How psychosocial interventions might work?

Psychosocial interventions do not address the underlying biological cause of infertility, but can improve healthy emotions, attitudes, and habits that ultimately enhance the QoL of couples and/or partners with infertility problems [10]. The mechanisms through which psychosocial interventions operate is not well understood but they target the cognitive, emotional, and relational processes, leading to desired changes in individuals’ mental health and functional outcomes [36]. The interventions grouped according to mode of delivery and inherent psychometric properties, such as counselling, psychodynamic therapy, cognitive behavioural therapy (CBT), mindfulness-based stress reduction (MBSR), humanistic therapy education, behavioural change techniques, yoga, and others, are widely recognized as effective for various psychosocial problems [37–40].

For example, counselling allows couples to process feelings of depression, anxiety and grief whilst being provided with access to emotional support from health professionals and experienced peers. Together, this creates a safe environment that allows individuals to express their concerns about problems they are going through, in this case infertility-related psychosocial problems [39]. In psychodynamic therapy, the focus is on uncovering unconscious thoughts and feelings, facilitating insight into emotional struggles through techniques like free association and dream analysis [40]. This process is complemented by the exploration of transference, where clients project past relational dynamics onto the therapist, allowing them to better understand their emotional responses [41]. Cognitive-behavioural therapy emphasizes cognitive restructuring, which involves identifying irrational thoughts and negative ideations and instead allows substitution with positive reinforcement coping strategies to meet conceived expectations [42]. It also incoporates behavioural activation, which encourages engagement in positive activities and thoughts that enhance emotional regulation and coping skills [43]. Adopting mechanisms such as cognitive restructuring in CBT that focuses on challenging cognitive distortions and fostering more adaptive thought patterns have been proved to be very effective [43]. Mindfulness-based stress reduction incorporates mindfulness meditation and yoga that helps individuals to manage daily stress and emotional pain, and builds resilence [44]. By fostering greater awareness and acceptance of one’s emotional state, MBSR can enhance coping skills for individuals facing infertility challenges [45].

Humanistic therapy education, however, prioritizes the therapeutic alliance by building a trusting relationship that fosters self-exploration, empowerment, and personal growth [46], whereas behavioural change techniques, such as exposure therapy and skill acquisition, help clients confront their fears and develop coping strategies [47]. Other interventions such as yoga aims to encourage the individual to focus on the present condition, and by so doing, it helps to alleviate anxiety [48]. This may help modulate infertile couples expectations following treatment and consequently alleviate the self-generated ideation of hopelessness [49]. Couples therapy focuses on addressing relationship issues, helping partners to navigate the emotional challenges of infertility together [50] and in assessing therapy sessions together tends to enhance couples ability to effectively communicate, resolve conflicts, and importantly develop shared coping strategies to improve emotional resilience and interpersonal relationships [50].

Central to the effectiveness of psychosocial interventions is the therapeutic alliance, characterized by trust, empathy, and open communication, which significantly enhance clients’ engagement and motivation [51]. Emotional processing further plays a crucial role, enabling clients to identify, express, and understand their emotions, ultimately promoting healing [52]. Additionally, self-awareness and insight gained through reflective exploration allow clients to integrate new-found understandings into their daily lives, facilitating lasting personal growth and improved mental health outcomes [53]. Understanding these mechanisms equips therapists to tailor their approaches to the needs of the client to enhance the effectiveness of the psychosocial intervention. Given the mechanisms through which psychosocial interventions work—such as improving communication, building trust, fostering amicable resolution of indifferences, and enhancing emotional bonds, these can improve marital intimacy, sexual satisfaction, and QoL of couples or individuals with infertility.

Rationale for this systematic review

Couples or persons with infertility problems are predisposed to societal stigma that negatively affects their psychological wellbeing. The decline in psychological resilience from infertility-related stress and events affects the social relationships, marital and sexual functions of individuals living with infertility [29]. Accumulated evidence, though fragmented, suggests an urgent need to manage infertility beyond medical care [54]. Adoption of psychosocial interventions may remediate psychological stress associated with the diagnosis, and management, of infertility [16,55–61].

