# Effectiveness of psychological interventions for smoking cessation among incarcerated population: A systematic review and meta-analysis

Mélanie Rome, Lucia Romo, Thibault Hennequin, Carine Meslot, Fanny Negre, Katia Illel, Oulmann Zerhouni, Xavier Laqueille

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## ► To cite this version:

Mélanie Rome, Lucia Romo, Thibault Hennequin, Carine Meslot, Fanny Negre, et al.. Effectiveness of psychological interventions for smoking cessation among incarcerated population: A systematic review and meta-analysis. Drug and Alcohol Dependence, 2025, 277, pp.112941. 10.1016/j.drugalcdep.2025.112941. hal-05355565

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Review

# Effectiveness of psychological interventions for smoking cessation among incarcerated population: A systematic review and meta-analysis

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Mélanie Rome<sup>a,b,*</sup>, Lucia Romo<sup>a,c,d</sup>, Thibault Hennequin<sup>b</sup>, Carine Meslot<sup>b</sup>, Fanny Negre<sup>a,e</sup>, Katia Illel<sup>b</sup>, Oulmann ZERHOUNI<sup>f,g</sup>, Xavier Laqueille<sup>a,b</sup>

ARTICLE INFO

Keywords:

Smoking cessation

Prison

Psychological intervention

Review

Meta-analysis

ABSTRACT

**Objective:** The prevalence of smoking is disproportionately high in correctional settings. This systematic review and meta-analysis evaluated the efficacy of psychological interventions in promoting smoking cessation among individuals who are currently incarcerated.

**Method:** We systematically searched four databases (PubMed, PsycInfo, Web of Science, and Embase) studies assessing the effectiveness of psychological interventions on smoking cessation. Two meta-analyses were conducted: (1) abstinence rates between psychological interventions and control groups in Randomised Controlled Trials (RCTs), and (2) a pooled analysis of abstinence rates after psychological intervention across follow-up periods. Meta-regression was used to examine the effect of follow-up duration on abstinence.

**Results:** 11 studies (N = 2688), including seven RCTs, met the inclusion criteria. Psychological interventions were associated with a significant increase in smoking abstinence compared with control conditions (OR = 3.41, 95 % CI [1.34, 8.70], z = 2.57; p < .05). Yet, this effect was highly heterogeneous (I² = 85.4 %) and became non-significant after adjustment for publication bias (OR = 1.60, 95 % CI [0.51, 5.07]). The pooled post-intervention abstinence rate across follow-up periods was 18.8 % (95 % CI [15.5, 22.1]), with moderate to high heterogeneity (I² = 56.1 %, τ² = 0.1356). Meta-regression revealed a significant decline in abstinence rates over time (log-odds = -0.0099, SE = 0.0051, *p* = 0.050).

**Conclusions:** Psychological interventions show promise but are limited by high heterogeneity, small sample sizes, and methodological weaknesses. More rigorous and adequately powered studies are needed to establish their effectiveness in correctional settings.

# 1. Introduction

Tobacco smoking is the leading cause of premature and preventable death worldwide, killing over 7 million people annually (World Health Organization (WHO), 2025). Smoking prevalence is significantly higher in correctional settings than in the general population. For example, 73 % of incarcerated adults in France were smoking in 2024 compared with 23 % of the general adult population (Le Nézet et al., 2025; Spilka et al., 2024). Similar disparities exist internationally: in the United States, 70–80 % of inmates smoke versus 11.5 % of adults (Cornelius

et al., 2023; Garver-Apgar et al., 2023), and before the adoption of smoke-free policies in England and Wales, prison smoking prevalence reached approximately 80 % compared with 14.7 % in the community (UK Health Security Agency, 2018). This markedly higher prevalence highlights the urgent public health importance of smoking cessation in prisons. However, achieving and maintaining abstinence in correctional settings presents unique challenges.

Many factors associated with the prison environment can impede smoking cessation: high stress, social pressure, positive perceptions of smoking, social functions and boredom (Lankenau, 2001; Picot-Ngo

<footnote><sup>a</sup> Université Paris Nanterre, Laboratoire CLIPSYD, Nanterre 92000, France</footnote>

<footnote><sup>b</sup> GHU Paris Psychiatrie et Neurosciences, Centre hospitalier Sainte-Anne, Service d'addictologie, Paris 75014, France</footnote>

<footnote><sup>c</sup> APHP, Hôpital Universitaire Raymond Poincaré, Garches 92380, France</footnote>

<footnote><sup>d</sup> Université Paris Saclay, INSERM CESP 1018 UPS, Villejuif 94807, France</footnote>

<footnote><sup>e</sup> APHP, Hôpitaux Universitaires Henri Mondor, DMU IMPACT, Hôpital Albert Chenevier, Service d'addictologie, Créteil 94010, France</footnote>

<footnote><sup>f</sup> Université Rouen Normandie, Normandie Univ, CRFDP UR 7475, Rouen 76000, France</footnote>

<footnote><sup>g</sup> Laboratoire Parisien de Psychologie Sociale, EA 4386 (équipe PS2C), Nanterre 92001, France</footnote>

<footnote>* Correspondence to: CLIPSYD (EA 4430), Université Paris Nanterre, 200 Avenue de la République, Nanterre Cedex 92001, France.
E-mail address: melanie.rome@ghu-paris.fr (M. Rome).</footnote>
et al., 2021; Richmond et al., 2006; van den Berg et al., 2014). In addition, common risk factors associated with smoking, such as other substance use disorders, psychiatric disorders, low educational attainment, poverty and impulsivity, are also highly prevalent among incarcerated individuals (Conklin et al., 2000; Cropsey et al., 2010; Papadodima et al., 2010). In this context, psychological interventions represent a theoretically appropriate approach because they target mechanisms implicated in tobacco use, including affect regulation, withdrawal, craving, and self-control (Baker et al., 2004; Carter and Tiffany, 1999; Hughes, 2007; Kassel et al., 2003; MacKillop et al., 2011). Given the well-documented psychological and social vulnerabilities of incarcerated populations, these approaches may be especially relevant in prisons.

Despite this rationale, psychological smoking cessation interventions remain underexplored in prisons. To our knowledge, only one systematic review has addressed the effectiveness of smoking cessation interventions in the incarcerated population (de Andrade and Kinner, 2016). Although this review provided relevant insights, it did not isolate psychological approaches or quantitatively synthesize their effects. No previous meta-analysis has focused exclusively on psychological smoking cessation interventions among incarcerated individuals to estimate pooled effect sizes.

The present review fills this methodological and conceptual gap by systematically assessing the effectiveness of psychological interventions

for smoking cessation among incarcerated individuals. Specifically, we conducted two meta-analyses: (1) to estimate the pooled relative effect of psychological interventions on biochemically verified abstinence compared to usual care or minimal intervention, expressed as a odds ratio; and (2) to estimate the absolute proportion of incarcerated who achieve biochemically verified abstinence following psychological interventions, regardless of the comparator. These two outcomes were selected to capture both between-group effects (intervention vs. control) and within-group abstinence rates after intervention, to provide a comprehensive assessment of intervention effectiveness.

## 2. Methodology

### 2.1. Registering protocol

The protocol for this review was registered in PROSPERO (CRD42024573161). This systematic review follows PRISMA guidelines for reporting (Page et al., 2021, see Fig. 1).

### 2.2. Search strategy

Four databases, Pubmed, PsycInfo, Web of Science, and Embase, were searched systematically. The final search was completed in April 2025. The search strategy combined Medical Subject Headings (MeSH)

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** PRISMA flow diagram of study selection. This figure presents the flow of information through the different phases of the systematic review, following the PRISMA 2020 guidelines. It illustrates the number of records identified through searches in four databases, the number of records screened after duplicate removal, the number of titles and abstracts screened, the number of full-text articles assessed for eligibility, and the number of studies included in the final review. Reasons for exclusion at the full-text screening stage are also reported.</figcaption></figure>
and free-text terms across three concepts: (1) incarcerated individuals ("prison*", "incarcerat*", "inmate*", "correctional facilities", "jail"), (2) tobacco use ("tobacco*", "tobacco smoking", "tobacco products", "nicotine", "smok*", "cigarette*"), and (3) psychological interventions ("psychological intervention", "psychological treatment", "counseling", "cognitive behavioral therapy", "CBT", "behavior therapy", "behavioral intervention*", "motivational interview*", "psychotherapy").

