The Relationship Between Social Support and Relapse Tendency Among Those Who Struggle With Drug Addiction: Multiple Mediators of Exercise Self-Efficacy and Health-Related Quality of Life

Abstract

Based on social support theory and exercise promotion health theory, we verified the mediating role of exercise self-efficacy and health-related quality of life in the relationship between social support and the relapse tendency of Chinese people who struggle with drug addiction. Samples who had received traditional Chinese health-promoting exercise interventions over 3 months were recruited from two drug rehabilitation centres in Zhejiang Province (n = 415). The participants completed the Social Support Rating Scale, Exercise Self-Efficacy Scale, Health Survey Short Form Questionnaire Chinese version, and Relapse Tendency Scale. Correlation analysis showed significant positive correlations between social support, exercise self-efficacy, and quality of life. In contrast, social support, exercise self-efficacy, and quality of life were negatively correlated with relapse tendency. In addition, intermediary effect analysis showed that social support has a direct negative predictive effect on relapse tendency in two ways: as an independent intermediary of exercise self-efficacy and as a chain intermediary of exercise self-efficacy and quality of life. Good exercise habits and adherence, as well as early establishment of social support, are beneficial not only for reducing craving and relapse behaviour but also for enhancing the quality of life of people who struggle with drug addiction, thereby facilitating the recovery efficacy for maintenance.

Keywords

social support relapse tendency drug addicts addiction quality of life exercise self-efficacy

Introduction

Drug addiction is a chronic relapsing disorder characterized by persistent drug-seeking and drug-taking behaviours (O’Brien & Thomas McLellan, 1996). Drug abuse has a severe impact on the health of patients and harms them and their families. It also poses a significant threat to social governance and public safety. Different kinds of drugs can inflict long-term health damage and thus reduce the lifespan of users. For example, cocaine causes severe cardiopulmonary toxicity over time, and chronic users of methamphetamine show not only dramatic changes in mood and behaviour but also psychotic symptoms (Glei & Preston, 2020). Deaths related to psychostimulants, such as methamphetamine and the combined use of the opioid fentanyl, have surged dramatically (Newman et al., 2021).

The World Drug Report 2021 showed that approximately 275 million people worldwide used drugs in 2020, and approximately 5.5% of the population aged 15–64 years had used drugs at least once in the past year, of which 13% (approximately 36.3 million people) suffered from drug abuse disorders. Between 2010 and 2019, the number of people abusing drugs increased by 22%, in part because of global population growth. Based on demographic projections, globally, the number of people who use drugs will increase by 11% by 2030. The report also stated that the COVID-19 epidemic has led to a more flexible service delivery model, innovative and adaptable drug prevention and treatment services, and the introduction or expansion of telemedicine services in many countries (UNODC, 2021). According to the National Drug Control Commission of China, in 2019, there were approximately 2.4 million registered people using drugs in China. Synthetic drugs have replaced opioids as the most commonly used drug in China in recent years, and the availability of novel psychoactive substances (NPS) has risen rapidly (Theunissen et al., 2022; Zhong et al., 2019). More than half are individuals who have relapsed and failed detoxification (National Narcotics Control Commission of China, 2019). Effective treatment strategies, including approved drug therapies, are needed but do not currently exist. The U.S. National Institute on Drug Abuse (NIDA) recommended that drug rehabilitation treatment outcome studies measure other domains, particularly cravings, psychosocial function, self-efficacy, quality of life (QoL), and social support (Tiffany et al., 2012). To reduce stigma and negative bias when talking about addiction, we use “people who struggle with drug addiction/patient” instead of “addict/drug abuser”.

Relapse after detoxification is a complex problem in global drug control and detoxification strategies. Relapse behaviour is the progression of substance use that interrupts the initiation or maintenance of abstinence (Hendershot et al., 2011). Research shows that the relapse rate in Western countries with advanced detoxification conditions and methods is more than 60% within six months (Njoroge, 2017), while relapse rates in developing countries are generally above 90% (Ramsewak et al., 2020; L. Zhang et al., 2019). Relapse tendency refers to the possibility and willingness of recurrent behaviour, an essential prerequisite for individuals to produce a specific behaviour (Krueger et al., 2000). The higher the recurrence tendency, the higher the possibility of relapse behaviour (Zeng & Wei, 2021). Preventing relapse is the key to detoxification for people with addictive behaviours (Maisto & Connors, 2006).

Studies have shown that the increased relapse frequency is related to the decreased perception of social support from family and friends and decreased total social support. Active social support (such as family relationships, communities, related institutions, and essential others) is vital in helping those struggling with drug problems eliminate or slow addictive behaviour. In 2018, China’s Ministry of Justice established a unified basic model of judicial administrative drug rehabilitation to help patients rebuild their social support system and promote exercise intervention as a replacement therapy for them. Relevant studies have shown that exercise has an impact on the stages of the drug addiction cycle (initiation, maintenance, relapse) (Morais et al., 2018). Moderate intensity aerobic exercise is effective in improving abstinence and reducing the relapse rate among patients (T. Zhang et al., 2021). Supervised short-term exercise intervention has achieved good results in physical and mental recovery, but how to translate these interventions into long-term, sustainable exercise remains uncertain. Individuals with high, generalized self-efficacy are more likely to benefit from exercise intervention rehabilitation programs than those with low self-efficacy in patients. According to the above-described background, the research question of this study was whether social support, exercise self-efficacy, and health status perceived by patients were associated with relapse tendency. To find answers to the research question, a cross-sectional survey was conducted.

