Next Article in Journal
Temple Syndrome: Clinical Findings, Body Composition and Cognition in 15 Patients
Next Article in Special Issue
Unconscious Conflict Adaptation of Heroin Abstainers
Previous Article in Journal
Integrated Relaxation Pressure (IRP) Distinguishes between Reflux-Predominant and Dysphagia-Predominant Phenotypes of Esophageal “Absent Contractility”
Previous Article in Special Issue
The Relationship between Coping and Expressed Emotion in Substance Users
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 11
Issue 21
10.3390/jcm11216288
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Loneliness and the Degree of Addiction to Shopping and Work among Polish Women: The Mediating Role of Depression

by
Kamila Rachubińska
1,
Anna Maria Cybulska
1,*,
Ewa Kupcewicz
2,
Alina Jurewicz
3,
Mariusz Panczyk
4,
Aneta Cymbaluk-Płoska
5,
Justyna Jurczak
6 and
Elżbieta Grochans
1
1
Department of Nursing, Pomeranian Medical University in Szczecin, 71-210 Szczecin, Poland
2
Department of Nursing, Collegium Medicum, University of Warmia and Mazury in Olsztyn, 14 C Zolnierska Street, 10-719 Olsztyn, Poland
3
Department of Clinical Nursing, Pomeranian Medical University in Szczecin, 71-210 Szczecin, Poland
4
Department of Education and Research in Health Sciences, Faculty of Health Science, Medical University of Warsaw, 00-581 Warsaw, Poland
5
Department of Gynecological Surgery and Gynecological Oncology of Adults and Adolescents, Pomeranian Medical University, 70-111 Szczecin, Poland
6
Department of Social Medicine and Public Health, Pomeranian Medical University in Szczecin, 71-210 Szczecin, Poland
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(21), 6288; https://doi.org/10.3390/jcm11216288
Submission received: 8 September 2022 / Revised: 16 October 2022 / Accepted: 22 October 2022 / Published: 25 October 2022
(This article belongs to the Special Issue Substance and Behavioral Addictions: Updates on Etiology and Clinical Interventions)
Browse Figures
Versions Notes

Abstract

:
(1) The aim of the research was to try to define the mediating role of depression in the relationship between addiction to shopping and work and loneliness, understood in terms of general loneliness among Polish women. (2) The study was conducted among 556 women. The research was carried out with the use of the diagnostic survey method, using the questionnaire technique: the De Jong Gierveld Loneliness Scale, the Purchasing Behavior Scale, the Work Addiction Risk Test, Beck Depression Inventory, and our own questionnaire. (3) Depression is a mediator in the relationship between the feeling of loneliness and the degree of addiction to shopping (β = −0.0246, z = −2.03, p = 0.043) and in the relationship between the feeling of loneliness and the degree of addiction to work (β = −0.0722, z = −4.002, p < 0.001). The direct impact of the feeling of loneliness on the degree of addiction to shopping (p = 0.237) and work (p = 0.576) is statistically insignificant. (4) Depression plays the role of a mediator between the feeling of loneliness and the degree of addiction to shopping and work. An increase in the level of depression increases the degree of addiction to shopping and work. The mediator’s participation lowers the loneliness feeling level.

1. Introduction

The rapid development of civilization is accompanied by many changes affecting human functioning. Technological development, wide access to positive stimuli, and the neglect of emotional control and self-awareness in the learning process make it difficult for individuals to control their behavior. Such a loss of control is associated with the development of addictions [1]. Therapists, doctors, and researchers more and more often encounter cases of compulsive behavior focused on a specific activity; apart from gambling or playing computer or internet games, more and more often the subjects of preoccupation are compulsive shopping, sexual activities, and work (workaholism) [2].
Human work changes with the progress of civilization. In addition to its economic and social function, it creates the area of our existence in which we are looking for opportunities for professional and personal development. Changes in the work process resulted from changes in the functioning of the Polish economy, i.e., teleworking, temporary work, or work performed under a mandate contract. Those changes increase job insecurity; increased competition in terms of reducing costs, producing goods, improving their quality and employee productivity, and automating the work processes. These activities lead to a further increase in job insecurity, thus to greater involvement of employees in the performance of their tasks. This situation may be caused by various reasons, including reducing the sense of job security, increase in the value of work caused by the persistence of a high unemployment rate in Poland for a long period or, finally, the tendency of employers to give high gratification to those employees who contribute to the realization of the company’s mission [2,3].
The term “workaholism” was proposed by a psychiatrist Wayna Oates in 1971 to describe compulsive behaviors and thoughts related to the performance of a professional job [4]. The phenomenon of work addiction is gaining more and more interest among psychologists and therapists due to the increasing scale of the problem and its negative consequences for the individual and their environment. The diagnostic criteria of compulsive behavior included in both the ICD and DSM classification can be applied to workaholism. The characteristics of these criteria largely describe the symptoms of work preoccupation and loss of control [5].
Research on the buying process has shown that shopping, often treated as a way of spending free time, is also a stimulus that provides short-term gratification [6]. However, making intensive and excessive purchases may consequently be detrimental and even destructive to the individual. Compulsive Buying Disorder has been classified as Impulse-Control Disorders, not elsewhere classified in the DSM-IV classification. In the DSM-5 classification, compulsive buying disorder is described in the context of behavioral addiction, such as an exercise or sex addiction, but it has not been formally defined due to insufficient data [7].
Shopaholism and workaholism are addictions, as are alcoholism, drug addiction, gambling, sex addiction, and network addiction. They are based on the irresistible need to perform a specific activity [8]. The only difference from drug addiction or alcoholism is that there is no physiological dependence. Compulsive buying and shopaholism are a different type of addiction called additive buying. A shopaholic behaves like any addict, knows that he is doing wrong, but he cannot get over it. Buying becomes a necessity, a compulsory behavior [9,10]. Loneliness plays a key role in the occurrence of addiction to shopping and work.
De Jong Gierveld [11] defines loneliness as an unpleasant, unacceptable situation of lack [quality] of certain social relations experienced by an individual. Loneliness is subjective and negative. It is the result of a cognitive assessment of the quantity and quality of existing relationships and the standards that an individual has for the relationship [12]. The results of the study by Cacioppo et al. showed that loneliness was a predictor of an increase in the level of depression [13]. Compulsive buyers have an increased level of social anxiety, alienation, a lowered level of self-esteem [14,15], and a high level of loneliness [16].
The first and most common psychiatric comorbid disease to look for in compulsive buying is depression. McElroy, Keck et al. (1994) [17] found that among 20 patients with compulsive buying disorder, 19 meet the DSM-III-R criteria for lifelong diagnosis of a major mood disorder, most often bipolar disorder [18].
Few studies have looked at the mechanisms underlying the relationship between workaholism and psychological problems, such as depression. A study by Yang et al. [19] showed a positive relationship between workaholism and depression. Workaholism, like other emerging behavioral addictions, such as Internet (gaming) addiction, is not a substance addiction and its causes, consequences, and mechanisms have not been well studied. Therefore, more theoretical and empirical research on workaholism is justified.
Rogowska et al. [20] found that gender plays an important role as mediator between workaholism and depression. Therefore, we eliminated gender as a variable in this research by focusing on the female population only. Research indicates that biological, social, and psychological factors contribute to the gender differences in the incidence of depression, which is confirmed by other studies [21]. Interestingly, it was found that in childhood, boys and girls equally report experiencing depression. However, in adulthood, women are two to three times more likely than men to be diagnosed with depression [22,23,24]. Chuick et al. [25] claim that men experience depression differently than women, which suggests that women internalize their depression more often, while men externalize it more often [26,27,28,29,30,31,32].
The aim of the research was to try to define the mediating role of depression in the relationship between addiction to shopping and work and loneliness, understood in terms of general loneliness among Polish women.

