Chrononutrition, diet quality and perceived stress: results from two population-based studies in Brazil

Crononutrição, qualidade da dieta e estresse percebido: resultados de dois estudos de base populacional no Brasil

Crononutrición, calidad de la dieta y estrés percibido: resultados de dos estudios poblacionales en Brasil

Abstract

Chrononutrition and diet quality appear to influence stress, however, evidence on this relationship needs to be better elucidated. We aim to assess the association between chrononutrition, diet quality, and perceived stress in adults from Southern Brazil. Population-based cross-sectional study conducted in two Brazilian cities, with individuals aged ≥18 years. Chrononutrition and diet quality were the exposure, and perceived stress was the outcome. To assess the associations, Poisson regression was used to calculate crude and adjusted prevalence ratio. 2,170 individuals were analyzed: 12.3% did not have breakfast, 27.5% had less than four meals per day, 30.4% presented poor diet quality, 14% referred irregular consumption of healthy foods, and 43.2% had regular consumption of unhealthy foods. Perceived stress was reported by 38.2% of the sample. Individuals who ate less than four meals per day, with poor diet quality, with regular consumption of unhealthy foods, and with irregular consumption of healthy foods were more likely to present perceived stress. These results may contribute to the development of joint actions on nutrition and mental health.

Key words:
Eating; Diet Quality; Mental Health; Pandemics

Resumo

Crononutrição e qualidade da dieta parecem influenciar o estresse, mas evidências precisam ser melhor elucidadas. O objetivo foi avaliar a associação entre crononutrição, qualidade da dieta e estresse percebido em adultos do Sul do Brasil. Estudo transversal de base populacional conduzido em duas cidades brasileiras, com indivíduos com ≥18 anos. Crononutrição e qualidade da dieta foram as exposições, estresse percebido foi o desfecho. Para avaliar as associações utilizou-se regressão de Poisson para calcular a razão de prevalência bruta e ajustada. Foram analisados 2.170 indivíduos: 12,3% não tomavam café da manhã, 27,5% faziam <4 refeições por dia, 30,4% tinham pior qualidade da dieta, 14% consumiam irregularmente alimentos saudáveis e 43,2% consumiam regularmente alimentos não saudáveis, 38,2% tinham estresse percebido. Aqueles que faziam <4 refeições por dia, com pior qualidade da dieta, consumo regular de alimentos não saudáveis e consumo irregular de alimentos saudáveis tiveram maior probabilidade de apresentar estresse. Os resultados poderão contribuir para o desenvolvimento de ações conjuntas em nutrição e saúde mental.

Palavras-chave:
Alimentação; Qualidade da Dieta; Saúde mental; Pandemias

Resumen

La crononutrición y la calidad de la dieta parecen influir en el estrés, pero es necesario dilucidar más la evidencia. El objetivo fue evaluar la asociación entre crononutrición, calidad de la dieta y estrés percibido en adultos del sur de Brasil. Estudio transversal de base poblacional realizado en dos ciudades brasileñas, con individuos ≥18 años de edad. La crononutrición y la calidad de la dieta fueron las exposiciones, y el estrés percibido fue el resultado. Para evaluar las asociaciones se utilizó la regresión de Poisson para calcular la razón de prevalencia cruda y ajustada. Se analizaron 2.170 individuos: el 12,3% no desayunaba, el 27,5% hacía <4 comidas al día, el 30,4% tenía una dieta de peor calidad, el 14% consumía alimentos saludables de forma irregular y el 43,2% consumía regularmente alimentos poco saludables. El 38,2% había percibido estrés. Aquellos que comían <4 comidas al día, tenían una calidad de dieta más pobre, consumían regularmente alimentos poco saludables y consumían de manera irregular alimentos saludables, tenían más probabilidades de experimentar estrés. Los resultados pueden contribuir al desarrollo de acciones conjuntas en nutrición y salud mental.

Palabras clave:
Comida; Calidad de la dieta; Salud mental; Pandemias

Introduction

Stress can be defined as a non-specific response, derived from actions that affect the individual’s physical and psychological integrity. Stress is responsible for increasing the activity of the hypothalamic-pituitary-adrenal (HPA) axis and, consequently, the plasma cortisol levels¹. During the COVID-19 pandemic, the occurrence of emotional disturbances, including stress, was commonly observed². A study, with participants from 57 countries including Mexico, Argentina, Canada, the United Kingdom, Germany, the United States, China, and Brazil, recruited via social media, reported an average perceived stress score of 19.08. This score reflects a moderate level of stress³. In Brazil, another study found that 34.2% of participants had symptoms of post-traumatic stress during the COVID-19 pandemic⁴.

Multiple biological, environmental, social, and behavioral factors, including already-known factors, such as violence, abuse, family conflicts, trauma, anxiety, and depression, may influence stress⁵. Recently, the hypothesis that the circadian cycle and stress may share biological mechanisms has been explored⁶. The circadian cycle, one of the main regulators of the body, can affect various metabolic processes, including the energy balance of an individual. This regulation may be influenced by behavioral factors, such as dietary intake, for example⁷.

Chrononutrition is the area that aims to associate the study of nutrition and chronobiology, through the analysis of the influence of the feeding moment on the circadian cycle and health. Chrononutrition considers three main aspects: regularity of meals, food frequency, and mealtime⁸. Diet quality similarly influences brain functioning, cognitive functions, and mental health^9,10. For instance, the consumption of ultra-processed foods, consistently associated with poor diet quality, has been correlated with an increase in depressive symptoms¹¹.

Even before the COVID-19 pandemic, urbanization had already induced changes in eating patterns, impacting the regularity and frequency of meals⁸. Skipping breakfast has been associated with weight gain¹², increasing the risk of cardiovascular diseases¹³. Conversely, a higher daily meal frequency appears linked to a lower prevalence of hypertension¹⁴, while a lower frequency of the population’s meals seems to increase this prevalence¹⁵. However, research on the association between chrononutrition and mental health is limited. Regarding diet quality, studies indicate a positive association between perceived stress and inadequate food consumption. Stressed individuals tend to have higher consumption of added sugar, saturated fats, processed meats, and nutritionally inadequate snacks^16,17.