The complete, reproducible search strings for all four databases are provided in Supplementary Appendix A.

## 2.3. Eligibility criteria

### 2.3.1. Context / study setting

Eligible studies had to be conducted in correctional facilities, defined as institutions where individuals are deprived of liberty following a judicial decision (e.g., prisons, jails, detention centers). To capture the diversity of institutional settings, studies conducted in facilities with either full or partial smoking bans were considered eligible, regardless of existing tobacco-related policies.

We included smoke-free prisons because individuals who smoked prior to incarceration remain at high risk of relapse after release, even if they have been abstinent for months due to institutional restrictions. Including such settings allowed us to capture this key target population for prison-based cessation or relapse-prevention interventions. Moreover, as smoke-free policies are increasingly common in correctional systems, their inclusion enhances the ecological validity and relevance of our findings.

### 2.3.2. Participants

Studies included a population of adult inmates (≥18 years) who smoked defined as individuals who have smoked on at least 15 days out of the past 30 days or have smoked daily before incarceration. This definition was designed to include individuals who were abstinent at the time of assessment due to smoking restrictions in smoke-free prison settings. Studies including other forms of tobacco (tobacco product) or dual use (e.g., tobacco and cannabis) were eligible only if the data associated with cigarettes could be clearly distinguished.

### 2.3.3. Study design

Eligible studies had to be full-text articles employing an experimental (e.g., randomized controlled trial), quasi-experimental (e.g., non-randomized controlled studies), or pre-experimental (e.g., pre-post without control group) design. Mixed methods studies were included provided that the outcomes relevant to our review were assessed quantitatively.

Rigorous RCTs remain scarce in prison settings. Therefore, we included quasi-experimental and pre-experimental designs to capture the full range of available evidence, and to obtain additional information on the feasibility and acceptability of psychological interventions in this context. As non-randomized studies could influence the pooled effect estimates, they were examined in the second analysis, and their higher risk of bias was formally assessed using the MINORS tool and reflected in the GRADE ratings.

Studies without quantitative data on smoking abstinence, the primary outcomes of this review, were excluded.

Studies reporting the same data in another publication were excluded to avoid duplication. Systematic reviews, meta-analyses, case studies, conference abstracts, dissertations and gray literature reports were also excluded. No restrictions were applied regarding country, language, or publication date.

### 2.3.4. Interventions

This review and meta-analyses focused on psychological interventions designed to promote smoking cessation or relapse

prevention among incarcerated individuals. Included approaches were Cognitive Behavioural Therapy (CBT), Motivational Interviewing (MI), behavioral therapy, counselling, stress management, and mindfulness-based programs. We deliberately excluded brief cessation treatments (BCTs), which involve minimal contact and limited therapeutic depth, as our interest lay in more intensive and psychologically grounded methods. Interventions could be delivered alone or alongside pharmacological treatments or environmental smoking restrictions, as long as the psychological component remained a core element. To ensure consistency in the description of interventions and support future replication, two trained reviewers independently coded each psychological protocol using the Behavior Change Technique Taxonomy version 1 (BCTTv1; Michie et al., 2013). We applied the taxonomy to all written descriptions and supplementary materials, marking a BCT as present only when explicit evidence supported its inclusion. This taxonomy-based approach standardizes terminology across studies and highlights key active ingredients that may drive efficacy in prison-based smoking cessation programs.

### 2.3.5. Comparator(s)/control

This review included both studies with control group and studies with no control group (i.e., pre-experimental design). In RCTs, comparators had to represent a condition where the specific psychological intervention was not delivered, such as usual care, no treatment, minimal advice, waiting list, educational material only or health education sessions.

## 2.4. Selection process

The article selection process was performed by two independent reviewers. Duplicates were removed via Zotero, and the remaining studies were screened on Rayyan in two stages: (1) title and abstract screening, followed by (2) full-text review. Disagreements between authors were resolved through discussion, with a third reviewer making the final decision when necessary. Studies were mainly excluded in the first screening for the following reasons: Non-eligible population (e.g., individuals not incarcerated, participants not meeting smoking-related inclusion criteria, or minors), absence of relevant outcome data (abstinence or smoking reduction), ineligible study design (e.g., reviews, qualitative studies), or interventions that did not target smoking cessation or lacked a psychological focus. Although Richmond et al. (2013) primarily focused on a pharmacological intervention, the study was included because it also independently evaluated a psychological component and provided outcome data on smoking abstinence, aligned with the aims of the meta-analysis.

## 2.5. Data collection process and extracted items

Data extraction was conducted by two independent reviewers and any discrepancies were resolved by a third author. From each eligible study, we extracted detailed information on:

(1) study characteristics (year, country, setting, study design, and duration);

(2) participant characteristics (sample size, mean age, gender distribution, inclusion criteria, and baseline smoking status);

(3) intervention components (intervention type, delivery format, frequency and duration, therapist qualification, behavioral techniques, and any concurrent pharmacotherapy);

(4) outcomes and follow-up timing.
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Summary of risk of bias assessment using the ROB-2 tool for included RCTs. This figure summarizes the risk of bias assessments for the included randomized controlled trials, conducted using the Cochrane Risk of Bias 2 (ROB-2) tool. It presents individual judgments across the five ROB-2 bias domains for each study. Color coding is used to represent the level of risk of bias: green indicates low risk of bias, yellow indicates some concerns, and red indicates high risk of bias. An overall judgment per study is also provided based on domain-level assessments.</figcaption></figure>

When multiple abstinence measures were reported (e.g., point-prevalence abstinence,<fnref n="1" /> continuous abstinence), we extracted all available measures. The primary outcome for the meta-analysis was biochemically verified point prevalence abstinence, validated by carbon monoxide breath testing or urinary cotinine levels. This outcome was selected because it was the most frequently reported across studies, maximizing comparability and statistical power.

When multiple abstinence metrics were reported, we applied the following decision rules hierarchically to ensure consistency:

(1) Prefer biochemically verified outcomes over self-reported outcomes.

(2) If multiple timepoints were reported, extract all and account for repeated measures in the meta-analytic models.

(3) Prefer intent-to-treat (ITT) estimates over per-protocol results.

Quit attempts and time to relapse were not extracted. Cigarette reduction was extracted in the initial data collection phase for a planned secondary analysis; however, due to the low number of studies reporting this outcome, the analysis was not conducted, and these data were not included in the synthesis (see “Meta-analytic strategies”).

When abstinence data were unclear or missing, we contacted study authors. A total of nine authors were contacted, of whom two responded, and one provided usable outcome data for inclusion in the meta-analysis.

The full list of extracted variables is provided in Supplementary Appendix B.

## 2.6. Study risk of bias assessment

Risk of bias for each study was assessed by the same reviewers who performed the data extraction. We assessed the primary outcome of smoking abstinence in each group for RCTs (psychological intervention vs. control) using the Cochrane Risk of Bias tool (RoB 2; (Minozzi et al., 2020). Non-randomised studies were assessed using the Methodological Index for Non-Randomised Studies (MINORS; Slim et al., 2003). In cases of disagreement between reviewers, a discussion was organised to reach a consensus.

## 2.7. Meta-analytic strategy

Two analyses were conducted using R (RStudio version 4.4.2): (1) an analysis of effect sizes for statistical comparisons of abstinence rates between psychological interventions and control groups in RCTs, (2) an analysis of abstinence rates for the intervention group at different follow-ups. The first analysis focused on RCTs comparing abstinence rates between psychological intervention groups and control groups, reporting either statistical comparison or raw data required for effect size calculation. The second analysis included all studies that reported the number of abstinent participants and the total number of individuals in the intervention group. If there were unreported data or if the effect size could not be calculated from raw data, the corresponding study was excluded from the quantitative analysis.

Initially, we had planned two analyses: an analysis of effect sizes comparing psychological interventions with a control group and an analysis of smoking reduction. An insufficient number of studies reported the data necessary for this second analysis, so we adapted our approach by integrating an analysis of post-intervention abstinence rates at several follow-up periods. This approach allowed us to incorporate studies with non-RCT designs into this analysis. In addition, the presence of repeated measures in the included studies allowed us to assess the stability of the effects of psychological interventions and to

<footnote n="1">¹ Point prevalence abstinence refers to not smoking during a short, specified period (e.g., past 7 days) at the time of follow-up, while continuous abstinence refers to sustained abstinence from smoking over a longer, uninterrupted period since a defined starting point (e.g., end of treatment).</footnote>
examine the evolution of smoking abstinence over time.

