Wang WH, Wu MY, Zhang XK, Wang X, Yuan XX, Ma L, Li L, Zhang L. Correlation of objective mobile phone use time and mobile phone addiction with depressive and anxiety symptoms in young adults. World J Psychiatry 2026; 16(5): 117104 [DOI: 10.5498/wjp.v16.i5.117104]
Corresponding Author of This Article
Lei Zhang, PhD, School of General Medicine, Xi’an Medical University, No. 48 Fenghao West Road, Lianhu District, Xi’an 710077, Shaanxi Province, China. zhl21443141@163.com
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Psychology, Social
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Observational Study
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May 19, 2026 (publication date) through May 5, 2026
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World Journal of Psychiatry
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2220-3206
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Wang WH, Wu MY, Zhang XK, Wang X, Yuan XX, Ma L, Li L, Zhang L. Correlation of objective mobile phone use time and mobile phone addiction with depressive and anxiety symptoms in young adults. World J Psychiatry 2026; 16(5): 117104 [DOI: 10.5498/wjp.v16.i5.117104]
Wen-Hua Wang, Lei Zhang, School of General Medicine, Xi’an Medical University, Xi’an 710077, China
Wen-Hua Wang, Xue Wang, Xiao-Xiao Yuan, Lei Zhang, Shaanxi Provincial Health Industry Association Service Center, Xi’an 710003, Shaanxi Province, China
Ming-Yang Wu, Department of Maternal and Child Health, Xiangya School of Public Health, Changsha 410078, Hunan Province, China
Xu-Kuan Zhang, Shijiazhuang Center for Disease Control and Prevention, Shijiazhuang 050011, Hebei Province, China
Le Ma, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
Lu Li, Department of Gastroenterology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004, Shaanxi Province, China
Lei Zhang, Shaanxi Medical Association, Xi’an 710003, Shaanxi Province, China
Author contributions: Wang WH and Wu MY contributed to the conception or design of the paper, drafted the manuscript, and they contributed equally to this manuscript and are co-first authors; Wang WH, Wu MY, Zhang XK, Wang X, Yuan XX, Ma L, and Zhang L contributed to the acquisition, analysis, or interpretation of data for the work; Wu MY, Li L, and Zhang L provided a critical review of the manuscript; Li L and Zhang L contributed equally to this manuscript and are co-corresponding authors. All authors have read and agreed to the published version of the manuscript and contributed to the article and approved the submitted version.
Supported by Natural Science Basic Research Program of Shaanxi Province, China, No. S2025-YBMS-1014.
Institutional review board statement: This study was approved by the Ethics Committee of The Second Affiliated Hospital of Xi’an Jiaotong University (Approval No. 2022248).
Informed consent statement: All participants gave electronic informed consent before enrolment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
Corresponding author: Lei Zhang, PhD, School of General Medicine, Xi’an Medical University, No. 48 Fenghao West Road, Lianhu District, Xi’an 710077, Shaanxi Province, China. zhl21443141@163.com
Received: November 28, 2025 Revised: January 21, 2026 Accepted: February 6, 2026 Published online: May 19, 2026 Processing time: 152 Days and 18.7 Hours
Abstract
BACKGROUND
The correlation between mobile phone use and the mental health of young adults remains a matter of intense scrutiny. While a substantial body of evidence implicates excessive mobile phone use and addiction in the exacerbation of depressive and anxiety symptoms, conflicting data suggest that these associations may be inconsistent or overestimated.
AIM
To explore the relationship between mobile phone use time and addiction and depressive and anxiety symptoms.
METHODS
Using a multi-center observational study design (2022-2023), we recruited 16668 participants from six universities. Mobile phone use time was captured through mobile phone use record screenshots, and mobile phone addiction was assessed utilizing the Mobile Phone Addiction Tendency Scale. Depressive and anxiety symptoms were estimated using the Self-rating Depression Scale and the Generalized Anxiety Disorder-7. Subsequently, logistic regression and restricted cubic spline regression models were used.
