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Article

Factors Associated with Perceived Change in Weight, Physical Activity, and Food Consumption during the COVID-19 Lockdown in Latin America

1
Facultad de Medicina, Universidad Continental, Lima 15000, Peru
2
Escuela de Posgrado, Universidad Peruana Cayetano Heredia, Lima 15074, Peru
3
Hospital Nacional Edgardo Rebagliati Martins, Lima 15072, Peru
4
Hospital Sub Regional Andahuaylas, Apurimac 03701, Peru
5
Departamento de Ciencias Médicas, Facultad de Ciencias de la Salud, Universidad Castilla La Mancha, 45600 Talavera de la Reina, Spain
6
Escuela de Medicina, Universidad Cesar Vallejo, Piura 22700, Peru
7
Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Universidad San Ignacio de Loyola, Lima 15024, Peru
8
South American Center for Education and Research in Public Health, Universidad Norbert Wiener, Lima 15046, Peru
9
Oficina de Epidemiología, Hospital Regional Lambayeque, Chiclayo 14012, Peru
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(3), 2382; https://doi.org/10.3390/su15032382
Submission received: 2 November 2022 / Revised: 15 January 2023 / Accepted: 16 January 2023 / Published: 28 January 2023
(This article belongs to the Special Issue Achieving Sustainable Development Goals in COVID-19 Pandemic Times)

Abstract

:
Quarantine and the restrictions necessitated by the COVID-19 pandemic have generated problems in nutrition and physical condition around the world. We aimed to determine factors associated with changes in perceived weight and lifestyle factors during the COVID-19 quarantine in Latin America, conducting a cross-sectional study based on a survey administered in more than a dozen countries during June–August 2020. Perceptions of weight changes and alterations in other habits were investigated and were associated with social factors and self-reported diseases. Of 8800 respondents, the majority perceived that they had gained weight and had been less physically active. Being female, living in Bolivia, obesity, and stress were factors associated with a higher perception of weight gain. A higher perception of physical activity was also associated with living in Chile, being of older age, being female, having diabetes, obesity, and stress. When living in Paraguay and Mexico, being female, obesity, anxiety, and stress were associated with a higher perception of unhealthy food consumption. When living in Bolivia, women, obesity, and stress were associated with a higher perception of consuming larger food portions. In conclusion, the perceived changes in weight and lifestyle during the pandemic were more evident in women, people with comorbidities, and those with emotional distress. Differences in the perception of weight changes were minimal among Latin American countries. This information suggests the possible metabolic implications in at-risk individuals that should be further addressed by researchers for timely intervention.

1. Introduction

It is undeniable that the COVID-19 pandemic brought many repercussions, which were the result of events such as social immobilization, the mandatory quarantine that dominated in many countries, and the fear that the disease generated in people, among other aspects that changed the behavior of society [1,2]. Consequences such as those that have emerged from this pandemic period were largely unexpected. Families’ obligations to stay at home resulted in a decrease in those physical activities that were performed in open spaces. These events also affected the obtaining of food due to COVID-19 restrictions, limiting food consumption, among others [3,4]. For this reason, changes have been documented in several areas regarding human behavior; one of them is possible weight gain or weight dysregulation [4,5]. In addition, sedentary lifestyles have been reported to be on the increase due to the lack of opportunities for physical activity in open areas or for the regular practice of any type of individual or group sport [6]. Likewise, anxiety and stress have been demonstrated an upward trend, which has possibly caused people to have an unhealthy and/or excessive diet as compensation [7,8]. To this could be added the fact that the recorded weight gain and weight loss may be associated with olfactory and gustatory deficits that are considered common symptoms in the long sequelae of acute COVID-19 [9]. In addition, reduced taste perception is associated with an increased body mass index (BMI); this correlation becomes more pronounced with the greater age of the subjects [10].
Previous literature has suggested that the pandemic had a serious influence on health behaviors in the general population [11], most especially in individuals with metabolic disorders, children, and adolescents [12,13,14]. This has been reported in countries such as Italy, where a survey was administered to 3533 people. Here, one out of every two participants stated that they perceived an increase in their body mass [5]. Another study in northern Italy showed that 92% of participants had a sedentary lifestyle, which also contributed to weight gain [15]. Furthermore, in Lithuania, a cross-sectional study of 2447 people reported that 49% of people had eaten more than usual and had ingested more sugary drinks. Hence, 32% of them gained weight, leading to their being overweight, obese, and having cardio-metabolic disorders, largely due to their sedentary lifestyle [16]. Information on perceived changes in weight and lifestyle are indirect measures of possible emotional distress and may allow the early detection of metabolic disorders. However, weight gain perception has not been reported in Latin America. This knowledge would lead to a better understanding of the lifestyle profiles of the population in this region and build a basis of knowledge that could identify individuals at risk of serious conditions, such as obesity and type 2 diabetes. It is also important to consider that these countries were seriously affected by the pandemic compared to other countries because of the fragility of their healthcare systems [17]. Therefore, we aimed to determine the factors associated with the perception of weight change and healthy habits during quarantine due to COVID-19 in Latin America.