2. Materials and Methods

2.1. Settings and Design

The study was conducted among 556 women. Before starting the project, the approval of the Bioethics Committee of the Pomeranian Medical University in Szczecin was obtained (Resolution No. KB-0012/518/12/16). The inclusion criteria for the study were: female gender, age > 18 years of age, place of residence beingWest Pomeranian Voivodeship, submission of informed written consent to participate in the study, completion of the provided set of questionnaires. The prepared sheets of the research tool were handed out by the author to women who were acquainted with the above-mentioned information and agreed to participate in the project. After agreeing to participate in the study, the respondents received a set of questionnaires. During the distribution of the questionnaires, the author answered all questions about the research and the prepared tools. The respondents could withdraw from the study at any time without giving any reason. The test was performed in about 15 min.

2.2. Research Instruments

The research was carried out with the use of the diagnostic survey method, using the questionnaire technique. In order to analyze the occurrence of behavioral addictions in adult women, three standardized and adapted to Polish conditions research tools and an own questionnaire were used.
The De Jong Gierveld Loneliness Scale (DJGLS) by J. De Jong Gierveld and F. Kamphuis, Polish adaptation by P. Grygiel et al. The scale for measuring the sense of loneliness. DJGLS is basically one-dimensional and measures a generalized sense of loneliness. It is a partially balanced tool, consisting of five positive items measuring satisfaction with interpersonal relationships and six negative items describing dissatisfaction with social contacts. The level of acceptance of individual statements was indicated by the respondents on a 5-point scale, from “definitely yes” to “definitely not”. After recoding the “negative” items, a higher total score indicates a more intense feeling of loneliness. The scale is characterized by a high level of reliability and homogeneity: Cronbach’s internal stability coefficient is 0.89, the value of the mean inter-positional correlation r = 0.42, and the H Loevinger homogeneity coefficient—0.47 [33,34].
The Scale of Shopping Behavior (SZZ) is a scale to determine the risk of shopaholism. The tool allows us to determine the overall result of purchasing behavior and its two factors, which are compulsion and lack of control, and reduction of tension and negative emotions. The scale consists of 16 items, rated on a five-point scale (from almost never—1 to almost always—5). The overall sum of the scores is between 16–80 points. The higher the score, the greater the tendency for shopaholism. A high tendency for shopaholism is evidenced by a result above 44, and a low tendency by result below 35 points. The results in the range of 35–44 points indicate a moderate tendency towards shopaholism. Internal compliance of SZZ was assessed using Cronbach’s alpha coefficient, which is 0.92 [35].
The Work Addiction Risk Test (WART) is a questionnaire that measures the symptoms of a workaholic’s behavior pattern. The tool consists of 25 items measuring the behavioral, cognitive, and emotional responses that are believed to constitute workaholism syndrome. Twenty-five statements are rated on a four-point scale for the frequency of symptoms of work addiction. The test person’s task is to indicate (by marking one of the four categorized answers) to what extent each of the statements relates to him. The questionnaire measures fully formed workaholism syndrome or job addiction risk depending on the score. The range of the scale is from 25 to 100 points. The result of the compulsion to work is indicated by a score above 56 points: a high score (67–100 points) is an indicator of being highly addicted, a medium score (57–66 points) represents a moderate work addiction. The indicator of the lack of addiction and the degree of risk of the addiction to work is a low score ranging from 25–56 points (the higher the score, the greater the likelihood of developing workaholism) [36,37,38].
The Beck Depression Inventory-BDI I-II is a questionnaire used for measuring the severity of depressive disorders, and was developed by Aaron Beck et al. The tool consists of 21 questions with four options to answer (attitude and symptoms). Each category describes a specific behavioral manifestation of depression. The level of depression is calculated by adding up the total number of points obtained. The results of the calculations were interpreted by referring to standardized divisions, 0–13-no depression or minimal symptoms of depression, 14–19-mild depression, 20–28-moderate depression, and 29–63-severe depression [39].

2.3. Participants

556 women participated in the study. The respondents accounted for 0.1% of the female population of the West Pomeranian Voivodeship in Poland. The mean age of the respondents was 34 years, and the median was 27 years. The largest group (40.3%) were women aged 20–30. Due to the small size of the selected subgroups, the analysis of socio-demographic variables (i.e., education and marital status) was based on the following classification criteria: education: higher and lower (secondary, vocational, primary), marital status: in a formal/informal relationship and single (maiden, divorced, widow).
Of the 556 respondents, 48.4% had higher education, and slightly more than half of them lower (51.6%). Women living in towns with less than 100,000 inhabitants accounted for over half of the respondents (52.3%). Most of the women declared staying in a formal/informal relationship (66.5%). The vast majority of the respondents, 89.2%, remained professionally active (Table 1).

2.4. Statistical Analysis

Quantitative data collected using standardized scales was presented using the parameters of descriptive statistics. The following were determined: mean, standard deviation (SD), range (min-max), and skewness. In order to obtain the distribution of symmetrical analyzed variables, they were transformed with the Box-Cox method [40]. A Box-Cox transformation is a transformation of non-normal variables into a normal shape. Normality is an important assumption for mediation analysis. The Generalized Linear Model Mediation Analysis was used to estimate the influence of the mediator (depressiveness) on the relationship between the independent variable and the dependent variable. A mediation model was fitted to each sample resulting in a bootstrap sample. Bootstrapping is a statistical method that utilizes random resampling with replacement to estimate a population parameter. This technique samples from a given dataset to estimate a parameter when it would otherwise be impossible [41].
Data were analyzed using IBM SPSS (Version 28, IBM Corp., Armonk, NY, USA) for the descriptive analyses, and Jamovi (Version 2.2.5, Jamovi Project, Sydney, Australia), with jAMM module to test the mediation model [42]. The jAMM package allows estimation of the direct and indirect effects of the independent variables on the dependent variables, by also examining all paths of the mediation model components (e.g., the associations between the independent variables and the mediator and the associations between the mediator and the dependent variables) [43]. For all analyses, a 5% level of statistical significance was set to reject the null hypothesis.

