Skip to main content
Download PDF

Examining the interplay between physical activity, problematic internet use and the negative emotional state of depression, anxiety and stress: insights from a moderated mediation path model in university students

BMC Psychology volume 12, Article number: 406 (2024) Cite this article

Abstract

Background

The aim of this study was to investigate the relationship between Problematic Internet Use (PIU), emotional states of stress, anxiety and depression, and the practice of physical activity among Tunisian students.

Methods

Cross-sectional data were collected from 976 university students aged 20.76 ± 1.63 years (46.8% female). They filled out an online survey comprised of a socio-demographic questionnaire, the depression, anxiety and stress scale– 21 items (DASS-21), the international physical activity questionnaire (IPAQ) and the compulsive internet use scale (CIUS). Students were divided, based on their economic levels, into three groups: low (n = 256, 26.23%), medium (n = 523, 53.59%) and high (n = 197, 20.18%).

Results

Mediation analysis: Indirect effects of IPAQ and gender on DASS-21 were highlighted: β= -0.18 (p < 0.01) and β= -0.04, P < 0.01) respectively. In addition, a significant and negative effect of IPAQ on CIUS was demonstrated (β = -0.45, P < 0.01). In addition, the effect of CIUS on DASS-21 was significant (β = 0.39, P < 0.01). Also, the effect of gender on CIUS was significant (β=-0.10, P < 0.01) However, its effect on DASS-21 was not significant (β = 0.05, p = 0.078). The total effect of IPAQ on DASS21 was significant (β= -0.52, p < 0.01) but the effect of Gender on DASS-21 was not significant (β = 0.01, p = 0.817). Moderation analysis: the results showed a significant moderation effect of the interaction between IPAQ and Gender on CIUS (β = 0.07, p < 0.01). However, it was not significant between Gender and CIUS on DASS-21 (β = 0.09, p = 0.390) and between IPAQ and Gender on DASS21 (β = 0.01, p = 0.736) Also, the interaction between IPAQ and CIUS did not have a significant moderation effect on DASS-21 (β = 0.15, p = 0.115).

Conclusions

Findings suggest that relationships between PIU and negative emotional state of depression, anxiety and stress are mediated via physical exercise. These results underscore the importance of the physical activity factor in the studies analyzing longitudinal effects of PIU on mental health outcomes.

Peer Review reports

Introduction

Due to its proven usefulness in the field of education, entertainment and especially the transmission of information, the use of the Internet has become a reflection of the degree of societal progress [1]. The Internet allows millions of Internet users to access an almost unlimited source of information. It contributes to the development of collaboration with other users, the democratization of knowledge and even the ability to work remotely. Given the sanitary conditions caused by the COVID-19 pandemic, the growth of internet use, which was already rapid before, has seen a clear acceleration in recent years and has therefore become a customary habit [2,3,4]. According to a recent study, Problematic Internet Use (PIU) affects 7.02% of the world’s population, with teenagers appearing to be particularly vulnerable [5].

Additionally, there are gender disparities in selected application categories which emphasize the need to explore the role of habits in understanding Problematic Internet Use (PIU). Notably, two distinct profiles have emerged: one characterized by the problematic use of video games, primarily among boys, and the other by the problematic use of social media, predominantly among girls [6]. Understanding how habits contribute to PIU is essential. Habits, defined as the actions that bring individuals happiness but disrupt their daily lives, have been identified as significant contributors to PIU [7]. Authors argue that habits leading to cognitive, social, and psychological distress can exacerbate PIU, transforming moderate internet use from a customary habit into a problematic behavior [7,8,9]. This transformation diminishes the educational and recreational aspects of internet use, leading to interference with daily life and potential addiction [1, 10]. Therefore, investigating the role of habits in PIU is crucial for developing targeted interventions and addressing the complexities of internet addiction among different demographic groups.

All uncontrollable behaviors related to the Internet, video games, excessive use of social media, passive search for information while ignoring other daily activities, can be included in PIU [1, 7, 8]. PIU is defined as “use of the Internet that creates psychological, social, school and/or work difficulties in a person’s life” [9]. In a recent meta-analysis that included 40 studies, PIU was associated with significant impairment in inhibitory control, stop cue task, go/no-go task, decision-making, and working memory [10, 11].

Moreover, problematic internet use correlates with a higher prevalence of mental health problems among adolescents, including depression, anxiety, and social isolation [12]. Even though PIU has become quite a serious mental health problem, it has not yet been classified as a disorder in the Diagnostic and Statistical Manual of Mental Disorders, but it has been found to be a condition that requires further clinical investigation [13]. A recent study found that video game stimuli activate the brain in the same way induced by drug cues [14]. These authors showed that excessive cue exposure can affect sensory processing ability and self-reflection.

Other studies have shown that Internet addiction gives rise to the withdrawal syndrome which has negative repercussions on the family environment [15]. PIU leads to an increase in individualism, a decrease in sociability and enculturation [16].

According to neuropsychological studies, several symptoms of Internet addiction are linked to executive functions, including inhibition, working memory, and cognitive flexibility [17,18,19,20]. Recent research has also revealed that people with PIU may have problems with attention, motor inhibition, decision-making [10]and depression [21, 22].

Several studies have shown that there is a strong link between gender and PIU [21, 23,24,25]. The majority of available data has shown that generally men have significantly higher internet addiction rates than women [26,27,28,29]. Men may be more inclined to explore the unknown, which may lead them to be more attracted to substances that indicate addiction, such as pornography, online games, and cybersex [30, 31]. However, other study presented results that showed no discernible gender difference [32].

Anxiety, stress, and depression are prevalent mental health challenges among university students, significantly impacting their well-being and academic performance. According to recent studies, a substantial proportion of university students experience symptoms of anxiety, stress, or depression within a given timeframe. For instance, according to a study conducted by Kavvadas et al. [33], the prevalence of stress, anxiety, and depression among university students reveals concerning levels of mental health challenges. The research indicates that 21.3% of participants reported severely increased levels of stress, while 64.0% reported normal to mild levels of stress. Similarly, concerning proportions were observed for anxiety, with 23.3% of participants experiencing severely increased levels, juxtaposed with 66.5% reporting normal to mild anxiety levels. Additionally, the study found that 25.1% of participants exhibited severely increased levels of depression, while 57.2% reported normal to mild depressive symptoms.

The impact of anxiety, stress, and depression on students extends beyond their academic performance, affecting their social relationships, overall quality of life, and future prospects. Moreover, the transition to university life, academic pressure, financial concerns, and social isolation can exacerbate these mental health challenges among students [34].

In response to these issues, there is growing recognition of the potential benefits of physical exercise for promoting mental health and well-being among university students. Numerous studies have demonstrated that regular physical exercise can have positive effects on mood regulation, stress reduction, anxiety management, and the alleviation of depressive symptoms [35,36,37].

The mechanisms underlying the beneficial effects of physical exercise on mental health are multifaceted. It stimulates the release of endorphins, neurotransmitters, and other neurochemicals that contribute to improved mood and stress resilience [38, 39]. Moreover, exercise promotes neuroplasticity in the brain, enhancing cognitive function and emotional regulation [40].

University students frequently suffer from social anxiety and academic pressure, which may increase their willingness to abuse the Internet as a momentary stress relief [41]. They use the internet excessively due to flexible hours and easy access. According to previous studies, between 8.6 and 40% of university students suffer from Internet addiction [41, 42]. A research on Brazilian university students revealed that 7.3% of them suffer from Internet addiction [43].

In addition, previous studies have found associations between PIU and mental health parameters such as stress, anxiety and depression in students [44,45,46,47]. For example, the study found that high Internet use was associated with high depression scores and high perceived stress scores [48].

University students frequently struggle with a lack of physical exercise due to their tendency to lead sedentary lifestyles as a result of spending too much time on their cell phones and social media [49].

