Association of Screen Time with Anxiety and Depressive Symptoms in College Students During COVID-19 Outbreak in Shanghai: Mediation Role of Sleep Quality

Abstract

The study aimed to investigate the associations between different types of screen time (ST) and anxiety and depressive symptoms in college students during the Coronavirus disease 2019 (COVID-19) outbreak in Shanghai, China; the potential mediation role of sleep quality was also examined. A total of 1,550 college students completed an online survey in May 2022. ST, Self-rating Anxiety Scale (SAS) score, Self-rating Depression Scale (SDS) score, Pittsburgh Sleep Quality Index (PSQI) score, and physical activity were self-reported. Multiple linear regression and mediation analysis were conducted. The results showed that more time spent in TV/movie viewing (>2 h/day) and recreational reading (>1 h/day) was associated with higher levels of anxiety, while more time spent in online social media (>2 h/day) was associated with a higher level of depressive symptoms. In contrast, time spent in online social media (1–2 h/day) was associated with a lower level of anxiety. Meanwhile, recreational reading (2–3 h/day) had a significant indirect effect on anxiety and depressive symptoms through sleep quality. During the COVID-19 outbreak, the associations of ST with anxiety and depressive symptoms varied by the type of screen viewing in college students. The associations of slightly excessive time spent on recreational reading with higher levels of anxiety and depressive symptoms were partially mediated by sleep quality.

Introduction

In March 2022, Coronavirus disease 2019 (COVID-19) broke out in Shanghai. To prevent a further spread of the outbreak, Shanghai implemented stay-at-home orders and social distancing measures. Meanwhile, schools and universities were locked down. Different from the situation during the COVID-19 outbreak in Wuhan, during the lockdown in Shanghai, college students took courses through online learning platforms at dormitories.¹ Accordingly, time spent on sedentary behaviors increased in college students, especially screen-based sedentary behaviors.² Screen time (ST) refers to time spent on engaging with screen-based devices (e.g., computer, smartphone, television), while being sedentary.^3,4 Excessive ST is not only associated with poorer eating habits,⁵ lower physical activity,⁶ and higher level of risk factors for cardiovascular diseases⁷ but also with mental disorders.⁸

Anxiety and depression are two disabling mental disorders,⁹ which may have a severely deleterious influence on social, occupational, and other areas of functioning.^10,11 Although evidence for the relationship between excessive ST and anxiety and depression is accumulating in youth,^12–14 investigations of ST and its mental health effects among college students are relatively sparse.

Some studies have reported that more screen exposure is associated with worse mental health among college students.^15–18 During the COVID-19 outbreak, significantly increased ST was significantly associated with higher levels of anxiety¹⁹ and depression.²⁰ It is worth noting that recent studies have suggested that not all ST is equally associated with health indicators. Different contents or types of screen viewing may be differently associated with mental health issues in youth^21–24 and cognitive function in older adults.²⁵ However, the abovementioned studies of college students did not discriminate between the effects of different types of screen viewing.

There are a couple of hypotheses existing in the literature explaining the effects of screen-based sedentary behavior on mental health, such as the displacement hypothesis and the upward social comparison hypothesis.²⁶ The displacement hypothesis posits that all ST is negatively associated with mental health, since it replaces some healthy behaviors and activities such as physical activity.²⁶ The upward social comparison hypothesis suggests that the effects of the content of the screen determine whether ST is deleterious to mental health.²¹ Therefore, it is important to investigate the association between different types of ST and mental health indicators among college students during the COVID-19 outbreak.

Sleep plays an important role in people's daily lives and may influence mental health.²⁷ According to the recommendations of the American Academy of Sleep Medicine and the Sleep Research Society, adults should get at least 7 hours of sleep a night for optimal health.²⁸ However, over 60 percent of college students had insufficient sleep in a study by Becker et al.²⁹ Poor sleep quality and excessive daytime sleepiness have been found to be common among college students.^30,31 Recently, studies have shown that excessive ST is associated with longer time to fall asleep, shorter sleep duration, and poor sleep quality.^7,32 Overexposure to screens at night could disrupt circadian rhythms and sleep.^33,34 Even worse, some studies have suggested that poorer sleep is associated with anxiety and depression.^{17,19,35–37}

Therefore, sleep may play a mediation role in the relationship between ST and mental health indicators, such as anxiety and depression. A mediating effect of sleep was found between ST and depressive symptoms.¹⁷ Furthermore, ST was indirectly associated with depressive symptoms through sleep duration.³⁸ The significant increase in ST would also contribute to poor sleep quality, resulting in heightened anxiety.¹⁹ In addition, there are relatively few studies of the relationship between college students' behavior and mental health during COVID-19 outbreak.

