Association between internet addiction and the risk of upper cross syndrome in Chinese college students: A cross sectional study

Abstract

It is well established that increased internet use is related to an increased risk of upper cross syndrome (UCS) among adolescents. The relationship between internet addiction (IA), a unique condition involving severe internet overuse, and UCS has, however, not been reported. This study aimed to investigate the association between IA and the risk of UCS among Chinese college students. This cross sectional study (n = 2552) was conducted in November 2020. IA status was evaluated using the 20-item Young’s Internet Addiction Test. IA was defined as internet addiction score ≥ 50 points. UCS was measured by means of a reference American College of Sports Medicine postural screening. Multiple logistic regression analysis was performed to determine association between IA categories (normal, mild, and moderate-to-severe) and UCS. Among all participants, the prevalence of UCS was 59.7%, male and female was reported by 42.2 and 24.8, respectively. The prevalence of IA was 67%, mild and moderate-to-severe of IA was reported by 42.2% and 24.8%, respectively. After adjusting for potential confounders (sex, age, single child, father’s educational level, mother’s educational level, smoking status, drinking status, Body mass index, physical activity, Sedentariness, and Depressive symptoms), the results showed significant differences in the risk of UCS among different IA categories. The odds ratios and 95% confidence intervals for UCS with IA categories were 1.000 (reference), 5.19 (4.27, 6.32), and 9.14 (7.14, 11.69), respectively (P for trends: < .001). This cross sectional study showed that severe IA was associated with a higher risk of UCS in Chinese college students. In future research, it will be necessary to explore causality regarding this relationship using interventional studies.

1. Introduction

Internet addiction is a behavioral disorder that results from excessive use of the internet. According to a report by the China Internet Network Information Center, the number of internet users in China reached 1.011 billion in 2021. While the internet plays an important role in learning, entertainment, and socializing, excessive or unlimited use can lead to internet addiction.^[1] It is widely recognized that internet addiction not only contributes to poor sleep quality,^[2] but may also be a significant risk factor for psychological disorders such as depression and anxiety.^[3] As a result, internet addiction has received considerable attention from researchers and the public worldwide.^[4] Additionally, due to their psychological and developmental characteristics, college students are particularly susceptible to internet addiction.^[5] Relevant studies show that approximately 16.8% of college students in the United States exhibit symptoms of internet addiction,^[3] while in Korea, an estimated 10% of college students are affected by internet addiction (IA).^[6] In Japan, approximately 8.1% of college students experience symptoms of IA.^[7] Chinese college students are more prone to internet addiction than students from other countries,^[8,9] with a prevalence rate of 23.7%.^[10]

Moreover, there is increasing evidence that excessive internet use can have negative effects on college students’ body image.^[11] Internet addiction can also lead to skeletal muscle pain in college students.^[12] This is due to the need to maintain an abnormal posture for extended periods of time, which can result in altered muscle tone and increased local pressure, leading to the development of skeletal muscle pain.^[13,14] The fatigue caused by excessive internet use is also one of the reasons why the body cannot maintain a normal posture.^[15] It is worth noting that abnormal body posture has been ranked as one of the top 3 factors affecting the physical and mental health of Chinese youth, along with obesity and myopia. However, it remains unclear whether internet addiction is associated with abnormal body posture among college students. We hypothesize that internet addiction may have adverse effects on upper cross syndrome (UCS) (a common classification of body posture abnormalities). To our knowledge, few studies have examined the relationship between internet addiction and abnormal body posture. One cross sectional study found that college students with internet addiction may be a contributing factor to body image dissatisfaction.^[16] Another study reported that smartphone addiction, which involves excessive internet use,^[17] is significantly associated with the development of neck disorders caused by abnormal neck posture.^[18] However, these studies did not find a significant relationship between internet addiction and UCS.

