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(Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-922818936)
Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-922818936Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-922818936, Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-922818936
COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC's 2020 Food & Health Survey
G. Tison, Robert Avram, Peter Kuhar, S. Abreau, G. Marcus, M. Pletcher, J. Olgin (2020)
Worldwide Effect of COVID-19 on Physical Activity: A Descriptive StudyAnnals of Internal Medicine
(2020)
Shelter-in-place” and “stay-at-home” orders
Lee (2012)
219Lancet, 380
(Center for Systems Science and Engineering at Johns Hopkins University. COVID-19 Dashboard. Published 2020. Accessed January 22, 2021. https://coronavirus.jhu.edu/map.html)
Center for Systems Science and Engineering at Johns Hopkins University. COVID-19 Dashboard. Published 2020. Accessed January 22, 2021. https://coronavirus.jhu.edu/map.htmlCenter for Systems Science and Engineering at Johns Hopkins University. COVID-19 Dashboard. Published 2020. Accessed January 22, 2021. https://coronavirus.jhu.edu/map.html, Center for Systems Science and Engineering at Johns Hopkins University. COVID-19 Dashboard. Published 2020. Accessed January 22, 2021. https://coronavirus.jhu.edu/map.html
(International Food Information Council. COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC’s 2020 Food & Health Survey. Published 2020. Accessed November 21, 2020. https://foodinsight.org/wp-content/uploads/2020/06/2020-Food-and-Health-Survey-.pdf)
International Food Information Council. COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC’s 2020 Food & Health Survey. Published 2020. Accessed November 21, 2020. https://foodinsight.org/wp-content/uploads/2020/06/2020-Food-and-Health-Survey-.pdfInternational Food Information Council. COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC’s 2020 Food & Health Survey. Published 2020. Accessed November 21, 2020. https://foodinsight.org/wp-content/uploads/2020/06/2020-Food-and-Health-Survey-.pdf, International Food Information Council. COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC’s 2020 Food & Health Survey. Published 2020. Accessed November 21, 2020. https://foodinsight.org/wp-content/uploads/2020/06/2020-Food-and-Health-Survey-.pdf
(Tison GH, Avram R, Kuhar P, . Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-266532598162)
Tison GH, Avram R, Kuhar P, . Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-266532598162Tison GH, Avram R, Kuhar P, . Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-266532598162, Tison GH, Avram R, Kuhar P, . Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-266532598162
(2012)
Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancyBDJ, 213
(FINRA. State “Shelter-in-place” and “stay-at-home” orders. Published 2020. Accessed May 9, 2020. https://www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place)
FINRA. State “Shelter-in-place” and “stay-at-home” orders. Published 2020. Accessed May 9, 2020. https://www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-placeFINRA. State “Shelter-in-place” and “stay-at-home” orders. Published 2020. Accessed May 9, 2020. https://www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place, FINRA. State “Shelter-in-place” and “stay-at-home” orders. Published 2020. Accessed May 9, 2020. https://www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place
(Xu H, Cupples LA, Stokes A, Liu C-T. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.458730646366)
Xu H, Cupples LA, Stokes A, Liu C-T. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.458730646366Xu H, Cupples LA, Stokes A, Liu C-T. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.458730646366, Xu H, Cupples LA, Stokes A, Liu C-T. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen.2018.458730646366
R. Trimble (2020)
COVID-19 Dashboard
Hanfei Xu, L. Cupples, A. Stokes, Ching‐Ti Liu (2018)
Association of Obesity With Mortality Over 24 Years of Weight HistoryJAMA Network Open, 1
