Abstract
Background:
Because of the impact of lifestyle on health, we assessed the correlation of diet, oral hygiene, and sleep habits with the severity of COVID-19 among patients in a referral hospital in Iran.
Methods:
In this case-control study, 582 participants (289 cases and 293 controls) were included. The case group consisted of patients diagnosed with COVID-19 infection, who were admitted to intensive care unit or died during hospitalization. The control group consisted of patients diagnosed with COVID-19 infection who were admitted to other wards or did not have serious problems and subsequently discharged in relatively good condition. Independent variables were lifestyle information, including habits and diet, oral and dental hygiene, sleep habits.
Results:
Adequate consumption of salt and white meat have been identified protective factor for severe COVID-19 infection (adjusted odds ratio [AOR] =0.52, 95% confidence interval [CI]: 0.34–0.78) (AOR = 0.34, 95% CI: 0.17–0.69), respectively. The findings indicated that brushing teeth after dinner was decreased the chance of contracting the severe COVID-19 infection (AOR = 0.62, 95% CI: 0.40–0.96). However, had dentures was linked to an increased risk of infection (AOR = 1.77, 95% CI: 1.16–2.70). The pain during sleep, feeling cold during sleep were associated with an elevated chance of severe infection (AOR = 2.09, 95% CI: 1.24–3.53) and (AOR = 1.85, 95% CI: 1.09–3.39), respectively. Also, the individuals who slept longer periods during the day exhibited a heightened probability of developing the severe form of COVID-19 (AOR = 1.21, 95% CI: 1.08–1.36). Furthermore, persistent, or intermittent cough increased the odds of the severe disease (AOR = 1.65, 95% CI: 1.01–2.69) (AOR = 4.45, 95% CI: 1.88–10.52).
Conclusions:
Our findings showed significant associations between the severity of COVID-19 infection and some aspects of lifestyle. Attention to these habits may change the course of such infections with minimum costs.
Keywords: Diet, Iran, life style, oral hygiene, sever COVID-19, sleep habits
Introduction
COVID-19 created a widespread pandemic and affected the world for nearly 4 years, the World Health Organization reported over 757 million confirmed cases, with more than 6.85 million deaths, by February 2023[1] and estimates revealed the pandemic caused between 14.8 and 14.9 million excess deaths in 2020 and 2021, respectively.[2,3] As of August 2023, over 7.6 million Iranians were infected, and more than 146,480 deaths occurred.[4] However, there are still many questions and uncertainties about its pathophysiology, risk factors for contracting and aggravating the disease, its treatment and control strategies.[5,6]
One of the problems in the management of this disease is the lack of standard methods for timely and correct diagnosis and the lack of adequate ability to estimate prognosis based on clinical characteristics and lifestyle. So, poor prognosis prediction is associated with high severity and mortality among patients with COVID-19.[7]
Most people infected with COVID-19 had no or only mild symptoms. However certain factors may lead to life-threatening conditions, with age, co-morbidities, and obesity influencing the severity of COVID-19. In this case, improving the immune system is a strategy to avoid health threats which is influenced by various factors. Following a nutritious diet and regular physical activity are key measures to maintain and strengthen the immune system,[8] and can be achieved by having a healthy lifestyle, including a good diet, regular exercise, a healthy weight, avoiding alcohol and smoking, adequate sleep, and stress management.[9]
Research has shown that an unhealthy diet, obesity, unusual sleep habits, and lack of physical activity can negatively affect the immune system and the body’s defense.[10] Based on the research, high diet quality was associated with a lower risk of COVID-19 and severe COVID-19.[11] However, oral health is an important component of well-being and periodontal symptoms were associated with COVID-19 severity.[12] Also, it also emphasizes the crucial role of sleep in the immune response that can affect the severity of diseases such as COVID-19.[13] for instance, a systematic review showed that poor sleep quality may increase the risk of mortality and the severity of COVID-19.[14]
Nevertheless, although there have been studies on the effect of lifestyle on the severity of COVID globally, the available information regarding these effective factors in Iran is rare and due to the difference in lifestyle such as eating habits in different countries, it may affect the health outcomes. So, raising the awareness of people and authorities about this issue could help to prevent the progression and mortality of similar diseases. Therefore, this study aimed to investigate the association of diet, oral hygiene, and sleep habits with the severity of COVID-19 in Iran.
