The relationship between high school English learners’ anxiety and their smartphone addiction: evidence from a daily diary approach

Background: The issue of smartphone addiction among high school English learners is becoming more and more significant in this era of information technology, and it is directly associated with their anxiety.

Methods: To examine the correlation between high school English learners’ anxiety level and the severity of smartphone addiction, the anxiety and smartphone addiction levels of high school English learners were explored. SPSS 26 and MPLUS 8.3 were utilized for data analysis. The sample selected by stratified random sampling comprised 248 high school English learners from a high school in Shanxi Province, and a diary survey was conducted for seven consecutive days, obtaining a total of 1,610 valid data points.

Results: The findings of the multilevel regression model indicated a substantial correlation between anxiety level and the severity of smartphone addiction among high school English learners. Anxiety level was a substantial favorable indicator of the severity of smartphone addiction; a notable disparity existed in the smartphone addiction levels of males and females; however, no significant difference was observed between the genders regarding the predicted influence of anxiety level on the severity of smartphone addiction.

Conclusions: This study examined the effect of anxiety levels on the severity of smartphone addiction and analyzed whether there were gender differences. Based on the results, this study proposes a study on anxiety and smartphone addiction interventions for high school English learners in basic education. The proposal offers evidence from both empirical and theory-based investigations to substantiate the healthy development of high school English learners.

1 Introduction

With the widespread use of smartphones, they have become an important tool for language learning (1, 2). For English learners, audiovisual materials on smartphones can help learners immerse themselves in real-life environments (3, 4), thereby enhancing their English proficiency (5). While smartphones have numerous positive effects on English learning, it cannot be denied that they also have negative impacts on learning (6). As reliance on smartphones continues to grow (7), the risk of smartphone addiction(SA) inevitably increases (8), particularly among adolescents (9–11).

Mark D Griffiths (12) argues that smartphone addiction is a form of behavioral addiction. This addiction exhibits characteristic features of addiction, including pleasurable sensations, tolerance, withdrawal symptoms, and relapse (12, 13). Smartphone addiction can lead users to become excessively obsessed with smartphone use, resulting in intense and persistent cravings and dependence, which may harm their psychological and social functioning (14). Currently, gender differences in SA symptoms have become one of the key areas of focus for researchers. Research has not reached a consensus on whether men or women are more prone to SA (15–19). However, regardless of the results, it is undeniable that gender has become a factor that must be considered in analyzing SA.

Research indicates that there may be numerous factors contributing to SA symptoms. For example, personality traits (20), emotional and cognitive needs (21, 22), and habitual smartphone use (23). Anxiety(AN), as a common negative emotion (24), is considered an important contributing factor to SA symptoms. AN primarily manifests as uncontrollable worry and fear (25, 26), which may lead to physical illnesses, social difficulties, academic burnout, and other physiological or psychological issues (27–29), particularly prevalent among adolescents (30, 31), and foreign language learners are no exception (32). This study treats AN symptoms as a negative emotion.

To alleviate AN symptoms, many adolescents overuse smartphones to distract themselves. A link has been established between AN and SA symptoms (33–35). Smartphones, as tools for social interaction, information retrieval, and entertainment and relaxation, can effectively alleviate AN symptoms in learners (36–38). However, while most researchers have extensively studied AN and SA among students of different age groups (39–41), However, studies systematically collecting data using continuous observation diary methods for this group are scarce, and few studies have conducted in-depth explorations of the relationship between these two variables using multilevel regression models.

Therefore, this study aims to use diary methods and data model analysis to explore the complex relationship between AN among middle school English learners and SA, as well as the impact of gender differences. Based on the research objectives and theoretical framework, the following hypotheses were formulated:

H1: The severity of SA among high school English learners has gender differences.

H2: High school English learners’ AN positively predicts the severity of SA.

2 Methodology

2.1 Participants

The data for this study were obtained from a broader research initiative, employing random stratified sampling in Shanxi Province, China, with a total number of 248 senior high school students. After data collection, the researcher checked the validity of the completed questionnaires; total valid questionnaires were 230. Here is the formula to determine the sample size:

$$n=\frac{Z^{2}p\left(1-p\right)}{e^2}$$

n is the sample size, Z is the standard error, p is the variance of the population estimate, and e is the permissible error.

