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JNFS 2023, 8(4): 619-630 |
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Toupchian O, Soltani S, Hosseini-Marnani E, Eslami F, Poorbarat S, Clark C C T, et al . Lifestyle Changes and COVID-19 Infection: A Cross-Sectional Study. JNFS 2023; 8 (4) :619-630
URL: http://jnfs.ssu.ac.ir/article-1-708-en.html
URL: http://jnfs.ssu.ac.ir/article-1-708-en.html
Omid Toupchian 
, Sepideh Soltani , Elham Hosseini-Marnani , Fatemeh Eslami , Salar Poorbarat , Cain C. T. Clark , Javad Heshmati , Rezvan Rajabzadeh , Shima Abdollahi *
, Sepideh Soltani , Elham Hosseini-Marnani , Fatemeh Eslami , Salar Poorbarat , Cain C. T. Clark , Javad Heshmati , Rezvan Rajabzadeh , Shima Abdollahi *
Department of Nutrition, School of Public Health¬, North Khorasan University of Medical Sciences, Bojnurd, Iran
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Introduction
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Lifestyle Changes and COVID-19 Infection: A Cross-Sectional Study
Omid Toupchian ; PhD1, Sepideh Soltani ; PhD2, Elham Hosseini-Marnani ; MSc3,4, Fatemeh Eslami ; BSc1,5, Salar Poorbarat ; BSc5, Cain C. T. Clark ; PhD6, Javad Heshmati ; PhD7, Rezvan Rajabzadeh ; PhD8 &
Shima Abdollahi ; PhD*1
Omid Toupchian ; PhD1, Sepideh Soltani ; PhD2, Elham Hosseini-Marnani ; MSc3,4, Fatemeh Eslami ; BSc1,5, Salar Poorbarat ; BSc5, Cain C. T. Clark ; PhD6, Javad Heshmati ; PhD7, Rezvan Rajabzadeh ; PhD8 &
Shima Abdollahi ; PhD*1
1 Department of Nutrition, School of Public Health, North Khorasan University of Medical Sciences, Bojnurd, Iran;
2 Yazd Cardiovascular Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 3 The University of Adelaide, Adelaide Medical School- Faculty of Health and Medical Sciences, Adelaide, Australia.; 4The University of Adelaide, Centre of Research Excellence in Translating Nutritional Science to Good Health- Faculty of Health and Medical Sciences, Adelaide, Australia; 5 Student Research Committee, North Khorasan University of Medical Sciences, Bojnurd, Iran; 6 Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, U.K; 7 Songhor Healthcare Center, Kermanshah University of Medical Sciences, Kermanshah, Iran; 8 Medical School, North Khorasan University of Medical Sciences, Bojnurd, Iran.
2 Yazd Cardiovascular Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 3 The University of Adelaide, Adelaide Medical School- Faculty of Health and Medical Sciences, Adelaide, Australia.; 4The University of Adelaide, Centre of Research Excellence in Translating Nutritional Science to Good Health- Faculty of Health and Medical Sciences, Adelaide, Australia; 5 Student Research Committee, North Khorasan University of Medical Sciences, Bojnurd, Iran; 6 Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, U.K; 7 Songhor Healthcare Center, Kermanshah University of Medical Sciences, Kermanshah, Iran; 8 Medical School, North Khorasan University of Medical Sciences, Bojnurd, Iran.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: COVID-19 pandemic has evidently influenced people's lifestyle, particularly their health. In this study, the authors examined the association between dietary intake and lifestyle changes, and COVID-19 infection in adults living in Bojnurd, Iran. Methods: In this cross-sectional study conducted on 4425 adults from Bojnurd city, Iran, regarding changes in food consumption, physical activity, sleep duration, and the history of COVID-19 infection; data were collected online using a researcher-designed questionnaire. The associations between lifestyle changes and COVID-19 infection were assessed by multivariate- adjusted logistic regression models. Results: There were significant associations between lower odds of COVID-19, increased legumes consumption (OR: 0.76; 95% CI: 0.61, 0.96), and increased physical activity (OR: 0.74; 95% CI: 0.57, 0.95) during the pandemic; this was while increased intakes of refined grain (OR: 1.32; 95% CI: 1.06, 1.63), butter oil (OR: 1.34; 95% CI: 1.03, 1.73), processed meat (OR: 1.36; 95% CI: 1.01, 1.82), fast foods (OR: 1.65; 95% CI: 1.13, 2.40), honey (OR: 1.34; 95% CI: 1.10, 1.64), and coffee (OR: 1.61; 95% CI: 1.24, 2.09) were associated with higher odds of infection. Moreover, higher sleep duration (OR: 1.25; 95% CI: 1.02, 1.52), increased intake of multivitamins/minerals (OR: 1.66; 95% CI: 1.35, 2.05), vitamin D (OR: 1.22; 95% CI: 1.01, 1.47), and vitamin C (OR: 1.52; 95% CI: 1.26, 1.84) were significantly associated with higher odds of infection, compared to the cases with no change. Conclusion: Increased intake of refined grain and high-fat foods may be associated with lower odds of infection. However, the cross-sectional design of the present study precludes causal inferences. Keywords: Diet; COVID-19; Physical activity; Sleep habit; Cross-sectional studies |
|
| Article history: Received:3 Sep 2022 Revised: 19 Oct 2022 Accepted: 19 Oct 2022 |
||
| *Corresponding author: sh.abd6864@yahoo.com Department of Nutrition, School of Public Health, North Khorasan University of Medical Sciences, Bojnurd, Iran. Postal code: 74877-94149 Tel: + 58 32240571 |
Introduction
On December 2019, a new type of coronavirus disease broke out in Wuhan, China, and sparked a global pandemic, on March 11, 2020 (Silva, 2020, Song et al., 2020). The disease was termed COVID-19, which predominantly attacks lungs (both upper and lower). Symptoms of the disease include fever, fatigue, body aches, cough, and shortness of breath (Pullen et al., 2020). Globally, according to the latest report of World Health Organization (WHO) (Ammar et al., 2020), as of 14 October 2022, 620 million people were infected, and 6.5 million died.
To prevent the spread of COVID-19, governments carried out sanitization and disinfectant protocols, and WHO advised home quarantine and maintaining social distance (Velavan and Meyer, 2020). Travel between high-risk cities was banned, and gatherings were limited. Subsequently, many jobs were shut down or virtualized, and online distance learning prevailed. Previous studies demonstrated that despite preventing the spread of the virus, these changes yielded detrimental effects on mental and physical health; lower physical/outdoor activities because of the time spent on home-cooked sweets, restricted access to grocery shopping centers causing lower consumption of fresh foods, altered sleep patterns, and feeling anxious due to the constant exposure to stressful news (Banerjee and Rai, 2020). Stress-coping behaviors may also develop habits like overeating, especially high-fat sugary foods and beverages (Koball et al., 2012, Yannakoulia et al., 2008). This condition may prevent individuals from having a healthy lifestyle.
