A lthough mortality for cardiovascular disease (CVD) has declined for several decades, heart disease and stroke continue to be the leading causes of death, disability, and high healthcare costs. Unhealthy behaviors related to CVD risk (eg, smoking, sedentary lifestyle, and unhealthful eating habits) remain highly prevalent. The high rates of overweight, obesity, and type 2 diabetes mellitus (T2DM); the persistent presence of uncontrolled hypertension; lipid levels not at target; and the ≈18% of adults who continue to smoke cigarettes pose formidable challenges for achieving improved cardiovascular health.1,2 It is apparent that the performance of healthful behaviors related to the management of CVD risk factors has become an increasingly important facet of the prevention and management of CVD. 3In 2010, the American Heart Association (AHA) made a transformative shift in its strategic plan and added the concept of cardiovascular health.2 To operationalize this concept, the AHA targeted 4 health behaviors in the 2020 Strategic Impact Goals: reduction in smoking and weight, healthful eating, and promotion of regular physical activity. Three health indicators also were included: glucose, blood pressure (BP), and cholesterol. On the basis of the AHA Life's Simple 7 metrics for improved cardiovascular health, <1% of adults in the United States follow a healthful eating plan, only 32% have a normal body mass index, and > 30% have not reached the target levels for lipids or BP. National Health and Nutrition Examination Survey (NHANES) data revealed that people who met ≥6 of the cardiovascular health metrics had a significantly better risk profile (hazard ratio for all-cause mortality, 0.49) compared with individuals who had achieved only 1 metric or none.2 The studies reviewed in this statement targeted these behaviors (ie, smoking, physical activity, healthful eating, and maintaining a healthful weight) and cardiovascular health indicators (ie, blood glucose, lipids, BP, body mass index) as the primary outcomes in the clinical trials testing mobile health (mHealth) interventions.eHealth, or digital health, is the use of emerging communication and information technologies, especially the Internet, to improve health and health care 4 (Table 1). mHealth, a subsegment of eHealth, is the use of mobile computing and communication technologies (eg, mobile phones, wearable sensors) for health services and information.4,5 mHealth technology uses techniques and advanced concepts from an array of disciplines, for example, computer science, electrical and

Background:The Diabetes Prevention Program (DPP) lifestyle intervention reduced the incidence of type 2 diabetes mellitus (DM) among high-risk adults by 58%, with weight loss as the dominant predictor. However, it has not been adequately translated into primary care. Methods:We evaluated 2 adapted DPP lifestyle interventions among overweight or obese adults who were recruited from 1 primary care clinic and had pre-DM and/or metabolic syndrome. Participants were randomized to (1) a coach-led group intervention (n = 79), (2) a selfdirected DVD intervention (n = 81), or (3) usual care (n=81). During a 3-month intensive intervention phase, the DPP-based behavioral weight-loss curriculum was delivered by lifestyle coach-led small groups or homebased DVD. During the maintenance phase, participants in both interventions received lifestyle change coaching and support remotely-through secure email within an electronic health record system and the American Heart Association Heart360 website for weight and physical activity goal setting and self-monitoring. The primary outcome was change in body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) from baseline to 15 months.Results: At baseline, participants had a mean (SD) age of 52.9 (10.6) years and a mean BMI of 32.0 (5.4); 47% were female; 78%, non-Hispanic white; and 17%, Asian/ Pacific Islander. At month 15, the meanϮSE change in BMI from baseline was Ϫ2.2Ϯ0.3 in the coach-led group vs Ϫ0.9 Ϯ 0.3 in the usual care group (P Ͻ .001) and Ϫ1.6Ϯ0.3 in the self-directed group vs usual care (P=.02). The percentages of participants who achieved the 7% DPPbased weight-loss goal were 37.0% (P=.003) and 35.9% (P=.004) in the coach-led and self-directed groups, respectively, vs 14.4% in the usual care group. Both interventions also achieved greater net improvements in waist circumference and fasting plasma glucose level. Conclusion:Proven effective in a primary care setting, the 2 DPP-based lifestyle interventions are readily scalable and exportable with potential for substantial clinical and public health impact.

