Unique and cumulative effects of lifestyle-related behaviors on depressive symptoms among Chinese adolescents

Abstract

Background:

It is well known that some lifestyle-related behaviors are related to depressive symptoms, but the unique and cumulative effects of lifestyle-related behaviors on depressive symptoms among Chinese adolescents are still controversial.

Aims:

The aims of this study were to examine the unique and cumulative effects of lifestyle-related behaviors on depressive symptoms among Chinese adolescents, and explored the potential influences of gender difference on these associations.

Methods:

We conducted a cross-sectional study among 3967 Chinese adolescents aged 11 to 19 from Jilin, China during September and October of 2018. Students reported their lifestyle factors including sleep duration, time spent on computer, time spent on television, time spent on homework, eating breakfast, smoking, drinking, physical activity, and outdoor activity. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CES-D).

Results:

The prevalence of depressive symptoms was 28.2% among Chinese adolescents. Multivariate logistic regression analysis revealed that sleep duration <8 hour/day, time spent on homework ⩾3 hour/day, skipping breakfast, alcohol use, physical activity <3 days/week, and outdoor activity <2 hour/day were positively associated with depressive symptoms in both girls and boys. Time spent on computer ⩾2 hour/day was an independent risk predictor for depressive symptoms in males, while smoking only showed higher risk of depressive symptoms in females. There was an additive relationship between the lifestyle risk index scores and the risk of depressive symptoms for both genders, the relationship being strongest among females.

Conclusion:

The important role of lifestyle factors should be taking into consideration when create intervention programs to prevent and reduce depressive symptoms among adolescents. In addition, preventive interventions may need to focus on gender-informed approaches when targeting multiple lifestyle factors.

Keywords

Depressive symptoms lifestyle factors sleep duration smoking alcohol adolescents

Introduction

Depression is a global public health concern and is expected to be the highest global burden of disease by 2030 (Malhi & Mann, 2018). The incidence rate increases during adolescence, with youth between the age 13 and 18 years at particular risk (Gunnell et al., 2018; Avenevoli et al., 2015). Evidence suggests that depression during adolescence not only associates with immense personal and family suffering, it also causes other adverse outcomes, such as academic failure (Brière et al., 2015), impaired cognitive functions (Vijayakumar et al., 2016), poor interpersonal relationships (Verboom et al., 2014), and substance abuse (Wilkinson et al., 2016). Furthermore, more than half of adolescent suicide victims had depressive disorder, making depression the most common cause of suicide (Barnett, 2016). Thus, identifying risk and protective factors that influence depressive symptoms is crucial to reducing the burden of depressive disorders.

It is widely known that living a healthy life can be beneficial for one’s well-being. A healthy lifestyle means to engage in regular physical activity, refrain from smoking, limit alcohol consumption, eat on time, and get adequate sleep, etc. These behaviors can lead to both better physical health and mental well-being (Tanihata et al., 2015). Adolescence is an important transitional period of physical and psychological developments. During this period, the establishment of lifestyle habits is developed which can significantly impact on immediate and long-term physical and mental health. Therefore, unhealthy lifestyle factors among juveniles become a public health concern (Carli et al., 2014). With advances in technology, screen time, including watching television, using computers and other electronic products, becomes a central component of daily lives for adolescents. The American Academy of Pediatrics recommends no more than 2 hour/day of screen time for children and adolescents (American Academy of Pediatrics. Committee on Public Education, 2001). However, a study showed that 51.5% of adolescents spent more than 2 hours on screen time per day in China (Ye et al., 2018). Sleep time is also an important living habit that influences the health of adolescents. Insufficient sleep, delayed sleep-wake behavior, and sleep disturbances are common among youth and adolescents around the world. The National Sleep Foundation has recommended 8 to 10 hours sleep everyday for teenagers (Hirshkowitz et al., 2015). However, many adolescents sleep less than 8 hours, especially on weekdays (Adolescent Sleep Working Group et al., 2014). Substance abuse, like cigarettes and alcohol, is commonly initiated and established during adolescence. In China, smoking and drinking among adolescents have been commonly reported (Wang et al., 2019). Physical inactivity has also been identified as a major public health problem.

