Associations between Dietary Behaviors and Physical Activity and Mental Health Disorders among Chinese Residents Aged 18-60: A Cross-Sectional Study

doi:10.21203/rs.3.rs-4899132/v1

Download PDF

Research Article

Associations between Dietary Behaviors and Physical Activity and Mental Health Disorders among Chinese Residents Aged 18-60: A Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-4899132/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective

Our aim was to study the associations of dietary behavior and physical activity intensity with anxiety and depressive symptoms in Chinese adults, as well as their dose‒response relationships.

Method

The data originated from the Psychology and Behavior Investigation of Chinese Residents (PBICR) project. We categorized poor dietary behavior and physical activity intensity based on the Dietary Guidelines for Chinese Residents (2022) and the IPAQ-7 questionnaire. Poor dietary behavior encompassed five aspects: sugary drink consumption, alcohol intake, skipping breakfast, eating out, and dieting. Physical activity was classified into three levels: low, medium, and high. Ordered logistic regression was used to estimate the adjusted odds ratios and 95% confidence intervals for depressive and anxiety symptoms. A restricted cubic spline analysis further explored the dose‒response relationship between anxiety/depression rates and unhealthy dietary behaviors.

Results

A total of 43.61%, 45.42%, and 52.29% of people with low, moderate and high physical activity levels, respectively, suffer from anxiety. A total of 55.76%, 51.73%, 58.25%, 53.68%, and 64.51% of people with bad eating habits, such as sugary drinks, drinking, skipping breakfast, eating out, suffer from anxiety. Individuals who engage in low, moderate and high levels of physical activity experience depression at a rate of 54.29%, 57.36%, and 63.90%, respectively. Individuals who consumed sugary drinks or alcohol, frequently skipped breakfast, dined out frequently, or were on a diet exhibited depression rates of 68.40%, 62.91%, 65.22%, and 76.22%, respectively. After adjusting for the same variables, the types of unhealthy eating behaviors and levels of physical activity were positively correlated with anxiety: sugary drinks (aOR = 1.38; 95% CI = 1.27, 1.41), alcohol consumption (aOR = 1.31; 95% CI = 1.21, 1.41), skipping breakfast (aOR = 1.65; 95% CI = 1.51, 1.80), eating out of home (aOR = 1.19; 95% CI = 1.09, 1.30), diet (aOR = 1.95; 95% CI = 1.75, 2.16), and physical activity level (mid: aOR = 1.02; 95% CI = 0.95, 1.11; high: aOR = 1.95; 95% CI = 1.36, 1.47), as well as the same changes in depressive moods.

Conclusion

The increasing risk of anxiety and depression is associated with a growing number of unhealthy dietary habits, resulting in a commensurate increase in these mental health concerns.

dietary behaviors

physical activity

anxiety

depression

Mental health disorders, including anxiety and depression, represent a significant and escalating global health challenge. In 2019, 290.2 million individuals experienced depressive disorder, while 301.39 million individuals experienced anxiety disorder^[1],[2]. A growing body of evidence has shown that anxiety and depression may manifest as symptoms such as low mood, diminished interest, poor eating and sleeping and can lead to disability, dementia, and even suicide^{[3], [4], [5]}. Lifestyles such as dietary behaviors and physical activity are modifiable factors that affect mental health^{[6, 7]}.

With the rapid development of society, the evolution of lifestyles and the increase in the amount of processed food, people's eating habits have gradually changed. Research indicates that some food habits, such as consuming sugary drinks^[8], skipping breakfast^[9] and ordering takeaways^[10], can contribute to mental health issues. These actions frequently have the potential to cause issues such as weight gain and metabolic diseases, which can worsen psychological conditions such as anxiety and sadness.

Furthermore, studies have demonstrated that physical activity triggers the body to release endorphins and dopamine, which function in the central nervous system to process emotions and control mood. Elevated blood levels of endorphins and dopamine facilitate stress relief and lessen the symptoms of depression and anxiety. A healthy physical activity regimen frequently improves mental health^{[11, 12]}.

China, which has the second-largest population globally, is facing a growing burden of mental disorders^[13]. A national report indicated a 10.6% detection rate for depression risk and a 15.8% detection rate for anxiety risk^[14]. Existing research in this area on Chinese populations has focused mainly on a particular group of people, such as adolescents^[15], older adults^{[16, 17]}, working populations^[18] and university students^{[19, 20]}, and few studies have investigated the associations between dietary behavior, physical activity intensity, and anxiety or depression in Chinese adults. Nonetheless, adults are better able to take charge of their own lives, exhibit more self-control, and are better suited to directly changing their lifestyles—such as their nutritional status and level of physical exercise. A better understanding of the roles of these lifestyle factors in the onset and progression of adult depressive symptoms is necessary, especially given their changeable nature and potential for use in the prevention or treatment of anxiety and depressive symptoms.

Therefore, in the present study, we assessed anxiety and depressive symptoms in adult participants using a structured questionnaire with data from the PBICR programme. We defined poor dietary behavior and physical activity intensity by the Dietary Guidelines for Chinese Residents (2022) and the International Physical Activity Questionnaire (IPAQ-7) and defined poor dietary behavior as five dimensions: consumption of sugary drinks, alcohol intake, skipping breakfast, dining out, and engaging in dieting practices. Physical activity was divided into three levels: low, medium, and high. Our aim was to study the associations of dietary behavior and physical activity intensity with anxiety and depressive symptoms in Chinese adults, as well as their dose‒response relationships.

1 Study Design

The data were derived from the Psychology and Behavior Investigation of Chinese Residents (PBICR) project. PBICR spanned from June 20, 2022, to August 31, 2022, encompassing 31 provinces (including autonomous regions and municipalities directly under the central government) of China. This study employed a robust multistage sampling method across 148 cities, 202 districts and counties, 390 townships/towns/streets, and 780 communities/villages. Using China's population pyramid as a reference, quota sampling was meticulously performed on selected residents in ten cities. This process encompassed attributes such as gender, age, and urban/rural distribution to ensure that the resulting samples mirrored the demographic characteristics of the overall population.

