# Effect of the COVID-19 Epidemic on Body Mass Index: Findings from a Large Survey during Wuhan Lockdown

Authors: Weidong Wang; Youfa Wang; Yisong Hu
Journal: Health & Social Care in the Community
 (2023)

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## Abstract

This study aimed to explore the effect of the Wuhan lockdown on body mass index (BMI) among Wuhan residents as well as factors including food habits affected and changes in physical activity mediating the association. As a survey of the Wuhan lockdown, a random digit dialing method was used to sample residents aged 18 years and older. A computer-aided telephone interview was applied to survey 11,223 residents in Wuhan City in July 2020. Ordinary least squares (OLS) regression and mediation analysis were used to analyze the influence of the Wuhan lockdown on BMI. Demographic characteristics, lifestyle characteristics, and health status differed statistically significantly between residents living in and those who left Wuhan during the lockdown (mean BMI, 23.05 ± 0.04 kg/m2 and 22.34 ± 0.06 kg/m2, respectively). The mean BMI among residents living in Wuhan was 0.42 kg/m2 higher (P < 0.001) than that of residents who left Wuhan during the lockdown, as determined by multivariate OLS regression analysis. Residents living in Wuhan during the lockdown had higher BMI among those aged 18–29 and 30–44 years (P < 0.001), with no difference among those aged 45+ years. The association between living in Wuhan and increased BMI was the strongest among residents who were overweight/obese. The mediation analysis results showed an indirect effect path with living in Wuhan (through decreased physical activity and increasing BMI) and the direct effect of living in Wuhan during the lockdown. The lack of association between living in Wuhan and increased BMI among residents aged 45+ years was due to the indirect influence of living in Wuhan affecting food habits. The findings indicated that the lockdown in Wuhan city increased residents’ BMI, especially among those who were young or overweight/obese, partly because of decreased physical activity.


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## Body

## 1. Introduction

To decrease the infection risk of COVID-19 in the population, many countries have initiated home quarantine policies at the early outbreak stage. Such policies were very effective in China. However, quarantine policies may negatively influence population health because of changing lifestyles of the people in the long run.Some studies have shown that home quarantine may increase individuals’ risks of being overweight/obese. Homestay reduces access to outdoor sports activities [1] and increases sitting [2] and screen time [3]. Concerning food intake during home quarantine, people may be more likely to change their dietary habits or patterns [4, 5], especially in populations with lower socioeconomic status [6] and vulnerable groups (e.g. pregnant women) [7–9]. These are the main risk factors for overweight/obesity [10]. Weight gained during a short time may be difficult to lose and therefore last for a very long time [1]. Obesity is a global epidemic that is associated with many serious illnesses. A recent meta-analysis showed that COVID-19 patients with obesity presented with a more severe disease condition and higher fatality rate on average [11, 12]. To deal with the current situation (i.e. continuous emerging variant strain virus and the increasing number of both new and fatal cases of COVID-19), it is of immense practical significance to study the effects of home quarantine policies on overweight and obesity on a population level during the COVID-19 pandemic, especially in China.Wuhan was one of the most affected cities and has implemented the most comprehensive home quarantine policy on a global scale during the early epidemic period. The novel nucleic acid detection system implemented in May 2020 marked this city’s key milestone in COVID-19 epidemic elimination. Our data were collected through a telephone survey among Wuhan residents and our study was conducted on a large scale. Thus, the survey location and timing were typical, and the sample selection and data collection procedure were rigorous and valid. Especially, the survey population was unique in that 30% of residents left Wuhan City before the lockdown (as reflected within our representative random sample), which naturally divided the whole city population into two comparable groups.A survey within a large-scale population in Wuhan city was conducted to confirm the effect of the COVID-19 lockdown on trends in population BMI, to explore effect differences according to population characteristics, and to examine the possible mediating factors, including food habits affected and changes in physical activity associated with these effects. We aimed to provide research evidence for coping with COVID-19 and fighting the obesity epidemic.


## 2. Methods

### 2.1. Study Design and Participants

The survey was conducted in Wuhan City, China, from July 3rd to July 31st, 2020 by the National Research Center at Renmin University of China. The respondents were residents living in Wuhan aged 18 years and older before the lockdown. Random digit dialing based on available cell phone registry information was used for sampling. The sampling frame was the Wuhan cell phone number segment dataset, available from the Ministry of Industry and Information Technology. A computer-aided telephone interview platform was used to administer the survey by trained interviewers. All respondents were confirmed with regard to eligibility criteria (age and family location) before conducting the formal interviews. Each telephone call was recorded and reviewed by the survey supervisor to ensure data quality. A total of 11,223 questionnaires were collected, of which 11,206 were eligible for the final data analysis after excluding a few respondents who lacked information on key variables.The questionnaire included the following components: (1) evaluations about government countermeasures during the COVID-19 epidemic; (2) risk assessments about the COVID-19 epidemic and self-protection measures; (3) queries as to whether the respondent lived in Wuhan City during the lockdown; (4) queries as to whether the respondent underwent nucleic acid detection for their COVID-19 status; (5) the COVID-19 status of the respondents’ family members; (6) the COVID-19 status within their community; (7) respondent’s psychological and physical health status during the epidemic period; and (8) demographic characteristics.All participants provided oral informed consent before the telephonic survey. The study was approved by the biomedical ethics review board of the Health Science Center at Xi’an Jiaotong University (approval number: 2020-1235). It was conducted in accordance with the Declaration of Helsinki and its later amendments.


