| Literature DB >> 35057460 |
Seok Tyug Tan1, Chin Xuan Tan2, Seok Shin Tan3.
Abstract
INTRODUCTION: The coronavirus disease 2019 (COVID-19) isolation has altered individuals' food purchasing behaviour and dietary intake patterns. Therefore, this study aims to investigate the changes in dietary intake patterns and their impacts on the weight status of young adults in Malaysia during the COVID-19 lockdown.Entities:
Keywords: COVID-19; dietary intake patterns; weight status; young adults
Mesh:
Year: 2022 PMID: 35057460 PMCID: PMC8778075 DOI: 10.3390/nu14020280
Source DB: PubMed Journal: Nutrients ISSN: 2072-6643 Impact factor: 5.717
Socio-demographic characteristics and weight status of the respondents during the COVID-19 lockdown.
| Variable | Frequency, | Mean ± Standard Deviation |
|---|---|---|
| Gender | - | |
| Male | 378 (36.2) | |
| Female | 667 (63.8) | |
| Age (years old) | 22.66 ± 2.47 | |
| 18–24 | 843 (80.7) | |
| 25–30 | 202 (19.3) | |
| Ethnicity | - | |
| Malay | 621 (59.4) | |
| Chinese | 170 (16.3) | |
| Indian | 230 (22.0) | |
| Others | 24 (2.3) | |
| Marital status | - | |
| Single | 978 (94.4) | |
| Married | 50 (4.8) | |
| Divorced/Widowed | 8 (0.8) | |
| Educational attainment | - | |
| No formal education/Primary | 18 (1.7) | |
| Secondary | 137 (13.1) | |
| Tertiary | 890 (85.2) | |
| Weight status (kg) | ||
| Sustained weight | 156 (14.9) | 0 |
| Weight loss | 379 (36.3) | −4.90 ± 4.38 |
| Weight gain | 510 (48.8) | 4.06 ± 3.23 |
| BMI (kg/m2) | ||
| Before the pandemic | ||
| Underweight | 186 (17.8) | |
| Normal | 443 (42.4) | 22.78 ± 4.87 a |
| Overweight | 159 (15.2) | |
| Obese | 257 (24.6) | |
| During the pandemic | ||
| Underweight | 162 (15.5) | |
| Normal | 450 (43.1) | 22.85 ± 4.70 a |
| Overweight | 166 (15.9) | |
| Obese | 267 (25.6) |
a Mean difference was tested with a paired samples t-test. Different letters indicate significant difference at p < 0.05.
Changes in dietary intake patterns of young adults in Malaysia during the COVID-19 lockdown.
| Food Groups/Items | ∆Dietary Intakes | ||
|---|---|---|---|
| Reduced Intake, | Remained the Same, | Increased Intake, | |
| Level 1 | |||
| Fruits | 159 (15.2) | 413 (39.5) | 473 (45.3) |
| Vegetables | |||
| Dark green vegetables | 153 (14.6) | 510 (48.8) | 382 (36.6) |
| Other vegetables | 134 (12.8) | 536 (51.3) | 375 (35.9) |
| Level 2 | |||
| Cereals and grains | 165 (15.8) | 511 (48.9) | 369 (35.3) |
| Tubers | |||
| White tubers and roots | 247 (23.7) | 617 (59.0) | 181 (17.3) |
| Vitamin A-rich tubers | 162 (15.5) | 523 (50.1) | 360 (34.4) |
| Level 3 | |||
| Legumes | 238 (22.8) | 590 (56.4) | 217 (20.8) |
| Fish and shellfish | 206 (19.7) | 537 (51.4) | 302 (28.9) |
| Flesh meats | 157 (15.0) | 521 (49.9) | 367 (35.1) |
| Eggs | 106 (10.1) | 487 (46.6) | 452 (43.3) |
| Milk and dairy products | 191 (18.3) | 474 (45.3) | 380 (36.4) |
| Level 4 | |||
| Oils and fats | 247 (23.6) | 572 (54.8) | 226 (21.6) |
| Sugars and sweets | 277 (26.5) | 404 (38.7) | 364 (34.8) |
| Salts | 177 (16.9) | 535 (51.2) | 333 (31.9) |
| Plain water | 69 (6.6) | 347 (33.2) | 629 (60.2) |
Partial correlation between ∆ dietary intake patterns and weight gain during the COVID-19 lockdown.
