Abstract
Background & aims
COVID-19 is a respiratory syndrome caused by SARS-CoV-2. In the absence of effective treatment and vaccines, social distance (SD) is essential to reduce the spread of the virus. However, SD generates several behavioral changes, especially in psychological (i.e., emotions and depressive symptoms) and lifestyle-related parameters (i.e., diet, physical activity, and sleep), and the aged people could be more susceptible to COVID-19 SD-related effects (i.e., loneliness, stress, fear, concerns about life, etc.). As such, we aimed to explore the relationship between lifestyle- (physical exercise practice and sleep quality) and psychological-related factors with the dietary practices derived from the Brazilian National Food Guide of older adult during the COVID-19 SD.
Methods
A web-based survey was conducted in São Paulo, Brazil. Data were collected between August and December 2020 (a period of social isolation due to the COVID-19 pandemic). The questions were extracted from validated questionnaires to verify dietary practices (score of adhesion to the food guide for the Brazilian population), sleep quality (by Pittsburgh questionnaire), emotional food-related dimensions (by Three-factor Eating Questionnaire), and depression symptoms (by Beck's questionnaire).
Results
Results were from 229 aged people (mean age 66.5 ± 6.02 years old; mean BMI 27.5 ± 4.32 kg/m2; mean sleep duration 8.23 ± 1.41 h; Pittsburg sleep quality score 6.55 ± 4.83; dietary practice score 42.5 ± 6.47). In the adjusted-linear regression model, we verified that physical exercise practice at home (β = 2.179; 95% CI: 0.599 to 3.758; p = 0.007) during COVID-19 SD was positively associated with better dietary practices in aged people. In contrast, emotional eating was negatively associated with dietary practices (β = −0.051; 95% CI: −0.092 to - 0.009; p = 0.015).
Conclusion
We conclude that in aged people, physical exercise practice at home during COVID-19 SD was positively associated with better dietary practices, while emotional eating was negatively associated.
Keywords: COVID-19, Dietary practices, Physical exercise, Depressive symptoms
1. Introduction
To reduce SARS-CoV-2 transmission, social distance (SD) was imposed in several countries [1]. It is well known that SD decreases virus spreading and early short-term government-imposed SD permits healthcare systems to prepare for an increasing COVID-19 burden [1]. Despite being the better method to reduce virus transmission in the absence of the vaccine, SD-related adverse effects are complex, leading to mental-associated problems [[2], [3], [4]]. Dealing with COVID-19 SD depends on several factors (i.e., socioeconomic level, perceived stress, friends, family, age, etc.); therefore, SD tolerance is singular. Still, some countries suffered more from SD than others. In Brazil, due to political problems, scientific denial, and lack of vaccines until early 2021, SD duration was extended, raising the odds of psychological-, dietary- and physical activity-related problems [[5], [6], [7], [8], [9], [10], [11]].
Several published studies throughout 2020 and 2021 [[12], [13], [14]] showed that COVID-19 SD promotes health problems (i.e., physical, and mental), mainly due to increased stress and negative emotional perception. It was observed that people in SD reduced their physical activity level (i.e., transport, leisure, general daily activities) and exercise practices (i.e., endurance and resistance exercise) [[5], [6], [7], [8], [9], [10], [11]]. Furthermore, depressive symptoms increased during COVID-19 SD, especially in early stages [15]. These negative changes can be exacerbated in aged people [16]. Earlier studies showed that aged people present higher depressive symptoms than young adults [17,18], which aggravate several behaviors, such as sleep quality, physical activity, and dietary practices.
Dietary practices are related to several eating-related procedures [19,20]. Notably, unhealthy dietary practices predispose to greater consumption of ultra-processed food, increasing the risk of metabolic disorders and worsening pre-existing diseases [19,20]. Considering that lifestyle- and psychological-related factors could affect dietary food-associated factors, we believe that physical exercise practice, sleep quality, and depressive symptoms will be able to affect the dietary practices of aged people during COVID-19 home confinement.
The present study aimed to explore the relationship between lifestyle- (physical activity and sleep) and psychological-related factors with the dietary practices derived from the Brazilian National Food Guide of aged adults during the COVID-19 social distance.
2. Methods
2.1. Study type, participants, and procedure
This is a cross-sectional, descriptive, and exploratory web-based survey with a non-probabilistic sample, composed by aged people living in São Paulo. The study protocol and procedures were carried out following the last revised Helsinki Declaration [21], and approved by the Human Research Ethics Committee of the São Camilo University Center (number: 4.159.923/2020). All participants provided their consent to participate in the study.
The study was conducted with women or men aged ≥60 years, without distinction of race, social class, or gender. It is essential to mention that “older adults” are 60 or older under Brazilian law [22]. There was no restriction by disease; the participants freely reported which disease they had. Still, participants needed access to the questionnaire via any device (i.e., cell phone, tablet, or computer), autonomy, and the ability to read, interpret text, and answer questions. The participants were requested to answer the questionnaire only if they were confined at home and leaving the house only for essential services (e.g., market, pharmacy, and hospital).
The study was conducted between August and December 2020, when the State of São Paulo/Brazil had an average rate of home confinement of 45.5% (39–52%). During this period, vaccines were not yet available in Brazil. This research was disseminated in social media (i.e., Instagram and Facebook), and all the options available for the researchers to contact potential participants were used. The “snowball” sampling method was used to recruit more participants. An anonymous online questionnaire (Microsoft Forms®) was used for data collection, including all validated questionnaires proposed for research, without changing the original version. During the data collection period, the researchers were available to answer any questions from the participants by phone, messages, or e-mail.
2.2. Measures
2.2.1. Dietary practices
The Food Practices Measurement Scale (FPMS) consists of 24 items that exemplify dietary practices (planning, domestic organization, food choice, eating practices) in agreement or disagreement with the recommendations of the Dietary Guidelines for the Brazilian Population (DGBP) [19,20]. The possible answers to each item are as follows: strongly disagree; I disagree; I agree; strongly agree, whether or not they endorse such a practice in their daily lives. The scale's score (Likert-type scale) is computed by the simple sum of the answers to which values from 0 to 3 are assigned, varying from 0 to 72 as the maximum value. The 13 items aligned with the recommendations of the DGBP are scored so that the answer with the complete agreement is the one with the highest value (strongly agree = 3 points); the 11 items opposed to the DGBP recommendations are scored inversely (strongly disagree = 3 points). For classification purposes, two cutoff points were defined, based on score percentiles, generating three score ranges: first tertile (<32 points); second tertile (32–41 points); third tertile (>41 points). The higher the score, the greater the participant's adherence to the DGBP recommendations. Among the questions which considered best dietary practices, the following topics were included: (i) meal planning; (ii) variation of food consumption; (iii) meal preparation; (iv) meals at home and eating at the table; (v) eating meals calmly; and (vi) consumption of fruits, nuts, organic vegetables, and preference for the purchase of food by local suppliers. In contrast, questions related to the worst dietary practices were as follows: (i) the use of mealtimes to carry out other activities; (ii) eating meals at the work or study table, on the sofa or in bed; (iii) consumption of sugary drinks, chocolates, candies; eating snacks between meals; (iv) replacement of main meals such as lunch and dinner; (v) frequency of consumption of sandwiches and pizzas and frequency of consumption of fast foods in general.