UCS is a comprehensive condition characterized by an abnormal pattern of upper limb movements caused by tightness of the levator scapulae, upper trapezius, and pectoralis minor muscles, and occasionally tightness of the pectoralis major muscle, coupled with weakness of all the deep muscles of the neck (cervical) flexors, lower trapezius, and rhomboids muscles.^[19] Since UCS is one of the leading causes of skeletal muscle pain and body image dissatisfaction,^[20] we conducted a cross sectional study to explore possible relationships between internet addiction and the risk of UCS in a large sample of Chinese college students.

2. Materials and methods

2.1. Study participants

The Changchun Normal University Physical Fitness and Health study is a prospective ongoing study to assess the association between physical fitness and health status of college students at Changchun Normal University (Changchun, Northeast China). Changchun Normal University has approximately 22,000 full-time students and offers an extensive set of academic disciplines.

In 2020 (baseline period), approximately 2798 non-sports students were randomly selected from 20 schools/colleges. We invited all the randomly selected participants (n = 2798) to attend a physical fitness program in order to participate in the study. Written informed consent were obtained from participants and their parents, or from the legal guardians for those aged < 16 years. We excluded participants who did not complete the body posture screening (n = 97) and questionnaire (n = 149). After these exclusions, this study included a total of 2552 participants (male: 621) with an age range of 16 to 24 (mean 19.2, standard deviation 1.1) years (Fig. 1). Ethics approval was obtained from the Institutional Review Board of the College of Physical Education of Changchun Normal University (ethical approval number: NENU20201108).

Figure 1.

Research flowchart.

2.2. Sample size calculation

The study sample size was calculated using the following formula:

$$\text{N}={\left(u_{\text{}\text{}\alpha \text{}\text{}}/\text{}\text{}\delta \text{}\text{}\right)}^{2}p\left(1-p\right)$$

Where N = sample size.

p = prevalence of UCS.

u_α = 1.96.

δ = 0.03.

In a survey of the body posture of Chinese college students, the prevalence of UCS among college students was expected to be 57.6%.^[21] Therefore, it was estimated that the study should include a minimum total sample size of 1042 participants.

2.3. Assessment of internet addiction

Assessment of IA was obtained using the internet addiction test (IAT) Test, which covers an individual’s Internet use habits, their thoughts about the Internet, as well as the influence of Internet use on their lives (such as compulsive use, withdrawal, related problems at school, work, or sleep). The IAT is a 20-item test with responses ranging from 1 to 5 (with possible sum of scores from 20 to 100). Higher scores represent a more severe state of IA. The following cutoff points were used to distinguish between internet usage categories: normal (0–30 points), mild (31–49 points), and moderate-to severe (50–100 points). The reliability and validity of the Chinese version of IAT were described in previous studies.^[22,23] In the current study, Cronbach α for this test was 0.907.

2.4. Assessment of UCS

Assessment of UCS was obtained using the evaluation of ACSM (The American College of Sports Medicine). This study refers to the American College of Sports Medicine’s use of a hanging postural assessment form and measuring tape to assess UCS in college students by a professional appraiser.^[24] Participants with any of the 5 features mentioned above were defined as having UCS. Inconsistent distance between the bilateral acromion and the 7th cervical vertebra; The distance between the medial edge of the scapula and the spine is greater than the width of the test subject’s own 4 fingers; The distance from C1 to C7 is greater than the width of the test subject’s own 4 fingers; From the side, the test subject’s earlobe is in front of the acromion; The upper corner of the test subject’s ear and the corner of the eye are not at the same level.^[25]

2.5. Relevant covariates

Demographic variables included sex, age (continuous variable), single child (yes or no), father’s educational level (senior high school or less, college or postgraduate), and mother’s educational level (senior high school or less, college or postgraduate). Lifestyle factors included smoking status (smoker or nonsmoker), drinking status (drinker or nondrinker), sedentariness status (more than 8 hours/day or not). Demographic variables and lifestyle factors were assessed via a self-administered questionnaire. Levels of physical activity (PA) were assessed by the International Physical Activity Questionnaire.^[26] Total daily PA was calculated by METs × hour/day.^[27] Depressive symptoms were assessed according to the Chinese version of the Self-Rating Depression Scale.^[28] This scale include 20 items, which are defined as either positive or negative. The participant is required to score each item on a scale of 1 to 4. The sum of scores for the 20 items range from 20 to 80, with greater values indicating increased severity of depression. In the present study, a total of score > 50 indicates depressive symptoms.^[29] The Cronbach α coefficient for the scale is 0.740.