3. Results

Over the course of statistical analyses, mean values of the analyzed variables were established. The De Jong Gierveld Loneliness Scale measured a generalized feeling of loneliness. The mean value of the scores obtained by the respondents on the Sense of Loneliness Scale was 34.01 ± 3.87.
The mean scores obtained in the Scale of Shopping Behavior are 27.00 ± 10.77. The WART questionnaire measures the symptoms of a workaholic behavior pattern. The respondents achieved an average of 53.00 ± 12.24 points. Beck Depression Inventory-BDI I-II measures symptoms of depression. The mean value of the scores is 34.00 ± 3.87 (Table S1).

Mediation Analysis (Generalized Linear Model Mediation Analysis, Model Parameters Estimated Using Bootstrap Method)

In order to check whether depression is a significant mediator of the relationship between loneliness (De Jong Gierveld) and the overall assessment of the degree of addiction to shopping (SZZ) and work (WART), a mediation analysis was carried out.
In the mediation model adopted, it has been shown that depression is a mediator between the feeling of loneliness and the degree of addiction to work and shopping. The increase in the level of depression increased the degree of addiction to shopping. The mediator’s participation lowered the level of the feeling of loneliness.
Model 1 (Figure 1).
The results of the conducted analysis revealed the existence of a statistically significant mediation. Depressiveness is a mediator in the relationship between the feeling of loneliness and the degree of addiction to shopping (β = −0.0246, z = −2.03, p = 0.043). The mediation effect accounts for 29.1% of the variability of the dependent variable.
The relationship between loneliness and depression is negative (β = −0.2252, z = −4.64, p < 0.001). The relationship between depression and addiction to shopping is positive (β = 0.1092, z = 2.42, p = 0.015). The increase in the level of depression increased the degree of addiction to shopping. The mediator’s participation lowered the level of the feeling of loneliness. Loneliness was not a significant predictor of shopping addiction. The direct impact of the feeling of loneliness on the degree of addiction to shopping is statistically insignificant (p = 0.237) (Table 2).
Model 2 (Figure 2).
The results of the conducted analysis revealed the existence of a statistically significant mediation. Depression is a mediator in the relationship between the feeling of loneliness and the degree of addiction to work (β = −0.0722, z = −4.002, p < 0.001). The mediation effect accounts for 75.1% of the variability of the dependent variable.
The relationship between the feeling of loneliness and depression is negative (β = −0.2252, z = −4.70, p < 0.001). The relationship between depression and work addiction is positive (β = 0.3207, z = −7.524, p < 0.001). The increase in the level of depression increased the degree of addiction to work. The mediator’s participation lowered the level of the feeling of loneliness. Loneliness was not a significant predictor of work addiction. The direct impact of the feeling of loneliness on the degree of addiction to work is statistically insignificant (p = 0.576) (Table 3).

4. Discussion

Therapists, doctors, and researchers more and more often encounter cases of compulsive behavior focused on a specific activity. Apart from gambling or playing computer or internet games, increasingly the subjects of preoccupation are compulsive shopping, sexual activities, and work (workaholism) [5].
To the best of our knowledge, this is the first study to investigate the mediating role of depression in the relationship between shopping, work addiction, and loneliness. The results obtained in this study provided the following contribution to the literature.
Research in the field of behavioral addictions has shown that people who over-engage in certain activities usually struggle with problematic social lives and often experience a lack of social support and a feeling of loneliness [44,45,46,47]. Not much is known regarding social functioning of people addicted to shopping compared to other addictions. Based on previous studies [48,49,50], it can be expected that addiction to shopping is also associated with loneliness. Compulsive buying can be a way to escape the feeling of alienation, but it can also contribute to the escalation of interpersonal conflicts through the constant intensification of behavior [44].
The average score on the SZZ scale was 30.44, which indicates a low risk of shopaholism. Mueller et al. [51] noted a prevalence of addiction to purchasing of 6.9%. Age was inversely proportional to the prevalence of compulsive buying. Addicts showed greater depression than non-addicts [51,52,53]. Otero-López and Villardefrancos [50] showed the occurrence of addiction to purchases with the frequency of 7.1%. Female gender, depression, anxiety, and younger age were predictors of compulsive buying. Most studies have reported higher prevalence rates in women than in men [18,51,52,54].
In the mediation model adopted, it has been shown that depression is a mediator between the feeling of loneliness and the degree of addiction to shopping. The increase in the level of depression increased the degree of addiction to shopping. The mediator’s participation lowered the level of the feeling of loneliness. Other studies have shown similar results [14,15]. Compulsive buyers showed an increased level of social anxiety, decreased self-esteem, and a high sense of loneliness [14,15,16]. Thus, it can be concluded that psychological factors (e.g., feeling of loneliness) may be an important predictor of compulsive buying.
Research by Uzarska et al. [44] showed a positive correlation between social anxiety and loneliness and addiction to shopping, which may result from the fact that people who engage in excessive spending have problems with maintaining close relationships. This is consistent with previous research on addiction to shopping [48,49,50], as well as with other addictions [46,55].
Lejoyeux et al. [27] conducted research on the prevalence of shopaholism among hospitalized patients who showed features of a severe depressive episode. The addicts most often were younger people, women, and people who were single. People displaying compulsive buying were characterized by a greater number of depressive symptoms assessed using the German version of the Short Health Mood Scale (PHQ-9) [51]. Black et al. [56] demonstrated in their research that compulsive buyers were accompanied by mood disorders, anxiety, depressive symptoms, or ADHD throughout their lives. The results of the research by Suresh et al. [57] showed that psychological factors, such as loneliness, depression, low self-esteem, and anxiety, were positively associated with Internet addiction and compulsive online buying.
The main findings revealed a clear mediation: it has been shown that depression is a mediator between the feeling of loneliness and the degree of addiction to work. The increase in the level of depression increased the degree of addiction to work. The mediator’s participation lowered the level of the feeling of loneliness. Loneliness was not a significant predictor of work addiction.
Depression is one of the most common causes of work disability in industrialized countries [58,59,60,61]. A Finnish study showed that 50% of men and 28% of women with the first episode of depression were diagnosed with OCPD/APD [62,63]. First, workaholism is associated with increased stress and a disturbed work-life balance. On the other hand, stress is positively associated with depression [64]. According to the study by Yang et al. [19], respondents for whom professional career was more important than maintaining work-life balance were characterized by a higher level of depression. This is in line with previous studies [65].
Mental disorders, such as anxiety and depression, may increase the risk of addiction [62,63]. They can lead to addiction and vice versa [64,65,66,67,68]. Many studies have previously reported an association between anxiety, depression, and workaholism [69,70,71,72,73,74,75,76]. In addition, it is known that workaholism (in some cases) may result from an attempt to reduce the unpleasant feeling of anxiety and depression. Hard work is praised and honored in modern society and thus serves as the legitimate behavior of individuals to combat or mitigate negative feelings, and to feel better and increase self-esteem [77,78].
The average score on the WART scale was 53.46 points; a score in the range of 25–56 points indicates a low risk of workaholism, but the higher the number of points, the greater the risk of addiction.
Work addiction is associated with higher levels of stress at work and outside [79], sleep disturbances [80,81,82] and decreased well-being [82,83,84,85]. In contrast, chronic stress is a well-recognized risk factor for major depression as well as for many other disorders and non-communicable diseases [79]. Similar results were obtained by other authors [66,83,84,86,87]. The importance of the relationship between work addiction and depression and loneliness gains a new perspective after taking into account the socioeconomic costs of chronic stress [74,87,88,89]. Andreassen et al., have shown in their research that workaholism has a significant influence on the mediation between work-related stress and health-related outcomes, including emotional exhaustion, somatic symptoms, social dysfunction, and insomnia [90].
Depression seems to be significantly related to workaholism, leading directly to work addiction (and vice versa) [68,91,92]. The relationship between workaholism and depression has been demonstrated in studies conducted in various professional and cultural contexts [66,69,70,93,94]. Haar and Roche [89] found that both work commitment and joy at work were associated with anxiety and depression. Houlfort et al. [94] found that depression was positively correlated with an obsessive passion for work. Similarly, Nie and Sun [70] found a significant correlation between workaholism and depression. It is worth mentioning that a cross-sectional survey on a large sample of 16,426 employees showed positive and significant correlations between workaholism and all the examined symptoms of mental disorders, including depression [66]. The results revealed a partial mediating role of burnout in the relationship between work addiction and depression. Hard work is valued in society. In this way, it can serve as justified and rationalized behavior of individuals in order to reduce negative feelings, feel better, and raise self-esteem [66]. Consequently, employers should be aware of the negative consequences of workaholism and understand that overworking does not equal productivity. Employers should adapt working conditions by establishing standards and values that ensure that both labor productivity and work-health balance are maintained, and by offering their employees training in time and stress management [81].
Workaholism, like other emerging behavioral addictions, such as Internet (gaming) addiction, is not a substance addiction, and its causes, consequences, and mechanisms have not been well studied. Therefore, further theoretical and empirical research on workaholism is justified [19].
These results provide empirical insight into the social functioning of people addicted to shopping and work. Loneliness and social anxiety with the mediating role of depression make it impossible for people to seek social support and deal with various problems. As a result, the lack of social support, along with the mediating role of depression may favor non-adaptive coping through compulsive shopping or workaholism. At the same time, addictions may cause conflicts with the social environment, which favors further isolation from relatives [44]. Therefore, it seems important to continue the research and conduct more detailed analysis of social predictors of addiction to shopping and work.

