ACCEL | American College of Cardiology Extended Learning

What’s News? The Latest in mHealth

Digital health or mHealth (as in mobile health) is used to describe the new technologies that bring health and fitness to patients via diet and exercise applications (or apps) or wearable technologies, such as those that measure steps, calories burned, and stairs climbed.

How accurate are they? Recently investigators evaluated four different products and all the activity monitors tested were accurate in their step detection over the variety of different surfaces tested (natural lawn grass, gravel, ceramic tile, tarmacadam/asphalt, linoleum), when wearing both running shoes and hard-soled dress shoes.1 A recent systematic review concurred, suggesting high validity for devices used to track steps taken, but lower validity for energy expenditure and measurement of time spent sleeping.2 In general, 2,000 steps equal about 1 mile.

Elizabeth A. Jackson, MD, an assistant professor of medicine at the University of Michigan Health Center, said tht “there is real evidence to suggest that people can change behavior (i.e., regular physical activity) and evidence that these devices can change dietary behavior when combined with peer support.”

When you are done with this issue of CardioSource WorldNews, go to the September issue of ACCEL and listen to the interview with Bonnie J. Spring, PhD, who discusses data demonstrating how valuable this technology can be in getting patients to make positive changes in their diet and activity habits.

This is an important topic: as the $3 trillion health care industry moves more towards consumer choice, increasing numbers of individuals are taking an active role in their overall health and wellness. Consumers are taking on more risk for managing their own care, and the industry is responding in kind by rolling out new products and services that empower them to do so. One problem: the wave of sophisticated wearables and self-diagnosis tools may be off target. As the saying goes, all that glitters is not gold: there are tons of great-looking apps out there that don’t do much.

David E. Conroy, MD, noted recently that more than half of American adults own smartphones, and half of those owners use some type of fitness app. He and his team identified the 100 top-selling health and fitness apps in the Apple iTunes and Google Play marketplaces.³ They looked for any of 93 possible behavior-changing techniques in the apps, including social support, instructions, demonstration, feedback, goal settings, prompt, and self-monitoring of behavior. Overall, there was an average of about seven (OK, it was 6.6) such techniques per app.

The good news: there is a base of evidence supporting some of those behavior-changing techniques Conroy and colleagues evaluated; the bad news is that’s not what the most popular apps are providing. Most of them had attractive interfaces, sure, but the apps favored behavior-changing techniques with a modest evidence base over others with more established evidence of efficacy. Overall, almost all of the apps are busy trying to find ways to help the user connect with Facebook, Twitter, and Instagram rather than promote active self-monitoring by users.

Don’t necessarily blame the consumer here. In late June, a survey demonstrated that people don’t crave the latest fitness wearable. Their overwhelming preference is for simple applications that provide and organize information.

The survey of 500 people was conducted for PwC’s Strategy& (that’s not a random abbreviation and a typo, by the way!). With spelling and spacing issues taken care of, here is what the survey found:

The five most preferred features were out-of-pocket cost estimators, simple access to health records (both online and mobile), mobile post-care instructions, online appointment scheduling with in-network providers, and a centralized payment portal to both health plan and provider.

The second category, labeled as “Nice to Have,” included things that would enhance current health plans, such as telehealth and mobile consultations, personalized health and wellness predictors, and ratings tools for quality transparency.

Even though wearables linking to health records and remote monitoring systems may be flashy (and enticing to health care providers and necessary to gather data to evaluate their worth), consumers rank those among the least important, along with health goal-management programs and interactive tools for self-diagnosis.

And people are not shy about sharing: 97% of those surveyed said they are willing to share personal health data, with just a few ranking data privacy as an important feature of a health plan.

Tele-Health Ready Made for CVD

We mentioned tele-health in passing. In June 2016, the Agency for Healthcare Research and Quality released a report indicating that the top chronic conditions for telehealth success were cardiovascular disease (CVD) and respiratory disease. Investigators conducted a systematic review and found “a large, broad evidence base about the effectiveness of telehealth, including over 200 systematic reviews and hundreds of primary studies published since 2006.”4 They identified a substantial amount of evidence, including 58 systematic reviews that covered several important clinical focus areas and met their inclusion criteria.

