Similarities and differences in the psychological factors associated with generalised problematic internet use, problematic social media use, and problematic online gaming

Comprehensive Psychiatry 134 (2024) 152512 Available online 26 June 2024 0010-440X/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Similarities and differences in the psychological factors associated with generalised problematic internet use, problematic social media use, and problematic online gaming Magdalena S´ anchez-Fern´ andez a , * , Mercedes Borda-Mas a , Zsolt Horvath b , c , Zsolt Demetrovics b , c , d , ** a Department of Personality, Assessment and Psychological Treatment, Faculty of Psychology, University of Seville (Universidad de Sevilla), Seville, Spain b Institute of Psychology, ELTE E¨ otv¨ os Lor´ and University, Budapest, Hungary c Centre of Excellence in Responsible Gaming, University of Gibraltar, Gibraltar, Gibraltar d College of Education, Psychology and Social Work, Flinders University, Adelaide, Australia ARTICLE INFO Keywords: Problematic internet use Internet addiction Social media Online gaming Psychological factors College students ABSTRACT Problematic online behaviours are a concern among university students. Although previous research has identified that psychological distress, cognitive distortions, conscientiousness and neuroticism traits, motor impulsivity, and emotion regulation strategies have a significant direct effect on generalised problematic Internet use (GPIU), problematic social media use (PSMU), and problematic online gaming (POG), it is still necessary to determine the extent to which these psychological factors, taken together, are associated with each of the problematic online behaviours. In a sample of 690 Spanish university students, the present study examined the relationship of these psychological factors with GPIU, PSMU, and POG. Correlation and regression analyses were performed. Results reported that (1) high psychological distress, low conscientiousness trait and high motor impulsivity were common associated factors of GPIU and PSMU, but not of POG; (2) high levels of cognitive distortions and cognitive reappraisal were common associated factors of GPIU, PSMU and POG; and (3) expressive suppression and neuroticism trait had no effect on any of the three problematic online behaviours. The findings will help to develop effective prevention and intervention strategies for each problematic online behaviour in the university context. 1. Introduction In the past few decades, the exponential growth of the Internet has brought a parallel increase in concern about problematic Internet use (PIU). Being a university student increases the probability of engaging in this potentially harmful behaviour [1,2], which correlates with adverse effects on academic performance and health ([3,4,5]; [6,7]). Problematic Internet use (PIU) is defined as a behaviour pattern involving dysfunctional Internet usage marked by a lack of control, the emergence of adverse outcomes, and obsessive thoughts when access to the Internet is unavailable [8]. This term accommodates a wide range of maladaptive behaviours online, including addictive tendencies, without presenting sufficient evidence of clinical-level impairment to justify terms like “addiction” [9]. Beyond the severity level, PIU comprises a variety of problematic online activities. In fact, Davis [10] differentiated between generalised and specific pathological Internet use. Generalised PIU (GPIU) includes a broader set of online behaviours without a clearly specified objective, while specific PIU (SPIU) refers to a specific area of online activity (e.g., gaming, social media, gambling, and pornography). In recent years, a detailed study of different problematic online behaviours is being advocated to conceptualise PIU as a spectrum of Internetmediated behaviours with common and specific characteristics [11,12]. In the current study, we will focus on GPIU and the two most prevalent SPIUs among university students (i.e., problematic social media use (PSMU) and problematic online gaming (POG)) [13]. Although they are distinct behaviours, previous studies have found that they are related * Corresponding author. ** Corresponding author at: Institute of Psychology, ELTE E¨ otv¨ os Lor´ and University, Budapest, Hungary. E-mail addresses: [email protected] (M. S´ anchez-Fern´ andez), [email protected] (M. Borda-Mas), [email protected] (Z. Horvath), zsolt.demetrovics@ gmail.com (Z. Demetrovics). Contents lists available at ScienceDirect Comprehensive Psychiatry journal homepage: www.elsevier.com/locate/comppsych https://doi.org/10.1016/j.comppsych.2024.152512 Comprehensive Psychiatry 134 (2024) 152512 2 ([14,15,16]; [17]). Understanding the factors that lead to these behaviours is essential for the formulation of prevention and intervention initiatives. Different authors suggest that problematic online behaviours are related to common and distinct risk factors ([6; 92]). Consistently, previous studies have investigated how these factors are associated with GPIU and PSMU [18,19,20,21], GPIU and POG [22,23], PSMU