Background
The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) outbreak a pandemic, since it is spreading rapidly worldwide. It has affected more than 200 countries around the world [
1]. It is a new disease that attacks the human respiratory system and can cause mild to severe illness [
2]. Elderly and people with underlying medical conditions, such as cardiovascular diseases, hypertension, diabetes and cancer, are at a higher risk of death as a result of contracting COVID-19 [
3‐
6]. In addition, it can cause severe complications among people with obesity-related conditions [
7].
There is insufficient information concerning the risk factors that can lead to severe illness and there is no vaccine or specific treatment to prevent or cure the disease. Therefore, WHO recommendations have focused on avoiding contracting the virus through the practice of good hygiene, social distancing and only leaving the house when necessary [
8]. On the 24th of February, the Kuwait Ministry of Health announced the first coronavirus cases. At the time of writing this report, in Kuwait, the number of confirmed COVID-19 cases had reached 75,697 and there had been more than 494 registered deaths [
9].
In line with international action and in order to counter the spread of COVID-19, the government postponed study in all schools and universities and suspended work, a part from that of the emergency services. In addition, all malls and local businesses such as salons and gyms were closed [
9]. Kuwait imposed a partial nationwide curfew on the 22nd of March 2020 until further notice. The government then imposed a total lockdown from the 10th to the 31st of May 2020. Furthermore, people were also encouraged to eat a healthy and balanced diet, be physically active and maintain a healthy lifestyle to support their immune system during these difficult times. The COVID-19 pandemic and government measures to stem its spread resulted in increased stress induced by the disruption of daily routine, along with fear and anxiety regarding the spread of the disease and its consequences for people’s finances, work, family and personal matters.
The relationship between stress and emotional eating is well established. Previous studies have shown an association between stress and the amount of food consumed [
10‐
13]. It has been shown that people under stress crave more high fat and high sugar foods, since the body under stress requires more energy to function [
14]. In addition, the body increases storage of abdominal fat [
15].
It has been hypothesized that the increase in unstructured time and the psychological impact resulting from the enforced quarantine might induce changes in dietary habits and lifestyle. Therefore, the primary aim of this study was to investigate the effects of the COVID-19 outbreak during the lockdown on eating habits and other health-related behaviours among adults in Kuwait. Second is to examine the demographic variation in eating habits and lifestyle.
Results
Participants’ characteristics and weight status based on BMI category
The socio-demographic characteristics of the study participants are presented in Table
1. In total, 415 adults participated in this descriptive cross-sectional study with a mean age of 38.47 ± 12.73 years; most of them were females, numbering 285 (68.7%). The average BMI was 28.52 ± 6.741 kg/m
2, which is indicative of the overweight category according to the definition of the WHO (male 30.36 ± 6.40 kg/m
2, female 27.68 ± 6.73 kg/m
2,
p < 0.001). The majority of the sample was Kuwaiti. With regard to marital status, a majority of the sample were married, followed by single, divorced and widowed. In terms of education level, just over three quarters of the participants were highly educated.
Table 1
Socio-demographic characteristics of the study participants
Age (years) | Mean ± SD (minimum–maximum) | 38.47 ± 12.73 18–73 |
Gender | Male | 130 (31.3) |
Female | 285 (68.7) |
BMI | Underweight | 7 (1.7) |
Normal | 116 (28) |
Overweight | 154 (37.2) |
Obese | 137 (33.1) |
Nationality | Kuwaiti | 376 (90.6) |
Non-Kuwaiti | 39 (9.4) |
Marital status | Single | 132 (31.8) |
Married | 235 (56.6) |
Divorced | 43 (10.4) |
Widow | 5 (1.2) |
Education level | Less than high school | 4 (1) |
High school | 30 (7.2) |
Special courses | 4 (1) |
Diploma | 60 (14.5) |
Bachelor | 221 (53.3) |
Postgraduate | 96 (23.1) |
Smoking habit | Yes | 82 (19.8) |
No | 333 (80.2) |
Meal patterns before and during the pandemic
There was a drop in the reported number of times the majority of participants ate per day, from 4 times before the pandemic to 3 times during, although this was non-significant. Lunch remained the main reported meal before and during COVID-19. There was no significant change in the reported number of those skipping breakfast (
p = 0.055), a meal snack (
p = 0.255) and lunch (
p = 0.830) between these two periods (
p = 0.055). It was noticed that breakfast was picked as the most commonly skipped meal among participants during both periods. There was, however, significantly less skipping of the meal snack between lunch and dinner during COVID-19 than prior to COVID-19 (OR = 0.49 (95% CI 0.29–0.81),
p = 0.006). In addition, compared to before COVID-19, people were much more likely to usually have a late-night snack or meal during COVID-19 (OR = 3.57 (95% CI 1.79–7.26),
p < 0.001). The vast majority of participants, both before and during the pandemic, had their main meal freshly made. However, during the pandemic, there was a significant increase in the percentage of participants who had their main meal freshly made (OR = 59.18 (95% CI 6.55–1400.76),
p = 0.001). Furthermore, the percentage of people who obtained their main meal from a restaurant also noticeably reduced, although this was not significant. Before the pandemic, 49% of the participants were more likely to consume fast food 1–2 times per week, while during COVID-19 up to 82% reported not consuming fast food (
p < 0.001). There was no significant difference in terms of the person who prepared the food for the family before and during COVID-19. However, there was an increase in the percentage of participants who cooked for themselves, as well as wives who cooked during COVID-19, accompanied by a reduction in the percentage of participants who relied on a housekeeper for cooking during COVID-19. With regard to stress eating, it was noticed that the percentage of participants who described themselves as usually engaging in stress eating increased, although no significant differences were detected between both periods (Table
2). Only age and educational level had a significant effect among participants (see Supplementary Table A, Additional File
1).
