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Published in final edited form as: Addict Res Theory. 2023 Jun 20;32(3):153–159. doi: 10.1080/16066359.2023.2224964

Improving Social Recovery Capital Research To Enhance Clinical Utility: A Proposed Agenda

Samuel N Meisel 1,2, Emily A Hennessy 3, Jordan Jurinsky 4, John F Kelly 3
PMCID: PMC11299860  NIHMSID: NIHMS1917881  PMID: 39109166

Abstract

Social recovery capital (SRC) is the combination of social resources that can be used to initiate and sustain addiction recovery through friends, family, and peers. Broadly, understanding one’s SRC allows us to get a sense of where one has social support for recovery and where there may be social barriers to their recovery process. SRC is often a vital component of many people’s recovery journey, yet our understanding of how best to use this concept in research and practice remains underdeveloped. To improve understanding of the role of social recovery capital and strategies to measure and increase it, we present a roadmap involving a five-pronged research agenda to: (1) Refine the measurement of social recovery capital; (2) Model the complexity of social recovery capital empirically; (3) Integrate personality science with social recovery capital research; (4) Optimize evidence-based behavior change techniques of social recovery capital; and (5) Incorporate an intersectional framework when examining or applying social recovery capital. Overall, this five-pronged research agenda seeks to enhance the clinical utility of SRC research to maximize the impact of SRC on one’s recovery.

Keywords: Behavior Change Techniques, Personality Science, Precision Medicine, Social Recovery Capital

Recovery capital refers to the availability and accumulation of resources that support substance use recovery (Cloud & Granfield, 2008). Social recovery capital (SRC), one facet of recovery capital, represents the variety of recovery supports generated through relationships with others (Cloud & Granfield, 2008; Granfield & Cloud, 1999, 2001). It can include positive and recovery-supportive relationships, opportunities for sober social activities, and linkages to resources helpful for recovery. Importantly, indicators of SRC are pre-treatment predictors and mediators of substance use treatment outcomes (e.g., Kelly et al., 2014; Litt et al., 2009), as well as predictors of recovery outside of formal treatment (Pongsavee et al., 2021). Broadly, SRC can identify a wide breadth of social resources (and conversely, point to potential social gaps/barriers) experienced during a recovery journey. Yet, because SRC is a broad framework, there is a need for a research agenda which purposely bridges this framework with precision medicine (i.e., the effort to match specific treatments to specific individuals) to guide clinical practice and promote an individual’s recovery capital.

In this think piece, we present a roadmap for how to take the more abstract, theoretical aspects of SRC and apply them to better understand (through research) and support (through practice) recovering individuals. To do so, we draw from several disciplines, including personality and clinical sciences, that each provide a new lens of understanding SRC. First, we provide an overview of SRC. Next, we present a five-pronged research agenda for advancing precision medicine efforts with respect to SRC research (Kranzler & McKay, 2012). These agendas are intended to facilitate the process of matching an individual’s unique profile of SRC (i.e., strengths and deficits) and characteristics (e.g., personality) to specific aspects of behavior change in formal treatment or outside of formal treatment that promote SRC and recovery.

Overview of Social Recovery Capital

Recovery capital is the breadth of resources necessary to support a recovery journey, regardless of one’s treatment history status, and is typically conceptualized as being comprised of personal, social, and community recovery capital (Granfield & Cloud, 1999). Recovery capital theory generally suggests that having a stronger connection to, admiration/emulation of, and a larger number of recovery-supportive social influences should produce better outcomes (Best & Hennessy, 2022; Hennessy, 2017). According to Granfield and Cloud (1999), SRC is all the resources available to an individual in recovery through the “structure and reciprocal functions of social relationships within which they are embedded” (p. 180). Thus, SRC facilitates an individual to bond with their family and others in their community, and, in turn, these bonds lead to accessing further resources through one’s network. White and Cloud (2008) further suggested that evidence of SRC in one’s network is indicated by several social factors including the “willingness of intimate partners and family members to participate in treatment, the presence of others in recovery within the family and social network, access to sober outlets for sobriety-based fellowship/leisure, and relational connections to conventional institutions” (p. 2). These definitions suggest there are a variety of ways that SRC may be evident in one’s life and in how it interacts with other internal (personal) and external (community) recovery capital domains. Thus, there is a foundational assumption of heterogeneity in SRC with respect to the multiple indicators that comprise it and the interactive nature of SRC with other recovery capital domains.

