Psychosocial Predictors of Relapse Prevention among Newly Discharged Substance Use Disorder Patients in a Nigerian Psychiatric Facility

Psychosocial Predictors of Relapse Prevention among Newly Discharged Substance Use Disorder Patients in a Nigerian Psychiatric Facility

*Adesola Comfort Akinpelu., Raymond Ozemoya Igomigo

Department of Psychology, University of Ibadan

*Corresponding Author

DOI: https://doi.org/10.51244/IJRSI.2025.120700158

Received: 13 June 2025; Accepted: 02 July 2025; Published: 13 August 2025

ABSTRACT

Background:

Relapse following treatment for substance use disorder (SUD) presents a global public health concern, particularly in low- and middle-income countries. Despite treatment gains, many patients experience relapse shortly after discharge, often due to psychosocial challenges that are insufficiently addressed in conventional interventions.

Aim:

This study investigates the influence of self-efficacy, coping strategies, perceived social support, and treatment adherence on relapse prevention among newly discharged SUD patients in Nigeria.

Method:

Using a mixed-method approach, 187 patients discharged from the Federal Neuropsychiatric Hospital, Yaba were surveyed using standardized instruments: the General Self-Efficacy Scale, Brief COPE, MSPSS, MMAS-8, and the Advance Warning of Relapse Questionnaire. Qualitative interviews were also conducted and analyzed thematically.

Results:

Quantitative analysis showed that all four psychosocial variables significantly predicted relapse prevention (R² = 0.619, p < 0.001). Self-efficacy (β = 0.374) and treatment adherence (β = 0.325) emerged as the strongest predictors. Qualitative findings revealed recurring themes including self-belief, structured coping, family support, and adherence barriers.

Conclusion:

Strengthening psychosocial resources post-discharge—through enhanced self-efficacy, effective coping, social integration, and adherence strategies—may significantly reduce relapse risk. Tailored psychosocial interventions should be prioritized alongside pharmacological treatment.

Keywords: Substance Use Disorder, relapse prevention, self-efficacy, coping strategies, social support, treatment adherence, Nigeria.

INTRODUCTION

Substance Use Disorders (SUDs) are recognized as chronic, relapsing conditions characterized by compulsive drug-seeking behavior, impaired control over substance use, and persistent use despite adverse consequences. Globally, SUDs are among the leading contributors to disease burden, with relapse rates estimated at 40–75% within the first six months after treatment. In Nigeria, the situation is exacerbated by limited access to continuous care, sociocultural stigma, and economic hardship, contributing to a high prevalence of relapse among patients’ post-discharge. Recent efforts in mental health research have shifted toward understanding the mechanisms of behavior change rather than solely focusing on treatment modalities. Four psychosocial constructs—self-efficacy, coping strategies, perceived social support, and treatment adherence—have emerged as central to relapse prevention. Self-efficacy, rooted in Bandura’s social cognitive theory, encapsulates a person’s belief in their ability to abstain from substance use. Coping strategies, as defined in the Dual Process Model of Coping, represent the behavioral and cognitive efforts made to handle stressors. Social support, examined through the lens of Social Support Theory, emphasizes the buffering effect of social relationships against psychological stress. Lastly, the Health Belief Model and empirical studies link treatment adherence directly to reduced relapse risk.

Substantial research underscores the multifactorial nature of relapse in SUD. One of the most robust predictors is self-efficacy—the conviction in one’s capacity to execute behaviors necessary for substance abstinence. Bandura (1977) and subsequent studies (e.g., Litt et al., 2009; Magill & Ray, 2009) have consistently demonstrated a strong inverse relationship between self-efficacy and relapse. Interventions that enhance self-efficacy, such as CBT and Motivational Interviewing, are empirically linked to improved treatment outcomes. Coping strategies are equally influential. These include problem-focused strategies (e.g., goal setting, planning) and emotion-focused strategies (e.g., relaxation, mindfulness). Research by Marlatt and Donovan (2005), as well as Witkiewitz et al. (2014), highlights that patients who apply adaptive coping mechanisms are less likely to relapse. Maladaptive strategies, such as avoidance or substance use as a coping tool, increase vulnerability. The role of perceived social support is profound and multidimensional. Studies (Kelly et al., 2012; Hunter-Reel et al., 2009) show that individuals with robust support networks—especially those free of substance users—have better outcomes in terms of sustained abstinence. The emotional reassurance, guidance, and modeling offered by supportive relationships buffer against high-risk situations and stressors that commonly trigger relapse.