2. Materials and Methods

We conducted a cross-sectional study in twelve Latin American countries from June to August 2020, during the lockdown period. People aged over 18 years, who stated that they had resided in the surveyed country during the quarantine period, who agreed to take part in the study, and who answered the four questions on their perception of weight and lifestyle changes were included. People who responded inadequately or with repetitive patterns to some of the questions were excluded (an exclusion of less than 10%).
This population was accessed using a convenience sampling method, through a virtual survey written in Spanish and posted on the Google Forms platform (see the Supplementary Materials). We chose this option because almost all these countries had a mandatory quarantine and the participants could not attend health centers or other facilities, which prevented us from taking measurements properly. However, this methodology is valid according to the procedure followed in other research because it allows the researcher to determine the perceptions of the respondent regarding weight gain or loss. This data collection methodology was also used by Di Renzo et al., who disseminated the survey through their institutional and private social networks [5]. Similarly, Kriaucioniene et al. distributed the link provided to access their questionnaire through social networks, such as Facebook and specific institutions’ websites [16]. In the United Arab Emirates, Ismail et al. also used this online survey distribution methodology in their study [18].
The DASS-21 questionnaire was used to measure depression, anxiety, and stress. This instrument is composed of 21 items and has shown adequate reliability for anxiety (Cronbach’s α = 0.82), depression (Cronbach’s α = 0.076), and stress (Cronbach’s α = 0.75) [19]. The categorization of each mental disorder ranges from normal to mild, moderate, severe, and extremely severe. For depression, the cutoff points for each category are 0–9, 10–13, 14–20, 21–27, and 28+. For anxiety, the cutoff points are 0–7, 8–9, 10–14, 15–19, and 20+. For stress, they are 0–14, 15–18, 19–25, 26–33, and 37+.
The dependent variables were the perception of body weight change (“I have the perception that I have gained weight during these months”), change in sedentary lifestyle (“I have the perception that I have been less physically active than before”), an increase in the amount of food intake (“I have had the perception that I have consumed larger amounts of food”), and an increase in junk food intake as part of the diet (“I have had the perception that I have consumed more unhealthy food”) during the pandemic. These variables were measured using a 5-point Likert scale (from 1 = “Strongly disagree” to 5 = “Strongly agree”). The independent variables were age, sex, country of residence, self-reported comorbidity (heart disease, obesity, and diabetes), depression, anxiety, and stress.
All these questions were extracted from the virtual survey posted on the Google Forms platform. Then, quality control was performed to select the population that would be included in the study. Subsequently, this was exported to a database in Stata (version 11.1) for later analysis and for the presentation of results.
In the descriptive statistical analysis, frequencies and percentages were estimated for each of the four main questions (which were taken individually for the survey). These descriptive analyses were performed in both forms, globally and according to the country where the survey was administered. Age was expressed as the median and 25th–75th percentiles. Then, a multivariate analysis was performed, wherein the prevalence ratios (PR), 95% confidence intervals (95%CI), and p-values were estimated using generalized linear models, the Poisson family of distribution, the log link function, and robust variance. A statistical significance level of 5% was used. Variables that did not reach statistical significance in the bivariate model did not enter the final (multivariate) model. The variance inflation factor (VIF) was estimated to assess the multicollinearity.
This study is a secondary data analysis of a sports nutrition diploma course approved by the institutional review board of the Universidad Privada Antenor Orrego, Lima, Peru, with code no. 0234-2020-UPAO. The primary study [20] complied with the considerations of the Declaration of Helsinki, and informed consent was obtained from the participants on the initial page of the online survey.