5. Limitations and Implications for Professional Practice

The divagations presented in this study on the role of depression as a mediator in the relationship between addiction to shopping, work, and loneliness among Polish women identified certain limitations and implications for professional practice. The main strengths of this study were the inclusion of important and reliable psychometric tools. Moreover, the presented data was enriched by using open-ended questions in addition to standardized tools. Consequently, the study significantly enriches the existing literature on behavioral addictions and provides further insight into the nature of shopping and work addiction, and its relationship to mental health and well-being. The presented research also has its limitations, including the sample size. The Polish sample was not representative, which places restrictions on the possibility of generalization to other populations and the exclusion of the male population. Since the cross-sectional research design does not allow for causal inference, it is necessary to design a causal experiment to fully clarify the role of the independent variables and the mediator.
The model did not test other potentially important predictors of behavioral addiction development, i.e., anxiety, stress, and insomnia. Despite the limitations, this study provides important findings and may be a starting point for wider research on the impact of psychological factors on the occurrence of addictions to shopping and work among Polish women. From a theoretical point of view, our findings are important as they provide additional insight into the relationship between workaholism and depression. Mental health professionals should be aware of the relationship between depression, workaholism, and shopaholism, and gender, in order to implement appropriate preventive programs and accurately select the target group of therapy.

6. Conclusions

In the mediation model adopted, it was shown that depressiveness plays the role of a mediator between the feeling of loneliness and the degree of addiction to shopping and work. The increase in the level of depression increased the degree of addiction to shopping and work. The mediator’s participation lowered the level of the feeling of loneliness. Loneliness was not a significant predictor of addiction to shopping and work. It is noteworthy that there are other variables (such as marital/romantic satisfaction, quality of friendship, hobbies/leisure time, impulsivity, emotional regulation) that could increase the explanatory capacity of the model in future studies. There is a need to include activities aimed at identifying psychological factors influencing the occurrence of addictions to shopping and work among women. It seems important to be able to use psychological help when needed. It is also necessary to take institutional preventive measures to prevent the occurrence of behavioral addictions among women.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm11216288/s1, Table S1. Description of primary variables and after Box-Cox transformation for N = 55.

Author Contributions

Conceptualization, K.R. and E.G.; methodology, A.M.C.; software, E.K.; validation, A.M.C. and E.K.; formal analysis, M.P.; investigation, A.J.; resources, J.J.; data curation, K.R.; writing—original draft preparation, E.G.; writing—review and editing, J.J.; visualization, M.P.; supervision, E.G.; project administration, A.C.-P.; funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by Bioethics Committee of the Pomeranian Medical University in Szczecin was obtained (Resolution No. KB-0012/518/12/16).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the first author.

