Skip to Content
MDPIMDPI

Advanced

ObesitiesObesities
PDF
  • Article
  • Open Access

4 December 2025

10 Pages

Trends in Anthropometric and Cardiometabolic Risk Factor Changes Among Health Professionals: A 3-Year Follow-Up Study in Taiwan

,
,
and
1
Occupational Safety and Health Office, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan
2
Division of Occupational Medicine, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan
3
School of Public Health, National Defense Medical Center, Taipei 114, Taiwan
*
Author to whom correspondence should be addressed.
Obesities2025, 5(4), 89;https://doi.org/10.3390/obesities5040089
Version Notes
Order Reprints

Abstract

Objectives: The purpose of this study is to evaluate the trend of anthropometric and cardiometabolic risk (CMRs) changes among health professionals over a three-year period at a medical center in Taiwan. Study Design: A 3-year follow-up cohort study design. Methods: This cohort study was conducted from 2019 to 2022 in a single healthcare center. The participants underwent annual physical check-ups for three consecutive years. CMRs were measured using standard methods and weight status change was measured using BMI. We used McNemar test and Wilcoxon Sign Rank test to evaluate the differences within and between subgroups. We used logistic regression to examine the risk of increased CMRs among subgroups of different weight status changes. Results: A total of 2217 participants (1641 females and 576 males) were included in this study, with a mean age of 40.2 ± 10.2 years. During this period, 72 (4.4%) female participants’ weight status changed from normal weight to overweight or obese and 530 (32.3%) remained overweight or obese. Among males, the proportion was 6.8% and 61.1%, respectively (p < 0.01). Participants who remained overweight or obese have more adverse CMRs. Compared to remained normal weight male subjects, the mean systolic blood pressure (131.0 ± 18.1 mmHg) and fasting blood glucose (94.4 ± 13.5 mg/dL) were higher in remained overweight or obese subjects (p < 0.001). Among females, those who remained overweight or obese have 4.01 (95% CI 2.92–5.51) times higher risk for abnormal diastolic blood pressure and 2.98 (95% CI 2.05–4.32) times higher risk for abnormal blood glucose compared to those with remained normal weight. Conclusions: Participants who remained overweight or became obese had more adverse CMRs such as high blood pressure, hyperglycemia, and dyslipidemia during the 3-year follow-up period.

1. Introduction

Obesity has been an epidemic worldwide for the last few decades. The prevalence has been increasing alarmingly and affects more than 40% of the adults in the US and worldwide [1,2]. The prevalence of obesity globally tripled over the last four decades [3]. Similarly to global trends, obesity rates increased from 11.8% to 22.1% in the past 20 years in Taiwan [4]. In Australia, the prevalence of obesity has increased from 19% to 32% over the past 30 years, along with a corresponding rise in obesity-related chronic diseases [5].
Subjects with a normal BMI but excess body fat percentage have a higher prevalence of diabetes, hypertension, and metabolic syndrome compared to both normal weight and normal body fat subjects [6]. The central adiposity (as by waist circumference) and visceral adiposity (measured by DEXA or MRI) could predict cardiometabolic risks and the combination may increase its effects [7]. Obesity is associated with multiple comorbidities, particularly cardiometabolic diseases, such as hypertension, dyslipidemia, atherosclerotic heart disease, heart failure, and diabetes. Losing body weight and excess visceral adiposity is necessary to minimize the risk of cardiometabolic diseases [8].
Obesity is associated with the development of atherosclerotic heart disease and heart failure in later life [9]. Cardiovascular diseases risk depends on the severity of the risk factor as well as the duration of exposure. Obesity is not only a strong cardiovascular disease risk factor but also augments the risk of disease by its duration [10]. Driven by elevated fasting glucose (7.3%) and blood pressure (5.2%), metabolic syndrome prevalence increased by 3.5% for men and 3.0% for women, from 2019 to 2020. Higher BMI and insufficient sleep or low activity raised the risk of developing metabolic syndromes [11].
Obesity, an independent risk factor, amplifies the risks of noncommunicable diseases and severe respiratory viral infections [12]. Coincidently, this study took place during the COVID-19 pandemic, where globally, healthcare workers under pandemic pressure face mental and physical health challenges [13]. Unhealthy lifestyle changes, such as poor dietary patterns, inactivity, and inadequate sleep, heighten their risks for weight gain, obesity, and related cardiometabolic complications [14].
The purpose of this study is to evaluate the weight change in a three-year follow-up period among healthcare professionals in Taiwan which covered the COVID-19 pandemic. We also assess cardiometabolic risk factors change among different weight status change subgroups and call for intervention to help healthcare professionals address weight gain and the increase in cardiometabolic risks.