2.2.2. Depression symptoms
The Beck Depression Inventory-II (BDI-II) is a 21-item classification self-report that measures the characteristics and symptoms of depression. The questionnaire consisted of 21 depressive-related manifestations in the last 15 days, classified on an increasing scale from 0 to 3, generating total scores ranging from 0 to 63. Higher scores indicate higher levels of depression. The suggested thresholds for the levels of severity were as follows: 0–13, minimal/no depression; 14–19, mild depression; 20–28, moderate depression; and 29–63, severe depression symptoms [17,23,24].
2.2.3. Eating behavior
The Three-Factor Eating Questionnaire (TFEQ)-R21 (translated and validated for the Brazilian population) was used to analyze aspects related to eating behavior [25]. The TFEQ-21 is a self-assessment questionnaire developed to measure cognitive and behavioral components of eating: emotional eating (EE), cognitive restraint (CR), and binge eating (BE). EE includes six items that measure the propensity to overeat in response to negative emotions, such as anxiety, depression, and loneliness. CR includes six items concerning several factors related to food intake and body mass changes. BE includes nine items that analyze the individual's tendency to ingest high amounts of food, generally triggered by some external stimulus during a short time. Higher scores were indicative of more EE, CR, and BE.
2.2.4. Sleep duration and quality
Sleep time and duration were assessed with the translated and validated version of the Pittsburgh Sleep Quality Index (PSQI) [26,27]. The questionnaire consists of 19 self-reported questions measuring seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disorders, use of sleeping medication, and daytime sleep dysfunction in the previous month. The scores of the seven components are added up, resulting in the global score, ranging from 0 to 21 points. A PSQI score of >5 indicates low overall sleep quality.
2.2.5. Physical exercise practice
The exercise was assessed as follows: (i) were you exercising before the start of the home confinement due to the COVID-19 pandemic? and (ii) are you exercising regularly at home during the COVID-19 pandemic? The answer possibilities were yes or no.
2.2.6. Covariates and confounders
In an interview, the following variables were collected: birth sex (male and female), age (in years), self-report of body weight (kg) and height (meters), marital status (married, single, or widower), smoking (yes or no), living with chronic disease (yes or no), type of disease (volunteers could openly describe which diseases they had), living with two diseases or more (yes or no), assistance in buying or preparing meals (yes or no), schooling level (primary education, complete high school, and complete college or more), current job status, and socioeconomic status by family income. Family income was considered the minimum wage (MW) received per month (pm) in Brazil, being 1.100 R$ or 201 US$, considering that the mean dollar conversion value during this period (August and December 2020) is 5,45 R$.
2.3. Statistical analysis
The Shapiro-Wilk test investigated data normality. Continuous and normally distributed data are presented as mean and standard deviation, while continuous and non-parametric data are presented as the median, minimum, and maximum values. The categorical data were presented as absolute and relative values. Comparisons between groups were conducted with Student's t-test or Mann-Whitney U-test. Distribution analysis was performed with the Chi-square test, and Pearson's correlation test was applied for continuous variables. The regression models were constructed in three steps, as follows:
First step: single models. We performed simple regression models including dietary practices as the dependent variable and independent variables were (i) beck score categories (no, mild, or moderate/severe depressive symptoms); (ii) sleep quality (poor vs. good sleep quality) (iii) eating behavior (i.e., emotional eating, cognitive restraint, binge eating); (iv) physical exercise practice before and during social distancing (yes or no).
Second step: multiple model. To be included in multiple model, the variables tested in the single models must have complied with the following conditions: (i) p-value ≤0.20; (ii) biological plausibility; (ii) absence of multicollinearity analyzed by the variance inflation factor.
Third step: inserting the confounding variables. The variables inserted were: sex (male vs. female), marital status (married, single, or widower), smoking (yes or no), living with chronic disease (yes or no), living with two diseases or more (yes or no), body mass index (continuous), age (in years), assistance in buying or preparing meals (yes or no), schooling level (primary education, complete high school, and complete college or more), current job status, and socioeconomic status by family income.
We performed multiple and adjusted linear regression due to better fit according to the Akaike information criterion (AIC) [28,29]. The lowest value obtained by AIC indicates that the assumption of normality of residues has better been met. AIC estimates the quality of each model compared with the other models proposed [28,29]. All statistical analysis was performed using Jamovi 2.3.21 version. The significance level was set at p < 0.05 for all analyses.
3. Results
Table 1 displays the characteristics of the sample. Three hundred and nine subjects answered the questionnaire voluntarily and anonymously, and after carefully reading of all answers, 229 older adults (142 women and 87 men) were included in the final sample. The mean age was 66.5 (±6.02; 60–93) years old. Stratified by sex, women's mean age was 65.4 (±5.50; 60–89) years old and men's mean age was 68.2 (±6.44; 60–93) years old.
Table 1.
