scholarly journals Social Distancing as a Health Behavior: County-level Movement in the United States During the COVID-19 Pandemic is Associated with Conventional Health Behaviors

2020 ◽  
Author(s):  
Kyle J. Bourassa ◽  
David Sbarra ◽  
Avshalom Caspi ◽  
Terrie Moffitt
Keyword(s):  
Physical Activity ◽  
United States ◽  
Health Behavior ◽  
Health Behaviors ◽  
Health Behavior Change ◽  
The United States ◽  
Movement Behavior ◽  
County Level ◽  
Social Contacts ◽  
Social Distancing

Background: Social distancing—when people reduce their physical movement and limit social contacts beyond their immediate household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support social distancing public health efforts and research is promising, but empirical evidence supporting this approach is needed. Purpose: We examined whether one type of social distancing behavior—reductions in movement outside the home—was associated with conventional health behaviors. Method: We examined the association between GPS-derived movement behavior in 2,858 counties in United States from March 1st to April 7th, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors. Results: Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater decreases in movement outside the home during the initial phases of the pandemic in the United States. Conclusions: Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing during the COVID-19 pandemic.

scholarly journals Social Distancing as a Health Behavior: County-Level Movement in the United States During the COVID-19 Pandemic Is Associated with Conventional Health Behaviors

2020 ◽  
Vol 54 (8) ◽  
pp. 548-556 ◽  
Author(s):  
Kyle J Bourassa ◽  
David A Sbarra ◽  
Avshalom Caspi ◽  
Terrie E Moffitt
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Health Behaviors ◽  
Close Contact ◽  
Health Behavior Change ◽  
The United States ◽  
Movement Behavior ◽  
County Level ◽  
Social Distancing ◽  
The Usa

Abstract Background Social distancing—when people limit close contact with others outside their household—is a primary intervention available to combat the COVID-19 pandemic. The importance of social distancing is unlikely to change until effective treatments or vaccines become widely available. However, relatively little is known about how best to promote social distancing. Applying knowledge from social and behavioral research on conventional health behaviors (e.g., smoking, physical activity) to support public health efforts and research on social distancing is promising, but empirical evidence supporting this approach is needed. Purpose We examined whether one type of social distancing behavior—reduced movement outside the home—was associated with conventional health behaviors. Method We examined the association between GPS-derived movement behavior in 2,858 counties in USA from March 1 to April 7, 2020 and the prevalence of county-level indicators influenced by residents’ conventional health behaviors. Results Changes in movement were associated with conventional health behaviors, and the magnitude of these associations were similar to the associations among the conventional health behaviors. Counties with healthier behaviors—particularly less obesity and greater physical activity—evidenced greater reduction in movement outside the home during the initial phases of the pandemic in the USA. Conclusions Social distancing, in the form of reduced movement outside the home, is associated with conventional health behaviors. Existing scientific literature on health behavior and health behavior change can be more confidently used to promote social distancing behaviors during the COVID-19 pandemic.

scholarly journals Effect of depression on health behavior among myocardial infarction survivors in the United States

2020 ◽  
Vol 10 (4) ◽  
pp. 222-231
Author(s):  
Brooke Nicholson ◽  
Shawn Morse ◽  
Terra Lundgren ◽  
Nina Vadiei ◽  
Sandipan Bhattacharjee
Keyword(s):  
Physical Activity ◽  
United States ◽  
Myocardial Infarction ◽  
Health Behavior ◽  
Health Behaviors ◽  
Smoking Status ◽  
The United States ◽  
Blood Cholesterol ◽  
Behavioral Risk ◽  
Cross Sectional

Abstract Introduction The purpose of this study was to evaluate the effect of depression on health behavior among myocardial infarction (MI) survivors. Methods This retrospective, cross-sectional study used publicly available 2015 Behavioral Risk Factor Surveillance System (BRFSS) data. Our study sample includes adults aged 50 years or older who completed the 2015 BRFSS survey and reported having MI. The BRFSS participants with a yes response to the question, Has a doctor, nurse, or other health care professional ever told you that you had a heart attack, also called a myocardial infarction? were identified as MI survivors. The presence or absence of depression among MI survivors was identified using a similar question. Health behaviors, the dependent variable of this study, included physical activity, smoking status, alcohol use, body mass index, last flu immunization, last physical checkup, last blood cholesterol check, heavy drinking, and vegetable and fruit consumption. Univariate (χ2 tests) and multivariable (binomial logistic regression) analyses were used to assess the differences in health behaviors between MI survivors with or without depression. Results Our final study sample consists of 20 483 older adults with MI among whom 5343 (26.19%) reported having depression. Multivariable analyses reveal MI survivors with depression are more overweight, have less physical activity, and have higher likelihood of smoking but less odds of consuming alcohol compared to MI survivors without depression. Discussion In this nationally representative sample of adults aged over 50 years in the United States, MI survivors with depression exhibited poorer health behaviors compared to those without depression.

scholarly journals Fear of Cancer Recurrence: A Model Examination of Physical Symptoms, Emotional Distress, and Health Behavior Change