and POG ([24,25]; [26]), and GPIU, PSMU, and POG [27,28,16], in the same sample. These studies have found differences in the association of these problematic online behaviours with psychosocial variables, and, in addition, they indicate that they might all be associated with decreased psychosocial well-being and some shared personality traits. Focussing on the psychological risk factors common to the three behaviours, theories and empirical studies on GPIU, PSMU, and POG were reviewed to determine the risk factors to be included in this study. According to the compensatory internet use model [29,30], problematic online behaviours may reflect compensatory mechanisms deployed to alleviate negative mood states and cope with demands of their environment. Consistently, recent systematic reviews focused on university students have found that depression, negative affect, and life stress are associated with GPIU (S´ anchez-Fern´ andez et al., 2022), and negative affectivity is associated with PSMU and POG (S´ anchez-Fern´ andez & Borda-Mas, 2022). Furthermore, the authors of this study, using the network analysis approach, found that depression symptoms were associated with GPIU and POG and anxiety symptoms with PSMU in a sample of university students [15]. In general, psychological distress is probably a common risk for different types of PIU, which needs further examination by applying other statistical approaches. On the other hand, Davis’ [10] cognitive-behavioural model argues that psychological distress is a necessary distal cause in the development of PIU such that individuals cope with negative emotional states by engaging excessively in online activities, such as social media [31] or online games (Melodia et al., 2020). However, this psychological distress alone cannot cause people to be affected by PIU and is only a necessary factor in its aetiology. Maladaptive cognitions are sufficient proximal causes of PIU [32]. Prior studies have identified that the level of cognitive distortions is a relevant variable related to problematic online behaviours ([33,34,35,36]; Shen et al., 2022; [37,38]). The recent person-affect-cognition-execution interaction model (I- PACE, [39]) goes beyond psychopathology in the aetiology of specific Internet use disorders and proposes that individuals have characteristics that make them more vulnerable to addictive use of certain types of apps or sites, such as personality factors. The meta-analysis by Kayis¸ et al. [40] established that although, in general, online problem behaviours are positively correlated with neuroticism and negatively correlated with openness to new experiences, conscientiousness, extraversion, and agreeableness, the largest effect size was found between PIU and conscientiousness, followed by the association between PIU and neuroticism. On the other hand, impulsivity was identified as a risk factor for problematic online behaviours [31,41,42], and, specifically, a longitudinal study has shown that motor impulsivity predicts PIU in college students [43]. In fact, prior to this study, the authors analysed the effect of motor impulsivity on GPIU, PSMU and POG in a sample of university students, finding significant effects of this variable on the three problematic online behaviours [44]. Furthermore, the I-PACE model also proposes that people who have a higher vulnerability to emotional distress (as predisposing factors), in combination with dysfunctional coping strategies, may be more likely to react to a need for emotional regulation when faced with a stressful situation. This interaction could lead to an increase in using the Internet application/site of choice if the individual has the expectation that using the Internet will alleviate their emotional state. Previous studies have shown that difficulties in emotion regulation significantly contributed to the risk of developing GPIU [45], PSMU [46,47,48,49], and POG [50,51,52,53]. More research is needed to determine the extent to which these psychological factors, taken together, are associated with each of the problematic online behaviours. In view of the above, we aimed to examine the relationship between GPIU, PSMU, and POG by assessing a series of potential associated psychological factors (i.e., psychological distress, cognitive distortions, conscientiousness and neuroticism traits, motor impulsivity, and emotion regulation strategies) in the same sample of university students. 