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Objective: To assess levels of self-efficacy for health-related behaviour change and its correlates in patients with TIA or ischemic stroke., Methods: In this prospective cohort study, 92 patients with TIA or ischemic stroke completed questionnaires on self-efficacy for health-related behaviour change and fear, social support and depressive symptoms. Relations between fear, social support, depressive symptoms, cognitive impairment, vascular risk factors and history and demographic characteristics and low-self-efficacy were studied with univariable and multivariable logistic regression., Results: Median total self-efficacy score at baseline was 4 (IQR 4-5). Older age (OR 1.05, 95% CI 1.01-1.09), depressive symptoms (OR 1.09, 95% CI 1.03-1.16), presence of vascular history (OR 2.42, 95% CI 0.97-6.03), higher BMI (OR 1.15, 95% CI 1.01-1.30), fear (OR 1.06, 95% CI 1.01-1.12) and low physical activity (OR 1.49, 95% CI 1.01-2.21) were significantly associated with low self-efficacy., Conclusion: Patients with recent TIA or ischemic stroke report high self-efficacy scores for health-related behaviour change. Age, vascular history, more depressive symptoms, higher BMI, less physical activity and fear were correlates of low self-efficacy levels., Practice Implications: These correlates should be taken into account in the development of interventions to support patients in health behaviour change after TIA or ischemic stroke.

Background: Many online interventions designed to promote health behaviors combine multiple behavior change techniques (BCTs), adopt different modes of delivery (MoD) (eg, text messages), and range in how usable they are. Research is therefore needed to examine the impact of these features on the effectiveness of online interventions., Objective: This study applies Classification and Regression Trees (CART) analysis to meta-analytic data, in order to identify synergistic effects of BCTs, MoDs, and usability factors., Methods: We analyzed data from Webb et al. This review included effect sizes from 52 online interventions targeting a variety of health behaviors and coded the use of 40 BCTs and 11 MoDs. Our research also developed a taxonomy for coding the usability of interventions. Meta-CART analyses were performed using the BCTs and MoDs as predictors and using treatment success (ie, effect size) as the outcome., Results: Factors related to usability of the interventions influenced their efficacy. Specifically, subgroup analyses indicated that more efficient interventions (interventions that take little time to understand and use) are more likely to be effective than less efficient interventions. Meta-CART identified one synergistic effect: Interventions that included barrier identification/ problem solving and provided rewards for behavior change reported an average effect size that was smaller (ḡ=0.23, 95% CI 0.08-0.44) than interventions that used other combinations of techniques (ḡ=0.43, 95% CI 0.27-0.59). No synergistic effects were found for MoDs or for MoDs combined with BCTs., Conclusions: Interventions that take little time to understand and use were more effective than those that require more time. Few specific combinations of BCTs that contribute to the effectiveness of online interventions were found. Furthermore, no synergistic effects between BCTs and MoDs were found, even though MoDs had strong effects when analyzed univariately in the original study.

Objective: Many health-promoting interventions combine multiple behavior change techniques (BCTs) to maximize effectiveness. Although, in theory, BCTs can amplify each other, the available meta-analyses have not been able to identify specific combinations of techniques that provide synergistic effects. This study overcomes some of the shortcomings in the current methodology by applying classification and regression trees (CART) to meta-analytic data in a special way, referred to as Meta-CART. The aim was to identify particular combinations of BCTs that explain intervention success., Method: A reanalysis of data from Michie, Abraham, Whittington, McAteer, and Gupta (2009) was performed. These data included effect sizes from 122 interventions targeted at physical activity and healthy eating, and the coding of the interventions into 26 BCTs. A CART analysis was performed using the BCTs as predictors and treatment success (i.e., effect size) as outcome. A subgroup meta-analysis using a mixed effects model was performed to compare the treatment effect in the subgroups found by CART., Results: Meta-CART identified the following most effective combinations: Provide information about behavior-health link with Prompt intention formation (mean effect size ḡ = 0.46), and Provide information about behavior-health link with Provide information on consequences and Use of follow-up prompts (ḡ = 0.44). Least effective interventions were those using Provide feedback on performance without using Provide instruction (ḡ = 0.05)., Conclusions: Specific combinations of BCTs increase the likelihood of achieving change in health behavior, whereas other combinations decrease this likelihood. Meta-CART successfully identified these combinations and thus provides a viable methodology in the context of meta-analysis.