Several studies investigated changes in lifestyle during the pandemic; they demonstrated that during COVID-19 people were more likely to eat unhealthy (Ammar et al., 2020b, Scarmozzino and Visioli, 2020, Sidor and Rzymski, 2020) and canned foods (Janssen et al., 2021). However, the situation may be quite different due to the raised public awareness about health and boosting immune systems. A study in Spain showed adherence to a Mediterranean diet was increased during the pandemic confinement, compared with previous habits (Rodríguez-Pérez et al., 2020). In another study, physical activity (PA) was reduced by around 34%, although dietary habits remained stable or improved in older adults in Finland (Lehtisalo et al., 2021).
Change in lifestyle can affect susceptibility to infectious diseases, primarily through affecting the immune system (Calder, 2020, Shi et al., 2020). A large cohort study in UK reported higher incidence of COVID-19 in people who had sedentary lifestyles, smoked, and were obese (Hamer et al., 2020). The results from a cross-sectional study also revealed higher rates of severe infection in patients with lower levels of PA, and less severity in patients with a healthier dietary pattern (Tavakol et al., 2021).
In Iran, the severity of infection was more complicated due to the imposition of sanctions on people facing severe inflation and losing their jobs (Abdoli, 2020). So far, no study has investigated dietary intake and other risk factors in relation to COVID-19 infection. Therefore, the authors aimed to examine the changes in lifestyle (including diet, physical activity, sleep duration, and smoking) and their association with COVID-19 infection.
Materials and Methods
Study design and participants: The present cross-sectional study was conducted on adults from Bojnurd, Iran, in July 2021. No inclusion and exclusion criteria were applied, except for age (18 years old and above). Anyone interested in participating in the study, after watching a recorded video about the objectives of the survey and reading the informed consent, could access the questionnaire through an electronic link created by survey.porsline.ir. To avoid duplication of data, the authors asked only one adult person in each household to complete the questionnaire (preferably the head of the household). The study was conducted in agreement with the Helsinki declaration, and all data were collected anonymously and kept confidential. The questionnaire was available from May 4th for one month, and social networks (including Instagram, Telegram, WhatsApp, and Facebook) and emails were used to recruit for the study. For this purpose, the invitation video along with the link of the questionnaire were shared on the most visited pages/groups related to North Khorasan, such as news channels, medical channels, and COVID-19 information channels on the social networks. Moreover, the announcement was released in the context of Roshd educational network, a designed platform for virtual education of students during the pandemic.
As old people use the internet less frequently than young individuals, participants were asked to interview with them and complete the questionnaire for relatives and acquaintances who did not have regular access to the internet.
Questionnaire: A 60-item self-administered questionnaire was designed, containing three sections: 1) socio-demographic characteristics, including age, sex, history of chronic disease, place of residence (urban/rural), education, sleep duration, weight change during the pandemic, income, marital status, living status, family size, and main source of income; 2) changes in lifestyle (PA, smoking, and sleep), and 3) dietary intake during and after the pandemic. They were also asked about whether their family members have been diagnosed with COVID-19. All the sections were based on multiple choice close-ended questions, except for age. Participants were asked to choose an option, based on the changes due to COVID-19 outbreak (decreased, increased, and unchanged).
Ethical considerations: This study was conducted in agreement with Helsinki declaration, and all the data were collected anonymously and kept confidentially. The study protocol was also approved by the Medical Ethics Committee of North Khorasan University of Medical Sciences (IR.NKUMS.REC.1400.026).
Data analyses: Descriptive statistics of the participants’ characteristics are presented as frequency and percentages. Chi-square test was performed to analyze the difference between socio-demographic factors and COVID-19. Stepwise multivariate logistic regression analyses were performed to explore the associations between changes in dietary intake, PA, and sleep [(1) no change/constant, (2) increase, (3) decrease]) and confirmed/unconfirmed diagnosis of COVID-19. The results of logistic regression analyses were expressed as crude and adjusted model (age, sex, food purchasing power, marital status, household composition, family member, place of residence, income, main source of income, and education). For all analyses, p-values less than 0.05 were considered significant. All analyses were performed with SPSS for Windows, version 16 (Chicago, Illinois).
Results
From 4425 completed questionnaires, 97 were excluded due to incomplete information. 675 participants were diagnosed with COVID-19 since beginning of the pandemic. More than half of the participants were married (nearly 58%), and most of the households had 3-4 members. More than 90% of the participants lived in families with monthly incomes below the poverty line, whilst half of the participants reported no change in their weight during the pandemic. Other main characteristics of the participants with COVID-19 infection are described in Table 1.
The multivariate adjusted model revealed a significant association between lower odds of COVID-19, increased legumes consumption (OR: 0.76; 95% CI: 0.61, 0.96), and increased PA (OR: 0.74; 95% CI: 0.57, 0.95) during the pandemic. This was while the increased intake of refined grain (OR: 1.32; 95% CI: 1.06, 1.63), butter oil (OR: 1.34; 95% CI: 1.03, 1.73), processed meat (OR: 1.36; 95% CI: 1.01, 1.82), fast foods (OR: 1.65; 95% CI: 1.13, 2.40), honey (OR: 1.34; 95% CI: 1.10, 1.64), and coffee (OR: 1.61; 95% CI: 1.24, 2.09) was associated with higher odds of infection. There was also a significant relationship between increased sleep duration and higher odds of infection (OR: 1.25; 95% CI: 1.02, 1.52). Furthermore, it was found that participants with an increased intake of multivitamins/ minerals (OR: 1.66; 95% CI: 1.35, 2.05), vitamin D (OR: 1.22; 95% CI: 1.01, 1.47), and vitamin C (OR: 1.52; 95% CI: 1.26, 1.84) was associated with higher odds of COVID-19 infection, compared to participants who did not change their intake (Table 2).
To prevent the spread of COVID-19, governments carried out sanitization and disinfectant protocols, and WHO advised home quarantine and maintaining social distance (Velavan and Meyer, 2020). Travel between high-risk cities was banned, and gatherings were limited. Subsequently, many jobs were shut down or virtualized, and online distance learning prevailed. Previous studies demonstrated that despite preventing the spread of the virus, these changes yielded detrimental effects on mental and physical health; lower physical/outdoor activities because of the time spent on home-cooked sweets, restricted access to grocery shopping centers causing lower consumption of fresh foods, altered sleep patterns, and feeling anxious due to the constant exposure to stressful news (Banerjee and Rai, 2020). Stress-coping behaviors may also develop habits like overeating, especially high-fat sugary foods and beverages (Koball et al., 2012, Yannakoulia et al., 2008). This condition may prevent individuals from having a healthy lifestyle.
