The role of self-regulation resources in the relationship between self-compassion and nicotine dependence

Abstract

The aim of this study was to examine the role of self-compassion in nicotine dependence within the framework of the Self-Regulation Resource Model (SRRM). The study included 331 adults aged 18 years and older. Participants were classified into three groups based on smoking status (current smokers, former smokers, and non-smokers). Differences in gender, education level, self-compassion, positive affect, negative affect, and health self-efficacy were examined across smoking status groups. The proposed SRRM-based mediation model was tested among current smokers (N = 164). The findings indicated that self-compassion was indirectly associated with nicotine dependence through negative affect and health self-efficacy, whereas positive affect did not show a significant mediating effect. Overall, the results highlight the role of emotional regulation and health self-efficacy as key self-regulation resources linked to nicotine dependence. These findings suggest that strengthening self-regulation resources may be relevant for smoking cessation efforts.

Keywords

affect health self efficacy nicotine dependence self-compassion self-regulation resource model

Introduction

Nicotine dependence is a significant global public health issue, characterized by complex biological, psychological, and social factors that make cessation difficult (World Health Organization [WHO], 2024a). Approximately 890 million people aged 15 years and over smoke worldwide, representing approximately 15% of the adult population (WHO, 2024a). In Türkiye, the rate of smoking in people aged 15 years and over is 31.2% (WHO, 2023). Smoking is not only an individual problem but also causes serious threats to public health. It is among the main risk factors of many fatal diseases such as respiratory diseases, cardiovascular diseases, stroke and diabetes (WHO, 2024b). Despite a global decline in smoking rates, the disease burden linked to tobacco use is expected to remain high in coming years (WHO, 2024a). Therefore, developing effective public health policies and implementing comprehensive prevention programs are crucial (WHO, 2024a). Against this broader public health context, examining psychological differences associated with smoking behavior provides a useful perspective for understanding variability in smoking patterns.

Smoking status is commonly used in clinical and population-based research to reflect meaningful differences in individuals’ psychological functioning. Prior studies have shown that smoking status is linked to negative affect (Steinberg et al., 2015) self-regulation processes relevant to the persistence or cessation of health behaviors (Smith et al., 2016), and psychological adjustment during the smoking cessation process (Liu et al., 2020). Accordingly, examining group differences based on smoking status provides a useful descriptive framework for examining how psychological self-regulation resources differ across smoking histories.

In addition to physiological dependence, smoking cessation is deeply influenced by psychosocial dynamics. Accordingly, psychological interventions such as counseling and cognitive-behavioral therapy have proven effective in helping individuals overcome nicotine dependence (Hartmann-Boyce et al., 2021; Nian et al., 2023). More recently, research has increasingly focused on self-compassion-based approaches, which offer promise in reducing negative self-judgment, increasing emotional resilience, and sustaining motivation throughout the cessation process (Kelly et al., 2010; O’Loughlin et al., 2025; Terry and Leary, 2011).

To fully grasp how self-compassion may impact addictive behaviors like smoking, it is important to understand the concept itself. According to Neff (2003), self-compassion consists of three interrelated dimensions: self-kindness, which involves treating oneself with care in times of distress or failure rather than avoidance; common humanity, the recognition that suffering and imperfection are part of the shared human experience rather than isolating or judging oneself; and mindfulness, the balanced awareness of one’s emotions and experiences without overidentifying with them (Neff, 2023). Together, these elements promote a more supportive and less judgmental inner dialog.

Self-compassion has been closely linked to self-regulation capacity, defined as an individual’s ability to guide and regulate their behavior in alignment with long-term goals, particularly in the health domain. A growing body of research supports this link, showing that individuals high in self-compassion demonstrate greater emotional balance, increased self-esteem, a stronger ability to cope with stress and shaping health behaviors (Abraham et al., 2000; Krieger et al., 2015; Neff and Knox, 2016; Wong et al., 2021). Furthermore, a recent meta-analysis found that self-compassion was positively associated with physical health and health-promoting behaviors, especially when supported by structured, multi-session interventions (Phillips and Hine, 2021). Conversely, inadequate self-regulation has been associated with unhealthy behaviors, including smoking (Kelly et al., 2010), alcohol abuse (Muraven et al., 2002), sedentary lifestyles (Martin Ginis and Bray, 2010), and disordered eating (Hofmann et al., 2007; Sproesser et al., 2011; Vohs and Heatherton, 2000; Ward and Mann, 2000). These findings suggest that self-compassion may enhance individuals’ capacity to resist maladaptive impulses and adopt healthier behaviors by strengthening underlying regulatory mechanisms.