All analysis were performed in R with the meta (Balduzzi et al., 2019) and metafor packages (Viechtbauer, 2010).

For the first analysis, effect sizes were calculated as Odds Ratios (ORs) using MetaConvert (Gosling et al., 2025), directly from results of statistical tests in the studies, or from raw data (e.g., number of abstainers in the control group, in the intervention group and sample size). Only two studies reported outcomes results at multiple follow-up time points. We selected the 3-month follow-up to compute effect sizes, ensuring consistency with the time frames reported in the rest of the sample. A random-effects model (Restricted Maximum Likelihood - REML) was used to account for heterogeneity between studies. REML was selected over other estimators (e.g., DerSimonian and Laird) because it provides more accurate and less biased estimates of between-study variance ($\tau^2$), particularly in cases of high heterogeneity and small sample sizes. To ensure approximate normality of the effects, the ORs were transformed into logORs using R Studio. Heterogeneity was assessed via Cochrane's Q, $\tau^2$, and $I^2$ tests. For easier interpretation, results were presented in a forest plot with exponentiated individual ORs, Confidence Intervals (CIs) and combined effect (see Fig. 3). We also performed a leave-one-out sensitivity analysis to identify whether any studies significantly influenced the results. Potential small-study effects were examined using a funnel plot and statistically tested with Egger's weighted regression test to assess significance of the asymmetry (two-sided, $\alpha = 0.10$). When the test indicated significant asymmetry, we applied Duval and Tweedie's nonparametric trim-and-fill procedure (random effects, L-trim) to estimate the number of potentially missing studies and compute an adjusted pooled effect size. PET and PEESE were not applied, as the number of included studies was insufficient for reliable estimation.

In the second analysis, results of studies reporting repeated measures were categorised into three follow-up periods: short-term (<3 months), medium-term (3–6 months) and long-term (>6 months). Abstinence rates were calculated by dividing the number of abstinent participants at follow-up by the total number of participants initially assigned to the intervention group, in accordance with an intention-to-treat approach. When a study only reported the percentage of abstinence, frequency was estimated using the initial sample of the group. For this analysis, we used a random-effects multivariate model (REML) to account for heterogeneity and repeated measures in the included studies. Compound Symmetry (CS) was chosen to model intra-study dependence, given its suitability for datasets with a limited number of studies. Estimated proportions were transformed into logit values (PLO) to approximate a symmetrical distribution of the data. The standard errors were calculated from estimated variance (vi). To measure heterogeneity, we used

the $I^2$, $\tau^2$ and Cochran's Q tests. For the presentation of the results, the logits and their 95 % confidence intervals were back-transformed via the inverse logit and reported as percentages for clarity. Additionally, a meta-regression was performed to evaluate the effect of follow-up time (in weeks) on abstinence rates. The leave-one-out analysis was conducted to verify the robustness of the results synthesis. We were unable to conduct risk of bias analysis because the limited number of studies and the presence of repeated measurements increasing the risk of biasing the results.

# 3. Results

## 3.1. Study selection

A total of 296 records were identified across the four databases. After removing 101 duplicates, 195 studies were screened based on title and abstract. 169 articles were excluded at this stage, leaving 26 articles for the full-text review. Ten articles, reporting 11 studies, were included in our analysis (see Fig. 1). Table 1 summarizes the studies included in the systematic review, presenting abstinence rates and statistical results as originally reported by the authors.

## 3.2. Descriptive summary of included studies

### 3.2.1. Participant characteristics

A total of 2688 participants across the 11 studies were included (2182 men, 296 women and 10 transgender participants). The average age was 35.20 years (SD = 3.00). 49.2 % (SD = 1.4 %) of participants were white (n = 6; Clarke et al., 2013; Cropsey et al., 2008; Edinger et al., 1978 [Study 1 and Study 2]; Valera et al., 2020; Winkelman et al., 2021) and 58.2 % (SD = 18.0 %) had not completed high school (n = 6; Clarke et al., 2013; Cropsey et al., 2008; Jalali et al., 2015; Jin et al., 2021; Richmond et al., 2006; Valera et al., 2020).

Regarding participant's offending history, 38.2 % had been institutionalised during childhood (n = 2; (Richmond et al., 2013; Richmond et al., 2006). On average, the sentence length was 2.79 years (SD = 2.48) (n = 4; Jalali et al., 2015; Jin et al., 2021; Richmond et al., 2013; Valera et al., 2020).

Average duration of exposure to smoking was 19.4 years (SD = 3.3), with an age of initiation at 15.77 years (SD = 2.29) (n = 7; Clarke et al., 2013; Cropsey et al., 2008; Edinger et al. [Study 1 and Study 2], 1978; Jalali et al., 2015; Richmond et al., 2006, 2013; Valera et al., 2020). They had tried to quit smoking an average of 2.87 times (SD = 0.71) (n = 4; Cropsey et al., 2008; Jalali et al., 2015; Richmond et al., 2006, 2013).

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Forest plot of between-group effect sizes for smoking abstinence in RCTs. This forest plot presents the effect sizes of the five studies included in this meta-analysis, as well as the overall effect size. Each line represents a study, with its effect size expressed as an odds ratio (OR) and 95 % confidence intervals. An OR > 1 indicates an effect in favor of the psychological intervention compared to the control group. Study weights are based on sample size, and pooled estimates were computed using a random-effects model (REML). Heterogeneity statistics ($I^2$, $\tau^2$, and p-value) are also reported in the figure. Detailed sample sizes and numbers abstinent (n/N) for each group are reported in Table 1.</figcaption></figure>
<table><caption>**Table 1**

