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Article

Association of Unhealthy Behaviors with Self-Harm in Chinese Adolescents: A Study Using Latent Class Analysis

by
Rong Yang
1,2,3,4,†,
Danlin Li
1,2,3,5,†,
Run Tian
6,
Jie Hu
1,2,3,
Yanni Xue
1,2,3,
Xuexue Huang
1,2,3,
Yuhui Wan
1,2,3,
Jun Fang
1,2,3,7,8,* and
Shichen Zhang
1,2,3,*
1
Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, Hefei 230032, China
2
MOE Key Laboratory of Population Health Across Life Cycle, Hefei 230032, China
3
Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei 230032, China
4
Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
5
School of Public Health, Medical College of Soochow University, Suzhou 215123, China
6
Department of Occupational and Environmental Health, School of public health, Guilin Medical University, Guilin 541199, China
7
Department of Toxicology, School of Public Health, Anhui Medical University, Hefei 230032, China
8
Faculty of Pharmaceutical Science, Sojo University, Ikeda 4-22-1, Kumamoto 860-0082, Japan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Trauma Care 2021, 1(2), 75-86; https://doi.org/10.3390/traumacare1020008
Submission received: 24 March 2021 / Revised: 15 July 2021 / Accepted: 22 July 2021 / Published: 26 July 2021
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Abstract

:
Previous studies have demonstrated the link between individual unhealthy behaviors and self-harm, but little is known about the influence of multiple unhealthy behaviors on self-harm among adolescents. This study aims to identify the potential patterns of unhealthy behaviors and to examine their associations with self-harm, which may become a useful tool for the screening of self-harm in adolescents. A total of 22,628 middle school students (10,990 males and 11,638 females) in six cities was enrolled in this study by multistage stratified cluster sampling from November 2015 to January 2016. Latent class analysis (LCA) was performed based on five kinds of unhealthy behaviors (unhealthy losing weight (ULW), tobacco use (TU), alcohol use (AU), screen time (ST), and mobile phone dependence (MPD)). Multivariate logistic regressions were used to examine associations between identified subgroups and self-harm. Four subgroups of unhealthy behaviors were identified. Class 1 (71.2%) had the lowest engagement in unhealthy behaviors. Class 2 ((ULW/MPD), 22.3%) had a relatively high prevalence of ULW and MPD. Class 3 ((TU/AU/ST), 3.2%) had a relatively high prevalence of TU, AU, and ST. Class 4 (3.3%) consistently engaged in unhealthy behaviors. Compared to class 1, class 2 (ULW/MPD), class 3 (TU/AU/ST), and class 4 showed OR (95%CI) values of 2.101 (1.964–2.248), 2.153 (1.839–2.520), and 3.979 (3.407–4.645) (p < 0.001 for each), respectively. Class 1, class 2 (ULW/MPD), and class 3 (TU/AU/ST) engagement in unhealthy behaviors was associated with increased self-harm. These findings strongly suggested that self-harm prevention efforts focusing on multiple unhealthy behaviors should be seriously considered for early detection of self-harm.

1. Introduction

Self-harm refers to the intentional self-inflicted destruction of body tissue without suicidal intent and for purposes not socially sanctioned. Forms of self-harm include cutting, skin carving, burning, severe scratching/abrading, and punching/hitting [1]. Self-harm has been shown to predict future suicide ideation and attempts above other established risk factors, such as depressive symptoms and previous suicidality [2]. The prevalence of lifelong health problems in adolescents with self-harm is 17–60% [3]. The international prevalence of self-harm was about 17.2% among adolescents, while that of Chinese middle school students was as high as 22.7% [4,5]. Therefore, self-harm among adolescents needs to be paid special attention.
The known risk factors of self-harm, including genetic, biological, mental disorder, psychological, and environmental factors, are often internalizing or difficult to modify [6]. Traditional health risk behaviors include specific forms of behavior, such as unintentional injury, absence of physical activity, risky sexual behavior, substance abuse, psychiatric addiction, and so on, which are proven to be associated with increased susceptibility to specific diseases or ill health [7,8]. Apart from the above, “new” behaviors such as Internet use, videogame playing, and mobile phone dependence are becoming increasingly common in adolescents [9,10]. In this study, we adopted “unhealthy behaviors” to define the above behaviors. Adolescence is a period in which people face great changes in behaviors and psychology. In this sensitive stage of growth, adolescents are curious and impulsive about new things, and with their peers, engaging in unhealthy behaviors [11]. Several studies have investigated the negative associations between various unhealthy behaviors and self-harm [9,12,13,14]. For instance, students who frequently use the internet and smoke are more prone to self-harm [10]. In depressed adolescents receiving outpatient treatment, alcohol use patterns predicted both deliberate self-harm and suicidality at one-year follow-up [15]. Laxative abuse was associated with self-harm in individuals with eating disorders [16]. Our previous research results found that the risk of self-harm among middle school students with mobile phone dependence was 2.062 times higher than the control group [9]. Furthermore, watching videos of self-harm-related behaviors can induce teenagers to imitate video content and make corresponding behaviors [17]. More than 2 h of weekend screen time per day by middle school students is a risk factor for self-harm [18]. Hawton et al. found that watching TV and playing computer for a long time not only had bad effects on the mental health of young people but also was a risk factor for repeated self-harm and suicide [6].
Thus, it is important to account for these multifaceted features of unhealthy behaviors to examine their association with self-harm among adolescents. Nevertheless, unhealthy behaviors often co-occur, which increases the risk of chronic disease incidence and mortality, and the co-occurrence of different unhealthy behaviors always not only shows the additive effects but exhibits a synergistic effect [19,20,21,22]. However, most research has routinely focused on single behaviors but not investigated the interaction of multiple behaviors and their impact on self-harm. In this context, to elucidate the effect of multifaceted features of unhealthy behaviors on self-harm among adolescents, in this study, we included a comprehensive assessment of the extent to which adolescents engaged in each unhealthy behavior. We aimed to understand the potential patterns of observed behaviors and their influences on self-harm.
Previous studies have shown that the cluster pattern of unhealthy behaviors in adolescents is not the same but is of heterogeneity [23,24,25]. Heterogeneity is known to be present when a population can be separated into distinct subpopulations or clusters. In the case of unobserved heterogeneity, the subpopulation membership must be inferred from the data. In this context, the subpopulations are termed latent classes since subpopulation membership is unobserved. Emerging statistical techniques such as latent class analysis (LCA) allow multiple behaviors or multiple dependent variables to be investigated together. Segmenting a heterogeneous population into relatively homogenous unobserved subgroups based on behavioral profiles may improve the scope, utilization, and efficacy of interventions by targeting multiple modifiable unhealthy behaviors simultaneously [26].
Based on Jessor’s risk behavior theory, unhealthy behaviors tend to co-occur in youth perhaps because they share a common motivation of thrill seeking [27]. Additionally, previous studies have found multiple clustering patterns for unhealthy behaviors and single unhealthy behavior was associated with self-harm among adolescents. We hypothesized that clusters of students engaging in more unhealthy behaviors will be more likely to report increased self-harm. The primary purpose of this study was to use LCA to examine the distinct unhealthy behavior patterns in a sample of Chinese adolescents. In addition, we also examined the relationship between the identified subgroups and self-harm.

