Associations Between Cardiovascular Health and Health-Related Quality of Life, Behavioral Risk Factor Surveillance System, 2013
CME ACTIVITY — Volume 13 — July 28, 2016
Erika C. Odom, PhD; Jing Fang, MD; Matthew Zack, MD; Latetia Moore, PhD; Fleetwood Loustalot, PhD
Suggested citation for this article: Odom EC, Fang J, Zack M, Moore L, Loustalot F. Associations Between Cardiovascular Health and Health-Related Quality of Life, Behavioral Risk Factor Surveillance System, 2013. Prev Chronic Dis 2016;13:160073. DOI: http://dx.doi.org/10.5888/pcd13.160073.
The American Heart Association established 7 cardiovascular health metrics as targets for promoting healthier lives. Cardiovascular health has been hypothesized to play a role in individuals’ perception of quality of life; however, previous studies have mostly assessed the effect of cardiovascular risk factors on quality of life.
Data were from the 2013 Behavioral Risk Factor Surveillance System, a state-based telephone survey of adults 18 years or older (N = 347,073). All measures of cardiovascular health and health-related quality of life were self-reported. The 7 ideal cardiovascular health metrics were normal blood pressure, cholesterol, body mass index, not having diabetes, not smoking, being physically active, and having adequate fruit or vegetable intake. Cardiovascular health was categorized into meeting 0–2, 3–5, or 6–7 ideal cardiovascular health metrics. Logistic regression models examined the association between cardiovascular health, general health status, and 3 measures of unhealthy days per month, adjusting for age, sex, race/ethnicity, education, and annual income.
Meeting 3 to 5 or 6 to 7 ideal cardiovascular health metrics was associated with a 51% and 79% lower adjusted prevalence ratio (aPR) of fair/poor health, respectively (aPR = 0.49, 95% confidence interval [CI] [0.47–0.50], aPR = 0.21, 95% CI [0.19–0.23]); a 47% and 72% lower prevalence of ≥14 physically unhealthy days (aPR = 0.53, 95% CI [0.51–0.55], aPR = 0.28, 95% CI [0.26–0.20]); a 43% and 66% lower prevalence of ≥14 mentally unhealthy days (aPR = 0.57, 95% CI [0.55–0.60], aPR = 0.34, 95% CI [0.31–0.37]); and a 50% and 74% lower prevalence of ≥14 activity limitation days (aPR = 0.50, 95% CI [0.48–0.53], aPR = 0.26, 95% CI [0.23–0.29]) in the past 30 days.
Achieving a greater number of ideal cardiovascular health metrics may be associated with less impairment in health-related quality of life.
Cardiovascular disease (CVD) is the leading cause of death in the United States, accounting for about 1 in 3 deaths annually (1). The economic burden of CVD accounts for over $120 billion per year in lost productivity costs from premature illness and death (2). However, CVD mortality rates have declined during the past 4 decades. Nationally, about half of the downward shift from 1980 to 2000 in coronary heart disease, a major subcategory of heart disease, was attributable to population declines in CVD risk factors and improved health behaviors (3). The primary risk factors for CVD are well known and in 2010, the American Heart Association (AHA) collated these risk factors into a composite measure of cardiovascular health, known as Life’s Simple 7 (4). The 7 cardiovascular health metrics (CVHM) include body mass index (BMI), smoking status, physical activity, a measure of dietary intake, glucose, blood pressure, and cholesterol. Poor, intermediate, and ideal ranges were developed for each of the 7 CVHM. Findings from large, prospective studies consistently indicate that individuals with a higher number of ideal CVHMs have lower risk of ischemic heart disease, cardiovascular mortality, and all-cause mortality than individuals with 0 to 1 ideal CVHMs (5,6). Yet, fewer than 3.5% (range, 0.1%–3.3%) of US adults have ideal levels of all 7 CVHM (5–7).
