Lift Yourself Up: The Short-Term Associations Between Strength Training and Mood States and the Longer Term Development of Physical Capital and “Grit” Among People Recovering From Substance Use Disorders

Abstract

Mind Body Connect is a charity that delivers community-based physical exercise (PE) classes to people in recovery from substance use disorders. This study aimed to identify the contribution that strength training played in the development of positive short- and long-term psychological attributes for people recovering from substance use disorders by assessing (a) the short-term associations between participation in group exercise classes (N = 43, t = 2,940) and (b) the prospective long-term association between strength development and individuals’ self-assessed level of “grit” among participants for whom strength training became a habitual activity (N = 13, median 8 weeks supported training). Participants completed demographic, Profile of Mood States—Adapted Short Form, and Short Grit Scale questionnaires before and after each strength training session over a period of 45 noncompulsory training sessions between February 2020 and March 2021. Weekly training logs recording the weight lifted were normalized into a measure of physical capital development. Short-term associations were tested using multilevel regression models predicting change in mean Grit score with interactions for gender; long-term associations were assessed through the use of latent growth models. We found that training sessions were associated with weak to moderate reductions ( $β$ = −0.112 to −0.533) in many negative mood states but elevated fatigue ( $β_{MEN}$ = 1.071, $β_{WOMEN}$ = 0.397). Latent growth in recorded strength was strongly associated with latent growth in self-assessed grit ( $β$ = 0.674). The findings suggest that PE classes and supported long-term strength training may assist with the development of recovery capital.

Keywords

recovery recovery capital addiction substance use disorder strength training physical exercise grit POMS mood

People suffering from substance use disorders often reside at society’s fringes. The disorders intersect with systemic inequality and long-term disadvantage. They are often in poor physical health and suffering from anxiety, depression, and low self-esteem (De Leon, 2000). Physical exercise (PE) has been consistently found to contribute to recovery from addiction (Patterson et al., 2022). A number of neurobiological mechanisms by which this is achieved have been proposed, including the alleviation and management of depressive symptoms of and anxiety or cognitive impairments related to addiction (Morais et al., 2018).

However, engagement with PE varies and intersects with several different biological, social, relational, and psychological mechanisms depending on its modality, which likely contribute to recovery but are not simply reducible to neurobiological changes. The conceptualization and measurement of PE engagement in existing studies tend to be limited, ignoring or assume that why people engage in sport and PE matters less than neurobiological mechanisms.

There is considerable overlap between the environments in which PE is conducted, especially where it is facilitated by experts as an intervention with a specific target population, and environments which demonstrate positive outcomes on recovery. For example, the benefits of psychotherapeutic interventions, including group therapy, are well documented (Boisvert et al., 2008; Pooler et al., 2014; Tracy & Wallace, 2016). While the purpose of exercise groups with populations in recovery is not to facilitate therapeutic discussion, the setting may enable participants to form and reify positive social identities characterized by resilience that can promote recovery (Buckingham et al., 2013), or support the establishment of mutually gratifying relationships with others (Flores, 2001).

The capacity to replace patterns of behavior that contribute to substance misuse with healthy behavior patterns and the ability to engage in mind–body relaxation and other forms of self-care have been identified as key determinants in successful abstention (Bowen et al., 2014). Individually motivated engagement with PE; the setting, management, and completion of sports-related goals; and actual and perceived physical mastery can replace negative behavior patterns in the long term due to the required discipline and consistent pursuit, reflecting investment in self-care (Melemis, 2015). Strength-related training, similarly, can be imagined to contribute to the regulation of strong emotions in the short term, with its emphasis on focus and breathing patterns (Melemis, 2015).

