Psychiatry Investig Search

CLOSE


Psychiatry Investig > Volume 20(10); 2023 > Article
Zhang: Smartphone Addiction Among University Students’ During the Post-COVID-19 Era: The Role of Emotional Intelligence and Future Anxiety

Abstract

Objective

Coronavirus disease-2019 (COVID-19) pandemic-related psychological symptoms can lead to smartphone addiction (SPA) risk and other behavioral disorders, thus impacting individuals’ mental health and well-being. The present study aims to propose a mediation model to investigate the association between emotional intelligence (EI) and SPA, and the mediating role of future anxiety (FA) during the post-COVID-19 era.

Methods

An online questionnaire including the Emotional Intelligence Scale, the Dark Future Scale, and the Smartphone Addiction Scale among university students from China, was conducted between September 14 and November 22, 2022. Finally, 1,154 valid questionnaires were collected. The reliability and confirmatory factor analysis results showed that all three scales had good reliability and validity.

Results

Structural Equation Model demonstrated that EI significantly and negatively influenced SPA (β=0.211, p<0.001), university students’ FA significantly and positively effected SPA (β=0.315, p<0.001), EI significantly predicted SPA in university students, and FA partially mediated the association between EI and SPA. The mediation effect of FA was 0.110, which accounted for 34.27% of the total effect. Bootstrap results furthermore tested the rigor of the mediating effect.

Conclusion

These findings broaden our understanding regarding the relationship between EI and SPA and the mediating role of FA, providing new sights for educators on how to reduce the risk of SPA when confronting the ongoing and possible future pandemics.

INTRODUCTION

Smartphone addiction (SPA), also known as “problematic smartphone use” or “smartphone use disorder,” refers to the maladaptive or excessive smartphone use accompanied by symptoms resembling substance-related dependence and associated functional impairment on the users [1]. Owing to its significant relations with academic performance [2], mental health [3,4], and satisfaction with life [5,6], university students’ SPA has gained much attention theoretically and practically. Many factors, such as personality traits, sociodemographic and psychological variables, could lead to SPA [7-9]. Yet, with the outbreak of coronavirus disease-2019 (COVID-19), the frequency and dependence of university students on smartphones has being increased due to the COVID-19 related control measures (such as lockdowns, social distancing, and home quarantine), which might result in the risk of SPA [10-13]. According to an online survey among 6,157 undergraduates in Jordan, the prevalence of SPA was 62.4% during COVID-19 quarantine [14]. A recent systematic review and meta-analysis included 495 articles from 64 countries conducted by Meng et al. [15], found that among the digital addictions the prevalence estimates were 26.99% for SPA.
Past findings have suggested that the fear of COVID-19 [16], COVID-19 phobia [17], anxiety regarding COVID-19 infection [18], perceived stress [19], and COVID-19 victimization experience [20] are all considered as significant positive predictors of SPA during the context of COVID-19 pandemic. For instance, in a sample of Türkiye 773 adults, Kayis et al. [16] found that the fear of COVID-19 influenced mental well-being via loneliness and SPA. However, the potential implications in context of post-COVID-19 era remain largely unknown. Since December 6, 2022, Chinese government had changed its previous zero COVID policy, particularly no lockdown measures and no compulsory nucleic acid test, which means that everyone in China may contract the virus. In this sense, examining SPA and its potential mechanism is more urgent and important in the post-COVID-19 era.
Additionally, past research has also reported anxiety symptom severity and depression psychopathology are the chief determinants regarding addictive behavior of problematic smartphone users [21-23]. However, some research found different results. For example, in a sample of Italian college students, De Pasquale et al. [24] reported that anxiety and fear of COVID-19 were the predictors of perceived vulnerability to disease but not the predictors of risk of SPA. The conflicting results need further research with regard to the effects of anxiety on SPA, since these studies were conducted mainly before and during the outbreak of COVID-19. Considered the long-term influences of COVID-19 on economic, social, and individual mental health, it is necessary to examine the effect of anxiety on SPA in the long time perspective. As a kind of anxiety, recently, future anxiety (FA) and its effects on mental well-being and addictive behaviors is being inquired by some research [20,25]. Consisting with this, the present study would further examine the influence of FA on SPA in the context of post-COVID-19.
Also, few existing studies have found that emotional intelligence (EI) is a significant protective factors in decreasing the risk of SPA [26-28]. Emotionally intelligent individuals normally have good relationships with others, are better able to deal with emergent events and daily stress, and are least risk of indulge in addictive smartphone and other social media use. To the best of our knowledge, however, no studies have investigated the interactive effects of EI and FA on SPA during the post-COVID-19 era. Therefore, the present study attempted to fill this gap. The findings of this empirical study could help understand the crucial factors affecting university students’ SPA, providing new sights for educators on how to reduce the risk of SPA when confronting the ongoing and possible future pandemics.

EI and SPA

As a construct that captures individual differences in identifying, understanding, processing, and regulating their own and other’s emotional experiences, EI can be assessed in two distinct methods: “trait” EI and “ability” EI [29]. The former is evaluated via self-report, while the latter through measures of maximal performance. The two conceptualizations of EI are complementary not conflicting. The present study will adopt the “trait” EI in which EI is considered as a distinct component of people’s personality, including individual’s perceived ability to understand and regulate their emotions and to cope with stressful and emotional challenges occurring in their life.
Subsequent studies have largely reported that EI is a key determinant of physical and mental health, psychological adjustment, and quality of life in a variety of occupational settings [30,31]. As to students, EI could negatively correlated with burnout and anxiety levels [32], and positively predict well-being [33]. In the field of addictive behavior, a systematic review conduced by Kun and Demetrovics [30] found that a lower level of EI is associated with more intensive smoking, alcohol use, and illicit drug use.
With respect to SPA, few studies have examined the role of EI. According to Beranuy et al. [26], apart from psychological distress, perceived EI could significantly predict maladaptive use of Internet and mobile phone. Likewise, Van Deursen et al. [27] emphasized that self-regulated individuals who are able to understand emotions and regulate feelings are better adjusted psychologically, are more unlikely to adversely affected by SPA. Mascia et al. [28] suggested that adolescents’ EI negatively influenced addictive smartphone behavior, which in turn influenced their well-being and quality of life. Overall, these facts suggest that university students with lower levels of EI may possess a higher risk of developing SPA.
Thus, based on the aforementioned findings, the present study proposed the first hypothesis as follows: Hypothesis 1, EI would exert a significant negative prediction effect on SPA.

