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Recovery and Recovery Capital: Aligning Measurement with Theory and Practice


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Although most conceptualizations of recovery from substance use disorders place abstinence at their core, the process of recovery also includes improvements to individual well-being that go beyond abstinence or remission. The process of recovery is heterogeneous and is often exemplified in different ways for different people and in different contexts. In this chapter, we argue that the process of initiating and sustaining long-term recovery is dynamic and can vary across individuals. Recovery being dynamic means that what recovery looks like for a given individual may shift across time and context. Similarly, one person may base their recovery on very different daily routines than another person. As a result, the resources used to initiate and sustain recovery, referred to as recovery capital, also necessarily vary by time, context, and person. Although a simple premise, this argument has profound impact on measurement: both the process of recovery and transactions with recovery capital must be measured on timescales that make it possible to analyze changes in the recovery process as they unfold within each individual. That is, our measurement of recovery should allow description of the process and outcomes of successful recovery for each individual, and the ways that those processes may change (i.e., the dynamics of the process) in that person over time and context. We must also ensure that our understanding of recovery capital is sufficiently robust to capture the way that the resources that support recovery and recovery well-being vary between and within individuals over time and across contexts.
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109© Springer Nature Switzerland AG 2021
J. M. Croff, J. Beaman (eds.), Family Resilience and Recovery from Opioids
and Other Addictions, Emerging Issues in Family and Individual Resilience,
Chapter 6
Recovery andRecovery Capital: Aligning
Measurement withTheory andPractice
H.HarringtonCleveland, TimothyR.Brick, KylerS.Knapp,
Great efforts have been made to understand who is in successful recovery from
substance use disorders (SUD)– that is, to understand the characteristics of indi-
viduals who are successful in recovery. Recovery is an ongoing and dynamic pro-
cess; it is possible to be successful, relapse, and then again be successful. A focus
on recovery as an ongoing process suggests that understanding when recovery is
successful for each individual and how processes that sustain recovery may change
over time may be more important than understanding who is successful in a moment.
In this chapter, we argue that successful recovery is a dynamic process comprised of
more components than abstinence. We propose that researchers interested in addic-
tion recovery should adopt measurement, modeling, and intervention paradigms
that match the multicomponent environmental and within-person nature of recov-
ery. Specically, researchers should adoptmeasurement that captures the dynamics
of change within the process of recovery for different individuals across many tim-
escales and contexts; that utilizes the concept of recovery capital, the set of resources
that support recovery; and that models the ongoing transactions between recovery
status and recovery capital. In Sect. 1, we set out the importance of recovery and
examine recent denitions of the term. In Sect. 2, we dene recovery capital and
discuss current strategies and future directions for understanding it and its interac-
tions with the process of recovery. Section 3 examines current approaches to mea-
suring recovery and contrasts this with the denitions of recovery reviewed in Sect.
1. Section 4 describes measurement approaches for modeling recovery capital that
H. H. Cleveland () · T. R. Brick · K. S. Knapp
Department of Human Development and Family Studies, The Pennsylvania State University,
University Park, PA, USA
J. M. Croff
National Center for Wellness and Recovery, Oklahoma State University Center for Health
Sciences, Tulsa, OK, USA
contribute to the maintenance of recovery well-being. To make our argument more
concrete, Sect. 5 describes recovery support programs that are tailored for different
populations and different contexts, setting out how the dynamics of recovery and
recovery capital might be assessed within these programs.
1 Recovery: What Is It?
“Recovery” is a nontechnical term used in both nonprofessional and professional
SUD settings to describe a state of health and functioning that follows the cessation
of addictive substance use, typically involving abstinence from use (White 1998).
Over the last two decades, recognition of the importance of recovery has led several
academic, professional, and government organizations to develop formal denitions
for recovery. Several of these denitions are provided in Table6.1. At the core of
most of these denitions is the idea that although abstinence from the addictive
substance may be required, recovery also requires addressing underlying causes of
SUD and improving individual well-being beyond nonuse or remission. Similarly,
the ongoing nature of recovery is evident in denitions describing recovery as a
“state…of health” (ASAM 2005) and a “lifestyle” (Betty Ford Institute Consensus
Panel 2007; see Table6.1, which provides the same denitions presented in Kelly
Table 6.1 Prominent addiction recovery denitions
Source Year Denition
American Society of
Addiction Medicine
2005 A patient is in a “state of recovery” when he or she has reached a
state of physical and psychological health such that his/her
abstinence from dependency-producing drugs is complete and
Betty Ford Institute
Consensus Panel
2006 A voluntarily maintained lifestyle characterized by sobriety,
personal health, and citizenship
Center for Substance
Abuse Treatment
2005 Recovery from alcohol and drug problems is a process of change
through which an individual achieves abstinence and improved
health, wellness, and quality of life
SAMHSA 2011 Recovery from mental disorders and substance use disorders is a
process of change through which individuals improve their health
and wellness, live a self-directed life, and strive to reach their full
Scottish Government 2008 A process through which an individual is enabled to move on
from their problem drug use, towards a drug-free life as an active
and contributing member of society
UK Drug Policy
2008 The process of recovery from problematic substance use is
characterized by voluntarily sustained control over substance use
which maximizes health and well-being and participation in the
rights, roles, and responsibilities of society
H. H. Cleveland et al.
and Hoeppner 2015) and the dynamic nature repeatedly reasserted by descriptions
of recovery as a “process” or a “process of change” (e.g., CSAT 2005; SAMHSA
2011). Although abstinence or sobriety is a feature of most denitions, so too are
physical and psychological health, wellness, quality of life, and societal engage-
ment. The overall sense is that recovery must be understood as a transformational
dynamic process that unfolds over time and encompasses development and mainte-
nance of a broad set of behavioral, cognitive, social, and physical characteristics.
Consistent with this broader conceptualization, Kaskutas etal. (2014) note that the
Big Book of AA describes a program of recovery and that abstinence requires devel-
oping a “new way of living.Given these denitions, we will argue later in this
chapter that measurement of recovery should not only go beyond monitoring non-
use to capture other factors related to health and well-being but that it should be
undertaken in a way that sheds light on the dynamic processes that underlie and
constitute recovery as it is lived.
Conceptually, Kelly and Hoeppner (2015) raise several questions that serve as an
excellent guide to highlight the ambiguities present in the denitions presented in
Table6.1. First, is recovery a single common process? If so, recovery across
individuals would essentially be the same. If not, individuals with qualitatively dif-
ferent versions of recovery could still be equally engaged in the process of recovery.
Second, what place does personal health and wellness have in recovery? Denitions
put forth by CSAT (2005) and SAMHSA (2011; see Table6.1) both describe recov-
ery as a process of change for which the outcome is individuals improving their
health and achieving improved health. In contrast, other denitions situate well-
being as part of recovery, rather than an outcome. For example, the Betty Ford
Institute’s (2007) denition lists personal health as a characteristic of recovery.
Similarly, ASAM states that a “state of recovery” is a “state of physical and psycho-
logical health.These denitions’ different orientations toward well-being create
uncertainty regarding whether improved well-being is an outcome or end goal of
recovery or a component part intrinsic to the larger process of recovery.
Third, what is the role of civic engagement or engagement with others generally?
Denitions from the Betty Ford Institute (2007) and both the Scottish government
and the UK Drug Policy Commission (2008) include civic engagement, participat-
ing in or contributing to society as part of recovery, but these are not included in the
other denitions. Although there may be good reasons to believe that isolation may
threaten many individuals’ recoveries, existing denitions do not completely align
regarding whether a recovery built on solitude is possible.
