Assessment of the Impact of COVID-19 Lockdown on the Nutritional Status and Lipid Profile of Employees in a Teaching Hospital in Rome: A Retrospective Cohort Study

Biochemical and Anthropometric Outcomes in Paediatric Patients with Heterozygous Familial Hypercholesterolemia after COVID-19 Pandemic Lockdowns: An Exploratory Analysis

The COVID-19 pandemic lockdowns affected the lifestyles of children and adolescents, leading to an increase in childhood obesity. Paediatric patients with familial hypercholesterolemia (FH) may be more susceptible to lockdown effects due to their increased cardiovascular risk. However, data are lacking. We investigated the effect of lockdowns on the metabolic profile of paediatric patients with FH. Blood lipids and anthropometry measured in September 2021-April 2022 were retrospectively compared with pre-pandemic values. Thirty participants were included (1-16 years; 57% female). From baseline to post-pandemic, median [P25, P75] blood LDL-C concentration was 125 [112, 150] mg/dL vs. 125 [100, 147] mg/dL (p = 0.894); HDL-C was 58 [52, 65] mg/dL vs. 56 [51, 61] mg/dL (p = 0.107); triglycerides were 64 [44, 86] mg/dL vs. 59 [42, 86] mg/dL (p = 0.178). The BMI z-score did not change significantly (0.19 [-0.58, 0.89] vs. 0.30 [-0.48, 1.10], p = 0.524). The lack of deterioration in metabolic profiles during lockdowns is positive, as some deterioration was expected. We speculate that patients and caregivers were successfully educated about healthy lifestyle and dietary habits. Our results should be interpreted with caution since the study sample was small and heterogeneous. Multicentre research is needed to better understand the impact of lockdowns on this population.

Dietary habits, lifestyles, and overall adherence to 2018 WCRF/AICR cancer prevention recommendations among adult women in the EPIC-Florence cohort: Changes from adulthood to older age and differences across birth cohorts

OBJECTIVES, SETTING AND PARTICIPANTS

We aimed to examine changes in dietary habits, lifestyles (e.g., smoking, physical activity levels, and alcohol intake), anthropometry, other individual health-relevant characteristics, and overall adherence to 2018 WCRF/AICR cancer prevention recommendations, among women enrolled in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Florence cohort.

DESIGN AND MEASUREMENTS

We fitted age- and energy intake-adjusted generalized linear models to describe (a) changes occurring over a person's lifetime in the transition from adulthood to older age, and (b) differences between women aged 56-60 years belonging to two birth cohorts spaced apart by around 25 years (born in 1933-1941 vs. 1958-1964).

RESULTS

Dietary habits and overall adherence to cancer prevention recommendations improved among women (n = 3,309) followed from adulthood to older age (mean age 47.4 and 71.8 years, respectively), despite increases in the prevalence of adiposity and sedentary lifestyle. Women in the younger birth cohort (n = 163) showed significantly greater overall adherence to cancer prevention recommendations than in the older birth cohort (n = 355), but had more often a positive smoking history and an average larger waist circumference.

CONCLUSION

A trend toward better adherence to cancer prevention recommendations emerged when analyzing adult-to-older-age trajectories and differences across birth cohort, yet some critical issues were also identified. Continuous monitoring is essential to detect changing prevention needs and adapt public health policies and practices.

Impact of the COVID-19 pandemic lockdown on Body Mass Index: a three-year follow up study in 6,156 Chinese college students

Background

The novel coronavirus disease 2019 as the most pervasive and consequential pandemic in recent years, has exerted significant impacts on human health, including aspects related to body weight. Objectives: This study aims to assess the influence of the lockdown measures implemented during the COVID-19 pandemic on Chinese college students' Body Mass Index (BMI) through a three-year cohort study.

Methods

We recruited 6156 college students (n = 4,248, 69% male, and n = 1,908, 31% female, with an average age of 18.68 ± 0.86 yr.) from a University in China to participate in this three-year cohort study. All of the subjects took the same physical fitness tests from 2019 to 2021 (pre-lockdown, during lockdown and post-lockdown). Participants' height and weight data were objectively measured by Tongfang Health Fitness Testing Products 5000 series. A paired t-test was performed in the analysis.

Results

During the lockdown, there is 4.2% increase of BMI among the college student (p<0.001). Moreover, males had a greater overall mean BMI rate increase of 4.74% (p<0.001) than females (2.86%, p<0.001). After the lockdown, there is 0.94% increase of BMI among the college student (p<0.001). However, females had a greater overall mean BMI rate increase of 1.49% (p<0.001) than males (0.72%, p<0.001). During this period, the obese and overweight group's growth rate from 2019 to 2020 was smaller than the normal and underweight group, which were 2.94% (p<0.001), 3.90% (p<0.001), 4.44% (p<0.001) and 5.25% (p<0.001), respectively.

Conclusion

BMI increased both during and post-lockdown periods among Chinese college students. However, during the lockdown, participants with higher BMI groups appeared to have a diminished BMI growth rate compared to those with lower BMI. After the lockdown, participants with higher BMI levels appeared to have an augmented BMI growth rate. Public policy action is needed to increase the level of physical activity of Chinese college students and take action to improve students' physical fitness performance after the lockdown.