### 2.2. Study Variables

(1)
The main outcome measures evaluated in this study were self-reported weight and height, which were used for calculating BMI. BMI was classified according to Chinese cut-off points, classifying underweight and obesity as a BMI of <18.5 kg/m2, ≥24 and <28 kg/m2, and ≥28 kg/m2, respectively [13].(2)
The main independent variable evaluated in this study was whether the respondent lived in Wuhan during the lockdown. Because the lockdown began just before the spring festival in China, many Wuhan residents would have returned to their hometowns. The respondents were asked whether they lived in Wuhan during the lockdown period (from January 23rd to April 8th of 2020).(3)
The main study covariates were social and demographic characteristics, including sex, age, marital status, educational attainment, hometown location, and household income per capita in 2019. Sex was coded as a dummy variable (1 = male. 0 = female), age was coded as a categorical variable (0 = 18–29 years, 1 = 30–44 years, 2 = 45–59 years, 3 = over 60 years), and marital status was categorized as 0 = married, 1 = single, and 2 = divorced or widowed. Educational attainment had five categories as follows: 0 = junior high school or below, 1 = high school, 2 = junior college, 3 = undergraduate college, and 4 = graduate school or higher. Hometown location was divided into three groups (0 = urban region, 1 = suburban region, 2 = rural region). Household income per capita in 2019 was categorized into four groups with three cut-off points as follows: 10,000, 50,000, and 100,000 RMB.(4)
Other covariates evaluated in this study were lifestyle and illness-associated covariates. Respondents’ health status as affected by the COVID-19 pandemic was evaluated as a continuous variable that was rated on a three-point scale as follows: “1 = became worse,” “2 = stayed about the same,” and “3 = became better.” Chronic diseases included hypertension, diabetes, cardiovascular disease, and other types of chronic diseases. The self-reported prevalence proportion of chronic disease was very low. The number of cigarettes smoked during lockdown compared with that before the COVID-19 epidemic was categorized into the following four groups: “0 = never smoke or quit smoking,” “1 = increased a lot or a little,” “2 = stayed about the same,” and “3 = decreased a lot or a little.” Alcohol consumption during the lockdown, as compared with that before the COVID-19 epidemic, was categorized into the following four groups: “0 = never drank or quit drinking,” “1 = increased a lot or a little,” “2 = stayed about the same,” and “3 = decreased a lot or a little.”(5)
The current study considered two mediator variables associated with food habits and physical activity. The degree of respondents’ food habits as affected by the COVID-19 pandemic was evaluated as a continuous variable that was rated on a three-point scale as follows: “1 = great impact,” “2 = little impact,” and “3 = no impact.” Physical activity during the lockdown, as compared with that before the COVID-19 epidemic, was categorized into the following five groups: “0 = increased a lot,” “1 = increased a little,” “2 = stayed about the same,” “3 = decreased a little,” and “4 = decreased a lot.”


### 2.3. Statistical Analysis

First, the chi-squared test was used to compare social and demographic characteristics as well as lifestyle characteristics and health status among residents living in and residents who left Wuhan during the lockdown. Second, means ± standard errors and proportion values were used to describe and compare BMI distributions among residents living in and residents who left Wuhan during the lockdown.Third, univariate and multivariable ordinary least squares (OLS) regression analysis was conducted to explore the association between living in Wuhan during the lockdown and BMI. Adjusted variables in the multivariable OLS regression analysis included sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, respondent’s health status as affected by the COVID-19 pandemic, experiencing at least one kind of chronic disease, changes in smoking habits during the lockdown, and changes in drinking habits during the lockdown. Analyses stratified analysis by sex, age, and BMI grouping were likewise performed in the multivariable OLS regression analyses.Finally, food habits affected and changes in physical activity mediating the association between living in Wuhan during the lockdown period and BMI were explored according to previously reported methodology [14]. Briefly, there were several steps for the mediation analysis. Firstly, the previous statistical relationship analysis (model 3) between BMI and Wuhan lockdown was the basis for further mediation exploration. Secondly, multivariate regression analysis (model 4 and model 5) was conducted to examine the effect of the Wuhan lockdown on each mediator, including food habits affected or physical activity change. Thirdly, model 6 was constructed based on model 3 by adding two mediators, including food habits affected and physical activity change. The mediation effect was examined using the “sgmediation” code in Stata. The bootstrap method was used to calculate the confidence intervals of the associated products of the mediation analyses. Mediation analyses were stratified by sex, age, and BMI grouping. When examining the mediating effects in the analysis, sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and drinking habits were controlled.All statistical analyses were carried out using Stata statistical software (version 16.0, College Station, TX, USA).


## 2.1. Study Design and Participants

The survey was conducted in Wuhan City, China, from July 3rd to July 31st, 2020 by the National Research Center at Renmin University of China. The respondents were residents living in Wuhan aged 18 years and older before the lockdown. Random digit dialing based on available cell phone registry information was used for sampling. The sampling frame was the Wuhan cell phone number segment dataset, available from the Ministry of Industry and Information Technology. A computer-aided telephone interview platform was used to administer the survey by trained interviewers. All respondents were confirmed with regard to eligibility criteria (age and family location) before conducting the formal interviews. Each telephone call was recorded and reviewed by the survey supervisor to ensure data quality. A total of 11,223 questionnaires were collected, of which 11,206 were eligible for the final data analysis after excluding a few respondents who lacked information on key variables.The questionnaire included the following components: (1) evaluations about government countermeasures during the COVID-19 epidemic; (2) risk assessments about the COVID-19 epidemic and self-protection measures; (3) queries as to whether the respondent lived in Wuhan City during the lockdown; (4) queries as to whether the respondent underwent nucleic acid detection for their COVID-19 status; (5) the COVID-19 status of the respondents’ family members; (6) the COVID-19 status within their community; (7) respondent’s psychological and physical health status during the epidemic period; and (8) demographic characteristics.All participants provided oral informed consent before the telephonic survey. The study was approved by the biomedical ethics review board of the Health Science Center at Xi’an Jiaotong University (approval number: 2020-1235). It was conducted in accordance with the Declaration of Helsinki and its later amendments.