Several validated measures assess the key dimensions of SRC, including the Assessment of Recovery Capital, ARC (Groshkova et al., 2013) and the Recovery Capital Questionnaire, RCQ (Burns & Marks, 2013). These measures ask about feeling happy or satisfied with relationships, feeling supported by relationships, questions specific to the existence and quality of different types of relationships, having accountability, responsibility or commitments to/with others, and access to recovery advice/information from others. Although designed to reflect the multiple domains of recovery capital (Groshkova et al., 2013), factor analytic work has not yielded strong support of the ARC, and it may not be sensitive to cultural or other geographic differences (Bowen et al., 2020). The RCQ social capital domain has not demonstrated strong predictive validity (Hennessy, 2017). Considering these limitations, refinement of the measurement of SRC has been identified as an important area of future research (Best & Hennessy, 2022). Accordingly, the first prong in this research agenda focuses on refining the measure of SRC.

Prong 1: Refine the Measurement of Social Recovery Capital

Social capital is difficult to measure (Adam & Rončević, 2003; Best & Hennessy, 2021; Claridge, 2004). Although SRC measures cover many of its key dimensions, these measures assume that all forms of SRC operate equally as their scoring protocols give the same weight to all items in their summary scores. However, the importance of the nature and quality of these relationships and social engagements vary between individuals. Some individuals may find material support (e.g., loan) but not emotional support (e.g., reassurance) through their networks (or vice versa). Others may feel emotionally supported, but their relationships do not provide access to tangible recovery resources/help. Alternatively, one could have a supportive recovery group in their lives, but that could be their only source of social capital. Any of these gaps could constitute major recovery barriers, but the measures do not capture these types of nuances. Furthermore, sociological research suggests that individuals need both strong (i.e., close relationships involving high levels of trust, loyalty, and emotional support) and weak social ties (i.e., casual relationships involving low emotional support) to garner the full benefit of resources from their networks (Durkheim, 1951; Granovetter, 1973), but this aspect is not captured by these scales.

The focus on strengths in these measures also neglects the fact that while individuals may have recovery-supportive individuals and activities in their lives, some of these things may simultaneously present barriers. A growing body of research demonstrates the dual nature of relationships. For example, qualitative studies with recovering adolescents suggest that youth may continue to spend time with their friends who use substances and serve as potential recovery risk factors because they knew these individuals prior to initiating substances, had shared connections with them through their substance use, or these friends were caring, honest, understanding, and fun (e.g., Jurinsky et al., 2022; Passetti et al., 2008). Or an individual could have relationships with family members that provide childcare during treatment sessions but may also act in non-supportive ways (e.g., using substances around them, creating a stressful environment). Additionally, one could have friends and family who generally support their recovery when spending time together one-on-one, but who are not supportive in larger social settings (e.g., a party).

Thus, the challenge for measures of SRC is to both (1) capture a level of detail that reflects the complex nature of SRC and (2) use the information generated to maximize its predictive validity. One measure that can capture the complex nature of SRC is social identity mapping (SIM; Beckwith et al., 2018). Rather than more typical questionnaire type assessment formats, the SIM approach uses a series of structured questions that produces a visual map of the individual within their perceived social network. An individual identifies groups they are part of, the number of and behavior of group members, time spent with those groups and group members, and other relational aspects (e.g., group importance, level of identification with group). One advantage to using SIM’s visual approach versus traditional survey measures (e.g., important people and activities questionnaire; Clifford & Longabaugh, 1991) is that it may prompt deeper reflection and more accurate data of both the positive and negative aspects of SRC within the same group. The production of a pictorial social “gestalt” that facilitates deeper cognitive-affective processing may also have therapeutic value. Once the positive and negative aspects of SRC are collected with a SRC measure, like the SIM, a challenge is how to incorporate this information in a model that maximizes its predictive validity (i.e., appropriately summarizes one’s SRC to predict key recovery-related outcomes).