Several studies investigated changes in lifestyle during the pandemic; they demonstrated that during COVID-19 people were more likely to eat unhealthy (Ammar et al., 2020b, Scarmozzino and Visioli, 2020, Sidor and Rzymski, 2020) and canned foods (Janssen et al., 2021). However, the situation may be quite different due to the raised public awareness about health and boosting immune systems. A study in Spain showed adherence to a Mediterranean diet was increased during the pandemic confinement, compared with previous habits (Rodríguez-Pérez et al., 2020). In another study, physical activity (PA) was reduced by around 34%, although dietary habits remained stable or improved in older adults in Finland (Lehtisalo et al., 2021).
Change in lifestyle can affect susceptibility to infectious diseases, primarily through affecting the immune system (Calder, 2020, Shi et al., 2020). A large cohort study in UK reported higher incidence of COVID-19 in people who had sedentary lifestyles, smoked, and were obese (Hamer et al., 2020). The results from a cross-sectional study also revealed higher rates of severe infection in patients with lower levels of PA, and less severity in patients with a healthier dietary pattern (Tavakol et al., 2021).
In Iran, the severity of infection was more complicated due to the imposition of sanctions on people facing severe inflation and losing their jobs (Abdoli, 2020). So far, no study has investigated dietary intake and other risk factors in relation to COVID-19 infection. Therefore, the authors aimed to examine the changes in lifestyle (including diet, physical activity, sleep duration, and smoking) and their association with COVID-19 infection.
Materials and Methods
Study design and participants: The present cross-sectional study was conducted on adults from Bojnurd, Iran, in July 2021. No inclusion and exclusion criteria were applied, except for age (18 years old and above). Anyone interested in participating in the study, after watching a recorded video about the objectives of the survey and reading the informed consent, could access the questionnaire through an electronic link created by survey.porsline.ir. To avoid duplication of data, the authors asked only one adult person in each household to complete the questionnaire (preferably the head of the household). The study was conducted in agreement with the Helsinki declaration, and all data were collected anonymously and kept confidential. The questionnaire was available from May 4th for one month, and social networks (including Instagram, Telegram, WhatsApp, and Facebook) and emails were used to recruit for the study. For this purpose, the invitation video along with the link of the questionnaire were shared on the most visited pages/groups related to North Khorasan, such as news channels, medical channels, and COVID-19 information channels on the social networks. Moreover, the announcement was released in the context of Roshd educational network, a designed platform for virtual education of students during the pandemic.
As old people use the internet less frequently than young individuals, participants were asked to interview with them and complete the questionnaire for relatives and acquaintances who did not have regular access to the internet.
Questionnaire: A 60-item self-administered questionnaire was designed, containing three sections: 1) socio-demographic characteristics, including age, sex, history of chronic disease, place of residence (urban/rural), education, sleep duration, weight change during the pandemic, income, marital status, living status, family size, and main source of income; 2) changes in lifestyle (PA, smoking, and sleep), and 3) dietary intake during and after the pandemic. They were also asked about whether their family members have been diagnosed with COVID-19. All the sections were based on multiple choice close-ended questions, except for age. Participants were asked to choose an option, based on the changes due to COVID-19 outbreak (decreased, increased, and unchanged).
Ethical considerations: This study was conducted in agreement with Helsinki declaration, and all the data were collected anonymously and kept confidentially. The study protocol was also approved by the Medical Ethics Committee of North Khorasan University of Medical Sciences (IR.NKUMS.REC.1400.026).
Data analyses: Descriptive statistics of the participants’ characteristics are presented as frequency and percentages. Chi-square test was performed to analyze the difference between socio-demographic factors and COVID-19. Stepwise multivariate logistic regression analyses were performed to explore the associations between changes in dietary intake, PA, and sleep [(1) no change/constant, (2) increase, (3) decrease]) and confirmed/unconfirmed diagnosis of COVID-19. The results of logistic regression analyses were expressed as crude and adjusted model (age, sex, food purchasing power, marital status, household composition, family member, place of residence, income, main source of income, and education). For all analyses, p-values less than 0.05 were considered significant. All analyses were performed with SPSS for Windows, version 16 (Chicago, Illinois).
Results
From 4425 completed questionnaires, 97 were excluded due to incomplete information. 675 participants were diagnosed with COVID-19 since beginning of the pandemic. More than half of the participants were married (nearly 58%), and most of the households had 3-4 members. More than 90% of the participants lived in families with monthly incomes below the poverty line, whilst half of the participants reported no change in their weight during the pandemic. Other main characteristics of the participants with COVID-19 infection are described in Table 1.
The multivariate adjusted model revealed a significant association between lower odds of COVID-19, increased legumes consumption (OR: 0.76; 95% CI: 0.61, 0.96), and increased PA (OR: 0.74; 95% CI: 0.57, 0.95) during the pandemic. This was while the increased intake of refined grain (OR: 1.32; 95% CI: 1.06, 1.63), butter oil (OR: 1.34; 95% CI: 1.03, 1.73), processed meat (OR: 1.36; 95% CI: 1.01, 1.82), fast foods (OR: 1.65; 95% CI: 1.13, 2.40), honey (OR: 1.34; 95% CI: 1.10, 1.64), and coffee (OR: 1.61; 95% CI: 1.24, 2.09) was associated with higher odds of infection. There was also a significant relationship between increased sleep duration and higher odds of infection (OR: 1.25; 95% CI: 1.02, 1.52). Furthermore, it was found that participants with an increased intake of multivitamins/ minerals (OR: 1.66; 95% CI: 1.35, 2.05), vitamin D (OR: 1.22; 95% CI: 1.01, 1.47), and vitamin C (OR: 1.52; 95% CI: 1.26, 1.84) was associated with higher odds of COVID-19 infection, compared to participants who did not change their intake (Table 2).