3. Results

Of the 8800 respondents, the median age was 22, 59.4% were women, 53.4% came from Peru, 4% had heart disease, 1.7% had diabetes, and 6.4% had obesity. Severe depressive symptoms were experienced by 10%, severe anxiety by 13.4%, and severe stress by 9.3% of individuals (Table 1).
The main perception was of weight gain. Out of the total number of participants, 57% agreed that they had gained weight during quarantine (24% strongly agreed and 33% agreed). In addition, another main perception was to have performed less physical activity (18% strongly agreed and 29% agreed). Figure 1 shows the variations in responses by sex.
Multivariate analysis showed that, compared to Peru, participants from Paraguay (adjusted prevalence ratio, aPR: 1.12; 95% CI: 1.03–1.22) and Colombia (aPR: 1.15; 95% CI: 1.04–1.26) had a higher perception of having gained weight. Being female (aPR: 1.24; 95% CI: 1.19–1.30), obesity (aPR: 1.35; 95% CI: 1.26–1.45), and stress (aPR: 1.16; 95% CI: 1.06–1.28) were also significantly associated with a higher perception of weight gain, adjusted for depression and anxiety (Table 2). The log pseudo-likelihood value was −6276 and the Akaike information criterion (AIC) value was 1.65. No multicollinearity was shown (mean VIF = 1.18).
It was found that participants from Chile (aPR: 1.11; 95%CI: 1.05–1.18; p-value = 0.001) and Bolivia (aPR: 1.10; 95%CI: 1.01–1.19; p-value = 0.023) had a higher perception of having done less physical activity compared to participants from Peru, adjusted for past history of heart disease, depression, and anxiety. Those who were older (aPR: >1.00; 95%CI: >1.00–>1.00; p-value < 0.001), women (aPR: 1.12; 95%CI: 1.08–1.17; p-value < 0.001), and those with diabetes (aPR: 1.17; 95%CI: 1.04–1.32; p-value = 0.008), obesity (aPR: 1.09; 95%CI: 1.02–1.17; p-value = 0.016), and stress (aPR: 1.22; 95%CI: 1.13–1.33; p-value < 0.001) had a higher perception of having done less physical activity, adjusted for past history of heart disease, depression, and anxiety (Table 3). The log pseudolikelihood value was −6769 and the AIC value was 1.79. No multicollinearity was shown (mean VIF = 1.17).
Participants from Chile (aPR: 1.11; 95% CI: 1.02–1.21; p-value = 0.019), Paraguay (aPR: 1.22; 95% CI: 1.11–1.34; p-value < 0.001), Mexico (aPR: 1.20; 95% CI: 1.08–1.32; p-value < 0.001), and Bolivia (aPR: 1.13; 95% CI: 1.00–1.27; p-value = 0.045) had a higher perception of having consumed more unhealthy food during quarantine compared to participants from Peru, adjusted for diabetes and depression. Women (aPR: 1.21; 95% CI: 1.14–1.28; p-value < 0.001), and those with obesity (aPR: 1.29; 95% CI: 1.18–1.40; p-value < 0.001), anxiety (aPR: 1.13; 95% CI: 1.02–1.24; p-value = 0.013), and stress (aPR: 1.17; 95% CI: 1.05–1.30; p-value = 0.005) also had a higher perception of having consumed more unhealthy food during quarantine, after adjustment for diabetes and depression (Table 4). The log pseudo-likelihood value was −5858 and the AIC value was 1.54. No multicollinearity was shown (mean VIF = 1.17).
Compared to Peru, participants from Paraguay (aPR: 1.18; 95%CI: 1.09–1.28; p-value < 0.001) and Bolivia (aPR: 1.22; 95%CI: 1.12–1.34; p-value < 0.001) had a higher perception of having consumed more unhealthy food during quarantine, adjusted for diabetes and depression. Older participants (aPR: >1.00; 95% CI: >1.00–>1.00; p-value = 0.003), female sex (aPR: 1.21; 95% CI: 1.15–1.28; p-value < 0.001), obesity (aPR: 1.21; 95%CI: 1.11–1.31; p-value < 0.001), and stress (aPR: 1.24; 95%CI: 1.13–1.37; p-value < 0.001) were also significantly associated with a higher perception of having consumed more food portions during the quarantine, adjusted for depression and anxiety (Table 5). The log pseudo-likelihood value was −6190 and the AIC value was 1.63. No multicollinearity was shown (mean VIF = 1.17).

