Background and aims Behavioural smoking cessation trials have used comparators that vary considerably between trials. Although some previous meta‐analyses made attempts to account for variability in comparators, these relied on subsets of trials and incomplete data on comparators. This study aimed to estimate the relative effectiveness of (individual) smoking cessation interventions while accounting for variability in comparators using comprehensive data on experimental and comparator interventions. Methods A systematic review and meta‐regression was conducted including 172 randomised controlled trials with at least 6 months follow‐up and biochemically verified smoking cessation. Authors were contacted to obtain unpublished information. This information was coded in terms of active content and attributes of the study population and methods. Meta‐regression was used to create a model predicting smoking cessation outcomes. This model was used to re‐estimate intervention effects, as if all interventions have been evaluated against the same comparators. Outcome measures included log odds of smoking cessation for the meta‐regression models and smoking cessation differences and ratios to compare relative effectiveness. Results The meta‐regression model predicted smoking cessation rates well (pseudo R2 = 0.44). Standardising the comparator had substantial impact on conclusions regarding the (relative) effectiveness of trials and types of intervention. Compared with a ‘no support comparator’, self‐help was 1.33 times (95% CI = 1.16–1.49), brief physician advice 1.61 times (95% CI = 1.31–1.90), nurse individual counselling 1.76 times (95% CI = 1.62–1.90), psychologist individual counselling 2.04 times (95% CI = 1.95–2.15) and group psychologist interventions 2.06 times (95% CI = 1.92–2.20) more effective. Notably, more elaborate experimental interventions (e.g. psychologist counselling) were typically compared with more elaborate comparators, masking their effectiveness. Conclusions Comparator variability and underreporting of comparators obscures the interpretation, comparison and generalisability of behavioural smoking cessation trials. Comparator variability should, therefore, be taken into account when interpreting and synthesising evidence from trials. Otherwise, policymakers, practitioners and researchers may draw incorrect conclusions about the (cost) effectiveness of smoking cessation interventions and their constituent components. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Motivation is an integral factor in substance use treatment and long-term recovery. However, it is unclear what role intrinsic and extrinsic motivation play across different treatment modalities. A meta-analysis (N = 84) was performed to estimate the pooled effect size of Motivational Interviewing (MI; primarily targeting intrinsic motivation) and contingency management (CM; primarily targeting extrinsic motivation) at different follow-up periods. Collapsed across all substance types, CM had a significant effect at 3-month follow-up, only. In contrast, MI had a significant effect at 6-month follow-up, only. CM had small and medium effects on multiple substances at 3-month follow-up (i.e., tobacco, marijuana, stimulants, polysubstances), but not at 6-month follow-up. MI had 1 significant medium effect at 3-month follow-up (i.e., marijuana), but several significant small effects at 6-month follow-up (i.e., alcohol, tobacco, polysubstances). This meta-analysis suggests that both CM and MI promote reductions in a range of substances, even several months after the intervention concludes. Further, these results provide some evidence that extrinsically focused CM may produce medium follow-up effects in the short run, but intrinsically focused MI may produce small but durable follow-up effects. However, this interpretation is complicated by the differences between the MI and CM studies that preclude statistical tests comparing effect sizes, and few studies assessed motivation itself. Future researchers should investigate how motivational dynamics impact lasting outcomes in substance use treatment. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
Background and aims Behavioural smoking cessation trials have used comparators that vary considerably between trials. Although some previous meta‐analyses made attempts to account for variability in comparators, these relied on subsets of trials and incomplete data on comparators. This study aimed to estimate the relative effectiveness of (individual) smoking cessation interventions while accounting for variability in comparators using comprehensive data on experimental and comparator interventions. Methods A systematic review and meta‐regression was conducted including 172 randomised controlled trials with at least 6 months follow‐up and biochemically verified smoking cessation. Authors were contacted to obtain unpublished information. This information was coded in terms of active content and attributes of the study population and methods. Meta‐regression was used to create a model predicting smoking cessation outcomes. This model was used to re‐estimate intervention effects, as if all interventions have been evaluated against the same comparators. Outcome measures included log odds of smoking cessation for the meta‐regression models and smoking cessation differences and ratios to compare relative effectiveness. Results The meta‐regression model predicted smoking cessation rates well (pseudo R2 = 0.44). Standardising the comparator had substantial impact on conclusions regarding the (relative) effectiveness of trials and types of intervention. Compared with a ‘no support comparator’, self‐help was 1.33 times (95% CI = 1.16–1.49), brief physician advice 1.61 times (95% CI = 1.31–1.90), nurse individual counselling 1.76 times (95% CI = 1.62–1.90), psychologist individual counselling 2.04 times (95% CI = 1.95–2.15) and group psychologist interventions 2.06 times (95% CI = 1.92–2.20) more effective. Notably, more elaborate experimental interventions (e.g. psychologist counselling) were typically compared with more elaborate comparators, masking their effectiveness. Conclusions Comparator variability and underreporting of comparators obscures the interpretation, comparison and generalisability of behavioural smoking cessation trials. Comparator variability should, therefore, be taken into account when interpreting and synthesising evidence from trials. Otherwise, policymakers, practitioners and researchers may draw incorrect conclusions about the (cost) effectiveness of smoking cessation interventions and their constituent components. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Systematic Review Question»Systematic review of interventions
