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. 2023 May 15;15(5):2985–2998.

Prevalence of mobile phone addiction among medical students: a systematic review

Anqi Song 1, Guiling Song 2, Huanyan Wang 3, Qichao Niu 1, Guisen Yin 4, Hua Chen 5, Faisal UL Rehman 6
PMCID: PMC10250977  PMID: 37303637

Abstract

The incidence and factors related to mobile phone addiction among Chinese medical students were analyzed through meta-analysis. Chinese literature databases (such as China Knowledge Network and VIP Information Resource System) and English literature databases (such as PubMed and Web of Science) were searched for cross-sectional studies on the incidence and factors related to mobile phone addiction, and the required data were extracted. Meta-analysis was performed using a random effects model with RevMan 5.3 statistical software, and publication bias was tested with Stata 12.0. A total of 20 studies were included, including 36,365 study subjects. Among them, there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. The results of the meta-analysis showed that the combined OR values (95% CI) of the factors were: gender 1.070 (1.030-1.120), residence 1.118 (1.090-1.146), school type 1.280 (1.241-1.321), mobile phone use time 1.098 (1.068-1.129), sleep quality 1.280 (1.288-1.334), self-perception of learning 0.737 (0.710-0.767), and family relationship 0.821 (0.791-0.852). The study showed that being a male student from cities and towns, being at a vocational college, excessive use of mobile phones, and poor sleep quality were the risk factors for mobile phone addiction among medical students in China. Positive self-perception of learning and family relationships were protective factors, and more related factors are still controversial and need to be further explored and confirmed.

Keywords: Mobile phone addiction, medical students, China, influencing factors, meta-analysis

Introduction

Mobile phone addiction, also known as mobile phone anxiety and mobile phone syndrome, is a behavioral addiction characterized by the compulsive and excessive use of smartphones that may result in decreased social functioning and psychological and behavioral disorders in patients [1]. According to a relevant study, there were 1.50978 billion mobile phone users in China as of June 2018, and smartphones have surpassed other Internet access devices in popularity [2]. The number of mobile phone users across the three primary telecommunications providers in China reached 1.67 billion in 2022, and the number of 5G mobile phone users reached 455 million, according to a report of the Ministry of Industry and Information Technology on the economic performance of the communications industry in the first half of the year, and this report also found that the majority of mobile phone users were young and middle-aged, with the mobile phone ownership rate of college students exceeding 90% [3]. Medical schools should help students comprehend, in light of such facts and trends, that mobile phones serve primarily as productivity aids, with entertainment functions making up a relatively minor portion of their overall impact on study, work, and life.

Some medical students have gotten addicted to their phones because of their excessive usage in the last several years, which has been increased by the pandemic, the demands of online courses, and the long hours spent at home away from the constraints of the group environment [4]. According to the findings, there is a strong correlation between medical students’ mobile phone use and a variety of mental health issues (including but not limited to interpersonal sensitivity, paranoia, depression, anxiety, hostility, and compulsion), all of which have a chilling effect on their academic performance and have now emerged as a social public health concern [5]. As the future pillar of the healthcare industry, medical students have a heavy learning task, and their mobile phone addiction should receive closer attention. Therefore, it is of great significance for understand the current situation and risk factors of mobile phone addiction among medical students in China and take corresponding preventive measures to reduce the rate of mobile phone addiction. The meta-analysis method was used in this study to systematically and quantitatively evaluate the literature previously published related to mobile phone addiction and associated factors in medical students to explore the relationship between various factors and Chinese medical students’ mobile phone addiction, and the risk ratio (OR) was used to assess the strength of the relationship between various factors and mobile phone addiction in order to provide a basis for prevention of phone addiction.

Materials and methods

Source of information/data

The data related to Chinese medical students’ mobile phone addiction published in official journals before June 2022 was searched in Chinese literature databases such as CNKI, Weipu Information Resources System, Wanfang Database, and a few others, along with English literature databases such as PubMed and Web of Sciences. At the same time, a further query was made through literature tracing.