The excessive and compulsive of the Internet use has a negative influence on individual mental health [16, 50,51,52,53,54,55]. Moreover, a previous study conducted on Korean adolescents indicated a negative association between level of physical activity and risk of PIU via the mediation of sleep satisfaction and stress [54]. The temporal allocation of time reserved for Internet use in spite of that allocated to physical activity led to a negative association between engagement in physical exercise and problematic Internet use [49]. This association stems from the fact that individuals with PIU tendencies have a disposition to reserve significant time for internet use, which substitutes for opportunities to engage in physical activity [56]. However, physical activity may have an inhibiting effect on problematic Internet use through its positive influence on negative emotional state.

Despite the large number of studies that have focused on the PIU, physical activity as well as on negative emotional state, to our knowledge, no study has addressed the association between these three concepts and specifically in the Arab world. Specially, Tunisia has one of the highest mobile phone subscriber rates in Africa with more than 15.6 million mobile lines in use [57] and PIU may affect users. In addition, recent studies in Tunisia have revealed that a considerable number of university students who use the Internet are vulnerable in terms of mental health [58].

The present study may shed light on interventions aimed at alleviating PIU and negative emotional state, ultimately improving mental health.

Accordingly, the aim of this study is to examine the links between PIU, negative emotional state (stress, anxiety and depression), gender, and the practice of physical activity among Tunisian students. We hypothesized that PIU would be influenced by gender and that there would be a positive correlation between negative emotional state and PIU. Also, the relationship between PIU and negative emotional state measured on the DASS21 are mediated and moderated via physical activity.

Materials and methods

Data collection and procedure

From November 10 to December 12, 2022, cross-sectional data were gathered using a survey created online using the Google Forms tool. An english language version of the survey is available as a supplementary file.

The questionnaire includes two sections: the first includes the socio-demographic questionnaire while the second includes the CIUS, the DASS21 and the subjective measurement of physical activity IPAQ.

The socio-demographic questionnaire includes age, gender, economic level (low/medium/high), place of residence of students (university hostel, residence with family, rental house with friends). In addition, the students were questioned by the following question on the use of the Internet: “What mainly use the Internet? “. The responses were coded into three responses (to play, to access social networks, or other purpose).

For the purpose of disseminating the questionnaire and including the most target individuals possible, we employed a snowball sampling technique to gather data from Tunisian students who were active on Facebook. Initially, Facebook student groups were used to deliver invitations to complete an informed consent form through individual Google Gmail accounts.

The responders then requested that their friends participate in the poll. To be able to manage various replies, this process enables the creation of a specialized vote box. We employed this environment, which is based on the Cloud Computing technology used by the Google application and enables for a single answer per user. To protect user confidentiality, privacy, and security, the usage of this method necessitates having a Google email account, and it forbids access to Internet Protocol (IP) addresses of users. There was no collection of personal data in the answer form (e.g., names, home addresses, email addresses, and phone numbers). The study complies with the CHERRIES (Recommended Standards for Conducting and Reporting Internet Surveys) guidelines.

Any student Facebook user who is at least 18 years old, resides in Tunisia, and speaks Arabic as their first language meets the inclusion requirements. To preserve the same social and cultural setting at the time of the survey, however, participants who do not live in the nation are not included in the research.The exclusion criteria involve subjects who have mental disorders (extremely severe scores) according to DASS21.

To obtain an adequate number of participants, we used an online calculator [59] used in previous studies referring to the number of students in Tunisia. Recent statistics reveal that the number of students in Tunisian universities is nearly 300,000 students [60]. A minimum number of 664 students is required (66% response rate, 5% margin of error, and 50% proportion with a 99% CI). This research has been approved by the Research Ethics Committee of the Institute of Sport and Physical Education at Kef, Jendouba University in Tunisia’s.

Instruments

The DASS-21 questionnaire

To assess stress, anxiety, and depression, an Arabic version of the DASS-21 was used. This version of the DASS-21 demonstrated a good reliably and validity across studies [61,62,63].

The DASS-21 is a self-assessment questionnaire that contains 21 items divided into three subscales. Each subscale consists 7 items that assess symptoms of depression (items 3, 5, 10, 13, 16, 17, 21), anxiety (items 2, 4, 7, 9, 15, 19, 20) and stress (items 1, 6, 8, 11, 12, 14, 18). Participants rate the severity of each symptom over the course of the past week on a four-point scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). The total score for each subscale ranges from 0 to 21, with higher scores indicating higher levels of depression, anxiety, and stress [64, 65]. The DASS-21’s psychometric properties were adequate, as proven by its good internal consistency reliability (Cronbach’s alpha ranged around 0.74 to 0.93) in studies that included both clinical and non-clinical populations [66], Additional information on the scoring of the DASS 21 is illustrated in additional file 1.

The arabic short version of the international physical activity questionnaire

To assess physical activity, the short Arabic version of the IPAQ scale (www.IPAQ.ki.se) was used. The tool was used in sevrel research among Tunisians and covers three types of activities: vigorous-intensity activities, moderate-intensity activities and walking. Participants report the number of days per week and the number of minutes per day they spent on each activity [67, 68]. The score for each type of physical activity in Metabolic Equivalent intensity in minutes per week (MET-min/wk ) is calculated by multiplying the number of minutes per week devoted to each category of activity by the MET value of this activity (walking = 3 0.3 × minutes of walking × days of walking; moderate activity = 4.0 × minutes of moderate activity × days of moderate activity; vigorous activity = 8.0 × minutes of vigorous activity × days of vigorous activityFurthermore, the calculation of adequate vigorous activity was based on no less than three days of vigorous activity exceeding 20 min per day. The same is true for sufficiently moderate activity and walking, which was calculated based on a five-day minimum of at least 30 min of moderate-intensity walking per day. Based on the scoring protocol provided by the IPAQ ( www.IPAQ.ki.se ) [69], levels of physical activity can be divided into three categories: inactive, minimally active, and health-enhancing physically active. IPAQ has demonstrated robust psychometric characteristics that have been well established in a number of populations [70, 71], particularly in the Arab population [72].

The arabic version of the compulsive internet use scale (CIUS)

We used the version translated by the CISU Arabic language translation and back-translation method. This version was identical to the initial version of the instrument which is designed to assess the severity of Internet addiction and/or compulsive, pathological or other Internet use that could be considered problematic. The 14 items on the CIUS-14 range in frequency from 0 (“never”) to 4 (“very often”). Higher scores indicate more severe PIUs; values range from 0 to 56. The original CIUS demonstrated sufficient concurrent, factorial, and validity as well as good reliability ( Cronbach’s alpha ranged between.89 and.90) [73].

Moreover, the Arabic version of the questionnaire demonstrated robust psychometric qualities with an internal consistency index that was satisfactory (α = 0.78), while the exploratory factor analysis suggested a one-factor solution [74].

Statistical analysis

The open-source free software Jamovi (version 2.3.21.0, Australia) was used to examine the data. Descriptive statistics of the demographic data were produced for the preliminary analysis, and assumptions about the normal distribution were confirmed.

We rigorously assessed the DASS-21 questionnaire’s reliability, including total score, stress, depression, and anxiety subscales, along with CIUS, using Cronbach’s alpha, McDonald’s omega, and Guttman lambda 6. This comprehensive approach ensures a solid understanding of the instruments’ consistency for future research and clinical applications.

The relationship between latent variables was evaluated by the Pearson correlation coefficients for DASS21 and CIUS SCORES. Moreover, Spearman coefficients was used to exam the relationships for gender and IPAQ. To examine these associations, we used low (< 0.35), moderate (0.36–0.67), and strong (> 0.67) thresholds for the correlation coefficient.

Seeing that the data did not deviate significantly from a normal distribution, correlation analysis using structural equation modelling (SEM) was performed.

We tested the structural model using the unweighted least squares. Several goodness-of-fit indices were used as criteria for the selection of the above model. We used χ2/df < 5, GFI, CFI, NFI, TLI > 0.90, SRMR, and RMSEA < 0.08 as the model fit index evaluation standards.

We performed bootstrapping with 5,000 samples for the analysis of moderate mediation considered the conditioning parameter from the mean and standard deviation (–1SD, +1SD) through the statistical package jamovi Advanced Mediation Models (JAMM).

We used a multinomial logistic regression to explore the effects of CIUS, Gender and IPAQ on the three mental health outcomes (stress, anxiety and depression).