Social support includes potentially useful information or resources that may positively impact health and well-being (BENSCHOP et al., 2009). Social psychologists have proven that support measures for the general population are not enough for specific groups, and specific support methods are necessary for people with mental illness and with substance use problems (Md Hatah et al., 2015). In this article, social support is referred to as services provided by others and perceived by the recipient as helpful, emotional, and tangible assistance (Ban et al., 2021). Therefore, given many previous studies on the relationship between social support and relapse tendency, we assume that social support can strongly predict relapse tendency.

Self-efficacy refers to the ability of individuals to initially decide to make behavioural changes and successfully adhere to specific healthy behaviours, such as abiding by a sports training program (Bandura, 1997). Exercise training is a high-benefit low-risk intervention for most patients. In recent years, there has been an increase in studies on physical activity and the risk of relapse (Motaghinejad et al., 2015; K. Wang et al., 2019; T. Zhang et al., 2020). Exercise can also enable individuals with drug addiction to achieve better fitness results and improve their psychological states, such as positive changes in personality and psychological characteristics, including emotional improvement, self-confidence, and willpower. Exercise can change individuals’ attitudes towards life and drugs, enhance their willpower and desire to detoxify or develop drug resistance, and prevent relapse (Victor et al., 2016; T. Zhang et al., 2021). We hypothesize that exercise self-efficacy is an intermediary between social support and relapse tendency.

The health and quality of life of those who struggle with drug use are receiving more attention in China (G. Zhang et al., 2018). HRQoL is an essential aspect of human health, embedded in one’s physical health, mental state, social relations, personal beliefs, and relationship with environmental characteristics (Lu et al., 2014). The size, tightness, intensity, and type of social support network will directly affect the social support received by the individual, which may directly or indirectly affect their health status (Bi et al., 2022; Morey et al., 2021). The quality of life and mental health of those with drug dependencies are often lower than those of people without substance use disorders (Zivari-Rahman et al., n.d.). However, the impact of HRQoL on the utilization of health services of those with substance use disorders in China has not been reported.

Lack of support from family or friends is widely recognized as a risk factor for drug addiction and reduces QoL (Guimarães et al., 2013). As a risk factor for relapse, lack of social support may have a dual impact. Due to the increase in psychological distress and social exclusion, recession and unemployment increase the possibility of drug abuse or relapse (FEASTER et al., 2010; Nagelhout et al., 2017). Drug rehabilitation should focus on simple abstinence and strive to restore patients' QoL and physical and mental health (Giménez-Meseguer et al., 2020). Having a high degree of social support can improve QoL, eliminate drug dependence, and reduce relapse. Therefore, given many previous studies on the relationship between social support and QoL, we assume that social support will positively impact QoL, and QoL may play an intermediary role in the effect of social support on relapse tendency.

People with higher self-efficacy are more confident in performing tasks, which helps to reduce the pressure on caregivers and improve their health (Kershaw et al., 2015). There is a negative correlation between self-efficacy, self-esteem, and many aspects of individual mental health (Dule, 2021; Kroll et al., 2007). Exercise can improve self-esteem and cognitive function, reduce social withdrawal, and improve QoL (Mikkelsen et al., 2017). Therefore, more attention needs to be paid to their daily self-management habits and quality of life to improve their exercise compliance. However, there is no research on the relationship between exercise self-efficacy and relapse tendency. We hypothesize that exercise self-efficacy and HRQoL play a chain intermediary role between social support and relapse tendency.

In conclusion, this study aims to explore the influence of social support on relapse tendency, test the independent intermediary role and chain intermediary role of exercise self-efficacy and HRQoL, and provide valuable suggestions for promoting the overall rehabilitation of those struggling with drug addiction.

Methods

Participants and Procedures

A cross-sectional survey study was conducted in two drug rehabilitation centres in Hangzhou city, China, in April 2020. This study adopted a random cluster sampling method. All participants volunteered to participate in the offline test. The test emphasized anonymity, confidentiality, and the use of data only for scientific research. Each test was approximately 30 minutes long. The inclusion criteria were as follows: (1) aged 18 years or above, (2) illegal drug-dependent use (assessed by the Chinese version of the Addiction Severity Index) and (3) medium-intensity traditional Chinese health-promoting exercise interventions lasting at least three months. The exclusion criteria consisted of (1) cognitive impairment and ability to complete the questionnaire independently, (2) serious diseases that prevent participation in physical activities, and (3) undergoing psychotropic drug treatment. A total of 430 questionnaires were distributed; 15 incomplete questionnaires were excluded from the analysis, leaving 415 valid questionnaires (96.5%). The participants included 300 men (72.3%) and 115 women (27.7%). Their ages ranged from 20 to 62 years (M = 38.5, SD = 8.13). Of the participants, 233 (56.1%) were addicted to methamphetamine, 170 (41.0%) to heroin, and 12 (2.9%) abused marijuana or other drugs. In terms of history of relapse, 94 of the participants (22.7%) were first-time drug users, 208 (50.1%) relapsed 1-3 times, and 113 (27.2%) relapsed more than 3 times. A variance analysis found no significant differences in gender and marital status. The research protocol was approved by the Institutional Ethical Review Board of Zhejiang Police Vocational College. Informed consent was obtained from each participant.

Instruments

Social Support Rating Scale

Xiao (1994) developed the SSRS, which has 10 items and three dimensions: subjective support (four items), objective support (three items), and support utilization (three items). While subjective support reflects the perceived interpersonal network that an individual can count on, objective support signifies the degree of actual support an individual received in the past. Support utilization refers to the pattern of behaviour that an individual utilizes when seeking social support. The response options of the eight items ranged from 1 to 4. One item among them has five subquestions; each question was scored from 1 to 4. Two items were scored 0–9 based on the support resources. Social support scores ≤ 44 are classified as a low support level, while those > 44 are classified as a high support level. The Cronbach alpha coefficient was .769 in the present sample. Each item was scored using a four-point Likert scale. Item scores of the SSRS were computed by summation, generating a total support score ranging from 12 to 66, a subjective support score ranging from 8 to 32, an objective support score ranging from 1 to 22, and a support-seeking behaviour score ranging from 3 to 12.