The survey was carried out through the Wenjuanxing network platform, the most popular survey software in China (https://www.wjx.cn/). Participants accessed a dedicated link to complete the questionnaire. In cases where respondents possessed cognitive abilities but lacked the ability to complete the questionnaire, the researcher conducted individual interviews and subsequently addressed the questions on their behalf. This study was approved by the Institutional Review Committee of Ji’nan University, Guangzhou, China (JNUKY-2021-018). All the participants fully understood the study and voluntarily signed informed consent forms.

2 Participants

The inclusion criteria were as follows: ① age ≥ 12 years; ②nationality of the People's Republic of China; ③ permanent resident in China (with an annual absence from home ≤ 1 month); ④ voluntary enrollment in the study and successful completion of the informed consent form; ⑤completion of the online questionnaire either on their own or with the help of the investigator; and ⑥understanding the meaning of each entry in the questionnaire.

The exclusion criteria were as follows: ① experiencing delirium or mental abnormalities; ② experiencing cognitive dysfunction; and ③ currently involved in other similar research projects. ; ④ Individuals expressing unwillingness to cooperate.

A total sample size of 21,916 individuals was included, and the research scope of this study included residents aged 18–60 years. Invalid data, such as age nonconformity, logical errors, and abnormal levels of physical activity, were excluded, resulting in the completion of the survey by 14,358 people (Fig. 1).

3 Assessment of Dietary Behaviors and Physical Activity Intensity

3.1 Dietary Behaviors

Poor dietary behaviors were defined as follows^[21]: ①Preference for sugar beverages: characterized by an average consumption exceeding four bottles (1,200 ml) per week in the past year; ②Drink: indicated by a habitual intake of alcoholic beverages (including beer, yellow wine, white wine, red wine, etc.); ③Skipping breakfast: defined as eating breakfast no more than twice in the past 7 days; ④Preference for takeaway: characterized by consuming takeaway meals/eating out (excluding cafeteria) more than three times in the past 7 days; and ⑤On diet: indicated by engaging in intermittent fasting/lightfasting behaviors during the past year.

3.2 Physical activity intensity

The PBICR follow-up interview questionnaire collected data on the quantity and duration of highly physically demanding activities (such as lifting heavy objects, plowing, aerobic exercise, etc.), moderately vigorous physical activities (such as lifting light objects, tai chi, sprinting, etc.), and light physical activities (such as walking) carried out by the respondents during the previous week. First, using the metabolic equivalent (MET) assignment of each physical activity in the short version of the IPAQ (International Physical Activity Questionnaire), we estimated the respondents’ energy expenditure regarding physical activity over one week. The METs for light-intensity, moderate-intensity, and high-intensity physical activity were 3.3, 4.0, and 8.0, respectively. The intensity level of physical activity is determined by multiplying the MET value assigned to the particular activity type by the frequency per week (d/w) and amount of time per day (min/d). The sum of these three intensity levels represents the total physical activity level. Physical activity was classified into three categories based on the IPAQ rubric: low-intensity physical activity (< 600 METs/min per week), moderate-intensity physical activity (600–3000 METs/min per week), and high-intensity physical activity (> 3000 METs/min per week). To enable comparative analyses with international studies on physical activity levels, two categories of physical activity levels were established: low intensity and moderate-to-high intensity^[22].

4 Anxiety and Depression

As a widely used self-report anxiety questionnaire, the 7-item Generalized Anxiety Disorder Scale (GAD-7) has demonstrated robust reliability and validity in general population studies^[23]. In the context of this research, the GAD-7 served as a concise screeing tool to identify anxiety levels, employing seven items rated on a 4-point Likert scale ranging from “not at all” to “nearly every day”. The total score, ranging from 0 to 21, reflects the severity of anxiety symptoms, with higher scores indicating greater severity. Specifically, scores of 0–4 denote no symptoms, 5–9 indicate mild symptoms, 10–14 indicate moderate symptoms, and 15–21 signify severe symptoms. In this study, we considered a score greater than 5 to be the criterion for determining the presence of anxiety symptoms. In addition, the Cronbach’s α was 0.954 in this study^[24].

The Patient Health Questionnaire-9 (PHQ-9), designed to screen for depression in accordance with the criteria of the Diagnostic and Statistical Manual of Mental Disorders, has been identified as a highly reliable screening tool^{[25, 26]}. Each of the nine questionnaire items is rated based on frequency, ranging from 'not at all' to 'nearly every day.' The total score ranges from 0 to 27, with higher scores indicating more severe depression. Specifically, scores between 0 and 4 indicate no symptoms, 5–9 indicate mild symptoms, 10–14 denote moderate symptoms, and 15–27 signify severe symptoms. We also set 5 as the cutoff score in this study. The Cronbach’s α of the PHQ-9 was 0.939.

5 Covariates

This study included demographic covariates, including a range of sociodemographic factors: age, sex, urban‒rural status, educational status (illiterate, primary, junior high, high school, university and above), occupation (student, employed, unemployed/retired), region (east, central, west), marital status (unmarried/divorced/widowed, married), living alone or not, mode of healthcare (no healthcare, with healthcare), and per capita monthly income (less than 3,000, between 3,000 and 6,000, 6,000 and over).

6 Statistical analysis

The quantity and percentage of categorical variables, as well as the mean and standard deviation of continuous variables, were calculated using descriptive statistics. We applied the chi-square test to compare anxiety and depression symptoms among dietary behaviors and physical activity intensity. Ordered logistic regression was used to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for depressive and anxiety symptoms, dietary behaviors and physical activity intensity. Covariates were adjusted in Model I and Model II: age, sex, marital status, urban or rural status, education, career status, residential status, insurance status, and per capita monthly household income. Model I included the variables number of poor dietary behaviors and physical activity intensity, and Model II included the variables type of poor dietary behaviors and physical activity intensity. Furthermore, a restricted cubic spline was performed to explore the dose–response relationship between anxiety and depression rates and the number of dementia behaviors. All tests in this study were two-sided with a significance level of P < 0.05. Participants were categorized by sex and age for subgroup analysis. SAS 9.4 (Inc.) and R 4.2.2 (drc package) statistical software were used to perform all the statistical analyses.

