adapting adopters (positive attitude, not meeting fidelity targets, active influence), (3) passive non-adopters (negative attitude, not meeting fidelity targets, passive influence), and (4) active non-adopters (negative attitudes, not meeting fidelity targets, active influence). The average percent agreement among the three facilitators for classification was 75%. Qualitative data support distinct patterns of perceptions across the classifications. A positive shift in classification was observed for 67% of cases between the mid-point and final classification. Finally, we generated an expanded classification approach to consider additional combinations of the three factors beyond those observed in this study. Conclusions: Data from this study support the ability to apply the classification approach with moderate to high reliability and to use the approach to tailor facilitation toward improved implementation. Findings suggest the potential of our approach for wider application and potential to improve tailoring of implementation strategies such as facilitation.

Background: Despite the potential for Early Care and Education (ECE) settings to promote healthy habits, a gap exists between current practices and evidence-based practices (EBPs) for obesity prevention in childhood.Methods: We will use an enhanced non-responder trial design to determine the effectiveness and incremental cost-effectiveness of an adaptive implementation strategy for Together, We Inspire Smart Eating (WISE), while examining moderators and mediators of the strategy effect. WISE is a curriculum that aims to increase children’s intake of carotenoid-rich fruits and vegetables through four evidence-based practices in the early care and education setting. In this trial, we will randomize sites that do not respond to low-intensity strategies to either (a) continue receiving low-intensity strategies or (b) receive high-intensity strategies. This design will determine the effect of an adaptive implementation strategy that adds high-intensity versus one that continues with low-intensity among non-responder sites. We will also apply explanatory, sequential mixed methods to provide a nuanced understanding of implementation mechanisms, contextual factors, and characteristics of sites that respond to differing intensities of implementation strategies. Finally, we will conduct a cost effectiveness analysis to estimate the incremental effect of augmenting implementation with high-intensity strategies compared to continuing low-intensity strategies on costs, fidelity, and child health outcomes.Discussion: We expect our study to contribute to an evidence base for structuring implementation support in real-world ECE contexts, ultimately providing a guide for applying the adaptive implementation strategy in ECE for WISE scale-up. Our work will also provide data to guide implementation decisions of other interventions in ECE. Finally, we will provide the first estimate of relative value for different implementation strategies in this setting.Trial registration: NCT05050539; 9/20/21.

This cluster randomized controlled trial aimed at overweight and obese children compared three treatments. Two psychoeducation interventions for parents and children were conducted: Family Lifestyle (FL) focused on food and physical activity; Family Dynamics (FD) added parenting and healthy emotion management. A third Peer Group (PG) intervention taught social acceptance to children. Crossing interventions yielded four conditions: FL, FL + PG, FL + FD, and FL + FD + PG—compared with the control. Longitudinal BMI data were collected to determine if family- and peer-based psychosocial components enhanced the Family Lifestyle approach. Participants were 1st graders with BMI%ile >75 (n = 538: 278 boys, 260 girls). Schools were randomly assigned to condition after stratifying for community size and percent American Indian. Anthropometric data were collected pre- and post-intervention in 1st grade and annually through 4th grade. Using a two-level random intercept growth model, intervention status predicted differences in growth in BMI or BMI-M% over three years. Children with obesity who received the FL + FD + PG intervention had lower BMI gains compared to controls for both raw BMI (B = −0.05) and BMI-M% (B = −2.36). Interventions to simultaneously improve parent, child, and peer-group behaviors related to physical and socioemotional health offer promise for long-term positive impact on child obesity.