2.6. Statistical analysis

All categorical variables were presented as proportions and were compared by logistic regression analysis. The occurrence of UCS was used as a dependent variable and categories of IA were used as independent variables. Multiple logistic regression analysis was also used to examine the relationship between categories of IA and occurrence of UCS. Model 1 was the crude univariate model; Model 2 was adjusted for sex and age; Model 3 was additionally adjusted for demographic variables and lifestyle factors, as outlined above. Significance was set at P < .05 for 2-sided tests. All tests were performed using IBM SPSS Statistics 24.0 software (IBM SPSS Inc., Chicago, IL).

3. Results

The participants baseline characteristics according to IA categories are presented in Table 1. Participants with severe IA reported older age (P for trends: = .031), a lower PA level (P for trends: < .001) and higher frequency of Sedentariness (P for trends: = .042). No other significant differences were observed across categories of IA.

Table 1.

Participants’ characteristics according to IA at baseline.

N = 2552	Normal group (n = 871)	Mild group (n = 1048)	Moderate and severe group (n = 633)	P for trends*
Age	19.07 (19.00, 19.14)	19.10 (19.02, 19.18)	19.22 (19.15, 19.30)	.031
Sex(male)	233 (26.8)	249 (23.8)	139 (22.0)	.087
BMI, kg/m2	21.28 (20.96, 21.60)	20.93 (20.72, 21.14)	21.22 (20.90, 21.54)	.136
Only 1 child, %	447 (51.3)	491 (46.9)	307 (48.5)	.147
Father education, %				.244
Senior high school or less	577 (66.2)	730 (69.7)	438 (69.2)
College or postgraduate	294 (33.8)	318 (30.3)	195 (30.8)
Mother education, %				.726
Senior high school or less	641 (73.6)	788 (75.2)	472 (74.6)
College or postgraduate	230 (26.4)	260 (24.8)	161 (25.4)
PA, MET·h·d-1	52.37 (49.82, 54.92)	24.56 (22.92, 26.19)	17.97 (16.04, 19.90)	.000
Smoking status, %				.865
Smoker	71 (8.2)	92 (8.8)	52 (8.2)
Nonsmoker	800 (91.8)	956 (91.2)	581 (91.8)
Drinking status, %				.787
Drinker	372 (42.7)	463 (44.2)	279 (44.1)
Nondrinker	499 (57.3)	585 (55.8)	354 (55.9)
Sedentariness, %				.042
≤8h	797 (91.5)	971 (92.7)	564 (89.1)
>8h	74 (8.5)	77 (7.3)	69 (10.9)
Depressive symptoms (SDS ≥ 50), %	69 (7.9)	68 (6.5)	53 (8.4)	.291

BMI = body mass index, IA = internet addiction, PA = physical activity, SDS = Self-Rating Depression Scale.

^*P value were assessed using chi-square-test for categorical variables and analysis of variance for continuous variables.

† Continuous variables were expressed as mean (95% CI) and categorical variables were expressed as n (%).

Among all participants, risk of IA and UCS was reported by 67.0% and 59.7%. Figure 2 shows the significant relationships between IA and risk of UCS in the multivariate logistic regression models. The odds ratios (95% confidence intervals) for UCS in each IA category (normal, mild, and moderate-to severe) in Model 3 were 1.00 (reference), 5.19 (4.27,6.32), and 9.14 (7.15,11.69), respectively (P for trends: < .001).

Figure 2.

Relationships of internet addiction with risk of UCS among Chinese college students. UCS = upper cross syndrome.