Results

Preliminary analysis

Table 1 indicates that there were 976 students who completed the questionnaires. The entire students meet the inclusion criteria and not have any problematic mental disoders according to DASS 21 scores. Participants’ average ages were 20.76 ± 1.63 years, with 52.46% (n = 512) of them being female and 464 (47.54%) male. All participants were Muslims and had on-going access to the internet.

Students were divided according to economic levels into low (n = 256, 26.23%), medium (n = 523, 53.59%) and high (n = 197, 20.18%). 31.66% of the students resided in university hostels, 34.73% with their families, while the rest of the students reside in rental houses with their colleagues.

Regarding the main use of the internet, 29.82% of the students declared that they use the internet to play games, 51.84% mainly use the internet to access social networks, while 18.34% of the students use the internet for other purposes such as watching online videos, gambling, learning and scientific researches.

Concerning the physical activity, 39.86% of the students practice a weak physical practice, 44.47% a moderate physical practice and 5.68% a rigorous physical practice.

Table 1 Sociodemographics, Internet main use, and physical activity practice
Full size table

The examination of the internal consistency reliability of the DASS-21 questionnaire and CIUS yielded excellent outcomes (see Table 2). Across various measures, the instruments demonstrated strong reliability. Cronbach’s alpha, McDonald’s Omega and Guttman Lambda 6 coefficients all exceeded the commonly recommended threshold of 0.80 for good, indicating high internal consistency. Overall, the comprehensive approach to reliability assessment underscores the excellent reliability of the DASS-21 and CIUS instruments.

Table 2 Internal Consistency Reliabilty of DASS-21 and CIUS
Full size table

As shown in Table 3, correlation coefficients indicate a moderate negative association between CIUS sores and IPAQ physical activity measurement (r=-0.49, p < 0.01). Also, a moderate positive association was demonstrated between CIUS and DASS21 (r = 0.54, p < 0.01). While a weak correlation was demonstrated between CIUS and gender (r=-0.24, p < 0.01). In addition, the results demonstrated a moderate negative relationship between IPAQ scores and DASS21 scores (r=-0.52, p < 0.01) and an association between IPAQ scores and gender (r = 0.31, p < 0.01). On the other hand, gender was weakly associated with DASS21 scores (r=-0.16, p < 0.01).

Table 3 Descriptive statistics and correlations coefficients
Full size table

In the SEM analysis (Fig. 1), the goodness of fit indices of the study mediation model were found to be significant (χ2 (624, N = 976) = 2246; P < 0.001; χ2/df = 3.60; GFI = 0.99; CFI = 0. 97; NFI = 0.96; TLI = 0.97; SRMR = 0.071; RMSEA = 0.052 [0.049-0.054].

Fig. 1
figure 1

Relationship between IPAQ, CIUS, Gender and DASS21 score’s

Full size image

Mediation effects

We provide an overview of all mediation effects in the path analysis in Table 4. All three predictor variables (IPAQ, Gender and CIUS) significantly affected DASS21 scores; IPAQ (ß = −0.46, p ≤ 0.01), Gender (ß = −0.1, p ≤ 0.001) and CIUS (ß = 0.39, p ≤ 0.01).

Indirect effects of IPAQ and gender on DASS21 were highlighted: β= -0.18 (p < 0.01) and β= -0.04 (P < 0.01) respectively. In addition, a significant and negative effect of IPAQ on CIUS was demonstrated (β = -0.45, P < 0.01). In addition, the effect of CIUS on DASS21 was significant (β = -0.45, P < 0.01). Also, the effect of gender on CIUS was significant (β=-0.10, P < 0.01) However, its effect on DASS21 was not (β = 0.05, p = 0.078). The total effect of IPAQ on DASS21 was significant (β= -0.52, p < 0.01) but the effect of Gender on DASS21 was not significant (β = 0.01, p = 0.817).

Table 4 Direct and indirect effects
Full size table

Moderation

The results showed a significant moderation effect of the interaction between IPAQ and Gender on CIUS (β = 0.07, p < 0.01). However, it was not significant between Gender and CIUS on DASS21 (β = 0.09, p = 0.390) and between IPAQ and Gender on DASS21 (β = 0.01, p = 0.736) Also, the moderation effect was not significant in the interaction between IPAQ and CIUS on DASS21 (β = 0.15, p = 0.115) (see Table 5).

Table 5 Moderation effects of gender and CIUS
Full size table

Higher Compulsive Internet Use Scale (CIUS) score correlates with an increased risk of adverse mental health outcomes (Table 6). For those classified with normal stress levels, each unit increase in CIUS is associated with a decrease in the odds (B = -0.506, SE = 0.178, p = 0.004, AOR = 0.603, 95% CI [0.425, 0.854]), while for normal anxiety, the association shows a negative trend but is not significant (B = -0.238, SE = 0.210, p = 0.257, AOR = 0.788, 95% CI [0.522, 1.190]). Higher CIUS scores are significantly associated with greater odds of moderate to severe stress (B = 0.935, SE = 0.138, p < 0.001, AOR = 2.546, 95% CI [1.943, 3.337]) and anxiety (B = 0.719, SE = 0.171, p < 0.001, AOR = 2.052, 95% CI [1.468, 2.869]). Low physical activity (IPAQ1) is linked with a higher likelihood of moderate to severe stress (B = 1.600, SE = 0.331, p < 0.001, AOR = 4.954, 95% CI [2.589, 9.480]), anxiety (B = 1.610, SE = 0.363, p < 0.001, AOR = 5.005, 95% CI [2.459, 10.185]), and depression (B = 0.882, SE = 0.355, p = 0.013, AOR = 2.417, 95% CI [1.206, 4.842]).

Table 6 Multionomial Logistic regression for stress depression, and anxiety
Full size table

Discussion

The present study demonstrated that there was a significant and a strong positive correlation between the negative emotional state assessed by DASS 21 and the PIU. Our finding are in line with a previous study [75], which indicated that there was a significant and strong positive correlation between mental health assessed by the Mental Health Inventory and PIU, while [76] showed a statistically significant weak relationship between psychological health evaluated by the General Health Questionnaire and PIU.

According to our model, there were overall associations between PIU and negative emotional state issues, which was consistent with the findings of earlier research [77,78,79,80,81, 81, 82]. For example, a recent study conducted on 524 students (78.60% female, mean age 24 [SD 3] years old) demonstrated that the severity of PIU was linked to a number of mental health issues [80].

The present study showed that the relationship between negative emotional state levels and PIU among college students are directly proportional.

Other previous studies have also found positive correlations between PIU and stress. A recent investigation of 433 Turkish students revealed an increase in stress levels among students as the level of PIU increased [83]. To manage negative emotions, students use their smartphones to access the Internet. Playing and chatting online is going to make this internet use problematic [84]. In addition, a Chinese study proved that students who are under stress use the Internet in a problematic way [85].

Expanding on these findings, it becomes evident that the relationship between PIU and stress is multifaceted and warrants further exploration. For instance, while the Turkish study highlights a direct correlation between PIU and stress levels, it is essential to delve deeper into the underlying mechanisms driving this association. According to Carmona [86], the escapism offered by online activities, provides temporary relief from stressors but ultimately perpetuates a cycle of dependence and heightened stress. Additionally, cultural factors and societal norms surrounding technology usage may play a significant role in shaping individuals’ susceptibility to PIU under stress [87]. In addition, several previous studies suggest associations between depression and PIU [76, 88,89,90,91]. For example, a study conducted on Chinese college students found that increased internet usage time is associated with a higher level of depression scores among Chinese college students [91]. Similarly, a recent cross-sectional study of 619 university students (92.9% female and 7.1% male) with an average age of 22 ± 3 years demonstrated that depressive symptoms were significantly higher among students with PIU. In other words, the results of structural equational modeling analysis revealed that PIU significantly influences depressive symptoms [45].