This study aimed to investigate the association between different types of ST and anxiety and depressive symptoms among college students during the COVID-19 lockdown in Shanghai. Furthermore, it examined the potential mediation role of sleep. We proposed the following hypotheses: (a) Excessive ST is associated with higher levels of anxiety and depressive symptoms and (b) sleep quality indirectly mediates the relationship between ST and anxiety and depressive symptoms.

Methods

Participants

This cross-sectional study was conducted in May 2022 during the COVID-19 outbreak in Shanghai, China. In total, 1,550 college students were recruited from several universities in Shanghai, including both undergraduate and postgraduate students. The participants in the study met the following criteria: (a) Having Internet access and screen-based devices and (b) no diagnosed mental disorder. The participants completed the online questionnaire through the Wenjuanxing platform (wjx.cn). Of the 1,550 participants, 1,453 participants aged 17–30 years (M_age = 21.47, SD = 2.60; 54.4% female) provided valid responses and were included in the statistical analyses. The study protocol was approved by the Institutional Review Board for Human Research Protections of Shanghai Jiao Tong University. All participants provided informed consent and indicated that they volunteered to participate in the study.

Measures

Screen time

ST was self-reported through the question, “On a typical day, how many hours do you spend using screen-based electronic devices for the following activities?” The four types of screen-based activities were TV/movie viewing, online social media (e.g., WeChat/Douyin/live streaming), recreational reading (e.g., web fiction/web browsing), and video gaming. The time spent was classified into five categories: “≤1 hour,” “1–2 hours,” “2–3 hours,” “3–4 hours,” and “>4 hours.”

Self-rating Anxiety Scale

The Self-rating Anxiety Scale (SAS) was compiled by Zung³⁹ and was used for assessing the participants' subjective perception of anxiety. The Chinese version of the SAS has been translated and validated.⁴⁰ The Cronbach's alpha coefficient is 0.93.⁴¹ The Cronbach's alpha coefficient for SAS in this study is 0.89.

The SAS consists of 20 items (e.g., “I feel more nervous and anxious than usual,” “I feel that everything is all right and nothing bad will happen”) and each item is scored between 1 and 4. The participants choose one of the options: “None or A little of the time” to “Most or All of the time.” The total rough score ranges from 20 to 80. The total rough score is multiplied by 1.25 represents the standard score, which ranges from 25 to 100, and a higher score indicates a greater tendency to anxiety.

Self-rating Depression Scale

The Self-rating Depression Scale (SDS) was also compiled by Zung⁴² and the Chinese version is widely used for reflecting the perceived level of depression of participants.⁴³ The Cronbach's alpha coefficient is 0.86.⁴⁴ The Cronbach's alpha coefficient for SDS in this study is 0.72. The number of items and score settings for SDS are the same as for SAS, including 20 items (e.g., “I feel downhearted and blue,” “I am restless and can't keep still”), with each item scored from 1 to 4. The total score is multiplied by 1.25 to obtain a standard depression severity score, which ranges from 25 to 100. The higher the score indicates the worse of depressive symptoms.

Pittsburgh Sleep Quality Index

Pittsburgh Sleep Quality Index (PSQI) was compiled by Buysse et al.⁴⁵ and has been translated into Chinese.⁴⁶ The Cronbach's alpha coefficient is 0.85.⁴⁶ The PSQI is widely used for assessing the sleep quality of the participants in the last month through 7 components consisting of 19 individual items, including sleep quality, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction (e.g., “During the past month, what time have you usually gone to bed at night?,” “During the past month, what time have you usually gotten up in the morning”). Each component is scored between 0 and 3 and the total score ranges from 0 to 21. A higher total score indicates poorer sleep quality.

