1. Introduction

Addiction is defined as a chronic, relapsing brain disorder. Substance misuse is a significant global issue, particularly in developed countries. The most commonly abused substances are alcohol and illicit drugs [1]. In 2020, an estimated 284 million people (5.6%) aged 15–67 had used a drug in the last 12 months [2,3]. This fact represents a 26% increase compared to 2010 [4]. Global estimates of drug users include 209 million for cannabis, 61 million for opioids, 34 million for amphetamines, and 20 million for cocaine and ecstasy [4]. The World Health Organization (WHO) estimated that 283 million people had alcohol use disorders worldwide in 2016 [1]. The most dangerous substance is opioids, which are the leading cause of drug overdose deaths, as tolerance decreases after a period of abstinence during the relapse phase [5,6,7]. Relapse rates for substance use, ranging from 40% to 93% within the first six months after treatment, highlight the need for relapse-sensitive care and additional treatment methods [1].

Relapse in substance use is a concept applied across all disciplines in health and behavioral science, particularly in the field of addiction. It refers to a return to substance use after an individual has previously managed to control or altogether quit the addiction. Nicotine, heroin, and alcohol have shown similar relapse rates over one year, ranging from 80% to 95% [8].

Various mechanisms can trigger relapse in drug and alcohol use, including stress, high-risk situations, failure to cope with temptation, and craving [9].

Several methods exist to prevent relapse from addiction to high-risk substances such as drugs, alcohol, tobacco, or gambling. These methods can be categorized into pharmacological and non-pharmacological approaches.

Pharmacological treatments work by targeting specific neurotransmitters in the brain to reduce cravings, withdrawal symptoms, and the reinforcing effects of addictive substances or behaviors. Naltrexone or acamprosate are prescribed for alcohol addiction; bupropion or varenicline for smoking cessation; and methadone or buprenorphine for opioid addiction [10,11,12,13].

Non-pharmacological approaches to relapse prevention include cognitive behavioral therapy (CBT), motivational interviewing, peer support groups, mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies such as yoga, acupuncture, and sound therapy. An innovative and thoroughly researched strategy involves using cutting-edge virtual reality technology to reduce the risk of relapse, revolutionizing the field of addiction intervention and prevention.

The purpose of this meta-analysis is to highlight significant new developments in the field of high-risk alcohol and drug addiction relapse, focusing on various study populations worldwide, across different age groups, and including individuals who have received pharmacological and non-pharmacological interventions during detoxification for relapse prevention.

The objectives of this paper are to explore and evaluate recent advancements in relapse prevention strategies for individuals recovering from high-risk addictions to substances such as alcohol, opioids, and illicit drugs. The research aims to identify and synthesize key findings across diverse populations and age groups, focusing on the effectiveness of pharmacological and non-pharmacological interventions in reducing relapse rates during and after detoxification.

2. Materials and Methods

All methodologies adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) [14] to execute this study.

2.1. Data Collection

A comprehensive literature search was conducted across medical, psychiatric, and psychological databases for studies published between January 2013 and December 2023. Multiple electronic databases were systematically explored, including PubMed, Cochrane Library, Google Scholar, Semantic Scholar, and Consensus. The search strategy utilized the following key terms: ‘Addiction relapse prevention’, ’Drug relapse prevention’, and ‘Alcohol relapse prevention’, combined with the Boolean operator ’OR’ to ensure a broad retrieval of relevant studies.

The studies incorporated in the meta-analysis fulfilled the inclusion criteria:

• Participants: studies that include patients diagnosed with alcohol use disorder (AUD) and high-risk drug addiction who were enrolled in relapse prevention programs. Participants were selected based on predefined eligibility criteria, including the severity of addiction, willingness to participate, and engagement in structured relapse prevention interventions.

• Study Design: Studies were selected based on specific inclusion criteria, such as publication date (e.g., studies published within the last 10 years), peer-reviewed status, and language (English only). These criteria were established to ensure the inclusion of high-quality, recent, and accessible evidence. Randomized trials were prioritized to minimize bias and establish causal relationships, while the cross-sectional study provided additional insights into population characteristics and trends.

• Intervention: Participants received various interventions, including pharmacological (e.g., medications like naltrexone or acamprosate) and non-pharmacological approaches (e.g., cognitive-behavioral therapy, motivational interviewing, and contingency management). The selection of interventions was based on their evidence-based efficacy in relapse prevention and their applicability to the target population.

• Outcomes: The studies reported key outcomes such as gender distribution, type of addiction (alcohol vs. drug), and the effectiveness of interventions in reducing relapse rates. The primary outcome measure was the average relapse period, reported in months. Secondary outcomes included adherence to treatment, quality of life, and adverse effects of interventions.

The inclusion criteria ensured methodological rigor and relevance to the research question. Randomized clinical trials were prioritized to reduce selection bias and confounding factors. However, potential sources of bias, such as publication bias (the tendency to publish only positive results) and heterogeneity in intervention protocols across studies, were acknowledged. A comprehensive search strategy was employed to address these, including gray literature and unpublished studies where possible. Additionally, while limited in establishing causality, the cross-sectional research provided valuable descriptive data on patient demographics and addiction profiles.

2.2. Study Selection

Studies were independently assessed for inclusion based on titles, keywords, and abstracts. A workflow diagram was created to illustrate the research process for literature screening and study selection (Figure 1).

_{Figure 1. Flow diagram of preferred reporting items and the exclusion criteria.}

2.3. Data Extraction

The data were extracted as follows: country of research and year of publication, type of study, number of participants, mean age of participants, gender distribution (percentage of females and males), type of substance use issue, average relapse period of patients in each study, and the specific relapse prevention intervention used (Figure 1).

2.4. Data Synthesis and Analysis

The extracted data were analyzed using Python 3 in Google Colaboratory, employing libraries such as pandas, statsmodels, matplotlib, seaborn, and scipy.stats. The analysis included descriptive statistics and regression models examining relationships between the mean relapse period, average age, and gender distribution (percentage of males and females). Additionally, the study presents the results of hypothesis testing, linear regression trends over the years, and the distribution of patients based on the type of substance use. A significance level of p < 0.05 was considered the threshold for statistical significance in all analyses, indicating that the probability of the observed results occurring by chance is less than 5%.

3. Results

A workflow chart for study selection was prepared following the Preferred Reporting Items for Systematic Review and meta-analysis guidelines [14]. The titles and abstracts of 934 articles were screened; 12 studies [10,11,13,15,16,17,18,19,20,21,22,23] fulfilled all inclusion criteria and included 2162 patients. Table 1 summarizes the studies’ characteristics.

Table 1. Characteristics of studies.

All selected studies addressed issues related to substance or alcohol abuse. The most frequently reported substances included combinations of opioids, heroin, cocaine, methamphetamine, and marijuana. The studies encompassed a wide range of pharmacological and non-pharmacological interventions, such as mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies.

An analysis of the demographic data across the studies showed that participants ranged in age from 18 to 70 years, with a mean age of 41.43 years in the meta-analysis. Regarding gender distribution, the data reinforce the well-documented trend that addictive behaviors are more prevalent among men. Specifically, 70% of participants were male, while 30% were female (Figure 2).

Figure 2. Gender distribution.

3.1. Correlation of the Mean Period of Relapse in Studies over Other Characteristics

The initial line of analysis focused on determining the distribution of participants across the studies based on the type of substance use. The findings revealed that the majority of patients were high-risk drug users (Figure 3).

Figure 3. Distribution of the number of participants according to the type of substance use.

To analyze how different participant characteristics influence the mean relapse period, the best-fitting model, the Ordinary Least Squares (OSL) regression model, was selected based on the dataset.

The model evaluating the relationship between mean age and relapse period demonstrated an R-squared value of 0.442, indicating that age accounts for 44.2% of the variance in relapse duration—a moderate explanatory power. The associated F-statistic (8.724) and p-value (0.0131) confirm the statistical significance of this model at the 5% level, suggesting that age is a meaningful predictor. In contrast, gender-related models (both male and female percentages) yielded lower R-squared values (0.171 and 0.147, respectively) and non-significant p-values (>0.05), indicating a weaker and statistically inconclusive relationship with relapse duration. Additionally, an ANOVA test evaluating intervention type revealed a highly significant F-statistic (2.195 × 10²⁸) with a p-value < 0.0001, emphasizing the strong impact of intervention strategies on relapse outcomes. These analyses support the conclusion that age and intervention type are the most statistically relevant predictors of relapse duration in the examined population.