RESULTS
The 3258 (19.5%) participants exhibited depressive symptoms, and 4590 (27.5%) reported anxiety symptoms. A significant U-shaped relationship was found between mobile phone use time and depressive symptoms (P for non-linearity < 0.001). Compared to the reference group (6-9 hours/day), the adjusted odds of depressive symptoms were significantly higher among students with screen times of 0-3 hours/day [odds ratio (OR) = 1.18, 95% confidence interval (CI): 1.05-1.33], 9-12 hours/day (OR = 1.14; 95%CI: 1.02-1.27) and ≥ 12 hours/day (OR = 1.26, 95%CI: 1.10-1.45), respectively. Mobile phone use time demonstrated a positive association with anxiety symptoms. Mobile phone addiction exhibited higher risks for both depressive symptoms (OR = 3.53; 95%CI: 3.25-3.84) and anxiety symptoms (OR = 3.73; 95%CI: 3.46-4.03) than those without mobile phone addiction.
CONCLUSION
Excessive mobile phone use time and addiction were positively correlated with depressive and anxiety symptoms, whereas overly short mobile phone use time was associated with an increased risk of depressive symptoms in college students. Interventions aimed at guarding against excessive mobile phone use time and preventing mobile phone addiction may be a vital approach to improve college students’ mental health.
Core Tip: This multi-center observational study of 16668 university students reveals a U-shaped relationship between objectively measured mobile phone use time and depressive symptoms, indicating that both overly short (0-3 hours/day) and long (≥ 9 hours/day) usage increase risk. Mobile phone use time was positively associated with anxiety symptoms. Notably, mobile phone addiction, posed a substantially higher risk for both depressive and anxiety symptoms than mobile phone use time alone, underscoring the critical need for interventions targeting mobile phone use time and addiction.
Citation: Wang WH, Wu MY, Zhang XK, Wang X, Yuan XX, Ma L, Li L, Zhang L. Correlation of objective mobile phone use time and mobile phone addiction with depressive and anxiety symptoms in young adults. World J Psychiatry 2026; 16(5): 117104
The prevalence of mental disorders among youth has continuously risen over the past two decades, and addressing youths’ mental health concerns like depressive and anxiety symptoms has emerged as a public health priority in numerous countries[1-4]. The factors contributing to the escalation in mental disorders among youth are multifaceted and complex. As youth are spending increasing time immersed in a digital world through their mobile phone[5,6], the impact of mobile phone use on mental well-being has been the subject of extensive debate[7-9]. It appears to be a double-edged sword for youths. Some literature suggests that excessive mobile phone use time and mobile phone addiction are associated with depressive and anxiety symptoms[10,11]. Nevertheless, other studies suggest that the negative mental health effects associated with mobile phone use might be exaggerated[9,12]. In fact, two studies suggested that people abstaining from mobile phones are more likely to suffer from depressive and anxiety symptoms[13,14]. In contrast to the positive and negative mental health impacts identified in above studies, Kim et al[15] did not find any connections between mobile phone use with mood disorders. Consistent with the contradictory findings from empirical research, several theoretical frameworks offer different perspectives. According to the displacement theory proposed by Kraut et al[16], excessive mobile phone use may come at the expense of in-person social interaction, sleep, and physical exercise, potentially harming mental health. Conversely, the conservation of resources theory posits that technological advancements, including mobile phones, can facilitate access to social and psychological resources, thereby promoting mental well-being[17].