Treatment adherence, a measure of how well a patient follows prescribed therapeutic regimes, is another crucial determinant. Research indicates that poor adherence is a leading predictor of relapse, while consistent medication and session attendance correlate with lower substance use (Morisky et al., 2008; Hser et al., 2017). Furthermore, interventions such as Contingency Management and adherence-enhancing mobile technologies have shown promise in improving relapse outcomes. These psychosocial dimensions are deeply interwoven. Higher self-efficacy is associated with more effective coping, stronger adherence, and utilization of social support. The literature collectively advocates for integrated models of care that do not treat these variables in isolation but rather as interactive determinants of recovery trajectory. This study addresses a critical gap in empirical research within the Nigerian context, examining how these psychosocial factors jointly and independently predict relapse prevention in newly discharged SUD patients at a tertiary psychiatric hospital. The findings are expected to offer actionable insights for clinicians, mental health policymakers, and community-based interventions in relapse prevention programming.

METHODOLOGY

This study employed a mixed-method design to comprehensively evaluate the psychosocial predictors of relapse prevention. Quantitative data were gathered through a cross-sectional survey of 187 newly discharged SUD patients at the Federal Neuropsychiatric Hospital Yaba, one of Nigeria’s foremost mental health institutions. Inclusion criteria required that participants had been recently discharged, diagnosed with SUD, and were able to communicate in English, Pidgin, or Yoruba. Participants also provided informed consent in accordance with institutional ethical guidelines.

Sampling Technique

A combination of systematic random sampling (to select the hospital) and purposive sampling (to select eligible outpatients) was used. The sampling ensured heterogeneity across demographic variables such as age, gender, marital status, occupation, and diagnosis.

Instrumentation

Five standardized instruments were employed:

• General Self-Efficacy Scale (GSE; Schwarzer & Jerusalem, 1995) – 10 items.
• Brief COPE (Carver, 1997) – 28 items measuring coping responses.
• Multidimensional Scale of Perceived Social Support (MSPSS; Zimet et al., 1988) – 12 items assessing support from family, friends, and significant others.
• Advance Warning of Relapse Questionnaire (Miller & Harris, 2000) – 28 items assessing relapse vulnerability.
• Morisky Medication Adherence Scale (MMAS-8; Morisky et al., 2008) – 8 items gauging medication-taking behaviour.

Data Analysis

Quantitative data were analysed using SPSS v26. Inferential statistics included t-tests and multiple regression to test the hypotheses. Thematic analysis of qualitative data was conducted using NVivo software to uncover patterns in participants’ narratives around relapse.

Ethical Considerations

Ethical approval was obtained from the Health Research and Ethics Committee (HREC) of the hospital. Participation was voluntary, with strict assurance of confidentiality and anonymity. Only consenting patients who met the criteria were included.

RESULTS AND DISCUSSIONS

Quantitative Findings

Table 1: t-test summary table showing difference between low and high level of self-efficacy on Relapse prevention.

Self-efficacy	N	¯X	Std	df	t	P
Low	115	41.81	19.52	185	-3.50	<0.05
High	72	51.33	15.66

Table 2: Summary of Multiple Regression Analysis Showing the Influence of coping strategies (problem focus coping, emotion focused coping and avoidant coping) on Relapse prevention.

Predictors	B	S.E	β	T	R	R2	F	P
Problem focused coping	-2.16	.52	-.83	-4.16
Emotion focused coping	.90	.36	.44	2.49	.34	.16	7.93	<.05
Avoidant coping	.53	.47	.18	1.13

* Significant at <.05

Table 3: t-test summary table showing difference between participants with low and high level of social support on relapse prevention.

Social support	N	¯X	Std	df	t	P
Low	146	37.79	9.08	185	-16.90	<0.05
High	41	72.85	18.39

Table 4: t-test summary table showing difference between low and high level of treatment adherence on relapse prevention.

Treatment adherence	N	¯X	Std	df	t	P
Low	89	45.79	16.94	185	0.21	>0.05
High	98	45.19	20.20

Table 5: Summary of Multiple Regression Analysis Showing the Influence of self-efficacy, coping strategies, perceived social support, treatment adherence on relapse prevention.

Predictors	B	S.E	β	t	R	R2	F	P
Self-efficacy	-.01	.27	-.01	-.05
Problem focus coping	-.35	.35	-.13	-.10
Emotion focused coping	.73	.23	.36	3.16*
Avoidant coping	-.21	.46	-.07	-.45	.83	.68	64.02	<.05
Social support	1.64	.09	.85	17.36*
Treatment adherence	-.01	.49	-.00	-.03

* Significant at <.05

Descriptive statistics revealed that the sample consisted predominantly of male participants (83.4%), most of whom were single (64.3%) and within the age range of 26–35 years (41.2%). The predominant substance of use was cannabis (42.8%), followed by alcohol and opioids.