Inclusion criteria: ① Full text of the literature was available; ② Medical students in the literature included junior college students, undergraduate and graduate students; ③ All medical students in the literature received a mobile phone addiction index scale assessment [6]; ④ The literature should provide statistical index OR value and 95% CI; ⑤ The literature was evaluated by the relevant quality evaluation standard, including 11 parameters in total with a total score of 11 points; the higher the score, the better the literature quality; and literature with a score ≥7 points was included [7].

Exclusion criteria: ① Unpublished papers; ② Lack of complete data in the literature or poor text usability; ③ Statistical method errors in the literature.

Data extraction

Articles were chosen and rated based on the inclusion and exclusion criteria. From each piece of literature, the date of publication, the first author, region, total number of subjects, number of mobile phone addiction cases, average age of research subjects, OR value to evaluate the strength of the relationship between influencing factors, 95% CI, and other information used for quality evaluation were extracted.

Statistical methods

The data were organized, and a database was created in accordance with the criteria of this meta-analysis. With RevMan 5.3 statistical software, a meta-analysis was conducted to determine the correlation between each research component and Internet addiction. The effect size was represented by the odds ratio (OR) and its 95% confidence interval (CI). In the included literature, statistical heterogeneity was qualitatively analyzed using the Q-test. The combined statistics were calculated using the fixed-effects model if there was no significant variability (P>0.05) across studies and the random-effects model if there was significant variability (P<0.05). The published findings of the literature were assessed using the funnel plot, the Begg rank correlation method, and the Egger linear regression method in Stata 12.0, and there was no publication bias if P>0.05.

Results

Basic characteristics of research literature

Preliminary search yielded 184 potentially relevant papers, and finally 20 papers met the inclusion criteria and were included in the analysis (Figure 1) [8-27]. Of these, 15 (75.00%) were in Chinese, and the other 5 (25.00%) were in English. Eleven of these studies focused on the correlation between gender and cell phone addiction (47.83%), 10 on the correlation between residence and cell phone addiction (43.48%), 10 on the correlation between the amount of time spent on mobile phones and addiction (43.48%), 10 on the correlation between self-perception of learning and cell phone addiction in teaching (43.48%), 10 on the correlation between cell phone addiction and sleep quality (43.48%), and 10 on the correlation between family relationships and cell phone addiction (43.48%), as shown in Table 1.

Figure 1.

Figure 1

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Flow chart of searching and screening literature.

Table 1.