This study explores the possible impact of PE on two potentially positive changes in psychological state that may contribute to recovery. In the short term, we explore the potential impact of group PE training on mood states of a population in early recovery (0–1 year abstinent). We propose that identifying positive changes in mood state as a result of PE may represent a positive route to recovery management, especially in the case of men who engage less with mindfulness-based therapies for the development of mind–body relaxation skills identified as effective ways to prevent relapse (Katz & Toner, 2013). In the longer term, we assess the association between growth in physical strength and growth in self-assessed “grit,” defined by Duckworth (2016) as “perseverance and passion for long-term goals,” which we view as a possible positive contributor to the accrual of psychological characteristics and a social identity characterized by resilience that may promote long-term recovery.

Logic Model

Our logic model is formalized in Figure 1. We propose that activities associated with strength training, including group-based training and individually pursued strength training, can be broken down into a range of factors that promote mechanisms with a positive association with recovery. Participation involved being in a group setting with peers in recovery. Long-term engagement with the classes, and with exercise routines more generally, required engagement with short- and long-term goal setting, documentation, and reflection on progress. These activities are theorized to have a positive effect on recovery mechanisms through a number of routes: neurobiological changes that might attenuate mood states that can make recovery challenging; the development of relationships with peers and trainers; the development and recognition of positive social identities associated with PE; and the wider development of various forms of capital.

Figure 1

Logic Model for the Contribution of PE Classes and Supported Training to Recovery Mechanisms

There are relatively few theoretical frameworks which consolidate the disparate contributions of neurobiological, social, and psychological factors of recovery covered above. Cloud and Granfield’s (2008) recovery capital model has traction in the field of addiction (Hennessy, 2017). It employs classical Bourdieusian conceptualizations of economic, social, and cultural capital, with the addition of human capital to account for “self-identity, confidence in expressing one’s own opinions, and emotional intelligence” (Gauntlett, 2011, p. 4). Access to therapies and treatments, as well as observed individual traits, socioeconomic conditions, and developments that support recovery from addiction, can be assigned to concepts of capital to understand its multidimensional nature.

However, prior to revision by Fitzgerald (2017), the recovery capital model failed to adequately reflect that “physical” capital might include both the role of neurobiological differences or development and the role that the social and relational formation and maintenance of bodies through sporting, leisure, and other activities might have on recovery. For example, one potential consequence of physical capital accrual might be the desire to avoid its loss through physically destructive behaviors. This dimension is argued to not be adequately captured by the existing concepts of human, economic, social, and cultural capital. Physical capital is a concept through which the “potential benefits of exercise upon recovery can be articulated” (Fitzgerald et al., 2021, p. 1331).

In addition to the proposed neurobiological benefits of exercise (Morais et al., 2018; Rawson et al., 2015), recovery capital and specifically physical capital provide a conceptual framework to explore whether people in recovery might benefit from participating in PE. Exercising in a group setting potentially confers a positive social identity (Buckingham et al., 2013; Fitzgerald et al., 2021) and forms new positive social relationships (Pooler et al., 2014). Strength training specifically can serve as a physiological and psychological affirmation of a person’s ability to achieve tangible self-improvement goals (Fitzgerald, 2017). In this sense, short- and long-term PE in a community setting is theorized to contribute to the development of recovery capital. It is also important to recognize that “recovery” in many cases is not a binary threshold, but one which for many people requires continuous recovery capital replenishment to avoid relapse (Fitzgerald, 2017). As such, improving our understanding of what activities might maintain and develop recovery capital is valuable for health promotion practice.

Setting

An opportunity sample was taken from 45 group-training sessions delivered between February 2020 and March 2021 by the charity Mind Body Connect (MBC), an organization which specializes in providing exercise intervention for marginalized groups and has a close relationship with the research team (Fitzgerald et al., 2021). All participants of these MBC classes were informed about the research and invited to participate. Due to the difficulty accessing people in recovery and the novel nature of the proposed research, an opportunity sample from an existing research partner was deemed the most feasible approach. Participants were engaged in early recovery programs in two community settings. The first, Phoenix Futures (PF), is a residential drug and alcohol rehabilitation center. PF conceptualizes recovery as abstinence-based but encompassing broader psychosocial development of interpersonal skills and coping strategies learned through peer interaction. The second setting, the Sheffield Treatment And Recovery Team (START), is a National Health Service that offers assessment and interventions to adults experiencing problems or misuse of drugs and alcohol.