Current findings exhibit the diversity of these perspectives, but remains inadequately explored. The majority prior studies have focused on either mobile phone use time or mobile phone addiction in isolation, and often on a small scale[9,11,12,15,18]. Nonetheless, these two elements serve as distinct indicators of mobile phone engagement[19]. Mobile phone addiction is a pathological construct characterized by compulsive use, impaired control, and functional impairment[20]. Conversely, screen time is regarded as a neutral measure of behavioral exposure. Theoretically, significant differences exist in how mobile phone addiction and screen time relate to mental health outcomes. Therefore, it is essential to analyze mobile phone addiction alongside screen time within a unified framework to thoroughly understand the overall effects of mobile phone use on mental health. Studies simultaneously examining both mobile phone addiction and mobile phone use time are still relatively scarce. Moreover, the pervasive use of mobile phones in daily life highlights the necessity for rigorous and objective methods capable of effectively capturing the genuine relationships between mobile phone use and mental well-being. However, much of the current research relies on participants' self-reported mobile phone use time[21-23]. This is an important limitation because prior studies showed that self-reported mobile phone use does not correlate well with objectively measured mobile phone use[24-26], which is especially inappropriate for people with psychological problems[24]. What’s more, due to the lack of objectively measured data in prior research, mobile phone use was always regarded as categorical variables to assess their connection with mental health. There is limited understanding of the relationships between various levels of mobile phone use and mental health risk.
The current large-scale study takes a more comprehensive approach by examining both mobile phone use time and mobile phone addiction. To mitigate self-reporting bias and enhance data precision, we objectively quantified mobile phone use time via screen captures of participants’ mobile phone use records. This rigorous methodology enabled us to elucidate the associations, specifically potential dose-response relationships, linking mobile phone use to depressive and anxiety symptoms.
MATERIALS AND METHODS
Study design and participants
In this study, we gathered data from a representative group of college students located in Shaanxi province, positioned in Northwest China, which has been described previously[27]. First, 6 universities were chosen at random from a pool of 57 universities in Shaanxi province in China. Afterward, we randomly chose 2 to 4 classes from various colleges and academic years within each university we sampled. All students in the selected classes were encouraged to take part in the survey. Prior to the investigation, we conducted a two-phase training session to clarify the objectives and procedures to the students. This included training two representatives from each selected class, followed by additional training for these class representatives to inform all students within their respective classes. Of the 20165 undergraduates invited to participate, 16668 were included in the final analysis after providing valid screenshots of their mobile phone use records.
Measures
Mobile phone use time: We gathered mobile phone use time by collecting participants’ mobile phone use record screenshots. A fortnight prior to the initiation of data collection, students were directed to activate the internal screen-time monitoring function, thereby guaranteeing the capture of precise mobile phone use time. Subsequently, we offered detailed instructions for capturing screenshots on various brands of mobile phones, requiring them to submit screenshots that displayed their mobile phone use time from the previous week. Participants were instructed to take and upload mobile phone use screenshots while they filled out the questionnaire. Trained research assistants conducted a thorough review of all uploaded screenshots, with particular attention to the submission timestamp of the questionnaire and the mobile phone usage depicted in the screenshots. Additionally, a comparison of usage reporting formats across Android and iOS systems revealed consistency in their core measurement principles, both based on screen-on time, indicating a good comparability between the two operating systems for the measurement of mobile phone use time. The participants involved were divided into 5 distinct categories, which were 0-3 hours/day, 3-6 hours/day, 6-9 hours/day, 9-12 hours/day, and ≥ 12 hours/day.
Mobile phone addiction: The Mobile Phone Addiction Tendency Scale (MPATS), designed by Xiong et al[28] was utilized to evaluate mobile phone addiction. The scale consisted of 16 items across four dimensions: Salience, withdrawal symptoms, social comfort, and mood changes. Items were rated on a 5-point Likert scale, yielding a total score range of 16 to 80. A cut-off score of 48 or higher was used to classify mobile phone addiction. The Cronbach’s α coefficient was 0.94.
Depressive symptoms: We evaluated depressive symptoms through the Self-Rating Depression Scale[29]. This scale consists of 20 questions, each rated from 1 to 4, capturing emotional symptoms over the past week. We transformed the total score of the Self-Rating Depression Scale into a standardized score by multiplying it by 1.25. A higher total score implies more depressive symptoms, and a standard score exceeding 50 indicates the presence of depressive symptoms[30]. The Cronbach’s α coefficient was 0.88.