2. Materials and Methods

2.1. Study Design and Participants

This study adopted a three-year follow-up cohort study design in a single healthcare center, focusing on healthcare professionals employed at a medical center in Southern Taiwan. Health check-ups and assessments were conducted for healthcare professionals who had been working at the medical center from 2019 to 2022. The subjects were recruited after they completed the 2019 physical check-up and then followed up annually. Repeated physical check-up was conducted in 2022. The inclusion criteria include those who completed these two physical check-ups and exclusion criteria were those who only conducted one physical check-up, suffered AMI, stroke, or other major diseases in the recent 6 months. This is a de-named and de-identified dataset obtained from our hospital and the research ethical approval was obtained from the Kaohsiung Veterans General Hospital Institutional Review Board (IRB) according to our national regulations (KSVGH24-CT9-14). All data were not identified during and after collection and were assessed by researchers from 12 September 2024 to 30 June 2025. The KSVGH IRB approved the study with waive informed consent form.

2.2. Anthropometric and Body Compositions Measurements

Anthropometric characteristics including body weight (BW), body height, waist circumference (WC), and body fat (BFAT) were measured using standard methods. Body height was measured using a digital stadiometer that recorded to the nearest 0.5 cm. BW was measured to the nearest 0.1 kg using a standard beam balance scale. When measuring height, the subjects were barefoot and wore only light indoor clothing. BFAT was obtained when subjects stood barefoot on the electrodes using a segmental body composition analyzer (TBF-410, Tanita Corp., Tokyo, Japan), which uses bioimpedance analysis (BIA) to measure body fat. The waist circumference (WC) was measured using standard methods suggested by the WHO (World Health Organization). The WC was measured to the nearest 0.1 cm at the midpoint between the margin of the last rib and the iliac crest of the ilium. We used standard formula BW (kg) divided by the square of the body height (m2) to calculate the body mass index (BMI).

2.3. Cardiometabolic Risk Measurements

Blood pressure was obtained after the subjects rested for five to ten minutes. In a seated position, the subjects’ arms were positioned at the same height as the heart and inserted into cuffs of appropriate sizes. Two measurements were recorded, and the mean value of the two recordings was used for data analysis.
After 10–12 h of overnight fasting, 10 mL of venous blood was collected from the subjects using a venous container. The plasma and serum were separated from the blood within one hour and stored at −80 °C until analysis. The plasma glucose concentration of each subject was analyzed immediately after blood sampling and was determined using the glucose oxidase method using the Beckman Glucose Analyzer II (Beckman Instruments, Fullerton, CA, USA). We measured the total cholesterol (TC) level using an esterase oxidase method and the triglyceride (TG) level using an enzymatic procedure with a Hitachi 7150 auto-analyzer (Hitachi, Tokyo, Japan). Levels of high-density lipoprotein-cholesterol (HDL-C) and low-density lipoprotein-cholesterol (LDL-C) were measured using an enzymatic method.
During our investigation, we applied a categorization framework to classify participants’ BMIs into distinct weight categories. Specifically, in Taiwan, individuals were categorized underweight if their BMI was less than 18.5 kg/m2, normal weight within the range of 18.5–23.9 kg/m2, overweight if it was between 24.0 and 26.9 kg/m2, and considered obese if it was greater or equal to 27.0 kg/m2 [15].
To assess various health parameters, we followed the 2022 guidelines provided by the Health Promotion Administration, Ministry of Health and Welfare (MOHW) in Taiwan [15]. These guidelines were served as benchmarks for evaluating different aspects of the participants’ health. For instance, waist circumference was considered elevated if the measurement is greater than or equal to 90 cm in males, and greater than or equal to 80 cm in females. Systolic blood pressure (SBP) readings were elevated if it is greater than or equal to 130 mmHg, while diastolic blood pressure (DBP) was considered elevated if it is greater than or equal to 85 mmHg.
Regarding lipid profiles, HDL-C was classified as low if it is less than 40 mg/dL in males and less than 50 mg/dL in females. Elevated LDL-C was defined as greater than or equal to 130 mg/dL. Additionally, fasting blood glucose (AC) was regarded as elevated if it is greater than or equal to 100 mg/dL; triglyceride (TG) was considered elevated if it is greater than or equal to 150 mg/dL; and total cholesterol (TC) was classified as elevated if greater than or equal to 200 mg/dL.
By utilizing these standardized criteria, we comprehensively evaluated the participants’ health status and discerned meaningful associations between various health indicators and BMI categories within our study population. The standardized guidelines enabled a systematic assessment of potential cardiometabolic risks in relation to weight status.