| Table 1. Characteristics of the questionnaires regarding COVID -19 infection. | ||||
| Variables | COVID-19 non-infected | COVID-19 infected | Total | P-valuea |
| Sex | ||||
| Male | 924 (25.4)b | 159 (23.6) | 1083 (25.3) | 0.31 |
| Female | 2714 (74.6) | 516 (76.4) | 3230 (74.6) | |
| Physical activity duration per day (min) | 0.003 | |||
| Less than 30 | 1108 (30.9) | 249 (37.8) | 1357 (32.0) | |
| 30-60 | 1194 (33.3) | 190 (28.9) | 1384 (32.6) | |
| 60-120 | 803 (22.4) | 129 (19.6) | 932 (22.0) | |
| More than 120 | 481 (13.4) | 90 (13.7) | 571 (13.5) | |
| Sleep duration (hour) | 0.073 | |||
| Less than 2 | 141 (3.9) | 16 (24.0) | 157 (3.6) | |
| 2-4 | 89 (2.4) | 12 (1.8) | 101 (2.3) | |
| 4-6 | 246 (6.7) | 59 (8.8) | 305 (7.1) | |
| 6-8 | 1405 (38.5) | 250 (37.1) | 1655 (38.3) | |
| 8-10 | 1463 (40.1) | 269 (39.9) | 1732 (40.1) | |
| More than 10 | 305 (8.4) | 68 (10.1) | 373 (8.6) | |
| Using the electronic devise (hour) | <0.001 | |||
| Less than 2 | 1110 (30.7) | 165 (24.7) | 1275 (29.7) | |
| 2-4 | 1020 (28.2) | 182 (27.3) | 1202 (28.0) | |
| 4-6 | 677 (18.7) | 128 (19.2) | 805 (18.8) | |
| 6-8 | 446 (12.3) | 102 (15.3) | 548 (12.8) | |
| 8-10 | 215 (5.9) | 43 (6.4) | 258 (6.0) | |
| More than 10 | 151 (4.2) | 47 (7.0) | 198 (4.6) | |
| Smoking | 0.045 | |||
| I never smoked | 2626 (85.9) | 522 (89.8) | 3148 (86.5) | |
| I have been smoking since the past | 156 (5.1) | 16 (2.8) | 172 (4.7) | |
| Decreased | 209 (6.8) | 33 (5.7) | 242 (6.7) | |
| Increased | 67 (2.2) | 10 (1.7) | 77 (2.1) | |
| Weight change during the pandemic | 0.034 | |||
| Without change | 1820 (49.8) | 301 (44.6) | 2121 (49.0) | |
| Decreased | 490 (13.4) | 93 (13.8) | 583 (13.5) | |
| Increased | 1344 (36.8) | 281 (41.6) | 1625 (37.5) | |
| Food intake during pandemic | 0.008 | |||
| Without change | 2297 (62.9) | 389 (57.9) | 2686 (62.1) | |
| Decreased | 475 (13.0) | 86 (12.7) | 561 (13.0) | |
| Increased | 879 (24.1) | 200 (29.6) | 1079 (24.9) | |
| Marital status | <0.001 | |||
| Single | 1540 (42.4) | 189 (27.9) | 1729 (40.1) | |
| Married | 2035 (56.0) | 464 (68.4) | 2499 (57.9) | |
| Widowed | 19 (0.5) | 5 (0.7) | 24 (0.6) | |
| Divorced | 41 (1.1) | 20 (2.9) | 61 (1.4) | |
| Household composition | <0.001 | |||
| Couple and children | 1926 (52.7) | 452 (66.9) | 2378 (54.9) | |
| Living with parents | 1666 (45.6) | 199 (29.4) | 1865 (43.1) | |
| One person | 32 (0.9) | 12 (1.8) | 44 (1.0) | |
| Extended family | 19 (0.5) | 8 (1.2) | 27 (0.6) | |
| Nonfamily households | 10 (0.3) | 5 (0.7) | 15 (0.3) | |
| Family size (number) | <0.001 | |||
| ≤ 2 | 121 (3.3) | 44 (6.5) | 165 (3.8) | |
| 3-4 | 2205 (60.3) | 439 (64.8) | 2644 (61) | |
| 5-6 | 1237 (33.8) | 179 (26.4) | 1416 (32.7) | |
| 7 ≥ | 95 (2.6) | 15 (2.2) | 110 (2.5) | |
| Income (million Rial) | <0.001 | |||
| < 1 | 588 (16.5) | 54 (8.2) | 642 (15.2) | |
| 1-2 | 822 (23.0) | 86 (13.0) | 908 (21.5) | |
| 3-5 | 1036 (29.0) | 228 (34.5) | 1264 (29.9) | |
| 6- 8 | 496 (13.9) | 124 (18.8) | 620 (14.7) | |
| 8-10 | 338 (9.5) | 81 (12.3) | 419 (9.9) | |
| 10-15 | 177 (5.0) | 60 (9.1) | 237 (5.6) | |
| 15- 20 | 58 (1.6) | 14 (2.1) | 72 (1.7) | |
| 20 ≥ | 52 (1.5) | 13 (2.0) | 65 (1.5) | |
| a: Chi-square test; b: n(%) | ||||
| Table 2. The association between dietary and lifestyle factors and COVID-19 infection. | ||||
| Food groups | Participants/ event (Number) |
Crude | Model 1 | Model 2 |
| Dairy | 4326/ 674 | |||
| No Change | 2938/ 470 | 1 | 1 | 1 |
| Decreased | 555/ 100 | 1.13 (0.88, 1.45) | 1.13 (0.88, 1.45) | 1.21 (0.93, 1.57) |
| Increased | 833/ 104 | 0.75 (0.59, 0.95) | 0.76 (0.60, 0.96) | 0.85 (0.66, 1.09) |
| Cookies and sweets | 4322/ 675 | |||
| No Change | 2632/ 397 | 1 | 1 | 1 |
| Decreased | 1047/ 156 | 0.94 (0.76, 1.16) | 0.94 (0.76, 1.16) | 0.94 (0.75, 1.17) |
| Increased | 643/ 122 | 1.27 (1.00, 1.60) | 1.27 (1.00, 1.60) | 1.21 (0.95, 1.54) |
| Refined grain | 4323/ 673 | |||
| No change | 3052/ 460 | 1 | 1 | 1 |
| Decreased | 315/ 52 | 0.95 (0.67, 1.33) | 0.96 (0.68, 1.35) | 1.19 (0.83, 1.71) |
| Increased | 956/161 | 1.14 (0.93, 1.40) | 1.15 (0.94, 1.41) | 1.32 (1.06, 1.63) |
| Red meat | 4315/ 674 | |||
| No change | 2817/ 457 | 1 | 1 | 1 |
| Decreased | 807/ 110 | 0.79 (0.62, 1.00) | 0.79 (0.62, 1.00) | 0.93 (0.73, 1.20) |
| Increased | 691/ 107 | 1.02 (0.81, 1.30) | 1.03 (0.82, 1.31) | 1.18 (0.92, 1.51) |
| Poultry | 4319/ 672 | |||
| No change | 2804/ 446 | 1 | 1 | 1 |
| Decreased | 755/ 105 | 0.81 (0.64, 1.03) | 0.82 (0.64, 1.05) | 0.97 (0.75, 1.25) |
| Increased | 760/ 121 | 0.99 (0.79, 1.24) | 1.00 (0.79, 1.25) | 1.11 (0.88, 1.41) |
| Egg | 4321/ 674 | |||
| No change | 2800/ 436 | 1 | 1 | 1 |
| Decreased | 556/ 90 | 1.01 (0.78, 1.31) | 1.02 (0.78, 1.32) | 1.23 (0.94, 1.62) |
| Increased | 965/ 148 | 0.96 (0.78, 1.19) | 0.97 (0.79, 1.21) | 0.99 (0.79, 1.23) |