## 2.2. Study Variables

(1)
The main outcome measures evaluated in this study were self-reported weight and height, which were used for calculating BMI. BMI was classified according to Chinese cut-off points, classifying underweight and obesity as a BMI of <18.5 kg/m2, ≥24 and <28 kg/m2, and ≥28 kg/m2, respectively [13].(2)
The main independent variable evaluated in this study was whether the respondent lived in Wuhan during the lockdown. Because the lockdown began just before the spring festival in China, many Wuhan residents would have returned to their hometowns. The respondents were asked whether they lived in Wuhan during the lockdown period (from January 23rd to April 8th of 2020).(3)
The main study covariates were social and demographic characteristics, including sex, age, marital status, educational attainment, hometown location, and household income per capita in 2019. Sex was coded as a dummy variable (1 = male. 0 = female), age was coded as a categorical variable (0 = 18–29 years, 1 = 30–44 years, 2 = 45–59 years, 3 = over 60 years), and marital status was categorized as 0 = married, 1 = single, and 2 = divorced or widowed. Educational attainment had five categories as follows: 0 = junior high school or below, 1 = high school, 2 = junior college, 3 = undergraduate college, and 4 = graduate school or higher. Hometown location was divided into three groups (0 = urban region, 1 = suburban region, 2 = rural region). Household income per capita in 2019 was categorized into four groups with three cut-off points as follows: 10,000, 50,000, and 100,000 RMB.(4)
Other covariates evaluated in this study were lifestyle and illness-associated covariates. Respondents’ health status as affected by the COVID-19 pandemic was evaluated as a continuous variable that was rated on a three-point scale as follows: “1 = became worse,” “2 = stayed about the same,” and “3 = became better.” Chronic diseases included hypertension, diabetes, cardiovascular disease, and other types of chronic diseases. The self-reported prevalence proportion of chronic disease was very low. The number of cigarettes smoked during lockdown compared with that before the COVID-19 epidemic was categorized into the following four groups: “0 = never smoke or quit smoking,” “1 = increased a lot or a little,” “2 = stayed about the same,” and “3 = decreased a lot or a little.” Alcohol consumption during the lockdown, as compared with that before the COVID-19 epidemic, was categorized into the following four groups: “0 = never drank or quit drinking,” “1 = increased a lot or a little,” “2 = stayed about the same,” and “3 = decreased a lot or a little.”(5)
The current study considered two mediator variables associated with food habits and physical activity. The degree of respondents’ food habits as affected by the COVID-19 pandemic was evaluated as a continuous variable that was rated on a three-point scale as follows: “1 = great impact,” “2 = little impact,” and “3 = no impact.” Physical activity during the lockdown, as compared with that before the COVID-19 epidemic, was categorized into the following five groups: “0 = increased a lot,” “1 = increased a little,” “2 = stayed about the same,” “3 = decreased a little,” and “4 = decreased a lot.”


## 2.3. Statistical Analysis

First, the chi-squared test was used to compare social and demographic characteristics as well as lifestyle characteristics and health status among residents living in and residents who left Wuhan during the lockdown. Second, means ± standard errors and proportion values were used to describe and compare BMI distributions among residents living in and residents who left Wuhan during the lockdown.Third, univariate and multivariable ordinary least squares (OLS) regression analysis was conducted to explore the association between living in Wuhan during the lockdown and BMI. Adjusted variables in the multivariable OLS regression analysis included sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, respondent’s health status as affected by the COVID-19 pandemic, experiencing at least one kind of chronic disease, changes in smoking habits during the lockdown, and changes in drinking habits during the lockdown. Analyses stratified analysis by sex, age, and BMI grouping were likewise performed in the multivariable OLS regression analyses.Finally, food habits affected and changes in physical activity mediating the association between living in Wuhan during the lockdown period and BMI were explored according to previously reported methodology [14]. Briefly, there were several steps for the mediation analysis. Firstly, the previous statistical relationship analysis (model 3) between BMI and Wuhan lockdown was the basis for further mediation exploration. Secondly, multivariate regression analysis (model 4 and model 5) was conducted to examine the effect of the Wuhan lockdown on each mediator, including food habits affected or physical activity change. Thirdly, model 6 was constructed based on model 3 by adding two mediators, including food habits affected and physical activity change. The mediation effect was examined using the “sgmediation” code in Stata. The bootstrap method was used to calculate the confidence intervals of the associated products of the mediation analyses. Mediation analyses were stratified by sex, age, and BMI grouping. When examining the mediating effects in the analysis, sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and drinking habits were controlled.All statistical analyses were carried out using Stata statistical software (version 16.0, College Station, TX, USA).


## 3. Results

### 3.1. Demographic Characteristics

Table1 presents the demographic characteristics of the study sample; 55.5% of the respondents were male, and 24.7% were aged over 45 years. A total of 63.7% of the respondents were married and 36.3% had a bachelor’s degree; 73.7% of respondents originated from urban “hometowns,” and 47.7% of the population had a household income per capita of over 10,000 RMB in 2019.Table 1
Demographic characteristics of study participants. Wuhan, China, 2020.
VariableGroupTotal (%)N = 11,206Wuhan (%)N = 8,110Other regionsa(%)N = 3,096χ2PSexMale55.554.757.88.78170.003Female44.545.342.2Age18–29 years31.826.446.0667.1500<0.00130–44 years43.543.044.845+ years24.730.69.3Marital statusMarried63.767.653.5334.1606<0.001Unmarried32.027.244.6Divorced/widowed4.35.22.0Education attainmentJunior high school or less9.39.48.991.1694<0.001High school20.722.615.6Junior college23.823.225.3Undergraduate college36.335.837.5Graduate school or higher10.09.012.7Hometown locationUrban region73.774.172.639.1995<0.001Suburban region19.319.818.0Rural region7.06.19.4Household income per capita in 20190–9,999 RMB18.920.115.828.1900<0.00110,000–49,999 RMB47.746.950.150,000–99,000 RMB19.519.519.5100,000+ RMB13.813.514.6a “other regions” include all regions outside of Wuhan City.The demographics of the sample were compared between residents living in and residents who left Wuhan during the lockdown. The results showed that, compared with the population living in Wuhan during the lockdown, the residents who left Wuhan were more likely to be male (P = 0.003) and were also younger (P < 0.001), more likely to be unmarried (P < 0.001), more highly educated (P < 0.001), more likely to have lived in a rural area (P < 0.001), and more likely to have household income per capita of over 10,000 RMB in 2019 (P < 0.001).Table2 shows differences in health status between residents living in Wuhan and those who left Wuhan during the lockdown. The proportion of people whose health status worsened during the pandemic was higher among those staying in Wuhan (P < 0.001). A higher proportion of residents with at least one chronic disease, including hypertension, diabetes, cardiovascular disease, and other chronic diseases, had stayed in Wuhan (P < 0.001). Concerning comparative evaluations of lifestyle changes, a lower percentage of people were those who never smoked or quit smoking (P = 0.035), a higher percentage had never drunk or had quit drinking (P < 0.001), a higher percentage had experienced little or no impact on food habits (P < 0.001), and a higher percentage had experienced a decrease in physical activity during the lockdown period (P < 0.001) among those who stayed in Wuhan.Table 2
Comparison of lifestyle and health status between residents living in and leaving Wuhan during the lockdown. Wuhan, China, 2020.
VariableGroupTotal (%)N = 11,206Wuhan (%)N = 8,110Other regions (%)N = 3,096χ2PRespondent’s health statusBecame worse7.38.25.037.6581<0.001Stayed about the same86.686.188.0Became better6.15.77.0Chronic diseaseHypertension6.98.72.4138.8104<0.001Diabetes2.22.70.839.4893<0.001Cardiovascular disease1.82.30.444.8090<0.001Other types of chronic disease9.510.66.739.2762<0.001At least one chronic disease17.019.89.6166.3291<0.001Smoking changeNever smoked or quit smoking74.674.076.28.62780.035Increased a lot or a little4.04.23.3Stayed about the same10.811.19.9Decreased a lot or a little10.710.710.6Alcohol drinking changeNever drank or quit drinking60.861.957.920.8320<0.001Increased a lot or a little2.82.53.5Stayed about the same20.920.122.8Decreased a lot or a little15.515.415.8Food habits affectedGreat impact14.315.611.071.0466<0.001Little impact55.756.353.9No impact30.028.135.1Physical activity changeIncreased a lot4.04.13.844.4543<0.001Increased a little8.38.18.9Stayed about the same24.422.928.5Decreased a little27.227.825.7Decreased a lot36.037.133.1