A theoretical model that captures this process is the Self-Regulation Resource Model (SRRM) developed by Sirois (2015). This model proposes that self-compassion is linked to health behaviors indirectly through its impact on three core self-regulatory resources: positive affect, negative affect, and health self-efficacy. Specifically, individuals with higher self-compassion are more likely to experience elevated positive affect and a stronger sense of health-related self-efficacy, while simultaneously experiencing lower negative affect. These emotional and cognitive resources, in turn, mediate the effect of self-compassion on health behavior engagement. Empirical studies have supported these relationships in the context of behaviors such as healthy eating, alcohol moderation, and regular exercise (Sirois, 2015).

In addition to its theoretical clarity, the SRRM offers practical insights into how individuals set, pursue, and revise health-related goals. In order to engage in health behaviors, it is important to have self-regulation skills. Self-regulation involves not only goal setting and progress monitoring, but also emotional management and motivation maintenance. Self-compassion supports these processes by helping individuals remain non-defensive when facing setbacks, reevaluate harmful goals, adhere to treatment regimens, and manage difficult emotions (Terry and Leary, 2011). For instance, individuals with lower self-compassion tend to report higher depressive symptoms, increased binge eating, and poorer overall health perception (O’Loughlin et al., 2025). Supporting this perspective, Kelly et al. (2010) found that a self-compassion-based intervention helped individuals reduce smoking over time by promoting more adaptive self-regulation strategies, particularly among individuals with low readiness for change and high self-criticism.

While much of the existing literature has focused on health-promoting behaviors, relatively limited research has applied the SRRM to health-risk behaviors such as smoking. Accordingly, the present study examines the role of self-regulation resources in the relationship between self-compassion and nicotine dependence within the framework of the SRRM. Specifically, the study investigates whether positive affect, negative affect, and health self-efficacy are associated with nicotine dependence as potential psychological pathways linking self-compassion to maladaptive health behaviors. Smoking status (current smoker, ex-smoker, non-smoker) was also considered to provide contextual information regarding the distribution of self-compassion and self-regulation resources across different smoking histories.

The hypotheses were formulated to address two complementary aims. The first aim was descriptive and comparative in nature and focused on examining differences across smoking status groups. Accordingly, Hypotheses 1–6 tested whether smoking status differed by gender and educational level, and whether self-compassion, positive affect, negative affect, and health self-efficacy varied between smokers, ex-smokers, and non-smokers. These hypotheses were included to provide contextual information regarding the distribution of self-regulation resources across different smoking histories.

The second aim was theory-driven and focused on testing the associations proposed by the SRRM, with nicotine dependence treated as the primary outcome variable. In line with this aim, Hypotheses 7–10 examined the associations between self-compassion and nicotine dependence and tested the mediating roles of positive affect, negative affect, and health self-efficacy among current smokers. The hypotheses of the study are presented below.

H₁: Smoking status significantly differs by gender.

H₂: Smoking status significantly differs by educational status.

H₃: The mean self-compassion score significantly differs between smokers, ex-smokers, and non-smokers.

H₄: The mean positive affect score significantly differs between smokers, ex-smokers, and non-smokers.

H₅: The mean negative affect score significantly differs between smokers, ex-smokers, and non-smokers.

H₆: The mean health self-efficacy score significantly differs between smokers, ex-smokers, and non-smokers.

H₇: Self-compassion is negatively associated with nicotine dependence.

H₈: Positive affect has a mediating role in the relationship between self-compassion and nicotine dependence.

H₉: Negative affect has a mediating role in the relationship between self-compassion and nicotine dependence.

H₁₀: Health self-efficacy has a mediating role in the relationship between self-compassion and nicotine dependence.

Methods

Sample

The sample of the study consists of 331 participants aged 18 and over who completed an online survey via Google Forms between July 2023 and March 2024. Among the participants, 49.5% were current smokers, 16.6% reported that they had quit smoking, and 33.8% were non-smokers. Ethical approval was obtained from the appropriate institutional ethics committee (Date: 02.06.2023; Meeting Number: 134).