Basic characteristics of research literature

First author, Publication year Area of study Age of subjects Sample size Mobile phone addiction cases Incidence rate of mobile phone addiction Adjustment factors
Liu H [8], 2022 southwest China 19.16±1.21 2182 866 39.69 Mental health, Occupational identity, Cell phone use before bed, Perceived research stress
Ren LS [9], 2017 South China 20.30±1.58 619 87 14.05 Origin, Total ULS-8 score of loneliness, daily mobile Internet access time, Monthly mobile phone expenses
Xu J [10], 2012 East China 19.24±1.74 5122 449 8.77 Gender, living expenses, time spent on the mobile Internet, purpose of Internet use
HUANG ZH [11], 2021 South China 19.84±1.36 10357 6154 59.42 Gender, school type, residence, age, major, type of residence, time spent on mobile phone, family relationship, sleep quality
ZUO K [12], 2019 Northwest China 20.14±1.83 380 78 20.53 Gender, residence, grade, time spent on mobile phone, self-perception of learning, frequency of using mobile phone in class, integration with classmates, social comfort, school type, self-perception in learning, family relationship
XIE F [13], 2019 Northwest China 22.01±2.03 493 327 66.33 Residence, Recent exams, depression, self-perception in learning, family relationship, sleep quality
LI X [14], 2018 East China 20.25±1.33 1328 250 18.83 Gender, major, school type, residence, life satisfaction, interpersonal relationships, self-perception in learning
Tang J [15], 2014 Central China 20.03±1.79 755 45 1.32 Gender, Emotions, Family Situation, Stressful Life Events
LI F J [16], 2018 Central China 19.83±1.28 2687 160 5.95 Residence, time spent on mobile phones, self-perception in learning, school type, family relationship, sleep quality
HAO L [17], 2016 East China 20.16±1.73 680 92 13.53 Gender, Origin, school type, time spent on mobile phone, family relationship, self-perception in learning
CONG JN [18], 2016 East China 20.41±1.39 567 31 5.46 Gender, residence, school type, family structure, family relationship, mobile phone usage time, integration of classmates, self-perception in learning
ZHOU XY [19], 2015 Northwest China 20.53±1.67 601 117 19.47 School type, integration of classmates, philosophy and pursuit of life, non-mobile recreational activities, anxiety, sleep quality
CHEN YJ [20], 2014 North China 19.06±1.28 5485 704 12.84 Gender, residence, family relationship, family structure, loneliness, professional satisfaction
ZHANG Q [21], 2016 East China 19.87±1.54 901 175 19.42 Gender, introversion and extroversion, popular culture effect, depression, neuroticism, self-perception in learning
Feng Z [22], 2022 East China 20.46±1.85 450 36 8.00 Depression, sleep quality, professional satisfaction
LIU H [23], 2011 Northwest China 20.47±1.67 442 131 29.84 Loneliness, online entertainment, school type
HOU J [24], 2021 South China 19.61±1.72 545 173 31.74 Social anxiety, Negative emotional information, Attentional Bias, Internet use purpose
JIANG XJ [25], 2019 North China 20.38±18.42 475 264 55.50 Bad mood, sleep quality, generalized anxiety, professional satisfaction, mobile phone use time
NIE GH [26], 2019 East China 20.14±1.92 1198 195 16.3 Gender, residence, depression, sleep quality, school type, time spent on mobile phone, self-perception of learning, family relationship
Lam LT [27], 2015 Northeast China 19.87±1.54 1098 263 23.95 Internet time, video game time, bad mood, stress
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Meta analysis results of each research factor

A total of 36,365 research subjects from 20 studies were included in this analysis, and there were 10,597 cases of mobile phone addiction overall, with an incidence of 29.14%. We first tested the heterogeneity of each study element included in the literature using I2 statistics; the findings revealed that I2 values were >50% and P values were <0.05. Thus, we chose the random effect model for the meta-analysis. The findings revealed that among Chinese medical students, poor sleep quality, male gender, urban residence, vocational college type, and mobile phone usage time ≥4 hours were risk factors for mobile phone addiction (OR>1, P<0.05). Good family ties and a positive self-perception were protective factors against mobile phone addiction in Chinese medical students (OR<1, P<0.05), as shown in Table 2 and Figures 2, 3, 4, 5, 6, 7 and 8.

Table 2.

Meta-analysis results of each research factor

Research factor Number of studies Heterogeneity test results Meta-analysis results
I2 Value PH value Incidence (95% CI)
Gender 11 86.50% <0.001 1.070 (1.030-1.120)
Residence 10 98.00% <0.001 1.118 (1.090-1.146)
School type 10 98.90% <0.001 1.280 (1.241-1.321)
Phone usage time 10 96.00% <0.001 1.098 (1.068-1.129)
Self-perception in learning 10 97.50% <0.001 0.737 (0.710-0.767)
Family relationship 10 96.60% <0.001 0.821 (0.791-0.852)
Sleep quality 10 95.20% <0.001 1.280 (1.228-1.334)
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Figure 2.

Figure 2

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Gender and mobile phone addiction.

Figure 3.

Figure 3

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Residence and mobile phone addiction.

Figure 4.

Figure 4

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School types and mobile phone addiction.

Figure 5.

Figure 5

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Phone use time and mobile phone addiction.

Figure 6.

Figure 6

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Self-perception in learning and mobile phone addiction.

Figure 7.

Figure 7

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Family Relationships and mobile phone addiction.

Figure 8.

Figure 8

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Sleep quality and mobile phone addiction.

