Skip main navigation

Self‐Care for the Prevention and Management of Cardiovascular Disease and Stroke

A Scientific Statement for Healthcare Professionals From the American Heart Association
Originally published of the American Heart Association. 2017;6:e006997



    Self‐care is defined as a naturalistic decision‐making process addressing both the prevention and management of chronic illness, with core elements of self‐care maintenance, self‐care monitoring, and self‐care management. In this scientific statement, we describe the importance of self‐care in the American Heart Association mission and vision of building healthier lives, free of cardiovascular diseases and stroke. The evidence supporting specific self‐care behaviors such as diet and exercise, barriers to self‐care, and the effectiveness of self‐care in improving outcomes is reviewed, as is the evidence supporting various individual, family‐based, and community‐based approaches to improving self‐care. Although there are many nuances to the relationships between self‐care and outcomes, there is strong evidence that self‐care is effective in achieving the goals of the treatment plan and cannot be ignored. As such, greater emphasis should be placed on self‐care in evidence‐based guidelines.

    Imagine a world in which cardiovascular disease (CVD) is not the No. 1 cause of death decade after decade because self‐care is pushed to the top of the hierarchy of best practices to managing health. Now, imagine the more probable scenario in which a “perfect storm” of an aging population,1 increased numbers of individuals with multiple CVD risk factors,2 and increased prevalence of people with multiple chronic conditions3, 4 converge to create a healthcare crisis5 because self‐care has been ignored. The latter scenario is the reality we are facing as fragmented, episodic, acute care remains a major focus of the healthcare system, whereas primordial and primary disease prevention fostered by optimal self‐care receive far less attention. The purpose of this scientific statement is to synthesize the evidence for the effectiveness of self‐care in preventing, delaying, and managing CVD and stroke. We delineate the self‐care skills and knowledge required to achieve these goals, discuss barriers to engagement in good self‐care, and present optimal strategies whereby healthcare providers and systems can support individuals and families as they engage in self‐care. Although our focus is on adults, we set the stage by describing the need for self‐care at all life stages.

    Self‐Care Defined

    Self‐care is fundamental to maintenance of health, and prevention and management of chronic illnesses.6, 7 The World Health Organization definition of self‐care7 and other recent definitions focus primarily on healthy people.8 In this article, we use a definition of self‐care from the Theory of Self‐care of Chronic Illness that addresses both the prevention and management of chronic illness, with core elements of self‐care maintenance, self‐care monitoring, and self‐care management.9 In this definition, self‐care is a process whereby individuals and their families maintain health through health‐promoting practices and managing illness. People who engage in self‐care maintenance adhere to those behaviors needed to maintain physical and emotional stability. Self‐care monitoring involves a process of observing oneself for changes in signs and symptoms—body listening. Self‐care management is used by people as they respond to signs and symptoms when they occur.

    Similar self‐care activities are important whether one is concentrating on the prevention or management of CVD or stroke. With the onset of CVD or stroke, there are several additional elements of self‐care that need to be incorporated into the health regimen. Categorically, these self‐care behaviors can be mapped with key elements of the Theory of Self‐care of Chronic Illness.9 Based on current evidence‐based guidelines and complementary reviews, self‐care maintenance, monitoring, and management of the common CVDs of hypertension,10, 11, 12, 13 heart failure (HF),6, 14, 15, 16 stroke,17, 18, 19 atrial fibrillation (AF),20, 21, 22 coronary artery disease,23, 24 and peripheral artery disease25 are presented in Table 1.

    John Wiley & Sons, Ltd

    Table 1. Self‐Care of Common Cardiovascular Disorders Using the Middle‐Range Theory of Self‐Care Nomenclature

    HypertensionHeart FailureStrokeAtrial FibrillationCoronary Heart DiseasePeripheral Artery Disease
    Self‐care maintenance
    Continued cardiovascular health behaviors
    Smoking cessationXXXXXX
    Maintain normal body mass indexXXXXXX
    Routine physical activityXXXXXX
    Maintain healthy dietXXXXXX
    Maintain low cholesterolXXXXXX
    Maintain normal blood pressureXXXXXX
    Maintain normal fasting plasma glucoseXXXXXX
    Reduce dietary sodium intakeXXXXXX
    Decrease alcohol useXXXXXX
    Gaining knowledge
    Seek information about the conditionXXXXXX
    Determine where to get more informationXXXXXX
    Understand self‐care requirementsXXXXXX
    Adherence to condition‐specific treatments
    Take medications as prescribedXXXXXX
    Know normal and side effects of treatmentsXXXXXX
    Understand why treatment is prescribedXXXXXX
    Learn how to adjust to specific dietary recommendations
    Attend cardiac/other rehabilitationXXX
    Keep schedule appointments and contact providers as neededXXXXXX
    Self‐care monitoring
    Know common signs and symptomsXXXXX
    Know signs and symptoms of worsening disease (eg, stroke or heart failure)XXXXXX
    Know signs and symptoms of complications (eg, bleeding from anticoagulation)XXXXX
    Routine (daily) blood pressure measurementXXX
    Routine (daily) weight measurementXXX
    Establish routine for monitoring signs and symptomsXXXXXX
    Self‐care management
    Distinguish among cardiovascular symptoms and non–life‐threatening conditionsXXXXXX
    Have a plan of what to do when signs and/or symptoms occurXXXXXX
    Further reduce dietary sodiumXX
    Increase diureticX
    Take nitroglycerinX
    Adjust anticoagulationX
    Evaluate the effectiveness of treatmentXXXXXX
    Know when and which provider to call when signs and/or symptoms occurXXXXXX