When the 95% confidence interval, Z is 1.96. p is taken to be 0.5 for prudent estimation. The permissible error is 0.01. The formula calculation shows that at least 97 participants are needed for this study. Sample size is higher than 97. The study participants consisted of high school students who selected Physics, Chemistry, and Biology for the National College Entrance Examination to pursue admission in General Higher Education. The participants were teenagers aged 15 to 18 years (mean age = 16.4); 112 (48.70%) were male, and 118 (51.30%) were female. The sample comprised 160 (69.57%) students who lived in urban areas and 70 (30.43%) in rural areas. Furthermore, informed agreement was obtained from both the students and their parents before to the experiment, and the study’s goal was elucidated to the participants. The participants agreed to providing anonymized data for the current study.

2.2 Procedure

This study had three principal phases. During the initial phase, participants signed a written informed permission form and completed a questionnaire intended to gather demographic and baseline data. Those who finished the questionnaire and supplied their assent were eligible to participate in a diary tracking survey. In the second phase, the diary tracking survey was conducted for seven consecutive days using a paper diary checklist for data collection. The collected data provided demographic information, including details of gender, date of birth, grade level, family residence, and parental occupation. The baseline questionnaire and diary inventory consisted of scales pertaining to SA and AN. Data of all participating students were finally collected on June 15, 2024, with daily diary reports on campus between 17:30-18:00 pm. Reports were submitted daily. Finally, the responses to the diary questionnaire were recorded by the researcher.

2.3 Measures

2.3.1 Daily anxiety scale

The Negative Mood Scale proposed by Clark and Watson (42) and compiled by Antony et al. (43), which includes three dimensions: AN, depression, and stress, was used in the study; it has 21 items, but, in this study, only the subscale “AN” was considered. After discussion among the authors, some of the wording was modified to accommodate the diary measurements (e.g. “I don’t seem to feel any pleasantness or relief at all today”). Participants self-assessed utilizing a 4-point Likert scale, with 1 indicating ‘does not meet’ and 4 denoting ‘always meets.’ This study reported Cronbach’s alpha coefficients for the scale ranging from 0.890 to 0.967 for each day, with an overall coefficient of 0.945 throughout the 7 days.

2.3.2 Daily Smartphone Addiction Scale

The Smartphone Addiction Scale-Short Version (SAS-SV), developed by Kwon et al. (44), is a short version of the Smartphone Addiction Scale, which consists of 10 items (e.g., “I will change my study program today because of using my smartphone”). Following deliberation, certain phrasing was altered to align with the diary measure. Participants evaluated themselves utilizing a Likert scale, with responses spanning from 1 (strongly disagree) to 6 (strongly agree). In the present research, the Cronbach’s alpha coefficients for the scale varied from 0.900 to 0.977 daily, yielding a cumulative Cronbach’s alpha coefficient of 0.961 throughout the 7 days.

2.4 Data analyses

The data of this study had a nested structure, i.e., the daily survey data of the study participants (within-individual level, Level 1) were nested within the overall data of the individuals (between-individual level, Level 2). Therefore, a multilevel model was required for data visualization.

The data was processed with SPSS 26.0 and Mplus 8.3. First, data was collected using the self-reporting technique, and in order to avoid common method bias due to subjective reporting, Harman’s one-factor test was employed to assess common method biases (CMB). Second, descriptive statistics were analyzed for all samples to understand the sample composition. Additionally, the relationship among AN and dependency on smartphones was analyzed utilizing Pearson’s coefficient. to explain the relationship between variables more scientifically. In addition, Mplus8.3 was used to determine the intra-class correlation coefficient (ICC) for each variable to authenticate the suitability of the data in this research for multilevel evaluation. At last, Mplus 8.3 was additionally employed to develop a multilevel regression model to test the relationship between AN level and the severity of SA.

3 Results

3.1 Common method biases analysis

To verify the data’s reliability, the CMB was determined utilizing the Harman one-way test prior to data processing. By testing the 17 items in the questionnaire related to the two variables, the results showed that the three factors had eigenvalues greater than 1, and the maximum factor variance explained was 38.717%, which did not reach the critical value of 40%, and hence, no serious common variance bias was found (45).

3.2 Descriptive statistics and correlation analysis

The outcomes of descriptive statistical methods and correlation analysis between AN level and the severity of SA among students are displayed in Table 1. Table 1 illustrates that there are 230 responders., the mean value of SA in high students was 3.78 with a standard deviation of 0.72 and an intra-individual standard deviation of 0.94. The mean value of AN was 3.92 with a standard deviation of 0.66 and an intra-individual standard deviation of 0.86.

Table 1

Table 1. Descriptive statistics and correlation analysis.