Evidence on the relationship and mechanisms by which diet and mental health interact needs to be better elucidated. In this way, studies assessing the association of different dietary variables with mental health, specifically stress, are extremely necessary nowadays⁹. Therefore, this study aimed to assess the association between chrononutrition, diet quality, and perceived stress in adults using data from population-based studies conducted amid the COVID-19 pandemic in Southern Brazil.

Methods

Setting, study sample, and data collection

This is a cross-sectional population-based study with data from the project entitled “Mental COVID: impact of COVID-19 on the mental health of the population”, conducted in two Brazilian Southern cities: Criciúma (Santa Catarina state) and Rio Grande (Rio Grande do Sul state). Criciúma has approximately 217,311 inhabitants, a Human Development Index (HDI) of 0.788, a population density of 815.87 inhabitants per km², and a literacy rate of 96,8%, with 59,9% of residents being adults and 15,7% being elderly^18-20. Rio Grande has 211,965 inhabitants, an HDI of 0.744, a population density of 72.79 inhabitants per km², and a literacy rate of 96,1%, with 55,5% of residents being adults and 19,8% being elderly^18-20. This study was conducted amid the COVID-19 outbreak, between October 2020 and January 2021, with individuals aged 18 years or older who were living in the urban areas of both cities. Subjects who were physically or cognitively unable to complete the survey were excluded.

The sampling process was conducted in two stages, based on the 2010 Brazilian Demographic Census²¹. First, primary units (census tracts) were randomly selected. Then, secondary units (households) were randomly chosen from the previously selected census tracts. The total number of households was sampled proportionally to the sector size. In Criciúma, 60 census tracts were sampled, resulting in 15,765 households, 607 of them included in this study. In Rio Grande, 90 census tracts were sampled, resulting in 29,734 households, 900 of them included in the study. All adults living in the selected households were invited to participate in the study. If the residents were not present in the household, two interview attempts were made. If after three attempts, the residents were still not found, the next household on the right was selected.

The sample size calculation was performed with parameters including 80% power, a 95% confidence interval (95%CI), a 50% prevalence for the outcome, a sampling error of five percentage points, a design effect of 1.5, and a 20% increment to account for potential losses and refusals. Ultimately, each study required a sample size of 690 individuals.

Trained personnel were responsible for conducting face-to-face household interviews, who wore personal protective equipment during the fieldwork to avoid SARS-CoV-2 infection. A single, precoded, and standardized questionnaire was used. Data collection was performed using tablets and the RedCap® software. Each interview lasted 30 minutes on average.

Chrononutrition

Chrononutrition was assessed by two of its three aspects, frequency and mealtime, using the following variables: number of daily meals (frequency) and making breakfast (an indirect indicator of the time of the first meal)^14,22. The individuals interviewed should answer “yes” or “no” for each of the six daily meals (breakfast, morning snack, lunch, afternoon snack, dinner, and evening snack). The variable number of daily meals was dichotomized into <4 and ≥4 meals per day since the main daily meals are usually three (breakfast, lunch, and dinner). The idea of this categorization was to compare individuals who have consumed snacks between the main meals with those who have only eaten the main meals¹⁴.

Diet quality

Diet quality was evaluated by a frequency index of intake of healthy (legumes, vegetables, fruits, and milk) and unhealthy (soda and artificial juice, candies, and red meat) foods. To build it, the following questions were asked to the participants: “How many days a week do you usually eat…?”, followed by the foods: “at least one type of raw or cooked vegetable such as lettuce, tomato, cabbage, carrot, chayote, eggplant, zucchini (not including potatoes or cassava)”, “legumes such as beans, lentils, peas”, “fruit”, “milk (not including vegetable milk such as soy, almonds, chestnuts, rice)”, “soda or artificial juice”, “sweet foods, such as ice cream, chocolates, cakes, cookies or sweets”, and “red meat (beef, pork)”.

A score from 0 to 4 points was attributed according to the answer’s options for each question: “never”, “almost never”, “1 to 2 days a week”, “3 to 4 days a week”, “5 to 6 days a week” and “daily (including Saturday and Sunday)”. For healthy foods, the higher the frequency of intake the lower the score, varying from 0 (daily) to 4 (never or almost never). For unhealthy foods, the higher the intake the higher the score, also varying from 0 (never or almost never) to 4 (daily) (Chart 1). The final score was the sum of the intake score of each food, varying from 0 (better quality) to 28 points (worst quality), a little different from the original score proposed by Francisco et al.²³ (maximum of 32 points), as raw and cooked vegetables were evaluated using the same question. We then categorized the diet quality into tertiles. The 1^st tertile corresponded to the best diet quality and the 3^rd to the worst diet quality^23,24.

Chart 1
Healthy and unhealthy food consumption score from the diet quality variable.

Foods	0	1	2	3	4
Legumes	Daily	5 to 6 days a week	3 to 4 days a week	1 to 2 days a week	Never or almost never
Vegetables	Daily	5 to 6 days a week	3 to 4 days a week	1 to 2 days a week	Never or almost never
Fruits	Daily	5 to 6 days a week	3 to 4 days a week	1 to 2 days a week	Never or almost never
Milk	Daily	5 to 6 days a week	3 to 4 days a week	1 to 2 days a week	Never or almost never
Soda	Never or almost never	1 to 2 days a week	3 to 4 days a week	5 to 6 days a week	Daily
Artificial juice	Never or almost never	1 to 2 days a week	3 to 4 days a week	5 to 6 days a week	Daily
Candies	Never or almost never	1 to 2 days a week	3 to 4 days a week	5 to 6 days a week	Daily
Red meat	Never or almost never	1 to 2 days a week	3 to 4 days a week	5 to 6 days a week	Daily

• Source: Adapted from Franscisco et al.²³ and Meller et al.²⁴.