In analyzing the influence of gender, an R-squared value of 0.171 was observed, indicating that the percentage of male participants explains approximately 17.1% of the variability in the mean relapse period. The F-statistic for the relationship between the male group and the relapse period was 2.264, with a corresponding p-value of 0.161. This suggests the model is not statistically significant at the conventional 0.05 significance level. A 95% confidence interval ([0.025, 0.975]) provides a range of plausible values for the actual population coefficient.

In the model examining the relationship between the female gender and the mean relapse period, the coefficient for the percentage of female participants was −0.0338. This indicates that the expected mean relapse period decreases by approximately 0.0338 months for each one-unit increase in the percentage of females. The confidence interval reflects the standard error [0.926, −0.025]. An R-squared value of 0.147 suggests that the proportion of female participants can explain about 14.7% of the variability in the mean relapse period. The F-statistic for this model was 1.901, with a corresponding p-value of 0.195, indicating that the model does not reach statistical significance at the conventional 0.05 level.

3.2. Effect of Interventions in Different Types of Addiction

The ANOVA test was chosen as the statistical test to evaluate the effect of different interventions on each study’s mean relapse period registration.

The sum of squares for the factor intervention type was 52.77, representing the portion of the variability in the mean relapse period explained by the different intervention categories. The F-statistic for the intervention type was approximately 2.20 × 10²⁸, indicating an extremely high test statistic value used to assess the overall significance of the intervention type on the mean relapse period.

The probability associated with the F-statistic (PR(>F)) for the intervention type factor was approximately 5.26 × 10⁻¹⁵, indicating a highly significant result. This suggests that the likelihood of obtaining the observed F-statistic under the null hypothesis—assuming no effect of intervention type on the mean relapse period—is extremely low. The absence of an F-statistic and associated p-value for the residuals indicates insufficient information to assess the significance of the residual variability.

Linear regression analysis of the mean relapse period across publication years did not reveal an increasing trend, suggesting that the evolution of therapeutic approaches has not significantly extended the average relapse period (Figure 4). Furthermore, a decline in research interest on relapse prevention methods was noted over the past two decades, with the majority of studies published between 2014 and 2018.

Figure 4. Regression of mean relapse period over the years.

The forest plot illustrates relapse outcomes across multiple studies (Figure 5). Effect sizes represent the difference in relapse duration between treatment groups, with error bars indicating the confidence intervals. Studies such as Mahajan (2020) [15] and Rong (2016) [22] reported longer relapse periods, whereas others like Glasner (2016) [18] demonstrate shorter durations. The plot highlights substantial variability in relapse outcomes across studies, suggesting possible differences in treatment efficacy or methodological approaches.

Figure 5. Forest plot of the mean time of relapse (months) across multiple studies [10,11,12,13,15,16,17,18,19,20,21,22,23].

4. Discussion

This study conducted a comprehensive meta-analysis of 12 studies to examine key aspects of high-risk alcohol and drug addiction relapse across diverse populations worldwide, spanning various age groups and including individuals who received pharmacological and non-pharmacological interventions for relapse prevention during the detoxification phase.

The primary finding regarding the effect of mean age on relapse prevention is statistically significant, with a p-value of 0.0131 in the regression model. An R-squared value of 0.442 indicates that approximately 44.2% of the variance in the mean relapse period is explained by age. Notably, the studies by Gonzales (2012) [24] and Satre (2011) [25] offer valuable insights into relapse dynamics. The results suggest that younger individuals are more responsive to relapse prevention interventions for alcohol and drug addiction, highlighting the nuanced and complex nature of relapse within this demographic [24,25,26,27].

Age can influence various factors associated with relapse, including psychological resilience, comorbidities, social dynamics, and treatment responses. Understanding these can inform strategies that optimize recovery outcomes. Research shows that older adults often experience complex health profiles, frequently with higher rates of comorbidity, which can amplify the risk of relapse [28]. Young adults may respond well to technology-based solutions, such as smartphone apps that help monitor mood and provide just-in-time adaptive interventions based on behavioral triggers [29]. These technologies can effectively engage younger populations in their recovery and prevent relapses by offering real-time support and resources tailored to their needs [30].

The findings highlight that no single factor can independently predict relapse among youth [25]. While individual-level factors significantly influence the initiation and maintenance of substance use, a wide range of social and environmental influences also play a critical role in this process [31,32]. Therefore, understanding the complex interplay between personal characteristics, social dynamics, and broader environmental factors is essential for comprehending the developmental trajectories of relapse among youth undergoing treatment [24,33,34,35]. Rehabilitation has been linked to poorer outcomes over 5–9 years of consumption, particularly among individuals aged 40 and above at the study’s outset. In such cases, rehabilitation may indicate a higher risk of relapse or more severe substance-related issues within this population [25,36,37].

Emerging treatment approaches—such as virtual reality (VR) and digital medicine—offer new perspectives in relapse prevention [38,39,40]. Huang (2021) observed that VR therapy was more effective in preventing relapse among younger individuals compared to adults [41]. VR therapy enhances the sense of presence, allowing individuals to engage with simulated environments actively [41]. Digital interventions encompass a variety of strategies, including psychological therapies, cognitive function enhancement programs, and innovative technologies such as VR and biofeedback/neurofeedback. The primary appeal of digital medicine lies in its accessibility and convenience. As these technologies advance and become more widely adopted, digital medicine is expected to provide cost-effective alternatives to traditional medical services [41,42,43].

Regarding the impact of gender, the regression model suggests that a higher percentage of male participants may be associated with a longer mean relapse period; however, this effect is not statistically significant at the conventional 0.05 significance level. The model accounts for approximately 17.1% of the variability in the mean relapse period, but the overall significance remains questionable. Similarly, the model analyzing the percentage of female participants explains about 14.7% of the variance. The constant term has a coefficient of 4.324 with a standard error of 0.926. The coefficient for the percentage of females is −0.034, with a standard error of 0.025, but this result is not statistically significant (p = 0.195), and the overall model significance remains uncertain (p = 0.195).

Becker (2016) suggests that women may be more vulnerable to addiction, with a faster progression from initial use to dependence on both drugs and alcohol compared to men [44]. Additionally, women are reported to be more sensitive to the effects of stress and interpersonal difficulties in the context of alcohol addiction and relapse [44,45]. However, a 2021 review of clinical studies challenges this view, finding no consistent evidence that women are more vulnerable than men to psychostimulants, opioids, or related relapse. The available data do not support significant gender differences in craving or relapse rates [46]. On the other hand, research shows that women experience different antecedents and risks associated with substance abuse compared to men. For instance, women are more often influenced by personal relationships and social dynamics, such as stress from marriage, feelings of depression, and relationship-based substance use, which can markedly elevate their relapse potential [47,48]. Greenfield et al. emphasize that the reasons for female relapse are frequently tied to their psychosocial contexts, fundamentally differing from the external situational factors more often cited by male substance users [49]. This illustrates a need for gender-sensitive treatment approaches that consider the relational and emotional factors impacting women specifically. Moreover, studies indicate that, while women may initially engage in substance use for reasons like mood regulation and emotional coping, men are more likely to use substances for experimentation and social acceptance [50]. This fundamental difference carries through to treatment and relapse scenarios. It has been found that women are less likely to relapse after treatment compared to men, mainly when they obtain sufficient social and familial support. Yet, when they do relapse, it tends to occur in connection with intimate partner dynamics or familial stress, highlighting the intersectionality of gender and social situations in SUDs [48,51]. For instance, women often report higher levels of distress associated with family conflicts compared to men, amplifying the risk of SUD relapse. This contrasts with men’s relapse triggers, which are often tied to social factors such as living alone or peer pressure [52].

The findings highlight a clear emphasis on analyzing the distribution of participants based on the type of substance used. Notably, the results indicate a predominance of high-risk drug users within the study population. This observation calls for further exploration of how substance type may influence treatment outcomes and emphasizes the need for tailored interventions targeting this high-risk subgroup. According to the European Drug Report 2023, the most commonly consumed drug was cannabis, followed by cocaine and crack, amphetamines, heroin, and other substances [53]. Additionally, a study from the United States reported that the prevalence of individuals engaging in both alcohol and drug co-use was 5.6% [54].

Our study underscores the multifaceted nature of the factors influencing relapse periods, highlighting the need for further research into additional variables that may contribute to the observed outcome variability.