2. Materials and Methods

2.1. Participants and Procedures

We designed a school-based cross-sectional study from November 2015 to January 2016. The study was approved by the Ethics Committee of Anhui Medical University (1 Mar 2014; approval number 20140087). All subjects participated in the study upon receiving informed consent from their parents. The participants were recruited from junior and senior high schools located in six cities in China, including both urban and rural regions, by using convenient cluster sampling. The sampling steps are as follows: firstly, six cities were selected by convenient sampling. These cities were Shenyang (capital of Liaoning Province), Xinxiang (north of Henan Province), Yangjiang (southwest coast of Guangdong Province), Chongqing (one of China’s four direct-controlled municipalities), Ulanchap (the central Inner Mongolia Autonomous Region), and Bengbu (a northeastern city of Anhui province). Most of these stations are located in densely populated eastern and southern China. Then, eight schools (two rural junior and two senior schools, two urban junior schools, and two senior schools) were selected in each region based on the stratified cluster sampling. Lastly, four to six classes were selected randomly from each grade in each school. The sample size calculation formula for cross-sectional studies was used to calculate the minimum theoretical sample size for this study (N = Za2*P(1−P)/d2) [28]. According to previous study, the prevalence of self-harm among Chinese middle school students was 22.7% [5]. Cluster sampling has a large sampling error, the sample size should be increased by about half, and there is a possibility of sample shedding [29]. The sample size should be a minimum of 1,963 using the prevalence of 22.7%. The exclusion criteria include the following: (1) participants who refused to participate in the study; and (2) participants with a history of psychiatric disorder (anxiety, depression, eating disorder, sleep disorders, and so on) reported by parents. In total, 23,137 students took part in this survey. After excluding the incomplete questionnaires with missing data >5% (n = 509), there were 22,628 valid questionnaires with an efficiency rate of 97.8%.
A questionnaire survey was administered in the classroom by researcher staff and teachers with guidance and explanations on each item for the participants. The students were allowed to withdraw from the study if they were not willing to participate. Students completed a self-report questionnaire during 20–30 min in the classroom.

2.2. Design of Questionnaires

Socio-demographic variables were recorded as follows: age, gender, registered residence, household structure, accommodation type, parental educational level, and self-reported family economic situation.
Table A1 showed the main measures. Self-harm was assessed by the Adolescent Non-Suicidal Self-Injury Assessment Questionnaire, which included eight items (hit, cut, fire, bite, pull hair, bang head, a toxic substance, and pinch or scratch), such as “Have you ever hit yourself in the past 12 months?”. All the response options were “yes” or “no”. When the answer was “yes” for any single item or more, the students were judged as having self-harm behaviors in the past 12 months. The reliability and validity have been demonstrated in a previous study, in which the Cronbach’s alpha coefficient was reported to be 0.776 [30]. The Cronbach’s α coefficient for this study was 0.779.
Tobacco use, alcohol use, and unhealthy weight loss were assessed with three questions. Tobacco use was assessed by asking “During the past 30 days, how many days did you smoke cigarettes?” with response options of 0 = 0 day; 1 = 1 to 9 days; 2 = 10 to 19 days; or 3 = 20 to 30 days, and alcohol use was assessed by asking “During the past 30 days, on how many days did you have at least one drink of alcohol?” with response options of 0 = 0 day; 1 = 1 to 9 days; 2 = 10 to 19 days; or 3 = 20 to 30 days [27,31]. Unhealthy weight loss was assessed with the following question, “During the past 30 days, have you taken any diet pills or diet tea without a doctor’s advice to lose weight?”, with response options of 0 = 0 time; 1 = 1 time; 2 = 2 to 3 times; 3 = 4 and over 4 times. For the item, participants could choose 0 as no and other options as yes [31,32]. The validity of adolescent self-reported data on behaviors related to tobacco, alcohol, and drug use has been assessed [33].
The screen time of the participants was assessed by the self-report question, “On weekdays, how much time do you spend playing games or doing things unrelated to study on the computer every day on average?”. According to the standard of the American Academy of Pediatrics and previous studies [34,35,36], screen time >2 h/day is defined as too long screen time. Mobile phone dependence was measured by the Self-rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) [37]. It consists of 13 items with responses on a 5-point Likert scale (never, occasionally, sometimes, often, and always) and covers three dimensions including six questions for withdrawal symptoms (e.g., “If I don’t have a phone, I will feel overwhelmed”), four questions for craving (e.g., “I always feel that I don’t have enough time to use my phone”), and three questions for physical and mental health status (e.g., “Too much mobile phone use leads to insufficient sleep”). The Cronbach’s alpha coefficient for this study was 0.923. According to the previous studies, participants in this study were categorized as mobile phone dependent when this score was ≥P75 [9,38,39].

2.3. Statistical Analysis

The database was entered by EpiData 3.1 (The EpiData Association, Odense, Denmark) and analyzed in Mplus (Version 7.4, Muthén & Muthén, Los Angeles, CA, USA) and SPSS 23.0 (SPSS Inc., Chicago, IL, USA). Firstly, the Chi-square test was performed to assess group differences concerning their statistical significance. Secondly, LCA was applied to identify unhealthy behavior patterns. Finally, the multivariable logistic regression models were used to examine the associations of subclasses of unhealthy behaviors and self-harm. For the main variable (unhealthy behaviors and self-harm), we used mode to supplement missing categorical data.
LCA was carried out in Mplus to explore the most likely number of classes based on the five unhealthy behaviors. The optimal model was determined based on six model fit indexes: Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted Bayesian information criterion (aBIC), Lo–Mendell–Rubin (LMR), bootstrapped likelihood ratio test (BLRT), and entropy. LMR and BLRT were used to make a comparison between the estimated model and a model with k-1 class, or a class with k equaling the number of classes [40]. For the LMR and BLRT, a low and significant p-value imply that the estimated model is superior to the model with one less class [40]. The AIC, BIC, and aBIC are commonly used for comparing different counterpart models, with the lowest value on each indicator suggesting a best-fitting model [41].