Publication bias analysis

The publication bias was statistically assessed using funnel plots. The findings revealed that the funnel plots of the variables in Table 2 were essentially symmetrical; the P value of the Begg test was >0.05; the Egger test results revealed that the P values of gender, residence, school type, mobile phone use time, integration of classmates, family structure, family relationship, and sleep quality were all >0.05. The stated findings suggest that the included literature was free of any publication bias, as shown in Table 3 and Figures 9, 10, 11, 12, 13, 14 and 15.

Table 3.

Publication bias analysis

Research factor Number of studies Begg rank correlation test Egger Linear Regression
ZB/PB tE/PE
Gender 11 0.00/1.000 0.13/0.900
Residence 10 0.72/0.474 0.07/0.948
School Type 10 0.18/0.858 0.33/0.749
Phone usage time 10 1.77/0.076 1.70/0.133
Self-perception in learning 10 1.25/0.210 2.06/0.056
Family relationship 10 0.72/0.474 1.96/0.064
Sleep quality 10 0.94/0.348 0.99/0.353
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Figure 9.

Figure 9

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Publication bias regarding gender.

Figure 10.

Figure 10

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Publication bias regarding residence.

Figure 11.

Figure 11

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Publication bias regarding school type.

Figure 12.

Figure 12

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Publication bias regarding phone use time.

Figure 13.

Figure 13

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Publication bias regarding self-perception in learning.

Figure 14.

Figure 14

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Publication bias regarding family relationship.

Figure 15.

Figure 15

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Publication bias regarding sleep quality.

Discussion

Mobile phones are popular because they play a crucial role in modern society, including communication, payment, and online entertainment, thus resulting in an increase in mobile phone usage [28]. In recent years, as a result of the pandemic, mobile phones have become an essential entertainment tool for college students participating in outdoor activities. Simultaneously, due to the increased demand for online courses, mobile phones have emerged as a significant learning tool [29]. A study from Hong Kong estimated that 38.5% of adults were addicted to their mobile phones [30]. In 2016, a study recruited 1,441 undergraduates at Wannan Medical College and assessed the phenomena of mobile phone addiction among medical students using the Smartphone Addiction Scale (SAS-SV) revealed that 29.8% of medical students (29.3% of females and 30.3% of males) had smartphone addiction [31]. In a study conducted by Long in 2015, which included 1062 college students who used smartphones, it was discovered that the prevalence of mobile phone addiction among these students was 21.3% [32]. A total of 36,365 subjects were included in this study, and there were 10,597 cases of mobile phone addiction with an incidence of 29.14%. Identifying risk factors and formulating prevention and control measures are particularly critical for preventing mobile phone addiction and ensuring the physical and mental health of medical students.

There are many factors related to mobile phone addiction, and it is often difficult to include all factors in a single study. At the same time, there are certain differences between studies, which makes it difficult to apply the results of literature research to clinical practice. The results from a meta-analysis are more in line with the facts since they are the outcome of a quantitative combined analysis of several studies on the same scientific subject. A total of 20 studies on factors related to mobile phone addiction among Chinese medical students with different sample sizes, different regions, and different times were included in this study. The Meta results found that male gender, junior/vocational medical school, urban residence, and the use of mobile phones for ≥4 hours, and poor sleep quality were risk factors for mobile phone addiction in Chinese medical students. Good self-esteem in learning and harmonious family relationship were protective factors for mobile phone addiction in Chinese medical students, indicating that gender, residence, school type, mobile phone use time, sleep quality, self-perception in learning, and family relationship are closely related to mobile phone addiction in Chinese medical students.

