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 Authors were contacted to obtain unpublished information.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)
BACKGROUND: Group therapy offers individuals the opportunity to learn behavioural techniques for smoking cessation, and to provide each other with mutual support.
OBJECTIVES: To determine the effect of group-delivered behavioural interventions in achieving long-term smoking cessation.
SEARCH METHODS: We searched the Cochrane Tobacco Addiction Group Specialized Register, using the terms 'behavior therapy', 'cognitive therapy', 'psychotherapy' or 'group therapy', in May 2016.
SELECTION CRITERIA: Randomized trials that compared group therapy with self-help, individual counselling, another intervention or no intervention (including usual care or a waiting-list control). We also considered trials that compared more than one group programme. We included those trials with a minimum of two group meetings, and follow-up of smoking status at least six months after the start of the programme. We excluded trials in which group therapy was provided to both active therapy and placebo arms of trials of pharmacotherapies, unless they had a factorial design.
DATA COLLECTION AND ANALYSIS: Two review authors extracted data in duplicate on the participants, the interventions provided to the groups and the controls, including programme length, intensity and main components, the outcome measures, method of randomization, and completeness of follow-up. The main outcome measure was abstinence from smoking after at least six months follow-up in participants smoking at baseline. We used the most rigorous definition of abstinence in each trial, and biochemically-validated rates where available. We analysed participants lost to follow-up as continuing smokers. We expressed effects as a risk ratio for cessation. Where possible, we performed meta-analysis using a fixed-effect (Mantel-Haenszel) model. We assessed the quality of evidence within each study and comparison, using the Cochrane 'Risk of bias' tool and GRADE criteria.
MAIN RESULTS: Sixty-six trials met our inclusion criteria for one or more of the comparisons in the review. Thirteen trials compared a group programme with a self-help programme; there was an increase in cessation with the use of a group programme (N = 4395, risk ratio (RR) 1.88, 95% confidence interval (CI) 1.52 to 2.33, I(2) = 0%). We judged the GRADE quality of evidence to be moderate, downgraded due to there being few studies at low risk of bias. Fourteen trials compared a group programme with brief support from a health care provider. There was a small increase in cessation (N = 7286, RR 1.22, 95% CI 1.03 to 1.43, I(2) = 59%). We judged the GRADE quality of evidence to be low, downgraded due to inconsistency in addition to risk of bias. There was also low quality evidence of benefit of a group programme compared to no-intervention controls, (9 trials, N = 1098, RR 2.60, 95% CI 1.80 to 3.76 I(2) = 55%). We did not detect evidence that group therapy was more effective than a similar intensity of individual counselling (6 trials, N = 980, RR 0.99, 95% CI 0.76 to 1.28, I(2) = 9%). Programmes which included components for increasing cognitive and behavioural skills were not shown to be more effective than same-length or shorter programmes without these components.
AUTHORS' CONCLUSIONS: Group therapy is better for helping people stop smoking than self-help, and other less intensive interventions. There is not enough evidence to evaluate whether groups are more effective, or cost-effective, than intensive individual counselling. There is not enough evidence to support the use of particular psychological components in a programme beyond the support and skills training normally included.
BACKGROUND: Individuals with current or past depression are often smokers who are more nicotine dependent, more likely to suffer from negative mood changes after nicotine withdrawal, and more likely to relapse to smoking after quitting than the general population, which contributes to their higher morbidity and mortality from smoking-related illnesses. It remains unclear what interventions can help them to quit smoking.
OBJECTIVES: To evaluate the effectiveness of smoking cessation interventions, with and without specific mood management components, in smokers with current or past depression.
SEARCH METHODS: In April 2013, we searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, other reviews, and asked experts for information on trials.
SELECTION CRITERIA: Criteria for including studies in this review were that they had to be randomised controlled trials (RCTs) comparing smoking cessation interventions in adult smokers with current or past depression. Depression was defined as major depression or depressive symptoms. We included studies where subgroups of participants with depression were identified, either pre-stated or post hoc. The outcome was abstinence from smoking after six months or longer follow-up. We preferred prolonged or continuous abstinence and biochemically validated abstinence where available.