Taken together, these denitions exhibit ambiguity concerning the homogeneity
vs. heterogeneity of the recovery process across individuals, contexts, and time.
Moreover, there are also ambiguities around how to consider those aspects of recov-
ery that go beyond nonuse and are more focused on thriving and striving to improve
one’s condition, as well as the relationships between individuals, their recovery, and
the broader society.
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
2 Recovery Capital
The construct of “recovery capital” provides a framework for examining resources
that support the process of recovery. This concept is relatively new– at least for-
mally– in the eld of recovery research (Graneld and Cloud 1999, 2004). On the
most basic level, recovery capital includes all resources someone has access to and
the capacity of the individual to use these resources to support their recovery:
including social, physical, human, and cultural capital. Similar to recovery itself,
recovery capital is also complex and dynamic.
In dening recovery capital, Graneld and Cloud (1999, 2004) distinguish
between social and physical capital. Social capital is the sum of resources that peo-
ple have as a result of their relationships and includes both support from and obliga-
tions to groups to which they belong. Physical capital includes tangible assets such
as property and money that may increase recovery options, such as being able to pay
for better treatment options (e.g., medication-assisted treatments for opioid and
alcohol use disorders) and opportunities to access posttreatment settings (e.g., sup-
portive recovery housing). Physical capital and social capital can mutually inuence
each other. For example, an individual might use physical resources (e.g., money) to
move away from networks of users and seek out opportunities to add recovering
friends to their social networks. Alternatively, an individual might rely on a network
of friends to help make ends meet nancially. Despite this mutual inuence, the two
are still conceptually independent. Being nancially able to move to a new town
may create opportunities to add recovering or supportive friends to one’s social
network, but it does not directly create or maintain those relationships.
Human capital includes education, skills, positive health, aspirations, hopes,
“grit,” and other personal resources that enable individuals to prosper. Education is
a particularly important aspect of human capital that supports recovery, because
education increases the potential for productive and professional engagement.
Similarly, interpersonal skills can have clear impact on someone’s ability to build
and maintain relationships that may provide additional support.
Cultural capital includes the values, beliefs, and attitudes that link individuals to
prosocial norms and identities and aid in reconceptualizing oneself– both within
the recovery community and within general society. This reconceptualization is
closely tied to the psychological construct of mattering, an important aspect of self-
concept, which denes how human beings have a need to be signicant to others,
attend to others, and to serve as a resource to others (Elliott etal. 2004). Indeed, this
concept interacts with the process and denitions of recovery presented above.
Negative self-concept is a dening characteristic of substance use disorder
(Dumont& Vamos 1975), and reconceptualization is a critically important hallmark
of transition to recovery dependent on cultural capital and interaction with others.
The analogy of capital is an important one. Physical capital (e.g., net worth) can
take many forms: a person might have money in the form of cash or as in invest-
ment; these can be applied in different ways. Cash has a tremendous inuence on a
person’s ability to purchase goods or to deal with a sudden debt. By contrast,
H. H. Cleveland et al.
investments might not be immediately useful to purchase goods, but can provide
benets over the longer term. In the same way, recovery capital may take on very
different forms, each of which may be more or less suited to a given person, context,
and time, and each of which may provide benets that only become evident at dif-
ferent points in time. This variability makes the consistent measurement of recovery
capital and modeling its effects on recovery more difcult than might be immedi-
ately obvious.
2.1 Relationship Between Recovery andRecovery Capital
The relationship between recovery and recovery capital is characterized by a recip-
rocal process. On one hand, such relationships can lead to benecial trajectories
with increased capital sparking improved recovery, which allows the individual to
accumulate more capital. On the other hand, an individual who faces repeated
recovery challenges may quickly burn through recovery capital (e.g., by overbur-
dening social supporters), each time leading to fewer resources available for the
next challenge.During active addiction, the reciprocal process between an individ-
ual’s own state of wellbeing, or lack thereof, and their increasingly damaged social
relationships may contribute to what has been referred to as “rock bottom.Variance
in individual capital is present from before the onset of substance use disorder
through the recovery process, resulting in differential success in the process of
recovery. The following examples set out different ways in which recovery capital
may support or undercut recovery.
Some individuals with substance use addictions have substantial nances,
insurance policies, opportunities for higher quality and longer treatment and post-
treatment care, and the ability to move away from their old set of using peers to
form new peer groups of nonusers and individuals in recovery. By contrast, others
do not have the same resources for treatment and may be tied via nancial concerns
to a single job and location and may therefore nd it difcult to avoid social groups
with substance-using peers, and potential cascades of triggers to use, negative affect,
and cravings. Not all differences in recovery capital are external to the individual or
even stable over time. For example, individuals who are committed to their recover-
ies may be more likely to attend support group meetings and develop supportive
social networks. These investments can create more resources for individuals to
draw upon over time as they do the daily work of maintaining well-being and/or
dealing with acute threats to their recoveries. Conversely, an individual who does
not invest in their recovery will likely have less recovery capital to draw upon
when needed.
The observation that recovery and recovery capital are integrated and interactive
is not unique to this chapter. For example, the central idea of Kelly and Hoeppner’s
(2015) proposed biaxial formulation of recovery is that as remission becomes more
stable and longer-term, there are (generally) improvements to recovery capital.
Similarly, the more recovery capital is accrued, in the form of intrapersonal
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
capabilities and interpersonal support, the more likely remission will continue.
Accordingly, growth along these axes (i.e., remission and recovery capital) is linked
in a reciprocal fashion. Kelly and Hoeppner (2015) propose that the link between
them is mediated by ability to cope with stressors and triggers. Consistent with our
view, Kelly and Hoeppner emphasize that recovery is dynamic and involves chang-
ing relationships between stressors and individual capacities. The centrality of the
transactions among people’s characteristics, triggers, and resources to our under-
standing of recovery capital underscores the need for methods that capture these
within-person processes.
3 Measurement Approaches forStudying Recovery
If recovery is multifaceted and dynamic, it follows that measurement strategies
should be implemented that are designed to capture dynamic and multifaceted pro-
cesses. Kaskutas etal. (2014) empirically assessed several recovery domains, focus-
ing on both discovering the most common specic elements of recovery and
identifying differences among the unique pathways taken by individuals. Their
study utilized 47 items drawn from the World Health Organization (WHO) scales
and recruited participants from diverse recovery settings. Results revealed four
dened domains of recovery: abstinence in recovery (i.e., no use/misuse of drugs),
essentials of recovery (i.e., responding to mistakes and negative feelings), enriched
recovery (i.e., contributing to family/society), and spirituality of recovery (i.e.,
being grateful). These four domains cover similar ground as those proposed by
SAMHSA in 2011, which include purpose (similar to spirituality), community
(similar to enriched recovery), and health (related to both abstinence and negative
feelings), and therefore provide data-driven support for SAMHSA’s domains.
However, the Kaskutas etal. (2014) study also revealed that some of these unique
elements, such as essentials of recovery, were especially important to some sub-
groups of people in recovery. They also found differences in engagement in these
distinct strategies between individuals, varying by characteristics such as experi-
ence in treatment and time in recovery. Their ndings suggest that these four pri-
mary domains may vary in importance across individuals, contexts, and time in
Differences in factor scores across contextual and individual variables highlight
the need for measurement that can assess differences both between individuals and
contexts and across time within a single individual. Each of the four factors identi-
ed by Kaskutas etal. (2014) may not only differ across people (e.g., some indi-
viduals are better able to deal with mistakes and negative feelings than others), but
they may also vary within people by context (e.g., individuals being better able to
deal with mistakes and negative feelings within some spaces or social groups than
in others), and time (e.g., individuals being better able to deal with mistakes and
negative feelings on same days than on others).