There are growing evidences suggest that lifestyle factors are associate with depressive symptoms (Cairns et al., 2014). Past research showed that substance abuse, such as smoking and drinking, is associated with higher levels of depressive symptoms (Pedrelli et al., 2016). Sleep deprivation and skipping breakfast also increase adolescents’ vulnerability to depression (Ojio et al., 2016; Ren et al., 2020). In addition, an excessive amount of time spent on sedentary behaviors, such as long screen time and high homework load, is related to higher risk of depressive symptoms (Hallgren et al., 2018). On contrary, frequent sports participation can be useful for treating and preventing depression (Schuch et al., 2018). According to recent knowledge, any physical activity level, including low levels (e.g. walking <150 minutes/weeks), can prevent future depression (Mammen & Faulkner, 2013). These modifiable lifestyle factors are deemed to be useful targets for the treatment and prevention of depressive symptoms.

However, the existing evidences on the associations of these lifestyle factors with depression are inconsistent. For instance, the relationship between smoking and depressive symptoms is still controversial, although most studies have found that smokers are more likely to have depression than nonsmoker (Stubbs et al., 2018; Xu et al., 2016; Furihata et al., 2018). In addition, the evidence that links sport participations during adolescence to lower levels of depression is equivocal (Cairns et al., 2014). Inconsistent results might be caused by the heterogeneity of study samples, exposure and outcome assessment, and various confounders. Moreover, previous studies focused on the relationships between screen time and depressive symptoms (Wang et al., 2019), while less attention was paid to the other sedentary behavior, such as doing homework. More research exploring context specific sedentary behavior is still needed, so as to provide important insights into the underlying relationships between different types of sedentary behavior and depressive symptoms. Another drawback of the current studies is that the combined effects of multiple lifestyle factors on depressive symptoms are understudied. Furthermore, few studies have assessed gender-specific associations between lifestyle behaviors and depressive symptoms, which is critical for sex-specific risk identification and intervention.

To present a more comprehensive understanding of the effects of lifestyle factors on depressive symptoms among Chinese adolescents, the current study considered nine different lifestyle factors that may play a significant role in developing depressive symptoms. We aimed to investigate the unique and cumulative effects of lifestyle-related behaviors on depressive symptoms among Chinese adolescents by controlling confounding factors and identify the influences of gender factor on this relationship.

Methods

Study design and participants

We conducted this cross-sectional survey on adolescents in Jilin Province, China, from September to October in 2018. A stratified cluster random sampling method was applied to recruit participants. First, all cities in Jilin Province were divided into three categories according to the level of economic development in 2018, and then we randomly selected one city from each category. Second, based on the proportions of schools, we selected four junior high schools (i.e. grade 7–9) and three senior high schools (i.e. grade 10–12) randomly in each of the three cities, enrolled 21 school in total. Finally, two classes were randomly selected from each grade at each selected school. A total of 3,967 sets of data were collected in the end, after eliminated invalid responses, with a response rate of 94.5%. The age of the participants ranged from 11 to 19 years (mean = 14.9 years, SD = 1.8).

Prior to data collection, we conveyed the study objectives and procedures to students, parents, teachers, and other relevant personnels. Students understood that their participations in the study were voluntarily and all their personal information will be protected by the researchers. We obtained consents from parents and schools and approvals from students for conducting the survey. This study was approved by the Ethics Committee of the School of Public Health, Jilin University.

Measures

Depressive symptoms

The prevalence of depressive symptoms was measured using the Center for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977). The scale contains 20 items, which covered four dimensions named depressed affect, absence of positive affect, somatic activity/inactivity, and interpersonal difficulties. All items were responded on a four-point Likert scale ranging from 0 = rarely or none of the time (<1 day) to 3 = almost or all of the time (5–7 days). After reverse-coding four items, we calculated a sum of the scores for the 20 items. Higher scores indicate more severe depressive symptoms, with a maximum score of 60. Based on the suggested cut-off, participants who scored 16 points or more were considered to be at high risk for depressive symptoms in the present study (Radloff, 1991; Song et al, 2008). In this study, Cronbach’s α coefficient was .84.

Lifestyle factors

The following questions about lifestyle factors were included in the questionnaire.

