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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

BackgroundOne-third of US adults, 86 million people, have prediabetes. Two-thirds of adults are overweight or obese and at risk for diabetes. Effective and affordable interventions are needed that can reach these 86 million, and others at high risk, to reduce their progression to diagnosed diabetes.ObjectiveThe aim was to evaluate the effectiveness of a fully automated algorithm-driven behavioral intervention for diabetes prevention, Alive-PD, delivered via the Web, Internet, mobile phone, and automated phone calls.MethodsAlive-PD provided tailored behavioral support for improvements in physical activity, eating habits, and factors such as weight loss, stress, and sleep. Weekly emails suggested small-step goals and linked to an individual Web page with tools for tracking, coaching, social support through virtual teams, competition, and health information. A mobile phone app and automated phone calls provided further support. The trial randomly assigned 339 persons to the Alive-PD intervention (n=163) or a 6-month wait-list usual-care control group (n=176). Participants were eligible if either fasting glucose or glycated hemoglobin A1c (HbA1c) was in the prediabetic range. Primary outcome measures were changes in fasting glucose and HbA1c at 6 months. Secondary outcome measures included clinic-measured changes in body weight, body mass index (BMI), waist circumference, triglyceride/high-density lipoprotein cholesterol (TG/HDL) ratio, and Framingham diabetes risk score. Analysis was by intention-to-treat.ResultsParticipants’ mean age was 55 (SD 8.9) years, mean BMI was 31.2 (SD 4.4) kg/m2, and 68.7% (233/339) were male. Mean fasting glucose was in the prediabetic range (mean 109.9, SD 8.4 mg/dL), whereas the mean HbA1c was 5.6% (SD 0.3), in the normal range. In intention-to-treat analyses, Alive-PD participants achieved significantly greater reductions than controls in fasting glucose (mean –7.36 mg/dL, 95% CI –7.85 to –6.87 vs mean –2.19, 95% CI –2.64 to –1.73, P<.001), HbA1c (mean –0.26%, 95% CI –0.27 to –0.24 vs mean –0.18%, 95% CI –0.19 to –0.16, P<.001), and body weight (mean –3.26 kg, 95% CI –3.26 to –3.25 vs mean –1.26 kg, 95% CI –1.27 to –1.26, P<.001). Reductions in BMI, waist circumference, and TG/HDL were also significantly greater in Alive-PD participants than in the control group. At 6 months, the Alive-PD group reduced their Framingham 8-year diabetes risk from 16% to 11%, significantly more than the control group (P<.001). Participation and retention was good; intervention participants interacted with the program a median of 17 (IQR 14) of 24 weeks and 71.1% (116/163) were still interacting with the program in month 6.ConclusionsAlive-PD improved glycemic control, body weight, BMI, waist circumference, TG/HDL ratio, and diabetes risk. As a fully automated system, the program has high potential for scalability and could potentially reach many of the 86 million US adults who have prediabetes as well as other at-risk groups.Trial RegistrationClinicaltrials.gov NCT01479062; https://clinicaltrials.gov/ct2...

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BACKGROUND No studies have comprehensively examined the prevalence of dyslipidemia, a major risk factor for cardiovascular disease, among diverse racial/ethnic minority groups. The primary aim of this study was to identify racial/ethnic differences in dyslipidemia among minorities including Asian Americans (Asian Indian, Chinese, Filipino, Japanese, Korean or Vietnamese), Mexican Americans, and African Americans compared to Non-Hispanic Whites (NHWs). METHODS AND RESULTS Using a three-year cross-section (2008–2011), we identified 169,430 active primary care patients (35 years or older) from an outpatient health care organization in Northern California. Age-standardized prevalence rates were calculated for three dyslipidemia subtypes: high TG (fasting lab ≥150 mg/dL), low HDL-C (fasting lab <40 [men] and <50 [women] mg/dL), and high LDL-C (fasting lab ≥130 mg/dL or taking LDL-lowering agents). Odds ratios were calculated using multivariable logistic regression, adjusting for patient characteristics (age, measured BMI, smoking). Compared to NHWs, every minority subgroup had increased prevalence of high TGs, except African Americans. Most minority groups had increased prevalence of low HDL-C, except for Japanese and African Americans. The prevalence of high LDL-C was increased among Asian Indians, Filipinos, Japanese, and Vietnamese, compared to NHWs. CONCLUSIONS Minority groups, except for African Americans, were more likely to have high TG/low HDL-C dyslipidemia. Further research is needed to determine how racial/ethnic differences in dyslipidemia affect racial/ethnic differences in cardiovascular disease rates.