Characteristics of the studies included in the review.</caption><thead><tr><th>REFERENCE</th><th>COUNTRY AND PRISON SETTING</th><th>SAMPLE (N, % MALE, MEAN AGE, ± SD)</th><th>STUDY DESIGN</th><th>PSYCHOLOGICAL INTERVENTION (TYPE, FORMAT, DURATION, SESSIONS)</th><th>CONTROL CONDITION (TYPE)</th><th>ABSTINENCE RATE PPA<sup>†</sup> (%) INTERVENTION</th><th>ABSTINENCE RATE PPA (%) CONTROL</th><th>STATISTICAL COMPARISON (INTERVENTION VS. CONTROL)</th><th>RISK OF BIAS (ROB 2<sup>§</sup> / MINORS<sup>10)</sup>)</th></tr></thead><tbody><tr><td>Edinger et al. [Study 1], 1978</td><td>USA, Federal correctional institution</td><td>N = 9, 100%, 23.5 (SD NR<sup>1</sup>)</td><td>Pre-post design</td><td>Self control treatment, group sessions, 4 weeks (90 min/ session), weekly</td><td>N/A<sup>5</sup></td><td>N/A</td><td>N/A</td><td>N/A</td><td>MINORS: 4/16</td></tr><tr><td>Edinger et al. [Study 2], 1978</td><td>USA, Federal correctional institution</td><td>N = 24, 100%, 25.5 (SD NR)</td><td>Comparative experimental study without control</td><td>Self control treatment, group sessions, 10 weeks (30 min/ session), weekly</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>MINORS: 3/16</td></tr><tr><td>Richmond et al., (2006)</td><td>Australia, Maximum security prison</td><td>N = 30, 100%, 35.0 ± 10.0</td><td>Pre-post design</td><td>brief CBT<sup>2</sup>, individual counseling, 2 weeks (30 min/ session), weekly</td><td>N/A</td><td>n = 30<br>5 M: 37.0 %<br>6 M: 26.0 %</td><td>N/A</td><td>NR</td><td>MINORS: 7/16</td></tr><tr><td>Cropsey et al., (2008)</td><td>USA, Women's correctional facility</td><td>N = 360, 0%, 33.8 ± 9.0</td><td>RCT</td><td>Mood management training based on behavioral therapy, group sessions, 10 weeks (duration NR), weekly</td><td>Waitlist</td><td>n = 250<br>Post: 18.4 %<br>3 M: 16.8 %<br>6 M: 14.0 %<br>12 M: 11.6 %<br>(reported; raw data unavailable)</td><td>n = 289<br>Post: NR<br>3 M: NR<br>6 M: 2.8 %<br>12 M: NR<br>(cross-over at 6 M)</td><td>Post-intervention: NR<br>3 M: NR<br>6 M: χ<sup>2</sup>(1) = 23.0, p < .001<br>12 M: NR (cross-over at 6 M)</td><td>ROB 2: High risk</td></tr><tr><td>Clarke et al., (2013)</td><td>USA, Correctional facility<br>Smoke free prison</td><td>N = 247, 65.2%, 35.6 ± 9.2</td><td>RCT</td><td>Intensive Behavioral Intervention: CBT and MI<sup>3</sup>, individual counseling, 6 weeks prior to release (30–60 min/ session), weekly</td><td>Wellness video viewing control</td><td>n = 122<br>3wks (post-release): 25.0 %<br>3 M (post-release): 11.5 %<sup>8</sup></td><td>n = 125<br>3wks (post-release): 7.2 %<br>3 M (post-release): 2.4 %<sup>8</sup></td><td>3wks (post-release): OR = 4.4 [2.0–9.7]<br>3 M (post-release): OR = 5.3 [1.4–23.8]<sup>7</sup></td><td>ROB 2: High risk</td></tr><tr><td>Richmond et al., (2013)</td><td>Australia, Unspecified facility</td><td>N = 425, 100%, 33.5 ± 10.2</td><td>RCT</td><td>brief CBT, individual counseling, 2 session (3rd week after and 5th–6th week after quit date) (30 min/session), weekly</td><td>Alternative treatment<sup>4</sup>: brief CBT, Placebo, Quitline, NRT</td><td>Pharmacotherapy (n = 206)<br>3 M: 27.7 %<br>6 M: 19.4 %<br>12 M: 12.1 %</td><td>Psychological intervention (n = 219)<br>3 M: 19.6 %<br>6 M: 14.2 %<br>12 M: 14.6 %</td><td>3 M: OR = 1.57 [1.00–2.46]<br>6 M: OR = 1.46 [0.87–2.44]<br>12 M: OR = 0.81 [0.46–1.42]</td><td>ROB 2: Some concerns</td></tr><tr><td>Naik et al., (2014)</td><td>India, Central prison</td><td>N = 600, 100%, Age NR</td><td>RCT</td><td>MI, NR</td><td>NR</td><td>n = 300<br>6 M: 16 %</td><td>n = 300<br>6 M: NR</td><td>6 M: NR</td><td>ROB 2: High risk</td></tr><tr><td>Jalali et al., (2015)</td><td>Iran, Central prison<br>Partial smoking ban</td><td>N = 213, 100%, 37.6 ± 8.8</td><td>RCT</td><td>MI, individual counseling, 5 weeks (30 min/ session), weekly</td><td>No treatment</td><td>n = 71<br>Post: NR<br>2 M: NR</td><td>n = 71<br>Post: NR<br>2 M: NR</td><td>Post: NR<br>2 M: NR</td><td>ROB 2: High risk</td></tr><tr><td>Valera et al., (2020)</td><td>USA, Prison facility</td><td>N = 177, 94.4%, 43.0 ± 10.3</td><td>Pre-post design</td><td>Cognitive social learning, CBT and MI, group sessions, 6 weeks (90 min/ session), weekly</td><td>N/A</td><td>n = 177<br>1 M: NR</td><td>N/A</td><td>N/A</td><td>MINORS: 6/16</td></tr><tr><td>Jin et al., (2021)</td><td>Australia, Unspecified facility<br>Smoke free prison</td><td>N = 557, 91.6%, 34.3 ± 9.5</td><td>RCT</td><td>SNAP<sup>4</sup> intervention (based on MI), individual counseling, 1 session (45–60 min/ session)</td><td>Usual care</td><td>n = 266<br>3 M: 31.2 %</td><td>n = 291<br>3 M: 27.1 %</td><td>3 M: RR = 1.14 [0.82–1.57]</td><td>ROB 2: High risk</td></tr><tr><td>Winkelman et al., (2021)</td><td>USA, Petrial county jail<br>Smoke free prison</td><td>N = 46, 91%, 38.2 ± 9.0</td><td>RCT</td><td>Type NR, individual counseling, 1 session (60 min)</td><td>Alternative treatment: BHE, NRT</td><td>n = 23<br>1wk: NR<br>3wks: 11.9 %</td><td>n = 23<br>1wk: NR<br>3wks: 10.6 %</td><td>1wks: NR<br>3wks: OR = 1.13 [0.14–9.07]</td><td>ROB 2: High risk</td></tr></tbody></table>

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<table><caption>**Table 1 (continued)**</caption><thead><tr><th>REFERENCE</th><th>COUNTRY AND PRISON SETTING</th><th>SAMPLE (N, % MALE, MEAN AGE, ± SD)</th><th>STUDY DESIGN</th><th>PSYCHOLOGICAL INTERVENTION (TYPE, FORMAT, DURATION, SESSIONS)</th><th>CONTROL CONDITION (TYPE)</th><th>ABSTINENCE RATE PPA' (%) INTERVENTION</th><th>ABSTINENCE RATE PPA (%) CONTROL</th><th>STATISTICAL COMPARISON (INTERVENTION VS. CONTROL)</th><th>RISK OF BIAS (ROB 2<sup>9</sup> / MINORS<sup>10</sup>)</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td>and up to 4 telephone sessions (10–20 min/ session) at 1,7, 14 and 21 days after release</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

The table summarizes key characteristics and main findings of each included study. All data, including abstinence rates and statistical comparisons, are presented as reported in the original publications. Methodological quality was assessed using the ROB 2 tool for randomized studies and the MINORS tool for non-randomized studies.

At baseline, patients smoked an average of 21.81 cigarettes per day (SD = 3.18) (n = 8; Cropsey et al., 2008; Edinger et al. [Study 1 and Study 2], 1978; Jalali et al., 2015; Naik et al., 2014; Richmond et al., 2006, 2013; Valera et al., 2020). The average score obtained on the Fagerström Test for Nicotine Dependence was 5.11 (SD = 0.16) out of a total score of 10 (n = 3; Clarke et al., 2013; Jalali et al., 2015; Winkelman et al., 2021).

### 3.2.2. Study design and setting

Studies were conducted in four countries, primarily in the USA (n = 6) (Clarke et al., 2013; Cropsey et al., 2008; Edinger et al. [Study 1 and Study 2], 1978; Valera et al., 2020; Winkelman et al., 2021), followed by Australia (n = 3) (Jin et al., 2021; Richmond et al., 2006, 2013), India (n = 1) (Naik et al., 2014), and Iran (n = 1) (Jalali et al., 2015).

Seven studies were RCTs (Clarke et al., 2013; Cropsey et al., 2008; Jalali et al., 2015; Jin et al., 2021; Naik et al., 2014; Richmond et al., 2013; Winkelman et al., 2021), three had a pre-post design (Edinger et al. [Study 1], 1978; Richmond et al., 2006; Valera et al., 2020) and one study had a comparative experimental design without control group (Edinger et al. [Study 2], 1978).

### 3.2.3. Control condition

The RCTs used a wide variety of control conditions: waitlist control group (Cropsey et al., 2008), wellness video intervention (Clarke et al., 2013), usual care (Jin et al., 2021), Brief Health Education (BHE) with Nicotine Replacement Therapy (Winkelman et al., 2021). One study provided no treatment (Jalali et al., 2015), and another did not specify its control condition (Naik et al., 2014). One study (Richmond et al., 2013) focused on an intervention combining pharmacological treatment with a psychological intervention. The control group received the same psychological intervention, a placebo, and had access to a quit line.

### 3.2.4. Prison characteristics

Studies were conducted in different prison settings. Three studies were conducted in general correctional institutions (Clarke et al., 2013; Edinger et al. [Study 1 and Study 2], 1978), one in a maximum-security prison (Richmond et al., 2006), and one in a women's facility (Cropsey et al., 2008). Three of these institutions had implemented a smoking ban throughout the facility (Clarke et al., 2013; Jin et al., 2021; Winkelman et al., 2021), and one in specific areas (Jalali et al., 2015). Three studies did not specify their prison setting or smoking policies (Jin et al., 2021; Richmond et al., 2013; Valera et al., 2020).

