The severity of mobile phone addiction and its relationship with quality of life in Chinese university students

Introduction

The use of smartphone has exponentially increased in recent years. In 2018, the number of smartphone users has exceeded 2.5 billion people worldwide, while the corresponding figure in China reached around 713 million (Statista Research Department, 2019). Because of their availability, relatively cheap price, and convenience in use, smartphones are quickly replacing laptop and desk-top computers as the preferred method of accessing the internet. Although smartphones have a wide range of functionality that enhances productivity, excessive mobile phone use could lead to negative health outcomes, such as poor sleep quality, headache, tiredness (Zarghami et al., 2015), wrist and neck pain, blurred vision (Rai et al., 2016), sleep quality (Sahin et al., 2013), anxiety, insomnia (Jenaro et al., 2009), and depression (Thomee, Harenstam & Hagberg, 2011), all of which could lead to low quality of life (QOL).

Excessive mobile phone use increases the risk of mobile phone addiction, which is characterized by typical symptoms of addiction (Billieux et al., 2015). The diagnostic criteria of mobile phone addiction remains controversial. Other terms are also used to describe this construct, including addiction of mobile phone use, excessive mobile phone use, compulsive mobile phone use, problematic mobile phone use, pathological mobile phone use, and compensatory mobile phone use (De-Sola Gutierrez, Rodriguez De Fonseca & Rubio, 2016). Mobile phone addiction and Internet addiction share similar features. Mobile phone addiction is occasionally described using the criteria of internet addiction, as both of them are characterized by dependance, tolerance, withdrawal symptoms, and social problems (Beard, 2005).

The China Internet Network Information Center (CINIC) has reported that there were 854 million internet users in mainland China in 2019; of them, 99.1% accessed the Internet via mobile phones, and people aged 20–29 years accounted for the highest proportion of internet users (24.6%) (The China Internet Network Information Centre, 2019). Chinese university students on average spend over 5 h a day on mobile phone and around 79% of students use their mobile phones during class (MyCOS Research Institute, 2018).

There is a lack of research on the association of the severity of mobile phone addiction and QOL in Chinese university students, which gave us the impetus to examine the demographic characteristics and QOL of Chinese university students with excessive mobile phone use. This study also included participants from two special administrative regions of China, the former European colonies of Macau and Hong Kong that have different socioeconomic background compared to mainland China (Wang et al., 2018; Xiang et al., 2008). We hypothesized that certain demographic characteristics would influence the severity of mobile phone use, and more excessive mobile phone use would be associated with poorer QOL in Chinese students.

Materials and Methods

Results

Altogether, 2,523 students have been invited to participate in the study; 2,312 students (928 from Macau, 938 from mainland China, and 446 from Hong Kong) completed the assessments, yielding a participation rate of 91.6%. No significant difference was observed between students who completed and those who did not complete the survey in terms of gender and age.

Table 1 shows the of sociodemographic characteristics of the whole sample and by study sites. Table 2 displays the association between mean MPAS score and demographic characteristics, the Pearson correlation coefficient between actual sleep hours and MPAS score was −0.161 (p < 0.001). Table 3 shows the association between the severity of mobile phone addiction and QOL. MPAS score was significantly and negatively associated with all QOL domains after controlling for covariates, that is, those with more excessive mobile phone use were more likely to have poor QOL in all domains. Multiple linear regression analyses revealed that compared to students in mainland China, students in Macao (β = 2.74, p < 0.001) and Hong Kong (β = 2.86, p < 0.001) were more likely to have excessive mobile phone use (Table 4). Further, higher academic pressure (β = 1.99, p < 0.001) and poor academic performance (β = 1.41, p < 0.001) were both positively, while male gender (β = −2.99, p < 0.001), greater interest in academic major (β = −2.15, p < 0.001) and longer sleep duration (β = −1.13, p < 0.001) were negatively associated with excessive mobile phone use.