    Of 8760 hours in a year, patients spend only around 10 hours or 0.001% of their time with healthcare providers, meaning all other health maintenance, monitoring, and management activities are done by individuals or patients and their families as self‐care activities outside of the clinical or hospital setting. The basic self‐care activities important in CVD and stroke prevention and management are captured in the American Heart Association (AHA) “Life's Simple 7” (ie, smoking cessation, maintenance of body mass index [BMI], physical activity, healthy diet, maintaining low cholesterol, maintaining normal blood pressure [BP], and maintaining normal fasting plasma glucose).8 These behaviors have been shown to reduce incident stroke,26 HF,27 venous thromboembolism,28 and chronic kidney disease,29 and even incident cognitive impairment30 and non‐CVD.31

    Self‐Care as a Decision‐Making Process

    Self‐care is most commonly understood as a naturalistic decision‐making process in which persons engage for the purpose of maintaining health and managing acute and chronic illness.9, 32, 33, 34, 35 Self‐care decision making is a complicated process. Better understanding of the nature of self‐care decision making will help clinicians understand how to better teach self‐care to their patients and to understand how self‐care fails and how to improve it. Naturalistic decision making has been used to explain the process of self‐care in individuals with CVD, most commonly HF, as well as other chronic illnesses.35, 36, 37, 38

    The naturalistic decision‐making framework explains that, in real‐world settings, people make decisions that are meaningful and familiar to them.39 These real‐world decisions are complex; they involve uncertainty, ambiguity, dynamically evolving conditions, missing information, time stress, and high stakes. These decisions may also have ill‐defined, shifting, or competing goals and involve multiple individuals. The naturalistic decision‐making process explains how individuals make decisions given this complexity and how they develop the skills necessary to succeed when faced with similar situations.40 Naturalistic decision making emphasizes how individuals use their experience and personal values in decision making.39 Experience emerges from situational awareness,41 or perception of the situation, as well as comprehension of the significance of a specific situation. One's experience with the situation, each option, and past response create a set of patterns that include relevant actions and expected outcomes associated with each possible response.36 In this way, past experience and personal values lead to the actions taken in specific situations.42

    Section Summary

    • Self‐care is defined as a naturalistic decision‐making process addressing both the prevention and management of chronic illness, with core elements of self‐care maintenance, self‐care monitoring, and self‐care management.

    The Coming Chronic Disease Healthcare Crisis

    Worldwide, we are experiencing an unprecedented increase in the age of the population attributed to decreased fertility, particularly in developed countries, and increased life expectancy.43 Life expectancy increased globally by 20 years from 1950 to 2000 and is expected to increase by another 10 years by 2050.43 By 2050, the population aged ≥60 years will double; 25% of the population will be >60 years of age; 20% >65 years of age; and close to 5% will be >85 years of age.2 By most projections, an increase in morbidity affected years will accompany the increase in life expectancy. Aging is a major risk factor for noncommunicable chronic conditions, particularly CVD and stroke, so this “silver tsunami”44 will result in huge numbers of elderly individuals with multiple chronic conditions.4 By 2050, the number of people with chronic conditions will escalate by 40%, and the number of older people with disabilities from chronic conditions will double.44 Currently, at least 66% of all deaths worldwide are attributable to noncommunicable diseases, the most common of which is CVD, and this percentage is projected to increase in coming years.

    CVD is the primary cause of death in men and women in the United States and worldwide.45, 46 Multiple lines of research convincingly demonstrate that preventing CVD is economically, socially, and humanly superior to even the best medical treatment of manifest CVD.47, 48, 49 Using sophisticated modeling, 1 group compared CVD prevention versus treatment for a population aged 30 to 84 years with risk‐factor levels, event rates, current behavior patterns, levels of treatment, and mortality rates resembling those of the US population. In the target population, 44% of all deaths were attributable to heart disease. Management of CVD risk factors before an event would have prevented or postponed 33% of these deaths. This compared with prevention of only 8% of deaths if “perfect care” was used during an acute event. These data provide strong support for the importance of self‐care in preventing CVD and further events.47

    Section Summary

    • A globally aging population will result in huge numbers of elderly individuals with multiple chronic conditions, including CVD and stroke, by 2050.