2. Method 2.1. Participants and procedure Between October 2022 and January 2023, a cross-sectional study was conducted at a university in Andalusia, Spain. The sample was recruited through convenience sampling. Questionnaires were administered via an online survey distributed by teaching staff. Inclusion criteria were: over 17 years of age, enrolled in a degree programme at this university, having a smartphone or any other device with Internet access, having a good command of the Spanish language, and giving informed consent to participate. Participation was voluntary. A robust data set comprising 697 responses to the questionnaire was gathered, with each item required to be completed before proceeding to subsequent sections, ensuring no incomplete entries. All participants met the inclusion criteria, except six participants who were excluded due to their failure to sign the informed consent form. Furthermore, one response was excluded due to the detection of atypical responses during questionnaire completion. The final sample consisted of 690 university students (408 women, 272 men and 10 non-binary; M age =21.29 years, SD age =3.80; range =17 to 40 years). Following that, only responses from participants who had utilised social media were considered for questionnaires assessing PSMU (N =690), and only responses from participants who had engaged in online video gaming were selected for questionnaires measuring POG (N =454). The study received approval from the Ethics Committee of this university and adhered to the principles of the Declaration of Helsinki (Internal code: 1346-N-22; Date of approval: 28 September 2022). 2.2. Measures This data collection assessed GPIU, PSMU, POG, and several personality characteristics and psychological dimensions in addition to sociodemographic and internet use data. Here, we only present the measures used in the current analysis, while other measures and analyses with different focuses can be found elsewhere [44,15]. CIUS-14 (Compulsive Internet Use Scale, [54]; Spanish version: [55]) was used to evaluate GPIU. This 14-item questionnaire is based on a 5- point Likert scale from 0 (never) to 4 (very frequently). In the present study, Spanish CIUS-14 showed high levels of internal consistency ( α = 0.88, ω =0.88). BSMAS (Bergen Social Media Addiction Scale, [25]; Spanish version: [56]) was used to assess PSMU. This 6-item test can be answered by using a 5-point Likert scale from 1 (very rarely) to 5 (very often). In the present sample, the Spanish BSMAS showed elevated levels of internal consistency ( α =0.80, ω =0.81). IGDS9-SF (Internet Gaming Disorder Scale–Short Form, [57]; Spanish version: [58]) was used to measure POG. This 9-item questionnaire is based on a 5-point Likert scale from 1 (never) to 5 (very often). In the present study, Spanish IGDS9-SF showed elevated levels of internal consistency ( α =0.86, ω =0.86). DASS-21 (Depression, Anxiety, and Stress Scale-21, [59]; Spanish version: [60]) assesses psychological distress through three specific subscales, pertaining to depression (DASS-D), anxiety (DASS-A), and stress (DASS-S). Due to the strong interrelationship between constructs (r =0.69–0.82), in the present study, the total score of DASS-21 was used to measure psychological distress (PD) [61,62]. This 21-item questionnaire is answered using a 4-point scale from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time). In the M. S´ anchez-Fern´ andez et al. Comprehensive Psychiatry 134 (2024) 152512 3 present study, the Spanish version of DASS-21 showed very high levels of internal consistency ( α =0.95, ω =0.95). IPA (Automatic Thoughts Inventory, from the Spanish ‘Inventario de Pensamientos Autom´ aticos’, [63]) measures the frequency of automatic negative thoughts and 15 cognitive distortions (e.g., personalisation, catastrophism, overgeneralisation). In this study, an overall score was used to evaluate the general level of cognitive distortions. This 45-item test is based on a 5-point Likert scale from 1 (never) to 5 (very often). In the present study, IPA showed elevated levels of internal consistency ( α =0.88, ω =0.88). Mini-IPIP (Mini International Personality Item Pool-Five-Factor Model, [64]; Spanish version: [65]) assesses the Big Five Personality Traits [66] (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience). This 20-item questionnaire is answered through a 5-point scale from 1 (very inaccurate) to 5 (very accurate). In the present study, only the Conscientiousness (C) and Neuroticism (N) subscales were used. The C and N subscales showed close-to-adequate levels of internal consistency (C: α =0.65, ω =0.67; N: α =0.66, ω = 0.67). The BIS-11 (Barratt Impulsiveness Scale, [67]; Spanish version: [68]) assesses impulsivity through three factors: attentional, motor, and nonplanning. This 30-item questionnaire is answered through a 5-point scale from 0 (rarely) to 4 (always). In the present study, only the motor impulsivity (MI) subscale was used. The MI subscale showed high levels of internal consistency ( α =0.80, ω =0.81). ERQ (Emotion Regulation Questionnaire, [69]; Spanish version: [70]) was used to assess emotion regulation strategies through two subscales: expressive suppression (ES, 4 items), and cognitive reappraisal (CR, 6 items). This 10-item questionnaire is based on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). In the present study, ERQ showed elevated levels of internal consistency across its two subscales (ES: α =0.83, ω =0.85; CR: α =0.86, ω =0.86). 