MBC recognizes recovery as requiring more than abstinence and employs a community as method approach that relies upon peer interaction, most notably through exercise peers: service users who have gained qualifications, interned with MBC coaches, and, in a few cases, been employed by MBC and worked in the wider sport and fitness industry. Classes were delivered to three distinct client groups. One was for PF’s “main” rehabilitation center, a second was for PF’s “re-entry” center for service users who have completed their rehabilitation program and are transitioning back into society. The final was for START’s service users who are within the community and typically at the beginning of their recovery. Classes were held at Structure Sport and Fitness, a gym based in Sheffield, England. Classes began and ended with participants completing the study questionnaires (described in the Method section) and logging the compound lift they performed as well as any variation in the lift, the number of sets, number of repetitions, and weight.

Research activities received ethical approval on a monthly basis through MBC’s independent ethical review procedure, where exercise peers, current practitioners, and senior academic trustees provide input on ongoing research studies as part of a review committee to ensure participants are treated ethically according to the service’s principles. This review process ensured that participant approval was sought routinely as new service users joined, with data collection activities continually reviewed in response to trainers’ observations on whether the research was affecting service user engagement. This contrasts with fixed “procedural” ethical processes that offer minimal participant involvement or reactivity to changing circumstances. Participation in data collection was voluntary and not a condition of receiving a service from MBC. Service users were briefed on the purpose of the research and their right to withdraw at the beginning of each training session, and were debriefed upon completion of data collection.

Data

Data were gathered from 43 individuals (male; N = 32, female; N = 11) across the 45 classes, resulting in 1,477 (male; N = 1,359, female; N = 118) unique weightlifting entries, Grit score responses, and before and after Profile of Mood States—Adapted Short Form (POMS-ASF) measurements. Forty-two participants described their ethnicity as “White British” and one as “mixed heritage.” While not representative, this (98%) White British sample reflects that the vast majority (85%) of clients in addiction treatment in England are White British with “other white” being the next most common ethnicity (4%: Public Health England 2017, p. 7). The non-White ethnic group accounted for fewer than 2% of service users during 2016–2017 (Public Health England, 2017, p. 7). The majority (75%) of our sample was aged 29–39, with the youngest being 22 years and the oldest 59 years. For our analysis of strength and grit trajectories, we excluded participants who recorded fewer than five training sessions. Thirteen individuals met the inclusion criteria for the trajectory analysis. Of these, one was female. Sample descriptive statistics are included in Table 1.