Exercise Self-Efficacy Scale

We used the recently verified Chinese version of the ESES (Y. Liu et al., 2016). The ESES is an instrument used to evaluate confidence in being physically active (Bay et al., 2018). The scale consists of 10 items, and each item is scored on a four-point Likert scale ranging from 1 to 4 (1 = not at all true, 2 = rarely true, 3 = moderately true, 4 = always true). The best possible ESES score is 40 points. The ESES is a validated instrument with high internal consistency and scale integrity (Kroll et al., 2007). The Cronbach alpha coefficient was .879 in the present sample.

Health Survey Short Form Questionnaire (SF-36) Chinese version

This is a self-rating scale with good reliability and validity widely used in the HRQoL measurement of the general population, diseased populations, and others (Ware et al., 1993; Xie et al., 2009). The scale included physical (PCS, Physical Component Summary) and mental domains (MCS, Mental Component Summary). The PCS score was obtained from four dimensions: physical functioning (PF), physical roles (RP), bodily pain (BP), and general health (GH). The MCS score was obtained from four dimensions: vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH). The total score in each of the eight domains can be converted into a 0–100 scale, with higher scores indicating better HRQoL. The Cronbach alpha coefficient was acceptable for the PCS (.885) in the current study and the MCS (.784).

Relapse Tendency Scale

The RTS was compiled by Geng Wenxiu (W. Zhu & Geng, 2002). This scale consists of 18 items, including self-assessment of confidence in detoxification, current drug influence, objective environment, degree of physical and mental damage, and support system, with a score of six levels (0 being “almost impossible” and 5 being “very easy”). The higher the total score, the higher the relapse tendency (Xia et al., 2022). In this study, there was excellent internal consistency in this questionnaire (Cronbach’s α = 0.856).

Analysis Strategy

In this study, SPSS 25.0 and AMOS 21.0 were used for descriptive analysis, correlation analysis, and regression analysis. The significance level of all of the variables was determined as α = 0.05. The PROCESS program developed by Hayes examined the mediation model, and the percentage bootstrap method of deviation correction was used to test the significance of the mediating effect. In this study, gender and age were used as control variables, and 95% unbiased correction confidence intervals were constructed by SPSS plug-in PROCESS 3.4 according to the bootstrap method (5000 samples were extracted). Model 4 in PROCESS 3.4 was selected to examine the separate mediating effect of exercise self-efficacy and QoL. Model 6 was then selected to test the chain mediating effect between these factors.

Results

Bivariate Correlations Between Variables of Interest

The average, standard deviation, and correlation coefficient of each variable are shown in Table 1. Social support is significantly positively correlated with exercise self-efficacy and HRQoL and negatively correlated with relapse tendency. Relapse tendency is negatively correlated with social support, exercise self-efficacy, and HRQoL. HRQoL is significantly positively correlated with exercise self-efficacy. These results are in line with hypothetical expectations.

Table 1.

Correlations, Means, and Standard Deviation of Variables in the Study (N = 415).

Variables	M	SD	1	2	3	4
1. Social support	26.13	7.24	1
2. Exercise self-efficacy	2.60	.73	.584**	1
3. HRQoL	52.51	14.32	.251**	.346**	1
4. Relapse tendency	3.41	1.33	−.420**	−.419**	−.0347**	1

p < .05, ** p < .01, ***p < .001, the same below.

Test for Mediating Effect

As shown in Table 2, the mediating effect analysis was carried out using the SPSS macro program PROCESS compiled by Hayes (Preacher & Hayes, 2008). Controlling for gender and age, the mediating effects of exercise self-efficacy and HRQoL on the relationship between social support and relapse tendency were analysed. Multiple regression analyses showed that social support had a direct and positive predictive effect on exercise self-efficacy (β = 0.585, p < .001), and exercise self-efficacy had a direct and positive predictive effect on HRQoL (β = 0.309, p < .001). When social support, exercise self-efficacy, and HRQoL predicted relapse tendency at the same time, they all had significant negative effects on relapse tendency (β = −0.244, p < .001; β = −0.205, p < .001; β = −0.216, p < .001, respectively).

Table 2.

Multiple Regression Analysis Between Variables (N = 415).

Regression Equation		Overall Model Fit			Regression Coefficient Significance
Outcome	Predictor	R	R ²	F	β	t
Exercise self-efficacy	Social support	.590	.349	71.47	.585	14.491***
HRQoL	Social support	.360	.129	14.85	.073	1.269
HRQoL	Exercise self-efficacy				.309	5.344***
Relapse tendency	Social support	.519	.269	29.35	−.244	−4.595***
	Exercise self-efficacy				−.205	−3.737***
	HRQoL				−.216	−4.711***

As shown in Table 3, the nonparametric percentile bootstrap method was used to test the multiple mediation effects. The results showed that the mediating effect of exercise self-efficacy and HRQoL was significant, and the mediating effect value was −0.323. Specifically, multiple mediation effects are generated through three mediation pathways. In indirect effect 1 (−0.222), the bootstrap 95% confidence interval does not contain 0, indicating that the mediating effect of exercise self-efficacy is significant. In indirect effect 2 (−0.029), the bootstrap 95% confidence interval contains 0, indicating that the mediating effect of HRQoL is not significant. In indirect effect 3 (−0.072), the bootstrap 95% confidence interval does not contain 0, indicating that exercise self-efficacy and HRQoL have a significant chain-mediating effect between social support and relapse tendency. Furthermore, the relative effect (ratio of total indirect effect to total effect) was 41.78%. The specific pathways by which social support affects relapse tendency are shown in Figure 1.