DATA COLLECTION AND ANALYSIS: When possible, we estimated pooled risk ratios (RRs) with the Mantel-Haenszel method (fixed-effect model). We also performed subgroup analyses, by length of follow-up, depression measurement, depression group in study, antidepressant use, published or unpublished data, format of intervention, level of behavioural support, additional pharmacotherapy, type of antidepressant medication, and additional nicotine replacement therapy (NRT).
MAIN RESULTS: Forty-nine RCTs were included of which 33 trials investigated smoking cessation interventions with specific mood management components for depression. In smokers with current depression, meta-analysis showed a significant positive effect for adding psychosocial mood management to a standard smoking cessation intervention when compared with standard smoking cessation intervention alone (11 trials, N = 1844, RR 1.47, 95% CI 1.13 to 1.92). In smokers with past depression we found a similar effect (13 trials, N = 1496, RR 1.41, 95% CI 1.13 to 1.77). Meta-analysis resulted in a positive effect, although not significant, for adding bupropion compared with placebo in smokers with current depression (5 trials, N = 410, RR 1.37, 95% CI 0.83 to 2.27). There were not enough trial data to evaluate the effectiveness of fluoxetine and paroxetine for smokers with current depression. Bupropion (4 trials, N = 404, RR 2.04, 95% CI 1.31 to 3.18) might significantly increase long-term cessation among smokers with past depression when compared with placebo, but the evidence for bupropion is relatively weak due to the small number of studies and the post hoc subgroups for all the studies. There were not enough trial data to evaluate the effectiveness of fluoxetine, nortriptyline, paroxetine, selegiline, and sertraline in smokers with past depression.
Twenty-three of the 49 trials investigated smoking cessation interventions without specific components for depression. There was heterogeneity between the trials which compared psychosocial interventions with standard smoking cessation counselling for both smokers with current and past depression. Therefore, we did not estimate a pooled effect. One trial compared nicotine replacement therapy (NRT) versus placebo in smokers with current depression and found a positive, although not significant, effect (N = 196, RR 2.64, 95% CI 0.93 to 7.45). Meta-analysis also found a positive, although not significant, effect for NRT versus placebo in smokers with past depression (3 trials, N = 432, RR 1.17, 95% CI 0.85 to 1.60). Three trials compared other pharmacotherapy versus placebo and six trials compared other interventions in smokers with current or past depression. Due to heterogeneity between the interventions of the included trials we did not estimate pooled effects.
AUTHORS' CONCLUSIONS: Evidence suggests that adding a psychosocial mood management component to a standard smoking cessation intervention increases long-term cessation rates in smokers with both current and past depression when compared with the standard intervention alone. Pooled results from four trials suggest that use of bupropion may increase long-term cessation in smokers with past depression. There was no evidence found for the use of bupropion in smokers with current depression. There was not enough evidence to evaluate the effectiveness of the other antidepressants in smokers with current or past depression. There was also not enough evidence to evaluate the group of trials that investigated interventions without specific mood management components for depression, including NRT and psychosocial interventions.
AIMS: To update our prior meta-analysis that showed past major depression (MD+) to be unrelated to smoking cessation outcome. METHODS: Eligible trials included 14 from our original review and 28 identified through an updated systematic review (2000–2009). We coded for assessment of past MD, exclusion for recent MD episode (MDE; ≤6 months versus no exclusion), duration/modality of cognitive behavioral treatment (CBT; face-to-face versus self-help) and other factors. To minimize influence of experimental treatments that may selectively benefit MD+ smokers we analyzed placebo/lowest intensity control arms only. Study-specific ORs for the effect of past MD on short-term (≤3 months) and long-term (≥6 months) abstinence were estimated and combined using random effects. Two-way interaction models of past MD with study methodology and treatment factors were used to evaluate hypothesized moderators of the past MD-abstinence association. RESULTS: MD+ smokers had 17% lower odds of short-term abstinence (<i>n</i> = 35, OR = 0.83, 95% CI = 0.72–0.95, <i>P</i> = 0.009) and 19% lower odds of long-term abstinence (<i>n</i> = 38, OR = 0.81, 95% CI = 0.67–0.97, <i>P</i> = 0.023) than MD-smokers after excluding the sole study of varenicline because of its antidepressant properties. The association between past MD and abstinence was affected by methodological (recent MDE exclusion, type of MD assessment) and treatment (CBT modality) factors. Conclusions Past major depression has a modest adverse effect on abstinence during and after smoking cessation treatment. An increased focus on the identification of effective treatments or treatment adaptations that eliminate this disparity in smoking cessation for MD+ smokers is needed. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
BACKGROUND: Acupuncture, hypnotherapy, and aversive smoking are the most frequently studied alternative smoking cessation aids. These aids are often used as alternatives to pharmacotherapies for smoking cessation; however, their efficacy is unclear.
