Initiating and maintaining behavior change is key to the prevention and treatment of most preventable chronic medical and psychiatric illnesses. The cultivation of mindfulness, involving acceptance and nonjudgment of present-moment experience, often results in transformative health behavior change. Neural systems involved in motivation and learning have an important role to play. A theoretical model of mindfulness that integrates these mechanisms with the cognitive, emotional, and self-related processes commonly described, while applying an integrated model to health behavior change, is needed. This integrative review (1) defines mindfulness and describes the mindfulness-based intervention movement, (2) synthesizes the neuroscience of mindfulness and integrates motivation and learning mechanisms within a mindful self-regulation model for understanding the complex effects of mindfulness on behavior change, and (3) synthesizes current clinical research evaluating the effects of mindfulness-based interventions targeting health behaviors relevant to psychiatric care. The review provides insight into the limitations of current research and proposes potential mechanisms to be tested in future research and targeted in clinical practice to enhance the impact of mindfulness on behavior change.

PURPOSE: To evaluate whether dispositional mindfulness (defined as the ability to attend nonjudgmentally to one's own physical and mental processes) is associated with obesity and central adiposity. METHODS: Study participants (n = 394) were from the New England Family Study, a prospective birth cohort, with median age 47 years. Dispositional mindfulness was assessed using the Mindful Attention Awareness Scale (MAAS). Central adiposity was assessed using dual-energy X-ray absorptiometry (DXA) scans with primary outcomes android fat mass and android/gynoid ratio. Obesity was defined as body mass index > /=30 kg/m(2). RESULTS: Multivariable-adjusted regression analyses demonstrated that participants with low vs. high MAAS scores were more likely to be obese (prevalence ratio for obesity = 1.34 (95 % confidence limit (CL): 1.02, 1.77)), adjusted for age, gender, race/ethnicity, birth weight, childhood socioeconomic status, and childhood intelligence. Furthermore, participants with low vs. high MAAS level had a 448 (95 % CL 39, 857) g higher android fat mass and a 0.056 (95 % CL 0.003, 0.110) greater android/gynoid fat mass ratio. Prospective analyses demonstrated that participants who were not obese in childhood and became obese in adulthood (n = 154) had -0.21 (95 % CL -0.41, -0.01; p = 0.04) lower MAAS scores than participants who were not obese in childhood or adulthood (n = 203). CONCLUSIONS: Dispositional mindfulness may be inversely associated with obesity and adiposity. Replication studies are needed to adequately establish whether low dispositional mindfulness is a risk factor for obesity and adiposity.

OBJECTIVE: In 2010, the American Diabetes Association (ADA) added hemoglobin A1c (A1C) to the guidelines for diagnosing type 2 diabetes. However, existing models for predicting diabetes risk were developed prior to the widespread adoption of A1C. Thus, it remains unknown how well existing diabetes risk prediction models predict incident diabetes defined according to the ADA 2010 guidelines. Accordingly, we examined the performance of an existing diabetes prediction model applied to a cohort of African American (AA) and white adults from the Coronary Artery Risk Development Study in Young Adults (CARDIA). RESEARCH DESIGN AND METHODS: We evaluated the performance of the Atherosclerosis Risk in Communities (ARIC) diabetes risk prediction model among 2,456 participants in CARDIA free of diabetes at the 2005-2006 exam and followed for 5 years. We evaluated model discrimination, calibration, and integrated discrimination improvement with incident diabetes defined by ADA 2010 guidelines before and after adding baseline A1C to the prediction model. RESULTS: In the overall cohort, re-estimating the ARIC model in the CARDIA cohort resulted in good discrimination for the prediction of 5-year diabetes risk (area under the curve [AUC] 0.841). Adding baseline A1C as a predictor improved discrimination (AUC 0.841 vs. 0.863, P = 0.03). In race-stratified analyses, model discrimination was significantly higher in whites than AA (AUC AA 0.816 vs. whites 0.902; P = 0.008). CONCLUSIONS: Addition of A1C to the ARIC diabetes risk prediction model improved performance overall and in racial subgroups. However, for all models examined, discrimination was better in whites than AA. Additional studies are needed to further improve diabetes risk prediction among AA. long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

The purpose of this review is to provide (1) a synopsis on relations of mindfulness with cardiovascular disease (CVD) and major CVD risk factors, and (2) an initial consensus-based overview of mechanisms and theoretical framework by which mindfulness might influence CVD. Initial evidence, often of limited methodological quality, suggests possible impacts of mindfulness on CVD risk factors including physical activity, smoking, diet, obesity, blood pressure, and diabetes regulation. Plausible mechanisms include (1) improved attention control (e.g., ability to hold attention on experiences related to CVD risk, such as smoking, diet, physical activity, and medication adherence), (2) emotion regulation (e.g., improved stress response, self-efficacy, and skills to manage craving for cigarettes, palatable foods, and sedentary activities), and (3) self-awareness (e.g., self-referential processing and awareness of physical sensations due to CVD risk factors). Understanding mechanisms and theoretical framework should improve etiologic knowledge, providing customized mindfulness intervention targets that could enable greater mindfulness intervention efficacy.

BACKGROUND: The impact of a healthy lifestyle on risk of heart failure (HF) is not well known. OBJECTIVES: The objectives of this study were to evaluate the effect of a combination of lifestyle factors on incident HF and to further investigate whether weighting each lifestyle factor has additional impact. METHODS: Participants were 84,537 post-menopausal women from the WHI (Women's Health Initiative) observational study, free of self-reported HF at baseline. A healthy lifestyle score (HL score) was created wherein women received 1 point for each healthy criterion met: high-scoring Alternative Healthy Eating Index, physically active, healthy body mass index, and currently not smoking. A weighted score (wHL score) was also created in which each lifestyle factor was weighted according to its independent magnitude of effect on HF. The incidence of hospitalized HF was determined by trained adjudicators using standardized methodology. RESULTS: There were 1,826 HF cases over a mean follow-up of 11 years. HL score was strongly associated with risk of HF (multivariable-adjusted hazard ratio [HR] [95% confidence interval (CI)] 0.49 [95% CI: 0.38 to 0.62], 0.36 [95% CI: 0.28 to 0.46], 0.24 [95% CI: 0.19 to 0.31], and 0.23 [95% CI: 0.17 to 0.30] for HL score of 1, 2, 3, and 4 vs. 0, respectively). The HL score and wHL score were similarly associated with HF risk (HR: 0.46 [95% CI: 0.41 to 0.52] for HL score; HR: 0.48 [95% CI: 0.42 to 0.55] for wHL score, comparing the highest tertile to the lowest). The HL score was also strongly associated with HF risk among women without antecedent coronary heart disease, diabetes, or hypertension. CONCLUSIONS: An increasingly healthy lifestyle was associated with decreasing HF risk among post-menopausal women, even in the absence of antecedent coronary heart disease, hypertension, and diabetes. Weighting the lifestyle factors had minimal impact. Elsevier Inc. All rights reserved.