Pearson’s product-moment relationship analysis was employed to investigate the correlations among the model parameters at Level 1 and Level 2, with correlation coefficients at Level 2 above the diagonal line (N = 230) and correlation coefficients at Level 1 below the diagonal line (N = 1610). It was found that AN level had a substantial positive correlation with the severity of SA (r = 0.478, p < 0.01).

3.3 Intra-class correlation coefficient analysis

ICC is the ratio of interindividual variation to total variation and indicates the proportion of all variation explained by the interindividual variation. In this study, the null model was used to calculate the intragroup correlation coefficient for each variable (46).

Table 2 indicates that the ICC for SA was 0.616, indicating that 61.6% of the variation came from inter-individual differences and 38.4% of the variation came from intra-individual differences. The ICC for AN was 0.593, indicating that 59.3% of the values were attributed to the inter-individual variation and 40.7% of the values were attributed to the intra-individual variation. The ICC values for all variables were greater than 0.059, indicating that within-group similarities could not be ignored, thus allowing for multilevel analysis (47).

Table 2

Table 2. Intra-group correlation coefficients.

3.4 Regression analysis

Using students’ gender as a moderator, the relationship between AN and SA was investigated. After determining the Level 1 predictive effect of AN on SA, the following model was built:

$$Y_{ij}={\beta }_{0j}+{\beta }_{1j}{X}_{ij}+{ϵ}_{ij}$$

Where ${ϵ}_{ij}\sim N \left(0, {\sigma }^2\right)$.

The differences between males and females at different levels of SA and the predictive effect of AN on SA were determined through Level 2, using students’ gender as a predictor variable. The modeling constructs are as follows:

$${\beta }_{0j}={\gamma }_{00}+{\gamma }_{01}{W}_j+{\mu }_{oj}$$

$${\beta }_{1j}={\gamma }_{10}+{\gamma }_{11}{W}_j+{\mu }_{1j}$$

Where $\left[\begin{array}{c}{\mu }_{oj}\\ {\mu }_{1j}\end{array}\right]\sim N\left[\left[\begin{array}{c}0\\ 0\end{array}\right], \left[\begin{array}{cc}{\tau }_{00}& {\tau }_{01}\\ {\tau }_{10}& {\tau }_{11}\end{array}\right]\right]$.

Let $Var\left({\beta }_{0j}\right)=Var\left({\mu }_{0j}\right)={\tau }_{00}$, $Var\left({\beta }_{1j}\right)=Var\left({\mu }_{1j}\right)={\tau }_{11}$, $Cov\left({\beta }_{0j}, {\beta }_{1j}\right)$ = ${\tau }_{01}$.

In this model, the intercept and slope of Level 1 are determined by the Level 2 factor, i.e., all changes in Level 1 are reflected in Level 2, and the variance exists in Level 2. Therefore, Level 1 results only output the residual variance (σ2), which is valued at 0.240. Male students had a SA level of 1.175, according to the results shown in Table 3, and there was a substantial difference between male and female students in terms of SA (β = 0.906, SE = 0.356, p < 0.05). AN level in males was a significant positive predictor of SA (β = 0.552, SE = 0.136, p < 0.001), but the predictive effect of AN on SA of males and females did not differ significantly (β = -0.158, SE = 0.086). That is, there was no gender difference in the predictive effect of anxiety level on the severity of smartphone addiction. The overall variance of each student’s AN was 3.364, and the overall variance of each student’s slope was 0.192. Thus, males and females’ levels of SA differed significantly from one another; additionally, males’ and females’ levels of AN significantly and positively predicted the severity of SA.

Table 3

Table 3. Multi-level regression results.

4 Discussion

4.1 Discussion of results

As smartphones have become an integral part of life, SA among adolescents has become a challenging public health issue today. This study used a diary method to explore the role of anxiety levels of high school English learners in influencing their SA symptoms and whether there were gender differences. The results of the study indicated that anxiety level of high school English learners was a predictor of smartphone addiction severity. There was a significant difference in the level of smartphone addiction between males and females, but no significant difference between the genders was observed in the predictive effect of anxiety level on the severity of smartphone addiction. The findings contribute to a deeper understanding of the relationship between anxiety levels and the severity of SA among high school English language learners.

First, the results of this study are consistent with H1 that there is a significant gender difference in the severity of SA among high school English learners. It has been shown that the difference in SA symptoms between genders is statistically significant (48–50). The possible reason for this is that males and females have differences in smartphone app usage. Among them, males prefer mobile games while females favor social media applications (51). This reflects the fact that females have more need to communicate and maintain relationships through smartphones for socialization purposes (52, 53). Therefore, if SA symptoms are to be reduced among high school English learners, educators need to take into account the gender differences of students.