H. H. Cleveland et al.
Behavioral response to protect and build recovery will vary by timescale. The
relevant timescale for linkages between intrapersonal states, such as craving or neg-
ative mood, and behavioral reactions, and subsequent outcomes, might be hours or
minutes. Behaviors might result in release of endorphins and improved sleep,
therein reinforcing a positive behavior cascade which started from a negative emo-
tion. By contrast, civic engagement requires repeated behavioral engagements to
lead to positive results. The result of civic engagement may be less helpful in
addressing short-term negative affect, but may strengthen a person’s recovery iden-
tity and reduce risk of relapse in the coming months and years. The experiences of
dealing with negative emotion, and subsequent engagement in positive behaviors,
may, over time, reduce the strength of substance craving, as new behavioral patterns
take hold. During this transition, engagement in long-term outcomes, like civic
engagement, may increase in importance for sustained recovery. The recovery pro-
cess requires engagement across domains, which yield benets at different times-
cales. Therefore, it is necessary for measurement approaches to capture engagement
across domains at different times as well.
4 Measurement Approaches forStudying Recovery Capital
andIts Relationship toRecovery
Although studies assessing broadly dened views of recovery itself are sparse, the
measurement of recovery capital has received greater attention, with a focus on
understanding its different domains. A recent systematic review of the literature by
Hennessy (2017) found 35 unique studies examining recovery capital, with sample
sizes ranging from 4 to 703 participants. Findings from this review indicated that
recovery capital has been studied in a range of diverse populations, yet conceptual-
ization of the key domains and quantitative measurement have been inconsistent.
For example, the rst attempt to measure recovery capital (Sterling etal. 2008)
focused on personal and social capital with a heavy emphasis on spirituality, but did
not demonstrate ability to predict recovery success across participants. Another
instrument, the Recovery Capital Questionnaire (RCQ; Burns and Marks 2013),
assesses four domains: social, physical, human, and community capital. Both of
these tools showed measurement validity but have not been utilized in applied
The Assessment of Recovery Capital (ARC; Groshkova etal. 2013) and other
measures subsequently developed from it changed this trend, being used in applied
studies of recovery focused on targets besides scale development. The ARC assesses
a variety of domains, including substance use and sobriety, psychological and phys-
ical health, community involvement, social support, and more. However, rather than
providing evidence of multiple domains, principal components analysis suggested
that a single-component structure provided the best t to the data. Vilsaint etal.
(2017) drew from the 50-item pool of the ARC to test a reduced version of the ARC
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
assessing a single unied dimension: the Brief Assessment of Recovery Capital
(BARC-10). Results revealed that this new measure demonstrated internal consis-
tency, concurrent and predictive validity, and measurement invariance across loca-
tion and gender, suggesting that it is a strong measure to distinguish differences in
recovery capital at a between-persons level.
The REC-CAP was designed as a broader tool that incorporates the ARC as one
of its component parts, alongside other measures assessing recovery goals, engage-
ment, and motivation, to create a more holistic assessment of recovery capital (Best
etal. 2016). The instrument is primarily intended to identify strengths and barriers
to recovery by capturing individuals’ amounts of personal, social, and community
recovery capital. Unlike other measures such asthe ARC, which are largely used for
research purposes, the REC-CAP is designed for use in various recovery settings for
monitoring of progress by both the participant and professional or nonprofessional
staff and can inform recovery planning over time (Best etal. 2016, 2017).
Each of these measures (with the possible exception of the REC-CAP) is vali-
dated using data collected at a between-persons level. That is, they assess each per-
son at a single point in time and cannot distinguish differences between people from
differences in, for example, stage of recovery. As a result, these measures are likely
to focus on those aspects that differentiate people from each other– called interin-
dividual variability in the literature. Future directions should include measurement
of changes in capital within individuals to assess whether capital can increase,
decrease, or change form over time throughout an individual’s recovery journey,
and the study of interindividual variability misses those changes. This second set of
variability is termed intraindividual variability (Nesselroade& Ram 2004).
Measures that focus on interindividual differences will often miss crucial factors
that matter within a given person’s development (Molenaar 2004). Therefore, efforts
are needed to develop, test, and utilize measures that assess recovery capital longi-
tudinally in ecologically valid ways (e.g., measuring it in context as it is unfolding
rather than only in the lab or at one sitting using retrospective recall).
Modern technological tools have begun to provide a means of assessing this type
of intraindividual change. New approaches to measurement, referred to collectively
as “slice of life methods” (Smyth etal. 2017), focus on collecting intensive longitu-
dinal data about a single individual across time while they live their everyday lives.
Two common approaches are the daily diary method and ecological momentary
assessment. Daily diary methods ask participants to provide a single report each day
describing their experiences throughout the day (Bolger etal. 2003). These once-a-
day reports require a relatively small burden on the part of the participant but pro-
vide a clear view of the within-person changes and patterns that occur on a
day-to-day level.
Within-day processes may still, however, be important to recovery and recovery
capital. For example, negative social interactions at midday may have a detrimental
impact on later day mood and craving (see Cleveland and Harris 2010). Conversely,
positive support from a friend in recovery, an engagement in social recovery capital,
may increase the salience of an individual’s own recovery commitment for that day
and reduce the impact of a negative social experience on end of day mood or
H. H. Cleveland et al.
craving. Ecological momentary assessment approaches use technological tools (fre-
quently smartphones, in modern studies) to request feedback from participants sev-
eral times a day (Stone and Shiffman 1994). These studies permit researchers to
examine differences in recovery, as well as recovery capital within and between
days. Applying mixed-effects and multilevel models (Pritikin etal. 2017; Oravecz
and Brick 2019) to these types of data allows researchers to identify the phenomena
that matter at different times and contexts for a single individual, as well as those
that differentiate individuals from one another. In the case of the former, for exam-
ple, an individual might be strongly affected by negative social experiences when
he/she has low positive mood, but not when that individual is experiencing high
amounts of positive mood. EMA approaches also allow researchers to investigate
between-person differences in such within-person processes. For example, some
people may be highly reactive to negative social experiences, leading to strong
impacts on mood and craving from only a single interaction, while another might
show much less reactivity. Even more interesting, this reactivity may also change
within-person across time, with different patterns of change for each individual.
These patterns may be driven by the process of recovery itself or may be in response
to outside factors, such as social interactions, current location or environment, and
the accessibility of support services.