The forest plot of this meta-analysis visually summarizes individual studies’ effect sizes and confidence intervals, offering insights into the comparative effectiveness of various interventions in prolonging time to relapse. Each effect size reflects the magnitude of the difference in relapse duration between treatment groups, while the confidence intervals indicate the precision of these estimates. Notably, studies such as Mahajan (2020) and Rong (2016) exhibit larger effect sizes, suggesting substantial differences in relapse times favoring the treatment groups [15,22]. In contrast, studies like Glasner (2016) demonstrate smaller effect sizes, indicating less pronounced differences or potentially non-significant effects [18].

The variability in relapse times observed across studies may be attributed to multiple factors, including differences in study populations, intervention protocols, follow-up durations, and methodological designs [10]. Heterogeneity in patient demographics, severity of addiction, comorbid conditions, and treatment adherence can all influence relapse outcomes, contributing to the dispersion of effect sizes. Furthermore, variations in the type and intensity of interventions—from pharmacotherapy and psychotherapy to holistic or lifestyle-based approaches—may impact relapse rates and further underscore the diversity of findings across studies.

Understanding the diversity of relapse outcomes illustrated in the forest plot carries significant implications for clinical practice. Clinicians must account for the heterogeneous nature of patient populations and their varied responses to treatment when designing and implementing personalized intervention strategies [55,56,57]. Identifying interventions associated with larger effect sizes—as demonstrated in studies such as Mahajan (2020) and Rong (2016)—can guide treatment selection and optimization efforts [15,22]. Conversely, studies reporting minimal or null effects, such as Glasner (2016), highlight the need to critically assess the efficacy of existing interventions and explore alternative therapeutic approaches [18].

In addition to established pharmacological and non-pharmacological methods, increasing attention is being directed toward digital relapse prevention strategies [58,59,60]. Emerging research explores the use of virtual reality (VR) as a tool to support relapse prevention, offering unique benefits such as enhanced self-awareness, behavioral monitoring within simulated environments, and the opportunity for individuals to adopt new perspectives through avatar-based experiences [61,62,63,64,65,66]. These innovations may provide practitioners with deeper insights into the recovery process while offering patients immersive, personalized support during critical stages of relapse prevention.

One of the primary challenges associated with implementing VR interventions in mental health and rehabilitation is the requirement for significant resources, including financial investment, infrastructure, and trained personnel [67,68]. Despite its promise, developing high-quality VR applications necessitates substantial time and expertise, which can delay deployment within clinical settings. Furthermore, practitioners often must navigate the complexities of patient training and familiarization with VR tools, which can hinder immediate effectiveness. These challenges are compounded by the evolving nature of VR technology, which may lead to frequent updates and modifications, creating an additional burden for healthcare providers who wish to effectively incorporate these innovations into their practices. Another critical challenge is the ethical and clinical validation of VR applications. As VR technologies advance, questions regarding informed consent, data privacy, and the potential for unintended psychological effects during exposure to virtual environments become essential. For VR therapies targeted at treating conditions like PTSD or anxiety disorders, clinicians must ensure that exposure techniques do not retraumatize patients, particularly in vulnerable populations [69,70]. Additionally, ensuring robust safety protocols for monitoring patient reactions in a VR setting is imperative, though the immersive nature of the technology may inadvertently detract from direct human interaction.

Additional studies on alcohol relapse prevention and craving have provided valuable insights into the effectiveness of combining VR interventions with CBT [71,72,73,74,75]. VR represents a novel technique that complements traditional treatment approaches and has shown the potential to elicit cravings through controlled exposure to alcohol-related environments. However, while promising, the superiority of VR in assessment and relapse management still requires further empirical validation [75]. High-fidelity simulations offer potential therapeutic benefits but also pose challenges, including the risk of overstimulation or triggering. Nevertheless, the VR approach is a powerful tool for developing personalized interventions, marking a promising frontier in psychiatry and psychology [76,77].

The limitations of this meta-analysis include the relatively small number of studies available in this field, the inherent challenges of enrolling individuals with addiction into clinical trials, and the limited quality and consistency of data reported in the included studies.

This meta-analysis is subject to limitations, including potential publication bias and methodological heterogeneity across the included studies, which may affect the generalizability and consistency of the findings.

We have noted a reduction in relapse prevention research output since 2018. This downturn may stem from various overlapping causes, such as evolving focus areas within addiction science, financial constraints limiting support for long-term studies, and increasing ethical or regulatory hurdles—especially when working with high-risk populations. Furthermore, challenges in maintaining participant engagement and continuity throughout studies can impede reliable data gathering. These issues point to an underexplored field that merits deeper examination to better understand its consequences for developing effective strategies to prevent relapse. Future research should move beyond basic demographic profiling to explore the complex interplay between intervention type, social determinants, and individualized treatment needs. Integrating these multidimensional factors into large-scale randomized controlled trials could yield more nuanced insights into relapse prevention and contribute to improved outcomes for diverse populations affected by substance use disorders.

5. Conclusions

This meta-analysis highlights that, while age emerged as a statistically significant predictor of relapse duration, it should not be viewed in isolation. Our findings indicate that intervention type—mainly the distinction between pharmacological and non-pharmacological methods—is essential in influencing relapse outcomes, as demonstrated by highly significant ANOVA results. Interventions such as mindfulness-based relapse prevention (MBRP), cognitive behavioral therapy, and emerging digital tools like virtual reality have shown promising variability in effectiveness, suggesting that tailored treatment approaches may enhance long-term recovery. The influence of gender in relapse prevention appears to be multifaceted, with current evidence suggesting that, while statistical significance remains limited, gender-specific psychosocial factors may influence shaping relapse risk and treatment responsiveness. Additionally, although not directly measured in all studies, the impact of social and environmental factors—such as family support, peer influence, and gender-specific psychosocial dynamics—warrants more profound attention. These contextual variables, often underrepresented in statistical models, may mediate or moderate the effectiveness of clinical interventions and should be considered essential elements in designing relapse prevention strategies.

Introduction

Addiction is characterized as a chronic, relapsing brain disorder. Substance misuse represents a significant global concern, particularly in developed nations. In 2020, an estimated 284 million individuals aged 15–67 reported drug use within the preceding 12 months, marking a 26% increase since 2010. Global estimates indicate substantial populations using cannabis, opioids, amphetamines, cocaine, and ecstasy. The World Health Organization estimated 283 million people worldwide had alcohol use disorders in 2016. Opioids are considered the most dangerous substance due to their association with overdose deaths, as tolerance diminishes significantly after a period of abstinence during relapse. High relapse rates for substance use, often ranging from 40% to 93% within six months of treatment, underscore the necessity for sensitive care and diverse treatment modalities.

Relapse in substance use refers to a return to substance use after an individual has achieved control over or cessation of the addiction. This concept is broadly applied across health and behavioral sciences. Nicotine, heroin, and alcohol have demonstrated comparable relapse rates over a one-year period, typically between 80% and 95%. Various mechanisms can trigger a return to drug and alcohol use, including stress, high-risk situations, difficulty managing temptation, and intense cravings.

Strategies for preventing relapse from addiction to high-risk substances, such as drugs, alcohol, tobacco, or gambling, are broadly categorized into pharmacological and non-pharmacological approaches. Pharmacological treatments target specific brain neurotransmitters to mitigate cravings, alleviate withdrawal symptoms, and reduce the reinforcing effects of addictive substances or behaviors. Examples include naltrexone or acamprosate for alcohol addiction, bupropion or varenicline for smoking cessation, and methadone or buprenorphine for opioid addiction. Non-pharmacological approaches encompass cognitive behavioral therapy (CBT), motivational interviewing, peer support groups, mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies like yoga or acupuncture. An innovative and increasingly studied method involves the use of virtual reality technology to reduce relapse risk, representing a significant advancement in addiction intervention.

This meta-analysis was conducted to highlight recent developments in high-risk alcohol and drug addiction relapse, specifically examining various study populations globally, across different age groups, and including individuals who received pharmacological and non-pharmacological interventions during detoxification for relapse prevention. The objectives included exploring and evaluating advancements in relapse prevention strategies for individuals recovering from high-risk addictions, identifying key findings across diverse demographics, and assessing the effectiveness of interventions in reducing relapse rates during and after detoxification.

Materials and Methods

All methodologies adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) to ensure a rigorous study execution.

Data Collection

A comprehensive literature search was performed across medical, psychiatric, and psychological databases for studies published between January 2013 and December 2023. Electronic databases such as PubMed, Cochrane Library, Google Scholar, Semantic Scholar, and Consensus were systematically explored. The search strategy utilized key terms including 'Addiction relapse prevention', 'Drug relapse prevention', and 'Alcohol relapse prevention', combined with the Boolean operator 'OR' to ensure broad retrieval of relevant studies.