| Food Groups/Items 1 | Partial Correlation, rpartial 2 | |
|---|---|---|
| Level 1 | ||
| Fruits | 0.007 | 0.818 |
| Vegetables | ||
| Dark green vegetables | −0.027 | 0.393 |
| Other vegetables | −0.017 | 0.589 |
| Level 2 | ||
| Cereals and grains | 0.105 | 0.001 |
| Tubers | ||
| White tubers and roots | 0.028 | 0.370 |
| Vitamin A-rich tubers | −0.027 | 0.376 |
| Level 3 | ||
| Legumes, nuts and seeds | 0.002 | 0.943 |
| Fish and shellfish | 0.029 | 0.356 |
| Flesh meats | 0.052 | 0.095 |
| Eggs | 0.050 | 0.107 |
| Milk and dairy products | 0.031 | 0.313 |
| Level 4 | ||
| Oils and fats | 0.140 | <0.001 |
| Sugars and sweets | 0.085 | 0.006 |
| Salts | 0.039 | 0.206 |
| Plain water | −0.053 | 0.088 |
1 Dietary intakes were dummy coded as 0 = reduced intakes/remained the same and 1 = increased intakes. 2 Partial correlation with the adjustment of gender, age, ethnicity, marital status and educational attainment. 3 Variable which portrays a p-value of less than 0.25 (p < 0.25) was included into hierarchical multiple regression model.
Predictors of weight gain during the COVID-19 lockdown.
| ∆Dietary Intakes 1 | B (SE) | β | 95% CI | |
|---|---|---|---|---|
| Level 2 | ||||
| Cereals and grains | 0.088 (0.036) | 0.084 | 0.015 | 0.017–0.160 |
| Level 3 | ||||
| Flesh meats | 0.009 (0.040) | 0.009 | 0.815 | −0.069–0.087 |
| Eggs | 0.024 (0.038) | 0.024 | 0.533 | −0.051–0.099 |
| Level 4 | ||||
| Oils and fats | 0.150 (0.046) | 0.123 | 0.001 | 0.059–0.241 |
| Sugars and sweets | 0.020 (0.038) | 0.019 | 0.596 | −0.055–0.096 |
| Salts | −0.042 (0.040) | −0.039 | 0.293 | −0.121–0.036 |
| Plain water | −0.102 (0.035) | −0.100 | 0.003 | −0.171–−0.034 |
1 With the adjustment of gender, age, ethnicity, marital status and educational attainment. 2 Significant difference was considered at p < 0.05.
Partial correlation between ∆ dietary intake patterns and weight loss during the COVID-19 lockdown.
| Food Groups/Items 1 | Partial Correlation, rpartial 2 | |
|---|---|---|
| Level 1 | ||
| Fruits | 0.014 | 0.661 |
| Vegetables | ||
| Dark green vegetables | −0.010 | 0.746 |
| Other vegetables | −0.005 | 0.859 |
| Level 2 | ||
| Cereals and grains | 0.184 | <0.001 |
| Tubers | ||
| White tubers and roots | 0.016 | 0.598 |
| Vitamin A-rich tubers | −0.004 | 0.889 |
| Level 3 | ||
| Legumes, nuts and seeds | 0.049 | 0.115 |
| Fish and shellfish | 0.026 | 0.405 |
| Flesh meats | 0.069 | 0.027 |
| Eggs | 0.025 | 0.420 |
| Milk and dairy products | 0.051 | 0.097 |
| Level 4 | ||
| Oils and fats | 0.144 | <0.001 |
| Sugars and sweets | 0.110 | <0.001 |
| Salts | 0.025 | 0.419 |
| Plain water | −0.014 | 0.650 |
1 Dietary intakes were dummy coded as 0 = increased intakes/remained the same and 1 = reduced intakes. 2 Partial correlation with the adjustment of gender, age, ethnicity, marital status and educational attainment. 3 Variable which portrays a p-value of less than 0.25 (p < 0.25) was included into hierarchical multiple regression model.
Predictors of weight loss during the COVID-19 lockdown.
| ∆Dietary Intakes 1 | B (SE) | β | 95% CI | |
|---|---|---|---|---|
| Level 2 | ||||
| Cereals and grains | 0.205 (0.042) | 0.156 | <0.001 | 0.122–0.288 |
| Level 3 | ||||
| Legumes, nuts and seeds | 0.023 (0.038) | 0.020 | 0.542 | −0.051–0.098 |
| Flesh meats | −0.006 (0.045) | −0.004 | 0.902 | −0.095–0.083 |
| Milk and dairy products | −0.020 (0.042) | −0.016 | 0.645 | −0.103–0.064 |
| Level 4 | ||||
| Oils and fats | 0.103 (0.041) | 0.091 | 0.012 | 0.022–0.183 |
| Sugars and sweets | 0.039 (0.038) | 0.036 | 0.296 | −0.035–0.113 |
1 With the adjustment of gender, age, ethnicity, marital status and educational attainment. 2 Significant difference was considered at p < 0.05.