Background: Prevention of weight gain has been suggested as an important strategy in the prevention of obesity and people who are overweight are a specifically important group to target. Currently there is a lack of weight gain prevention interventions that can reach large numbers of people. Therefore, we developed an Internet-delivered, computer-tailored weight management intervention for overweight adults. The focus of the intervention was on making small (100 kcal per day), but sustained changes in dietary intake (DI) or physical activity (PA) behaviors in order to maintain current weight or achieve modest weight loss. Self-regulation theory was used as the basis of the intervention. Objective: This study aims to evaluate the efficacy of the computer-tailored intervention in weight-related anthropometric measures (Body Mass Index, skin folds and waist circumference) and energy balance-related behaviors (physical activity; intake of fat, snacks and sweetened drinks) in a randomized controlled trial. Methods: The tailored intervention (TI) was compared to a generic information website (GI). Participants were 539 overweight adults (mean age 47.8 years, mean Body Mass Index (BMI) 28.04, 30.9% male, 10.7% low educated) who where recruited among the general population and among employees from large companies by means of advertisements and flyers. Anthropometric measurements were measured by trained research assistants at baseline and 6-months post-intervention. DI and PA behaviors were assessed at baseline, 1-month and 6-month post-intervention, using self-reported questionnaires. Results: Repeated measurement analyses showed that BMI remained stable over time and that there were no statistically significant differences between the study groups (BMI: TI=28.09, GI=27.61, P=.09). Similar results were found for waist circumference and skin fold thickness. Amount of physical activity increased and intake of fat, snacks and sweetened drinks decreased during the course of the study, but there were no differences between the study groups (eg, fat intake: TI=15.4, GI=15.9, P=.74). The first module of the tailored intervention was visited by almost all participants, but only 15% completed all four modules of the tailored intervention, while 46% completed the three modules of the general information intervention. The tailored intervention was considered more personally relevant (TI=3.20, GI=2.83, P=.001), containing more new information (TI=3.11, GI=2.73, P=.003) and having longer texts (TI=3.20, GI=3.07, P=.01), while there were no group differences on other process measures such as attractiveness and comprehensibility of the information (eg, attractive design: TI=3.22, GI=3.16, P=.58). Conclusions: The online, computer-tailored weight management intervention resulted in changes in the desired direction, such as stabilization of weight and improvements in dietary intake, but the intervention was not more effective in preventing weight gain or modifying dietary and physical activity behaviors than generic information. A possible reason for the absence of intervention effects is sub-optimal use of the intervention and the self-regulation components. Further research is therefore needed to gain more insight into how the intervention and exposure to its contents can be improved. Trial Registration: NTR1862; http://apps.who.int/trialsearch/trial.aspx?trialid=NTR1862 [ABSTRACT FROM AUTHOR]

Objective: To assess determinants of intention to change health-related behavior and actual change in patients with TIA or ischemic stroke.Methods: In this prospective cohort study, 100 patients with TIA or minor ischemic stroke completed questionnaires on behavioral intention and sociocognitive factors including perception of severity, susceptibility, fear, response-efficacy and self-efficacy at baseline. Questionnaires on physical activity, diet and smoking were completed at baseline and at 3 months. Associations between sociocognitive factors and behavioral intention and actual change were studied with multivariable linear and logistic regression.Results: Self-efficacy, response efficacy, and fear were independently associated with behavioral intention, with self-efficacy as the strongest determinant of intention to increase physical activity (aBeta 0.40; 95% CI 0.12-0.71), adapt a healthy diet (aBeta 0.49; 95% CI 0.23-0.75), and quit smoking (aBeta 0.51; 95% CI 0.13-0.88). Intention to change tended to be associated with actual health-related behavior change.Conclusion: Self-efficacy, fear, and response-efficacy were determinants of intention to change health-related behavior after TIA or ischemic stroke.Practice Implications: These determinants of intention to change health-related behavior after TIA or ischemic stroke should be taken into account in the development of future interventions promoting health-related behavior change in these group of patients. [ABSTRACT FROM AUTHOR]