Studies were included if they involved participants diagnosed with alcohol use disorder (AUD) and high-risk drug addiction enrolled in relapse prevention programs. Participant selection was based on predefined criteria, encompassing addiction severity, willingness to participate, and engagement in structured relapse prevention interventions. Study designs were selected based on specific inclusion criteria, such as publication date, peer-reviewed status, and English language. Randomized trials were prioritized to minimize bias and establish causal relationships, while cross-sectional studies offered insights into population characteristics and trends. Participants received various pharmacological (e.g., naltrexone, acamprosate) and non-pharmacological interventions (e.g., cognitive-behavioral therapy, motivational interviewing). Key reported outcomes included gender distribution, type of addiction, the effectiveness of interventions in reducing relapse rates, with the primary outcome being the average relapse period in months. Secondary outcomes included treatment adherence, quality of life, and adverse effects of interventions. The inclusion criteria ensured methodological rigor, acknowledging potential biases such as publication bias and heterogeneity in intervention protocols, which were addressed by employing a comprehensive search strategy.

Study Selection

Studies were independently assessed for inclusion based on titles, keywords, and abstracts. A workflow diagram was utilized to illustrate the research process for literature screening and study selection.

Data Extraction

The following data were extracted from each study: country of research and year of publication, type of study, number of participants, mean age of participants, gender distribution (percentage of females and males), type of substance use issue, average relapse period of patients, and the specific relapse prevention intervention utilized.

Data Synthesis and Analysis

The extracted data were analyzed using Python 3 in Google Colaboratory, incorporating libraries such as pandas, statsmodels, matplotlib, seaborn, and scipy.stats. The analysis included descriptive statistics and regression models to examine relationships between the mean relapse period, average age, and gender distribution. Additionally, the study presents results from hypothesis testing, linear regression trends over the years, and the distribution of patients based on the type of substance use. A significance level of p < 0.05 was considered the threshold for statistical significance in all analyses.

Results

A workflow chart guided study selection in accordance with PRISMA guidelines. Out of 934 screened articles, 12 studies met all inclusion criteria, encompassing 2162 patients. These studies universally addressed substance or alcohol abuse, with frequently reported substances including opioids, heroin, cocaine, methamphetamine, and marijuana. A wide array of pharmacological and non-pharmacological interventions, such as mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies, were employed across the studies. Participant demographics indicated an age range from 18 to 70 years, with a meta-analysis mean age of 41.43 years. Gender distribution reinforced the trend of higher addiction prevalence among men, with 70% of participants being male and 30% female. The initial analysis revealed that the majority of patients were high-risk drug users.

The Ordinary Least Squares (OLS) regression model was selected to analyze how participant characteristics influence the mean relapse period. The model assessing the relationship between mean age and relapse period yielded an R-squared value of 0.442, indicating that age accounts for 44.2% of the variance in relapse duration. The associated F-statistic (8.724) and p-value (0.0131) confirmed the statistical significance of this model, suggesting age is a meaningful predictor.

In contrast, models evaluating gender-related factors (both male and female percentages) showed lower R-squared values (0.171 and 0.147, respectively) and non-significant p-values (>0.05), indicating a weaker and statistically inconclusive relationship with relapse duration. For the male group, the F-statistic was 2.264 with a p-value of 0.161. For the female group, the coefficient was −0.0338, with an F-statistic of 1.901 and a p-value of 0.195, neither reaching conventional statistical significance.

An ANOVA test, employed to evaluate the effect of different interventions on the mean relapse period, revealed a highly significant F-statistic (approximately 2.20 × 10^28) with an associated probability (PR(>F)) of approximately 5.26 × 10^−15. This emphasizes the strong impact of intervention strategies on relapse outcomes.

Linear regression analysis of the mean relapse period across publication years did not indicate an increasing trend, suggesting that advancements in therapeutic approaches have not significantly prolonged the average relapse period. Furthermore, a decline in research interest concerning relapse prevention methods was observed over the past two decades, with the majority of studies published between 2014 and 2018. The forest plot visually summarized relapse outcomes across multiple studies, illustrating substantial variability in relapse durations, with some studies reporting longer relapse periods and others demonstrating shorter durations, suggesting differences in treatment efficacy or methodological approaches.

Discussion

This meta-analysis synthesized data from 12 studies to examine high-risk alcohol and drug addiction relapse across diverse populations, age groups, and intervention types (pharmacological and non-pharmacological) during detoxification. A primary finding indicated that mean age significantly predicted relapse prevention, with a p-value of 0.0131 and an R-squared of 0.442, suggesting that younger individuals may exhibit greater responsiveness to relapse prevention interventions. Age can influence various factors associated with relapse, including psychological resilience, comorbidities, social dynamics, and treatment responses. Understanding these factors can inform strategies that optimize recovery outcomes. Additionally, the analysis revealed a highly significant impact of intervention type on relapse outcomes, emphasizing the critical role of different therapeutic approaches.

Regarding the influence of gender, regression models indicated that while a higher percentage of male participants might correlate with a longer mean relapse period, this association lacked statistical significance. Similarly, the percentage of female participants did not demonstrate a statistically significant relationship with the mean relapse period. However, existing literature suggests women may experience different antecedents and risks associated with substance use, often influenced by personal relationships, stress, depression, and intimate partner dynamics, which can elevate relapse potential. Conversely, some research challenges the notion of consistent gender differences in craving or relapse rates for psychostimulants or opioids, highlighting the complex and sometimes contradictory nature of findings in this area. The study also noted a predominance of high-risk drug users within the included population, underscoring the necessity for targeted interventions for this subgroup.

Emerging treatment approaches, such as virtual reality (VR) and digital medicine, offer new perspectives in relapse prevention. VR therapy has shown potential in enhancing self-awareness, behavioral monitoring within simulated environments, and adopting new perspectives through avatar-based experiences, particularly for younger individuals. Digital interventions, including psychological therapies and cognitive function enhancement programs, offer accessibility and convenience, presenting cost-effective alternatives to traditional services. However, implementing VR interventions faces challenges, including significant resource requirements (financial, infrastructure, trained personnel), the need for substantial time and expertise in developing high-quality applications, and complexities in patient training. Ethical and clinical validation of VR applications also raises concerns regarding informed consent, data privacy, potential for retraumatization, and the imperative for robust safety protocols.

The findings underscore the multifaceted nature of factors influencing relapse periods and the variability observed across studies. This variability can be attributed to differences in study populations, intervention protocols, follow-up durations, and methodological designs. Clinicians must consider this heterogeneity in patient populations and their varied responses when designing personalized intervention strategies. Identifying interventions associated with larger effect sizes can guide treatment selection and optimization.

A noted reduction in relapse prevention research output since 2018 may stem from evolving focus areas within addiction science, financial constraints, and increasing ethical or regulatory hurdles. These issues indicate an underexplored field requiring deeper examination to develop effective relapse prevention strategies. This meta-analysis is subject to limitations, including a relatively small number of available studies, inherent challenges in enrolling individuals with addiction into clinical trials, and inconsistencies in reported data quality. Future research should extend beyond basic demographic profiling to explore the complex interplay between intervention type, social determinants, and individualized treatment needs. Integrating these multidimensional factors into large-scale randomized controlled trials could yield more nuanced insights into relapse prevention and contribute to improved outcomes for diverse populations affected by substance use disorders.

Conclusions

This meta-analysis reveals that while age serves as a statistically significant predictor of relapse duration, its influence should be considered within a broader context. The findings underscore the critical role of intervention type—specifically the distinction between pharmacological and non-pharmacological methods—in influencing relapse outcomes. Tailored treatment approaches, including mindfulness-based relapse prevention (MBRP), cognitive behavioral therapy, and emerging digital tools like virtual reality, demonstrate variable but promising effectiveness, suggesting their potential to enhance long-term recovery. The impact of gender on relapse prevention is multifaceted; although direct statistical significance remains limited in some models, literature suggests that gender-specific psychosocial factors may significantly shape relapse risk and treatment responsiveness. Additionally, the often-underrepresented impact of social and environmental factors, such as family support and peer influence, warrants increased attention as essential elements in designing comprehensive relapse prevention strategies.