2.3. Statistical analysis For the distribution of variables, the absolute values of skewness ranged from 0.053 (CR) to 2.360 (POG), and the absolutes values of kurtosis ranged from 0.116 (PD) to 8.146 (POG). According to Kim [71], with the criteria of skewness <2 and kurtosis <7 for large sample sizes, the distribution can be considered normal for all variables, except for POG. Firstly, means, standard deviations, reliabilities (Cronbach’s alpha and McDonald’s omega), and correlations among the study variables were calculated. Pearson’s correlation was used for all variables, except for POG, where Spearman’s correlation was employed due to the nonnormality of IGDS9-SF. Three multiple linear regression models were conducted using the enter method, with the three different problematic behaviours (i.e., GPIU, PSMU, and POG) as outcome variables. The following factors were included in each regression model: age, gender, psychological distress, cognitive distortions, conscientiousness and neuroticism traits, motor impulsivity, and emotion regulation strategies (i.e., expressive suppression and cognitive reappraisal). Unstandardised (B) regression coefficients with the corresponding standard errors (SE), standardised regression coefficients (β), F value, and R 2 coefficient were reported. Statistical assumptions of linear regression analysis (normality of residuals, linearity, independence of residuals [GPIU: DWT =1.95; PSMU: DWT =2.04; POG: DWT =1.95], homoscedasticity [i.e., random scatterplot between residuals and predicted outcome values], and nonmulticollinearity [Tolerance =0.45–0.99; VIF =1.01–2.20]) were fulfilled. Due to the violation of the normality of the residuals assumption, the B and SE coefficients were estimated using the bootstrapping method. This involved generating 5000 samples from different bootstraps to estimate the coefficients, ensuring a bias-corrected confidence interval of 95%. To interpret the strength of the associations, the following cut-off points were used for the correlation coefficients: small (<0.2), medium (0.2–0.3), and large (>0.3) [72]. On the other hand, the following cut-off points were used for the beta coefficients: very small (<0.1), small (0.1–0.3), medium (0.3–0.5), and large (≥0.5) [73]. JASP statistical software was used for the analyses [74]. The minimum sample size was determined using the G*Power calculator [75]. In the context of regression analysis, considering an expected effect size of 0.10, an alpha level of 0.05, a power of 0.80, and a total of nine predictive factors, the software indicated a minimum sample size of 166. The required minimum was surpassed. 3. Results Of the 690 participants, the majority were females, Bachelor students, under 20 years of age, from Engineering and Architecture, had a medium economic level, lived with a family member, used social media, and played online games (Table 1). Table 2 shows the means and standard deviations for the study variables. The mean scores were low for the three problematic online behaviours, for psychological distress and for cognitive distortions, and medium for conscientiousness, expressive suppression, and cognitive reappraisal. GPIU, PSMU, and POG were positively correlated (Table 2). The correlations were strong between GPIU and PSMU, medium between GPIU and POG, and weak between PSMU and POG. GPIU, PSMU, and POG were positively correlated with PD, CD, N, ES, and CR. Strong associations were found for PD with PSMU and GPIU, for CD with PSMU Table 1 Characteristics of the study participants (N =690). Characteristics Frequency % Gender Male 272 39.4 Female 408 59.1 Non-binary 10 1.4 Age (years) <20 360 52.2 20–25 258 37.4 26–30 46 6.7 31–35 14 2.0 >35 12 1.7 Educational degree (studying) Bachelor 604 87.5 Master 58 8.4 Doctoral 28 4.1 Field of knowledge Sciences 126 18.3 Sciences Health sciences 113 16.4 Social sciences 142 20.6 Arts and humanities 84 12.2 Engineering and Architecture 225 32.6 Academic performance Below average 55 8.1 Average 540 79.3 Above average 86 12.6 Income level Low 152 22.0 Medium 340 49.3 High 198 28.7 Employment status Unemployed 414 60.0 Unemployed in search of employment 137 19.9 Part-time employed 102 14.8 Full-time employed 37 5.4 Residence With a family member 398 57.7 In a student residence/flat 237 34.3 With a couple/alone 55 8.0 Social media use Yes 690 100 No 0 0 Gaming online Yes 459 66.5 No 231 33.5 M. S´ anchez-Fern´ andez et al. Comprehensive Psychiatry 134 (2024) 152512 4 and GPIU, and N with PSMU and GPIU. Medium associations were identified for MI with GPIU, PSMU and POG, ES with GPIU and POG, between N and POG, and between CD and POG. Lastly, weak associations were detected for CR with GPIU, PSMU, and POG, and between ES and PSMU. Moreover, GPIU, PSMU, and POG were negatively correlated with C, with strong, medium and weak associations, respectively. On the other hand, PD was positively correlated with CD, N, MI and ES, and negatively correlated with C; CD was negatively correlated with C and positively correlated with N, MI and ES; C was negatively correlated with N, MI and ES; N was positively correlated with MI, ES and CD; MI was positively correlated with CR, and ES was positively correlated with CR. Of these correlations, the association between PD and CD, and between these variables and N were strong. The associations between PD and ES, PD and C, PD and MI, CD and MI, CD and ES, C and N, C and MI, N