Research Letter | Public Health Body Weight Changes During Pandemic-Related Shelter-in-Place in a Longitudinal Cohort Study Anthony L. Lin, MD; Eric Vittinghoff, PhD; Jeffrey E. Olgin, MD; Mark J. Pletcher, MD, MPH; Gregory M. Marcus, MD, MAS Introduction Supplemental content Author affiliations and article information are As of January 22, 2021, there were more than 98 million confirmed cases of severe acute respiratory listed at the end of this article. syndrome coronavirus 2 (SARS-CoV-2), more than 24 million of which are attributed to the US alone. Recent surges in SARS-CoV-2 and the threat of a second wave have prompted many states to reconsider reopening timelines. During the initial US surge, 45 out of 50 state governments issued shelter-in-place (SIP) orders from March 19, 2020, to April 6, 2020, to slow disease transmission. The initial SIP coincided with an observed decrease in daily step counts, likely reflective of changes in physical activity and patterns of daily living, as well as concurrent self-reported increases in snacking and overeating. We therefore sought to investigate ambulatory weight changes of a longitudinal cohort during initial SIP orders to better understand the possible downstream health implications of prolonged SIP. Methods This cohort study was approved by the University of California, San Francisco (UCSF) institutional review board, and informed consent was obtained from all participants. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. We performed a longitudinal analysis of data obtained from February 1 to June 1, 2020, from participants in the Health eHeart Study who volunteered to report weight measurements from their Bluetooth-connected smart scale (Fitbit [Fitbit Inc] or iHealth [iHealth Labs Inc]). Additional study design and study population selection details are in the eAppendix in the Supplement. Demographic characteristics and medical conditions were obtained via online surveys. Race and ethnicity of participants were assessed via self-report at time of enrollment. Weight change before and after SIP was studied via a linear mixed-effects model with a spline point at the day SIP orders were issued for each state. Random intercepts, random slopes, and first-order autoregressive residuals were used to track within-group changes for each participant. A 2-tailed P < .05 was considered statistically significant. All analyses were performed using R version 4.0.0 (R Project for Statistical Computing) from February to May 2020. Results A total of 7444 weight measurements from 269 unique study participants (residing in 37 states and Washington, District of Columbia) were collected during the study period, with a mean (SD) of 28 (24) weight measurements per participant. Of 269 study participants, 130 (48.3%) were men and 207 (77.0%) were White individuals; and age data was available for 169 participants (62.8%) with a mean (SD) age of 51.9 (17.3) years. Baseline characteristics are displayed in the Table. As illustrated in the Figure, post-SIP participants experienced steady weight gain at a rate of 0.27 kg every 10 days (95% CI, 0.17 to 0.38 kg per 10 days; P < .001), irrespective of geographic location or comorbidities. Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536 (Reprinted) March 22, 2021 1/4 JAMA Network Open | Public Health Body Weight Changes During Pandemic-Related Shelter-in-Place in a Longitudinal Cohort Study Figure. Mean Weight Change After Shelter-in-Place for the Study Population 0.4 0.2 –0.2 Figure data normalized as weight above or below each participant’s median weight in kilograms. Shaded areas –0.4 0 7 14 21 28 denote the 95% CI for the mean weight of study Time from shelter-in-place, d participants after shelter-in-place. Table. Demographic and Clinical Characteristics of Participants Patients, No. (%) Below median weight Above median weight Characteristics (n = 134) (n = 135) P value Age, mean (SD), y 50.6 (18.8) 52.0 (15.6) NA Sex Female 75 (56) 42 (31) <.001 Male 47 (35) 83 (61) Not reported or unknown 12 (9) 10 (7) .81 Race Black or African American 5 (4) 4 (3) .99 White 100 (75) 107 (79) .45 Asian 7 (5) 1 (1) .07 Identified as 2 or more races 6 (4) 5 (4) .99 Other 4 (3) 7 (5) .55 Not reported or unknown 12 (9) 11 (8) >.99 Ethnicity Hispanic 6 (4) 10 (7) .45 Not reported or unknown 12 (9) 12 (9) >.99 Geographical region West 51 (38) 42 (31) .29 Midwest 21 (16) 21 (16) >.99 Northeast 23 (17) 26 (19) .77 South 39 (29) 46 (34) .46 Medical comorbidities Hypertension 35 (26) 66 (49) <.001 Hyperlipidemia 46 (34) 54 (40) .40 Diabetes 8 (6) 18 (13) .66 Coronary artery disease 9 (7) 10 (7) >.99 Congestive heart failure 5 (4) 2 (1) .44 Atrial fibrillation 9 (7) 20 (15) .05 Chronic obstructive pulmonary 4 (3) 2 (1) .67 disease Sleep apnea 9 (7) 31 (23) <.001 History of myocardial infarction 6 (4) 6 (4) >.99 History of stroke 3 (2) 3 (2) >.99 Abbreviation: NA, not applicable. Weight, mean (SD), kg 68.1 (9.1) 101.0 (15.4) NA Body mass index is calculated as