2.4. Statistical Methods

Statistical analysis of the data was conducted using IBM SPSS Statistics version 26.0. Chi-square tests were used to analyze the relationship between two categorical variables; one-way analysis of variance (ANOVA) was utilized to assess differences among multiple groups; and logistic regression was applied to explore the relationships between dependent and independent variables. To minimize gender inference, the study segregated male and female participants. The significance level (α) was set at 0.05. Weight status change from 2019 to 2022 was designated as the independent variable, and subjects were stratified into different weight status change scenarios (from normal weight to normal weight (NN), from overweight/obesity to normal weight (ON), from normal weight to overweight/obesity (NO), from overweight/obesity to overweight/obesity (OO)), with gender serving as a differentiating factor. The NN group was established as the reference group, and repeated-measures analysis was performed to compare the results of measurements, which includes waist circumference, SBP, DBP, fasting blood glucose, total cholesterol, triglyceride, LDL-C, and HDL-C levels, among the ON, NO, and OO subgroups, to infer the relationships between the various groups and the risk of cardiovascular diseases.

3. Results

3.1. Weight Status Change from 2019 to 2022

This study included a total of 2217 participants, comprising 1641 females and 576 males. In 2019, the average age among total participants was 40.2 ± 10.2 years; the age range is between 24 and 65 years. Among male participants, the average age was 42.6 ± 10.5 years, while the average for female participants was 39.9 ± 10.0 years.
Table 1 shows the prevalence of different weight statuses in 2019 and 2022. Between 2019 and 2022, the prevalence of normal weight males showed a slight decrease, declining from 33.7% to 32.1%. The proportion of overweight males increased from 34.0% to 37.2%, while the obesity rate decreased from 32.3% to 30.7%. Conversely, females exhibited different trends. The prevalence of normal weight increased from 60.8% in 2019 to 63.3% in 2022. The proportion of overweight females decreased slightly, declining from 20.8% in 2019 to 18.8% in 2022, and the prevalence of obesity also slightly decreased from 18.4% to 17.9% between 2019 and 2022.
Table 1. Prevalence of different weight status using BMI during 2019 and 2022 with gender-specification (n = 2217).
This indicates that more than half of the male participants were either overweight or obese in 2019. In contrast, among females, this proportion was 39.2%, which was relatively lower. However, by 2022, this proportion among males had risen to 67.9%, while it slightly decreased to 36.7% among females. The weight status changes indicate a significant increase in overweight and obesity among male healthcare professionals during the COVID pandemic, while females showed a relatively stable trend.
The distribution of weight status changes from 2019 to 2022 among different weight status subgroups with gender-specification are presented in Table 2. For example, among 194 normal weight male participants in 2019, 155 (79.9%) remained normal weight; 38 (19.6%) and 1 (0.5%) became overweight and obese, respectively, in 2022. Among 997 normal weight female participants in 2019, 925 (92.8%) remained in normal weight; and only 67 (6.8%) and 5 (0.5%) became overweight and obese in 2022. After the study period, 72 (7.2%) female participants changed from normal weight to overweight or obese, while approximately 530 (82.3%) maintained overweight or obesity status during this period (p < 0.001). Among male participants, 20.1% (39/194) changed from normal weight to overweight or obese, and 92.1% (352/382) of males maintained overweight or obesity status during this period (p < 0.001).
Table 2. Weight status changes among different weight status subgroups from 2019 to 2022 with gender-specification (n = 2217).