Materials and Methods
A case-control study was designed and conducted between 2020 and 2021 at the referral hospital in Kerman, located in the southeast Iran.
Study population
According to different frequencies of probable risk factors observed in this study, based on an expert’s opinion the frequency of the minimum and maximum variables of 0.2 and 0.5 in the control group and at least 1.5 times these frequencies considered in the case group. Considering α = 0.05 and β = 0.9, the maximum sample size for each group was 285 individuals that obtained by G-Power software. With a 5% non-response rate, the sample size was increased to 300 individuals per group. The study population consisted of patients with confirmed COVID-19 infection, as determined by a positive PCR test result, who were admitted to the hospital between 2020 and 2021 and were at least 18 years of age. The case group consisted of patients diagnosed with COVID-19 infection who were admitted to the intensive care unit or died during hospitalization. The control group consisted of patients diagnosed with COVID-19 infection who were admitted to other wards or didn’t have serious problems and subsequently discharged in relatively good condition. According to the patients’ hospital information, we extracted the information of the people and the characteristics of the people, and the people who agreed to participate in the study were included in the study, and this work continued until the desired sample size was obtained.
Data collection
The data were collected from patient’s medical records and questionnaires. The initial step involved retrieving the list of patients from the hospital. Following the extraction of their names and phone numbers, the patients were contacted. After explaining the study and its objectives, the patients were given the option to participate. If they chose to do so, an appointment was scheduled, and they were asked to complete a questionnaire. In the event of a patient’s death, an interview was conducted with one of their family members, such as children or spouse in form of verbal autopsy.
The questionnaire, which was based on standard questionnaires (Life Satisfaction Questionnaire, health-promoting lifestyle profile, and The Pittsburgh Sleep Quality questionnaire.[15,16,17] in each domain, included 33 variables and 180 questions that covered demographic and lifestyle information, including habits and diet, oral and dental hygiene, sleep habits, and more. The questionnaire was evaluated by five experts in this field to assess its validity. The content validity index and content validity rate were found to be >0.7, and the reliability was established through the completion of 30 initial questionnaires as a pilot study within the target population and the Cronbach’s alpha coefficients were used to assess reliability, resulting in a reliability value of 0.722.
Statistical analysis
Descriptive statistics, including frequencies and percentages, were reported for demographic variables, dietary habits, oral hygiene, and sleep characteristics. These variables were compared between two groups (cases and controls) using either a Chi-square or Fisher’s exact test. Multivariable logistic regression models were constructed to investigate that factors were associated with severe COVID-19 infection, adjusting for age, gender, marital status, and education. The results included adjusted odds ratios along with 95% confidence intervals and a P value. These data were analyzed using Stata version 14.1 (College Station, Texas). The threshold for statistical significance was a P < 0.05.
Results
A total of 289 cases were analyzed, of which 169 (58.5%) were man. In the control group, 130 out of 293 participants were man 130 (44.4%). The mean and standard division of age of the cases was 62.1 ± 1.1 (76.1% over 50 years old) and for controls were 48.3 ± 0.9 (46.1% over 50 years old). Most of participants in both the case and control groups were married (n = 213, 87.3%) and (n = 255, 87.1%), respectively. Moreover, the percent of individuals in both the case and control groups who had completed at least 12 years of formal education were (n = 35, 12.2%) and (n = 90, 30.8%), respectively. The results showed that there was a significant difference in the distribution of demographic characteristics, including sex, age, and formal education, between the case and control groups (P < 0.05) [Table 1].
Table 1.