and MI, N and ES, N and CR, and between ES and CR were medium. Finally, the correlations between CD and C, between C and ES, and between MI and CR were weak. The results of the multiple linear regression models for GPIU, PSMU, and POG are presented in Table 3. The regression analysis for GPIU showed that male gender (β =0.07, p <.05), high PD (β =0.16, p <.01), high CD (β =0.25, p <.001), low C (β = − 0.17, p <.001), high MI (β = 0.09, p <.05), and high CR (β =0.15, p <.001) were significantly associated with higher rates of GPIU. The regression analysis for PSMU found that high PD (β =0.20, p <.001), high CD (β =0.14, p <.01), low C (β = − 0.14, p <.001), high MI (β =0.11, p <.05), and high CR (β = 0.10, p <.01) were significantly associated with high levels of PSMU. The regression analysis for POG found that male gender (β =0.34, p < .001), high CD (β =0.22, p <.001), and high CR (β =0.12, p <.01) were significantly associated with high levels of POG. High CD and high CR were common factors associated with higher rates of GPIU, PSMU, and POG. High PD, low C and high MI were specific factors associated with high levels of GPIU and PSMU. Finally, being male was a specific factor associated with higher rates of GPIU and POG. The effect sizes were small in all cases, except for the association of male gender with POG, which was medium. 4. Discussion Currently, problematic online behaviours have become a cause of concern among university students due to their negative impact on health and academic performance. Knowing the risk and protective factors of each of these behaviours is necessary in the creation of actions Table 2 Mean, Standard Deviations (SD), reliabilities, and Pearson/Spearman correlations of study variables. Measures 1 2 3 (rho) 4 5 6 7 8 9 10 1. GPIU (r) – 2. PSMU (r) 0.72*** – 3. POG (rho) 0.29*** 0.24*** – 4. PD (r) 0.38*** 0.39*** 0.19** – 5. CD (r) 0.45*** 0.38*** 0.30*** 0.71*** – 6. C (r) −0.31*** −0.25*** −0.20*** −0.22*** −0.28*** – 7. N (r) 0.36*** 0.31*** 0.21*** 0.62*** 0.66*** −0.22*** – 8. MI (r) 0.26*** 0.26*** 0.17*** 0.27*** 0.28*** −0.30*** 0.19*** – 9. ES (r) 0.24*** 0.14*** 0.21*** 0.23*** 0.32*** −0.17*** 0.26*** 0.09* – 10. CR (r) 0.16*** 0.09** 0.11** −0.04 0.06 0.02 0.17*** −0.01 0.26*** – M 17.58 11.69 11.57 20.18 90.93 13.23 9.83 4.33 13.14 22.76 SD 9.66 4.65 4.31 13.72 28.29 3.18 3.02 3.18 5.87 7.84 Range 0–47 6–30 9–42 0–63 45–202 4–20 4–20 0–15 4–27 6–42 α 0.88 0.80 0.86 0.95 0.95 0.65 0.66 0.80 0.83 0.86 ω 0.88 0.81 0.86 0.95 0.95 0.67 0.67 0.81 0.85 0.86 α : Cronbach’s alpha coefficient, ω : omega coefficient, M: mean, SD: standard deviation, r: Pearson correlation coefficient, rho: Spearman correlation coefficient, GPIU: generalised problematic Internet use, PSMU: problematic social media use, POG: problematic online gaming, PD: psychological distress, CD: cognitive distortions, C: conscientiousness, N: neuroticism, MI: motor impulsivity, ES: expressive suppression, CR: cognitive reappraisal, ** p <.01 *** p <.001 (2-tailed). Table 3 Multiple regression analysis.a GPIU (N ¼680 a ) PSMU (N ¼680 a ) POG (N ¼452 a ) 95% CI of B 95% CI of B 95% CI of B B SE B β Lower Upper B SE B β Lower Upper B SE B β Lower Upper Age −0.16 0.08 −0.06 −0.32 0.01 −0.06 0.04 −0.05 −0.14 0.03 0.19 0.06 0.04 −0.04 0.19 Male gender vs. female gender 1.46 0.69 0.07* 0.11 2.81 −0.57 0.35 −0.06 −1.26 0.12 3.18 0.43 0.34*** 2.34 4.02 Psychological distress 0.11 0.04 0.16** 0.03 0.19 0.07 0.02 0.20*** 0.03 0.11 0.04 0.02 0.10 −0.01 0.09 Cognitive distortions 0.08 0.02 0.25*** 0.05 0.12 0.02 0.01 0.14** 0.01 0.04 0.04 0.01 0.23*** 0.02 0.06 Conscientiousness −0.52 0.11 −0.17*** −0.73 −0.31 −0.20 0.05 −0.14*** −0.31 −0.09 −0.09 0.07 −0.06 −0.22 0.05 Neuroticism −0.02 0.21 −0.00 −0.39 0.43 0.07 0.11 0.04 −0.14 0.28 −0.06 0.13 −0.04 −0.32 0.19 Motor impulsivity 0.27 0.11 0.09* 0.06 0.48 0.16 0.05 0.11** 0.06 0.27 0.13 0.07 0.08 −0.01 0.26 Expressive suppression 0.05 0.08 0.03 −0.12 0.21 −0.02 0.04 −0.03 −0.11 0.06 0.04 0.05 0.05 −0.06 0.12 Cognitive reappraisal 0.20 0.04 0.15*** 0.11 0.27 0.06 0.02 0.10** 0.02 0.10 0.07 0.03 0.12** 0.02 0.14 R 2 0.29 0.22 0.24 F 30.64*** 20.84*** 15.29*** GPIU: generalised problematic Internet use, PSMU: problematic social media use, POG: problematic online gaming, B: Unstandardized regression coefficients, SE: standard errors, β: standardised regression coefficients. *p ≤.05 ** p <.01 ***p <.001. Note. Bootstrapping based on 5000 replicates. B, SE and p coefficients estimate is based on the median of the bootstrap distribution. a Non-binary gender has not been taken into account in the analyses due to lack of representativeness. M. S´ anchez-Fern´ andez et al. Comprehensive Psychiatry 134 (2024) 152512 5 for their prevention and treatment. This study aimed to examine how the same set of psychological factors (i.e., psychological distress, cognitive distortions, conscientiousness and neuroticism traits, motor impulsivity, and emotion regulation strategies) have an effect on three problematic online behaviours after controlling for age and gender. The regression analyses identified that (1) a high level of psychological