Physical activity

Physical activity was assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF).⁴⁷ The Chinese version of the IPAQ-SF has been translated and validated.⁴⁸ The IPAQ-SF records the time spent on moderate and vigorous intensity activity, walking (min/week) over the past 7 days. According to the IPAQ-SF rules, the metabolic equivalents (METs) for vigorous-intensity activity, moderate-intensity activity, and walking respectively were 8, 4, and 3.3 METs.⁴⁷ The total physical activity score (METs-min/week) was calculated as the sum of the participants' time spent on moderate and vigorous intensity activity and walking (min/week) multiplied by the corresponding METs.

Statistical analyses

The statistical analyses were performed using SPSS 26.0 (IBM, USA). First, the descriptive analyses were performed to present characteristics of gender differences through the T-test or Chi-square test. Second, multiple linear regression was used to test the associations between ST, sleep quality, anxiety, and depressive symptoms. Third, PROCESS 3.4 in SPSS was used to test mediation effects of ST and mental health outcomes,⁴⁹ with bias-corrected bootstrap 95% confidence interval (CI) in 5,000 bootstrap samples. If the CI for the indirect effects did not include zero, a significant mediating effect was identified. p < 0.05 was considered significant.

Results

Descriptive statistics

Overall, 1,453 college students were included in the analysis. Among them, 663 (45.6 percent) were males and 790 (54.4 percent) were females. As shown in Table 1, the mean age of the participants was 21.47 years (SD: 2.60). For the sleep quality, the mean score was 4.21 (SD: 2.68). For the mental health outcomes, the mean score of SAS was 44.82 (SD: 11.89), and the mean score of SDS was 51.15 (SD: 11.09). For the four types of screen-based activities, most students self-reported between 1 and 4 h/day (ranging from 37.4 to 71.0 percent), while fewer students had more than 4 h/day of different ST (ranging from 4.6 to 20.3 percent). Compared to male students, female students reported poorer sleep quality, higher anxiety, and more severe depressive symptoms. Significant differences were also present in gender distribution for all different types of total ST (p < 0.001).

Table 1.

Characteristics of the Participants (N = 1,453)

	Male (n = 663)		Female (n = 790)		Total (N = 1,453)		p
	Mean or n	SD or %	Mean or n	SD or %	Mean or n	SD or %	p
Age (years)	21.62	2.73	21.34	2.49	21.47	2.60	0.045
TV/movie viewing							<0.001
≤1 h	257	38.8%	218	27.6%	475	32.7%
1 < h ≤ 2	193	29.1%	223	28.2%	416	28.6%
2 < h ≤ 3	98	14.8%	172	21.8%	270	18.6%
3 < h ≤ 4	44	6.6%	88	11.1%	132	9.1%
>4 h	71	10.7%	89	11.3%	160	11.0%
Online social media							<0.001
≤1 h	89	13.4%	38	4.8%	127	8.7%
1 < h ≤ 2	195	29.4%	184	23.3%	379	26.1%
2 < h ≤ 3	165	24.9%	239	30.3%	404	27.8%
3 < h ≤ 4	96	14.5%	152	19.2%	248	17.1%
>4 h	118	17.8%	177	22.4%	295	20.3%
Recreational reading							<0.001
≤1 h	418	63.0%	424	53.7%	842	57.9%
1 < h ≤ 2	113	17.0%	186	23.5%	299	20.6%
2 < h ≤ 3	64	9.7%	92	11.6%	156	10.7%
3 < h ≤ 4	29	4.4%	60	7.6%	89	6.1%
>4 h	39	5.9%	28	3.5%	67	4.6%
Video gaming							<0.001
≤1 h	208	31.4%	464	58.7%	672	46.2%
1 < h ≤ 2	179	27.0%	134	17.0%	313	21.5%
2 < h ≤ 3	152	22.9%	109	13.8%	261	18.0%
3 < h ≤ 4	59	8.9%	45	5.7%	104	7.2%
>4 h	65	9.8%	38	4.8%	103	7.1%
Sleep qualityPSQI (scores)	4.00	2.72	4.39	2.63	4.21	2.68	0.006
AnxietySAS (scores)	43.87	12.31	45.61	11.48	44.82	11.89	0.005
Depressive SymptomsSDS (scores)	49.85	11.36	52.23	10.75	51.15	11.09	<0.001
Physical activityMET-min/week	2222.82	2663.83	1955.02	2272.68	2077.22	2462.64	0.042

MET, metabolic equivalents; PSQI, Pittsburgh Sleep Quality Index; SAS, Self-rating Anxiety Scale; SD, standard deviation; SDS, Self-rating Depression Scale.