### 3.2.5. Psychological interventions

Psychological interventions were mainly based on Cognitive Behavioural Therapy (CBT), alone (n = 2; Richmond et al., 2006, 2013) or combined with mood management training (Cropsey et al., 2008), motivational interviewing (Clarke et al., 2013), or cognitive social learning (Valera et al., 2020). Motivational interviewing alone was evaluated in three studies (Jalali et al., 2015; Jin et al., 2021; Naik et al., 2014). Two studies used a self-control treatment (Edinger et al. [Study 1 and Study 2], 1978). One study did not specify its theoretical framework (Winkelman et al., 2021).

Interventions lasted an average of 4.27 sessions (range 1–10). For interventions consisting of more than one session, the majority were conducted weekly (n = 7; Clarke et al., 2013; Cropsey et al., 2008; Edinger et al. [Study 1 and Study 2], 1978; Jalali et al., 2015; Richmond et al., 2006; Valera et al., 2020), except for Richmond et al. (2013) which included one session during the week of quit date and one session 2–3 weeks later. Most of interventions were delivered individually (n = 6; Clarke et al., 2013; Jalali et al., 2015; Jin et al., 2021; Richmond et al., 2006, 2013; Winkelman et al., 2021), while others were in group format (n = 4) (Cropsey et al., 2008; Edinger et al. [Study 1 and Study 2], 1978; Valera et al., 2020). One study did not specify its format (Naik et al., 2014).

Several studies combined psychological intervention with NRT alone (n = 6; Cropsey et al., 2008; Jalali et al., 2015; Richmond et al., 2013; Valera et al., 2020; Winkelman et al., 2021), or combined with Bupropion (Richmond et al., 2006). In total, 7 out of the 11 included studies used a combination of psychological and pharmacological treatments.

The 28 distinct BCTs identified across psychological intervention are presented in Table 2. The most frequently used BCTs were Action planning (1.4), Behavior substitution (8.2), Problem solving (1.2), Goal Setting (1.1) and Information about health consequences (5.1). These BCTs were present in at least six interventions. Conversely, a few BCTs such as Review outcome goals (1.7), Behavior cost (14.1), and Imaginary punishment (16.1) were identified in only one intervention. The number of BCTs per intervention ranged from 2 to 19, which may reflect heterogeneity in intervention complexity, but is also likely influenced by

<footnote n="1">¹ NR = Not Reported</footnote>

<footnote n="2">² CBT = Cognitive Behavioral Therapy</footnote>

<footnote n="3">³ MI = Motivational Interviewing</footnote>

<footnote n="4">⁴ SNAP = Brief intervention on Smoking, Nutrition, Alcohol and Physical inactivity for smoking relapse prevention</footnote>

<footnote n="5">⁵ N/A = Not Applicable</footnote>

<footnote n="6">⁶ In this study, the intervention group received pharmacotherapy and the control group received a psychological intervention (inverse of other included studies).</footnote>

<footnote n="7">⁷ PPA = Point Prevalence Abstinence</footnote>

<footnote n="8">⁸ Abstinence rate measured using continuous abstinence (CA)</footnote>

<footnote n="9">⁹ ROB 2 = Cochrane Risk of Bias tool</footnote>

<footnote n="10">¹⁰ MINORS = Methodological Index for Non-Randomised Studies</footnote>
<table><caption>**Table 2**

Behavior Change Techniques (BCTs) across included interventions.</caption><thead><tr><th></th><th>Edinger<br>et al.<br>[Study 1],<br>1978</th><th>Edinger<br>et al.<br>[Study 2],<br>1978</th><th>Richmond<br>et al.,<br>(2006)</th><th>Cropsey<br>et al.,<br>(2008)</th><th>Clarke<br>et al.,<br>(2013)</th><th>Richmond<br>et al.,<br>(2013)</th><th>Naik<br>et al.,<br>(2014)</th><th>Jalali<br>et al.,<br>(2015)</th><th>Valera<br>et al.,<br>(2020)</th><th>Jin<br>et al.,<br>(2021)</th><th>Winkelman<br>et al., (2021)</th></tr></thead><tbody><tr><td>1.1 Goal setting<br>(behavior)</td><td>x</td><td>x</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>1.2 Problem solving</td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>1.3 Goal setting<br>(outcome)</td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1.4 Action planning</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>1.5 Review behavior<br>goal(s)</td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1.7 Review outcome<br>goal(s)</td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2.1 Monitoring of<br>behavior by others<br>without feedback</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2.3 Self-monitoring of<br>behavior</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>2.4 Self-monitoring of<br>outcome(s) of<br>behavior</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td></td></tr><tr><td>3.1 Social support<br>(unspecified)</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>4.1 Instruction on how<br>to perform the<br>behavior</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>4.2 Information about<br>antecedents</td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>5.1 Information about<br>health consequences</td><td>x</td><td></td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>5.2 Salience of<br>consequences</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>8.2 Behavior<br>substitution</td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>8.7 Graded tasks</td><td>x</td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>9.2 Pros and cons</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>9.3 Comparative<br>imagining of future<br>outcomes</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>10.9 Self-reward</td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td></td></tr><tr><td>10.11 Future<br>punishment</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>11.1 Pharmacological<br>support</td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td>x</td></tr><tr><td>11.2 Reduce negative<br>emotions</td><td>x</td><td></td><td></td><td>x</td><td></td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>12.2 Restructuring the<br>social environment</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>12.3 Avoidance/<br>reducing exposure<br>to cues for the<br>behavior</td><td>x</td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>13.2 Framing/<br>reframing</td><td></td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>14.1 Behavior cost</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>15.4 Self-talk</td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td><td></td></tr><tr><td>16.1 Imaginary<br>punishment</td><td>x</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

variation in the completeness and transparency of intervention description across studies.

### 3.2.6. Primary outcome

The primary outcome measure in all studies was smoking abstinence, except for Edinger et al. [Study 1 and Study 2] (1978), who focused on smoking reduction. Abstinence was mainly measured via point prevalence abstinence (n = 6; Clarke et al., 2013; Cropsey et al., 2008; Jalali et al., 2015; Naik et al., 2014; Valera et al., 2020; Winkelman et al., 2021), or continuous abstinence (n = 1; Jin et al., 2021), or both (n = 2; Richmond et al., 2006, 2013).

Abstinence was bioverified in all studies assessing this outcome, mainly by exhaled CO (n = 7; Cropsey et al., 2008; Jin et al., 2021; Naik

et al., 2014; Richmond et al., 2006, 2013; Valera et al., 2020; Winkelman et al., 2021). One study associated exhaled CO with level of carboxyhemoglobin (COHB) (Naik et al., 2014), while another used exclusively urinary cotinine levels (Clarke et al., 2013).

### 3.3. Results of individual studies

#### 3.3.1. Abstinence rate comparisons between groups (RCTs)

Five RCTs (N = 1810) were included in the analysis comparing abstinence rates of psychological interventions with control groups (Clarke et al., 2013; Cropsey et al., 2008; Jin et al., 2021; Naik et al., 2014; Winkelman et al., 2021). Study by Richmond et al. (2013) was excluded because the psychological intervention was used as the control
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Forest plot of abstinence rates in psychological intervention groups, categorized by short-, medium-, and long-term follow-up periods. This forest plot shows smoking abstinence rates following psychological interventions across different follow-up periods: short-term (<3 months), medium-term (3–6 months), and long-term (>6 months). In addition to pooled estimates for each follow-up category, a global estimate was calculated by aggregating all time points from the included studies. Each study is represented by a point estimate with a 95 % confidence interval. Abstinence rates were transformed to the logit scale for meta-analytic computations, back-transformed to proportions using the inverse logit function, and expressed as percentages to improve interpretability in the forest plot. Heterogeneity statistics ($I^2$, $\tau^2$, and p-value) are reported for each subgroup and for the overall analysis. Pooled estimates were calculated using a random-effects model (REML). Detailed sample sizes and numbers abstinent (n/N) for each group are reported in Table 1.</figcaption></figure>

group (see method). Jalali et al. (2015) was also excluded because data required for the analysis was not reported.