4. Discussion

This study showed that the majority of respondents perceived themselves to have gained weight and to have been less physically active. Participants in Bolivia, females, and those with obesity and stress were associated with a higher perception of weight gain. A higher perception of lesser physical activity was associated with living in Chile, older age, being female, and having diabetes, obesity, and stress. Living in Paraguay and Mexico, being female, and having obesity, anxiety, and stress were associated with a higher perception of unhealthy food consumption. Living in Bolivia, being female, and having obesity and stress were associated with a higher perception of consuming more food portions.
Regarding the perception of physical activity, a systematic review of 16 studies reported that a decrease in physical activity had a negative impact on physical and mental health during the period of confinement [21]. Social isolation may especially accelerate physical and psychological decline in vulnerable populations, including older adults [22]. Another study in patients with diabetes indicated that physical activity is necessary to maintain glycemia at adequate levels [23]. It has also been reported that physical activity is an adequate tool by which to maintain physical and mental health during social isolation [24,25]. Likewise, the American College of Sports Medicine recommends that the practice of physical activity should be seen as an adjuvant factor in the treatment of COVID-19 [26]. Therefore, all these reports show how important it is to maintain regular physical activity during the stage of confinement.
On the other hand, previous research has shown that the decrease in physical exercise is associated with sedentary behaviors, which leads to an increased risk of different diseases or the worsening of pre-existing conditions of the cardiovascular, muscular, pulmonary, and nervous systems and, indirectly, with the endocrine, digestive, immune, and renal systems [24,27]. A study conducted on obese outpatients in northern Italy reported that there was an increase in weight in the first month of COVID-19 confinement [15]. This increase was related to a low cultural level, anxiety/depression, a lack of healthy food choices, higher quantities of snack consumption, and a decrease in physical activity and training [15].
It was observed in this study that the older the age of the participant, the less physical activity was perceived. This result could also be seen in a study conducted in Spain [28], where it was found that depression, fatigue, and social relationships directly affected the physical activity performed by older adults during quarantine. Living alone reduced the respondents’ physical activity levels during the pandemic, which suggests that this factor could influence physical well-being by affecting mental health. In addition, the study reported that older adults had a greater perception of overeating [15]. This result was similar to other research results [29] showing that decreased physical activity had a direct relationship with overeating as a way of coping with the stress caused by the pandemic.
In our study, people with obesity reported having gained weight more frequently than those without this condition. A previous study reported that BMI was positively correlated with age during confinement due to the pandemic (p < 0.01). This correlation was still significant after adjustments for sex, education, exercise, and sleep. In addition, participants with obesity or those who were overweight gained an average weight of 1 kg and 0.7 kg, respectively (p < 0.05) [30].
It was evident that participants with diabetes more frequently reported being less physically active in comparison with people without this condition. This result was similar to a study in which a significant increase in the daily hours of sitting and physical inactivity was observed [31]. Regarding time spent walking, the same study showed a significant decrease during quarantine compared to the amount in previous periods. In addition, a decrease in the average weekly time spent performing any type of moderate physical activity was shown [31].
Participants with obesity more frequently reported less physical activity during the last few months, compared to those without obesity. Similarly, one study reported that the factors associated with decreased physical activity during confinement were weight gain perception (PR 2.01; 95% CI: 1.35–3.25) and being overweight (PR 1.80; 95% CI: 1.17–2.76) [32]. Likewise, participants with obesity reported having an increased food intake more frequently, compared to those without obesity. This result coincides with a study that showed a general increase in carbohydrate consumption [33]. Sugar-sweetened beverages, ice cream, and homemade or packaged sweet foods showed an increase in daily or weekly consumption of up to 70%. It was also observed that homemade pizza, fresh bread, pasta, and rice were the most consumed foods by the Italian and Spanish populations [33]. Likewise, another study showed an increase (28.4%) in the intake of caloric and salty foods during the initial containment period in France [34]. In addition, participants with obesity more frequently reported having increased their intake of junk food, compared to those without obesity. Something similar was reported in Spain, Italy, Brazil, Colombia, and Chile since there was a 19% increase in the consumption of these foods per week in 820 adolescents from those countries [33].
In participants who reported symptoms of stress, the perception of having gained weight was 16% more frequent than in those without this emotional response. One study evidenced a significantly higher weight gain and BMI increase in people with anxiety and depression, with a weight gain greater than 3.18 kg (p < 0.001) and a BMI increase greater than 1.21 (p < 0.001) [15]. Furthermore, participants with stress more frequently reported less physical activity than those without such an emotional response (22%). A study conducted during the pandemic reported that physical inactivity was associated with loneliness (odds ratio (OR) 1.32; 95% CI 1.02–1.70), sadness (OR 1.34; 95% CI 1.01–1.77), and anxiety (OR 1.71; 95% CI 1.30–2.25) [35]. In addition, it was observed that people with depression who reported physical inactivity presented with a higher probability of loneliness and sadness [35].
It was also reported that participants with stress and anxiety more frequently reported having increased their junk food intake by 17% and 13%, respectively. Previously, a study conducted during the quarantine in Brazilian adults showed that a previous diagnosis of depression increased the likelihood of consuming ultra-processed foods (OR 1.49; 95% CI 1.21–1.83) [36]. This is added to the fact that we found that patients with stress had a higher frequency (24%) of having perceived an increase in the amount of food eaten. This is similar to a study that showed that, during confinement, mood negatively affected eating, with differences for sex (affirmative responses in 39% of women, vs. 29% of men). The negative effect of mood was greater in participants with obesity, reaching 62% [30].
This research has several limitations. First, there is a measurement bias since it was not possible to have an objective measurement for weight gain or loss, which is justified by the current context of self-reporting. Although other studies have performed the same technique, this measurement should be ratified in subsequent studies. Second, there is a selection bias, given that the sampling was made using a non-probability method. This prevents us from extrapolating the findings to the entire Latin American population. Third, the cross-sectional design limits causal inferences between variables. Fourth, the classification of individuals according to weight was limited by the lack of a more detailed question. Fifth, disproportionate responses across countries occurred because most participants were from Peru, the main location of the study authors. The original study idea was conceived in this place, but then, new collaborators from Latin American countries were included to expand the study hypothesis. Sixth, the COVID-19 restrictions established by each Latin American country were different, possibly based on economic and cultural differences. This could have generated more emotional distress in some individuals than others and, therefore, a deeper perception of weight and lifestyle changes. Nevertheless, we have managed to capture a wide diversity of data, drawn from participants residing in more than a dozen countries in the region. Therefore, this research offers important baseline results throughout Latin America. This can be useful for various specialists and institutions when patients return to their doctor’s office and then exhibit complications derived from a probable increase in weight and the impairment of their metabolism, due to the influence of the pandemic.
This study has shown changes in weight and lifestyle during the pandemic, which could have long-term repercussions for the health of individuals. No data, as far as we are concerned, were published in this regard in the Latin American population. The findings provide baseline results to compare in future research, showing how this context has influenced serious public health concerns, such as obesity, hypertension, and type 2 diabetes. It also allows researchers to classify potentially at-risk groups and countries that would need to implement various interventions related to lifestyle.

5. Conclusions

Our results show that, during quarantine, the perception of changes in body weight and health habits were different according to the country of residence, sex, age, obesity, and exposure to stress. The perceived change in weight and lifestyle during the pandemic were more evident in women, in people with comorbidities, and in those suffering emotional distress. Differences in perception were minimal between Latin American countries. This information suggests the possible metabolic implications in at-risk individuals that should be further addressed by researchers for timely intervention.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15032382/s1.