As shown in Fig. 1, a total of 14358 adults were included in this study, of whom 6667 (46.43%) had anxiety symptoms, including mild anxiety symptoms in 4788 (33.35%), moderate anxiety symptoms in 1452 (10.11%), and severe anxiety symptoms in 427 (2.97%). A total of 8285 (57.70%) participants were assessed as having depressive symptoms, of which 5201 (36.22%) participants were mildly depressed, 1936 (13.48%) participants were moderately depressed and 1148 (8.00%) participants were highly depressed. According to the baseline characteristics of the participants (Table 1), those aged 18–34 years, female, Student, living in Eastern China, rural, with a high level of education and income, unmarried/divorced/widowed, living alone and having insurance had a greater prevalence of anxiety symptoms. The results are similar to those in Table 2. Having or not having health insurance is a special factor. As shown in Table 1, having insurance had a lower prevalence of depressive symptoms. There were significant differences among the different groups in terms of age, sex, education status, region, career status, monthly household income per capita, marital status, living alone and having healthy insurance (P < 0.05).

Table 1

The statistical description of categorical variables and the anxiety level of the study samples.
Categorical variables	Anxiety, n(%)
	n		anxiety level			P value
	n	Overall	Mild	Moderate	Severe	P value
Total	14358	6667(46.43)	4788(33.35)	1452(10.11)	427(2.97)
Age/years						< 0.001
18–34	7331	3699(50.46)	2526(34.46)	884(12.06)	289(3.94)
35–44	2800	1310(46.79)	996(35.57)	245(8.75)	69(2.46)
45–60	4227	1658(39.22)	2569(29.95)	323(7.64)	69(1.63)
Sex						< 0.001
Male	6871	3091(44.99)	2144(31.20)	754(10.97)	193(2.81)
Female	7487	3576(47.76)	2644(35.31)	698(9.32)	234(3.13)
Education						< 0.001
Primary school and below	1032	423(40.99)	313(30.33)	88(8.53)	22(2.13)
Middle school	2400	964(40.17)	694(28.92)	215(8.96)	55(2.29)
High school	2568	1235(48.09)	880(34.27)	277(10.79)	78(3.04)
University and above	8358	4045(48.40)	2901(34.71)	872(10.43)	272(3.25)
Region*						0.001
Eastern China	4454	2162(48.54)	1513(33.97)	493(11.07)	156(3.50)
Central China	4189	1942(46.36)	1416(33.80)	418(9.98)	108(2.58)
Western China	5707	2557(44.80)	1857(32.54)	540(9.46)	160(2.80)
Place of residence						0.024
Urban	10596	4850(45.77)	3494(32.97)	1037(9.79)	319(3.01)
Rural	3762	1817(48.30)	1294(34.40)	415(11.03)	108(2.87)
Career status						< 0.001
Student	4041	2077(51.40)	1409(34.87)	503(12.45)	165(4.08)
Employed	6915	3058(44.22)	2274(32.89)	622(8.99)	162(2.34)
Retired/Unoccupied	3402	1532(45.03)	1105(32.48)	327(9.61)	100(2.94)
Per capita monthly household income, yuan						< 0.001
≤ 3000	4320	2096(48.52)	1477(34.19)	463(10.72)	156(3.61)
3001–6000	5947	2601(43.74)	1917(32.23)	553(9.30)	131(2.20)
≥ 6000	4091	1970(48.15)	1394(34.07)	436(10.66)	140(3.42)
Marital status						< 0.001
Unmarried/Divorced/widowed	6026	3098(51.41)	2087(34.63)	747(12.40)	264(4.38)
Married	8332	3569(42.83)	2701(32.42)	705(8.46)	163(1.96)
Whether living alone						< 0.001
No	12330	5618(45.56)	4081(33.10)	1209(9.81)	328(2.66)
Yes	2028	1049(51.73)	707(34.86)	243(11.98)	99(4.88)
Whether having health Insurance						< 0.001
No	939	504(53.67)	312(33.23)	144(15.34)	48(5.11)
Yes	13419	6163(45.93)	4476(33.36)	1308(9.75)	379(2.82)
*Missed 8

Table 2

The statistical description of categorical variables and the depression level of the study samples
	Depression, n(%)
	n		depression level			P value
	n	Overall	mild	moderate	severe	P value
Total	14358	8285(57.70)	5201(36.22)	1936(13.48)	1148(8.00)
Age/years						< 0.001
18–34	7331	4594(62.67)	2773(37.83)	1090(14.87)	731(9.97)
35–44	2800	1574(56.21)	1020(36.43)	369(13.18)	185(6.61)
45–60	4227	2117(50.08)	1408(33.31)	477(11.28)	185(5.49)
Sex						< 0.001
Male	6871	3835(55.81)	2312(33.65)	933(13.58)	590(8.59)
Female	7487	4450(59.44)	2889(38.59)	1003(13.40)	558(7.45)
Education						< 0.001
Primary school and below	1032	471(45.64)	294(28.49)	112(10.85)	65(6.30)
Middle school	2400	1251(52.13)	790(32.92)	302(12.58)	159(663)
High school	2568	1508(58.72)	942(36.68)	363(14.14)	203(7.90)
University and above	8358	5055(60.48)	3175(37.99)	1159(13.87)	721(8.63)
Region*						< 0.001
Eastern China	4454	2673(60.01)	1668(37.45)	608(13.65)	397(8.91)
Central China	4189	2456(58.63)	1570(37.48)	564(13.46)	322(7.69)
Western China	5707	3149(55.18)	1962(34.38)	763(13.37)	424(7.43)
Place of residence						0.241
Urban	10596	6084(57.42)	3851(36.34)	1397(13.18)	836(7.89)
Rural	3762	2201(58.51)	1350(35.89)	539(14.33)	312(8.29)
Career status						< 0.001
Student	4041	2636(65.23)	1614(39.94)	611(15.12)	411(10.17)
Employed	6915	3778(54.63)	2445(35.36)	865(12.51)	468(6.77)
Retired/Unoccupied	3402	1871(55.00)	1142(33.57)	460(13.52)	269(7.91)
Per capita monthly household income, yuan						< 0.001
≤ 3000	4320	2576(59.63)	1534(35.51)	650(15.05)	392(9.07)
3001–6000	5947	3262(54.85)	2121(35.67)	724(12.17)	417(7.01)
≥ 6000	4091	2447(59.81)	1546(37.79)	562(13.74)	339(8.29)
Marital status						< 0.001
Unmarried/Divorced/widowed	6026	3877(64.34)	2284(37.90)	954(15.83)	639(10.60)
Married	8332	4408(52.90)	2917(35.01)	982(11.79)	509(6.11)
Whether living alone						< 0.001
No	12330	7005(56.81)	4485(36.37)	1604(13.01)	916(7.43)
Yes	2028	1280(63.12)	716(35.31)	332(16.37)	232(11.44)
Whether having health Insurance						< 0.001
No	939	595(63.37)	300(31.95)	161(17.15)	134(14.27)
Yes	13419	7690(57.31)	4901(36.52)	1775(13.23)	1014(7.56)
*Miss 8