Sample characteristics of older adults during COVID-19 social distance (n = 229).
| Parameters (mean ± SD or frequency) | Whole sample (n = 229) | Women (n = 142) | Men (n = 87) | p-value between sex |
|---|---|---|---|---|
| Age (y) | 66.5 ± 6.02 | 65.4 ± 5.50 | 68.2 ± 6.44 | <0.01 |
| Weight (kg) | 75.1 ± 15.0 | 70.3 ± 12.5 | 83.1 ± 15.4 | <0.01a |
| Height (m) | 1.65 ± 0.08 | 1.60 ± 0.06 | 1.72 ± 0.06 | <0.01 |
| BMI (kg/m2) | 27.5 ± 4.32 | 27.3 ± 4.50 | 27.8 ± 4.02 | 0.368 |
| Dietary practices score | 42.5 ± 6.47 | 43.7 ± 6.87 | 40.5 ± 5.22 | <0.01a |
| Lower dietary practices score | 11 (4.8) | 8 (72.7) | 3 (27.3) | 0.003 |
| Moderated dietary practices score | 89 (38.8) | 43 (48.3) | 46 (51.7) | |
| Higher dietary practices score | 129 (56.3) | 91 (70.5) | 38 (29.5) | |
| p-value | <0.001 | |||
| Beck score | 8.48 ± 7.27 | 9.32 ± 7.98 | 7.10 ± 5.72 | 0.06a |
| BDI-II categories n (%) | ||||
| Minimal or no depression | 171 (74.7) | 101 (59.1) | 70 (40.9) | 0.182 |
| Mild depression | 35 (15.3) | 23 (65.7) | 12 (34.3) | |
| Moderate or severe depression | 23 (10) | 18 (78.3) | 5 (21.7) | |
| p-value | <0.001 | |||
| EE score | 25.4 ± 25.8 | 31.8 ± 28.1 | 14.8 ± 17.0 | <0.01 |
| BE score | 25.2 ± 19.5 | 29.0 ± 20.5 | 19.1 ± 16.0 | <0.01a |
| CR score | 43.7 ± 22.2 | 46.6 ± 21.7 | 39.0 ± 22.4 | 0.012 |
| Sleep duration (h) | 8.23 ± 1.41 | 8.24 ± 1.45 | 8.21 ± 1.35 | 0.860 |
| Sleep efficiency (%) | 84.4 ± 12.2 | 81.8 ± 13.30 | 88.7 ± 9.27 | <0.001 |
| Pittsburg score | 6.55 ± 4.83 | 7.39 ± 4.51 | 5.20 ± 5.06 | <0.001 |
| PSQI categories n (%) | ||||
| Good sleep quality | 118 (51.5) | 61 (43) | 57 (65.5) | <0.001 |
| Poor sleep quality | 111 (48.5) | 81 (57) | 30 (34.5) | |
| p-value | 0.655 | |||
| Physical exercise practice categories n (%) | ||||
| Before SD | 0.432 | |||
| Yes | 140 (61) | 84 (60) | 56 (40) | |
| Not | 89 (38.9) | 58 (65.2) | 31 (34.8) | |
| p-value | <0.001 | |||
| During SD | 0.220 | |||
| Yes | 104 (45.4) | 60 (57.7) | 44 (42.3) | |
| Not | 125 (54.6) | 82 (65.6) | 43 (34.4) | |
| p-value | 0.165 | |||
| Diseases n (%) | 0.006 | |||
| Yes | 139 (60.7) | 96 (69.1) | 43 (30.9) | |
| Not | 90 (39.3) | 46 (51.1) | 44 (48.9) | |
| p-value | 0.001 | |||
| Disease type n (%) | 0.009 | |||
| Cardiovascular disease | 9 (3.9) | 5 (55.6) | 4 (44.4) | |
| Hypertension | 48 (20.9) | 31 (64.6) | 17 (35.4) | |
| Dyslipidemia | 5 (2.1) | 4 (80) | 1 (20) | |
| Diabetes Mellitus | 17 (7.4) | 8 (47.1) | 9 (52.9) | |
| Other diseases (i.e., arthritis, osteopenia, disc herniation). | 60 (26.2) | 48 (80) | 12 (20) | |
| p-value | <0.001 | |||
| Living with two or more diseases n (%) | 0.269 | |||
| Yes | 31 (13.5) | 22 (71) | 9 (29) | |
| Not | 198 (86.5) | 120 (60.6) | 78 (39.4) | |
| p-value | <0.001 | |||
| Smoking status n (%) | 0.441 | |||
| Yes | 20 (8.7) | 14 (70) | 6 (30) | |
| Not | 209 (91.2) | 128 (61.2) | 81 (38.8) | |
| p-value | <0.001 | |||
| Schooling level n (%) | 0.014 | |||
| Primary School | 59 (25.8) | 35 (59.3) | 24 (40.7) | |
| High School | 32 (14) | 13 (40.6) | 19 (59.4) | |
| College or higher | 138 (60.3) | 94 (68.1) | 44 (31.9) | |
| p-value | <0.001 | |||
| Income per month n (%) | 0.013 | |||
| ≤1–3 MW/pm | 47 (20.5) | 36 (76.6) | 11 (23.4) | |
| >3–5 MW/pm | 59 (25.8) | 40 (67.8) | 19 (32.2) | |
| >5 MW/pm | 123 (53.7) | 66 (53.7) | 57 (46.3) | |
| p-value | <0.001 | |||
| Are you currently working? n (%) | <0.001 | |||
| Yes | 79 (34.5) | 34 (43) | 45 (57) | |
| Not | 150 (65.5) | 108 (72) | 42 (28) | |
| p-value | <0.001 | |||
| Marital Status n (%) | <0.001 | |||
| Married | 137 (59.8) | 67 (48.9) | 70 (51.1) | |
| Single | 66 (28.8) | 51 (77.3) | 15 (22.7) | |
| Widower | 26 (11.4) | 24 (92.3) | 2 (7.7) | |
| p-value | <0.001 | |||
| Assistance for purchasing foodstuffs? n (%) | 0.835 | |||
| Yes | 33 (14.4) | 21 (63.6) | 12 (36.4) | |
| Not | 196 (85.6) | 121 (61.7) | 75 (38.3) | |
| p-value | <0.001 | |||
| Assistance for preparing meals? n (%) | <0.001 | |||
| Yes | 25 (10.9) | 5 (20) | 20 (80) | |
| Not | 204 (89.1) | 137 (67.2) | 67 (32.8) | |
| p-value | <0.001 | |||
Legend: EE = emotional eating; BE = Binge eating; CR = Cognitive restraint. Independent sample T-test was applied to verify differences between groups by gender. When Levene's test was significant (p < 0.05), we applied the Mann–Whitney U test, represented by “a.” Chi-square test was applied for frequencies. In bold, significant values considering p ≤ 0.05.
3.1. Dietary practices
The mean dietary practices score was 43.7 (±6.87) for women and 40.5 (±5.22) for men (p < 0.01). Considering the categories for adherence to the proposed DGBP dietary practices, 11 (4.8%), 89 (38.8%), and 129 (56.3%) participants showed low, moderate, and high adherence, respectively. Furthermore, women (n = 91; 70.5%) adhered more to DGBP recommendations than men (n = 38; 29.5%).