Associations between ST and mental health outcomes

Table 2 shows the associations between the time spent of four screen activities and anxiety and depressive symptoms. For ST of TV/movie viewing, individuals who engage in TV/movie viewing for 2 to 3 hours (β = 4.82, 95% CI: 3.09–6.55), 3 to 4 hours (β = 4.62, 95% CI: 2.39–6.86), and more than 4 hours per day (β = 7.83, 95% CI: 5.75–9.90) demonstrated elevated levels of anxiety in comparison to those who watch TV/movies for less than 1 hour per day. The associations persisted after further adjusting for gender, age, physical activity, and other three ST. In addition, compared to TV/movie viewing ≤1 h/day, spending more than 4 hours on TV/movie viewing per day was associated with higher levels of depressive symptoms (β = 2.94, 95% CI: 1.41–4.48). However, the association did not persist after further adjusting for gender, age, physical activity, and other three ST (p > 0.05).

Table 2.

Associations Between Screen Time and Anxiety and Depressive Symptoms

	Anxiety (SAS)		Depressive Symptoms (SDS)
	Model 1 ^a	Model 2 ^b	Model 1 ^a	Model 2 ^b
	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
TV/movie viewing
≤1 h (Ref.)
1 < h ≤ 2	0.41 (−1.12 to 1.93)	0.34 (−1.17 to 1.85)	0.20 (−0.93 to 1.33)	0.06 (−1.07 to 1.19)
2 < h ≤ 3	4.82^*** (3.09 to 6.55)	3.99^*** (2.24 to 5.74)	1.15 (−0.13 to 2.43)	0.63 (−0.67 to 1.93)
3 < h ≤ 4	4.62^*** (2.39 to 6.86)	3.11^** (0.82 to 5.41)	1.40 (−0.25 to 3.05)	0.42 (−1.29 to 2.13)
>4 h	7.83^*** (5.75 to 9.90)	6.06^*** (3.83 to 8.28)	2.94^*** (1.41 to 4.48)	1.43 (−0.23 to 3.09)
Recreational reading
≤1 h (Ref.)
1 < h ≤ 2	3.01^*** (1.48 to 4.54)	2.37^** (0.84 to 3.89)	−0.45 (−1.58 to 0.68)	−0.71 (−1.84 to 0.42)
2 < h ≤ 3	5.55^*** (3.56 to 7.53)	4.78^*** (2.79 to 6.77)	1.56^* (0.10 to 3.02)	1.24 (−0.24 to 2.72)
3 < h ≤ 4	5.16^*** (2.62 to 7.69)	3.11^* (0.52 to 5.70)	1.21 (−0.66 to 3.08)	0.09 (−1.84 to 2.02)
>4 h	9.52^*** (6.63 to 12.41)	7.25^*** (4.19 to 10.30)	3.24^** (1.11 to 5.37)	1.40 (−0.87 to 3.67)
Online social media
≤1 h (Ref.)
1 < h ≤ 2	−2.43^* (−4.80 to −0.06)	−2.75^* (−5.06 to −0.44)	0.95 (−0.76 to 2.67)	0.85 (−0.88 to 2.57)
2 < h ≤ 3	0.89 (−1.46 to 3.24)	−0.60 (−2.93 to 1.73)	2.21^* (0.51 to 3.91)	1.78^* (0.04 to 3.51)
3 < h ≤ 4	2.37 (−0.15 to 4.89)	−0.38 (−2.91 to 2.15)	3.04^** (1.22 to 4.87)	2.35^* (0.46 to 4.24)
>4 h	1.74 (−0.71 to 4.19)	−1.31 (−3.81 to 1.19)	3.48^*** (1.70 to 5.25)	2.51^** (0.64 to 4.37)
Video gaming
≤1 h (Ref.)
1 < h ≤ 2	−0.73 (−2.32 to 0.86)	−0.61 (−2.19 to 0.97)	0.30 (−0.85 to 1.45)	0.53 (−0.64 to 1.71)
2 < h ≤ 3	2.05^* (0.36 to 3.75)	0.98 (−0.73 to 2.69)	1.21 (−0.02 to 2.43)	1.07 (−0.20 to 2.34)
3 < h ≤ 4	1.51 (−0.94 to 3.96)	−0.27 (−2.71 to 2.17)	2.17^* (0.40 to 3.94)	1.76 (−0.05 to 3.58)
>4 h	2.54^* (0.07 to 5.00)	−1.32 (−3.94 to 1.29)	2.29^* (0.51 to 4.07)	1.09 (−0.85 to 3.04)