Table 3 displays the distribution of poor dietary behaviors and physical activity intensity in patients with anxiety and depression. A total of 43.61%, 45.42%, and 52.29% of people with low, moderate and high physical activity levels, respectively, suffer from anxiety. A total of 55.76%, 51.73%, 58.25%, 53.68%, and 64.51% of people with bad eating habits, such as sugary drinks, drinking, skipping breakfast, eating out and eating one’s diet, suffer from anxiety, and people with low physical activity levels suffer from anxiety. Individuals who engage in low, moderate and high levels of physical activity experience depression at a rate of 54.29%, 57.36%, and 63.90%, respectively. Individuals who consumed sugary drinks or alcohol, frequently skipped breakfast, dined out frequently, or were on a diet exhibited depression rates of 68.40%, 62.91%, 65.22%, and 76.22%, respectively.

Table 3

Distribution of poor dietary behaviors and physical activity intensity in patients with anxiety and depression
		Anxiety						Depression
	Overall	mild (%)	moderate(%)		severe(%)	P value	Overall	mild (%)	moderate (%)	severe (%)	P value
Dietary Behavior
sugary drinks	55.76	36.34	14.82	4.59		< .0001	68.40	37.92	17.83	12.64	< .0001
drunk	51.73	36.59	11.74	3.39		< .0001	62.91	37.74	15.52	9.65	< .0001
skip breakfast	58.25	34.94	17.79	5.51		< .0001	69.45	34.68	12.43	15.81	< .0001
eat out	53.68	36.74	11.82	5.13		< .0001	65.22	38.73	15.30	11.19	< .0001
on diet	64.51	32.58	9.45	2.51		< .0001	76.22	35.78	12.97	7.02	< .0001
Physical activity level						0.005					< .0001
low	43.61	31.29	8.79	3.43			54.29	34.40	12.05	7.83
mid	45.42	33.99	8.78	2.65			57.36	37.57	12.58	7.21
high	52.29	36.05	13.75	2.51			63.90	37.99	16.84	9.07

We present the longitudinal associations between the frequency of poor dietary behavior and the risk of depressive symptoms and anxiety symptoms in Table 4. Model I shows that after adjusting for variables such as age, gender, marital status, urban and rural status, education status, career status, residential status, insurance status and per capita monthly household income, compared with those with good dietary behavior, people with less poor dietary behavior had lower odds of developing anxiety (1: adjusted odds ratio [aOR] = 1.46; 95% confidence level [95% CI] = 1.35, 1.57; 2: aOR = 2.02; 95% CI = 1.84, 2.23; 3: aOR = 2.75; 95% CI = 2.38, 3.18; 4: aOR = 4.73; 95% CI = 3.54, 6.31; 5: aOR = 8.81; 95% CI = 3.87, 20.06). Less poor dietary behavior was protective against depression (1: aOR = 1.52; 95% CI = 1.41, 1.63; 2: aOR = 2.12; 95% CI = 1.94, 2.33; 3: aOR = 2.83; 95% CI = 2.45, 3.25; 4: aOR = 5.80; 95% CI = 4.26, 7.71; 5: aOR = 17.44; 95% CI = 7.39, 41.15). Model II included the types of unhealthy eating behaviors and levels of physical activity after adjusting for the same variables, and the results showed that the types of unhealthy eating behaviors and levels of physical activity were positively correlated with anxiety severity: sugary drinks (aOR = 1.38; 95% CI = 1.27, 1.41), drinks (aOR = 1.31; 95% CI = 1.21, 1.41), skipping breakfast (aOR = 1.65; 95% CI = 1.51, 1.80), eating out of home (aOR = 1.19; 95% CI = 1.09, 1.30), diet (aOR = 1.95; 95% CI = 1.75, 2.16), and physical activity level (mid: aOR = 1.02; 95% CI = 0.95, 1.11; high: aOR = 1.95; 95% CI = 1.36, 1.47), as well as showing the same changes in depressive moods.

