Prevalence and Distinct Correlates of Smartphone Addiction, Depressive Symptoms, and Their Comorbidity in College Students
Article information
Abstract
Objective
This study aimed to assess the prevalence of comorbid of smartphone addiction and depressive symptoms and identify their common and distinct risk factors among college students.
Methods
A total of 8,347 Chinese college students participated in the study by completing questionnaires that assessed individual, family, school, and peer factors, as well as smartphone addiction and depressive symptoms. Multivariate logistic regression was conducted to explore the risk of smartphone addiction only, depressive symptoms only, and their comorbidity.
Results
The prevalence rates of smartphone addiction only, depressive symptoms only, and their comorbidity were 30.5%, 8.9%, and 25.3%, respectively. Higher frequency of mobile phone use in lessons and lower level of school belonging were associated with the increased risk of all three disease outcomes. There was a dose-response relationship between the number of risk exposures and the three disease outcomes.
Conclusion
The more risk factors of college students were exposed to, the more likely they were to develop smartphone addiction only, depressive symptoms only, and their comorbidity. Targeted preventive measures and solutions should be implemented to reduce the occurrence of smartphone addiction and depressive symptoms.
INTRODUCTION
College students are undergoing a significant transition as they progress from adolescence to adulthood [1], making them more vulnerable to behavioral and psychological health problems due to their immaturity and lower self-regulatory skills [2]. Depressive symptom is one of the most prevalent psychiatric illnesses in this population. The pooled prevalence of generalized depressive symptoms among college students was reported to be 33.6% [3]. Concurrently, smartphone addiction is currently recognized as a public health problem, especially among college students [4]. It is defined by the excessive use of smartphones and an inability to regulate that use [5]. As a subtype of internet addiction [6], it exhibits neurobiological features akin to other addictive behaviors [7], including preoccupation, loss of control, tolerance, withdrawal, and relapse [8]. Epidemiological studies have highlighted a high prevalence of smartphone addiction among college students, ranging from 12.8% in Spain [4] to 59% in Egypt [9], with a reported prevalence of 52.8% in China [10].
Given the high prevalence of depressive symptoms and smartphone addiction among college students, researchers have increasingly focused on the potential interplay between the two conditions [11,12]. Several studies have documented a positive relationship between depressive symptoms and smartphone addiction [11]. Individuals with depressive symptoms use their smartphones repeatedly to alleviate their mood symptoms, which in turn leads to smartphone addiction [13]. Conversely, depressive symptoms may be a side-effect of smartphone addiction, which can cause neuroplastic changes in the brain and result in mood disorders [14]. Moreover, a bidirectional relationship has also been found that two conditions mutually reinforce each other’s adverse consequences [12]. This bidirectionality is consistent with the broader literature on psychiatric comorbidity, which shows that addictive disorders often co-occur with depressive symptoms [15]. For instance, 25% of individuals with substance use disorder have a comorbid depressive disorder [16]. Based on the bidirectional models of comorbidity, the ongoing and interactive effects of psychiatric disorders account for increased rates of comorbidity [17]. Therefore, we thought that the comorbidity between smartphone addiction and depressive symptoms might exist. However, this comorbidity has not yet been well studied.
While direct research on the comorbidity between smartphone addiction and depressive symptoms is limited, insights can be drawn from studies on internet addiction. Previous studies have observed comorbidity between internet addiction and depressive symptoms, with prevalence rates ranging from 0% to 75% [18]. According to common factor models, such high rates of comorbidity result from shared vulnerabilities to both disorders [17], such as dysfunctions in dopaminergic pathways [19] and transcription factors like brain-derived neurotrophic factor (BDNF) [15]. In addition, males [20], weekly hours of internet use [21], and negative peer relationships [22] have been identified as predictors of the increased risk of comorbidity between internet addiction and depressive symptoms. Given the conceptual and behavioral overlap features between internet and smartphone use [23], we thought that smartphone addiction and depressive symptoms might share similar biological mechanisms and risk profiles.
Notably, individuals with comorbidity showed more severe psychopathological impairments [24]. It is often associated with poor prognosis, leading to more severe clinical features and a greater medical burden than either condition alone [25]. Understanding the occurrence of this comorbidity and recognizing its associated profiles are critical for the proper prevention and treatment of behavioral addiction and psychological problems [15]. Therefore, it is necessary to analyze the prevalence of comorbidity between smartphone addiction and depressive symptoms, as well as the related factors. However, research comparing the correlates of smartphone addiction only, depressive symptoms only, and comorbidity among college students is lacking.
To address these gaps, this study aimed to assess the prevalence of comorbid smartphone addiction and depressive symptoms and to identify the shared and distinct factors of single and comorbid problems among college students. The outcomes of this study will provide a foundation for future research on comorbidity and enhance our understanding of the relationship between smartphone addiction and depressive symptoms. Moreover, the findings will offer valuable insights for developing preventive strategies, thereby facilitating early identification and intervention to improve the mental health outcomes of college students.
METHODS
Procedures
This cross-sectional study was conducted from October to November 2022. We adopted convenience sampling to select four universities in the Chinese provinces of Jilin, Liaoning, Shandong, and Shanxi. Cluster convenience sampling was used to recruit participants from each of these universities. This study was approved by the Ethics Committee of Shandong University (LL20210102). Participants had signed an electronic informed consent form. The purpose of the research was explained to participants before the survey. This online survey was anonymous. All participants were voluntary, and all data were confidential.
Participants
The valid sample included 8,347 college students (5,203 females and 3,144 males) whose age ranged from 16 to 25 years (M=19.78±1.60). Freshmen accounted for over one-third of the participants (n=3,074, 36.8%). The majority of students came from families with more than one child (n=5,588, 66.9%). Further details are presented in Table 1.
Sample characteristics by smartphone addiction only, depressive symptoms only, and their comorbidity (N, %)
Measures
The self-reported questionnaire was composed of seven sections, encompassing socio-demographic, individual, familial, peer-related, and school-related factors, in addition to standardized scales for smartphone addiction and depressive symptoms. In our study, comorbidity refers to the occurrence of smartphone addiction and depressive symptoms at the same time within the same assessment period.
Socio-demographic factors
Ten demographic factors were included as follows: sex, grade, family residence, ethnic groups, one-child family, self-reported family income, father’s educational level, mother’s educational level, father’s occupation, and mother’s occupation. The occupation was classified into four groups: unemployment (no-jobs and stay-at-home), blue-collar jobs (occupations relating to transport and equipment operator; and occupations unique to primary industry, processing, manufacturing, and building industry), service jobs (occupations relating to trades, sales, and service), and white-collar jobs (occupations relating to management, natural and applied sciences, health, social sciences, education, religion, art, culture and recreation; and occupations relating to business, finance, administration).
Individual factors
The question “How often do you use your smartphone in lessons?” is used to measure the frequency of smartphone use in classroom. The question “How much time do you spend using the internet every day?” is used to assess the daily hours of internet usage.
Family factor
Parenting styles were divided into positive and negative. Emotional warmth and understanding can be classified as positive parenting styles. Negative parenting styles include punishment and sternness, excessive interference and protection, preference, rejection, and denial. The question “How is your relationship with your parents?” is used to measure the parent-child relationship.
Peer factors
The question “How is your relationship with your classmates (except roommates)?” is used to identify the classmate relationship of college students. The question “How is your relationship with your roommates?” is used to measure the roommate relationship of participants.
School factors
School belonging was determined by the question: “How is your sense of school belonging?” Courses about internet or media at school were assessed by the question: “Does your school provide courses in Internet or media information?”
Smartphone Addiction Scale-Short Version
The Smartphone Addiction Scale-Short Version [26] contains 10 items to assess the level of smartphone addiction among college students. This self-report instrument uses a 6-point Likert-scale from “strongly disagree” (coded 1) to “strongly agree” (coded 6). The total score ranges from 10 to 60. Higher scores represent a high risk of smartphone addiction. The cutoff value differs by sex. In this study, smartphone addiction was defined as 31 points for males and 33 points for females [10]. The Cronbach’s alpha in the current sample was 0.939.
Center for Epidemiologic Studies Depression
The Center for Epidemiologic Studies Depression is a 20-item self-report instrument developed by Radloff [27] to assess depressive symptoms. This scale measures the frequency of related depressive symptoms over the past week. Items are scored on a 4-point Likert-scale ranging from 0 to 3, with 3 representing the highest frequency. There are 4 items that require reverse coding. The total scores vary from 0 to 60. This study used 20 as the cut-off value to define clinical depressive symptoms [28]. The Cronbach’s alpha in the current sample was 0.922.
Statistical analysis
SPSS 24.0 (IBM Corp.) was used to analyze data. We calculated number and percentages of smartphone addiction only, depressive symptom only, and their comorbidity based on factors related to different domains (individual, family, school, and peer). We used chi-squared test to assess whether smartphone addiction only, depressive symptom only, and their comorbidity differed significantly in different groups. Multivariate logistic regression models were used to determine the prediction of potential risk factors for smartphone addiction only, depressive symptom only, and their comorbidity to identify potential risk factors, respectively. We used Bonferroni correction to calculate the adjusted p-value for multiple tests. Box-plot was used to illustrate the correlation between risk factors and outcomes.
RESULTS
Characteristics of study sample
The prevalence of smartphone addiction was 30.5%. Of the total sample, 8.9% of the respondents had experienced depressive symptoms during the past week. As for the comorbidity of smartphone addiction and depressive symptoms, 25.3% of participants reported these symptoms. In univariate analysis, there were significant differences in disease outcomes related to sex (χ2=11.81, p=0.008), grade (χ2=41.73, p<0.001), family residence (χ2=16.66, p=0.001), ethnic groups (χ2=13.61, p=0.003), one-child family (χ2=49.79, p<0.001), self-reported family income (χ2=92.65, p<0.001), father’s educational level (χ2=21.58, p=0.001), mother’s educational level (χ2=40.62, p<0.001), father’s occupation (χ2=31.99, p<0.001), mother’s occupation (χ2=33.03, p<0.001), frequency of mobile phones use in lessons (χ2=310.89, p<0.001), daily hours of internet usage (χ2=230.42, p<0.001), parenting styles (χ2=282.38, p<0.001), parent-child relationship (χ2=301.04, p<0.001), classmate relationship (χ2=286.92, p<0.001), roommate relationship (χ2=313.22, p<0.001), school belonging (χ2=481.81, p<0.001), and courses related to internet or media at school (χ2=43.40, p<0.001). More details are shown in Table 1.
Multivariable analysis of risk factors
The significant predictors in univariate analysis were entered into the multivariate logistic regression models (Table 2). Participants who were not from one-child family (p<0.001), reported higher frequency of mobile phones use in lessons (p<0.001), more average internet use time every day (p<0.001), received negative parenting style (p<0.05), poor classmate relationship (p<0.05), lower school belonging (p<0.001) were at greater risk of smartphone addiction. Students who reported lower-middle-level family income (p<0.05), higher frequency of mobile phones use in lessons (p<0.05), negative parenting style (p<0.001), poor parent-child relationship (p<0.001), poor roommate relationship (p<0.001), and lower school belonging (p<0.001) were more likely to be diagnosed with depressive symptoms. Males (p<0.01), Chinese Han nationality (p<0.05), participants from families with two or more children (p<0.001), mother with junior college and above diploma (p<0.05), lower-middle-level self-reported family income (p<0.01), frequency of mobile phones use in lessons (p<0.001), internet use time >7 hour every day (p<0.01), negative parenting style (p<0.001), poor parent-child relationship (p<0.001), poor classmate relationship (p<0.001), poor roommate relationship (p<0.001), and lower school belonging (p<0.001) were observed to be the significant risk factors of the comorbidity of smartphone addiction and depressive symptoms.
Multivariate logistic regression models for risk of smartphone addiction only, depressive symptoms only, and their comorbidity
Then, we adopted the Bonferroni correction to calculate the adjusted p-value (0.0167) for multiple tests. Students not from one-child family and higher average internet use time every day were associated with smartphone addiction and comorbid smartphone addiction and depressive symptoms. Poor parent-child relationship, poor roommate relationship, and negative parenting style had significant associations with risk of depressive symptoms and comorbid smartphone addiction and depressive symptoms. Higher frequency of mobile phones use in lessons and lower school belonging were related to higher possibility of smartphone addiction only, depressive symptoms only, and their comorbidity. Males, participants with lower-middle-level self-reported family income, and poor classmate relationship were more likely to have the comorbidity of smartphone addiction and depressive symptoms.
Relationships between the risk exposures and disease outcomes
Figure 1 illustrates the association between the number of risk factors and the likelihood of having smartphone addiction alone, depressive symptoms alone, and their comorbidity. The model of associations exhibited a dose-response relationship between the number of risk exposures and the likelihood of three disease outcomes. Specifically, students exposed to none of the factors had the lowest risk of having smartphone addiction alone, depressive symptoms alone, and their comorbidity (risk varied from 8% to 32%). Conversely, students who were exposed to all the factors had the highest probability of suffering from smartphone addiction alone (63%), depressive symptoms alone (65%), and their comorbidity (92%).
Relationships between the number of study risk factors and the likelihood of having smartphone addiction only, depressive symptoms only, and their comorbidity. A: Association between risk factors and smartphone addiction. B: Association between risk factors and depressive symptoms. C: Association between risk factors and comorbidity.
DISCUSSION
This study examined the prevalence of smartphone addiction alone, depressive symptoms alone, and their comorbidity among college students, as well as associated factors. The results indicated that the prevalence of smartphone addiction was 30.5%, while the comorbidity was 25.3%, exceeding the 8.9% with depressive symptoms alone among college students. Four, five, and ten risk factors were associated with smartphone addiction only, depressive symptoms only, and their comorbidity, respectively. Higher frequency of mobile phone use in lessons and lower level of school belonging were the common predictors of all three disease outcomes. Generally, exposure to more risk factors increased the likelihood of these outcomes, with a 92% probability of comorbidity among medical students exposed to all ten risk factors.
Compared with other studies, our study found that college students had a slightly higher prevalence of smartphone addiction alone (30.5%) but a lower rate of depressive symptoms alone (8.9%). A systematic review showed that the prevalence of smartphone addiction in young people was 23.3% [29]. This highlights potential public health concerns linked to smartphone use among college students [30]. Furthermore, the prevalence of depressive symptoms only in our study sample was lower than that in previous studies, such as in Nigeria (9%) [31], Iran (37%) [32], and Africa (40.1%) [3]. The discrepancies in the figures can be attributed to the use of different measurement methods and sample sizes. Notably, compared to the study on the comorbidity of internet addiction and mood symptoms (13.6%) [33], college students have slightly higher rate of the comorbidity of smartphone addiction and depressive symptoms (25.3%). One study found that the prevalence of the comorbid depressive symptoms among individuals with addictive behaviors was 2.59 times higher than among individuals without addictive behaviors [25]. The high comorbidity rate indicates a growing mental health crisis among college students. Depressive symptoms with comorbid addictive behavior may lead to greater academic impairment, reduced quality of life, and huge psychiatric burden [25]. Addressing smartphone addiction and depressive symptoms in isolation may overlook their interaction. Therefore, it is crucial to recognize and address this comorbidity in order to prevent severe clinical outcomes in college students.
Our findings revealed that smartphone addiction and depressive symptoms share overlapping pathogenic factors. After correction for multiple tests, males had a higher risk of comorbid smartphone addiction and depressive symptoms, which is consistent with previous studies on internet addiction and depressive symptoms [34,35]. Compared to females, males tend to spend more time on online gaming by using smartphones, adopt social interaction avoidance strategies [36], and are more prone to addiction. However, some studies have reported the opposite conclusion [37,38]. Meanwhile, other studies have found no sex difference in the comorbidity of addictive behaviors and depressive symptoms [30,39]. Thus, these inconsistent findings need to be further explored to determine how sex affects this comorbidity.
Notably, our study demonstrated that students from families with more than one child were a significant predictor of suffering from both smartphone addiction only and comorbidity. This support prior research indicating that students with siblings are more susceptible to addictive behaviors [40] and depressive symptoms [2]. Moreover, lower-middle-level family income is a high risk of depressive symptoms only and comorbidity. Previous findings have indicated that high-income levels are significantly associated with a lower risk of depressive symptoms [41,42]. Besides, individuals from families with lower-middle-level income are more susceptible to smartphone addiction [43]. One possible reason for this is that students from low-income families see smartphones as a way to escape from the psychological burden of their daily life [43]. However, this finding is inconsistent with other studies that students from higher income families spend more time on their smartphones [44].
This study identified a link between excessive internet daily use (>7 hour) and smartphone addiction, consistent with previous research [45]. Poor self-control and self-regulation in smartphone use can foster problematic habits [46]. Excessive internet daily use was also a contributing factor to the comorbid smartphone addiction and depressive symptoms, potentially explained by the compensatory internet use theory [13]. Individuals may use smartphones excessively to cope with negative emotional situations and to fulfill psychological needs that may not be met in the real world [13]. In addition, the frequency of mobile phone use in lessons was correlated with all three outcomes. College students with smartphone addiction tend to use their phones frequently in class [47]. As highlighted in an integrative review, excessive smartphone use in lessons distracts students from their focus, impairs their academic performance, and may increase their vulnerability to depression [48].
Consistent with previous studies, poor parent-child relationships were related to elevated depressive symptoms among college students [49,50] and to the co-occurrence of depressive symptoms and smartphone addiction. Emotional warmth is a fundamental aspect of a good parent-child relationship [51]. Those with strained parent-child relationships often exhibit poorer emotion regulation skills and perceive less emotional support, making them more vulnerable to depressive symptoms [49]. Furthermore, negative parenting styles were associated with depressive symptoms only. Individuals who experienced less parental warmth and more parental rejection were at an increased risk of depression [52]. In line with the study on the comorbidity on internet addiction and depressive symptoms [53], negative parenting styles were also the risk factors for the overlap of smartphone addiction and depressive symptoms. Satisfying basic psychological needs is crucial for adolescent development [54]; however, negative parenting styles, such as parental rejection, can undermine these needs [55]. Lacking family support and facing emotional rejection, students are more prone to psychological issues and may turn to excessive smartphone use to compensate for these unmet needs, which can lead to addictive behavior.
In addition, we also found that poor roommate relationships were associated with depressive symptoms. Both poor classmate relationship and poor roommate relationship were linked with the risk of comorbid smartphone addiction and depressive symptoms. College students are heavily influenced by their peers in school [56]. Students with poor peer relationships may experience less social support and greater isolation, leading to diminished social connectedness [57]. Lower social connectedness has been found to be correlated with mental health problems [58] and higher health-risk behaviors. Notably, school belonging was a significant predictor of all three outcomes. In line with prior research, college students with a strong school belonging were less likely to develop smartphone addiction [59]. A sense of belonging influences individuals’ cognition, emotions, and behaviors [60]. When the sense of belonging cannot satisfy students’ needs in school, a series of negative outcomes could occur, such as depressive symptoms and addictive behaviors.
Strengths and limitations
Depressive symptoms often co-occur with addictive behaviors, but the comorbidity of smartphone addiction has not been well studied. There is little evidence on the shared and unique factors underlying this overlap. Thus, the value of our study lies in comparing the prevalence and risk factors of smartphone addiction alone, depressive symptoms alone, and their comorbidity in a large sample of college students. The dose-response relationship between the number of risk exposures and the likelihood of all three disease outcomes was also determined. These findings enrich the field of psychiatric comorbidity and offer practical implications for the early screening and targeted intervention of high-risk student populations.
There are some limitations to be noted. Firstly, although this study had a large sample size, the generalizability may be influenced by the convenience sampling to select our targeted universities. Multi-stage cluster sampling should be considered in future studies to include students from a variety of universities. Secondly, the cause-and-effect relationship between risk factors and disease outcomes cannot be assessed due to the cross-sectional design. Longitudinal studies should be carried out in the future to confirm this association. Thirdly, this study did not include certain variables related to smartphone addiction and depressive symptoms, such as university and majors, personality traits, and history of mental health problems, and so on. Future research should consider other potential variables to provide deeper insights into the contributing factors of smartphone addiction and depressive symptoms. Finally, smartphone addiction and depressive symptoms were measured by self-reported questionnaires rather than clinical diagnoses. Thus, recall bias could not be ruled out.
In conclusion, our study revealed a high prevalence of the comorbidity of smartphone addiction and depressive symptoms among college students. There are common and unique factors associated with smartphone addiction alone, depressive symptoms alone, and their comorbidity. Increased exposure to risk factors heightened the likelihood of developing smartphone addiction only, depressive symptoms only, and their comorbidity. High priority should be given to identifying the factors that are closely related to the pathogenesis of smartphone addiction and depressive symptoms among college students. Targeted preventive measures and solutions should be implemented. We should fully consider internal and external protective factors for college students to reduce the occurrence of smartphone addiction and depressive symptoms.
Notes
Availability of Data and Material
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Author Contributions
Conceptualization: Yan Shi, Xiwu Zhang. Data curation: Yan Shi. Formal analysis: Yan Shi. Funding acquisition: Xiwu Zhang. Investigation: Yan Shi. Methodology: Yan Shi, Xiwu Zhang. Supervision: Xiwu Zhang. Writing— original draft: Yan Shi. Writing—review & editing: Xiwu Zhang.
Funding Statement
This research was supported by the Natural Science Foundation of Shandong Province (ZR2022QB030).
Acknowledgments
None