Additionally, we assessed the regularity of consumption for both healthy and unhealthy diet markers, employing the same questions and categorizations as the diet quality score. The distinction lies in evaluating the frequency of food consumption. Individuals reporting the consumption of at least one food item from the healthy or unhealthy groups at least five times a week were categorized as having a regular consumption of the corresponding food markers. Conversely, those consuming at least one food item from these groups less than five times a week were categorized as having irregular consumption²⁴.

Perceived stress

The perceived stress scale, previously validated for the Brazilian population²⁵, was used to evaluate stress. It is a self-reported measure designed to deal with the degree to which situations in an individual’s life are appraised as stressful. It was developed as a 14-item scale that assessed the perception of stressful experiences over the previous month using a Likert-type scale from 0 to 4 corresponding to the answer options “never”, “almost never”, “sometimes”, “fairly often”, and “very often”. The total score consisted of the sum of points, ranging from 0 (lower stress) to 56 points (higher stress). We categorized the total score in quintiles and those individuals classified in the fourth or fifth quintile were considered stressed. This approach to classifying stress has already been used in other published studies²⁶.

Sociodemographic characteristics, pandemic-related behavioral, and health variables

We also used socioeconomic and demographic characteristics as well as pandemic-related behaviors and health-related characteristics as covariables of our study. They are described in Chart 2.

Chart 2
Description of the covariables studied.

Variables	Description
Sex	Male/female
Age group (years)	Categorized by age group: 18-29, 30-39, 40-49, 50-59, ≥60.
Skin color	Collected as white, black, brown, yellow, indigenous, and dichotomized into white and others.
Marital status	Collected as single, married, separated, widow/widower and categorized in single, married, and separated/widow (combination of the last two categories).
Schooling	Elementary school, high school, and university education.
Wealth index	Built using the analysis of main components, using the variables: number of rooms, number of bathrooms, freezer, clothes dryer, computer, air conditioning, internet, and number of cars in the household. The result was categorized as tertiles.
Living alone	Identify by the question “Counting you, how many people live in this household?”. Individuals who responded that they lived alone were categorized as “yes” and those who did not live alone as “no”.
Got unemployed	The question “How did social distancing due to the COVID-19 pandemic affect your occupation/work?”, with answers options: “I did not work before and I continued without working”, “I continued working normally”, “I continued working, but at home (home office), “I started working during the pandemic” and “I lost my job or stopped working”, was asked. Those who responded that they lost their jobs or stopped working were categorized as “yes” and the others as “no”.
Started working from home	The same question of the previous variable was used. Individuals who responded “I continued working, but at home (home office)” were categorized as “yes” and the others as “no”.
Adherence to social distance	The following question was used: “During the period of social distancing, when only essential services were open, did you leave home?” considering the answers options no and yes.
Infodemic	Assessed using the question: “How many times a day do you search for or receive information about COVID-19?” with the answer options many times a day, a few times a day, few times a day, only once a day, and a few times a week. Infodemic was considered to occur when an individual searched for or received information about COVID-19 many times a day66.
Fear of COVID	Assessed by Fear of COVID-19 scale, which is a screening tool developed by Ahorsu et al.67 and validated for use in Brazil68. The score was divided into quintiles and those individuals in the highest quintile were classified as having higher fear of COVID-19.
Going to bars or restaurants	Identify by the question “Did you go to bars, restaurants, and malls?”, with answer options: No, yes.
Go out for physical activity	Identify through the question “Did you go out to do activities of leisure/physical activity?”, with answer options: No, yes.
Contact with someone infected	Identify by the question: “Have you had close personal contact with someone diagnosed with COVID-19?”. Categorized as: No, yes.
Presence of COVID-19 symptoms	The presence of the symptoms of cough, sore throat, fever, difficulty breathing, tiredness, diarrhea, loss of taste/smell, chills, and headache were asked and summed. The presence of any reported symptoms was considered “yes” and negative responses for all symptoms as “no”.
Tested positive for COVID-19	Identify using the question “What was the test result?”, with answer options: negative, positive, do not know, ignored. An affirmative response for the positive result was considered “yes”.
Using online tools to talk to friends and family	Identify through the question “Did you interact with your friends and/or family using online tools?”, with answer options: No, yes.
Excess weight	Classified by body mass index (BMI) ≥25 Kg/m² for adults69 and ≥27 Kg/m² for elderly people70. The BMI was calculated with self-reported data on weight and height. Categorized in no, yes.
Physical activity	Assessed using the International Physical Activity Questionnaire (IPAQ) long version, which evaluates commuting, as well as moderate and vigorous physical activities71. Classified in “<150 minutes/week” and “≥150 minutes/week” according to the World Health Organization72.
Sleep quality	Self-reported by the question: “How do you consider the quality of your sleep?”, with answers options: very good, good, regular, poor, very poor.
Sleep duration	It was calculated using the information of the following questions: “What time do you usually sleep during the week (Monday to Friday)?” and “What time do you usually wake up during the week (Monday to Friday)?”. The sleep duration was classified as inadequate when <7 or ≥9 hours per day, and as adequate when between 7 and 8 hours per day)73. Data on weekend sleep duration were unavailable since there were no corresponding questions in the baseline survey questionnaire. Typically, when evaluating self-reported sleep duration, questions focus on weekdays to capture usual patterns, considering that routines often change on weekends.
Food insecurity	Assessed by the short version of the Brazilian Food Insecurity Scale74. It is a scale of five questions corresponding to the last three months, used to screen households in food insecurity. Households with at least one affirmative answer to any of the five questions were categorized as having food insecurity (“yes”). Households with negative responses to all questions were considered to have no food insecurity (“no”).
Self-reported sadness	Assessed through the faces scale75, composed of seven faces ranging from very happy (face 1) to very sad (face 7). Individuals who chose faces 5, 6, or 7 were categorized as having feelings of sadness (“yes”), while the ones who chose faces 1, 2, 3, and 4 were classified as not having such feelings (“no”).
Depressive symptoms	Assessed using the Patient Health Questionnaire-9 (PHQ-9)76. It is a questionnaire with nine questions that assess symptoms for major depressive episodes, such as anhedonia, lack of energy, depressed mood, sleep problems, weight and appetite changes, worthlessness, guilt and restless feelings, and suicidal thoughts, in the previous two weeks. Each question has a Likert-type scale, varying from 0 (never) to 3 (almost every day) points. Individuals who choose as answers “once or more a week” (2 points) or “almost every day” (3 points) for any question, and “less than once a week” (1 point) or “once or more a week” (2 points) or “almost every day” (3 points) for the question on suicidal thoughts, were considered to have a positive response. After, the score was added up and those who presented ≥9 points were considered as having a positive screening for depressive symptoms76.