Table 4

Results of the multicategorical logistic regression of poor dietary behaviors and physical activity intensity with anxiety and depression
	Anxiety		Depression
	Model I	Model Ⅱ	Model I	Model Ⅱ
No. of poor dietary behavior
0	1.00		1.00
1	1.46 (1.35 ~ 1.57)		1.52 (1.41 ~ 1.63)
2	2.02 (1.84 ~ 2.23)		2.12 (1.94 ~ 2.33)
3	2.75 (2.38 ~ 3.18)		2.83 (2.45 ~ 3.25)
4	4.73 (3.54 ~ 6.31)		5.80 (4.36 ~ 7.71)
5	8.81 (3.87 ~ 20.06)		17.44 (7.39 ~ 41.15)
Dietary Behavior
sugary drinks		1.38 (1.27 ~ 1.41)		1.49 (1.38 ~ 1.62)
drunk		1.31 (1.21 ~ 1.41)		1.32(1.23 ~ 1.43)
skip breakfast		1.65 (1.51 ~ 1.80)		1.74 (1.59 ~ 1.89)
eat out		1.19 (1.09 ~ 1.30)		1.16 (1.07 ~ 1.26)
on diet		1.95 (1.75 ~ 2.16)		1.99 (1.79 ~ 2.21)
Physical activity level
low	1.00	1.00	1.00	1.00
mid	1.03 (0.95 ~ 1.11)	1.02(0.95 ~ 1.11)	1.06 (0.98 ~ 1.14)	1.05 (0.98 ~ 1.13)
high	1.37 (1.27 ~ 1.48)	1.36(1.26 ~ 1.47)	1.41 (1.31 ~ 1.52)	1.39 (1.29 ~ 1.50)
* Adjusted variables: age, gender, marital status, urban and rural status, education, career status, residential status, insurance status, per capita monthly household income; Model I: effect of number of poor eating behaviors versus physical activity on anxiety and depression; Model II: effect of types of poor eating behaviors versus physical activity on anxiety and depression

Dose–response relationships between the number of poor dietary behaviors and the risk of depressive and anxiety symptoms are displayed in Fig. 2. A J-shaped relationship between poor dietary behaviors and depressive symptoms and anxiety symptoms is shown in Fig. 2. As the number of poor dietary categories increased, the detection rate of depression and anxiety also showed an upward trend. Subgroup analysis demonstrated whether these associations altered after stratification by sex and age (Table 5, Table 6).

Table 5

Associations of poor dietary behavior and physical activity level with depressive and anxiety symptoms according to age.
	Anxiety			Depression
	18–34	35–44	45–60	18–34	35–44	45–60
No. of poor dietary behavior
0	1.00	1.00	1.00	1.00	1.00	1.00
1	1.49 (1.34 ~ 1.66)	1.47 (1.24 ~ 1.74)	1.44 (1.25 ~ 1.65)	1.57 (1.42 ~ 1.74)	1.45 (1.23 ~ 1.71)	1.48 (1.29 ~ 1.69)
2	2.02 (1.79 ~ 2.29)	2.43 (1.94 ~ 3.04)	1.78 (1.43 ~ 2.22)	2.17 (1.93 ~ 2.45)	2.32 (1.86 ~ 2.88)	1.89 (1.54 ~ 2.33)
3	2.64 (2.22 ~ 3.13)	2.85 (1.94 ~ 4.20)	3.75 (2.43 ~ 5.78)	2.92 (2.47 ~ 3.45)	2.28 (1.56 ~ 3.32)	3.45 (2.26 ~ 5.26)
4	4.61 (3.34 ~ 6.36)	4.12 (1.93 ~ 8.79)	8.84 (1.76 ~ 44.56)	6.14 (4.46 ~ 8.44)	4.02 (1.92 ~ 8.44)	8.65 (1.75 ~ 42.74)
5	12.35 (5.03 ~ 30.33)	2.53 (0.30 ~ 21.75)	-	22.33 (8.52 ~ 58.55)	9.11 (1.16 ~ 71.40)	-
Physical activity level
low	1.00	1.00	1.00	1.00	1.00	1.00
mid	0.95 (0.86 ~ 1.06)	1.09 (0.91 ~ 1.31)	1.13 (0.97 ~ 1.31)	0.97 (0.87 ~ 1.07)	1.10 (0.93 ~ 1.31)	1.19 (1.04 ~ 1.37)
high	1.22 (1.09 ~ 1.35)	1.44 (1.21 ~ 1.72)	1.66 (1.42 ~ 1.93)	1.24 (1.12 ~ 1.38)	1.54 (1.30 ~ 1.82)	1.69 (1.46 ~ 1.95)

Table 6

Associations of poor dietary behavior and physical activity level with depressive and anxiety symptoms among males and females.
	Anxiety		Depression
	Males	Females	Males	Females
No. of poor dietary behavior
0	1.00	1.00	1.00	1.00
1	1.43 (1.28 ~ 1.61)	1.50 (1.35 ~ 1.67)	1.51 (1.35 ~ 1.68)	1.55 (1.40 ~ 1.71)
2	2.04 (1.79 ~ 2.34)	2.01 (1.75 ~ 2.31)	2.19 (1.93 ~ 2.49)	2.05 (1.79 ~ 2.36)
3	2.85 (2.35 ~ 3.46)	2.58 (2.07 ~ 3.23)	2.84 (2.35 ~ 3.42)	2.82 (2.27 ~ 3.51)
4	4.39 (3.00 ~ 6.42)	5.15 (3.28 ~ 8.07)	5.50 (3.79 ~ 7.99)	6.10 (3.91 ~ 9.50)
5	7.50 (2.77 ~ 20.33)	11.50 (2.66 ~ 49.71)	18.87 (6.53 ~ 54.52)	11.92 (2.71 ~ 52.32)
Physical activity level
low	1.00	1.00	1.00	1.00
mid	1.12 (0.99 ~ 1.25)	0.96 (0.87 ~ 1.07)	1.18 (1.05 ~ 1.32)	0.96 (0.87 ~ 1.07)
high	1.55 (1.39 ~ 1.74)	1.20 (1.08 ~ 1.35)	1.53 (1.38 ~ 1.70)	1.30 (1.17 ~ 1.45)

In the present study, we investigated the correlation between poor dietary behaviors and physical activity levels and anxiety and depressive symptoms. Overall, 46.43% of the participants in our sample reported having anxiety symptoms, and 57.70% reported having depressive symptoms. In addition, eating out, skipping breakfast, consuming sugary drinks, being inebriated, consuming a diet, and engaging in high-intensity exercise were associated with higher rates of anxiety and depression in both the general population and distinct age and gender subgroups. In addition, dose‒response relationships imply that the more unhealthy eating habits a person engages in, the worse their anxiety and depression seem to become.