### 3.2. BMI Distribution

We compared BMI distribution between residents who stayed in and who left Wuhan during the lockdown. The results showed the mean BMI values of 23.05 ± 0.04 kg/m2 and 22.34 ± 0.06 kg/m2, respectively, for residents who stayed in or left Wuhan (P < 0.001).Figure1 shows BMI distributions based on Chinese adults’ overweight or obesity standards. Compared with an underweight prevalence of 6.8% among people living in Wuhan during the lockdown, the underweight proportion was higher (9.7%) in those who left Wuhan during the lockdown. The percentages of overweight or obese people were 35.5% and 27.9% for residents living in and those who left Wuhan during the lockdown, respectively.Figure 1
Weight status among residents living in and residents who left Wuhan during the epidemic period. Wuhan, China, 2020.Table3 presents BMI distributions by social and demographic characteristics. The mean BMI was higher among the population that stayed in Wuhan as compared to those who left Wuhan during the epidemic when evaluating this association within categories of sex, age, marital status, educational attainment, hometown location, and household income per capita in 2019. Still, no statistically significant differences were found between the two groups in the subpopulations of those aged over 45 years, those who were divorced/widowed, those who had moved to Wuhan from rural hometowns, or those with a household income per capita of over 100,000 RMB in 2019 (all P > 0.05).Table 3
Comparison of BMI distribution between residents living in and those who left Wuhan, by social and demographic characteristics. Wuhan, China, 2020.
VariableGroupWuhan,N = 8110mean (SE)Other regions,N = 3096mean (SE)t/FPSexMale24.1 (0.05)23.4 (0.08)7.0677<0.001Female21.8 (0.05)20.8 (0.08)9.3046<0.001Age18–29 years22.0 (0.08)21.5 (0.09)3.6819<0.00130–44 years23.3 (0.06)22.9 (0.09)2.98330.00345+ years23.7 (0.06)23.6 (0.17)0.31660.752Marital statusMarried23.4 (0.05)23.0 (0.08)3.9922<0.001Unmarried22.2 (0.08)21.5 (0.09)5.0837<0.001Divorced/widowed23.3 (0.17)22.5 (0.35)1.63170.103Education attainmentJunior high school or less23.6 (0.12)23.1 (0.20)2.14550.032High school23.3 (0.08)22.5 (0.14)4.5065<0.001Junior college23.1 (0.08)22.3 (0.13)5.0751<0.001Undergraduate college22.8 (0.07)22.2 (0.11)4.5848<0.001Graduate school or higher22.7 (0.13)22.1 (0.15)3.35640.001Hometown locationUrban region23.1 (0.05)22.3 (0.07)8.5401<0.001Suburban region23.0 (0.09)22.5 (0.16)3.10320.002Rural region22.7 (0.14)22.5 (0.23)0.50790.612Household income per capita in 20190–9,999 RMB23.1 (0.09)22.1 (0.16)5.5208<0.00110,000–49,999 RMB23.0 (0.06)22.2 (0.09)6.9979<0.00150,000–99,999 RMB23.1 (0.09)22.5 (0.13)3.6626<0.001100,000+ RMB23.1 (0.11)22.8 (0.16)1.71030.087Total23.0 (0.04)22.3 (0.06)9.5002<0.001


### 3.3. Multivariate Analysis

Univariate OLS regression analyses showed that the mean BMI of residents living in Wuhan was 0.71 kg/m2 higher than that of respondents who had left Wuhan during the lockdown (P < 0.001). After adjusting for sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and alcohol drinking habits within the multivariate OLS regression analyses, the mean BMI among residents living in Wuhan during the lockdown period was still 0.42 kg/m2 (P < 0.001) higher than among those who had left.Univariate and multivariate OLS regression analyses were conducted on the association between BMI and living in Wuhan during the epidemic period according to sex, age, and BMI grouping (FileS1). Living in Wuhan was associated with a BMI increase in male and female residents within both univariate and multivariate OLS regression analyses (P < 0.001). After stratifying by age group, this association remained in the subpopulations aged 18–29 or 30–44 years (P < 0.001) when adjusting for sex, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and alcohol drinking habits. BMI increase was also statistically significantly associated with living in Wuhan during the lockdown period among overweight or obese residents (P < 0.05), but not in underweight residents.