Sleep duration. Adolescents reported their usual sleep duration at night in a typical week based on the following question: ‘how many hours do you sleep habitually at night?’ Following the recommendations of the American Academy of Sleep Medicine (Paruthi et al., 2016), adolescents who had less than 8 hours of sleep on an average night were considered as having insufficient sleep.

Time spent on television and computer. We used the following question: ‘on an average school day, how many hours do you watch television?’ ‘On an average school day, how many hours do you use computer?’ Both screen behavior variables were each dichotomized as ⩾2 hour/day versus not, consistent with previous studies (Houghton et al., 2015; Zink et al., 2020).

Time spent on homework. We used the following question: ‘on an average school day, how many hours do you spend on your homework?’ The present study categorized time spent on homework as ‘<1 hour/day’, ‘1-hour/day’, and ‘⩾ 3 hour/day’.

Breakfast intake. We used the following question to assess how frequently the participants ate breakfast: ‘How often do you eat breakfast?’ The question had three response options: ‘never’, ‘sometimes’, and ‘every day’ (Manios et al., 2015). The responses were recoded as ‘Yes’ (every day) or ‘No’ (other options) for analysis.

Tobacco and alcohol. Tobacco and alcohol intake were investigated by asking ‘Have you ever tried cigarette smoking, even one or two puffs? (response yes or no)’ and ‘During your life, have you ever had at least one drink of alcohol? (response yes or no),’ respectively.

Physical activity. Physical activity was measured based on the question: ‘During the past week, how many days did you do exercise that increased your heart rate or breathing rate for a total of at least 60 minutes?’ Eight response options were available ranging from ‘0’ to ‘7’ days, and the frequency of physical activity was divided into three categories: high physical activity (⩾3 days/week), low physical activity (1–2 days/week), and no physical activity (0 day/week) (Wang et al., 2018).

Outdoor activity. We used the following question: ‘During the past week, how many hours did you spend on outdoor activities during the day?’ The present study categorized time spent on outdoor activities as ‘<1 hour/day’, ‘1-hour/day’, and ‘⩾ 2 hour/day’.

Potential confounder measures

The potential confounders we considered including residence (urban, suburb), sex (males, females), age (11–13, 14–16, or 17–19 years), single child status (yes, no), living arrangement with parents (living with both parents, living only with father, living only with mother, or not living with both parents), perceived family economic status (above average, average, or below average), father’s education level (primary school or below, middle school, or university or above), and mother’s education level (primary school or below, middle school, or university or above).

Lifestyle risk index

To evaluate the combined effects of multiple lifestyle risk factors on depressive symptoms, we constructed an overall lifestyle risk index. In this study, the lifestyle risk index score was calculated via aggregation of all assessed lifestyle risk factors. The threshold was determined based on existing literature on dose–response relationships between lifestyle factors and depressive symptoms and our own analyses. Unhealthy lifestyle choices with respect to the lifestyle risk index score included: short sleep duration (<8 hour/night), long television time (⩾2 hour/day), long computer time (⩾2 hour/day), long homework time (⩾3 hour/day), not eating breakfast every day, being smoker, alcohol consumption, low physical activity (<3 days/week), low outdoor activity (<1 hour/day). Lifestyle risk index score was constructed by summing up the scores of lifestyle components obtained by each participant as described above. Assuming that subjects in the high risk categories of the mentioned components got the score of 1 and others got the score of 0. Therefore, a lifestyle risk index score ranged from 0 to 9.

Statistical analysis

Analyses were conducted using SPSS 24.0 statistical software. Percentage for categorical variables were used to describe the distribution of lifestyle factors, depressive symptoms, and confounders. Chi-square tests were used to compare the differences in depressive symptoms between different demographic and family factors, and tested differences in the distribution of lifestyle factors between males and females. Univariate and multivariate logistic regression analyses were performed to examine the unique and cumulative effects of lifestyle-related behaviors on depressive symptoms. The results were expressed as odds ratios (OR) and 95% confidence intervals (CI). A p-value <.05 was considered to be statistically significant.

Results

Participant characteristics

The descriptive characteristics of the study sample are shown in Table 1. The sample consisted of 1914 males (48.2%) and 2053 females (51.8%). The age of the students ranged from 11 to 19 years (mean = 14.9 years, SD = 1.8). Nearly half of the students (49.7%) resided in urban area, and more than half of the students (59.4%) were the only child in the family. Most of the students (76.0%) lived with both parents. The majority of the students (80.7%) reported average level family economic status. The education level of parents was concentrated in middle school category (father: 75.3%, mother: 72.4%).

