key: cord-0955644-53uimpkq authors: Seal, Adam; Schaffner, Andrew; Phelan, Suzanne; Brunner‐Gaydos, Hannah; Tseng, Marilyn; Keadle, Sarah; Alber, Julia; Kiteck, Isabelle; Hagobian, Todd title: COVID‐19 pandemic and stay‐at‐home mandates promote weight gain in US adults date: 2021-11-21 journal: Obesity (Silver Spring) DOI: 10.1002/oby.23293 sha: b145aee480629f2836dd17fdb6495db114a9903b doc_id: 955644 cord_uid: 53uimpkq OBJECTIVE: The purpose of this study was to prospectively examine the effect of state stay‐at‐home mandates on weight of US adults by BMI over 3 months during COVID‐19. METHODS: US adults completed an online questionnaire containing demographics, weight, physical activity, sedentary time, fruit/vegetable intake, depressive symptoms, stress, and sleep at baseline (May 2020) and after 3 months (August 2020). RESULTS: Participants gained 0.6 kg (76.7‐77.3 kg, p = 0.002). A total of 26% of those with obesity gained > 2 kg compared with 14.8% of those with normal weight (p < 0.001). A total of 53.3% of individuals with obesity maintained weight within 2 kg compared with 72.5% of those with normal weight (p < 0.001). Greater weight gain was related to longer stay‐at‐home mandates (β = 0.078, p = 0.010), lower baseline minutes of physical activity per day (β = −0.107, p = 0.004), greater declines in minutes of physical activity per day (β = −0.076, p = 0.026), depressive symptoms (β = 0.098, p = 0.034), and greater increases in time preparing food (β = 0.075, p = 0.031). CONCLUSIONS: US adults gained weight, and stay‐at‐home mandates were associated with atypical weight gain and greater reported weight gain in individuals with obesity over 3 months. Basic demographic information was collected, including age, sex, race, ethnicity, income, education, smoking habits, and marital status. Length of state-mandated pandemic-related restrictions was determined using the Johns Hopkins Coronavirus Resource Center (11). Mandate length was measured from the beginning of the participants' state-issued mandate to the date of reopening within the 3-month time frame of the study. Height at baseline and weight at both time points were self-reported (12) . BMI (kilograms per meters squared) was calculated and used to categorize weight status (underweight: < 18.5; healthy weight: 18.5-24.9; overweight: 25-29.9; obesity: ≥ 30). Weight change was computed as the difference between study entry and 3 months. As in previous literature, weight change was used to classify participants into four categories: those who lost more than 2 kg, those who maintained weight between ±2 kg, those who gained any weight (>0 kg), and those who gained more than 2 kg (13) . Depressive symptoms was measured using the Center for Epidemiological Studies Depression Scale (CES-D) (14) . The questionnaire focuses on feelings and behaviors during the previous week. Responses range from "rarely or none of the time" to "most or all of the time." Higher composite scores represent the presence of more symptomatology, with possible scores ranging from 0 to 60. CES-D scores of 16 or more are often used as criteria for risk of depression. Stress was measured using the four-item Perceived Stress Scale (15) . The questionnaire focuses on feelings and thoughts during the previous month and requires respondents to indicate how often they have felt a variety of stressors, ranging from "never" to "very often." Higher composite scores indicate higher stress levels, with possible scores ranging from 0 to 16. Perceived vulnerability to COVID-19 was assessed as a What is already known? ► Previous research has shown that COVID-19-related mandates are associated with body weight gain, and that this gain may disproportionately affect individuals with overweight or obesity. ► Longer mandate length was associated with more 3month weight gain. ► Greater weight gain was related to more depressive symptoms and engaging in less physical activity and more time preparing food. ► Future research is needed to examine long-term effects of stay-at-home orders on weight and related behavioral and psychosocial parameters to inform potential targets of lifestyle interventions. sum of responses to five questions regarding chances and seriousness of infection, frequency of worrying of infection, perception of controlling infection, and ability to prevent infection. Five response options were provided, ranging from "not at all" to "very likely," which were subsequently numbered from 1 to 5 and then summed. Higher scores represent increased worry of COVID-19 infection. Dietary habits were measured using the National Cancer Institute's Fruit and Vegetable Screener (16) . Five questions regarding frequency of fruit and vegetable intake were used to create a composite score of ingestion over the prior 7 days. Responses ranged from "never" to "5+ times per day," which were given numerical values and then summed. Higher scores indicate more consumption of fruits and vegetables, with possible scores ranging from 0 to 30. Participants were also asked to estimate how many sodas and alcoholic beverages they consumed over the previous week as well as how many hours each day were spent on food preparation and cooking. Physical activity and sedentary time were measured using the 2017 Nurses' Health Study Physical Activity Questionnaire (17) . For physical activity, participants were asked to estimate how much time during the past week they spent walking, jogging, running, performing other aerobic exercise, lower intensity exercise, other