### 3.4. Mediation Analysis

Model 4 used food habits affected as a dependent variable, as shown in Table4, which illustrates that food habits were more strongly affected by residents living in Wuhan. Moreover, this effect existed within the total population and various sex, age, and BMI groupings (all P < 0.05). Model 5 uses physical activity change as the dependent variable. The result shows that more residents living in Wuhan experienced a decrease in physical activity during the pandemic compared to people who had left Wuhan; this effect was found among men (30–44 years) and those with a normal weight and overweight/obesity. Model 6 was constructed based on model 3 by adding the following two mediators: food habits affected and physical activity change. This mediation model presents results on the direct effects of living in Wuhan during the lockdown period on BMI increases within the study population and according to sex, age, and overweight/obesity groupings.Table 4
Mediation analysis on factors mediating the association between BMI (body mass index) and living in Wuhan, by total, sex, age group, and weight status during the lockdown period. Wuhan, China, 2020.
GroupVariableModel 3a(BMI)Model 4b(Δ food habits affected)Model 5c(Δ physical activity change)Model 6d(BMI)β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)PTotalLiving in Wuhan0.42 (0.28–0.56)<0.0010.12 (0.09–0.15)<0.0010.10 (0.05–0.15)<0.0010.42 (0.28–0.56)<0.001Food habits affected——————−0.08 (−0.18–0.02)0.133Physical activity change——————0.10 (0.04–0.15)0.001∗MaleLiving in Wuhan0.41 (0.21–0.60)<0.0010.11 (0.07–0.15)<0.0010.11 (0.04–0.17)0.0010.40 (0.21–0.60)<0.001Food habits affected——————−0.03 (−0.16–0.10)0.642Physical activity change——————0.06 (−0.02–0.13)0.142FemaleLiving in Wuhan0.40 (0.20–0.60)<0.0010.14 (0.10–0.18)<0.0010.09 (0.01–0.16)0.0240.41 (0.21–0.61)<0.001Food habits affected——————−0.13 (−0.27–0.02)0.100Physical activity change——————0.14 (0.06–0.22)0.00118–29 yLiving in Wuhan0.46 (0.23–0.70)<0.0010.12 (0.08–0.17)<0.0010.04 (−0.04–0.11)0.3440.47 (0.24–0.70)<0.001Food habits affected——————−0.10 (−0.28–0.09)0.310Physical activity change——————0.08 (−0.03–0.19)0.16530–44 yLiving in Wuhan0.45 (0.25–0.66)<0.0010.13 (0.09–0.17)<0.0010.15 (0.08–0.22)<0.0010.43 (0.23–0.64)<0.001Food habits affected——————0.02 (−0.14–0.17)0.847Physical activity change——————0.12 (0.04–0.21)0.005∗45+yLiving in Wuhan0.15 (−0.18–0.49)0.3740.12 (0.04–0.20)0.0020.07 (−0.07–0.21)0.3010.18 (−0.16–0.51)0.297Food habits affected——————−0.20 (−0.38–0.03)0.023∗Physical activity change——————0.06 (−0.04–0.16)0.270UnderweightLiving in Wuhan−0.03 (−0.15–0.10)0.6640.16 (0.06–0.26)0.0020.02 (−0.14–0.19)0.799−0.02 (−0.15–0.10)0.697Food habits affected——————−0.02 (−0.12–0.08)0.702Physical activity change——————0.01 (−0.04–0.06)0.726Normal weightLiving in Wuhan0.08 (−0.00–0.17)0.0580.13 (0.09–0.16)<0.0010.08 (0.02–0.15)0.0120.08 (−0.00–0.17)0.058Food habits affected——————−0.03 (−0.09–0.02)0.259Physical activity change——————0.06 (0.02–0.09)0.001Overweight/ObesityLiving in Wuhan0.31 (0.08–0.54)0.0080.11 (0.06–0.17)<0.0010.12 (0.02–0.21)0.0140.30 (0.07–0.53)0.011Food habits affected——————0.06 (−0.08–0.20)0.379Physical activity change——————0.06 (−0.02–0.14)0.123aModel 3 in Table 4 is adjusted for sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and drinking habits. bThe dependent variable in model 4 is food habits affected, adjusted for the same covariates as in model 3. cThe dependent variable in model 5 is physical activity change, adjusted for the same covariates as in model 3. dThe dependent variable in model 6 is BMI, adjusted for two more variables (food habits affected and physical activity change) as compared with model 3.The indirect effect proportions of living in Wuhan leading to a decrease in physical activity and consequently to an increase in BMI were 2.3% and 4.1%, respectively, among the study population and in the 30–44 years age group. In addition, an indirect influence of living in Wuhan affecting food habits, which led to a BMI decrease, was found in the 45 and above age group.