Prong 2: Model the Complexity of Social Recovery Capital Empirically

Maximizing the predictive validity of SRC requires accounting for the multiple domains that comprise SRC and how they interact with one another to influence recovery. Figure 1 depicts multiple SRC indicators of one’s social environment. Person 1 and Person 2 in Figure 1 both engage in multiple recovery supportive hobbies and each report having many friends who use substances, few friends who do not use substances, few family members who use substances, and many family members who do not use substances. The main difference between these individuals is the number of friends they have who are supportive. If a study only examines some of the SRC domains, such as the number of friends or substance use by family members, it misses the potentially important difference between these individuals – the number of friends that are supportive of recovery. Persons 3 and 4 differ such that Person 3 is characterized by a risky friend environment and protective familial and recreational environments whereas Person 4 has a protective peer environment but risky familial and recreational environments. Again, only examining one social domain (e.g., family) may lead to an inaccurate characterization of an individual’s SRC and how it relates to recovery. Importantly, although Figure 1 depicts the complexity in modeling the multiple indicators of SRC simultaneously to maximize its predictive validity, this figure is an oversimplification. Even if an individual has many friends who use substances and a family with a large proportion of members with substance use disorders, the presence of a single, supportive best friend may be sufficient to overcome those barriers (Schofield et al., 2015). Further, the figure does not capture important features of relationships such as admiration, influence, respect, conflict, and closeness with network members.

Figure 1.

Figure 1.

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A conceptual depiction of the heterogeneous possibilities of SRC indicators. Using analytic strategies that capture this heterogeneity across individuals is needed to maximize the predictive utility of SRC.

Fortunately, existing data analytic techniques provide one avenue to maximize the predictive validity of a SRC measure. One possible analytic approach is mixture modeling, which identifies distinct combinations (i.e., subgroups) of indicators. Imagine that Figure 1 was expanded to represent the SRC indicators from a measure of SRC, such as SIM, for 300 people. Mixture modeling could identify distinct combinations of these SRC indicators (i.e., summarize the SIM) and group individuals based on these combinations. In turn, these groups summarizing combinations of SRC indicators could then be used to predict outcomes. Several studies have used mixture modeling techniques to capture the heterogeneity in individuals’ social capital (e.g., Ahlborg et al., 2019; Hennessy, 2018). For example, in a sample of adolescents, Ahlborg et al. (2019) found distinct profiles of combinations of social cohesion, social support, family, school, peer relationships, and indicators of social capital, and these classes were differentially associated with health complaints. The use of analytic techniques that can simultaneously capture the multiple social domains that comprise SRC may help uncover the most critical elements, or combinations of elements, and thereby improve the predictive validity of SRC in relation to recovery as well as improve and inform future SRC measurement.

Prong 3: Integrate Personality Science with Social Recovery Capital

Addiction science researchers have proposed that individuals should be matched to appropriate treatment based on personality and etiological risk factors for substance use (Boness & Witkiewitz, 2022). Thus, accurately measuring and appropriately modeling the complexity of SRC may be insufficient to identify which skills best promote SRC for specific individuals. Imagine, that a therapist has two clients, Bill and Linda, who initiate treatment for a substance use disorder (SUD) with what appears to be the same type and level of self-reported SRC1. They both report recovery-supportive relationships with three close friends, and they are engaged in a peer support group. Although Bill and Linda present with very similar SRC according to objective standards, the therapist notices their distinct interpersonal styles. Whereas Bill is antagonistic and has limited openness to new experiences, Linda is extroverted, willing to meet new people and try new experiences. Would it be reasonable for Bill and Linda’s therapist to interpret their clients’ baseline scores of SRC as having an equal weight in directing the treatment approach? Would it also be reasonable for this therapist to expect that these two clients would be similarly successful in building SRC or engaging with their recovery supports? Modeling these complexities with a sufficient sample size is needed to inform the answer to these clinically-relevant questions.

