In meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to... Show moreIn meta-analysis, heterogeneity often exists between studies. Knowledge about study features (i.e., moderators) that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other's effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-packagemetacart, which provides user-friendly functions to conduct meta-CART analyses in R. This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples. Show less
Self-efficacy is an important determinant of health behaviour. Digital interventions are a potentially acceptable and cost-effective way of delivering programmes of health behaviour change at scale... Show moreSelf-efficacy is an important determinant of health behaviour. Digital interventions are a potentially acceptable and cost-effective way of delivering programmes of health behaviour change at scale. Whether behaviour change interventions work to increase self-efficacy in this context is unknown. This systematic review and meta-analysis sought to identify whether automated digital interventions are associated with positive changes in self-efficacy amongst non-clinical populations for five major health behaviours, and which BCTs are associated with that change. A systematic literature search identified 20 studies (n = 5624) that assessed changes in self-efficacy and were included in a random-effects meta-analysis. Interventions targeted: healthy eating (k = 4), physical activity (k = 9), sexual behaviour (k = 3) and smoking (k = 4). No interventions targeting alcohol use were identified. Overall, interventions had a small, positive effect on self-efficacy . The effect of interventions on self-efficacy did not differ as a function of health behaviour type (Q-between = 7.3704, p = .061, df = 3). Inclusion of the BCT 'information about social and environmental consequences' had a small, negative effect on self-efficacy . Whilst this review indicates that digital interventions can be used to change self-efficacy, which techniques work best in this context is not clear. Show less
PurposeHealthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention... Show morePurposeHealthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults.MethodData from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions.ResultsFor healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with self-monitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking.ConclusionThis study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities. Show less