2019 ◽  
Vol 15 (9) ◽  
pp. e787-e797 ◽  
Author(s):  
Daniel L. Hall ◽  
Rachel B. Jimenez ◽  
Giselle K. Perez ◽  
Julia Rabin ◽  
Katharine Quain ◽  
...  
Keyword(s):  
Physical Activity ◽  
Health Behavior ◽  
Behavior Change ◽  
Health Behaviors ◽  
Emotional Distress ◽  
Cancer Recurrence ◽  
Health Behavior Change ◽  
Physical Symptoms ◽  
Fear Of Cancer Recurrence ◽  
Fear Of Cancer

PURPOSE: Fear of cancer recurrence is highly prevalent among adult survivors of cancer. The role of fear of recurrence in the emotional distress of survivors of cancer, as well as health behaviors that may directly affect their health, remains unclear. To advance oncology practice, this study sought to examine the extent to which fear of recurrence stemming from physical symptoms accounts for emotional distress in a large sample of adult survivors of cancer and to extend the model to explain postdiagnosis self-reported health behavior change. METHODS: In 2016, 258 survivors of cancer at an academic hospital completed a survey of psychosocial needs. Items assessed physical symptoms (checklist), fear of cancer recurrence (Assessment of Survivor Concerns), emotional distress (anxiety and depressed mood), and health behaviors (current alcohol use, physical activity, diet, and sunscreen use, as well as changes after cancer diagnosis) informed by National Comprehensive Cancer Network survivorship guidelines. Indirect effects regression models accounting for relevant covariates (age and treatment history) used 5,000-iteration bootstrapping. RESULTS: Higher fear of cancer recurrence was associated with greater number of physical symptoms ( P < .001), greater emotional distress ( P < .05), lower moderate or vigorous physical activity ( P < .05), higher sunscreen use ( P < .05), and postdiagnosis increases in alcohol use ( P < .01) and reductions in physical activity ( P < .01). Fear of cancer recurrence models accounted for almost half of the variance in distress of survivors of cancer ( R2 = 0.44, P < .001) and, to a lesser yet significant extent, changes in alcohol consumption ( R2 = 0.09, P < .001) and physical activity ( R2 = 0.06, P = .003). CONCLUSION: Fear of cancer recurrence plays a central role in the emotional distress and key health behaviors of survivors of cancer. These findings support fear of cancer recurrence as a potential target for emotional health and health behavior change interventions.

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

10.2196/23902 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23902
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

scholarly journals Factors Associated with Initiation and Sustenance of Stress Management Behaviors in Veterinary Students: Testing of Multi-Theory Model (MTM)

2020 ◽  
Vol 17 (2) ◽  
pp. 631 ◽  
Author(s):  
Vinayak K. Nahar ◽  
Julia K. Wells ◽  
Robert E. Davis ◽  
Elizabeth C. Johnson ◽  
Jason W. Johnson ◽  
...  
Keyword(s):  
United States ◽  
Health Behavior ◽  
Behavior Change ◽  
Stress Management ◽  
Health Behavior Change ◽  
The United States ◽  
Theory Model ◽  
Veterinary Students ◽  
Cross Sectional ◽  
Management Behaviors

Veterinary students across the United States face the challenge of stress during school every day. When managed improperly, stress can become chronic and manifest in physical and emotional consequences. The purpose of this study was to examine the utility of the multi-theory model (MTM) of health behavior change in predicting the initiation and sustenance of stress management behaviors among veterinary students. A cross-sectional design was used to study the efficacy of the MTM in predicting initiation and sustenance of stress management behaviors among veterinary students at a private College of Veterinary Medicine in the Southeast United States. Researchers collected data using a 54-item valid and reliable survey. Only students who did not already engage in daily stress management behaviors were included in the study. After recruitment and exclusion, a total of 140 students remained and participated in the study. Hierarchical multiple regression revealed that, for initiation of stress management behaviors, 49.5% of the variance was explained by depression, academic classification, and behavioral confidence. Regarding sustenance of stress management behaviors, 50.4% of the variance was explained by perceived stress, depression, academic classification, and emotional transformation. MTM serves as a promising framework for predicting initiation and sustenance of health behavior change. Based on the results of this study, interventions aimed to promote stress management behaviors in veterinary students should focus on the MTM constructs of behavioral confidence and emotional transformation.