Brailovskaia and Margraf [92] suggests that excessive internet use, characterized by PIU, can lead to a sedentary lifestyle, thereby reducing the likelihood of engaging in regular physical activity. Individuals who spend significant amounts of time online may prioritize screen time over exercise, leading to decreased physical activity levels. This sedentary behavior can contribute to physical health problems such as obesity, cardiovascular issues, and muscular-skeletal disorders, all of which can exacerbate symptoms of depression.

Conversely, regular physical activity has been shown to have a positive impact on mental health, including reducing symptoms of depression. Exercise triggers the release of endorphins, neurotransmitters that promote feelings of well-being and happiness, while also reducing levels of stress hormones like cortisol. Engaging in physical activity can provide a healthy outlet for managing stress and negative emotions, potentially mitigating the risk of developing depression [93].

Also, previous studies have advanced the theory that anxious people use online interactions to compensate for poor real-life relationships and shown a strong association between PIU and anxiety symptoms [6, 94]. In contrast, previous studies have found no links between PIU and symptoms of anxiety [76, 95, 96].

Additionally, according to Svensson and all [97]., regular physical activity has been demonstrated to positively impact anxiety levels. Exercise triggers the release of endorphins, chemicals in the brain that promote feelings of well-being and relaxation, while also reducing levels of stress hormones such as cortisol. Participating in physical activity can serve as a constructive outlet for managing stress and anxiety, potentially alleviating symptoms.

A number of studies have found a link between mental health quality and physical activity [98,99,100,101,102,103] and highlited that poor mental health was related to weak physical activity [104]. Our present research agrees with a previous study [105] and reveals that physical activity had a decisive moderating role in the link between PIU and the negative emotional state of university students. In other words, students who have a higher PIU are also more likely to have unfavorable negative emotional state indices, which is partly explained by lower levels of physical activity.

Our study agrees with previous research [106,107,108] stating that PIU can impact the prevalence of physical activity. For example, the results of the study by [106] reinforce the results of the present study by revealing that there is a significant correlation between physical activity levels and PIU. The results indicate that the scores of the Cognitive-Behavioral Physical Activity Questionnaire are observed to be significantly lower in participants who spend more of their free time on the Internet compared to those who spend their free time playing sports [106]. Similarly, the authors showed that participants who spend their time playing sports are likely to express a lower PIU than those who spend their free time on the Internet. On the other hand, a recent study conducted on a sample of Vietnamese university students revealed that Internet addiction has no effect on sports practice [109].

Several previous studies have found a correlation between gender and severity of PIU. A recent meta-analytic study reported an overall gender difference [110]. The results of the present study agree with other previous studies and reveal that women are likely to express a higher PIU than men [111,112,113]. For example, Casaló & Escario [111] and Machimbarrena et al. [113] reported that Spanish women outscored men on PIU severity however, the present study departs from earlier surveys that have suggested a propensity for men to exhibit a higher level of problematic Internet use (PIU) compared to women [114,115,116,117,118,119]. For example, a study in populations from the United States, United Kingdom and Australia found that women had significantly lower scores in terms of PUI severity compared to men [114].

Our study highlighted a role of physical activity moderation on the association between PIU and negative emotional state outcomes measured on the DASS21. In contrast, no such moderating effect was found for the relationship between physical activity and negative emotional state parameters. Similarly, gender did not act as a moderating variable for the relationship between physical activity and PIU.

In line with these results, a moderation analysis indicated that physical exercise moderated the relationship between PIU and psychological symptoms [120].

Our study could not highlight the role of gender as a moderator between physical practice and negative emotional state. In contrast, gender appears to be a moderator between physical activity and mental health in adolescents [121].

Furthermore, other previous research contradicts our results and claims to find no significant associations between gender and PIU severity [122,123,124,125,126,127]. However, to best our knowledge, no moderation effect has been examined in previous studies.

The results of this research indicate that the practice of physical activity among Tunisian women is lower than that of men. Our results agree with those of other previous studies [128,129,130,131]. For example, a recent study conducted on an Arabic sample revealed that gender differences in the prevalence of physical activity were evident [130]. Likewise, a previous study demonstrated that Saudi men have been shown to be more active than women [129]. Moreover, cross-cultural studies have revealed that young Arab women are less active than British ones [128, 131].

Limitation of study

Our study has several limitations

The first limitation concerns the cross-sectional design of the study, which may limit the generalizability of the results. In addition, the snowball sampling method and the line design may prove to have methodological limitations.

PIU may vary by gender, as previous studies have suggested that women use social media heavily while men use the internet more for gambling. Future studies should incorporate questionnaires that involve gaming disorders and social media addiction.

The current study did not control for mental health disorders and medication use among participants. these variables may affect the association between physical practice, PIU, and negative emotional state. A question regarding mental health problems and medication use should be considered in previous research in this topic.

Finally, previous studies have found that the mental health measurement tools used influence the results of associations between PIU and mental health indicators [132, 133]. Therefore, it is necessary to use other measures of negative emotional state other than the DASS21.

Conclusion

Our finding proved the relationship between the practice of physical activity and PIU. Also, physical practice appears to moderate the link between PIU and mental health. While gender was linked to PIU. In addition, a direct association of PIU with mental health outcomes has been demonstrated.

Data availability

The datasets utilized and/or examined in this study can be obtained from the corresponding author upon a reasonable request.

Abbreviations

DASS-21:

The depression, anxiety and stress scale– 21 items

IPAQ:

The international physical activity questionnaire

CIUS:

The compulsive internet use scale

PIU:

Problematic Internet Use

IP:

Internet Protocol

JAMM:

Statistical package jamovi Advanced Mediation Models

χ2:

Chi-Square

GFI:

The goodness of fit index

CFI:

The comparative fit index

NFI:

Normed Fit Index

TLI:

Tucker-Lewis index

SRMR:

Standardized Root Mean Square Residual

RMSEA:

Root mean square error of approximation

References

  1. Lozano-Blasco R, Robres AQ, Sánchez AS. Internet addiction in young adults: a meta-analysis and systematic review. Comput Hum Behav. 2022;:107201.

  2. Hernández C, Cottin M, Parada F, Labbé N, Núñez C, Quevedo Y, et al. Watching the world from my screen: a longitudinal evaluation of the influence of a problematic use of the internet on depressive symptomatology. Comput Hum Behav. 2022;126:106995.

    Article  Google Scholar 

  3. Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, et al. Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Compr Psychiatry. 2020;100:152180.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sun Y, Li Y, Bao Y, Meng S, Sun Y, Schumann G, et al. Brief report: increased addictive internet and substance use behavior during the COVID-19 pandemic in China. Am J Addict. 2020;29:268–70.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pan Y-C, Chiu Y-C, Lin Y-H. Systematic review and meta-analysis of epidemiology of internet addiction. Neurosci Biobehav Rev. 2020;118:612–22.

    Article  PubMed  Google Scholar 

  6. Lavoie C, Dufour M, Berbiche D, Therriault D, Lane J. The relationship between problematic internet use and anxiety disorder symptoms in youth: specificity of the type of application and gender. Comput Hum Behav. 2023;140:107604.

    Article  Google Scholar 

  7. Kayiş AR, Satici SA, Yilmaz MF, Şimşek D, Ceyhan E, Bakioğlu F. Big five-personality trait and internet addiction: a meta-analytic review. Comput Hum Behav. 2016;63:35–40.

    Article  Google Scholar 

  8. Park SM, Lee JY, Kim YJ, Lee J-Y, Jung HY, Sohn BK, et al. Neural connectivity in internet gaming disorder and alcohol use disorder: a resting-state EEG coherence study. Sci Rep. 2017;7:1–12.

    PubMed  PubMed Central  Google Scholar 

  9. Beard KW, Wolf EM. Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychol Behav. 2001;4:377–83.

    Article  PubMed  Google Scholar 

  10. Ioannidis K, Hook R, Goudriaan AE, Vlies S, Fineberg NA, Grant JE, et al. Cognitive deficits in problematic internet use: meta-analysis of 40 studies. Br J Psychiatry. 2019;215:639–46.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Stieger S, Wunderl S. Associations between social media use and cognitive abilities: results from a large-scale study of adolescents. Comput Hum Behav. 2022;135:107358.