Twelve reviews covered CVD and an additional eight dealt with diabetes. The former included studies of telehealth for the management of heart failure, acute care and follow-up for myocardial infarction, management of patients with implantable defibrillators, and primary and secondary prevention of coronary disease; the latter included management of type 1, type 2, and gestational diabetes and a target range of activities from regulating glucose levels to promoting physical activity.

CVD/remote patient monitoring had the most evidence followed by psychotherapy and behavioral health. The most consistent benefit for telehealth has been for communication and counseling or remote monitoring in chronic conditions such as cardiovascular and respiratory disease, with improvements in outcomes such as mortality, quality of life, and reductions in hospital admissions.

Given sufficient evidence of effectiveness for these topics, the report concludes that future research should shift to implementation and practice-based research.


  1. O’Connell S, ÓLaighin G, Kelly L, et al. PLoS One 2016;11:e0154956.
  2. Evenson KR, Goto MM, Furberg RD. Int J Behav Nutr Phys Act 2015;12:159.
  3. Yang CH, Maher JP, Conroy DE. Am J Prev Med 2015;48:452-5.
  4. Totten AM, Womack DM, Eden KB, et al. Telehealth: Mapping the Evidence for Patient Outcomes From Systematic Reviews. Technical Brief No. 26. AHRQ Publication No.16-EHC034-EF. Rockville, MD: Agency for Healthcare Research and Quality; June 2016.

Save the Obese Heart From Failure and Arrhythmias

Obesity and diabetes are each independent risk factors for cardiovascular disease, new onset heart failure (HF), and atrial fibrillation (AF). Comorbidities consistently makes things worse, so it is becoming more challenging to treat patients with cardiovascular disease today as comorbidities keep piling up.

It was almost 15 years ago when the Framingham Heart Study demonstrated that body mass index (BMI) was associated with risk of HF and obesity; specifically, a doubling of the risk for heart failure.1 A few years later, Fonarow et al. reported an obesity paradox: at least for hospitalized patients with HF, higher BMI was associated with lower in-hospital mortality risk.2

Subsequently, Fonarow and colleagues evaluated waist circumference, an alternative anthropometric index of obesity that is more specific to abdominal adiposity.3 Again, the obesity paradox was apparent: bigger waist circumference, high BMI, and the combination of both were each associated with improved outcomes in an advanced HF cohort and the effect was seen in both men and women.4 Even after adjusting for age, sex, blood urea nitrogen, blood pressure, creatinine, sodium, heart rate, and dyspnea at rest, BMI quartile still predicted mortality risk. For every 5 U increase in BMI, the odds of risk-adjusted mortality were 10% lower.

While there is still considerable debate, the issue appears to be one of both fitness and fatness and the obesity paradox seems largely limited to patients with low fitness, whereas those with better fitness have a good prognosis, period, and no clear obesity paradox is apparent.5 Indeed, in one analysis of 10 studies jointly assessing the impact of cardiorespiratory fitness and BMI on all-cause mortality, fitness was more important than fatness for long-term mortality.6

The Framingham study has also revealed a lot about the impact of diabetes and the risk of HF. Compared with people without diabetes, HF risk is twice as high in diabetic males, five times higher in diabetic females, four times higher in young diabetic males, and eight times greater in young diabetic females.7

Indeed, while CAD and hypertension have traditionally been considered among the most important modifiable risk factors for the development of HF, Horwich and Fonarow have highlighted the importance of increasingly prevalent metabolic risk factors: glucose, diabetes, obesity, and the metabolic syndrome.8

As for AF, obesity is an independent risk factor, with approximately 2.4-fold excess risk.9 The risk of AF rises progressively with increasing BMI and weight is one of the major factors in the Framingham AF risk calculator. Importantly, obese patients present with AF approximately 10 years younger than those in a healthy weight category. Plus, some studies have shown that diabetes is also associated with increased risk of AF (1.4-fold).

One implication of the obesity and diabetes epidemics in the United States: full employment for cardiologists specializing in HF and electrophysiology.

Therapeutic Approaches (Sound Familiar?)

There are therapeutic approaches for reducing the risk of cardiovascular events, HF, and AF in patients with obesity and patients with diabetes.