• Source: Authors.

Statistical analyses

Absolute and relative frequencies were used to describe the characteristics of the sample. Crude analysis of the association of chrononutrition, diet quality, and perceived stress with the covariables, was assessed using Fisher’s exact test with a significance level of 5%.

Fisher’s exact test was also used to assess the association of chronutrition and diet quality with perceived stress. To evaluate whether crude associations of chrononutrition and diet quality with stress were independent of possible confounders, adjusted Poisson regression models were used, presenting the p-value corresponding to the Wald test for heterogeneity. Regression results were reported as prevalence ratio (PR) with their corresponding 95% confidence intervals (CI). To identify the possible confounders, we used a level hierarchical model of analysis²⁷. Level 1 (distal) included demographic characteristics: sex, skin color, and age; Level 2 included socioeconomic and demographic characteristics as well as job situation: wealth index, schooling, marital status, living alone, getting unemployed, started working from home; Level 3 (proximal) included pandemic-related behavioral as well as health-related characteristics: adherence to social distance, going to bars or restaurants, go out for physical activity, contact with someone infected, infodemic, tested positive for COVID-19, fear of COVID-19, presence of COVID-19 symptoms, using online tools to talk to friends and family, excess weight, physical activity, sleep duration and quality, depressive symptoms, feeling of sadness and food insecurity. Variables were selected using the backward method considering each hierarchical level, and those variables with p-value<0.20 remained in the final model (Figure 1).

Figure 1
Hierarchical model of the association between chrononutrition, diet quality and perceived stress.

All analyses were performed in STATA version 12.1 using the svy prefix, which considers the complexity of the sampling process and the effect of the study design.

Ethical Aspects

This study was approved by the Research Ethics Committee of the Federal University of Rio Grande [protocol number 4.055.737]. Verbal informed consent was obtained from all subjects and was witnessed and formally recorded.

Results

A total of 2,170 individuals were analyzed (response rate of 75%). Most of them were female (59.7%), self-reported white skin color (84.0%), and did not live alone (89.4%). About a quarter (26.1%) had university education, 31.2% were 60 years old or older, and 49.1% were married (Table 1). Less than 10% became unemployed amid COVID-19 pandemic, while over 90% continued to work in person. More than 80% reported not going out for physical activity during this period. The prevalence of infodemic was 22.0% and of fear of COVID-19 was 19.1% (Table 2). More than half presented excess weight (59.1%) and approximately a quarter were physically active (≥150 minutes/per week) (24.7%). Concerning sleep characteristics, 9.6% reported poor or very poor sleep quality and 53.8% presented inadequate sleep duration. Felling of sadness and depressive symptoms were, respectively, observed in 16.1% and 13.0% of the interviewees (Table 3).

Table 1
Distribution of demographic and socioeconomic characteristics of the individuals aged 18 years or older. Criciúma-SC and Rio Grande-RS, 2020-2021 (n=2,170).

Variables		n	%
Sex
	Male	875	40.3
	Female	1,295	59.7
Age (years)
	18-29	394	18.2
	30-39	335	15.4
	40-49	354	16.3
	50-59	409	18.9
	≥60	678	31.2
Skin color
	White	1,815	84.0
	Others	347	16.0
Wealth index (tertile)a
	First (poor)	719	34.7
	Second	673	32.5
	Third (richer)	680	32.8
Schooling
	Elementary	881	41.4
	High school	692	32.5
	University education	555	26.1
Marital status
	Single	751	34.6
	Married	1,066	49.1
	Separated/widow	353	16.3
Live alone
	No	1,880	89.4
	Yes	222	10.6

• ^a
  Variable with highest number of missing data: 4.5% (n=98).
• Source: Authors.

Table 2
Distribution of pandemic-related variables in individuals aged 18 years or older. Criciúma-SC and Rio Grande-RS, 2020-2021 (n=2,170).

Variables		n	%
Got unemployed
	No	2,010	92.6
	Yes	160	7.4
Started working from home
	No	2,002	92.3
	Yes	168	7.7
Adherence to social distancing
	No	75	3.5
	Yes	2,095	96.5
Infodemic
	No	1,692	78.0
	Yes	478	22.0
Fear of COVID-19a
	No	1,740	80.9
	Yes	412	19.1
Went to bars or restaurants
	No	1,965	90.5
	Yes	205	9.5
Go out for physical activity
	No	1,769	81.5
	Yes	401	18.5
Contact with someone infected
	No	1,617	74.5
	Yes	553	25.5
Presence of COVID-19 symptoms
	No	1,667	76.9
	Yes	500	23.1
Positive test for COVID-19
	No	2,023	93.2
	Yes	147	6.8
Using online tools to talk to friends and family
	No	426	19.6
	Yes	1,744	80.4

• ^a
  Variable with highest number of missing data: 0.8% (n=18).
• Source: Authors.

Regarding chrononutrition, 12.3% of the individuals usually skipped breakfast and 27.5% had less than four meals a day (Table 3). Younger (p<0.001) and single individuals (p=0.017), those who had COVID-19 symptoms (p=0.027), who did not use online tools to talk to friends and family (p=0.038), who practiced <150 minutes of physical activity per week (p=0.016), and had poor sleep quality (p=0.011), depressive symptoms (p<0.001) and sadness (p=0.002) presented higher frequency of skipping breakfast. Higher prevalence of having less than 4 meals a day was associated with being male (p=0.040), younger (p<0.001), single (p<0.001), non-white (p<0.001), having better education (p=0.001), not living alone (p=0.005) and not adhering to social distance (p=0.022), referring infodemic (p=0.003) and fear of COVID-19 (p=0.037), having gone to bars or restaurants amid the pandemic (p<0.001) and had contacted with someone infected (p<0.001), being with excess weight (p=0.006) and depressive symptoms (p<0.001) (Table 4).

