| Literature DB >> 35898932 |
Hana Wei Jun Chen1, Roy Rillera Marzo1, Herlina Anton1, Mohammed A Abdalqader1, Visveshwarran Rajasekharan1, Mohammed Faez Baobaid1, Hazian Hamzah1, Hooi Chia Tang1, Hassan Omar Ads1.
Abstract
Background: Due to the global surge in COVID-19 cases, numerous countries have enforced lockdowns of varying stringency. Social isolation and stay-at-home orders have detrimental effects on one's lifestyle and dietary habits. This study aimed to assess the dietary patterns, food preferences, shopping behavior and weight gain during the lockdown among students in a private university in Malaysia. Design andEntities:
Keywords: COVID-19; dietary habits; eating habits; lockdown; online shopping; weight gain
Year: 2022 PMID: 35898932 PMCID: PMC9310253 DOI: 10.4081/jphr.2021.2921
Source DB: PubMed Journal: J Public Health Res ISSN: 2279-9028
Sociodemographic and anthropometric profile (n = 426).
| Variables | n | % |
|---|---|---|
| Age group | ||
| 19-24 | 270 | 63.4 |
| 25-29 | 130 | 30.5 |
| ≥30 | 26 | 6.1 |
| Gender | ||
| Female | 238 | 55.9 |
| Male | 188 | 44.1 |
| Ethnicity | ||
| Malay | 215 | 50.5 |
| Chinese | 67 | 15.7 |
| Indian | 49 | 11.5 |
| Others | 95 | 22.3 |
| Household income group
| ||
| Low (B40) | 52 | 12.2 |
| Middle (M40) | 218 | 51.2 |
| High (T20) | 156 | 36.6 |
| Education level | ||
| Diploma | 88 | 20.7 |
| Degree | 306 | 71.8 |
| Postgraduate | 32 | 7.5 |
| Employment status | ||
| Working remotely | 40 | 9.4 |
| Working in rotating basis | 71 | 16.7 |
| Working at workplace | 35 | 8.2 |
| Not working | 280 | 65.7 |
| Residential area during lockdown | ||
| Urban | 331 | 77.7 |
| Rural | 95 | 22.3 |
| Living arrangements (during lockdown) | ||
| Alone | 39 | 9.2 |
| With family | 195 | 45.8 |
| With relatives | 37 | 8.7 |
| With friends | 155 | 36.4 |
| Body Mass Indexb (during lockdown) (kg/m2) | ||
| Underweight (<18.5 kg/m2) | 48 | 11.3 |
| Normal weight (18.5 – 22.9 kg/m2) | 248 | 58.2 |
| Overweight (≥23.0 – 27.4 kg/m2) | 93 | 21.8 |
| Obesity (≥27.5 kg/m2) | 37 | 8.7 |
B40 (
Body Mass Index classification.
Figure 1.(A) Food consumption rate among respondents during the lockdown (n = 426). (B) Food consumption rate among respondents with BMI =18.5 – 22.9kg/m2 (n = 248). (C) Food consumption rate for respondents with BMI ≥ 27.5 kg/m2 (n = 37). (D) Food consumption rate among respondents with BMI <18.5 kg/m2 (n = 48). (E) Food consumption rate for respondents with BMI ≥ 23.0 – 27.4 kg/m2 (n = 93).
Figure 2(A) Increased food consumption among respondents during the lockdown (n = 426). (B) Increased snacking among respondents during the lockdown (n = 426). (C) Increased cooking among respondents during the lockdown (n = 426).
Figure 3(A) Increased food consumption among different BMI groups during the lockdown (n = 426). (B) Increased snacking among different BMI groups during the lockdown (n = 426). (C) Increased cooking among different BMI groups during the lockdown (n = 426).
Figure 4(A) Fears during grocery shopping and food contact (n = 426). (B) Shopping patterns during the lockdown (n = 426). (C) Increased online food ordering during the lockdown (n = 426).
