scholarly journals Development and First Year Results of a Psychosocial Surveillance System for Chronic Disease Related Health Behaviors

2003 ◽  
Vol 1 (SI) ◽  
pp. 54-64 ◽  
Author(s):  
Jay E. Maddock ◽  
Carrie S. Marshall ◽  
Claudio R. Nigg ◽  
Jodi D. Barnett
Keyword(s):  
Physical Activity ◽  
Health Promotion ◽  
Behavior Change ◽  
Health Behaviors ◽  
Tobacco Use ◽  
Surveillance System ◽  
The United States ◽  
Behavioral Risk ◽  
Surveillance Systems ◽  
Target Behaviors

Chronic diseases account for 7 out of 10 deaths in the United States and 60% of the Nation’s health care expenses. Tobacco use, lack of physical activity and poor nutrition account for one third of US mortality. Behavioral surveillance systems such as the Behavioral Risk Factor Surveillance System (BRFSS) provide information on rates of behavior in the population and among different demographic categories. While these systems are essential for health promotion they do not assist the health educator in understanding psychosocial factors which may be related to the rates. A psychosocial surveillance system can aid in understanding the behavior change process and in the readiness of the population for behavior change. Results can assist states and localities in targeting health promotion messages and programs and can help in the allocation of often scarce health promotion funds. In 2000, the Hawaii Department of Health launched the Healthy Hawaii Initiative, a statewide program to reduce tobacco use, increase physical activity, and improve nutrition. As part of the evaluation, researchers at the University of Hawaii implemented a psychosocial surveillance system for the three target behaviors to assess changes in hypothesized mediators including stage of change, self efficacy, attitude and social norms. A random digit dial survey was conducted in the Spring and Fall of 2002 with 4,706 and 4,555 participants, respectively. Results show stability in the demographic characteristics and health behaviors of the sample but changes in the psychosocial variables. Several possible areas for interventions and messaging are demonstrated. A psychosocial surveillance system can be an important tool for health promotion and can lead to better understanding of health behaviors and attitudes.

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.

Physical Health Promotion through Modern Technologies

2016 ◽  
pp. 889-907
Author(s):  
Sandul Yasobant
Keyword(s):  
Physical Activity ◽  
Health Promotion ◽  
Health Behaviors ◽  
Physical Health ◽  
Interactive Video ◽  
Future Research ◽  
Positive Health ◽  
Physical Activity Interventions ◽  
Future Research Directions

Health promotion and the maintenance of the quality of life are realized recently. Advancement in technologies offer new possibilities for both the promotion of positive health behaviors that were unimaginable even a decade ago. Though promoting physical activity has been proven an important component of health promotion by many researchers, still a lot of efforts on how to improve physical activity being provided by group of researchers. Technology such as pedometers, accelerometers, and heart rate monitors have been used to promote physical activity for years. Newer technologies such as global positioning system (GPS), geographic information systems (GIS), interactive video games, and persuasive technology, Internet-based physical activity interventions have been used recently to promote and change exercise behavior. This chapter seeks to provide a complete insight of technologies used to changing health behaviors especially physical health promotion and will take a forward to analyses all the issues while using these technologies and future research directions.

Evaluating Chronic Disease Surveillance Systems: Lessons Learned From Applying a Framework to Assess the Alaska Behavioral Risk Factor Surveillance System

2020 ◽  
Vol 41 (3) ◽  
pp. 420-431
Author(s):  
Katie Cueva ◽  
Andrea Fenaughty ◽  
Jessica Aulasa Liendo ◽  
Samantha Hyde-Rolland
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Lessons Learned ◽  
Behavioral Risk ◽  
Surveillance Systems ◽  
Behavioral Risk Factor ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

Chronic diseases with behavioral risk factors are now the leading causes of death in the United States. A national Behavioral Risk Factor Surveillance System (BRFSS) monitors those risk factors; however, there is a need for national and state evaluations of chronic disease surveillance systems. The Department of Health and Human Services/Centers for Disease Control and Prevention (CDC) has developed a framework on evaluating noncommunicable disease–related surveillance systems; however, no implementation of this framework has yet been published. This article describes the process of, and offers lessons learned from, implementing the evaluation framework to assess the Alaska BRFSS. This implementation evaluation may inform assessments of other state and regional chronic disease surveillance systems and offers insight on the positive potential to consult key stakeholders to guide evaluation priorities.

scholarly journals The FLASHE Study: A Publicly Available Data Resource

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1337-1337
Author(s):  
Linda Nebeling ◽  
Laura Dwyer ◽  
April Oh
Keyword(s):  
Physical Activity ◽  
Health Behaviors ◽  
The United States ◽  
National Sample ◽  
Study Data ◽  
Accelerometer Data ◽  
Sedentary Behaviors ◽  
Data Resource ◽  
Neighborhood Socioeconomic Status ◽  
Funding Sources

Abstract Objectives The Family Life, Activity, Sun, Health and Eating (FLASHE) survey was conducted in 2014 to collect information on eating, physical activity, sedentary behaviors, and behavioral correlates from a national sample of parent-adolescent dyads in the United States. It is a publicly available data resource which can be used to research questions about psychosocial, generational, household and neighborhood correlates of health behaviors. Methods Data were collected from dyads of caregivers and their adolescent children (ages 12–17) (n = 1072 completed dyads) between April – October 2014. Two surveys for each respondent: one on diet-related behaviors and one on physical activity-related behaviors were collected. An additional 407 dyads wore an accelerometer for seven days and completed an activity log. A ‘geoFLASHE’ dataset used parent-provided address information for home and school to geocode these locations and compute a set of variables applied to several different neighborhood definitions, including both circular and street-network buggers with distances ranging from 400 – 1200 meters. Accelerometer variables were computed for a subset of adolescents who wore an Actigraph Gt3x+ for seven days and includes estimates from raw and activity counts data. Training webinars on dyadic analytical methods, models and applications were provided. Results The geoFLASHE dataset includes variables for neighborhood, socioeconomic status factor, factor scores for built environment characteristics (high density, older neighborhood, and short commutes), and other variables for each buffer configuration. The adolescent accelerometer dataset offers summary variables of accelerometer data and minute-level estimates of light, moderate, and vigorous activity using Crouter, Chandler, and GGIR processing methods. Additionally, training webinar, questionnaires, survey data files and codebooks are available. Conclusions The FLASHE study data resources can be used to understand cancer-related health behaviors in family dyads. Funding Sources FLASHE study was funded by the National Cancer Institute under contract number HHSN2612012000391 issued to Westat, Inc.

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