Second, the study confirmed that H2, the daily anxiety level of both males and females, positively predicted their daily SA symptoms. On the one hand, high school English learners have foreign language anxiety such as reading, writing, and listening anxiety in English learning, which can cause students to develop negative emotions (54, 55). According to the negative reinforcement emotional processing model, it is known that high school English learners develop addictive behaviors in order to escape or reduce the negative emotions caused by English anxiety (56). Smartphones are characterized by instant gratification, easy accessibility, and attention demand (57), which can provide individuals with diversified entertainment and effectively ease their anxiety (58), increasing the likelihood of students’ SA. When a student’s need to alleviate anxiety is met, he or she will continue to use the smartphones more frequently (59). On the other hand, in the field of English language learning, female English language learners are more prone to English anxiety due to greater emotional sensitivity compared to males (60), but females are more adept at language learning and language use (61). This may alleviate females’ lack of emotional control over English anxiety. Therefore, there was no significant difference between high school English language learners in the relationship between anxiety levels and the severity of SA. It should be noted that the present study only examined whether there were gender differences between anxiety levels on SA symptoms among high school English learners, and due to the limited data, it was not possible to discuss whether gender differences would have an impact on the correlation between single anxiety items in foreign languages, such as listening, speaking, reading, and writing, and SA, which could be explored in future studies.

4.2 Implications

This study offers distinctive insights on SA with significant theoretical and practical ramifications for frontline teachers. In terms of theoretical implications, this study was unique in that it explored the effect of anxiety levels on the severity of smartphone addiction and analyzed whether there were gender differences. In terms of practical implications, this study provides targeted insights for preventing smartphone addiction due to anxiety in language learners. Faced with anxiety during language learning, males will tend to use relaxation as a way of coping, while females tend to seek companionship and think positively (62). Therefore, teachers can alleviate the symptoms of smartphone addiction in students according to their gender differences. Teachers can also choose topics that students enjoy in class, using slower speech, simple words and grammar, thus easing the severity of smartphone addiction by reducing anxiety. This in turn encourages students to engage in self-talk and active participation in the classroom in English (63). In addition, educational researchers can improve the excessive use of smartphones by students from a psychological point of view. For example, teachers can enhance communication with students to understand the source of their AN and help them overcome their SA by alleviating their anxiety levels.

4.3 Limitations and suggestions for future research

Some restrictions apply to this research. First, all participants in this study were sophomores from a school in Shanxi Province, China, and hence the findings cannot be generalized. Future research can select students from other districts for the study. Second, this study proved that there is a significant gender difference in students’ smartphone addiction, but the size of the gender difference was not clarified due to the limitations of the research methodology. In addition, the number of topics that participants had to report in this diary study was high, which may have affected their motivation to participate in the study and hence affected the reliability of the questionnaire. In the future, we will improve this study in several ways. First, we will seek a more comprehensive research methodology to clarify the magnitude of specific gender differences between language learner anxiety and smartphone addiction. Second, researchers can we will expand the scope of the study by recruiting participants from different schools in different districts and even from different school years. In addition, researchers could explore the relationship between the dimensions of anxiety and other constructs as a way to more fully explore the relationship between anxiety and smartphone addiction. While the relationship between AN level and the severity of SA may still be controversial, this study could provide future researchers with some insights and necessary evidence.

5 Conclusions

In order to investigate the relationship between AN and SA, this study utilized an intensive longitudinal diary method research design to follow high school English learners for seven consecutive days. The findings revealed that AN level was a strong positive predictor of the severity of SA and that there was a substantial difference in the degree of SA between males and females.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Ethics Committee of Department of Foreign languages, Xinzhou Normal University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.

Author contributions

CZ: Data curation, Investigation, Project administration, Validation, Writing – original draft, Writing – review & editing. YH: Data curation, Investigation, Validation, Writing – original draft, Writing – review & editing. BL: Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. JW: Data curation, Investigation, Validation, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. The work was funded by Shanxi Provincial Higher Education Teaching Reform and Innovation Project, grant number J2021590 and Shanxi Provincial Education Science Planning Office [GH-19078], Chinese Association for Higher Education:2023 Higher Education Scientific Research Key Project (23JS0304).