Introduction

Addiction is understood as a long-term brain disorder that can involve relapses. The misuse of substances represents a major global challenge, especially in developed nations. The most commonly misused substances include alcohol and illicit drugs. In 2020, approximately 284 million individuals between 15 and 67 years old had used drugs in the past year, marking a 26% increase from 2010. Global estimates show significant numbers of users for cannabis, opioids, amphetamines, cocaine, and ecstasy. The World Health Organization estimated 283 million people worldwide had alcohol use disorders in 2016. Opioids are particularly dangerous, being a leading cause of overdose deaths due to decreased tolerance after periods of abstinence. Relapse rates for substance use, often between 40% and 93% within six months post-treatment, underscore the critical need for effective relapse prevention strategies.

Relapse in substance use refers to returning to drug or alcohol use after a period of control or abstinence. This concept is central to health and behavioral sciences, particularly in addiction studies. Studies have shown similar high relapse rates, ranging from 80% to 95% over one year, for substances such as nicotine, heroin, and alcohol.

Various factors can trigger a relapse in drug and alcohol use, including stress, exposure to high-risk situations, difficulty coping with temptation, and intense cravings.

Efforts to prevent relapse from addiction to high-risk substances like drugs, alcohol, tobacco, or gambling typically fall into two main categories: pharmacological and non-pharmacological approaches. Pharmacological treatments aim to reduce cravings, withdrawal symptoms, and the rewarding effects of addictive substances by targeting specific brain neurotransmitters. Examples include naltrexone or acamprosate for alcohol addiction, bupropion or varenicline for smoking cessation, and methadone or buprenorphine for opioid addiction.

Non-pharmacological strategies for relapse prevention include cognitive behavioral therapy (CBT), motivational interviewing, peer support groups, mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies such as yoga, acupuncture, and sound therapy. A notable and well-researched strategy involves using advanced virtual reality (VR) technology to help reduce relapse risk, representing a significant advancement in addiction intervention. This meta-analysis aims to highlight new developments in high-risk alcohol and drug addiction relapse, examining various study populations globally, across different age groups, and involving individuals who received both pharmacological and non-pharmacological interventions during detoxification for relapse prevention.

Materials and Methods

This study followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). A comprehensive search of medical, psychiatric, and psychological databases was conducted for studies published from January 2013 to December 2023. Multiple electronic databases, including PubMed, Cochrane Library, Google Scholar, Semantic Scholar, and Consensus, were systematically explored using keywords such as 'Addiction relapse prevention', 'Drug relapse prevention', and 'Alcohol relapse prevention', combined with 'OR'.

Studies included in the meta-analysis met specific criteria: participants had to be diagnosed with alcohol use disorder (AUD) and high-risk drug addiction, enrolled in relapse prevention programs, and selected based on predefined eligibility concerning addiction severity and willingness to participate. Study designs were prioritized based on publication date (within the last 10 years), peer-reviewed status, and language (English only) to ensure high-quality and recent evidence. Randomized trials were favored to minimize bias and establish cause-and-effect relationships, while cross-sectional studies offered additional insights into population characteristics.

Interventions included a range of pharmacological approaches, such as medications like naltrexone or acamprosate, and non-pharmacological methods, including cognitive-behavioral therapy, motivational interviewing, and contingency management. Intervention selection was based on evidence of efficacy in relapse prevention and applicability to the target population. Key outcomes reported in the studies included gender distribution, type of addiction (alcohol vs. drug), and the effectiveness of interventions in reducing relapse rates, with the primary outcome being the average relapse period in months. Secondary outcomes included treatment adherence, quality of life, and adverse effects of interventions.

The inclusion criteria aimed for methodological rigor and relevance to the research question. While randomized clinical trials were prioritized to reduce bias, potential sources of bias, such as publication bias and variations in intervention protocols across studies, were acknowledged. A broad search strategy, including gray literature when possible, was employed to address these concerns. Although limited in establishing causality, cross-sectional research provided valuable descriptive data on patient demographics.

Study selection involved independently assessing articles based on titles, keywords, and abstracts. Data extraction included details such as the country and year of research, study type, number of participants, mean age, gender distribution, type of substance use, average relapse period, and the specific relapse prevention intervention used. The extracted data were analyzed using Python 3 in Google Colaboratory, utilizing libraries like pandas, statsmodels, matplotlib, seaborn, and scipy.stats. The analysis encompassed descriptive statistics and regression models to examine relationships between the mean relapse period, average age, and gender distribution. Hypothesis testing, linear regression trends over the years, and the distribution of patients by substance use type were also presented. A statistical significance threshold of p < 0.05 was applied to all analyses.

Results

A systematic screening of 934 articles resulted in 12 studies, encompassing 2162 patients, that met all inclusion criteria for the meta-analysis. All selected studies addressed issues related to substance or alcohol abuse, most frequently involving combinations of opioids, heroin, cocaine, methamphetamine, and marijuana. The studies featured a wide array of pharmacological and non-pharmacological interventions, including mindfulness-based relapse prevention (MBRP), psychoeducation, and holistic therapies.

Analysis of the demographic data revealed participant ages ranging from 18 to 70 years, with a mean age of 41.43 years across the meta-analysis. Regarding gender distribution, the data aligned with established trends, showing that addictive behaviors were more prevalent among men, with 70% of participants being male and 30% female.

Initial analysis focused on the distribution of participants based on the type of substance used, revealing that the majority of patients were high-risk drug users. To assess how different participant characteristics influenced the mean relapse period, the Ordinary Least Squares (OLS) regression model was chosen. The model examining the relationship between mean age and relapse period demonstrated moderate explanatory power, with an R-squared value of 0.442, indicating that age accounted for 44.2% of the variance in relapse duration. Its statistical significance (p = 0.0131) confirmed age as a meaningful predictor. In contrast, gender-related models (both male and female percentages) showed weaker and statistically inconclusive relationships with relapse duration, with lower R-squared values (0.171 and 0.147, respectively) and non-significant p-values (>0.05).

An ANOVA test evaluating the effect of intervention type revealed a highly significant F-statistic (approximately 2.20 × 1028) with a p-value < 0.0001, strongly emphasizing the impact of intervention strategies on relapse outcomes. This suggests that the type of intervention significantly influences the mean relapse period. However, linear regression analysis of the mean relapse period across publication years did not indicate an increasing trend, suggesting that therapeutic advancements have not significantly extended the average relapse period. Additionally, a decline in research interest in relapse prevention methods was observed over the past two decades, with the majority of studies published between 2014 and 2018.

The forest plot visually summarized relapse outcomes across the included studies, showing individual effect sizes and confidence intervals. Effect sizes represented the difference in relapse duration between treatment groups. Studies by Mahajan (2020) and Rong (2016) reported longer relapse periods, indicating a substantial difference in relapse times favoring their treatment groups. Conversely, studies such as Glasner (2016) demonstrated shorter durations or less pronounced effects. The plot highlighted considerable variability in relapse outcomes across studies, suggesting potential differences in treatment effectiveness or methodological approaches.

Discussion

This meta-analysis of 12 studies explored key aspects of high-risk alcohol and drug addiction relapse across diverse populations globally, considering both pharmacological and non-pharmacological interventions during detoxification. A primary finding was the statistically significant effect of mean age on relapse prevention, with an R-squared value of 0.442, indicating that age explained approximately 44.2% of the variance in the mean relapse period. The results suggest that younger individuals may be more responsive to relapse prevention interventions for alcohol and drug addiction. Age can influence various relapse-associated factors, including psychological resilience, comorbidities, social dynamics, and treatment responses, with older adults often facing complex health profiles that may increase relapse risk. Younger adults may benefit from technology-based solutions like smartphone apps for real-time support and tailored resources.

Regarding the influence of gender, regression models for both male and female participant percentages yielded lower R-squared values and non-significant p-values, suggesting a weaker and statistically inconclusive relationship with relapse duration. Research on gender and addiction vulnerability presents conflicting views; some suggest women may progress faster to dependence, while a 2021 review found no consistent evidence of greater vulnerability for women regarding psychostimulants, opioids, or related relapse rates. However, gender-specific psychosocial factors are important. Women's relapse triggers are often tied to personal relationships, marriage stress, depression, or family conflicts, differing from men's triggers, which are more frequently related to external situational factors like living alone or peer pressure. This highlights the need for gender-sensitive treatment approaches.

The study identified a predominance of high-risk drug users within the study population, calling for tailored interventions specific to substance type. The European Drug Report 2023 noted cannabis as the most consumed drug, followed by cocaine, crack, amphetamines, and heroin, while a U.S. study reported a 5.6% prevalence of alcohol and drug co-use. These findings underscore the complex nature of factors influencing relapse periods and the need for further research into additional variables that contribute to outcome variability beyond basic demographics.

