Weight Loss During the Intensive Intervention Phase of the Weight-Loss Maintenance Trial

Hollis, Jack F.; Gullion, Christina M.; Stevens, Victor J.; Brantley, Phillip J.; Appel, Lawrence J.; Ard, Jamy D.; Champagne, Catherine M.; Dalcin, Arlene; Erlinger, Thomas P.; Funk, Kristine L.; Laferriere, Daniel; Lin, Pao Hwa; Loria, Catherine M.; Samuel‐Hodge, Carmen D.; Vollmer, William M.; Svetkey, Laura P.

doi:10.1016/j.amepre.2008.04.013

Cited by 276 publications

(353 citation statements)

References 58 publications

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“…Race-related factors have been found to influence health behaviors. Health behavior profiles are less favorable among nonwhite groups compared with nonHispanic whites, 229,230,234 with a greater prevalence of unhealthy diets, inactivity, obesity, and medication nonadherence and lower healthcare use. Nonwhite patients are significantly more likely to be nonadherent to statins and antihypertensive medications, [235][236][237] and medication nonadherence has been shown to mediate the relationship between ethnicity and CVD outcomes.…”

Section: Behavioral Mechanismsmentioning

confidence: 99%

“…227 A study of inequalities in BMI and smoking behaviors in 70 countries concluded that a global trend exists toward an increasing burden of chronic disease risk among people of lower SEP as countries become more urban, 228 and another study showed that standard weight loss interventions may be less effective in blacks. 229,230 An English study that examined social inequalities of CVD risk factors in men found a significant increase in social inequality for smoking status and limited physical activity between 1998 and 2006, with increases in inequality over time resulting from improvements for those in higher socioeconomic classes. 231 However, other studies have suggested that differences in health behaviors may, at least partially, stem from differences in stressors and psychosocial resources associated not only with economic factors but also with race and social and residential environments.…”

Section: Behavioral Mechanismsmentioning

confidence: 99%

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Social Determinants of Risk and Outcomes for Cardiovascular Disease

Havranek¹,

Mujahid²,

Barr³

et al. 2015

Circulation

1,101

732

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Section: Behavioral Mechanismsmentioning

confidence: 99%

Section: Behavioral Mechanismsmentioning

confidence: 99%

Social Determinants of Risk and Outcomes for Cardiovascular Disease

Havranek¹,

Mujahid²,

Barr³

et al. 2015

Circulation

1,101

732

View full text Add to dashboard Cite

“…17,20 After 6 months on the DASH program, average weight loss in the intervention group was 5.8 kg in both trials, with standard deviations of 5.8 in the PREMIER trial and 4.4 in the larger WLM trial. Because of its greater sample size of 1685 participants, we based our effect estimates on the WLM trial.…”

Section: Intervention Effectivenessmentioning

confidence: 99%

“…[17][18][19] It emphasizes consumption of fruit, vegetables, low fat dairy products, whole grains, poultry, fish and nuts and the reduced consumption of fats, red meat, sweets and sugar-containing beverages. The trials that evaluated the DASH diet 17,19,20 also recommended 180 minutes per week of moderate intensity physical activity in conjunction with the diet.…”

Section: Interventionsmentioning

confidence: 99%

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Cost-effectiveness of diet and exercise interventions to reduce overweight and obesity

et al. 2011

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Objective: To analyze whether two dietary weight loss interventionsFthe dietary approaches to stop hypertension (DASH) program and a low-fat diet programF would be cost-effective in Australia, and to assess their potential to reduce the disease burden related to excess body weight. Design: We constructed a multi-state life-table-based Markov model in which the distribution of body weight influences the incidence of stroke, ischemic heart disease, hypertensive heart disease, diabetes mellitus, osteoarthritis, post-menopausal breast cancer, colon cancer, endometrial cancer and kidney cancer. The target population was the overweight and obese adult population in Australia in 2003. We used a lifetime horizon for health effects and costs, and a health sector perspective for costs. We populated the model with data identified from Medline and Cochrane searches, Australian Bureau of Statistics published catalogues, Australian Institute of Health and Welfare, and Department of Health and Ageing. Outcome measures: Disability adjusted life years (DALYs) averted, incremental cost-effectiveness ratios (ICERs) and proportions of disease burden avoided. ICERs under AUS$50 000 per DALY are considered cost-effective. Results: The DASH and low-fat diet programs have ICERs of AUS$12 000 per DALY (95% uncertainty range: Cost-savingF68 000) and AUS$13 000 per DALY (Cost-savingF130 000), respectively. Neither intervention reduced the body weight-related disease burden at population level by more than 0.1%. The sensitivity analysis showed that when participants' costs for time and travel are included, the ICERs increase to AUS$75 000 per DALY for DASH and AUS$49 000 per DALY for the low-fat diet. Modest weight loss during the interventions, post-intervention weight regain and low participation limit the health benefits. Conclusion: Diet and exercise interventions to reduce obesity are potentially cost-effective but have a negligible impact on the total body weight-related disease burden.

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