Description and comparison of demographic variables in case and control groups
| Variables | Control (n=293), n (%) | Case (n=289), n (%) | P |
|---|---|---|---|
| Gender | |||
| Man | 130 (44.4) | 169 (58.5) | 0.001 |
| Woman | 163 (55.6) | 120 (41.5) | |
| Age (years) | |||
| Mean±SD | 48.3±0.9 | 62.1±1.1 | <0.001 |
| ≤30 | 49 (16.7) | 21 (7.3) | |
| 31–50 | 108 (36.9) | 48 (16.6) | |
| >50 | 136 (46.4) | 220 (76.1) | |
| Marital status | |||
| Married | 255 (87.1) | 213 (87.3) | 0.927 |
| Single | 38 (12.9) | 31 (12.7) | |
| Formal educations (years) | |||
| Illiterate | 52 (17.8) | 94 (32.6) | <0.001 |
| <12 | 150 (51.4) | 159 (55.2) | |
| 12–16 | 68 (23.3) | 26 (9.1) | |
| >16 | 22 (7.5) | 9 (3.1) |
Diet and eating habits
The comparison of diet and eating habits between the case and control groups showed that there was a significant difference in the consumption of salt, red meat, white meat, fatty foods, dairy products, fried foods, steamed foods, having breakfast, no regular diet or having junk food, and speed of eating (P < 0.05) [Table 2].
Table 2.
Description and comparison of diet and eating habits in case and control groups
| Variables about consumptions | Control (n=293), n (%) | Case (n=289), n (%) | P |
|---|---|---|---|
| Salt | |||
| Low | 107 (36.5) | 153 (52.9) | <0.001 |
| Intermediate | 142 (48.4) | 102 (35.3) | |
| High | 44 (15.1) | 34 (11.8) | |
| Sugar | |||
| Low | 127 (43.3) | 154 (53.3) | 0.055 |
| Intermediate | 109 (37.2) | 87 (30.1) | |
| High | 57 (19.5) | 48 (16.6) | |
| Red meat | |||
| Low | 70 (23.9) | 100 (34.8) | 0.001 |
| Intermediate | 177 (60.4) | 166 (57.8) | |
| High | 46 (15.7) | 21 (7.4) | |
| White meat | |||
| Low | 33 (11.2) | 54 (18.7) | 0.021 |
| Intermediate | 201 (68.6) | 192 (66.4) | |
| High | 59 (20.2) | 43 (14.9) | |
| Beans | |||
| Low | 47 (16.1) | 50 (17.3) | 0.303 |
| Intermediate | 214 (73.0) | 218 (75.4) | |
| High | 32 (10.9) | 21 (7.3) | |
| Fruit | |||
| Low | 42 (14.4) | 41 (14.2) | 0.981 |
| Intermediate | 169 (57.7) | 165 (57.1) | |
| High | 82 (27.9) | 83 (28.7) | |
| Vegetables | |||
| Low | 112 (38.2) | 92 (31.8) | 0.267 |
| Intermediate | 129 (44.1) | 142 (49.1) | |
| High | 52 (17.7) | 55 (19.1) | |
| Fatty food | |||
| Low | 192 (65.5) | 223 (77.4) | 0.004 |
| Intermediate | 72 (24.6) | 41 (14.2) | |
| High | 29 (9.9) | 24 (8.4) | |
| Dairy | |||
| Low | 60 (20.4) | 58 (20.1) | 0.023 |
| Intermediate | 158 (53.9) | 128 (44.3) | |
| High | 75 (25.7) | 103 (35.6) | |
| Soft drinks | |||
| Low | 164 (55.9) | 184 (64.1) | 0.105 |
| Intermediate | 70 (23.9) | 51 (17.8) | |
| High | 59 (20.2) | 52 (18.1) | |
| Drink water with food | |||
| No | 222 (75.8) | 216 (74.8) | 0.774 |
| Yes | 71 (24.2) | 73 (25.2) | |
| Fried food | |||
| No | 146 (50.0) | 170 (58.8) | 0.033 |
| Yes | 146 (50.0) | 119 (41.2) | |
| Grilled food | |||
| No | 129 (44.2) | 139 (48.1) | 0.343 |
| Yes | 163 (55.8) | 150 (51.9) | |
| Steam food | |||
| No | 181 (61.9) | 152 (52.6) | 0.022 |
| Yes | 111 (38.1) | 137 (47.4) | |
| Having regular breakfast | |||
| No | 50 (17.1) | 27 (9.3) | 0.006 |
| Yes | 242 (82.9) | 262 (90.7) | |
| Do not have a proper diet or eat junk food | |||
| No | 211 (72.3) | 253 (87.5) | <0.001 |
| Yes | 81 (27.7) | 36 (12.5) | |
| Eating speed | |||
| Slow | 218 (74.4) | 245 (84.7) | 0.002 |
| Fast | 75 (25.6) | 44 (15.3) |
The results of the multivariable regression analysis, adjusted based on age, gender, marital status, and education, indicated that consuming an adequate amount of salt was as a protective factor for severe COVID-19 infection (adjusted odds ratio [AOR] = 0.52, 95% confidence interval [CI]: 0.34–0.78). Additionally, the consumption of white meat, such as fish and poultry, has been identified as a protective factor (AOR = 0.34, 95% CI: 0.17–0.69) [Table 3].