    A Life Course Approach to CVD

    By the time CVD is manifest, it usually has been a silent condition for years.50 Thus, it is essential that self‐care of CVD risk factors is addressed as early as possible, not waiting for the emergence of overt CVD. Many advocate taking a “life course” approach to the prevention of chronic illnesses beginning with primordial prevention, because it is clear that the seeds of most noncommunicable diseases are planted very early in the life course.44, 51, 52

    Epidemiological data suggest that events in the perinatal period, and possibly even in the periconception period, are associated with an increased risk of chronic diseases in later life.53 This “fetal programming,” also known as “developmental origins of health and disease,” describes the process whereby a nutritional or endocrine event during a critical period of development results in an increased later risk of chronic disease.54, 55 Low‐birth‐weight (LBW) infants have a higher risk for coronary heart disease, cerebrovascular disease, metabolic syndrome, type 2 diabetes mellitus, and hypertension as adults than normal‐birth‐weight infants56, 57, 58 that is not explained by lifestyle factors such as smoking, diet, employment, alcohol, or exercise.57, 59 LBW may be caused by fetal, placental, or maternal factors, but, in many cases, the cause is not identified.

    A number of potentially modifiable maternal risk factors have been associated with LBW, including young or old age (<17 years or >35 years), low BMI (<20 kg/m2), alcohol or drug use, smoking, and poor nutrition. Preeclampsia is associated with LBW and a higher risk of stroke, hypertension, and obesity as well as evidence of widespread vascular dysfunction in the offspring.60, 61, 62, 63 Maternal obesity also predicts childhood obesity in the offspring.53 Even maternal exercise during gestation may influence cardiometabolic status in the fetus and infant.64 An intriguing study from the Helsinki Birth Cohort 1934–1944 reported that offspring of women who were preeclamptic compared with those of women with normotensive pregnancies have an increased risk for adulthood abnormalities in adaptive functioning, mental well‐being, and later depressive symptoms, all of which might affect self‐care in adulthood.65, 66

    Other periods during the life course are receiving increased attention for the appearance of risk factors that must be addressed early. For example, pregnancy is considered a “natural stress test” that uncovers risk for future CVD among mothers.67 Women with hypertension or preeclampsia during pregnancy have more than double the risk for a future CVD or cerebrovascular disease death or diagnosis than women who do not have these conditions during pregnancy.68 LBW among offspring or delivery of a preterm baby place the mother at increased risk for CVD.52 Early self‐care during this window of opportunity could stave off the development of CVD.

    Maternal risk factors place infants and adolescents at higher than normal risk for early development of CVD risk factors and higher risk of early disease.69 Thus, self‐care must address maternal risk factors for LBW, primordial prevention (eg, not starting smoking, avoiding overweight, being active, eating a heart healthy and low‐sodium diet), and the early appearance of cardiometabolic risk factors in LBW offspring. For example, childhood obesity is rising as are other risk factors, such as sedentary lifestyle, and early self‐care intervention to prevent or manage these risk factors is needed. Primordial prevention of CVD risk factors is best addressed with multifaceted, population‐based strategies involving increased education beginning in elementary schools or earlier, improvements in environmental infrastructure (eg, more sidewalks, parks, recreation centers, and easier access to healthful food choices), and regulatory initiatives (eg, tighter control of tobacco products, reduction in use of simple sugars and trans fats in foods). Such strategies are particularly important because, as this review highlights, it may be that only through primordial prevention will we achieve a marked reduction in the incidence and prevalence of CVD.

    Failure of Routine Care to Promote Self‐Care

    Teaching and supporting self‐care should be a major activity in our healthcare system. Yet, complexities in its conceptualization and practice result in underappreciation of self‐care by clinicians and healthcare systems. As a consequence, clinicians have not emphasized self‐care, and the vast majority of people do not perform self‐care behaviors well.16, 70, 71

    Self‐care research and clinical efforts have been hindered by the perceptions of both patients and providers that pharmacological interventions are more effective than lifestyle change.72, 73 Widespread failure of clinicians to follow, or give more than token attention to, CVD prevention guidelines has resulted in little change or worsening in CVD risk factors over time in many countries and points to a compelling need for a greater emphasis on self‐care.74 Further evidence of this failure is evident in practice‐based approaches to CVD risk reduction.75, 76 Some reasons for the lack of effectiveness of clinical efforts to influence CVD risk include the limited training of providers about patient education and use of effective behavior change strategies, lack of time for patient encounters, lack of support in the clinic environment for a self‐care–based approach, growth of healthcare systems focusing on care of acute events with little appreciation of the chronicity of most conditions, and better reimbursement for treatment than for prevention.77

    Our clinical and societal focus on an illness‐ or disease‐driven model of episodic care has resulted in what Goldman et al call “investing in sickness rather than health.”49 This investment and our changing demography have resulted in people living longer with multiple chronic conditions that are not well controlled because self‐care is at the heart of control of chronic illnesses.