The forest plot in this meta-analysis illustrated significant variability in relapse outcomes across studies, with some interventions demonstrating larger effects on relapse duration (e.g., Mahajan, Rong) and others showing smaller or non-significant effects (e.g., Glasner). This variability can be attributed to differences in study populations, intervention protocols, follow-up durations, and methodological designs. Heterogeneity in patient demographics, addiction severity, comorbid conditions, and treatment adherence can all impact relapse outcomes. For clinical practice, understanding this diversity is crucial for designing personalized intervention strategies, selecting effective treatments, and critically assessing existing ones.

Increasing attention is being paid to digital relapse prevention strategies, including virtual reality (VR). VR offers unique benefits such as enhanced self-awareness, behavioral monitoring in simulated environments, and opportunities for new perspectives through avatar-based experiences. However, implementing VR interventions in mental health and rehabilitation faces challenges, including the need for significant financial resources, infrastructure, and trained personnel. Ethical and clinical validation is also crucial, particularly concerning informed consent, data privacy, and the potential for unintended psychological effects or retraumatization in vulnerable populations. Despite these challenges, VR combined with traditional therapies like CBT shows promise for developing personalized interventions. Limitations of this meta-analysis include the relatively small number of available studies, challenges in enrolling individuals with addiction into clinical trials, and inconsistencies in reported data quality. A notable reduction in relapse prevention research output since 2018 suggests an underexplored field that warrants deeper examination to develop more effective prevention strategies.

Conclusions

This meta-analysis indicates that while age emerged as a statistically significant predictor of relapse duration, its influence should be considered alongside other factors. The type of intervention, specifically the distinction between pharmacological and non-pharmacological methods, significantly impacts relapse outcomes. Approaches like mindfulness-based relapse prevention (MBRP), cognitive behavioral therapy, and innovative digital tools such as virtual reality show promising variability in effectiveness, suggesting that individualized treatment strategies can enhance long-term recovery. The influence of gender in relapse prevention is multifaceted; current evidence, though limited in statistical significance, suggests that gender-specific psychosocial factors notably shape relapse risk and treatment responsiveness. Additionally, crucial social and environmental factors, such as family support, peer influence, and gender-specific psychosocial dynamics, warrant more focused attention. These contextual variables, often not fully captured in statistical models, may mediate the effectiveness of clinical interventions and are essential for designing comprehensive relapse prevention strategies. Future research should move beyond basic demographic profiling to explore the complex interplay between intervention type, social determinants, and individualized treatment needs.

Introduction

Addiction is understood as a long-lasting brain disorder marked by recurring cycles of substance use. Misuse of substances is a major global concern, especially in developed nations. The substances most often misused are alcohol and illegal drugs. In 2020, about 284 million people between the ages of 15 and 67 had used a drug in the previous year, which was a 26% increase from 2010. Globally, 209 million people used cannabis, 61 million used opioids, 34 million used amphetamines, and 20 million used cocaine and ecstasy. The World Health Organization estimated that 283 million people worldwide had alcohol use disorders in 2016. Opioids are considered the most dangerous substance, as they are the leading cause of drug overdose deaths because tolerance lessens after a period of not using, making a return to use highly risky. Relapse rates for substance use are high, ranging from 40% to 93% within the first six months after treatment, showing a strong need for care that addresses relapse and offers new treatment methods.

Relapse in substance use means returning to drug or alcohol use after a person has successfully managed to control or stop their addiction. This concept applies across all health fields, especially in addiction. Nicotine, heroin, and alcohol have shown similar high relapse rates over one year, typically between 80% and 95%. Several factors can lead to relapse in drug and alcohol use, including stress, challenging situations, an inability to resist urges, and intense cravings.

Various methods exist to prevent relapse from addiction to high-risk substances such as drugs, alcohol, tobacco, or gambling. These methods are generally divided into two main categories: pharmacological approaches, which involve medications, and non-pharmacological approaches, which do not. Pharmacological treatments target specific brain chemicals to reduce cravings, withdrawal symptoms, and the rewarding effects of addictive substances or behaviors. For example, naltrexone or acamprosate are prescribed for alcohol addiction, bupropion or varenicline for smoking cessation, and methadone or buprenorphine for opioid addiction.

Non-pharmacological approaches to preventing relapse include cognitive behavioral therapy (CBT), motivational interviewing, support groups, mindfulness-based relapse prevention, psychoeducation, and holistic therapies like yoga or acupuncture. An innovative and well-researched strategy involves using advanced virtual reality (VR) technology to lower the risk of relapse, offering a new direction in addiction treatment and prevention.

This meta-analysis aims to highlight important new developments in preventing relapse for high-risk alcohol and drug addiction. It looks at various groups of people globally, across different age ranges, and includes individuals who received both medication-based and non-medication-based treatments during detoxification to prevent relapse. The objectives are to explore and assess recent advancements in relapse prevention for individuals recovering from addictions to substances like alcohol, opioids, and illicit drugs. The research seeks to identify and combine key findings from diverse populations and age groups, focusing on how effective different treatments are in reducing relapse rates during and after detoxification.

Materials and Methods

All methods followed the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) to conduct this study. A thorough search of medical, psychiatric, and psychological databases was performed for studies published between January 2013 and December 2023. Electronic databases like PubMed, Cochrane Library, Google Scholar, Semantic Scholar, and Consensus were systematically searched using key terms such as ‘Addiction relapse prevention’, ’Drug relapse prevention’, and ‘Alcohol relapse prevention’, combined with ’OR’ to ensure a wide search for relevant studies.

The studies included in the meta-analysis met specific criteria: participants were diagnosed with alcohol use disorder (AUD) and high-risk drug addiction and were part of relapse prevention programs. Participants were chosen based on specific eligibility criteria, including addiction severity, willingness to participate, and involvement in structured relapse prevention programs. Studies were selected based on publication date (within the last 10 years), peer-reviewed status, and language (English only). These criteria ensured high-quality, recent, and accessible evidence. Randomized trials were favored to minimize bias and establish cause-and-effect relationships, while cross-sectional studies offered additional insights into population characteristics and trends. Participants received various treatments, including medications (e.g., naltrexone or acamprosate) and non-medication approaches (e.g., cognitive-behavioral therapy, motivational interviewing). Treatment selection was based on proven effectiveness in relapse prevention and suitability for the target population. Key outcomes reported included gender distribution, type of addiction (alcohol vs. drug), and how effective interventions were at reducing relapse rates. The main outcome measured was the average time until relapse, reported in months. Secondary outcomes included treatment adherence, quality of life, and any negative effects of the interventions.

The inclusion criteria ensured methodological strictness and relevance to the research question. Randomized clinical trials were prioritized to reduce selection bias and confusing factors. However, potential sources of bias, such as publication bias (where only positive results are published) and differences in treatment plans across studies, were acknowledged. A wide search strategy was used to address these, including unpublished studies when possible. Additionally, while limited in showing cause, cross-sectional research provided useful descriptive data on patient demographics and addiction profiles.

Studies were independently evaluated for inclusion based on their titles, keywords, and abstracts. A diagram was created to show the research process for screening literature and selecting studies (Figure 1). Data extracted included the country and year of research, study type, number of participants, average age, gender distribution (percentage of females and males), type of substance use, average relapse period, and the specific relapse prevention method used (Figure 1).

The extracted data were analyzed using Python 3 in Google Colaboratory, using various libraries. The analysis included descriptive statistics and regression models that examined the relationships between the average relapse period, average age, and gender distribution. The study also presents results from hypothesis testing, linear regression trends over the years, and the distribution of patients based on the type of substance use. A significance level of p < 0.05 was used for all analyses, meaning there was less than a 5% chance of the results happening by accident.

Results

A chart detailing the study selection process was prepared following the Preferred Reporting Items for Systematic Review and Meta-analysis guidelines. After screening the titles and abstracts of 934 articles, 12 studies met all inclusion criteria, involving 2162 patients. Table 1 provides a summary of the characteristics of these studies.

All selected studies focused on substance or alcohol abuse. The most common substances reported included combinations of opioids, heroin, cocaine, methamphetamine, and marijuana. The studies covered a wide range of medication-based and non-medication-based treatments, such as mindfulness-based relapse prevention, psychoeducation, and holistic therapies. An analysis of participant demographic data showed ages ranging from 18 to 70 years, with an average age of 41.43 years in the meta-analysis. Regarding gender, the data confirmed that addictive behaviors are more common among men, with 70% of participants being male and 30% female (Figure 2).