Iterative near-term forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level in the United States

2020 ◽  
Author(s):  
Kenneth Newcomb ◽  
Morgan E. Smith ◽  
Rose E. Donohue ◽  
Sebastian Wyngaard ◽  
Caleb Reinking ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
The State ◽  
Data Driven ◽  
Effective Control ◽  
County Level ◽  
Movement Restriction ◽  
Social Distancing ◽  
Social Interventions ◽  
Near Term

Abstract The control of the initial outbreak and spread of SARS-CoV-2/COVID-19 by the implementation of unprecedented population-wide non-pharmaceutical mitigation measures has led to remarkable success in dampening the pandemic globally. With many countries easing or beginning to lift these measures to restart activities presently, concern is growing regarding the impacts that such reopening of societies could have on the subsequent transmission of the virus. While mathematical models of COVID-19 transmission have played important roles in evaluating the general population-level impacts of these measures for curbing virus transmission, a key need is for models that are able to effectively capture the effects of the spatial and social heterogeneities that drive the epidemic dynamics observed at the local community level. Iterative near-term forecasting that uses new incoming epidemiological and social behavioural data to sequentially update locally-applicable transmission models can overcome this gap, potentially leading to better predictions and intervention actions. Here, we present the development of one such data-driven iterative modelling tool based on publically-available data and an extended SEIR model for forecasting SARS-CoV-2 at the county level in the United States, and demonstrate, using data from the state of Florida, how this tool can be used to explore the outcomes of the social measures proposed for containing the course of the pandemic as a result of easing the initially imposed lockdown in the state. We provide comprehensive results showing the use of the locally identified models for accessing the impacts and societal tradeoffs of using specific strategies involving movement restriction, social distancing and mass testing, and conclude that while it is absolutely vital to continue with these measures over the near-term and likely to the end of March 2021 in all counties for containing the ongoing pandemic before less socially-disruptive vaccination strategies come into play, it could be possible to lift the more disruptive movement restriction/social distancing measures by end of December 2020 if these are accompanied by widespread testing and contact tracing. Our findings further show that such intensified social interventions could potentially also bring about the control of the epidemic in low and some medium incidence counties first, supporting the development and deployment of a geographically-phased approach to reopening the economy of Florida. We have made our data-driven forecasting system publicly available for policymakers and health officials to use in their own locales, with the hope that a more efficient coordinated strategy for controlling SARS-CoV-2 state-wide, based on effective control of viral transmission at the county level, can be developed and successfully implemented.

scholarly journals Differential Effects of Intervention Timing on COVID-19 Spread in the United States

2020 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Metropolitan Areas ◽  
Human Mobility ◽  
The United States ◽  
Aggressive Response ◽  
Control Measures ◽  
County Level ◽  
Social Distancing ◽  
Us Counties

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 61.6% [95% CI: 54.6%-67.7%] of reported infections and 55.0% [95% CI: 46.1%-62.2%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

scholarly journals Prediction of COVID-19 Social Distancing Adherence (SoDA) on the United States county-level

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Myles Ingram ◽  
Ashley Zahabian ◽  
Chin Hur
Keyword(s):  
United States ◽  
Prediction Model ◽  
Mobile Phone ◽  
The United States ◽  
Population Based ◽  
Mobile Phone Data ◽  
County Level ◽  
Social Distancing ◽  
Work From Home ◽  
First Case

AbstractSocial distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p < 0.00001) and owner-occupied housing unit rate (β = −0.322, p < 0.00001) were the most positively correlated and negatively correlated features to SoDA, respectively. Counties with higher per capita income, older persons, and more suburban areas were positively associated with adherence while counties with higher African American population, high obesity rate, earlier first COVID-19 case/death, and more Republican-leaning residents were negatively correlated with adherence. The base model predicted county SoDA with 90.8% accuracy. The model using only COVID-19-related features predicted with 64% accuracy and the model using the top 25 most substantial features predicted with 89% accuracy. Our results indicate that economic features, health features, and a few other features, such as political affiliation, race, and the time since the first case/death, impact SoDA on a countywide level. These features, combined, can predict adherence with a high level of confidence. Our prediction model could be utilized to inform health policy planning and potential interventions in areas with lower adherence.

Testing a Theoretical Model of Immigration Transition and Physical Activity

2015 ◽  
Vol 29 (3) ◽  
pp. 177-188 ◽  
Author(s):  
Sun Ju Chang ◽  
Eun-Ok Im
Keyword(s):  
Physical Activity ◽  
United States ◽  
Theoretical Model ◽  
Length Of Stay ◽  
Health Behaviors ◽  
Hispanic Women ◽  
The United States ◽  
Asian Women ◽  
Country Of Birth ◽  
Level Of Acculturation

The purposes of the study were to develop a theoretical model to explain the relationships between immigration transition and midlife women’s physical activity and test the relationships among the major variables of the model. A theoretical model, which was developed based on transitions theory and the midlife women’s attitudes toward physical activity theory, consists of 4 major variables, including length of stay in the United States, country of birth, level of acculturation, and midlife women’s physical activity. To test the theoretical model, a secondary analysis with data from 127 Hispanic women and 123 non-Hispanic (NH) Asian women in a national Internet study was used. Among the major variables of the model, length of stay in the United States was negatively associated with physical activity in Hispanic women. Level of acculturation in NH Asian women was positively correlated with women’s physical activity. Country of birth and level of acculturation were significant factors that influenced physical activity in both Hispanic and NH Asian women. The findings support the theoretical model that was developed to examine relationships between immigration transition and physical activity; it shows that immigration transition can play an essential role in influencing health behaviors of immigrant populations in the United States. The NH theoretical model can be widely used in nursing practice and research that focus on immigrant women and their health behaviors. Health care providers need to consider the influences of immigration transition to promote immigrant women’s physical activity.

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