distress was a specifically associated factor of high levels of GPIU and PSMU; (2) a high level of cognitive distortions was a commonly associated factor of high levels of GPIU, PSMU, and POG; (3) a low level of conscientiousness trait was a specifically associated factor of high levels of GPIU and PSMU; (4) a high level of motor impulsivity was a specific associated factor of high levels of GPIU and PSMU; and (5) a high level of cognitive reappraisal emotion regulation strategies was a common associated factor of GPIU, PSMU, and POG. Furthermore, the findings revealed that, taken together, psychological distress, cognitive distortions, conscientiousness trait, and emotion regulation strategies contributed significantly to the variance in GPIU (29%), PSMU (22%) and POG (24%). These findings suggest a relative overlap between the three investigated PIUs and that they are partly associated with different psychological variables. Regarding control variables, there was no important effect of age on GPIU, PSMU, or POG, although there was a significant effect of gender on GPIU and POG (not in PSMU). Male gender was associated with GPIU and POG, with a stronger effect on the latter. This finding is supported by other studies [76,45,77], and supports the hypothesis that gender is a specific non-shared risk factor in problematic online behaviours [25]. Moreover, it may reflect that there is a male orientation toward activities with aggressive and competitive content related to gaming [78,2]. Correlational results revealed that both GPIU and the two SPIU are correlated. Higher levels of GPIU were found to correlate with higher levels of PSMU and POG. The correlation was stronger in the former, which may be due to the fact that, although GPIU may include inappropriate use of the two specific activities, social media is one of the main Internet uses among university students [79,80], with students who have high levels of GPIU also having high levels of PSMU. In addition, PSMU and POG are correlated, which may be due to the fact that they share common mechanisms, thus people with certain characteristics are more likely to make inappropriate uses of different online activities [39]. However, the correlation between the two SPIU studied was weak, which may be related to motivations for use. While social affiliation is a crucial element in social media behaviour, online gaming is likely to be based on motivations such as immersion, escapism, and personal achievement [81,82,83,84,85]. Moreover, significant correlations were found between these three problematic online behaviours and the psychological variables studied, ranging from strong (between GPIU and cognitive distortions) to weak (between the three PIU and cognitive reappraisal). Regarding the regression results, partially consistent with our expectations, psychological distress was a specific associated factor of GPIU and PSMU. This aligns with the model of compensatory Internet use, indicating that problematic online behaviours might serve as compensatory strategies to alleviate negative moods and manage environmental stressors [29,30]. However, psychological distress was not found to have a direct effect on POG. This could be due to the fact that in this study, psychological distress was assessed using the DASS-21 scale, which assesses symptoms of depression, stress and anxiety, and, among these, it is the symptoms of depression that have a direct effect on POG [13,86]. Another possible explanation is that gender plays a moderating role in the association between psychological distress and POG. Bonnaire and Baptista [87] showed that, although depression was a risk factor for POG in both men and women, anxiety only acted as a risk factor for men. Furthermore, this result could be explained by the fact that, in the case of POG, gratifying gaming motives (e.g., fun-seeking, skill development, social contacts), which are more closely associated with wellbeing [88], might counterbalance the effects of motives more linked to coping with negative emotional states (e.g., escapism) in university students. Consequently, the level of psychological distress may not directly impact POG. In addition, the effect of psychological distress on POG may be fully mediated by other variables, such as cognitive distortions. In fact, as expected, the results also showed that the level of cognitive distortions was a common risk factor for the three problematic online behaviours. These findings are consistent with Davis’ [10] cognitive-behavioural model, which argues that psychological distress is a necessary distal cause in the development of PIU, in a way that individuals cope with negative emotional states by engaging excessively in online activities. However, in the case of GPIU and PSMU, psychological distress has a significant direct effect even when cognitive distortions are taken into account. This could be explained because the behaviours of using the Internet, in general, or social media, in particular, become easily accessible through electronic devices such as mobile phones, promoting passive use [89], which would leave no