Table 3.

Indirect Effect of Social Support on Relapse Tendency via Exercise Self-Efficacy and HRQoL (N = 415).

Path	Coefficient	Relative effect (%)	Boot SE	Boot LLCI	Boot ULCI
Total indirect effect	−.323	41.78	.066	−.452	−.195
Indirect effect 1 (social support→Exercise self-efficacy→Relapse tendency)	−.222	28.67	.067	−.353	−.091
Indirect effect 2 (social support→HRQoL→Relapse tendency)	−.029	3.77	.024	−.081	.017
Indirect effect 3 (social support→Exercise self-efficacy→HRQoL→Relapse tendency)	−.072	9.33	.023	−.123	−.034

Note. LLCI = Lower limit of confidence interval; ULCI = Upper limit of confidence interval.

Figure 1.

Path analysis for the social support-exercise self-efficacy-HRQoL-relapse tendency model. Values are shown as standardized coefficients.

Discussion

This study’s results showed a significant positive correlation between social support and exercise self-efficacy and a nonsignificant positive correlation between social support and HRQoL. We also found a significant negative correlation between social support and relapse tendency. The results showed that social support played a positive role in promoting the exercise efficacy of those struggling with drug addiction. At the same time, social support also positively promoted physical and mental health and reduced relapse tendency.

Many studies have confirmed that there is a significant relationship between social support and relapse tendency or a significant negative correlation, which is consistent with the results of this study (Caravaca-Sánchez & García-Jarillo, 2020; Massah et al., 2016; Rodríguez-Sáez et al., 2021). Some studies have used different social support scales and come to the same conclusion, such as a significant negative correlation between family support and relapse tendency (Li Liu et al., 2022; Xia et al., 2022; Zeng & Tan, 2021). In addition, this study found that exercise self-efficacy and QoL were significantly negatively correlated with relapse tendency, indicating that exercise self-efficacy acquired with social support (such as training guidance and obtaining positive comments) and QoL (physical and mental health) play an essential role in promoting health and reducing the tendency of relapse among those with substance use disorders.

This study and previous studies show that QoL improvements contribute to reducing drug abuse (Manning et al., 2019). According to the self-medication hypothesis (Khantzian, 1997), relapse may occur as a coping mechanism for managing mental distress, pain, or complex/severe psychosocial issues. Individuals are more likely to reduce their drug use only after finding alternate solutions that improve their QoL. Those who abuse drugs that have poor physical and/or mental health (a substantial proportion of the population) and, more generally, those with poor QoL, often must find ways to reduce relapse. A study found that it was efficient to use exercise to improve the QoL and social interaction among patients dependent on methamphetamine (D. Zhu et al., 2016). Exercise is one of the adjustable strategies to promote a healthy lifestyle and can reduce the incidence of drug abuse. Traditional sports and exercise routines have shown promise as interventions for drug addiction, with suggestions that exercise may attenuate craving states and reduce relapse through interactions with the neurotransmitters active in neural reward pathways, including dopamine and glutamate. Medium-to-high intensity and long-term exercise can encourage the brain to secrete endorphins, resulting in a sense of comfort and pleasure (Kilani et al., 2020). Based on our findings, we believe that the beneficial effects of exercise interventions on QoL should be further explored.

Further results showed that the influence of social support on relapse tendency occurs through the independent intermediary role of exercise self-efficacy and the chain intermediary effect of sports self-efficacy and QoL. However, QoL does not play an independent intermediary role. Most clinical studies have found that social support can significantly positively predict QoL (S. M. Davis et al., 2019; Grills-Taquechel et al., 2011; Jessani et al., 2021; Z. Wang et al., 2022); however, we have not reached a consistent conclusion among those struggling with substance abuse issues. Social support is not always positive and can actually be quite stressful or work against being free from drugs (e.g., domestic violence, negative family relations, peer pressure). In the field of criminology, some scholars believe that social support may not always have a beneficial effect on those struggling with drugs (Deng et al., 2020). Generally, positive social support can have a buffering effect on stress responses, resulting in reduced effects of drugs of abuse. QoL includes two dimensions, physical and mental functioning. Both are harmed by drug addiction because of the negative physical (e.g., bodily pain and physical weakness) and mental (e.g., depression, anxiety, and disruption of family relations) consequences of addiction (Eshagh Afkari et al., 2013).

Social support is a reliable predictor of physical health, but social support and physical health were not associated with adverse mental health outcomes (E. B. Davis et al., 2021). We found that three aspects of social support are of great significance to the QoL of those struggling with drugs. In addition to employment and community integration support, dignity and caring support are critical to those with substance use disorders. In general, the self-esteem of these individuals is significantly lower than that of the general population (Gossop, 1976). A qualitative study shows that taking drugs can give users a feeling of guilt or self-hatred, damaging their self-esteem and self-image (Butler et al., 2010). This difference may also be related to gender. Gender affects psychological functioning, so females struggling with drug use, being more sensitive than males, are more vulnerable to psychological damage. This provides a new conceptual framework for future research to measure the social support needed by this specific group and complements the existing theory of social support. Meanwhile, it also provides some references and guidance for improved interventions.