Table 1.

The general characteristics of the study population.

Variables	Total	Depressive symptoms	p
Variables	N (%)	n (%)	p
Residence
Urban	1973 (49.7)	581 (29.4)	.084
Suburb	1994 (50.3)	538 (27.0)	.084
Sex
Males	1914 (48.2)	499 (26.1)	.004
Females	2053 (51.8)	620 (30.2)	.004
Age (years)
11–13	1077 (27.1)	238 (22.1)	<.001
14–16	1953 (49.2)	559 (28.6)
17–19	937 (23.6)	322 (34.4)
Single child in the family
Yes	2358 (59.4)	648 (27.5)	.218
No	1609 (40.6)	471 (29.3)	.218
Living arrangement with parents
Living with both parents	3015 (76.0)	799 (26.5)	<.001
Living only with father	287 (7.2)	101 (35.2)
Living only with mother	419 (10.6)	127 (30.3)
Not living with both parents	246 (6.2)	92 (37.4)
Family economic status
Above average	171 (4.3)	48 (28.1)	<.001
Average	3201 (80.7)	853 (26.6)
Below average	595 (15.0)	218 (36.6)
Father’s education level
Primary school or below	474 (11.9)	153 (32.3)	.025
Middle school	2986 (75.3)	842 (28.2)
University or above	507 (12.8)	124 (24.5)
Mother’s education level
Primary school or below	592 (14.9)	190 (32.1)	.035
Middle school	2872 (72.4)	802 (27.9)
University or above	503 (12.7)	127 (25.2)

The results of the chi-square tests indicated that depressive symptoms were significantly different among different groups of students based on sex, age, living arrangement with parents, family economic status, father’s education level, and mother’s education level (p < .05). These factors were adjusted for in subsequent analyses. However, residence and single child status were not associated with the depressive symptoms (p > .05).

Distribution of lifestyle factors

Gender-specific distributions of the main variables are specified in Table 2. About 28.2% students (26.1% for boys vs 30.2% for girls) have depressive symptoms. The distribution of eight lifestyle factors, lifestyle risk index and depressive symptoms were significantly different between males and females (p < .05), except for eating breakfast every day (p > .05).

Table 2.

Distribution of lifestyle factors, lifestyle risk index and depressive symptoms.

Lifestyle factors	Total	Males		Females		p
Lifestyle factors	N (%)	n	%	n	%	p
Sleep duration (per night)
⩾8 hour	1,662 (41.9)	882	46.1	780	38.0	<.001
<8 hour	2,305 (58.1)	1,032	53.9	1,273	62.0
Time spent on television (per day)
<2 hour	3,093 (78.0)	1,525	79.7	1,568	76.4	.012
⩾2 hour	874 (22.0)	389	20.3	485	23.6
Time spent on computer (per day)
<2 hour	3,545 (89.4)	1,666	87.0	1,879	91.5	<.001
⩾2 hour	422 (10.6)	248	13.0	174	8.5
Time spent on homework (per day)
<1 hour	705 (17.8)	407	21.3	298	14.5	<.001
1-hour	2,292 (57.8)	1,114	58.2	1178	57.4
⩾3 hour	970 (24.5)	393	20.5	577	28.1
Eating breakfast every day
Yes	2,636 (66.4)	1,300	67.9	1,336	65.1	.058
No	1,331 (33.6)	614	32.1	717	34.9
Tobacco use
No	3,503 (88.3)	1,596	83.4	1,907	92.9	<.001
Yes	464 (11.7)	318	16.6	146	7.1
Alcohol use
No	2,562 (64.6)	1,101	57.5	1,461	71.2	<.001
Yes	1,405 (35.4)	813	42.5	592	28.8
Physical activity (per week)
0 day	1,005 (25.3)	375	19.6	630	30.7	<.001
1–2 day	1,342 (33.8)	596	31.1	746	36.3
⩾3 day	1,620 (40.8)	943	49.3	677	33.0
Outdoor activity (per day)
<1 hour	1,249 (31.5)	509	26.6	740	36.0	<.001
1-hour	1,512 (38.1)	752	39.3	760	37.0
⩾2 hour	1,206 (30.4)	653	34.1	553	26.9
Lifestyle risk index
0	268 (6.8)	151	7.9	117	5.7	.001
1	605 (15.3)	320	16.7	285	13.9
2	852 (21.5)	424	22.2	428	20.8
3	903 (22.8)	406	21.2	497	24.2
4	749 (18.9)	333	17.4	416	20.3
5	384 (9.7)	187	9.8	197	9.6
6–9	206 (5.2)	93	4.9	113	5.5
Depressive symptoms
High	1,119 (28.2)	499	26.1	620	30.2	.004
Low	2,848 (71.8)	1,415	73.9	1,433	69.8