3.2. Weight Status Change and Cardiometabolic Risk

Table 3 demonstrates the changes in waist circumference, blood pressure, blood glucose, and blood lipid profiles among different weight status change subgroups from 2019 to 2022. In the male OO group, they had significant differences in cardiometabolic risk factors, including waist circumference, systolic and diastolic blood pressure, fasting blood glucose, triglyceride, and HDL-C when compared with NN group (all p < 0.001, except AC, p < 0.05) (Table 3). Female participants across all groups showed a significant difference in waist circumference (p < 0.001). The female NO group had differences in waist circumference, total cholesterol, triglyceride, LDL-C, and HDL-C when compared to NN group (p < 0.01). The female OO group showed differences in waist circumference, blood pressure, fasting blood glucose, triglyceride, LDL-C, and HDL-C when compared to NN group (p < 0.001) (Table 3).
Table 3. (a) Distributions (mean ± SD) of waist circumference, blood pressure, blood glucose, and blood lipid profiles among male participants by weight status change from 2019 to 2022 (n = 576). (b) Distributions (mean ± SD) of waist circumference, blood pressure, blood glucose, and blood lipid profiles among female participants by weight status change from 2019 to 2022 (n = 1641).
Table 4 presents the Odds Ratio (OR) and 95% confidence interval (CI) of cardiometabolic risk factors on different weight status change subgroups, using NN as reference group, with gender-specification. For males, the logistic regression analysis revealed that the OO group had the highest risk of abnormal waist circumference, with an OR of 60.3 (95% CI 24.1–150.9) when compared with NN group. Among females, the OO group also had a higher risk of abnormal waist circumference, with an OR of 30.4 (95% CI 22.6–41.0) when compared with NN group. This suggests that abnormal waist circumference was one of the most significant health issues among healthcare professionals during the COVID pandemic.
Table 4. Logistic regression of different weight status change subgroups on abnormal waist circumference, blood pressure, blood glucose, and blood lipid profiles with gender-specification (NN as reference group).

4. Discussion

Our study defined obesity as BMI ≥ 27 which reveals a notable increase in male healthcare professionals’ obesity rates from 66.3% to 67.9% during the three-year period, indicating susceptibility to weight gain. In contrast, female obesity rates remained stable, with over 20% losing weight. We found that weight status increase among male healthcare professionals was associated with increasing cardiometabolic risk factors. This research underscores the varied trend of healthcare professionals’ weight dynamics and cardiometabolic health risks, emphasizing challenges faced by overweight/obese males. This provides some insights that during the pandemic with stay-at-home or lockdown, we need to pay attention to weight change and cardiometabolic risks.
However, our study has certain limitations. The sample was derived from a single medical center in southern Taiwan, which may not be fully representative of all healthcare professionals across Taiwan. Furthermore, our research covers only a 3-year period, limiting our ability to provide insights into long-term changes. It is difficult to explain this characteristic using a simple or single reason. Information about dietary pattern, physical activity, and lifestyles change during this period was also very important for weight status and cardiometabolic risk change. The disease severity of COVID-19, change in work hours, and variation in job contents may explain the difference between male and female. However, we did not collect enough information for these issues. This is one of the potential limitations of our study. Despite these limitations, our conclusions remain valuable and offer essential insights into the health status of healthcare professionals.
Akter et al. observed a substantial increase in overweight individuals, rising from 5.19% before the COVID-19 pandemic to 34.9% during it, indicating significant weight gain among 4.4% of the population since the pandemic’s onset [16]. Sociodemographic and physical activity-related factors, including gender, age, residence, occupation, lack of outdoor space, and exercise habits, did not significantly influence BMI changes during the pandemic [16]. In Belgium, 28.6% of individuals aged 18 and above reported weight gain in the first six weeks of lockdown, with 56.9% maintaining stability and 14.5% reporting weight loss. The prevalence of weight gain increased with higher BMI categories as follows: 9.9% for underweight, 23.4% for normal weight, 31.4% for overweight, and 38.7% for obese [17]. Some possible reasons, such as the stay-at-home with change in lifestyles, lockdown with less social events, stress of job, and the potential change in physical activity could explain the change of overweight and obesity prevalence between males and females. In other Asian countries like South Korea, Lee et al. found a 2.5% increase in obesity prevalence in 2020, with a 1.4% rise in severe obesity compared to 2019 [18]. Keeping pandemic-induced restrictions in mind, they might decline physical activity and change dietary habits and outdoor activities. Overweight or obese individuals, regardless of gender, were more likely to decrease their physical activity levels during the pandemic [19]. However, this is hard to explain by a simple or single reason. Possible reasons include that staying-at-home, quarantines, and lockdown may be associated with change in dietary patterns, physical activity, and lifestyles. This challenge intensified for healthcare professionals, whose extended work hours, driven by the heightened demand for medical services, could adversely affect their physical health and work–life balance. In our study, it provides some insights that during the pandemic with stay-at-home or lockdown, we need to pay attention to weight change and cardiometabolic risks.
Individuals maintaining an overweight or obese status faced a higher risk of cardiovascular metabolic disorders. Male participants exhibited higher average systolic blood pressure (131.0 ± 18.1 mmHg) and fasting blood glucose (94.4 ± 13.5 mg/dL) compared to those with remained normal weight. It is noteworthy that among females maintaining overweight or obesity status, the risk of abnormal diastolic blood pressure was 4.01 times higher, and the risk of abnormal blood glucose was 2.98 times higher relative to those maintaining normal weight.
Interestingly, our research revealed unique trends including male healthcare professionals’ weight changes and cardiometabolic risks versus some other studies. This suggested that male healthcare professionals should be more careful about their weight status and cardiometabolic risk changes. These variations may stem from regional and sample differences, and specific circumstances. Further investigations are needed to elucidate the reasons for underlying inconsistencies across studies.
In conclusion, the general recommendations, such as lifestyle modification, body weight maintenance, and preventive health measures, should be included for both male and female healthcare professionals to ensure the physical health of healthcare professionals. However, ensuring adequate support for healthcare workers, including appropriate health promotion intervention, counseling, and stress management is critical in addressing the substantial physical and mental health challenges faced by healthcare workers during pandemic. Our study underscores the necessity of targeted interventions and policies to address alarming issues related to weight gain and cardiometabolic risks.