The mean age of the total sample was 34.85 years (SD = 11.93), ranging from 18 to 71 years. Among current smokers, who constituted the sample for the mediation analyses, the mean age was 32.88 years (SD = 10.70), with an age range of 18–63 years.

The sample size was determined based on participant availability during the data collection period, which is consistent with previous studies examining self-compassion and self-regulation processes in health-related behaviors. Given the focus on indirect effects, bootstrap-based mediation analysis was employed, as this approach is considered appropriate for detecting mediation effects in studies with moderate sample sizes and is less sensitive to distributional assumptions (Hayes, 2017; Preacher and Hayes, 2008).

Participants were eligible for inclusion if they were aged 18 years or older and provided informed consent to participate in the study. All survey items were set as mandatory in the online data collection platform; therefore, no missing data were present for the variables included in the analyses. Participants who completed the full survey were included in descriptive and group comparison analyses. Mediation analyses were restricted to participants who reported current smoking behavior and completed the Fagerström Test for Nicotine Dependence, as nicotine dependence is conceptually and empirically relevant only for current smokers.

Measures

Informed Consent Form: The Informed Consent Form provided participants with essential information about the research, including its purpose, participation requirements, procedures, and duration. Assurance was given that the research was completely voluntary and that all information and collected data would remain confidential. Participants were asked to confirm that they had read and agreed to the terms before proceeding with the study.

Demographic Information Form: This form was used to collect demographic information such as age, gender, and educational background. It also included items assessing smoking status and prior quit attempts.

Self-Compassion Scale: Developed by Neff (2003; α = 0.92) is a self-report scale consisting of 26 items measuring 6 sub-dimensions: self-compassion, self-judgment, self-kindness, common humanity, isolation, mindfulness, over-identification. Higher scores on the scale indicate higher levels of self-compassion. Items are rated on a five-point Likert scale ranging from 1 (Almost never) to 5 (Almost always). An example item is: “I try to be understanding and patient toward those aspects of my personality I don’t like.”

The Turkish validity and reliability study of the scale was conducted by Akın et al. (2007; α = 0.94). The internal consistency coefficients for the Turkish version of the subscales were as follows: self-compassion (0.94), self-judgment (0.94), common humanity (0.87), isolation (0.89), mindfulness (0.92), and over-identification (0.94).

Although the scale is theoretically multidimensional, a total self-compassion score was used in the present study. This approach is consistent with previous research examining self-compassion as a global self-regulation resource and aligns with the SRRM, which conceptualizes self-compassion as an overarching construct influencing health-related outcomes (Sirois, 2015).

Positive and Negative Affect Scale: This 20-item self-report scale was developed by Watson et al. (1988; α = 0.88 for positive affect and α = 0.85 for negative affect). It is a five-point Likert-type scale measuring two dimensions: 10 items assess positive affect and 10 items assess negative affect. Items are rated on a five-point Likert scale ranging from 1 (Very slightly or not at all) to 5 (Extremely). An example item for positive affect is: “Interested,” and an example item for negative affect is: “Distressed.”

The Turkish version of the scale was validated by Gençöz (2000), with internal consistency coefficients of 0.83 for positive affect and 0.86 for negative affect.

Control Beliefs Inventory: It is a self-report scale developed by Sirois (2004; α = 0.84) aiming to measure individuals’ perceptions of their control over their health. The original scale consists general control, chance control, symptom control and health self-efficacy sub-dimensions. In the present study, only the health self-efficacy subscale was used. This subscale evaluates individuals’ feelings of competence and confidence in performing behaviors necessary to maintain and protect their health. Higher scores indicate greater health-related self-efficacy. The subscale consists of eight items rated on a six-point Likert scale. The health self-efficacy subscale consists of eight items rated on a six-point Likert scale ranging from 1 (Strongly disagree) to 6 (Strongly agree). An example item is: “I am able to meet the challenge of following a healthy routine.”

Validity and reliability studies were conducted by the researchers for health self-efficacy, which is a sub-dimension of the Control Beliefs Inventory. Principal Component Analysis was used to evaluate construct validity. The fact that the Kaiser Meyer Olkin coefficient, which evaluates the adequacy of the sample, is high 0.87 (Tabachnick and Fidell, 2013) and the Bartlett test result, which tests the validity of the factor analysis, is significant (χ² 998.626, p < 0.001) shows that the data set is suitable for factor analysis. According to the factor analysis results, a unidimensional structure with an eigenvalue of 3.91 was observed and explained 48.90% of the total variance. The total internal consistency coefficient for the scale was found to be 0.84.