Anxiety symptoms: The Generalized Anxiety Disorder-7 was employed to assess participants’ anxiety symptoms[31]. The scale includes seven questions scored from 0 to 3. The overall score can range from 0 to 21, where a greater score suggests increased anxiety symptoms. Anxiety symptoms were identified when the score exceeded 4. The Cronbach’s α coefficient was 0.95.
Covariates: Sociodemographic information included gender, grade, race, registered permanent residence, siblings, and parental educational attainment. Negative life events assessed included family disasters, hospitalization experiences, failed exams, and failed relationships. Participants who answered “ever” during the past year were deemed to have negative life events. Existing literature indicates that health-related lifestyles were both associated with mobile phone use and mental health outcomes[32,33], and may serve as confounders in the relationship between mobile phone use and mental health. Referring to a previous study[34], health-related lifestyles included current smoking, current drinking, physical activity, and rational diet. Individuals who had used at least one cigarette or consumed one glass of wine within the past month were categorized as current smokers or current drinkers. The assessment of physical activity was conducted using The International Physical Activity Questionnaire Short Form[35], categorizing activity levels into low, moderate, and high levels. Irrational diet was characterized by participants’ eating red meat on a daily basis or consuming fruits and vegetables less frequently than once a day.
Statistical analysis
We summarized categorical data as frequencies and proportions, whereas continuous measures were reported as means with standard deviations. We applied binary logistic regression analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for depressive and anxiety symptoms with three models. In model 1, we adjusted for sociodemographic information. In model 2, we further adjusted for negative life events. For model 3, health-related lifestyles were additionally adjusted. We also employed a restricted cubic splines regression model featuring four knots positioned at the 5th, 35th, 65th, and 95th percentiles to flexibly model the relationship. Then, we tested for interactions of mobile phone use time and mobile phone addiction with depressive and anxiety symptoms.
For sensitive analyses, we further ran a multivariable model that additionally mutually adjusted for depressive and anxiety symptoms. To verify the robustness of these results, we further applied logistic regression models weighted by gender. In all tests, statistical significance was defined as a 2-sided P < 0.05. All analyses utilized R version 4.0.2.
RESULTS
Of the 16668 participants, 5881 (35.3%) were male and 10787 (64.7%) were female. 16169 (97.0%) were Han nationals, 4940 (29.6%) were from single-child families, and 8993 (54.0%) from rural. Among these, 3258 (19.5%) had depressive symptoms and 4590 (27.5%) had anxiety symptoms. The mobile phone use time was 6.98 ± 4.11 hours/day, with 5821 (34.9%) participants spending more than 9 hours/day on mobile phone and 3707 (22.2%) spending less than 3 hours/day on mobile phone. We also revealed that 4826 (29.0%) participants qualified as having mobile phone addiction, and the MPATS score was 39.3 ± 12.9 (Table 1).
Table 1 Characteristics of the study population, n (%)/mean ± SD.
In three models adjusting for different factors, mobile phone usage duration exhibited a U-shaped relationship with depressive symptoms, whereas mobile phone use time showed a positive association with anxiety symptoms (Table 2). In the full model, which controlled for sociodemographic factors, negative life events, and health-related lifestyles (Table 2, model 3), mobile phone use time showed a stronger U-shaped association with depressive symptoms. Compared to participants with 6-9 hours/day of mobile phone use time, those with 0-3 hours/day (OR = 1.18; 95%CI: 1.05-1.33), 9-12 hours/day (OR = 1.14; 95%CI: 1.02-1.27) and ≥ 12 hours/day (OR = 1.26; 95%CI: 1.10-1.45) of mobile phone use time had higher odds of depressive symptoms. Meanwhile, we detected a positive connection between mobile phone use time and anxiety symptoms in the fully adjusted model. Compared for participants with 6-9 hours/day of mobile phone use time, those with 9-12 hours/day (OR = 1.10; 95%CI: 1.01-1.21) and ≥ 12 hours/day (OR = 1.29; 95%CI: 1.14-1.46) demonstrated significantly higher odds of anxiety symptoms. Similar results were observed in model 1 and model 2 (Table 2).