Deep neural network for prediction of diet quality among doctors and nurses in North China during the COVID-19 pandemic

Objective

The COVID-19 pandemic has placed unprecedented pressure on front-line healthcare workers, leading to poor health status, especially diet quality. This study aimed to develop a diet quality prediction model and determine the predictive effects of personality traits, socioeconomic status, lifestyles, and individual and working conditions on diet quality among doctors and nurses during the COVID-19 pandemic.

Methods

A total of 5,013 doctors and nurses from thirty-nine COVID-19 designated hospitals provided valid responses in north China in 2022. Participants' data related to social-demographic characteristics, lifestyles, sleep quality, personality traits, burnout, work-related conflicts, and diet quality were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a diet quality prediction model among doctors and nurses during the COVID-19 pandemic.

Results

The mean score of diet quality was 46.14 ± 15.08; specifically, the mean scores for variety, adequacy, moderation, and overall balance were 14.33 ± 3.65, 17.99 ± 5.73, 9.41 ± 7.33, and 4.41 ± 2.98, respectively. The current study developed a DNN model with a 21-30-28-1 network framework for diet quality prediction. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE were 0.928, 0.048, 0.004, and 0.065, respectively. Among doctors and nurses in north China, the top five predictors in the diet quality prediction model were BMI, poor sleep quality, work-family conflict, negative emotional eating, and nutrition knowledge.

Conclusion

During the COVID-19 pandemic, poor diet quality is prevalent among doctors and nurses in north China. Machine learning models can provide an automated identification mechanism for the prediction of diet quality. This study suggests that integrated interventions can be a promising approach to improving diet quality among doctors and nurses, particularly weight management, sleep quality improvement, work-family balance, decreased emotional eating, and increased nutrition knowledge.

Comparison of the Clinical and Metabolic Characteristics of Patients With Acute Coronary Syndromes Between the Pre- and Post-lockdown Periods

INTRODUCTION

In 2020, the SARS-CoV-2 pandemic outbreak required restrictive measures to limit the spread of the virus. This study aimed to assess how changes in dietary habits and lifestyle associated with such measures have affected the characteristics of patients with acute coronary syndromes (ACS) in the post-lockdown period. In particular, we evaluated if the incidence of ACS was higher in younger patients, who were more negatively affected by lockdown measures.

METHODS

We analysed 609 ACS patients and compared the clinical, laboratory, and angiographic characteristics of those admitted six months before lockdown (n = 312) and those admitted in the same six-month period after lockdown. Moreover, we compared several anthropometric and laboratory data between pre- and post-lockdown in younger (≤55 years old) and older patients.

RESULTS

The incidence of ACS in young adults (≤55 years) was significantly higher in the post- vs. pre-lockdown period (17.5% vs. 10.9%, p = 0.019). A trend to a higher percentage of ST-elevation myocardial infarction (STEMI) was observed in the post-lockdown period together with a significantly lower incidence of non-STEMI (p = 0.033). Moreover, in the post-lockdown period, we observed in younger patients a significant increase in weight, body mass index, admission glycaemia, and triglycerides while in older patients, these parameters were significantly reduced.

CONCLUSION

The lockdown may have negatively affected cardiovascular risk, thus increasing the incidence of ACS, particularly in younger patients who probably underwent more relevant lifestyle changes, with several consequent anthropometric and metabolic alterations. Such evidence should be considered to take preventive measures in case a new state of emergency occurs.

Life Habits of Healthcare Professionals during the Third Wave of COVID-19: A Cross-Sectional Study in a Spanish Hospital

(1) Background: To describe sleep quality, eating behaviour and alcohol, tobacco and illicit drug use among healthcare staff in a Spanish public hospital. (2) Methods: Cross-sectional descriptive study examining sleep quality (Pittsburg Sleep Quality Index), eating behaviour (Three Factor Eating Questionnaire (R18)), tobacco and drug use (ESTUDES questionnaire) and alcohol use (Cut down, Annoyed, Guilty, Eye-opener). (3) Results: 178 people, of whom 87.1% (155) were women, with an average age of 41.59 ± 10.9 years. A total of 59.6% of the healthcare workers had sleep problems, to a greater or lesser degree. The average daily consumption was 10.56 ± 6.74 cigarettes. The most commonly used drugs included cannabis, occasionally used by 88.37%, cocaine (4.75%), ecstasy (4.65%) and amphetamines (2.33%). A total of 22.73% of participants had increased their drug use, and 22.73% had increased their consumption during the pandemic, with beer and wine accounting for 87.2% of drinks consumed during this period. (4) Conclusions: In addition to the psychological and emotional impact already demonstrated, the COVID-19 crisis has repercussions on sleep quality, eating behaviour and alcohol, tobacco and drug consumption. Psychological disturbances have repercussions on physical and functional aspects of healthcare workers. It is feasible that these alterations are due to stress, and it is necessary to act through treatment and prevention as well as promote healthy habits.

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

Objective

The COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.

Methods

The present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants' data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.

Results

Of participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R ², MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.

Conclusion

During the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.