Table 3.
The results of multivariable logistic regression for investigating the effect of diet and eating habits on the severe Covid-19 infection in Kerman, Iran
| Variables | Category | Adjusted OR (95% CI) | P |
|---|---|---|---|
| Salt | Low | Ref | |
| Intermediate | 0.52 (0.34–0.78) | 0.003 | |
| High | 0.77 (0.45–1.38) | 0.322 | |
| Sugar | Low | Ref | |
| Intermediate | 0.70 (0.46–1.07) | 0.117 | |
| High | 0.89 (0.52–1.51) | 0.595 | |
| Red meat | Low | Ref | |
| Intermediate | 0.79 (0.34–1.02) | 0.083 | |
| High | 0.72 (0.37–1.40) | 0.350 | |
| White meat | Low | Ref | |
| Intermediate | 0.68 (0.44–1.04) | 0.078 | |
| High | 0.34 (0.17–0.69) | 0.002 | |
| Beans | Low | Ref | |
| Intermediate | 1.11 (0.67–1.88) | 0.650 | |
| High | 1.03 (0.46–2.27) | 0.938 | |
| Fruit | Low | Ref | |
| Intermediate | 0.98 (0.56–1.69) | 0.869 | |
| High | 1.29 (0.70–2.38) | 0.464 | |
| Vegetables | Low | Ref | |
| Intermediate | 1.56 (1.02–2.38) | 0.039 | |
| High | 1.88 (1.08–3.28) | 0.025 | |
| Fatty food | Low | Ref | |
| Intermediate | 0.59 (0.23–1.53) | 0.287 | |
| High | 0.93 (0.48–1.83) | 0.853 | |
| Dairy | Low | Ref | |
| Intermediate | 0.77 (0.47–1.27) | 0.323 | |
| High | 1.28 (0.74–2.21) | 0.367 | |
| Soft drinks | Low | Ref | |
| Intermediate | 0.67 (0.41–1.10) | 0.116 | |
| High | 1.01 (0.61–1.69) | 0.940 | |
| Drink water with food | No | Ref | |
| Yes | 1.31 (0.84–2.05) | 0.218 | |
| Fried food | No | Ref | |
| Yes | 1.15 (0.78–1.68) | 0.461 | |
| Grilled food | No | Ref | |
| Yes | 1.10 (075–1.60) | 0.614 | |
| Steam food | No | Ref | |
| Yes | 0.87 (0.59–1.29) | 0.507 | |
| Having regular breakfast | No | Ref | |
| Yes | 0.87 (0.49–1.55) | 0.643 | |
| Do not have a proper diet or eat junk food | No | Ref | |
| Yes | 1.42 (0.87–2.31) | 0.155 | |
| Eating speed | Slow | Ref | |
| Fast | 1.29 (0.79–2.10) | 0.318 |
Adjusted for age, sex, marital status, and educations. CI=Confidence interval; OR=Odds ratio
Oral hygiene
A comparative analysis of the case and control groups concerning oral hygiene revealed significant differences between the two groups about brushing after breakfast, brushing after dinner, flossing after dinner, restoring teeth, and having dentures (P < 0.05) [Table 4].