The mediating role of FA

With the outbreak of COVID-19, extant studies have indicated that the COVID-19 related psychological symptoms, such as depression, distress, loneliness, insomnia, and anxiety, are all associated with SPA during the COVID-19 epidemic [34-40]. Effectively, as regards to anxiety symptoms, which is the most prevalent psychological symptoms during the COVID-19 epidemic [41,42], different kinds of anxiety, such as social anxiety [43,44], attachment anxiety [45]. cyberchondria [46], and online social anxiety [47], were founded to have a significant and positive effect on SPA. According to previous studies, time perspective was also a important predictor of addiction behaviors [48-51]. For instance, one of these studies carried out by Przepiorka and Blachnio [49] among 756 Internet users suggested that time perspective was a predictor of two kinds of addiction: Internet addiction and Facebook addiction.
In this vein, time perspective should be put in addictive behaviors studies [52,53]. In contrast to the concept of anxiety, which focuses on how people react to current events and personal experiences, FA involves a more remote personal future. FA refers to a state in which an individual feels uncertainty, fear, worry, and concern about what may happen in the future [54]. Studies have found that the COVID-19 pandemic has increased anxiety regarding the future by considerably disrupting individuals’ lives, exacerbating economic instabilities and social problems, and generating anticipatory fears, which in turn increases anxiety and uncertainty regarding the future [55,56]. Therefore, in the post-COVID-19 era, the FA resulting from the COVID-19 related experience might be more urgent. For example, in a sample of 478 students in Poland, Przepiorka et al. [57] indicated that FA significantly and positively predicted problematic new media use. More recently, in a sample of 840 Chinese college students, Chen et al. [20] revealed that both FA and COVID-19 victimization experience was significantly related with mobile phone addiction. Thus, the present study proposed the second hypothesis as follows: Hypothesis 2, Higher levels of FA would significantly and positively predict SPA among university students.
Additionally, several researchers have also examined the potential link between EI and anxiety [58,59]. Prior studies have posited that lower levels of EI are significantly associated with anxiety, stress, and depression, and may impact on subsequent development of social and emotional issues [26,60,61]. In this vein, when facing stressful situations and challenging events, individuals with low levels of EI are more likely to use maladaptive coping strategies and to face challenges negatively [30]. In the academic context, Fiorilli et al. [62] founded that students with lower levels of EI were more likely to experience anxiety when dealing with challenging school events, which in turn enhanced their overall risk of school burnout. By contrast, Mavroveli et al. [63] have found that adolescents with higher levels of EI are generally less predisposed to developing depressive or somatic symptoms, and are more likely to successfully cope with difficult events. Overall, these results support the status of EI as a fundamental antecedent variable with a strong effect on students’ school adjustment outcomes and cognitive behaviors. Specifically, individuals with high EI levels tend to express positive and optimistic evaluations of their future situations compared to their low emotionally intelligent peers.
Moreover, previous research has suggested that anxiety or FA often plays an essential mediating role. For example, Romano et al. [64] posited that students’ anxiety performed as a mediator between alexithymia and academic burnout. Similarly, Fiorilli et al. [62] founded that academic anxiety mediated the association between trait EI and school burnout. Zhan et al. [47] suggested that both online social anxiety and cyber danger belief mediated the influence of personality on cellphone addiction during the COVID-19 outbreak. Hao et al. [65] indicated that anxiety mediated the association between academic burnout and problematic smartphone use during the COVID-19 pandemic. Paredes et al. [25] found that FA had a mediating effect between the perceived threat of COVID-19 and mental well-being. A recent study conducted by Przepiorka et al. [57] with 478 students in Poland reported that FA partially mediated the link between procrastination and mobile phone addiction. More recently, Chen et al. [20] revealed that FA fully mediated the association between COVID-19 victimization experience and mobile phone addiction. Similarly, Ma et al. [66] found that depression, anxiety, and stress could mediate the relationship between resilience and mobile phone addiction among 1,751 Chinese adolescents. Therefore, Hypothesis 3 was proposed as follows: Hypothesis 3, FA would have a mediating effect on the relationship between EI and SPA among university students.

The present study

Based on the above literature discussion, the present study will construct a mediated model to examine the relationship between EI and SPA during the post-COVID-19 era, which is shown in Figure 1.

METHODS

Participants

The Ethics Committee of the Zhoukou Normal University approved the present study (2022ZKNU0912). The procedure of study was conducted in accordance with the Declaration of Helsinki [67], and all participants voluntarily filled out questionnaires. The data were collected and analyzed anonymously.
A questionnaire survey was conducted at two universities from China, one in Zhoukou city, and the other in Shenyang city between September 14 and November 22, 2022. Participants were enrolled by means of convenience sampling with the help of four assistants. Before the formal survey, the training was given to the four assistants, and the scientific purpose, voluntariness and anonymity of the research were told to the participants. During the break, questionnaires were distributed on the online through Wechat. Since no questionnaire was submitted until all items were completed, so there was no uncompleted questionnaire, and 1,223 questionnaires were gathered. The entire questionnaire took about 15 minutes. After excluding 69 invalid questionnaires, because the answering time was too low (i.e., completed in lower than 120 s), finally 1,154 valid questionnaires were collected, with an effective rate of 94.36%.

Measures

The Smartphone Addiction Scale

To measure SPA, the shorten version of Smartphone Addiction Scale (SAS-10) developed by Kwon et al. [68] was used. The responses are rated on a 5-point Likert scale ranging from 1 (not at all) to 5 (always), with higher scores indicating higher levels of SPA. The sample item is: “I won’t be able to stand not having a smartphone.”

The Emotional Intelligence Scale

EI was assessed with Wong and Law’s Scale (WLEIS-16) [69]. The Chinese version has shown satisfactory psychometric properties [70]. This scale is a 16 items self-report measure with a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (completely agree), with higher scores suggesting higher levels of EI, including four dimensions: self-emotion appraisal, others’ emotion appraisal, use of emotion, and regulation of emotion. The sample item is: “I am a good observer of others’ emotions.”

Future Anxiety Scale

To measure FA, the Dark Future scale developed by Zaleski et al. [71] was used. This single dimension scale consists of five items. The responses are rated on a 5-point Likert scale, with scores ranging from 1 (decidedly false) to 5 (decidedly true), and higher scores indicates higher degrees of FA. The sample item is: “I am afraid that in the future my life will change for the worse.”

Procedure and data analysis

The statistical procedure for this study ran as follow. First, Harman’s single factor was used to test the common method variance (CMV) [72]. Then, participants’profile, descriptive statistics and correlation analysis was used with IBM SPSS 21.0 (IBM Corp., Armonk, NY, USA). Second, confirmatory factor analysis (CFA) was performed to test the structural validity of the measurement model [73]. Then the fit and path coefficients of the hypothesized mediation model were assessed by the Structural Equation Model (SEM) with AMOS 21.0 (IBM Corp., Armonk, NY, USA), not least since it can test associations between many factors simultaneously [74]. Finally, to further test the rigor of mediating effect, the bias-corrected nonparametric percentile bootstrap method with 5,000 times resampling was used. According to Preacher and Hayes [75], compared with the traditional causal steps, the method of bootstrapping shows greater statistical power.