While group interventions for weight management have been shown to be efficacious, adherence is often low, especially among men. This pilot study seeks to test whether group interventions using web-based group video conferencing (VC) technology is effective for weight loss. We adapted a 12-week curriculum based on the Diabetes Prevention Program, and delivered this intervention to a small group of men (BMI ≥30 kg/m 2 ), using web-based group VC. Participants were randomized to intervention (n=32) or delayed-intervention control group (n=32). The intervention group lost 3.5 % (95 % CI 2.1 %, 4.9 %) of their initial body weight. Difference in mean weight loss was 3.2 kg (p=0.0002) and mean BMI decrease was 1.0 kg/m 2 (p=0.0010) between the two groups. Virtual small groups may be an effective means of allowing face-to-face group interaction, while overcoming some barriers to access.

BackgroundEffective lifestyle interventions targeting high-risk adults that are both practical for use in ambulatory care settings and scalable at a population management level are needed.ObjectiveOur aim was to examine the potential effectiveness, feasibility, and acceptability of delivering an evidence-based Electronic Cardio-Metabolic Program (eCMP) for improving health-related quality of life, improving health behaviors, and reducing cardiometabolic risk factors in ambulatory care high-risk adults.MethodsWe conducted a randomized, wait-list controlled trial with 74 adults aged ≥18 years recruited from a large multispecialty health care organization. Inclusion criteria were (1) BMI ≥35 kg/m2 and prediabetes, previous gestational diabetes and/or metabolic syndrome, or (2) BMI ≥30 kg/m2 and type 2 diabetes and/or cardiovascular disease. Participants had a mean age of 59.7 years (SD 11.2), BMI 37.1 kg/m2 (SD 5.4) and were 59.5% female, 82.4% white. Participants were randomized to participate in eCMP immediately (n=37) or 3 months later (n=37). eCMP is a 6-month program utilizing video conferencing, online tools, and pre-recorded didactic videos to deliver evidence-based curricula. Blinded outcome assessments were conducted at 3 and 6 months postbaseline. Data were collected and analyzed between 2014 and 2015. The primary outcome was health-related quality of life. Secondary outcomes included biometric cardiometabolic risk factors (eg, body weight), self-reported diet and physical activity, mental health status, retention, session attendance, and participant satisfaction.ResultsChange in quality of life was not significant in both immediate and delayed participants. Both groups significantly lost weight and reduced waist circumference at 6 months, with some cardiometabolic factors trending accordingly. Significant reduction in self-reported anxiety and perceived stress was seen in the immediate intervention group at 6 months. Retention rate was 93% at 3 months and 86% at 6 months post-baseline. Overall eCMP attendance was high with 59.5-83.8% of immediate and delayed intervention participants attending 50% of the virtual stress management and behavioral lifestyle sessions and 37.8-62.2% attending at least 4 out of 7 in-person physical activity sessions. The intervention received high ratings for satisfaction.ConclusionsThe technology-assisted eCMP is a feasible and well-accepted intervention and may significantly decrease cardiometabolic risk among high-risk individuals.Trial RegistrationClinicaltrials.gov NCT02246400; https://clinicaltrials.gov/ct2/show/NCT02246400 (Archived by WebCite at http://www.webcitation.org/6h6mWWokP)

Moderate and severe obesity (BMI ≥35 kg/m2) affect 15% of US adults, with a projected increase over the next two decades. This study reviews evidence of behavioral lifestyle interventions for weight loss in this population. We searched PubMed, PsychInfo, CINAHL®, and Scopus through February 2016 for experimental and quasi-experimental studies that tested a dietary and/or physical activity intervention with a behavioral modification component versus a comparator; and had ≥six-month follow-up and a weight-related primary outcome. Twelve studies representing 1,862 participants (mean BMI 37.5–48.3, mean age 30–54 years) were included. Nine studies compared different behavioral interventions and three tested behavioral intervention(s) versus pharmacological or surgical treatments. Among the 25 behavioral interventions in the 12 studies, 18 reported percent of participants achieving clinically significant weight loss up to 12 months (32–97% achieving 5% or 3–70% achieving 10%). Three studies measured other cardiometabolic risk factors, but showed no significant risk reduction. Seven interventions with greater effectiveness (i.e., at least 31% achieving ≥10% or 62% achieving ≥5% weight loss up to one year) included multiple components (diet, physical activity, and behavioral strategies), long duration (e.g., one year), and/or intensive contacts (e.g., inpatient stays for clinic-based interventions, weekly contacts for community-based ones). Evidence for the effectiveness of behavioral interventions versus pharmacological or surgical treatment was limited. Comprehensive and intensive behavioral interventions can result in clinically significant, albeit modest, weight loss in this obese subpopulation but may not result significant improvements in other cardiometabolic risk factors. More research on scalable and sustainable interventions is needed.