| Fish | 4318/ 671 | |||
| No change | 2868/ 477 | 1 | 1 | 1 |
| Decreased | 993/ 133 | 0.76 (0.61, 0.94) | 0.76 (0.61, 0.94) | 0.81 (0.65, 1.02) |
| Increased | 457/ 61 | 0.77 (0.57, 1.04) | 0.77 (0.57, 1.04) | 0.77 (0.56, 1.05) |
| Salty snacks | 4316/ 674 | |||
| No change | 2371/ 381 | 1 | 1 | 1 |
| Decreased | 1322/ 177 | 0.80 (0.65, 0.98) | 0.81 (0.66, 0.99) | 0.91 (0.73, 1.12) |
| Increased | 623/ 116 | 1.19 (0.94, 1.51) | 1.19 (0.94, 1.51) | 1.22 (0.95, 1.57) |
| Olive and olive oil | 4297/ 673 | |||
| No change | 3399/ 539 | 1 | 1 | 1 |
| Decreased | 525/ 74 | 0.86 (0.65, 1.13) | 0.87 (0.66, 1.14) | 0.94 (0.71, 1.25) |
| Increased | 373/ 60 | 0.92 (0.67, 1.25) | 0.93 (0.68, 1.27) | 0.84 (0.61, 1.15) |
| Butter oil | 4310/ 671 | |||
| No change | 3111/ 475 | 1 | 1 | 1 |
| Decreased | 648/ 96 | 0.91 (0.71, 1.17) | 0.92 (0.72, 1.18) | 1.06 (0.82, 1.38) |
| Increased | 551/100 | 1.20 (0.94, 1.54) | 1.21 (0.95, 1.55) | 1.34 (1.03, 1.73) |
| Processed meat | 4300/ 674 | |||
| No change | 2607/ 414 | 1 | 1 | 1 |
| Decreased | 1305/ 181 | 0.88 (0.72, 1.07) | 0.88 (0.73, 1.08) | 0.92 (0.75, 1.13) |
| Increased | 388/ 79 | 1.32 (1.00, 1.75) | 1.34 (1.01, 1.77) | 1.36 (1.01, 1.82) |
| Fast food | 4291/ 671 | |||
| No change | 2321/ 334 | 1 | 1 | 1 |
| Decreased | 1761/ 287 | 1.13 (0.94, 1.35) | 1.13 (0.94, 1.35) | 1.00 (0.83, 1.21) |
| Increased | 209/ 50 | 1.73 (1.21, 2.46) | 1.78 (1.25, 2.54) | 1.65 (1.13, 2.40) |
| Alcoholic drinking | 3970/ 624 | |||
| No change | 3426/ 549 | 1 | 1 | 1 |
| Decreased | 446/ 55 | 0.78 (0.57, 1.07) | 0.78 (0.75, 1.07) | 1.01 (0.73, 1.40) |
| Increased | 98/ 20 | 1.24 (0.71, 2.16) | 1.28 (0.73, 2.23) | 1.39 (0.76, 2.54) |
| Energetic drinks | 4123/ 650 | |||
| No change | 3408/ 535 | 1 | 1 | 1 |
| Decreased | 527/ 88 | 1.09 (0.84, 1.41) | 1.10 (0.85, 1.42) | 1.30 (0.99, 1.71) |
| Increased | 188/ 27 | 0.97 (0.63, 1.51) | 0.99 (0.64, 1.54) | 1.13 (0.71, 1.79) |
| Sweet beverage | 4284/ 669 | |||
| No change | 2467/ 406 | 1 | 1 | 1 |
| Decreased | 1323/ 175 | 0.77 (0.63, 0.93) | 0.77 (0.63, 0.94) | 0.83 (0.67, 1.02) |
| Increased | 494/ 88 | 1.00 (0.77, 1.31) | 1.02 (0.78, 1.33) | 1.09 (0.82, 1.45) |
| Water | 4315/ 673 | |||
| No change | 2274/ 362 | 1 | 1 | 1 |
| Decreased | 233/ 32 | 0.86 (0.58, 1.28) | 0.87 (0.58, 1.29) | 1.11 (0.73, 1.68) |
| Increased | 1808/ 279 | 0.90 (0.76, 1.08) | 0.91 (0.76, 1.09) | 0.95 (0.79, 1.15) |
| Tea | 4310/ 672 | |||
| No change | 2554/ 378 | 1 | 1 | 1 |
| Decreased | 412/ 56 | 0.87 (0.63, 1.21) | 0.89 (0.64, 1.22) | 1.15 (0.82, 1.61) |
| Increased | 1344/ 238 | 1.19 (1.00, 1.44) | 1.20 (0.99, 1.44) | 1.13 (0.93, 1.37) |
| Coffee | 4245/ 666 | |||
| No change | 3198/ 474 | 1 | 1 | 1 |
| Decreased | 576/ 84 | 1.01 (0.78, 1.31) | 1.01 (0.78, 1.31) | 1.17 (0.89, 1.54) |
| Increased | 471/ 108 | 1.66 (1.30, 2.12) | 1.70 (1.32, 2.17) | 1.61 (1.24, 2.09) |
| Nuts | 4296/ 671 | |||
| No change | 2567/ 398 | 1 | 1 | 1 |
| Decreased | 750/ 106 | 0.89 (0.70, 1.13) | 0.89 (0.70, 1.13) | 0.96 (0.75, 1.23) |
| Increased | 979/ 167 | 1.07 (0.87, 1.32) | 1.07 (0.87, 1.32) | 0.99 (0.79,1.23) |
| Honey | 4319/ 671 | |||
| No change | 2660/ 377 | 1 | 1 | 1 |
| Decreased | 538/ 73 | 0.93 (0.70, 1.24) | 0.94 (0.71, 1.25) | 1.10 (0.82, 1.47) |
| Increased | 1121/ 221 | 1.54 (1.27, 1.86) | 1.53 (1.26, 1.85) | 1.34 (1.10, 1.64) |
| Fruits | 4325/ 673 | |||
| No change | 1896/ 292 | 1 | 1 | 1 |
| Decreased | 499/ 64 | 0.74 (0.54, 1.00) | 0.74 (0.55, 1.01) | 0.91 (0.66, 1.26) |
| Increased | 1930/ 317 | 1.03 (0.86, 1.24) | 1.04 (0.86, 1.24) | 1.05 (0.87, 1.27) |
| Vegetable | 4322/ 675 | |||
| No change | 2438/ 387 | 1 | 1 | 1 |
| Decreased | 519/ 86 | 0.97 (0.74, 1.27) | 0.97 (0.74, 1.28) | 1.09 (0.82, 1.45) |
| Increased | 1365/ 202 | 0.86 (0.71, 1.04) | 0.86 (0.71, 1.04) | 0.87 (0.71, 1.06) |
| Beans | 4328/ 676 | |||
| No change | 3065/ 512 | 1 | 1 | 1 |
| Decreased | 297/ 43 | 0.78 (0.54, 1.13) | 0.79 (0.55, 1.14) | 1.01 (0.69, 1.49) |
| Increased | 966/ 121 | 0.73 (0.59, 0.91) | 0.73 (0.59, 0.91) | 0.76 (0.61, 0.96) |
| Physical activity | 4244/ 658 | |||
| No change | 1357/ 249 | 1 | 1 | 1 |
| Decreased | 1384/ 190 | 0.74 (0.60, 0.91) | 0.74 (0.60, 0.91) | 0.79 (1.63, 0.98) |
| Increased | 932/ 129 | 0.71 (0.56, 0.91) | 0.72 (0.56, 0.92) | 0.74 (0.57, 0.95) |
| Smoking | 3639/ 581 | |||
| No change | 3320/ 538 | 1 | 1 | 1 |
| Decreased | 242/ 33 | 0.94 (0.64, 1.39) | 0.95 (0.65, 1.41) | 1.35 (0.89, 2.05) |
| Increased | 77/ 10 | 0.57 (0.24, 1.33) | 0.59 (0.25, 1.39) | 0.55 (0.21, 1.42) |
| Social media | 4319/ 670 | |||
| No change | 1174/ 170 | 1 | 1 | 1 |
| Decreased | 339/ 29 | 0.58 (0.38, 0.90) | 0.59 (0.38, 0.91) | 0.75 (0.48, 1.18) |
| Increased | 2806/ 471 | 1.15 (0.94, 1.40) | 1.15 (0.94, 1.40) | 1.11 (0.90, 1.37) |