3.1.1. Depressive symptoms
The mean Beck questionnaire score for women was 9.32 (±7.98) and 7.10 (±5.72) for men (p < 0.01). Of all aged people evaluated, 171 (74.7%) had no depressive symptoms, 35 (15.3%) and 23 (10%) had mild or moderate depressive symptoms, respectively. The frequency of depressive symptoms did not differ between genders (X2 = 3.41; p = 0.182).
3.1.2. Sleep-related parameters
The PSQI score was 6.55 ± 4.83 for the entire sample, with 7.39 ± 4.51 for women and 5.20 ± 5.06 for men (p < 0.001). The mean sleep duration was 8.23 ± 1.41 h, 8.24 ± 1.45 h for women, and 8.21 ± 1.35 h for men (p = 0.860). Mean sleep efficiency was 84.4 ± 12.2%, 81.8 ± 13.30% for women, and 88.7 ± 9.27% for men (p < 0.001). The classification of good sleep (n = 118; 51.5%) and poor sleep (n = 111; p = 48.5) was homogeneous in the sample. However, women (n = 81; 57%) had poorer sleep quality (X2 = 11.0; p < 0.001) than men (n = 30; 34.5%).
3.1.3. Physical exercise
Among all assessed volunteers, 140 subjects practiced physical exercise before COVID-19 SD, and 104 subjects practiced it at home during COVID-19 SD. No differences were observed in the distribution of exercise practice before (X2 = 0.617; p = 0.432) or during (X2 = 1.51; p = 0.220) social distancing by males and females.
Other characteristics of the sample (i.e., presence of diseases, type of diseases, level of education, monthly family income, smoking, assistance in buying foodstuff and preparing meals, etc.) are also described in Table 1.
Table 2 presents the regression model considering dietary practices as the dependent variable. The multiple regression model explained the variability in dietary practices by 26.1% (adjusted R2: 0.261; F = 11.1 [220,8]; p < 0.001), and the adjusted-regression model explained the variability of dietary practices by 38.4% (adjusted R2 = 0.384; F = 7.18 [205,23]; p < 0.001). The Durbin-Watson test revealed no autocorrelation (DW: 2.15; p = 0.252). In the multiple model, physical exercise at home during COVID-19 SD was the factor most associated with better dietary practices (β = 2.240; p = 0.009), followed by physical exercise practice before SD (β = 1.789; p = 0.037) and CR (β = 0.108; p < 0.001). In the adjusted-regression model, physical exercise at home during COVID-19 SD (B = 2.179; p = 0.007) was the factor most associated with better dietary practices, followed by CR (B = 0.088; p < 0.001). In contrast, EE (β = − 0.051; p = 0.015) was negatively associated with dietary practices.
Table 2.
Regression analysis examining the associations between assessed variables and dietary practices (n = 229).
| Parameters | Β |
95% CI |
p-value |
β |
95% CI |
p-value |
β |
95% CI |
p-value |
|
|---|---|---|---|---|---|---|---|---|---|---|
| Univariate regression | Multiple model | Confounders-adjusted model | ||||||||
| Beck score categories | ||||||||||
| Without depression symptoms (reference) | 1.00 | 1.00 | 1.00 | |||||||
| Mild depression symptoms | −2.67 | −5.01 to -0.327 | 0.026 | −1.689 | −3.870 to 0.490 | 0.128 | −1.632 | −3.668 to 0.403 | 0.115 | |
| Moderate or severe depression symptoms | −2.59 | −5.39 to 0.214 | 0.070 | −0.074 | −2.740 – 2.592 | 0.956 | 0.427 | −2.172 – 3.028 | 0.746 | |
| Sleep quality by PSQI | ||||||||||
| ≤5 PSQI (reference) | 1.00 | |||||||||
| >5 PSQI | −1.38 | −3.06 to 0.303 | 0.108 | −0.727 | −2.365 to 0.910 | 0.382 | −1.368 | −2.912 to 0.176 | 0.082 | |
| Physical exercise practice before social distancing | ||||||||||
| Did not do physical exercise before social distancing (reference) | 1.00 | |||||||||
| Performed exercise before social distancing | 3.81 | 2.15 to 5.47 | <0.001 | 1.789 | 0.110 to 3.468 | 0.037 | 1.548 | −0.112 to 3.209 | 0.067 | |
| Physical exercise practice during social distancing | ||||||||||
| Did not do physical exercise at home (reference) | 1.00 | |||||||||
| Performed exercise at home | 4.38 | 2.78 to 5.98 | <0.001 | 2.240 | 0.572 to 3.908 | 0.009 | 2.179 | 0.599 to 3.758 | 0.007 | |
| TFEQ-r21 score | ||||||||||
| CR (score) | 0.117 | 0.082 to 0.152 | <0.001 | 0.108 | 0.073 to 0.142 | <0.001 | 0.088 | 0.055 to 0.121 | <0.001 | |
| BE (score) | −0.051 | −0.094 to -0.008 | 0.018 | −0.014 | −0.070 to 0.042 | 0.627 | −0.011 | −0.063 to 0.041 | 0.677 | |
| EE (score) | −0.035 | −0.067 to -0.002 | 0.034 | −0.028 | −0.070 to 0.014 | 0.191 | −0.051 | −0.092 to -0.009 | 0.015 | |
Legend: β = Beta; EE = emotional eating; BE = Binge eating; CR = Cognitive restraint; BMI = Body Mass Index; CI = Confidence Interval. Multiple variable linear regression model was applied to verify the associations between assessed variables and dietary practices. The model was adjusted by sex (male vs. female), marital status (married, single, or widower), smoking (yes or no), living with chronic disease (yes or no), living with two diseases or more (yes or no), body mass index (continuous), age (in years), assistance in buying or preparing meals (yes or no), schooling level (primary education, complete high school, and complete college or more), current job status, and socioeconomic status by family income. Family income was considered the minimum wage (MW) received per month (pm) in Brazil, being 1.100 R$ or 201 US$, considering that the mean dollar conversion value during this period (August and December 2020) is 5,45 R$. Thus, category 1 (≤1–3 MW/pm), category 2 (>3–5 MW/pm), and category 3 (>5 MW/pm).
Supplementary Table 1 shows correlation analyses between eating practice score, PSQI score, sleep quality, EE, CR, BE, and BDI-II.
4. Discussion
The present study explores the relationship between eating behavior, lifestyle-related variables (sleep quality and exercise practice), and depressive symptoms with dietary practices during COVID-19 SD in aged people. During COVID-19 SD, physical exercise practice at home were the factor most positively related to better dietary practices, while EE was negatively associated. In contrast, depressive symptoms or sleep quality were not associated with dietary practices. Our study design does not permit assuming causality; however, we speculated that those who maintained their exercise practices during COVID-19 SD preserved or improved dietary practices.