Fagerström Nicotine Dependence Test: Developed by Heatherton et al. (1991; α = 0.83), this six-item self-report scale measures the severity of nicotine dependence. Items assess smoking-related behaviors such as time to first cigarette after waking and daily cigarette consumption.

The Turkish version of the scale was validated by Uysal et al. (2004; α = 0.56). Items are scored using dichotomous and ordinal response formats, and total scores range from 0 to 10. Total scores range from 0 to 10 and are categorized into three levels: low dependence (0–4 points), moderate dependence (5–6 points), and high dependence (7–10 points). Higher scores indicate a greater severity of nicotine addiction. In the present study, the scale demonstrated sufficient variability among current smokers to support its use as a continuous outcome variable in mediation analyses.

Statistical analyses

All data were analyzed using SPSS 29.0. Student t-tests, Pearson correlation analyses, and linear regression analyses (using the enter method) were employed. The threshold for statistical significance was set at p < 0.05. Descriptive analyses were conducted to summarize the demographic characteristics of the sample. Correlation analyses were conducted to explore the relationships among the study variables (see Table 4).

To examine the indirect effects of self-compassion on nicotine dependence, mediation analyses were performed using the bootstrapping method with 5000 resamples, as outlined by Preacher and Hayes (2008). Hayes’ PROCESS macro for SPSS (Model 4) was used to test the simultaneous mediating effects of the self-regulation variables. Indirect effects were evaluated using bias-corrected 95% confidence intervals (see Table 5).

Results

Results are presented in two stages. The first stage reports findings on group differences according to smoking status. The second stage presents results from analyses testing the SRRM.

Table 1 presents the demographic characteristics of the participants. Of the 331 individuals who participated in the study, 61.6% were female and 37.8% were male. Regarding smoking status, 49.5% were current smokers, 16.6% had quit smoking, and 33.8% were non-smokers. Among current smokers, 52.4% were female and 46.3% were male.

Table 1.

Sociodemographic characteristics of participants.

Variables	N	%
Gender
Male	125	37.8
Female	204	61.6
Other	2	0.6
Educational status
Primary school	3	0.9
Middle school	4	1.2
High school	52	15.7
Undergraduate/graduate	272	82.2
Smoking status
Smoker	164	49.5
Non-smoker	112	33.8
Ex-smoker	55	16.6
Smoking cessation attempt
No	50	15.1
Yes, once	68	20.5
Yes, more than once	101	30.5
I don’t smoke	112	33.8

Table 2 presents group comparisons based on smoking status. A significantly higher proportion of men reported smoking compared to women (p = 0.002, p < 0.01). Additionally, individuals with lower levels of education (primary or middle school) were more likely to be smokers, although the overall education-based difference reached marginal significance (p = 0.046, p < 0.05).

Table 2.

Smoking status by gender and educational level.

Variables	Smoker	Non-smoker	p
Gender
Male	76 (46.3%)	49 (29.3%)	0.002 **
Female	86 (52.4%)	118 (70.7%)
Other	2 (1.2%)	0 (0.0%)
Educational status
Primary school	3 (1.8%)	0 (0.0%)	0.046 *
Middle school	4 (2.4%)	0 (0.0%)
High school	28 (17.1%)	24 (14.4%)
Undergraduate/graduate	129 (78.7%)	143 (85.6%)

Note. p-values based on Chi-square test.

The italic values in Table 2 represent p values. Statistically significant values are indicated as follows: *p < 0.05, **p < 0.01.

In order to further investigate differences among all smoking status groups, a one-way ANOVA was conducted with smoking status (smoker, ex-smoker, non-smoker) as the independent variable and each of the following as dependent variables: health self-efficacy, positive affect, negative affect, and self-compassion. As shown in Tables 3 and 4, smoking status had a statistically significant effect on all dependent variables.

Table 3.

One-way ANOVA results for study variables by smoking status.