Author Contributions

Y.-R.C. was responsible for writing the protocol and report, conducting the search, extracting and analyzing data, interpreting results, updating reference lists. N.-F.C. was responsible for designing the review protocol and screening potentially eligible studies, interpreting results, creating tables, and making the final manuscript. D.-M.W. conducted the statistical analyses, creating tables, and contributed to the design of the protocol. W.-C.S. was responsible for conducting the search, screening potentially eligible studies, and helping with the final report. All authors have read and agreed to the published version of the manuscript.

Funding

This study is partially supported by KSVGH 112-126.

Institutional Review Board Statement

Approved by the Kaohsiung Veterans General Hospital Institutional Review Board (IRB) according to our national regulations (KSVGH24-CT9-14), 9 September 2024.

Data Availability Statement

The datasets used and analyzed during the current study available from the corresponding author (Prof. Nain-Feng Chu, chuepi369@gmail.com) on reasonable request.

Acknowledgments

The authors thank Winnie Chu for her English correction.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Centers for Disease Control and Prevention (CDC). National Health and Nutrition Examination Survey 2017–March 2020 Prepandemic Data Files Development of Files and Prevalence Estimates for Selected Health Outcomes; National Health Statistics Reports; National Center for Health Statistics: Hyattsville, MD, USA, 2023. Available online: https://stacks.cdc.gov/view/cdc/106273 (accessed on 10 November 2024).
  2. World Health Organization (WHO). Obesity and Overweight. Updated 1 March 2024. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 10 November 2024).
  3. Swinburn, B.A.; Kraak, V.I.; Allender, S.; Atkins, V.J.; Baker, P.I.; Bogard, J.R.; Brinsden, H.; Calvillo, A.; De Schutter, O.; Devarajan, R.; et al. The global syndetic of obesity, undernutrition, and climate change: The Lancet Commission report. Lancet 2019, 393, 791–846. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  4. Chang, H.C.; Yang, H.C.; Chang, H.Y.; Yeh, C.J.; Chen, H.H.; Huang, K.C.; Pan, W.H. Morbid obesity in Taiwan: Prevalence, trends, associated social demographics, and lifestyle factors. PLoS ONE 2017, 12, e0169577. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Australian Institute of Health and Welfare. Overweight and Obesity. 2022. Available online: https://www.aihw.gov.au/reports/overweight-obesity/overweight-and-obesity/contents/overweight-and-obesity (accessed on 30 September 2024).
  6. Jia, A.; Xu, S.; Xing, Y.; Zhang, W.; Yu, X.; Zhao, Y.; Ming, J.; Ji, Q. Prevalence and cardiometabolic risk of normal weight obesity in Chinese population: A nationwide study. Nutr. Metab. Cardiovasc. Dis. 2018, 28, 1045–1053. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Ruano, G.R.; Nogueira, G.A.; Dadson, P.; Ferreira, S.R.G.; Sapienza, M.T.; Velloso, L.A.; Monfort-Pires, M. Abdominal obesity and cardiometabolic risk markers: A comparative analysis of waist circumference, dual-energy X-ray absorptiometry, and magnetic resonance imaging techniques. Nutr. Metab. Cardiovasc. Dis. 2025, 35, 103801. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  8. Valenzuela, P.L.; Carrera-Bastos, P.; Castillo-Garcia, A.; Leiberman, D.E.; Santos-Lozano, A.; Lucia, A. Obesity and the risk of cardiometabolic diseases. Nat. Rev. Cardiol. 2023, 20, 475–494. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  9. Dash, S. Obesity and cardiometabolic disease: Insights from genetic studies. Can. J. Cardiol. 2025, 41, 1715–1726. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  10. Sidhu, S.K.; Aleman, J.O.; Heffron, S.P. Obesity duration and cardiometabolic disease. Arterioscler. Thromb. Vasc. Biol. 2023, 43, 1764–1774. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  11. Kass, D.A.; Duggal, P.; Cingolani, O. Obesity could shift severe COVID-19 disease to younger ages. Lancet 2020, 395, 1544–1545. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  12. Daniels, N.F.; Burrin, C.; Chan, T.; Fusco, F. A systematic review of the impact of the first year of COVID-19 on obesity risk factors: A pandemic fueling a pandemic? Curr. Dev. Nutr. 2022, 6, nzac011. [Google Scholar] [CrossRef] [Scilit]
  13. Shechter, A.; Diaz, F.; Moise, N.; Anstey, D.E.; Ye, S.; Agarwal, S.; Birk, J.L.; Brodie, D.; Cannone, D.E.; Chang, B.; et al. Psychological distress, coping behaviors, and preferences for support among New York healthcare workers during the COVID-19 pandemic. Gen. Hosp. Psychiatry 2020, 66, 1–8. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  14. Scarmozzino, F.; Visioli, F. COVID-19 and the subsequent lockdown modified dietary habits of almost half the population in an Italian sample. Foods 2020, 9, 675. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  15. Taiwan Ministry of Health and Welfare. Obesity Prevalence in Recent 4 Years (2018–2021). 2021. Available online: https://www.mohw.gov.tw/lp-3265-2.html (accessed on 30 September 2024).
  16. Akter, T.; Zeba, Z.; Hosen, I.; Al-Mamun, F.; Mamun, M.A. Impact of the COVID-19 pandemic on BMI: Its changes in relation to socio-demographic and physical activity patterns based on a short period. PLoS ONE 2022, 17, e0266024. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  17. Drieskens, S.; Berger, N.; Vandevijvere, S.; Gisle, L.; Braekman, E.; Charafeddine, R.; De Ridder, K.; Demarest, S. Short-term impact of the COVID-19 confinement measures on health behaviours and weight gain among adults in Belgium. Arch. Public Health 2021, 79, 22. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  18. Lee, M.N.; Choi, Y.S.; Kim, S.D. The Leading Factors of Obesity and Severe Obesity in Korean Adults during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2023, 19, 12214. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  19. do Prado, W.L.; Lofrano-Prado, M.C.; Christofaro, D.G.D.; Santana, C.C.A.; Cucato, G.G.; Jackson, M.; Shumate, G.; Shumate, S.; Correia, M.D.A.; Botero, J.P.; et al. The effects of COVID-19 stay-at-home orders on physical activity of people with obesity. Einstein 2022, 20, eAO6727. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Nutritional Approaches to Enhance GLP-1 Analogue Therapy in Obesity: A Narrative Review
Previous
Evaluating Dose Titration in Semaglutide and Tirzepatide for Weight Loss: A Retrospective Academic Call Center Study
Next