Table 4.
Description and comparison of oral hygiene in case and control groups
| Variables | Control (n=293), n (%) | Case (n=289), n (%) | P |
|---|---|---|---|
| Brushing teeth after breakfast | |||
| No | 177 (61.3) | 223 (77.4) | <0.001 |
| Yes | 122 (38.7) | 65 (22.6) | |
| Use dental floss after breakfast | |||
| No | 281 (96.9) | 280 (97.2) | 0.817 |
| Yes | 9 (3.1) | 8 (2.8) | |
| Brushing teeth after lunch | |||
| No | 256 (87.9) | 262 (90.9) | 0.240 |
| Yes | 35 (12.1) | 26 (9.1) | |
| Use dental floss after lunch | |||
| No | 283 (97.2) | 284 (98.6) | 0.251 |
| Yes | 8 (2.8) | 4 (1.4) | |
| Brushing teeth after dinner | |||
| No | 155 (53.3) | 181 (62.8) | 0.019 |
| Yes | 136 (46.7) | 107 (37.2) | |
| Use dental floss after dinner | |||
| No | 257 (88.3) | 278 (96.5) | <0.001 |
| Yes | 34 (11.7) | 10 (3.5) | |
| Doing teeth extraction | |||
| No | 18 (21.9) | 17 (33.3) | 0.147 |
| Yes | 64 (78.1) | 34 (66.7) | |
| Doing teeth restoration | |||
| No | 21 (22.3) | 18 (45.0) | 0.008 |
| Yes | 73 (77.7) | 22 (55.0) | |
| Doing dental implants | |||
| No | 2 (40.0) | 5 (83.3) | 0.137 |
| Yes | 3 (60.0) | 1 (16.7) | |
| Having decay teeth | |||
| No | 253 (87.2) | 246 (87.5) | 0.913 |
| Yes | 37 (12.8) | 35 (12.5) | |
| Halitosis | |||
| No | 269 (92.8) | 270 (95.7) | 0.126 |
| Yes | 21 (7.2) | 12 (4.3) | |
| Bleeding gums | |||
| No | 280 (96.5) | 275 (97.5) | 0.496 |
| Yes | 10 (3.5) | 7 (2.5) | |
| Oral infection | |||
| No | 277 (95.5) | 271 (95.8) | 0.887 |
| Yes | 13 (4.5) | 12 (4.2) | |
| Aphthous stomatitis | |||
| No | 271 (93.4) | 273 (96.5) | 0.099 |
| Yes | 19 (6.6) | 10 (3.5) | |
| Dentures | |||
| No | 222 (76.5) | 137 (47.7) | <0.001 |
| Yes | 68 (23.5) | 150 (52.3) |
The findings indicated that the habit of brushing teeth after dinner was decreased the chance of contracting the severe COVID-19 infection (AOR = 0.62, 95% CI: 0.40–0.96). However, the use of dentures was linked to an increased risk of infection (AOR = 1.77, 95% CI: 1.16–2.70) [Table 5].
Table 5.