Variables	SS	df	MS	F	p
Positive affect
Between groups	937.21	2	468.60	9.61	0.001
Within groups	15,848.05	325	48.76
Total	16,785.26	327
Negative affect
Between groups	1909.75	2	954.88	13.93	0.001
Within groups	22,277.86	325	68.55
Total	24,187.61	327
Health self-efficacy
Between groups	3016.14	2	1508.07	28.45	0.001
Within groups	17,385.96	328	53.01
Total	20,402.10	330
Self-compassion
Between groups	16.20	2	8.10	16.25	0.001
Within groups	163.52	328	0.50
Total	179.73	330

Note. One-way ANOVA results for each variable.

SS: sum of squares; MS: mean square.

p < 0.05, p < 0.01.

Table 4.

Bonferroni-adjusted pairwise comparisons of study variables by smoking status.

Variables		Smoker^a	Non-Smoker^b	Ex-Smorker^c		p ^a-b	p ^a-c	p ^b-c
Positive Affect	Mean ± SD	28.63 ± 7.06	32.39 ± 6.95	29.76 ± 6.83	0.001**	0.001**	0.544	0.058
Negative Affect	Mean ± SD	24.78 ± 8.93	19.5 ± 7.29	21.33 ± 8.14	0.001**	0.001**	0.025*	0.343
Health Self-Efficacy	Mean ± SD	29.63 ± 7.46	35.91 ± 7.05	35.09 ± 7.18	0.001**	0.001 **	0.001**	0.766
Self-Compassion	Mean ± SD	3.03 ± 0.73	3.52 ± 0.68	3.28 ± 0.69	0.001**	0.001**	0.060	0.087

Note. One-way ANOVA Test & Bonferroni corrected pairwise comparisons. Superscripts (a, b, c) indicate group categories. p-values are presented in italics.

p < 0.01.

Specifically, smoking status had a significant effect on positive affect, F(2, 331) = 9.61, p < 0.001. Post hoc tests indicated that non-smokers reported significantly higher positive affect than smokers (p < 0.01) and ex-smokers (p < 0.01), while the difference between smokers and ex-smokers was not statistically significant (p = 0.54).

For negative affect, a statistically significant effect of smoking status was found, F(2, 331) = 13.93, p < 0.001. Post hoc tests showed that smokers had significantly higher negative affect compared to both non-smokers (p < 0.01) and ex-smokers (p = 0.03), while the difference between non-smokers and ex-smokers was not significant (p = 0.34).

The analysis revealed a statistically significant effect of smoking status on health self-efficacy, F(2, 331) = 28.45, p < 0.001. Post hoc tests indicated that ex-smokers reported significantly higher health self-efficacy scores than current smokers (p < 0.01), but significantly lower scores than non-smokers (p < 0.05).

Finally, the analysis revealed a statistically significant effect of smoking status on self-compassion, F(2, 331) = 16.24, p < 0.001. Post hoc tests indicated that non-smokers reported significantly higher self-compassion compared to smokers (p < 0.01), whereas differences between smokers and ex-smokers (p = 0.06) and between ex-smokers and non-smokers (p = 0.08) were not statistically significant.

Following the findings on group differences, analyses testing the theoretical model were conducted. As the proposed model focused on nicotine dependence, correlational analyses were examined only among current smokers. Table 5 reports the Pearson correlation coefficients among nicotine dependence, self-compassion, positive affect, negative affect, and health self-efficacy. Nicotine dependence showed a positive correlation with negative affect (r = 0.23, p < 0.01), and negative correlations with both health self-efficacy (r = −0.32, p < 0.001) and self-compassion (r = −0.25, p < 0.001). There was no significant correlation between nicotine dependence and positive affect (r = −0.01, p = 0.21).

Table 5.

Correlations related to study variables.

Variables	1	2	3	4	5
1. Nicotine dependence	1
2. Positive affect	−0.01	1
3. Negative affect	0.23*	−0.37**	1
4. Health self-efficacy	−0.32**	0.36**	−0.40*	1
5. Self-compassion	−0.25**	0.48**	−0.58**	0.50**	1

p < 0.01; **p < 0.001.

Mediation analyses were conducted using the bootstrapping method (5000 resamples), following Preacher and Hayes (2008) procedure, to examine whether positive affect, negative affect, and health self-efficacy mediate the relationship between self-compassion and nicotine dependence and these results are shown in Table 6.

Table 6.

Mediating role of positive affect, negative affect and health self-efficacy in the effect of self-compassion on nicotine dependence.