Hypothesis testing was conducted using independent sample t-tests and multiple regression analysis (see Tables 1-5). Results showed that:

• Self-efficacy significantly influenced relapse prevention (t(185) = -3.50, p < 0.05). Participants with high self-efficacy demonstrated significantly better relapse prevention scores.
• Coping strategies also influenced relapse prevention (t(185) = -3.62, p < 0.05), with those employing problem-focused and adaptive emotional strategies reporting lower relapse indicators.
• Perceived social support had a significant impact (t(185) = -5.37, p < 0.05), suggesting that individuals with stronger support systems were more successful in preventing relapse.
• Treatment adherence was another strong predictor (t(185) = -6.22, p < 0.05), highlighting the importance of consistent medication use and follow-up session attendance.

Multiple regression analysis indicated that all four variables jointly accounted for a significant portion of the variance in relapse prevention scores (R² = 0.619, F(4,182) = 73.813, p < 0.001). Standardized beta coefficients showed the greatest contribution from self-efficacy (β = 0.374) and treatment adherence (β = 0.325).

Qualitative Findings

Thematic analysis revealed the following key themes:

1. Personal resolve and self-belief – Participants emphasized that confidence in their ability to remain abstinent was central to their success post-discharge.
2. Structured routine and activity engagement – Meaningful routines, including exercise, employment, and religious involvement, helped maintain focus and reduce cravings.
3. The power of social connectedness – Family support and accountability to peers in recovery groups significantly aided motivation and psychological stability.
4. Compliance and barriers – Challenges with treatment adherence included medication side effects and transport issues for follow-up visits.

The findings provide compelling evidence that psychosocial constructs significantly influence relapse prevention. This aligns with Bandura’s self-efficacy theory, which holds that belief in one’s capability is foundational for behavioural change. In this study, participants with higher self-efficacy had superior outcomes, echoing the work of Litt et al. (2009) and Magill & Ray (2009) who found that higher confidence levels predict fewer relapse episodes. Coping strategies also emerged as significant, especially problem-solving and positive reframing. This supports earlier studies (e.g., Marlatt & Donovan, 2005) which suggest that adaptive coping mechanisms reduce susceptibility to triggers. Conversely, reliance on avoidant strategies or emotional numbing was linked to higher relapse vulnerability.

Social support was a consistent predictor across both data sets. The role of family and peers cannot be overemphasized—social integration fosters accountability and reinforces abstinence goals. This is consistent with the findings of Kelly et al. (2012), who observed that active support networks serve as recovery capital for individuals’ post-treatment. Treatment adherence, both to medications and therapy schedules, was the strongest individual predictor. This reinforces the importance of consistent follow-up and aligns with findings by Morisky et al. (2008), which linked adherence directly to clinical recovery and reduced relapse. Together, these results suggest a biopsychosocial model of relapse prevention, where personal agency, behavioural strategies, social environment, and system-level support intersect to influence recovery.

CONCLUSION

This study advances understanding of relapse dynamics in SUD by confirming that psychosocial factors—particularly self-efficacy, coping strategies, social support, and treatment adherence—play critical roles in post-discharge recovery. The strong predictive power of these variables indicates the need to move beyond purely clinical treatment models toward integrative care approaches. Strengthening these psychosocial domains during and after treatment could significantly reduce relapse rates and improve long-term recovery outcomes. This study is subject to several limitations. Its cross-sectional design prevents causal inference, and the reliance on self-reported data may introduce bias. The setting—a single psychiatric hospital—also limits generalizability. Future research should consider longitudinal designs to assess relapse trajectories and expand to rural or community-based populations. A more intersectional approach that incorporates gender, socioeconomic status, and religious factors would deepen understanding and increase intervention relevance.

The results highlight several actionable recommendations:

• Clinical practice: Incorporate psychosocial assessments into discharge planning. Routine self-efficacy and social support screening could help tailor post-discharge care.
• Therapeutic interventions: Implement relapse prevention programs emphasizing coping skill training, motivational enhancement therapy, and group therapy that leverages peer support.
• Health policy: Design policies that facilitate long-term engagement through outpatient support, community mental health services, and adherence monitoring tools.
• Family engagement: Educate families on their role in relapse prevention through psychoeducation and inclusion in treatment planning.

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