Associations of different lifestyle factors and depressive symptoms

Results of the logistic regression analysis regarding associations of the unique lifestyle factors with depressive symptoms are shown in Table 3. After adjusting for the other individual lifestyle factors and potential confounders, the multiple logistic regression analysis showed sleep duration <8 hour/night, time spent on homework ⩾3 hour/day, not eating breakfast every day, alcohol consumption, physical activity <3 days/week were positively associated with the increased likelihood of depressive symptoms for both genders. Time spent on computer was significantly associated with depressive symptoms among males only, while the association for tobacco use was statistically significant only for females. In addition, less than 1 hour of outdoor activity was associated with more severe depressive symptoms in males, while less than 2 hour of outdoor activity was associated with more severe depressive symptoms in females. There was a ‘dose–response’ relationship between the levels of lifestyle factors and depressive symptoms.

Table 3.

Unique effects of lifestyle factors on depressive symptoms among males and females.

Lifestyle factors	Males		Females
Lifestyle factors	Unadjusted OR (95%CI)	Adjusted OR (95%CI)	Unadjusted OR (95%CI)	Adjusted OR (95%CI)
Sleep duration (per night)
⩾8 hour	1.00	1.00	1.00	1.00
<8 hour	2.32 (1.87–2.88)***	1.93 (1.52–2.46)***	2.01 (1.63–2.46)***	1.72 (1.35–2.19)***
Time spent on television (per day)
<2 hour	1.00	1.00	1.00	1.00
⩾2 hour	1.27 (0.99–1.62)	1.28 (0.97–1.69)	1.03 (0.83–1.29)	0.87 (0.68–1.11)
Time spent on computer (per day)
<2 hour	1.00	1.00	1.00	1.00
⩾2 hour	1.61 (1.21–2.14)**	1.44 (1.04–1.99)*	1.58 (1.15-2.18)**	1.42 (0.99–2.04)
Time spent on homework (per day)
<1 hour	1.00	1.00	1.00	1.00
1-hour	0.75 (0.57–0.99)*	0.97 (0.72–1.31)	0.84 (0.62–1.14)	1.18 (0.84–1.66)
⩾3 hour	1.33 (0.97–1.84)	1.54 (1.09–2.19)*	1.22 (0.87–1.71)	1.53 (1.06–2.21)*
Eating breakfast every day
Yes	1.00	1.00	1.00	1.00
No	1.86 (1.51–2.30)***	1.50 (1.20–1.88)***	2.13 (1.76–2.59)***	1.86 (1.51–2.28)***
Tobacco use
No	1.00	1.00	1.00	1.00
Yes	1.40 (1.08–1.82)*	0.87 (0.64–1.18)	3.47 (2.46–4.88)***	2.40 (1.65–3.50)***
Alcohol use
No	1.00	1.00	1.00	1.00
Yes	1.98 (1.61–2.43)***	1.65(1.30–2.10)***	2.42 (1.98–2.96)***	1.83 (1.47–2.29)***
Physical activity (per week)
0 day	1.00	1.00	1.00	1.00
1–2 day	0.70 (0.53–0.93)*	0.89 (0.66–1.20)	0.67 (0.53–0.84)***	0.78 (0.61–1.00)
⩾3 day	0.57 (0.44–0.75)***	0.74 (0.56–0.99)*	0.54 (0.43–0.68)***	0.73 (0.56–0.95)*
Outdoor activity (per day)
<1 hour	1.00	1.00	1.00	1.00
1-hour	0.51 (0.39–0.65)***	0.59 (0.45–0.77)***	0.76 (0.61–0.94)*	0.90 (0.71–1.14)
⩾2 hour	0.45 (0.34–0.58)***	0.50 (0.38–0.67)***	0.59 (0.45–0.76)***	0.67 (0.50–0.88)**

p < .05, **p < .01, ***p < .001. OR = odds ratio; CI = confidence interval.