Table 1

Descriptive Statistics for Short-Term and Long-Term Data

Before/after training measures
Variable	N	M	SD	25th %	Mdn	75th %
Anger	2,940	0.22	0.493	0	0	0.25
Depression	2,924	0.425	0.519	0.143	0.143	0.571
Fatigue	2,946	0.948	0.853	0.167	0.667	1.5
Tension	2,948	0.514	0.509	0.2	0.2	0.65
Confusion	2,954	0.486	0.501	0.2	0.2	0.6
Vigor	2,927	1.776	0.485	1.4	1.8	2.2
Relaxation	2,954	1.808	0.872	1.25	1.75	2.5
Sex	N	%	No. of individuals	%
Male	2,718	92	32	74
Female	236	8	11	26
Long-term growth
Variable	N	M	SD	25th %	Mdn	75th %
Estimated deadlift growth (KG) intercept	13	126.552	33.39	98.817	118.457	145.733
Estimated deadlift growth (KG) slope	13	1.513	1.876	0.604	1.81	2.564
Average Grit score (0–4) intercept	13	2.138	0.623	1.733	2.121	2.51
Average Grit score (0–4) slope	13	0.037	0.059	0.002	0.052	0.067
Average anger POMS (0–4) intercept	13	0.327	0.366	0.033	0.152	0.544
Average anger POMS (0–4) slope	13	−0.013	0.061	−0.039	−0.013	0.01
Average depression POMS (0–4) intercept	13	0.525	0.423	0.225	0.349	0.81
Average depression POMS (0–4) slope	13	−0.02	0.038	−0.034	−0.018	0
Average fatigue POMS (0–4) intercept	13	0.979	0.679	0.262	0.9	1.337
Average fatigue POMS (0–4) slope	13	−0.009	0.11	−0.03	0.012	0.059
Average tension POMS (0–4) intercept	13	0.651	0.515	0.291	0.393	1.071
Average tension POMS (0–4) slope	13	−0.03	0.064	−0.034	−0.015	0.004
Average confusion POMS (0–4) intercept	13	0.59	0.397	0.214	0.487	1.029
Average confusion POMS (0–4) slope	13	−0.014	0.054	−0.031	−0.017	0.001
Average vigor POMS (0–4) intercept	13	1.774	0.362	1.627	1.747	1.843
Average vigor POMS (0–4) slope	13	−0.022	0.037	−0.03	−0.018	0.004
Average relaxation POMS (0–4) intercept	13	1.728	0.762	1.062	1.619	2.308
Average relaxation POMS (0–4) slope	13	−0.019	0.11	−0.065	−0.029	0.006
Total number of sessions	13	9.308	3.728	6	8	12

Note. POMS = Profile of Mood States, KG = Kilograms.

Classes began with participants completing a POMS-ASF questionnaire (Fitzgerald et al., 2021). At the end of each class, they completed a second POMS-ASF questionnaire, a short “grit” questionnaire (Duckworth & Quinn, 2009), and logged the compound lift they performed: variation, number of sets, number of repetitions, and weight. The response rate was 100%, but not every attendee was present at every session.

The POMS is a reliable and valid measurement tool that is widely used in clinical, psychological, and sports research (Di Corrado, 2017). Using Shacham’s (1983) Short Form POMS, Fitzgerald et al. (2021) developed the POMS-ASF version to work with recovering addicts in an exercise setting measuring seven mood subscales: Anger, Confusion, Depression, Fatigue, Tension, Vigor, and Relaxation. Each subscale consisted of 4–6 single word items, with 31 items in total. Respondents indicated how strongly they associated with each adjective immediately prior and immediately after each class, using 5-point Likert-type scales.

Grit is defined as the sustained application of effort toward a long-term goal (Duckworth, 2016). Its characteristics are passion, perseverance, and a willingness and doggedness to overcome challenges. The trait-level abilities required to sustain sobriety cross over with the characteristics associated with grit (Griffin et al., 2016; Pulliam, 2021). Duckworth and Quinn’s (2009) eight-item Short Grit Scale (Grit-S) is psychometrically robust (Lechner et al., 2019). Participants completed a Grit-S alongside their after-session POMS-ASF, responding on 5-point scales on whether they felt like the personal characteristics of grit were relatable. Following data collection, the items were computed into an average Grit score. Average linear change in Grit score over sessions was calculated for the analysis of trajectories.

We measured physical strength accumulation through participants’ weightlifting logs. Training goals and progress are meaningfully captured by performance on key lifts. The MBC coaching team recommended we focus upon the deadlift as it is typically the most difficult of the key lifts to learn (Kilgores & Rippetoe, 2007). The decision had two perceived benefits to the research and coaching team: first, it was nonintrusive, not interrupting participants’ regular training and exercise regimens. The logging of performance is central to strength training regimes. These data were not onerous for participants to record and were therefore envisioned to be met with higher adherence than a more artificial measure of strength that was not part of their routine. Second, performance on key lifts represented not only actual strength, but a metric of strength-based performance and capability that was meaningful to the participant, as performance on key lifts often forms the basis for many of the social exchanges and relationships in the setting. Discussions of the development of and achievements in strength are often centered on performance on key lifts, and are therefore likely also a more accurate measure of a person’s self-perceived or actualized strength.