Author Contributions

Conceptualization, C.R.M. and M.J.V.-G.; methodology, C.R.M. and M.J.V.-G.; formal analysis, C.R.M. and M.J.V.-G.; investigation, C.R.M., B.F., J.A.F.-T., K.P., C.J.P.-V., V.E.F.-R. and M.J.V.-G.; data curation, C.R.M.; writing—original draft, B.F., J.A.F.-T. and K.P.; writing—review and editing, C.R.M., B.F., J.A.F.-T., K.P., C.J.P.-V., V.E.F.-R. and M.J.V.-G.; visualization, C.J.P.-V. and V.E.F.-R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Universidad Privada Antenor Orrego, Lima, Peru (protocol code no. 0234-2020-UPAO).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The dataset generated and analyzed during the current study is not publicly available because the ethics committee has not provided permission/authorization to publicly share the data but they are available from the corresponding author upon reasonable request.

Acknowledgments

We are grateful to the Diploma in Sports Nutrition and Physical Activity, Lima, Peru, which developed this research as part of the “Integrating Project” course. We also thank Katherine Cantaro Bernardo, from the graduate school of the Universidad Peruana Cayetano Heredia, for the facilities she provided during the course. M.J.V.-G. was supported by the Fogarty International Center of the National Institutes of Mental Health (NIMH) under Award Number D43TW009343 and the University of California Global Health Institute (UCGHI).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Responses regarding the perceived changes in weight and lifestyle features during the first wave of the COVID-19 pandemic in Latin America, stratified according to sex.
Figure 1. Responses regarding the perceived changes in weight and lifestyle features during the first wave of the COVID-19 pandemic in Latin America, stratified according to sex.
Sustainability 15 02382 g001
Table 1. Characteristics of the participants.
Table 1. Characteristics of the participants.
CharacteristicsN (%)
Age *22 (20–30)
Sex
  Male3560 (40.6)
  Female5205 (59.4)
Country
  Peru4682 (53.4)
  Chile852 (9.7)
  Paraguay656 (7.5)
  Mexico531 (6.1)
  Colombia127 (1.5)
  Bolivia442 (5.0)
  Panama 396 (4.5)
  Ecuador300 (3.4)
  Costa Rica215 (2.5)
  El Salvador203 (2.3)
  Honduras185 (2.1)
  Guatemala99 (1.1)
  Other countries 82 (0.9)
Heart disease
  No8192 (96.0)
  Yes 344 (4.0)
Diabetes
  No8393 (98.3)
  Yes143 (1.7)
Obesity
  No7988 (93.6)
  Yes548 (6.4)
Depression
  ≤ Moderate 7557 (90.0)
  ≥ Severe843 (10.0)
Anxiety
  ≤ Moderate7313 (86.7)
  ≥ Severe1127 (13.3)
Stress
  ≤ Moderate 7583 (90.7)
  ≥ Severe779 (9.3)
* Age was expressed in median and 25th–75th percentiles.
Table 2. Factors associated with weight gain perception in the Latin American population during the first wave of the COVID-19 pandemic.
Table 2. Factors associated with weight gain perception in the Latin American population during the first wave of the COVID-19 pandemic.
VariableWeight ChangeBivariate Analysis
PR (95% CI) p-Value
Multivariate Analysis
PR (95% CI) p-Value
No, n (%)Yes, n (%)
Total4625 (52.6%)4175 (47.4%)-------
Country
  Peru2511 (53.6%)2171 (46.4%)Ref.Ref.
  Chile409 (48.0%)443 (52.0%)1.12 (1.04–1.20) 0.0021.07 (0.99–1.15) 0.080
  Paraguay300 (45.7%)356 (54.3%)1.17 (1.08–1.26) < 0.0011.12 (1.03–1.22) 0.007
  Mexico262 (49.3%)269 (50.7%)1.09 (0.99–1.19) 0.0521.04 (0.95–1.14) 0.370
  Colombia71 (55.9%)56 (44.1%)0.95 (0.78–1.16) 0.6190.91 (0.73–1.12) 0.366
  Bolivia201 (45.5%)241 (54.5%)1.18 (1.07–1.29) < 0.0011.15 (1.04–1.26) 0.004
  Panama 233 (58.8%)163 (41.2%)0.89 (0.79–1.00) 0.0550.88 (0.77–1.01) 0.060
  Ecuador174 (58.0%)126 (42.0%)0.91 (0.79–1.04) 0.1550.88 (0.76–1.02) 0.092
  Costa Rica120 (55.8%)95 (44.2%)0.95 (0.82–1.11) 0.5380.93 (0.78–1.09) 0.355