3. Results

3.1. Characteristics of Self-Harm

Table 1 presented the prevalence of self-harm by frequency characteristics, which was 32.1% of total enrolled participants. Self-harm was more common in males than females (35.2% vs. 29.1%; p < 0.001). Furthermore, statistical significance was found on grade, registered residence, boarding on school days, parents’ educational level, self-reported family economy, and the number of friends (p < 0.05 for each). Similar results were observed in most single self-harm behavior.

3.2. Latent Class Analysis of Unhealthy Behaviors

The prevalence of tobacco use, alcohol use, unhealthy weight losst, screen time, and mobile phone dependence was 2.8%, 16.8%, 4.8%, 16.3%, and 25.4%, respectively. Patterns of unhealthy behaviors were identified by LCA in Mplus to five classes. The five classes model did not replicate the best log-likelihood value and was therefore not considered further. The four-class model was chosen as the optimal model, for which bootstrap validation procedures also demonstrated a good fit (p < 0.001, Table 2).
Figure 1 showed the estimated probabilities of unhealthy behaviors among the four identified latent classes. Class 1 (71.2%, n = 16,184) was the lowest risk cluster, in which the students were unlikely to have unhealthy behaviors. In this class, few students reported unhealthy losing weight (1.8%), tobacco use (0.5%), alcohol use (8.6%), or screen time (8.1%), and none of them had mobile phone dependence. In contrast, class 4 was defined as the highest risk cluster which included 3.3% (n = 747) of the participants, in which the majority of adolescents engaged in unhealthy weight loss (30.1%), tobacco use (28.8%), alcohol use (88.6%), and screen time (69.6%). Moreover, all of them reported mobile phone dependence in this class (100%). Furthermore, we found two moderate risk classes (class 2 and class 3), which we defined as those engaging in at least one or two of the negative behaviors but not all. Class 2 (22.3%, n = 5055) was characterized by relatively little tobacco use (1.5%), alcohol use (16.1%), or screen time (23.9%), but relatively high percentages of unhealthy weight loss (9.1%) and mobile phone dependence (79.1%). Finally, 3.2% of the participants belonged to class 3 (n = 713), which was characterized by relatively little unhealthy weight loss (5.8%) and mobile phone dependence (21.1%), but relatively high percentages of tobacco use (20.9%), alcohol use (71.6%), and screen time (44.5%). There was generally a good distinction among the four classes in this optimal model, based on an overall entropy value of 0.728, indicating good classification quality based on the threshold of 0.7 suggested by Nagin [42].

3.3. Multivariate Logistic Regression Analyses

After extracting the four latent classes, individuals were assigned to different classes. As shown in Figure 2, factors that were considered to potentially interact were gender, grade, registered residence, accommodation type, parents’ educational level, self-reported family economic status, number of friends, and cities. Results from multivariate logistic regression analyses indicated the subgroups of unhealthy behaviors associated with self-harm. Compared with class 1, class 2, class 3, and class 4 were positively related to self-harm, with the OR (95%CI) of 2.101 (1.964–2.248), 2.153 (1.839–2.520), and 3.979 (3.407–4.645) (p < 0.001 for each), respectively.

4. Discussion

In the present study, we found the rate of self-harm to be 32.1%, which is higher than in developed countries (19.1%, 95% CI: 9.3–35.3%) [43]. Because the definition of self-harm, samples, and the assessment tools are different, the self-reported self-harm ranged from 7.9% to 73.7% among adolescents in the world [44,45,46,47,48]. A meta-analysis shows that the prevalence of self-harm among Chinese adolescents is increasing [5]. From 2015 to 2017, the prevalence reached 28.5% (95% CI: 25.3–31.8%) [5]. Furthermore, the prevalence increased with sample size, indicating the importance of an appropriate sample size [44,48]. We found that self-harm was more likely to occur in males, which was consistent with some previous studies [49], but opposite results were also reported [50]. The reasons for the gender differences in self-harm are still unclear, and further studies are needed to clarify this issue. We found that middle school students reported a higher rate of self-harm than high school students, which was similar to other studies [51,52]. In early–middle adolescence, impulsive choices tend to appear, leading to the occurrence of self-harm [53]. The different rate of self-harm between urban and rural students might be due to the economic development status, parental caregiving, or other social factors. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition points out that self-harm is motivated by satisfaction, solving interpersonal problems, alleviating negative thoughts or feelings, and generating positive emotions or feelings [3]. The oriental countries represented by China and Japan are a kind of collectivist culture [54]. Under the cultural background of dependence self-construction in the east, individuals emphasize collectivism and pay attention to group relations. Easterners may pay more attention to the function of self-punishment to repair interpersonal relations and restore social reputation. In our study, the students who had fewer friends and the boarding students showed a higher incidence rate of self-harm. These students may face more interpersonal problems leading to a higher prevalence of self-harm.
Using the LCA, this study identified four distinct subgroups of unhealthy behaviors using five behavior indicators in Chinese students. Students classified in the highest risk cluster (class 4) were likely to engage in multiple unhealthy behaviors such as unhealthy losing weight, tobacco use, alcohol use, screen time, and mobile phone dependence. Although the proportion of students in this class was small (3.3%), the problems cannot be ignored because of the large population base in China. Class 3 (22.3%) reported a high level of tobacco use, alcohol use, and screen time, which is consistent with previous research reporting the strong interaction between smoking and drinking behaviors [55,56]. Interestingly, our results showed that long screen time was included in the same class with drinking and smoking. Furthermore, class 2 (3.2%) comprised primarily of a higher prevalence of unhealthy weight loss and mobile phone dependence. Nevertheless, the previous studies did not find that unhealthy weight loss and mobile phone dependence were included in the same cluster by LCA analyses, although unhealthy weight loss and mobile phone dependence have known associated psychological behaviors [9,57]. Meanwhile, adolescents with unhealthy weight loss behaviors were more likely to engage in delinquency and suicide attempts [58], and it is thus reasonable to assume that unhealthy weight loss is associated with self-harm. Adolescents with comorbid unhealthy weight loss and mobile phone dependence may represent a clustering of traditional and “new” unhealthy behaviors. Surprisingly, screen time and mobile phone dependence were not included in the same class; this may be partly due to teenagers usually having less time to choose an electronic entertainment device during the period of high school. Therefore, screen time and mobile phone dependence did not appear in one class, and the reasons need to be clarified in the future. Hence, these findings support the notion that adolescents who engage in self-harm are not a homogenous class but rather a diverse class.
Our study also examined how these patterns of multiple unhealthy behaviors were associated with self-harm, and the findings support the notion that engaging in multiple unhealthy behaviors is associated with a higher risk of self-harm. To our knowledge, no studies have investigated the relationship of subclasses of unhealthy behaviors on self-harm in adolescents. Therefore, although the comparison of our results with findings from other studies is limited, this study provides important information on possible predictors of self-harm in adolescents. Although there was no direct evidence that subgroups of unhealthy behaviors were related to self-harm, some studies have examined the clustering of health risk behaviors and mental health status among college samples. Findings from these studies showed that students with greater numbers of unhealthy behaviors had increased risks for depression, anxiety, and greater self-perceived psychological stress [23,24,25], while psychological problems are an important factor leading to self-harm [59,60].
Moreover, understanding the association between unhealthy behaviors and self-harm is imperative for detecting the most at-risk subgroups. Identifying key patterns of unhealthy behaviors during adolescence can shed light on future directions for tailored intervention programs for this population. Across the behavioral cluster domains, some studies explored the effects of changing multiple behaviors simultaneously or sequentially. For interventions targeting tobacco and diet [61], tobacco and alcohol [62], and physical activity and diet [63], simultaneous versus sequential long-term outcomes indicated no significant difference. In an intervention targeting physical activity, tobacco, and dietary sodium, the sequential intervention was inferior to simultaneous intervention [64]. Furthermore, effectively treating two behaviors in an individual reduces medical costs by about 2000 dollars per year [65]. Consequently, it may be efficient to intervene in multiple behaviors at the same time. By targeting multiple health-risk behaviors simultaneously, individuals may be able to transfer their knowledge and experiences from one behavior to another if the domains share similarities [66]. The idea is that changes in one of these behaviors may result in a cascading effect, similar to previous research showing a corresponding decrease in marijuana and alcohol use when quitting smoking [66]. Success in changing one or more lifestyle behaviors may also increase one’s confidence or self-efficacy to improve risk behaviors for which individuals have low motivation to change. As such, health behavior change may serve as a gateway to the overall healthful lifestyle change.
This study employed emerging analytic methodology, LCA, and explored a wide array of unhealthy behaviors among a nationwide sample of Chinese adolescents. However, some limitations existed in this study. Firstly, not all kinds of unhealthy behaviors were included. Secondly, different samples may get different results, so the results of this study need to be verified by more studies in the future. Thirdly, this study focused on only one domain per behavior, for example, unhealthy weight loss was represented by the diet pills or diet tea, while tobacco use was assessed by the number of days smoked during the past 30 days. Finally, in terms of model fit, it should be noted that an entropy value of 0.728 means that more than 25% of our sample was not optimally classified. Further investigations to overcome these limitations are warranted.