Performance on key lifts was normalized to a predicted one-repetition, maximum effort deadlift weight in kilograms (DL-P1RM) for each session. We attempted to remove variance associated with lift type (deadlift, bench press, or squat), lift variation (e.g., trap-bar deadlift vs barbell deadlift), number of sets, and number of repetitions using a log-linear multilevel multiple regression model predicting kilograms lifted for each type of key lift by lift variation, number of sets, number of repetitions, and an interaction term between sets and repetitions, while also adjusting for age, sex, session number, and any other unmeasured individual-level random effects. This allowed us to predict 1RMs for each type of lift. We then estimated predicted DL-P1RMs using the predicted squat and bench press 1RMs using a second model, essentially adjusting the weight lifted for what we would expect the participant to be able to lift had they been performing a deadlift instead of a squat or a bench press based on the average differences between these three kinds of key lift.

We validated this approach by checking adherence of set and repetition adjustment curves with recognized strength programming/predictive models (Reynolds et al., 2006), and via scrutiny from MBC coaches who checked it aligned with their qualitative observations and general programming techniques. This created comparable predictions of DL-P1RM for each recorded key lift. Supplemental Appendix Figure 1 visualizes the effect of removing this variation.

With comparable measures of strength over subsequent sessions derived, we calculated linear trajectories of change in DL-P1RM as our measure of average change in strength between sessions for the 13 individuals with more than five training sessions. On average, DL-P1RM grew at a rate of 1.5 kg per session but ranged between a −2.31-kg-per-session decrease and a 5-kg-per-session increase. Three individuals had negative DL-P1RM average growth and 10 had positive DL-P1RM average growth.

Method

We examined the short-term and long-term effects of strength training on change in POMS score and grit trajectories. Multilevel linear regression models were used to estimate short-term effects of training on POMS score so that individual-level variance could be controlled for through the inclusion of random intercepts. Short-term effects models were estimated with an interaction effect between pretraining and posttraining measurement of POMS and sex, with marginal effects and robust standard errors derived for men and women.

To estimate the direct association between strength trajectory (average change in DL-P1RM per session) and Grit score trajectory (average change in Grit score per session), we used linear regression. Intercept values for grit were included as predictors to control for the fact that those with very high or very low grit before their engagement with the program may have had more extreme trajectories that could skew any association with strength.

Results

Short-Term Effects of Training on POMS Scores by Gender

Comparison of POMS scores suggests that the classes had a significant positive effect on mood (reflected by decreases in negative mood scores). Table 2 shows effect contrasts from seven multilevel linear regressions, moderated by sex, of average POMS score after training. Figure 2 visualizes the standardized changes for men and women with 95% confidence intervals.

Table 2

Average Change in Negative POMS Scores Before and After Training, Contrast Effects for Men and Women

POMS category	Sex	Change in POMS score after training		t	p
POMS category	Sex	B (SE)	(SE)	t	p
Anger	Female	−0.121 (0.051)	−0.245 (0.102)	−2.391	.055
	Male	−0.131 (0.015)	−0.266 (0.030)	−8.784	<.001
Depression	Female	−0.172 (0.051)	−0.331 (0.099)	−3.338	.003
	Male	−0.177 (0.015)	−0.341 (0.029)	−11.602	<.001
Fatigue	Female	0.339 (0.078)	0.397 (0.091)	4.349	<.001
	Male	0.914 (0.023)	1.071 (0.027)	39.734	<.001
Tension	Female	−0.271 (0.047)	−0.533 (0.092)	−5.795	<.001
	Male	−0.138 (0.014)	−0.271 (0.027)	−9.972	<.001
Confusion	Female	−0.163 (0.048)	−0.325 (0.095)	−3.418	.002
	Male	−0.056 (0.014)	−0.112 (0.028)	−3.986	<.001
Vigor	Female	−0.115 (0.051)	−0.238 (0.105)	−2.270	.075
	Male	−0.073 (0.015)	−0.151 (0.031)	−4.858	<.001
Relaxation	Female	−0.013 (0.079)	−0.015 (0.090)	−0.161	.998
	Male	0.126 (0.023)	0.144 (0.027)	5.416	<.001
		No. of sessions (POMS measurements)	No. of individuals
	Female	118 (236)	11
	Male	1,359 (2,718)	32