Association between preferred way to buy food and having fears over contracting SARS-CoV-2 via grocery shopping and food products contamination (n = 426).
| Fears over contracting SARS-CoV-2 | Preferred way to buy food | X2 | p-value | ||
|---|---|---|---|---|---|
| Order food online (n=254) | From nearby shops (n=97) | From supermarket (n=75) | |||
| During grocery shopping | |||||
| Yes | 231 | 66 | 48 | 46.036 | |
| Not sure | 12 | 10 | 6 | <0.001 | |
| No | 11 | 21 | 21 | ||
| Via food products contamination | |||||
| Yes | 187 | 48 | 34 | 32.225 | |
| Not sure | 23 | 13 | 9 | <0.001 | |
| No | 44 | 36 | 32 | ||
Observed weight changes after one
| Observed Weight Changes | n | % | Mean (kg) | SD (kg) | Range (kg) |
|---|---|---|---|---|---|
| Weight gain | 194 | 45.5 | 3.36 | 1.61 | 1 - 10 |
| Weight loss | 42 | 9.9 | 2.43 | 1.33 | 1 - 6 |
| No changes | 143 | 33.6 | - | - | - |
| Unsure | 47 | 11.0 | - | - | - |
Figure 5Weight changes among different BMI groups after one month of lockdown.
Association between sociodemographic characteristics, changes in dietary and shopping patterns with weight gain after one month lockdown (n=426).
| Variables | Weight gain | X2 | p-value | |
|---|---|---|---|---|
| Yes (n/%) (n = 194) | No (n/%) (n = 232) | |||
| Age group | ||||
| 19-24 | 139 | 131 | 16.827 | <0.001 |
| 25-29 | 40 | 90 | ||
| >30 | 15 | 11 | ||
| Gender | ||||
| Female | 122 | 116 | 7.116 | 0.008 |
| Male | 72 | 116 | ||
| Ethnicity | ||||
| Malay | 93 | 122 | 5.754 | 0.124 |
| Chinese | 25 | 42 | ||
| Indian | 24 | 25 | ||
| Others | 52 | 43 | ||
| Household income group | ||||
| Low (B40) | 11 | 41 | 14.443 | 0.001 |
| Middle (M40) | 109 | 109 | ||
| High (T20) | 74 | 82 | ||
| Education level | ||||
| Diploma | 32 | 56 | 3.827 | 0.148 |
| Degree | 146 | 160 | ||
| Postgraduate | 16 | 16 | ||
| Employment status | ||||
| Working remotely | 30 | 10 | 18.456 | <0.001 |
| Working in rotating basis | 27 | 44 | ||
| Working at workplace | 11 | 24 | ||
| Not working | 126 | 154 | ||
| Residential area | ||||
| Urban | 156 | 175 | 1.513 | 0.219 |
| Rural | 38 | 57 | ||
| Living arrangements | ||||
| Alone | 23 | 16 | 3.665 | 0.300 |
| With family | 83 | 112 | ||
| With relatives | 72 | 83 | ||
| With friends | 16 | 21 | ||
| Increased in food consumption | ||||
| Yes | 151 | 67 | 101.341 | <0.001 |
| No | 43 | 165 | ||
| Increased in snacking | ||||
| Yes | 161 | 74 | 111.512 | <0.001 |
| No | 33 | 158 | ||
| Increased in cooking | ||||
| Yes | 123 | 106 | 13.334 | <0.001 |
| No | 71 | 126 | ||
| Shopping patterns | ||||
| Order food or groceries online | 126 | 128 | 5.263 | 0.072 |
| Order food or groceries at shops nearby | 35 | 62 | ||
| Grocery shopping at mall | 33 | 42 | ||
| Increased online food ordering | ||||
| Yes | 151 | 152 | 7.806 | 0.005 |
| No | 43 | 80 | ||