Acknowledgments

We thank all participants for their participation and all the staff of the survey team for their efforts that made this study possible.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Koryczan, P.; Sęktas, M.; Dybczak, L.; Malicka, K. Uzależnienie od zakupów—Przegląd badań. In Badania i Rozwój Młodych Naukowców w Polsce—Nauki Humanistyczne i Społeczne; Nyćkowiak, J., Leśny, J., Eds.; Młodzi Naukowcy: Poland, Poznań, 2017; pp. 79–84. [Google Scholar]
  2. Dudek, B. Pracoholizm–szkodliwy skutek nadmiernego zaangażowania w pracę. Med. Pr. 2008, 59, 247–254. [Google Scholar] [PubMed]
  3. Dudek, B.; Hauk, M.; Merecz, D. Ocena pracoholizmu jako behawioralnej tendencji–polska adaptacja Kwestionariusza Mudracka i Naughtona. Med. Pr. 2011, 62, 127. [Google Scholar] [PubMed]
  4. Robinson, B.E. A typology of workaholics with implications for counselors. J. Addict. Offender. Couns 2000, 21, 34–48. [Google Scholar] [CrossRef]
  5. Rowicka, M. Uzależnienia behawioralne. Dialog Motywujący 2015, 53, 28–65. [Google Scholar]
  6. Mukhopadhyay, A.; Johar, G.V. Indulgence as self-reward for prior shopping restraint: A justification-based mechanism. J. Consult. Psychol. 2009, 19, 334–345. [Google Scholar] [CrossRef] [Green Version]
  7. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; APA: Washington, DC, USA, 2013. [Google Scholar]
  8. Gąsiorowska, A. Kupowanie kompulsywne–zjawisko, jego determinanty i konsekwencje: Przegląd badań. Przegląd Psychol. 2001, 44, 463–477. [Google Scholar]
  9. Bighiu, G.; Manolică, A.; Roman, C.T. Compulsive buying behavior on the internet. Proc. Econom. Financ. 2015, 20, 72–79. [Google Scholar] [CrossRef] [Green Version]
  10. Norberg, M.M.; David, J.; Crone, C.; Kakar, V.; Kwok, C.; Olivier, J.; Grisham, J.R. Determinants of object choice and object attachment: Compensatory consumption in compulsive buying–shopping disorder and hoarding disorder. J. Behav. Addict 2020, 9, 153–162. [Google Scholar] [CrossRef]
  11. De Gierveld, J. Developing and testing a model of loneliness. J. Pers. Soc. Psychol 1987, 53, 119–128. [Google Scholar] [CrossRef]
  12. Harnish, R.J.; Bridges, K.R.; Gump, J.T.; Carson, A.E. The maladaptive pursuit of consumption: The impact of materialism, pain of paying, social anxiety, social support, and loneliness on compulsive buying. Int. J. Ment. Health Addict. 2019, 17, 1401–1416. [Google Scholar] [CrossRef]
  13. Cacioppo, J.T.; Hawkley, L.C.; Thisted, R.A. Perceived social isolation makes me sad: Five year cross-lagged analysis of loneliness and depressive symptomatology in the Chicago health, aging, and social relations study. Psychol. Aging 2010, 25, 453–463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Black, D.W. Compulsive buying disorder: Definition, assessment, epidemiology and clinical management. CNS Drugs 2001, 15, 17–27. [Google Scholar] [CrossRef] [PubMed]
  15. King, A. Beyond propensities: Toward a theory of addictive consumption. In The Changing Market Environment: New Theories; Bernhardt, K.L., Ed.; American Marketing Association: Chicago, IL, USA, 1981; pp. 438–440. [Google Scholar]
  16. Faber, R.J.; O’Guinn, T.C. A clinical screener for compulsive buying. J. Consum. Res. 1992, 19, 459–469. [Google Scholar] [CrossRef]
  17. McElroy, S.L.; Keck, P.E.; Pope, H.G.; Smith, J.M.; Strakowski, S.M. Compulsive buying: A report of 20 cases. J. Clin. Psychiatry 1994, 55, 242–248. [Google Scholar] [PubMed]
  18. Weinstein, A.; Maraz, A.; Griffiths, M.D.; Lejoyeux, M.; Demetrovics, Z. Compulsive buying—Features and characteristics of addiction. In Neuropathology of Drug Addictions and Substance Misuse; Academic Press: Cambridge, MA, USA, 2016; pp. 993–1007. [Google Scholar] [CrossRef]
  19. Yang, X.; Qiu, D.; Lau, M.C.; Lau, J.T. The mediation role of work-life balance stress and chronic fatigue in the relationship between workaholism and depression among Chinese male workers in Hong Kong. J. Behav. Addict 2020, 9, 483–490. [Google Scholar] [CrossRef]
  20. Rogowska, A.M.; Zmaczyńska-Witek, B.; Olejniczak, P. Depression and workaholism in undergraduates: Examining gender as a moderator. J. Am. Coll Health 2020, 1–9. [Google Scholar] [CrossRef]
  21. Hyde, J.S.; Mezulis, A.H.; Abramson, L.Y. The ABCs of depression: Integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychol. Rev. 2008, 115, 291–313. [Google Scholar] [CrossRef] [Green Version]
  22. Simon, R.W.; Lively, K. Sex, anger and depression. Soc. Forces 2010, 88, 1543–1568. [Google Scholar] [CrossRef]
  23. Sloan, D.M.; Sandt, A.R. Gender differences in depression. Wom. Health 2006, 2, 425–434. [Google Scholar] [CrossRef] [Green Version]
  24. Burke, R.; Oberklaid, F.; Burgess, Z. Workaholism among Australian women psychologists: Antecedents and consequences. Wom. Manage Rev. 2004, 19, 252–259. [Google Scholar] [CrossRef]
  25. Chuick, C.D.; Greenfeld, J.M.; Greenberg, S.T.; Shepard, S.J.; Cochran, S.V.; Haley, J.T. A qualitative investigation of depression in men. Psychol. Men. Masc 2009, 10, 302–313. [Google Scholar] [CrossRef]
  26. Christenson, G.A.; Faber, R.J.; De Zwaan, M.; Raymond, N.C.; Specker, S.M.; Ekern, M.D.; Eckert, E.D. Compulsive buying: Descriptive characteristics and psychiatric comorbidity. J. Clin. Psychiatry 1994, 55, 5–11. [Google Scholar] [PubMed]
  27. Lejoyeux, M.; Tassain, V.; Solomon, J.; Ades, J. Study of compulsive buying in depressed patients. J. Clin. Psychiatry 1997, 58, 169–173. [Google Scholar] [CrossRef] [PubMed]
  28. Kessler, R.C.; Berglund, P.; Demler, O.; Koretz, D.; Merikangas, K.R.; Rush, A.J.; Walters, E.E.; Wang, P.S. The epidemiology of major depressive disorder: Results from the National Comorbidity Survey Replication (NCS-R). JAMA 2003, 289, 3095–3105. [Google Scholar] [CrossRef]
  29. Kuehner, C.C. Gender differences in unipolar depression: An update of epidemiological findings and possible explanations. Acta Psychiatr. Scand. 2003, 108, 163–174. [Google Scholar] [CrossRef]