Individual results of this analysis are presented in Fig. 3 as odds ratios (ORs) with their 95 % confidence intervals. Cropsey et al. (2008) and Naik et al. (2014) observed the highest effects of psychological interventions on smoking cessation at a six-month follow-up, with respective ORs of 8.21 [1.93, 34.87] and 9.33 [3.93, 22.17]. However, these results were imprecise, as emphasised by very wide CIs. Clarke et al. (2013) observed a high and significant effect in favour of the psychological intervention at three-week follow-up (OR = 4.39 [1.99,

9.69]), but CI, although more restricted than in previous studies, also indicated variability in results. Jin et al. (2021) showed that the psychological intervention modestly increased the chances of quitting smoking at the 3-month follow-up (OR = 1.22 [0.84, 1.75]), but CI included 1, the effect was therefore not significant. In contrast, Winkelman et al. (2021) concluded that there was no effect of the intervention at three-month follow-up (OR = 0.75 [0.07, 7.60]), but wide CI did not provide reliable conclusions. Heterogeneity in effect sizes and high CIs indicates some uncertainty in these findings.
### 3.3.2. Abstinence rates in psychological intervention groups across follow-up periods

Seven studies (N = 1708) measured abstinence rates following psychological intervention (see Fig. 4). Studies that did not report these data were excluded from the analysis (Edinger et al. [Study 1 and Study 2], 1978; Jalali et al., 2015; Valera et al., 2020). Abstinence data from the control group of Richmond et al. (2013), which assessed the psychological intervention, were included in this analysis.

Short-term abstinence (≤ 1 month) ranged from 13.0 % to 25.4 %, with the lowest rate observed by Winkelman (2021) at one week post-intervention (95 % CI [4.3, 33.5]) and the highest rate by Clarke et al. (2013) at three weeks (95 % CI [18.5, 33.9]). Cropsey estimated an abstinence rate of 18.4 % at post-intervention (95 % CI [14.1, 23.7]).

Abstinence in the medium term (>1 and ≤6 months) varied between 13.0 % and 33.3 %. The lowest rate was found again by Winkelman et al. (2021) (95 % CI [4.3, 33.5]) at 3 months, with exactly the same results as those reported in the short term. Richmond et al. (2006) obtained the highest rate at 5 months (95 % CI [19.0, 51.6]). At 3 months, abstinence rate of Richmond et al. (2013) was 19.6 % (95 % CI [14.9, 25.4]). Cropsey et al. (2008) found a rate of 16.8 % (95 % CI [12.7, 22.0]) at 3 month, which is slightly lower than the short-term rate. Jin et al. (2021) reported the second highest rate in medium term with 31.2 % abstinence at 3 months after the psychological intervention (95 % CI [25.9, 37.0]).

Long-term abstinence (≥ 6 months) ranged from 14.0 % to 23.3 %. The lowest rate was recorded by Cropsey et al. (2008) at 6 months follow-up (95 % CI [10.2, 18.9]), showing a further decline compared to earlier time points. In contrast, Richmond et al. (2006) observed the highest abstinence rate at this same follow-up (95 % CI [11.6, 41.5]), despite a decrease relative to the 5-month assessment. At 6 months, Naik et al. (2014) reported an abstinence rate of 16.0 % (95 % CI [12.3, 20.6]) and Richmond et al. (2013) observed a similar rate of 14.2 % (95 % CI [10.1, 19.4]). Yet, at 12-month follow-up, the proportion had significantly increased, with 14.6 % of participants who were abstinent (95 % CI [10.5, 19.9]).

## 3.4. Meta-analytic results

### 3.4.1. Abstinence rate comparisons between groups (RCTs)

We observed a substantial overall effect of psychological interventions on smoking abstinence, as estimated by the random effects model (REML) (OR = 3.41, 95 % CI [1.34, 8.70], z = 2.57; *p* < .05). However, the wide confidence intervals reflect uncertainty regarding the precision of the overall effect estimate. Substantial and statistically significant heterogeneity observed between studies ($I^2$ = 85.4 %, $\tau^2$ = 0.8121, *p* < .0001) highlight high variability in treatment efficacy across interventions. Regarding risk of bias analysis, asymmetry observed via funnel plot was not statistically confirmed by Egger's test (t = 1.29, df = 3, *p* = 0.288). As the test has limited statistical power due to the small number of studies (n = 5), these results should be interpreted with caution. To adjust asymmetry, the Trim-and-Fill method added two hypothetical studies. After correction, the combined effect was reduced and no longer significant (OR = 1.60, 95 % CI [0.51, 5.07], z = 0.80, *p* = .423), and substantial heterogeneity persisted despite the adjustment ($I^2$ = 88.88 %, $\tau^2$ = 2.04, $\tau$ = 1.43). These results suggest that the observed overall effect may be overestimated due to potential publication bias, with the apparent benefit possibly inflated by small or unpublished studies.

The leave-one-out sensitivity analysis identified Jin et al. (2021) study as the most influent, its exclusion led to a significant increase in overall effect (OR = 5.67, 95 % CI [3.07; 10.46], *p* < 0.0001) and a substantial decrease in heterogeneity ($I^2$ = 38.2 %).

Finally, we conducted a sensitivity analysis comparing studies conducted in smoke-free versus non-smoke-free prison settings. Although the two smoke-free studies showed lower odds ratios, this difference was not statistically significant according to meta-regression (*p* = 0.298).

Abstinence rate across follow-up

<figure><img src="image_7.png" /></figure>

**Fig. 5.** Meta-regression analysis of smoking abstinence rates across follow-up duration. This figure displays the results of a meta-regression examining the association between follow-up duration (in weeks) and smoking abstinence rates reported across the psychological intervention arms of the included studies. This analysis was conducted as part of the second meta-analytic model, which synthesized abstinence rates expressed as proportions. Each dot represents a study or time point, plotted according to its follow-up duration on the x-axis and its corresponding smoking abstinence rate (expressed as a percentage) on the y-axis. The solid curve represents the fitted meta-regression line, estimated using a random-effects meta-regression model. Abstinence rates were logit-transformed prior to analysis and back-transformed for presentation as percentages. This figure illustrates the trend in abstinence rates over time and helps explore whether the effectiveness of psychological interventions varies according to the length of follow-up.

### 3.4.2. Abstinence rates in psychological intervention groups across follow-up periods

Analysis of the seven studies using the multivariate random-effects model showed a combined abstinence rate of 18.8 % (95 % CI [15.5, 22.1]). Heterogeneity was moderate to high ($I^2$ = 56.1 %, $\tau^2$ = 0.1356), and significant between studies (Q = 45.779, *p* < 0.0001). A very strong intra-study correlation between repeated measurements was observed (*p* = 0.9621). In addition, the statistical comparison of subgroups (short, medium and long-term) revealed a significant difference between the follow-up periods in abstinence rate (QM(df = 2) = 7.04, *p* = 0.030). In short term, overall abstinence was 20.1 % (95 % CI [14.4, 25.7]), with low and non-significant heterogeneity ($I^2$ = 21.4 %, $\tau^2$ = 0.0412, *p* = 0.198), indicating that the results in this time period showed few variations. In the medium term, abstinence was slightly higher (22.5 %, 95 % CI [15.3, 29.6]) compared to other follow-up periods. Heterogeneity was also higher and significant for this time period ($I^2$ = 54.5 %, $\tau^2$ = 0.1455, *p* = 0.006). Long-term abstinence was the lowest of the time period with an overall abstinence rate estimated at 14.9 % (95 % CI [12.7, 17.1]), with low heterogeneity ($I^2$ = 0 %, $\tau^2$ = 0, *p* = 0.705). Fig. 6 illustrates pooled abstinence rates across short-, medium-, and long-term follow-up windows.