Classically, the three pillars of AF management have been anticoagulation for prevention of thromboembolism, rhythm control, and rate control. In both prevention and management of AF, a growing body of evidence supports an increased role for comprehensive cardiac risk factor modification. This includes weight loss and cardiovascular exercise in the prevention and management of AF.

Recently in JACC, Miller et al. reviewed the growing evidence supporting aggressive risk factor management, especially weight loss.10 Doing so produces benefits relating to AF prevention, better AF management, and a reduction in AF complications, including stroke.

Gregg C. Fonarow, MD, is co-chief of the division of cardiology at the University of California, Los Angeles, and director the Ahmanson–UCLA Cardiomyopathy Center. He goes further than Miller et al.: for all the issues we’ve been discussing, the top three recommendations he gives to cardiologists are help prevent obesity, help prevent obesity, and help prevent obesity.

Based on his own work, it is also best if you identify overweight and obesity, measuring both weight and waist circumference. Assess risk factors present in all patients and initiate proven cardioprotective therapies. For patients with obesity and patients with diabetes, that would include angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, statin therapy, blood pressure control, and sodium-glucose co-transporter 2 inhibitors, a class of prescription medicines that are approved by the U.S. Food and Drug Administration for use with diet and exercise to lower blood glucose in adults with type 2 diabetes.

Clinicians should recommend regular moderate exercise to patients with AF, not only to reduce AF, but also for its overall cardiovascular benefits.

Overall, the evidence suggests that intentional weight loss improves outcomes in patients with ischemic heart disease, diabetes, and AF. While losing weight improves quality of life in HF patients, Dr. Fonarow said more studies are necessary to determine whether intentional weight loss will improve outcomes in patients with HF.


  1. Kenchaiah S, Evans JC, Levy D, et al. N Engl J Med. 2002;347:305-13.
  2. Fonarow GC, Srikanthan P, Costanzo MR, Cintron GB, Lopatin M; ADHERE Scientific Advisory Committee and Investigators. Am Heart J. 2007;153:74-81.
  3. Clark AL, Fonarow GC, Horwich TB. J Card Fail. 2011;17:374-80.
  4. Clark AL, Chyu J, Horwich TB. Am J Cardiol. 2012;110:77-82.
  5. Lavie CJ, McAuley PA, Church TS, Milani RV, Blair SN.
  6. J Am Coll Cardiol. 2014;63:1345-54.
  7. Barry VW, Baruth M, Beets MW, Durstine JL, Liu J, Blair SN. Prog Cardiovasc Dis. 2014;56:382-90.
  8. Kannel WB, McGee DL. JAMA. 1979;241:2035-8.
  9. Horwich TB, Fonarow GC. J Am Coll Cardiol. 2010;55:283-93.
  10. Magnani JW, Hylek EM, Apovian CM. Circulation. 2013;128:401-5.
  11. Miller JD, Aronis KN, Chrispin J, et al. J Am Coll Cardiol. 2015;66:2899-906.

MI-GENES: Do Patients Want to Know their Genetic Risk?
(Does it Matter?)

Do you want to know? For that matter, do your patients want to know?

To reduce risk of acute coronary syndromes, the benefits of a healthy lifestyle are clear, but genetics can still stack the deck. The question is: what happens if you turn the cards over and see your genetic specifics?

We’re learning more and more about what could be in the cards. Through DNA genotyping, investigators recently tested 54,003 coding-sequence variants covering 13,715 human genes in up to 72,868 patients with coronary artery disease (CAD) and 120,770 controls without CAD. They found that some genetic variants or SNPS (single nucleotide polymorphisms; pronounced “snips”) offer a natural advantage in protecting against heart disease while others portend a distinct disadvantage.

There are 46 SNPS that have been associated with susceptibility to CAD. They searched genetic variants that altered proteins to identify those that appeared to influence heart disease risk, as errors in proteins can have major physiological consequences.

For the study, published in the New England Journal of Medicine, researchers – including Iftikhar J. Kullo, MD, of the Mayo Clinic – identified genes already shown to confer an advantage or a vulnerability in protecting against heart disease risk.1 Two new ones stood out: ANGPTL4 and SVEP1.