5. Conclusions

The present study identified four subgroups of unhealthy behaviors and highlighted their associations with self-harm among Chinese adolescents. Our findings reveal a significant relationship between clustering of unhealthy behaviors with negative self-harm outcomes, specifically, students with greater likelihoods of engaging in unhealthy weight loss, tobacco use, alcohol use, screen time, and mobile phone dependence were most likely to exhibit self-harm. Understanding how self-harm may appear in the context of the interactions of multiple unhealthy behaviors will be important and helpful for institutions to create targeted interventions for those students who may potentially be at higher risk for self-harm. Further studies are warranted to prove and improve the efficacy of interventions focusing on multiple unhealthy behaviors.

Author Contributions

Conceptualization, S.Z.; methodology, R.Y. and D.L.; formal analysis, R.Y. and D.L.; investigation, R.Y., D.L., J.H., R.T., Y.X. and X.H.; writing—original draft preparation, R.Y. and D.L.; writing—review and editing, S.Z. and J.F.; supervision, S.Z., Y.W. and J.F.; project administration, S.Z., Y.W. and J.F.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (81402699) and the Natural Science Foundation in Higher Education of Anhui (KJ2020A0209).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Anhui Medical University (1 March 2014; approval number 20140087).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

We thank all participants and the schools involved in this study.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. The main measures.
Table A1. The main measures.
VariableMeasures
Self-harmAdolescent Non-suicidal Self-injury Assessment Questionnaire
Unhealthy weight lossDuring the past 30 days, have you taken any diet pills or diet tea to lose weight?
Tobacco useDuring the past 30 days, how many days did you smoke cigarettes?
Alcohol useDuring the past 30 days, on how many days did you have at least one drink of alcohol?
Screen timeThe average hours on weekdays spent playing games or doing things unrelated to study on the computer every day.
Mobile phone dependenceSelf-rating Questionnaire for Adolescent Problematic Mobile Phone Use