	Bootstrap estimates		95% CI		R ²	F	p
Pathways	B	SE	Lower	Upper	R ²	F	p
Stage I.
Self-Compassion → Nicotine dependence	−0.04	0.01	−0.06	−0.01	0.06	10.68	0.001**
Stage II.
Self-Compassion → Positive Affect	0.18	0.02	0.14	0.21	0.23	97.71	0.001***
Self-Compassion → Negative Affect	−0.26	0.02	−0.30	−0.22	0.34	167.76	0.001***
Self-Compassion → Health Self-Efficacy	0.20	0.02	0.17	0.24	0.21	86.62	0.001***
Stage III.
Positive Affect → Nicotine dependence	−0.04	0.03	−0.10	0.02	0.003	1.55	0.21
Negative Affect → Nicotine dependence	0.08	0.03	0.03	0.13	0.05	9.39	0.003**
Health Self-Efficacy → Nicotine dependence	−0.32	0.23	−1.44	−0.53	0.01	2.75	0.001***
Stage IV.
Direct Effect	−0.02	0.02	−0.05	0.02			0.32
Indirect Effect Positive Affect	0.01	0.01	−0.01	0.02
Indirect Effect Negative Affect	−0.01	0.01	−0.03	0.01
Indirect Effect Health Self-Efficacy	−0.02	0.01	−0.03	−0.01
Total Effect	−0.04	0.01	−0.06	−0.02			0.001***

Note. The analysis was conducted in four stages. Indirect effects represent mediation pathways. *p < 0.05, **p < 0.01, ***p < 0.001.

As a first stage, the direct effect of self-compassion variable on nicotine dependence was tested. The results showed that self-compassion had a negative and significant direct effect on nicotine dependence (p < 0.01). This suggests that individuals with high self-compassion are less likely to experience nicotine dependence. Second step analyzed the effect of self-compassion variable on positive affect, negative affect and health self-efficacy. The effect of self-compassion on positive affect was found to be positive and significant (p < 0.01). The effect of self-compassion on negative affect was negative and significant (p < 0.01). The effect of self-compassion on health self-efficacy was positive and significant (p < 0.01). In the third step, the indirect effect of self-compassion on nicotine dependence was tested through the mediating variables of negative affect and health self-efficacy. The indirect effect of negative affect on nicotine dependence was found to be negative and significant (p < 0.01). The indirect effect of health self-efficacy on nicotine dependence was also negative and significant (p < 0.05). In the fourth step, the explanatory power of the whole model was assessed. The model significantly explained the indirect effects of self-compassion on nicotine dependence through negative affect and health self-efficacy. The overall significance of the model was tested and R² = 0.06, which means that self-compassion explains 6% of nicotine dependence. The total effect of the model was significant (p < 0.01).

In result, the findings of the study in Figure 1 showed that the direct effect of self-compassion on nicotine dependence was not significant. However, the effect of self-compassion in relation to lower levels of nicotine dependence emerged indirectly through negative affect and health self-efficacy. It is seen that negative affect is associated with higher levels of nicotine dependence, while health self-efficacy is associated with lower levels of nicotine dependence. Positive affect has no significant indirect effect on nicotine dependence. In this case, it was determined that health self-efficacy and negative affect played a mediating role in the relationship between self-compassion and nicotine dependence, and the mediating effect of positive affect was not significant.

Discussion

The main aim of this study is to examine the role of self-compassion on nicotine dependence within the framework of SRRM (Sirois, 2015). A secondary aim was to explore differences in self-compassion, positive affect, negative affect, and health self-efficacy among smokers, ex-smokers, and non-smokers. The findings showed that non-smokers reported higher levels of self-compassion and positive affect, lower levels of negative affect, and higher health self-efficacy compared to current smokers. In addition, ex-smokers exhibited lower negative affect and higher health self-efficacy than smokers. Analyses testing the theoretical model further indicated that self-compassion was not directly associated with nicotine dependence; rather, its association operated indirectly through negative affect and health self-efficacy. Positive affect did not demonstrate a mediating role in this relationship.