Table 2
Multivariate analysis between two periods using logistic regression for meal pattern
How many times a day do you eat? | One time | 5 (1.2) | 4 (1.0) | - | - |
Two times | 56 (13.5) | 43 (10.4) | 0.96 (0.24–4.08) | 0.29 (0.05–1.68) |
Three times | 124 (29.9) | 112 (27.0) | 1.13 (0.29–4.66) | 0.36 (0.06–1.99) |
Four times | 131 (31.6) | 104 (25.1) | 0.99 (0.26–4.10) | 0.36 (0.06–2.02) |
Five times | 80 (19.3) | 89 (21.4) | 1.39 (0.36–5.79) | 0.43 (0.08–2.47) |
Six or more | 19 (4.6) | 63 (15.2) | 4.14 (1.00–18.26) | 0.88 (0.14–5.70) |
What meal would you consider to be your main meal? | Dinner | 49 (11.8) | 102 (24.6) | 1.10 (0.44–2.60) | 1.56 (0.44–5.25) |
Breakfast | 78 (18.8) | 58 (14.0) | 0.39 (0.16–0.93) | 0.42 (0.12–1.41) |
Lunch | 279 (67.2) | 238 (57.3) | 0.45 (0.19–1.01) | 0.66 (0.20–2.07) |
Other | 9 (2.2) | 17 (4.1) | - | - |
Skipping meal breakfast | No | 254 (61.2) | 242 (58.3) | - | - |
Yes | 161 (38.8) | 173 (41.7) | 1.13 (0.85–1.49) | 0.62 (0.38–1.01) |
Skipping meal snack (breakfast and lunch) | No | 281 (67.7) | 308 (74.2) | - | - |
Yes | 134 (32.3) | 107 (25.8) | 0.73 (0.54–0.98) | 0.77 (0.49–1.21) |
Skipping meal lunch | No | 378 (91.1) | 358 (86.3) | - | - |
Yes | 37 (8.9) | 57 (13.7) | 1.63 (1.05–2.54) | 0.93 (0.48–1.81) |
Skipping meal snack between lunch and dinner | No | 295 (71.1) | 363 (87.5) | - | - |
Yes | 120 (28.9) | 52 (12.5) | 0.35 (0.24–0.50) | 0.49 (0.29–0.81) |
Skipping meal dinner | No | 296 (71.3) | 331 (79.8) | - | - |
Yes | 119 (28.7) | 84 (20.2) | 0.63 (0.46–0.87) | 0.64 (0.38–1.08) |
None skipping meal | No | 400 (96.4) | 392 (94.5) | - | - |
Yes | 15 (3.6) | 23 (5.5) | 1.56 (0.81–3.10, p = 0.187) | 0.58 (0.22–1.53) |
How likely are you to have a late night snack or meal? (past 10 pm) | Never | 89 (21.4) | 65 (15.7) | - | - |
Rarely | 135 (32.5) | 82 (19.8) | 0.83 (0.55–1.27) | 0.77 (0.45–1.32) |
Occasionally | 140 (33.7) | 136 (32.8) | 1.33 (0.90–1.98) | 1.65 (0.96–2.87) |
Usually | 51 (12.3) | 132 (31.8) | 3.54 (2.26–5.62) | 3.57 (1.79–7.26) |
How is your main meal prepared? | Freshly made | 306 (73.7) | 386 (93.0) | 13.88 (2.68–254.35) | 59.18 (6.55–1400.76) |
Restaurant | 61 (14.7) | 9 (2.2) | 1.62 (0.26–31.45) | 11.91 (1.11–305.06) |
Microwave | 37 (8.9) | 19 (4.6) | 5.65 (0.98–107.13) | 22.56 (2.29–556.04) |
None | 116 (28.0) | 341 (82.2) | - | - |
Number of times a week you consume fast food | Other | 11 (2.7) | 1 (0.2) | - | - |
1–2/week | 205 (49.4) | 57 (13.7) | 0.09 (0.07–0.13) | 0.09 (0.06–0.14) |
3–4/week | 72 (17.3) | 9 (2.2) | 0.04 (0.02–0.08) | 0.04 (0.02–0.09) |
5 or more/week | 22 (5.3) | 8 (1.9) | 0.12 (0.05–0.27) | 0.10 (0.03–0.28) |
Who prepares and cooks in your family | By my self | 86 (20.7) | 133 (32.0) | 1.55 (0.58–4.11) | 1.43 (0.37–5.50) |
Husband | 1 (0.2) | 2 (0.5) | 2.00 (0.16–47.71) | 6.96 (0.29–279.42) |
Wife | 59 (14.2) | 70 (16.9) | 1.19 (0.44–3.23) | 0.96 (0.25–3.72) |
Father | 0 (0.0) | 1 (0.2) | - | - |
Mother | 82 (19.8) | 74 (17.8) | 0.90 (0.34–2.43) | 1.05 (0.27–4.11) |
Grandparents | 9 (2.2) | 6 (1.4) | 0.67 (0.16–2.65) | 1.06 (0.15–6.98) |
Housekeeper | 169 (40.7) | 120 (28.9) | 0.71 (0.27–1.87) | 0.64 (0.17–2.40) |
Other | 9 (2.2) | 9 (2.2) | - | - |
Do you eat when you feel stressed, unhappy, angry, or bored? | Never | 100 (24.1) | 98 (23.6) | - | - |
Rarely | 103 (24.8) | 87 (21.0) | 0.86 (0.58–1.28) | 0.96 (0.57–1.62) |
Occasionally | 149 (35.9) | 143 (34.5) | 0.98 (0.68–1.41) | 1.12 (0.69–1.83) |
Usually | 63 (15.2) | 87 (21.0) | 1.41 (0.92–2.17) | 1.18 (0.62–2.28) |
Food group patterns
There was no significant difference among participants in terms of the weekly frequency of consumption of red meat, chicken, processed meat, canned fish, fruits, vegetables, bread, milk, cooking fat and most snack foods, both before and during the pandemic.
