Study design and participants
This retrospective observational study enrolled youth from two universities (Hunan Traditional Chinese Medical College, Hunan, China, and Medical College of Jinhua Polytechnic, Zhejiang, China) that performed the compulsory Chinese National Student Physical Fitness Standard (CNSPFS) battery between December 1, 2019, to January 20, 2020, when the government issued sanctioned lockdowns and social distancing and completed a follow-up online questionnaire beginning on May 1, 2020, and ending on May 23, 2020. A total of 14,059 university students who were free of chronic diseases and had completed the CNSPFS were screened. Of these, 13,013 participants (response rate of 93.2%) completed the online questionnaire. The participants who replied with questionnaires of poor quality were excluded (n = 124). A total of 12,889 participants were finally recruited in the study. All baseline data were extracted from the CNSPFS system, follow-up data were collected from a professional survey platform (https://www.wjx.cn). The criteria of poor quality were: (1) ID information in the CNSPFS system did not match that in the follow-up questionnaire; or (2) The 81 question survey was completed in less than three minutes.This study received ethical approval from the Medical Ethics Committee of Xiangya Hospital, Central South University (REC reference: 202005126). All participants gave electronic consent before enrolment in the study, which was conducted in accord with the principles of the Declaration of Helsinki and participants were anonymized and codified to protect their personal information.
Characteristics of Lockdown
The first lockdown order in China was delivered on January 23rd, 2020 by the government of Wuhan, Hubei province, followed by the other cities and provinces of China. The main requirements included: (1) all individuals were ordered to stay home or at their place of residence, except for permitted work, local shopping or other permitted errands, or as otherwise authorized. (2) all schools, sports facilities, entertainment, and recreational venues, personal care and beauty services, and the majority of factories and markets were closed.
During the period of data collection, the participants involved would continue their studies through online learning at the beginning of their second semester of teaching. There were no imminent academic examinations in either time point of data collection.
Weight change
Baseline weight was assessed by staff members of the two respective universities. This was done in accordance with CNSPFS standards for all university students across China, using scales and after the removal of shoes/coats. Follow-up body weight was obtained via questionnaire. Concerning the determination of follow-up body weight, all participants were asked to measure their body weight in the morning right after waking up, in a state of fasting, shoes off, no large coat. Weight change was calculated by subtracting baseline weight from follow-up weight and the results were verified according to perceived weight change. Weight change was further classified as no significant change (-0.9 to 0.9 kg), mild decrease (-3.9 to -1.0 kg), moderate decrease (-6.9 to -4.0 kg), major decrease (≤ -7.0 kg), mild increase (1.0 to 3.9 kg), moderate increase (4.0 to 6.9 kg), major increase (≥ 7.0 kg).
Physical activity
Physical activity was classified as exercise and free-living activity. Exercise habits were collected through questions regarding exercise pre- and post- lockdown, these included frequency, duration, and intensity of aerobic exercise as well as strength exercise, in a modified version with added items of the International Physical Activity Questionnaire-Long form-Chinese (IPAQ-LC), which had shown adequate reliability and reasonable validity for use in Chinese students(8). Exercise volume was expressed in MET-hr/wk, calculated according to the American College of Sports Medicine (ACSM) metabolic equations prior to and during the lockdown.(9) Sedentary time, defined as any waking behavior where energy expenditure is ≤ 1.5 MET’s while reclining or sitting, included the number of hours a day spent using a computer or mobile phone(9) and was expressed as hr/d.
Dietary habits
In this study, food composition was not assessed due to the high participant burden of such a questionnaire and data collection limitations. The present study evaluated the dietary habits, including breakfast and lunch frequency, alcoholic drinks per week, and snacking times per day. Breakfast and lunch were categorized into three groups: less than once a week, two to six times per week, every day. Snacking was also categorized into three groups: no snacking, snacking once a day (day or late-night), snacking twice per day (day and late-night). Alcohol habits were recorded as the number of drinks, one drink was defined as drinking 100 ml wine or liquor, or 1 glass/bottle/can of beer.
COVID-19 induced stress, depression and anxiety
The question used to measure COVID-19 induced stress was designed based on previous research(10): How concerned are you about yourself, or family members/friends being infected by COVID-19? To which the possible answers were: None; Mild; Moderate; Major; Severe. Two psychological indexes were also used, Becks’ Depression Inventory, second edition (BDI-II)(11) and the State-Trait Anxiety Inventory (STAI) (Form Y-1).(12)
Statistical analysis
The primary outcome (dependent variable) of the present study was weight change over the lockdown, the independent variables were COVID-19 induced stress, depression, anxiety, physical activity, and dietary habits. Paired t-test and Wilcoxon signed-rank test were used for assessment in mean difference between baseline and follow-up of continuous and ordinal variables respectively. The independent relationships of COVID-19 induced stress, depression, anxiety, physical activity, and dietary habits to weight changes within the lockdown, using multivariable linear or binary logistic regression models and accounting for within individual repeated measures were assessed accordingly. Potential nonlinear effects of decreases versus increases in each variable were evaluated by modeling changes in indicator categories, with “no change” as the reference. Multivariable models were used to adjust for sex, age, baseline weight, physical activity, dietary habits, and psychological status simultaneously. To minimize confounding from geolocation and baseline overweight/obesity, university- and BMI-stratified multivariate linear regression were used. Additionally, results were analyzed as relative weight changes (percent), and weight change was a binary variable (weight-gain versus weight loss). Analyses were carried out with the use of SAS software, version 9.4 (SAS Institute), a two-tailed alpha level of 0.05 was considered significant.