4. Discussion

In this cross sectional study of Chinese college students, multivariate logistic analyses were conducted to investigate the relationship between IA and the risk of UCS. The results showed that severe IA was significantly and independently associated with a higher risk of UCS, even after adjusting for potential confounding factors.

Although the exact etiology of the association between IA and UCS is not yet known, we explored 2 possible reasons. A plausible explanation for the association between IA and UCS is the prolonged abnormal posture holding during prolonged internet or smartphone use. Studies have shown that such prolonged use can result in rounded shoulders and a hunched back, as well as a forward head posture, and abnormal scapular dynamics have been observed in the upper limb activity of individuals with IA.^[30] From an anatomical standpoint, the direction of the physiological curvature of the cervical and thoracic segments determines, to some extent, the sitting posture of the human body while concentrating on the Internet, with neck extension and flexion of the thoracic spine area.^[31] It is possible that individuals with IA use the internet for longer periods on a daily basis, which increases the risk of developing UCS. During internet or smartphone use, such as while chatting with friends by text messaging or playing games online, users often remain in a fixed position for extended periods, gripping their smartphones with their heads extended forward, shoulders internally rotated, and hunchbacked. Such abnormal postural habits can lead to decreased muscle tone in the back and elevated ventral muscle tone.^[31] Over time, imbalances in contralateral muscle strength and tone can lead to the development of UCS.

Indeed, decreased physical activity could also play a role in the association between IA and UCS. The previously mentioned study showed that severe IA was associated with less physical activity,^[32] and another study found that decreased physical activity was significantly associated with an increase in the rate of abnormal body posture among adolescents.^[33] Reduced physical activity can lead to weakened muscle strength and imbalanced muscle development, which can in turn lead to altered movement patterns and poor posture.^[34] Over time, these poor habits become ingrained, and can disrupt proper movement and breathing patterns, leading to a negative cycle of worsening posture.^[35] Additionally, from a perspective of motor learning and control, a lack of exercise can result in the body having less control over the deeper muscles compared to the superficial muscles. This leaves the deeper muscles around the scapula and the weaker side muscles unexercised, leading to an imbalance between the deep back and pectoral muscles, which is a contributing factor to UCS.^[36] The combination of chronic lack of exercise and IA can lead to muscle stiffness, musculoskeletal pain, and neck pain, which can further perpetuate the cycle of poor posture and pain.^[37] Overall, decreased physical activity can be a significant contributing factor to UCS in IA individuals.

In the present study, other potential confounding factors that might influence the results were analyzed, including sex, age, single child status, educational level of each parent, smoking status, drinking status, PA, Body mass index, Sedentariness, and depressive symptoms. Even after adjusting for these potential confounders, a significant association between IA and UCS remained, indicating that this association was independent.

There are several limitations to our study. First, the present work was a population-based cross sectional study and therefore the causative link between IA and UCS could not be established. Secondly, history of trauma and surgery could also contribute to the risk of developing UCS. Therefore, future studies need to examine participants past medical history. Finally, the results may not be representative of the Chinese general college students; therefore, further investigations with larger sample sizes are needed to confirm our findings.

5. Conclusion

This cross sectional study indicates that severe IA is associated with higher risk of UCS. In future research, it will be necessary to explore the causative links between IA and UCS with prospective cohort or other interventional studies.

Acknowledgments

The authors would like to thank all participants for participating in the study research and provided informed consent for the analysis of their data. Furthermore, we also want to thank the staff at Changchun Normal University for their dedicated work.

Author contributions

Conceptualization: Chaowei Zhang, Guang Yang.

Data curation: Chaowei Zhang.

Formal analysis: Jingyu Zhang.

Methodology: Guang Yang.

Project administration: Guang Yang.

Resources: Jingyu Zhang, Guang Yang.

Software: Jingyu Zhang.

Writing – original draft: Chaowei Zhang.

Writing – review & editing: Guang Yang.