Multiple healthy behaviors and optimal self-rated health: Findings from the 2007 Behavioral Risk Factor Surveillance System Survey☆
Introduction
Unhealthy behaviors are the modifiable risk factors for the development of leading chronic diseases, including cardiovascular diseases (CVDs) and type 2 diabetes (Fine et al., 2004, Noar et al., 2008, Prochaska et al., 2008b). In particular, cigarette smoking, excessive drinking, physical inactivity, and inadequate daily consumption of fruits and vegetables are four modifiable unhealthy lifestyle behaviors that contribute to the development of many chronic diseases or conditions, such as heart attack, angina or coronary heart disease, stroke, and type 2 diabetes in the adult population (Fine et al., 2004, Greenlund et al., 2004, Khaw et al., 2008, Orozco et al., 2008, WHO, 2007). Individuals who have more than one unhealthy behavior are often at heightened risk for severe health consequences, including developing chronic conditions, co-morbidities, complications, disabilities, and premature death (Ford et al., 2009, Khaw et al., 2008). Nevertheless, many unhealthy behaviors are interrelated and amenable to interventions. In addition, the success in changing one unhealthy behavior may increase individuals' self-efficacy and motivation to modify other unhealthy behaviors (Eriksson et al., 2006a, Prochaska et al., 2008b, Schwarzer, 2008). Accumulating evidence shows that multiple health behavior change (MHBC) is an effective prevention strategy for individuals at risk for or already diagnosed with CVDs or diabetes (Eriksson et al., 2006b, Gaede et al., 2003, Goldstein et al., 2004, Jones et al., 2003, Jrgensen et al., 2003, Praet & van Loon, 2009). Interventions that address multiple health behaviors simultaneously or sequentially could not only confer increased efficacy, decreased likelihoods of co-morbidity, and improved quality of life, but could also prevent initiation of high-risk behaviors, reduce health care burden and socioeconomic costs among adult population (Edington, 2001, Eriksson et al., 2006a, Johnson et al., 2008, Ketola et al., 2000, Mills et al., 2007, Norris et al., 2001, Orozco et al., 2008, Prochaska et al., 2008b, Prochaska et al., 2006, Smith et al., 2005).
Self-rated health (SRH) is a validated, overall health indicator that is highly predictive of future morbidity and mortality, functional decline, and health care utilization among adults including those with CVDs and diabetes (DeSalvo et al., 2006, Idler & Benyamini, 1997, Idler et al., 2000, Jylha, 2009). Several previous studies evaluated the relationship between number of unhealthy behaviors and suboptimal SRH (i.e., fair or poor) (Li et al., 2008, Pisinger et al., 2009, Tsai et al., 2010b). To the best of our knowledge, no studies have evaluated an association between the number of healthy behaviors (i.e., cumulative and clustering patterns) and optimal SRH (i.e., excellent, very good, or good) pertaining to the general population of U.S. adults—especially those with CVDs or diabetes conditions. Population-based epidemiologic studies can provide important observational evidence to inform healthier lifestyle choices that foster optimal health and well-being among individuals and at-risk populations. Therefore, the aim of this study was to examine the association between the number of healthy behaviors (i.e., not currently smoking, not currently drinking excessively, physically active, and consuming fruits and vegetables five or more times per day) and optimal SRH among adults overall, as well as adults with CVDs or diabetes in the United States by using the data from the 2007 Behavioral Risk Factor Surveillance System (BRFSS) survey.
Section snippets
Participants
The BRFSS is the largest ongoing, state-based, random-digit-dialed telephone survey that collects information on health-related risk behaviors, preventive health practices, and health care access primarily related to chronic disease and injury (CDC, 2009a). With a survey median cooperation rate of 72.1% in 2007, a total of 430,912 individuals aged 18 years and older from all 50 states, the District of Columbia, Guam, Puerto Rico, and the Virgin Islands participated in the survey (CDC, 2009a).