## 3.1. Demographic Characteristics

Table1 presents the demographic characteristics of the study sample; 55.5% of the respondents were male, and 24.7% were aged over 45 years. A total of 63.7% of the respondents were married and 36.3% had a bachelor’s degree; 73.7% of respondents originated from urban “hometowns,” and 47.7% of the population had a household income per capita of over 10,000 RMB in 2019.Table 1
Demographic characteristics of study participants. Wuhan, China, 2020.
VariableGroupTotal (%)N = 11,206Wuhan (%)N = 8,110Other regionsa(%)N = 3,096χ2PSexMale55.554.757.88.78170.003Female44.545.342.2Age18–29 years31.826.446.0667.1500<0.00130–44 years43.543.044.845+ years24.730.69.3Marital statusMarried63.767.653.5334.1606<0.001Unmarried32.027.244.6Divorced/widowed4.35.22.0Education attainmentJunior high school or less9.39.48.991.1694<0.001High school20.722.615.6Junior college23.823.225.3Undergraduate college36.335.837.5Graduate school or higher10.09.012.7Hometown locationUrban region73.774.172.639.1995<0.001Suburban region19.319.818.0Rural region7.06.19.4Household income per capita in 20190–9,999 RMB18.920.115.828.1900<0.00110,000–49,999 RMB47.746.950.150,000–99,000 RMB19.519.519.5100,000+ RMB13.813.514.6a “other regions” include all regions outside of Wuhan City.The demographics of the sample were compared between residents living in and residents who left Wuhan during the lockdown. The results showed that, compared with the population living in Wuhan during the lockdown, the residents who left Wuhan were more likely to be male (P = 0.003) and were also younger (P < 0.001), more likely to be unmarried (P < 0.001), more highly educated (P < 0.001), more likely to have lived in a rural area (P < 0.001), and more likely to have household income per capita of over 10,000 RMB in 2019 (P < 0.001).Table2 shows differences in health status between residents living in Wuhan and those who left Wuhan during the lockdown. The proportion of people whose health status worsened during the pandemic was higher among those staying in Wuhan (P < 0.001). A higher proportion of residents with at least one chronic disease, including hypertension, diabetes, cardiovascular disease, and other chronic diseases, had stayed in Wuhan (P < 0.001). Concerning comparative evaluations of lifestyle changes, a lower percentage of people were those who never smoked or quit smoking (P = 0.035), a higher percentage had never drunk or had quit drinking (P < 0.001), a higher percentage had experienced little or no impact on food habits (P < 0.001), and a higher percentage had experienced a decrease in physical activity during the lockdown period (P < 0.001) among those who stayed in Wuhan.Table 2
Comparison of lifestyle and health status between residents living in and leaving Wuhan during the lockdown. Wuhan, China, 2020.
VariableGroupTotal (%)N = 11,206Wuhan (%)N = 8,110Other regions (%)N = 3,096χ2PRespondent’s health statusBecame worse7.38.25.037.6581<0.001Stayed about the same86.686.188.0Became better6.15.77.0Chronic diseaseHypertension6.98.72.4138.8104<0.001Diabetes2.22.70.839.4893<0.001Cardiovascular disease1.82.30.444.8090<0.001Other types of chronic disease9.510.66.739.2762<0.001At least one chronic disease17.019.89.6166.3291<0.001Smoking changeNever smoked or quit smoking74.674.076.28.62780.035Increased a lot or a little4.04.23.3Stayed about the same10.811.19.9Decreased a lot or a little10.710.710.6Alcohol drinking changeNever drank or quit drinking60.861.957.920.8320<0.001Increased a lot or a little2.82.53.5Stayed about the same20.920.122.8Decreased a lot or a little15.515.415.8Food habits affectedGreat impact14.315.611.071.0466<0.001Little impact55.756.353.9No impact30.028.135.1Physical activity changeIncreased a lot4.04.13.844.4543<0.001Increased a little8.38.18.9Stayed about the same24.422.928.5Decreased a little27.227.825.7Decreased a lot36.037.133.1


## 3.2. BMI Distribution

We compared BMI distribution between residents who stayed in and who left Wuhan during the lockdown. The results showed the mean BMI values of 23.05 ± 0.04 kg/m2 and 22.34 ± 0.06 kg/m2, respectively, for residents who stayed in or left Wuhan (P < 0.001).Figure1 shows BMI distributions based on Chinese adults’ overweight or obesity standards. Compared with an underweight prevalence of 6.8% among people living in Wuhan during the lockdown, the underweight proportion was higher (9.7%) in those who left Wuhan during the lockdown. The percentages of overweight or obese people were 35.5% and 27.9% for residents living in and those who left Wuhan during the lockdown, respectively.Figure 1
Weight status among residents living in and residents who left Wuhan during the epidemic period. Wuhan, China, 2020.Table3 presents BMI distributions by social and demographic characteristics. The mean BMI was higher among the population that stayed in Wuhan as compared to those who left Wuhan during the epidemic when evaluating this association within categories of sex, age, marital status, educational attainment, hometown location, and household income per capita in 2019. Still, no statistically significant differences were found between the two groups in the subpopulations of those aged over 45 years, those who were divorced/widowed, those who had moved to Wuhan from rural hometowns, or those with a household income per capita of over 100,000 RMB in 2019 (all P > 0.05).Table 3
Comparison of BMI distribution between residents living in and those who left Wuhan, by social and demographic characteristics. Wuhan, China, 2020.
VariableGroupWuhan,N = 8110mean (SE)Other regions,N = 3096mean (SE)t/FPSexMale24.1 (0.05)23.4 (0.08)7.0677<0.001Female21.8 (0.05)20.8 (0.08)9.3046<0.001Age18–29 years22.0 (0.08)21.5 (0.09)3.6819<0.00130–44 years23.3 (0.06)22.9 (0.09)2.98330.00345+ years23.7 (0.06)23.6 (0.17)0.31660.752Marital statusMarried23.4 (0.05)23.0 (0.08)3.9922<0.001Unmarried22.2 (0.08)21.5 (0.09)5.0837<0.001Divorced/widowed23.3 (0.17)22.5 (0.35)1.63170.103Education attainmentJunior high school or less23.6 (0.12)23.1 (0.20)2.14550.032High school23.3 (0.08)22.5 (0.14)4.5065<0.001Junior college23.1 (0.08)22.3 (0.13)5.0751<0.001Undergraduate college22.8 (0.07)22.2 (0.11)4.5848<0.001Graduate school or higher22.7 (0.13)22.1 (0.15)3.35640.001Hometown locationUrban region23.1 (0.05)22.3 (0.07)8.5401<0.001Suburban region23.0 (0.09)22.5 (0.16)3.10320.002Rural region22.7 (0.14)22.5 (0.23)0.50790.612Household income per capita in 20190–9,999 RMB23.1 (0.09)22.1 (0.16)5.5208<0.00110,000–49,999 RMB23.0 (0.06)22.2 (0.09)6.9979<0.00150,000–99,999 RMB23.1 (0.09)22.5 (0.13)3.6626<0.001100,000+ RMB23.1 (0.11)22.8 (0.16)1.71030.087Total23.0 (0.04)22.3 (0.06)9.5002<0.001


## 3.3. Multivariate Analysis

Univariate OLS regression analyses showed that the mean BMI of residents living in Wuhan was 0.71 kg/m2 higher than that of respondents who had left Wuhan during the lockdown (P < 0.001). After adjusting for sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and alcohol drinking habits within the multivariate OLS regression analyses, the mean BMI among residents living in Wuhan during the lockdown period was still 0.42 kg/m2 (P < 0.001) higher than among those who had left.Univariate and multivariate OLS regression analyses were conducted on the association between BMI and living in Wuhan during the epidemic period according to sex, age, and BMI grouping (FileS1). Living in Wuhan was associated with a BMI increase in male and female residents within both univariate and multivariate OLS regression analyses (P < 0.001). After stratifying by age group, this association remained in the subpopulations aged 18–29 or 30–44 years (P < 0.001) when adjusting for sex, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and alcohol drinking habits. BMI increase was also statistically significantly associated with living in Wuhan during the lockdown period among overweight or obese residents (P < 0.05), but not in underweight residents.