References

  1. Favazza, A.R. Bodies under Siege: Self-Mutilation and Body Modification in Culture and Psychiatry, 2nd ed.; Johns Hopkins: London, UK, 1996. [Google Scholar]
  2. Guan, K.; Fox, K.R.; Prinstein, M.J. Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample. J. Consult. Clin. Psychol. 2012, 80, 842–849. [Google Scholar] [CrossRef] [Green Version]
  3. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; American Psychiatric Association Press: Washington, DC, USA, 2000. [Google Scholar]
  4. Swannell, S.V.; Martin, G.; Page, A.; Hasking, P.; John, N.J.S. Prevalence of Nonsuicidal Self-Injury in Nonclinical Samples: Systematic Review, Meta-Analysis and Meta-Regression. Suicide Life-Threat. Behav. 2014, 44, 273–303. [Google Scholar] [CrossRef] [PubMed]
  5. Lang, J.; Yao, Y. Prevalence of nonsuicidal self-injury in chinese middle school and high school students. Medicine 2018, 97, e12916. [Google Scholar] [CrossRef]
  6. Hawton, K.; Saunders, K.E.A.; O’Connor, R.C. Self-harm and suicide in adolescents. Lancet 2012, 379, 2373–2382. [Google Scholar] [CrossRef]
  7. World Health Organization. Health Promotion Glossary; World Health Organization: Geneva, Switzerland, 1998; Available online: https://www.who.int/healthpromotion/about/HPR%20Glossary%201998.pdf?ua (accessed on 31 August 2020).
  8. Ji, C.Y. Adolescent health risk behavior monitoring: An important frontier of school health work. Chin. J. Sch. Health 2009, 30, 99–105, (In Chinese with English abstract). [Google Scholar]
  9. Li, D.; Yang, R.; Wan, Y.; Tao, F.; Fang, J.; Zhang, S. Interaction of health literacy and Problematic Mobile Phone Use and Their Impact on Non-Suicidal Self-Injury among Chinese Adolescents. Int. J. Environ. Res. Public Health 2019, 16, 2366. [Google Scholar] [CrossRef] [Green Version]
  10. Throuvala, M.A.; Griffiths, M.D.; Rennoldson, M.; Kuss, D.J. School-based Prevention for Adolescent Internet Addiction: Prevention is the Key. A Systematic Literature Review. Curr. Neuropharmacol. 2019, 17, 507–525. [Google Scholar] [CrossRef]
  11. Mayne, S.L.; Virudachalam, S.; Fiks, A.G. Clustering of unhealthy behaviors in a nationally representative sample of U.S. children and adolescents. Prev. Med. 2020, 130, 105892. [Google Scholar] [CrossRef]
  12. Wu, J.; Liu, H. Features of Nonsuicidal Self-Injury and Relationships with Coping Methods among College Students. Iran. J. Public Health 2019, 48, 270–277. [Google Scholar] [CrossRef]
  13. Martiniuk, A.; Chen, H.-Y.; Glozier, N.; Patton, G.; Senserrick, T.; Williamson, A.; Woodward, M.; Ivers, R. High alcohol use a strong and significant risk factor for repetitive self-harm in female and male youth: A prospective cohort study. Am. J. Drug Alcohol Abus. 2015, 41, 465–473. [Google Scholar] [CrossRef]
  14. Nagata, J.M.; Garber, A.K.; Tabler, J.; Murray, S.B.; Bibbins-Domingo, K. Differential Risk Factors for Unhealthy Weight Control Behaviors by Sex and Weight Status Among, U.S. Adolescents. J. Adolesc. Health 2018, 63, 335–341. [Google Scholar] [CrossRef] [PubMed]
  15. Tuisku, V.; Pelkonen, M.; Kiviruusu, O.; Karlsson, L.; Marttunen, M. Alcohol use and psychiatric comorbid disorders predict deliberate self-harm behaviour and other suicidality among depressed adolescent outpatients in 1-year follow-up. Nord. J. Psychiatry 2011, 66, 268–275. [Google Scholar] [CrossRef] [PubMed]
  16. Tozzi, F.; Thornton, L.M.; Mitchell, J.; Fichter, M.M.; Klump, K.L.; Lilenfeld, L.R.; Reba, L.; Strober, M.; Kaye, W.H.; Bulik, C.M. Features Associated With Laxative Abuse in Individuals With Eating Disorders. Psychosom. Med. 2006, 68, 470–477. [Google Scholar] [CrossRef]
  17. Çimen, İ.D.; Coşkun, A.; Etiler, N. Non-suicidal self-injury behaviors’ features and relationship with adolescents’ daily life activities and mental status. Turk. J. Pediatrics 2017, 59, 113–121. [Google Scholar] [CrossRef] [Green Version]
  18. Fu, J.L.; Wan, Y.H.; Sun, Y.; Tao, S.M.; Zu, P.; An, J.; Wang, X.; Tao, F.B. Relation of screen time and psychological sub-health to self-harm behavior in adolescents. Chin. Ment. Health J. 2013, 27, 468–472, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  19. Choi, S.H.; Duffy, S.A. Analysis of health behavior theories for clustering of health behaviors. J. Addict. Nurs. 2017, 28, 203–209. [Google Scholar] [CrossRef] [PubMed]
  20. Krokstad, S.; Ding, D.; Grunseit, A.C.; Sund, E.R.; Holmen, T.L.; Rangul, V.; Bauman, A. Multiple lifestyle behaviours and mortality, findings from a large population-based Norwegian cohort study—The HUNT Study. BMC Public Health 2017, 17, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Prochaska, J.J.; Prochaska, J.O. A review of multiple health behavior change interventions for primary prevention. Am. J. Lifestyle Med. 2011, 5, 208–221. [Google Scholar] [CrossRef]
  22. Loef, M.; Walach, H. The combined effects of healthy lifestyle behaviors on all cause mortality: A systematic review and meta-analysis. Prev. Med. 2012, 55, 163–170. [Google Scholar] [CrossRef]
  23. Ye, Y.-L.; Wang, P.-G.; Qu, G.-C.; Yuan, S.; Phongsavan, P.; He, Q.-Q. Associations between multiple health risk behaviors and mental health among Chinese college students. Psychol. Health Med. 2015, 21, 377–385. [Google Scholar] [CrossRef]
  24. Dodd, L.J.; Al-Nakeeb, Y.; Nevill, A.; Forshaw, M.J. Lifestyle risk factors of students: A cluster analytical approach. Prev. Med. 2010, 51, 73–77. [Google Scholar] [CrossRef]