METHODS: We carried out a random effect meta-analysis of randomized controlled trials to determine the efficacy of alternative smoking cessation aids. We systematically searched the Cochrane Library, EMBASE, Medline, and PsycINFO databases through December 2010. We only included trials that reported cessation outcomes as point prevalence or continuous abstinence at 6 or 12 months.
RESULTS: Fourteen trials were identified; 6 investigated acupuncture (823 patients); 4 investigated hypnotherapy (273 patients); and 4 investigated aversive smoking (99 patients). The estimated mean treatment effects were acupuncture (odds ratio [OR], 3.53; 95% confidence interval [CI], 1.03-12.07), hypnotherapy (OR, 4.55; 95% CI, 0.98-21.01), and aversive smoking (OR, 4.26; 95% CI, 1.26-14.38).
CONCLUSION: Our results suggest that acupuncture and hypnotherapy may help smokers quit. Aversive smoking also may help smokers quit; however, there are no recent trials investigating this intervention. More evidence is needed to determine whether alternative interventions are as efficacious as pharmacotherapies.
BACKGROUND: Placebo interventions are often claimed to substantially improve patient-reported and observer-reported outcomes in many clinical conditions, but most reports on effects of placebos are based on studies that have not randomised patients to placebo or no treatment. Two previous versions of this review from 2001 and 2004 found that placebo interventions in general did not have clinically important effects, but that there were possible beneficial effects on patient-reported outcomes, especially pain. Since then several relevant trials have been published.
OBJECTIVES: Our primary aims were to assess the effect of placebo interventions in general across all clinical conditions, and to investigate the effects of placebo interventions on specific clinical conditions. Our secondary aims were to assess whether the effect of placebo treatments differed for patient-reported and observer-reported outcomes, and to explore other reasons for variations in effect.
SEARCH STRATEGY: We searched the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library Issue 4, 2007), MEDLINE (1966 to March 2008), EMBASE (1980 to March 2008), PsycINFO (1887 to March 2008) and Biological Abstracts (1986 to March 2008). We contacted experts on placebo research, and read references in the included trials.
SELECTION CRITERIA: We included randomised placebo trials with a no-treatment control group investigating any health problem.
DATA COLLECTION AND ANALYSIS: Two authors independently assessed trial quality and extracted data. We contacted study authors for additional information. Trials with binary data were summarised using relative risk (a value of less than 1 indicates a beneficial effect of placebo), and trials with continuous outcomes were summarised using standardised mean difference (a negative value indicates a beneficial effect of placebo).
MAIN RESULTS: Outcome data were available in 202 out of 234 included trials, investigating 60 clinical conditions. We regarded the risk of bias as low in only 16 trials (8%), five of which had binary outcomes.
In 44 studies with binary outcomes (6041 patients), there was moderate heterogeneity (P < 0.001; I2 45%) but no clear difference in effects between small and large trials (symmetrical funnel plot). The overall pooled effect of placebo was a relative risk of 0.93 (95% confidence interval (CI) 0.88 to 0.99). The pooled relative risk for patient-reported outcomes was 0.93 (95% CI 0.86 to 1.00) and for observer-reported outcomes 0.93 (95% CI 0.85 to 1.02). We found no statistically significant effect of placebo interventions in four clinical conditions that had been investigated in three trials or more: pain, nausea, smoking, and depression, but confidence intervals were wide. The effect on pain varied considerably, even among trials with low risk of bias.