The results of multivariable logistic regression for investigating the effect of oral hygiene on the severe Covid-19 infection in Kerman, Iran
| Variables | Category | Adjusted OR (95% CI) | P |
|---|---|---|---|
| Brushing teeth after breakfast | No | Ref | |
| Yes | 0.84 (0.54–1.29) | 0.437 | |
| Use dental floss after breakfast | No | Ref | |
| Yes | 0.43 (0.15–1.24) | 0.122 | |
| Brushing teeth after lunch | No | Ref | |
| Yes | 0.91 (0.49–1.69) | 0.786 | |
| Use dental floss after lunch | No | Ref | |
| Yes | 0.96 (0.27–3.4) | 0.952 | |
| Brushing teeth after dinner | No | Ref | |
| Yes | 0.62 (0.40–0.96) | 0.034 | |
| Use dental floss after dinner | No | Ref | |
| Yes | 0.64 (0.29–1.45) | 0.293 | |
| Doing teeth extraction | No | Ref | |
| Yes | 0.60 (0.25–1.41) | 0.247 | |
| Doing teeth restoration | No | Ref | |
| Yes | 0.48 (0.18–1.23) | 0.129 | |
| Doing dental implants | No | Ref | |
| Yes | 0.88 (0.01–50.6) | 0.953 | |
| Having decay teeth | No | Ref | |
| Yes | 1.01 (0.56–1.81) | 0.294 | |
| Halitosis | No | Ref | |
| Yes | 1.26 (0.56–2.82) | 0.570 | |
| Bleeding gums | No | Ref | |
| Yes | 1.10 (0.37–3.24) | 0.849 | |
| Oral infection | No | Ref | |
| Yes | 1.09 (0.42–2.84) | 0.844 | |
| Aphthous stomatitis | No | Ref | |
| Yes | 1.47 (0.58–3.72) | 0.411 | |
| Dentures | No | Ref | |
| Yes | 1.77 (1.16–2.70) | 0.008 |
Adjusted for age, sex, marital status, and educations. CI=Confidence interval; OR=Odds ratio
Sleeping habits
A comparison of sleep and wakefulness habits in the case and control groups revealed significant differences between the two groups in several variables, including afternoon sleep, the experience of pain during sleep, the feeling of coldness during sleep, dyspnea, coughing during sleep, and the mean hours slept per day (P < 0.05) [Table 6].
Table 6.
Description and comparison of sleeping habits in case and control groups.
| Variables | Control (n=293), n (%) | Case (n=289), n (%) | P |
|---|---|---|---|
| Sleep in afternoon | |||
| No | 31 (10.7) | 19 (6.7) | 0.005 |
| Some | 94 (32.4) | 67 (23.4) | |
| Always | 165 (56.9) | 200 (69.9) | |
| Sleeping late at night | |||
| No | 148 (51.4) | 154 (54.1) | 0.074 |
| Some | 89 (30.9) | 66 (23.1) | |
| Always | 51 (17.7) | 65 (22.8) | |
| Waking up early | |||
| No | 199 (69.1) | 192 (67.4) | 0.182 |
| Some | 63 (21.8) | 54 (18.9) | |
| Always | 26 (9.1) | 39 (13.7) | |
| Waking up several times at night | |||
| No | 145 (50.3) | 131 (46.3) | 0.102 |
| Some | 99 (34.4) | 89 (31.4) | |
| Always | 44 (15.3) | 63 (22.3) | |
| Nightmare disorder | |||
| No | 157 (54.5) | 165 (57.9) | 0.208 |
| Some | 105 (36.4) | 86 (30.2) | |
| Always | 26 (9.1) | 34 (11.9) | |
| Snoring | |||
| No | 143 (49.6) | 134 (47.1) | 0.189 |
| Some | 71 (24.7) | 59 (20.7) | |
| Always | 74 (25.7) | 92 (32.2) | |
| Have pain | |||
| No | 188 (65.3) | 153 (53.7) | <0.001 |
| Some | 60 (20.8) | 54 (18.9) | |
| Always | 40 (13.9) | 78 (27.4) | |
| Feeling cold | |||
| No | 205 (71.5) | 195 (68.4 | 0.017 |
| Some | 57 (19.8) | 44 (15.5) | |
| Always | 25 (8.7) | 46 (16.1) | |
| Feeling warm | |||
| No | 163 (56.8) | 180 (63.2) | 0.282 |
| Some | 69 (24.1) | 56 (19.6) | |
| Always | 55 (19.1) | 49 (17.2) | |
| Dyspnea | |||
| No | 222 (77.3) | 200 (70.1) | 0.003 |
| Some | 44 (15.4) | 38 (13.4) | |
| Always | 21 (7.3) | 47 (16.5) | |
| Cough in sleep | |||
| No | 210 (73.1) | 185 (64.9) | <0.001 |
| Some | 69 (24.1) | 57 (20.1) | |
| Always | 8 (2.8) | 43 (15.1) | |
| Average sleep | 7.49±0.8 | 8.66±0.11 | 0.002 |
Our findings indicate that the pain during sleep was associated with an elevated chance of infection (AOR = 2.09, 95% CI: 1.24–3.53). Similarly, the experience of feeling cold during sleep was also linked to an increased likelihood of infection (AOR = 1.85, 95% CI: 1.09–3.39). Individuals who cough intermittently or persistently exhibit a heightened susceptibility to the severe form of the COVID-19 compared to those who do not cough (AOR = 1.65, 95% CI: 1.01–2.69) (AOR = 4.45, 95% CI: 1.88–10.52), respectively. Furthermore, the data indicated that individuals who slept for longer periods during the day exhibited a heightened probability of developing the severe form of COVID-19. (AOR = 1.21, 95% CI: 1.08–1.36) [Table 7].