Figure 2

Summary of Short-Term Effect Sizes on Aggregate POMS With 95% Confidence Intervals

Participants saw significant decreases in Anger, Depression, Tension, and Confusion. The standardized values for these measures ranged from −0.533 to −0.112, with a mean change of around −0.3, indicating a small-to-moderate improvement after training. The greatest improvement in mood state was for tension among female participants (β = −0.533), reflecting a moderate decrease in feelings of panic, anxiety, worry, and nervousness, followed by depression (β = −0.331). For men, the greatest decrease in negative mood states was for depression (β = −0.331) followed by anger (β = −0.266). Men, but not women, also had slightly better relaxation scores after exercising (β = 0.144). Fatigue increased after training; for men, this effect was large (β = 1.071) whereas for women it was more moderate (β = 0.397). Both genders reported worse feelings of vigor (standardized mood state decreased by −0.151 and −0.238, respectively), indicating feeling somewhat less lively, energetic, active, and alert.

Longer Term Effects of Training on Grit

Table 3 presents results from a regression model predicting linear Grit score change over time by linear DL-P1RM change over time, with a control for Grit score intercept. This model suggests that, for each 1-kg increase per session in a participant’s DL-P1RM, there was an average increase in Grit score per session of 0.021. Standardized model results suggest that this association was moderate to strong in size (β = 0.674). The faster a participant’s strength increased, the faster their self-assessed “grit” improved, indicating greater improvements in a sense of the self as being focused, resilient, goals-orientated, and diligent.

Table 3

Regression of Growth in Grit Score on Growth in Strength

Parameter	B (SE)	β (SE)	Z	p
Grit score intercept	−0.021 (0.019)	−0.222 (0.202)	−1.101	.271
Deadlift KG growth per session	0.021 (0.006)	0.674 (0.202)	3.341	<.001
Constant	0.050 (0.043)	0 (0.193)	1.171	.242
R ²	0.476

KG = Kilograms.

Discussion

Our findings of significantly reduced anger, confusion, depression, and tension following a single class regardless of sex mirror those found by Fitzgerald et al. (2021) following much less vigorous therapeutic yoga sessions. This perhaps suggests that exercise classes such as those delivered by MBC might be beneficial for short-term alleviation of negative mood states that might raise the risk of relapse or other challenges in recovery, possibly through similar mechanisms to those found in research on mind–body relaxation, control, and mindfulness (Bowen et al., 2014).

We found evidence for a potentially strong relationship between increases in strength and increases in grit over repeated sessions. We cautiously suggest that this relationship may be representative of the long-term benefits people in recovery are likely to experience from training on aggregate, including the manifestation of new, positive social identities based on resilience, diligence, focus, and self-efficacy which may be a significant part of a person’s recovery capital that can promote and maintain their recovery (Buckingham et al., 2013). Training resulting in greater strength and a more positive self-perception of capabilities may potentially be as important a protective and restorative factor as the act of exercising itself. To voluntarily embrace hardship and challenge in such fashion is the cornerstone of grit development (Duckworth, 2016).