Table 3
Distribution of health outcomes and dietary variables in individuals aged 18 years or older. Criciúma-SC and Rio Grande-RS, 2020-2021 (n=2,170).

Variables		n	%
Excess weighta
	No	815	40.9
	Yes	1,178	59.1
Physical activity (minutes per week)
	<150	1,624	75.3
	≥150	532	24.7
Sleep quality
	Very good/good	1,509	69.5
	Regular	453	20.9
	Poor/very poor	208	9.6
Sleep duration
	Inadequate	1,167	53.8
	Adequate	1,003	46.2
Perceived stress
	No	1,330	61.8
	Yes	824	38.2
Depressive symptoms
	No	1,879	87.0
	Yes	280	13.0
Feeling of sadness
	No	1,816	83.9
	Yes	349	16.1
Food insecurity
	No	1,528	70.5
	Yes	639	29.5
Breakfast
	No	268	12.3
	Yes	1,902	87.7
Daily meals
	<4	597	27.5
	≥4	1,573	72.5
Diet quality (tertile)
	Tertile 1 (better)	912	42.1
	Tertile 2	596	27.5
	Tertile 3 (poor)	659	30.4
Irregular consumption of healthy foods
	No	1,866	86.0
	Yes	304	14.0
Regular consumption of unhealthy foods
	No	1,232	56.8
	Yes	936	43.2

• ^a
  Variable with highest number of missing data: 8.2% (n=177).
• Source: Authors.

Table 4
Prevalence of chrononutrition and diet quality according to sociodemographic characteristics, pandemic-related variables and health outcomes of individuals aged 18 years or older. Criciúma-SC and Rio Grande-RS, 2020-2021 (n=2,170).

Variables		Not having breakfast		Less than 4 meals a day		Poor diet quality		Regular consumption of unhealthy foods		Irregular consumption of healthy foods
		%	p-valuea	%	p-valuea	%	p-valuea	%	p-valuea	%	p-valuea
Sex			0.350		0.040		<0.001		0.004		0.797
	Male	14.9		45.8		35.7		30.7		12.8
	Female	13.4		41.3		26.7		25.2		12.2
Age (years)			<0.001		<0.001		<0.001		0.064		<0.001
	18-29	19.3		61.9		42.4		32.2		21.1
	30-39	16.1		51.8		41.3		25.7		13.1
	40-49	16.9		49.1		37.6		29.9		11.6
	50-59	10.8		36.3		23.3		27.1		11.7
	≥60	10.3		29.1		18.6		24.6		7.7
Skin color			0.540		<0.001		0.047		0.540		0.344
	White	14.2		41.4		29.5		27.2		12.0
	Non-whiteb	13.0		52.4		34.9		28.8		13.8
Wealth index (tertile)			0.449		0.919		0.700		<0.001		0.924
	First (poor)	15.3		43.1		29.9		33.2		12.7
	Second	14.2		43.1		32.0		23.3		12.0
	Third (richer)	12.9		44.1		31.0		25.7		12.6
Schooling			0.109		0.001		<0.001		0.322		0.121
	Elementary	12.1		39.1		25.8		27.5		10.7
	High school	14.7		48.0		35.8		29.0		13.4
	University education	15.9		45.3		31.8		25.2		13.9
Marital status			0.017		<0.001		<0.001		0.213		<0.001
	Single	16.9		53.2		36.8		28.8		16.6
	Married	12.7		38.8		28.7		27.9		10.8
	Separated/widow	11.9		35.1		21.9		23.8		7.9
Live alone			0.739		0.005		0.021		0.270		0.099
	No	14.0		44.5		31.4		27.8		12.9
	Yes	14.9		34.7		23.9		24.3		9.0
Got unemployed			0.186		0.858		0.438		0.199		0.040
	No	13.7		43.2		30.2		27.2		11.9
	Yes	17.5		42.5		33.1		31.9		17.5
Started working from home			0.302		0.689		0.611		0.044		0.855
	No	13.8		43.0		30.3		28.1		12.4
	Yes	16.7		44.6		32.1		20.8		11.9
Adherence to social distancing			0.128		0.022		0.036		0.003		0.925
	No	20.0		56.0		41.3		42.7		12.0
	Yes	13.8		42.7		30.0		27.0		12.4
Infodemic			0.137		0.003		0.546		0.862		0.996
	No	14.6		41.5		30.7		27.4		12.3
	Yes	11.9		49.2		29.3		27.8		12.3
Fear of COVID-19			0.594		0.037		0.673		0.003		0.959
	No	13.8		42.3		30.3		26.1		12.5
	Yes	14.8		47.9		31.4		33.2		12.4
Went to bars or restaurants			0.365		<0.001		0.005		0.057		0.335
	No	13.8		41.9		29.5		26.9		12.6
	Yes	16.1		55.1		39.0		33.1		10.2
Go out for physical activity			0.442		0.834		0.805		0.649		0.804
	No	13.7		43.1		30.3		27.3		12.3
	Yes	15.2		43.6		30.9		28.4		12.7
Contact with someone infected			0.289		<0.001		0.383		0.231		0.965
	No	14.5		40.7		29.9		26.8		12.4
	Yes	12.7		50.4		31.9		29.5		12.3
Presence of COVID-19 symptoms			0.027		0.050		0.025		0.378		0.403
	No	13.1		42.0		29.1		27.0		12.0
	Yes	17.0		47.0		34.4		29.0		13.4
Positive test for COVID-19			0.523		0.144		0.382		0.298		0.063
	No	14.1		43.6		30.6		27.8		12.7
	Yes	12.2		37.4		27.2		23.8		7.5
Using online tools to talk to friends and family			0.038		0.549		0.242		<0.001		0.211
	No	17.1		41.9		28.1		38.7		10.6
	Yes	13.2		43.5		31.0		24.8		12.8
Excess weight			0.669		0.006		0.009		0.010		0.159
	No	14.6		39.2		27.5		24.2		11.3
	Yes	13.9		45.5		32.9		29.4		13.4
Physical activity (minutes per week)			0.016		0.078		0.003		0.196		0.261
	<150	15.1		44.2		32.1		28.3		12.7
	≥150	10.9		39.8		25.4		25.4		10.9
Sleep quality			0.011		0.300		0.001		0.001		0.058
	Very good/good	12.5		42.2		28.1		25.2		11.3
	Regular	17.2		46.4		34.9		32.7		14.3
	Poor/very poor	17.8		43.0		37.7		33.2		15.9
Sleep duration			0.153		0.972		0.385		0.389		0.004
	Inadequate	15.0		43.1		29.6		28.3		14.2
	Adequate	12.9		43.2		31.3		26.6		10.2
Depressive symptoms			<0.001		<0.001		<0.001		0.024		0.029
	No	12.9		41.0		28.6		26.8		11.8
	Yes	21.8		57.0		43.4		33.2		16.4
Feeling of sadness			0.002		0.946		<0.001		0.020		0.118
	No	13.0		43.2		28.9		26.6		11.9
	Yes	19.2		43.4		38.5		32.6		14.9
Food insecurity			0.257		0.520		0.430		0.046		0.003
	No	13.5		42.7		29.5		26.3		11.0
	Yes	15.3		44.2		31.7		30.5		15.6