Model 1 unadjusted linear regression models.

Model 2 association between ST and mental health outcome was adjusted for gender, age, physical activity, and other three ST.

p < 0.05; ^**p < 0.01; ^***p < 0.001.

CI, confidence interval; Ref., Reference group; SAS, Self-rating Anxiety Scale; SDS, Self-rating Depression Scale; ST, screen time.

For ST of recreational reading, engaging in recreational reading for 1 to 2 hours (β = 3.01, 95% CI: 1.48–4.54), 2 to 3 hours (β = 5.55, 95% CI: 3.56–7.53), 3 to 4 hours (β = 5.16, 95% CI: 2.62–7.69), and more than 4 hours per day (β = 9.52, 95% CI: 6.63–12.41) were associated with higher levels of anxiety. The associations persisted after further adjusting for gender, age, physical activity, and other three ST. Compared to recreational reading ≤1 h/day, engaging in recreational reading for 2 to 3 hours (β = 1.56, 95% CI: 0.10–3.02), and more than 4 hours per day (β = 3.24, 95% CI: 1.11–5.37) were associated with higher levels of depressive symptoms. The associations did not persist after further adjusting for gender, age, physical activity, and other three ST (p > 0.05).

For ST of online social media, compared to spending less than 1 hour on online social media, engaging for 1 to 2 hours per day was associated with a lower level of anxiety (β = −2.43, 95% CI: −4.80 to −0.06). The associations remained after further adjusting for gender, age, physical activity, and other three ST. However, using online social media for 2 to 3 hours (β = 2.21, 95% CI: 0.51–3.91), 3 to 4 hours (β = 3.04, 95% CI: 1.22–4.87), and more than 4 hours per day (β = 3.48, 95% CI: 1.70–5.25) were associated with higher levels of depressive symptoms. The associations persisted after further adjusting for gender, age, physical activity, and other three ST.

For ST of video gaming, compared to video gaming ≤1 h/day, playing video games for 2 to 3 hours (β = 2.05, 95% CI: 0.36–3.75), and more than 4 hours per day (β = 2.54, 95% CI: 0.07–5.00) were associated with higher levels of anxiety. The associations did not persist after further adjusting for gender, age, physical activity, and other three ST (p > 0.05). Compared to video gaming ≤1 h/day, playing video games for 3 to 4 hours (β = 2.17, 95% CI: 0.40–3.94), and more than 4 hours per day (β = 2.29, 95% CI: 0.51–4.07) were associated with higher levels of depressive symptoms. The associations also did not persist after further adjusting for gender, age, physical activity, and other three ST (p > 0.05).

Associations between ST and sleep quality

The associations between different types of ST and sleep quality are shown in Table 3. Compared to TV/movie viewing ≤1 h/day, engaging in TV/movie viewing for 3 to 4 hours (β = 0.53, 95% CI: 0.02–1.05) and more than 4 hours per day (β = 0.60, 95% CI: 0.12–1.07) were significantly correlated with poorer sleep quality. However, the associations did not persist after further adjusting for gender, age, physical activity, and other three ST (p > 0.05). Compared to recreational reading ≤1 h/day, engaging in recreational reading for 3 to 4 hours (β = 0.70, 95% CI: 0.25–1.16) and more than 4 hours per day (β = 0.82, 95% CI: 0.16–1.48) were significantly correlated with poorer sleep quality. The associations persisted after further adjusting for gender, age, physical activity, and other three ST. However, online social media and video gaming showed no correlation with sleep quality (p > 0.05).