Nearly half of the participants, both before and during COVID-19, consumed red meat 1–2 times per week (49.4% and 47.5% respectively).
With regard to chicken consumption, nearly half of the participants, both before and during COVID-19, consumed chicken 3–4 times per week (41.4% and 39.5% respectively).
There was an overall significant reduction in the frequency of consumption of fish and seafood. More participants preferred to consume fish and seafood the most, once to twice weekly compared to never, both before and during COVID-19 (OR = 0.25, 95% CI 0.15–0.40, p < 0.001). Furthermore, there was a great increase in the percentage of participants who reported that they did not eat any fish or seafood (from 10.6 to 26.5%).
More than half of the participants never consumed canned fish (52.8%, 58.1%), and the majority never consumed processed meat (68.4%, 69.4%) before and during. With regard to fruit consumption, the most commonly reported frequency was 1 serving per day both before and during. In terms of vegetable consumption, the most commonly reported frequency was 1 serving per day both before and during. It can be seen from the data in Table
3 that most participants did not meet the USDA minimum recommended daily intake of fruits and vegetables of 5 servings a day (2 servings of fruits and 3 servings of vegetables). A total of 76.9% and 73.8% of participants did not meet the fruit consumption recommendation both before and during respectively. Moreover, about 86% of participants did not meet the vegetable consumption recommendation both before and during the pandemic.
Table 3
Multivariate analysis between two periods using logistic regression for food groups pattern
Red meat | Never | 32 (7.7) | 42 (10.1) | - | - |
Less than 1/w | 72 (17.3) | 86 (20.7) | 0.91 (0.52–1.58) | 1.00 (0.51–1.95) |
1–2/w | 205 (49.4) | 197 (47.5) | 0.73 (0.44–1.20) | 0.97 (0.52–1.81) |
3–4/w | 94 (22.7) | 75 (18.1) | 0.61 (0.35–1.05) | 0.80 (0.40–1.59) |
5–6/w | 5 (1.2) | 9 (2.2) | 1.37 (0.43–4.83) | 1.27 (0.33–5.24) |
7 or more | 3 (0.7) | 2 (0.5) | 0.51 (0.06–3.24) | 0.82 (0.09–6.48) |
I don’t know | 4 (1.0) | 4 (1.0) | 0.76 (0.17–3.44) | 1.27 (0.21–7.79) |
Chicken | Never | 15 (3.6) | 20 (4.8) | - | - |
Less than 1/w | 18 (4.3) | 27 (6.5) | 1.12 (0.46–2.77) | 1.85 (0.64–5.40) |
1–2/w | 149 (35.9) | 143 (34.5) | 0.72 (0.35–1.45) | 1.35 (0.55–3.26) |
3–4/w | 172 (41.4) | 164 (39.5) | 0.72 (0.35–1.44) | 1.24 (0.51–2.99) |
5–6/w | 44 (10.6) | 47 (11.3) | 0.80 (0.36–1.75) | 1.14 (0.43–2.99) |
7 or more | 13 (3.1) | 11 (2.7) | 0.63 (0.22–1.80) | 0.72 (0.20–2.55) |
I don’t know | 4 (1.0) | 3 (0.7) | 0.56 (0.10–2.92) | 0.48 (0.06–3.77) |
Fish and sea food | Never | 44 (10.6) | 110 (26.5) | - | - |
Less than 1/w | 131 (31.6) | 137 (33.0) | 0.42 (0.27–0.64) | 0.36 (0.22–0.58) |
1–2/w | 195 (47.0) | 143 (34.5) | 0.29 (0.19–0.44) | 0.25 (0.15–0.40) |
3–4/w | 36 (8.7) | 18 (4.3) | 0.20 (0.10–0.38) | 0.15 (0.07–0.31) |
5–6/w | 1 (0.2) | 1 (0.2) | 0.40 (0.02–10.26) | 0.23 (0.01–9.79) |
7 or more | 2 (0.5) | 1 (0.2) | 0.20 (0.01–2.14) | 0.17 (0.01–2.14) |
I don’t know | 6 (1.4) | 5 (1.2) | 0.33 (0.09–1.16) | 0.27 (0.05–1.34) |
Processed meat | Never | 284 (68.4) | 288 (69.4) | - | - |
Less than 1/w | 74 (17.8) | 67 (16.1) | 0.89 (0.62–1.29) | 0.92 (0.61–1.40) |
1–2/w | 38 (9.2) | 29 (7.0) | 0.75 (0.45–1.25) | 0.75 (0.41–1.33) |
3–4/w | 13 (3.1) | 21 (5.1) | 1.59 (0.79–3.32) | 1.82 (0.81–4.18) |