• ^a
  Exact Fisher’s test. ^bBlack, brown, yellow, indigenous skin color.
• Source: Authors.

Concerning diet quality, 30.4% had poor diet quality, 14% referred irregular consumption of markers of a healthy diet, and 43.2% had regular consumption of markers of an unhealthy diet (Table 3). Higher prevalence of poor diet quality was found in individuals males (p<0.001), younger (p<0.001), non-white (p=0.047), single (p<0.001), with better education (p<0.001), who did not live alone (p=0.021), did not adhere to social distancing (p=0.036), went to bars or restaurants (p=0.005), had COVID-19 symptoms (p=0.025) and excess weight (p=0.009), practiced <150 minutes per week of physical activity (p=0.003) and referred poor sleep quality (p=0.001), depressive symptoms (p<0.001) and sadness (p<0.001). Regular consumption of unhealthy foods was more frequent in males (p=0.004), with lowest wealth index (p<0.001), who did not start working from home during the pandemic (p=0.044), did not adhere to social distancing (p=0.003), referred fear of COVID-19 (p=0.003), did not use online tools to talk to friends and family (p<0.001), had excess weight (p=0.010), poor sleep quality (p=0.001), depressive symptoms (p=0.024), sadness (p=0.020) and food insecurity (p=0.046). Younger (p<0.001) and single individuals (p<0.001), who became unemployed (p=0.040), had inadequate sleep duration (p=0.004), depressive symptoms (p=0.029), and food insecurity (p=0.003) presented higher prevalence of irregular consumption of healthy foods (Table 4).

Perceived stress was reported by 38.2% of the sample. Men (p<0.001), single (p<0.001), younger (p<0.001), and non-white individuals (p=0.027) presented a higher prevalence of stress. Individuals with poor wealth index (p<0.001), who have become unemployed (p<0.001), who had gone to bars or restaurants (p=0.002), who were afraid of COVID-19 (p<0.001), who had contact with someone infected by COVID-19 (p=0.008), and symptoms of the infection (p<0.001) also presented higher prevalence of stress. Moreover, a higher frequency of stress was present in those who did not report infodemic (p<0.001) and did not use online tools to talk to friends and family (p<0.001). Higher prevalence of stress was also related to the practice of <150 minutes per week of physical activity (p=0.038), with poor or very poor sleep quality (p<0.001) and inadequate sleep duration (p<0.001), the presence of depressive symptoms (p<0.001) and feelings of sadness (p<0.001), and with food insecurity (p<0.001) (Table 5).

Table 5
Prevalence of perceived stress according to sociodemographic characteristics, pandemic-related variables and health outcomes of individuals aged 18 years or older. Criciúma-SC and Rio Grande-RS, 2020-2021 (n=2,170).

Variables		Perceived stress
		%	p-valuea
Sex			<0.001
	Male	43.1
	Female	31.1
Age (years)			<0.001
	18-29	48.5
	30-39	39.5
	40-49	42.7
	50-59	36.4
	≥60	30.4
Skin color			0.027
	White	37.1
	Non-whiteb	43.4
Wealth index (tertile)			<0.001
	First (poor)	47.5
	Second	37.1
	Third (richer)	31.3
Schooling			0.736
	Elementary	36.9
	High school	37.8
	University education	39.0
Marital status			<0.001
	Single	44.0
	Married	34.5
	Separated/widow	37.5
Live alone			0.097
	No	39.2
	Yes	33.5
Got unemployed			<0.001
	No	36.6
	Yes	59.2
Started working from home			0.708
	No	38.4
	Yes	36.9
Adherence to social distancing			0.061
	No	48.6
	Yes	37.9
Infodemic			<0.001
	No	40.3
	Yes	30.9
Fear of COVID-19			<0.001
	No	32.3
	Yes	62.7
Went to bars or restaurants			0.002
	No	37.2
	Yes	48.3
Go out for physical activity			0.731
	No	38.4
	Yes	37.5
Contact with someone infected			0.008
	No	36.6
	Yes	43.0
Presence of COVID-19 symptoms			<0.001
	No	34.1
	Yes	51.8
Positive test for COVID-19			0.311
	No	38.0
	Yes	42.2
Using online tools to talk to friends and family			<0.001
	No	48.8
	Yes	35.7
Excess weight			0.656
	No	37.2
	Yes	38.2
Physical activity (minutes per week)			0.038
	<150	39.5
	≥150	34.5
Sleep quality			<0.001
	Very good/good	32.3
	Regular	49.0
	Poor/very poor	58.0
Sleep duration			<0.001
	Inadequate	42.1
	Adequate	33.8
Depressive symptoms			<0.001
	No	31.4
	Yes	83.2
Feeling of sadness			<0.001
	No	32.3
	Yes	69.3
Food insecurity			<0.001
	No	34.9
	Yes	46.3

• ^a
  Exact Fisher’s test. ^bBlack, brown, yellow, indigenous skin color.
• Source: Authors.