    Section Summary

    • The seeds of most noncommunicable diseases such as CVD are planted very early in the life course, requiring a massive shift in the focus on treatment of acute events to an early emphasis on self‐care.

    Self‐Care Behaviors

    In this section, we address self‐care behaviors at the individual, family, and community levels. The roots of health lie in behavior, genetics, social circumstances, health care, and environmental exposures.78 Inadequate or unavailable medical therapy is thought to contribute little (around 10%) to illness and disease, whereas the predominant force is behavior, contributing ≈40% to overall health.78 Other determinants of health include genetics and stress. Social circumstances and environment contribute another ≈20% to health.78 Thus, it is clear that improving self‐care at the individual, family, and community levels could produce a major impact in health.

    Individual‐Level Self‐Care Behaviors

    Autonomy, understanding self‐care, and self‐responsibility.

    Our healthcare system is built on the assumption that individuals seeking care will comply with healthcare providers’ recommendations. Accountability is fostered in the context of keeping appointments and taking prescribed medications while following provider advice. The complex tasks of preventing chronic illnesses such as CVD and stroke commonly are addressed with provision of advice to “lose weight, get more activity, stop smoking” without provision of the knowledge, skills, and long‐term support needed for people to be successful with these self‐care behaviors. Training in self‐care is not common in the current healthcare system (for either providers or patients), and there is little time for development of strong and respectful patient‐provider partnerships.

    Given the lack of emphasis on self‐care in the healthcare system today, most patients expect that healthcare providers hold the responsibility for patients’ health.79 Thus, both healthcare providers and patients will need a major change in expectations about their respective roles in the prevention and management of CVD and stroke. Recently, the National Academy of Medicine (formerly the Institute of Medicine) wrote a white paper, “Vital Directions for Health and Health Care,” which addresses the major problems with healthcare systems today and changes that need to be made. One of their major focuses was “Empower People—Democratize Action for Health.”78 With this focus comes the charge to develop a healthcare system in which patients and families are informed, empowered, and engaged in their own health care and that they are promoted as partners in making healthcare decisions and in ensuring that these decisions are commensurate with their goals for health and life.78

    Knowledge of health status.

    Knowledge of health status and awareness of risk is an essential first step in self‐care of CVD. Individuals need to understand their current health status and their risk for future conditions in order to engage in adequate self‐care. Such knowledge requires development of a partnership with all healthcare providers who are willing to promote self‐care by keeping patients informed about their health status, thoroughly explaining all risk factors, and providing patients with access to appropriate sources of further information (eg, reputable web sources, books, and media outlets).

    The “Know Your Numbers” campaign was designed to encourage people to determine their risk for CVD.80 Five risk factors are targeted: BP, total cholesterol, high‐density lipoprotein cholesterol, blood glucose, and BMI. The goal is for people to determine whether any of these factors are abnormal, allowing them to take appropriate action to reduce their risk for CVD. A similar campaign is used for people with pre–diabetes mellitus or type 2 diabetes mellitus.81 There have been no studies to determine the effectiveness of these specific campaigns; however, the “Know Your Numbers” program in Australia to increase BP awareness resulted in a majority of people with hypertension seeking medical follow‐up.82


    Self‐care related to diet differs from some other preventative lifestyle behaviors in that a new behavior is not required, such as exercise, but rather existing behaviors are modified. This can make dietary self‐care challenging because it involves changing habitual behaviors that are embedded in culture and may have social consequences. Equally challenging are what can appear to be frequent changes to dietary recommendations, which may be frustrating to those attempting to follow guidelines. This may be particularly true in respect to changes in long‐standing recommendations. One example is the change in current guidelines that no longer specify a maximum daily intake of dietary cholesterol, which people have spent the past 45 years struggling to limit.83 Thus, it is important that people have confidence that the recommendations they are given are worth the effort to follow. The recent switch in focus away from restriction of specific nutrients to promoting heart‐healthy dietary patterns84 may increase stability in recommendations and reduce future frustration. It is important to note, however, that the recommended dietary patterns are still intended to restrict intake of specific nutrients, namely added sugars, trans fats, saturated fats, and sodium, indicating that these nutrients continue to be implicated in development of CVD.