CMV Test

Before formal analysis, since all variables were measured with self-reported scale among the same participants, there may be the CMV problem. In order to cope with this problem, Harman’s one-factor test was used. More specifically, the Bartlett’s test of sphericity reached significance (p<0.001), and unrotated factor analysis indicated that the Kaiser-Meyer- Olkin was 0.907 (more than 0.8). Furthermore, a total of 7 factors were extracted from the factor analysis, and the explanatory power of the first factor was 27.067%, without passing 50% threshold [72], indicating that the CMV problem did not affect the study results.

RESULTS

Participants’ profile

A total of 1,154 participants were enrolled in this study, the results of participants’ profile were shown in Table 1. As shown, 525 (45.5%) were male students and 629 (54.5%) were female students. As to age, 40 (3.5%) were 17-year-old, 369 (32.0%) were 18 years, 328 (28.4%) were 19 years, and 417 (36.1%) were 20 years. As regard to grade, the sample included 561 (48.6%) freshmen, 281 (24.4%) sophomores, 282 (24.4%) juniors, and 30 (2.6%) seniors. In terms of major, 425 (36.8%) were arts and humanities, 320 (27.7%) were science, and 409 (35.4%) were engineering.

Reliability and validity assessment of measurement model

The Smartphone Addiction Scale

The results of CFA of the SAS-10 was displayed in Table 2. As shown, the factor loadings of SAS-10 were ranging from 0.474 to 0.716, the composite reliability (CR) value was 0.848 (more than 0.7), and the average variance extracted (AVE) value was 0.361. Even though the AVE is less than the threshold of 0.5, however, according to Fornell and Larcker [76], if the CR value is greater than 0.6, the convergent validity of the scale is still acceptable. The Cronbach’s α was 0.845, greater than 0.7, indicating the good reliability [76].

The Emotional Intelligence Scale

The results of CFA of the WLEIS-16 was displayed in Table 3. As shown, the factor loadings of WLEIS-16 were ranging from 0.628 to 0.885 (more than 0.5), the CR values were ranging from 0.850 to 0.891 (more than 0.7), and AVE values were ranging from 0.589 to 0.673 (more than 0.5). All the values exceeded the standard value, indicating the high convergent validity. The Cronbach’s α of each dimension was between 0.845 and 0.888, greater than 0.7, indicating the good reliability [76].

Future Anxiety Scale

Table 4 presents the results of CFA of the Future Anxiety Scale (FAS-5). The standardized factor loadings ranged from 0.740 to 0.865 (more than 0.5), indicating satisfactory validity. The CR value was 0.896 (more than 0.7), and the AVE value was 0.634 (more than 0.5), indicating satisfactory convergent validity. The Cronbach’s α of the FAS-5 was 0.896 (more than 0.7). These values indicated an acceptable fit of the measurement model to the observed data [76].

Discriminant validity

According to Fornell and Larcker [76], the square root of AVE was used to test the discrimination validity. As displayed in Table 5, the square root of AVE of each dimension was greater than the correlation coefficient of each dimension, indicating the high discriminant validity.

Descriptive statistics and correlation analysis of main variables

Descriptive statistics and correlation analysis were shown in Table 6. The statistical results showed that university students had a high or mean level of EI (mean=3.592), FA (mean=2.759), and SPA (mean=2.869). In addition, the results indicated that EI negatively and significantly correlated with SPA (r=-0.259, p<0.001); EI negatively and significantly correlated with FA (r=-0.286, p<0.001); and FA positively and significantly correlated with SPA (r=0.348, p<0.001).

Structural model

Prior studies have reported that age might a potential variable that could relate with SPA [8]. Thus, before examining the proposed mediation model, the single factor analysis of variance was used to analyze the SPA in different age groups. The results showed that there was no significant difference in SPA among university students in different age groups (F=1.897, p>0.05). Therefore, age is not a variable associated with SPA in the present study. To test the proposed mediation model in Figure 1, SEM analysis was conduced. Specifically, the selected absolute, incremental and parsimonious model-fit indices, i.e., root mean square of error approximation=0.069, goodness of fit index=0.911, comparative fit index=0.907, normed fit index=0.891, Tucker-Lewis index=0.893, and chi-square/degree of freedom=6.235, all satisfied the criteria, indicating a good fit of the proposed model [74].
The results of the structural relationships were displayed in Figure 2. As shown, EI could significantly and negatively predict SPA (β=-0.211, p<0.001); FA could significantly and positively predict SPA (β=0.315, p<0.001); and EI significantly and negatively predicted FA (β=-0.349, p<0.001). The result suggested that FA exerted a partially mediating effect between university students’ EI and their levels of SPA.
To examine the stability of the mediation model results, the bias-corrected nonparametric percentile bootstrap method with 5,000 times resampling was further performed. The bootstrap results were shown in Table 7. The results suggested that the indirect effect, i.e., EI → FA → SPA was -0.110 with 95% confidence interval (CI, -0.148 to -0.079), indicating a significant mediating effect. While the direct effect of EI on SPA was -0.211, with 95% CI (-0.301 to -0.125), indicating a partial mediating effect. Furthermore, the total effect value, i.e., the sum of the indirect effect and the direct effect, was -0.321 with 95% CI (-0.404 to -0.238). The mediation effect accounted for 34.27% of the total effect.