Contemporary personality perspectives argue that personality shapes how individuals interact with their social environments, which has significant implications for SRC practice and research (Hopwood et al., 2022; Wright et al., 2020). To date, theoretical accounts of SRC (e.g., Granfield & Cloud, 2001; Neale & Stevenson, 2015) have not focused on how individual differences in personality traits influence SRC in recovery. This is a notable gap in SRC research considering evidence syntheses have demonstrated consistent associations between personality traits and friendship quality (Harris & Vazire, 2016), romantic relationship functioning (Malouff et al., 2010), and community involvement (Lodi-Smith & Roberts, 2007). Moreover, individual differences in personality traits alter the magnitude of the association between having peers who use substances and an individual’s own substance use (Scalco & Colder, 2017). These findings highlight the importance of considering how individual differences in personality traits may amplify or buffer associations between SRC and treatment outcomes.

Incorporating personality science into the study of SRC aligns with growing efforts to adopt a precision medicine approach to recovery science (Kranzler & McKay, 2012). Individual differences in personality traits may help us better understand which interventions or intervention ingredients best maximize SRC for an individual seeking SUD treatment or maintaining recovery.

Prong 4: Optimize Evidence-Based Behavior Change Techniques of Social Recovery Capital

Multiple treatments such as Network Supportive treatment (Litt et al., 2009), motivational enhancement and cognitive behavior therapy (MET-CBT; Meisel et al., 2021), family therapy (Henderson et al., 2009), 12-step based programs (Kelly et al., 2014), multicomponent programs (Best & Lubman, 2017), and pharmacotherapy (Meisel et al., 2022) are associated with improvements in aspects of SRC (e.g., increasing social support for abstinence, decreases in time with using peers; Kelly et al., 2014; Litt et al., 2009; Meisel et al., 2021). Moreover, studies of individuals outside of formal treatment also demonstrate that changes in indicators of SRC are a central predictor of recovery (Pongsavee et al., 2021).

Oftentimes addiction treatments, formal or informal (e.g., use of a mobile application), consist of several active components to address various aspects of behavior change. Studies assessing multiple behavior change techniques (BCTs)2 simultaneously using between-condition comparison are unable to identify which specific BCTs yield improvements in SRC (Magill & Longabaugh, 2013). For example, consider the use of five session MET-CBT (Sample & Kadden, 2001) for SUDs. Even in this brief intervention, MET (e.g., relational change ingredients, directional change ingredients), and CBT (e.g., resisting peer influence, altering negative thoughts, positive activity scheduling, building social supports) each have multiple BCTs that may effectively increase SRC on their own. Addressing the “black box” issue of behavior change, the limited understanding of which specific BCTs promote behavior change, is critical to strengthening and scaling SUD interventions (Magill & Longabaugh, 2013).

Knowing which BCTs in multicomponent interventions, like MET-CBT, as well as in natural recovery increase indicators of SRC will enhance our understanding of how to promote improvements in SRC. An expanded understanding of why a BCT implemented as part of an intervention or used in settings outside of formal treatment promotes SRC may help identify potential turning points in an individual’s recovery journey (Hallgren et al., 2018). For example, if research indicates that positive activity scheduling (i.e., action planning) enhances SRC, studies of individuals outside of formal treatment should repeatedly assess positive activity scheduling to capture subsequent shifts in SRC. An established evidence base of the BCTs that promote SRC will enable future research that examines whether certain techniques are more or less effective based on individual differences in personality (Boness & Witkiewitz, 2022). In sum, knowledge of the BCTs that promote SRC will be instrumental in advancing precision medicine efforts.