weight in kilograms Body mass index, mean (SD) 24.1 (3.0) 32.9 (5.6) NA divided by height in meters squared. JAMA Network Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536 (Reprinted) March 22, 2021 2/4 Mean weight change from each participant’s median weight, kg JAMA Network Open | Public Health Body Weight Changes During Pandemic-Related Shelter-in-Place in a Longitudinal Cohort Study These results translate into approximately 1.5 lb of weight gain every month (to convert kilograms to pounds, divide by 0.45). Discussion Weight is a clinically relevant health outcome that is independently associated with all-cause mortality. It is also a helpful proxy for physical activity, another measurement associated with all-cause mortality. In analyzing weight trends around initial SIP, we found a significant increase in weight over the post-SIP period at a rate of roughly a pound and a half weight gain per month following SIP. Although this may not appear clinically important, prolonged effects as have occurred with the pandemic might lead to substantial weight gain. Because of the reliance on Bluetooth-connected scales and weight measurements during SIP from participants of the Health eHeart study, reduction of overall sample size is a limitation to this study. Although idiosyncratic characteristics of those who happen to own a Bluetooth-connected scale may limit the study’s generalizability, following individuals over time to assess their objectively measured weight changes during SIP diminishes threats to internal validity. It is important to recognize the unintended health consequences SIP can have on a population level. The detrimental health outcomes suggested by these data demonstrate a need to identify concurrent strategies to mitigate weight gain, such as encouraging healthy diets and exploring ways to enhance physical activity, as local governments consider new constraints in response to SARS- CoV-2 and potential future pandemics. ARTICLE INFORMATION Accepted for Publication: January 29, 2021. Published: March 22, 2021. doi:10.1001/jamanetworkopen.2021.2536 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Lin AL et al. JAMA Network Open. Corresponding Author: Gregory M. Marcus, MD, MAS, 505 Parnassus Ave, M1180B, San Francisco, CA 94143 (greg.marcus@ucsf.edu). Author Affiliations: Department of Medicine, University of California, San Francisco (Lin); Department of Epidemiology and Biostatistics, University of California, San Francisco (Vittinghoff, Pletcher); Division of Cardiology, Department of Medicine, University of California, San Francisco (Olgin, Marcus). Author Contributions: Dr Marcus had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Lin, Olgin, Marcus. Acquisition, analysis, or interpretation of data: Lin, Vittinghoff, Pletcher, Marcus. Drafting of the manuscript: Lin, Olgin, Marcus. Critical revision of the manuscript for important intellectual content: Lin, Vittinghoff, Olgin, Pletcher. Statistical analysis: Lin, Vittinghoff. Obtained funding: Olgin, Pletcher, Marcus. Administrative, technical, or material support: Olgin, Marcus. Supervision: Olgin, Marcus. Conflict of Interest Disclosures: None reported. REFERENCES 1. Center for Systems Science and Engineering at Johns Hopkins University. COVID-19 Dashboard. Published 2020. Accessed January 22, 2021. https://coronavirus.jhu.edu/map.html 2. FINRA. State “Shelter-in-place” and “stay-at-home” orders. Published 2020. Accessed May 9, 2020. https:// www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place JAMA Network Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536 (Reprinted) March 22, 2021 3/4 JAMA Network Open | Public Health Body Weight Changes During Pandemic-Related Shelter-in-Place in a Longitudinal Cohort Study 3. Tison GH, Avram R, Kuhar P, et al. Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173(9):767-770. doi:10.7326/M20-2665 4. International Food Information Council. COVID-19 pandemic transforms the way we shop, eat and think about food, according to IFIC’s 2020 Food & Health Survey. Published 2020. Accessed November 21, 2020. https:// foodinsight.org/wp-content/uploads/2020/06/2020-Food-and-Health-Survey-.pdf 5. Xu H, Cupples LA, Stokes A, Liu C-T. Association of obesity with mortality over 24 years of weight history: findings from the Framingham Heart Study. JAMA Netw Open. 2018;1(7):e184587. doi:10.1001/jamanetworkopen. 2018.4587 6. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT; Lancet Physical Activity Series Working Group. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219-229. doi:10.1016/S0140-6736(12)61031-9 SUPPLEMENT. eAppendix. Supplementary Methods JAMA Network Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536 (Reprinted) March 22, 2021 4/4 Supplemental Online Content Lin AL, Vittinghoff E, Olgin JE, Pletcher MJ, Marcus GM. Body weight changes during pandemic-related shelter-in-place in a longitudinal cohort study. JAMA Netw Open. 2021;4(3):e212536. doi:10.1001/jamanetworkopen.2021.2536 eAppendix. Supplementary Methods This supplemental material has been provided by the authors to give readers additional information about their work. © 2021 Lin AL et al. JAMA Network Open. eAppendix. Supplementary Methods The Health eHeart Study is an internet-based longitudinal cardiovascular e-cohort launched on March 8, 2013 and is still ongoing. Enrollment of participants was done worldwide via lay press, social media, email campaigns, and word-of-mouth. All English-speaking adults aged 18 years and over with an active email address were eligible for enrollment. Enrollment was voluntary and no incentives were offered for study participation. Consent was obtained electronically through the study website. Upon enrollment, participants were prompted to complete a series of baseline questionnaires that asked about basic demographics, family history, medical comorbidities, social demographics, and health behaviors. Age was self-reported as a continuous integer that participants provided on enrollment. Self-identified sex was dichotomized as male or female. Self-identified race was categorized as Black or African American, White, Asian or Pacific Islander, or other, which gave participants an opportunity to identify as another race. Ethnicity was dichotomized as Hispanic or not. Geographic region was based on self-reported zip codes provided by participants. Medical comorbidities were ascertained via self-report and participants were asked to report diagnoses of hypertension, hyperlipidemia, diabetes, coronary artery disease, congestive heart failure, atrial fibrillation, chronic obstructive pulmonary disease, sleep apnea, a history of myocardial infarction, and a history of stroke. For any of the baseline questionnaires, participants were allowed to leave any question unanswered should they wish not to share that information. © 2021 Lin AL et al. JAMA Network Open. The study includes the capability to synchronize a Bluetooth-connected smart scales from FitBit (i.e. Fitbit Aria Air, Fitbit Aria 2, etc.) or iHealth (i.e. iHealth Lite Wireless Body Analysis Scale, etc.). Participants with smart scales could opt to voluntarily contribute their weight data to the Health eHeart Study. A total of 2,336 unique participants have contributed 577,933 weight measurements through a Bluetooth-connected smart scale since the feature was introduced. Shelter-in-place recommendations were made in 45 out of 50 states throughout the United States from March 19, 2020 to April 6, 2020 in response to the global SARS- CoV-2 pandemic. Health eHeart participants who contributed at least 1 weight measurement before and 1 measurement after their state-specific shelter-in-place order was placed were included in this study, which reduced the study sample size to 269 unique participants. Continuous variables were presented as means and standard deviations and compared using t-tests, while categorical variables were presented as frequencies (percentages) and compared using the chi-squared test. As participants volunteered weight measurements from their home smart scales only when performed, a weight was not recorded for all participants for every day in the study period. Therefore, a linear mixed effects model was employed to enable analysis of aggregate body weight changes during this time period. A spline point at the day shelter-in-place orders were issued for each state was used to assess for any changes in body weight immediately following state mandates promoting shelter-in-place. The use of random intercepts, random slopes, and first-order autoregressive residuals was employed to allow the model to account for variability in baseline weight and different rates of body weight change between participants during the © 2021 Lin AL et al. JAMA Network Open. study period. A two-tailed p-value of <0.05 was considered statistically significant. All analyses were done using R version 4.0.0. © 2021 Lin AL et al. JAMA Network Open.
JAMA Network Open – American Medical Association
Published: Mar 22, 2021
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