space for maladaptive cognitions. This is why it could be thought that, in the development of online problem behaviours, GPIU and PSMU have become faster and more automatic maladaptive coping methods than POG. Moreover, the associations between SPIU and psychological distress are dynamic and can be influenced by situational factors [90]. On the other hand, the conscientiousness trait was a protective factor for GPIU and PSMU, in a way that university students with low conscientiousness have higher levels of GPIU and PSMU. This is in line with previous literature [91,92] and with the I-PACE model [39], which goes beyond psychopathology in the aetiology of specific Internet-use disorders and proposes that personal characteristics make individuals more susceptible to addictive use of certain types of apps or sites, such as personality factors. Given that conscientiousness is characterised by high self-control, a high sense of responsibility, compliance with social norms or principles, consistency in work, and organisation and orderliness [93], and that it is positively related to academic achievement [94] and relationship quality [95], it is unlikely that students exhibiting high levels of conscientiousness might permit important aspects of their lives to be adversely impacted by the Internet and/or social media. Nevertheless, no significant effect of this personality trait on POG was found. This finding is consistent with the meta-analytic review by Chew [96], who found that the association between POG and conscientiousness was moderated by age, with the association diminishing as age advanced. Therefore, during childhood and adolescence, this personality trait may reduce the risk of POG, although this effect could decrease in adulthood. We did not find a direct effect of the neuroticism trait on any of the online behaviours. This finding could be explained by the fact that although significant correlations have been found between this personality trait and online problem behaviours, in line with previous literature [40], the effect could be mediated by other variables introduced in our model. Therefore, although trait neuroticism could be a variable that would increase individuals’ prior vulnerability to developing problematic online behaviours [39], the effect could be mediated by cognitive distortions or psychological distress that would make them use different online functions in a maladaptive way. In fact, a recent study suggests that, although among the Big Five personality traits, neuroticism and conscientiousness are the most influential on PIU, conscientiousness directly impacts PIU, while the influence of neuroticism is mediated by the level of psychological distress [97]. Motor impulsivity was a risk factor for GPIU and PSMU, in a way that university students with high motor impulsivity have higher levels of GPIU and PSMU. This is consistent with previous literature [43,31,41,42], as well as the previous study by the authors [44], and serves as empirical support for the I-PACE model that proposes that inhibitory control deficits are involved in the development of addictive behaviours [39]. However, no significant effect of motor impulsivity on POG was found. This could be due to the fact that, in agreement with previous findings regarding the lack of a significant effect of psychological distress on POG, in the case of video game activity, as it is a more M. S´ anchez-Fern´ andez et al. Comprehensive Psychiatry 134 (2024) 152512 6 conscious activity that requires higher levels of attention, the effect of motor impulsivity is being mediated by cognitive distortions. In fact, a previous study finds that the effect of impulsivity on POG is mediated by maladaptive cognitions related to internet gaming [98]. Finally, in terms of emotion regulation, cognitive reappraisal strategies were found to be a common risk factor in the three PIUs. Initially, this result seemed to diverge from expectations, as cognitive reappraisal (reframing an emotional situation) has been proposed as a positive emotion regulation strategy (i.e., it is related to greater psychological well-being) [69]. However, experimental studies suggest that certain contextual factors, such as the timing of reappraisal or the characteristics of the stressors, could determine the adaptiveness and/or efficacy of such a strategy in modulating emotions ([99,100]; [101]). Sheppes and Meiran [99] showed that late cognitive reappraisal acts as a maladaptive strategy. In addition, Sheppes and Meiran [100] found that late cognitive reappraisal led to a depletion of self-control resources. This finding is important, since self-control has been reported as a protective factor for inappropriate Internet use [102,77,103,104]. Moreover, [101] found that, when the emotional situation is controllable, greater use of reappraisal may contribute to diminished psychological well-being. Therefore, a maladaptive use of cognitive reappraisal strategies may increase the risk of PIU, which is consistent with previous literature [50]. However, we did not find a direct effect of expressive suppression strategies on any of