  30. Marek, K.; Białoń, P.; Wichowicz, H.; Melloch, H.; Nitka-Siemińska, A. Przesiewowa ocena rozpowszechnienia objawów depresyjnych i lękowych wśród studentów Akademii Medycznej w Gdańsku. Psychiatria 2005, 2, 217–224. [Google Scholar]
  31. Sullivan, R.A. Fatalistic Beliefs and Sociocultural Factors as Predictors of Depression and Hopelessness in a Diverse College Sample. Ph.D. Thesis, University of La Verne, La Verne, CA, USA, 2016. [Google Scholar]
  32. Airenti, G. The development of anthropomorphism in interaction: Intersubjectivity, imagination, and theory of mind. Front. Psychol. 2018, 9, 2136. [Google Scholar] [CrossRef]
  33. Grygiel, P.; Humenny, G.; Rębisz, S.; Świtaj, P.; Sikorska, J. Validating the Polish Adaptation of the 11-Item De Jong Gierveld Loneliness Scale. Eur. J. Psychol. Assess 2013, 29, 129. [Google Scholar] [CrossRef]
  34. De Jong Gierveld, J.; Van Tilburg, T. The De Jong Gierveld Short Scales for Emotional and Social Loneliness: Tested on Data from 7 Countries in the UN Generations and Gender Surveys. Eur. J. Ageing 2010, 7, 121–130. [Google Scholar] [CrossRef] [Green Version]
  35. Ogińska-Bulik, N. Osobowość Typu D: Teoria i Badania; Wydawnictwo Wyższej Szkoły Humanistyczno-Ekonomicznej: Łódź, Poland, 2009. [Google Scholar]
  36. Wojdyło, K. Kwestionariusz Pracoholizmu (WART)–Adaptacja narzędzia i wstępna analiza własności psychometrycznych. Now. Psychol. 2005, 4, 71–84. [Google Scholar]
  37. Robinson, B.E.; Phillips, B. Measuring workaholism: Content validity of the work addiction risk test. Psychol. Rep. 1995, 77, 657–658. [Google Scholar] [CrossRef] [PubMed]
  38. Robinson, B.E.; Post, P. Validity of the work addiction risk test. Percept. Mot. Ski. 1994, 78, 337–338. [Google Scholar] [CrossRef] [PubMed]
  39. Zawadzki, B.; Popiel, A.; Pragłowska, E. Charakterystyka Psychometryczna Polskiej Adaptacji Kwestionariusza Depresji BDI-II Aarona, T. Becka. Psychol. Etiol. Genet. 2009, 19, 71–94. [Google Scholar]
  40. Atkinson, A.C.; Riani, M.; Corbellini, A. The box–cox transformation: Review and extensions. Stat. Sci. 2021, 36, 239–255. [Google Scholar] [CrossRef]
  41. Lockwood, C.M.; MacKinnon, D.P. Bootstrapping the standard error of the mediated effect. In Proceedings of the 23rd annual meeting of SAS Users Group International, Nashville, TN, USA, 22–25 March 1998; pp. 997–1002. [Google Scholar]
  42. The Jamovi Project. Jamovi (Version 2.2) Computer Software. 2021. Available online: https://www.jamovi.org (accessed on 7 September 2022).
  43. Gallucci, M. jAMM: Jamovi Advanced Mediation Models. Jamovi Module. 2020. Available online: https://jamovi-amm.github.io/ (accessed on 7 September 2022).
  44. Uzarska, A.; Czerwiński, S.K.; Atroszko, P.A. Measurement of shopping addiction and its relationship with personality traits and well-being among Polish undergraduate students. Curr. Psychol. 2021, 1–17. [Google Scholar] [CrossRef]
  45. Bozoglan, B.; Demirer, V.; Sahin, I. Loneliness, self-esteem, and life satisfaction as predictors of internet addiction: A cross-sectional study among Turkish university students. Scand. J. Psychol. 2013, 54, 313–319. [Google Scholar] [CrossRef]
  46. Ste-Marie, C.; Gupta, R.; Derevensky, J.L. Anxiety and social stress related to adolescent gambling behaviour. Int. Gambl. Stud. 2002, 2, 123–141. [Google Scholar] [CrossRef]
  47. Trevorrow, K.; Moore, S. The association between loneliness, social isolation and Women’s electronic gaming machine gambling. J. Gambl. Stud. 1998, 14, 263–284. [Google Scholar] [CrossRef]
  48. O’Guinn, T.; Faber, R.J. Compulsive buying: A phenomenological exploration. J. Consum. Res. 1989, 16, 147–157. [Google Scholar] [CrossRef]
  49. Schlosser, S.; Black, D.W.; Repertinger, S.; Freet, D. Compulsive buying. Demography, phenomenology, and comorbidity in 46 subjects. Gen. Hosp. Psychiatry 1994, 16, 205–212. [Google Scholar] [CrossRef]
  50. Wang, J.; Xiao, J.J. Buying behavior, social support and credit card indebtedness of college students. Int. J. Consum. Stud. 2009, 33, 2–10. [Google Scholar] [CrossRef]
  51. Mueller, A.; Mitchell, J.E.; Black, D.W.; Crosby, R.D.; Berg, K.; de Zwaan, M. Latent profile analysis and comorbidity in a sample of individuals with compulsive buying disorder. Psychiatry Res. 2010, 178, 348–353. [Google Scholar] [CrossRef] [PubMed]
  52. Mueller, A.; Claes, L.; Mitchell, J.E.; Faber, R.J.; Fischer, J.; de Zwaan, M. Does compulsive buying differ between male and female students? Pers. Individ. Differ. 2011, 50, 1309–1312. [Google Scholar] [CrossRef]
  53. Mueller, A.; Mitchell, J.E.; Peterson, L.A.; Faber, R.J.; Steffen, K.J.; Crosby, R.D.; Claes, L. Depression, materialism, and excessive Internet use in relation to compulsive buying. Compr. Psychiatry 2011, 52, 420–424. [Google Scholar] [CrossRef] [PubMed]
  54. Otero-López, J.M.; Villardefrancos, E. Prevalence, sociodemographic factors, psychological distress, and coping strategies related to compulsive buying: A cross sectional study in Galicia, Spain. BMC Psychiatry 2014, 14, 101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Dobrean, A.; Păsărelu, C.R. Impact of social media on social anxiety: A systematic review. In New Developments in Anxiety Disorders; Durbano, F., Marchesi, B., Eds.; InTech: Rijeka, Croatia, 2016; pp. 129–149. [Google Scholar] [CrossRef] [Green Version]
  56. Black, D.W.; Shaw, M.; McCormick, B.; Bayless, J.D.; Allen, J. Neuropsychological performance, impulsivity, ADHD symptoms, and novelty seeking in compulsive buying disorder. Psychiatry Res. 2012, 200, 581–587. [Google Scholar] [CrossRef] [Green Version]
  57. Suresh, A.S.; Biswas, A. A study of factors of internet addiction and its impact on online compulsive buying behaviour: Indian millennial perspective. Glob. Bus. Rev. 2020, 21, 1448–1465. [Google Scholar] [CrossRef]