DISCUSSION

Main findings

Drawing upon previous research, in a sample of 1,154 university students in China, the aim of the present study was to investigate the role of EI and FA in forming university students’ SPA during the post-COVID-19 era.
First, as is illustrated in Figure 2, the present findings confirmed Hypothesis 1, supporting that EI are negative stimuli that have a significant predictive effect on SPA. This result is consistent with previous studies reporting that EI correlated negatively with burnout and anxiety levels [32], that EI could significantly predict maladaptive use of Internet and mobile phone [26], and that lower levels of EI are associated with addictive disorders [30]. Aligning with those results, the present study further found the significant relationship between EI and SPA among university students during the post-COVID-19 era.
The COVID-19 pandemic has been exacerbating economic and social problems, leading to increased clinical, psychological and social unfavorable outcomes among different groups, which in turn increases the risk of SPA [39,40,77-79]. As to university students, since their limited social relationships and lifestyle [80-84], when they experience psychological problems and mental disorders, they are more likely to use smartphones or other social medias to escape negative emotions and traumatic events. Moreover, extant studies have revealed that increased EI could decrease the risk of burnout, depression, and psychological distress [31,32]. In this sense, although individuals with psychological symptoms tend to overuse their smartphones frequently, the levels of SPA could be decreased by enhancing their EI abilities. Indeed, emotionally intelligent students, who are more capable of knowing how to appraise their own and others’ emotions consistently and deal with emotional problems effectively, are less vulnerable to developing addictive behaviors.
Second, our results indicated that FA was positively related to SPA, thus supporting Hypothesis 2. Like other psychological symptoms, this result is in line with previous studies reporting that depression was positively associated with SPA [37,85], anxiety was positively associated with SPA [35,86], anxiety disorders was positively associated with substance use disorders [38,87], and psychological distress was positively associated with internet addiction [88]. Based on previous studies, the present study furthermore add up time perspective as a significant predictor of addictive behaviors [48,49]. That is, university students with a higher level of FA may exhibit stronger tendencies to become addicted so as to find consolation for their anxiety or worry.
The results of the present study can be explained in the light of the uses and gratifications theory [89], which postulates the recipient’s active role and selection of social media for the gratification of specific needs. That is, the effect of the social media depends on the user’s subjective intentions and characteristics. During the post-COVID-19 era, the ongoing pandemic has been disrupting university students’ normal lives. In particular, increased interpersonal alienation levels has result in increased time spent with social medias and problematic lifestyles [13]. Anxious university students may have repetitive negative thinking regarding the future and low sense of self-control [90-92]. Smartphone could create a virtual word full of interactivity, demassification, and hypertextuality, which might satisfy their safety and self-actualization needs. In this vein, smartphone use may relieve their negative mood, which in turn may enhance addictive tendencies. Moreover, negative attitude towards the future is related to avoidant coping strategies, which may also increase the tendency to use their smartphones to escape reality into the online world in problem situations [88].
Third, supporting Hypothesis 3, the results indicated that FA played a mediating role between EI and SPA. That is, EI could indirectly affect the university students’ levels of SPA through FA. The results are in agreement with those of previous studies indicating that FA mediated the association between the perceived threat of COVID-19 and mental wellbeing [65], between procrastination and mobile phone addiction [57], and between COVID-19 victimization experience and mobile phone addiction [23]. Similarly, the mediating role of past negative and present fatalistic time perspective orientations was found in the relationship between attention-deficit hyperactivity disorder symptoms and addictive Facebook use [51]. Consisting with the future time perspective, the present study further broadens the mediating role of FA between EI and SPA, showing that the effects of university students’ EI on their SPA could be partially compromised by their high levels of FA.
Regarding the nature of mediating of FA in the contest of SPA, past studies reported different views. For example, according to Przepiorka et al. [57], FA partially mediated the link between procrastination and mobile phone addiction among 478 students in Poland; while Chen et al. [20] revealed that FA fully mediated the association between COVID-19 victimization experience and mobile phone addiction among 840 Chinese college students. Using a sample of 1,154 university students in China, the present study reported the partial mediating role of FA in the EI-SPA link. The different mediating role of FA in relation to SPA could attribute to the individuals’ changing knowledge and information regarding the COVID-19 during the different phase of pandemic. At least in the post-COVID-19 era, although the effects of COVID-19 are profound, anxiety symptoms resulted from the COVID-19, such as FA, are not last forever, in particular for individuals who have enhanced levels of EI.
Speculatively, these results could be explained by the fact that individuals with higher levels of EI have better self-awareness, paying attention to oneself and others’ emotions, and using them to manage their relationships and improve adaption coping mechanisms such as problem-solving and stress management. All these strategies could be conducive to reduce the risk of anxiety and SPA [58]. By contrast, individuals with lower levels of EI would tend to think about the future negatively, feel uncertain about what may happen to them in future, find no meaning in life, and are more likely to escape into smartphone or indulge in others social medias [93]. Using these new social medias might relieve their negative feelings about future tasks and decisions. Thus, the present study significantly extends the findings of earlier research by considering FA in explaining SPA.

Practical implications

The present study could provide some practical reference for education administrators and instructors to respond to emergency public health events such as COVID-19. First, considered that university students’ EI could decrease their levels of SPA, it is suggested that instructors should integrate EI training programs into mental health courses so as to improve university students’ EI ability and minimize the vulnerability of university students to develop SPA.
Second, instructors should guide university students to properly confront COVID-19 epidemic and develop an proper understanding. For instance, education administrators should hold lectures on COVID-19 knowledge, scientifically interpret the current development of the epidemic, guide university students to accurately understand the threat to health and to have reasonable thinking in similar public health events, so as to alleviate students’ anxiety regarding the future.

Limitations and future research

Like other studies, some limitations should be noted so that future research could address them. First, since the present study was cross-sectional, the sample using the convenience sampling method was merely collected among the Chinese university students, it might have inferential limitations. To generalize the results, thus, a longitudinal cross-cultural study should be conducted, the sampling method should be improved, and range of participants, particularly populations with cultural differences must be considered.
Second, this study used a self-report questionnaire to measure the variables, which could have resulted in socially-desirable answers. This might especially be important in relation to EI. A note for further research is to measure EI with other methods, such as performance-based method.
Third, this study used a 5-point Likert self-reported scale to measure SPA without distinguishing different types of use, and one factor loading and AVE value were low than 0.5. For future studies, to measure smartphone use more precisely, different types of smartphone use, e.g., process use and social usage, should be taken into account [3,94]; to improve the validity of data, a particular application that can track smartphone screen time could also be employed [95].
Forth, as extant studies showed many factors could influence university students’ levels of SPA, the present study only examined the mediating role of FA in the association between EI and SPA among university students, considered the longterm effects of COVID-19 on individuals’ anxiety symptom, further studies should examine the role of other mediators, such as fear of missing out, self-regulation, and positive mental health [96-98], in the link between FA and SPA.

Conclusion

The COVID-19 has been changing people’s lives in many aspects profoundly. As for university students, the frequency and dependence on smartphones has being increased due to the COVID-19 related control measures, thus researchers should pay more attention to examine the factors and mechanism influencing university students’ SPA in the post-COVID-19 era. Based on prior studies, the present study constructed a mediation model. As expected, a significant correlation was observed among EI, FA, and SPA. SEM analysis showed that EI had a significant negative predictive effect on SPA and FA could significantly and positively predict university students’ levels of SPA. Moreover, FA mediated the link between EI and SPA. Taken together, the findings could theoretically enrich our understanding and knowledge regarding the risk of SPA, i.e., EI considered as a protective variable, FA considered as a risk variable, and the predictive effect of EI on SPA could partially mediated by FA. To design intervention measures to decrease university students’ SPA, educators and policy makers should design EI training programs in public health courses and guide university students to accurately understand the COVID-19 and similar public affairs. In the post-COVID-19 era, considering everyone has the potential risk of becoming the victims of COVID-19, more COVID-19 related symptoms and its interactive effects in relation to EI on smartphone or other social media addiction risk should be carried out in future studies.