Table 3.

Association Between Screen Time and Sleep Quality

	Sleep quality (PSQI)
	Model 1 ^a	Model 2 ^b
	β (95% CI)	β (95% CI)
TV/movie viewing
≤1 h (Ref.)
1 < h ≤ 2	−0.11 (−0.46 to 0.24)	−0.15 (−0.50 to 0.20)
2 < h ≤ 3	0.30 (−0.10 to 0.70)	0.15 (−0.26 to 0.55)
3 < h ≤ 4	0.53^* (0.02 to 1.05)	0.25 (−0.28 to 0.79)
>4 h	0.60^* (0.12 to 1.07)	0.30 (−0.22 to 0.82)
Recreational reading
≤1 h (Ref.)
1 < h ≤ 2	0.24 (−0.12 to 0.59)	0.17 (−0.18 to 0.53)
2 < h ≤ 3	0.70^** (0.25 to 1.16)	0.69^** (0.22 to 1.15)
3 < h ≤ 4	0.58 (−0.01 to 1.16)	0.41 (−0.19 to 1.01)
>4 h	0.82^** (0.16 to 1.48)	0.74^* (0.03 to 1.44)
Online social media
≤1 h (Ref.)
1 < h ≤ 2	−0.40 (−0.94 to 0.14)	−0.45 (−0.99 to 0.09)
2 < h ≤ 3	0.04 (−0.49 to 0.57)	−0.12 (−0.66 to 0.42)
3 < h ≤ 4	0.21 (−0.36 to 0.78)	−0.04 (−0.63 to 0.54)
>4 h	0.40 (−0.15 to 0.96)	0.17 (−0.41 to 0.75)
Video gaming
≤1 h (Ref.)
1 < h ≤ 2	−0.25 (−0.61 to 0.11)	−0.16 (−0.53 to 0.20)
2 < h ≤ 3	−0.01 (−0.39 to 0.38)	−0.06 (−0.45 to 0.34)
3 < h ≤ 4	−0.22 (−0.78 to 0.33)	−0.35 (−0.92 to 0.21)
>4 h	−0.08 (−0.64 to 0.47)	−0.48 (−1.09 to 0.12)

Model 1 unadjusted linear regression models.

Model 2 association between ST and sleep quality was adjusted for gender, age, physical activity, and other three ST.

Ref., Reference group; PSQI, Pittsburgh Sleep Quality Index.

p < 0.05; ^**p < 0.01.

Testing for mediation effects

As shown in Table 4, after adjusting for covariates, the recreational reading of 2 to 3 hours had a significant indirect effect on anxiety (indirect effect: 1.48, 95% CI: 0.46–2.52) and depressive symptoms (indirect effect: 0.27, 95% CI: 0.06–0.54) among college students through sleep quality. However, TV/movie viewing, online social media, and video gaming showed no indirect effect on both mental health outcomes.

Table 4.

Mediation Effect of Sleep Quality on the Relationship Between Screen Time and Anxiety and Depressive Symptoms