The dietary practices questionnaire has been applied in studies for the Brazilian population because it considers the extent of adhesion to the Brazilian food guide recommendations. Four components are assessed (i) planning, related to the individual's dedication to diet; (ii) household organization, which incorporates home organization for the food requirement and preparation at home; (iii) modes of eating, especially the environment satisfactoriness, time and attention reserved to the act of eating; (iv) food choices, mainly ultra-processed foods, for example, the substitution of main meals, consumption of sugary drinks, and snacks between meals [19,20,30]. Our sample had moderate (n = 89; 38.8%) and high (n = 129; 56.3%) dietary practice scores. The mean score of 42.5 is above what is considered most appropriate (>41). Nevertheless, our sample includes people with high schooling level (n = 138; 60.3%), with high income per month (n = 123; 53.7%), and married people (n = 137; 59.8%), factors that favor better dietary practice scores.
Food intake concern's has increased during COVID-19 SD, mainly due to nutrient-related effects on several risk factors (i.e., glycaemia, fat depot, immune system competence, etc.) associated with COVID-19 severity and mortality [31]. Besides, poor dietary practices favor weight gain in aged people, a factor associated with harmful outcomes [32]. Higher dietary practices scores are related to lower sugary drinks, high sugar, or fat snacks, processed and ultra-processed food intake, accompanied by a high intake of healthy-related foods (i.e., fruits, vegetables, whole-grain, and sugar-free foods). Moreover, a higher score denotes better planning and food preparation, as well as a calmer act of eating and more attention paid to the meal [19,20].
Unlike our results, previous studies have shown that sleep related-problems (i. e., excessive daytime sleepiness and insomnia) contribute to changes in food intake, which can negatively impact the nutritional status of aged people [[33], [34], [35]]. Another study in Brazil that evaluated factors associated with better dietary practices showed that better sleep quality was positively associated with dietary practices, while vespertine chronotype was negatively associated [6]. In our sample, 58 (25.3%) subjects exhibited depressive symptoms. Likewise, Fhon et al. [36] found that 62.9% of the aged people assessed in Peru exhibited symptoms of depression during COVID-19 SD, which were also associated with sleep problems. Studies in other countries (i.e., Spain and the United States) have also shown that these populations were psychologically affected by COVID-19 SD, leading to depressive symptoms [37,38].
During COVID-19 SD, higher energy intake and lower physical activity levels were associated with weight gain among Chinese people [39]. Recently, a systematic review and meta-analysis published by NG et al. [40] showed that during the COVID-19 pandemic, people living in countries that applied a lockdown strategy decreased their physical activity [40]. According to this data, we asked about physical exercise practices before and during COVID-19 SD. Our data revealed that aged people decreased their exercise practices, such as in other studies [41,42]. Bevilacqua et al. [43] evaluated two studies (HCS and HEAF). In the HCS study, 65 males and 60 females (82.4–86.6 years old) reported being 47% less physically active during the pandemic than before; still, 18% reported eating less. Data from HEAF study with 1086 men and 1383 women (62.0–69.3 years old) showed that 44% of participants were less active than before the pandemic, 16% said that their diet was less healthy, and 19% reported eating more than before [43].
In the scenario of health-related effects influenced by of physical exercise, current literature centralizes on the role of exercise in contributing to a negative energy balance through energy expenditure increase [44,45]; nevertheless, the effect of physical exercise on food intake is controversial. For several reasons, higher physical activity levels and exercise practices are positive for health-related issues [46]. Moreover, beyond metabolic-related effects, physical exercise enhances dietary choices [47]. The effects of physical exercise on food intake depend on several factors (i. e., sex, age, volume, intensity, training status, etc.); even so, literature is derived primarily from young adults. It is believed that physical exercise enhances satiety sensitivity, improving appetite control, but there are several studies that do not corroborate the effect of long-term physical exercise on downregulated appetite [[48], [49], [50]]. Hubner; Boron; Koehler et al. [51] conducted a systematic review and meta-analysis to verify the effect of physical exercise on the appetite of aged people. The authors inserted 15 reports with “good” quality judgement following the risk of bias assessment. The study revealed that exercise decreases leptin in aged people. Regardless, any observed effect was related to energy intake and body weight [51].
The FPMS questionnaire enabled us to get information beyond simple food intake because several queries are related to day-to-day dietary practices, which are critical to nutrition health-associated effects, especially in aged people [52]. Thus, regardless of changes in energy intake or in body composition due to physical exercise, better dietary practices could improve other health-related parameters. Our data, at least in part, corroborates with previous studies. For instance, Joo et al. [47] verified that 15-week exercise training (i. e., 30 min of aerobic exercise at 65–85% of age- and gender-specific maximum heart rate reserve) improves the dietary pattern of young adults. For example, they observed that participants who achieved the prescribed exercise protocol showed more adhesion to the prudent dietary pattern. Likewise, a longer exercise duration was associated with a decrease in preferences for Western diet patterns [47]. In aged people, Ferreira et al. [53] observed that Traditional dietary pattern was positively associated with physical exercise practice in an Brazillian cross-sectional study. Likewise, Stefan et al. [54] verified the association of dietary pattern of aged people (by Elderly Dietary Index Score) with physical activity. Regression model was adjusted for BMI, self-rated health, sleep quality, socioeconomic status, psychological distress, and chronic diseases. The intake of health-related foods (i. e., fish, cereals, fruits, legumes) was associated with adequate physical exercise level. Besides, other studies verified a potential relationship between lower physical activity levels and poor diet-related outcomes [55].
Interestingly, we observed (supplementary data) a positive (although weak) correlation between EEand the depressive symptoms score (r = 0.281; p < 0.001), and a negative (although weak) correlation between dietary practices and depressive symptoms (r=−0.247; p < 0.001). Recently, Kaner et al. [56] showed that EE was associated with depression symptoms in young adults. In adjusted-regression analysis, EE-related effect on dietary practices is small; however, it is known that deading with stressful situations is singular. At the same time, some peoples increase food intake, while others decrease [57]. Food intake derived from stress occurs by several mechanisms. Increased cortisol levels could impact non-homeostatic (i.e., mesolimbic reward pathways) and homeostatic systems (i.e., ghrelin and leptin) [[57], [58], [59], [60]]. Similarly, cortisol can affect hypothalamic neuropeptides (i.e., orexigenic neuropeptide Y, agouti-related peptide, proopiomelanocortin, etc.) [57]. Additionally, high levels of anxiety, sadness, worries, and irritability could impact food intake [61]. Moreover, several previous studies describe food intake-related disturbances in aged people (i.e., poor palatability and central nervous system signaling) [62].