Although CVD events like heart failure, heart attack, and stroke are the typical measures of illness examined in studies linking the CVHM and health outcomes, health-related quality of life (HRQOL) is also an important measure of cardiovascular illness (8–10). HRQOL indicates patients’ perceptions of their general, physical, and mental health status and describes health burden in a population. Many studies suggest that people with at least one CVD risk factor, such as diabetes, hypertension, high cholesterol, or obesity, report less than “good” HRQOL (8,9); however, less is known about the association between cardiovascular health and HRQOL (10). The primary objective of this investigation was to determine the association between 7 ideal CVHMs and HRQOL (ie, self-reported general health status and 3 measures of unhealthy days) in the 2013 Behavioral Risk Factor Surveillance System (BRFSS).
We used data from the 2013 BRFSS, an ongoing, state-based telephone survey of the noninstitutionalized US population aged ≥18 years (N = 491,773; weighted N = 246,024,416). Participants are selected using a stratified, multistage probability sampling design, incorporating random-digit–dialing methods for data collection via both landline and cellular telephones. BRFSS uses iterative proportional fitting to improve representation of the respondents to the entire US adult population based on sex, age, race/ethnicity, county, region, telephone service (landline, cellular phone, or both), tenure (renting or owning a home), marital status, and education (11). In 2013, the median state response rate was 46.4%, with a range of 29.0% to 60.3% across different states (12). After excluding individuals with missing data on any outcome, predictor, or control measure (except income), a total analytic sample of 347,073 (71%; weighted n = 156,525,839) remained. Compared with excluded individuals, a higher proportion of people in the analytic sample had a high income (44% vs 25%, >$50k), were more educated (30% vs 18%, college educated), were older (22.5% vs 11.8%, 65 or older), and less racially/ethnically diverse (69.1% vs 56.8% white).
HRQOL was measured using 4 self-reported indicators: 1) general health status (“Would you say that in general your health is excellent, very good, good, fair, or poor?”); 2) physically unhealthy days per month (“Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?”); 3) mentally unhealthy days per month (“Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”); and 4) days per month of activity limitation (“During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?”). The HRQOL indicators and their validity have been extensively described (13). We dichotomized responses for general health status as “fair/poor” or “excellent/very good/good.” Additionally, we dichotomized each of the remaining indicators into mutually exclusive groups depending on whether an individual reported 14 or more unhealthy days or fewer than 14 unhealthy days (α = 0.70). Previous studies define 14 or more unhealthy days as a meaningful cut point for those reporting substantially impaired HRQOL (8,14). We also calculated average unhealthy days, indicating the mean number of physically or mentally unhealthy days per month (ie, a maximum of 30 days).
Proxy indicators for CVHM included participant self-report of BMI, current smoking status, physical activity, consumption of fruits or vegetables, diabetes, high blood pressure, and high cholesterol ( Appendix). Relevant BRFSS questions for the CVHMs used in this study were based on AHA standards and have been used in previous analyses (4,7). Responses for each of the 7 CVHM were dichotomized as ‘0’ for not meeting the ideal or ‘1’ for meeting the ideal status for that individual metric and were based on self-report. The seven CVHM are summed for a score, with a range of 0 to 7. For the purposes of this study, we created a 3-level categorical score of cardiovascular health (CVH composite score) to indicate meeting ideal status on 0 to 2, 3 to 5, and 6 to 7 CVHM. Ideal smoking status included those who had not smoked at least 100 cigarettes during their lifetime or who smoked at least 100 cigarettes during their lifetime but were not currently smoking. Ideal physical activity included meeting weekly aerobic recommendations of 150 or more minutes of moderate-intensity activity, or 75 or more minutes of vigorous intensity activity, or an equivalent combination. Fruit and vegetable intake was reported via a 6-item screener on consumption of 100% fruit juice, whole fruit, dried beans, dark green vegetables, orange vegetables, and other vegetables during the previous month. Individuals were classified as having an ideal diet if their consumption met or exceeded age- and sex-specific federal fruit or vegetable intake recommendations for those with a sedentary lifestyle (15). The indicators for hypertension, high cholesterol, and diabetes were categorized as “no” (ideal) or “yes” based on self-report.
Sociodemographic control variables included age group (≥18–24, 25–34, 35–44, 45–54, 55–64, ≥65), sex (male, female), race/ethnicity (non-Hispanic whites, non-Hispanic blacks, non-Hispanic Asian, non-Hispanic American Indian/Alaskan Natives, Hispanics, non-Hispanic persons of other races), education (<high school diploma, high school diploma, some college, ≥college graduate), and annual household income (<$25K, ≥$25K to $50K, >$50K).