| Sleep duration | 4317/ 673 | |||
| No change | 2453/ 363 | 1 | 1 | 1 |
| Decreased | 552/ 85 | 1.05 (0.80, 1.38) | 1.06 (0.81, 1.38) | 1.17 (0.88, 1.54) |
| Increased | 1312/ 225 | 1.17 (0.97, 1.41) | 1.18 (0.98, 1.43) | 1.25 (1.02, 1.52) |
| Intake of multivitamin- mineral intake |
4256/ 667 | |||
| No change | 3055/ 435 | 1 | 1 | 1 |
| Decreased | 368/ 43 | 0.81 (0.57, 1.16) | 0.82 (0.58, 1.17) | 1.02 (0.71, 1.49) |
| Increased | 833/ 189 | 1.80 (1.47, 2.19) | 1.80 (1.48, 2.20) | 1.66 (1.35, 2.05) |
| Intake of vitamin D supplement |
4283/ 671 | |||
| No change | 2496/ 350 | 1 | 1 | 1 |
| Decreased | 406/ 46 | 0.79 (0.57, 1.12) | 0.80 (0.57, 1.12) | 1.03 (0.72, 1.47) |
| Increased | 1381/ 275 | 1.49 (1.25, 1.79) | 1.50 (1.24, 1.78) | 1.22 (1.01, 1.47) |
| Intake of vitamin A supplement |
4260/ 666 | |||
| No change | 3115/ 480 | 1 | 1 | 1 |
| Decreased | 351/ 46 | 0.82 (0.58, 1.16) | 0.83 (0.59, 1.18) | 1.06 (0.74, 1.53) |
| Increased | 794/ 140 | 1.14 (0.91, 1.41) | 1.14 (0.92, 1.42) | 1.11 (0.88, 1.40) |
| Intake of vitamin C supplement |
4275/ 664 | |||
| No change | 2681/ 358 | 1 | 1 | 1 |
| Decreased | 355/ 48 | 1.00 (0.71, 1.40) | 1.02 (0.72, 1.43) | 1.31 (0.92, 1.88) |
| Increased | 1239/ 258 | 1.72 (1.43, 2.06) | 1.72 (1.43, 2.07) | 1.52 (1.26, 1.84) |
| Intake of omega-3 supplement |
4238/ 659 | |||
| No change | 3281/ 509 | 1 | 1 | 1 |
| Decreased | 374/ 50 | 0.84 (0.61, 1.17) | 0.84 (0.61, 1.18) | 1.11 (0.79, 1.58) |
| Increased | 583/ 100 | 1.19 (0.93, 1.51) | 1.20 (0.94, 1.52) | 1.11 (0.86, 1.43) |
| Model 1: Adjusted for age and sex; Model 2: Adjusted for age, sex, food purchasing power, marriage status, household composition, family member, place of residence, income, main source of income, and education | ||||
Discussion
This paper examined the association between lifestyle factors and COVID-19 infection in Bojnurd, Iran. The increased consumption of legumes and increased PA was negatively associated with the odds of COVID-19 infection. However, increased consumption of refined grain, butter oil, processed meats, fast foods, honey, caffeine, and more sleep duration contributed to higher odds of COVID-19 infection. Surprisingly, higher intake of multivitamin, vitamin D, and vitamin C supplements was associated with higher odds of infection.
Adequate nutrition is essential for strengthening the immune system and may improve protection against COVID-19 infection and its complications (Calder and Jackson, 2000, EFSA Panel on Dietetic Products and Nutrition and Allergies, 2016, Keusch, 2003, Watson, 1984). It is well known that under nutrition with insufficient energy, protein, and nutrient intake is related to poor immune function (Katona and Katona-Apte, 2008), while over nutrition is associated with impaired lung function (Dietz and Santos-Burgoa, 2020, Melo et al., 2014), secretion of inflammatory mediators (Hauner, 2005), and cytokine storm, leading to acute respiratory syndrome and organs dysfunction (Muscogiuri et al., 2020). A balanced diet to meet nutritional needs, containing both plant-based foods and animal resources, in accordance with healthy nutritional guidelines, can improve immune responses and help body to fight against infection (Cena and Calder, 2020).
In concordance with some previous studies (Abdulah and Hassan, 2020, Kim et al., 2021), this study revealed a significant association between higher consumption of legumes and lower odds of COVID-19 infection. Legumes are solid sources of protein, dietary fiber, as well as nutraceutical compounds (Singh et al., 2017). Indeed, there are several sources of evidence supporting the beneficial effect of legumes on obesity (Kim et al., 2016), diabetes mellitus (Becerra-Tomás et al., 2018), dyslipidemia (Ha et al., 2014), high blood pressure, and CVD (Grosso et al., 2017).
There was also a significant association between increased PA and lower odds of COVID-19 infection. A similar finding was observed in a study with 48,440 participants, where inactivity was positively linked with severe COVID-19 infection (Sallis et al., 2021). PA is acknowledged as an indispensable part of healthy lifestyle; although, it is suggested higher PA may increase pro-inflammatory cytokines in muscles, but not in the circulation (Peake et al., 2015). Regular PA has been shown to enhance immune response, lung capacity, muscle strength, mental health (Buitrago-Garcia et al., 2020), and reduce systemic inflammation (Sallis et al., 2021), lockdown-induced emotional stress (Celorio-Sardà et al., 2021) and COVID-19 complications (Nieman and Wentz, 2019). On the other hand, it is plausible that people who are more active are leaner, and therefore, follow a healthier lifestyle, compared with less active individuals.