5 Where Recovery Happens: Recovery Support Services
Most conventional research on recovery has begun by searching for metrics indicat-
ing the success of SUD treatment in a context like a residential rehabilitation facil-
ity, or rather, following release from such treatment. Yet, the adoption of recovery as
an ongoing process has driven two paradigmatic shifts: rst, the move from a focus
on symptoms/pathology to a wellness framework, and second, the move from a
perspective that recovery is the aftermath of treatment to a perspective thatrecov-
eryis an ongoing process that requires chronic/continuing care (White 2006; Laudet
2011). These paradigm shifts also suggest a movement from measurement of recov-
ery in terms of xed treatment outcomes (such as relapse within the rst 90 days of
completing treatment) to measurement of metrics indicative of positive growth and
continued maintenance. This shift in conceptualizing the measurement of recovery
is not only due to the multifaceted and individualized nature of recovery but also
reects the reality that many who are engaged in recovery do so without treatment
(Sobell etal. 2000). Thus, someone being actively involved in recovery does not
assume they have graduated from treatment. Indeed, a nationally representative
study by Kelly and colleagues found that only 28% of people who report being in
recovery went to any form of formal treatment (Kelly etal. 2017). Even within the
28% whose path to recovery involves formal treatment, only half did so via residen-
tial treatment, which is undoubtedly the most commonly studied setting. More com-
monly, recovery is found via involvement in mutual help support groups or the
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
growing number of recovery centers and sober living environments, many of which
have only been available in the last 15–20years (Kelly etal. 2017).
Accordingly, research efforts must explore contexts beyond formal treatment set-
tings. In order to generate lasting effects, it is critical to understand the recovery
support programs that provide the support necessary for short-term recovery main-
tenance and the opportunities to develop other aspects of recovery capital necessary
for building long-term recovery. This point is well made by Kelly etal. (2017), who
point out that in contrast to the great volume of treatment center work, there is rela-
tively little work on recovery support services, such as collegiate recovery commu-
nities (see Cleveland etal. 2010) and recovery housing (see Polcin and Borkman
2008; Jason and Ferrari 2010).
Below we rst distinguish between professional and peer-delivered recovery
support services and then review two different types of programs that support recov-
ery in different settings: Collegiate Recovery Communities and Recovery Housing.
Broader reviews of such programs exist in other places (see Laudet and Humphreys
2013). Our purpose here is twofold: (1) set out different types of programs and con-
texts in a fashion that illustrates the differences in recovery capital provided by these
programs targeting different populations and different contexts and (2) discuss how
research and evaluation of these different program settings can be tailored to capture
the dynamic transactions between individuals and the aspects of recovery capital
that are offered by and can be cultivated within these programs.
5.1 Distinguishing Between Professional andPeer-Delivered
Recovery Support Services
Recovery support programs typically help individuals address a hierarchy of needs
that could adversely affect recovery. These needs are greatest in the beginning of the
recovery process and addressing them creates the scaffolding on which recovery
capital is built. The hierarchy of needs addressed are most notably in the domains of
employment, education, social relationships, and housing (Laudet and Humphreys
2013). Recovery support services can be provided by either professionals or peers.
Professional recovery support services frequently consist of recovery management
checkups (RMC) that monitor clients’ status, address relapse risks, and link clients
to services during gaps in employment. Staff often use motivational interviewing
techniques to help clients recognize relapse risks and further their engagement
activities. These services both link individuals to recovery capital and help them
develop the tools to access recovery capital resources.
Peer-delivered recovery support services are dened as the process of giving and
receiving nonprofessional, nonclinical assistance to achieve long-term recovery
(Bassuk etal. 2016). This support is provided by peers with experiential knowledge
who are in recovery themselves and serve as both models and advisors for others in
recovery (Borkman 1999). These individuals can be either paid or volunteer, and
H. H. Cleveland et al.
their qualications for providing support vary across settings. Some have required
durations of drug/alcohol abstinence to qualify for peer recovery coach credentials.
For example, Tennessee’s Certied Recovery Specialist requires individuals be in
recovery and take 40h of dedicated training; Pennsylvania requires 75h of training.
The services that are provided are delivered in various forms (e.g., one-on-one ser-
vices, group settings) and in different contexts, such as recovery community cen-
ters, faith-based institutions, jails and prisons, social service centers, and addiction
treatment agencies. This support will include helping the individual set recovery
goals and develop and maintain a recovery plan. The supporting peer can connect
the individual with recovery resources in the community and serve as an advocate
for the individual in these settings. In other words, peers are simultaneously recov-
ery capital themselves and guides for individuals regarding how to identify and
cultivate recovery capital.
Research protocols within these settings should be tailored to address the spe-
cic recovery capital needs of the recovering population being studied and the spe-
cic sources of recovery support that are provided by the program setting, whether
formal or informal. Specically, the frequency of different recovery-supportive (as
well as recovery-challenging) events should be considered. For example, an indi-
vidual may have daily Twelve-Step meetings, weekly scheduled meetings with a
recovery specialist, and numerous– but unscheduled and irregularly spaced– inter-
actions with a sponsor. In professional-led settings, such as MAT outpatient settings
with counseling, protocols could assess the craving experiences of the patients as
well as the situations that are linked to higher or lower levels of craving. Accordingly,
measurement paradigms should ensure that recovery metrics are assessed frequently
enough to capture their variability across time and to understand the events that are
supportive and detrimental to recovery.
5.1.1 Collegiate Recovery Communities
Some recovery support programs are highly tailored to specic populations and set-
tings. A prime example is Collegiate Recovery Communities (CRC). These pro-
grams directly support the recovery needs of populations engaged in higher
education. Education can directly contribute to recovery capital by increasing
opportunities for employment and meaningful engagement in society, both of which
can help promote recovery. CRCs also address the recovery needs of their partici-
pants in a developmentally targeted fashion. One of the difculties younger (i.e.,
teen and young adult) individuals in recovery face is the lack of t between the
conventional mutual help group context and their own experiences (see Russell,
Cleveland, and Wiebe 2010). CRCs are tailored to the developmental challenges
being negotiated by these young individuals generally, and the specic challenges
they encounter as they try to maintain recovery in their educational settings.
Like most recovery support, CRCs are generally informed by Twelve-Step tenets.
Their general goal is to provide supportive communities that deliver developmen-
tally appropriate support for their members. CRCs conventionally provide a drop-in
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
center, where members can spend time between classes. The communities are sup-
ported by program staff, who provide relapse prevention and life skills workshops
as well as work with members of the community to organize sober recreational
activities and service opportunities (Harris, Baker, and Cleveland 2010; Laudet and
Humphreys 2013).
CRCs differ substantially in terms of size (from only a few to over 100 members)
and in the characteristics of their members, such as demographics, time in recovery,
and drug of choice. They also differ in the scale of the role of program staff. In all
cases the staff provides support for the community, although program staff play a
more direct supervisory and even therapeutic role in some cases. The degree to
which communities differ in direct vs. indirect role of staff also manifests in whether
and how they provide and regulate recovery housing. Some programs provide on-
campus housing including on-site clinical professionals. Others provide recovery
housing on campus, but do not provide professional staff to supervise residents.
These housing contexts may have a senior member of the community act as lead
resident. Still others do not provide housing at all but work to match members of the
community together as housemates in private rentals. Still other programs provide
no assistance with housing. Full descriptions of CRCs are available in Laudet etal.
(2015) and Cleveland etal. (2007). Program variability in size, average length of
recovery, and level of support needs to be considered when attempting to under-
stand how recovery develops and is maintained within these settings.
5.1.2 Recovery Housing
Recovery residences are residential self-help communal living facilities that con-
tribute to continued abstinence by providing a network of others who share absti-
nence goals and creating conditions to gain employment skills (Gómez etal. 2014).
These residences also encourage social support, civic engagement, and physical
health and well-being. These residences are open to a wider range of individuals
than those served by CRCs, whose reach is limited to individuals who are able to
pursue higher education and have the nancial ability to do so. Recovery residences
recruit from either community treatment or criminal justice agencies and require
abstinence, mutual aid, meeting attendance, getting and keeping employment, and
contributing to the common upkeep and well-being of the residence (Cano etal.