Results
The age-standardized prevalence of reporting optimal SRH was 83.5%, 55.6%, and 56.3%, among adults overall, and adults with CVDs or diabetes, respectively (Table 2). Regardless of status of CVDs or diabetes, a greater prevalence of optimal SRH was found among participants who had a college education, were employed, were non-excessive drinkers, were physically active, or had no history of arthritis, when compared to their respective counterparts (p < 0.001 for χ2 test). Additionally, the
Discussion
Whereas several previous studies demonstrated a graded relationship between the number of unhealthy behaviors and suboptimal SRH, our study extends previous research by assessing the association between optimal SRH and multiple healthy behaviors specific to the general population of U.S. adults, as well adults with CVDs or diabetes. Furthermore, several population representative estimates, including the prevalence of optimal SRH, as well as the clustering patterns of healthy behaviors among
Conclusions
The findings of this study indicate that number of healthy behaviors is associated with optimal SRH among adults, especially adults with CVDs or diabetes. These findings reinforce the support for identifying and implementing clinical and population-based intervention strategies that effectively promote multiple lifestyle healthy behaviors among adults.
Conflict of interest statement
No potential conflicts of interest relevant to this article were reported.
Acknowledgments
The authors of this study sincerely thank BRFSS coordinators for all participating states and territories, the Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia, for making data available.
References (66)
- et al.
A meta-analysis of alcohol consumption and the risk of 15 diseases
Prev. Med.
(2004) - et al.
Physician screening for multiple behavioral health risk factors
Am. J. Prev. Med.
(2004) - et al.
Is physical activity a gateway behavior for diet? Findings from a physical activity trial
Prev. Med.
(2008) - et al.
Prevalence of multiple chronic disease risk factors. 2001 National Health Interview Survey
Am. J. Prev. Med.
(2004) - et al.
Multiple behavioral risk factor interventions in primary care. Summary of research evidence
Am. J. Prev. Med.
(2004) - et al.
Transtheoretical model-based multiple behavior intervention for weight management: effectiveness on a population basis
Prev. Med.
(2008) What is self-rated health and why does it predict mortality? Towards a unified conceptual model
Soc. Sci. Med.
(2009)- et al.
Self-rated health showed a graded association with frequently used biomarkers in a large population sample
J. Clin. Epidemiol.
(2006) - et al.
Clustering of cardiovascular disease risk factors and health-related quality of life among US adults
Value Health
(2008) - et al.
Self-rated health, chronic diseases, and symptoms among middle-aged and elderly men and women
J. Clin. Epidemiol.
(2002)
Applying health behavior theory to multiple behavior change: Considerations and approaches
Prev. Med.
Addressing multiple behavioral health risks in primary care. Broadening the focus of health behavior change research and practice
Am. J. Prev. Med.
The relationship between lifestyle and self-reported health in a general population: the Inter99 study
Prev. Med.
Associations of physical activity with smoking and alcohol consumption: a sport or occupation effect?
Prev. Med.
Physical activity as a strategy for maintaining tobacco abstinence: a randomized trial
Prev. Med.
Multiple health behavior change research: an introduction and overview
Prev. Med.
Integrating individual and public health perspectives for treatment of tobacco dependence under managed health care: a combined stepped-care and matching model
Ann. Behav. Med.
Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio
BMC Med. Res. Methodol.
Common factors predicting long-term changes in multiple health behaviors
J. Health Psychol.
Socioeconomic disparities in health in the United States: what the patterns tell us
Am. J. Public Health
Self-Rated Fair or Poor Health Among Adults with Diabetes - United States, 1996-2005
MMWR Morb. Mortal. Wkly. Rep.
Center for Disease Control and Prevention. Cigarette Smoking Among Adults—United States, 2006
MMWR Morb. Mortal. Wkly. Rep.
Behavioral Risk Factor Surveillance System
The Guide to Community Preventive Services
Moving toward paradigm-shifting research in health disparities through translational, transformational, and transdisciplinary approaches
Am. J. Public Health
The science of eliminating health disparities: summary and analysis of the NIH summit recommendations
Am. J. Public Health
Mortality prediction with a single general self-rated health question. A meta-analysis
J. Gen. Intern. Med.
U.S. Department of Health and Human Services. Healthy people 2010
2008 physical activity guidelines for Americans
Emerging research: a view from one research center
Am. J. Health Promot.
A randomized trial of lifestyle intervention in primary healthcare for the modification of cardiovascular risk factors
Scand. J. Public Health
A randomized trial of lifestyle intervention in primary healthcare for the modification of cardiovascular risk factors
Scand. J. Public Health
Healthy living is the best revenge: findings from the European Prospective Investigation Into Cancer and Nutrition-Potsdam study
Arch. Intern. Med.
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Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.