## 3.4. Mediation Analysis

Model 4 used food habits affected as a dependent variable, as shown in Table4, which illustrates that food habits were more strongly affected by residents living in Wuhan. Moreover, this effect existed within the total population and various sex, age, and BMI groupings (all P < 0.05). Model 5 uses physical activity change as the dependent variable. The result shows that more residents living in Wuhan experienced a decrease in physical activity during the pandemic compared to people who had left Wuhan; this effect was found among men (30–44 years) and those with a normal weight and overweight/obesity. Model 6 was constructed based on model 3 by adding the following two mediators: food habits affected and physical activity change. This mediation model presents results on the direct effects of living in Wuhan during the lockdown period on BMI increases within the study population and according to sex, age, and overweight/obesity groupings.Table 4
Mediation analysis on factors mediating the association between BMI (body mass index) and living in Wuhan, by total, sex, age group, and weight status during the lockdown period. Wuhan, China, 2020.
GroupVariableModel 3a(BMI)Model 4b(Δ food habits affected)Model 5c(Δ physical activity change)Model 6d(BMI)β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)PTotalLiving in Wuhan0.42 (0.28–0.56)<0.0010.12 (0.09–0.15)<0.0010.10 (0.05–0.15)<0.0010.42 (0.28–0.56)<0.001Food habits affected——————−0.08 (−0.18–0.02)0.133Physical activity change——————0.10 (0.04–0.15)0.001∗MaleLiving in Wuhan0.41 (0.21–0.60)<0.0010.11 (0.07–0.15)<0.0010.11 (0.04–0.17)0.0010.40 (0.21–0.60)<0.001Food habits affected——————−0.03 (−0.16–0.10)0.642Physical activity change——————0.06 (−0.02–0.13)0.142FemaleLiving in Wuhan0.40 (0.20–0.60)<0.0010.14 (0.10–0.18)<0.0010.09 (0.01–0.16)0.0240.41 (0.21–0.61)<0.001Food habits affected——————−0.13 (−0.27–0.02)0.100Physical activity change——————0.14 (0.06–0.22)0.00118–29 yLiving in Wuhan0.46 (0.23–0.70)<0.0010.12 (0.08–0.17)<0.0010.04 (−0.04–0.11)0.3440.47 (0.24–0.70)<0.001Food habits affected——————−0.10 (−0.28–0.09)0.310Physical activity change——————0.08 (−0.03–0.19)0.16530–44 yLiving in Wuhan0.45 (0.25–0.66)<0.0010.13 (0.09–0.17)<0.0010.15 (0.08–0.22)<0.0010.43 (0.23–0.64)<0.001Food habits affected——————0.02 (−0.14–0.17)0.847Physical activity change——————0.12 (0.04–0.21)0.005∗45+yLiving in Wuhan0.15 (−0.18–0.49)0.3740.12 (0.04–0.20)0.0020.07 (−0.07–0.21)0.3010.18 (−0.16–0.51)0.297Food habits affected——————−0.20 (−0.38–0.03)0.023∗Physical activity change——————0.06 (−0.04–0.16)0.270UnderweightLiving in Wuhan−0.03 (−0.15–0.10)0.6640.16 (0.06–0.26)0.0020.02 (−0.14–0.19)0.799−0.02 (−0.15–0.10)0.697Food habits affected——————−0.02 (−0.12–0.08)0.702Physical activity change——————0.01 (−0.04–0.06)0.726Normal weightLiving in Wuhan0.08 (−0.00–0.17)0.0580.13 (0.09–0.16)<0.0010.08 (0.02–0.15)0.0120.08 (−0.00–0.17)0.058Food habits affected——————−0.03 (−0.09–0.02)0.259Physical activity change——————0.06 (0.02–0.09)0.001Overweight/ObesityLiving in Wuhan0.31 (0.08–0.54)0.0080.11 (0.06–0.17)<0.0010.12 (0.02–0.21)0.0140.30 (0.07–0.53)0.011Food habits affected——————0.06 (−0.08–0.20)0.379Physical activity change——————0.06 (−0.02–0.14)0.123aModel 3 in Table 4 is adjusted for sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and drinking habits. bThe dependent variable in model 4 is food habits affected, adjusted for the same covariates as in model 3. cThe dependent variable in model 5 is physical activity change, adjusted for the same covariates as in model 3. dThe dependent variable in model 6 is BMI, adjusted for two more variables (food habits affected and physical activity change) as compared with model 3.The indirect effect proportions of living in Wuhan leading to a decrease in physical activity and consequently to an increase in BMI were 2.3% and 4.1%, respectively, among the study population and in the 30–44 years age group. In addition, an indirect influence of living in Wuhan affecting food habits, which led to a BMI decrease, was found in the 45 and above age group.