5–6/w | 2 (0.5) | 4 (1.0) | 1.97 (0.38–14.31) | 1.10 (0.18–9.19) |
7 or more | 0 (0.0) | 1 (0.2) | - | - |
I don’t know | 4 (1.0) | 5 (1.2) | 1.23 (0.32–5.02) | 1.56 (0.25–11.85) |
Canned fish | Never | 219 (52.8) | 241 (58.1) | - | - |
Less than 1/w | 132 (31.8) | 99 (23.9) | 0.68 (0.49–0.94) | 0.79 (0.55–1.13) |
1–2/w | 46 (11.1) | 48 (11.6) | 0.95 (0.61–1.48) | 1.26 (0.77–2.07) |
3–4/w | 10 (2.4) | 15 (3.6) | 1.36 (0.61–3.19) | 1.50 (0.61–3.77) |
5–6/w | 1 (0.2) | 3 (0.7) | 2.73 (0.35–55.33) | 1.11 (0.06–50.08) |
7 or more | 0 (0.0) | 1 (0.2) | - | - |
I do not know | 7 (1.7) | 8 (1.9) | 1.04 (0.37–3.01) | 0.98 (0.27–3.53) |
Fruit | None | 33 (8.0) | 38 (9.2) | - | - |
Less than 1/d | 129 (31.1) | 121 (29.2) | 0.81 (0.48–1.38) | 0.76 (0.41–1.41) |
1/d | 157 (37.8) | 147 (35.4) | 0.81 (0.48–1.36) | 0.82 (0.44–1.55) |
2/d | 62 (14.9) | 76 (18.3) | 1.06 (0.60–1.89) | 1.16 (0.57–2.35) |
3/d | 17 (4.1) | 18 (4.3) | 0.92 (0.41–2.08) | 1.29 (0.49–3.38) |
4 or more | 9 (2.2) | 7 (1.7) | 0.68 (0.22–2.01) | 0.71 (0.19–2.51) |
I do not know | 8 (1.9) | 8 (1.9) | 0.87 (0.29–2.61) | 1.06 (0.25–4.45) |
Vegetables | None | 29 (7.0) | 34 (8.2) | - | - |
Less than 1/d | 93 (22.4) | 96 (23.1) | 0.88 (0.50–1.56) | 1.09 (0.57–2.09) |
1/d | 151 (36.4) | 140 (33.7) | 0.79 (0.46–1.36) | 0.97 (0.51–1.85) |
2/d | 85 (20.5) | 89 (21.4) | 0.89 (0.50–1.59) | 0.92 (0.46–1.82) |
3/d | 32 (7.7) | 38 (9.2) | 1.01 (0.51–2.01) | 1.02 (0.45–2.31) |
4 or more | 17 (4.1) | 9 (2.2) | 0.45 (0.17–1.14) | 0.46 (0.15–1.34) |
I do not know | 8 (1.9) | 9 (2.2) | 0.96 (0.33–2.86) | 1.02 (0.27–3.90) |
Bread group | Other | 10 (2.4) | 7 (1.7) | - | - |
White | 178 (42.9) | 199 (48.0) | 1.60 (0.60–4.48) | 1.44 (0.46–4.72) |
Brown/brown seeds | 183 (44.1) | 164 (39.5) | 1.28 (0.48–3.60) | 1.13 (0.37–3.65) |
Whole wheat | 39 (9.4) | 40 (9.6) | 1.47 (0.51–4.40) | 1.55 (0.47–5.31) |
Seeds | 0 (0.0) | 0 (0.0) | - | - |
None | 5 (1.2) | 5 (1.2) | 1.43 (0.29–7.12) | 2.33 (0.39–14.37) |
Milk group | None | 97 (23.4) | 99 (23.9) | - | - |
Whole milk | 124 (29.9) | 128 (30.8) | 1.01 (0.70–1.47) | 1.15 (0.76–1.76) |
Semi-skimmed | 99 (23.9) | 103 (24.8) | 1.02 (0.69–1.51) | 1.14 (0.73–1.78) |
Skimmed | 56 (13.5) | 47 (11.3) | 0.82 (0.51–1.33) | 1.09 (0.64–1.87) |
Soy milk | 4 (1.0) | 5 (1.2) | 1.22 (0.32–5.08) | 1.93 (0.45–8.71) |
Almond milk | 21 (5.1) | 15 (3.6) | 0.70 (0.34–1.43) | 0.94 (0.40–2.18) |
Other (rice/goat milk) | 11 (2.7) | 13 (3.1) | 1.16 (0.49–2.76) | 1.07 (0.41–2.84) |
Do not know | 3 (0.7) | 5 (1.2) | 1.63 (0.39–8.14) | 2.60 (0.37–23.68) |
Fat type | None | 19 (4.6) | 13 (3.1) | - | - |
Butter | 21 (5.1) | 19 (4.6) | 1.32 (0.52–3.43) | 1.14 (0.39–3.40) |
Vegetable oil | 234 (56.4) | 241 (58.1) | 1.51 (0.73–3.19) | 1.58 (0.69–3.75) |
Olive oil | 109 (26.3) | 107 (25.8) | 1.43 (0.68–3.11) | 1.49 (0.64–3.61) |
Ghee/lard | 7 (1.7) | 8 (1.9) | 1.67 (0.49–5.90) | 1.52 (0.40–5.95) |
Others | 9 (2.2) | 10 (2.4) | 1.62 (0.52–5.20) | 1.59 (0.45–5.77) |
Do not know | 16 (3.9) | 17 (4.1) | 1.55 (0.58–4.20) | 1.86 (0.59–6.00) |
Type of snack |
Biscuit | No | 346 (83.4) | 320 (77.1) | - | - |
Yes | 69 (16.6) | 95 (22.9) | 1.49 (1.06–2.11) | 1.86 (1.26–2.75) |
Crisps | No | 289 (69.6) | 284 (68.4) | - | - |
Yes | 126 (30.4) | 131 (31.6) | 1.06 (0.79–1.42) | 0.90 (0.63–1.27) |
Chocolate | No | 236 (56.9) | 229 (55.2) | - | - |
Yes | 179 (43.1) | 186 (44.8) | 1.07 (0.81–1.41) | 1.12 (0.83–1.53) |