Table 7.
The results of multivariable logistic regression for investigating the effect of sleep habits on the severe COVID-19 infection in Kerman, Iran
| Variables | Category | Adjusted OR (95% CI) | P |
|---|---|---|---|
| Sleep in afternoon | No | Ref | |
| Some | 1.17 (0.56–2.46) | 0.667 | |
| Always | 1.65 (0.81–3.33) | 0.160 | |
| Sleeping late at night | No | Ref | |
| Some | 0.70 (0.45–1.11) | 0.141 | |
| Always | 1.30 (0.80–2.13) | 0.282 | |
| Waking up early | No | Ref | |
| Some | 0.88 (0.54–1.43) | 0.622 | |
| Always | 1.49 (0.81–2.76) | 0.197 | |
| Waking up several times at night | No | Ref | |
| Some | 1.02 (0.53–1.28) | 0.401 | |
| Always | 1.40 (083–2.37) | 0.199 | |
| Nightmare disorder | No | Ref | |
| Some | 1.05 (0.62–1.45) | 0.819 | |
| Always | 1.54 (0.81–2.90) | 0.181 | |
| Snoring | No | Ref | |
| Some | 1.00 (0.43–1.37) | 0.186 | |
| Always | 1.18 (0.75–1.84) | 0.468 | |
| Have pain in night sleep | No | Ref | |
| Some | 1.02 (0.55–1.50) | 0.735 | |
| Always | 2.09 (1.24–3.53) | 0.006 | |
| Feeling cold | No | Ref | |
| Some | 0.72 (0.43–1.22) | 0.323 | |
| Always | 1.85 (1.09–3.39) | 0.046 | |
| Feeling warm | No | Ref | |
| Some | 0.71 (0.44–1.15) | 0.168 | |
| Always | 1.24 (0.69–2.23) | 0.463 | |
| Dyspnea | No | Ref | |
| Some | 1.66 (0.87–3.18) | 0.149 | |
| Always | 1.77 (0.95–3.31) | 0.070 | |
| No | Ref | ||
| Cough in sleep | Some | 1.65 (1.01–2.69) | 0.041 |
| Always | 4.45 (1.88–10.52) | 0.001 | |
| Liner effect of sleeping hours per day | 1.21 (1.08–1.36) | 0.001 |
Adjusted for age, sex, marital status, and education. CI=Confidence interval; OR=Odds ratio
Discussion
In this study, we investigate the effect of lifestyle components such as eating habits, oral health, and sleep habits on the severity of COVID-19. This study is of an exploratory type and many habits have been tried to be investigated so that if significant and strong correlations are shown, hypotheses can be generated and the effects can be investigated in specific, precise, and targeted studies. Therefore, among the examined variables related to eating habits, 3 variables, oral hygiene 2 variables, and sleep 4 variables were significant.
Our finding demonstrated that among eating habits and diet, consuming an intermediate amount of salt and white meat had a protective effect on the severity of COVID-19 infection. However, vegetables are considered as a risk factor. Based on the results of our study, related to oral hygiene, brushing teeth after dinner can reduce the severity of Covid-19, while people with dentures are more likely to get severe disease. Regarding sleeping habits, people who always have pain during sleep, feel cold, and cough in sleep, experience a more severe form of COVID-19. Also, a longer average sleep hours increases the chance of more severe COVID-19.