We believe interpretation of short-term training effects on POMS scores can be fairly confident within this participant group, as the analysis does not rely on our experimental measure of strength and has a far larger number of observations. However, longer term analyses should be interpreted with greater caution due to the small convenience sample. Nevertheless, this study contributes to a paucity of long-term studies on strength development and self-perceived grit which we hope will promote further research. Further research utilizing a causal research design and representative sampling frame would be required to confidently assert our conclusions, but consideration should be given to how an experimental design may undermine the supportive and relational aspects of PE delivery that are core to MBC’s principles. Study designs that focus on delineating between the disparate contributions of the social, relational, physical, and neurobiological dimensions of PE on recovery would enable the development of more effective health interventions.

People often enter recovery with severely depleted social, economic, human, and physical capital which requires time to build up in the form of recovery capital (Fitzgerald, 2017). This article has aimed to elucidate some of the processes by which group PE might promote the development of social, human, and physical capital theorized to be essential to recovery.

Implications for Practice

Efforts to support people in making positive changes to health or wellbeing are often short term: characterized by prescribed numbers of sessions delivered through transient programs or evidenced by studies that only consider pretreatment and posttreatment effects and, as a result, rarely see “interventions” turn into sustained behavior changes (Graham, 2010). In this study, we have tried to measure and assess the impact of potential short-term and longer term associations between PE and mechanisms of recovery, finding a degree of positive support for both. These findings should inform practice where PE interventions are prescribed or recommended for people in recovery, stressing that while there is additional evidence of significant short-term positive effects on mood states which can support the day-to-day maintenance of recovery, professional training to effectively develop strength over time, above and beyond the participation in group exercise, can also lead to the cultivation of “grit”: beliefs and characteristics which might support recovery in the long term.

Supplemental Material

sj-docx-1-hpp-10.1177_15248399241245051 – Supplemental material for Lift Yourself Up: The Short-Term Associations Between Strength Training and Mood States and the Longer Term Development of Physical Capital and “Grit” Among People Recovering From Substance Use Disorders

Supplemental material, sj-docx-1-hpp-10.1177_15248399241245051 for Lift Yourself Up: The Short-Term Associations Between Strength Training and Mood States and the Longer Term Development of Physical Capital and “Grit” Among People Recovering From Substance Use Disorders by Christopher Fitzgerald, Calum Webb and Christopher McNally in Health Promotion Practice

Footnotes

Authors’ Note:

Christopher Fitzgerald is Director of Mind Body Connect (registered charity No. 1173834).

ORCID iD

Calum Webb

Supplemental Material

Supplemental material for this article is available online at .

References

Boisvert

R. A.

Martin

L. M.

Grosek

Clarie

A. J.

(2008). Effectiveness of a peer-support community in addiction recovery: Participation as intervention. Occupational Therapy International, 15(4), 205–220.

Bowen

Witkiewitz

Clifasefi

S. L.

Grow

Chawla

Hsu

S. H.

Carroll

H. A.

Harrop

Collins

S. E.

Lustyk

M. K.

Larimer

M. E.

(2014). Relative efficacy of mindfulness-based relapse prevention, standard relapse prevention, and treatment as usual for substance use disorders: A randomized clinical trial. JAMA Psychiatry, 71(5), 547–556.

Buckingham

S. A.

Frings

Albery

I. P.

(2013). Group membership and social identity in addiction recovery. Psychology of Addictive Behaviors, 27(4), 1132–1140.

Cloud

Granfield

(2008). Conceptualizing recovery capital: Expansion of a theoretical construct. Substance Use & Misuse, 43(12–13), 1971–1986.

De Leon

. (2000). The therapeutic community: Theory, model, and method. Springer.

Di Corrado

. (2017). Biological underpinnings of mood and the role of physical exercise. Sport Sciences for Health, 13(3), 461–468.

Duckworth

A. L.

(2016). Grit: The power of passion and perseverance (Vol. 234). Scribner.

Duckworth

A. L.

Quinn

P. D.

(2009). Development and validation of the Short Grit Scale (GRIT-S). Journal of Personality Assessment, 91(2), 166–174.

Fitzgerald

(2017). Capitalising upon the physical: Exercise and addiction recovery [Unpublished doctoral thesis, University of Sheffield].