Table 6 shows the association between chrononutrition, diet quality, and perceived stress. In the crude model, both aspects of chrononutrition were associated with perceived stress. Individuals who usually skipped breakfast and who ate less than four meals per day presented a higher prevalence of perceived stress. However, after adjustment for confounders, only the frequency remained associated. Individuals who reported eating less than four meals per day were 19% more likely to have perceived stress when compared to those who usually ate four or more meals a day (PR=1.19, CI95% 1.07;1.32).

Table 6
Crude and adjusted analysis of association between chrononutrition, diet quality and perceived stress in individuals aged 18 years or older during the COVID-19 pandemic. Criciúma-SC and Rio Grande-RS, 2020-2021.

		Perceived stress
		Crude analysesa		Adjusted analysesb
		%	p-value	PR (95% CI)	p-value
Breakfast			0.043		0.695
	No	44.0		1.04 (0.85;1.28)
	Yes	37.5		1.00
Daily meals			<0.001		0.001
	<4	48.6		1.19 (1.07;1.32)
	≥4	34.4		1.00
Diet quality (tertile)			<0.001		0.001
	Tertile 1 (best)	32.1		1.00
	Tertile 2	37.3		1.07 (0.93;1.23)
	Tertile 3 (worst)	47.6		1.25 (1.09;1.43)
Regular consumption of unhealthy foods			<0.001		0.021
	No	54.0		1.00
	Yes	35.7		1.13 (1.02;1.25)
Irregular consumption of healthy foods			<0.001		0.002
	No	44.1		1.00
	Yes	33.8		1.25 (1.09;1.44)

• PR: Prevalence ratio. 95%CI: 95% confidence interval. ^aExact Fisher’s test. ^bAdjusted Poisson regression for sex, age, skin color, wealth index, schooling, marital status, live alone, got unemployed, started working from home, adherence to social distancing, infodemic, fear of COVID-19, went to bars or restaurants, go out for physical activity, contact with someone infected, presence of COVID-19 symptoms, positive test for COVID-19, using online tools to talk to friends and family, excess weight, physical activity, sleep quality, sleep duration, perceived stress, depressive symptoms, feeling of sadness and food insecurity.
• Source: Authors.

The association between diet quality and perceived stress showed that, after adjustment for confounders, poor diet quality was associated with a higher prevalence of perceived stress (PR=1.25, CI95% 1.10;1.43). Regarding the association of food markers consumption with perceived stress, irregular consumption of healthy foods was associated with a higher prevalence of perceived stress (PR=1.25, CI95% 1.09;1.44). In turn, regular consumption of unhealthy foods was associated with an increase of 13% in the prevalence of perceived stress (PR=1.13, CI95% 1.02;1.25) (Table 6).

Discussion

This study, which aimed to assess the association of chrononutrition and diet quality with perceived stress amid the COVID-19 pandemic, showed important findings. Less than four meals per day, irregular consumption of healthy foods, regular consumption of unhealthy foods, and poor diet quality increased the prevalence of perceived stress, independent of the variables included as potential confounders in the analysis model.

The relationship between dietary intake and mental health has been explored by other studies, in which poor diet quality was linked to worse mental health, although most papers consider depression as the outcome. Akbaraly et al.²⁸ showed that women with higher scores on the Healthy Eating Index were less likely to have depressive symptoms when compared to women with lower scores. Liu et al.²⁹ observed that the intake of processed foods was positively associated with depression and perceived stress. Research with data from low and middle-income countries showed that fruit intake was associated with lower odds of depression and anxiety, while vegetable intake was associated with lower risk of depression³⁰.

Regarding perceived stress, a cross-sectional study observed that a higher intake of saturated fats and added sugars was associated with a higher stress score¹⁶. In the United Kingdom, lower adherence to a Mediterranean Diet was related to higher levels of stress in reproductive-age women¹⁷.

Our results agree with the literature. Poor diet quality, including irregular consumption of healthy foods and regular consumption of unhealthy foods, was associated with a higher prevalence of perceived stress. Nonetheless, our study has the advantage of being carried out amid the COVID-19 pandemic. The COVID-19 pandemic, with its unpredictability and numerous changes in daily life, left the population vulnerable to mental health issues, including stress³¹. While data on dietary changes during this period show contradictions, there is evidence of an increase in the number of daily snacks and meals. This increase is often associated with poor nutrition quality, primarily as a coping strategy for the stressful environment of the pandemic^32,33.

On the other hand, studies evaluating the association between chrononutrition and stress are almost unknown. An Italian study conducted with elderly adults evaluated the influence of time-restricted feeding on mental health. Aged-stratified analysis showed that individuals aged 70 and over, who had time-restricted feeding of 8 hours presented less mental distress than those without time-restrict feeding, but this association disappeared after controlling for having breakfast. Moreover, there was no association between time-restricted feeding and mental health after adjusting for confounding variables in the non-stratified analysis by age³⁴.

Circadian circle and stress systems are related for being responsible for energy and metabolic regulation and are involved mainly by neural mechanisms. While stressful actions activate the HPA axis and release catecholamines and glucocorticoids, the circadian circle receives information from the external environment through clocks located in peripheral tissues. This external information sends signals to the circadian clock located in the central nervous system (CNS), regulating the metabolism. These signals can activate circadian clocks from the HPA axis, releasing glucocorticoids and creating a stress response. Diet and feeding behaviors are external influencers of the circadian circle, also known as zeitgebers^6,35.