Background: Early childhood educators (ECEs) often use detrimental feeding practices and are slow to implement positive feeding practices. Nevertheless, few studies have aimed to understand and change ECEs’ feeding practices. This gap needs to be addressed because implementation (i.e., adding new, evidence-based practices) and de-implementation (i.e., stopping low-value or harmful practices) are distinct processes that require unique strategies.Methods: We will develop a de-implementation strategy for detrimental feeding practices using evidence-based quality improvement (EBQI) sessions to engage stakeholders and draw on the Niven process model for de-implementation. Then, we will investigate the effects of the de-implementation strategy in a proof-of-principle study. The de-implementation strategy will be evaluated in 2 partnering childcare agencies using a pre-post, within-site design. For our primary outcome, we will interview educators throughout the school year to assess the feasibility and acceptability of the intervention and survey them with standard measures for assessing feasibility and acceptability. For secondary outcomes, we will investigate its effects on the use of detrimental and evidence-based feeding practices by teachers and impacts on child BMI and diet.Discussion: The current study will establish the feasibility and acceptability of our de-implementation approach and will provide preliminary data toward 3 predicted secondary outcomes: (1) decreased detrimental feeding practices by ECEs, (2) increased adoption of and fidelity to nutrition promotion practices, and (3) improved child dietary outcomes. These results are expected to contribute to the uptake and sustainability of mealtime interventions to improve the diets of young children. Results will also apply to the field of implementation science by informing processes for developing de-implementation approaches in a community setting.

SYNOPSIS Objective: Early childhood weight predicts later weight, so there is need for longitudinal research to identify key factors in the development of childhood obesity, especially among vulnerable populations. Design: This study examines the links between parenting styles and child obesity across three waves (Mages = 6.90, 7.34, and 8.28 years) and the moderating factor of mother ethnicity in a sample of 356 children and their mothers (78.5% European American; 14.8% American Indian). Results: Growth curve SEM showed that high-permissive parenting predicted higher initial weight than the average permissive reference group and low permissive parenting group. Children of high authoritarian mothers began at a non-significantly different weight relative to the average authoritarian and low authoritarian reference groups, but later increased their weight, primarily from wave 1 to 2. An interaction between mother ethnicity and authoritative parenting was found for the autonomy granting subscale. Children of American Indian mothers with low levels of autonomy granting had the highest initial levels of weight compared to children of parents in the average and high autonomy granting reference groups and by wave 3 had an average increase close to the cutoff for being overweight. Conclusions: Parenting style appears to play a role in predicting child weight longitudinally, and this relation is moderated by the ethnicity of the child’s mother. This study is also the first to show measurement invariance in the Parenting Styles and Dimensions Questionnaire among American Indian mothers.

Researchers face many decisions in developing a measurement tool and protocol for monitoring fidelity to complex interventions. The current study uses data evaluating a nutrition education intervention, Together, We Inspire Smart Eating (WISE), in a preschool setting to explore issues of source, timing, and frequency of fidelity monitoring. The overall study from which these data are drawn was a pre/post design with an implementation-focused process evaluation. Between 2013 and 2016, researchers monitored fidelity to evidence-based components of the WISE intervention in 49 classrooms in two Southern states. Data collectors obtained direct assessment of fidelity on a monthly basis in study classrooms. Research staff requested that educators provide indirect assessment on a weekly basis. We used mean comparisons (t-tests), correlations (Pearson’s r), and scatterplots to compare the direct and indirect assessments. No mean comparisons were statistically different. Correlations of direct and indirect assessments of the same component for the same month ranged between − 0.51 (p = 0.01) and 0.54 (p = 0.001). Scatterplots illustrate that negative correlations can be driven by individuals who are over reporting (i.e., self-report bias) and that near zero correlations approximate the ideal situation (i.e., both raters identify high fidelity). Our findings illustrate that, on average, observed and self-reports may seem consistent despite weak correlations and individual cases of extreme over reporting by those implementing the intervention. The nature of the component to which fidelity is being monitored as well as the timing within the context of the intervention are important factors to consider when selecting the type of assessment and frequency of fidelity monitoring. NCT03075085 Registered 20 February 2017. Trial registration corresponds to the funding that supported the writing of this manuscript, not the data collection. The original study was not a trial and was collected without registration. However, the data reported here provided foundational preliminary data for the trial.