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1.
Addiction ; 118(10): 1835-1850, 2023 10.
Article in English | MEDLINE | ID: mdl-37132077

ABSTRACT

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 Authors were contacted to obtain unpublished information.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.


Subject(s)
Smoking Cessation , Humans , Smoking Cessation/methods , Behavior Therapy/methods , Counseling , Cost-Effectiveness Analysis
2.
Psychol Health ; : 1-17, 2022 Jul 23.
Article in English | MEDLINE | ID: mdl-35876093

ABSTRACT

OBJECTIVE: Reporting of the content and delivery characteristics of comparator interventions in published articles is often incomplete. This study examines the feasibility and validity of two methods for collecting additional information on comparator interventions from trial authors. METHODS & MEASURES: In a systematic review of smoking cessation trials (IC-Smoke), all trial authors were asked to send unpublished comparator intervention materials and complete a specially-developed comparator intervention checklist. All published and additionally obtained information from authors were coded for behaviour change techniques (BCTs) and other characteristics (type of comparator, provider, provider training, delivery mode and treatment duration). To assess representativeness, we assessed the amount of additional information obtained from trial authors compared with the amount that was published. We examined known-group and convergent validity of comparator intervention data when using only published or also unpublished information. RESULTS: Additional information were obtained from 91/136 (67%) of trial authors. Representativeness, known-group and convergent validity improved substantially based on the data collected by means of the comparator intervention checklist, but not by requesting authors to send any existing comparator materials. CONCLUSIONS: Requesting authors for unpublished comparator intervention data, using specially-developed checklists and unpublished materials, substantially improves the quality of data available for systematic reviews.

3.
Health Psychol Rev ; 15(2): 195-213, 2021 06.
Article in English | MEDLINE | ID: mdl-31906781

ABSTRACT

Despite its importance, underreporting of the active content of experimental and comparator interventions in published literature has not been previously examined for behavioural trials. We assessed completeness and variability in reporting in 142 randomised controlled trials of behavioural interventions for smoking cessation published between 1/1996 and 11/2015. Two coders reliably identified the potential active components of experimental and comparator interventions (activities targeting behaviours key to smoking cessation and qualifying as behaviour change techniques, BCTs) in published, and in unpublished materials obtained from study authors directly. Unpublished materials were obtained for 129/204 (63%) experimental and 93/142 (65%) comparator groups. For those, only 35% (1200/3403) of experimental and 26% (491/1891) of comparator BCTs could be identified in published materials. Reporting quality (#published BCTs/#total BCTs) varied considerably between trials and between groups within trials. Experimental (vs. comparator) interventions were better reported (B(SE) = 0.34 (0.11), p < .001). Unpublished materials were more often obtained for recent studies (B(SE) = 0.093 (0.03), p = .003) published in behavioural (vs. medical) journals (B(SE) = 1.03 (0.41), p = .012). This high variability in underreporting of active content compromises reader's ability to interpret the effects of individual trials, compare and explain intervention effects in evidence syntheses, and estimate the additional benefit of an experimental intervention in other settings.


Subject(s)
Smoking Cessation , Behavior Therapy , Delivery of Health Care , Humans , Randomized Controlled Trials as Topic
4.
Ann Behav Med ; 53(6): 583-591, 2019 05 03.
Article in English | MEDLINE | ID: mdl-30239563

ABSTRACT

BACKGROUND: The behavior change technique (BCT) taxonomy v1 is often used in systematic reviews for identifying active components of interventions. Its utility could be enhanced by linking BCTs to specific target behaviors and qualifying BCT delivery style. PURPOSE: To determine whether behavioral targets and delivery styles of BCTs can be coded reliably and to determine the utility of coding these characteristics. METHODS: As part of a large systematic review of 142 smoking cessation trials, two researchers independently coded publicly and privately held intervention and comparator group materials, specifying the behavioral target (quitting, abstinence, medication adherence, or treatment engagement) and delivery style (tailored vs. not tailored; active participation vs. passive receipt) of each BCT. RESULTS: Researchers coded 3,843 BCTs, which were reliably attributed to behavioral targets (AC1 = 0.92, PABAK = 0.91). Tailoring (AC1 = 0.80, PABAK = 0.74) and participation (AC1 = 0.71, PABAK = 0.64) were also coded reliably. There was considerable variability between groups in quitting and abstinence BCTs (ranges: 0-41; 0-18) and in tailoring and participation (ranges: 0-20; 0-32), but less variability for medication adherence and treatment engagement (ranges: 0-6; 0-7). CONCLUSIONS: Behavioral targets and delivery styles of BCTs can be reliably identified and occur with sufficient frequency in smoking cessation trials for inclusion in quantitative syntheses (e.g., meta-regression analyses). Systematic reviewers could consider adopting these methods to evaluate the impact of intervention components targeting different behaviors, as well as the benefits of different BCT delivery styles.


Subject(s)
Behavior Therapy , Outcome and Process Assessment, Health Care , Randomized Controlled Trials as Topic , Smoking Cessation , Systematic Reviews as Topic , Humans
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