Nuts | No | 283 (68.2) | 276 (66.5) | - | - |
Yes | 132 (31.8) | 139 (33.5) | 1.08 (0.81–1.44) | 1.05 (0.75–1.45) |
Vegetables and fruits | No | 300 (72.3) | 284 (68.4) | - | - |
Yes | 115 (27.7) | 131 (31.6) | 1.20 (0.89–1.62) | 1.28 (0.90–1.82) |
Brown/brown seeded bread was the most frequently consumed type of bread before COVID-19 (44.1%), followed closely by white bread (42.9%). On the other hand, white bread was the most frequently consumed during (48%), followed by brown/brown seeded bread (39.5%). With regard to the type of milk consumed, whole milk was the most frequently consumed milk (29.9%, 30.8%), followed by semi-skimmed milk (23.9%, 24.8%), before and during respectively. Furthermore, vegetable oil was the most frequently used fat for cooking (56.4%, 58.1%), followed by olive oil (26.3%, 25.8%), before and during.
With regard to favourite snacks, the most commonly reported snacks by the participants were chocolate followed by nuts and crisps before and during respectively. There was a significant between-subject effect of gender on food group patterns among participants (
p < 0.01), but not for BMI, age, marital status, nationality, smoking status and educational level (see Supplementary Table B, Additional File
1).
Beverage consumption habits
There was no significant difference in the number of beverages consumed per day among the participants for Arabic coffee, tea, fizzy drinks, energy drinks, fruit drinks, herbal tea and water, both before and during the pandemic. However, most participants consumed Americano coffee 1–2 times per day compared to none per day, both before and during COVID-19 (OR = 0.5, 95% CI 0.40–0.85,
p = 0.005). Likewise, participants were less likely to drink fresh juice 1–2 times per day compared to none per day, both before and during COVID-19 (OR = 0.51, 95% CI 0.32–0.83,
p = 0.006) (Table
4). BMI and smoking status were noted to be the only parameters to have a significant between-subject effect among participants (
p = 0.027 and
p = 0.001 respectively) (see Supplementary Table C, Additional File
1).
Table 4
Multivariate analysis between two periods using logistic regression for beverage consumption habits
Americano coffee | None | 94 (22.7) | 131 (31.6) | - | - |
Less than 1 | 100 (24.1) | 106 (25.5) | 0.76 (0.52–1.11) | 0.81 (0.54–1.21) |
1–2/d | 172 (41.4) | 137 (33.0) | 0.57 (0.40–0.81) | 0.58 (0.40–0.85) |
3–4/d | 36 (8.7) | 23 (5.5) | 0.46 (0.25–0.82) | 0.53 (0.28–0.99) |
5–6/d | 8 (1.9) | 12 (2.9) | 1.08 (0.43–2.84) | 2.19 (0.76–6.69) |
More than 6/d | 5 (1.2) | 6 (1.4) | 0.86 (0.25–3.07) | 0.71 (0.17–2.88) |
Arabic coffee | None | 192 (46.3) | 227 (54.7) | - | - |
Less than 1 | 72 (17.3) | 57 (13.7) | 0.67 (0.45–0.99) | 0.73 (0.48–1.12) |
1–2/d | 64 (15.4) | 56 (13.5) | 0.74 (0.49–1.11) | 0.74 (0.47–1.15) |
3–4/d | 44 (10.6) | 32 (7.7) | 0.62 (0.37–1.01) | 0.62 (0.36–1.06) |
5–6/d | 24 (5.8) | 23 (5.5) | 0.81 (0.44–1.49) | 0.90 (0.47–1.72) |
More than 6/d | 19 (4.6) | 20 (4.8) | 0.89 (0.46–1.73) | 0.83 (0.40–1.70) |
Tea | None | 117 (28.2) | 114 (27.5) | - | - |
Less than 1 | 112 (27.0) | 103 (24.8) | 0.94 (0.65–1.37) | 1.08 (0.72–1.62) |
1–2/d | 131 (31.6) | 137 (33.0) | 1.07 (0.75–1.53) | 1.34 (0.91–1.98) |
3–4/d | 44 (10.6) | 41 (9.9) | 0.96 (0.58–1.57) | 1.14 (0.66–1.97) |
5–6/d | 7 (1.7) | 12 (2.9) | 1.76 (0.68–4.88) | 2.34 (0.83–6.99) |
More than 6/d | 4 (1.0) | 8 (1.9) | 2.05 (0.63–7.86) | 2.11 (0.55–9.19) |