Consistent with hypotheses H₁–H₆, the analysis revealed significant differences: non-smoker participants were more self-compassionate, experienced more positive affect, experienced less negative affect, and had higher health self-efficacy perceptions. In addition, ex-smoker participants reported lower negative affect and higher health self-efficacy perceptions compared to current smokers. The main finding of the study suggests that self-compassion does not have a direct effect on smoking behavior, but this effect is mediated by emotional and cognitive processes. More specifically, lower negative affect and higher self-efficacy were found to mediate the relationship between self-compassion and smoking behavior reduction, supporting hypotheses H₉ and H₁₀. In contrast, no mediating effect of positive affect was observed, and thus hypothesis H₈ was not supported.

The relationship between nicotine dependence and affect has been the subject of frequent studies, especially in the context of smoking cessation and maintenance (Baker et al., 2004; Kahler et al., 2015; Langdon et al., 2016). Negative affect is at the basis of the motivations that cause smoking (Baker et al., 2004). In their study, Langdon et al. (2016) found a high risk of smoking on days when negative affect increased in the attempt to quit smoking. It is observed that those with low positive affect have negative results in the smoking cessation process (Kahler et al., 2015). In a study conducted with a large sample in Türkiye, significant differences were found between smokers and non-smokers. In parallel with the findings of the current study, it was found that participants with use had less positive affect and higher negative affect than non-smokers (Ünübol and Hızlı Sayar, 2019).

Self-efficacy also appears to be a determining factor in the smoking cessation process. It was observed that participants with high self-efficacy perception had successful smoking cessation attempts compared to those without (Gallus et al., 2023). In a study conducted in Türkiye, it was found that self-efficacy scores of smokers were negatively correlated with daily cigarette consumption rates (Karanci, 1992). In a follow-up study in which the self-efficacy perceptions of participants who quit smoking and those who did not quit smoking were evaluated, no significant difference was observed in self-efficacy perceptions between participants who quit smoking and those who did not quit smoking. The same study showed that participants who quit smoking in a 10-week period increased their self-efficacy scores as they continued not to smoke (Clyde et al., 2019). The results of the current study are in parallel with these findings; it was observed that as the health self-efficacy perception of individuals increased, lower levels of nicotine dependence severity were observed.

The main finding of the study indicates that self-compassion does not have a direct effect on nicotine dependence; rather, this association is mediated by negative affect and health self-efficacy. More specifically, lower negative affect and higher self-efficacy were found to mediate the relationship between self-compassion and nicotine dependence, supporting hypotheses H₉ and H₁₀. In contrast, no mediating effect of positive affect was observed, and thus hypothesis H₈ was not supported.

The findings of the study are consistent with the Self-Regulation Resource Model, which suggests that self-regulation resources play a critical role in maintaining or reducing maladaptive behaviors. Studies have shown that there is a relationship between smoking behavior and self-regulation and self-compassion (Biber and Ellis, 2019; Kelly et al., 2010). A study investigating the effects of self-compassion intervention on smoking cessation shows that self-compassionate images provide a useful tool to increase self-regulation power. Of particular note is the finding that resorting to self-compassionate imagery when faced with the urge to smoke reduces daily smoking behavior, particularly in people who are self-critical and less inclined to quit (Kelly et al., 2010).

Systematic review studies examining the effect of self-compassion on health behaviors also point to similar results. Biber and Ellis (2019) study revealed that self-compassion plays a positive role in many health-related self-regulation processes such as eating disorder symptoms, overeating, physical activity, smoking cessation, and self-care behaviors. Accordingly, the findings of the current study also show that self-compassion strengthens individuals’ ability to manage their negative emotions in the context of nicotine dependence.

The findings of the current study are in line with Sirois (2015) study examining how self-compassion shapes health behaviors. Sirois (2015) proposes that self-compassion influences health behaviors primarily through self-regulation resources such as negative affect and health self-efficacy, while the mediating role of positive affect is limited or less consistent. Similarly, consistent with Sirois (2015), this study found no significant mediating effect of positive affect in the relationship between self-compassion and nicotine dependence, suggesting that positive affect may play a limited role in both health-promoting and health-risk behaviors. This may be due to the fact that the empirical support for the mediating role of positive affect remains limited in the existing literature, especially in the context of health-risk behaviors. However, there is an important difference between this study and Sirois’ study: Sirois (2015) focused on supporting positive health-promoting behaviors, whereas the current study examined the mechanisms underlying nicotine dependence, a health-risk behavior. This difference suggests that the SRRM may be useful in promoting healthy behaviors and in decreasing health-risk behaviors.