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The author has no potential conflicts of interest to disclose.

Funding Statement

None

ACKNOWLEDGEMENTS

The author would like to thank all instructors and students that have participated in the study.

Figure 1.
The proposed mediation model. EI, emotional intelligence; FA, future anxiety; SPA, smartphone addiction; H, hypothesis.
pi-2023-0021f1.jpg
Figure 2.
The mediation model. ***p<0.001. EI, emotional intelligence; FA, future anxiety; SPA, smartphone addiction.
pi-2023-0021f2.jpg
Table 1.
Demographic profile of participants
Variable Value (N=1,154)
Gender
 Male 525 (45.5)
 Female 629 (54.5)
Age
 17 yr 40 (3.5)
 18 yr 369 (32.0)
 19 yr 328 (28.4)
 20 yr 417 (36.1)
Grade
 Freshman 561 (48.6)
 Sophomore 281 (24.4)
 Junior 282 (24.4)
 Senior 30 (2.6)
Major
 Arts and Humanities 425 (36.8)
 Science 320 (27.7)
 Engineering 409 (35.4)

Values are presented number (%).

Table 2.
CFA results of SAS-10
Dimension No. Factor loading CR AVE Cronbach’s α
Smartphone addiction 1 0.527 0.848 0.361 0.845
2 0.610
3 0.540
4 0.571
5 0.703
6 0.716
7 0.594
8 0.474
9 0.652
10 0.575

CFA, confirmatory factor analysis; SAS-10, shorten version of Smartphone Addiction Scale; CR, composite reliability; AVE, average variance extracted

Table 3.
CFA results of WLEIS-16
Dimension No. Factor loading CR AVE Cronbach’s α
Self-emotion appraisal 1 0.717 0.865 0.617 0.862
2 0.828
3 0.862
4 0.726
Others’ emotion appraisal 1 0.776 0.891 0.673 0.888
2 0.885
3 0.874
4 0.738
Use of emotion 1 0.628 0.850 0.589 0.845
2 0.815
3 0.810
4 0.801
Regulation of emotion 1 0.798 0.881 0.650 0.880
2 0.821
3 0.767
4 0.837

CFA, confirmatory factor analysis; WLEIS-16, Wong and Law Emotional Intelligence Scale; CR, composite reliability; AVE, average variance extracted

Table 4.
CFA results of FAS-5
Dimension No. Factor loading CR AVE Cronbach’s α
Future anxiety 1 0.762 0.896 0.634 0.896
2 0.842
3 0.865
4 0.764
5 0.740

CFA, confirmatory factor analysis; FAS-5, Future Anxiety Scale; CR, composite reliability; AVE, average variance extracted

Table 5.
Discriminant validity of main variables
Dimension M SD 1 2 3 4 5 6
1. EI-SEA 3.684 0.660 0.785
2. EI-OEA 3.642 0.684 0.510*** 0.820
3. EI-UOE 3.534 0.704 0.545*** 0.474*** 0.767
4. EI-ROE 3.506 0.723 0.485*** 0.386*** 0.506*** 0.806
5. FA 2.759 0.939 -0.227*** -0.141*** -0.262*** -0.264*** 0.796
6. SPA 2.869 0.691 -0.181*** -0.165*** -0.219*** -0.244*** 0.348*** 0.601

N=1,154.

*** p<0.001;

numbers are the square root of the average variance extracted (AVE), numbers in the lower diagonal denote the correlation coefficients.

M, mean; SD, standard deviation; EI-SEA, self-emotion appraisal; EI-OEA, others’ emotion appraisal; EI-UOE, use of emotion; EI-ROE, regulation of emotion; FA, future anxiety; SPA, smartphone addiction

Table 6.
Descriptive statistics and correlation analysis of main variables
Variable M SD EI FA SPA
EI 3.592 0.542 1
FA 2.759 0.939 -0.286*** 1
SPA 2.869 0.691 -0.259*** 0.348*** 1

N=1,154.

*** p<0.001.

M, mean; SD, standard deviation; EI, emotional intelligence; FA, future anxiety; SPA, smartphone addiction

Table 7.
The results of bootstrap with 5,000 times resampling
Path Estimate 95% confidence interval
Indirect effect -0.110 -0.148 to -0.079
Direct effect -0.211 -0.301 to -0.125
Total effect -0.321 -0.404 to -0.238