	Anxiety, ^a β (95% CI)		Depressive symptoms, ^a β (95% CI)
	Direct effect	Indirect effect	Direct effect	Indirect effect
TV/movie viewing
Ref. ≤1 h				—
1 < h ≤ 2	0.66 (−0.78 to 1.86)	−0.32 (−1.06 to 0.43)	0.12 (−1.00 to 1.24)	−0.06 (−0.22 to 0.08)
2 < h ≤ 3	3.67 (2.16 to 5.18)^b	0.32 (−0.51 to 1.16)	0.57 (−0.72 to 1.86)	0.06 (−0.10 to 0.23)
3 < h ≤ 4	2.56 (0.58 to 4.55)^b	0.55 (−0.62 to 1.75)	0.32 (−1.38 to 2.01)	0.10 (−0.12 to 0.35)
>4 h	5.41 (3.49 to 7.34)^b	0.64 (−0.52 to 1.88)	1.31 (−0.34 to 2.96)	0.12 (−0.10 to 0.42)
Recreational reading
Ref. ≤1 h
1 < h ≤ 2	1.99 (0.68 to 3.31)^b	0.37 (−0.39 to 1.12)	−0.78 (−1.90 to 0.35)	0.07 (−0.07 to 0.23)
2 < h ≤ 3	3.30 (1.57 to 5.02)^b	1.48 (0.46 to 2.52)^b	0.97 (−0.50 to 2.44)	0.27 (0.06 to 0.54)^b
3 < h ≤ 4	2.22 (−0.02 to 4.46)	0.89 (−0.68 to 2.63)	−0.07 (−1.99 to 1.84)	0.16 (−0.13 to 0.54)
>4 h	5.66 (3.01 to 8.30)^b	1.59 (−0.34 to 3.62)	1.11 (−1.15 to 3.37)	0.29 (−0.06 to 0.77)
Online social media
Ref. ≤1 h
1 < h ≤ 2	−1.78 (−2.78 to 0.22)	−0.97 (−2.24 to 0.21)	1.03 (−0.68 to 2.74)	−0.18 (−0.47 to 0.04)
2 < h ≤ 3	−0.35 (−2.36 to 1.67)	−0.25 (−1.51 to 0.93)	1.82 (0.10 to 3.54)^b	−0.05 (−0.30 to 0.19)
3 < h ≤ 4	−0.28 (−2.48 to 1.91)	−0.10 (−1.43 to 1.20)	2.37 (0.49 to 4.24)^b	−0.02 (−0.28 to 0.24)
>4 h	−1.68 (−3.85 to 0.48)	0.37 (−1.02 to 1.74)	2.44 (0.59 to 4.29)^b	0.07 (−0.19 to 0.36)
Video gaming
Ref. ≤1 h
1 < h ≤ 2	−0.26 (−1.62 to 1.11)	−0.35 (−1.06 to 0.39)	0.60 (−0.57 to 1.77)	−0.07 (−0.22 to 0.07)
2 < h ≤ 3	1.10 (−0.38 to 2.58)	−0.12 (−0.97 to 0.75)	1.09 (−0.17 to 2.35)	−0.02 (−0.19 to 0.14)
3 < h ≤ 4	0.49 (−1.62 to 2.60)	−0.76 (−2.06 to 0.55)	1.90 (0.10 to 3.71)^b	−0.14 (−0.40 to 0.10)
>4 h	−0.28 (−2.54 to 1.99)	−1.05 (−2.49 to 0.44)	1.28 (−0.65 to 3.22)	−0.19 (−0.50 to 0.07)

Adjusted for gender, age, physical activity, and other three ST.

A significant indirect effect.

Ref., Reference group.

Discussion

This study examined the associations of four types of ST with anxiety and depressive symptoms in college students during the COVID-19 outbreak in Shanghai. The findings indicate that associations between ST and anxiety and depression varied by the type of screen viewing. The associations between slightly excessive time spent on recreational reading with higher levels of anxiety and depressive symptoms were partially mediated by sleep quality.

This study is one of the few to explore the relationship between different types of ST with anxiety and depressive symptoms among college students amidst the COVID-19 pandemic. During the COVID-19 lockdown in Shanghai, over 20 percent of college students reported >2 hours of ST every day, spent on TV/movie viewing, recreational reading, online social media, and video gaming.

During the pandemic, people often used online media to meet social and psychological needs that were otherwise not met, thus causing a significant rise in ST.⁵⁰ Meanwhile, additional ST may also have been required by people using the Internet to keep up with real-time news about the outbreak so that they could be prepared in time.⁵¹ Furthermore, this study found that the four types of ST were not equally associated with anxiety and depressive symptoms. Specifically, >2 h/day of TV/movie viewing and >1 h/day of recreational reading were associated with higher levels of anxiety among college students, while >2 h/day of online social media was associated with more severe depressive symptoms. The findings generally support part of the displacement hypothesis that excessive ST has adverse effects on mental health.

