Interactions of problematic mobile phone use and psychopathological symptoms with unintentional injuries: a school-based sample of Chinese adolescents.

BACKGROUND: Unintentional injuries are a major contributor to morbidity and mortality in adolescents. Mobile phone use in certain circumstances (e.g., driving, cycling, walking) and mental health conditions are risk factors for unintentional injury. However, research on the interactions between problematic mobile phone use (PMPU) and psychopathological symptoms in unintentional injuries is limited. The present study aimed to determine the prevalence of unintentional injuries (road traffic injuries, pedestrian collisions, and falls) and examined interactions of PMPU and psychopathological symptoms with unintentional injuries in a school-based sample of Chinese adolescents.
METHODS: A total of 14,221 students (6915 middle school students and 7306 high school students) were randomly selected from 32 schools in four cities in China in 2012. The sample comprised 6712 boys and 7509 girls with a mean age of 15.12 years (standard deviation 1.89 years). PMPU, psychopathological symptoms, and unintentional injuries were measured with validated instruments. Chi-square tests and multivariable logistic regression were used to analyze the rates of unintentional injuries, the relationship with PMPU and psychopathological symptoms, and the interactions of PMPU and psychopathological symptoms with unintentional injuries.
RESULTS: The prevalence of road traffic injuries, pedestrian collisions, and falls were 4.9, 16.2, and 10.1 %, respectively. The rates of unintentional injuries were higher among students with PMPU and psychopathological symptoms. Interaction analysis indicated that psychopathological symptoms were associated with a greater increase in the likelihood of unintentional injuries for adolescents with PMPU than for those without PMPU.
CONCLUSIONS: The findings indicate that unintentional injuries in adolescents are an important public health issue in China that merit further research. Intervention programs must consider the adolescents' behavioral and psychological health.

Background

Mobile phones are now considered an essential part of everyday life, embraced by all age groups and demographics. The Ministry of Industry and Information Technology reported that in 2011 there were nearly 1 billion mobile phone subscribers in China; the number of subscribers increased by more than 81 million to over 940 million in the first 8 months of the year [1]. However, problematic mobile phone use (PMPU) has rapidly increased as a risk behavior worldwide. PMPU is defined as an inability to regulate one’s use of the mobile phone, which eventually involves negative consequences in daily life [2]. In a Korean sample, Lee et al. reported that 16 % of middle school students were “addicted” to their phones [3]. In a Tunisian population, Halayem et al. reported that 26 % of participants suffered from excessive mobile phone use [4]. A study in the United Kingdom found the prevalence of PMPU among 1026 students was 10 %, with the typical problematic users being adolescents aged 11–14 years [5].

In addition, the negative effects of PMPU should not be underestimated. The risk of distraction from mobile phones has grown dramatically over recent years, with this distraction connected to unintentional injuries, such as involvement in road traffic accidents or pedestrian injuries. Road traffic accident rates are higher for adolescent drivers than for any other age group, and driving while distracted might contribute to these high accident rates. A study in the United States found that nearly half of high school students aged ≥16 years reported texting while driving during the past 30 days, with this group more likely to engage in additional risky motor vehicle behaviors [6]. Zhao et al. noted that a greater reported frequency of mobile phone use while driving was likely to increase the overall risk of accident involvement [7]. Mobile phone use while riding a bicycle may also increase the accident risk for cyclists [8]. Furthermore, mobile phones have been found to have a negative effect on perception, potentially forming a threat to cyclist traffic safety [9].

Pedestrian injury and unintentional falls are major public health issues. Stavrinos et al. found that college students frequently talked on the phone while walking, and have increased pedestrian injury rates compared with other age groups. They conducted two experiments, with the results suggesting mobile phone conversations distracted pedestrians to a level that compromised safety [10]. Another study suggested that pedestrian behavior was generally riskier when participants were using mobile Internet and crossing the street than when crossing the street with no distraction. The hypothetical mechanism suggested for pedestrian risk was that the increased cognitive load caused by a distracting device reduced the efficacy of the cognitive process in perceiving relevant environmental conditions at roadside and making safe decisions to initiate motoric processes [11].

The number of mobile phone-related injuries has increased, and more research is required to evaluate the risks caused by mobile phone distraction in all aspects of life [12]. Studies of falls have mainly focused on older adults, and research on falls in children or adolescents is lacking. Globally, almost 50 % of the total number of disability-adjusted life years lost to falls occurred in children under 15 years of age [13]. Falls are gaining attention as a public health issue and may be associated with distraction from mobile phones; therefore, falls were included as an outcome in the present study.