REFERENCES

1. Elhai JD, Levine JC, Dvorak RD, Hall BJ. Non-social features of smartphone use are most related to depression, anxiety and problematic smartphone use. Comput Hum Behav 2017;69:75-82.
crossref
2. Abbasi GA, Jagaveeran M, Goh YN, Tariq B. The impact of type of content use on smartphone addiction and academic performance: physical activity as moderator. Technol Soc 2021;64:101521
crossref
3. Kil N, Kim J, McDaniel JT, Kim J, Kensinger K. Examining associations between smartphone use, smartphone addiction, and mental health outcomes: a cross-sectional study of college students. Health Promot Perspect 2021;11:36-44.
crossref pmid pmc pdf
4. Xu T, Sun X, Jiang P, Chen M, Yue Y, Dong E. Effects of cell phone dependence on mental health among college students during the pandemic of COVID-19: a cross-sectional survey of a medical university in Shanghai. Front Psychol 2022;13:920899
crossref pmid pmc
5. Lepp A, Barkley JE, Karpinski AC. The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Comput Hum Behav 2014;31:343-350.
crossref
6. Jiang W, Luo J, Guan H, Jiang F, Tang YL. Problematic mobile phone use and life satisfaction among university students during the COVID-19 pandemic in Shanghai, China. Front Public Health 2022;9:805529
crossref pmid pmc
7. Duan L, He J, Li M, Dai J, Zhou Y, Lai F, et al. Based on a decision tree model for exploring the risk factors of smartphone addiction among children and adolescents in China during the COVID-19 pandemic. Front Psychiatry 2021;12:652356
crossref pmid pmc
8. van Oosten JM, Vandenbosch L, Peter J. Predicting the use of visually oriented social media: the role of psychological well-being, body image concerns and sought appearance gratifications. Comput Hum Behav 2023;144:107730
crossref
9. Kheradmand A, Amirlatifi ES, Rahbar Z. Personality traits of university students with smartphone addiction. Front Psychiatry 2023;14:1083214
crossref pmid pmc
10. Caponnetto P, Inguscio L, Valeri S, Maglia M, Polosa R, Lai C, et al. Smartphone addiction across the lifetime during Italian lockdown for COVID-19. J Addict Dis 2021;39:441-449.
crossref pmid
11. Zhao J, Ye B, Yu L. Peer phubbing and Chinese college students’ smartphone addiction during COVID-19 pandemic: the mediating role of boredom proneness and the moderating role of refusal self-efficacy. Psychol Res Behav Manag 2021;14:1725-1736.
crossref pmid pmc pdf
12. Güldal Ş, Kılıçoğlu NA, Kasapoğlu F. Psychological flexibility, coronavirus anxiety, humor and social media addiction during COVID-19 pandemic in Turkey. Int J Adv Couns 2022;44:220-242.
crossref pmid pmc pdf
13. Popescu AM, Balica RȘ, Lazăr E, Bușu VO, Vașcu JE. Smartphone addiction risk, technology-related behaviors and attitudes, and psychological well-being during the COVID-19 pandemic. Front Psychol 2022;13:997253
crossref pmid pmc
14. Saadeh H, Al Fayez RQ, Al Refaei A, Shewaikani N, Khawaldah H, Abu-Shanab S, et al. Smartphone use among university students during COVID-19 quarantine: an ethical trigger. Front Public Health 2021;9:600134
crossref pmid pmc
15. Meng SQ, Cheng JL, Li YY, Yang XQ, Zheng JW, Chang XW, et al. Global prevalence of digital addiction in general population: a systematic review and meta-analysis. Clin Psychol Rev 2022;92:102128
crossref pmid
16. Kayis AR, Satici B, Deniz ME, Satici SA, Griffiths MD. Fear of COVID-19, loneliness, smartphone addiction, and mental wellbeing among the Turkish general population: a serial mediation model. Behav Inf Technol 2022;41:2484-2496.
crossref
17. Chopdar PK, Paul J, Prodanova J. Mobile shoppers’ response to Covid-19 phobia, pessimism and smartphone addiction: does social influence matter? Technol Forecast Soc Change 2022;174:121249
crossref pmid
18. Al Qudah MF, Albursan IS, Hammad HI, Alzoubi AM, Bakhiet SF, Almanie AM, et al. Anxiety about COVID-19 infection, and its relation to smartphone addiction and demographic variables in Middle Eastern countries. Int J Environ Res Public Health 2021;18:11016
crossref pmid pmc
19. Peng Y, Zhou H, Zhang B, Mao H, Hu R, Jiang H. Perceived stress and mobile phone addiction among college students during the 2019 coronavirus disease: the mediating roles of rumination and the moderating role of self-control. Pers Individ Dif 2022;185:111222
crossref pmid
20. Chen L, Li J, Huang J. COVID-19 victimization experience and college students’ mobile phone addiction: a moderated mediation effect of future anxiety and mindfulness. Int J Environ Res Public Health 2022;19:7578
crossref pmid pmc
21. Kliestik T, Scott J, Musa H, Suler P. Addictive smartphone behavior, anxiety symptom severity, and depressive stress. Anal Metaphys 2020;19:45-51.
crossref
22. Porter T, Potcovaru AM, Zauskova A, Rowland Z, Grupac M. Smartphone addiction risk, anxiety symptom severity, and depression psychopathology. Rev Contemp Philos 2020;19:57-63.
crossref
23. Green M, Kovacova M, Valaskova K. Smartphone addiction risk, depression psychopathology, and social anxiety. Anal Metaphys 2020;19:52-58.
crossref
24. De Pasquale C, Pistorio ML, Sciacca F, Hichy Z. Relationships between anxiety, perceived vulnerability to disease, and smartphone use during coronavirus disease 2019 pandemic in a sample of Italian college students. Front Psychol 2021;12:692503
pmid pmc
25. Paredes MR, Apaolaza V, Fernandez-Robin C, Hartmann P, Yañez-Martinez D. The impact of the COVID-19 pandemic on subjective mental well-being: the interplay of perceived threat, future anxiety and resilience. Pers Individ Dif 2021;170:110455
crossref pmid
26. Beranuy M, Oberst U, Carbonell X, Chamarro A. Problematic internet and mobile phone use and clinical symptoms in college students: the role of emotional intelligence. Comput Hum Behav 2009;25:1182-1187.
crossref
27. Van Deursen AJ, Bolle CL, Hegner SM, Kommers PA. Modeling habitual and addictive smartphone behavior: the role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Comput Hum Behav 2015;45:411-420.

