Injury Risk and Influencing Factors of Substance Use and Problematic Internet Use Among Emerging Adults in Beijing, China.

doi:10.21203/rs.3.rs-4934481/v1

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Research Article

Injury Risk and Influencing Factors of Substance Use and Problematic Internet Use Among Emerging Adults in Beijing, China.

https://doi.org/10.21203/rs.3.rs-4934481/v1

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Background

This study aimed to explore the impact of substance use (SU), problematic internet use (PIU), and their comorbidity on unintentional and intentional injuries among emerging adults in Beijing.

Methods

This cross-sectional study targeted emerging adults from two universities (undergraduates and postgraduates) in Beijing to ensure comprehension ability and voluntary participation. Commencing in July 2023, an on-site survey employed an anonymous electronic questionnaire accessible through a QR code. Descriptive measures, analysis of variance (ANOVA), chi-square tests, and logistic regression were used for statistical analysis. Logistic regression analyses were used to explore the risk of injury in different populations and to further analyze the factors influencing injury in different populations.

Results

Among the 1629 participants, substance use was categorized into 1045 low-risk (64.15%), 525 medium-risk (32.22%), and 59 (3.62%) high-risk users. For problematic internet use, participants were divided into non-PIU (n = 777, 47.70%), mild-moderate PIU (n = 819, 50.28%), and severe PIU (n = 33, 2.02%) groups. The comorbid group had a significantly greater risk of intentional injuries, with an adjusted odds ratio (aOR) of 2.08 (95% CI: 1.44–3.01). Patients in the comorbid group demonstrated increased risks of non-suicidal self-injury (NSSI) and suicidal ideation. The comorbid group had the highest risk for NSSI (aOR = 6.75, 95% CI = 3.15–14.48) and suicidal ideation (aOR = 3.49, 95% CI = 2.54–4.81). The demographic, psychological and social factors associated with injury risk varied among substance use, problematic internet use, and comorbid groups.

Conclusion

This study unveiled the prevalence of substance use and problematic internet use, each manifesting distinct risk profiles, among individuals in the emerging adulthood stage. Both substance use and problematic internet use independently contributed to increased risks of non-suicidal self-injury and suicidal ideation. Notably, the comorbid group faced the highest vulnerability, emphasizing the compounding effect of these behaviors. Improving the mental health of young people, the quality of their sleep, appropriate stress release and the control of bullying in schools may play a significant role in the occurrence of intentional injuries. Furthermore, it is even more important to intervene with emerging adulthood who are younger, live in urban areas, have neurotic personalities and female groups.

emerging adulthood

substance use

problematic internet use

unintentional injuries

intentional injuries

Emerging adulthood is defined as a distinct time period between the ages of 18 and 25 where individuals transition from adolescence to young adulthood¹. Globally, emerging adults are commonly challenged by substance use (SU) and problematic internet use (PIU). Substance use was defined as undesirable behaviors such as drug abuse, alcoholism, or smoking. Problematic Internet use is defined as excessive Internet use that adversely affects an individual's mental health, social adjustment, learning, and work and is also referred to as Internet addiction, pathological Internet use, and cyber addiction, respectively². These two are not only behaviorally interconnected, but are also closely linked to injury conditions³.

Emerging adults commonly face serious issues during Substance Use (SU) and Problematic Internet Use (PIU), often resulting in physical injuries. Studies have shown that emerging adults tend to lose their judgment of danger and risk and are prone to impulsive and aggressive behaviors. This leads to injuries such as traffic accidents⁴ and violent behavior⁵. Moreover, problematic internet use might lead to decreased interpersonal skills and increased risk of schoolwork and cyberbullying^{6, 7}. This practice has had negative impacts on study and life, as well as depression, anxiety, and other mental health problems⁸. Furthermore, emerging adults were more likely to be at risk for injuries when faced with both SU and PIU^{9, 10}.

However, there is still a relative dearth of research in China on the integrated relationship between SU, PIU, and injury among emerging adulthood. Stoddard et al. examined the relationship between SU and violence¹¹, and Lam et al. focused on whether PIU increased the risk of non-suicidal self-injury (NSSI)¹². Existing studies have tended to focus on the independent relationship between SU or PIU and particular injuries, without fully considering the comorbid relationship and multiple injuries. Therefore, this study aimed to explore the effects of SU and PIU on multiple injuries. The study also further analyzed the influencing factors on the population at high risk of injury. Through these findings, we could develop targeted interventions and prevention strategies to help individuals who are experiencing harm as a result of SU and PIU.

Participants and Procedures

A cross-sectional research design was chosen for this study, and the participants were enrolled in two general universities in Beijing, including undergraduate and postgraduate. The respondents were assured that they understood the survey information, participated voluntarily, and were excluded the suspended students (due to physical and mental illness) and foreign students. The on-site survey was scheduled to begin in July 2023. All test questions were completed before submission, thus ensuring the completeness of the survey respondents' information. The questionnaires were required to be completed within one week to ensure timely checking of questionnaire quality. The ethical review board of Capital Medical University approved this study.

Measurements

Participants were assessed for injuries, substance use, and problematic internet use. Other covariates included basic demographic characteristics, mental health status, personality traits, sleep quality, stress, peer bullying, and childhood trauma. We used these scales, which included the Self-Rating Injury Scale, Beck Scale for Suicide Ideation (BSI-CV), Car Relax Alone Forget Friends Trouble (CRAFFT) substance use scale, Internet Addiction Test for Chinese (IAT), Symptom Checklist 90 Chinese Version (SCL-90), Neuroticism Extroversion Five-Factor Inventory (NEO-FFI), Pittsburgh Sleep Quality Index (PSQI) questionnaire, Perceived Stress Scale (PSS-14), Delaware Bullying Victimization Scale (DBVS-S), and Childhood Trauma Questionnaire (CTQ).

Demographic Information

The demographic information included gender, age, body mass index (BMI), sexual orientation, sex identity, ethnicity, family income, mother's education, and father's education.