There has also been a growing interest in the potential influence of mental health factors on the risk for unintentional injuries. Poor psychological symptoms include inattention, impulsion, errors in perception and judgment, and psychomotor dysfunction. All of these are potential mechanisms that may explain how poor psychological symptoms increase the risk of unintentional injuries [14]. A cross-sectional study showed that participants with depressive symptoms had a higher prevalence of unintentional injuries [15]. Another study indicated that pre-existing depression was associated with unintentional injury in rural Australians [16]. Furthermore, psychological symptoms were shown to influence young drivers to engage in mobile phone use. Weller et al. investigated if risk perception could predict the tendency of younger drivers to engage in calling while driving, with lower risk perception toward calling while driving associated with higher risk driving behavior [17]. Perceived risk has also been negatively correlated with answering a phone call while driving [18]. It may be that people who use mobile phones while driving are more inclined to ignore the associated injury risks because of their own errors in perceived control. As psychological symptoms influence mobile phone use in driving behavior, they may also increase the risk of mobile phone-related injuries.

Given the evidence that unintentional injuries among adolescents are associated with PMPU and with psychopathological symptoms, the present study aimed to examine (1) associations of PMPU and psychopathological symptoms with self-reported unintentional injuries and (2) whether the interaction of PMPU and psychopathological symptoms increased the likelihood of unintentional injuries among Chinese adolescents.

Methods

Study design and participants

The study protocol was approved by the Ethics Committee of Anhui Medical University. Data for the present analysis were collected in 2012. Participants were recruited from public middle and high schools (32 schools in total) from four cities (Shenyang, Xinxiang, Guangzhou, and Chongqing) in China. Students were randomly selected using stratified cluster random sampling in two steps. The first step was the selection of eight schools that met inclusion criteria: located in urban and rural areas; represented different levels of quality of education; included one key middle school, one general middle school, one provincial demonstration high school, and one city demonstration high school from urban areas as well as two general middle schools, one general high school and one city demonstration high school from rural areas. The second step involved randomly selecting three classes in each grade from each school, giving a total of 288 classes. All students in these classes (n = 14,665) were invited to participate, and 14,221 provided useable data (response rate =97.0 %). Data collection was completed in classroom settings. Teachers, school administrators, and staff distributed questionnaires to the participating students and instructed them to complete the survey anonymously. Most students took approximately 30–45 min to complete the questionnaire. Written informed consent was obtained from all participants (students and parents) before completing the survey.

Materials

Demographic information

Participants were asked about their city, sex, grade, registered permanent residence, any siblings, perceived family economic status, parental expectations, academic performance, number of friends, and cigarette and alcohol use. To determine registered permanent residence, participants were asked “Where are you from?” Responses were categorized as urban or rural. Siblings were determined by the question: “Are there any siblings in your family?” Responses were recoded as “Yes” or “No.” Perceived family economic status was measured with the question “How well off is your family compared with other families in your region?” Responses were recoded into three categories: low, medium, and high. Academic performance was measured with the question “How do you feel about the level of your academic performance in your class?” Response options were “Low,” “Medium,” and “High.” Participants’ number of friends was measured with the question “How many close friends do you have?” Responses were recoded into four categories: none, 1–2, 3–5, and ≥6. Measurement of cigarette and alcohol use was based on the Young Risk Behavior Surveillance System questionnaire [19]. Cigarette smoking was measured with the question “During the past 30 days, on the days you smoked, how many cigarettes did you smoke per day?” Smoking was defined as smoking at least one cigarette per day. Alcohol drinking was measured with the question “During the past 30 days, on how many days did you have at least one drink of alcohol?” Alcohol drinking was defined as drinking at least once during the past 30 days.

PMPU

We assessed PMPU with the Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU), which is a standardized questionnaire appropriate for evaluating PMPU in adolescents developed by Tao et al. [20] (Additional file 1). The SQAPMPU comprises 13 items (e.g., “I lose sleep due to the time I spend on my mobile phone,” “I become irritable if I have to switch off my mobile phone for meetings, dinner engagements, or at the movies,” “I feel lost without my mobile phone”) and covers three dimensions including withdrawal symptoms, craving, and physical and mental health status. Responses to each item are on a five-point Likert scale (1 = Not true at all, 2 = Slightly true, 3 = Moderately true, 4 = Strongly true, and 5 = Extremely true). The validity and reliability of the SQAPMPU have been previously examined. Exploratory factor analysis showed the variance cumulative contribution rate was 59.13 %. Confirmatory factor analysis showed that the value of Root Mean Square Error Approximate was 0.067, and the values of fit indices (Norm Fitting index, Relative Fitting Index, Comparative Fitting Index, Goodness of Fit Index, and Adjusted Goodness of Fit Index) were above 0.9. All of the indicators were significant and had good construct validity. To determine the concurrent validity of the SQAPMPU, correlations between the SQAPMPU and the Mobile Phone Problem Use Scale developed by Bianchi and Phillips [21] were investigated in 238 students. The results showed a significant correlation, with a Pearson correlation coefficient of 0.862 (P < 0.001).

The Cronbach’s alpha coefficient and split-half reliability were 0.87 and 0.80, respectively. In the present study, the Cronbach’s alpha coefficient was 0.94. The scores ranged between 13 and 65, with the 75th percentile used as the cutoff point, meaning scores were recoded as “No” (