We estimated the prevalence and 95% confidence intervals (CIs) of HRQOL measures for selected sociodemographic characteristics, the 7 CVHM, and the CVH composite score. Prevalence estimates were age-standardized to the 2000 US standard population, except for those associated with specific age groups. Finally, we used multiple logistic regression to examine the association between meeting ideal cardiovascular health and the likelihood of reporting poor general health and each measure of unhealthy days, adjusting for age, sex, education, race, and income. About 10% of the BRFSS survey sample had missing information on income; therefore, a “missing” category was created for the income variable (ie, <$25K, ≥$25K-$50K, >$50K, missing). There were 8 logistic regression models for general health status and each measure of unhealthy days — 7 for each CVHM as the primary predictor and 1 for the CVH composite score as the primary predictor. For all models, we estimated model-adjusted prevalence ratios (aPR) on average marginal predictions (16). Because multiple comparisons were made on the HRQOL variables, we used the Bonferroni correction, the most conservative approach for declaring significance. Differences were significant if P < .0063. We used SAS 9.3 and SAS-callable SUDAAN (SAS Institute, Inc.) with design variables and sampling weights to account for the complex survey design.
Overall, 16.1% of adults reported their general health status as fair or poor, a proportion that increased with age, was higher in women than men, and higher in Hispanics than other racial/ethnic groups. The prevalence of fair/poor health decreased with higher education and income. Similar patterns occurred by sex, education, and income across the remaining measures of HRQOL. The overall prevalence for 14 or more physically unhealthy days was 11.3%; 14 or more mentally unhealthy days, 10.8%; and 14 or more activity limitation days, 7.6% (Table 1). Unlike the other unhealthy days indicators, the percentage of adults with 14 or more mentally unhealthy days generally decreased with age.
For each of the 7 CVHM, the prevalence of ideal cardiovascular health ranged from 16.4% (met fruit or vegetable intake) to 87.8% (no history of diabetes; Table 2). Although 16.5% of individuals reported meeting 0 to 2 CVHM, 69.4% met 3 to 5 CVHM and 14.1% of adults met 6 or more CVHM. Only 2.4% of individuals met ideal cardiovascular health for all 7 metrics. In general, there was an inverse relationship between CVHM and HRQOL; the prevalence of poor general health or unhealthy days was 1.5 to 3 times as high among adults who reported not meeting ideal cardiovascular health for each metric (except for meeting fruit or vegetable intake; Table 2). Adults meeting 0 to 2 CVHM reported an average of 11.3 (standard error [SE], 0.21) unhealthy days per month; adults meeting 3 to 5 CVHM reported an average of 6.0 (SE, 0.05) unhealthy days; adults meeting 6 to 7 CVHM reported an average 3.6 (SE, 0.07) unhealthy days.
The prevalence of fair/poor health status was nearly 10 times as great among adults who met only 0 to 2 CVHM than among adults who met 6 to 7 CVHMs. Similarly, compared with adults with 6 to 7 CVHM, the prevalence of 14 or more physically unhealthy days was 6 times greater among adults who met only 0 to 2 CVHM; 4 times greater among adults reporting 14 or more mentally unhealthy days and 0 to 2 CVHM; and, 8 times greater among adults reporting 14 or more activity limitation days and 0 to 2 CVHM (Table 2).