28. Mascia ML, Agus M, Penna MP. Emotional intelligence, self-regulation, smartphone addiction: which relationship with student well-being and quality of life? Front Psychol 2020;11:375
crossref pmid pmc
29. Davis SK, Humphrey N. The influence of emotional intelligence (EI) on coping and mental health in adolescence: divergent roles for trait and ability EI. J Adolesc 2012;35:1369-1379.
crossref pmid pdf
30. Kun B, Demetrovics Z. Emotional intelligence and addictions: a systematic review. Subst Use Misuse 2010;45:1131-1160.
crossref pmid
31. Ke T, Barlas J. Thinking about feeling: using trait emotional intelligence in understanding the associations between early maladaptive schemas and coping styles. Psychol Psychother 2020;93:1-20.
crossref pmid pdf
32. Romano L, Tang X, Hietajärvi L, Salmela-Aro K, Fiorilli C. Students’ trait emotional intelligence and perceived teacher emotional support in preventing burnout: the moderating role of academic anxiety. Int J Environ Res Public Health 2020;17:4771
crossref pmid pmc
33. Carvalho VS, Guerrero E, Chambel MJ. Emotional intelligence and health students’ well-being: a two-wave study with students of medicine, physiotherapy and nursing. Nurse Educ Today 2018;63:35-42.
crossref pmid
34. Lăzăroiu G, Kovacova M, Siekelova A, Vrbka J. Addictive behavior of problematic smartphone users: the relationship between depression, anxiety, and stress. Rev Contemp Philos 2020;19:50-56.
crossref
35. Ge J, Liu Y, Cao W, Zhou S. The relationship between anxiety and depression with smartphone addiction among college students: the mediating effect of executive dysfunction. Front Psychol 2023;13:1033304
crossref pmid pmc
36. Islam MS, Sujan MSH, Tasnim R, Mohona RA, Ferdous MZ, Kamruzzaman S, et al. Problematic smartphone and social media use among Bangladeshi college and university students amid COVID-19: the role of psychological well-being and pandemic related factors. Front Psychiatry 2021;12:647386
crossref pmid pmc
37. Adachi M, Takahashi M, Shinkawa H, Mori H, Nishimura T, Nakamura K. Longitudinal association between smartphone ownership and depression among schoolchildren under COVID-19 pandemic. Soc Psychiatry Psychiatr Epidemiol 2022;57:239-243.
crossref pmid pdf
38. Andrade ALM, Di Girolamo Martins G, Scatena A, Lopes FM, de Oliveira WA, Kim HS, et al. The effect of psychosocial interventions for reducing co-occurring symptoms of depression and anxiety in individuals with problematic internet use: a systematic review and metaanalysis. Int J Ment Health Addict 2022;Jun 3 [Epub]. https://doi.org/10.1007/s11469-022-00846-6.
crossref
39. Zhang C, Hao J, Liu Y, Cui J, Yu H. Associations between online learning, smartphone addiction problems, and psychological symptoms in Chinese college students after the COVID-19 pandemic. Front Public Health 2022;10:881074
crossref pmid pmc
40. Zhao J, Ye B, Yu L, Xia F. Effects of stressors of COVID-19 on Chinese college students’ problematic social media use: a mediated moderation model. Front Psychiatry 2022;13:917465
crossref pmid pmc
41. Jin L, Hao Z, Huang J, Akram HR, Saeed MF, Ma H. Depression and anxiety symptoms are associated with problematic smartphone use under the COVID-19 epidemic: the mediation models. Child Youth Serv Rev 2021;121:105875
crossref pmid
42. Jiang Y. Problematic social media usage and anxiety among university students during the COVID-19 pandemic: the mediating role of psychological capital and the moderating role of academic burnout. Front Psychol 2021;12:612007
crossref pmid pmc
43. Sertbaş K, Çutuk S, Soyer F, Akkuş ÇZ, Aydoğan R. Mediating role of emotion regulation difficulties in the relationship between social anxiety and problematic internet use. Psihologija 2020;53:291-305.
crossref
44. Annoni AM, Petrocchi S, Camerini AL, Marciano L. The relationship between social anxiety, smartphone use, dispositional trust, and problematic smartphone use: a moderated mediation model. Int J Environ Res Public Health 2021;18:2452
crossref pmid pmc
45. Zhang W, Zhou F, Zhang Q, Lyu Z. Attachment anxiety and smartphone addiction among university students during confinement: teacher-student relationships, student-student relationships and school connectedness as mediators. Front Public Health 2022;10:947392
crossref pmid pmc
46. Köse S, Murat M. Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Arch Psychiatr Nurs 2021;35:563-570.
crossref pmid
47. Zhan Z, Wei Q, Hong JC. Cellphone addiction during the Covid-19 outbreak: how online social anxiety and cyber danger belief mediate the influence of personality. Comput Human Behav 2021;121:106790
crossref pmid pmc
48. Chittaro L, Vianello A. Time perspective as a predictor of problematic Internet use: a study of facebook users. Pers Individ Differ 2013;55:989-993.
crossref
49. Przepiorka A, Blachnio A. Time perspective in internet and facebook addiction. Comput Hum Behav 2016;60:13-18.
crossref
50. Kim J, Hong H, Lee J, Hyun MH. Effects of time perspective and selfcontrol on procrastination and internet addiction. J Behav Addict 2017;6:229-236.
crossref pmid pmc
51. Settanni M, Marengo D, Fabris MA, Longobardi C. The interplay between ADHD symptoms and time perspective in addictive social media use: a study on adolescent facebook users. Child Youth Serv Rev 2018;89:165-170.
crossref
52. Zimbardo PG, Boyd JN. Putting time in perspective: a valid, reliable individual-differences metric. J Pers Soc Psychol 1999;77:1271-1288.
crossref
53. Marengo D, Angelo Fabris M, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addict Behav 2022;126:107204
crossref pmid
54. Zaleski Z. Future anxiety: concept, measurement, and preliminary research. Pers Individ Differ 1996;21:165-174.
crossref
55. Drouin M, McDaniel BT, Pater J, Toscos T. How parents and their children used social media and technology at the beginning of the COVID-19 pandemic and associations with anxiety. Cyberpsychol Behav Soc Netw 2020;23:727-736.
crossref pmid
56. Cohen S, Nica E. COVID-19 pandemic-related emotional anxiety, perceived risk of infection, and acute depression among primary care providers. Psychosociological Issues Hum Resour Manage 2021;9:7-20.
crossref
57. Przepiorka A, Blachnio A, Cudo A. Procrastination and problematic new media use: the mediating role of future anxiety. Curr Psychol 2023;42:5169-5177.
crossref pdf
58. Kousha M, Bagheri HA, Heydarzadeh A. Emotional intelligence and anxiety, stress, and depression in Iranian resident physicians. J Family Med Prim Care 2018;7:420-424.
crossref pmid pmc
59. Liu M, Ren S. Moderating effect of emotional intelligence on the relationship between rumination and anxiety. Curr Psychol 2018;37:272-279.
crossref pdf
60. Mikolajczak M, Petrides KV, Hurry J. Adolescents choosing self-harm as an emotion regulation strategy: the protective role of trait emotional intelligence. Br J Clin Psychol 2009;48:181-193.
crossref pmid
61. Shen S, Tang T, Shu H, Wang S, Guan X, Yan X, et al. Linking emotional intelligence to mental health in Chinese high school teachers: the mediating role of perceived organizational justice. Front Psychol 2022;12:810727
crossref pmid pmc
62. Fiorilli C, Farina E, Buonomo I, Costa S, Romano L, Larcan R, et al. Trait emotional intelligence and school burnout: the mediating role of resilience and academic anxiety in high school. Int J Environ Res Public Health 2020;17:3058
crossref pmid pmc
63. Mavroveli S, Petrides KV, Rieffe C, Bakker F. Trait emotional intelligence, psychological well‐being and peer‐rated social competence in adolescence. Br J Dev Psychol 2007;25:263-275.
crossref
64. Romano L, Buonomo I, Callea A, Fiorilli C. Alexithymia in young people’s academic career: the mediating role of anxiety and resilience. J Genet Psychol 2019;180:157-169.
crossref pmid
65. Hao Z, Jin L, Huang J, Lyu R, Cui Q. Academic burnout and problematic smartphone use during the COVID-19 pandemic: the effects of anxiety and resilience. Front Psychiatry 2021;12:725740
crossref pmid pmc
66. Ma A, Yang Y, Guo S, Li X, Zhang S, Chang H. The impact of adolescent resilience on mobile phone addiction during COVID-19 normalization and flooding in China: a chain mediating. Front Psychol 2022;13:865306
crossref pmid pmc
67. Goodyear MD, Krleza-Jeric K, Lemmens T. The Declaration of Helsinki. BMJ 2007;335:624-625.
crossref pmid pmc
68. Kwon M, Kim DJ, Cho H, Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PLoS One 2013;8:e83558.
crossref pmid pmc
69. Wong CS, Law KS. The effects of leader and follower emotional intelligence on performance and attitude: an exploratory study. Leadersh Q 2002;13:243-274.
crossref
70. Kong F. The validity of the wong and law emotional intelligence scale in a Chinese sample: tests of measurement invariance and latent mean differences across gender and age. Pers Individ Differ 2017;116:29-31.
crossref
71. Zaleski Z, Sobol-Kwapinska M, Przepiorka A, Meisner M. Development and validation of the dark future scale. Time Soc 2019;28:107-123.
crossref pdf
72. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003;88:879-903.
crossref pmid
73. Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull 1988;3:411-423.
crossref
74. Kline RB. Principles and practice of structural equation modeling (3rd ed). New York: Guilford Press; 2010.

75. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 2008;40:879-891.
crossref pmid pdf
76. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981;18:39-50.
crossref pdf
77. Hu T, Wang Y, Lin L, Tang W. The mediating role of daytime sleepiness between problematic smartphone use and post-traumatic symptoms in COVID-19 home-refined adolescents. Child Youth Serv Rev 2021;126:106012
crossref pmid pmc
78. Serra G, Lo Scalzo L, Giuffrè M, Ferrara P, Corsello G. Smartphone use and addiction during the coronavirus disease 2019 (COVID-19) pandemic: cohort study on 184 Italian children and adolescents. Ital J Pediatr 2021;47:150
crossref pmid pmc pdf
79. Liu H, Zhou Z, Huang L, Zhu E, Yu L, Zhang M. Prevalence of smartphone addiction and its effects on subhealth and insomnia: a crosssectional study among medical students. BMC Psychiatry 2022;22:305
crossref pmid pmc pdf
80. Lian L, You X, Huang J, Yang R. Who overuses smartphones? Roles of virtues and parenting style in smartphone addiction among Chinese college students. Comput Hum Behav 2016;65:92-99.
crossref
81. Hosen I, Al Mamun F, Sikder MT, Abbasi AZ, Zou L, Guo T, et al. Prevalence and associated factors of problematic smartphone use durthe COVID-19 pandemic: a Bangladeshi study. Risk Manag Healthc Policy 2021;14:3797-3805.
pmid pmc
82. Freitas BHBM, Gaíva MAM, Diogo PMJ, Bortolini J. Relationship between lifestyle and self-reported smartphone addiction in adolescents in the COVID-19 pandemic: a mixed-methods study. J Pediatr Nurs 2022;65:82-90.
crossref pmid pmc
83. Hu Q, Liu Q, Wang Z. Meaning in life as a mediator between interpersonal alienation and smartphone addiction in the context of Covid-19: a three-wave longitudinal study. Comput Human Behav 2022;127:107058
crossref pmid
84. Potas N, Açıkalın ŞN, Erçetin ŞŞ, Koçtürk N, Neyişci N, Çevik MS, et al. Technology addiction of adolescents in the COVID-19 era: mediating effect of attitude on awareness and behavior. Curr Psychol 2022;41:1687-1703.
crossref pmid pdf
85. Yang X, Hu H, Zhao C, Xu H, Tu X, Zhang G. A longitudinal study of changes in smart phone addiction and depressive symptoms and potential risk factors among Chinese college students. BMC Psychiatry 2021;21:252
crossref pmid pmc pdf
86. Song Y, Sznajder K, Cui C, Yang Y, Li Y, Yang X. Anxiety and its relationship with sleep disturbance and problematic smartphone use among Chinese medical students during COVID-19 home confinement — A structural equation model analysis. J Affect Disord 2022;296:315-321.
crossref pmid
87. Soyka M. Comorbidity of anxiety disorders and substance use. In: Dom G, editor. Co-occurring addictive and psychiatric disorders. Berlin: Springer Berlin Heidelberg, 2015, p. 149-160.

88. McNicol ML, Thorsteinsson EB. Internet addiction, psychological distress, and coping responses among adolescents and adults. Cyberpsychol Behav Soc Netw 2017;20:296-304.
crossref pmid pmc
89. Ruggiero TE. Uses and gratifications theory in the 21st century. Mass Commun Soc 2000;3:3-37.
crossref
90. Cho HY, Kim DJ, Park JW. Stress and adult smartphone addiction: mediation by self-control, neuroticism, and extraversion. Stress Health 2017;33:624-630.
crossref pmid pdf
91. Brailovskaia J, Stirnberg J, Rozgonjuk D, Margraf J, Elhai JD. From low sense of control to problematic smartphone use severity during Covid-19 outbreak: the mediating role of fear of missing out and the moderating role of repetitive negative thinking. PLoS One 2021;16:e0261023.
crossref pmid pmc
92. Li J, Zhan D, Zhou Y, Gao X. Loneliness and problematic mobile phone use among adolescents during the COVID-19 pandemic: the roles of escape motivation and self-control. Addict Behav 2021;118:106857
crossref pmid pmc
93. Zhao H, Rafik-Galea S, Fitriana M, Song T. Meaning in life and smartphone addiction among Chinese female college students: the mediating role of school adjustment and the moderating role of grade. Front Psychol 2023;14:1092893
crossref pmid pmc
94. Zhao N, Zhou G. COVID-19 stress and addictive social media use (SMU): mediating role of active use and social media flow. Front Psychiatry 2021;12:635546
crossref pmid pmc
95. Hodes LN, Thomas KG. Smartphone screen time: inaccuracy of selfreports and influence of psychological and contextual factors. Comput Hum Behav 2021;115:106616
crossref
96. Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Hum Behav 2013;29:1841-1848.
crossref
97. Błachnio A, Przepiorka A. Dysfunction of self-regulation and self-control in Facebook addiction. Psychiatr Q 2016;87:493-500.
crossref pmid pdf
98. Brailovskaia J, Balcerowska JM, Precht LM, Margraf J. Positive mental health mediates the association between insomnia symptoms and addictive social media use in Germany and Poland. Comput Hum Behav 2023;143:107676
crossref
TOOLS
Share:
Facebook Twitter Linked In Google+
METRICS Graph View
  • 0 Crossref
  •   Scopus
  • 1,332 View
  • 35 Download


ABOUT
AUTHOR INFORMATION
ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
Editorial Office
#522, 27, Seochojungang-ro 24-gil, Seocho-gu, Seoul 06601, Korea
Tel: +82-2-717-0892    E-mail: psychiatryinvest@gmail.com                

Copyright © 2024 by Korean Neuropsychiatric Association.

Developed in M2PI

Close layer
prev next