Some previous studies showed that excessive sitting or screen viewing was associated with worse mental health indicators in college students.^16,52–54 The findings are also in accordance with previous studies conducted in the context of the COVID-19 outbreak. For example, Zhang et al. found that higher total ST was associated with a higher level of depressive symptoms during the COVID-19 outbreak among Chinese college students.²⁰ Another related study in the context of COVID-19 showed that the pandemic led to increased ST (including TV viewing, smartphones use, and computer use) and reduced quality of life in adults, which was associated with higher levels of anxiety and depressive symptoms.⁵⁵

However, varying durations of engagement with online social media exhibited diverse effects on mental health. The finding that >2 h/day of online social media was associated with more severe depression might be explained by the social comparison hypothesis, since excessive ST increases the opportunities for users to make social comparisons.²⁶ The social comparison hypothesis states that, with the lack of objective measures of self-evaluation, individuals tend to evaluate themselves by comparing themselves to others.⁵⁶ In social media use, users tend to show positive information, including appearance, life, and achievements, while upward comparisons with others can decrease one's self-esteem levels and further increase depressive symptoms.^57–59

An interesting finding in this study is that 1–2 hours of daily online social media were associated with reduced anxiety in college students. The findings indicate that moderate levels of online social media use may alleviate anxiety in college students during the COVID-19 lockdown. There are a couple of potential explanations for the observed association. People may have used social media to distribute reassuring information and gain self-efficacy and enjoyment, which may have relieved anxiety.^60,61 In addition, people showed authentic self-presentation on social media (Facebook), which was associated with positive psychological outcomes.⁶² Social media serves as a platform to promote social connectedness and enable people to find groups with similar interests and ideas, thus creating a sense of belonging.⁶³

A systematic review showed that positive interactions on social media, more social support and social connectedness, were associated with lower levels of anxiety and depressive symptoms.⁶⁴ Furthermore, social media is seen as one of the means of mediating attributes after emphasizing its instrumental nature. Individuals may feel a sense of achievement for completing a job or task, which has a mitigating effect on anxiety.⁶⁵ Therefore, our findings not only support the associations of excessive time on screen-based behaviors with increased scores of anxiety and depressive symptoms but also extend the previous studies by showing that associations between ST and anxiety and depression vary by the contents and duration of screen viewing.

This study also observed that different types of ST were differentially associated with sleep quality. Recreational reading of 2 to 3 h/day and recreational reading >4 h/day were associated with poorer sleep quality among college students, whereas TV/movie viewing, online social media, and video gaming were not associated with sleep quality. Furthermore, sleep quality partially mediated the associations of slightly excessive time spent on recreational reading with higher levels of anxiety and depressive symptoms.

The findings are consistent with those in the study by Chen et al., who found that sleep quality mediated the association between ST changes and anxiety among young adults during the COVID-19 pandemic.¹⁹ To some extent, the findings of our study also support recent studies in children and adolescents, which have demonstrated that sleep mediates the associations between ST and more severe depressive symptoms.^17,38,66 Therefore, the findings suggest that sleep may be an intervention target to reduce the associations of ST with increased levels of anxiety and depression.

The study also has limitations. First, due to the nature of the cross-sectional study design, the observed associations do not reflect a causal relationship. Second, this study was conducted in the context of the COVID-19 outbreak. Therefore, the findings cannot be directly translated into normal conditions. However, it was also important to study the associations of ST with anxiety and depression during the special period of time. Third, this study did not collect information on socioeconomic status, which might be a potential covariate.

Despite these limitations, this study has its strengths. The participants were from various universities in Shanghai and had a high response rate and good cooperation with the questionnaire. In addition, the college student population in this study expands on previous related studies with larger groups of children and adolescents. Furthermore, the study extends the evidence by exploring the indirect effects of sleep on the relationship between ST and mental health. Future studies are needed to explore these relationships through a longitudinal study design.

Conclusion

During the COVID-19 outbreak, the associations between ST and anxiety and depressive symptoms varied by the type of screen viewing in college students. Excessive time spent on TV/movie viewing and recreational reading was associated with higher anxiety, while excessive time spent on online social media was associated with higher depression. The associations of slightly excessive time spent on recreational reading with higher levels of anxiety and depressive symptoms were partially mediated by sleep quality.

Footnotes

Acknowledgments

The authors wish to thank all those who participated in this study.

Authors' Contributions

T.H. designed the study. Y.S., Y.L., J.Z., L.K., and J.G. conducted data collection. Y.L., Y.S., and K.Z. managed the data and conducted the statistical analyses. Y.L. and T.H. prepared the original draft, and Y.S., J.Z., L.K., and J.G. revised the article. All authors contributed to and have approved the final article.

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

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