As an additional analysis, a meta-regression was conducted to assess how abstinence rates varied according to the time interval (in weeks) between the intervention and the outcome measurement (see Fig. 5). The model revealed a negative association between time since intervention and abstinence. At post-intervention (week 0), the model-estimated abstinence rate was 23.39 % (log-odds = - 1.19, SE = 0.17, *p* < 0.0001). Each additional week was associated with a 0.18 % reduction of abstinence (log-odds = - 0.0099, SE = 0.0051, *p* = 0.050), corresponding to an approximate decrease of 0.7–0.8 % per month. After 6 months, abstinence rate had dropped to 18.8 % and after one year to 14.1 %. Compared to the initial model, inclusion of follow-up time improved adjustment (AIC = 14.91 vs. 15.66) and slightly reduced inter-study variance ($\tau^2$ = 0.1150 vs. 0.1356). However, heterogeneity remained significant (QE(df = 11) = 36.5156, *p* < 0.0001), indicating that follow-up duration alone did not account for the
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Pooled abstinence rates across follow-up windows. This figure presents pooled abstinence rates at short-term (<3 months), medium-term (3–6 months), and long-term (>6 months) follow-up intervals. Based on 13 assessments across seven studies, abstinence rates were 20.1 % in the short term, 22.5 % in the medium term, and 14.9 % in the long term.</figcaption></figure>

variability across studies.

### 3.5. Risk of bias in studies

Among the 11 studies, seven RCTs were evaluated using ROB 2.0 (Clarke et al., 2013; Cropsey et al., 2008; Jalali et al., 2015; Jin et al., 2021; Naik et al., 2014; Richmond et al., 2013; Winkelman et al., 2021) and the remaining studies by the MINORS (n = 4; Edinger et al. [Study 1 and Study 2], 1978; Richmond et al., 2006; Valera et al., 2020).

The ROB 2.0 assessment revealed an overall high risk of bias in six studies, whereas one study was assessed as having some concerns (see Fig. 2). Highest risks of bias were found in domains of missing outcome data and measurement of outcomes, where five studies out of seven were rated as high risk of bias. Deviations from intended interventions and selection of reported results showed moderate concerns in most studies. Five studies were assessed as having some concerns in the randomisation

process and two studies showed a low risk. In addition, two studies showed risks of bias in four of the five domains evaluated by the tool, indicating significant methodological weakness.

The MINORS evaluation revealed methodological limitations across several studies, with scores ranging from 3 to 7 out of a maximum of 16, and an average of 5 (see Table 3). Most studies clearly stated their aim and follow-up periods were generally adequate for the outcomes assessed. Two studies included consecutive patients, indicating that all eligible participants were enrolled in sequence during the recruitment period. However, notable methodological weaknesses were observed in key items: prospective data collection, calculation of sample size prior to recruitment, protocol established before the study was launched, endpoints appropriate to the aim of the study and unbiased assessment of study endpoint. Also, all studies had an attrition rate above 5 %.

<table><caption>**Table 3**

Methodological quality assessment of non-randomized studies using the</caption><thead><tr><th></th><th>Clearly stated aim</th><th>Inclusion of consecutive patients</th><th>Prospective collection of data</th><th>Endpoints appropriate to the aim of the study</th><th>Unbiased assessment of the study endpoint</th><th>Follow-up period appropriate to the aim of the study</th><th>Loss to follow up less than 5 %</th><th>Prospective calculation of the study size</th><th>Total score</th></tr></thead><tbody><tr><td>Edinger et al. [Study 1] (1978)</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>0</td><td>4/16</td></tr><tr><td>Edinger et al. [Study 2] (1978)</td><td>2</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>3/16</td></tr><tr><td>Richmond et al. (2006)</td><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>2</td><td>0</td><td>0</td><td>7/16</td></tr><tr><td>Valera et al. (2020)</td><td>2</td><td>2</td><td>0</td><td>1</td><td>0</td><td>1</td><td>0</td><td>0</td><td>6/16</td></tr></tbody></table>

### MINORS tool

Each item is scored from 0 (not reported), 1 (reported but inadequate), to 2 (reported and adequate), with a maximum possible score of 16. Higher scores indicate better methodological quality.
<table><caption>**Table 4**
GRADE Assessment of certainty of evidence for primary outcomes.</caption><thead><tr><th>Outcome</th><th>Study design(s)</th><th>No. of studies</th><th>Participants</th><th>Effect (95 % CI)</th><th>Certainty of evidence</th><th>Key reasons for downgrading / comments</th></tr></thead><tbody><tr><td>Biochemically verified abstinence (psychological vs control)</td><td>RCT</td><td>5</td><td>1 810</td><td>OR = 3.41 (1.34 – 8.70)</td><td>Very low</td><td>Risk of bias: 5/5 trials high RoB.<br>Inconsistency: substantial heterogeneity (I² = 85.4 %)<br>Imprecision: wide confidence intervals (95 % CI: 1.34–8.70).<br>Publication bias: effect lost significance after Trim-and-Fill (OR = 1.60, 0.51 – 5.07).</td></tr><tr><td>Absolute abstinence after any psychological intervention</td><td>RCT + non-RCT</td><td>7</td><td>2 265</td><td>18.8 % (15.5 – 22.1)</td><td>Very low</td><td>Study design: includes non-randomised studies (baseline = Low)<br>Risk of bias: two non-RCTs MINORS ≤ 7/16; all RCTs high RoB.<br>Inconsistency: moderate heterogeneity (I² = 56 %).<br>Indirectness: follow-up windows vary across trials.</td></tr></tbody></table>

**Note.** Certainty of evidence follows GRADE: High, Moderate, Low, Very low.

This table summarizes the overall certainty of evidence for the two main outcomes assessed in the review, based on the GRADE framework. For each outcome, we report the study design, number of studies and participants, effect size with 95 % confidence interval, the overall certainty rating, and key reasons for downgrading. Certainty was rated as very low for both outcomes, due to risk of bias, inconsistency, imprecision, and publication bias or indirectness.

### 3.6. Certainty of evidence (GRADE assessment)

We used the GRADE framework to assess the overall certainty of evidence for the two main outcomes. Both were rated as very low certainty, due to multiple sources of bias and inconsistency. A summary table is provided in Table 4.

#### 3.6.1. First outcome: biochemically verified abstinence (psychological vs. control)

Based on 5 RCTs (N = 1810), the pooled OR was 3.41 (95 % CI: 1.34–8.70). Certainty was downgraded due to high risk of bias in all included trials, wide confidence intervals (95 % CI: 1.34–8.70), substantial heterogeneity (I² = 85.4 %), and potential publication bias (effect lost significance after Trim-and-Fill: OR = 1.60, 95 % CI: 0.51–5.07).

#### 3.6.2. Second outcome: absolute abstinence after psychological intervention

Based on 7 studies (5 RCTs, 2 non-RCTs; N = 2265), the pooled abstinence rate was 18.8 % (95 % CI: 15.5–22.1). Certainty was downgraded due to inclusion of non-RCTs high risk of bias across all RCTs (5/5 rated as high risk) and both non-RCTs (MINORS ≤ 7/16), moderate heterogeneity (I² = 56 %), and variability in outcome definitions and follow-up durations.

### 4. Discussion

This systematic review and meta-analysis identified 11 studies (N = 2688) from four countries that examined psychological interventions for smoking cessation in prison populations. Although the findings suggest a potential association between psychological interventions and smoking abstinence, the evidence should be interpreted with caution due to methodological limitations and publication bias. Across the five RCTs, the pooled effect size was OR = 3.41 (95 % CI [1.34, 8.70], z = 2.57; p < .05). Yet, heterogeneity was substantial (I² = 85.4 %, τ² = 0.8121, p < .0001). After correction for publication bias, the effect was attenuated and no longer significant (OR = 1.60, 95 % CI [0.51, 5.07], z = 0.80, p = .423).

While the evidence remains limited and the effect size was reduced after accounting for publication bias, the results suggest that psychological interventions could play a role in supporting smoking cessation in prison populations. Social Cognitive Theory (Bandura, 1986) highlights three mechanisms, self-efficacy, behavioural modelling and outcome expectancies, that are routinely targeted in effective community-based programmes. Meta-analytic evidence shows that higher quit-specific self-efficacy substantially increases the likelihood of

long-term abstinence (Gwaltney et al., 2009), and that both motivational interviewing and cognitive-behavioural skills training raise quit rates in part by strengthening that construct (Hettema and Hendricks, 2010; Lancaster and Stead, 2017). Translating these insights to correctional settings suggests that programmes combining MI-style exploration of outcome expectancies with structured coping-skills practice (e.g., managing high-risk cues) could bolster inmates' confidence to resist smoking, even in restrictive and cue-rich environments. Beyond cessation itself, psychological interventions may foster coping skills and stress reduction, which could indirectly support long-term abstinence. Future trials should therefore not only include validated measures of self-efficacy and perceived behavioural control but also consider broader psychosocial outcomes such as coping and stress management, to refine theory-driven intervention design.