In the logistic regression models, after controlling for sociodemographic variables, meeting ideal cardiovascular health was inversely associated with HRQOL (Table 3). The association between cardiovascular health and fair/poor health ranged from a 9% lower prevalence among individuals who met fruit or vegetable intake (aPR = 0.91 vs those who did not meet fruit or vegetable intake, 95% CI [0.87–0.95]) to a 56% lower prevalence of fair/poor health among people who did not have a history of diabetes (aPR = 0.44 vs those who reported being told they have diabetes, 95% CI [0.43–0.45]). The association between cardiovascular health and 14 or more physically unhealthy days ranged from a 19% lower prevalence among individuals who reported a normal BMI (aPR = 0.81 vs those with overweight/obese BMI or BMI <18.5, 95% CI [0.78–-0.85]) to a 46% lower prevalence among individuals who did not have a history of diabetes (aPR = .54 vs those who reported being told they have diabetes, 95% CI [0.52–0.56]). The association between cardiovascular health and 14 or more mentally unhealthy days ranged from a 8% lower prevalence among individuals who met fruit or vegetable intake (aPR = 0.92 vs those who did not meet fruit or vegetable intake, 95% CI [0.86–0.97]) to a 44% lower prevalence among individuals who did not currently smoke (aPR = 0.56 vs those who reported smoking, 95% CI [0.53–0.58]). Finally, the association between cardiovascular health and 14 or more activity limitation days ranged from a 16% lower prevalence among individuals who reported a normal BMI (aPR = 0.84 vs those with overweight/obese BMI or BMI <18.5, 95% CI [0.79–0.88]) to a 49% lower prevalence among individuals reporting moderate/vigorous physical activity (aPR = 0.51 vs those who reported not meeting physical activity recommendations, 95% CI [0.48–0.53]). There was no association between meeting fruit or vegetable intake goals and 14 or more physically unhealthy days or 14 or more activity limitation days.
Compared with adults meeting 2 or fewer CVHM, meeting 3 to 5 or 6 to 7 CVHM was associated with 51% and 79% lower prevalence of fair/poor health respectively (aPR = 0.49, 95% CI [0.47–0.50], aPR = 0.21, 95% CI [0.19–0.23]) ; 47% and 72% lower prevalence of 14 or more physically unhealthy days respectively (aPR = 0.53, 95% CI [0.51–0.55], aPR = 0.28, 95% CI [0.26–0.30]); 43% and 66% lower prevalence of 14 or more mentally unhealthy days respectively (aPR = 0.57, 95% CI [0.55–0.60], aPR = 0.34, 95% CI [0.31–0.37]); 50% and 74% lower prevalence of 14 or more activity limitation days respectively (aPR = 0.50, 95% CI [0.48–0.53], aPR = 0.26, 95% CI [0.23–0.29]) . Additionally, the adjusted prevalence ratio for meeting 6 to 7 CVHM was nearly half the adjusted prevalence ratio for meeting 3 to 5 CVHM across each of the poor HRQOL measures.
Our study examined the association between cardiovascular health and HRQOL measures in a population-based surveillance system. Adults meeting 0 to 2 CVHM reported an average of 11.3 unhealthy days per month; adults meeting 3 to 5 CVHM reported an average of 6.0 unhealthy days; adults meeting 6 to 7 CVHM reported an average of 3.6 unhealthy days. Compared with people meeting 0 to 2 CVHM, people meeting 6 or more CVHM were significantly less likely to report poor general health, 14 or more physically or mentally unhealthy days, and 14 or more days of activity limitation, suggesting that meeting ideal cardiovascular health recommendations may have a cumulative impact on various self-reported measures of health. Although the directionality and contributions of CVHM on HRQOL cannot be assessed, the association with cardiovascular health is evident and stable even in adjusted models.
Previous research has consistently demonstrated the benefits of improvements in cardiovascular health on traditional outcome measures, like heart disease and stroke mortality (5,6). Our results extend these findings by demonstrating the possible beneficial effect of cardiovascular health on HRQOL, another aspect of disease burden. Studies that have examined HRQOL as an outcome measure have primarily explored the association with cardiovascular risk factors (8,9,14,17,18). Yet, a recent study using the National Health and Nutrition Examination Survey (NHANES) also showed an association between cardiovascular health and quality of life ─ compared with those in poor CVH, individuals in intermediate CVH were 44% less likely to report being in fair or poor health, and individuals in ideal CVH were 71% less likely to report being in fair or poor health (19). Likewise, findings from the Look AHEAD trial, an intensive lifestyle intervention for people with type 2 diabetes, show that lifestyle modifications that support cardiovascular health (ie, including dietary changes) are associated with fewer hospitalizations, fewer medications, lower health care costs, and better quality of life, suggesting that cardiovascular health may be associated with HRQOL in ways beyond decreasing cardiovascular disease and disability (20,21).