Addressing infertility is an integral part of sexual and reproductive health and rights [62]. However, in many countries, policies and services on infertility are inadequate. Tackling infertility and its negative psychosocial impact is essential for the achievement of Sustainable Development Goal (SDG) 3 [63], which aims to ensure healthy lives and well-being for everyone at all ages, and SDG 5, which seeks to attain gender equality and empower all women and girls. Furthermore, providing psychosocial interventions for infertility is fundamental for realizing the human rights related to the highest attainable standard of physical and mental health and the ability to make decisions [64]. This is likely to enhance their self-esteem, and, likely to improve their participation in leadership roles, and potentially improve their decision-making about infertility [65]. Some primary studies have investigated the role of psychosocial interventions on marital intimacy and sexual satisfaction, but the results are fragmented and inconclusive [66,67]. Therefore, there is a need for a systematic review to collate existing studies and obtain a pooled estimate of the effectiveness of psychosocial interventions for persons with infertility problems.

To ensure this systematic review is not duplicating existing reviews, searches were conducted in relevant databases, and three existing systematic reviews that investigated psychosocial interventions among infertile couples were retrieved [66–68]. One of these systematic reviews investigated anxiety and depression [68], another, somewhat outdated review, investigated only sexual satisfaction [67], and the last review focused on the Middle East context [66]. None of the earlier systematic reviews was as comprehensive as the current systematic review. This review aims to assess the effect of psychosocial interventions in improving sexual satisfaction, marital satisfaction, marital intimacy, sleep quality and infertility-related QoL. It also seeks to explore (psychometric) characteristics of available psychosocial interventions including feasibility in resource-limited settings, adaptability to other LMICs settings, cost of the intervention. Additionally, the review examines the level of expertise required to deliver the intervention, whether the intervention can be delivered in peripheral health facilities by non-specialist healthcare providers and acceptability by patients.

A comprehensive synthesis of studies on psychosocial interventions for infertility within LMICs will help to objectively and rationally uncover the strengths and limitations in existing psychosocial interventions. The characteristics of existing interventions and the contexts in which they were administered need to be explored for adaptability and feasibility in sub-Saharan African countries. This is expected to help conceptualize care and improve marital intimacy, sexual satisfaction, and QoL of individuals with infertility in LMICs settings as recommended by the World Health Organization (WHO) [69]. The main objective of this systematic review is to systematically synthesize existing data captured from the literature and other sources and provide robust evidence on the availability and effectiveness of psychosocial interventions to improve the well-being of people with infertility problems in LMICs. Specifically, the study aims to: 1) assess the effectiveness of documented psychosocial interventions for improving QoL, marital intimacy, and sexual satisfaction among individuals living in LMICs going through infertility problems, 2) assess the quality of life of individuals living in LMICs going through infertility, 3) assess (psychometric) characteristics of the documented psychosocial interventions in LMICs, and 4) assess psychological concerns of people going through infertility problems in LMICs.

Patient and public involvement

The review questions and outcome measures have been developed in collaboration with the relevant patient and consumer involvement and are informed by their priorities, experiences, and preferences in line with the GRIPP2 reporting checklists [73]. The results of the review will be shared with the relevant wider client communities who will also be involved in the dissemination of the results.

Types of studies

Randomized controlled trials (RCTs), quasi-RCTs, and Observational studies (cohort, case-control cross-sectional studies) reporting on any psychosocial intervention used to improve QoL, marital intimacy, and sexual satisfaction in people with infertility problems living in LMICs as defined by ScHARR [77], will be eligible for inclusion. Reviews will not be considered for inclusion; however, we will snowball review papers for any potentially primary eligible studies missed in our primary searches and consider them for inclusion. Where the study reports a country or regional estimate without a well-defined sample (a representative sample or sub-sample of the source population) or data from secondary analyses, it will not be eligible for inclusion. Expert opinions, commentaries, newsletters, case series, and case studies will be excluded. Studies that assessed psychosocial problems such as depression, anxiety, stress, self-esteem but did not investigate sexual satisfaction, marital intimacy and, fertility-related QoL will be excluded.