Across the 13 follow-up observations pooled from the seven included studies, 18.8 % of participants achieved biochemically verified abstinence, a figure that aligns with the outcomes observed in general population samples undergoing behavioral interventions (Lemmens et al., 2008, 2008; Lindson et al., 2019; Nian et al., 2023; Piasecki, 2006). Stratifying by follow-up length revealed a clear temporal gradient: abstinence was 20.1 % in the short term (assessments <3 months), 22.5 % in the medium term (3–6 months), and 14.9 % in the long term (>6 months). The difference between these windows was statistically significant (Q² = 7.04, p = 0.030), highlighting the difficulty of sustaining cessation in correctional settings over time. Further analysis confirmed a gradual decline in abstinence rates (log-odds = -0.0099, p = .050), suggesting an attenuated effect of treatment with longer follow-up. However, it is important to note that relapse trends in the general population tend to be even more pronounced (Brandon et al., 1990; Hughes et al., 2008), even in interventions specifically designed for relapse prevention (Livingstone-Banks et al., 2019). It seems that further exploration of additional moderators and mechanisms of intervention response could be beneficial in order to better tailor cessation programs to this high-risk population.

#### 4.1. Limitations

It is important to interpret these findings with caution due to methodological limitations.

We observed substantial heterogeneity across studies, reflecting differences in design, interventions, and populations, which limits the generalisability of our results.

Nine of the eleven eligible studies were conducted in high-income countries (United States and Australia). Only two trials came from low- or middle-income countries (India and Iran), where differences in
resources, healthcare systems, and norms surrounding tobacco use may limit the generalizability of our findings to other correctional settings. In addition, the pooled sample was predominantly male (81.2% across studies), which reflects the demographic profile of most incarcerated individuals but limits confidence in the applicability of our estimates to women or gender-diverse individuals.

The overall evidence base was also limited in scope and quality. Only 11 studies were eligible, including seven RCTs, and six of these RCTs were rated as high risk in at least one ROB 2.0 domain. Non-randomized studies obtained low MINORS scores (ranging from 3 to 7 out of 16), further reflecting methodological weaknesses. Because most studies were classified as high risk, sensitivity analyses excluding them were not feasible. These limitations were nevertheless accounted for in the GRADE assessment, which rated both outcomes as very low certainty due to bias, imprecision, and inconsistency. Reporting of follow-up outcomes was also inconsistent and often incomplete, with several trials omitting medium- or long-term assessments, reducing the ability to evaluate sustained abstinence. Moreover, most studies (7 out of 11) combined psychological interventions with pharmacotherapy (e.g., NRT or bupropion), which introduces a confounding factor and complicates interpretation.

Implementation in prison settings also presents specific challenge. Contextual constraints such as limited staff availability, frequent inmate transfers, and inconsistent institutional support may compromise intervention fidelity and reduce participant retention. Variability in delivery quality, including differences in counselor training and the presence of institutional smoking bans, may also have influenced study outcomes.

Finally, subgroup analyses by age, sex, or type of therapy would have been highly informative. However, the small number of studies included in the meta-analyses, combined with substantial heterogeneity in intervention types, did not allow for such analyses to be conducted meaningfully. We were also unable to conduct a publication bias analysis for the post-psychological intervention abstinence rate analysis due to the limited number of studies (n = 7) and the repeated measures involved in this analysis. Our initial plan to investigate smoking reduction was also abandoned due to insufficient data.

Beyond these analytical limitations, prison-based research may also be particularly vulnerable to publication bias, as studies with null or negative results could be less likely to be written up or published due to ethical, logistical, and access barriers. This possibility further underscores the need for cautious interpretation of our findings

## 4.2. Implications and future directions

Future trials should prioritize methodological rigor. The small number of existing studies, combined with high heterogeneity, highlights the need for research conducted in line with CONSORT guidelines (Bennett, 2005). Pre-registration of protocols and transparent reporting of methods are essential to ensure replicability. Increasing sample sizes and implementing strategies to improve retention in studies will help achieve sufficient statistical power and reduce attrition-related bias. To address the poor reporting of follow-up outcomes observed in several trials included in this review, we echo recent methodological guidance (e.g., the CONSORT-SR and Russell Standard for Smoking Cessation Trials) in calling for future prison-based cessation studies to adopt standardized outcome definitions (point prevalence, continuous abstinence, or prolonged abstinence), and uniform assessment windows (e.g., four weeks, six months, or 12 months). Complete outcome reporting, including denominators and reasons for missing data, is essential for robust meta-analysis and evidence-informed policy.

It is equally important to identify the active components of psychological interventions. The Behaviour Change Technique Taxonomy v1 (Michie et al., 2013) provides a standardized framework to systematically code intervention components, which facilitates the identification of the active ingredients of psychological interventions. Using this

taxonomy would enhance replicability and facilitate comparative effectiveness analyses across studies. For instance, the efficacy of techniques such as goal setting, self-monitoring, and social support in facilitating smoking cessation outcomes among incarcerated individuals may vary, and these techniques should be isolated in future designs. The systematic classification of the components of psychological interventions allows for a more precise identification of active ingredients, facilitates their replication and provides a common language between researchers and practitioners. Among the BCTs, self-incentives have shown promising results in incarcerated populations (Brown et al., 2025), offering a low-cost and practical strategy to enhance intervention effectiveness.

Scalability and sustainability also need careful evaluation. Limited financial and human resources are common constraints in correctional institutions. Assessing cost-effectiveness and logistical feasibility will therefore be essential to ensure that evidence-based interventions can be adopted on a larger scale and integrated into standard correctional health services.

Finally, conducting RCTs in correctional settings raises unique ethical considerations. The methodological literature highlights challenges such as ensuring that consent is fully informed and perceived as voluntary and addressing continuity of care after release, a critical determinant of long-term abstinence, which should be carefully incorporated into the design and reporting of future trials (Gostin et al., 2007; Simpson et al., 2025).

The implementation of these methodological enhancements is expected to yield substantial contributions to the field, primarily in the form of high-quality studies and the evaluation of psychological interventions for smoking cessation in the incarcerated population.

## 5. Conclusion

This review provides the first quantitative synthesis of psychological interventions for smoking cessation in incarcerated populations. While the pooled results suggest a favourable effect of psychological interventions, this benefit may be overestimated due to potential publication bias. Moreover, the small number of high-quality studies and substantial heterogeneity further limit the robustness and generalisability of the findings, warranting cautious interpretation. Future studies should employ rigorous methodologies to ensure more robust generalisable conclusions. Considering the high smoking prevalence in this population and the health burden, further research is urgently needed.

### Authorship statement

All authors have made substantial contributions to the following: (1) the conception and design of the study, the extraction of data, or the analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content; (3) final approval of the version to be submitted for publication.

We confirm that no other individuals meet the authorship criteria but are not listed. Furthermore, we confirm that the order of authorship has been agreed upon by all authors involved.

### CRediT authorship contribution statement

**Xavier Laqueille:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.
**Oulmann ZERHOUNI:** Writing – review & editing, Methodology, Formal analysis.
**Katia Illel:** Supervision, Funding acquisition.
**Fanny Ngre:** Writing – review & editing, Data curation.
**Carine Meslot:** Writing – review & editing, Validation, Methodology, Formal analysis.
**Thibault Hennequin:** Writing – review & editing, Investigation, Formal analysis, Data curation.
**Lucia Romo:** Writing – review & editing, Supervision, Project administration, Methodology, Data curation,
Conceptualization. **Mélanie Rome:** Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Publication statement

The work described in this manuscript has not been published previously. The article is not under consideration for publication elsewhere. If accepted, the article will not be published elsewhere in the same form, either in English or in any other language, including electronically.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT to assist with the translation into English and improve the language. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.

## Funding

This systematic review was funded by the Regional Health Agency (Agence Régionale de la Santé, ARS) through a dedicated fund for addiction prevention and treatment. The funder had no role in the design, conduct, analysis, or reporting of this work.

## Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Melanie Rome reports financial support was provided by Regional Health Agency Ile-de-France. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

# Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.drugalcdep.2025.112941.

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