Specific findings are as follows: (1) The incidence of mobile phone addiction in male medical students is higher than that in females, as men are more obsessed with mobile games. Smartphones have online game functions, which can meet the psychological needs of some gamers, thereby increasing the risk of mobile phone addiction. Women, on the other hand, pay more attention to interpersonal relationships. Through social interaction, they can feel warm and positive emotions and thus get support from others. Therefore, the rate of mobile phone addiction is relatively low in females. (2) Medical students from cities and towns have earlier access to smartphones, a higher prevalence of smartphones, and more reliance on mobile phones in daily life, resulting in a higher frequency of use of mobile phones by urban students. At the same time, mobile phones are an important way for college students to engage in social interaction; however, smartphones give medical students with convenient and novel information to achieve global connection, hence boosting the mobile phone addiction rate of urban medical students. (3) Compared to undergraduate and graduate medical students, junior/vocational medical students have different academic performances, may have worse study habits, and their self-control ability is relatively poor. Affected by the pandemic, online courses are an important learning method for many medical students, but online courses have also further created regulatory loopholes, inability to supervise students’ learning in a timely and effective manner, and junior/vocational college students may be less able to effectively control mobile phone use, resulting in mobile phone addiction. (4) Mobile phone use time ≥4 h. Frequency and duration of mobile phone use are important indicators to evaluate phone addiction. Medical students who use mobile phones for a long time can increase their dependence on phones, indulge in the use of mobile phones, use up a lot of time, and harm their daily learning and life. (5) Lee et al. used the longitudinal data of the Korean Children and Youth Group Survey of the National Youth Policy Institute (2011-2013) to study a total of 1125 students, and the generalized estimating equation analysis showed that high mobile phone addiction (mobile phone addiction score >20 points) increases the risk of poor sleep quality, which is consistent with the conclusion of this study [33]. Sleep is an important life activity, and good sleep quality can help medical students regain sufficient energy to cope with the study and life of the next day. Medical students with poor sleep quality have difficulty falling asleep at night and may use mobile phones to pass the time, thereby increasing the frequency of mobile phone use, prolonging the use of mobile phones, and causing addiction. At the same time, mobile phone addiction may impair medical students’ sleep quality by squeezing out their sleep time, which is harmful to both physical and mental health. (6) Poor academic performance among medical students with low self-esteem may be due to their dissatisfaction with their majors and lack of motivation to work hard. Instead, they spend time using smartphones for entertainment, which lowers their grades and creates a vicious cycle of disinterest in studying and addiction to mobile phones. (7) The stability or harmony of family relationships has a significant impact on everyone’s behavioral addiction. Parental conflict in marriage can directly result in individual psychological issues and unhappiness in family relationships. This can create a stable and negative evaluation system, which can result in adaptive issues like loneliness and low self-esteem. As a result, they turn to virtual friends on their mobile phones for emotional support to meet their psychological needs, which leads to mobile phone addiction. Therefore, mobile phone addiction is a social problem that cannot be ignored. It is closely related to the family, school and society, as well as the psychological, physiological and personality characteristics of medical students. Based on these preceding factors, social departments can implement focused interventions to lower the incidence of mobile phone addiction among Chinese medical students.

This study used funnel plot, Begg rank correlation test and Egger linear regression method to evaluate publication bias. The results found that the results of Begg rank correlation test and Egger test were all P>0.05, and the funnel plots were basically symmetrical, which confirmed that the research results were relatively reliable. However, this study still has some limitations, such as: ① Some information cannot be obtained from the literature, such as the occurrence of mobile phone addiction among students at different schools and ages; ② All the included studies were cross-sectional studies, and the causal relationship between research factors and mobile phone addiction of Chinese medical students could not be determined.

To sum up, research shows that male gender, urban residence, vocational college study, mobile phone usage duration, and poor sleep quality were risk factors. At the same time, family harmony and positive self-esteem in education were protective factors against mobile phone addiction among Chinese medical students.

Acknowledgements

I would like to thank all the classmates and colleagues who have helped me, as well as the 2nd Affiliated Hospital of Harbin Medical University, the Affiliated Hospital of Qingdao University, and Qingdao Bioman Biomedical Technology Co., LTD for their help. This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. The research was funded by the 2022 basic scientific research, business cost research project of Heilongjiang provincial colleges and universities research on the countermeasures of synergetic education of ideological and political courses and “The ideological and political curriculum” in medical colleges and universities to improve the professional ethics of medical students.

Disclosure of conflict of interest

None.

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