  El Salvador119 (58.6%)84 (41.4%)0.89 (0.76–1.05) 0.1800.89 (0.74–1.06) 0.178
  Honduras114 (61.6%)71 (38.4%)0.83 (0.69–0.99) 0.0450.84 (0.69–1.02) 0.075
  Guatemala57 (57.6%)42 (42.4%)0.91 (0.73–1.15) 0.4520.87 (0.68–1.11) 0.250
  Other countries40 (48.8%)42 (51.2%)1.10 (0.89–1.37) 0.3611.02 (0.81–1.29) 0.847
Age (years) *22 (20–30)22 (20–30)1.00 (0.99–1.00) 0.487Did not enter the model
Sex
  Male2092 (58.7%)1468 (41.2%)Ref.Ref.
  Female2513 (48.3%)2692 (51.7%)1.25 (1.20–1.31) < 0.0011.24 (1.19–1.30) < 0.001
Heart disease
  No4334 (52.9%)3858 (47.1%)Ref.
  Yes 169 (49.1%)175 (50.9%)1.08 (0.97–1.20) 0.155Did not enter the model
Diabetes
  No4434 (52.8%)3959 (47.2%)Ref.
  Yes69 (48.3%)74 (51.7%)1.10 (0.93–1.29) 0.256Did not enter the model
Obesity
  No4307 (53.9%)3681 (46.1%)Ref.Ref.
  Yes196 (35.8%)352 (64.2%)1.39 (1.30–1.49) < 0.0011.35 (1.26–1.45) < 0.001
Depression
  ≤ Moderate 4037 (53.4%)3520 (46.6%)Ref.Ref.
  ≥ Severe354 (42.0%)489 (58.0%)1.25 (1.17–1.33) < 0.0011.05 (0.96–1.15) 0.275
Anxiety
  ≤ Moderate3934 (53.8%)3379 (46.2%)Ref.Ref.
  ≥ Severe484 (43.0%)643 (57.0%)1.23 (1.17–1.31) < 0.0011.06 (0.98–1.16) 0.120
Stress
  ≤ Moderate 4083 (53.8%)3500 (46.2%)Ref.Ref.
  ≥ Severe298 (38.3%)481 (61.7%)1.34 (1.26–1.42) < 0.0011.16 (1.06–1.28) 0.002
Prevalence ratios (left), 95% confidence intervals (within parentheses), and p-values (right) were obtained with generalized linear models (Poisson family, log link function, and robust variances). * Age was expressed as median and 25th–75th percentile. Ref.: Reference value.
Table 3. Factors associated with the perception of having performed less physical activity, as reported in a Latin American population during the first wave of the COVID-19 pandemic.
Table 3. Factors associated with the perception of having performed less physical activity, as reported in a Latin American population during the first wave of the COVID-19 pandemic.
VariableLess Physical ActivityBivariate Analysis
PR (95%CI) p-Value
Multivariate Analysis
PR (95%CI) p-Value
No n (%)Yes n (%)
Total3761 (42.7%)5039 (56.3%)-------
Country
  Peru2021 (43.2%)2661 (56.8%)Ref.Ref.
  Chile291 (34.2%)561 (65.8%)1.16 (1.10–1.22) <0.0011.11 (1.05–1.18) 0.001
  Paraguay262 (39.9%)394 (60.1%)1.06 (0.99–1.13) 0.1071.05 (0.97–1.13) 0.207
  Mexico233 (43.9%)298 (56.1%)0.99 (0.91–1.07) 0.7540.97 (0.90–1.06) 0.546
  Colombia61 (48.0%)66 (52.0%)0.91 (0.77–1.08) 0.2990.90 (0.75–1.07) 0.246
  Bolivia169 (38.2%)273 (61.8%)1.09 (1.01–1.17) 0.0351.10 (1.01–1.19) 0.023
  Panama 185 (46.7%)211 (53.3%)0.94 (0.85–1.03) 0.1860.95 (0.86–1.06) 0.367
  Ecuador149 (49.7%)151 (50.3%)0.89 (0.79–0.99) 0.0390.90 (0.80–1.01) 0.076
  Costa Rica99 (46.1%)116 (53.9%)0.95 (0.84–1.08) 0.4180.91 (0.80–1.05) 0.201
  El Salvador97 (47.8%)106 (52.2%)0.92 (0.80–1.05) 0.2150.90 (0.78–1.04) 0.158
  Honduras97 (52.4%)88 (47.6%)0.84 (0.72–0.98) 0.0230.88 (0.76–1.03) 0.123
  Guatemala50 (50.5%)49 (49.5%)0.87 (0.71–1.06) 0.1770.85 (0.69–1.05) 0.134
  Other countries35 (42.7%)47 (57.3%)1.01 (0.84–1.22) 0.9300.96 (0.78–1.18) 0.714
Age (years) *22 (20–28)22 (20–31)1.00 (1.00–1.00) <0.0011.00 (1.00–1.00) < 0.001
Sex
  Male1664 (46.7%)1896 (53.3%)Ref.Ref.
  Female2081 (40.0%)3124 (60.0%)1.13 (1.08–1.17) < 0.0011.12 (1.08–1.17) < 0.001
Heart disease
  No3531 (43.1%)4661 (56.9%)Ref.Ref.
  Yes130 (37.8%)214 (62.2%)1.09 (1.00–1.19) 0.0380.98 (0.89–1.08) 0.748
Diabetes
  No3620 (43.1%)4773 (56,9%)Ref.Ref.
  Yes41 (28.7%)102 (71.3%)1.25 (1.13–1.39) < 0.0011.17 (1.04–1.32) 0.008
Obesity
  No3464 (43.4%)4524 (56.6%)Ref.Ref.
  Yes197 (36.0%)351 (64.0%)1.13 (1.06–1.21) < 0.0011.09 (1.02–1.17) 0.016
Depression
  ≤ Moderate 3284 (43.5%)4263 (56.5%)Ref.Ref.