In this study, exercise self-efficacy plays an intermediary role in the influence of social support on relapse tendency, which showed that the effect of exercise intervention is the same as that of social support, which can effectively reduce the relapse tendency of those struggling with substance use disorders. Motaghinejad et al. and Strickland et al. also believed that physical activity could reduce relapse tendency and the relapse rate both for animal models and humans (Motaghinejad et al., 2015; Strickland et al., 2016). Exercise has also been indicated as a therapy for drug addiction, either in isolated exercise-based interventions or in combination with other addiction treatment strategies (Chen et al., 2022). For example, Schwinn et al. investigated the self-efficacy of women with substance use disorders and found that women with low self-efficacy are more likely to relapse (Schwinn et al., 2016). We speculate that exercise self-efficacy is related to emotions, and those with substance use disorders willing to adhere to rehabilitation exercises have improved emotions, forming a virtuous circle and reducing relapse. Kresina et al. found that different treatments, especially methadone, must be accompanied by behavioural therapy to reduce recurrence (F. Kresina, 2012). We suggest psychological counselling, exercise training, and other behavioural therapy instead of relying solely on drug treatment. To ensure that everyone is able to exercise, the patient’s fitness, pain level, and access to exercise need to be measured first, as unsafe neighbourhoods, high rates of gym membership or limited space or money for exercise will reduce exercise adherence and efficacy. We recommend moderate intensity traditional Chinese health-promoting exercises, such as Baduanjin, Tai Chi, and Wuqinxi (Jia et al., 2022).

This study also discussed the chain mediation between exercise self-efficacy and HRQoL. Long-term drug abuse leads to poor physical functioning, which is also the main factor affecting the psychological function of those with drug addiction (Liu Liu et al., 2018). The level of exercise self-efficacy has a reliable correlation with persisting exercise and has a negative correlation with psychological disturbance and physical discomfort (Hötting et al., 2012). Zhu et al. used drug addiction/dependence (QOL-DA) to assess QoL, and the results showed that it was very effective to use exercise to improve QoL and social interaction among those with drug addictions (D. Zhu et al., 2016).

There are some limitations to this study. First, this study used cross-sectional data, and future studies may adopt a longitudinal design approach to detect the causal relationships between variables. Second, our participants were from mainland China, and the results may not apply to other countries and regions. Third, this study found that exercise self-efficacy partially mediates social support and relapse tendency. Whether other factors, such as specific physical activities, drug abuse history, and improved QoL (including environmental and social domains), moderate or mediate the associations between social support and relapse tendency warrants future research.

The present study shows that social support plays a positive role in promoting exercise self-efficacy and the physical and mental health of those with substance use problems and reduces the tendency of relapse. In particular, exercise self-efficacy acts as an independent mediator of the relationship between social support and relapse tendency, and there is a chain intermediary between exercise self-efficacy and HRQoL. Rehabilitation treatment of those struggling with drug addiction ultimately aspires to improve their QoL by reducing or ceasing their drug use. However, the association between these treatment outcomes should be researched further. Treatment interventions emphasizing appropriate activity and exercise levels, social support, and managing health perceptions may reduce cravings and relapse in those struggling with drug use.

Footnotes

Declaration of Conflicting interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by 2022 Soft Science Research Program of Zhejiang Province, 2022C35056.

Ethical Approval

The studies were reviewed and approved by the ethics committee of Zhejiang Police Vocational College. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Data Availability

The original contributions presented in the study are included in the article/Supplementary Materials, and further inquiries can be directed to the corresponding author.

ORCID iD

Dongming Jia

Author Biographies

Dongming Jia is currently an Assistant Professor in the Department of Criminal Justice at Zhejiang Police Vocational College. His research spans drug policy, social support for drug addicts, sports rehabilitation training, and health management of chronic diseases.

Kai Zhang (PhD) is an Assistant Professor in the Department of Correction Education at National Police University for Criminal Justice. Major: law. Research field: law on drug crime and community correction.

Yuming Xu (PhD) is a Professor of Hangzhou Normal University. Director of National Virtual Simulation Experiment Teaching Center for Scientific Fitness and Sports Skills. National fitness public service expert. Major: Kinesiology, Sport and Exercise Science. Research field: Skeletal muscle function, Physical Fitness and Intelligent Management of Sports Health, Intelligent sports product research and development.

References

Ban

(2021). The effect of fear of progression on quality of life among breast cancer patients: The mediating role of social support. Health and Quality of Life Outcomes, 19(1), 178. https://doi.org/10.1186/s12955-021-01816-7

Bandura

(1997). Self-efficacy: The exercise of control American Psychological Association. 23, 604.

Bay

Sandberg

Thilén

Wadell

Johansson

(2018). Exercise self-efficacy in adults with congenital heart disease. IJC Heart & Vasculature, 18, 7–11. https://doi.org/10.1016/j.ijcha.2017.12.002

Benschop

Stevenson

M. A.

Dahl

Morris

R. S.

French

N. P.

(2009). Informing surveillance programmes by investigating spatial dependency of subclinical Salmonella infection. Epidemiology and Infection, 137(9), 1348–1359. https://doi.org/10.1017/s0950268809002106

Lin

Fan

(2022). Influence of social support network and perceived social support on the subjective wellbeing of mothers of children with autism spectrum disorder. Frontiers in Psychology, 13(1), 835110. https://doi.org/10.3389/fpsyg.2022.835110

Butler

Wheeler

Sheridan

(2010). Physical and psychological harms and health consequences of methamphetamine use amongst a group of New Zealand users. International Journal of Mental Health and Addiction, 8(3), 432–443. https://doi.org/10.1007/s11469-009-9213-5

Caravaca-Sánchez

García-Jarillo

(2020). Perceived social support, resilience and consumption of psychoactive substances amongst inmates in prisons. Revista Española de Sanidad Penitenciaria, 22(2), 75–79. https://doi.org/10.18176/resp.00013

Chen

Xiong

Yang

(2022). Potential effects of Nrf2 in exercise intervention of neurotoxicity caused by methamphetamine oxidative stress. Oxidative Medicine and Cellular Longevity, 2022(1), 4445734–4445815. https://doi.org/10.1155/2022/4445734

Davis

E. B.

McElroy-Heltzel

S. E.

Lemke

A. W.

Cowden

R. G.

VanderWeele

T. J.

Worthington

E. L.

Glowiak

K. J.

Shannonhouse

L. R.

Davis

D. E.

Hook

J. N.

Van Tongeren

D. R.

Aten

J. D.

(2021). Psychological and spiritual outcomes during the COVID-19 pandemic: A prospective longitudinal study of adults with chronic disease. Health Psychology, 40(6), 347–356. https://doi.org/10.1037/hea0001079

10.