In 158 trials with continuous outcomes (10,525 patients), there was moderate heterogeneity (P < 0.001; I2 42%), and considerable variation in effects between small and large trials (asymmetrical funnel plot). It is therefore a questionable procedure to pool all the trials, and we did so mainly as a basis for exploring causes for heterogeneity. We found an overall effect of placebo treatments, standardised mean difference (SMD) -0.23 (95% CI -0.28 to -0.17). The SMD for patient-reported outcomes was -0.26 (95% CI -0.32 to -0.19), and for observer-reported outcomes, SMD -0.13 (95% CI -0.24 to -0.02). We found an effect on pain, SMD -0.28 (95% CI -0.36 to -0.19)); nausea, SMD -0.25 (-0.46 to -0.04)), asthma (-0.35 (-0.70 to -0.01)), and phobia (SMD -0.63 (95% CI -1.17 to -0.08)). The effect on pain was very variable, also among trials with low risk of bias. Four similarly-designed acupuncture trials conducted by an overlapping group of authors reported large effects (SMD -0.68 (-0.85 to -0.50)) whereas three other pain trials reported low or no effect (SMD -0.13 (-0.28 to 0.03)). The pooled effect on nausea was small, but consistent. The effects on phobia and asthma were very uncertain due to high risk of bias. There was no statistically significant effect of placebo interventions in the seven other clinical conditions investigated in three trials or more: smoking, dementia, depression, obesity, hypertension, insomnia and anxiety, but confidence intervals were wide.
Meta-regression analyses showed that larger effects of placebo interventions were associated with physical placebo interventions (e.g. sham acupuncture), patient-involved outcomes (patient-reported outcomes and observer-reported outcomes involving patient cooperation), small trials, and trials with the explicit purpose of studying placebo. Larger effects of placebo were also found in trials that did not inform patients about the possible placebo intervention.
AUTHORS' CONCLUSIONS: We did not find that placebo interventions have important clinical effects in general. However, in certain settings placebo interventions can influence patient-reported outcomes, especially pain and nausea, though it is difficult to distinguish patient-reported effects of placebo from biased reporting. The effect on pain varied, even among trials with low risk of bias, from negligible to clinically important. Variations in the effect of placebo were partly explained by variations in how trials were conducted and how patients were informed.
OBJECTIVE: To synthesize the evidence on the effectiveness of smoking-cessation interventions by type of provider. METHODS: A random effects meta-regression was estimated to examine the effect of provider and whether the intervention contained nicotine replacement therapy (NRT), on the intervention's relative risk of quitting as compared to placebo or usual care from studies published in databases from inception to 2000. Thirty additional studies not included in the previous 1996 and 2000 U.S. Public Health Service clinical practice guidelines were used to provide the most comprehensive analysis to date of the comparative effectiveness of different types of providers in interventions for smoking cessation that have been published. RESULTS: The effectiveness without NRT follows: psychologist (1.94, 95% confidence interval [CI]: 1.04-3.62); physician (1.87, CI=1.42-2.45); counselor (1.82, CI=0.84-3.96); nurse (1.76, CI=1.21-2.57); unknown (1.27, CI=0.57-2.82); other (1.18, CI=0.67-2.10); and self-help (1.28, CI=0.89-1.82). Effectiveness of most providers increased by almost twofold with the use of NRT. CONCLUSIONS: Smoking-cessation interventions without NRT delivered by psychologists, physicians, or nurses are all effective. NRT increases the effectiveness of most providers.
BACKGROUND: Aversion therapy pairs the pleasurable stimulus of smoking a cigarette with some unpleasant stimulus. The objective is to extinguish the urge to smoke.
OBJECTIVES: This review has two aims: First, to determine the efficacy of rapid smoking and other aversive methods in helping smokers to stop smoking; Second, to determine whether there is a dose-response effect on smoking cessation at different levels of aversive stimulation.
SEARCH STRATEGY: We searched the Cochrane Tobacco Addiction Group specialised register (latest search date October 2009) for studies which evaluated any technique of aversive smoking.
SELECTION CRITERIA: Randomized trials which compared aversion treatments with 'inactive' procedures or which compared aversion treatments of different intensity for smoking cessation. Trials must have reported follow up of least six months from beginning of treatment.
DATA COLLECTION AND ANALYSIS: We extracted data in duplicate on the study population, the type of aversion treatment, the outcome measure, method of randomization and completeness of follow up.
The outcome measure was abstinence from smoking at maximum follow up, using the strictest measure reported by the authors. Subjects lost to follow up were regarded as smokers. Where appropriate, we performed meta-analysis using a fixed effect model.