  ≥ Severe284 (33.7%)559 (66.3%)1.17 (1.11–1.24) < 0.0011.04 (0.97–1.12) 0.294
Anxiety
  ≤ Moderate 3187 (43.6%)4126 (56.4%)Ref.Ref.
  ≥ Severe398 (35.3%)729 (64.7%)1.15 (1.09–1.20) < 0.0011.00 (0.93–1.07) 0.947
Stress
  ≤ Moderate 3324 (43.8%)4259 (56.2%)Ref.Ref.
  ≥ Severe226 (29.0%)553 (71.0%)1.26 (1.20–1.32) < 0.0011.22 (1.13–1.33) < 0.001
Prevalence ratios (left), 95% confidence intervals (within parentheses), and p-values (right) were obtained with generalized linear models (Poisson family, log link function, and robust variances). Age was taken in its quantitative format. * Age was expressed as median and 25th–75th percentile. Ref.: Reference value.
Table 4. Factors associated with the perception of having consumed more unhealthy food, as reported in a Latin American population during the first wave of the COVID-19 pandemic.
Table 4. Factors associated with the perception of having consumed more unhealthy food, as reported in a Latin American population during the first wave of the COVID-19 pandemic.
VariableMore Consumption of Unhealthy FoodBivariate Analysis
PR (95% CI) p-Value
Multivariate Analysis
PR (95% CI) p-Value
No, n (%)Yes, n (%)
Total5202 (59.1%)3598 (40.9%)-------
Country
  Peru2865 (61.2%)1817 (38.8%)Ref.Ref.
  Chile467 (54.8%)385 (45.2%)1.16 (1.07–1.26) <0.0011.11 (1.02–1.21) 0.019
  Paraguay335 (51.1%)321 (48.9%)1.26 (1.16–1.37) <0.0011.22 (1.11–1.34) <0.001
  Mexico277 (52.2%)254 (47.8%)1.23 (1.12–1.36) <0.0011.20 (1.08–1.32) <0.001
  Colombia68 (53.5%)59 (46.5%)1.20 (0.99–1.45) 0.0641.16 (0.95–1.42) 0.135
  Bolivia245 (55.4%)197 (44.6%)1.15 (1.03–1.28) 0.0141.13 (1.00–1.27) 0.045
  Panama 256 (64.7%)140 (35.3%)0.91 (0.79–1.05) 0.1850.94 (0.81–1.09) 0.401
  Ecuador192 (64.0%)108 (36.0%)0.93 (0.79–1.08) 0.3430.89 (0.75–1.06) 0.184
  Costa Rica117 (54.4%)98 (45.6%)1.17 (1.01–1.37) 0.0361.15 (0.98–1.35) 0.091
  El Salvador125 (61.6%)78 (38.4%)0.99 (0.83–1.18) 0.9130.99 (0.82–1.20) 0.957
  Honduras127 (68.7%)58 (31.3%)0.81 (0.65–1.00) 0.0530.83 (0.66–1.04) 0.108
  Guatemala68 (68.7%)31 (31.3%)0.81 (0.60–1.08) 0.1530.86 (0.64–1.15) 0.298
  Other countries 44 (53.7%)38 (46.3%)1.19 (0.94–1.51) 0.1401.09 (0.84–1.42) 0.512
Age (years) *22 (20–30)22 (20–30)0.99 (0.99–1.00) 0.145Did not enter the model
Sex
  Male2265 (63.6%)1295 (36.4%)Ref.Ref.
  Female2917 (56.0%)2288 (44.0%)1.20 (1.14–1.27) < 0.0011.21 (1.14–1.28) < 0.001
Heart disease
  No4856 (59.3%)3326 (40.7%)Ref.
Yes202 (58.7%)142 (41.3%)1.01 (0.89–1.15) 0.836Did not enter the model
Diabetes
  No4984 (59.4%)3409 (40.6%)Ref.Ref.
  Yes74 (51.8%)69 (48.2%)1.19 (1.00–1.41) 0.0491.15 (0.95–1.38) 0.154
Obesity
  No4805 (60.2%)3183 (39.8%)Ref.Ref.
  Yes253 (46.2%)295 (53.8%)1.35 (1.24–1.47) < 0.0011.29 (1.18–1.40) < 0.001
Depression
  ≤ Moderate 4546 (60.2%)3011 (39.8%)Ref.Ref.
  ≥ Severe404 (47.9%)439 (52.1%)1.31 (1.22–1.40) < 0.0011.06 (0.95–1.17) 0.306
Anxiety
  ≤ Moderate4437 (60.7%)2876 (39.3%)Ref.Ref.
  ≥ Severe547 (48.5%)580 (51.5%)1.31 (1.23–1.39) < 0.0011.13 (1.02–1.24) 0.013
Stress
  ≤ Moderate 4589 (60.5%)2994 (39.5%)Ref.Ref.
  ≥ Severe350 (44.9%)429 (55.1%)1.39 (1.30–1.49) < 0.0011.17 (1.05–1.30) 0.005
Prevalence ratios (left), 95% confidence intervals (within parentheses), and p-values (right) were obtained with generalized linear models (Poisson family, log link function, and robust variances). * Age was expressed as the median and 25th–75th percentiles. Ref.: Reference value.
Table 5. Factors associated with the perception of having consumed more food portions, as reported in the Latin American population during the first wave of the COVID-19 pandemic.
Table 5. Factors associated with the perception of having consumed more food portions, as reported in the Latin American population during the first wave of the COVID-19 pandemic.