10.

Fitzgerald

Barley

Hunt

Klasto

S. P.

West

(2021). A mixed-method investigation into therapeutic yoga as an adjunctive treatment for people recovering from substance use disorders. International Journal of Mental Health and Addiction, 19(4), 1330–1345.

11.

Flores

P. J.

(2001). Addiction as an attachment disorder: Implications for group therapy. International Journal of Group Psychotherapy, 51(1 Special issue), 63–81.

12.

Gauntlett

(2011). Making is connecting: The social meaning of creativity, from DIY and knitting to YouTube and Web 2.0. Polity Press.

13.

Graham

(2010). Where is the future in public health? The Milbank Quarterly, 88(2), 149–168.

14.

Griffin

M. L.

McDermott

K. A.

McHugh

R. K.

Fitzmaurice

G. M.

Weiss

R. D.

(2016). Grit in patients with substance use disorders. The American Journal on Addictions, 25(8), 652–658.

15.

Hennessy

(2017). Recovery capital: A systematic review of the literature. Addiction Research & Theory, 25(5), 349–360.

16.

Katz

Toner

(2013). A systematic review of gender differences in the effectiveness of mindfulness-based treatments for substance use disorders. Mindfulness, 4, 318–331.

17.

Kilgore

Rippetoe

(2007). Redefining fitness for health and fitness professionals. Journal of Exercise Physiology Online, 10(2), 34–39.

18.

Lechner

C. M.

Danner

Rammstedt

(2019). Grit (effortful persistence) can be measured with a short scale, shows little variation across socio-demographic subgroups, and is associated with career success and career engagement. PLoS ONE, 14(11), e0224814.

19.

Melemis

S. M.

(2015). Focus: Addiction: Relapse prevention and the five rules of recovery. The Yale Journal of Biology and Medicine, 88(3), 325–332.

20.

Morais

A. P. D.

Pita

I. R.

Fontes-Ribeiro

C. A.

Pereira

F. C.

(2018). The neurobiological mechanisms of physical exercise in methamphetamine addiction. CNS Neuroscience & Therapeutics, 24(2), 85–97.

21.

Patterson

M. S.

Spadine

M. N.

Graves Boswell

Prochnow

Amo

Francis

A. N.

Russell

A. M.

Heinrich

K. M.

(2022). Exercise in the treatment of addiction: A systematic literature review. Health Education & Behavior, 49(5), 801–819.

22.

Pooler

D. K.

Qualls

Rogers

Johnston

(2014). An exploration of cohesion and recovery outcomes in addiction treatment groups. Social Work With Groups, 37(4), 314–330.

23.

Public Health England. (2017). Adult substance misuse statistics from the National Drug Treatment Monitoring System.

24.

Pulliam

J. S.

(2021). Predictors of the treatment retention and quality of recovery from opioid use disorder using buprenorphine in medication-assisted treatment: A pilot study of the effects of hope, grit, and readiness to change [Doctoral dissertation, Liberty University].

25.

Rawson

R. A.

Chudzynski

Gonzales

Mooney

Dickerson

Ang

Dolezal

Cooper

C. B.

(2015). The impact of exercise on depression and anxiety symptoms among abstinent methamphetamine-dependent individuals in a residential treatment setting. Journal of Substance Abuse Treatment, 57, 36–40.

26.

Reynolds

J. M.

Gordon

T. J.

Robergs

R. A.

(2006). Prediction of one repetition maximum strength from multiple repetition maximum testing and anthropometry. The Journal of Strength & Conditioning Research, 20(3), 584–592.

27.

Shacham

(1983). A shortened version of the Profile of Mood States. Journal of Personality Assessment, 47(3), 305–306.

28.

Tracy

Wallace

S. P.

(2016). Benefits of peer support groups in the treatment of addiction. Substance Abuse and Rehabilitation, 7, 143–154.

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