| Literature DB >> 32992623 |
Roberta Zupo1, Fabio Castellana1, Rodolfo Sardone1, Annamaria Sila1, Vito Angelo Giagulli2, Vincenzo Triggiani3, Raffaele Ivan Cincione4, Gianluigi Giannelli5, Giovanni De Pergola1,6.
Abstract
The world is currently struggling to face the coronavirus pandemic (COVID-19), and many countries have imposed lockdowns and recommended quarantine to limit both the spread of the virus and overwhelming demands for medical care. Direct implications include the disruption of work routines, boredom, depression, increased calorie consumption, and other similar harmful effects. The present narrative review article briefly analyzes the preliminary effects of the quarantine lifestyle from the standpoint of dietary habits. In six different databases, we searched for original articles up to 10 August 2020, assessing eating habits among populations during the COVID-19 pandemic, and recorded any change in the intake of major food categories, as well as changes in body weight. The research strategy yielded 364 articles, from which we selected 12 articles that fitted our goal. Our preliminary findings revealed a sharp rise of carbohydrates sources consumption, especially those with a high glycemic index (i.e., homemade pizza, bread, cake, and pastries), as well as more frequent snacks. A high consumption of fruits and vegetables, and protein sources, particularly pulses, was also recorded, although there was no clear peak of increase in the latter. Data concerning the consumption of junk foods lacked consistency, while there was a decreased alcohol intake and fresh fish/seafood consumption. As a possible connection, people gained body weight. Therefore, in the realistic perspective of a continuing global health emergency situation, timely preventive measures are needed to counteract obesity-related behaviors in the long-term, so as to prevent further health complications.Entities:
Keywords: COVID-19; body weight; diet; dietary changes; obesity
Mesh:
Year: 2020 PMID: 32992623 PMCID: PMC7579065 DOI: 10.3390/ijerph17197073
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Search strategy used in the US National Library of Medicine (PubMed) and Medical Literature Analysis and Retrieval System Online (MEDLINE), according to the selected descriptors.
| Strategy | Descriptors Used |
|---|---|
| # 1 | (diet[tiab]) OR (feeding[tiab])) OR (habits[tiab]) OR (dietary lifestyle[tiab]) OR (dietary habits[tiab]) OR (dietary [tiab]) OR (dietary pattern[tiab]) OR (dietary behavior[tiab]) OR (food[tiab]) OR (foods[tiab]) OR (food habits[tiab]) OR (nutritional habits[tiab]) OR (eating habits[tiab]) OR (eating[tiab]) |
| # 2 | (change[tiab]) OR (changes[tiab]) OR (modifications[tiab]) OR (alterations[tiab]) OR (alteration[tiab]) OR (different[tiab]) OR (differences [tiab]) |
| # 3 | (sars cov 2[tiab]) OR (covid 19[tiab]) OR (severe acute respiratory syndrome coronavirus 2[tiab]) |
| # 4 | # 1 AND # 2 AND # 3 |
Figure 1Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram of search procedure.
Descriptive characteristics of included studies.
| Study Details | |||||||
|---|---|---|---|---|---|---|---|
| Ref. | Authors, Year | Sample Size | Study Group | Study Design | Country | Method of Estimating Intake | Dietary Assessment Method |
| [ | Sidor et al., 2020 | 1097 | 18–71 years | Cross-sectional | Poland | Daily/weekly frequency | Online questionnaire |
| [ | Górnicka et al., 2020 | 2.381 | 18+ years | Cross-sectional | Poland | Daily/weekly frequency | Online questionnaire |
| [ | Ghosh et al., 2020 | 150 | Middle-aged adults | Longitudinal | India | Daily frequency | Phone interview |
| [ | Di Renzo et al., 2020 | 3533 | 12–86 years | Cross-sectional | Italy | Daily/weekly frequency | Online questionnaire Eating Habits and Lifestyle Changes in COVID19 lockdown (EHLC-COVID-19) |
| [ | Romeo-Arroyo et al., 2020 | 600 | 18–68 years | Cross-sectional | Spain | Weekly frequency | Online questionnaire |
| [ | Pellegrini et al., 2020 | 150 | 18–75 years | Cross-sectional | Italy | Daily/weekly frequency | Online questionnaire |
| [ | Wang et al., 2020 | 2289 | 18–81 years | Cross-sectional | China | Weekly frequency | Online questionnaire |
| [ | Rodríguez-Pérez et al., 2020 | 7514 | >18 years | Longitudinal | Spain | Daily/weekly frequency | Online questionnaire |
| [ | Pietrobelli et al., 2020 | 41 | 6–18 years | Longitudinal | Italy | Daily frequency | Phone interview |
| [ | Reyes-Olavarría et al., 2020 | 700 | 18–62 years | Cross-sectional | Chile | Daily/weekly frequency | Online questionnaire |
| [ | Ruiz-Roso et al., 2020 | 820 | 10–19 years | Longitudinal | Italy, Spain, Chile, Colombia, Brazil | Weekly frequency | Online questionnaire (National School Health Survey—PeNSE survey) |
| [ | Scarmozzino et al., 2020 | 1.929 | − | Cross-sectional | Italy | Weekly frequency | Online questionnaire |
Dietary characteristics of the study findings by principal carbohydrate sources (sugary foods, fruit, vegetables, and cereals), body weight changes (gain, loss, none) and overall findings.