VariableMore Consumption of Food PortionsBivariate Analysis
PR (95% CI) p-Value
Multivariate Analysis
PR (95% CI) p-Value
No, n (%)Yes, n (%)
Total4722 (53.7%)4078 (46.3%)-------
Country
  Peru2546 (54.4%)2136 (45.6%)Ref.Ref.
  Chile456 (53.5%)396 (46.4%)1.02 (0.94–1.10) 0.6421.00 (0.92–1.09) 0.957
  Paraguay293 (44.7%)363 (55.3%)1.21 (1.12–1.30) <0.0011.18 (1.09–1.28) <0.001
  Mexico281 (52.9%)250 (47.1%)1.03 (0.94–1.14) 0.5181.00 (0.91–1.11) 0.974
  Colombia61 (48.0%)66 (52.0%)1.14 (0.96–1.35) 0.1331.12 (0.94–1.34) 0.197
  Bolivia193 (43.7%)249 (56.3%)1.23 (1.13–1.35) <0.0011.22 (1.12–1.34) <0.001
  Panama 233 (58.8%)163 (41.2%)0.90 (0.80–1.02) 0.0980.91 (0.80–1.03) 0.147
  Ecuador165 (55.0%)135 (45.0%)0.99 (0.87–1.12) 0.8350.98 (0.85–1.12) 0.750
  Costa Rica127 (59.1%)88 (40.9%)0.90 (0.76–1.06) 0.1940.92 (0.77–1.09) 0.350
  El Salvador126 (62.1%)77 (37.9%)0.83 (0.70–0.99) 0.0430.84 (0.69–1.01) 0.065
  Honduras114 (61.6%)71 (38.4%)0.84 (0.70–1.01) 0.0670.83 (0.68–1.01) 0.066
  Guatemala61 (61.6%)38 (38.4%)0.84 (0.65–1.08) 0.1780.83 (0.64–1.08) 0.167
  Other countries47 (57.3%)35 (42.7%)0.94 (0.73–1.20) 0.6060.80 (0.60–1,08) 0.141
Age (years) *22 (20–31)22 (20–29)1.00 (1.00–1.00) < 0.0011.00 (1.00–1.00) 0.003
Sex
  Male2106 (59.2%)1454 (40.8%)Ref.Ref.
  Female2600 (50.0%)2605 (50.0%)1.23 (1.17–1.29) < 0.0011.21 (1.15–1.28) < 0.001
Heart disease
  No4390 (53.6%)3802 (46.4%)Ref.
  Yes195 (56.7%)149 (43.3%)0.93 (0.83–1.06) 0.272Did not enter the model
Diabetes
  No4509 (53.7%)3884 (46.3%)Ref.
  Yes76 (53.2%)67 (46.8%)1.01 (0.85–1.21) 0.890Did not enter the model
Obesity
  No4341 (54.3%)3647 (45.7%)Ref.Ref.
  Yes244 (44.5%)304 (55.5%)1.22 (1.12–1.31) < 0.0011.21 (1.11–1.31) < 0.001
Depression
  ≤ Moderate 4125 (54.6%)3432 (45.4%)Ref.Ref.
  ≥ Severe372 (44.1%)471 (55.9%)1.23 (1.15–1.31) < 0.0010.97 (0.88–1.06) 0.509
Anxiety
  ≤ Moderate 4032 (55.1%)3281 (44.9%)Ref.Ref.
  ≥ Severe492 (42.7%)635 (56.3%)1.26 (1.19–1.33) < 0.0011.08 (0.99–1.18) 0.080
Stress
  ≤ Moderate 4183 (55.2%)3400 (44.8%)Ref.Ref.
  ≥ Severe297 (38.1%)482 (61.9%)1.38 (1.30–1.47) < 0.0011.24 (1.13–1.37) < 0.001
Prevalence ratios (left), 95% confidence intervals (within parentheses), and p-values (right) were obtained with generalized linear models (Poisson family, log link function, and robust variances). Age was taken in its quantitative format. * Age was expressed as the median and 25th–75th percentile. Ref.: Reference value.
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Mejia, C.R.; Farfán, B.; Figueroa-Tarrillo, J.A.; Palomino, K.; Pereira-Victorio, C.J.; Failoc-Rojas, V.E.; Valladares-Garrido, M.J. Factors Associated with Perceived Change in Weight, Physical Activity, and Food Consumption during the COVID-19 Lockdown in Latin America. Sustainability 2023, 15, 2382. https://doi.org/10.3390/su15032382

AMA Style

Mejia CR, Farfán B, Figueroa-Tarrillo JA, Palomino K, Pereira-Victorio CJ, Failoc-Rojas VE, Valladares-Garrido MJ. Factors Associated with Perceived Change in Weight, Physical Activity, and Food Consumption during the COVID-19 Lockdown in Latin America. Sustainability. 2023; 15(3):2382. https://doi.org/10.3390/su15032382

Chicago/Turabian Style

Mejia, Christian R., Bertha Farfán, Jorge A. Figueroa-Tarrillo, Kely Palomino, César Johan Pereira-Victorio, Virgilio E. Failoc-Rojas, and Mario J. Valladares-Garrido. 2023. "Factors Associated with Perceived Change in Weight, Physical Activity, and Food Consumption during the COVID-19 Lockdown in Latin America" Sustainability 15, no. 3: 2382. https://doi.org/10.3390/su15032382

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