    Key components of current dietary recommendations84 center around eating a varied, nutrient‐dense diet that includes all vegetable subgroups: dark green, red and orange, legumes, and starchy. Also recommended are fruits, especially whole fruits, grains, particularly whole grains, fat‐free or low‐fat dairy, and protein from seafood, poultry, lean meats, eggs, nuts, seeds, and soy products. Unsaturated fat oils, such as soybean, corn, olive, canola, and safflower, are recommended.85 The 2 most commonly recommended diets that achieve these recommendations are the Dietary Approaches to Stop Hypertension (DASH) and Mediterranean‐style diets.85 The recommendations can also easily be achieved with vegan or vegetarian diets as long as attention is paid to obtaining all essential amino acids and minerals.84, 85

    Although adherence to dietary recommendations has improved over the past 10 years, it still remains low at ≤60% of the population.84 Although considerable research has been conducted testing the effect of various diets on cardiovascular health, studies to promote dietary adherence are limited. In a recent review of dietary advice interventions for healthy adults to improve CVD risk factors, most interventions focused on nutrient reductions, such as fats or sodium, or nutrient increases, such as fruits, vegetables, or fiber.86 None focused on the currently recommended “whole diet” approach. Higher‐intensity interventions with more personal contact were more effective than low‐intensity interventions. Dietary advice appeared to be more effective for people with known or perceived risk for CVD who are more likely to be receptive to advice. Effectiveness of specific strategies was not identified in the review. In another recent review, strategies to change dietary behavior for prevention and management of chronic illness identified feedback, telephone follow‐up after education sessions, provision of nutritional tools such as menus, and contracting as the most promising interventions.87 However, none of the interventions was clearly superior, primarily because of the low quality of the studies.

    Published examples of how to apply current dietary guidelines are based on Western foods.84, 85 Cultural considerations are important when planning self‐care strategies for everyone given the multiplicity of cultures and the effect of culture on diet.88 For example, in many cases, the diets of first‐generation immigrants are more heart‐healthy than the typical Western diet on arrival in the United States, but gradually change to a more Western‐style diet over time.89 In this case, strategies are needed to promote adoption of healthy components of the Western diet, rather than the large portion sizes or commercially prepared and convenience foods. For immigrants who maintain a diet from their home culture, the underlying principles of the guidelines can be applied by focusing on the selection of comparable foods.

    Weight control.

    Maintaining a healthy body weight is an important self‐care behavior and current dietary guidelines emphasize the importance of weight control.84 This is achieved by consuming appropriate serving sizes of recommended foods to meet estimated caloric demand. Estimating and tracking caloric intake is difficult over the course of a typical day and therefore not a successful strategy for weight control. Fortunately, serving sizes for specific food categories are provided in the guidelines for each level of caloric intake. Estimating serving sizes can be quickly mastered after a brief period of measuring out portion sizes. Individual caloric need is based on activity level, indicating that self‐care strategies for increasing activity carry equal importance with diet in body weight maintenance.

    Weight loss for people who are obese has been a long‐standing recommendation for cardiovascular health.90 It should be noted that with the exception of bariatric surgery, no interventions have been associated with substantial weight loss sustained over time. So, setting initial weight loss targets of 5% to 10% can have a clinically significant impact on CVD risk.91 Self‐care for weight loss is rarely achieved alone. Most successful lifestyle interventions for weight loss require participation in a program, either individually or in a group, for ≥6 months.91 Long‐term weight maintenance may require an ongoing support system. Computer‐based interventions are an attractive alternative to in‐person programs for convenience and cost. However, the evidence suggests that, whereas these programs are better than passive interventions such as pamphlets or a manual, they are not equal in effectiveness to in‐person programs.92 However, the majority of these studies were not highly interactive, so it is possible that interventions that provide greater interaction may be equally effective to in‐person programs.

    Physical activity and exercise.

    Aerobic exercise is a self‐care behavior with incontrovertible health benefit, including systemic improvement in oxygen consumption, endothelial function, inflammation, BP, and insulin resistance as well as improvement in functional status, sleep quality, and quality of life.93 The importance of physical activity on mortality risk was highlighted in the Oslo II study.94 Elderly men who engaged in 30 minutes of physical activity 6 d/w had a 40% mortality risk reduction at 12‐year follow‐up—a benefit comparable to that observed with smoking cessation.

    There is increasing evidence that inactivity or sedentary behavior is independently associated with negative outcomes independent of overall exercise or physical activity levels. In patients with symptomatic chronic HF, physical inactivity and high levels of daily television screen time are associated with greater risk of all‐cause and cardiac mortality whereas modest exercise is associated with reduced all‐cause and cardiac mortality.95 In patients with diabetes mellitus, every 60 min/d increase in objectively measured sedentary behavior resulted in a 13% increase in mortality, independent of physical activity.96 However, in adults without past CVD, higher physical activity levels and not sedentary behavior were related to lower CVD mortality.97

    Cardiac rehabilitation teaches and encourages self‐care and is recommended in clinical practice guidelines for patients with acute myocardial infarction or coronary revascularization,98 other cardiac surgical procedures, chronic HF,15 stroke,99 and peripheral artery disease.100 Traditional models of cardiac rehabilitation and structured exercise therapy have required a supervised setting in a healthcare environment to achieve the greatest benefit. However, there is increasing evidence that structured programs that take place in a home‐ or community‐based environment may be as effective and more accessible to patients with CVD, who are often older and more debilitated.101, 102, 103 The efficacy of these programs is augmented by incorporation of strategies such as health coaching and activity tracking. Despite the demonstrated benefits of cardiac rehabilitation, only 20% to 30% of eligible patients participate and referral is significantly lower in women than men.104 Ades et al105 estimate that if participation in cardiac rehabilitation increased to 70% of eligible patients, 12 000 lives and 87 000 hospitalizations could be saved in the first year alone.