Acknowledgments

We would like to thank the students who completed the questionnaire for their contributions to our research. We would also like to thank those who assisted with language revision.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Luo Y and Watts M. Exploration of university students’ lived experiences of using smartphones for English language learning. Comput Assisted Lang Learn. (2024) 37:608–33. doi: 10.1080/09588221.2022.2052904

Crossref Full Text | Google Scholar

2. Xia J and Gao X. Parental involvement in Chinese preschool children’s mobile-assisted foreign language learning. Porta Linguarum Rev Interuniversitaria Didáctica Las Lenguas Extranjeras. (2022), 65–81. doi: 10.30827/portalin.vi.23840

Crossref Full Text | Google Scholar

3. Andujar A. Benefits of mobile instant messaging to develop ESL writing. System. (2016) 62:63–76. doi: 10.1016/j.system.2016.07.004

Crossref Full Text | Google Scholar

4. Hwang G-J and Fu Q-K. Trends in the research design and application of mobile language learning: A review of 2007–2016 publications in selected SSCI journals. Interactive Learn Environments. (2019) 27:567–81. doi: 10.1080/10494820.2018.1486861

Crossref Full Text | Google Scholar

5. Hui L, Teng LS, and Guo F. Modeling the relationship between digital nativity and Smartphone usage in learning English as a foreign language contexts. Front Psychol. (2023) 13:1053339. doi: 10.3389/fpsyg.2022.1053339

PubMed Abstract | Crossref Full Text | Google Scholar

6. Metruk R. Confronting the challenges of MALL: distraction, cheating, and teacher readiness. Int J Emerging Technol Learn (iJET). (2020) 15:4–14. doi: 10.3991/ijet.v15i02.11325

Crossref Full Text | Google Scholar

7. Elamin NO, Almasaad JM, Busaeed RB, Aljafari DA, and Khan MA. Smartphone addiction, stress, and depression among university students. Clin Epidemiol Global Health. (2024) 25:101487. doi: 10.1016/j.cegh.2023.101487

Crossref Full Text | Google Scholar

8. Demirci K, Akgönül M, and Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict. (2015) 4:85–92. doi: 10.1556/2006.4.2015.010

PubMed Abstract | Crossref Full Text | Google Scholar

9. Lee H, Kim JW, and Choi TY. Risk factors for smartphone addiction in korean adolescents: smartphone use patterns. J Korean Med Sci. (2017) 32:1674. doi: 10.3346/jkms.2017.32.10.1674

PubMed Abstract | Crossref Full Text | Google Scholar

10. Walsh D. Ten commonly asked computer questions … and answers Part 2. Dentistry Today. (2007) 26:122–4.

Google Scholar

11. Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, and Schaub MP. Smartphone use and smartphone addiction among young people in Switzerland.. J Behav Addict. (2024) 4(4):4. doi: 10.1556/2006.4.2015.037

PubMed Abstract | Crossref Full Text | Google Scholar

12. Griffiths MD. Technological addictions. Clin Psychol Forum. (1995) 76:14–9. doi: 10.53841/bpscpf.1995.1.76.14

Crossref Full Text | Google Scholar

13. Griffiths MD. Behavioural addiction: An issue for everybody? Employee Councelling Today. (1996) 8:19–25. doi: 10.1108/13665629610116872

Crossref Full Text | Google Scholar

14. Lin Y-H, Chang L-R, Lee Y-H, Tseng H-W, Kuo TBJ, and Chen S-H. Development and validation of the smartphone addiction inventory (SPAI). PloS One. (2014) 9:e98312. doi: 10.1371/journal.pone.0098312

PubMed Abstract | Crossref Full Text | Google Scholar

15. Aljomaa SS, Al.Qudah MF, Albursan IS, Bakhiet SF, and Abduljabbar AS. Smartphone addiction among university students in the light of some variables. Comput Hum Behav. (2016) 61:155–64. doi: 10.1016/j.chb.2016.03.041

Crossref Full Text | Google Scholar

16. Gökçearslan Ş, Uluyol Ç, and Şahin S. Smartphone addiction, cyberloafing, stress and social support among university students: A path analysis. Children Youth Serv Rev. (2018) 91:47–54. doi: 10.1016/j.childyouth.2018.05.036

Crossref Full Text | Google Scholar

17. Son H-G, Cho HJ, and Jeong K-H. The effects of korean parents’ Smartphone addiction on korean children’s smartphone addiction: moderating effects of children’s gender and age. Int J Environ Res Public Health. (2021) 18:6685. doi: 10.3390/ijerph18136685

PubMed Abstract | Crossref Full Text | Google Scholar

18. Swalih MM, Mathew FP, and Sulphey MM. Adaptation of smartphone addiction scale. ST THERESA J OF HUMANITIES AND Soc Sci. (2019) 5:22–41.