Self-Rating Injury Scale The Self-Rating Injury Scale was used to assess the occurrence of injuries. Injury was defined as (1) a visit to a medical institution for treatment and diagnosis of one or more injuries and (2) an injury caused by more than one day of suspension from school, rest, or limitation of activity. An injury was defined as either of these. Participants self-assessed the occurrence of injuries based on the above definitions. Injury was divided into two broad categories: unintentional injury and intentional injury. We took a step further by investigating the subcategories of intentional injuries, including non-suicidal self-injury, suicidal ideation, suicidal attempts, attempted suicide and interpersonal violence. Suicidal ideation was measured using the Beck Suicide Scale¹³. Unintentional and other intentional injuries were dichotomized according to whether they were experienced (yes/no).

CRAFFT When assessing substance abuse (alcohol, cannabis, and other illicit drugs, including illegal, over-the-counter and prescription drugs) and its consequences, we used the CRAFFT scale developed by Knight¹⁴. The CRAFFT questionnaire included into two parts, part A and part B. Next, ask the patient the three past-12-month frequency-of-use questions exactly as they are worded in Part A of the CRAFFT 2.1. If the patient answers “0” to all the opening frequency questions, ask the CAR question only. If the patient reports any days of use on any of the frequency questions, ask the full set of six CRAFFT questions. We analyzed substance use by categorizing participants into three groups: low, medium, and high risk. Low risk was defined as one that reports no use of alcohol or drugs in the past 12 months and answers no to the car question. Moderate risk could be met in two ways: no use of alcohol or drugs in the past 12 months and yes to the car question, or any use of alcohol or drugs in the past 12 months and CRAFFT score of 0 or 1. High risk was defined as any use of alcohol or drugs in the past 12 months and have a CRAFFT total score of 2 or more. Finally, the low-risk was classified as not having substance use group.

IAT We measured participants’ PIU using the Internet Addiction Test (IAT)¹⁵. The IAT consists of 20 items with a total score ranging from 20 to 100 and evaluates the impact of Internet use in daily life. The items are rated on a five-point Likert scale (from 1- not at all to 5- always). IAT scores between 20 and 49 reflect average online users, scores between 50 and 79 reflect problem users, and scores between 80 and 100 indicate significant internet use problems². In this study, we applied a cutoff for the PIU of 50 or more. The split-half reliability and Cronbach’s alpha (α) coefficient were 0.86 and 0.90, respectively.

SCL-90 The SCL-90 is usually used to assess mental health¹⁶. It comprises 90 items and 10 subscales to assess somatization, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoia, psychoticism, and additional status (mainly including sleep and eating conditions)¹⁷. Each item is scored on a five-point scale ranging from 0 (not at all) to 4 (extremely). Each item was assessed using a 1–5 Likert scale. The lower the score is, the better the mental health status is¹⁸.

NEO-FFI The NEO-FFI is a questionnaire that measures personality traits and comprises five subscales: neuroticism, extroversion, openness, agreeableness, and conscientiousness¹⁹. Each subscale included 12 items, and every item was scored based on a 5-point Likert scale ranging from 1 (strong disagreement) to 5 (strong agreement). The NEO-FFI provides a quick, reliable, and accurate measure of the five domains of adult (aged 17 years and older) personality²⁰.

PSQI Sleep quality was assessed using the standardized Pittsburgh Sleep Quality Index (PSQI) questionnaire²¹. This scale is a 19-item self-report questionnaire that includes seven subscales: sleep latency, sleep quality, habitual sleep efficiency, sleep duration, daytime dysfunction, use of sleeping medications, and sleep disturbances²¹. Each component was rated from 0 to 3, and the global score was computed by summing the scores of the seven subscales. The total score ranged from 0 to 21 points, and higher scores indicated poorer sleep quality.

PSS-14 The Perceived Stress Scale-14²² was used to measure the level of perceived stress. The PSS-14 is a 14-item measure of the degree to which individuals perceive situations as stressful and measures levels of distress and ability to cope²³. The higher the score is, the greater the perceived pressure.

DBVS-S This study used the Chinese version of the Delaware Bullying Victimization Scale to assess school bullying victimization in adolescents²⁴. The scale has a total of 17 items divided into four subscales—verbal, physical, social/relational, and cyberbullying victimization—of which the 13th item was not included in the data analysis as a screening item. The items of the scale are scored on a 6-point Likert scale, where 0 means "never", and 5 means "every day", with higher scores indicating more bullying. The use of the Bullying Scale has been validated in Chinese adolescents, and the Cronbach’s alpha in the original Chinese version was 0.91.

CTQ CTQ score was assessed using the Childhood Trauma Questionnaire (CTQ)²⁵. The CTQ is a 28-item self-reported assessment of traumatic events that an individual may or may not have experienced during his or her lifetime and of the frequency of those events. The measure yields a total score indicating the overall severity of childhood trauma and five subscales assessing emotional abuse, emotional neglect, physical abuse, physical neglect, and sexual abuse. Total CTQ scores ranged from 25, indicating no endorsement of childhood trauma, to 125, indicating severe levels of childhood trauma.

Data Analysis

We first estimated unadjusted frequencies for demographic characteristics, psychological and social factors, and injury situations in subsamples of individuals with low-risk SU, medium-risk SU, high-risk SU, non-PIU, mild-moderate PIU, and severe PIU. The population was combined into four groups: those with only substance use experience (SU group), only problematic Internet use (PIU group), both substance use experience and problematic Internet use (comorbid group), and those without substance use experience or problematic Internet use (control group). The risk of injury occurrence (unintentional injury, NSSI, suicidal ideation, attempted suicide, and interpersonal violence) was calculated for the SU, PIU, and comorbid group compared to the control population by performing logistic regression postadjusting for demographic variables (age, gender, BMI, ethnicity, household income and family status, and place of residence). In addition, we further conducted three separate sets of logistic regression analyses for SU, PIU, and comorbid populations to estimate aORs and 95% CIs for demographic, psychological and social and social factors associated with meaningful injury outcomes in these three samples. A two-tailed test with p < 0.05 was considered to indicate statistical significance. We used SPSS 22.0 and R 3.4.1 software for all the statistical analyses.