Inadequate food intake and behavior can disrupt metabolic homeostasis. Nutritional inadequate meals (in composition and time) can disrupt the secretion of glucocorticoids such as cortisol, increasing it to superior levels from those normally indicated by the CNS³⁶. Glucocorticoids, along with other hormones such as glucagon and ghrelin, are released during fasting status. As previously stated, its secretion is controlled by circadian clocks, including those existing in CNS, and is responsible for signaling the peripheral tissues indicating the feeding time^36,37.

Glucocorticoids increase their levels right after fasting beginning³⁸. This could be due to the activation of the HPA axis, especially in short periods of fasting³⁷, once this status is seen as a stressful condition. In response, adrenocorticotrophin hormone is released to stimulate cortisol production, a stress hormone. During fasting, glucocorticoids act in favor of lipolysis, protein degradation, and glucose synthesis³⁹. Therefore, is possible to hypothesize that less than four meals a day might be accompanied by longer periods of fasting, favoring the secretion of stress response mediators, such as glucocorticoids.

Regarding diet quality, it is important to establish that healthy dietary patterns are essential to prevent non-communicable chronic diseases^40,41. These patterns are generally higher in plant-based foods, including fruits, vegetables, whole grains, legumes, seeds, and nuts, and lower in animal-based foods, processed meats, and ultra-processed, high-fat, and high-sugar foods⁴⁰. Ultra-processed foods are industrially manufactured containing food additives and little or no whole foods. They usually have high amounts of trans fat and sugar, and low amounts of fiber, vitamins, and minerals⁴². Cross-sectional analysis of the Longitudinal Study of Adult Health Brazil pointed out that high ultra-processed food intake was associated with higher C-reactive protein (CRP) levels in women⁴³.

Meals high in saturated fat can increase inflammatory markers, including CRP levels⁴⁴, interleukin-6 (IL-6)⁴⁵, tumor necrosis factor-alpha⁴⁵, intercellular adhesion molecule-1 (ICAM-1)^44,46, and vascular cell adhesion molecule-1⁴⁴. Also, meals high in saturated fat increase nuclear factor kappa B (NFκB) activation⁴⁷. In line with this, palmitic acid (the most abundant saturated fatty acid in the Western diet) activates the proinflammatory response in microvascular endothelial cells, elevating the expression of IL-6, IL-8, ICAM-1, and toll-like receptor 2⁴⁸. This effect occurs, at least in part, by TLR4 - NFκB signaling pathway⁴⁸. A high-fat meal intake also alters the expression of circadian clock-, inflammation-, and oxidative stress-related genes in human skeletal muscle⁴⁹. A high-fat, high-carbohydrate meal raises endotoxin (lipopolysaccharide) levels postprandially and induces an inflammatory response in normal-weight men⁵⁰.

On the other hand, healthy eating patterns are associated with lower circulating concentrations of inflammatory markers^51,52. A study showed that two years of a Mediterranean-style diet, with daily consumption of whole grains, fruits, vegetables, nuts, and olive oil, reduced CRP, IL-6, and IL-18 compared to the control diet⁵³. Also, the dietary intake alters the human gut microbiota^54,55, with a Mediterranean diet and dietary components, such as fruits, vegetables, fiber, and polyphenols, positively modulating the gut microbiota, decreasing inflammation, and improving the gut barrier^56,57.

Circadian rhythm disruption can lead to gut microbiota dysbiosis, with an increase in pathobionts and a decrease in beneficial bacteria, in addition to the disrupted gut barrier and blood-brain barrier⁵⁸. The microbiota rhythms are regulated by diet and feeding time, which can alter both microbiota and gut activity, affecting host immune and metabolic function⁵⁹. The disruption to microbiota homeostasis (dysbiosis) can alter innate and adaptive immunity, contributing to inflammation⁶⁰. Also, the gut microbiota is essential for modulating brain functions^61,62. Indeed, the microbiota can impact several neurological diseases, including stress and stress-related disorders⁶³. Literature shows that host feeding rhythms synchronize with the microbiota to promote rhythms in intestinal innate immunity that anticipate exogenous microbial exposure⁶⁴. A preclinical study evidenced that the microbiota coordinates with the circadian clock to generate rhythms in the expression of genes that govern lipid metabolism and absorption⁶⁵.

It is important to highlight the methodological limitations of our study. The cross-sectional design does not allow establishing causality and temporality of associations, which increases the risk of reverse causality. Moreover, the assessment of perceived stress was through a screening tool and cannot be used as a diagnostic. However, the PSS instrument is widely used in population-based studies due to its easy application^25,26. Finally, we were only able to evaluate two of the three chrononutrition aspects, once data on regularity of food was not available in the applied questionnaire.

As a strength, this is a population-based study conducted amid the COVID-19 pandemic with a sampling process conducted in two stages, representing an important epidemiological assessment. In addition, unlike most studies conducted during the pandemic, interviews take place face-to-face, at the participants’ households. Moreover, chrononutrition is a recent topic of study and there are few studies evaluating its association with stress in literature. Finally, surveys conducted during the pandemic have provided insights into how such an event can impact various aspects of people’s lives. This enables the planning of policies and actions targeted at the most affected individuals, and it also contributes to the accumulation of knowledge. This information will be valuable in addressing possible future pandemic events.

In conclusion, the study showed that less than four meals per day, poor diet quality, irregular consumption of healthy foods, and regular consumption of unhealthy foods were associated with a higher prevalence of stress in two Brazilian cities amid the COVID-19 pandemic. These findings demonstrated an important association between dietary aspects and stress, which can contribute to the development of joint actions on nutrition and mental health.

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• Funding
  This work was supported by the Fundação de Amparo à pesquisa do Estado do Rio Grande do Sul (FAPERGS, Brasil) [Edital nº 06/2020].

• Chief editors:
  Maria Cecília de Souza Minayo, Romeu Gomes, Antônio Augusto Moura da Silva

Publication Dates

• Publication in this collection
  25 Apr 2025
• Date of issue
  Apr 2025

History

• Received
  25 Oct 2023
• Accepted
  19 Mar 2024
• Published
  21 Mar 2024