Participants

Individuals (women or couples) with infertility problems living in a LMIC who received psychosocial intervention aimed to improve their QoL, marital intimacy, marital satisfaction, and sexual satisfaction. Infertility is defined by the World Health Organization (WHO) as a disorder of the male or female reproductive system that leads to failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse [74]. This systematic review will include women and couples diagnosed with primary infertility where a couple has never been able to conceive a pregnancy after a minimum of 12 months of attempting to do so through unprotected intercourse. It will also include those with secondary infertility, characterized as the inability to conceive or carry a baby to term after 12 months of unprotected intercourse, in a woman who has previously had a baby without fertility treatments. Participants must have received any form of psychosocial intervention that sought to improve their marital intimacy, marital and sexual satisfaction, and overall fertility-related QoL.

Studies focusing exclusively on men with infertility issues will not be eligible for inclusion.

Intervention

Psychosocial interventions, may include support programs, counseling, CBT, educational interventions, acceptance and commitment therapy (ACT), and relaxation techniques. Interventions may be women, couples, group, or Internet-based. Interventions will be stratified by format, frequency, duration, delivery mode, setting, provider qualifications and theoretical underpinnings. Pharmacological interventions will be excluded. Intervention characteristics to be examined include availability, effectiveness, ease of delivery/application, preference, patient understanding, contextual relevance, type of health professional able to apply it, appropriateness, feasibility and adaptability for use in the sub-Saharan African setting. These characteristics are usually influenced by culture, participant factors, or the context in which the intervention was originally developed. Adaptability of the intervention refers to the ease of modifying aspects of the intervention to make it relevant to the target population, thereby ensuring that the purpose of the intervention is achieved. Any other information or characteristics that further describe the psychosocial interventions will be considered.

Control

• Placebo
• Pharmacological intervention.
• Routine/standard fertility.

Outcomes

Searches for the identification of studies

The following electronic databases will be searched: PubMed, SCOPUS, CINAHL Complete, LILACS, Cochrane CENTRAL and PsycINFO (EBSCO), and Google Scholar from 2000 to 31^st March 2025, without any language restriction, using all the relevant search terms and their synonyms, singular and plural forms, and spelling variations, together with the individual countries adapted from the ScHARR 2022 classification of LMICs. We structured the search around three key concepts: infertility, psychosocial interventions, and name of each LMIC, included a combination of both index terms (e.g., MeSH terms in PubMed) and free-text terms to capture relevant variations in terminology. We have incorporated Medical Subject Headings (MeSH) and other controlled vocabulary where applicable, alongside text words to enhance sensitivity and carefully combined terms using Boolean operators (AND, OR) to ensure both breadth and precision. The revised strategy has been calibrated by running test searches to ensure it will retrieve all relevant studies”. The search strategy for PubMed has been reported in Table 1 and it will be adapted for use in the other databases. We will also search HINARI, African Journals Online, Conference Proceedings, Preprints and Thesis Repositories. Reference lists of relevant studies including relevant systematic reviews will be searched to retrieve studies missed by our searches. Experts in the field will be contacted for completed but unpublished studies.

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Table 1. Search strategy for PubMed (to be adapted for the other databases).

https://doi.org/10.1371/journal.pone.0335068.t001

Managing the search results and selecting studies

All studies retrieved from the various sources will be documented in a table and exported to Rayyan for de-duplication and study selection. A pre-tested study selection flow chart developed from the inclusion/exclusion criteria (Fig 1) will be used for study selection. At least two reviewers will screen titles and abstracts against the study selection flowchart to select all potentially relevant studies. Full documents or texts will be sought for these studies for final assessment and selection against our pre-specified eligibility criteria. Studies that meet all the pre-specified inclusion criteria will be included in the systematic review. Any disagreements between reviewers will be resolved through discussion.