Demographic and Social Characteristics

The analyzed sample consisted of 1629 individuals (404 males and 1225 females) with a mean (SD) age of 20.31 (2.78) years. The demographic characteristics of the sample and between-group comparisons by substance use and problematic Internet use were presented in Tables 1 and 2, respectively. Substance use was categorized into 1045 low-risk (64.15%), 525 medium-risk (32.22%), and 59 high-risk (3.62%) users according to the CRAFFT assessment scale. Compared to the low-risk group, the individuals in the medium- and high-risk groups were more likely to have a higher mean age and higher SCL, be from an urban area and single-parent family (Table 1). In addition, participants were categorized into a non-PIU group (n = 777, 47.70%), a mild-moderate PIU group (n = 819, 50.28%), and a severe PIU group (n = 33, 2.02%). Compared with the non-PIU group, more PIU youth had differences in sexual orientation and gender identity, significantly poorer sleep quality, greater psychological symptoms and stress scores, and experience of childhood trauma. In terms of personality, individuals in the severe PIU group reported higher levels of neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness (Table 2). Regarding injury outcomes, the patients in the SU and PIU groups were more likely to have unintentional injuries, NSSI, suicidal ideation, or interpersonal violence than were those in the non-SU and non-PIU groups.

Table 1

Comparison of Basic Demographic Characteristics, Social and Psychological Factors, and Injuries of College Students with Substance Use Among Emerging Adults
Variable	Total	Low-Risk	Medium-Risk	High-Risk	P value
Variable	(n = 1629)	(n = 1045)	(n = 525)	(n = 59)	P value
Age, years (mean (SD))	20.31 (2.78)	20.04 (2.66)	20.76 (2.90)	21.20 (3.10)	< 0.001
BMI, kg/m² (mean (SD))	22.26 (5.48)	22.13 (5.53)	22.46 (5.48)	22.74 (4.41)	0.410
Pressure (mean (SD))	32.35 (9.67)	31.60 (9.67)	33.05 (9.30)	39.44 (9.52)	< 0.001
Personality trait (mean (SD))
Neuroticism	23.20 (5.40)	23.32 (5.41)	22.88 (5.32)	24.07 (5.94)	0.141
Extraversion	26.38 (5.52)	26.26 (5.51)	26.70 (5.53)	25.53 (5.61)	0.163
Openness to experiencer	21.75 (5.85)	21.50 (6.00)	22.21 (5.55)	22.07 (5.71)	0.070
Agreeableness	22.31 (4.79)	22.29 (4.85)	22.28 (4.63)	22.75 (5.28)	0.772
Conscientiousness	27.03 (5.11)	27.09 (5.10)	26.99 (5.13)	26.22 (5.18)	0.436
Sleep quality (mean (SD))	5.48 (3.68)	5.02 (3.50)	6.31 (3.84)	8.03 (3.93)	< 0.001
SCL-90 score (mean (SD))	121.54 (43.58)	118.76 (40.57)	123.32 (45.19)	154.98 (62.72)	< 0.001
Gender (%)					< 0.001
Male	404 (24.80)	221 (21.15)	166 (31.62)	17 (28.81)
Female	1225 (75.20)	824 (78.85)	359 (68.38)	42 (71.19)
Gender identity (%)					0.097
Cisgender	1471 (90.30)	952 (91.10)	470 (89.52)	49 (83.05)
Transgender	158 (9.70)	93 (8.90)	55 (10.48)	10 (16.95)
Sexual orientation (%)					0.469
Heterosexual	1269 (77.90)	823 (78.76)	404 (76.95)	42 (71.19)
Bisexual	193 (11.85)	118 (11.29)	64 (12.19)	11 (18.64)
Homosexuality	63 (3.87)	38 (3.64)	21 (4.00)	4 (6.78)
Asexual	104 (6.38)	66 (6.32)	36 (6.86)	2 (3.39)
Ethnic (%)					0.806
Han	1299 (79.74)	837 (80.10)	414 (78.86)	48 (81.36)
Minority	330 (20.26)	208 (19.90)	111 (21.14)	11 (18.64)
Monthly per capita family income, yuan (%)					0.127
<5000	621 (38.12)	424 (40.57)	177 (33.71)	20 (33.90)
5000–9999	562 (34.50)	362 (34.64)	182 (34.67)	18 (30.51)
10000–14999	235 (14.43)	139 (13.30)	84 (16.00)	12 (20.34)
15000–19999	107 (6.57)	64 (6.12)	40 (7.62)	3 (5.08)
20000–29999	45 (2.76)	25 (2.39)	18 (3.43)	2 (3.39)
≥ 30000	59 (3.62)	31 (2.97)	24 (4.57)	4 (6.78)
Residence (%)					0.0112
Urban	1059 (65.01)	661 (63.25)	350 (66.67)	48 (81.36)
Rural	570 (34.99)	384 (36.75)	175 (33.33)	11 (18.64)
Mother’s education level (%)					0.001
Master and above	78 (4.79)	43 (4.11)	28 (5.33)	7 (11.86)
College and bachelor	574 (35.24)	348 (33.30)	200 (38.10)	26 (44.07)
Senior high school	385 (23.63)	250 (23.92)	119 (22.67)	16 (27.12)
Junior high school	362 (22.22)	232 (22.20)	121 (23.05)	9 (15.25)
Elementary school and below	230 (14.12)	172 (16.46)	57 (10.86)	1 (1.69)
Father’s education level (%)					0.155
Master and above	109 (6.69)	62 (5.93)	43 (8.19)	4 (6.78)
College and bachelor	628 (38.55)	397 (37.99)	203 (38.67)	28 (47.46)
Senior high school	377 (23.14)	235 (22.49)	125 (23.81)	17 (28.81)
Junior high school	371 (22.77)	249 (23.83)	114 (21.71)	8 (13.56)
Elementary school and below	144 (8.84)	102 (9.76)	40 (7.62)	2 (3.39)
Family structure (%)					0.025
Native two-parent family	1450 (89.01)	945 (90.43)	456 (86.86)	49 (83.05)
Single-parent family	105 (6.45)	58 (5.55)	40 (7.62)	7 (11.86)
Reorganized family	60 (3.68)	33 (3.16)	26 (4.95)	1 (1.69)
Other	14 (0.86)	9 (0.86)	3 (0.57)	2 (3.39)
Peer bullying (%)					0.271
None	1323 (81.22)	858 (82.11)	421 (80.19)	44 (74.58)
Ever	306 (18.78)	187 (17.89)	104 (19.81)	15 (25.42)
Childhood trauma (%)					0.766
None	1145 (70.29)	735 (70.33)	371 (70.67)	39 (66.10)
Ever	484 (29.71)	310 (29.67)	154 (29.33)	20 (33.90)
Injury conditions (%)
Unintentional injury					0.003
None	1353 (83.06)	889 (85.07)	422 (80.38)	42 (71.19)
Ever	276 (16.94)	156 (14.93)	103 (19.62)	17 (28.81)
Non-suicidal self-injury					< 0.001
None	1538 (94.41)	998 (95.50)	494 (94.10)	46 (77.97)
Ever	91 (5.59)	47 (4.50)	31 (5.90)	13 (22.03)
Suicidal ideation					< 0.001
None	1135 (69.67)	759 (72.63)	353 (67.24)	23 (38.98)
Ever	494 (30.33)	286 (27.37)	172 (32.76)	36 (61.02)
Attempted suicide					0.001
None	1580 (96.99)	1022 (97.80)	506 (96.38)	52 (88.14)
Ever	49 (3.01)	23 (2.20)	19 (3.62)	7 (11.86)
Interpersonal violence					0.007
None	1601 (98.28)	1031 (98.66)	515 (98.10)	55 (93.22)
Ever	28 (1.72)	14 (1.34)	10 (1.90)	4 (6.78)