A positive association was observed between increased intake of refined grains, honey, processed meats, fast food, and butter oil, and higher odds of COVID-19 infection. It has been well established that adherence to a diet rich in refined carbohydrates and saturated fats is associated with obesity, metabolic syndrome, cardiovascular damage, and inflammation, which can predispose individuals to infections, as well as COVID-19 (Butler and Barrientos, 2020). A study demonstrated that higher consumption of sugary drinks was associated with higher odds of COVID-19 infection (Abdulah and Hassan, 2020). Indeed, accumulating evidence suggests that chronic consumption of high-glycemic carbohydrates and saturated or trans fats contribute to higher circulating levels of pro-inflammatory cytokines such as CRP, IL-6 and TNF-α (Bulló et al., 2013, Clarke et al., 2008, Liu et al., 2002, Mozaffarian et al., 2004). Moreover, Higher intake of honey was significantly related to increased risk of COVID-19 infection. On the contrary, several studies have reported beneficial effects of honey on COVID-19 via interaction on the entrance of the virus into the host cells (Abedi et al., 2021). Moreover, immune-boosting benefits of honey may be defined by its immunomodulatory, anti-thrombotic, anti-inflammation, and anti-oxidative properties (Hossain et al., 2020). The main explanation of the results may be related to, so-called, ‘food fraud’ in Iran regarding the honey industry, as it has been reported that available samples are artificially altered by feeding sugar and syrup of C4 origin to bees (Khansaritoreh et al., 2021). Moreover, energy intake and other potential confounding factors were not assessed in the present study, which makes it difficult to draw a firm association.
Another contradictory result of this study was higher odds of COVID-19 infection in association with increased caffeinated-drinks consumption. Conversely, in the UK Biobank, daily consumption of 2-3 cups of coffee was associated with lower risk of COVID-19 compared to 1 cup per day (Vu et al., 2021). Caffeinated-drinks (including coffee, types of tea) provide a large amount of polyphenols, and empirical evidence supports its anti-inflammatory properties (Barcelos et al., 2020, Oyewole, 2015, Santana-Gálvez et al., 2017) via decrease in inflammatory factors, such as CRP and IL- 6 (Wang et al., 2012). However, the observed association in this study might be confounded by added sugar or high-fat milk in the drinks, thereby, reducing its health benefits.
During COVID-19 pandemic, the use of supplements increased all over the world as a strategy to boost immunity (Hamulka et al., 2021). Sufficient levels of anti-oxidant vitamins, such as vitamin D and vitamin C, can decrease cytokine storm, which occurs in COVID-19 and is related to severe cases (Holford et al., 2020, Jain et al., 2020). Increased intake of nutritional supplements was associated with higher odds of infection; however, no form of causal association can be drawn, and the higher consumption of nutritional supplements may be due to treatment protocols in infected individuals.
It seems that quarantine measures put in place following the COVID-19 pandemic has disturbed sleep patterns. Furthermore, social distancing, working from home, and virtual education, all engender longer sleep periods (Smit et al., 2021). A cross-sectional study which presented the effect of lockdown on sleep duration, demonstrated that sleeping hours increased in more than 40 percent of children during restriction (Kaditis et al., 2021). According to the results of a longitudinal study, sedentary behaviors and sleep duration increased; while PA was lower among Hong-Kong-based adults during quarantine (Zheng et al., 2020). Conversely, some studies have reported sleep deprivation due to increased stress and anxiety during COVID-19 lockdown (Celorio-Sardà et al., 2021, Pérez-Rodrigo et al., 2020, Voitsidis et al., 2020). Both sleep deprivation and long sleep duration are associated with impaired immune response (Besedovsky et al., 2012, Bryant et al., 2004). However, those who experience long sleeping hours are probably less active, and follow an unhealthier lifestyle than those with healthy sleep cycles. It should be noted that the present study cannot discern causal inferences, and it may be because people who became infected slept more due to their medications.
This was the first study to examine the association between lifestyle changes and COVID-19 infection in Iran. Despite the novelty of this work, there were some limitations; the primary one was its cross-sectional design, which precludes causal inferences, and it is not clear whether the changes happened before or after the infection. Second, evaluation of lifestyle was based on a self-reported qualitative questionnaire, and results may have been influenced by over-reporting or under-reporting of the respondents. Third, COVID-19 pandemic may have caused a change in respondent’s behavior, but also increased the potential for recall bias. In addition, there are several confounding factors which were not considered, including compliance with COVID-19 health and safety protocols. Finally, the limitations related to online surveys may result in bias; the difficulty of reaching those who did not have Internet access, not knowing how to fill out the electronic questionnaire, being illiterate, lack of quality random sampling, and not having time to fill out a 60-item questionnaire, which caused some participants to quit.
Conclusion
This cross-sectional study demonstrates for the first time that dietary intake and lifestyle factors may be associated with increased or decreased odds of COVID-19 infections in an Iranian population. However, more research is needed to draw firm conclusions.
Acknowledgements
The authors would like to thank North Khorasan University of Medical Sciences for their financial support.
Conflict of interest
Authors declared no conflict of interest.
Funding
This work was funded by student research committee of North Khorasan University of Medical Sciences (grant number: 1400p1504).
Authors’ contributions
Toupchian O and Abdollahi S contributed to the study's conception and design. Soltani S, Hosseini-Marnani E, Eslami F, Poorbarat S, Heshmati J, and Rajabzade R prepared, collected, and analyzed data. The first draft of the manuscript was written by Toupchian O, and edited by Cain C.T. Clark. All the authors read and approved of the final manuscript.
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Zheng C, et al. 2020. COVID-19 pandemic brings a sedentary lifestyle in young adults: a cross-sectional and longitudinal study. International journal of environmental research and public health. 17 (17): 6035.
This paper examined the association between lifestyle factors and COVID-19 infection in Bojnurd, Iran. The increased consumption of legumes and increased PA was negatively associated with the odds of COVID-19 infection. However, increased consumption of refined grain, butter oil, processed meats, fast foods, honey, caffeine, and more sleep duration contributed to higher odds of COVID-19 infection. Surprisingly, higher intake of multivitamin, vitamin D, and vitamin C supplements was associated with higher odds of infection.
Adequate nutrition is essential for strengthening the immune system and may improve protection against COVID-19 infection and its complications (Calder and Jackson, 2000, EFSA Panel on Dietetic Products and Nutrition and Allergies, 2016, Keusch, 2003, Watson, 1984). It is well known that under nutrition with insufficient energy, protein, and nutrient intake is related to poor immune function (Katona and Katona-Apte, 2008), while over nutrition is associated with impaired lung function (Dietz and Santos-Burgoa, 2020, Melo et al., 2014), secretion of inflammatory mediators (Hauner, 2005), and cytokine storm, leading to acute respiratory syndrome and organs dysfunction (Muscogiuri et al., 2020). A balanced diet to meet nutritional needs, containing both plant-based foods and animal resources, in accordance with healthy nutritional guidelines, can improve immune responses and help body to fight against infection (Cena and Calder, 2020).