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Fig 1. Study selection flow chart.

https://doi.org/10.1371/journal.pone.0335068.g001

Data extraction and management

Data will be extracted using pretested data extraction form (S2 Table). The following information will be extracted: characteristics of the study including Study ID, country the study was conducted, year the study was conducted, sample size and setting (urban/rural); population variables such as age of participants, sex; type of psychosocial intervention (Supportive Program, Counselling, CBT, Educational Intervention, ACT and Relaxation Technique) all intervention characteristics such as format, intensity, frequency, number of sessions, duration of session, delivery mode, setting, provider qualifications, theoretical underpinnings and follow-up time; outcomes data (number achieving the desired effect of marital intimacy, sexual satisfaction, marital satisfaction, sleep quality as well as information on psychometric characteristics of the intervention such as cost, adaptability, feasibility, expertise required to deliver and intervention preference); and information on adverse events including type, frequency, duration, and timing of the occurrence of the adverse event. All data conversions will be made as part of the data extraction process before the data analysis. Any discrepancies between the data extractors will be discussed and resolved by consensus.

Assessment of quality of the included studies for risk of bias

The Cochrane risk-of-bias tool for randomized trials (RoB 2) (S3 Table) will be used to assess risk of bias of included RCTs in five main domains: 1) risk of bias arising from the randomization process, 2) risk of bias due to deviations from the intended interventions (effect of assignment to intervention) and risk of bias due to deviations from the intended interventions (effect of adhering to intervention), 3) missing outcome data, 4) risk of bias in measurement of the outcome, and 5) risk of bias in selection of the reported result. Each of the domains has a number of signalling questions with responses to each signalling questions being ‘Yes (N)’, ‘Probably Yes (PY)’, ‘Probably No (PN)’, ‘No (N)’, and ‘No Information (NI)’. The risk of bias in each trial will be judged as ‘Low’, ‘High’ or ‘Some concerns’. The results from the risk of bias assessment will be presented in a table with supporting statements from the primary studies. The risk of bias in non-randomised studies – of interventions will be assessed using ROBINS-Iv2 tool from seven domains: 1) risk of bias due to confounding, 2) risk of bias in classification of interventions, 3) risk of bias in selection of participants into the study (or into the analysis), 4) risk of bias due to deviations from intended interventions, 5) risk of bias due to missing data, 6) risk of bias arising from measurement of the outcome and 7) risk of bias in selection of the reported result. Each bias domain in ROBINS-I is addressed using a series of signalling questions to gather important information about the study and the analysis being assessed. Most signalling questions have response options ‘Yes’, ‘Probably yes’, ‘Probably no’, ‘No’ and ‘No information’, with ‘Yes’ and ‘Probably yes’ having the same implications for risk of bias and similarly for ‘No’ and ‘Probably no’. Some questions have additional response options (a ‘weak’ and a ‘strong’ version of ‘Yes’ or ‘No’) to help discriminate between higher and lower risk of bias. The risk of bias in each trial is judged as Low risk of bias (there is little or no concern about bias with regard to this domain), Moderate risk of bias (there is some concern about bias with regard to this domain, although it is not clear that there is an important risk of bias, Serious risk of bias (the study has some important problems in this domain: characteristics of the study give rise to a serious risk of bias) or Critical risk of bias (the study is very problematic in this domain: characteristics of the study give rise to a critical risk of bias, such that and the result should generally be excluded from evidence syntheses). The quality of observational studies will be assessed using Hoy’s quality assessment tool (S4 Table) in four risk of bias domains: selection, non-response, measurement, and data analysis. For each domain, two independent reviewers will evaluate and classify it as having a ‘low risk of bias’, ‘high risk of bias’, or ‘unclear risk of bias’. The overall quality of evidence will be classified as low, high, or unclear risk of bias based on each study. Discrepancies will be resolved through discussion between the reviewers.

Data analysis

The included studies will be summarized by their PICOS elements, including participant characteristics, study type, psychosocial interventions, and outcomes of interest. Review Manager v5.4 (and where necessary Stata version 18.0) will be used to analyze quantitative data. Measures of effect; Odds ratio (OR) or risk ratio (RR) will be utilized to present dichotomous data, while mean difference (MD) will be used to express continuous outcome data. Continuous outcomes data measured using different scales or instruments will be reported as standardized mean difference (SMD). All measures will be accompanied by their corresponding confidence intervals (CIs). The study-specific estimates will be used to determine standard deviations, where applicable, based on point estimates and the appropriate denominators, assuming a binomial distribution to assess heterogeneity between studies. Then the magnitude of heterogeneity between included studies will be assessed quantitatively using the index of heterogeneity (I² statistics). The range of I² values that will be considered for categorizing heterogeneity are: 25% for low, 50% for moderate, and ≥75% for significant heterogeneity. If the data from multi-country studies cannot be disaggregated because the results were initially pooled, these studies will be excluded from the meta-analysis.