We found that an adequate amount of salt can be a protective factor for COVID-19 severity. Evidence shows that diet and various micronutrients play a role in the immune system and studies suggested that balanced consumption of sodium is an essential factor for the normal function of the body,[18,19] and based on the underlying inflammation, sodium may contribute to protective immunity and thereby reduce the severity of diseases.[20] Since the function of the immune system is one of the important determinants in dealing with diseases and reducing their severity, adequate salt consumption may act as a protective factor. However, people who consume salts usual, are more likely to have a healthier diet and follow other health-related factors more appropriately and thus may have a better health status and experience a milder form of the disease. Additionally, white meat consumption was associated with reducing the severity of COVID-19. Because white meat, is often rich in protein, vitamins (such as B vitamins), and minerals (such as selenium and zinc), and help improve the immune system’s ability against infections, it may be a protective factor for contracting severe form of COVID-19.[21]
Unlike several studies that consider vegetables as protective factors for contracting many diseases,[22,23,24] In the present study, vegetable consumption was identified as a risk factor for the severe form of COVID-19. In interpreting this result, it is important to note that people who consume more vegetables may already have health problems preparing them for severe COVID-19. It is also possible that individuals who consume more vegetables do not have enough hygiene in washing and preparing vegetables, which affects their health status.
As we showed, regarding oral hygiene, health behaviors aimed at the prevention of severe form of the disease, however oral and dental injuries tend to increase the chance of severe disease, but most of them were not significant. Some components of oral health such as brushing teeth after dinner and dentures may affect the severity of COVID-19, in such a way that brushing teeth after dinner decreases the chance of disease severity, while dentures increase it. Several studies also provided evidence that oral health could have a potential impact on the severity of COVID-19.[25,26] The oral cavity is considered a potential reservoir for respiratory microorganisms, and severe cases of COVID-19 have been associated with secondary bacterial infections.[25,27] Therefore, brushing teeth, by cleaning the oral cavity from pathogens, can act as a protective factor for the severity of the disease. Considering that people who have dentures are more exposed to oral and dental infections and because these people are probably older and have other health problems,[28] the presence of dentures in this study is known as a risk factor for the severity of COVID-19.
Based on our findings, people who always have pain during sleep, feel cold, and cough in sleep and people with longer sleep hours per day, had a higher chance of getting severe COVID-19. Findings of a study conducted by Huang et al., revealed that lack of sleep had a strong influence on disease severity and prognosis.[13] The factors that have been declared as risk factors for the severity of COVID-19 in our study, which include pain, feeling cold, and cough in sleep are also among the components that can cause a lack of sleep for people. They also may be a result of other health condition that affect COVID-19 prognosis and severity. In addition, probably the people who have longer hours of sleep may be sicker or older people whose other health conditions are involved in their suffering from a more severe form of COVID-19.
Finally, our lifestyle plays an important role in determining the risk of disease and it has several dimensions. It is necessary to review in an exploratory manner to find clues to basic connections and it is possible to reduce the risk of developing a disease by adopting healthy habits in different aspects and making appropriate behavioral choices.[29]
Limitations
It is important to acknowledge the limitations of our study. As a case-control study, our findings may suffer from systematic errors, such as recall bias. Moreover, since our sample consisted solely of hospitalized patients, we cannot compare their profiles with those of mild COVID-19 cases. We also explored potential links between various lifestyle habits and the severity of COVID-19 infection, which may lead to incidental associations.
Conclusion
Our study highlighted several lifestyle components which affect COVID-19 severity. Consequently, lifestyle factors play an important role in determining the severity of COVID-19. By having a healthier lifestyle and paying attention to the determinants of health, we can significantly protect individuals and communities from the risk of severe consequences of similar diseases. These results emphasize the need to inform people to promote a healthy life so that effective planning can be vital in controlling future epidemics.
Conflicts of interest
There are no conflicts of interest.
Acknowledgment
We would like to express our great appreciation to all those who participated in this study, as well as to Afzalipour Hospital in Kerman for their invaluable assistance.
Funding Statement
Nil.
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