Table 2 Associations of mobile phone use time and mobile phone addiction with depressive and anxiety symptoms.
Smartphone use
Depressive symptoms OR (95%CI)
Anxiety symptoms OR (95%CI)
Model 1
Model 2
Model 3
Model 1
Model 2
Model 3
Mobile phone use time (hours/day), per 3 hours/day
We used restricted cubic splines to flexibly model and visualize the relationship of mobile phone use time with depressive and anxiety symptoms in college students. The plot showed a U-shaped relation between mobile phone use time and depressive symptoms, with a substantial reduction of the risk within the lower range of mobile phone use time, which reached the lowest risk around 5.58 hours/day and then increased thereafter (P for non-linearity < 0.001). Above 7.57 hours/day, the OR of per 3 hours/day mobile phone use time for depressive symptoms was 1.14 (95%CI: 1.05-1.24). Regarding the positive relation between mobile phone use time and anxiety symptoms, the risk of anxiety symptoms was relatively flat until around 7.57 hours/day of mobile phone use time and then started to increase afterward (P for non-linearity = 0.058). Above 7.57 hours/day, the OR of per hour/day mobile phone use time for anxiety symptoms was 1.14 (95%CI: 1.06-1.23; Figure 1).
Figure 1 Restricted cubic splines regression analysis of mobile phone use time and mobile phone addiction with depressive and anxiety symptoms.
A: Mobile phone use time and depressive symptoms; B: Mobile phone use time and anxiety symptoms; C: Mobile phone addiction and depressive symptoms; D: Mobile phone addiction and anxiety symptoms. Odds ratio and confidence interval indicate hazard ratio and confidence interval. In each plot, the solid line indicates how risk of depressive symptoms and anxiety symptoms varies as a function of mobile phone use time and Mobile Phone Addiction Tendency Scale score, while the dashed lines are confidence intervals. All results were adjusted for demographic information (gender, grade, race, registered permanent residence, siblings, and parental educational attainment), negative life events (family disasters, hospitalization experiences, failed exams, and failed relationships), and health-related lifestyles (current smoking, current drinking, physical activity, and rational diet). OR: Odds ratio; CI: Confidence interval; MPATS: Mobile Phone Addiction Tendency Scale.
When we used mobile phone addiction, we observed positive significant associations with depressive symptoms (OR = 3.53; 95%CI: 3.25-3.84) and anxiety symptoms (OR = 3.73; 95%CI: 3.46-4.03) in three models (Table 2). In the fully adjusted model, an increase of 5 points in the MPATS score was notably linked to greater likelihood of depressive symptoms (OR = 1.38; 95%CI: 1.35-1.40) and anxiety symptoms (OR = 1.40; 95%CI: 1.38-1.43). These associations remained consistent across models 1 and 2. Furthermore, analyses using restricted cubic splines demonstrated a non-linear dose-response relationship of MPATS score with depressive and anxiety symptoms (all P for non-linearity < 0.001), with the risk of depressive and anxiety symptoms relatively increasing as MPATS scores increased (Figure 1). We further identified positive interactions on mobile phone use time with mobile phone addiction on depressive and anxiety symptoms (Supplementary Table 1).
Gender-stratified results are detailed in Table 3. We observed gender differences in the associations of mobile phone use time with depressive and anxiety symptoms (all P for interaction < 0.05). Compared with 6-9 hours/day of mobile phone use time, those male participants with 0-3 hours/day of mobile phone use time exhibited a higher risk of depressive symptoms (OR = 1.24; 95%CI: 1.02-1.52), while no increased risk was observed in female participants. In contrast, compared with 6-9 hours/day of mobile phone use time, those female participants with 9-12 hours/day (OR = 1.17; 95%CI: 1.02-1.34) and ≥ 12 hours/day (OR = 1.36; 95%CI: 1.16-1.61) of mobile phone use time showed elevated odds of depressive symptoms, while no increased risk was observed in male participants. For mobile phone addiction, gender-specific differences in the relationship of MPATS score with depressive and anxiety symptoms were found (All P for interaction < 0.05), but not observed in the association of mobile phone addiction with depressive and anxiety symptoms (All P for interaction > 0.05).