2017). These residences provide both housing and stability for individuals embark-
ing on the recovery process outside of traditional treatment centers. One of the criti-
cal aspects of these programs is that they provide conditions for individuals to gain
useful employment skills (Gómez etal. 2014). Better employment helps support
recovery in several ways, including helping residents redevelop purpose and iden-
tity as well as connecting them to the world beyond themselves and their immediate
focus on recovery itself (see Burrow and Hill 2011). Accordingly, employment can
provide “bridging social capital,” a resource that is critical for avoiding social isola-
tion and supporting long-term recovery. For more information on recovery houses
as well as Oxford houses, which are self-governing recovery homes and their
H. H. Cleveland et al.
impact on recovery, see Cano etal. (2017), Polcin etal. (2010), Jason etal. (2006),
and Olson etal. (2009).
5.1.3 Measurement inCRCs andRecovery Housing
Research conducted within CRCs should assess both daily interactions within drop-
in centers as well as the (presumably less frequent) contact with staff. Assessments
should also aim to capture social interactions among members of the community
outside of the center and across the day. The nesting of these groups within the
broader university community means that non-SUD comparison groups can be
assembled that share a common set of contexts. Program events, such as recovery
workshops and alternative sober social events, could provide excellent “natural
experiments” by assessing differences between participants and nonparticipants
over the time period following the event, as well as examining the impact of attend-
ing these events on the well-being of participants immediately following engaging
in these events. Future research should consider both the impact of program and
social experiences on individuals’ well-being and the inuence of individuals’
mood on their frequency of engagement with formal program events and interac-
tions with community members.
Studies focused on recovery houses may have unique opportunities to study the
inuences of social forces and individual identity on (and of) recovery. The focus of
these residences on identity, employment, and social support provide an excellent
means to examine the way that overlapping spheres of social inuence (e.g., within
the recovery house, at work, and in the greater community) may interact with each
individuals’ recovery identity over time. Studies of these communities are also well-
suited to examine questions of community involvement and civic engagement.
6 Implications
6.1 Implications forUnderstanding Family Resilience
As has been described in detail in other chapters in this book, the recovery process
has implications beyond the individuals in recovery (Bradshaw etal. 2020; Hays-
Grudo etal. 2020; Ciciolla etal. 2020). Measurement of the recovery process, and
the context that supports that process, requires inclusion of social systems. Among
individuals with high recovery capital at the start of the process, these social sys-
tems may include immediate family. Immediate family systems may be conceptual-
ized as parents, spouses/partners, or children of individuals with substance use
disorders. Just as likely for individuals who experienced childhood trauma (Hays-
Grudo etal. 2020), family systems may be trusted and loved aunts, uncles, or other
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
individuals who are part of a made-family. Regardless of relationship, the substance
use disorder affects and destabilizes the whole family unit. The resilience of the
family unit aligns with the recovery capital of the individual and includes social,
physical, human, and cultural capital.
For researchers interested in understanding family and individual resilience in
the context of recovery, there are many methodological issues to consider.
Interpersonal assessments within family systems may improve understanding of
context, time, and individual characteristics on recovery and recovery capital.
However, these intensive measurements may be burdensome to participants and
their families– stopping one’s day four to six times to answer surveys is simply a
lot of effort. It is important for researchers interested in this approach, therefore, to
carefully select the questions that need to be asked and to take care to balance the
scientic gain of each question or survey against the burden for participants (Brick
etal. 2020). Particularly within the context of measuring multiple family members,
it is important to minimize the conict such measurement can cause within the fam-
ily system. Within the context of treatment for substance use disorder, our research
group has implemented a 12-day assessment protocol with four assessments per day
at early morning, late morning, early afternoon, and evening by carefully schedul-
ing data collection times to avoid treatment activities, keeping surveys short, and
providing exible response windows.
A second concern of particular importance in recovery settings is the require-
ment for privacy. The intensive data that can be collected with smartphones (e.g.,
GPS, heart rate, self-report, social context, etc.) adds a signicant risk to partici-
pants if data are publicly disclosed. Especially in recovery settings where some
patients may have social and family networks that are unaware of their prior use,
special care must be taken to ensure that privacy is maintained. While work is ongo-
ing to create privacy-preserving methods of data collection and analysis (see, e.g.,
Boker etal. 2015; Snoke etal. 2018), current best practices involve ensuring proper
data security, limiting data collection to only what is needed, avoiding personally
identifying data collection whenever possible, and removing any remaining identi-
ers at the rst opportunity.
Several other tools exist to assist researchers. Burst designs, which use short,
intensive bursts of measurement interspersed with times of less intense measure-
ment, can capture moment-to-moment variability within a person and the way it
changes across time during recovery without overburdening the participant with
long bursts of intensive measurement (see, e.g., Ram and Diehl 2015). For example,
a study could follow individuals from when they begin recovery until a year later,
but reduce burden by using four 21-day bursts of data collection at months 1, 4, 8,
and 12 to capture both within-person dynamics of recovery (assessed with the inten-
sive bursts) and how these dynamics shift across the year (by comparing patterns
across the bursts). Finally, passive measurement tools, such as wearable heart rate
wristbands, can be used to identify moments of risk (Osotsi etal. 2020), stress
(Hovsepian etal. 2015), and craving (Chatterjee etal. 2016). Perhaps more impor-
tantly, these tools can be used to deliver targeted assessment to capture both the
participant’s perspective and the more objective measures of stress and craving
(Bertz etal. 2018; Brick etal. 2020).
H. H. Cleveland et al.
6.2 Promising Applications
Best etal.’s (2016) qualitative evidence suggests that opportunities to build bridging
social capital vis-à-vis links to the local community beyond those in recovery is an
important aspect of recovery programs. A new and compelling program for recov-
ery support has been introduced in Blackpool, England. The Jobs, Friends, and
Housing program is a social enterprise supported by the local law enforcement in
Lancashire, UK.In addition to providing access to recovery activities in the eve-
nings and on weekends, the program provides training and employment in the con-
struction industry for people in recovery. The recovering individuals who are part of
the program work to build or renovate houses and either sell them for prot to be
reinvested in the Jobs, Friends, and Houses enterprise or rent them out as recovery
housing for others in recovery. Building recovery housing links members’ labor to
“giving back” to the community and helps them develop social capital and extend
their lives beyond the world of recovery.
The methodological challenges and opportunities presented by evaluating pro-
fessionally staffed recovery houses, Oxford Houses, and programs similar to Jobs,
Friends, and Housing are intriguing. These programs are designed to facilitate a
deep transition in individuals’ lives. They are designed to do so via providing impor-
tant components of recovery capital for individuals to access, benet from, and
further develop. Thus, evaluations of these programs that are primarily focused on
how many of their individual program attendees or community members are absti-
nent after nite periods of time, such as 30days, 6months, or a year, are missing an
opportunity to consider the dynamic social transactions through which recovery and
recovery capital are built. Rather, it is critical to understand the degree and quality
of their social interactions, with whom these interactions take place, where they take
place, and the value or lack thereof that individuals gain from these interactions.
The data collections and analyses need to align timescales with the dynamic nature
of social transactions and their impact on individuals’ well-being. For example, do
the recovery seminars/workshops provided by collegiate recovery programs or
recovery houses have short-term impacts on the daily well-being or recovery iden-
tity of community members? Do program experiences contribute to changes in the
characteristics of social networks (e.g., the number of abstinent individuals within a
social network) or– perhaps more importantly– the strategic and timely use of
members’ recovery support social networks? Just as technology can be used to
assess the dynamic nature of recovery itself and recovery capital, it can also be
deployed to assess how and when program components affect these processes.