| Authors, Year [Ref.] | Body Weight Changes | Carbohydrate Sources | Overall Findings | |||||
|---|---|---|---|---|---|---|---|---|
| Gain | Loss | None | Sugary Food | Fruit | Vegetables | Cereals | ||
| Ghosh et al., 2020 [ | 19% | 33% | 48% | 7% reported 25–50% more sugar intake | 20% reported 25–50% more fruits intake | 9% increased servings/day (3 or more) | 21% increased cereals intake (rice, grains) | Increased carbohydrate, snacking and fruit intake, and home cooked meals |
| Ruiz-Roso et al., 2020 [ | - | 47.4% increased sweet foods intake (>4/week) versus 40.6% pre-COVID-19. Ditto for sugar beverages (23.8% against 22.7%) | 58.6% increase (>4/week) versus 53.9% pre-COVID-19 | 70.8% increase (>4/week) versus 66.2% pre-COVID-19 | - | Increased pulses, fruit, and vegetables intake, and home cooked meals. Higher sweet food intake. The overall diet quality did not improve. | ||
| Rodríguez-Pérez et al., 2020 [ | 12% | - | 47% | Up to 21% decreased daily sweet beverages intake | Up to 18% increased daily intake | Up to 19% increased daily intake | - | Higher intake of fruits, vegetables, and pulses and lower intake of red meat, alcohol, and fried foods. |
| Pietrobelli et al., 2020 [ | - | Increase of sugary drinks (0.40 ± 0.90 to 0.90 ± 1.16 servings/day) | Increased (1.16 ± 0.74 to 1.39 ± 0.70 servings/day) | Increased (1.34 ± 0.74 to 1.27 ± 0.69 servings/day) | - | No changes in reported vegetables intake. Fruit intake increased. Potato chip, red meat, and sugary drink intakes increased significantly. | ||
| Sidor et al., 2020 [ | 29.9% | 18.6% | - | One-third consumed at least once or more/day | One-third did not consume fresh vegetables and fruits on a daily basis | One-third did not consume fresh vegetables and fruits on a daily basis | The majority (64.2%) consumed grains once or more/day | One-third of people surveyed did not consume fresh vegetables and fruits on a daily basis, while the same proportion admitted to consuming sweets at least once every day. Obese people surveyed tended to eat vegetables, fruits, and pulses less frequently, and salty foods, meat, and dairy more often. |
| Di Renzo et al., 2020 [ | 48.6% | 13.9% | 37.4% | 43% increased homemade sweets | 37.4% increase | 37.4% increase | Up to 40% increased (homemade pizza, fresh bread, cereals) | Increased homemade foods (e.g., sweets, pizza and bread), cereals, and pulses, and decreased fresh fish, packaged sweets and baked products, delivery foods and alcohol intake |
| Romeo-Arroyo et al., 2020 [ | - | Over 50% increased sweets intake | 35% increase | 30% increase | Increase of cereals (20%), pasta/rice (39%), and bread (36%) consumption | Increased baking, fruits, and vegetables intake | ||
| Scarmozzino et al., 2020 [ | 19.5% | - | 50.7% | 42.5% increased chocolate, cakes, and ice creams | 21.2% increase | 21.2% increase | - | Increased consumption of fresh fruit and vegetables. Decreased alcohol consumption |
| Górnicka et al., 2020 [ | - | 39.9% increased homemade pastries intake, 8.4% decreased sugar-sweetened beverages, and 5% decreased energy drink intake | 20.1% decreased servings/day consumption | Almost 19% decreased servings/day consumption | 16.3% increased whole grain products intake | Highly increased consumption of homemade pastries. Increased consumption of eggs, pulses, and cereals, as well as alcohol. Decreased fish, fruit, and vegetable intake. | ||
| Reyes-Olavarría et al., 2020 [ | 32% | 17% | 51% | - | 30.9% increased daily intake | 30.9% Increased daily intake | - | Increased fruit and vegetables consumption, and home cooked meals. Higher junk and fried foods intake. The overall diet quality did not improve. |
| Pellegrini et al., 2020 [ | Self-reported weight and BMI significantly increased by 1.51 kg | 72% reported equal or greater sweets consumption than pre-COVID-19 | 81% reported equal or greater consumption than pre-COVID-19 | 81% reported equal or greater consumption of cereals than pre-COVID-19 | Increased frequency of overall food intake. More sweets and snacks consumption. | |||
| Wang et al., 2020 [ | - | 30% reported consuming more vegetables and fruit, especially women | Increased consumption (250–400 g/day), especially men | Higher intake of fruits, vegetables, and cereals. Increased snacking frequency. | ||||
Dietary characteristics of the study findings by other major principal food sources. Data on home-cooking habits were also included.