In concordance with some previous studies (Abdulah and Hassan, 2020, Kim et al., 2021), this study revealed a significant association between higher consumption of legumes and lower odds of COVID-19 infection. Legumes are solid sources of protein, dietary fiber, as well as nutraceutical compounds (Singh et al., 2017). Indeed, there are several sources of evidence supporting the beneficial effect of legumes on obesity (Kim et al., 2016), diabetes mellitus (Becerra-Tomás et al., 2018), dyslipidemia (Ha et al., 2014), high blood pressure, and CVD (Grosso et al., 2017).
There was also a significant association between increased PA and lower odds of COVID-19 infection. A similar finding was observed in a study with 48,440 participants, where inactivity was positively linked with severe COVID-19 infection (Sallis et al., 2021). PA is acknowledged as an indispensable part of healthy lifestyle; although, it is suggested higher PA may increase pro-inflammatory cytokines in muscles, but not in the circulation (Peake et al., 2015). Regular PA has been shown to enhance immune response, lung capacity, muscle strength, mental health (Buitrago-Garcia et al., 2020), and reduce systemic inflammation (Sallis et al., 2021), lockdown-induced emotional stress (Celorio-Sardà et al., 2021) and COVID-19 complications (Nieman and Wentz, 2019). On the other hand, it is plausible that people who are more active are leaner, and therefore, follow a healthier lifestyle, compared with less active individuals.
A positive association was observed between increased intake of refined grains, honey, processed meats, fast food, and butter oil, and higher odds of COVID-19 infection. It has been well established that adherence to a diet rich in refined carbohydrates and saturated fats is associated with obesity, metabolic syndrome, cardiovascular damage, and inflammation, which can predispose individuals to infections, as well as COVID-19 (Butler and Barrientos, 2020). A study demonstrated that higher consumption of sugary drinks was associated with higher odds of COVID-19 infection (Abdulah and Hassan, 2020). Indeed, accumulating evidence suggests that chronic consumption of high-glycemic carbohydrates and saturated or trans fats contribute to higher circulating levels of pro-inflammatory cytokines such as CRP, IL-6 and TNF-α (Bulló et al., 2013, Clarke et al., 2008, Liu et al., 2002, Mozaffarian et al., 2004). Moreover, Higher intake of honey was significantly related to increased risk of COVID-19 infection. On the contrary, several studies have reported beneficial effects of honey on COVID-19 via interaction on the entrance of the virus into the host cells (Abedi et al., 2021). Moreover, immune-boosting benefits of honey may be defined by its immunomodulatory, anti-thrombotic, anti-inflammation, and anti-oxidative properties (Hossain et al., 2020). The main explanation of the results may be related to, so-called, ‘food fraud’ in Iran regarding the honey industry, as it has been reported that available samples are artificially altered by feeding sugar and syrup of C4 origin to bees (Khansaritoreh et al., 2021). Moreover, energy intake and other potential confounding factors were not assessed in the present study, which makes it difficult to draw a firm association.
Another contradictory result of this study was higher odds of COVID-19 infection in association with increased caffeinated-drinks consumption. Conversely, in the UK Biobank, daily consumption of 2-3 cups of coffee was associated with lower risk of COVID-19 compared to 1 cup per day (Vu et al., 2021). Caffeinated-drinks (including coffee, types of tea) provide a large amount of polyphenols, and empirical evidence supports its anti-inflammatory properties (Barcelos et al., 2020, Oyewole, 2015, Santana-Gálvez et al., 2017) via decrease in inflammatory factors, such as CRP and IL- 6 (Wang et al., 2012). However, the observed association in this study might be confounded by added sugar or high-fat milk in the drinks, thereby, reducing its health benefits.
During COVID-19 pandemic, the use of supplements increased all over the world as a strategy to boost immunity (Hamulka et al., 2021). Sufficient levels of anti-oxidant vitamins, such as vitamin D and vitamin C, can decrease cytokine storm, which occurs in COVID-19 and is related to severe cases (Holford et al., 2020, Jain et al., 2020). Increased intake of nutritional supplements was associated with higher odds of infection; however, no form of causal association can be drawn, and the higher consumption of nutritional supplements may be due to treatment protocols in infected individuals.
It seems that quarantine measures put in place following the COVID-19 pandemic has disturbed sleep patterns. Furthermore, social distancing, working from home, and virtual education, all engender longer sleep periods (Smit et al., 2021). A cross-sectional study which presented the effect of lockdown on sleep duration, demonstrated that sleeping hours increased in more than 40 percent of children during restriction (Kaditis et al., 2021). According to the results of a longitudinal study, sedentary behaviors and sleep duration increased; while PA was lower among Hong-Kong-based adults during quarantine (Zheng et al., 2020). Conversely, some studies have reported sleep deprivation due to increased stress and anxiety during COVID-19 lockdown (Celorio-Sardà et al., 2021, Pérez-Rodrigo et al., 2020, Voitsidis et al., 2020). Both sleep deprivation and long sleep duration are associated with impaired immune response (Besedovsky et al., 2012, Bryant et al., 2004). However, those who experience long sleeping hours are probably less active, and follow an unhealthier lifestyle than those with healthy sleep cycles. It should be noted that the present study cannot discern causal inferences, and it may be because people who became infected slept more due to their medications.
This was the first study to examine the association between lifestyle changes and COVID-19 infection in Iran. Despite the novelty of this work, there were some limitations; the primary one was its cross-sectional design, which precludes causal inferences, and it is not clear whether the changes happened before or after the infection. Second, evaluation of lifestyle was based on a self-reported qualitative questionnaire, and results may have been influenced by over-reporting or under-reporting of the respondents. Third, COVID-19 pandemic may have caused a change in respondent’s behavior, but also increased the potential for recall bias. In addition, there are several confounding factors which were not considered, including compliance with COVID-19 health and safety protocols. Finally, the limitations related to online surveys may result in bias; the difficulty of reaching those who did not have Internet access, not knowing how to fill out the electronic questionnaire, being illiterate, lack of quality random sampling, and not having time to fill out a 60-item questionnaire, which caused some participants to quit.
Conclusion
This cross-sectional study demonstrates for the first time that dietary intake and lifestyle factors may be associated with increased or decreased odds of COVID-19 infections in an Iranian population. However, more research is needed to draw firm conclusions.
Acknowledgements
The authors would like to thank North Khorasan University of Medical Sciences for their financial support.
Conflict of interest
Authors declared no conflict of interest.
Funding
This work was funded by student research committee of North Khorasan University of Medical Sciences (grant number: 1400p1504).
Authors’ contributions
Toupchian O and Abdollahi S contributed to the study's conception and design. Soltani S, Hosseini-Marnani E, Eslami F, Poorbarat S, Heshmati J, and Rajabzade R prepared, collected, and analyzed data. The first draft of the manuscript was written by Toupchian O, and edited by Cain C.T. Clark. All the authors read and approved of the final manuscript.
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Received: 2022/09/3 | Published: 2023/11/20 | ePublished: 2023/11/20
Received: 2022/09/3 | Published: 2023/11/20 | ePublished: 2023/11/20
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