Table 2

Comparison of Basic Demographic Characteristics, Social and Psychological Factors, and Injuries of Problematic Internet Use (PIU) Among Emerging Adults
Variable	Non-PIU (n = 777)	Mild-moderate PIU (n = 819)	Severe PIU (n = 33)	P value
Age, mean (SD), y	20.50 (3.04)	20.12 (2.50)	20.61 (2.94)	0.021
BMI, mean (SD), kg/m²	22.12 (5.37)	22.34 (5.54)	23.46 (6.39)	0.319
Pressure, mean (SD)	29.33 (9.53)	34.74 (8.75)	44.06 (9.14)	< 0.001
Personality trait (mean (SD))
Neuroticism	21.38 (5.69)	24.76 (4.35)	27.45 (7.38)	< 0.001
Extraversion	26.52 (6.45)	26.13 (4.22)	29.03 (8.87)	0.007
Openness to experiencer	21.08 (6.49)	22.24 (4.91)	25.30 (8.57)	< 0.001
Agreeableness	21.53 (5.22)	22.87 (3.90)	26.52 (8.74)	< 0.001
Conscientiousness	27.24 (5.88)	26.72 (4.03)	29.58 (8.19)	0.002
Sleep quality (mean (SD))	4.92 (3.65)	5.93 (3.63)	7.12 (4.08)	< 0.001
SCL-90 score (mean (SD))	109.40 (34.31)	131.79 (46.34)	153.12 (76.75)	< 0.001
Gender (%)				0.379
Male	203 (26.13)	195 (23.81)	6 (18.18)
Female	574 (73.87)	624 (76.19)	27 (81.82)
Gender identity (%)				0.001
Cisgender	711 (91.51)	736 (89.87)	24 (72.73)
Transgender	66 (8.49)	83 (10.13)	9 (27.27)
Sexual orientation (%)				< 0.001
Heterosexual	653 (84.04)	600 (73.26)	16 (48.48)
Bisexual	68 (8.75)	115 (14.04)	10 (30.30)
Homosexuality	22 (2.83)	40 (4.88)	1 (3.03)
Asexual	34 (4.38)	64 (7.81)	6 (18.18)
Ethnic (%)				0.339
Han	629 (80.95)	642 (78.39)	28 (84.85)
Minority	148 (19.05)	177 (21.61)	5 (15.15)
Monthly per capita family income, yuan (%)				0.334
<5000	271 (34.88)	335 (40.90)	15 (45.45)
5000–9999	276 (35.52)	277 (33.82)	9 (27.27)
10000–14999	114 (14.67)	116 (14.16)	5 (15.15)
15000–19999	60 (7.72)	45 (5.49)	2 (6.06)
20000–29999	25 (3.22)	20 (2.44)	0 (0.00)
≥ 30000	31 (3.99)	26 (3.17)	2 (6.06)
Residence (%)				0.183
Urban	519 (66.80)	516 (63.00)	24 (72.73)
Rural	258 (33.20)	303 (37.00)	9 (27.27)
Mother’s education level (%)				0.049
Master and above	37 (4.76)	38 (4.64)	3 (9.09)
College and bachelor	280 (36.04)	284 (34.68)	10 (30.30)
Senior high school	200 (25.74)	172 (21.00)	13 (39.39)
Junior high school	161 (20.72)	197 (24.05)	4 (12.12)
Elementary school and below	99 (12.74)	128 (15.63)	3 (9.09)
Father’s education level (%)				0.507
Master and above	55 (7.08)	51 (6.23)	3 (9.09)
College and bachelor	307 (39.51)	308 (37.61)	13 (39.39)
Senior high School	190 (24.45)	179 (21.86)	8 (24.24)
Junior high school	167 (21.49)	197 (24.05)	7 (21.21)
Elementary school and below	58 (7.46)	84 (10.26)	2 (6.06)
Family structure				0.098
Native two-parent family	698 (89.83)	726 (88.64)	26 (78.79)
Single-parent family	52 (6.69)	50 (6.11)	3 (9.09)
Reorganized family	21 (2.70)	35 (4.27)	4 (12.12)
Other	6 (0.77)	8 (0.98)	0 (0.00)
Peer bullying (%)				0.084
No	644 (82.88)	656 (80.10)	23 (69.70)
Yes	133 (17.12)	163 (19.90)	10 (30.30)
Childhood trauma (%)				< 0.001
No	585 (75.29)	545 (66.54)	15 (45.45)
Yes	192 (24.71)	274 (33.46)	18 (54.55)
Injury conditions (%)
Unintentional injury				0.002
No	666 (85.71)	665 (81.20)	22 (66.67)
Yes	111 (14.29)	154 (18.80)	11 (33.33)
Non-suicidal self-injury				< 0.001
No	756 (97.30)	755 (92.19)	27 (81.82)
Yes	21 (2.70)	64 (7.81)	6 (18.18)
Suicidal ideation				< 0.001
No	610 (78.51)	510 (62.27)	15 (45.45)
Yes	167 (21.49)	309 (37.73)	18 (54.55)
Attempted suicide				0.007
No	753 (96.91)	798 (97.44)	29 (87.88)
Yes	24 (3.09)	21 (2.56)	4 (12.12)
Interpersonal violence				0.004
No	765 (98.46)	806 (98.41)	30 (90.91)
Yes	12 (1.54)	13 (1.59)	3 (9.09)