the online behaviours. This result aligns with a prior investigation [105], and it may be due to the multi-faceted nature of emotion dysregulation [106]. More research is needed to explore whether additional emotion regulation strategies increase the risk of different PIUs. 4.1. Limitations and conclusions The present study has some limitations that should be considered in future research. Firstly, the sample was selected from one university in Spain through convenience sampling; therefore, the generalisability of the findings is limited. Future studies should attempt to replicate the present study in other university student samples from different countries and cultures to confirm these findings. Secondly, this study employed a cross-sectional design, which limited its causal inferences. Therefore, future studies should employ longitudinal studies to elucidate the temporal sequencing of these effects. Thirdly, based on the previous literature and to avoid response bias due to participant fatigue, only the personality traits of conscientiousness and neuroticism were considered in the regression models. For future studies, it would be advisable to incorporate the other three personality traits into the models. Fourthly, only self-report questionnaires were used, which have several limitations, such as memory recall or social desirability biases. In addition to these measures, corroboration through qualitative or mixed methods could improve the assessment of the variables studied. Finally, it should be noted that, following the guidelines of the prior literature that evaluated GPIU, the consideration of the specific activities that the participants had in mind when responding to the various items that comprise the CIUS instrument was omitted. This oversight hindered a detailed understanding of the overlaps between GPIU and the two specific behaviours under study. Looking forward to future research, the examination of specific behaviours is advocated. Additionally, for studies interested in GPIU, it is recommended to incorporate items into assessment tests that reference the online activities that participants are considering when responding to the items. This approach will enable more confident conclusions to be drawn regarding the overlaps between GPIU and other specific online problem behaviours. Despite these limitations, this study makes important contributions to the scientific literature. Although the association between psychological factors and different PIUs has been thoroughly investigated in previous studies, this research represents the first attempt to explore how these psychological variables influence three problematic online behaviours in the same sample of university students, allowing to analyse similarities and differences between them. The findings demonstrate that GPIU, PSMU, and POG were associated with cognitive distortions and cognitive reappraisal in university students, and GPIU and PSMU were also related to psychological distress, conscientiousness trait, and motor impulsivity. The results have provided empirical support for the hypothesis, stipulating that PIU can be conceptualised within a spectrum of distinct and related behaviours associated with both common and distinct antecedents ([107,39,108]; Starcevic & Billieux, 2017). Furthermore, this study has led to the conclusion that there are greater similarities between GPIU and PSMU, as evidenced by higher correlations and significant impacts of the same psychological variables. On the contrary, there are greater differences between the two first problematic behaviours and POG. This finding supports conceptions that argue that GPIU is a nonspecific disorder, while PSMU and POG are independent constructs that warrant a detailed analysis (Starcevic & Billieux, 2017). Additionally, it aligns with conceptions that separate POG from the PIU spectrum due to the distinct characteristics of the former [22]. Lastly, from a practical perspective, health and education professionals must incorporate these findings when considering possible prevention and treatment strategies to address each of the three problematic online behaviours studied in the university context. CRediT authorship contribution statement Magdalena S´ anchez-Fern´ andez: Writing – original draft, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. Mercedes Borda-Mas: Writing – review & editing, Supervision, Resources, Methodology, Investigation, Conceptualization. Zsolt Demetrovics: Writing – review & editing, Supervision, Funding acquisition, Conceptualization. Declaration of competing interest The University of Gibraltar received funding from the Gibraltar Gambling Care Foundation, an independent, not-for-profit charity, and donations from gambling operators through the LCCP RET process supervised by the UK Gambling Commission. None of these funding sources are related to this study, and the funding institutions/organisations had no role in the study design, data collection, analysis, interpretation, manuscript writing, or decision to submit the paper for publication. 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