    Article  Google Scholar 

  12. Özaslan A, Yıldırım M, Güney E, Güzel HŞ, İşeri E. Association between problematic internet use, quality of parent-adolescents relationship, conflicts, and mental health problems. Int J Ment Health Addict. 2022;20:2503–19.

    Article  Google Scholar 

  13. Edition F. Diagnostic and statistical manual of mental disorders. Am Psychiatr Assoc. 2013;21:591–643.

    Google Scholar 

  14. Weinstein A, Lejoyeux M. Neurobiological mechanisms underlying internet gaming disorder. Dialogues Clin Neurosci. 2022.

  15. Sha P, Sariyska R, Riedl R, Lachmann B, Montag C. Linking internet communication and smartphone use disorder by taking a closer look at the Facebook and WhatsApp applications. Addict Behav Rep. 2019;9:100148.

    PubMed  Google Scholar 

  16. Guo Y, Li Y, Ito N. Exploring the predicted effect of social networking site use on perceived social capital and psychological well-being of Chinese international students in Japan. Cyberpsychology Behav Soc Netw. 2014;17:52–8.

    Article  Google Scholar 

  17. Tseng Y-H, Chao H-H, Hung C-L. Effect of a Strategic Physical Activity Program on Cognitive Flexibility among children with internet addiction: a pilot study. Children. 2022;9:798.

    Article  PubMed  Google Scholar 

  18. Brand M, Young KS, Laier C. Prefrontal control and internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Front Hum Neurosci. 2014;:375.

  19. Fineberg NA, Menchón JM, Hall N, Dell’Osso B, Brand M, Potenza MN et al. Advances in problematic usage of the internet research–A narrative review by experts from the European network for problematic usage of the internet. Compr Psychiatry. 2022;:152346.

  20. Wegmann E, Müller SM, Turel O, Brand M. Interactions of impulsivity, general executive functions, and specific inhibitory control explain symptoms of social-networks-use disorder: an experimental study. Sci Rep. 2020;10:1–12.

    Article  Google Scholar 

  21. Diotaiuti P, Girelli L, Mancone S, Corrado S, Valente G, Cavicchiolo E. Impulsivity and depressive brooding in internet addiction: a study with a sample of Italian adolescents during covid-19 lockdown. Front Psychiatry. 2022;13.

  22. Yang X, Guo W-J, Tao Y-J, Meng Y-J, Wang H-Y, Li X-J, et al. A bidirectional association between internet addiction and depression: a large-sample longitudinal study among Chinese university students. J Affect Disord. 2022;299:416–24.

    Article  PubMed  Google Scholar 

  23. Yalçin YG. Investıgatıon of Internet addiction and anger expression styles of students of the Faculty of sports sciences. Pak J Med Health Sci. 2022;16:724–724.

    Google Scholar 

  24. Hassan T, Alam MM, Wahab A, Hawlader MD. Prevalence and associated factors of internet addiction among young adults in Bangladesh. J Egypt Public Health Assoc. 2020;95:1–8.

    Article  Google Scholar 

  25. Romero-Rodríguez J-M, Marín-Marín J-A, Hinojo-Lucena F-J, Gómez-García G. An explanatory model of problematic internet use of Southern Spanish university students. Soc Sci Comput Rev. 2022;40:1171–85.

    Article  Google Scholar 

  26. Alpaslan AH, Koçak U, Avci K, Uzel Taş H. The association between internet addiction and disordered eating attitudes among Turkish high school students. Eat Weight Disord-Stud Anorex Bulim Obes. 2015;20:441–8.

    Article  Google Scholar 

  27. Anand N, Thomas C, Jain PA, Bhat A, Thomas C, Prathyusha PV, et al. Internet use behaviors, internet addiction and psychological distress among medical college students: a multi centre study from South India. Asian J Psychiatry. 2018;37:71–7.

    Article  Google Scholar 

  28. Vigna-Taglianti F, Brambilla R, Priotto B, Angelino R, Cuomo G, Diecidue R. Problematic internet use among high school students: prevalence, associated factors and gender differences. Psychiatry Res. 2017;257:163–71.

    Article  PubMed  Google Scholar 

  29. Wang W, Li D, Li X, Wang Y, Sun W, Zhao L, et al. Parent-adolescent relationship and adolescent internet addiction: a moderated mediation model. Addict Behav. 2018;84:171–7.

    Article  PubMed  Google Scholar 

  30. Cervigón-Carrasco V, Schulze-Steinen L, Ballester-Arnal R, Billieux J, Gil-Juliá B, Giménez-Garcia C, et al. Attentional inhibitory control interference related to videogames, pornography, and TV series exposure: an experimental study in three independent samples. Comput Hum Behav. 2023;143:107683.

    Article  Google Scholar 

  31. Cypress Valkyrie Z. Cybersexuality in MMORPGs: virtual sexual revolution untapped. Men Masculinities. 2011;14:76–96.

    Article  Google Scholar 

  32. Holdoš J. Type D personality in the prediction of internet addiction in the young adult population of Slovak Internet users. Curr Psychol. 2017;36:861–8.

    Article  Google Scholar 

  33. Kavvadas D, Kavvada A, Karachrysafi S, Papaliagkas V, Chatzidimitriou M, Papamitsou T. Stress, anxiety, and Depression levels among University students: three years from the beginning of the pandemic. Clin Pract. 2023;13:596–609.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Cheung K, Tam KY, Tsang MH, Zhang LW, Lit SW. Depression, anxiety and stress in different subgroups of first-year university students from 4-year cohort data. J Affect Disord. 2020;274:305–14.

    Article  PubMed  Google Scholar 

  35. Herbert C, Meixner F, Wiebking C, Gilg V. Regular physical activity, short-term exercise, mental health, and well-being among university students: the results of an online and a laboratory study. Front Psychol. 2020;11:509.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Xiang M-Q, Tan X-M, Sun J, Yang H-Y, Zhao X-P, Liu L, et al. Relationship of physical activity with anxiety and depression symptoms in Chinese college students during the COVID-19 outbreak. Front Psychol. 2020;11:582436.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Grasdalsmoen M, Eriksen HR, Lønning KJ, Sivertsen B. Physical exercise, mental health problems, and suicide attempts in university students. BMC Psychiatry. 2020;20:175.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Alghadir AH, Gabr SA. Hormonal function responses to Moderate Aerobic Exercise in older adults with Depression. Clin Interv Aging. 2020;15:1271–83.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Drigas A, Mitsea E, Metacognition. Stress-relaxation Balance & related hormones. Int J Recent Contrib Eng Sci IT. 2021;9:4–16.

    Google Scholar 

  40. Liang J, Wang H, Zeng Y, Qu Y, Liu Q, Zhao F, et al. Physical exercise promotes brain remodeling by regulating epigenetics, neuroplasticity and neurotrophins. Rev Neurosci. 2021;32:615–29.

    Article  PubMed  Google Scholar 

  41. Al-Gamal E, Alzayyat A, Ahmad MM. Prevalence of I nternet Addiction and its Association with psychological distress and coping strategies among University students in J ordan. Perspect Psychiatr Care. 2016;52:49–61.

    Article  PubMed  Google Scholar 

  42. Servidio R. Exploring the effects of demographic factors, internet usage and personality traits on internet addiction in a sample of Italian university students. Comput Hum Behav. 2014;35:85–92.

    Article  Google Scholar 

  43. Marín Díaz V, Sampedro Requena BE, Vega Gea EM. Estudio psicométrico de la aplicación del internet addiction test con estudiantes universitarios españoles. Context Educ Rev Educ. 2017.

  44. Balhara YPS, Doric A, Stevanovic D, Knez R, Singh S, Chowdhury MRR, et al. Correlates of problematic internet use among college and university students in eight countries: an international cross-sectional study. Asian J Psychiatry. 2019;45:113–20.