Fizzy drinks | None | 263 (63.4) | 267 (64.3) | - | - |
Less than 1 | 91 (21.9) | 89 (21.4) | 0.96 (0.69–1.35) | 1.02 (0.70–1.49) |
1–2/d | 49 (11.8) | 44 (10.6) | 0.88 (0.57–1.37) | 1.05 (0.63–1.74) |
3–4/d | 8 (1.9) | 9 (2.2) | 1.11 (0.42–2.99) | 1.36 (0.44–4.24) |
5–6/d | 3 (0.7) | 4 (1.0) | 1.31 (0.29–6.72) | 1.11 (0.23–6.05) |
More than 6/d | 1 (0.2) | 2 (0.5) | 1.97 (0.19–42.54) | 2.31 (0.20–52.98) |
Energy drinks | None | 370 (89.2) | 382 (92.0) | - | - |
Less than 1 | 27 (6.5) | 21 (5.1) | 0.75 (0.41–1.35) | 0.61 (0.31–1.17) |
1–2/d | 16 (3.9) | 11 (2.7) | 0.67 (0.30–1.44) | 0.45 (0.17–1.13) |
3–4/d | 2 (0.5) | 1 (0.2) | 0.48 (0.02–5.08) | 0.43 (0.02–4.93) |
5–6/d | 0 (0.0) | 0 (0.0) | - | - |
More than 6/d | 0 (0.0) | 0 (0.0) | - | - |
Fruit drinks | None | 246 (59.3) | 250 (60.2) | - | - |
Less than 1 | 106 (25.5) | 102 (24.6) | 0.95 (0.68–1.31) | 1.11 (0.77–1.61) |
1–2/d | 61 (14.7) | 57 (13.7) | 0.92 (0.61–1.37) | 1.11 (0.70–1.77) |
3–4/d | 1 (0.2) | 5 (1.2) | 4.92 (0.79–94.62) | 7.30 (0.93–158.59) |
5–6/d | 0 (0.0) | 0 (0.0) | - | - |
More than 6/d | 1 (0.2) | 1 (0.2) | 0.98 (0.04–24.98) | 4.06 (0.14–121.29) |
Fresh juice | None | 227 (54.7) | 258 (62.2) | - | - |
Less than 1 | 122 (29.4) | 107 (25.8) | 0.77 (0.56–1.06) | 0.68 (0.48–0.96) |
1–2/d | 64 (15.4) | 44 (10.6) | 0.60 (0.39–0.92) | 0.51 (0.32–0.83) |
3–4/d | 2 (0.5) | 6 (1.4) | 2.64 (0.60–18.14) | 1.93 (0.36–15.82) |
5–6/d | 0 (0.0) | 0 (0.0) | - | - |
More than 6/d | 0 (0.0) | 0 (0.0) | - | - |
Herbal tea | None | 215 (51.8) | 228 (54.9) | - | - |
Less than 1 | 115 (27.7) | 101 (24.3) | 0.83 (0.60–1.15, p = 0.257) | 0.88 (0.61–1.25) |
1–2/d | 76 (18.3) | 78 (18.8) | 0.97 (0.67–1.40, p = 0.861) | 1.04 (0.70–1.55) |
3–4/d | 9 (2.2) | 5 (1.2) | 0.52 (0.16–1.54, p = 0.253) | 0.57 (0.17–1.74) |
5–6/d | 0 (0.0) | 3 (0.7) | 1997406.56 (0.00–NA, p = 0.977) | 1251915.00 (0.00–NA) |
More than 6/d | 0 (0.0) | 0 (0.0) | - | - |
Water | None | 22 (5.3) | 15 (3.6) | - | - |
Less than 1 | 83 (20.0) | 79 (19.0) | 1.40 (0.68–2.93, p = 0.367) | 1.56 (0.72–3.46) |
1–2/d | 133 (32.0) | 149 (35.9) | 1.64 (0.82–3.36, p = 0.162) | 1.75 (0.83–3.81) |
3–4/d | 101 (24.3) | 88 (21.2) | 1.28 (0.63–2.66, p = 0.502) | 1.38 (0.64–3.07) |
5–6/d | 76 (18.3) | 84 (20.2) | 1.62 (0.79–3.40, p = 0.192) | 1.84 (0.84–4.13) |
More than 6/d | 0 (0.0) | 0 (0.0) | - | - |
Physical activity and sleeping habits
With regard to practising a physical activity, before the pandemic, 18% of the participants exercised only in some seasons, compared to 20% who never did. During COVID-19, only 10% of participants reported exercising only in some seasons, compared to 39.5% who reported not exercising at all (OR = 0.36, 95% CI 0.21–0.62,
p < 0.001). There was, however, no significant difference in the number of hours of physical activity per week among the participants, both before and during the pandemic. Before COVID-19, 14% of participants spent their free time shopping compared to walking (87%). However, during COVID-19, this percentage significantly dropped to 2.2% compared to walking (11.6%) (OR = 0.19, 95% CI 0.07–0.45,
p < 0.001). The percentage of participants who spent more than 6 h per day watching TV or on a computer/phone increased from 16.1% before COVID-19 to 43.6% during COVID-19 (OR = 3.84, 95% CI 2.33–6.42,
p < 0.001), compared to spending just 1–2 h per day on these devices (Table
5).