Davis

S. M.

Lepore

S. J.

Dumenci

(2019). Psychometric properties and correlates of a brief scale measuring the psychological construct mattering to others in a sample of women recovering from breast cancer. Quality of Life Research, 28(6), 1605–1614. https://doi.org/10.1007/s11136-019-02118-8

11.

Deng

Liu

Chui

W. H.

(2020). Suicide attempts and perceived social support among Chinese drug users: The mediating role of self-esteem and depression. International Journal of Environmental Research and Public Health, 18(1), 208. https://doi.org/10.3390/ijerph18010208

12.

Dule

(2021). Psychological distress among Ethiopian pregnant women during COVID-19: Negative correlation with self-efficacy. Psychology Research and Behavior Management, 14, 1001–1010. https://doi.org/10.2147/prbm.s317961

13.

Eshagh Afkari

Ghasemi

Shojaeizadeh

Tol

Rahimi Foroshani

Taghdisi

M. H.

(2013). Comparison between family function dimensions and quality of life among amphetamine addicts and non- addicts. Iranian Red Crescent Medical Journal, 15(4), 356–362. https://doi.org/10.5812/ircmj.9947

14.

Feaster

D. J.

Burns

M. J.

Brincks

A. M.

Prado

Mitrani

V. B.

Mauer

M. H.

Szapocznik

(2010). Structural ecosystems therapy for HIV+ african-American women and drug abuse relapse. Family Process, 49(2), 204–219. https://doi.org/10.1111/j.1545-5300.2010.01318.x

15.

Giménez-Meseguer

Tortosa-Martínez

Cortell-Tormo

(2020). The benefits of physical exercise on mental disorders and quality of life in substance use disorders patients. Systematic review and meta-analysis. International Journal of Environmental Research and Public Health, 17(10), 3680. https://doi.org/10.3390/ijerph17103680

16.

Glei

D. A.

Preston

S. H.

(2020). Estimating the impact of drug use on US mortality, 1999-2016. Plos One, 15(1), Article e0226732. https://doi.org/10.1371/journal.pone.0226732

17.

Gossop

(1976). Drug dependence and self-esteem. International Journal of the Addictions, 11(5), 741–753. https://doi.org/10.3109/10826087609058810

18.

Grills-Taquechel

A. E.

Littleton

H. L.

Axsom

(2011). Social support, world assumptions, and exposure as predictors of anxiety and quality of life following a mass trauma. Journal of Anxiety Disorders, 25(4), 498–506. https://doi.org/10.1016/j.janxdis.2010.12.003

19.

Guimarães

A. F.

Carvalho

D. V. de

Machado

N. Á. A.

Baptista

R. A. N.

Lemos

S. M. A.

(2013). Risk of developmental delay of children aged between two and 24 months and its association with the quality of family stimulus. Revista Paulista de Pediatria, 31(4), 452–458. https://doi.org/10.1590/s0103-05822013000400006

20.

Hendershot

C. S.

Witkiewitz

George

W. H.

Marlatt

G. A.

(2011). Relapse prevention for addictive behaviors. Substance Abuse Treatment, Prevention, and Policy, 6(1), 17. https://doi.org/10.1186/1747-597x-6-17

21.

Hötting

Schauenburg

Röder

(2012). Long-term effects of physical exercise on verbal learning and memory in middle-aged adults: Results of a one-year follow-up study. Brain Sciences, 2(3), 332–346. https://doi.org/10.3390/brainsci2030332

22.

Jessani

Quadri

M. F. A.

Lefoka

El-Rabbany

Hooper

Lim

H. J.

Ndobe

Brondani

Laronde

D. M.

(2021). Oral health status and patterns of dental service utilization of adolescents in Lesotho, southern africa. Children, 8(2), 120. https://doi.org/10.3390/children8020120

23.

Jia

Zhou

(2022). Effectiveness of traditional Chinese health-promoting exercise as an adjunct therapy for drug use disorders: A systematic review and meta-analysis. Journal of Integrative and Complementary Medicine, 28(4), 294–308. https://doi.org/10.1089/jicm.2021.0285

24.

Kershaw

Ellis

K. R.

Yoon

Schafenacker

Katapodi

Northouse

(2015). The interdependence of advanced cancer patients’ and their family caregivers’ mental health, physical health, and self-efficacy over time. Annals of Behavioral Medicine, 49(6), 901–911. https://doi.org/10.1007/s12160-015-9743-y

25.

Khantzian

E. J.

(1997). The self-medication hypothesis of substance use disorders: A reconsideration and recent applications. Harvard Review of Psychiatry, 4(5), 231–244. https://doi.org/10.3109/10673229709030550

26.

Kilani

H. A.

Bataineh

M. F.

Al-Nawayseh

Atiyat

Obeid

Abu-Hilal

M. M.

Mansi

Al-Kilani

Al-Kitani

El-Saleh

Jaber

R. M.

Sweidan

Himsi

Yousef

Alzeer

Nasrallah

Al Dhaheri

A. S.

Al-Za’abi

Allala

Kilani

(2020). Healthy lifestyle behaviors are major predictors of mental wellbeing during COVID-19 pandemic confinement: A study on adult arabs in higher educational institutions. Plos One, 15(12), Article e0243524. https://doi.org/10.1371/journal.pone.0243524

27.