Unintentional and Intentional Injury Risk

The results of the study were shown in Table 3. The SU group and PIU group demonstrated a significantly increased risk of NSSI (aOR = 3.04, 95% CI = 1.24–7.42; aOR = 4.51, 95% CI = 2.14–9.48). In terms of suicidal ideation, the SU group had an increased risk of suicidal ideation (aOR = 1.76, 95% CI = 1.22–2.54), and there was also a significant association between the problematic Internet use group and suicidal ideation (aOR = 2.28, 95% CI = 1.71–3.05). The comorbid group had a greater risk of NSSI and suicidal ideation (aOR = 6.75, 95% CI = 3.15–14.48; aOR = 3.49, 95% CI = 2.54–4.81). For unintentional injuries, which were statistically significant only in the comorbid group, the adjusted odds ratio (aOR) and 95% confidence interval (CI) were 2.08 (1.44, 3.01). However, in terms of interpersonal violence and suicide attempts, no significant differences were found between the groups.

Table 3

Risk of five Injury Outcomes for SU, PIU, and Comorbid Status Compared to Control Group
Dependent variable	SU	PIU	Comorbid
Dependent variable	aOR (95% CI)	aOR (95% CI)	aOR (95% CI)
Number of participants	258	526	326
Unintentional injury	1.37 (0.90,2.09)	1.28 (0.91,1.81)	2.08 (1.44,3.01)
Non-suicidal self-injury	3.04 (1.24,7.42)	4.51 (2.14,9.48)	6.75 (3.15,14.48)
Suicidal ideation	1.76 (1.22,2.54)	2.28 (1.71,3.05)	3.49 (2.54,4.81)
Attempted suicide	2.09 (0.90,4.83)	0.96 (0.41,2.21)	2.00 (0.90,4.44)
Interpersonal violence	2.48 (0.77,7.97)	1.43 (0.49,4.20)	2.49 (0.85,7.34)
Adjusted age, gender, BMI, ethnic, monthly per capita family income, residence, family structure. SU = only substance use PIU = only problematic internet use Comorbid = Both substance use experience and problematic Internet use Control = without substance use experience or problematic Internet use CI = Confidence Interval

Demographic, Psychological and Social Factors Associated with the Risk of Unintentional and Intentional Injuries in Emerging Adulthood with SU, PIU, and Comorbid Groups

Table 4 showd that logistic regression analysis of NSSI in the SU group revealed that age, BMI, stress level, neuroticism, extroversion, agreeableness, conscientiousness, and sleep quality did not significantly differ (P > 0.05). The only significant correlation was between the SCL score (aOR = 1.02, 95% CI = 1.01–1.04, P = 0.046). Similarly, in terms of suicidal ideation, factors such as BMI, stress level, extroversion, agreeableness, conscientiousness, and sleep quality did not significantly differ (P > 0.05). Individuals with high neuroticism scores (aOR = 1.24, 95% CI = 1.09–1.41, P = 0.001), high SCL scores (aOR = 1.01, 95% CI = 1-1.03, P = 0.044), and experiences of peer bullying (aOR = 3.91, 95% CI = 1.26–12.08, P = 0.018) were more likely to have suicidal ideation. However, increasing age was negatively correlated with suicidal ideation (aOR = 0.8, 95% CI = 0.66–0.97, P = 0.027).

Table 4

Demographic, Psychological and Social Factors Associated with NSSI and Suicidal Ideation in SU Emerging Adults
Independent variable	NSSI		Suicidal ideation
Independent variable	aOR (95% CI)	P value	aOR (95% CI)	P value
Age, years	0.83(0.48,1.41)	0.489	0.80(0.66,0.97)	0.027
BMI, kg/m²	0.84(0.68,1.04)	0.109	0.95(0.86,1.05)	0.323
Pressure	0.90(0.79,1.04)	0.152	1.01(0.94,1.06)	0.954
Personality trait
Neuroticism	1.10(0.85,1.42)	0.487	1.24(1.09,1.41)	0.001
Extraversion	0.98(0.78,1.23)	0.845	0.90(0.79,1.04)	0.152
Openness to experiencer	1.23(0.98,1.54)	0.077	0.92(0.83,1.03)	0.144
Agreeableness	0.90(0.73,1.11)	0.323	1.04(0.92,1.18)	0.500
Conscientiousness	0.78(0.59,1.04)	0.087	0.93(0.82,1.04)	0.202
Sleep quality	0.99(0.76,1.29)	0.943	1.09(0.96,1.24)	0.183
SCL-90 score	1.02(1.01,1.04)	0.046	1.01(1.00,1.03)	0.044
Gender (reference = male)
Female	0.79(0.09,6.85)	0.834	0.60(0.22,1.63)	0.318
Ethnic (reference = Han)
Minority	0.87(0.12,6.51)	0.894	0.55(0.20,1.53)	0.254
Residential location (reference = urban)
Rural	1.31(0.18,9.48)	0.787	0.45(0.16,1.27)	0.130
Peer bullying (reference = No)
Yes	2.76(0.45,16.9)	0.271	3.91(1.26,12.08)	0.018
Childhood trauma (reference = No)
Yes	0.61(0.06,6.35)	0.678	1.33(0.44,4.05)	0.618
NSSI = non-suicidal self-injury SU = only substance use

For NSSI behaviors in the PIU group (Table 5), high neuroticism scores (aOR = 1.15, 95% CI = 1.01–1.3, P = 0.028) and high SCL scores (aOR = 1.01, 95% CI = 1-1.02, P = 0.002) were thought to be associated with the risk of NSSI. Residence in rural areas was a protective factor against NSSI behavior (aOR = 0.41, 95% CI = 0.17–0.98, P = 0.046). For suicidal ideation, the significantly associated factors included stress (aOR = 1.04, 95% CI = 1-1.07, P = 0.032), openness to personality (aOR = 0.92, 95% CI = 0.88–0.98, P = 0.005), sleep quality (aOR = 1.13, 95% CI = 1.05–1.21, P = 0.001), and SCL scores (aOR = 1.01, 95% CI = 1-1.02, P = 0.001). Age, BMI, sleep quality, extroversion, and agreeableness did not significantly differ (P > 0.05).