The current study has a few limitations. The analytic sample included a higher proportion of individuals who were older, white, had more education, and a higher income than those excluded from the sample; thus, the generalizability of the findings may be limited.
Second, no clinical measures of BMI, smoking, diabetes, hypertension, or cholesterol were collected; because of this, it is possible that some participants were miscategorized as having ideal cardiovascular health and that we overestimate true prevalence. On the other hand, some participants with diabetes, hypertension, or high cholesterol may not have been diagnosed, underestimating true prevalence. Lower prevalence estimates of CVD risk factors have been consistently reported in validation studies of BRFSS when comparing them to studies with direct physical measures (22,23). Third, in the current study we estimated whether participants consumed recommended amounts of fruits or vegetables; only 3% of the participants reported meeting recommendations for both fruits and vegetables. We defined ideal diet using this more flexible classification (ie, meeting recommendations for fruit intake or meeting recommendations for vegetable intake) to support more stable statistical modeling; however, this methodology overestimates diet quality. Additionally, in BRFSS, diet quality is not a comprehensive measure of dietary recall. Although AHA’s healthy diet score is made on the basis of multiple components of a healthy diet (ie, intake of fruits and vegetables, whole grains, sodium, sugar-sweetened beverages, and fish), fruit and vegetable intake has been used in previous studies as a proxy for a diet supporting cardiovascular health (7,24). Finally, our study was cross-sectional and provides only a snapshot of participants’ report of cardiovascular health and HRQOL, which does not allow conclusions to be drawn about causality. Thus, although we found that having a greater number of CVHM was associated with less impairment in HRQOL, people with impaired HRQOL may be more sedentary and less likely to participate in behaviors that support cardiovascular health — decreasing one’s ability to attain ideal recommendations for the CVHM.
In conclusion, the current study findings support an association between cardiovascular health and less impairment in general health and unhealthy days. Although BRFSS has methodological limitations with respect to the traditional AHA definition of cardiovascular health, these findings are consistent with other research. By the year 2030, lost productivity costs due to cardiovascular illness and death are projected to rise to more than $275 billion (2). Promoting cardiovascular health could help improve the quality of life for all Americans by reducing the number of physically and mentally unhealthy days that individuals experience and reducing societal costs due to lost productivity. Primary care providers should continue to encourage lifestyle modifications that are heart-healthy, including meeting diet and physical activity recommendations, and use HRQOL measures as a screening tool to regularly monitor improvements or declines in self-reported health.
The authors report no conflicts of interest or financial disclosures. 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.
Corresponding Author: Erika C. Odom, 4770 Buford Hwy, Bldg 102, Mail Stop F-77, Atlanta, GA 30341. Telephone: 770-488-8218. Email:firstname.lastname@example.org.
Jing Fang, Matthew Zack, Latetia Moore, Fleetwood Loustalot, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia.
- Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, et al. ; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association [published correction appears in Circulation 2013;127:e6–e245]. Circulation 2013;127(1):e6–245. CrossRefPubMed
- Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. ; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2015 update: a report from the American Heart Association. Circulation 2015;131(4):e29–322. CrossRef PubMed
- Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, et al. Explaining the decrease in US deaths from coronary disease, 1980–2000. N Engl J Med 2007;356(23):2388–98.CrossRef PubMed
- Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. ; American Heart Association Strategic Planning Task Force and Statistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association’s strategic Impact Goal through 2020 and beyond. Circulation 2010;121(4):586–613. CrossRef PubMed
- Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD; Atherosclerosis Risk in Communities Study Investigators. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol 2011;57:1690–6. CrossRef PubMed
- Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, et al. Trends in cardiovascular health metrics and associations with all-cause and CVD mortality among US adults. JAMA 2012;307(12):1273–83. CrossRef PubMed
- Fang J, Yang Q, Hong Y, Loustalot F. Status of cardiovascular health among adult Americans in the 50 States and the District of Columbia, 2009. J Am Heart Assoc 2012;1(6):e005371.CrossRef PubMed
- Chen HY, Baumgardner DJ, Rice JP. Health-related quality of life among adults with multiple chronic conditions in the United States, Behavioral Risk Factor Surveillance System, 2007. Prev Chronic Dis 2011;8(1):A09. http://www.cdc.gov/pcd/issues/2011/jan/09_0234.htm. Accessed February 11, 2016. PubMed
- Li C, Ford ES, Mokdad AH, Balluz LS, Brown DW, Giles WH. Clustering of cardiovascular disease risk factors and health-related quality of life among US adults. Value Health 2008;11(4):689–99. CrossRef PubMed
- Daviglus ML, Liu K, Pirzada A, Yan LL, Garside DB, Feinglass J, et al. Favorable cardiovascular risk profile in middle age and health-related quality of life in older age. Arch Intern Med 2003;163(20):2460–8. CrossRef PubMed
- Behavioral Risk Factor Surveillance System. 2013 comparability of data. Atlanta (GA): Centers for Disease Control and Prevention; 2013. http://www.cdc.gov/brfss/annual_data/2013/pdf/Compare_2013.pdf. Accessed August 24, 2015.