The present study has some limitations, such as (i) cross-section design, which means that it is not possible to infer causality among assessed variables; (ii) we did not evaluated the subjects in the very beginning of COVID-19 SD, but five months after its onset; (iii) data was obtained only by self-reported questionnaire; (iv) snow-ball method could increase the risk of sampling bias similarly to any nonrandom sampling method; (v) we did not insert specific questions about dementia or dysphagia; although none of these diseases have been reported by participants, it is a potential limitation because both affect dietary practices.
5. Conclusion
We conclude that physical exercise at home during COVID-19 SD was positively associated with better dietary practices of aged people living in São Paulo-Brazil. Nonetheless, EE was associated with lower dietary practices scores. This data corroborates previous findings and reinforces the importance of considering the relationship between physical exercise and dietary-related factors. Still, psychological well-being and EE need to be constantly monitored to better dietary practices management under stressful conditions, particularly in aged individuals who display myriad physiological and metabolic modifications.
Author contributions
Study design (MVLSQ and GCGC), data collection and curation (GCGC, CMSR, RSL, RML, and RGS), data analysis (MVLSQ and GCGC), manuscript preparation (MVLSQ and GCGC), manuscript critical review (MVLSQ).
Declaration of Competing Interest
The authors wish to confirm that there are no known conflicts of interest associated with this study and there has been no significant support for this publication that could have influenced its outcome.
Acknowledgment
We thank all research participants, the Nutrition undergraduate course coordinator Sandra Maria Chemin Seabra da Silva M.Sc., Professor Fernanda Patti Nakamoto Ph.D., and Centro Universitário São Camilo. This study had no grants or funding. We want to dedicate this study to everyone who died from COVID-19 and their family, especially Mr. Juarez Quaresma, Marcus Quaresma's father. Because of the scientific negationism in Brazil, the vaccine took a long time to be available, and unfortunately, numerous preventable deaths occurred.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.clnesp.2023.01.015.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Teslya A., Pham T.M., Godijk N.G., Kretzschmar M.E., Bootsma M.C.J., Rozhnova G. Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: a modelling study. PLoS Med. 2020;17 doi: 10.1371/journal.pmed.1003166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Didriksen M., Werge T., Nissen J., Schwinn M., Sorensen E., Nielsen K.R., et al. Impact of COVID-19 pandemic on sleep quality, stress level and health-related quality of life-A large prospective cohort study on adult Danes. Int J Environ Res Publ Health. 2021;18 doi: 10.3390/ijerph18147610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Smit A.N., Juda M., Livingstone A., U S.R., Mistlberger R.E. Impact of COVID-19 social-distancing on sleep timing and duration during a university semester. PLoS One. 2021;16 doi: 10.1371/journal.pone.0250793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Sepulveda-Loyola W., Rodriguez-Sanchez I., Perez-Rodriguez P., Ganz F., Torralba R., Oliveira D.V., et al. Impact of social isolation due to COVID-19 on health in older people: mental and physical effects and recommendations. J Nutr Health Aging. 2020;24:938–947. doi: 10.1007/s12603-020-1500-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Rogers A.M., Lauren B.N., Woo Baidal J.A., Ozanne E.M., Hur C. Persistent effects of the COVID-19 pandemic on diet, exercise, risk for food insecurity, and quality of life: a longitudinal study among U.S. adults. Appetite. 2021;167 doi: 10.1016/j.appet.2021.105639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Dos Santos Quaresma M.V., Marques C.G., Magalhaes A.C.O., Dos Santos R.V.T. Emotional eating, binge eating, physical inactivity, and vespertine chronotype are negative predictors of dietary practices during COVID-19 social isolation: a cross-sectional study. Nutrition. 2021;90 doi: 10.1016/j.nut.2021.111223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Marziali M.E., Card K.G., McLinden T., Wang L., Trigg J., Hogg R.S. Physical distancing in COVID-19 may exacerbate experiences of social isolation among people living with HIV. AIDS Behav. 2020;24:2250–2252. doi: 10.1007/s10461-020-02872-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Steele E.M., Rauber F., Costa C.D.S., Leite M.A., Gabe K.T., Louzada M., et al. Dietary changes in the NutriNet Brasil cohort during the covid-19 pandemic. Rev Saude Publica. 2020;54:91. doi: 10.11606/s1518-8787.2020054002950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lamarche B., Brassard D., Lapointe A., Laramee C., Kearney M., Cote M., et al. Changes in diet quality and food security among adults during the COVID-19-related early lockdown: results from NutriQuebec. Am J Clin Nutr. 2021;113:984–992. doi: 10.1093/ajcn/nqaa363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Deschasaux-Tanguy M., Druesne-Pecollo N., Esseddik Y., de Edelenyi F.S., Alles B., Andreeva V.A., et al. Diet and physical activity during the coronavirus disease 2019 (COVID-19) lockdown (March-May 2020): results from the French NutriNet-Sante cohort study. Am J Clin Nutr. 2021;113:924–938. doi: 10.1093/ajcn/nqaa336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Ammar A., Brach M., Trabelsi K., Chtourou H., Boukhris O., Masmoudi L., et al. Effects of COVID-19 home confinement on eating behaviour and physical activity: results of the ECLB-COVID19 international online survey. Nutrients. 2020;12 doi: 10.3390/nu12061583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gustafsson P.E., Nilsson I., San Sebastian M. Venerable vulnerability or remarkable resilience? A prospective study of the impact of the first wave of the COVID-19 pandemic and quarantine measures on loneliness in Swedish older adults with home care. BMJ Open. 2022;12 doi: 10.1136/bmjopen-2021-060209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Dunford S., Brooke J. Nurs Older People; 2022. Effects of social isolation and restrictions on older people during the COVID-19 pandemic. [DOI] [PubMed] [Google Scholar]