Table 5
Multivariate analysis between two periods using logistic regression for physical activity
Practising a physical activity | Never | 84 (20.2) | 164 (39.5) | - | - |
Only in some seasons | 78 (18.8) | 43 (10.4) | 0.28 (0.18–0.44) | 0.36 (0.21–0.62) |
Sometimes | 167 (40.2) | 147 (35.4) | 0.45 (0.32–0.63) | 0.74 (0.46–1.19) |
Always | 86 (20.7) | 61 (14.7) | 0.36 (0.24–0.55) | 1.09 (0.53–2.27) |
Hours practising PA per week | Less than 1 h or none | 203 (48.9) | 257 (61.9) | - | - |
1–2 h/w | 84 (20.2) | 75 (18.1) | 0.71 (0.49–1.01) | 0.86 (0.52–1.41) |
3–4 h/w | 61 (14.7) | 49 (11.8) | 0.63 (0.42–0.96) | 0.88 (0.48–1.61) |
More than 4 h/w | 67 (16.1) | 34 (8.2) | 0.40 (0.25–0.63) | 0.57 (0.26–1.24) |
Free time activity spend | Walking | 87 (21.0) | 48 (11.6) | - | - |
TV, music, computer, reading | 230 (55.4) | 339 (81.7) | 2.67 (1.82–3.97) | 1.40 (0.87–2.28) |
Sports | 37 (8.9) | 19 (4.6) | 0.93 (0.48–1.78) | 0.73 (0.34–1.57) |
Shopping | 61 (14.7) | 9 (2.2) | 0.27 (0.12–0.56) | 0.19 (0.07–0.45) |
Hours spend on computer/mobile/TV | 1–2 h/d | 126 (30.4) | 50 (12.0) | - | - |
3–4 h/d | 138 (33.3) | 79 (19.0) | 1.44 (0.94–2.22) | 1.32 (0.81–2.15) |
5–6 h/d | 84 (20.2) | 105 (25.3) | 3.15 (2.05–4.90) | 2.30 (1.39–3.82) |
More than 6 h/d | 67 (16.1) | 181 (43.6) | 6.81 (4.45–10.56) | 3.84 (2.33–6.42) |
Sleeping time habit | Night sleep | 268 (66.3) | 119 (29.7) | - | - |
Day sleep | 136 (33.7) | 282 (70.3) | 4.67 (3.48–6.31) | 3.99 (2.86–5.62) |
Sleeping amount | Mean (SD) | 7.1 (1.7) | 8.0 (2.1) | 1.29 (1.20–1.40) | 1.28 (1.17–1.40) |
With regard to sleeping habits, results indicated significant statistical differences before and during the pandemic, there was a decrease in the percentage of participants who slept during the night and a marked increase in the percentage of participants who slept during the day (OR = 3.99 (95% CI 2.86–6.62), p < 0.001).
There was a significant between-subject effect of BMI (
p = 0.001) and age (
p = 0.003) on physical activity, but not for smoking status, gender, marital status, nationality or educational level (see Supplementary Table D, Additional File
1).
Discussion
This study produces novel information about dietary habits and lifestyle behaviours in Kuwait during the period of COVID-19. This critical period resulted in not only serious public health consequences, but also severe economic and social consequences globally [
23]. Maintaining a healthy and balanced diet and being physically active are recommended in these difficult times to support the immune system [
1‐
3]. However, factors such as sudden lifestyle changes, anxiety, fear, stress and depression can influence food choices and everyday behaviours [
24]. The present findings seem to be consistent with other research that has observed changes in dietary habits [
25‐
27] and lifestyle behaviours [
28] during the pandemic. The findings of this study indicate some changes in daily life, including changes in some eating practices, physical activity and sleeping habits. Unhealthy meal patterns were detected in this study, such as skipping breakfast and late-night snacking. Both behaviours are likely associated with overweight and obesity [
29,
30]. Consistent with the literature [
31‐
34], this research found skipping breakfast was common among participants. It was noticed that the rate of skipping breakfast remained consistent, with a slight increase during the pandemic. Possible explanations for this behaviour include a lack of time, intentionally skipping breakfast to cut calories and a lack of appetite [
33]. However, other possible explanations for skipping breakfast during COVID-19 include staying up late, which leads to late-night snacking, and oversleeping during the day, as shown in the results. These findings reflect those of Okada et al. [
30], which was a study among 19,687 Japanese women that found a significant association between a late dinner or bedtime snack and skipping breakfast, as well as an association of this behaviour with overweight and obesity.
Despite the recommendation to reduce the intake of fats, sugar and salt during COVID-19 [
35] and avoid irregular snacking [
36], chocolate, nuts and crisps were reported to be the most commonly consumed snacks, and these are loaded with sugar, fat and salt. A similar finding was reported by Scarmozzino and Visioli [
25], who found that half of the participants of an Italian sample showed an increase in the consumption of both sweet and salty comfort foods during COVID-19. These results may be explained by the fact that feelings of boredom, anxiety and stress (likely heightened due to quarantine, as shown in the results) lead to higher consumption of energy-dense foods that are high in sugar and fat [
14,
37,
38]. Similar findings were also reported by Muscogiuri et al. [
39], who found that many people over-eat sugary and salty comfort foods for snacking due to stress induced by quarantine and that this habit may increase the risk of developing obesity. Furthermore, it has been demonstrated that there was a strong association between weight gain and self-reported anxiety/depression among patients during the pandemic [
26].
The results of this study indicate a rise in home cooking during COVID-19. Participants started cooking more themselves (or their wives and mothers did so), resulting in reduced reliance on a housekeeper for cooking purposes. These results match those observed by an American study [
40] that found about half of the participants reported they were cooking and baking more during the pandemic. Moreover, this result matches with the findings of Di Renzo [
41] and Scarmozzino and Visioli [
25] that there was an increased consumption of homemade foods such as desserts, bread and pizza during the lockdown among Italian residents.