The first part of the analysis looked at how participants were distributed across studies based on the type of substance they used. The findings showed that most patients were high-risk drug users (Figure 3). To understand how different participant characteristics affect the average relapse period, the Ordinary Least Squares (OLS) regression model was chosen as the best fit for the dataset. The model examining the relationship between average age and relapse period had an R-squared value of 0.442, meaning age accounted for 44.2% of the variation in relapse duration. The statistical significance of this model (F-statistic 8.724, p-value 0.0131) confirmed that age is a meaningful predictor. In contrast, models related to gender (both male and female percentages) had lower R-squared values (0.171 and 0.147, respectively) and were not statistically significant (p-values > 0.05), suggesting a weaker and inconclusive relationship with relapse duration. Additionally, an ANOVA test on intervention type showed a highly significant F-statistic (2.195 × 1028) with a p-value < 0.0001, indicating that intervention strategies strongly impact relapse outcomes. These analyses support the conclusion that age and intervention type are the most statistically relevant predictors of relapse duration in the studied population.

The ANOVA test was used to evaluate the effect of different treatments on each study’s average relapse period. The sum of squares for the intervention type was 52.77, which indicates how much of the variation in the average relapse period is explained by the different treatment categories. The F-statistic for intervention type was very high (approximately 2.20 × 1028), showing its strong overall significance on the average relapse period. The probability associated with this F-statistic was extremely low (approximately 5.26 × 10⁻15), indicating a highly significant result. This suggests that the observed F-statistic is very unlikely to occur if the intervention type had no effect on the average relapse period.

Linear regression analysis of the average relapse period over publication years did not show an increasing trend, suggesting that new treatment approaches have not significantly lengthened the average time to relapse (Figure 4). Also, a decrease in research interest in relapse prevention methods was noted over the past two decades, with most studies published between 2014 and 2018. The forest plot shows relapse outcomes across multiple studies (Figure 5). Effect sizes represent the difference in relapse duration between treatment groups, with error bars showing the confidence intervals. Some studies reported longer relapse periods, while others showed shorter durations. The plot highlights significant differences in relapse outcomes across studies, possibly due to differences in treatment effectiveness or research methods.

Discussion

This study conducted a thorough meta-analysis of 12 studies to examine key aspects of high-risk alcohol and drug addiction relapse across diverse populations worldwide. It included various age groups and individuals who received both medication-based and non-medication-based treatments for relapse prevention during the detoxification phase. The primary finding was that age significantly predicts relapse prevention, with approximately 44.2% of the variation in the average relapse period explained by age. Results suggest that younger individuals respond better to relapse prevention treatments for alcohol and drug addiction, highlighting the complex nature of relapse in this age group. Age can influence factors related to relapse, such as mental strength, other health issues, social dynamics, and how people respond to treatment. Older adults often have complex health conditions, which can increase their risk of relapse. Younger adults may respond well to technology-based solutions like smartphone apps that monitor mood and provide timely support. These technologies can effectively involve younger populations in their recovery and prevent relapses by offering real-time support tailored to their needs.

The findings indicate that no single factor can independently predict relapse among youth. While personal factors greatly influence starting and continuing substance use, many social and environmental influences also play a critical role. Therefore, understanding the complex interaction between personal traits, social dynamics, and wider environmental factors is essential for understanding how relapse develops among youth in treatment. For individuals aged 40 and above at the start of a study, rehabilitation has been linked to worse outcomes over 5–9 years of substance use, suggesting a higher risk of relapse or more severe substance-related issues in this older group. New treatment approaches like virtual reality (VR) and digital medicine offer new ways to prevent relapse. VR therapy has been found more effective in preventing relapse among younger individuals compared to adults, as it enhances the feeling of being present, allowing people to actively engage with simulated environments. Digital interventions, which include psychological therapies, brain function improvement programs, and technologies like VR, are appealing due to their accessibility and convenience. As these technologies advance, digital medicine is expected to offer cost-effective alternatives to traditional medical services.

Regarding the impact of gender, the analysis suggests that a higher percentage of male participants might be linked to a longer average relapse period, but this effect is not statistically significant. The model explains about 17.1% of the variability in the average relapse period, but its overall significance remains unclear. Similarly, the model for female participants explains about 14.7% of the variation, but this result is also not statistically significant. Some research suggests women might be more susceptible to addiction, with a faster progression from initial use to dependence compared to men. Additionally, women are reported to be more sensitive to the effects of stress and relationship difficulties in the context of alcohol addiction and relapse. However, a 2021 review of clinical studies challenges this, finding no consistent evidence that women are more vulnerable than men to certain stimulants, opioids, or related relapse, and no significant gender differences in craving or relapse rates. On the other hand, research shows that women experience different reasons and risks for substance use compared to men. For instance, women are often more influenced by personal relationships and social dynamics, such as stress from marriage, feelings of depression, and relationship-based substance use, which can significantly increase their potential for relapse. This highlights the need for gender-sensitive treatment approaches that consider the relational and emotional factors specifically affecting women.

This study emphasizes the importance of analyzing how participants are distributed based on the type of substance used. The results clearly show a majority of high-risk drug users in the study population. This finding suggests a need for further research into how substance type might affect treatment outcomes and highlights the importance of targeted interventions for this high-risk group. The European Drug Report 2023 indicates that cannabis was the most commonly consumed drug, followed by cocaine and crack, amphetamines, and heroin. Additionally, a study from the United States reported that 5.6% of individuals engaged in both alcohol and drug co-use. The findings highlight the complex nature of factors influencing relapse periods and indicate a need for more research into other variables that might contribute to the observed differences in outcomes.

The forest plot of this meta-analysis visually summarizes the impact and confidence intervals of individual studies, providing insights into how effective various treatments are at extending the time to relapse. Each effect size shows the magnitude of the difference in relapse duration between treatment groups, while the confidence intervals indicate how precise these estimates are. Studies reported longer relapse periods, suggesting substantial differences in relapse times that favored the treatment groups. In contrast, other studies showed smaller effects, indicating less clear differences or possibly no significant effects. The differences in relapse times observed across studies may be due to multiple factors, including variations in study populations, treatment protocols, follow-up durations, and research methods. Differences in patient demographics, addiction severity, co-occurring conditions, and treatment adherence can all affect relapse outcomes, contributing to the wide range of effects. Furthermore, variations in the type and intensity of treatments—from medication and therapy to holistic approaches—may impact relapse rates and further explain the diversity of findings across studies.

Understanding the variety of relapse outcomes shown in the forest plot has important implications for clinical practice. Healthcare providers must consider the diverse nature of patient populations and their varied responses to treatment when designing and implementing personalized intervention strategies. Identifying treatments associated with larger effects can help guide treatment selection and improvement efforts. Conversely, studies reporting minimal or no effects highlight the need to critically evaluate the effectiveness of existing treatments and explore alternative therapeutic approaches. In addition to established medication-based and non-medication-based methods, increasing attention is being paid to digital strategies for relapse prevention. Emerging research explores the use of virtual reality (VR) as a tool to support relapse prevention, offering unique benefits such as improved self-awareness, monitoring behavior in simulated environments, and the chance for individuals to gain new perspectives through avatar-based experiences. These innovations may provide practitioners with deeper insights into the recovery process while offering patients immersive, personalized support during critical stages of relapse prevention.

One of the main challenges with using VR interventions in mental health and rehabilitation is the significant resources required, including financial investment, infrastructure, and trained staff. Despite its promise, developing high-quality VR applications requires a lot of time and expertise, which can delay their use in clinical settings. Also, practitioners often need to manage the complexities of training patients and helping them get familiar with VR tools, which can hinder immediate effectiveness. These challenges are made more complex by the constantly changing nature of VR technology, which may lead to frequent updates, adding an extra burden for healthcare providers. Another critical challenge is the ethical and clinical validation of VR applications. As VR technologies advance, questions arise regarding informed consent, data privacy, and the potential for unintended psychological effects when exposed to virtual environments. For VR therapies aimed at conditions like PTSD or anxiety disorders, clinicians must ensure that exposure techniques do not cause re-traumatization, especially in vulnerable groups. Additionally, ensuring strong safety protocols for monitoring patient reactions in a VR setting is essential, even though the immersive nature of the technology might unintentionally reduce direct human interaction.