7 Conclusion andFuture Directions
Recovery is a continual process that requires ongoing change in a person’s social,
cognitive, and behavioral patterns to thrive during sustained abstinence from sub-
stance use and requires a tremendous amount of recovery capital. Treatment can
6 Recovery andRecovery Capital: Aligning Measurement withTheory andPractice
begin the process of abstinence and provide some small amount of recovery capital,
but falls far short of the capacities needed for sustained recovery success. This mis-
match between what recovery needs and what treatment alone provides is consistent
with the high percentage of posttreatment relapse as well as the high proportion of
individuals who nd recovery without treatment. This observation does not suggest
that treatment providers have nothing to offer. In contrast, treatment providers are
often the best positioned to guide individuals to the recovery support they need
(Best and Laudet 2010). Nonetheless, it is clear that treatment alone is not suf-
cient, and research priorities that disproportionately focus on treatment are mis-
guided. What we propose is a research agenda that focuses on a multifaceted view
of recovery and meaningfully considers that recovery is a within-person process
that varies across time, individual, and context. To match this complexity, methods
and analyses will have to capture and consider within- and between-person vari-
ability in these aspects. Further, more work is needed to study recovery and its
reciprocal relationship with recovery capital in the contexts in which recovery
occurs and across the various timescales on which recovery is built and maintained.
These studies will have to capture not only the multifaceted nature of recovery and
the complexity of within-person processes and how they shift across time, but also
the meaningful heterogeneity in which recovery capital is accumulated and spent
across different contexts (Breakout Box 6.1).
Breakout Box 6.1. Focus on Experience
I’ll never forget the day that my brother told me about his drug addiction. He
was 16. I came home one day from school and he was waiting for me in the
family room. He had tears streaming down his face as he told me that he
needed my help. He’d been using crack and he didn’t want to tell my parents.
He cried and slapped his head saying that he just wanted to kill himself. Once
my mother found out, she made arrangements for him to leave the state to live
with a relative to get away from the drugs. He left and things only got worse.
He began using opioids and eventually was arrested for a DUI.After he served
his time, he moved back home. Things were good for a few months. We were
very hopeful. We thought that things had gotten bad enough for him that he
would never go back to it. But then his behavior changed. He didn’t want to
be around. He found excuses to leave family parties early. He started stealing
from my parents and had brought drug dealers to their home. I feel that we
were slow to catch on. I don’t know why we didn’t approach him earlier. I
think we all wanted to believe that he had changed andthat he was nally
going to be safe and happy, but things couldn’t have been worse. He went to
jail again, this time for 60days. It was a relief. At least we knew he was safe,
that he wasn’t somewhere overdosing. Part of his probation terms included
completing an outpatient drug program. He has but I still expect him to end up
in jail again or overdosing. When my mother calls, I often worry it’s to tell me
he is dead. But there are times when I really feel hope for him. He is more
open about his addiction than he has been. I can see a change in his desire to
change. Even when he messes up, I think he tries to get back on track quicker.
Elizabeth– sister of a recovering opioid addict
H. H. Cleveland et al.
Glossary of Terms
Daily diary participants provide a single report each day describing their experi-
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etal. 2003)
Ecological momentary assessment approaches that use technological tools (fre-
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several times a daywith the goal of collecting information about experiences in
the context in which they occur close in time to the experiences
Intraindividual change change within an individual
Recovery nontechnical term used in both nonprofessional and professional SUD
settings to describe a state of health and functioning that follows the cessation of
addictive substance use, typically involving abstinence from use (White 1998);
a dynamic process
Recovery capital includes all resources someone has access to and the capacity of
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physical, human, and cultural capital
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... Consistent with this change, the operational definition of recovery is also undergoing a transformation in both clinical and research settings. To track progress in recovery, research needs a consensus on the preferred primary outcome measure (Cleveland et al., 2021). A number of measures have emerged as candidates, including the Assessment of Recovery Capital (Groshkova et al., 2013), World Health Organizations Quality of Life assessment (World Health Organization, 2004), the Addiction Severity Index (McLellan et al., 1992), the Treatment Effectiveness Assessment (Ling et al., 2012), and DSM symptoms of SUD (American Psychiatric Association, 2013). ...
... The Assessment of Recovery Capital is one measure that has recently been gaining momentum as a tool to track progress in recovery (Best and Laudet, 2010;Cleveland et al., 2021). Recovery capital is a relatively new conceptual framework that focuses on the amount of tangible and intangible resources (e.g., monetary resources, skills/attributes, health, social relationships, values/norms,) available to initiate and sustain recovery from SUDs (Granfield & Cloud, 1999). ...
Introduction Research defines recovery capital as the amount of tangible and intangible resources (e.g., human/personal, physical, social, and cultural) available to initiate and sustain recovery from substance use disorders (SUDs). An individual's amount of recovery capital is dynamic over time and influenced by a number of factors such as baseline amount at initiation of recovery/treatment, length of abstinence, access/availability of resources, and individual factors such as the decision to utilize available resources. Research has been proposed delay discounting (DD), which reflects an individual's relative preference for immediate versus delayed rewards, as a candidate behavioral marker for SUDs but has not yet examined it in the context of recovery capital, and DD may be an important aspect of human capital. Thus, the aim of the current study was to examine associations among recovery capital, DD, and length of abstinence. Methods The study included in its analysis data from 111 individuals in recovery from SUDs from the International Quit and Recovery Registry, an ongoing data collection program used to further scientific understanding of recovery. The study assessed recovery capital using the Assessment of Recovery Capital (ARC) and assessed discounting rates using an adjusting-delay task. The study team performed univariate linear regression to examine the relationship between total ARC score and demographic variables, length of abstinence, and DD. The research team performed a mediation analysis to understand the role of length of abstinence in mediating the relationship between DD and ARC score. Results Total ARC score was significantly negatively associated with DD and positively associated with length of abstinence, even after adjusting for covariates. Mediation analysis indicated that length of abstinence significantly partially mediated the relationship between DD and ARC score. Conclusion These findings support the characterization of DD as an important aspect of human capital and a candidate behavioral marker for SUDs. Future research may wish to investigate whether interventions designed to increase the value of future rewards also increase recovery capital.
... One measure that has gained momentum recently due to the increased interest in recovery science is the assessment of recovery capital (ARC; Best et al., 2021;Bowen et al., 2020;Cleveland et al., 2021;Sánchez et al., 2020). ARC refers to "the sum total of one's resources that can be brought to bear on the initiation and maintenance of substance misuse cessation" (Cloud & Granfield, 2008) and involves a wide range of components (e.g., physical, cultural, social, and human capitals) critical to various stages and types of recovery (Groshkova et al., 2013). ...
Background: Recovery from substance use disorders (SUDs) requires sustained and purposeful support to maintain long-term remission. Methods: This study investigated the association between assessment of recovery capital, household chaos, delay discounting (DD) and probability discounting (PD), and remission status among individuals in recovery from SUD. Data from 281 participants from the International Quit & Recovery Registry (IQRR), an ongoing online registry that aims to study the recovery process, were included in the analysis. Results: Lower DD rates and higher recovery capital were found among those in remission compared to those not in remission after controlling for demographics. In contrast, the association of household chaos and PD with remission status were insignificant. Overall, DD accounted for 20% of the total effect between the recovery capital and the remission status. Conclusion: This study contributes to the understanding of recovery as a multidimensional process, supports DD as a behavioral marker of addiction, and suggests areas for future research.