  25. Kwan, M.; Arbour-Nicitopoulos, K.P.; Duku, E.; Faulkner, G. Patterns of multiple health risk–behaviours in university students and their association with mental health: Application of latent class analysis. Health Promot. Chronic Dis. Prev. Can. 2016, 36, 163–170. [Google Scholar] [CrossRef] [Green Version]
  26. Hagenaars, J.A. Applied Latent Class Analysis; McCutcheons, A.L., Ed.; Cambridge University Press: Cambridge, UK, 2002. [Google Scholar]
  27. Jessor, R. Risk behavior in adolescence: A psychosocial framework for understanding and action. J. Adolesc. Health 1991, 12, 597–605. [Google Scholar] [CrossRef]
  28. Elwood, M. Critical Appraisal of Epidemiological Studies and Clinical Trials; Oxford University Press: New York, NY, USA, 2007. [Google Scholar]
  29. Lv, J.; He, P.P.; Tu, W.X.; Li, L.M. Estimation of sampling error on data from cluster sample survey. Zhonghua Liu Xing Bing Xue Za Zhi 2008, 29, 78–80. [Google Scholar]
  30. Wan, Y.H.; Liu, W.; Hao, J.H.; Tao, F.B. Development and evaluation on reliability and validity of adolescent non-suicidal self-injury assessment questionnaire. Chin. J. Sch. Health 2018, 39, 170–173, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  31. Kann, L.; McManus, T.; Harris, W.A.; Shanklin, S.L.; Flint, K.H.; Queen, B.; Lowry, R.; Chyen, D.; Whittle, L.; Thornton, J.; et al. Youth Risk Behavior Surveillance—United States, 2017. MMWR Surveill. Summ. 2018, 67, 1–114. [Google Scholar] [CrossRef] [PubMed]
  32. Eaton, D.K.; Kann, L.; Kinchen, S.; Shanklin, S.; Ross, J.; Hawkins, J.; Harris, W.A.; Lowry, R.; McManus, T.; Chyen, D.; et al. Youth risk behavior surveillanc, United States, 2009. MMWR Surveill. Summ. 2010, 59, 1–142. [Google Scholar] [PubMed]
  33. Brener, N.D.; Billy, J.O.; Grady, W.R. Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: Evidence from the scientific literature. J. Adolesc. Health 2003, 33, 436–457. [Google Scholar] [CrossRef] [Green Version]
  34. American Academy of Pediatries. Committee on public education. American Academy of Pediatrics: Children, adolescents, and television. Pediatrics 2001, 107, 423–426. [Google Scholar] [CrossRef] [Green Version]
  35. Wu, X.; Tao, S.; Zhang, Y.; Zhang, S.; Tao, F. Low Physical Activity and High Screen Time Can Increase the Risks of Mental Health Problems and Poor Sleep Quality among Chinese College Students. PLoS ONE 2015, 10, e0119607. [Google Scholar] [CrossRef] [Green Version]
  36. Cao, H.; Qian, Q.; Weng, T.; Yuan, C.; Sun, Y.; Wang, H.; Tao, F. Screen time, physical activity and mental health among urban adolescents in China. Prev. Med. 2011, 53, 316–320. [Google Scholar] [CrossRef] [PubMed]
  37. Tao, S.M.; Fu, J.L.; Wang, H.; Hao, J.H.; Tao, F.B. Development of self-rating questionnaire for adolescent problematic mobile phone use and the psychometric evaluation in undergraduates. Chin. J. Sch. Health 2013, 34, 26–29, (In Chinese with English abstract). [Google Scholar] [CrossRef]
  38. Tao, S.; Wu, X.; Zhang, Y.; Zhang, S.; Tong, S.; Tao, F. Effects of sleep quality on the association between problematic mobile phone use and mental health symptoms in Chinese college students. Int. J. Environ. Res. Public Health 2017, 14, 185. [Google Scholar] [CrossRef] [Green Version]
  39. Tao, S.; Wu, X.; Wan, Y.; Zhang, S.; Hao, J.; Tao, F. Interactions of problematic mobile phone use and psychopathological symptoms with unintentional injuries: A school-based sample of Chinese adolescents. BMC Public Health 2015, 16, 88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Muthén, L.K.; Muthén, B.O. Mplus User’s Guide, 7th ed.; Muthén & Muthén: Los Angeles, CA, USA, 2012; Available online: https://www.statmodel.com/download/usersguide/Mplus%20user%20guide%20Ver_7_r3_web.pdf (accessed on 15 March 2021).
  41. Carragher, N.; Adamson, G.; Bunting, B.; McCann, S. Subtypes of depression in a nationally representative sample. J. Affect. Disord. 2009, 113, 88–99. [Google Scholar] [CrossRef]
  42. Nagin, D. Group-Based Modeling of Development; Harvard University Press: London, UK, 2005. [Google Scholar]
  43. Lim, K.-S.; Wong, C.H.; McIntyre, R.S.; Wang, J.; Zhang, Z.; Tran, B.X.; Tan, W.; Ho, C.S.; Ho, R.C. Global Lifetime and 12-Month Prevalence of Suicidal Behavior, Deliberate Self-Harm and Non-Suicidal Self-Injury in Children and Adolescents between 1989 and 2018: A Meta-Analysis. Int. J. Environ. Res. Public Health 2019, 16, 4581. [Google Scholar] [CrossRef] [Green Version]
  44. Kidger, J.; Heron, J.; Lewis, G.; Evans, J.; Gunnell, D. Adolescent self-harm and suicidal thoughts in the ALSPAC cohort: A self-report survey in England. BMC Psychiatry 2012, 12, 69. [Google Scholar] [CrossRef] [Green Version]
  45. Chan, S.; Denny, S.; Fleming, T.; Fortune, S.; Peiris-John, R.; Dyson, B. Exposure to suicide behaviour and individual risk of self-harm: Findings from a nationally representative New Zealand high school survey. Aust. New Zealand J. Psychiatry 2017, 52, 349–356. [Google Scholar] [CrossRef] [PubMed]
  46. Ceylan, M.F.; Hesapcioglu, S.T.; Kasak, M.; Yavas, C.P. High prevalence of nonsuicidal self-injury, tattoos, and psychiatric comorbidity among male adolescent prisoners and their sociodemographic characteristics. Asian J. Psychiatry 2019, 43, 45–49. [Google Scholar] [CrossRef] [PubMed]
  47. Ömer, B.; Bildik, T.; Kabukçu-Başay, B.; Güngör, D.; Başay, Ö.; Farmer, R.F. Prevalence of non-suicidal self-injury and distinct groups of self-injurers in a community sample of adolescents. Soc. Psychiatry Psychiatr. Epidemiol. 2015, 50, 1163–1171. [Google Scholar] [CrossRef]