Prong 5: Incorporate an Intersectional Framework When Examining or Applying Social Recovery Capital

One critique of SRC is that it is rooted in the experiences of individuals with privilege and existing resources. This critique stems from the initial work on SRC, and recovery capital more broadly, being centered on White, middle-class, adult, males (Hennessy, 2017). Intersectionality, whose origins stem from Black feminist scholar-activists (e.g., Crenshaw, 1999; Collins, 1990), highlights the importance of attending to the multiple, intersecting identities (e.g., age, race, gender, income, sexual identity) and social positions of an individual simultaneously, and the privileges and disadvantage of those intersections (Rosenthal, 2016). A serious threat to SRC research is that a failure to adopt an intersectional framework will advance research that has limited generalizability and fails to support the diverse array of individuals with SUDs. To demonstrate this point, we briefly discuss race, income, and age to highlight the need for an intersectional approach to SRC research.

Race:

A growing literature base demonstrates the inequitable experiences of different races and ethnicities within the healthcare system overall, and more specifically on treatment and recovery outcomes. There are likely cultural differences that influence how individuals choose to engage with available services or disclose their experience with others (Pinedo et al., 2018). These experiences have important implications for how we operationalize SRC for different racial groups in both research and practice as we must consider who is asking the questions and how they are asking them.

Income:

Social supports may look different across different economic groups. For example, individuals from low-income backgrounds are characterized by few social supports (Cheney et al., 2016) and a larger reliance on their treatment providers for support (Zschau et al., 2016). Importantly, in a racially diverse and low-income sample, Bowen et al. (2020) demonstrated that the Assessment of Recovery Capital (ARC), a common measure of recovery capital, had significant theoretical and psychometric limitations. These findings highlight that what constitutes SRC as well as the elements most salient to SRC may differ among individuals from low-income backgrounds.

Developmental Stage and Age:

Developmental stage and age are an important intersectional factor. From this perspective, let’s consider adolescents in recovery. They begin in the system at a disadvantage. They are minors, who are oftentimes viewed as making poor choices, needing redirection by an adult provider. In these encounters, they may be given directives from an adult perspective without consideration for their perspective. This can be disempowering as it emphasizes their lack of agency at a time when developing agency is a key developmental goal. Ultimately, this is a disservice to their personhood and research has demonstrated that this approach is not as effective as a developmentally-tailored one.

As seen in these brief examples, adopting an intersectional framework requires listening to the voices and perspectives of individuals with diverse and intersecting identities. As depicted in Figure 2, Prong 5 should not be viewed as separate from Prongs 1–4, but rather as a core consideration that undergirds each. Centering the voices of individuals with intersecting identities will answer questions pertinent to Prongs 1–4 of our agenda: (1) Prong 1: Are the components of SRC the same or different for individuals with different intersecting identities? (2) Prong 2: Are the multiple elements of SRC equally important to individuals with different intersecting identities? (3) Prong 3: Are the personality facets that facilitate building SRC and advancing precision medicine efforts the same across individuals with different intersecting identities? (4) Prong 4: How do factors such as race, sex, economic opportunity, and class influence which BCTs are acceptable and effective for enhancing SRC?

Figure 2.

Figure 2.

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Integrative research agenda to advance precision medicine efforts for SRC.

Conclusions

SRC is often a vital component of recovery for many, yet our understanding of this concept for clinical applications is still underdeveloped. To advance a better understanding of the role of SRC and strategies to increase it, we have presented a five-pronged research agenda (Figure 2). In line with our agenda, adopting an intersectional framework that considers the diverse array of pathways through which individuals recover from SUDs (e.g., through formal treatments or natural recovery) will be critical to producing SRC that is maximally generalizable. We believe that each of the proposed research agenda prongs must be pursued to enhance the clinical applications of SRC and fully realize a precision medicine approach to SRC.

Acknowledgments

Author support was provided by NIAAA to Samuel N. Meisel (K99AA030030), Emily A. Hennessy (K01AA028536), and John F. Kelly (K24AA022136).

Footnotes

1

Personality science is equally relevant to SRC research for individuals who are not in formal treatment. Personality science will facilitate understanding how individual differences in personality facilitate or undermine building SRC outside of formal treatment.

2

Although full review of behavior change techniques is beyond the scope of this manuscript, readers should examine the behavior change taxonomy for additional considerations on measuring and analyzing intervention components (Michie et al., 2013).

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