Furthermore, the study detected a significant reduction in the frequency of fast-food consumption. It seems possible that this rise in home cooking is related to attempts to occupy the increased free time resulting from quarantine. Another explanation is that people wanted to eat healthier in reaction to the spread of COVID-19 and thus resorted to home cooking more frequently. Finally, it could be related to the reduced consumption of fast-food as a result of fears regarding the transmission of COVID-19, whether it be from unhygienic practices at restaurants or from the delivery driver. However, it is difficult to conclude that people ate more healthily during the pandemic just because they reported consuming more home cooked meals, especially if unhealthy foods were still in circulation.
Regarding food choices within the five main food groups assessed in this study, there were no significant changes in terms of red meat, chicken, type of fat, milk, bread, fruit and vegetables, before and during the pandemic, except in the case of fish and seafood. A study reported by Zhao et al. [
27] also observed low consumptions of fish during the lockdown among the Chinese population. This result was expected and can be explained by the fact that the fish markets in Kuwait were closed on April 2020 [
9] as a precautionary measure and so there was a lack of availability of fresh fish and seafood due to the absence of working fisherman during this period. In addition, it is often preferred to consume fish and seafood fresh, which may have further contributed to the reduction in their consumption.
Although the consumption of fruits and vegetables is recommended to support the immune system especially during the pandemic [
35], however, the results of this study show that more than 70% of the participants did not reach the minimum portions of fruits and vegetables recommended by the USDA of 5 portions a day [
42]. This result is in line with findings from other studies that reported a low consumption of fruits and vegetables among Kuwaiti adults [
43] such as the EMAN study [
44] and KNNS [
45]. These results are likely related to a lack of awareness of the current recommendation for the consumption of fruits and vegetables (unpublished data). A finding from two cross-sectional questionnaire studies among the UK’s population found an association between low knowledge of details of the 5-a-day recommendation and low consumption of fruits and vegetables [
46]. In addition, another possible explanation is a predisposition towards energy-dense foods that are high in sugar and fat for snacking, as shown in the results. Moreover, the limited availability of fruits and vegetables and restricted food store opening hours due to quarantine during the pandemic could have caused a reduction in the consumption of fruits and vegetables.
The results of this study did not show remarkable changes in beverage consumption habits among participants before and during the pandemic, except for Americano coffee and fresh juice, which both showed a reduction in consumption during COVID-19. Regarding Americano coffee, a possible explanation for the reduction in its consumption might be the closing of coffee shops as a precautionary measure during the pandemic. A study conducted in Kuwait that gives support to this explanation is Allafi et al. [
47], which found that most of its participants preferred to drink their coffee at coffee shops and that the most consumed type of coffee daily was Americano. The reduction in the consumption of fresh juice can possibly be explained by the negative impact of the pandemic on the availability of fruit and vegetables, since Kuwait has very limited agricultural production, particularly in terms of fruit. Moreover, fresh fruit and vegetables have short shelf lives. In addition, making fresh juice requires a larger amount of fruit and vegetables than when simply eating them. Another possible explanation is the limited access to grocery shopping, as elaborated on above.
It has been suggested that to enhance the immune system, it is important to be physically active and get enough sleep [
48]. In the present study, a noticeable reduction was found in the prevalence of physical activity during COVID-19, while time spent on sedentary behaviours increased, similar to the findings of Ammar et al. [
28]. This is most likely due to social distancing measures and the need for open spaces for people to be physically active [
49].
Based on the results, more than half of the participants met the recommended sleeping hours during the pandemic. However, 70% of the participants slept during the daytime instead of night-time. This result may be explained by the fact that quarantine may cause stress, which results in sleep disturbances and abnormal sleep patterns, or because of changes in daily routine. This may negatively affect the immune system [
50]. Moreover, it may increase food intake and increase the risk of developing obesity [
39].
Various challenges and difficulties were faced during the recruitment stage of the study. There was a lack of interest in the study accompanied by a lack of motivation to take part despite possible interest. This was particularly true during the early stage of the pandemic, likely due to the panic and stress induced by this unfamiliar situation resulting in people not giving priority to participation in the study and instead focusing on preparation for the pandemic in terms of stockpiling food and other necessities in the limited time available to do so because of the enforced partial quarantine and the measures put in place to organize access to daily grocery shopping. The lack of motivation to take part may have been further exacerbated by the length of the questionnaire. Furthermore, there was a lack of time on the side of the researchers to recruit for the study, since they were bound by the approach of the holy month of Ramadan, which was due to start on the 23rd of April, and it causes marked changes in usual food patterns.
Limitations of the study
It is acknowledged that the current study has some limitations. Firstly, all measurements, including height and weight, physical activity, dietary, smoking and sleeping habits, were self-reported. The poor informative status may increase information bias. Secondly, diet was only measured using questions that relied on daily or weekly frequency consumption; measuring of serving size was neglected. Moreover, the consumption substances that are specific dietary risk factors, such as fat, sodium and sugar, were not collected. Thirdly, although the study questionnaire was developed after a comprehensive review of literature, the tool was new, and this could add to the limitations. In addition, a web-based survey tool was used for its convenience during COVID-19 which may have led to selection bias. Most participants were highly educated. Furthermore, as a convenience sample was used in this study, the number of individuals who agreed to take part in the study could be one of the limitations.
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