Table 5

Demographic, Psychological and Social Factors Associated with NSSI and Suicidal Ideation in PIU Emerging Adults
Independent variable	NSSI		Suicidal ideation
Independent variable	aOR (95% CI)	P value	aOR (95% CI)	P value
Age, years	0.88(0.70,1.10)	0.261	0.94(0.83,1.07)	0.352
BMI, kg/m²	1.03(0.98,1.09)	0.243	1.02(0.99,1.06)	0.193
Pressure	1.01(0.96,1.07)	0.673	1.04(1.00,1.07)	0.032
Personality trait
Neuroticism	1.15(1.01,1.3)	0.028	1.07(1.00,1.14)	0.053
Extraversion	0.97(0.85,1.11)	0.669	0.96(0.89,1.03)	0.281
Openness to experiencer	0.92(0.84,1.02)	0.099	0.92(0.88,0.98)	0.005
Agreeableness	0.95(0.84,1.06)	0.343	0.97(0.90,1.04)	0.344
Conscientiousness	0.93(0.81,1.06)	0.270	0.99(0.92,1.06)	0.752
Sleep quality	1.09(0.97,1.22)	0.155	1.13(1.05,1.21)	0.001
SCL-90 score	1.01(1.00,1.02)	0.002	1.01(1.00,1.02)	0.001
Gender (reference = male)
Female	1.01(0.35,2.9)	0.981	1.05(0.58,1.91)	0.867
Ethnic (reference = Han)
Minority	0.71(0.27,1.92)	0.504	0.61(0.35,1.06)	0.079
Residence (reference = urban)
Rural	0.41(0.17,0.98)	0.046	0.63(0.39,1.02)	0.062
Peer bullying (reference = no)
Yes	1.21(0.47,3.14)	0.688	1.33(0.73,2.42)	0.347
Childhood trauma (reference = no)
Yes	1.29(0.55,3.04)	0.562	1.33(0.80,2.22)	0.269
NSSI = non-suicidal self-injury PIU = only problematic internet use

Among the comorbid group (Table 6), females with poorer sleep quality and neuroticism were at greater risk for suicidal ideation (aOR = 2.51, 95% CI = 1.14–5.52, P = 0.022; aOR = 1.13, 95% CI = 1.01–1.25, P = 0.026; and aOR = 1.18, 95% CI = 1.06–1.30, P = 0.002). In contrast, individuals living in rural areas were at relatively low risk for NSSI and suicidal ideation. No significant differences were found in unintentional injuries. By analyzing the above three groups, we found that for NSSI, key factors were mainly focused on psychological conditions. High neuroticism scores, poor sleep quality, and poor psychological condition were positively associated with suicidal ideation. Individuals living in rural areas, on the other hand, had a relatively low risk of NSSI and suicidal ideation.

Table 6

Demographic, Psychological and Social Factors Associated with NSSI and Suicidal Ideation in Comorbid Emerging Adults
Independent variable	NSSI		Suicidal ideation		Unintentional injury
Independent variable	aOR (95% CI)	P value	aOR (95% CI)	P value	aOR (95% CI)	P value
Age, years	0.83(0.60,1.15)	0.270	0.91(0.78,1.07)	0.257	1.03(0.87,1.21)	0.762
BMI, kg/m²	0.92(0.77,1.09)	0.328	0.96(0.88,1.05)	0.360	0.97(0.90,1.05)	0.506
Pressure	1.05(0.97,1.13)	0.210	1.02(0.97,1.07)	0.495	1.03(0.98,1.08)	0.248
Personality trait
Neuroticism	1.07(0.91,1.24)	0.424	1.18(1.06,1.30)	0.002	0.98(0.89,1.09)	0.755
Extraversion	1.03(0.86,1.24)	0.722	0.97(0.87,1.08)	0.552	1.02(0.92,1.14)	0.714
Openness to experiencer	0.92(0.81,1.04)	0.167	0.92(0.85,1.00)	0.060	0.96(0.89,1.04)	0.332
Agreeableness	1.07(0.92,1.25)	0.366	0.99(0.89,1.11)	0.891	1.03(0.93,1.15)	0.524
Conscientiousness	0.89(0.75,1.05)	0.152	0.97(0.88,1.07)	0.573	0.98(0.90,1.08)	0.714
Sleep quality	1.15(0.97,1.35)	0.104	1.13(1.01,1.25)	0.026	1.02(0.92,1.13)	0.762
SCL-90 score	1.01(1.00,1.02)	0.270	1.00(0.99,1.01)	0.566	1.00(1.00,1.01)	0.465
Gender (reference = male)
Female	1.75(0.41,7.50)	0.449	2.51(1.14,5.52)	0.022	1.14(0.52,2.51)	0.741
Ethnic (reference = Han)
Minority	0.76(0.22,2.67)	0.668	0.63(0.28,1.39)	0.251	0.61(0.27,1.38)	0.234
Residence (reference = urban)
Rural	0.08(0.01,0.45)	0.004	0.41(0.20,0.85)	0.016	1.01(0.49,2.06)	0.994
Peer bullying (reference = no)
Yes	1.73(0.56,5.33)	0.342	0.91(0.41,2.06)	0.828	0.60(0.25,1.41)	0.239
Childhood trauma (reference = no)
Yes	1.80(0.61,5.34)	0.288	1.65(0.80,3.41)	0.177	1.36(0.66,2.80)	0.405
NSSI = non-suicidal self-injury Comorbid = both substance use experience and problematic Internet use

In this study, we delved into the impact of substance use and problematic internet use on young individuals' multiple injuries. This is the first study in China to emphasize the differences in injuries associated with the co-occurrence of both SU and PIU compared to normal young adults. In addition, this study explored the impact of psychological and social factors associated with substance and internet use (i.e., mental health, childhood trauma, and peer bullying) on intentional injuries in young adults in China. These findings are instructive for reducing the incidence of injuries in China, particularly in emerging adults.