... Treatment intends to build recovery capital by addressing needs that could be detrimental to recovery early on (Cleveland et al., 2021). ...
... The positive association between recovery capital scores and substitute behaviors may relate to the availability of human recovery capital and the capacity to apply (alternative, adaptive) coping skills and solve problems in the context of high-risk situations [36]. Treatment intends to build recovery capital by addressing needs that could be detrimental to recovery early on [83]. ...
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The dynamics of substitute behaviors and associated factors remain poorly understood globally, and particularly in low- and middle-income contexts. This prospective study describes the prevalence and types of substitute behaviors as well as predictors, correlates, and motivations associated with substitution in persons (n = 137) admitted to residential substance use treatment in the Western Cape province of South Africa. The Brief Assessment of Recovery Capital, Overall Life Satisfaction scale, and an adapted version of the Addiction Matrix Self-report Measure were completed during and post-treatment. Results indicate that substitutes were employed consciously for anticipated appetitive effects, for time-spending, (re)connecting with others, and enjoyment. At follow-up, 36% of service users had substituted their primary substance(s) with another substance or behavior; 23% had relapsed and 40% had maintained abstinence. While some service users may be especially vulnerable to developing substitute behaviors, targeted prevention and intervention efforts can reduce this risk.
... Early life adversity also impacts the ability to recover from a substance use disorder. Early life adversity directly affects the recovery capital of an individual (Cleveland et al. 2020), often resulting in fewer social relationships to support recovery. Social relationships also impact the economic capital accessible to an individual with substance use disorder: treatment is expensive and often needs to be repeated for this chronic, relapsing condition. ...
In this chapter we discuss the roots of the opioid crisis and its relationship to work and the workplace. Opioid overdose mortality, in combination with increased deaths from alcohol and suicide, are devastating to American families across the nation. The profound impact on American workplaces includes compromising occupational safety and health, increased workers’ compensation and health insurance costs, rising absenteeism, and lost productivity. The President’s Council of Economic Advisors estimates that over a million workers are out of the workforce due to the opioid crisis. The impact on workers is equally profound including job loss, divorce and family disruption, and potential imprisonment, injury, illness, and death. Contained within this chapter are the review of several studies that document opioid mortality by occupation and industry that conclude that pain from occupational injuries, illnesses, and stress are important pathways to opioid misuse and addiction. A major focus is on the significant opportunity that effective workplace programs offer to prevent and respond to opioid misuse and addiction. Several key policy interventions are recommended including prevention of workplace physical and emotional pain, safe prescribing practices, alternative pain management methods, worker education and training, and moving away from stigmatizing punitive workplace substance abuse programs and replacing them with supportive programs.
Women are among the fastest growing populations of those with substance use disorders in the United States. Women face different social barriers than men in their access to treatment and recovery from these disorders. Differentially experienced barriers include greater child caregiving responsibilities, social sigma regarding motherhood and substance use, romantic partners who also use substances, experiences of violence and trauma, and, relatedly, symptoms of post-traumatic stress disorder. These barriers have been studied primarily by employing between-group approaches (e.g. comparing men and women) or between-persons approaches (e.g. cross-sectionally assessing the relationship between person-level PTSD symptoms and relapse). However, there are limited studies on women’s gender-specific experiences in recovery with the aim of elucidating within-person effects. Employing within-person designs, such as daily diary or ecological momentary assessments, has many advantages. These advantages include reducing retrospective bias, assessing temporality of processes that occur on a short time scale, and analyzing processes that may occur when individuals deviate from their personally normative experiences. Studying women’s experiences in recovery “as they are lived” will enable the development of interventions that are fine-tuned to the specific needs of each woman, and ultimately may help to reduce the suffering of women with substance use disorders.
Adverse childhood experiences (ACEs) are widely recognized as predictors of early onset of alcohol and other drug use, problematic substance use, and addiction (Anda et al., Eur Arch Psych Clin Neurosci 256(3):174–186, 2006; Campbell et al., Am J Prev Med 50(3):344–352, 2016; Merrick et al., J Am Med Assoc Pediat 172:1038–1044, 2018). ACEs can have profound and enduring effects on neurological development, and immune and metabolic systems, resulting in behavioral and epigenetic changes that can persist across generations. Research with animal models and humans indicates that early exposure to abuse, neglect, and other stressors alters brain development, decreasing the individual’s ability and capacity to manage stress and emotions and increasing the likelihood of dependence on mood-altering substances (Kirsch et al., Adver Resil Sci, 1–19, 2020). There are growing bodies of evidence that environments enriched in nurturing relationships and resources can buffer the effects of early adversity (Blaze and Roth 2015; Morris et al. 2018). Interventions such as Attachment Biobehavioral Catch-up (ABC) that promote secure attachment show promise for interrupting the intergenerational cycle of adversity and addiction. Policies and programs that ensure access to Protective and Compensatory Experiences (PACEs) may also promote resilience in families who have experienced ACEs and addiction (Hays-Grudo and Morris, Adverse and protective childhood experiences: A developmental perspective. American Psychological Association, Washington, DC, 2020).
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Background: Mobile health (mHealth) methods often rely on active input from participants, for example, in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet, the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and internet-of-things devices, in combination with statistical feature selection and adaptive interventions, have begun to make such things possible. Objective: In this paper, we introduced Wear-IT, a smartphone app and cloud framework intended to begin addressing current limitations by allowing researchers to leverage commodity electronics and real-time decision making to optimize the amount of useful data collected while minimizing participant burden. Methods: The Wear-IT framework uses real-time decision making to find more optimal tradeoffs between the utility of data collected and the burden placed on participants. Wear-IT integrates a variety of consumer-grade sensors and provides adaptive, personalized, and low-burden monitoring and intervention. Proof of concept examples are illustrated using artificial data. The results of qualitative interviews with users are provided. Results: Participants provided positive feedback about the ease of use of studies conducted using the Wear-IT framework. Users expressed positivity about their overall experience with the framework and its utility for balancing burden and excitement about future studies that real-time processing will enable. Conclusions: The Wear-IT framework uses a combination of passive monitoring, real-time processing, and adaptive assessment and intervention to provide a balance between high-quality data collection and low participant burden. The framework presents an opportunity to deploy adaptive assessment and intervention designs that use real-time processing and provides a platform to study and overcome the challenges of long-term mHealth intervention.
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With wearable, relatively unobtrusive health monitors and smartphone sensors, it is increasingly easy to collect continuously streaming physiological data in a passive mode without placing much burden on participants. At the same time, smartphones provide the ability to survey participants to provide “ground-truth” reporting on psychological states, although this comes at an increased cost in participant burden. In this paper, we examined how analytical approaches from the field of machine learning could allow us to distill the collected physiological data into actionable decision rules about each individual’s psychological state, with the eventual goal of identifying important psychological states (e.g., risk moments) without the need for ongoing burdensome active assessment (e.g., self-report). As a first step towards this goal, we compared two methods: (1) a k-nearest neighbor classifier that uses dynamic time warping distance, and (2) a random forests classifier to predict low and high states of affective arousal states based on features extracted using the tsfresh python package. Then, we compared random-forest-based predictive models tailored for the individual with individual-general models. Results showed that the individual-specific model outperformed the general one. Our results support the feasibility of using passively collected wearable data to predict psychological states, suggesting that by relying on both types of data, the active collection can be reduced or eliminated.