Kresina

F. T.

(2012). International expansion of the use of pharmacotherapies for the treatment of opioid dependence. Clinical and Experimental Pharmacology 10(11), 1727–1740. https://doi.org/10.4172/2161-1459.S5-001

28.

Kroll

Kehn

P.-S.

Groah

(2007). The SCI exercise self-efficacy scale (ESES): Development and psychometric properties. International Journal of Behavioral Nutrition and Physical Activity, 4(1), 34. https://doi.org/10.1186/1479-5868-4-34

29.

Krueger

N. F.

Reilly

M. D.

Carsrud

A. L.

(2000). Competing models of entrepreneurial intentions. Journal of Business Venturing, 15(5–6), 411–432. https://doi.org/10.1016/s0883-9026(98)00033-0

30.

Liu

Chui

Chen

(2018). Violent and non-violent criminal behavior among young Chinese drug users: A mixed methods study. International Journal of Environmental Research and Public Health, 15(3), 432. https://doi.org/10.3390/ijerph15030432

31.

Liu

Meng

Liu

(2022). The mediating role of social support in the relationship between parenting styles and adolescent drug abuse identification. Frontiers in Psychology, 12(1), 802408. https://doi.org/10.3389/fpsyg.2021.802408

32.

Liu

Wang

Dong

(2016). Reliability and validity of the Chinese version of exercise self-efficacy scale in stroke patients. Chinese Journal of Practical Nursing, 32, 974–977.

33.

C.-H.

Wang

P.-X.

Lei

Y.-X.

Luo

Z.-C.

(2014). Influence of health-related quality of life on health service utilization in Chinese rural-to-urban female migrant workers. Health and Quality of Life Outcomes, 12(1), 121. https://doi.org/10.1186/s12955-014-0121-4

34.

Maisto

S. A.

Connors

G. J.

(2006). Relapse in the addictive behaviors: Integration and future directions. Clinical Psychology Review, 26(2), 229–231. https://doi.org/10.1016/j.cpr.2005.11.009

35.

Manning

Garfield

J. B. B.

Lam

Allsop

Berends

Best

Buykx

Room

Lubman

D. I.

(2019). Improved quality of life following addiction treatment is associated with reductions in substance use. Journal of Clinical Medicine, 8(9), 1407. https://doi.org/10.3390/jcm8091407

36.

Massah

Azkhosh

Azami

Goodiny

A. A.

Doostian

Mousavi

S. H.

(2016). Students tendency toward illicit drug use: The role of perceived social support and family function in Iran. Iranian Journal of Psychiatry and Behavioral Sciences, https://doi.org/10.17795/ijpbs.8314

37.

Md Hatah

Lim

K. P.

Mohd Ali

Mohamed Shah

Islahudin

(2015). The influence of cultural and religious orientations on social support and its potential impact on medication adherence. Patient Preference and Adherence, 589(1), 589–596. https://doi.org/10.2147/ppa.s79477

38.

Mikkelsen

Stojanovska

Polenakovic

Bosevski

Apostolopoulos

(2017). Exercise and mental health. Maturitas, 106(1), 48–56. https://doi.org/10.1016/j.maturitas.2017.09.003

39.

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. https://doi.org/10.1111/cns.12788

40.

Morey

B. N.

Valencia

Park

H. W.

Lee

(2021). The central role of social support in the health of Chinese and Korean American immigrants. Social Science & Medicine, 284(1), 114229. https://doi.org/10.1016/j.socscimed.2021.114229

41.

Motaghinejad

Motevalian

Larijani

Khajehamedi

(2015). Protective effects of forced exercise against methylphenidate-induced anxiety, depression and cognition impairment in rat. Advanced Biomedical Research, 4(1), 134. https://doi.org/10.4103/2277-9175.161528

42.

Nagelhout

G. E.

Hummel

de Goeij

M. C. M.

de Vries

Kaner

Lemmens

(2017). How economic recessions and unemployment affect illegal drug use: A systematic realist literature review. International Journal of Drug Policy, 44(1), 69–83. https://doi.org/10.1016/j.drugpo.2017.03.013

43.

National Narcotics Control Commission of China (2019). Annual report on drug-using situation in China. Ministry of Public Security.

44.

Newman

A. H.

Jordan

C. J.

Bonifazi

Z.-X.

(2021). New drugs, old targets: Tweaking the dopamine system to treat psychostimulant use disorders. Annual Review of Pharmacology and Toxicology, 61(1), 609–628. https://doi.org/10.1146/annurev-pharmtox-030220-124205

45.

Njoroge

M. W.

(2017). Review on treatment of substance use disorders. Journal of Addiction Research & Therapy, 08(06). https://doi.org/10.4172/2155-6105.1000353

46.

O’Brien

Thomas McLellan

(1996). Myths about the treatment of addiction. The Lancet, 347(8996), 237–240. https://doi.org/10.1016/s0140-6736(96)90409-2

47.

Preacher

K. J.

Hayes

A. F.

(2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/brm.40.3.879

48.

Ramsewak

Putteeraj

Somanah

(2020). Exploring substance use disorders and relapse in Mauritian male addicts. Heliyon, 6(8), Article e04731. https://doi.org/10.1016/j.heliyon.2020.e04731

49.

Rodríguez-Sáez

J. L.

Martín-Antón

L. J.

Salgado-Ruiz

Carbonero

M. Á.

(2021). Socio-emotional variables linked to the consumption of drugs amongst university students of social sciences: A pilot study. International Journal of Environmental Research and Public Health, 18(9), 4502. https://doi.org/10.3390/ijerph18094502

50.

Schwinn

T. M.

Schinke

S. P.

Hopkins

Thom

(2016). Risk and protective factors associated with adolescent girls’ substance use: Data from a nationwide Facebook sample. Substance Abuse, 37(4), 564–570. https://doi.org/10.1080/08897077.2016.1154495

51.