Adverse advents will be categorized as non-serious or serious events. Given the lack of uniformity, and the fact that adverse events are usually poorly reported, we anticipate synthesizing adverse events outcome data narratively and reporting the results in tables, stratified by type of intervention, and providing clear description of patterns, context, frequency, duration, and timing of occurrence of the events. However, if data permit and the studies are sufficiently similar, we will compare event rates between intervention and control groups in meta-analysis and expressed as RR, OR, or risk difference (RD) using random-effects model. If the adverse events are rare, we will employ Peto odds ratio to estimate pooled event rates between the intervention and control. All estimates of adverse events will be reported with their 95% CIs.

Heterogeneity and subgroup analysis

We will determine the significance of heterogeneity using the p-value of the I² statistic, and a p-value <0.05 will be considered evidence of heterogeneity. Subgroup analysis will be performed where heterogeneity detected is significantly high and the possible sources will be explored. Heterogeneity will be assessed around variables such as sex, age of participants, study setting (hospital or community-based), geographical location (regions and subregions) and type of infertility. Sub-group analysis for RCTs will be done carefully in order not to break the randomization code of the trials.

Sensitivity analysis

This is a process of testing the robustness of the results. Data will be re-analysed to determine if the results are sensitive to specific review elements [84]. The domains to be considered are the quality of the included studies, sample size and meta-analysis technique applied.

Handling missing and incomplete data

An attempt will be made to extract sufficient data from all included studies. Where data on some pertinent variables are missing, the original authors of the primary studies will be contacted to see if they can provide the missing data. Where original authors are unable to provide the requested information or cannot be reached, we will not compute. Instead, the potential impact of missing data on the findings of the review will be addressed in the discussion section.

Grading level of evidence

The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach will be used to assess the overall quality of evidence generated from the review for each outcome. The pooled data of each study will be evaluated against the five GRADE considered; risk of bias, imprecision, inconsistency, indirectness, and publication bias, and will be graded as high, high, moderate, low, and very low levels of evidence accordingly [85,86]. High-quality evidence is interpreted as further research is very unlikely to change the confidence in the estimate of effect, while grading of very low quality implies an estimate of effect is very doubtful.

Ethics and dissemination

This study includes data from existing studies and does not require ethical approval. The findings of this systematic review will be made available to stakeholders, practitioners, patients and policy makers through presentation at scientific conferences and symposia. The review will be submitted for publication in peer-reviewed journal.

Strengths and limitations

The study will report the first comprehensive systematic review and meta-analysis in LMICs to identify available psychosocial interventions and determine their impact on infertile couples, with particular emphasis on marital intimacy, sexual satisfaction and quality of life. In order to minimize bias in the review process, we will employ a very comprehensive search strategy to retrieve studies from all relevant database and non-database sources, and select studies, extract data and assess quality in the included studies independently using validated tools. The review plans to assess certainty of the overall (pooled) evidence using GRADE. A potential limitation is the anticipated diversity of psychosocial interventions that will make direct comparisons difficult. However, we will classify the types of interventions used in primary studies carefully and perform subgroup analyses, where necessary possible, to explore the source of the heterogeneity and the subgroup that will benefit most from the interventions. We acknowledge that some of the primary studies will be of low quality, we will perform sensitivity analysis on the quality of studies domain to test the robustness of our pooled evidence.

Implications of the anticipated study findings

The findings from the proposed systematic review are expected to document psychosocial interventions in LMICs that improve marital intimacy and sexual satisfaction, ultimately impacting on the quality of life of people with infertility, particularly women. The findings will also identify knowledge gaps to inform future research and provide evidence-based solutions to improve psychosocial health care for people with infertility in LMICs.