A study including participants aged 18 years and older from 34 provincial areas in China showed that 27.9% of survey respondents experienced mental health symptoms, while 31.6% experienced anxiety, which is lower than the percentages reported in this study^[27]. The use of different scales for assessing common mental health and different standardizations are possible reasons for this. The results of this study show that the number of young people with anxiety (50.46%) and depression (46.79%) was greater, and the number of people with severe anxiety (3.94%) and severe depressive mood (9.97%) was also greater among adults. This result is similar to that of the 2020 China National Mental Health Development Report ^[28]. According to a study of global GBD data from 2019, adults, or those between the ages of 16 and 65, account for 80.6% of the disease burden worldwide related to mental disorders ^[1]. Since adults make up the majority of the labor force in society, the physical and mental well-being of this group has a significant influence on the aging trend ^[29].

Diet has a significant impact on mental health. According to the current study, eating poorly increases anxiety and depressive symptoms. There is a noticeable detrimental effect on people's experiences of anxiety and sadness when the number of unhealthy eating habits reaches three or more. Furthermore, drinking sugary drinks (OR = 1.38, 95% CI: 1.27–1.41) and being intoxicated (OR = 1.49, 95% CI: 1.38–1.62) had an impact on anxiety and depression symptoms in relation to the type of diet. Similar to our results, Oliván et al. demonstrated that consuming multiple carbonated or sugar-filled beverages on a daily basis is indicative of increased depression symptoms^[30]. Daily intake of carbonated or soft drinks, including sugar, was linked to an increased level of anxiety, according to a randomized controlled experiment. Additionally, daily consumption of carbonated or soft drinks containing sugar and nonnutritive sweeteners was linked to an increased level of sadness^[31]. Claims about alcohol consumption have been quite controversial; however, a 2018 study published in The Lancet showed that the safest amount of alcohol to consume is 0, with no drops recommended ^[32]. Tsai et al. showed that excessive drinkers are 1.8 times more likely than the general population to experience depressive symptoms ^[17]. In fact, although drinking alcohol and sugary drinks can stimulate hormone secretion in the body and produce excitement to a certain extent, as a dietary behavior, prolonged intake will still have an impact on mental health, and dependence on addiction will, on the contrary, cause psychological stress. Furthermore, consuming too much alcohol and sweets can harm one's organs and increase the risk of heart disease, stroke, and other conditions that can worsen anxiety and sadness^[33].

According to our study, anxiety and depression were present in more than 50% of the respondents who ate takeout, skipped breakfast, or consumed food. The results of this study revealed that these poor dietary behaviors are risk factors for anxiety and depression. A cross-sectional study demonstrated that people who rarely ate breakfast were most likely to have a profile of psychological issues (OR: 3.59; 95% CI: 2.52–5.11)^[34]. Zhang et al. reported that the odds ratios and 95% confidence intervals for depressive symptoms were 1.35 (1.08–1.68) and 1.40 (1.07–1.83) when breakfast consumers were compared to occasional consumers and skippers, respectively^[35]. Yun et al. reported a correlation between people's depression and how frequently they ate breakfast and ate out. ^[36]. Ling Zhang et al. assessed 3337 commuters in total and discovered a strong correlation between eating out frequently and a greater risk of mental symptoms, particularly when dining out for lunch and supper. The least likely individuals who experienced mental symptoms were those who ate at home, followed by those who ate in the canteen at work. ^[37].

Our study also revealed that compared to low-intensity physical activity, high-intensity physical exercise is more likely to result in anxiety and depression. This result varies in the current study. Earlier research revealed that people with moderate to high levels of physical exercise had far lower scores for anxiety and depression than people with lower levels of physical activity^[7]. Afterwards, evidence of a connection between physical activity and anxiety and depression was presented by Baumeister et al. Physical activity during exercise and during leisure time was linked to lower rates of anxiety and depression^[38]. The results of two meta-analyses have also shown that physical activity contributes to the reduction of anxiety and depression ^{[39, 12, 11]}. Later, a study conducted in 2022 by Ruilin Cao revealed that physical activity < 3 days/week and outdoor activity < 2 hours/day were positively associated with depressive symptoms in Chinese adolescents^[15]. Another prospective study revealed a negative correlation between new-onset depression and later depressive symptoms and between 4–7 hours of moderate–to–intense physical exercise per week. However, intense exercise for more than seven hours a week did not yield meaningful advantages ^[40]. This may be because adults made up the study population, which also suggests that adults who engage in weekly high-intensity physical activity may experience psychological stress, muscle soreness, excessive physical exhaustion, and a lack of energy to handle demanding tasks.

Our study is a large-sample, large-scale, current study with a good research design. For the first time, our study assessed the effects of eating behavior and physical activity levels on common mental disorders in adults from a national perspective, covering a wide range of ages. Moreover, the definitions of indicators such as physical activity level, anxiety and depression were based on international scales, which are more reliable. However, we must admit that there are some limitations of the study. First, this was a cross-sectional study, and the findings do not establish a causal relationship between poor eating behavior and anxiety and depression. Second, all the data were self-reported by the respondents and are subject to some bias. Third, the diet-related data were semiquantitative, which hindered the calculation of specific daily intakes of various food types.

In conclusion, our study underscores the imperative need for heightened awareness regarding the elevated prevalence of anxiety and depression among the Chinese population aged 18-60. Poor dietary habits such as liking sugary drinks, drinking alcohol, skipping breakfast, frequently eating takeaways, consuming food, and engaging in high-intensity physical activity are associated with adult depression and anxiety among the Chinese population aged 18-60. Specifically, the heightened risk of anxiety and depression aligns with an increasing number of poor dietary behaviors, with a proportional escalation in the severity of these mental health issues. Considering these poor dietary behaviors and physical activity intensity factors may be helpful in alleviating adult depression and anxiety symptoms.