Additional studies on alcohol relapse prevention and craving have provided valuable insights into the effectiveness of combining VR interventions with cognitive behavioral therapy (CBT). VR is a new technique that complements traditional treatment approaches and has shown the potential to trigger cravings through controlled exposure to alcohol-related environments. However, while promising, the superiority of VR in assessment and relapse management still needs further scientific proof. High-fidelity simulations offer potential therapeutic benefits but also present challenges, including the risk of overstimulation or triggering. Nevertheless, the VR approach is a powerful tool for developing personalized interventions, marking a promising new area in psychiatry and psychology.

The limitations of this meta-analysis include the relatively small number of available studies, the difficulties of enrolling individuals with addiction into clinical trials, and the limited quality and consistency of data reported in the included studies. This meta-analysis is also subject to potential publication bias and differences in methods across the included studies, which may affect how widely and consistently the findings apply. There has been a reduction in relapse prevention research since 2018. This decline may stem from various overlapping causes, such as changing focus areas within addiction science, financial limitations for long-term studies, and increasing ethical or regulatory challenges, especially when working with high-risk populations. Furthermore, difficulties in maintaining participant engagement throughout studies can hinder reliable data collection. These issues point to an underexplored field that requires deeper examination to understand its consequences for developing effective relapse prevention strategies. Future research should move beyond basic demographic profiling to explore the complex interaction between intervention type, social factors, and individualized treatment needs. Including these multiple factors in large-scale randomized controlled trials could provide more detailed insights into relapse prevention and lead to better outcomes for diverse populations affected by substance use disorders.

Conclusions

This meta-analysis highlights that while age emerged as a statistically significant predictor of relapse duration, it should not be considered in isolation. The findings indicate that the type of intervention—specifically the difference between medication-based and non-medication-based methods—is crucial in affecting relapse outcomes, as shown by highly significant statistical results. Treatments like mindfulness-based relapse prevention (MBRP), cognitive behavioral therapy, and new digital tools such as virtual reality have shown promising differences in effectiveness, suggesting that personalized treatment approaches may improve long-term recovery. The influence of gender in relapse prevention appears to be complex; current evidence suggests that, despite limited statistical significance, gender-specific social and psychological factors may play a role in shaping relapse risk and how individuals respond to treatment. Additionally, although not directly measured in all studies, the impact of social and environmental factors—such as family support, peer influence, and gender-specific social dynamics—warrants more in-depth attention. These contextual variables, often not fully represented in statistical models, may influence the effectiveness of clinical interventions and should be considered essential elements when designing relapse prevention strategies.

Introduction

Addiction is a long-term brain problem where people cannot stop using drugs or alcohol. It is a big issue around the world, especially in richer countries. Many people use substances in ways that hurt them. For example, in 2020, about 284 million people aged 15–67 had used drugs in the last year. This number went up by a lot since 2010. Common drugs include cannabis, opioids, amphetamines, and cocaine. Also, many people around the world have problems with alcohol. Opioids are very dangerous because they cause many deaths from overdose, especially if a person stops using them for a while and then starts again.

When someone goes back to using a substance after trying to stop, this is called a relapse. Many people who get help for addiction will relapse within six months. This shows that we need better ways to help people stay clean. Relapse is an idea that doctors and scientists use to understand addiction. It means a person starts using again after they had been able to control their use or stop completely. For example, people trying to quit nicotine, heroin, or alcohol have high relapse rates, often over 80% in one year.

Different things can cause a person to relapse, such as feeling stressed, being in tough situations, not being able to handle urges, or having strong cravings for the substance.

There are many ways to try and stop relapse from drugs, alcohol, tobacco, or gambling. These ways can be split into two main types: using medicine or using other kinds of help.

Medicine treatments work by changing how the brain acts to lower cravings, ease bad feelings from stopping, and make the substance feel less good. For example, specific medicines are given for alcohol addiction, smoking, or opioid addiction. Other ways to help prevent relapse include talk therapy, help from support groups, learning how to be mindful, and getting education about addiction. New ideas like using virtual reality (like computer games for your mind) are also being studied to help people with addiction.

This study looked at many other studies to find out what new things are being done to prevent relapse for people with high-risk alcohol and drug addictions. The study looked at different groups of people, ages, and whether they used medicine or other kinds of help.

Materials and Methods

This study looked at other research papers. The ways used to do this study followed special rules to make sure the work was done correctly.

Many studies were looked at from health and mind science journals. The search looked for papers published between 2013 and 2023. Keywords like "Addiction relapse prevention" were used to find the right studies.

Studies were chosen if they included people who had problems with alcohol or high-risk drugs and were in programs to prevent relapse. The studies also needed to be published in the last 10 years and written in English. Studies where people were chosen by chance were preferred to make sure results were fair.

Information was taken from these chosen studies. This included the country and year of the study, how many people were in it, their average age, how many men and women there were, what kind of substance problem they had, how long they went without relapsing, and what kind of help they received.

Computers were used to look at the collected information. This helped the study understand how things like age and gender were connected to how long people went without relapsing. The study considered results important if they were very likely to be true and not just by chance.

Results

Out of many studies found, 12 were chosen because they fit what was needed. These studies included 2162 people.

All the chosen studies were about problems with drugs or alcohol. The most common drugs mentioned were opioids, heroin, cocaine, methamphetamine, and marijuana. The studies looked at many types of help, like different medicines, mindfulness training, learning about addiction, and other whole-body therapies.

Looking at the people in the studies, their ages ranged from 18 to 70, with the average age being about 41. More men were in the studies than women, with about 70% being male and 30% female. This shows that more men often have addiction problems.

What Makes Relapse Time Different

The study looked at how different things might change how long people stayed clean. Most of the people in the studies were using high-risk drugs.

Age was found to explain almost half (44.2%) of how long people went without using again. This showed that age really does matter when it comes to relapse. However, gender (being male or female) did not show a clear link to how long people stayed clean in the same way.

How Treatments Worked for Different Addictions

The type of help or treatment people received made a big difference in how long they stayed clean. This was a very important finding.

Looking at studies over the years, it didn't seem like newer treatments made people stay clean longer on average. Also, there has been less research on how to stop relapse in recent years, especially since 2018.

A special chart showed how long people stayed clean in different studies. Some studies showed people stayed clean for a longer time after treatment, while others showed shorter times. This means that how well treatments worked was very different from one study to another.

Discussion

This study looked at 12 other studies to understand important parts of relapse for people with serious alcohol and drug addiction. It looked at different groups of people, their ages, and if they got medicine or other types of help.

A main finding was that age is important for how long someone stays clean. Older people often have more health problems, which can make relapse harder to avoid. Younger people might do better with new kinds of help, like phone apps that can give support when needed. However, no single thing alone can say if a young person will relapse. Friends, family, and surroundings are also very important.

When it comes to gender, the study did not find a clear link between being male or female and how long people stayed clean. Some past research suggested women might be more likely to relapse or relapse faster, especially due to stress or relationship problems. Other research says there's no strong proof of this. Still, it is known that women's reasons for relapse are often linked to their feelings and relationships, while men's reasons are more about outside situations like peer pressure. This means that help should be given in ways that fit the person, considering these differences.

The study also showed that most people in the studies were using high-risk drugs. This means we need to learn more about how different types of drugs and alcohol affect how well treatments work. New technologies, like virtual reality (VR), are starting to be used to help prevent relapse. VR can help people practice how to deal with urges in a safe space. This can be very good, especially for younger people.

However, using VR for help costs a lot of money and needs special training. It also takes time to make good VR programs. There are also important questions about how safe these programs are and how they affect people's minds. Even with these challenges, VR could be a powerful new way to help people avoid relapse. Overall, there are not enough studies on addiction and relapse, and the ones that exist are not always done in the same way. Also, less research has been done on relapse prevention in recent years. More studies are needed to understand all the different things that lead to relapse, including social factors like family and friends, so that better help can be given to everyone.

Conclusions

This study shows that age is a big reason for how long people stayed clean from addiction. The type of help someone gets, like medicine or talk therapy, also makes a big difference in stopping relapse. New tools like virtual reality show promise in helping people stay clean over time.

While gender's role in relapse is complex and not fully understood by numbers alone, it seems that how life and feelings affect men and women differently can play a part in their risk of relapse. Also, things like family support, friends, and personal feelings are very important. These hidden factors, which are not always seen in studies, can change how well treatments work. So, when planning ways to help people avoid relapse, these personal and social factors must be considered. More studies are needed to look at all these things together to find the best ways to help different people.