Table 3 Gender-specific associations of objectively measured mobile phone use time and mobile phone addiction with depressive and anxiety symptoms.
Smartphone use
Depressive symptoms, OR (95%CI)
P for interaction
Anxiety symptoms, OR (95%CI)
P for interaction
Male
Female
Male
Female
Mobile phone use time (hours/day), per 3 hours/day
Our findings remained robust in sensitivity analyses. In the gender-weighted logistic regression model, we found that both mobile phone use time and addiction retained a significant correlation with depressive and anxiety symptoms (Supplementary Table 2). In a mutually adjusted model including both depressive and anxiety symptoms, results were comparable (Supplementary Table 3).
DISCUSSION
The present study utilized objective monitoring of mobile phone use time and mobile phone addiction assessments to explore the correlation between mobile phone use and psychiatric symptoms within a large-scale student population. We found a U-shaped association with depressive symptoms, and a positive association of mobile phone use time with anxiety symptoms. In contrast, mobile phone addiction showed a strong positive association with depressive and anxiety symptoms.
Many epidemiologic studies have examined the relationship between mobile phone use and mental health, but the findings are mixed[36-40]. Most studies have reported the correlations of excessive mobile phone use time and addiction with the psychological health of adolescents[10,11,38,41]. A systematic review that included 40 studies showed that excessive mobile phone use time and mobile phone addiction were closely associated with depressive symptoms among college students[42]. The research conducted by Chen et al[37] highlighted a distinct link between mobile phone addiction and anxiety. A longitudinal study found that youth with mobile phone addiction had more emotional disorders and depressive symptoms in the next three years than those without such problems[21]. Conversely, conflicting data suggest that the detrimental psychological impact of mobile phone use might have been overstated[9,12]. Some studies indicated that there were no unfavorable associations between mobile phone use time and mental health, nor with any specific components[15,43,44]. For example, a study from Ontario discovered no links between mobile phone use with mood and anxiety disorders[15]. Notably, two investigations even indicated that individuals who do not use mobile phones appear more vulnerable to anxiety and depression[13,14]. Consequently, despite the correlation between mobile phone use and mental health having garnered substantial attention, it remains inadequately explored. However, most existing research uses participants’ self-reported mobile phone use time[21-23], a method prone to significant recall bias[24-26], and is not appropriate for people with psychological problems[24]. This study addresses a critical gap in the existing literature[6,45-47] as there is a marked scarcity of objective screen time data, particularly within the Chinese population, which currently ranks among the highest worldwide for mobile phone ownership and addiction[48]. A study with only 101 participants showed that depressive and anxiety symptoms severity were not related to objectively measured mobile phone use time[49]. In another study involving 13 families with adolescents diagnosed with clinical depression[50], significant correlations were observed of objective short message service frequency and average call duration with the psychometric scores of depressions and anxiety, with correlation coefficients ranging from 0.44 and 0.72. While the sample size of these studies was extremely limited.
Moreover, due to the lack of objective measurements and statistical methods, few investigations have empirically analyzed the dose-response relationship between mobile phone use and mental health. The present study systematically characterizes the links between mobile phone use time, mobile phone addiction, and its continuous changes with depressive and anxiety symptoms. Our findings on mobile phone addiction were in line with the previous findings, whereby we consistently observed a strong positive association with depressive and anxiety symptoms after accounting for important factors. As for mobile phone use time, our study added a new insight that excessive mobile phone use time is positively correlated with depressive and anxiety symptoms. Under the displacement theory proposed by Kraut et al[16], a strong urge to engage with devices such as mobile phones may result in heightened social isolation, as it diminishes both in-person interactions and size of one’s social circle[51], which results in poor mental health. Existing literature indicates that excessive mobile phone use time and addiction can displace critical health behaviors, including sleep, physical activity and social interaction[42,52], which are critical for maintaining psychological health[51]. Furthermore, mobile phones might impede self-regulation strategies[53], a deficit often linked to the exacerbation of depressive and anxiety symptoms.
Our research also emphasizes that overly short mobile phone use time is associated with a higher risk of depressive symptoms. The digital divide between mobile phone users and non-users may prevent people from crucial health resources, resulting in mental health disparities. Likewise, according to conservation of resources theory, mobile phones serve a crucial function in managing and sustaining various forms of psycho-social resources[17]. These resources help people to evade pressure-filled situations, maintain adequate resilience, and keep ample reserves of resources to tackle challenges[38,39,54]. Many studies post that rational mobile phone use can enhance social connection and support, fortify relationships, and alleviate feelings of loneliness or social isolation. However, it is important to recognize that extremely low mobile phone use time may not directly cause depressive symptoms. Rather, individuals experiencing depressive symptoms may exhibit anhedonia or social withdrawal, which could lead to a diminished interest in mobile phone activities or social interactions. Moreover, external factors that limit mobile phone use, such as strict parental controls and institutional restrictions, may act as confounding variables that simultaneously restrict usage and contribute to psychological distress. Future research should further incorporate these variables to elucidate the true relationship between mobile phone use and depressive symptoms.
Notably, we observed a gender-differentiated pattern. Male participants with overly short mobile phone use time exhibited a higher risk of depressive symptoms than females, while female participants with excessive mobile phone use time faced a greater risk of depressive symptoms than males. These differences may be attributed to the varying emphasis on interpersonal relationships and social engagement between genders, leading to distinct patterns of mobile phone application use. Research indicates that females are typically more inclined to utilize social media platforms to cultivate social connections and seek emotional support, whereas males often use mobile phones primarily for gaming and content consumption[55]. Such divergent use patterns may result in different psychological impacts associated with mobile phone use, which could, in turn, contribute to the observed gender differences in mental health outcomes. It suggests that overly short mobile phone use time interventions targeted at male youth and excessive mobile phone use time interventions targeted at female youth may have a greater association with depressive symptoms prevention. However, further research is needed to determine the causes underlying this gender difference.
This study is subject to certain limitations. First, although our statistical models adjusted for an extensive range of confounders, the potential impact of unmeasured confounders cannot be entirely ruled out. Second, a retrospective self-reported questionnaire to assess depressive and anxiety symptoms, which may result in reporting bias. Third, the cross-sectional nature of this study limits our ability to determine the causal relationship between mobile phone use time, mobile phone addiction, and the development of depressive and anxiety symptoms. Future research should utilize longitudinal designs or randomized controlled trials to evaluate the impact of mobile phone reduction interventions on mental health. Fourth, as this study focused exclusively on Chinese college students, the generalizability of our findings to other demographics is limited. Finally, we did not distinguish between specific functional types of mobile phone use, which may have distinct implications for mental health outcomes.
CONCLUSION
Our study of 16668 college students shows that excessive mobile phone use time and mobile phone addiction were associated with a higher risk of both depressive and anxiety symptoms in college students. Moreover, overly short mobile phone use time was related to depressive symptoms, rather than anxiety symptoms. Interventions and strategies focusing on preventing excessive mobile phone use time and mobile phone addiction may be a vital approach to improve college students’ mental health.
ACKNOWLEDGEMENTS
The authors would like to thank the Mental Health Education Centers of the six universities and all participants involved in this study.
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P-Reviewer: Nagamine T, PhD, Professor, Japan; Pandya A, PhD, Assistant Professor, India; Ulasoglu C, MD, Professor, Türkiye S-Editor: Zuo Q L-Editor: A P-Editor: Yu HG