Ethical Approval and consent to participate

This study was approved by the Institutional Review Committee of Ji’nan University, Guangzhou, China (JNUKY-2021-018). All the participants fully understood the study and voluntarily signed informed consent forms.

Funding

No funding.

Availability of data and materials:

Under reasonable requirements, the data and material of this study can be obtained from the corresponding author. The data are not publicly available due to privacy restrictions.

YANG F, LODDER P, HUANG N, et al. Thirty-year trends of depressive disorders in 204 countries and territories from 1990 to 2019: An age-period-cohort analysis[J/OL]. Psychiatry Research, 2023, 328: 115433. DOI:10.1016/j.psychres.2023.115433.
YANG X, FANG Y, CHEN H, et al. Global, regional and national burden of anxiety disorders from 1990 to 2019: results from the Global Burden of Disease Study 2019[J/OL]. Epidemiology and Psychiatric Sciences, 2021, 30: e36. DOI:10.1017/S2045796021000275.
Depression and Other Common Mental Disorders[EB/OL]. [2023-11-12]. https://www.who.int/publications-detail-redirect/depression-global-health-estimates.
STAFFORD J, CHUNG W T, SOMMERLAD A, et al. Psychiatric disorders and risk of subsequent dementia: Systematic review and meta-analysis of longitudinal studies[J/OL]. International Journal of Geriatric Psychiatry, 2022, 37(5): gps.5711. DOI:10.1002/gps.5711.
KADER M, PASTERNAK B, LIM C E, et al. Depression and anxiety-related disorders and suicide among Swedish male elite football players: a nationwide cohort study[J/OL]. British Journal of Sports Medicine, 2023: bjsports-2023-107286. DOI:10.1136/bjsports-2023-107286.
KRIS-ETHERTON P M, PETERSEN K S, HIBBELN J R, et al. Nutrition and behavioral health disorders: depression and anxiety[J/OL]. Nutrition Reviews, 2021, 79(3): 247–260. DOI:10.1093/nutrit/nuaa025.
BRUNES A, AUGESTAD L B, GUDMUNDSDOTTIR S L. Personality, physical activity, and symptoms of anxiety and depression: the HUNT study[J/OL]. Social Psychiatry and Psychiatric Epidemiology, 2013, 48(5): 745–756. DOI:10.1007/s00127-012-0594-6.
CHEN H, CAO Z, HOU Y, et al. The associations of dietary patterns with depressive and anxiety symptoms: a prospective study[J/OL]. BMC medicine, 2023, 21(1): 307. DOI:10.1186/s12916-023-03019-x.
LIU L, GUO C, LANG F, et al. Association of breakfast, total diet quality, and mental health in adolescents: a cross-sectional study of HBSC in Greece[J/OL]. European Journal of Pediatrics, 2023[2023-12-04]. https://doi.org/10.1007/s00431-023-05180-0. DOI:10.1007/s00431-023-05180-0.
CHEN M, XU X, LIU Y, et al. Association of eating habits with health perception and diseases among Chinese physicians: a cross-sectional study[J/OL]. Frontiers in Nutrition, 2023, 10: 1226672. DOI:10.3389/fnut.2023.1226672.
SCHUCH F B, STUBBS B, MEYER J, et al. Physical activity protects from incident anxiety: A meta-analysis of prospective cohort studies[J/OL]. Depression and Anxiety, 2019, 36(9): 846–858. DOI:10.1002/da.22915.
SCHUCH F B, VANCAMPFORT D, FIRTH J, et al. Physical Activity and Incident Depression: A Meta-Analysis of Prospective Cohort Studies[J/OL]. The American Journal of Psychiatry, 2018, 175(7): 631–648. DOI:10.1176/appi.ajp.2018.17111194.
HUANG Y, WANG Y, WANG H, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study[J/OL]. The Lancet. Psychiatry, 2019, 6(3): 211–224. DOI:10.1016/S2215-0366(18)30511-X.
China National Mental Health Development Report (2021–2022) [EB/OL]. [2023-11-12]. https://www.pishu.com.cn/skwx_ps/bookdetail?SiteID=14&ID=14414517.
CAO R, GAO T, REN H, et al. Unique and cumulative effects of lifestyle-related behaviors on depressive symptoms among Chinese adolescents[J/OL]. The International Journal of Social Psychiatry, 2022, 68(2): 354–364. DOI:10.1177/0020764021996739.
SHAO P, XU Y, PAN C W. Factors associated with and prevalence of depressive features amongst older adults in an urban city in eastern China[J/OL]. The South African journal of psychiatry: SAJP: the journal of the Society of Psychiatrists of South Africa, 2017, 23: 1064. DOI:10.4102/sajpsychiatry.v23i0.1064.
TSAI A C, CHI S H, WANG J Y. Cross-sectional and longitudinal associations of lifestyle factors with depressive symptoms in ≥ 53-year old Taiwanese - results of an 8-year cohort study[J/OL]. Preventive Medicine, 2013, 57(2): 92–97. DOI:10.1016/j.ypmed.2013.04.021.
BAI S, CHANG Q, YAO D, et al. Anxiety in Residents in China: Prevalence and Risk Factors in a Multicenter Study[J/OL]. Academic Medicine: Journal of the Association of American Medical Colleges, 2021, 96(5): 718–727. DOI:10.1097/ACM.0000000000003913.
XU Y, QI J, YANG Y, et al. The contribution of lifestyle factors to depressive symptoms: A cross-sectional study in Chinese college students[J/OL]. Psychiatry Research, 2016, 245: 243–249. DOI:10.1016/j.psychres.2016.03.009.
GAO C, SUN Y, ZHANG F, et al. Prevalence and correlates of lifestyle behavior, anxiety and depression in Chinese college freshman: A cross-sectional survey[J/OL]. International Journal of Nursing Sciences, 2021, 8(3): 347–353. DOI:10.1016/j.ijnss.2021.05.013.