    Smoking cessation.

    Never smoking and smoking cessation are essential self‐care behaviors with significant benefit to smokers and those exposed to tobacco smoke. Smokers who quit after a CVD event have better outcomes than those who continue to smoke, with health benefits that begin almost immediately. In those who quit, the risk of a recurrent event is the same as a nonsmoker within 3 years, and the risk of death from CVD is decreased by two thirds in former smokers versus those who continue to smoke.106, 107 Smoking reduction rather than cessation has little effect on clinical or biological markers, confirming that even minor exposure to tobacco smoke is harmful and illustrating the importance of complete cessation.108, 109, 110

    Smoking cessation is an extremely challenging self‐care behavior because of the addiction to tobacco, but medication can facilitate quitting,111 even in those who are not yet ready to quit. In one trial, patients who were not willing to quit in the next month, but were willing to reduce the number of cigarettes smoked, were randomized to varenicline or placebo and instructed to reduce the number of cigarettes smoked by 50%, 75%, and 100% over 12 weeks. At weeks 21 to 24, those randomized to the varenicline group had a 38% abstinence rate compared with 13% in the placebo group.112 Psychosocial interventions, behavioral interventions, telephone counseling, and self‐help materials are also effective in promoting smoking cessation, with the best results achieved with more intense interventions.113 Group therapy is as effective as individual therapy, especially when augmented by physician advice or nicotine replacement therapy.114 Healthcare providers who give brief advice to quit at every interaction with a smoker increase the odds of quitting by 34%,115 which is further enhanced with brief counselling.116 A combined approach to smoking cessation significantly increases the likelihood that a smoker will be able to quit.117 Patients who quit after an acute CVD event may be more successful at maintaining this self‐care behavior over time than patients with a slowly progressing chronic disease.118, 119 More research is needed to understand how best to help smokers quit.

    Alcohol use.

    The data on alcohol use are confusing and the public is unsure what constitutes good self‐care. Alcohol contributes to the development of hypertension and is associated with cardiomyopathy. Light‐to‐moderate alcohol consumption is protective in patients with stable ischemic heart disease. Current guidelines state that 1 or 2 drinks/d is reasonable for patients with stable ischemic heart disease.98 Similarly, current stroke guidelines suggest that stroke patients who drink heavily should reduce their alcohol consumption, but it may be reasonable for them to consume light‐to‐moderate amounts of alcohol.99 An inverse relationship was found between alcohol intake and acute coronary syndrome.120 Moderate drinking was associated with a lower mortality rate than abstaining or heavy drinking.120 Similar confusing results were found in diabetes mellitus,120, 121 incident HF,122 and ischemic and hemorrhagic stroke.123, 124 Given the potential negative health and societal consequences of alcohol dependence or abuse, people who do not currently drink alcohol should not be encouraged to begin drinking. Those who do consume alcohol should be informed of the potential risks and encouraged to consume alcohol in moderation.

    Medication adherence.

    Self‐care includes taking medicines as prescribed and responsible selection and use of nonprescription medicines. Medication adherence is most likely when patients are well informed about medications, including potential side effects, the potential for drug interactions, and when to contact their clinician to discuss discontinuing or changing medications. Responsible use also includes understanding the pros and cons of using nonprescription or over‐the‐counter medicines, following the label recommendations, and an ongoing evaluation of the benefits and effectiveness of the drug. Responsible use also involves informing healthcare providers of the use of both prescription and nonprescription drugs.

    Poor adherence to the medication regimen is a major factor in the inadequate achievement of CVD treatment goals.125, 126, 127 Nonadherence to cardiovascular medicine is associated with greater risk of adverse effects and inappropriate therapeutic drug escalations.126, 127, 128 Unfortunately, no clearly effective method of promoting medication adherence has been identified. Two recent systematic reviews found significant improvement in medication adherence through the use of text messages.129, 130, 131 Patel et al131 found that a mobile phone–based automated medication reminder system showed promise in improving medication adherence and BP control in individuals at high risk for CVD.