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This paper focuses on the privacy paradigm of providing access to researchers to remotely carry out analyses on sensitive data stored behind firewalls. We address the situation where the analysis demands data from multiple physically separate databases which cannot be combined. Motivating this problem are analyses using multiple data sources that currently are only possible through extension work creating a trusted user network. We develop and demonstrate a method for accurate calculation of the multivariate normal likelihood equation, for a set of parameters given the partitioned data, which can then be maximized to obtain estimates. These estimates are achieved without sharing any data or any true intermediate statistics of the data across firewalls. We show that under a certain set of assumptions our method for estimation across these partitions achieves identical results as estimation with the full data. Privacy is maintained by adding noise at each partition. This ensures each party receives noisy statistics, such that the noise cannot be removed until the last step to obtain a single value, the true total log-likelihood. Potential applications include all methods utilizing parameter estimation through maximizing the multivariate normal likelihood equation. We give detailed algorithms, along with available software, and both a real data example and simulations estimating structural equation models (SEMs) with partitioned data.
Neonatal abstinence syndrome (NAS) is a variable, complex, and incompletely understood spectrum of neonatal neurobehavioral dysregulation associated with prenatal drug exposure, most commonly opioids. Research on short- and long-term neurodevelopmental consequences of NAS is limited, and results are often confounded by polysubstance use during pregnancy (licit and illicit), prenatal care and nutrition, and medical complications, as well as ongoing exposure to environmental risks, including poverty and the quality of the caregiving environment. However, neonatal outcomes including symptom severity, need for and length of pharmacotherapy, and length of stay, may also be associated with the approach to screening and treatment of NAS symptoms. Although nonpharmacological care is the recommended first-line approach, there is no standardized procedure for NAS management or initiation of pharmacotherapy, and a wide range (27–91%) of neonates with NAS receive pharmacological interventions. Nonpharmacological treatments vary greatly in documented efficacy and include methods of “supportive care,” such as providing close relational experiences; swaddling; quiet, low-stimulation environment; and use of pacifiers. Recent research has suggested that family-centered NAS treatment approaches, such as rooming-in and the Eat, Sleep, Console (ESC) model, are associated with shorter lengths of stay and reduced need for pharmacotherapy, as well as increased rates of breastfeeding, skin-to-skin contact, and improved quality of caregiving and attachment. Together these outcomes are associated with better neurodevelopmental outcomes. Thus, family-centered approaches and treatment programs that keep mothers and infants together may be key to reducing morbidity and improving long-term outcomes for substance-exposed infants and their families.
Families are relational systems that influence and are influenced by the development of a substance use disorder (SUD). An SUD in the family is associated with shifts in a family’s power dynamics, rules, roles, and structure—among other adaptations—that negatively impact family member health and well-being. More attention on family members of individuals with SUD is needed to better understand how they are impacted by an SUD and how they can support their loved one in recovery, while also recovering themselves. Historically, family systems research on SUD has emphasized the behavioral constructs of codependency, enabling behaviors and personality characteristics as behavioral antecedents of how family members are impacted; only recently has research emerged that explores the physiological brain structure and function associated with impacted family members. This chapter reflects upon two studies that examined prefrontal cortex (PFC) functioning of family members as it relates to emotional cues of their loved one with an SUD. The PFC is the brain region associated with information processing, decision-making, and goal-oriented behavior and is therefore important as family members engage with their loved one with an SUD. This work supported altered PFC functioning in family members in response to cues of their loved -one with an SUD, and that PFC activation in response to such cues is associated with reported behavior of codependency. Overall, the literature reviewed and the studies discussed in this chapter demonstrate the importance of continued attention, research, and resources directed toward family members that can positively influence family members and family recovery.
“Core affect”—defined as momentary valence (pleasantness) and arousal (activation) levels—plays an important role in our emotional experiences. We examined the relationship between the “fast-timescale” (moment-to-moment) changes in core affect and “slow-timescale” (trait-level) indicators of emotional functioning. Results from an experience sampling study showed that daily valence and arousal baselines were positively related to emotional well-being. Furthermore, we found meaningful positive associations between fast-timescale core affect regulation and the habitual deployment of reappraisal as emotion regulation strategy.
Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.
Background: Alcohol and other drug (AOD) problems confer a global, prodigious burden of disease, disability, and premature mortality. Even so, little is known regarding how, and by what means, individuals successfully resolve AOD problems. Greater knowledge would inform policy and guide service provision. Method: Probability-based survey of US adult population estimating: 1) AOD problem resolution prevalence; 2) lifetime use of "assisted" (i.e., treatment/medication, recovery services/mutual help) vs. "unassisted" resolution pathways; 3) correlates of assisted pathway use. Participants (response=63.4% of 39,809) responding "yes" to, "Did you use to have a problem with alcohol or drugs but no longer do?" assessed on substance use, clinical histories, problem resolution. Results: Weighted prevalence of problem resolution was 9.1%, with 46% self-identifying as "in recovery"; 53.9% reported "assisted" pathway use. Most utilized support was mutual-help (45.1%,SE=1.6), followed by treatment (27.6%,SE=1.4), and emerging recovery support services (21.8%,SE=1.4), including recovery community centers (6.2%,SE=0.9). Strongest correlates of "assisted" pathway use were lifetime AOD diagnosis (AOR=10.8[7.42-15.74], model R2=0.13), drug court involvement (AOR=8.1[5.2-12.6], model R2=0.10), and, inversely, absence of lifetime psychiatric diagnosis (AOR=0.3[0.2-0.3], model R2=0.10). Compared to those with primary alcohol problems, those with primary cannabis problems were less likely (AOR=0.7[0.5-0.9]) and those with opioid problems were more likely (AOR=2.2[1.4-3.4]) to use assisted pathways. Indices related to severity were related to assisted pathways (R2<0.03). Conclusions: Tens of millions of Americans have successfully resolved an AOD problem using a variety of traditional and non-traditional means. Findings suggest a need for a broadening of the menu of self-change and community-based options that can facilitate and support long-term AOD problem resolution.
Background: In recent years, there has been recognition that recovery is a journey that involves the growth of recovery capital. Thus, recovery capital has become a commonly used term in addiction treatment and research yet its operationalization and measurement has been limited. Due to these limitations, there is little understanding of long-term recovery pathways and their clinical application. Methods: We used the data of 546 participants from eight different recovery residences spread across Florida, USA. We calculated internal consistency for recovery capital and wellbeing, then assessed their factor structure via confirmatory factor analysis. The relationships between time, recovery barriers and strengths, wellbeing and recovery capital, as well as the moderating effect of gender, were estimated using structural equations modelling. Results: The proposed model obtained an acceptable fit (χ(2) (141, N=546)=533.642, p<0.001; CMIN/DF=3.785; CFI=0.915; TLI=0.896; RMSEA=0.071). Findings indicate a pathway to recovery capital that involves greater time in residence ('retention'), linked to an increase in meaningful activities and a reduction in barriers to recovery and unmet needs that, in turn, promote recovery capital and positive wellbeing. Gender differences were observed. Conclusions: We tested the pathways to recovery for residents in the recovery housing population. Our results have implications not only for retention as a predictor of sustained recovery and wellbeing but also for the importance of meaningful activities in promoting recovery capital and wellbeing.