| Authors, Year [Ref.] | Junk/Fast Foods | Dressing Fat | Protein | Snacks | Alcohol | Home |
|---|---|---|---|---|---|---|
| Ghosh et al., 2020 [ | - | 5% increased overall fat intake (ghee, butter) | 3% increased overall protein intake (eggs, fish, meat, pulses, soybean) | 23% increase snacking frequency (>4/day) | 8% decreased daily intake by 25–50% versus 3% of increase | Widespread (97%) |
| Ruiz-Roso et al., 2020 [ | Up to 18.5% increase (>4/week) | - | 23.6% increased pulses intake (>4/week) against 22.8% before | - | ||
| Rodríguez-Pérez et al., 2020 [ | Up to 34% lower fast foods and processed meat weekly intake | 4% increase of olive oil daily intake | Up to 75% and 7% increase in weekly fish and pulses intake. Almost 2% decreased daily meat intake. | 37.6% increased snacking frequency | 57.3% decreased weekly intake. | 45.7% increase |
| Pietrobelli et al., 2020 [ | Increased potato chips intake (0.07 ± 0.24 to 0.61 ± 0.83 serving/day) | - | Increased daily red meat consumption (from 1.80 ± 1.53 to 3.46 ± 2.45) | - | ||
| Sidor et al., 2020 [ | Higher frequency | - | Dairy, meat, and pulses were consumed by the majority quite often during the week | Up to 60% increase | 14.6% increase | 62.3% increase |
| Di Renzo et al., 2020 [ | 29.8% decrease | - | Up to 15% increased eggs, meat, and pulses. Decreased fresh fish consumption -22% | - | 13% decreased wine and beer consumption | - |
| Romeo-Arroyo et al., 2020 [ | - | 30% increase of eggs, 39% increase of milk and dairy products, and 33% decrease of fish intake | - | 27% decreased alcoholic beverages | Widespread (between 4 and 6 on a 7-point scale of agreement) | |
| Scarmozzino et al., 2020 [ | - | 23.5% increased frequency | 36.8% decreased wine, beer, and liquors consumption | - | ||
| Górnicka et al., 2020 [ | 36.6% decrease | - | Increased low-fat meat and/or eggs (15.7%), pulses (13.9%), and milk and dairy products (20.8%) intake. Decreased fish and seafood (17%), and processed meat (17.7%) intake. | 19.7% decreased salty snacks | 18.1% increase | 48% increase |
| Reyes-Olavarría et al., 2020 [ | Increased junk and fried foods intake (1–2 times/week) | - | About 25% consumed red and white meat more than 3 times/week. 75.1% and 83.7% consumed fish and pulses 1–2 times/week. | - | 30% declared a daily consumption of alcohol | 59.6% increase |
| Pellegrini et al., 2020 [ | - | 81% reported equal or greater consumption of overall protein sources than pre-COVID-19 | 60.6% reported equal or greater snacking frequency | - | ||
| Wang et al., 2020 [ | - | Meats, dairy products, and eggs fulfilled the recommendation of the dietary guidelines for Chinese residents | About 30% reported an increased snacking frequency | - | ||
Figure 2Food trajectories triggered by the COVID-19 home confinement.
Details about reported trends on changes in physical activity level during the COVID-19 lockdown.
| Ref. | Authors, Year | Physical Activity Level | Study Design |
|---|---|---|---|
| [ | Sidor et al., 2020 | - | Cross-sectional |
| [ | Górnicka et al., 2020 | 43% decreased | Cross-sectional |
| [ | Ghosh et al., 2020 | 42% decreased | Longitudinal |
| [ | Di Renzo et al., 2020 | No significant difference between the percentage of people that did not train before (37.7%) or during (37.4%) the COVID-19 lockdown. However, a higher frequency of training during the emergency was found when compared to the previous period. | Cross-sectional |
| [ | Romeo-Arroyo et al., 2020 | - | Cross-sectional |
| [ | Pellegrini et al., 2020 | 79.3% did not do any physical activity or reduced their physical activity level compared to pre-COVID-19 | Cross-sectional |
| [ | Wang et al., 2020 | More than 50% decreased | Cross-sectional |
| [ | Rodríguez-Pérez et al., 2020 | 59.6% decreased | Longitudinal |
| [ | Pietrobelli et al., 2020 | Sports time decreased significantly by 2.30 ± 4.60 h/week | Longitudinal |
| [ | Reyes-Olavarría et al., 2020 | The highest percentage of subjects passed ≥6 h sitting or sedentary activities (54.4%) | Cross-sectional |
| [ | Ruiz-Roso et al., 2020 | - | Longitudinal |
| [ | Scarmozzino et al., 2020 | - | Cross-sectional |