Adjusted for other lifestyle factors and potential confounders (age group, living arrangement with parents, family economic status, father’s education level, mother’s education level).

Associations of lifestyle risk index and depressive symptoms

After adjusting for potential confounders, it showed no differences in the likelihood of depressive symptoms between students who were identified with one or without lifestyle risk factor among males. In addition, an additive relationship was observed between the lifestyle risk index and the likelihood of depressive symptoms for both genders. We found that the likelihood of develop depressive symptoms increased with the number of lifestyle risk factors, and the relationship being strongest among females. The results are shown in Table 4.

Table 4.

Cumulative effects of lifestyle risk factors on depressive symptoms among males and females.

Lifestyle risk index	Males			Females
Lifestyle risk index	DS (%)	Unadjusted OR (95%CI)	Adjusted OR (95%CI)	DS (%)	Unadjusted OR (95%CI)	Adjusted OR (95%CI)
0	6.6	1.00	1.00	6.0	1.00	1.00
1	10.6	1.68 (0.81–3.49)	1.68 (0.81–3.51)	18.9	3.67 (1.62–8.34)**	3.75 (1.64–8.54)**
2	21.7	3.91 (1.98–7.73)***	3.91 (1.96–7.76)***	21.5	4.30 (1.94–9.56)***	4.22 (1.89–9.44)***
3	29.6	5.92 (3.01–11.63)***	5.95 (3.00–11.81)***	29.8	6.66 (3.03–14.65)***	6.51 (2.93–14.47)***
4	38.7	8.92 (4.53–17.57)***	8.88 (4.45–17.72)***	39.4	10.23 (4.65–22.51)***	9.84 (4.41–21.95)***
5	37.4	8.44 (4.16–17.10)***	8.38 (4.06–17.27)***	45.7	13.22 (5.86–29.83)^***	13.01 (5.69–29.77)***
6–9	47.3	12.66 (5.92–27.06)***	12.26 (5.63–26.70)***	57.5	21.28 (9.09–49.80)^***	20.47 (8.60–48.75)***

p < .01. ***p < .001.

OR = odds ratio; CI = confidence interval; DS, depressive symptoms.

Adjusted for potential confounders (age group, living arrangement with parents, family economic status, father’s education level, mother’s education level).

Discussion

The present study investigated the unique and cumulative effects of lifestyle-related behaviors on depressive symptoms in adolescents of different genders. The results showed that factors like short duration of sleep, long homework time, skipping breakfast, drinking alcohol, less physical activities and outdoor activities were significantly associated with the higher risk of depressive symptoms for both genders.

The time spent on computer was an independent predictor for depressive symptoms in males, while smoking increased the risk of depressive symptoms in females. Further, we found that the risk of depressive symptoms increased with the number of unhealthy lifestyle factors for both genders.

The results of the present study suggested that short sleep duration was significantly associated with depressive symptoms for both genders, consistent with previous findings (Widome et al., 2019; Short et al., 2020). Several possible interpretations about the association between sleep duration and depressive symptoms have been proposed. One is that insufficient sleep may increase daytime fatigue, which can lead to adverse consequences like depression (Twenge et al., 2017; Chaiard et al., 2018; Chami et al., 2020). In addition, significant distress during daytimes, unable to focus, memory decline, and disturbed mood can also cause depression (Lo et al., 2016).

Another is that sleep deprivation alters the hypothalamic-pituitary-adrenal axis. This mechanism in the body which dysfunction can result in several mental problems including depression (Lippman et al., 2017). Some studies have shown that causal relationships between sleep and depressive symptoms are likely to be bi-directional. One of the symptoms of depression is disruption in normal sleep patterns (Rumble et al., 2015), and insufficient sleep can contribute to the development and worsening of depressive symptoms (Furihata et al., 2017). In short, sleep is essential to support mental health and emotional development during adolescence (Furihata et al., 2012). The education on the topic of the importance of high-quality sleep for good mental health should be prioritized in universal prevention programs.