  48. Tørmoen, A.J.; Myhre, M.; Walby, F.A.; Grøholt, B.; Rossow, I. Change in prevalence of self-harm from 2002 to 2018 among Norwegian adolescents. Eur. J. Public. Health 2020, 30, 688–692. [Google Scholar] [CrossRef]
  49. Barrocas, A.L.; Giletta, M.; Hankin, B.L.; Prinstein, M.; Abela, J.R.Z. Nonsuicidal Self-Injury in Adolescence: Longitudinal Course, Trajectories, and Intrapersonal Predictors. J. Abnorm. Child Psychol. 2014, 43, 369–380. [Google Scholar] [CrossRef]
  50. Whitlock, J.; Muehlenkamp, J.; Purington, A.; Eckenrode, J.; Barreira, P.; Abrams, G.B.; Marchell, T.; Kress, V.; Girard, K.; Chin, C.; et al. Nonsuicidal Self-injury in a College Population: General Trends and Sex Differences. J. Am. Coll. Health 2011, 59, 691–698. [Google Scholar] [CrossRef] [PubMed]
  51. Nock, M.K. Self-Injury. Annu. Rev. Clin. Psychol. 2010, 6, 339–363. [Google Scholar] [CrossRef]
  52. Wan, Y.H.; Hu, C.-L.; Hao, J.-H.; Sun, Y.; Tao, F.-B. Deliberate self-harm behaviors in Chinese adolescents and young adults. Eur. Child Adolesc. Psychiatry 2011, 20, 517–525. [Google Scholar] [CrossRef] [PubMed]
  53. Steinberg, L.; Albert, D.; Cauffman, E.; Banich, M.; Graham, S.; Woolard, J. Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Dev. Psychol. 2008, 44, 1764–1778. [Google Scholar] [CrossRef] [Green Version]
  54. Markus, H.R.; Kitayama, S. Culture and the self: Implications for cognition, emotion, and motivation. Psychol. Rev. 1991, 98, 224–253. [Google Scholar] [CrossRef]
  55. Meader, N.; King, K.; Moe-Byrne, T.; Wright, K.; Graham, H.; Petticrew, M.; Power, C.; White, M.; Sowden, A.J. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health 2016, 16, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Luo, J.; Agley, J.; Hendryx, M.; Gassman, R.; Lohrmann, D. Risk Patterns Among College Youth. Health Promot. Pract. 2014, 16, 132–141. [Google Scholar] [CrossRef] [PubMed]
  57. Stephen, E.M.; Rose, J.S.; Kenney, L.; Rosselli-Navarra, F.; Weissman, R.S. Prevalence and correlates of unhealthy weight control behaviors: Findings from the national longitudinal study of adolescent health. J. Eat. Disord. 2014, 2, 16. [Google Scholar] [CrossRef] [Green Version]
  58. Neumark-Sztainer, D.; Story, M.; French, S.A. Covariations of Unhealthy Weight Loss Behaviors and Other High-Risk Behaviors Among Adolescents. Arch. Pediatrics Adolesc. Med. 1996, 150, 304–308. [Google Scholar] [CrossRef] [PubMed]
  59. Herpertz, S.C.; Mueller, B.; Qunaibi, M.; Lichterfeld, C.; Konrad, K.; Herpertz-Dahlmann, B. Response to Emotional Stimuli in Boys With Conduct Disorder. Am. J. Psychiatry 2005, 162, 1100–1107. [Google Scholar] [CrossRef] [PubMed]
  60. You, J.; Leung, F. The role of depressive symptoms, family invalidation and behavioral impulsivity in the occurrence and repetition of non-suicidal self-injury in Chinese adolescents: A 2-year follow-up study. J. Adolesc. 2012, 35, 389–395. [Google Scholar] [CrossRef]
  61. Spring, B.J.; Doran, N.; Pagoto, S.L.; Schneider, K.L.; Pingitore, R.; Hedeker, D. Randomized Controlled Trial for Behavioral Smoking and Weight Control Treatment: Effect of Concurrent Versus Sequential Intervention. J. Consult. Clin. Psychol. 2004, 72, 785–796. [Google Scholar] [CrossRef] [Green Version]
  62. Joseph, A.M.; Willenbring, M.L.; Nugent, S.M.; Nelson, D.B. A randomized trial of concurrent versus delayed smoking intervention for patients in alcohol dependence treatment. J. Stud. Alcohol. 2004, 65, 681–691. [Google Scholar] [CrossRef] [PubMed]
  63. Vandelanotte, C.; De Bourdeaudhuij, I.; Sallis, J.F.; Spittaels, H.; Brug, J. Efficacy of sequential or simultaneous interactive computer-tailored interventions for increasing physical activity and decreasing fat intake. Ann. Behav. Med. 2005, 29, 138–146. [Google Scholar] [CrossRef] [PubMed]
  64. Hyman, D.J.; Pavlik, V.N.; Taylor, W.C.; Goodrick, G.K.; Moye, L. Simultaneous vs Sequential Counseling for Multiple Behavior Change. Arch. Intern. Med. 2007, 167, 1152–1158. [Google Scholar] [CrossRef] [PubMed]
  65. Edington, D.W. Emerging research: A view from one research center. Am. J. Health Promot. 2001, 15, 341–349. [Google Scholar] [CrossRef] [PubMed]
  66. Adlaf, E.M.; Demers, A.; Gliksman, L. Canadian Campus Survey 2004; Centre for Addiction and Mental Health: Toronto, ON, Canada, 2005. [Google Scholar]
Traumacare 01 00008 g001 550
Figure 1. Four classes of unhealthy behaviors of the best-fitting four-class pattern. Note. ULW, unhealthy weight loss; TU, tobacco use; AU, alcohol use; ST, screen time; MPD, mobile phone dependence.
Figure 1. Four classes of unhealthy behaviors of the best-fitting four-class pattern. Note. ULW, unhealthy weight loss; TU, tobacco use; AU, alcohol use; ST, screen time; MPD, mobile phone dependence.
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Traumacare 01 00008 g002 550
Figure 2. Adjusted odds ratios and 95% confidence intervals for the self-harm with latent subgroups of unhealthy behaviors. ★ latent subgroups; | gender; ✱ grade; × registered residence; ○ accommodation type; ◇ father’s educational level; ▽ mother’s educational level; ▲ self-reported family economic status; ● the number of friends. Multinomial logistical regression was used. *** p < 0.001, ** p < 0.01.
Figure 2. Adjusted odds ratios and 95% confidence intervals for the self-harm with latent subgroups of unhealthy behaviors. ★ latent subgroups; | gender; ✱ grade; × registered residence; ○ accommodation type; ◇ father’s educational level; ▽ mother’s educational level; ▲ self-reported family economic status; ● the number of friends. Multinomial logistical regression was used. *** p < 0.001, ** p < 0.01.