MAIN RESULTS: Twenty-five trials met the inclusion criteria. Twelve included rapid smoking and nine used other aversion methods. Ten trials included two or more conditions allowing assessment of a dose-response to aversive stimulation. The odds ratio (OR) for abstinence following rapid smoking compared to control was 2.01 (95% confidence intervals (CI): 1.36 to 2.95). Several factors suggest that this finding should be interpreted cautiously. A funnel plot of included studies was asymmetric, due to the relative absence of small studies with negative results. Most trials had a number of serious methodological problems likely to lead to spurious positive results. The only trial using biochemical validation of all self reported cessation gave a non-significant result.
Other aversion methods were not shown to be effective (OR 1.15, 95% CI 0.73 to 1.82). There was a borderline dose-response to the level of aversive stimulation (OR 1.67, 95% CI 0.99 to 2.81).
AUTHORS' CONCLUSIONS: The existing studies provide insufficient evidence to determine the efficacy of rapid smoking, or whether there is a dose-response to aversive stimulation. Milder versions of aversive smoking seem to lack specific efficacy. Rapid smoking is an unproven method with sufficient indications of promise to warrant evaluation using modern rigorous methodology.
OBJECTIVE: Apply a "best practices" model to evidence regarding group smoking cessation to inform organizational decisions about adopting such programs. The best-practices model attempts to integrate rigorous review of evidence with context and practical considerations important to organizations contemplating adoption. DATA SOURCES: First, we identified effective practices by systematic literature review with two blinded reviewers to (1) search databases (99.8% agreement), (2) hand search journals with five or more papers selected in first step (99.9% agreement), (3) search reference lists of included papers (99.4% agreement), and (4) contact published experts. Second, model programs, theory, and expert opinion suggested plausible practices. Finally, a practitioner-researcher advisory group suggested practical considerations affecting adoption decisions. STUDY SELECTION: All 67 studies included in the review met six requirements: (1) peer reviewed, (2) primary studies, (3) using experimental or quasi-experimental design, (4) compared one or more smoking-cessation interventions that involved two or more group sessions, (5) studied persons 18+ years old, and (6) reported > or =6-month point prevalence or continuous abstinence outcomes. DATA EXTRACTION: Two independent raters assessed study quality (89.5% agreement). Effective practices consistently exhibited a statistically significant effect. Plausible practices showed consistency across three types of evidence. An advisory group based practicality criteria on critical review and experience. DATA SYNTHESIS: Two practices were rated effective: multicomponent behavioral intervention and nicotine replacement therapy. Five practices received plausible ratings: components of behavioral skills, information about smoking, self-monitoring, social support, and four or more sessions of 60 to 90 minutes. The Advisory Group identified 11 practicality questions to assist organizations to make adoption decisions regarding effective and plausible practices. CONCLUSIONS: No research evidence guides potential smoking-cessation program adopters regarding program participants, providers, settings, or quality assurance. Reviews to influence practice must consider science and practice (context) to facilitate adoption of best practices.
From 444 studies published until 2002 that investigated the efficacy of hypnosis, 57 randomized clinical studies were selected that compared patients treated exclusively by hypnosis to an untreated control group (or to a group of patients treated by conventional medical procedures). The 57 studies were integrated into a meta-analysis that yielded a weighted average post-treatment effect size of d = 0.56 (medium effect size). For hypnotic treatment of ICD-10 codable disorders (32 studies) the calculation of the weighted mean effect size resulted in d = 0.63. These estimates are conservative since all variables of a given study were used. Most of the studies employed methods of the classic approach to hypnosis. In order to obtain an estimate to which extent non-clinical factors (design-quality, way of comparison of dependent variables) have an influence on the effect sizes, effect sizes were computed for all studies of the original 444 studies that reported the necessary statistical information (N = 133), For those studies with an average effect size of d = 1.07 a massive influence of non-clinical factors was demonstrated with a range from d = 0.56 for randomized studies with group comparisons to d = 2.29 for non-randomized studies using pre-post-comparisons. Out of the 57 randomized studies, only 6 reported numerical values for the correlation between hypnotic suggestibility and treatment outcome with a mean correlation of r = 0.44.
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)