Rare errors in ANGPTL4 were associated with reduced risk of CAD. The reduction varied from 14% for a small error in the gene to cutting risk by about 50% when an entire copy of the gene was disabled. Carriers of ANGPTL4 loss-of-function alleles had triglyceride levels that were 35% lower than those seen in people who did not carry a loss-of-function allele (p = 0.003).

The other gene, SVEP1, showed the opposite correlation: a rare error increased CAD risk by about 14%. While ANGPTL4 has been the subject of much study, SVEP1 is a bit of a mystery. An error in SVEP1 was linked to higher blood pressure in their study populations, but beyond that there are few clues to what it’s doing.

So, it might be good to know what an individual’s specific genes suggest in terms of added —or lesser—risk. You would think such knowledge would be valuable and inspire healthy lifestyle changes. Yeah, well, maybe not. A new study offers some insight into just how ingrained our habits and lifestyle actually are: investigators found no evidence that genetic tests change unhealthy habits.

A team of British researchers reviewed the results of 18 studies that looked at whether communicating DNA test results for conditions such as cancer and heart disease led people to make healthy changes.2 They found nothing to suggest that people adopted healthier behaviors, such as quitting smoking or eating more healthfully, after receiving their DNA results.

While individuals were not motivated to make healthy changes, at least there was no indication that knowledge of their genetic risk discouraged such changes. In other words, no inclination to look at troubling results and say, “My gene pool is a cesspool; I might as well drink and smoke more. Why not?”

The MI-GENES Clinical Trial

Does genetic risk at least factor into shared decision making when both physician and patient know the genetics score? Kullo and colleagues also conducted the MI-GENES (Myocardial Infarction Genes) study, looking at the value of incorporating a genetic risk score (GRS) in coronary heart disease (CHD) risk estimates to see whether the additional knowledge led individuals to lower their low-density lipoprotein cholesterol (LDL-C) levels.3

MI-GENES investigators enlisted 203 participants (45-65 years of age) at intermediate risk for CHD and not on statin therapy. They were randomly assigned to receive a standard 10-year probability of CHD risk score with or without the additional information from a GRS. Risk was disclosed by a genetic counselor, and then participants were stratified as having high or average/low GRS, followed by shared decision making regarding statin therapy with a physician.

At 6 months, the group who received both a standard risk score and a GRS had a lower LDL-C than the conventional 10-year CHD calculation alone (96.5 ± 32.7 vs. 105.9 ± 33.3 mg/dl; p = 0.04). Participants with a high genetics risk score had lower LDL-C levels (p = 0.02) compared to a standard 10-year risk estimation alone, but not significantly different from those who had an average/low GRS (p = 0.18). Statins were initiated more often in the individuals who got the results of their GRS (39% vs. 22% with the standard risk score alone; p < 0.01).

Granted, these were modest changes in LDL-C and disclosure of a GRS did not lead to significant differences in dietary fat intake, physical activity, or anxiety levels. Still, here is one study suggesting that genetic risk information for CHD can be used at the point of care to enable shared decision making regarding statin therapy, leading to a subsequent change in LDL-C levels.


  1. Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators. N Engl J Med. 2016;374:1134-44.
  2. Hollands GJ, French DP, Griffin SJ, et al. BMJ. 2016;352:i1102.
  3. Kullo IJ, Jouni H, Austin EE, et al. Circulation. 2016;133:1181-8.
Read the full September issue of CardioSource WorldNews at

Clinical Topics: Acute Coronary Syndromes, Arrhythmias and Clinical EP, Diabetes and Cardiometabolic Disease, Heart Failure and Cardiomyopathies, Prevention, Sports and Exercise Cardiology, Atrial Fibrillation/Supraventricular Arrhythmias, Acute Heart Failure, Exercise

Keywords: CardioSource WorldNews, Acute Coronary Syndrome, Atrial Fibrillation, Body Mass Index, Comorbidity, Coronary Artery Disease, Diabetes Mellitus, Energy Metabolism, Genotype, Heart Failure, Hospital Mortality, Hydrocarbons, Life Style, Motor Activity, Obesity, Poaceae, Polymorphism, Single Nucleotide, Research Personnel, Risk Factors, Running, Telemedicine

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