vigorous activity, and/or weight training each day. For sedentary time, participants estimated how much time during the past week they spent sitting, either away from or at home. Physical activity response options ranged from 0 to 11+ hours per day, and sedentary time response options ranged from 0 to 15+ hours per day. Hours were converted to minutes to create a composite score representing how many minutes of physical activity or sedentary time participants performed in the past week. Amount of sleep was estimated using a sliding bar scale; participants were asked to drag a bar to the number representing the average hours of sleep per night in the past week. All data were analyzed using JMP Pro 15 (SAS Institute). Summary statistics are reported as mean (SD). In order to ensure data quality, a preliminary exploratory data analysis was carried out for each of the study variables to look for outliers and ensure reported values were within logical ranges. Individual response values outside the range specifications (n = 402) were recoded as missing and were excluded from statistical models. Only participants who completed both the baseline and 3-month time points were included in the analysis. χ 2 and t tests were used to assess demographic differences between respondents to only the baseline time point and respondents to baseline and 3-month time points. A paired t test was used to assess unadjusted changes in weight from baseline to 3 months, and a χ 2 test with Bonferroni correction was used to assess differences in the percentage of each BMI category that lost >2 kg, maintained weight within 2 kg, and gained >2 kg (18) . A paired t test was also used to assess unadjusted changes in all diet, physical activity, and psychosocial variables. A multivariate regression was used to assess relationships between state-ordered stay-at-home mandate length, base- Participant characteristics and demographics are presented in Table 1 . A total of 4,088 potential participants were initially con- (Table 1 ). There were differences in age, sex, education, marital status, race, body weight, and ethnicity between baseline-only respondents and respondents to both time points (p < 0.05). On average, self-reported body weight modestly increased (+0.6 [0.5] kg) from baseline to 3-months (t = 3.06, p = 0.002, Figure 1 ). Approximately 30% of participants reported weight gain (>0 kg). A significantly higher percentage of individuals with obesity gained more than 2 kg (26%) compared with individuals of normal weight (14.8%; p < 0.001, Figure 2) . A significantly lower percentage of individuals with obesity maintained weight within 2 kg (53.3%) compared with individuals of normal weight (72.5%; p < 0.001, Figure 2 ). A total of 18.4% of participants reported significant weight gain of more than 2 kg, 15.9% lost more than 2 kg, and 65.7% maintained weight within 2 kg after 3 months. From baseline to 3 months, depressive symptoms significantly increased, stress modestly decreased, and physical activity minutes per day and sleep decreased (p < 0.05; Table 2 ). (Table 3) . In the current study, state-ordered stay-at-home measures, initially implemented to slow the spread of COVID-19, were associated with increased body weight in adults. More specifically, these data show an almost 1% (0.6 kg) increase in body weight over 3 months, with 18.4% of participants gaining more than 2 kg. This is one of the first studies, to our knowledge, to prospectively examine whether stayat-home mandates were related to weight gain (7) weight gain of 0.27 kg per 10 days during stay-at-home mandates (7). Bhutani et al. used self-report data and observed a 0.62 kg increase in weight, which is nearly identical to weight change reported in the current study (20) . Additionally, in the current study, we noted that a higher percentage of participants with obesity reported weight gain over 2 kg compared with individuals of normal weight. These find- and fruit and vegetable intake has decreased during the pandemic but did not relate these changes to weight gain (27, 28) . In the current study, increased depressive symptoms were positively related to greater weight gain, independent of activity, diet, and other behaviors. This is consistent with a previous retrospective study that has shown that worsening mood and depression during 1 and respondents to baseline only. Although these differences were small, they were statistically significant, possibly affecting generalizability. Additionally, a true baseline measurement would have been collected precisely before the pandemic and before stay-at-home mandates began; however, it was not possible to attain data before mandates were implemented. Weight changes may have already occurred between the date of initial lockdowns and our baseline survey. Also, weight was self-reported, and surveys for sleep and food preparation have not been validated. We observed that state stay-at-home mandates, designed to slow the spread of COVID-19, had unintended consequences of promoting weight gain that disproportionately impacted individuals with obesity. In light of these data, as COVID-19 restrictions are lifted, it may be even more important to support programs and lifestyle interventions to reduce body weight, increase physical activity, and promote mental health. Future studies will analyze the long-term impact of the COVID-19 pandemic on weight and related behaviors over multiple years.O to this work. 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