    Article  Google Scholar 

  45. Gecaite-Stonciene J, Saudargiene A, Pranckeviciene A, Liaugaudaite V, Griskova-Bulanova I, Simkute D, et al. Impulsivity mediates associations between problematic internet use, anxiety, and depressive symptoms in students: a cross-sectional COVID-19 study. Front Psychiatry. 2021;12:634464.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Mamun MA, Hossain MS, Siddique AB, Sikder MT, Kuss DJ, Griffiths MD. Problematic internet use in Bangladeshi students: the role of socio-demographic factors, depression, anxiety, and stress. Asian J Psychiatry. 2019;44:48–54.

    Article  Google Scholar 

  47. Zhai B, Li D, Li X, Liu Y, Zhang J, Sun W, et al. Perceived school climate and problematic internet use among adolescents: mediating roles of school belonging and depressive symptoms. Addict Behav. 2020;110:106501.

    Article  PubMed  Google Scholar 

  48. Derbyshire KL, Lust KA, Schreiber LR, Odlaug BL, Christenson GA, Golden DJ, et al. Problematic internet use and associated risks in a college sample. Compr Psychiatry. 2013;54:415–22.

    Article  PubMed  Google Scholar 

  49. Liu W, Chen J-S, Gan WY, Poon WC, Tung SEH, Lee LJ, et al. Associations of problematic internet use, weight-related self-stigma, and nomophobia with physical activity: findings from mainland China, Taiwan, and Malaysia. Int J Environ Res Public Health. 2022;19:12135.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Aboujaoude E. Problematic internet use: an overview. World Psychiatry. 2010;9:85.

    Article  PubMed  PubMed Central  Google Scholar 

  51. El Asam A, Samara M, Terry P. Problematic internet use and mental health among British children and adolescents. Addict Behav. 2019;90:428–36.

    Article  PubMed  Google Scholar 

  52. Islam MR, Tushar MI, Tultul PS, Akter R, Sohan M, Anjum R, et al. Problematic internet use and depressive symptoms among the school-going adolescents in Bangladesh during the COVID-19 pandemic: a cross-sectional study findings. Health Sci Rep. 2023;6:e1008.

    Article  PubMed  Google Scholar 

  53. Liu T, Potenza MN. Problematic internet use: clinical implications. CNS Spectr. 2007;12:453–66.

    Article  PubMed  Google Scholar 

  54. Park S. Associations of physical activity with sleep satisfaction, perceived stress, and problematic internet use in Korean adolescents. BMC Public Health. 2014;14:1143.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Spada MM. An overview of problematic internet use. Addict Behav. 2014;39:3–6.

    Article  PubMed  Google Scholar 

  56. Alaca N. The impact of internet addiction on depression, physical activity level and trigger point sensitivity in Turkish university students. J Back Musculoskelet Rehabil. 2020;33:623–30.

    Article  PubMed  Google Scholar 

  57. Jelleli H, Hindawi O, Rebhi M, Ben Aissa M, Saidane M, Saad AR et al. Psychometric evidence of the Arabic Version of Nomophobia Questionnaire among Physical Education Students. Psychol Res Behav Manag. 2023;:2383–94.

  58. Guelmami N, Saidane M, Aissa MB, Rebhi M, Chalghaf N, Azaiez F, et al. Multidimensional Assessment of Student Mental Health during the COVID-19 pandemic: a latent Profile Analysis integrating positive psychology. New Asian J Med. 2023;1:20–9.

    Article  Google Scholar 

  59. Sample Size Calculator by Raosoft, Inc. http://www.raosoft.com/samplesize.html. Accessed 18 Sep 2023.

  60. Tunisia. number of students in higher education. Statista. https://www.statista.com/statistics/1241023/number-of-students-enrolled-in-tertiary-education-in-tunisia/. Accessed 18 Sep 2023.

  61. Chin EG. 12-Item depression, anxiety, and stress scales (Dass-12): associations with Self-Report measures, a semi-strcutured interview. Behav Tasks. 2015;:108.

  62. Lee J, Lee E-H, Moon SH. Systematic review of the measurement properties of the Depression anxiety stress Scales–21 by applying updated COSMIN methodology. Qual Life Res. 2019;28:2325–39.

    Article  PubMed  Google Scholar 

  63. Makara-Studzińska M, Tyburski E, Za\luski M, Adamczyk K, Mesterhazy J, Mesterhazy A. Confirmatory factor analysis of three versions of the depression anxiety stress scale (DASS-42, DASS-21, and DASS-12) in Polish adults. Front Psychiatry. 2022;:2342.

  64. Ali AM, Ahmed A, Sharaf A, Kawakami N, Abdeldayem SM, Green J. The Arabic Version of the Depression anxiety stress Scale-21: cumulative scaling and discriminant-validation testing. Asian J Psychiatry. 2017;30:56–8.

    Article  Google Scholar 

  65. Ali AM, Green J. Differential item functioning of the arabic version of the Depression anxiety stress Scale-21 (DASS-21). JOJ Nurs Health Care. 2017;4:67–71.

    Google Scholar 

  66. Ahmed O, Faisal RA, Alim SMAHM, Sharker T, Hiramoni FA. The psychometric properties of the depression anxiety stress scale-21 (DASS-21) Bangla version. Acta Psychol (Amst). 2022;223:103509.

    Article  PubMed  Google Scholar 

  67. Alsalhe TA, Aljaloud SO, Chalghaf N, Guelmami N, Alhazza DW, Azaiez F, et al. Moderation effect of physical activity on the relationship between fear of COVID-19 and general distress: a pilot case study in arabic countries. Front Psychol. 2020;11:570085.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Guelmami N, Chalghaf N, Tannoubi A, Puce L, Azaiez F, Bragazzi NL. Initial psychometric evidence of physical inactivity perceived experience scale (Pipes): COVID-19 pandemic as a pilot study. Front Public Health. 2022;10:819052.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Al-Hazzaa HM. Health-enhancing physical activity among Saudi adults using the International Physical Activity Questionnaire (IPAQ). Public Health Nutr. 2007;10:59–64.

    Article  PubMed  Google Scholar 

  70. Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8:1–11.

    Article  Google Scholar 

  71. Vasheghani-Farahani A, Tahmasbi M, Asheri H, Ashraf H, Nedjat S, Kordi R. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian J Sports Med. 2011;2:106.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Regaieg S, Charfi N, Yaich S, Damak J, Abid M. The reliability and concurrent validity of a modified version of the international physical activity questionnaire for adolescents (IPAQ-A) in Tunisian overweight and obese youths. Med Princ Pract. 2016;25:227–32.

    Article  PubMed  Google Scholar 

  73. Meerkerk G-J, Van Den Eijnden RJ, Vermulst AA, Garretsen HF. The compulsive internet use scale (CIUS): some psychometric properties. Cyberpsychol Behav. 2009;12:1–6.

    Article  PubMed  Google Scholar 

  74. Khazaal Y, Chatton A, Atwi K, Zullino D, Khan R, Billieux J. Arabic validation of the compulsive internet use scale (CIUS). Subst Abuse Treat Prev Policy. 2011;6:32.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Zafar N, Kausar R, Pallesen S. Internet addiction, Insomnia and Mental Health Problems in University Students in Pakistan. Pak J Soc Clin Psychol. 2018;16:10–6.

    Google Scholar 

  76. Mamun MA, Hossain MS, Moonajilin MS, Masud MT, Misti JM, Griffiths MD. Does loneliness, self-esteem and psychological distress correlate with problematic internet use? A Bangladeshi survey study. Asia-Pac Psychiatry. 2020;12:e12386.

    Article  PubMed  Google Scholar 

  77. Capetillo-Ventura N, Juárez-Treviño M. Internet addiction in university medical students. Med Univ. 2015;17:88–93.

    Google Scholar 

  78. Grover S, Sahoo S, Bhalla A, Avasthi A. Problematic internet use and its correlates among resident doctors of a tertiary care hospital of North India: a cross-sectional study. Asian J Psychiatry. 2019;39:42–7.

    Article  Google Scholar 

  79. Kwak Y, Kim H, Ahn J-W. Impact of internet usage time on mental health in adolescents: using the 14th Korea youth risk behavior web-based survey 2018. PLoS ONE. 2022;17:e0264948.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Milasauskiene E, Burkauskas J, Podlipskyte A, Király O, Demetrovics Z, Ambrasas L, et al. Compulsive internet use scale: psychometric properties and associations with sleeping patterns, mental health, and well-being in Lithuanian medical students during the coronavirus disease 2019 pandemic. Front Psychol. 2021;12:685137.