Background: Electronic health records (EHRs) have been proposed as a sustainable solution for improving the quality of medical care. We assessed the association between EHR use, as implemented, and the quality of ambulatory care in a nationally representative survey. Methods: We performed a retrospective, crosssectional analysis of visits in the 2003 and 2004 National Ambulatory Medical Care Survey. We examined EHR use throughout the United States and the association of EHR use with 17 ambulatory quality indicators. Performance on quality indicators was defined as the percentage of applicable visits in which patients received recommended care. Results: Electronic health records were used in 18% (95% confidence interval [CI], 15%-22%) of the estimated 1.8 billion ambulatory visits (95% CI, 1.7-2.0 billion) in the United States in 2003 and 2004. For 14 of the 17 quality indicators, there was no significant difference in performance between visits with vs without EHR use. Categories of these indicators included medical management of common diseases, recommended antibiotic prescribing, preventive counseling, screening tests, and avoiding potentially inappropriate medication prescribing in elderly patients. For 2 quality indicators, visits to medical practices using EHRs had significantly better performance: avoiding benzodiazepine use for patients with depression (91% vs 84%; P = .01) and avoiding routine urinalysis during general medical examinations (94% vs 91%; P = .003). For 1 quality indicator, visits to practices using EHRs had significantly worse quality: statin prescribing to patients with hypercholesterolemia (33% vs 47%; P =.01). Conclusion: As implemented, EHRs were not associated with better quality ambulatory care.

Research is needed to better elucidate the relationship between obesity and depression, which has been most consistently demonstrated for women, but not for men. We examined exclusively a population‐based sample of US women who participated in the 2005 or 2006 National Health and Nutritional Examination Survey. Current depression was defined as having a score of ≥10 (a conventional threshold for moderate symptoms of depression) or meeting the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM‐IV) diagnostic criteria for major depression on the nine‐item Patient Health Questionnaire. Weight and height were measured and BMI was calculated. Waist circumference, a clinical measure of abdominal obesity, was also measured. BMI was positively associated with the probability of moderate/severe depressive symptoms (r = 0.49, P = 0.03) and major depression (r = 0.72, P < 0.0001). The probability curves increased progressively, beginning at BMI of 30. Degree of obesity was an independent risk factor for depression even within the obese population, and women in obesity class 3 (BMI ≥40) were at particular risk (odds ratio (OR) = 4.91, 95% confidence interval (CI): 1.17–20.57), compared to those in obesity class 1 (BMI 30 to <35). Abdominal obesity was positively associated with depressive symptoms, but not major depression, independent of general obesity (BMI). In addition to severe obesity, compromised physical health status, young or middle‐aged adulthood, low income, and relatively high education were also independently associated with greater odds of depressive symptoms among obese women. These characteristics may identify specific at‐risk subgroups of obese women in which hypothesized causal pathways and effective preventive and therapeutic interventions can be profitably investigated.

Background Latino immigrants have high rates of obesity and face barriers to weight loss. Objective Evaluate the effectiveness of a case-management (CM) intervention with and without community health workers (CHWs) for weight loss. Design Two-year, randomized controlled trial comparing two interventions to each other and to usual care (UC). Participants/setting Eligible participants included Latinos with a Body Mass Index of 30-60 and one or more heart disease risk factors. The 207 participants recruited from 2009-2011 had a mean age of 47 years and were mostly female (77%). At 24 months, 86% of the sample was assessed. Intervention The CM+CHW (n=82) and CM (n=84) interventions were compared to each other and to UC (n=41). Both included an intensive 12 month phase followed by 12 months of maintenance. The CM+CHW group received home visits. Main outcome measures Weight change at 24 months. Statistical Analyses Generalized estimating equations using intent-to-treat. Results At 6 months, mean weight loss in the CM+CHW arm was −2.1 kg (95% CI −2.8, −1.3) or −2% of baseline weight (−1%, −2%) compared to −1.6 kg (−2.4, −0.7; % weight change: −2%, −1%, −3%) in CM and −0.9 kg (−1.8, 0.1; % weight change: −1%, 0%, −2%) in UC. By 12 and 24 months, differences narrowed and CM+CHW was no longer statistically distinct. Men achieved greater weight loss than women in all groups at each time point (p<0.05). At 6 months, men in the CM+CHW arm lost more weight (−4.4 kg, −6.0, −2.7) compared to UC (−0.4 kg, −2.4, 1.5), but by 12 and 24 months differences were not significant. Conclusions Incorporation of CHWs may help promote early weight loss, especially among men, but it did not achieve weight maintenance. Social and environmental influences may need to be addressed to achieve sustained weight loss in Latino immigrant populations.