Nicotine dependence is a multifactorial condition shaped by the interplay of biological mechanisms, psychological processes, and social and environmental influences (Benowitz, 2010). Accordingly, the relatively modest proportion of variance explained by the present mediation model should be interpreted as reflecting the contribution of specific self-regulation resources rather than as an attempt to account for the full complexity of nicotine dependence. Within this framework, the SRRM does not aim to fully account for nicotine dependence, but rather to clarify particular psychological pathways—such as affect regulation and health self-efficacy—through which self-compassion may be associated with nicotine dependence.

In conclusion, this study reveals that self-compassion has no direct effect on nicotine dependence, but has an indirect effect through self-efficacy and emotional regulation processes. In particular, reducing negative affect and increasing self-efficacy appear to be critical factors in lower levels of nicotine dependence. These findings highlight the potential benefits of self-compassion-based strategies in smoking cessation interventions and provide an important framework for future research.

Future recommendations and limitations

Beyond the current findings, future research could explore the temporal dynamics of self-compassion and its predictive value for relapse prevention in nicotine dependence. Future research should further examine the mediating role of positive affect in the link between self-compassion and addictive behaviors, because existing findings are sparse and inconclusive. Additionally, experimental intervention studies comparing different self-compassion training modalities (e.g. mindfulness-based, imagery-based) could shed light on the most effective mechanisms for enhancing self-regulatory resources. Contextual moderators, such as health literacy or perceived social support, should also be examined to determine under what conditions self-compassion is most beneficial in regulating addictive behaviors. Finally, applying the current model to other forms of addiction may broaden its utility and inform integrated prevention strategies. Future studies should investigate whether the Self-Regulation Resource Model shows similar mechanisms in other addiction types such as alcohol use disorder, gambling disorder and substance use disorders. In particular, examining the role of self-regulation components such as affect and self-efficacy in different addiction contexts will provide a more comprehensive perspective on addiction prevention and relapse prevention processes.

The present study is a cross-sectional study, is an important limitation. In order to overcome this limitation, it is recommended to conduct longitudinal studies in the future and to examine the change in mediation relationships over time. Although the R² value was relatively low (0.06), this is not uncommon in mediation models examining complex behavioral phenomena where multiple unmeasured factors may influence the outcome (Hayes, 2017). Small R² values can still reflect meaningful indirect effects and important theoretical insights in psychological research. On the other hand, the generalizability of the findings may be limited due to the fact that the sample group consists largely of higher educated individuals and female participants. In order to overcome this situation, similar studies should be conducted with different cultures and larger samples. It is recommended to implement self-compassion-based interventions to strengthen coping skills with negative affect. In addition, it is believed that developing programs to increase health self-efficacy perception may strengthen the motivation to quit tobacco use.

With regard to sample characteristics, although the overall sample included a higher proportion of female participants, gender distribution within the subgroup of current smokers—the group on which the mediation model was tested—was relatively balanced, and gender differences in smoking-related variables were not pronounced. Therefore, the indirect associations observed in the mediation model are unlikely to be primarily driven by gender composition.

The generalizability of the findings may also be limited by the relatively high educational level of the participants. Higher educational attainment may be associated with greater health literacy and self-regulation capacities, which could have contributed to higher levels of self-compassion and health self-efficacy observed in the study (Cutler and Lleras-Muney, 2010). In addition, the use of an online survey may have introduced self-selection bias, as individuals with higher educational attainment and greater interest in health-related or psychological topics may have been more likely to participate (Bethlehem, 2010). Consequently, the findings may not fully generalize to heavier smokers or clinical populations with more severe nicotine dependence.

Conclusion

In conclusion, this study extends the scope of the model by applying the SRRM to nicotine dependence, showing that self-compassion indirectly linked to nicotine dependence through negative affect and health self-efficacy. These findings emphasize the importance of addressing both affective and cognitive factors in smoking cessation interventions and provide valuable information for both research and clinical practice.

Footnotes

Acknowledgements

The authors thank the participants for their generous contributions to this research.

ORCID iDs

Dilara Demircan

Meltem Güler

Ethical considerations

The ethical approval was obtained from Çukurova University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee for this study (Date: 02.06.2023 Meeting Number: 134).

Consent to participate

All participants provided informed consent prior to participation.

Consent for publication

Consent for publication is not applicable to this article as it does not contain any identifiable data.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Data availability statement

Datasets are available from the corresponding author.

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