    Article  PubMed  Google Scholar 

  81. Shadzi MR, Salehi A, Vardanjani HM. Problematic internet use, mental health, and sleep quality among medical students: a path-analytic model. Indian J Psychol Med. 2020;42:128–35.

    Article  PubMed  Google Scholar 

  82. Younes F, Halawi G, Jabbour H, El Osta N, Karam L, Hajj A, et al. Internet addiction and relationships with insomnia, anxiety, depression, stress and self-esteem in university students: a cross-sectional designed study. PLoS ONE. 2016;11:e0161126.

    Article  PubMed  Google Scholar 

  83. Unsar S, Kostak MA, Yilmaz S, Ozdinc S, Selda O, Unsar AS. Problematic internet use and stress levels in students of health and social sciences. Int J Caring Sci. 2020;13:438–47.

    Google Scholar 

  84. Feng Y, Ma Y, Zhong Q. The relationship between adolescents’ stress and internet addiction: a mediated-moderation model. Front Psychol. 2019;10:2248.

    Article  PubMed  Google Scholar 

  85. Lei H, Chiu MM, Li S. Subjective well-being and internet overuse: a meta-analysis of mainland Chinese students. Curr Psychol. 2020;39:843–53.

    Article  Google Scholar 

  86. Carmona J. Escaping the escapism: a grounded theory of the addiction and recovery process in online video gaming. Qual Rep. 2021;26:01–2188.

    Google Scholar 

  87. Dressler WW. Culture and the Stress Process. In: Singer M, Erickson PI, Abadía-Barrero CE, editors. A Companion to Medical Anthropology. 1st edition. Wiley; 2022. pp. 93–108.

  88. Jelenchick LA, Eickhoff J, Zhang C, Kraninger K, Christakis DA, Moreno MA. Screening for adolescent problematic internet use: validation of the problematic and risky internet use screening scale (PRIUSS). Acad Pediatr. 2015;15:658–65.

    Article  PubMed  Google Scholar 

  89. Kożybska M, Kurpisz J, Radlińska I, Skwirczyńska E, Serwin N, Zabielska P, et al. Problematic internet use, health behaviors, depression and eating disorders: a cross-sectional study among Polish medical school students. Ann Gen Psychiatry. 2022;21:1–9.

    Article  Google Scholar 

  90. Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: the role of social anxiety and need for social assurance in problematic use of Facebook. Cyberpsychology Behav Soc Netw. 2015;18:567–74.

    Article  Google Scholar 

  91. Zhou M, Ding X. Internet use, depression, and cognitive outcomes among Chinese adolescents. J Community Psychol. 2023;51:768–87.

    Article  PubMed  Google Scholar 

  92. Brailovskaia J, Rohmann E, Bierhoff H-W, Schillack H, Margraf J. The relationship between daily stress, social support and Facebook Addiction Disorder. Psychiatry Res. 2019;276:167–74.

    Article  PubMed  Google Scholar 

  93. Hallgren M, Owen N, Stubbs B, Vancampfort D, Lundin A, Dunstan D, et al. Cross-sectional and prospective relationships of passive and mentally active sedentary behaviours and physical activity with depression. Br J Psychiatry. 2020;217:413–9.

    Article  PubMed  Google Scholar 

  94. Amendola S, Cerutti R, Presaghi F. Symptoms of prolonged social withdrawal, problematic internet use, and psychotic-like experiences in emerging adulthood: a moderation model. Clin Neuropsychiatry. 2023;20.

  95. Lopez-Fernandez O, Romo L, Kern L, Rousseau A, Lelonek-Kuleta B, Chwaszcz J, et al. Problematic internet use among adults: a cross-cultural study in 15 countries. J Clin Med. 2023;12:1027.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Restrepo A, Scheininger T, Clucas J, Alexander L, Salum GA, Georgiades K, et al. Problematic internet use in children and adolescents: associations with psychiatric disorders and impairment. BMC Psychiatry. 2020;20:1–11.

    Article  Google Scholar 

  97. Svensson M, Brundin L, Erhardt S, Hållmarker U, James S, Deierborg T. Physical activity is associated with lower long-term incidence of anxiety in a population-based, large-scale study. Front Psychiatry. 2021;12:714014.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Ambrose PR, Cuca YP, Baguso GN, Hoffmann TJ, Dawson-Rose C. Resilience, physical activity, and depression in women living with HIV in the San Francisco Bay area: a cross-sectional study. J Assoc Nurses AIDS Care. 2022;33:202–10.

    Article  PubMed  Google Scholar 

  99. Kim J-H. Regular physical exercise and its association with depression: a population-based study short title: Exercise and depression. Psychiatry Res. 2022;309:114406.

    Article  PubMed  Google Scholar 

  100. Matias TS, Lopes MVV, da Costa BGG, Silva KS, Schuch FB. Relationship between types of physical activity and depression among 88,522 adults. J Affect Disord. 2022;297:415–20.

    Article  PubMed  Google Scholar 

  101. Pearce M, Garcia L, Abbas A, Strain T, Schuch FB, Golubic R et al. Association between Physical Activity and Risk of Depression: a systematic review and Meta-analysis. JAMA Psychiatry. 2022.

  102. Rutherford ER, Vandelanotte C, Chapman J, To QG. Associations between depression, domain-specific physical activity, and BMI among US adults: NHANES 2011–2014 cross-sectional data. BMC Public Health. 2022;22:1–9.

    Article  Google Scholar 

  103. Tian J, Kang Y, Liu P, Yu H. Effect of physical activity on Depression in patients with Parkinson’s Disease: a systematic review and Meta-analysis. Int J Environ Res Public Health. 2022;19:6849.

    Article  PubMed  Google Scholar 

  104. Guelmami N, Tannoubi A, Chalghaf N, Saidane M, Kong J, Puce L, et al. Latent Profile Analysis to Survey Positive Mental Health and Well-Being: a Pilot Investigation Insight Tunisian Facebook users. Front Psychiatry. 2022;13:824134.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Demenech LM, Domingues MR, Muller RM, Levien VR, Dumith SC. Internet addiction and depressive symptoms: a dose-response effect mediated by levels of physical activity. Trends Psychiatry Psychother. 2021.

  106. Alagoz N, Keski̇nkilic AU. The relationship between internet and game addiction and the levels of physical activity of the secondary education students. Med (Baltim). 2022;11:267–73.

    Google Scholar 

  107. AlMarzooqi MA, Alhaj OA, Alrasheed MM, Helmy M, Trabelsi K, Ebrahim A, et al. Symptoms of Nomophobia, Psychological Aspects, Insomnia and physical activity: a cross-sectional study of ESports players in Saudi Arabia. In: Healthcare. MDPI; 2022. p. 257.

  108. Lubis M, Handayani DOD. The relationship of personal data protection towards internet addiction: Cyber crimes, pornography and reduced physical activity. Procedia Comput Sci. 2022;197:151–61.

    Article  Google Scholar 

  109. Dang AK, Nathan N, Le QNH, Nguyen LH, Nguyen HLT, Nguyen CT, et al. Associations between internet addiction and physical activity among Vietnamese youths and adolescents. Child Youth Serv Rev. 2018;93:36–40.

    Article  Google Scholar 

  110. Su W, Han X, Jin C, Yan Y, Potenza MN. Are males more likely to be addicted to the internet than females? A meta-analysis involving 34 global jurisdictions. Comput Hum Behav. 2019;99:86–100.

    Article  Google Scholar 

  111. Casaló LV, Escario J-J. Predictors of excessive internet use among adolescents in Spain: the relevance of the relationship between parents and their children. Comput Hum Behav. 2019;92:344–51.

    Article  Google Scholar 

  112. Karaer Y, Akdemir D. Parenting styles, perceived social support and emotion regulation in adolescents with internet addiction. Compr Psychiatry. 2019;92:22–7.