Comparison of Demographics, Psychological, Social Factors On Injuries Among Substance Use and Problematic Internet Use Group

We categorized substance users into low-, medium-, and high-risk groups based on assessment scales and categorized participants into a non-PIU group, a mild-moderate PIU group, and a severe PIU group based on the severity of the Internet Addiction Test (IAT). We found that individuals with medium- and high-risk SU exhibited greater mean age, urban area background, and single-parent family status than did individuals in the low-risk group. These characteristics could be influenced by several factors. A high average age may indicate that a group is more likely to be exposed to a variety of different types of substances²⁶. Additionally, upperclass members might face the challenges of employment pressure and future uncertainty²⁷, factors that have been linked to EA' substance use and increase use²⁸. Urban areas provide easier access to illicit materials²⁹. A single-parent family could lead to weakened social support systems, and low incomes could disrupt the level of normative supervision and support for single parents, which increase a child's risk of SU³⁰. Certain family structures might result in adolescents experiencing higher levels of distress and negative consequences. These factors included residential mobility; changes in parenting styles; and biological relationships, such as parental remarriage and general social unrest^{31, 32}. Substance use is often used as a coping or avoidance mechanism when adolescents experience distress, anxiety, and negative emotions^{33, 34}.

In terms of problematic internet use, individuals in the PIU group demonstrated significant differences in sexual orientation, gender identity, sleep quality, and personality traits. In reality, discrimination based on sexual orientation and gender identity manifests in several ways, including verbal harassment, employment discrimination, and physical violence ³⁵. This made them more prone to PIU behaviors. Indulgence in virtual Internet environments can lead to social isolation, especially for sexual minorities, and may increase individuals’ identity anxiety and social stress online³⁶. Decreased sleep quality could be associated with excessive participation in online activities at night, which in turn negatively affects one’s biological clock and mental health ³⁷. In addition, we investigated personality traits in depth and found that individuals in the heavy PIU group were more likely to have higher levels of stress, neuroticism, extroversion, openness, agreeableness, and conscientiousness. This provided important clues for understanding the association between PIU and individual psychological traits. Emerging adults might feel uncomfortable in real life due to negative emotions and low self-esteem and, in turn, might seek virtual environments as an escape mechanism. They spend more time addicting to the internet and developing behavioral patterns that are closely tied to the internet, all of which affect their personality traits, especially neuroticism³⁸.

Risk of Injuries in Substance Use, Problematic Internet Use, and Comorbid Groups

In terms of injury outcomes, our study further revealed the serious impact of substance use and problematic internet use on an individual's health. Individuals in the SU or PIU subgroup were more likely to experience NSSI and suicidal ideation than were individuals in the low-SU and non-PIU subgroups. This finding reflected the independent negative effects of SU and PIU on mental health. Substance use could have direct effects on individuals through physiological and pharmacological pathways; e.g., alcohol consumption, tobacco use, and other SU behaviors impair psychological and neurocognitive development and increase the risk of academic failure, chronic disease, and psychiatric disorders^{39, 40}. PIU might promote suicidal ideation by triggering mental health problems ^41, such as anxiety and depression, through dependence on cognitive and emotional dimensions ⁴². Furthermore, it was of interest that individuals with both SU and PIU more prominently exhibited a risk for unintentional injury, NSSI and suicidal ideation. This finding reflected a positive correlation between these two issues, consistent with previous findings^{43, 44}. Initial SU and multiple SU were more likely to increase the risk of PIU⁴⁵. This might be because individuals with SU and PIU share similar vulnerable brain regions, including the dorsolateral and orbitofrontal cortices^{46, 47}. Similarly, PIU was found to be a significant predictor of SU and included smoking, alcohol use, and drug use⁴⁸. All of these factors increase the likelihood of injury.

Factors influencing injuries in SU, PIU, and Comorbid Groups

Based on the results of the aforementioned injuries, we further explored factors associated with intentional injuries within the affected population. Numerous factors were closely linked to NSSI and suicidal ideation, but their impact varied among different demographic groups. In summary, psychological conditions, neuroticism, and residing in urban areas were found to be associated with NSSI and suicidal ideation across multiple populations. Poor sleep quality showed a positive correlation with the occurrence of suicidal ideation. Additionally, age, gender, stress, peer bullying, and openness also influenced these variables⁴⁹. In conclusion, improving the mental health of young people, the quality of their sleep, appropriate stress release and the control of bullying in schools may play a significant role in the occurrence of intentional injuries. Furthermore, it is even more important to intervene with emerging adulthood who are younger, live in urban areas, have neurotic personalities and female groups.

For NSSI behaviors, high neuroticism scores were strongly associated with the development of NSSI in the PIU population. Neuroticism, one of the Big Five personality traits, is a partially heritable personality trait that includes negative affect⁵⁰ and increased sensitivity to stress⁵¹. Highly neurotic individuals might avoid or mitigate unpleasant and disgusting emotional experiences to some extent by resorting to NSSI behaviors⁵². Positive associations between high SCL and NSSI have been found in several populations. This reflected the strong link between mental health and self-injury. Emerging adults lacked effective coping strategies for dealing with negative emotions and were more likely to seek relief through NSSI behaviors. This could lead to more serious psychological and social consequences (shame, guilt) and self-criticism, which can lead to damaged social relationships and negative emotions, creating a vicious cycle⁵³. Among the IAT and comorbid groups, we found that emerging adults living in rural areas were less prone to NSSI behaviors than were those living in urban areas. This might be because urban EA have a heavier academic load, higher parental expectations, and encounter a variety of pressures as individuals evolve⁵⁴. As a result, individuals are prone to various psychological problems, including serious mental disorders, which means that NSSI behaviors are more likely to occur⁵⁵. In addition, we also observed a negative correlation between this protective effect and suicidal ideation in a comorbid population.