Dietary Guidelines for Chinese Residents (2022) [M/OL]. [2023-11-11]. https://www.schlandor.com/Upload/202209/20220905092023_5289.
LEE P H, MACFARLANE D J, LAM T H, et al. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review[J/OL]. The International Journal of Behavioral Nutrition and Physical Activity, 2011, 8: 115. DOI:10.1186/1479-5868-8-115.
SPITZER R L, KROENKE K, WILLIAMS J B W, et al. A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7[J/OL]. Archives of Internal Medicine, 2006, 166(10): 1092. DOI:10.1001/archinte.166.10.1092.
MOSSMAN S A, LUFT M J, SCHROEDER H K, et al. The Generalized Anxiety Disorder 7-item scale in adolescents with generalized anxiety disorder: Signal detection and validation[J]. Annals of Clinical Psychiatry: Official Journal of the American Academy of Clinical Psychiatrists, 2017, 29(4): 227-234A.
CHEN S, CHIU H, XU B, et al. Reliability and validity of the PHQ-9 for screening late-life depression in Chinese primary care[J/OL]. International Journal of Geriatric Psychiatry, 2010, 25(11): 1127–1133. DOI:10.1002/gps.2442.
COSTANTINI L, PASQUARELLA C, ODONE A, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): A systematic review[J/OL]. Journal of Affective Disorders, 2021, 279: 473–483. DOI:10.1016/j.jad.2020.09.131.
L S, ZA L, JY Q, et al. Prevalence of and Risk Factors Associated With Mental Health Symptoms Among the General Population in China During the Coronavirus Disease 2019 Pandemic[J/OL]. JAMA network open, 2020, 3(7)[2023-11-13]. https://pubmed.ncbi.nlm.nih.gov/32609353/. DOI:10.1001/jamanetworkopen.2020.14053.
China National Mental Health Development Report (2021–2022) [EB/OL]. [2023-11-12]. https://www.pishu.com.cn/skwx_ps/bookdetail?SiteID=14&ID=14414517.
WANG B, FRANK R, GLIED S. Lasting scars: The impact of depression in early adulthood on subsequent labor market outcomes[J/OL]. Health Economics, 2023, 32(12): 2694–2708. DOI:10.1002/hec.4747.
OLIVÁN-BLÁZQUEZ B, AGUILAR-LATORRE A, MOTRICO E, et al. The Relationship between Adherence to the Mediterranean Diet, Intake of Specific Foods and Depression in an Adult Population (45–75 Years) in Primary Health Care. A Cross-Sectional Descriptive Study[J/OL]. Nutrients, 2021, 13(8): 2724. DOI:10.3390/nu13082724.
CASTRO A, GILI M, VISSER M, et al. Soft Drinks and Symptoms of Depression and Anxiety in Overweight Subjects: A Longitudinal Analysis of an European Cohort[J/OL]. Nutrients, 2023, 15(18): 3865. DOI:10.3390/nu15183865.
GBD 2016 ALCOHOL COLLABORATORS. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016[J/OL]. Lancet (London, England), 2018, 392(10152): 1015–1035. DOI:10.1016/S0140-6736(18)31310-2.
MILLWOOD I Y, WALTERS R G, MEI X W, et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China[J/OL]. Lancet (London, England), 2019, 393(10183): 1831–1842. DOI:10.1016/S0140-6736(18)31772-0.
HAGHIGHATDOOST F, FEIZI A, ESMAILLZADEH A, et al. Breakfast skipping alone and in interaction with inflammatory based quality of diet increases the risk of higher scores of psychological problems profile in a large sample of Iranian adults[J/OL]. Journal of Nutritional Science, 2021, 10: e10. DOI:10.1017/jns.2020.62.
ZHANG L, YIN J, CAI X, et al. Association between eating behaviors and depressive symptoms in Chinese adults: a population-based cross-sectional study[J/OL]. Psychology, Health & Medicine, 2022, 27(5): 1176–1183. DOI:10.1080/13548506.2021.1922720.
YUN H, KIM D W, LEE E J, et al. Analysis of the Effects of Nutrient Intake and Dietary Habits on Depression in Korean Adults[J/OL]. Nutrients, 2021, 13(4): 1360. DOI:10.3390/nu13041360.
ZHANG L, XIE Y, LI B, et al. Psychiatric Symptoms and Frequency of Eating out among Commuters in Beijing: A Bidirectional Association?[J/OL]. Nutrients, 2022, 14(20): 4221. DOI:10.3390/nu14204221.
BAUMEISTER S E, LEITZMANN M F, BAHLS M, et al. Associations of Leisure-Time and Occupational Physical Activity and Cardiorespiratory Fitness With Incident and Recurrent Major Depressive Disorder, Depressive Symptoms, and Incident Anxiety in a General Population[J/OL]. The Journal of Clinical Psychiatry, 2017, 78(1): e41-e47. DOI:10.4088/JCP.15m10474.
Q W, J Z, H Y, et al. Prevalence and associated factors of anxiety among the population in an urban area of China: a cross-sectional study[J/OL]. BMJ open, 2022, 12(11)[2023-11-13]. https://pubmed.ncbi.nlm.nih.gov/36351738/. DOI:10.1136/bmjopen-2022-062431.
JIANG W, SUN L, HE Y, et al. Association between physical activity and depressive symptoms in adolescents: A prospective cohort study[J/OL]. Psychiatry Research, 2023, 329: 115544. DOI:10.1016/j.psychres.2023.115544.

No competing interests reported.

Download PDF

Editor assigned by journal
16 Aug, 2024
Submission checks completed at journal
13 Aug, 2024
First submitted to journal
12 Aug, 2024

You are reading this latest preprint version

Associations between Dietary Behaviors and Physical Activity and Mental Health Disorders among Chinese Residents Aged 18-60: A Cross-Sectional Study

Status:

Version 1

Abstract

Objective

Method

Results

Conclusion

Figures

Introduction

Materials and Methods

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1