Google Scholar

19. Yue H, Yue X, Liu B, Li X, Dong Y, and Bao H. Short version of the smartphone addiction scale: Measurement invariance across gender. PloS One. (2023) 18:e0283256. doi: 10.1371/journal.pone.0283256

PubMed Abstract | Crossref Full Text | Google Scholar

20. La Barbera D, La Paglia F, and Valsavoia R. Social network and addiction. Stud Health Technol Inf. (2009) 144:33–6. doi: 10.3389/conf.neuro.14.2009.06.054

Crossref Full Text | Google Scholar

21. Lee ZWY, Cheung CMK, and Thadani DR. An investigation into the problematic use of facebook. In: 2012 45th hawaii international conference on system sciences. (Maui, HI, USA: IEEE) (2012). p. 1768–76. doi: 10.1109/HICSS.2012.106

Crossref Full Text | Google Scholar

22. Pelling EL and White KM. The theory of planned behavior applied to young people’s use of social networking Web sites. Cyberpsychology Behavior: Impact Internet Multimedia Virtual Reality Behav Soc. (2009) 12:755–9. doi: 10.1089/cpb.2009.0109

PubMed Abstract | Crossref Full Text | Google Scholar

23. Alaceva C and Rusu L. Barriers in achieving business/IT alignment in a large Swedish company: What we have learned? Comput Hum Behav. (2015) 51:715–28. doi: 10.1016/j.chb.2014.12.007

Crossref Full Text | Google Scholar

24. Henry JD and Crawford JR. The short-form version of the Depression Anxiety Stress Scales (DASS-21): Construct validity and normative data in a large non-clinical sample. Br J Clin Psychol. (2005) 44:227–39. doi: 10.1348/014466505X29657

PubMed Abstract | Crossref Full Text | Google Scholar

25. Fernandez S. Anxiety disorders in childhood and adolescence: A primary care approach. Pediatr Ann. (2017) 46:e213–6. doi: 10.3928/19382359-20170522-01

PubMed Abstract | Crossref Full Text | Google Scholar

26. Mittal VA and Walker EF. Diagnostic and statistical manual of mental disorders. Psychiatry Res. (2011) 189:158–9. doi: 10.1016/j.psychres.2011.06.006

PubMed Abstract | Crossref Full Text | Google Scholar

27. Beesdo K, Bittner A, Pine DS, Stein MB, Höfler M, Lieb R, et al. Incidence of social anxiety disorder and the consistent risk for secondary depression in the first three decades of life. Arch Gen Psychiatry. (2007) 64:903–12. doi: 10.1001/archpsyc.64.8.903

PubMed Abstract | Crossref Full Text | Google Scholar

28. Schneider S and In-Albon T. Angststörungen und Phobien im Kindes- und Jugendalter. Psychotherapeut. (2010) 55:525–40. doi: 10.1007/s00278-010-0724-0

Crossref Full Text | Google Scholar

29. Weems CF, Hayward C, Killen J, and Taylor CB. A longitudinal investigation of anxiety sensitivity in adolescence. J Abnormal Psychol. (2002) 111:471–7. doi: 10.1037/0021-843X.111.3.471

PubMed Abstract | Crossref Full Text | Google Scholar

30. Kessler RC, Ruscio AM, Shear K, and Wittchen H-U. Epidemiology of anxiety disorders. In: Stein MB and Steckler T, editors. Behavioral neurobiology of anxiety and its treatment, vol. 2. Berlin, Heidelberg: Springer Berlin Heidelberg (2009). p. 21–35. doi: 10.1007/7854_2009_9

Crossref Full Text | Google Scholar

31. Stansfeld S, Smuk M, Onwumere J, Clark C, Pike C, McManus S, et al. Stressors and common mental disorder in informal carers – An analysis of the English Adult Psychiatric Morbidity Survey 2007. Soc Sci Med. (2014) 120:190–8. doi: 10.1016/j.socscimed.2014.09.025

PubMed Abstract | Crossref Full Text | Google Scholar

32. Hong J-C, Hwang M-Y, Tai K-H, and Chen Y-L. Using calibration to enhance students’ self-confidence in English vocabulary learning relevant to their judgment of over-confidence and predicted by smartphone self-efficacy and English learning anxiety. Comput Educ. (2014) 72:313–22. doi: 10.1016/j.compedu.2013.11.011