## 4. Discussion

Some studies have explored the influence of the COVID-19 pandemic on weight gain or overweight/obesity. However, few studies focused on the early stage of the outbreak when home quarantine policies were initiated strictly around the world. Moreover, there were methodological problems in some of these studies, especially with sample selection and data collection. For example, some studies were more qualitative or needed to recruit a sufficient sample size [15, 16]. Other quantitative studies with large sample sizes recruited respondents through the Internet or social media, and the questionnaires were self-administered; these could, therefore, not ensure sufficient sample representativeness and survey quality [17–22]. In addition, the home quarantine policy in most regions of China was incomplete, and conclusions based on these studies should be drawn with caution.After the outbreak of the COVID-19 epidemic, the Chinese government took decisive action by instituting a strict lockdown in Wuhan City for 76 days, from January 23rd to April 8th, 2020. Consequently, the early spring festival lockdown split the entire population into two groups naturally, which provided a historically unique survey field. However, conducting a face-to-face survey during the epidemic was impractical, and obtaining a sample frame for a web survey was impossible. Fortunately, using a Cloud-CATI platform developed at our research center, the survey was initiated quickly and smoothly during this special occasion. Approximately two months after the Wuhan lockdown was lifted, a random sample was selected by an RDD method to survey over 10,000 residents living in Wuhan. This study, to our knowledge, is the largest scale random sampling survey regarding the COVID-19 epidemic outbreak that has been conducted to date.Our study showed that the mean BMI was 0.71 kg/m2 higher in residents living in Wuhan compared to residents who had left Wuhan. The population living in Wuhan during the lockdown had a 1.9–2.2 kg higher BMI on average than those who had left Wuhan in reference to an average height of 169.7 cm and 158.0 cm in men and women, respectively, as reported by Chinese nutritional researchers and with respect to chronic disease status distributions as of 2020. These results were consistent with those of other studies conducted in China and abroad [15–17, 19, 20]. Because there are inevitable differences in study characteristics across populations, including study region, demographic and lifestyle characteristics, sampling methods, and study periods, the results might differ across investigations. However, our results have undoubtedly added more evidence about the association between choices in residential locations during the Wuhan lockdown and higher BMI levels among the residents of Wuhan City.The mean BMI among residents living in Wuhan was 0.42 kg/m2 (P < 0.001) higher than that of residents who had left Wuhan during the lockdown within a multivariate OLS regression analysis adjusted for sex, age, marital status, educational attainment, hometown location, household income per capita in 2019, health status as affected by the pandemic, chronic disease, and changes in smoking and alcohol drinking habits. This result was slightly higher than the 0.31 kg/m2 difference reported in a prior meta-analysis [23], indicating that we detected direct effects of living in Wuhan on BMI. Direct effects were found in both the male and female populations but not in those aged over 45 years or underweight or normal weight, which was consistent with the results of other studies [5, 24].It is well known academically that BMI increases are associated with changes in food habits and physical activity. Hence, this study explored the mediation effects of changes in food habits and physical activity on the association between living in Wuhan during the lockdown period and BMI. The results showed an indirect path of living in Wuhan with regard to decreases in physical activity leading to increased BMI in the total study population and in those aged 30–44 years, which meant that living in Wuhan had an indirect effect on BMI that was attributed to changes in physical activity in addition to the direct effects seen in the younger population. Moreover, an indirect path of living in Wuhan, acting through changes in food habits, which, in turn, led to BMI decreases, was found in individuals aged over 45 years. This meant that living in Wuhan decreased BMI in the middle-aged and older population because of its effects on food habits. The result was similar to a prior study finding of decreased weight occurring during the COVID-19 epidemic in an investigation conducted in Italy. Hence, we conclude that the middle-aged and older population should be monitored more closely with regard to their malnutrition status during the pandemic lockdown [25, 26].There are several strengths of this study. First, a random digital dialing method survey on a large scale was employed in Wuhan, which implemented the most comprehensive home quarantine policy for the most prolonged period of any city in China. The results were relatively high quality. Second, this is the first survey with a random sampling method to examine the association of weight gain with the COVID-19 lockdown in China based on large-scale population analysis adjusting multiple confounders. Third, this study provided new evidence for the COVID-19 lockdown increasing residents’ BMI, which was necessary for the prevention and early intervention of noncommunicable chronic disease.This study has several limitations. First, a random digital dialing method was used in the survey. Still, it was impossible to avoid nonresponse bias because of the low response rate inherent to CATI fieldwork and survey-based epidemiologic research. However, considering the large scale of this survey, our results (based on surveying over 10,000 residents) may be regarded as validated. Next, the respondents’ weight before the COVID-19 epidemic was not collected, hence precluding the possibility of comparative evaluations conducted before and after the lifting of the lockdown. We adjusted many weight-associated covariates to increase comparability between the residents living in Wuhan and those who left during the epidemic. In addition, because of the questionnaire length limit, some factors relevant to weight (such as psychological factors) were not included in this evaluation, which may be partly associated with higher BMI among those living in Wuhan during the epidemic. Furthermore, the food habits measurement was not added based on this telephone survey. However, this study focused on the degree of influence of food habits by COVID-19; hence, it is necessary to use innovative approaches and tools to assess them in the future [27, 28], especially in China with more than one billion Internet users. Based on a large-scale survey administered in Wuhan City, the results provide direct evidence for the effects of the pandemic lockdown on BMI and overweight/obesity.The findings should be paid close attention for the following reasons: (1) The survey result showed a moderate self-reported proportion of overweight/obesity in Wuhan City. However, the continuously increasing trend with respect to overweight/obesity in China and around the globe should not be neglected. Based on this background, the BMI increase caused by the pandemic lockdown in Wuhan City puts forward a new challenge for the future control and prevention of overweight/obesity. (2) Currently, the COVID-19 pandemic is ongoing in China and around the world. Moreover, meta-analyses have shown that individuals with obesity are more at risk for contracting COVID-19 and hospitalization, intensive care unit admission, and COVID-19-associated mortality. Hence, the prevention of obesity is significant to COVID-19 control and prevention. (3) According to the latest statistics, there have been over 3.4 billion doses of COVID-19 vaccinations administered nationwide, and over 1 billion people have been vaccinated. However, vaccines are less effective for people with obesity because of their weakened immune response [29]. Hence, based on our results, more educational workshops and prevention work should be conducted to avoid possible increases with regard to chronic disease burden and difficulties in COVID-19 prevention due to shifting distributions of overweight and obesity.


## 5. Conclusions

This study showed a relationship between the COVID-19 lockdown and overweight/obesity in Wuhan city in China, especially among the young or overweight/obese, partly due to decreased physical activity. In China’s social and economic development context, enhancing physical activity and nutrition intervention during the lockdown period is beneficial for preventing overweight/obesity. It further contributed to decreasing and delaying the development of noncommunicable chronic diseases among the population and reducing the related burden in China.



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*Source: 9482498-2023-02-10.xml*