- 14.Schmidt A., Ayoub M.F., de Souza Y.L.P., Guimaraes A.T.B., Foss M.P. COVID-19 pandemic and mental health of a sample of Brazilian caregivers of people with dementia. Dement Neuropsychol. 2021;15:448–457. doi: 10.1590/1980-57642021dn15-040004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Fancourt D., Steptoe A., Bu F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: a longitudinal observational study. Lancet Psychiatr. 2021;8:141–149. doi: 10.1016/S2215-0366(20)30482-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gokseven Y., Ozturk G.Z., Karadeniz E., Sari E., Tas B.G., Ozdemir H.M. The fear of COVID-19 infection in older people. J Geriatr Psychiatr Neurol. 2021 doi: 10.1177/08919887211002651. [DOI] [PubMed] [Google Scholar]
- 17.Kirk J.M., Magaziner J., Shardell M.D., Ryan A.S., Gruber-Baldini A.L., Orwig D., et al. Depressive symptom heterogeneity among older adults after hip fracture. Age Ageing. 2021;50(6):1943–1951. doi: 10.1093/ageing/afab168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Assariparambil A.R., Noronha J.A., Kamath A., Adhikari P., Nayak B.S., Shankar R., et al. Depression among older adults: a systematic review of South Asian countries. Psychogeriatrics. 2021;21:201–219. doi: 10.1111/psyg.12644. [DOI] [PubMed] [Google Scholar]
- 19.Gabe K.T., Jaime P.C. Development and testing of a scale to evaluate diet according to the recommendations of the Dietary Guidelines for the Brazilian Population. Publ Health Nutr. 2019;22:785–796. doi: 10.1017/S1368980018004123. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Gabe K.T., Jaime P.C. Dietary practices in relation to the Dietary guidelines for the brazilian population: associated factors among Brazilian adults, 2018. Epidemiol Serv Saude. 2020;29 doi: 10.5123/S1679-49742020000100019. [DOI] [PubMed] [Google Scholar]
- 21.World Medical A. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–2194. doi: 10.1001/jama.2013.281053. [DOI] [PubMed] [Google Scholar]
- 22.Tramujas Vasconcellos Neumann L., Albert S.M. Aging in Brazil. Gerontol. 2018;58:611–617. doi: 10.1093/geront/gny019. [DOI] [PubMed] [Google Scholar]
- 23.von Glischinski M., von Brachel R., Hirschfeld G. How depressed is "depressed"? A systematic review and diagnostic meta-analysis of optimal cut points for the Beck Depression Inventory revised (BDI-II) Qual Life Res. 2019;28:1111–1118. doi: 10.1007/s11136-018-2050-x. [DOI] [PubMed] [Google Scholar]
- 24.Gorenstein C., Andrade L. Validation of a Portuguese version of the beck depression inventory and the state-trait anxiety inventory in Brazilian subjects. Braz J Med Biol Res. 1996;29:453–457. [PubMed] [Google Scholar]
- 25.de Medeiros A.C.Q., Yamamoto M.E., Pedrosa L.F.C., Hutz C.S. The Brazilian version of the three-factor eating questionnaire-R21: psychometric evaluation and scoring pattern. Eat Weight Disord. 2017;22:169–175. doi: 10.1007/s40519-016-0256-x. [DOI] [PubMed] [Google Scholar]
- 26.Buysse D.J., Reynolds C.F., 3rd, Monk T.H., Berman S.R., Kupfer D.J. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatr Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4. [DOI] [PubMed] [Google Scholar]
- 27.Bertolazi A.N., Fagondes S.C., Hoff L.S., Dartora E.G., Miozzo I.C., de Barba M.E., et al. Validation of the Brazilian Portuguese version of the Pittsburgh sleep quality index. Sleep Med. 2011;12:70–75. doi: 10.1016/j.sleep.2010.04.020. [DOI] [PubMed] [Google Scholar]
- 28.Vrieze S.I. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) Psychol Methods. 2012;17:228–243. doi: 10.1037/a0027127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Dziak J.J., Coffman D.L., Lanza S.T., Li R., Jermiin L.S. Sensitivity and specificity of information criteria. Briefings Bioinf. 2020;21:553–565. doi: 10.1093/bib/bbz016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Gabe K.T., Jaime P.C. Convergent validity and invariance analysis of a scale to measure adherence to eating practices recommended by the Dietary Guidelines for the Brazilian Population. Rev Bras Epidemiol. 2022;25 doi: 10.1590/1980-549720220009.2. [DOI] [PubMed] [Google Scholar]
- 31.Naja F., Hamadeh R. Nutrition amid the COVID-19 pandemic: a multi-level framework for action. Eur J Clin Nutr. 2020;74:1117–1121. doi: 10.1038/s41430-020-0634-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Guo J., Marseglia A., Shang Y., Dove A., Grande G., Fratiglioni L., et al. Association between late-life weight change and dementia: a population-based cohort study. J Gerontol A Biol Sci Med Sci. 2022;3 doi: 10.1093/gerona/glac157. glac-157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Koc Okudur S., Soysal P. Excessive daytime sleepiness is associated with malnutrition, dysphagia, and vitamin D deficiency in older adults. J Am Med Dir Assoc. 2021;22:2134–2139. doi: 10.1016/j.jamda.2021.05.035. [DOI] [PubMed] [Google Scholar]
- 34.Soysal P., Smith L., Dokuzlar O., Isik A.T. Relationship between nutritional status and insomnia severity in older adults. J Am Med Dir Assoc. 2019;20:1593–1598. doi: 10.1016/j.jamda.2019.03.030. [DOI] [PubMed] [Google Scholar]
- 35.van Egmond L., Tan X., Sjogren P., Cederholm T., Benedict C. Association between healthy dietary patterns and self-reported sleep disturbances in older men: the ULSAM study. Nutrients. 2019;11 doi: 10.3390/nu11051029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Fhon J.R.S., Villanueva-Benites M.E., Gomez-Lujan M.D.P., Mocarro-Aguilar M.R., Arpasi-Quispe O., Peralta-Gomez R.Y., et al. The mental health of the Peruvian older adult during the COVID-19 pandemic. Int J Environ Res Publ Health. 2022;19 doi: 10.3390/ijerph192416893. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Picaza Gorrochategi M., Eiguren Munitis A., Dosil Santamaria M., Ozamiz Etxebarria N. Stress, anxiety, and depression in people aged over 60 in the COVID-19 outbreak in a sample collected in northern Spain. Am J Geriatr Psychiatr. 2020;28:993–998. doi: 10.1016/j.jagp.2020.05.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Stanton A.M., Goodman G.R., Blyler A., Kirakosian N., Labbe A.K., Robbins G.K., et al. Mental health, social connectedness, and fear during the COVID-19 pandemic: a qualitative perspective from older women with HIV. AIDS Behav. 