Strickland

J. C.

Abel

J. M.

Lacy

R. T.

Beckmann

J. S.

Witte

M. A.

Lynch

W. J.

Smith

M. A.

(2016). The effects of resistance exercise on cocaine self-administration, muscle hypertrophy, and BDNF expression in the nucleus accumbens. Drug and Alcohol Dependence, 163(1), 186–194. https://doi.org/10.1016/j.drugalcdep.2016.04.019

52.

Theunissen

E. L.

Kuypers

K. P. C.

Mason

N. L.

Ramaekers

J. G.

(2022). A comparison of acute neurocognitive and psychotomimetic effects of a synthetic cannabinoid and natural cannabis at psychotropic dose equivalence. Frontiers in Psychiatry, 13(1), 891811. https://doi.org/10.3389/fpsyt.2022.891811

53.

Tiffany

S. T.

Friedman

Greenfield

S. F.

Hasin

D. S.

Jackson

(2012). Beyond drug use: A systematic consideration of other outcomes in evaluations of treatments for substance use disorders. Addiction, 107(4), 709–718. https://doi.org/10.1111/j.1360-0443.2011.03581.x

54.

UNODC (2021). World drug report 2021. Office on Drugs and Crime.

55.

Victor

C. R.

Rogers

Woodcock

Beighton

Cook

D. G.

Kerry

S. M.

Iliffe

Whincup

Ussher

Harris

T. J.

(2016). What factors support older people to increase their physical activity levels? An exploratory analysis of the experiences of PACE-lift trial participants. Archives of Gerontology and Geriatrics, 67, 1–6. https://doi.org/10.1016/j.archger.2016.06.006

56.

Wang

Luo

Zhang

Ouyang

Zhou

(2019). Effect of physical activity on drug craving of women with substance use disorder in compulsory isolation: Mediating effect of internal inhibition. Frontiers in Psychology, 10(1), 1928. https://doi.org/10.3389/fpsyg.2019.01928

57.

Wang

Chang

Wang

(2022). The mediating effect of parenting stress on the relationship between social support and quality of life in parents of children with autistic spectrum disorder: A meta-analytic structural equation modeling. Frontiers in Psychiatry, 13(1), 713620. https://doi.org/10.3389/fpsyt.2022.713620

58.

Ware

J. E.

Snow

K. K.

Kosinski

Gandek

(1993). SF-36 health survey manual and interpretation guide. Boston New England Medical Centre.

59.

Xia

Gong

Wang

Mao

(2022). Family function impacts relapse tendency in substance use disorder: Mediated through self-esteem and resilience. Frontiers in Psychiatry, 13(1), 815118. https://doi.org/10.3389/fpsyt.2022.815118

60.

Xiao

(1994). Theoretical basis and research application of “social support rating scale”. Journal of Clinical Psychiatry, 2(1), 98–100.

61.

Xie

Wang

Zhou

(2009). Application of drug addicts quality of life scale in methadone maintenance treatment outpatients. Modern Preventive Medicine, 36(1), 2128–2129.

62.

Zeng

Tan

(2021). The relationship between the family functioning of individuals with drug addiction and relapse tendency: A moderated mediation model. International Journal of Environmental Research and Public Health, 18(2), 625. https://doi.org/10.3390/ijerph18020625

63.

Zeng

Wei

(2021). The relationship between the psychological capital of male individuals with drug abuse and relapse tendency: A moderated mediation model. Current Psychology, 1–10. https://doi.org/10.1007/s12144-021-02325-y

64.

Zhang

Yang

Zhang

Shan

Dai

Liu

(2018). Effect of community-based extension clinics of methadone maintenance therapy for opiate-dependent clients. Medicine, 97(47), Article e13323. https://doi.org/10.1097/md.0000000000013323

65.

Zhang

Guo

Fang

Lin

Zheng

Lin

Yang

Z. y.

Fang

J. Q.

(2019). Traditional Chinese medicine-guided dietary intervention for male youth undergoing drug detoxification: A randomized controlled trial. Evidence-Based Complementary and Alternative Medicine, 2019(1), 1–12. https://doi.org/10.1155/2019/3870316

66.

Zhang

Wang

Hurr

Luo

(2021). The relationship between different amounts of physical exercise, internal inhibition, and drug craving in individuals with substance-use disorders. International Journal of Environmental Research and Public Health, 18(23), 12436. https://doi.org/10.3390/ijerph182312436

67.

Zhang

Wang

Jiang

Chen

Luo

(2020). The effect of physical activity on drug cravings of drug addicts with AIDS: The dual mediating effect of internal inhibition. Frontiers in Psychology, 11, 2002. https://doi.org/10.3389/fpsyg.2020.02002

68.

Zhong

B.-L.

Xie

W.-X.

Zhu

J.-H.

Chen

(2019). Prevalence and correlates of suicide attempt among Chinese individuals receiving methadone maintenance treatment for heroin dependence. Scientific Reports, 9(1), 15859. https://doi.org/10.1038/s41598-019-52440-x

69.

Zhu

Dai

Wang

Zhou

(2016). Beneficial effects of Tai Chi for amphetamine-type stimulant dependence: A pilot study. The American Journal of Drug and Alcohol Abuse, 42(4), 469–478. https://doi.org/10.3109/00952990.2016.1153646

70.

Zhu

Geng

(2002). Study on risk factors of relapse in heroin addicts. Chinese Behavioral Medicine Science, 11(1), 20–22.

71.

Zivari-Rahman

Lesani

Shokouhi-Moqaddam

(n.d.). Comparison of mental health, aggression and hopefulness between student drug-users and healthy students (A study in Iran). Addiction & Health, 4(1–2), 36–42.