This was followed by a discussion of the impact of suicidal ideation in different populations. Among those in the SU population, emerging adults who experienced peer bullying were more likely to experience suicidal ideation. Patients typically report higher levels of negative health outcomes such as depression, anxiety, and other internalizing behaviors⁵⁶. Older students were less likely to report suicidal ideation than younger students were. This finding was consistent with previous research⁵⁷ and could be attributed to the more developed problem-solving skills of upperclassmen. Relative to underclasses, they might solve problems in a more rational, less impulsive, and destructive manner ⁵⁸. In the PIU population, we found that stress was associated with the development of suicidal ideation. Stress leads to long-term changes in brain morphology and biology, which include alterations in the functional status of various neurotransmitters⁵⁹ and in interneuronal signaling systems, which could lead to suicidal ideation⁶⁰. There was a negative correlation between individuals' greater openness to experience and suicidal ideation. This finding suggested that individuals with higher levels of openness might be more positive in terms of their mental health. Openness is generally considered to be a positive personality trait that represents greater acceptance of novelty, creativity, and change⁶¹. Individuals with high openness were more likely to experience positive emotions and were more sensitive to good things in life. This approach helps to alleviate negative emotions and reduce the risk of suicidal ideation⁶². Among those with comorbidities, females were more likely to have suicidal ideation, consistent with the results of related studies⁶³. This might be because women are more submissive, psychologically sensitive⁵⁷ and more prone to experience symptoms of depression and anxiety⁶⁴ than men are, which in turn increases the risk of suicidal ideation⁶⁵. In addition, females were more inclined to submit to the school system and experienced greater academic stress, further increasing the risk of suicidal ideation⁶⁶.

Associations between poor mental health, neuroticism, poor sleep quality, and suicidal ideation have been found in multiple populations. Numerous studies have shown that a greater burden of depression and anxiety symptoms is associated with a greater risk of suicidal ideation ^{37, 67}. The neuroticism association could reflect the influence of emotional stability on suicidal ideation ⁶⁸. Highly neurotic individuals tended to have low emotional regulation and were poor at seeking help from outside sources; therefore, they were prone to increased depression ⁶⁹. In addition, the association between neuroticism and psychological states might involve complex interactions that collectively influence the development of suicidal ideation⁷⁰. Poor sleep quality is one of the most important predictors of suicidal ideation⁷¹. One possible explanation was that poor sleep quality led to nighttime wakefulness, which could result in decreased frontal lobe function. Decreased frontal lobe function might lead to decreased problem solving and increased impulsive behavior, both of which could be associated with suicide risk⁷². Another possibility is that insomnia or nightmares may trigger perceptions of failure, which in turn leads to feelings of entrapment, isolation, despair, and ultimately suicide⁷³.

Strengths and limitations

To the best of our knowledge, this is the first analysis to examine the association of substance use, problematic internet use, and comorbidity with the multiple injuries among Chinese emerging adulthood. In addition, the study examined the factors influencing injuries in different populations. This study has several limitations. First, all of our surveys relied on self-reported data from students. Second, there may be concealment or underestimation of problematic internet use and substance use due to social expectations, self-presentation, and privacy protection. Third, the cross-sectional research design of this study did not confirm causal relationships between substance use, problematic internet use, and injury. Fourth, the broad applicability and representativeness of the findings may be somewhat limited because we relied on data from only two universities. This may lead to uncertainty about the applicability of our findings to different populations, different universities, or different regions of the student body.

In this study, we investigated the impact of substance use, problematic internet use, and their comorbidity on unintentional and intentional injuries. The results revealed significant associations between substance use and problematic internet use and between substance use and the likelihood of intentional injuries, especially within the comorbid group. Moreover, individuals in the comorbid group demonstrated a noteworthy risk of experiencing unintentional injuries. Additionally, we conducted a thorough analysis of demographic characteristics and psychological and social factors within the substance use, problematic internet use, and comorbid groups. The detailed data contribute to a comprehensive understanding of injuries among specific populations and lay solid groundwork for future intervention and prevention initiatives.

substance use

PIU

problematic internet use

BSI-CV

Beck Scale for Suicide Ideation

CRAFFT

Car Relax Alone Forget Friends Trouble substance use scale

IAT

Internet Addiction Test for Chinese

SCL-90

Symptom Checklist 90 Chinese Version

NEO-FFI

Neuroticism Extroversion Five-Factor Inventory

PSQI

Pittsburgh Sleep Quality Index questionnaire

PSS-14

Perceived Stress Scale

DBVS-S

Delaware Bullying Victimization Scale

CTQ

Childhood Trauma Questionnaire

BMI

body mass index

Ethics approval and consent to participate

The ethical review board of Capital Medical University approved this study (Z2023SY039). The data in the manuscript have been used with informed consent.

Consent for publication

Every author agrees to publish it.

Availability of data and material

Data are confidential data of this research group. If you want to view it, you need to apply.

Competing interests

The authors declare that they have no competing interests.

Funding

No funding was received for this manuscript.

Author Agreement

We hereby confirm that this manuscript has not been previously published and is not under consideration elsewhere. All authors have reviewed and approved the final version of the manuscript and have no conflicts of interest to disclose. Prof. Huiping Zhu and Peixia Cheng, the corresponding authors, take full responsibility for the research and writing process and will serve as the primary contact for correspondence regarding this submission.

Authors' contributions

Dr Zhu, and Cheng had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Zhu, and Cheng share last authorship.

Concept and design: Zhu, Cheng, Zhou, Li, Gao

Acquisition, analysis, or interpretation of data: Zhu, Cheng, Zhou, Li.

Drafting of the manuscript: Zhou, Li.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Zhou, Li, Cheng, Gao, Wang,.

Supervision: Zhu, Cheng.

Acknowledgements

None

Consent for publication section

The data in the manuscript have been used with informed consent.

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No competing interests reported.

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Injury Risk and Influencing Factors of Substance Use and Problematic Internet Use Among Emerging Adults in Beijing, China.

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Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Participants and Procedures

Measurements

Demographic Information

Data Analysis

Results

Demographic and Social Characteristics

Unintentional and Intentional Injury Risk

Discussion

Risk of Injuries in Substance Use, Problematic Internet Use, and Comorbid Groups

Factors influencing injuries in SU, PIU, and Comorbid Groups

Strengths and limitations

Conclusions

Abbreviations

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