Crossref Full Text | Google Scholar

33. Boumosleh JM and Jaalouk D. Depression, anxiety, and smartphone addiction in university students- A cross sectional study. PloS One. (2017) 12:e0182239. doi: 10.1371/journal.pone.0182239

PubMed Abstract | Crossref Full Text | Google Scholar

34. Geng Y, Gu J, Wang J, and Zhang R. Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. ournal Affect Disord. (2021) 293:415–21. doi: 10.1016/j.jad.2021.06.062

PubMed Abstract | Crossref Full Text | Google Scholar

35. Kim S-G, Park J, Kim H-T, Pan Z, Lee Y, and McIntyre RS. The relationship between smartphone addiction and symptoms of depression, anxiety, and attention-deficit/hyperactivity in South Korean adolescents. Ann Gen Psychiatry. (2019) 18:1. doi: 10.1186/s12991-019-0224-8

PubMed Abstract | Crossref Full Text | Google Scholar

36. Dou X, Lu J, Yu Y, Yi Y, and Zhou L. The impact of depression and anxiety on mobile phone addiction and the mediating effect of self-esteem. Sci Rep. (2024) 14:23004. doi: 10.1038/s41598-024-71947-6

PubMed Abstract | Crossref Full Text | Google Scholar

37. Kim K, Yee J, Chung JE, Kim HJ, Han JM, Kim JH, et al. Smartphone addiction and anxiety in adolescents – A cross-sectional study. Am J Health Behav. (2021) 45:895–901. doi: 10.5993/AJHB.45.5.9

PubMed Abstract | Crossref Full Text | Google Scholar

38. Sapacz M, Rockman G, and Clark J. Are we addicted to our cell phones? Comput Hum Behav. (2016) 57:153–9. doi: 10.1016/j.chb.2015.12.004

Crossref Full Text | Google Scholar

39. Abu Khait A, Menger A, Al-Atiyyat N, Hamaideh SH, Al-Modallal H, and Rayapureddy H. The association between proneness to smartphone addiction and social anxiety among school students and the mediating role of social support: A call to advance Jordanian adolescents’ Mental health. J Am Psychiatr Nurses Assoc. (2024) 31:183–96. doi: 10.1177/10783903241261047

PubMed Abstract | Crossref Full Text | Google Scholar

40. Ge J, Liu Y, Cao W, and 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. doi: 10.3389/fpsyg.2022.1033304

PubMed Abstract | Crossref Full Text | Google Scholar

41. de Oliveira Meneses M and Andrade EMLR. Relationship between depression, anxiety, stress and smartphone addiction in COVID-19 nursing students. Rev Latino-Americana Enfermagem. (2024) 32:e4056. doi: 10.1590/1518-8345.6764.4056

PubMed Abstract | Crossref Full Text | Google Scholar

42. Clark LA and Watson D. Tripartite model of anxiety and depression: Psychometric evidence and taxonomic implications. J Abnormal Psychol. (1991) 100:316–36. doi: 10.1037//0021-843x.100.3.316

PubMed Abstract | Crossref Full Text | Google Scholar

43. Antony MM, Bieling PJ, Cox BJ, Enns MW, and Swinson RP. Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. psychol Assess. (1998) 10:176–81. doi: 10.1037/1040-3590.10.2.176

Crossref Full Text | Google Scholar

44. Kwon M, Kim D-J, Cho H, and Yang S. The smartphone addiction scale: development and validation of a short version for adolescents. PloS One. (2013) 8:e83558. doi: 10.1371/journal.pone.0083558

PubMed Abstract | Crossref Full Text | Google Scholar

45. Podsakoff PM, MacKenzie SB, Lee J-Y, and Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J Appl Psychol. (2003) 88:879–903. doi: 10.1037/0021-9010.88.5.879

PubMed Abstract | Crossref Full Text | Google Scholar

46. Bliese PD. Group size, ICC values, and group-level correlations: A simulation. Organizational Res Methods. (1998) 1:355–73. doi: 10.1177/109442819814001

Crossref Full Text | Google Scholar

47. Cohen J. Statistical power analysis for the behavioral sciences. New York: Routledge (2013). doi: 10.4324/9780203771587

Crossref Full Text | Google Scholar

48. Hong F-Y, Chiu S-I, and Huang D-H. A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students. Comput Hum Behav. (2012) 28:2152–9. doi: 10.1016/j.chb.2012.06.020