  58. Andreassen, C.S. The Bergen Shopping Addiction Scale: Reliability and validity of a brief screening test. Front. Psychol. 2015, 6, 1374. [Google Scholar] [CrossRef] [Green Version]
  59. Villardefrancos, E.; Otero-López, J.M. Compulsive buying in university students: Its prevalence and relationships with materialism, psychological distress symptoms, and subjective well-being. Compr. Psychiatry 2016, 65, 128–135. [Google Scholar] [CrossRef]
  60. Zander, H. Impulsive behaviors in patients with pathological buying. J. Behav. Addict. 2014, 5, 457–464. [Google Scholar] [CrossRef] [Green Version]
  61. Atroszko, P.A.; Demetrovics, Z.; Griffiths, M.D. Work addiction, obsessive-compulsive personality disorder, burn-out, and global burden of disease: Implications from the ICD-11. Int. J. Environ. Res. Public Health 2020, 17, 660. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Raiskila, T.; Sequeiros, S.B.; Kiuttu, J.; Kauhanen, M.L.; Läksy, K.; Rissanen, P.; Vainiemi, K.; Tuulio-Henriksson, A.; Veijola, J.; Joukamaa, M. Obsessive-compulsive personality disorder is common among occupational health care clients with depression. J. Occup. Environ. Med. 2013, 55, 168–171. [Google Scholar] [CrossRef] [PubMed]
  63. Rössler, W.; Hengartner, M.P.; Ajdacic-Gross, V.; Angst, J. Predictors of burnout: Results from a prospective community study. Eur. Arch. Psychiatry Clin. Neurosci. 2015, 265, 19–25. [Google Scholar] [CrossRef] [PubMed]
  64. Henn, F.A.; Vollmayr, B. Stress models of depression: Forming genetically vulnerable strains. Neurosci. Biobehav. Rev. 2005, 29, 799–804. [Google Scholar] [CrossRef]
  65. Allen, T.D.; Finkelstein, L.M. Work–family conflict among members of full-time dual-earner couples: An examination of family life stage, gender, and age. J. Occup. Health Psych. 2014, 19, 376. [Google Scholar] [CrossRef] [Green Version]
  66. Andreassen, C.S.; Griffiths, M.D.; Sinha, R.; Hetland, J.; Pallesen, S. The relationships between workaholism and symptoms of psychiatric disorders: A large-scale cross-sectional study. PLoS ONE 2016, 11, e0152978. [Google Scholar] [CrossRef]
  67. Kessler, R.C.; Chiu, W.T.; Demler, O.; Walters, E.E. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry 2005, 62, 617–627. [Google Scholar] [CrossRef] [Green Version]
  68. Lieb, R. Epidemiological perspectives on comorbidity between substance use disorders and other mental disorders. In Co-Occurring Addictive and Psychiatric Disorders; Dom, G., Moggi, F., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 3–12. [Google Scholar] [CrossRef]
  69. Matsudaira, K.; Shimazu, A.; Fujii, T.; Kubota, K.; Sawada, T.; Kikuchi, N. Workaholism as a risk factor for depressive mood, disabling back pain, and sickness absence. PLoS ONE 2013, 8, e75140. [Google Scholar] [CrossRef]
  70. Nie, Y.; Sun, H. Why do workaholics experience depression? A study with Chinese University teachers. J. Health Psychol. 2016, 21, 10. [Google Scholar] [CrossRef]
  71. Clark, M.A.; Michel, J.S.; Zhdanova, L.; Pui, S.Y.; Baltes, B.B. All work and no play? A meta-analytic examination of the correlates and outcomes of workaholism. J. Manag. 2016, 42, 1836–1873. [Google Scholar] [CrossRef]
  72. Patel, A.S.; Bowler, M.C.; Bowler, J.L.; Methe, S.A. A meta-analysis of workaholism. Int. J. Bus. Manag. 2012, 7, 2–17. [Google Scholar] [CrossRef] [Green Version]
  73. Del, L.M.; Llorens, S.; Salanova, M.; Schaufeli, W. Validity of a brief workaholism scale. Psicothema 2010, 22, 143. [Google Scholar]
  74. Griffiths, M.D.; Demetrovics, Z.; Atroszko, P.A. Ten myths about work addiction. J. Behav. Addict. 2018, 7, 845–857. [Google Scholar] [CrossRef]
  75. Shimazu, A.; Demerouti, E.; Bakker, A.B.; Shimoda, K.; Kawakami, N. Workaholism and well-being among Japanese dual-earner couples: A spillover-crossover perspective. Soc. Sci. Med. 2011, 73, 399–409. [Google Scholar] [CrossRef] [PubMed]
  76. Shimazu, A.; Schaufeli, W.B. Is workaholism good or bad for employee well-being? The distinctiveness of workaholism and work engagement among Japanese employees. Ind. Health 2009, 47, 495–502. [Google Scholar] [CrossRef] [Green Version]
  77. Griffiths, M.D. A components model of addiction within a biopsychological framework. J. Subst. Use 2005, 10, 191–197. [Google Scholar] [CrossRef]
  78. Griffiths, M.D. Workaholism: A 21st century addiction. Psychologist. Bull. Br. Psychol. Soc. 2011, 24, 740–744. [Google Scholar]
  79. Atroszko, P.A. Work addiction as a behavioural addiction: Towards a valid identification of problematic behaviour. Aust. N. Z. J. Psychiatry 2019, 53, 284–285. [Google Scholar] [CrossRef] [Green Version]
  80. Dutheil, F.; Charkhabi, M.; Ravoux, H.; Brousse, G.; Dewavrin, S.; Cornet, T.; Pereira, B. Exploring the link between work addiction risk and health-related outcomes using job-demand-control model. Int. J. Environ. Res. Public Health 2020, 17, 7594. [Google Scholar] [CrossRef]
  81. Andreassen, C.S. Workaholism: An overview and current status of the research. J. Behav. Addict. 2014, 3, 1–11. [Google Scholar] [CrossRef] [Green Version]
  82. Kubota, K.; Shimazu, A.; Kawakami, N.; Takahashi, M.; Nakata, A.; Shaufeli, W.B. Association between workaholism and slepp problems among hospital nurses. Ind. Health 2010, 48, 1006240015. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Quinones, C.; Griffiths, M.D. Addiction to work A critical review of the workaholism constructs and recommendations for assessment. J. Psychosoc. Nurs. 2015, 35, 48–59. [Google Scholar] [CrossRef] [Green Version]
  84. Schaufeli, W.B.; Bakker, A.B.; Van der Heijden, F.M.M.A.; Prins, J.T. Workaholism, burnout and well-being among junior doctors: The mediating role of role conflict. Work Stress 2009, 23, 155–172. [Google Scholar] [CrossRef]
  85. Shimazu, A.; Schaufeli, W.B.; Kamiyama, K.; Kawakami, N. Workaholism vs. work engagement: The two different predictors of future well-being and performance. Int. J. Behav. Med. 2015, 22, 18–23. [Google Scholar] [CrossRef] [PubMed]