Our findings indicated that the time spent on computer rather than television was an independent factor associated with development of depressive symptoms. This conclusion was consistent with Goldfield et al. (2016). Previous studies have proved that watching television has no connection with depressive symptoms (Casiano et al., 2012; Arat, 2015). The results may be related to the fact that people often watch television with their families, which provide opportunities for social interactions. On the other hand, computer is more of a solitary activity. Notably, using the computer for more than 2 hours a day was associated with high risk of depressive symptoms only in male population in our study. This could be the result of the fact that boys and girls normally use computers for different purposes, which were reflected on their mental health status. Unfortunately, we could not assess this possibility in our sample, as we did not collect the information on the purposes of computer use. The device-specific associations observed in this study suggest that sedentary behavior may not be the primary explanation for the link between screen time and depressive symptoms. In other words, specific types of electronic gadgets use-driven negative affect may not be fully explained by physiological mechanisms. Instead, psychosocial mechanisms are more likely to explain the potential link. Furthermore, few previous studies have investigated the relationship between time spent on homework and depressive symptoms. Doing homework is not only an indispensable part of students’ daily life, but also a sedentary behavior that may be related to depressive symptoms. In our study, both boys and girls who spent 3 hours or more on homework had higher depressive symptoms. Compared to western countries (such as the United States and Australia), Chinese students spend more time studying and doing homework (Sun et al., 2012). This finding provides advice for schools and teachers to give students a reasonable amount of homework.

Breakfast has long been recognized as the most important meal of the day (Wang et al., 2019). We found that the association between skipping breakfast and depressive symptoms exists. This finding is consistent with previous studies that have suggested that those who occasionally or never eat breakfast were more likely to develop depressive symptoms than those who eat breakfast regularly (Ferrer-Cascales et al., 2018; Lee & Kim, 2019). This association could be explained by the effect of carbohydrate ingestion. When the level of blood glucose concentration falls below normal standard due to night time fast, cortisol will be released (Fishbein & Pease, 1994). Higher cortisol levels are associated with an increased risk of depression. However, carbohydrate supplementation after breakfast can fully relieve metabolic stress and attenuate cortisol excretion (Scharhag et al., 2006). In addition, adolescents who skip breakfast are missing a significant amount of nutrients. Nutrients are essential for maintaining physical and mental health, and nutrition deficiency can lead to fatigue and sleepiness in adolescents. As a result, their behaviors will be affected, and their academic performances and memory functions will be diminished (Smith & Richards, 2018; Mhurchu et al., 2013). On contrary, people who have breakfast every day could start the day with a higher energy level and lower stress. Considering psychological benefits, promoting breakfast consumption may be an important strategy to improve public health.

Among adolescents, cigarette smoking and alcohol consumption are common behaviors in modern society. Researchers often put these two behaviors together to discuss their relationships to mental health. There are several ways in which the positive association between smoking and development of depression can be explained. One explanation is that neurobiological changes induced by smoking may contribute to vulnerabilities to depression (Vulser et al., 2015). A second explanation is that the results are due to reverse causation meaning that individuals could start smoking because they developed symptoms of depression (Flensborg-Madsen et al., 2011). The self-medication hypothesis suggested that individuals with depression might use smoking as an emotion regulation strategy to cope with their depressive mood or reduce their negative affect (Audrain-McGovern et al., 2009). Although boys are more likely to engage in smoking, our study discovered that smoking is a risk factor for depressive symptoms only in females. This result also exists in some studies that have reported that the association between smoking and depressive symptoms seemed to be stronger in female adolescents than males (Arnold et al., 2014; Guo et al., 2016). One reason for this result may be related to the fact that smoking is more common among males, and they may engage in smoking not because of emotional problems, but as an effective mean of displaying masculinity. On contrary, smoking is more likely to be regarded as an unacceptable deviant behavior among females, so females are affected more by smoking. Additionally, previous studies have shown that, compared to males, females demonstrated greater reliance on emotion-focused coping strategies in response to negative distress, including greater reliance on cigarettes to relieve negative affects (Pang et al., 2015; Wray et al., 2015). Alcohol consumption is another problematic behavior related to depressive symptoms (Pedrelli et al., 2016). Consistent with most studies, our study suggested that alcohol consumption is associated with depressive symptoms. Similar to smoking, alcohol consumption can act as a coping strategy for reduce emotional distress among people with depression (Hogarth et al., 2018). In addition, evidences indicated that heavy drinking during youth has a negative effect on the brain. Thus increasing the risk for mood problems (Squeglia et al., 2015). Unlike smoking, there was no sex difference in drinking behavior in this study.