Crossref Full Text | Google Scholar

49. Li Y, Sallam MH, and Ye Y. The impact of WeChat use intensity and addiction on academic performance. Soc Behav Personality: Int J. (2019) 47:1–7. doi: 10.2224/sbp.7331

Crossref Full Text | Google Scholar

50. Potembska E. Gender and severity of symptoms of mobile phone addiction in Polish gymnasium, secondary school and university students Płeć a nasilenie objawów uzależnienia od telefonu komórkowego u uczniów polskich szkół gimnazjalnych, średnich i wyższych (2011). Available online at: https://www.semanticscholar.org/paper/Gender-and-severity-of-symptoms-of-mobile-phone-in-Potembska/28905dd46b74352bbfd05a1b11a3804282c185e4related-papers. (Accessed January 10, 2025).

Google Scholar

51. Arpaci I. Relationships between early maladaptive schemas and smartphone addiction: the moderating role of mindfulness. Int J Ment Health Addict. (2021) 19:778–92. doi: 10.1007/s11469-019-00186-y

Crossref Full Text | Google Scholar

52. Hong YP, Yeom YO, and Lim MH. Relationships between smartphone addiction and smartphone usage types, depression, ADHD, stress, interpersonal problems, and parenting attitude with middle school students. J Korean Med Sci. (2021) 36:e129. doi: 10.3346/jkms.2021.36.e129

PubMed Abstract | Crossref Full Text | Google Scholar

53. van Deursen AJAM, Bolle CL, Hegner SM, and Kommers PAM. 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) 45:411–20. doi: 10.1016/j.chb.2014.12.039

Crossref Full Text | Google Scholar

54. Cheng Y-S. A measure of second language writing anxiety: Scale development and preliminary validation. J Second Lang Writing. (2004) 13:313–35. doi: 10.1016/j.jslw.2004.07.001

Crossref Full Text | Google Scholar

55. Saito Y, Garza TJ, and Horwitz EK. Foreign language reading anxiety. Modern Lang J. (1999) 83:202–18. doi: 10.1111/0026-7902.00016

Crossref Full Text | Google Scholar

56. Baker TB, Piper ME, McCarthy DE, Majeskie MR, and Fiore MC. Addiction motivation reformulated: An affective processing model of negative reinforcement. psychol Rev. (2004) 111:33–51. doi: 10.1037/0033-295X.111.1.33

PubMed Abstract | Crossref Full Text | Google Scholar

57. Rozgonjuk D, Kattago M, and Täht K. Social media use in lectures mediates the relationship between procrastination and problematic smartphone use. Comput Hum Behav. (2018) 89:191–8. doi: 10.1016/j.chb.2018.08.003

Crossref Full Text | Google Scholar

58. Nekouei ZK, Doost HTN, Yousefy A, Manshaee G, and Sadeghei M. The relationship of Alexithymia with anxiety-depression-stress, quality of life, and social support in Coronary Heart Disease (A psychological model). J Educ Health Promotion. (2014) 3:68. doi: 10.4103/2277-9531.134816

PubMed Abstract | Crossref Full Text | Google Scholar

59. Parker BJ and Plank RE. A uses and gratifications perspective on the Internet as a new information source. Latin Am Business Rev. (2000) 18:43–9.

Google Scholar

60. Dewaele J-M and MacIntyre PD. The two faces of Janus? Anxiety and enjoyment in the foreign language classroom. Stud Second Lang Learn Teach. (2014) 4:237–74. doi: 10.14746/ssllt.2014.4.2.5

Crossref Full Text | Google Scholar

61. Yan JX and Horwitz EK. Learners’ Perceptions of how anxiety interacts with personal and instructional factors to influence their achievement in english: A qualitative analysis of EFL learners in China. Lang Learn. (2008) 58:151–83. doi: 10.1111/j.1467-9922.2007.00437.x

Crossref Full Text | Google Scholar

62. Kao P-C, Chen KT-C, and Craigie P. Gender differences in strategies for coping with foreign language learning anxiety. Soc Behav Personality: Int J. (2017) 45:205–10. doi: 10.2224/sbp.5771

Crossref Full Text | Google Scholar

63. Zarei MA, Alibakhshi G, and Nezakatgoo B. Strategies employed by EFL teachers to cope with language learners’ classroom anxiety. TESOL J. (2024) 15:e843. doi: 10.1002/tesj.843

Crossref Full Text | Google Scholar