    Article  PubMed  Google Scholar 

  113. Machimbarrena JM, González-Cabrera J, Ortega-Barón J, Beranuy-Fargues M, Álvarez-Bardón A, Tejero B. Profiles of problematic internet use and its impact on adolescents’ health-related quality of life. Int J Environ Res Public Health. 2019;16:3877.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Andreetta J, Teh MSc J, Burleigh TL, Gomez R, Stavropoulos V. Associations between comorbid stress and internet gaming disorder symptoms: are there cultural and gender variations? Asia-Pac Psychiatry. 2020;12:e12387.

    Article  PubMed  Google Scholar 

  115. Chi X, Hong X, Chen X. Profiles and sociodemographic correlates of internet addiction in early adolescents in southern China. Addict Behav. 2020;106:106385.

    Article  PubMed  Google Scholar 

  116. Kannan B, Karthik S, Pal GK, Menon V. Gender variation in the prevalence of internet addiction and impact of internet addiction on reaction time and heart rate variability in Medical College Students. J Clin Diagn Res. 2019;13.

  117. Oluwafemi OO, Bibire AH, Mebu VA, Dung PH, Aderibigbe JK. Conditional indirect effects of gender and school on internet use for academic activities and social-moral development among secondary school students in Nigeria. Cogent Soc Sci. 2020;6:1748478.

    Google Scholar 

  118. Tomaszek K, Muchacka-Cymerman A. Sex differences in the relationship between student school burnout and problematic internet use among adolescents. Int J Environ Res Public Health. 2019;16:4107.

    Article  PubMed  PubMed Central  Google Scholar 

  119. Yu L, Recker M, Chen S, Zhao N, Yang Q. The moderating effect of geographic area on the relationship between age, gender, and information and communication technology literacy and problematic internet use. Cyberpsychology Behav Soc Netw. 2018;21:367–73.

    Article  Google Scholar 

  120. Lin L, Liu J, Cao X, Wen S, Xu J, Xue Z, et al. Internet addiction mediates the association between cyber victimization and psychological and physical symptoms: moderation by physical exercise. BMC Psychiatry. 2020;20:1–8.

    Article  Google Scholar 

  121. Halliday AJ, Kern ML, Turnbull DA. Can physical activity help explain the gender gap in adolescent mental health? A cross-sectional exploration. Ment Health Phys Act. 2019;16:8–18.

    Article  Google Scholar 

  122. Arafa A, Mahmoud O, Abu Salem E. Excessive internet use and self-esteem among internet users in Egypt. Int J Ment Health. 2019;48:95–105.

    Article  Google Scholar 

  123. Costa RM, Patrão I, Machado M. Problematic internet use and feelings of loneliness. Int J Psychiatry Clin Pract. 2019;23:160–2.

    Article  PubMed  Google Scholar 

  124. Faltỳnková A, Blinka L, Ševčíková A, Husarova D. The associations between family-related factors and excessive internet use in adolescents. Int J Environ Res Public Health. 2020;17:1754.

    Article  PubMed  Google Scholar 

  125. Li Q, Dai W, Zhong Y, Wang L, Dai B, Liu X. The mediating role of coping styles on impulsivity, behavioral inhibition/approach system, and internet addiction in adolescents from a gender perspective. Front Psychol. 2019;10:2402.

    Article  PubMed  Google Scholar 

  126. Tateno M, Teo AR, Ukai W, Kanazawa J, Katsuki R, Kubo H, et al. Internet addiction, smartphone addiction, and Hikikomori trait in Japanese young adult: social isolation and social network. Front Psychiatry. 2019;10:455.

    Article  PubMed  PubMed Central  Google Scholar 

  127. Vally Z, Laconi S, Kaliszewska-Czeremska K. Problematic internet use, psychopathology, defense mechanisms, and coping strategies: a cross-sectional study from the United Arab Emirates. Psychiatr Q. 2020;91:587–602.

    Article  PubMed  Google Scholar 

  128. Al-Hazzaa HM, Al-Nakeeb Y, Duncan MJ, Al-Sobayel HI, Abahussain NA, Musaiger AO, et al. A cross-cultural comparison of health behaviors between Saudi and British adolescents living in urban areas: gender by country analyses. Int J Environ Res Public Health. 2013;10:6701–20.

    Article  PubMed  Google Scholar 

  129. Alrimali AM. Assessment of physical activity level, self-efficacy and perceived barriers to physical activity among adult Saudi women. J Taibah Univ Med Sci. 2023.

  130. Murtagh E, Shalash A, Martin R, Rmeileh NA. Measurement and prevalence of adult physical activity levels in arab countries. Public Health. 2021;198:129–40.

    Article  PubMed  Google Scholar 

  131. Samara A, Nistrup A, Al-Rammah TY, Aro AR. Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students. Int J Womens Health. 2015;:279–86.

  132. Cai H, Xi H-T, Zhu Q, Wang Z, Han L, Liu S, et al. Prevalence of problematic internet use and its association with quality of life among undergraduate nursing students in the later stage of COVID-19 pandemic era in China. Am J Addict. 2021;30:585–92.

    Article  PubMed  PubMed Central  Google Scholar 

  133. Shen Y, Wang L, Huang C, Guo J, De Leon SA, Lu J, et al. Sex differences in prevalence, risk factors and clinical correlates of internet addiction among Chinese college students. J Affect Disord. 2021;279:680–6.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Open access provided by Qatar National Library.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Higher Institute of Sport and Physical Education of Kef, University of Jendouba, Jendouba, Tunisia

    Hilmi jelleli, Mohamed Ben Aissa, Nabil Kaddech, Mouna Saidane, Noomen Guelmami & Ismail Dergaa

  2. Postgraduate School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

    Noomen Guelmami

  3. Laboratory for Industrial and Applied Mathematics (LIAM), York University, Toronto, ON, Canada

    Nicola L. Bragazzi

  4. Department of Health and Nursing Science, Faculty of Social and Health Sciences, Inland Norway University of Applied Sciences, Elverum, Norway

    Tore Bonsaksen

  5. Department of Health, Faculty of Health Studies, VID Specialized University, Stavanger, Norway

    Tore Bonsaksen

  6. Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia

    Feten Fekih-Romdhane

  7. Department of Psychiatry “Ibn Omrane”, Razi Hospital, Manouba, Tunisia

    Feten Fekih-Romdhane

  8. Primary Health Care Corporation (PHCC), Doha, P.O. Box 26555, Qatar

    Ismail Dergaa

Authors
  1. Hilmi jelleli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Ben Aissa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nabil Kaddech
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mouna Saidane
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Noomen Guelmami
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicola L. Bragazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tore Bonsaksen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Feten Fekih-Romdhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ismail Dergaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HJ took on the role of research design, field survey supervision, data management, statistical analyses, and paper writing, with primary responsibility for the final content. MBA also contributed to the research design, data management, statistical analyses, and paper writing. NK contributed to the research design and had primary responsibility for the final content. MS played a role in data management, statistical analyses, and paper writing. NG was involved in data management and statistical analyses. NLB supervised the field survey. TB contributed to the research design and supervised the data management. FFK provided supervision for the research design and manuscript revision. ID took on the responsibilities of paper writing and field survey supervision. All authors participated in the critical revision of the manuscript, and they read and approved the final version.

Corresponding author

Correspondence to Ismail Dergaa.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

The study design protocol adheres to the guidelines set forth by the Declaration of Helsinki for human experimentation, receiving official approval from the Ethics Committee of the University of Jendouba in Tunis, Tunisia, with the assigned number 30/2023. Prior to the collection of data, all participants willingly joined the study and gave informed consent, ensuring their understanding and agreement before enrolling in the study protocol.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it.The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

jelleli, H., Ben Aissa, M., Kaddech, N. et al. Examining the interplay between physical activity, problematic internet use and the negative emotional state of depression, anxiety and stress: insights from a moderated mediation path model in university students. BMC Psychol 12, 406 (2024). https://doi.org/10.1186/s40359-024-01736-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40359-024-01736-3

Keywords

BMC Psychology

ISSN: 2050-7283

Contact us