Discussion

To the best of our knowledge, this was the first study that examined the demographics and QOL in Chinese university students addicted to the use of mobile phone. The severity of mobile phone addiction was different across study sites. Compared to students in mainland China, their counterparts in Macao and Hong Kong were more likely to suffer from more excessive mobile phone use, which could be attributed to several reasons. First, there is difference in the availability of mobile phones between study sites; Macau has 206.43 mobile phones per 100 people, followed by Hong Kong (190.21 mobile phones per 100 people), while in mainland China the corresponding figure is just 64.04 mobile phones per 100 people (International Telecommunications Union, 2011). Second, due to different socio-economic background between the three locations, free Wi-Fi internet service was available in participating universities in Macau and Hong Kong, but not in those in mainland China, which could partly explain the more excessive mobile phone use in Macao and Hong Kong.

Male students were found less likely to have more excessive mobile phone use in this study, which is consistent with the findings in Korean students (Choi et al., 2015) and in other populations elsewhere (Jenaro et al., 2009; Sanchez-Martinez & Otero, 2009). The more frequent and excessive mobile phone use by female students is probably associated with the different patterns (Hans, 2006; Roberts, Yaya & Manolis, 2014) and purpose of mobile phone use between genders (Choi et al., 2015). Female students usually use mobile phones for conversations, personal messaging, and sending emails to maintain interpersonal relationship and broaden their social network (Bianchi & Phillips, 2005; Bonka, Robert & David, 2001; Fernandez-Villa et al., 2015; Junco, Merson & Salter, 2010; Langer et al., 2017); in contrast, for male students mobile phone is more often an entertainment tool (Hans, 2006). In addition, female students are more likely to use multimedia applications by smartphones (Chen et al., 2017).

Similar to previous findings (Lepp, Barkley & Karpinski, 2015), a positive association between poor academic performance (B = 1.41, p < 0.001) and excessive mobile phone use was found in this survey. Students with more excessive mobile phone prefer to use more superficial approach to learning (e.g., instrumental learning to meet the requirements of one’s learning outcomes), instead of a deep approach (e.g., to fully understand the content studied), which could result in poor academic performance (Rozgonjuk, Saal & Taht, 2018). Students with greater interest in their academic major were less likely to have excessive mobile phone use (B = −2.15, p < 0.001). Excessive mobile phone use may lead to concentration difficulties in class and doing homework leading to poor academic performance (Jacobsen & Forste, 2011; Junco & Cotten, 2012). With increased stress, self-control usually decreases, contributing to the risk of excessive mobile phone use (Cho, Kim & Park, 2017); in addition, students with high academic pressure may attempt to relax by using mobile phone frequently, which could further explain the positive association between heavy academic pressure and excessive mobile phone use. A negative association between sleep duration and excessive mobile phone use was also found in this study. All electronic devices could have a negative impact on sleep time and quality (Zarghami et al., 2015). Many people use mobile phone at bedtime and the blue wavelength light from screens could affect the regulation of melatonin, which is associated with shortened sleep time and poor sleep quality (Schuz et al., 2009).

Students with excessive mobile phone use are more likely have unhealthy and irregular life habits and sedentary behaviors and spend less time on physical exercise (Kim, Kim & Jee, 2015), ending up with various health problems, such as obesity or metabolic syndrome (Kautiainen et al., 2005), low back, neck and shoulder pain, blurred vision (Rai et al., 2016), and depression (Thomee, Harenstam & Hagberg, 2011). All these negative health outcomes lower QOL. Excessive mobile phone use had negative associations across all QOL domains in this survey.

The strengths of this study include the large sample size and multicenter design. However, there were also limitations. First, due to the cross-sectional design, causality between the severity of mobile phone addiction and its background variables could not be examined. Second, important pieces of information related to mobile phone use, such as duration and frequency of mobile phone use, time spent on different hobbies, economic status, personality and social support, were not recorded. Due to logistical reasons, participants were only recruited from Beijing and Jilin province, therefore the findings could not be generalized to all university students in mainland China. Third, the instruments were self-reported screening tools and not diagnostic tools. There has been no agreement on the definition and diagnostic criteria of mobile phone addiction, therefore, following other studies (Chiu, Hong & Chiu, 2013; Hong et al., 2019; Lu et al., 2019), only a self-reported scale on the severity of mobile phone addiction could be employed.

Conclusions

Due to the negative impact of excessive mobile phone use on all aspects of QOL, effective preventive measures, such as public education, should been developed for Chinese university students, especially for those in Macau and Hong Kong.

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