    Family‐Level Self‐Care

    Informal caregivers, typically family members, exert a significant influence on self‐care,132, 133 with lack of support identified as an important barrier to self‐care (Figure).134, 135, 136 The most supportive contributions from caregivers are positive responses to a perceived need in a specific situation.137 Family caregivers positively influence self‐care by facilitating adherence to medications and diet as well as vigilance in monitoring.138, 139 In HF, significant family‐level factors associated with patient self‐care include sex, quality of life, relationship type and quality, social support, and factors associated with aging or disease progression such as number of chronic conditions, impaired activities of daily living, cognition, number of hospitalizations, and duration of experience with the illness.140 However, even when caregivers are invested and involved in promoting self‐care, they are limited by their own lack of knowledge or skill.138

    Figure 1.

    Figure 1. Self‐care is often thought of as solely an individual‐level behavior, but, in reality, self‐care occurs at the individual, family, and community level.

    Recent studies have focused on couples or dyadic influences on self‐efficacy,133, 141 self‐care,140 and dyadic patterns in collaborating on self‐care.142 In HF, dyadic patterns of self‐care have been identified as novice and complementary, inconsistent and compensatory, or expert and collaborative.142 Self‐care self‐efficacy, or task‐specific confidence, is generally higher in informal caregivers than in patients.141 Caregivers who rate the quality of the relationship higher are more likely to score the best in self‐care self‐efficacy.133 Other investigators have found that when dyads disagree on self‐care, this disagreement causes distress and dysfunction in the couple.143, 144, 145 However, significant heterogeneity exists across dyads.141 In a recent joint scientific statement from the AHA, American College of Cardiology, and American Geriatrics Society, models of care that integrate caregivers into decision making was identified as a significant practice gap across guidelines.146

    Community‐Level Self‐Care

    The range of environmental factors that impact health and the ability of people to perform self‐care is remarkably broad. The World Health Organization European Healthy Cities program identified 12 key health determinants, including access to services, healthy food, open spaces, safe environments, healthy air, physical activity, and social cohesion.147

    A number of studies have attempted to correlate walkability in the built environment with body weight, generally using BMI calculations. A 2008 review found that neighborhoods with barriers to physical activity were associated with higher BMI.148 However, a more‐recent systematic review failed to find that the multicomponent, community‐wide interventions studied effectively increased physical activity for the population, although some studies with environmental components observed more people walking.149

    Community‐level dietary self‐care strategies should include considerations for the food environment (shops, supermarkets, cafes, takeaways, restaurants, and vending machines).150 Food deserts, defined as areas with limited availability of nearby food markets, are present in both urban and rural areas.151 These food deserts are believed to limit access to heart healthy foods and be associated with higher food costs. Recent evidence, however, does not support the hypothesis that food deserts in urban neighborhoods significantly affect eating or shopping habits of people living in those neighborhoods.152, 153 People in rural areas may be more affected; transportation may be a greater barrier in rural areas than transportation to stores nearby in urban neighborhoods.151, 153 Food‐cost disparities exist in rural areas, with the cost of healthier foods higher in areas with greater poverty and less population density than in more‐wealthy, high‐density counties.154 Similarly, counties with the poorest population health ratings had the highest‐cost foods compared with counties with the best population health ratings. Regardless of urban or rural location, individual food affordability and accessibility need to be considered. In these cases, careful assessment of access barriers and identification of community resources may be needed for individual success. The food environment in different countries can be vastly different. In England, the availability of fast‐food outlets around schools is thought to be an obstacle to establishing healthy eating habits.155 A link has been demonstrated between fast‐food availability and obesity in older children.156

    Section Summary

    • The evidence supporting individual‐ (eg, exercise), family‐ (eg, social support), and community‐level (eg, accessibility of healthy food) self‐care approaches is described.

    CVD Outcomes of Self‐Care

    In this section, we summarize the data supporting self‐care for hypertension, coronary heart disease, peripheral artery disease, stroke, AF, and HF. There are so many studies focused on individual self‐care behaviors that we summarize only meta‐analyses. A brief summary of mechanisms of effectiveness concludes this important section.


    Self‐measurement and monitoring of BP is a major emphasis in the self‐care of hypertension. In a recent meta‐analysis, there was evidence that self‐measurement of BP was superior to usual care (24 trials) in reducing systolic (−3.1 mm Hg) and diastolic (−2.0 mm Hg) BP at 6 months.157 In the meta‐analysis, additional support beyond self‐measurement (24 trials) resulted in even greater reductions in systolic (−3.4 to −8.9 mm Hg) and diastolic (−1.9 to −4.4 mm Hg) BP compared with usual care up to 12 months.157 Another meta‐analysis of 11 trials of self‐measurement of BP interventions versus usual care demonstrated a significant reduction in diastolic (weighted mean difference, −2.02; 95% confidence interval [CI], −2.93 to −1.11), but not systolic, BP.158 The researchers also provided evidence that self‐measurement of BP interventions compared with usual care (13 trials) improved antihypertensive medication adherence (standardized mean difference, 0.21; 95% CI, 0.08–0.34). A third meta‐analysis159 provided evidence that digital interventions designed to improve hypertension self‐care were superior to usual care in reducing systolic (weighted mean difference being −3.74  mm Hg [95% CI, −2.19 to −2.58]) and diastolic BP (−2.37  mm Hg [95% CI, 0.40 to −4.35]). Hence, there is evidence across multiple trials favoring self‐care in general, and self‐measurement of BP in particular, as efficacious means of reducing BP and improving medication adherence in hypertension. Another Cochrane review on self‐monitoring for improving control of BP in patients with hypertension is currently underway.160