  86. Bartczak, M.; Oginska-Bulik, N. Workaholism and mental health among Polish academic workers. Int. J. Occup. Saf. Erg. 2012, 18, 3–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Atroszko, P.A.; Pallesen, S.; Griffiths, M.D.; Andreassen, C.S. Work addiction in Poland: Adaptation of the Bergen Work Addiction Scale and relationship with psychopathology. Health Psychol. Rep. 2017, 5, 345–355. [Google Scholar] [CrossRef]
  88. Denizci Nazlıgül, M.; Arcan, K.; Griffiths, M.D. Psychometric Properties of the Turkish Version of the Bergen Work Addiction Scale. Int. J. Ment. Health Addict. 2021, 20, 3178–3190. [Google Scholar] [CrossRef]
  89. Hancock, M.G.; Balkin, R.S.; Reiner, S.M.; Williams, S.; Hunter, Q.; Powell, B.; Juhnke, G.A. Life balance and work addiction among NCAA administrators and coaches. Career. Dev. Q. 2019, 67, 264–270. [Google Scholar] [CrossRef]
  90. Andreassen, C.S.; Pallesen, S.; Torsheim, T. Workaholism as a mediator between work-related stressors and health outcomes. Int. J. Environ. Res. Public Health 2018, 15, 73. [Google Scholar] [CrossRef] [Green Version]
  91. Masonjones, A.J.; Cabieses, B. Alcohol, binge drinking and associated mental health problems in young urban Chileans. PLoS ONE 2015, 10, e0121116. [Google Scholar] [CrossRef] [Green Version]
  92. Iwata, M.; Ota, K.T.; Duman, R.S. The inflammasome: Pathways linking psychological stress, depression, and systemic illnesses. Brain Behav. Immun. 2013, 31, 105–114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Haar, J.; Roche, M. Three-way interaction effects of workaholism on employee well-being: Evidence from blue-collar workers in New Zealand. JMO 2013, 19, 134–149. [Google Scholar] [CrossRef]
  94. Houlfort, N.; Philippe, F.L.; Vallerand, R.J.; Ménard, J. On passion and heavy work investment: Personal and organizational outcomes. J. Manag. Psychol. 2014, 29, 25–45. [Google Scholar] [CrossRef]
Jcm 11 06288 g001 550
Figure 1. Mediation model 1. DJGLS-De Jong Gierveld Loneliness Scale, SZZ-Scale of Shopping Behaviour, BDI-Beck Depression Inventory–BDI I-II.
Figure 1. Mediation model 1. DJGLS-De Jong Gierveld Loneliness Scale, SZZ-Scale of Shopping Behaviour, BDI-Beck Depression Inventory–BDI I-II.
Jcm 11 06288 g001
Jcm 11 06288 g002 550
Figure 2. Mediation model 2 DJGLS-De Jong Gierveld Loneliness Scale, WART-Work Addiction Risk Test, BDI-Beck Depression Inventory–BDI I-II.
Figure 2. Mediation model 2 DJGLS-De Jong Gierveld Loneliness Scale, WART-Work Addiction Risk Test, BDI-Beck Depression Inventory–BDI I-II.
Jcm 11 06288 g002
Table
Table 1. General sociodemographic characteristics of the study group (n = 556).
Table 1. General sociodemographic characteristics of the study group (n = 556).
Sociodemographic Variablesn%
EducationLower (secondary, vocational, elementary)28751.6
Higher26948.4
Marital statusSingle (maiden, divorced, widow)18634.5
in a formal/informal relationship37066.5
Place of residence<100.000 inhabitants29352.7
≥100.000 inhabitants26347.3
Professional activityProfessionally active49689.2
Professionally inactive6010.8
n-number of cases, %-percentage of the total study group.
Table
Table 2. Indirect and Total Effects-mediation model 1.
Table 2. Indirect and Total Effects-mediation model 1.
TypeEffectEstimate95% C.I. Lower95% C.I.
Upper		βZp
IndirectDJGLS -> BDI -> SZZ−4.62 × 10−6−9.02 × 10−6−8.06 × 10−8−0.0246−2.030.043
ComponentDJGLS -> BDI -> SZZ−8.62 × 10−4−0.00123−5.03 × 10−4−0.2252−4.64<0.001
0.005369.0006 × 1040.009580.10922.420.015
DirectDJGLS -> SZZ−1.12 × 10−5−2.97 × 10−57.57 × 10−6−0.0598−1.180.237
TotalDJGLS -> SZZ−1.59 × 10−5−3.14 × 10−5−2.76 × 10−7−0.0844−1.990.046
DJGLS-De Jong Gierveld Loneliness Scale, SZZ-Scale of Shopping Behaviour, WART-Work Addiction Risk Test, BDI-Beck Depression Inventory–BDI I-II, p—significance level, β—regression coefficient. Note. Confidence intervals computed with method: Parametric bootstrap, betas are completely standardized effect sizes.
Table
Table 3. Indirect and Total Effects-Mediation model 2.
Table 3. Indirect and Total Effects-Mediation model 2.
TypeEffectEstimate95% C.I. (a) Lower95% C.I. (a) UpperβZp
IndirectDJGLS -> BDI -> WART−7.51 × 10−4−0.00110−3.67 × 10−4−0.0722−4.002<0.001
ComponentDJGLS -> BDI -> WART−8.62 × 10−4
0.871		−0.00121
0.64018		−4.88 × 10−4
1.09		−0.2252
0.3207		−4.700
7.524		<0.001
<0.001
DirectDJGLS -> WART−2.48 × 10−4−0.001086.59 × 10−4−0.0239−0.5590.576
TotalDJGLS -> WART−10.00 × 10−4−0.00186−1.38 × 10−4−0.0961−2.2740.023
DJGLS-De Jong Gierveld Loneliness Scale, SZZ-Scale of Shopping Behavior, WART-Work Addiction Risk Test, BDI-Beck Depression Inventory–BDI I-II, β—regression coefficient. Note. Confidence intervals computed with method: Parametric bootstrap, Betas are completely standardized effect sizes, p—significance level.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rachubińska, K.; Cybulska, A.M.; Kupcewicz, E.; Jurewicz, A.; Panczyk, M.; Cymbaluk-Płoska, A.; Jurczak, J.; Grochans, E. Loneliness and the Degree of Addiction to Shopping and Work among Polish Women: The Mediating Role of Depression. J. Clin. Med. 2022, 11, 6288. https://doi.org/10.3390/jcm11216288

AMA Style

Rachubińska K, Cybulska AM, Kupcewicz E, Jurewicz A, Panczyk M, Cymbaluk-Płoska A, Jurczak J, Grochans E. Loneliness and the Degree of Addiction to Shopping and Work among Polish Women: The Mediating Role of Depression. Journal of Clinical Medicine. 2022; 11(21):6288. https://doi.org/10.3390/jcm11216288

Chicago/Turabian Style

Rachubińska, Kamila, Anna Maria Cybulska, Ewa Kupcewicz, Alina Jurewicz, Mariusz Panczyk, Aneta Cymbaluk-Płoska, Justyna Jurczak, and Elżbieta Grochans. 2022. "Loneliness and the Degree of Addiction to Shopping and Work among Polish Women: The Mediating Role of Depression" Journal of Clinical Medicine 11, no. 21: 6288. https://doi.org/10.3390/jcm11216288

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop