Smartphones are advanced electronic media devices which are capable of processing more information than other phones. Smartphones include many features such as games, videos, Internet browser, social media applications, messaging, multimedia system, and navigation, in addition to their use for communication (Lo Coco et al., 2020). Furthermore, with the development of phone applications and technologies, it is easier for phones to satisfy people’s requirements for daily communication and entertainment (Fallahi-Khoshknab & Yahyazadeh, 2016). Globally, 90% of the population own mobile phones, of which 59% are smartphones (Pew Research Center, 2018). According to the 47th China statistical report on Internet development, by the end of December 2020, there were 986 million mobile phone users in China, which accounted for 99.7% of the total Chinese Internet users (China Internet Network Information Center).

A major proportion of Chinese mobile phone users consist of the young adults. Some studies indicated that young adults, especially college students, were more dependent on mobile phone in daily life (Carbonell et al., 2018). However, the overdependence on mobile phone raised the problems of excessive and problematic use of mobile phone and, more serious, addiction on mobile phone (Ewa et al., 2016). One study reported that 48.0% of undergraduate university students were identified as smartphone addicted (Aljomaa et al., 2016). A meta-analysis revealed that the prevalence of mobile phone addiction (MPA) among Chinese college students was 23.0% (Tao et al., 2018). Another study reported that the prevalence of MPA among Chinese undergraduates was 21.3% (Long et al., 2016).

Mobile phone addiction (MPA) as a new concept aroused a wide range of interests (Elhai et al., 2017; Elhai, Levine, et al., 2019; Elhai, Yang, et al., 2019). Mobile phone addiction refers to a state of obsession that the individual behavior is out of control results from the overuse of mobile phone, leading to the significant impairment physical state, psychological state, and social function (Kim et al., 2018). It is a relatively new concept which has not been included in DSM-5 or ICD-11, therefore, the accuracy of the term “mobile phone addiction” is being questioned at present. The terminologies like mobile phone addiction, problematic use, excessive use, and overuse of mobile phones have been interchangeably used across studies in current research as there are no said criteria till date for addictive use. While various limitations exist, the growing body of empirical research generally supports the concept of MPA as a genuine addictive disorder (Billieux et al., 2015). We therefore adopt the term mobile phone addiction (MPA) in our research. At the same time, we argue that research should focus on the negative consequences of MPA rather than determine whether it should be considered as a behavioral addiction.

Increasing evidences indicated that MPA among adolescents and young adults had become a matter of concern due to its high occurrence rate (Ka Chun et al., 2020; Lopez-Fernandez, 2017) and serious adverse impacts on their physical, psychological and sleep quality (Yen et al., 2009). MPA has been considered as an important inducement for a variety of psychological problems (Yang et al., 2019). MPA has been linked to anxiety, depression (Elhai, Levine, et al., 2019; Elhai, Yang, et al., 2019), social anxiety (Di Matteo et al., 2021), attention (Tsai, 2020), and loneliness (Darcin et al., 2015). Soni et al. demonstrated that adolescents with MPA also developed various sleeping problems (Soni et al., 2017). It is reported that MPA was associated with at least one sleep outcome, such as insomnia, excessive daytime sleepiness, poor sleep quality, and short sleep duration (Kang et al., 2020). Therefore, adolescents who are addicted to mobile phones may have more psychological distress due to poor sleep quality and high levels of procrastination induced by excessive or uncontrolled use of mobile phones. In addition, uncontrolled mobile phone use could also cause physical related issues, such as neck, shoulder, and hand pain (Vally & Hichami). However, few studies have found the influence of MPA on autonomic symptoms, such as palpitation, nausea, and being chill or hot. Therefore, presence of autonomic symptoms would be included in the scope of this study.

MPA is associated with the duration of mobile phone use (Lee et al., 2017). According to a study conducted in 4,434 adolescents in 2019, the failure to control usage time accounted for the highest percentage of the prevalence of MPA (National Information Society Agency, 2019). Rozgonjuk reported that smartphone usage duration was closely related to MPA (Rozgonjuk et al., 2018). In addition, increased duration of smartphone usage in adolescents was related to physical and psychological health problems, such as the development of depressive symptoms (Riehm et al., 2019), decreased general happiness, physical symptoms, and poor sleep quality (Buxton et al., 2015). However, there were few studies that focused on the interaction effect between MPA and duration of mobile phone use on health outcomes.

There is no denying that mobile phone provides significant benefits for our daily life in countless ways. Nonetheless, increased accessibility and easy availability of mobile phones among college students have shown negative impacts on mental well-being, sleep quality, and physical health outcomes (Khalil et al., 2016). Research on MPA has notably increased in recent years (Aljomaa et al., 2016; Darcin et al., 2016). However, there are also few researches on the interaction effects between MPA and duration of phone use on physical, psychological health and sleep quality of college students. Therefore, the present research sought to explore the interaction effects between MPA and duration of phone use on physical and psychological health. This study assists in establishing professional measures to prevent negative health consequences caused by MPA.

Methods

Participants and Produce

The study was a cross-sectional research that recruited undergraduate students from a university in Changchun, China. We have randomly selected 5 departments which included the School of Health, Faculty of Arts and Sciences, Faculty of Engineering, Faculty of Economics, and Administrative Sciences; 200 students were randomly selected from each department. According to Kendall’s principle of sample size calculation, the sample size is 5–10 times the number of questionnaire items (Chow et al., 2017). In this study, there is a total of 104 questionnaire items. The estimated sample size should fall in the range between 520 and 1040. Eventually, a total of 1141 questionnaires were distributed, and 1073 students responded to the survey. Students with missing information on the variables assessing MPA were removed from the sample (117 students). To ensure the quality of the data, students also were removed if they did not report their gender (10 students). After removing the invalid questionnaires, a total of 946 sets of data were included in the analysis, with a valid response rate of 88.2%.

The study was approved by Institutional Review Board of the university , and research procedures followed the protocol of the Declaration of Helsinki. The students were informed about the subject and objective of the study, and verbal consents of the students were taken.

Measures

Demographics and Lifestyle Behaviors

The sociodemographic characteristics were collected in the study including gender, age, and grades.

Lifestyle behaviors were measured by cigarette smoking, alcohol drinking, and physical exercise. These variables were collected by the participants’ self-reported responses to the questionnaire’s questions, such as “During the past 30 days, have you ever smoked cigarettes?”, “During past 30 days, did you have at least one glass of alcohol or drink containing alcohol?”, and “How often have you exercised frequently?”. The participants who answered “always (yes)” were segmented within the “smoker group” or “drinker group” or “exercise frequently,” and remaining participants were classified into “no tobacco users” or “no alcohol users” or “ no exercise frequently.”

Mobile Phone Addiction

The mobile phone addiction index scale (MPAI) was used to measure the severity of MPA of participants (Leung et al., 2008), which proven to have good reliability and validity among Chinese college student population (Cronbach’s α: 0.86). The MPAI, revised by Leung et al., is a survey regarding mobile phone use with a total of 17 questions. Each question was answered on a five-point scale . Higher score indicates more severe MPA among all questions. Items 3, 4, 5, 6, 8, 9, 14, and 15 were designed to be MPA screening questions. If participants responded to five or more questions with a three or above score, they were considered to be mobile phone addicts. The others were classified as non-phone addicts. The categories of mobile phone addiction were created by interquartile range of the total score of MPAS. Scores from lowest to Q25 were defined as “no addiction”, scores from Q25 to median were defined as “mild addiction”, scores from median to Q75 were defined as “moderate addiction”, and scores from Q75 to highest were defined as “severe addiction”.

Mobile Phone Use Patterns

Mobile phone use patterns were regarded as key factors to MPA (George et al., 2018). Mobile phone use patterns were measured by mobile phone use duration and habits. The question regarding the duration of mobile phone use was “During the weekdays of the last 30 days, for how many hours on average did you use a smartphone in one day?”. The mobile phone use time was measured as less than 4 hours and greater than 4 hours (China Internet Network Information Center, 2017). Questions regarding mobile phone use habits were “In the last 30 days, have you usually used a smartphone before going sleep (or after waking up)?” and “In the last 30 days, have you usually used a smartphone in class?”.

Sleep Quality

Pittsburg Sleep Quality Index (PSQI) was compiled by Buysse et al. in 1989 (Buysse et al., 1989), which consisted of 24 questions (19 self-report items and 5 others-report items) to assess the sleep quality of subjects in the past month. It was divided into seven different dimensions, such as daytime dysfunctions, subjective sleep quality, sleep duration, use of sleep medication, sleep latency, sleep disturbances, and habitual sleep efficiency. Each dimension ranges from 0 to 3. The total score of PSQI ranges from 0 to 21, with a higher score indicating poorer sleep quality. Participants with 5 or higher total PSQI score were identified with poor sleep quality. The internal consistency of the questionnaire presented a Cronbach’s α of 0.83 with a test–retest reliability of 0.85 a month later (Buysse et al., 1989). The Cronbach’s α of the PSQI questionnaire in this study was 0.72.

Psychological Status

We listed six items to estimate psychological status which included anxiety symptoms, depressive symptoms, loneliness, social anxiety, sad or hopeless, and impaired concentration. (1) “During the past one month, did you ever feel so sad or hopeless?” (2) “During the past one month, did you ever feel anxious?” (3) “During the past one month, did you ever feel depressed?” (4) “During the past one month, did you ever feel lonely?” (5) “During the past one month, did you ever feel impaired concentration?” (6) “During the past one month, did you ever feel social anxiety?” Participants could choose between the following responses: “yes” or “no.”

Physical Status

The physical status was collected with six items to assess whether the participant has the autonomic symptoms in the past month, such as “Have you ever had palpitation (nausea, dry mouth, being chill or hot, numbness, or asphyxia) in the past one month?”. Participants could choose between the following responses: “yes” or “no.”

Data Analysis

IBM SPSS Statistics 22.0 was applied to perform all the statistical analysis. Quantitative and qualitative measurements were summarized as mean ± standard deviation and n (%), respectively. The total MPAS score was compared between participants with and without variables of psychological, physical, and sleep problems by using independent sample t-test. Binary logistic regression models were used to examine the relationship between mobile phone addiction and psychological, physical, and sleep-related status and to evaluate the interaction of level of mobile phone addiction and mobile phone use time. Adjustment was made for confounding factors such as gender, grade, drinking, smoking, physical exercise, playing mobile phone before sleep, playing mobile phone after waking up, and playing mobile phone in class. P < 0.05 was considered statistically significant.

Results

The characteristics and mobile phone using behaviors of participants were represented in Table 1. The average age of participants was 20.7 ± 1.6 years old. 84.4% of the participants played mobile phones before sleep, and 66.5% played after waking up. Around 37.6% of the participants used mobile phones for more than 4 hours a day. 36.6% of the participants were identified with MPA. Around 23.3% of the participants had mild mobile phone addiction, and 23.6% had severe mobile phone addiction.

Table 1 Characteristics and mobile phone using behavior of the study participants
Full size table

Significant differences in total score of mobile phone addiction were found in psychological, physical, and sleep quality characteristics. Higher total MPAS score was found to be significantly associated with poor sleep quality (T = 7.64, P < 0.001), feeling anxious symptoms (T = 5.03, P < 0.001), feeling depressive symptoms (T = 5.51, P < 0.001), loneliness (T = 5.46, P < 0.001), social anxiety (T = 4.80, P < 0.001), impaired concentration (T = 7.01, P < 0.001), and sad or hopeless (T = 7.69, P < 0.001). Higher total MPAS score was also found to be significantly associated with palpitation (T = 3.79, P < 0.001), nausea (T = 5.05, P < 0.001), dry mouth (T = 4.03, P < 0.001), being chill or hot (T = 2.98, P = 0.003), numbness (T = 2.07, P = 0.039), and asphyxia (T = 3.04, P = 0.002) (see Table 2).

Table 2 Associations between total MPAS score and different participants variables (N = 946)
Full size table

To further analyze the relationships among all variables, we put them into logistics regression model. Binary logistic regression analysis revealed that the level of MPA was the influencing factor of anxiety symptoms, depressive symptoms, loneliness, social anxiety, impaired concentration, and sad or hopeless (P < 0.05), especially severe MPA. The effect of the duration of mobile phone use was found to be not significant with most psychological variables apart from sad or hopeless (OR = 1.39, P = 0.042). The interactive effect between DU and severe MPA had statistical significance in all psychological status (P < 0.05). All data are given in Table 3.

Table 3 Logistic regression model for interaction between DU and level of mobile phone addiction on psychological status
Full size table

Binary logistic regression analysis revealed that level of mobile phone addiction was associated with a higher risk of developing palpitation, nausea, and asphyxia (P<0.05). The interaction effect between DU and severe mobile phone addiction had higher odds of having palpitation (OR = 2.25, P = 0.001) and nausea (OR = 2.34, P = 0.001). All data are given in Table 4.

Table 4 Logistic regression model for interaction between DU and level of mobile phone addiction on physical status
Full size table

The results of a regression analysis examining the interactions effect of DU and level of MPA on sleep quality was shown in Table 5. There was a interactive effect between DU and level of MPA on sleep quality, sleep latency, sleep disturbances, sleep duration, and sleep dysfunctions (P < 0.001); college students with severe MPA who used mobile phone for more than 4 h per day were more likely to be with sleep problems.

Table 5 Logistic regression model for interaction between DU and level of mobile phone addiction on sleep problems
Full size table

Discussion

There was a large number of young people with MAP (36.6%) in this study sample, and the prevalence was higher than the number reported by other similar studies conducted in China (Long et al., 2016; Tao et al., 2018). Previous studies conducted on university students in Asia have used various measuring instruments, with prevalence of MAP or excessive smartphone use ranging from 19.1% to 36.5% (Chen et al., 2017; Kim et al., 2017). The prevalence of our study is higher than previous studies. These discrepancies could be explained by the use of different measure instruments, classification method used, and the difference in the timing of the study. Moreover, the inconsistencies might be due to the differences among participants in different studies. Regardless, all findings have come to the conclusion; that is, MPA is a widespread problem in the young population. The wide range of prevalence rate identified in various studies indicated that MPA was a potential health concern and requires more attentions.

Our study found that students with severe MPA were more likely to feel anxiety symptoms, depressive symptoms, loneliness, social anxiety, impaired concentration, and sad or hopeless. This phenomenon is consistent with previous research results (Darcin et al., 2015; Di Matteo et al., 2021; Elhai, Levine, et al., 2019; Elhai, Yang, et al., 2019; Hyun & Hwa, 2017; Tsai, 2020). Our study also revealed that more severe MPA and longer duration of smartphone use led to poorer psychological health status. College students spend plenty of time on their mobile phones, which occupies their effective time and reduces their time to participate in other social activities (Cho & Lee, 2017). Furthermore, the wireless feature on mobile phone allows students to engage in internet surfing anywhere at any time, which further drives them away from reality and drown in virtual world (Mehroof & Griffiths, 2010). These circumstances alienate students from face-to-face interaction in the real world and weaken their social skills and enlarge the gap between people (Zhang , 2016). When one’s degree of satisfaction of his social intercourse is lower than his expectation, the feeling of loneliness arises (Liu et al., 2016). Slowly, the psychological emptiness could lead to disappointment and depression, which aggravates social anxiety (Li et al., 2017). Coupled with competitive pressure and academic stress, students’ psychological needs cannot be met. In order to seek emotional support and sustenance, students utilize mobile phone to resolve, which exacerbates the addiction on mobile phone (Xue-Yuan & Fan, 2016). Ultimately, it comes into being a vicious circle.

The research results showed that people with more severe MPA were more prone to develop palpitations, nausea, and asphyxia. It is possible that electromagnetic fields generated by mobile phones may have influences on the autonomic nervous system (ANS), which modulates the function of the circulatory system (Andrzejak et al., 2008). That results in numbness of palpitations, nausea, and asphyxia. Some studies found that the autonomic symptoms get worse with longer duration of daily phone use, which could be considered signs of dose response (Durusoy et al., 2017; Khan et al., 2008). Our study revealed that the interaction between severe mobile phone addiction and more than 4 h mobile use time was more likely to cause autonomic symptoms among college students. The reason for this result was that heart rate variability (HRV) was under the direct control of the central and autonomic nervous systems, and it was one of the most important indicators of adaptive processes in the human organism (Singh et al., 2018). Young people may lose control of time with prolonged use of Internet on mobile phones (Turel et al., 2018). The ability to accurately determine the duration of human-controlled events is important for human to have high HRV (Ogden et al., 2019). Otherwise, it will increase the possibility of developing autonomic symptoms (Krivonogova et al., 2021).

We found that nearly 33.2% of our sample of the Chinese college students reported poor sleep quality. It was higher than that of Chinese university students (25.7%) and the general population (15.0%) reported by two meta-analytic reviews (Cao et al., 2017; Li et al., 2018). Considering the many negative health effects associated with sleep problems and the very large number of Chinese college students, this 33.2% prevalence figure suggests that the sleep problems of college students deserve preventive and clinical attentions. Moreover, this study found strong and significant relationship between MPA and sleep problems. MPA would cause poor sleep quality, long sleep latency, sleep disturbances, short sleep duration, and daytime dysfunctions. This is consistent with the previous research results (Kumar et al., 2019; Lin et al., 2017). Further research found that the interaction between MPA and duration of phone use increased the risk of developing sleep problems among college students. When the duration of phone time was more than 4 h, there was a cumulative adverse effect of MPA on sleep problems. Long-time phone use may directly reduce the user’s time on sleep, resulting in insufficient sleep. Second, exposured to the light of smartphone screens for a long time during bedtime could influence the onset time and secretion of melatonin, which in turn causes sleep–wake rhythm disorders (Liu et al., 2019).

In summary, education and dissemination of information about mobile phone use and healthy use time could greatly improve the psychological and physical health status of college students. College students with moderate MPA are at intermediate risk of becoming more severe MPA if they do not reduce its usage. Severe MPA and duration of mobile phone were associated with psychological and physical health of all students. These findings suggest that parents, school counselors, psychiatrists, and other mental health professionals should take steps toward preventing physical and psychological problems by considering mobile phone use patterns and severity of addiction when developing early detection tools and intervention programs in school or community settings.

There are several limitations to our study. First, we used a cross-sectional design, and causal relationships were not defined. Second, physical and psychological status was determined by the several designed questions. The results could be more reliable if they were measured through clinical examinations. Third, we used self-reported data, and information bias might be inevitable.

Conclusions

There was a high prevalence of MPA among college students in China. The findings of study indicated that MPA and the interaction between MPA and duration of mobile phone use had strong influences on people’s physical and psychological health status.