The results of the present study suggested that lack of exercise was significantly associated with depressive symptoms. Previous studies have shown that physical activity reduces depressive symptoms and physically active individuals have lower risk for depressive symptoms in the future compared to inactive people (Raatikainen et al., 2019; Schuch et al., 2018). In addition, a cohort study indicated that the efficacious amount of physical activity for reducing the onset of depressive symptoms was higher for men than women (Kim et al., 2019). Since our study did not accurately identify the specific thresholds of the impact of physical exercise on depression between females and males, we can only drew the conclusion that people engage in high-level physical exercises are at the lowest risk of depressive symptoms. In addition, our study did not find any difference between the level of depression of adolescents who did not exercise and those who did low-level physical exercise. This is inconsistent with previous studies suggesting that any physical activity level, including low levels (e.g. walking <150 minutes/weeks), may prevent people from developing depressive symptoms (Mammen & Faulkner, 2013). Similar to physical activity, more outdoor activities were associated with lower depressive symptoms in our sample. Our study showed that the outdoor activities can provide rich sensory experiences with positive effects on mental health (Lawton et al., 2017). Mathieu et al. reported that increasing time spent outdoors might increase total physical activity in youth, which could contribute to flourishing mental health (Bélanger et al., 2019). It is worth noting that females seemed to need more outdoor time than males to reduce the risk of depressive symptoms in our study.

In the present study, we observed that an additive relationship between the number of unhealthy lifestyle-related behaviors and depressive symptoms. The more lifestyle risk factors students reported, higher the likelihood of experiencing depressive symptoms. This result confirmed the cumulative risk hypothesis that the accumulation of multiple risk factors increases the possibility of adverse health conditions (Sameroff, 2000). In this study, the majority of students (77.9%) reported at least two risk factors, while 56.4% reported at least three. We should not only focus on the impact of specific lifestyle factors on depression, but also the amount of lifestyle risk factors. Furthermore, the additive relationship was strongest for females, which indicates that the relationship between individual risk factors and depressive symptoms is stronger among females.

This article provided more evidence on the effects of lifestyle factors on depression among Chinese adolescents. This study not only identifies the types of risk factors related to depressive symptoms, but also emphasizes that combination of multiple high-risk lifestyle factors and gender differences can greatly influence the condition of depression among adolescents. All in all, the results from this study will be instrumental in the development of evidence-based guidelines for reducing depressive symptoms in adolescents by adjusting their lifestyles. It can also help to strengthen school-based and family-based health promotion practices and develop strategies to help adolescents to improve their quality of lives.

Several study limitations should be noted when interpreting the results. First, since this is a cross-sectional study, no causal relationship can be draw from the data. More comprehensive longitudinal studies are needed to better understand the complexity and potentially reciprocal relationship between these variables. Second, reports of depressive symptoms via CES-D were used rather than a clinical assessment of depression. Similarly, all lifestyle factors were based on self-report questionnaires and may be subject to recall and social desirability bias, so future studies should use objective measures for more accurate data.

Conclusion

In summary, after control a wide range of confounders, adequate duration of sleep, appropriate time for homework, eating breakfast regularly, abstaining from alcohol, physical activities, and outdoor activities are associated with lower risk of depressive symptoms among Chinese adolescents in both genders. Smoking is an independent risk predictor for depressive symptoms in girls, while longer time of computer use can worsen the existing depressive symptoms in boys. In addition, the likelihood of depressive symptoms increased with the number of risky lifestyle factors. Lifestyle risk factors seem to have an additive effect on mental health.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the International Innovation Team of Jilin University [grant number 2019GJTD06].

ORCID iD

Ruilin Cao

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