Traumacare 01 00008 g002
Table
Table 1. Frequency characteristics of self-harm in Chinese adolescent (%).
Table 1. Frequency characteristics of self-harm in Chinese adolescent (%).
VariableHitPull HairBang HeadPinch or ScratchBitCutFireToxic SubstanceTotal
Gender
 Male1687 (15.4)1558 (14.2)2876 (26.2)1060 (9.6)636 (5.8)555 (5.1)562 (5.1)203 (1.8)3871 (35.2)
 Female1516 (13.0)1064 (9.1)1634 (14.0)1650 (14.2)948 (8.1)796 (6.8)374 (3.2)93 (0.8)3390 (29.1)
χ225.124139.819521.060110.15348.30232.24651.46540.08996.332
p<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
Grade
Middle school1888 (15.7)1557 (13.0)2516 (21.0)1609 (13.4)962 (8.0)864 (7.2)456 (3.8)192 (1.6)4132 (34.5)
High school1315 (12.4)1065 (10.0)1994 (18.7)1101 (10.4)622 (5.8)487 (4.6)480 (4.5)104 (1.0)3129 (29.4)
χ252.92148.48217.55650.18240.87369.1837.18916.94865.487
p<0.001<0.001<0.001<0.001<0.001<0.0010.007<0.001<0.001
Registered residence
Rural1629 (15.0)1372 (12.6)2264 (20.8)1385 (12.7)847 (7.8)660 (6.1)486 (4.5)149 (1.4)3672 (33.7)
Urban1574 (13.4)1250 (10.6)2246 (19.1)1325 (11.3)737 (6.3)691 (5.9)450 (3.8)147 (1.3)3589 (30.6)
χ211.44921.31310.03311.21919.7580.3345.7440.60726.357
p0.001<0.0010.0020.001<0.0010.5630.0170.436<0.001
Accommodation type
Boarding student1660 (14.7)1353 (12.0)2340 (20.7)1428 (12.6)791 (7.0)596 (5.3)486 (4.3)132 (1.2)3725 (32.9)
Commuting student1543 (13.6)1269 (11.2)2170 (19.2)1282 (11.3)793 (7.0)755 (6.7)450 (4.0)164 (1.5)3536 (31.3)
χ24.8352.9447.7798.7610.00520.0811.4053.5406.952
p0.0280.0860.0050.0030.941<0.0010.2360.0600.008
Father’s educational level a
<High school degree1836 (14.1)1539 (11.8)2665 (20.5)1558 (12.0)926 (7.1)752 (5.8)522 (4.0)159 (1.2)4271 (32.8)
≥High school degree1324 (14.0)1052 (11.2)1795 (19.0)1112 (11.8)633 (6.7)572 (6.1)389 (4.1)125 (1.3)2918 (31.0)
χ20.0202.4017.1470.1681.3720.8140.1830.4728.823
p0.8860.1210.0080.6820.2420.3670.6690.4920.003
Mother’s educational level b
<High school degree2020 (14.1)1713 (11.9)2909 (20.3)1749 (12.2)1044 (7.3)826 (5.8)584 (4.1)171 (1.2)4696 (32.8)
≥High school degree1136 (14.0)868 (10.7)1544 (19.0)933 (11.5)516 (6.4)498 (6.1)335 (4.1)115 (1.4)2491 (30.7)
χ20.0247.8255.0292.3396.7221.3620.0462.1029.751
p0.8760.0050.0250.1260.0100.2430.8300.1470.002
Self-reported family economy
Bad628 (19.4)510 (15.7)802 (24.8)524 (16.2)296 (9.1)230 (7.1)169 (5.2)63 (1.9)1235 (38.1)
General2107 (12.9)1751 (10.7)3054 (18.7)1816 (11.1)1062 (6.5)887 (5.4)587 (3.6)174 (1.1)5029 (30.8)
Good468 (15.4)361 (11.9)654 (21.5)370 (12.2)226 (7.4)234 (7.7)180 (5.9)59 (1.9)997 (32.8)
χ298.12266.98767.76665.84729.89731.97846.05327.05167.759
p<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001<0.001
Number of friends
≤2965 (17.5)735 (13.3)1218 (22.1)845 (15.3)473 (8.6)401 (7.3)251 (4.6)84 (1.5)1954 (35.4)
3–51278 (13.3)1042 (10.8)1808 (18.8)1099 (11.4)656 (6.8)517 (5.4)352 (3.7)96 (1.0)2998 (31.2)
≥6960 (12.8)845 (11.3)1484 (19.8)766 (10.2)455 (6.1)433 (5.8)333 (4.4)116 (1.5)2309 (30.8)
χ267.94822.41323.96283.31331.50223.2359.71312.48737.753
p<0.001<0.001<0.001<0.001<0.001<0.0010.0080.002<0.001
Statistical methods: Chi-square test. a 198 students had no information about their father; b 188 students had no information about their mother.
Table
Table 2. Model fit statistics for each of the fitted latent class analysis models.
Table 2. Model fit statistics for each of the fitted latent class analysis models.
Statistic 2345
df11172329
AIC77,820.87077,531.14677,470.02877,473.069
BIC77,909.16677667.60477,654.64777,705.851
aBIC77,874.20977,613.57877,581.55477,613.690
LMR-LRT<0.0001<0.0001<0.00010.0822
BLRT<0.0001<0.0001<0.00010.1935
Entropy0.5810.7800.7280.717
Classification probability0.16325
0.83675		0.21937
0.06633
0.71429		0.03301
0.71208
0.03151
0.22340		0.09475
0.01039
0.02417
0.83905
0.03164
Note. df, degrees of freedom; AIC, Akaike information criteria; BIC, Bayesian information criteria; aBIC, adjusted Bayesian information criteria; LMR-LRT, Lo–Mendell–Rubin likelihood ratio; BLRT, bootstrapped likelihood ratio test. Bold: Represents the selected model.
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Yang, R.; Li, D.; Tian, R.; Hu, J.; Xue, Y.; Huang, X.; Wan, Y.; Fang, J.; Zhang, S. Association of Unhealthy Behaviors with Self-Harm in Chinese Adolescents: A Study Using Latent Class Analysis. Trauma Care 2021, 1, 75-86. https://doi.org/10.3390/traumacare1020008

AMA Style

Yang R, Li D, Tian R, Hu J, Xue Y, Huang X, Wan Y, Fang J, Zhang S. Association of Unhealthy Behaviors with Self-Harm in Chinese Adolescents: A Study Using Latent Class Analysis. Trauma Care. 2021; 1(2):75-86. https://doi.org/10.3390/traumacare1020008

Chicago/Turabian Style

Yang, Rong, Danlin Li, Run Tian, Jie Hu, Yanni Xue, Xuexue Huang, Yuhui Wan, Jun Fang, and Shichen Zhang. 2021. "Association of Unhealthy Behaviors with Self-Harm in Chinese Adolescents: A Study Using Latent Class Analysis" Trauma Care 1, no. 2: 75-86. https://doi.org/10.3390/traumacare1020008

APA Style

Yang, R., Li, D., Tian, R., Hu, J., Xue, Y., Huang, X., Wan, Y., Fang, J., & Zhang, S. (2021). Association of Unhealthy Behaviors with Self-Harm in Chinese Adolescents: A Study Using Latent Class Analysis. Trauma Care, 1(2), 75-86. https://doi.org/10.3390/traumacare1020008

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