- Behavioral Risk Factor Surveillance System. 2013 summary data quality report. Atlanta (GA): Centers for Disease Control and Prevention; 2013. http://www.cdc.gov/brfss/annual_data/2013/pdf/2013_DQR.pdf. Accessed August 24, 2015.
- Measuring healthy days: population assessment of health-related quality of life. Atlanta (GA): Centers for Disease Control and Prevention; 2000. http://www.cdc.gov/ HRQOL/pdfs/mhd.pdf. Accessed August 24, 2015.
- Brown DW, Balluz LS, Heath GW, Moriarty DG, Ford ES, Giles WH, et al. Associations between recommended levels of physical activity and health-related quality of life. Findings from the 2001 Behavioral Risk Factor Surveillance System (BRFSS) survey. Prev Med 2003;37(5):520–8. CrossRef PubMed
- Moore LV, Dodd KW, Thompson FE, Grimm KA, Kim SA, Scanlon KS. Using Behavioral Risk Factor Surveillance System data to estimate the percent of the population meeting USDA food patterns fruit and vegetable intake recommendations. Am J Epidemiol 2015;181(12):979–88. CrossRef PubMed
- Bieler GS, Brown GG, Williams RL, Brogan DJ. Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. Am J Epidemiol 2010;171(5):618–23. CrossRefPubMed
- Reuben DB, Tinetti ME. Goal-oriented patient care—an alternative health outcomes paradigm. N Engl J Med 2012;366(9):777–9. CrossRef PubMed
- Mody RR, Smith MJ. Smoking status and health-related quality of life: as findings from the 2001 Behavioral Risk Factor Surveillance System data. Am J Health Promot 2006;20(4):251–8.CrossRef PubMed
- Allen NB, Badon S, Greenlund KJ, Huffman M, Hong Y, Lloyd-Jones DM. The association between cardiovascular health and health-related quality of life and health status measures among U.S. adults: a cross-sectional study of the National Health and Nutrition Examination Surveys, 2001-2010. Health Qual Life Outcomes 2015;13(1):152. CrossRef PubMed
- Dutton GR, Lewis CE. The Look AHEAD trial: implications for lifestyle intervention in type 2 diabetes mellitus. Prog Cardiovasc Dis 2015;58(1):69–75. CrossRef PubMed
- Espeland MA, Glick HA, Bertoni A, Brancati FL, Bray GA, Clark JM, et al. ; Look AHEAD Research Group. Impact of an intensive lifestyle intervention on use and cost of medical services among overweight and obese adults with type 2 diabetes: the action for health in diabetes. Diabetes Care 2014;37(9):2548–56. CrossRef PubMed
- Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular disease risk factors assessed by telephone survey: the Behavioral Risk Factor Survey. J Clin Epidemiol 1993;46(6):561–71. CrossRef PubMed
- Fahimi M, Link M, Mokdad A, Schwartz DA, Levy P. Tracking chronic disease and risk behavior prevalence as survey participation declines: statistics from the behavioral risk factor surveillance system and other national surveys. Prev Chronic Dis 2008;5(3):A80. PubMed
- Dauchet L, Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr 2006;136(10):2588–93.PubMed