2022:1–14. doi: 10.1007/s10461-022-03950-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Zhu Q., Li M., Ji Y., Shi Y., Zhou J., Li Q., et al. Stay-at-Home" lifestyle effect on weight gain during the COVID-19 outbreak confinement in China. Int J Environ Res Publ Health. 2021:18. doi: 10.3390/ijerph18041813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Ng T.K.Y., Kwok C.K.C., Ngan G.Y.K., Wong H.K.H., Zoubi F.A., Tomkins-Lane C.C., et al. Differential effects of the COVID-19 pandemic on physical activity involvements and exercise habits in people with and without chronic diseases: a systematic review and meta-analysis. Arch Phys Med Rehabil. 2022;103:1448–14465 e6. doi: 10.1016/j.apmr.2022.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Bennett G., Young E., Butler I., Coe S. The impact of lockdown during the COVID-19 outbreak on dietary habits in various population groups: a scoping review. Front Nutr. 2021;8 doi: 10.3389/fnut.2021.626432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Otaki N., Yokoro M., Yano M., Imamura T., Akita M., Tanino N., et al. Social contact impacts physical activity and sedentary behavior among older adults in Japan due to COVID-19. BMC Geriatr. 2022;22:491. doi: 10.1186/s12877-022-03188-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Bevilacqua G., D'Angelo S., Linaker C., Paul A., Bloom I., Zhang J., et al. Physical activity and diet in a global pandemic: an investigation of the impact of COVID-19 on factors relevant for musculoskeletal health at two different stages of the lifecourse. Front Endocrinol. 2022;13 doi: 10.3389/fendo.2022.882399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Silva A.M., Judice P.B., Carraca E.V., King N., Teixeira P.J., Sardinha L.B. What is the effect of diet and/or exercise interventions on behavioural compensation in non-exercise physical activity and related energy expenditure of free-living adults? A systematic review. Br J Nutr. 2018;119:1327–1345. doi: 10.1017/S000711451800096X. [DOI] [PubMed] [Google Scholar]
- 45.Giroux V., Saidj S., Simon C., Laville M., Segrestin B., Mathieu M.E. Physical activity, energy expenditure and sedentary parameters in overfeeding studies - a systematic review. BMC Publ Health. 2018;18:903. doi: 10.1186/s12889-018-5801-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Febbraio M.A. Exercise metabolism in 2016: health benefits of exercise - more than meets the eye. Nat Rev Endocrinol. 2017;13:72–74. doi: 10.1038/nrendo.2016.218. [DOI] [PubMed] [Google Scholar]
- 47.Joo J., Williamson S.A., Vazquez A.I., Fernandez J.R., Bray M.S. The influence of 15-week exercise training on dietary patterns among young adults. Int J Obes. 2019;43:1681–1690. doi: 10.1038/s41366-018-0299-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.King N.A., Caudwell P.P., Hopkins M., Stubbs J.R., Naslund E., Blundell J.E. Dual-process action of exercise on appetite control: increase in orexigenic drive but improvement in meal-induced satiety. Am J Clin Nutr. 2009;90:921–927. doi: 10.3945/ajcn.2009.27706. [DOI] [PubMed] [Google Scholar]
- 49.Apolzan J.W., Flynn M.G., McFarlin B.K., Campbell W.W. Age and physical activity status effects on appetite and mood state in older humans. Appl Physiol Nutr Metabol. 2009;34:203–211. doi: 10.1139/H08-150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Blundell J.E., Stubbs R.J., Hughes D.A., Whybrow S., King N.A. Cross talk between physical activity and appetite control: does physical activity stimulate appetite? Proc Nutr Soc. 2003;62:651–661. doi: 10.1079/PNS2003286. [DOI] [PubMed] [Google Scholar]
- 51.Hubner S., Boron J.B., Koehler K. The effects of exercise on appetite in older adults: a systematic review and meta-analysis. Front Nutr. 2021;8 doi: 10.3389/fnut.2021.734267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Drewnowski A., Shultz J.M. Impact of aging on eating behaviors, food choices, nutrition, and health status. J Nutr Health Aging. 2001;5:75–79. [PubMed] [Google Scholar]
- 53.Ferreira M.P.N., Previdelli A.N., Freitas T.I., Marques K.M., Goulart R.M.M., Aquino R.C. Dietary patterns and associated factors among the elderly. Revista Brasileira de Geriatria e Gerontologia. 2017:20. [Google Scholar]
- 54.Stefan L., Petrinovic L., Sporis G., Vrgoc G. Frequency of dietary intake and physical activity in older adults: a cross-sectional study. Nutrients. 2018;10 doi: 10.3390/nu10121960. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Mascherini G., Catelan D., Pellegrini-Giampietro D.E., Petri C., Scaletti C., Gulisano M. Changes in physical activity levels, eating habits and psychological well-being during the Italian COVID-19 pandemic lockdown: impact of socio-demographic factors on the Florentine academic population. PLoS One. 2021;16 doi: 10.1371/journal.pone.0252395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Kaner G., Yurtdas-Depboylu G., Calik G., Yalcin T., Nalcakan T. Evaluation of perceived depression, anxiety, stress levels and emotional eating behaviours and their predictors among adults during the COVID-19 pandemic. Publ Health Nutr. 2022:1–10. doi: 10.1017/S1368980022002579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Yau Y.H., Potenza M.N. Stress and eating behaviors. Minerva Endocrinol. 2013;38:255–267. [PMC free article] [PubMed] [Google Scholar]
- 58.Laugero K.D., Falcon L.M., Tucker K.L. Relationship between perceived stress and dietary and activity patterns in older adults participating in the Boston Puerto Rican Health Study. Appetite. 2011;56:194–204. doi: 10.1016/j.appet.2010.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Dallman M.F., Pecoraro N.C., La Fleur S.E., Warne J.P., Ginsberg A.B., Akana S.F., et al. Glucocorticoids, chronic stress, and obesity. Prog Brain Res. 2006;153:75–105. doi: 10.1016/S0079-6123(06)53004-3. [DOI] [PubMed] [Google Scholar]
- 60.Russell G., Lightman S. The human stress response. Nat Rev Endocrinol. 2019;15:525–534. doi: 10.1038/s41574-019-0228-0. [DOI] [PubMed] [Google Scholar]
- 61.Ulrich-Lai Y.M., Fulton S., Wilson M., Petrovich G., Rinaman L. Stress exposure, food intake and emotional state. Stress. 2015;18:381–399. doi: 10.3109/10253890.2015.1062981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Roberts S.B., Rosenberg I. Nutrition and aging: changes in the regulation of energy metabolism with aging. Physiol Rev. 2006;86:651–667. doi: 10.1152/physrev.00019.2005. [DOI] [PubMed] [Google Scholar]
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