    Coronary Heart Disease

    Cardiac rehabilitation is a commonly prescribed element of care in coronary heart disease. Karmali et al161 conducted a systematic review of interventions to improve uptake of cardiac rehabilitation in coronary heart disease. Eight of 10 studies included in this review increased the uptake of cardiac rehabilitation, 3 of 8 studies included improved adherence to cardiac rehabilitation, and no studies found a difference in quality of life.161 More recently, Janssen et al162 completed a meta‐analysis of lifestyle modification programs for patients with coronary heart disease versus usual care (23 trials). In this analysis, usual care was associated with worse outcomes compared to lifestyle modification programs, including all‐cause mortality (odds ratio, 1.34 [95% CI, 1.10–1.64]), cardiac mortality (odds ratio, 1.48 [95% CI, 1.17–1.88]), and the composite of cardiac readmissions and subsequent nonfatal infarctions (odds ratio, 1.35 [95% CI, 1.17–1.55]); improvements in dietary and exercise behavior were greater for programs that incorporated goal setting, self‐monitoring, planning, and feedback techniques.162 In summary, there is evidence across multiple trials to support self‐care interventions that are focused on lifestyle modifications as a means to improve clinical outcomes in coronary heart disease. In contrast, interventions focused solely on improving adherence to cardiac rehabilitation only seem to influence uptake, but not adherence behavior or quality of life.

    Peripheral Artery Disease

    Li et al141 recently reviewed the efficacy of structured home‐based exercise programs in patients with peripheral artery disease. Across 5 trials, structured home‐based exercise programs improved walking time (668 seconds [95% CI, 5.2–128.4]) and pain‐free walking time (57.8 seconds [95% CI, 20.4–95.1]), as well as mean difference in Walking Impairment Questionnaire distance (8.7; 95% CI, 3.9–13.5) and speed scores (8.1; 95% CI, 4.5–11.6). Hence, there is evidence across multiple trials that home‐based exercise programs improve walking ability in patients with peripheral artery disease. Moreover, there is an additional Cochrane review on disease management interventions for improving self‐care in lower‐limb peripheral artery disease currently underway.163


    A recent systematic meta‐review of 13 systematic reviews representing 101 individual trials of self‐care support interventions with stroke survivors provide high‐quality evidence to support self‐care.164 The researchers concluded that support for self‐care in the context of therapy rehabilitation delivered soon after a stroke resulted in short‐term (<1 year) improvement in activities of daily living and reductions in the risk of dependence and death poststroke.164 More recently, Fryer et al165 completed a meta‐analysis of 6 trials of stroke self‐care programs and provided evidence that such interventions improve quality of life (standardized mean difference, 0.34 [95% CI, 0.05–0.62]) and improve self‐efficacy (0.33 [95% CI, 0.04–0.61]) compared with usual care. Hence, there is evidence across multiple systematic reviews and multiple trials to support self‐care as a means of improving activities of daily living, quality of life, and self‐efficacy, as well as reducing dependency and premature death in stroke.

    Atrial Fibrillation

    Heneghan et al166 completed a meta‐analysis of self‐testing and self‐care of oral anticoagulation in AF. Across 11 trials, there was a significant reduction in thromboembolic events associated with self‐monitoring versus usual care (hazard ratio [HR], 0.51 [95% CI, 0.31–0.85]), but no difference for major hemorrhagic events (HR, 0.88 [95% CI, 0.74–1.06]) or death (HR, 0.82 [95% CI, 0.62–1.09]); younger patients (aged <55 years) and those with mechanical heart valves had the greatest reductions in thromboembolic events in response to self‐testing and self‐care of oral anticoagulation.166 ClarkeSmith et al167 subsequently completed a meta‐analysis of 8 trials focused on the efficacy of educational or behavioral intervention versus usual care in AF on time in therapeutic range of oral anticoagulation therapy. In this meta‐analysis, self‐monitoring did not improve time in therapeutic range compared with usual care (mean difference of 6.31 [95% CI, −5.63 to 18.25]).167 The researchers suggested that they may not have found benefit because 4 of the trials included mixed indication cohorts and 10 further trials were excluded because they did not provide AF‐specific data. They also suggest some patient‐specific factors that could have impacted intervention effectiveness (eg, older age and inaccurate beliefs). Although there is evidence across multiple trials that self‐testing and self‐care of oral anticoagulation reduces the risk of thromboembolic events at large, more research is needed into mechanisms other than time in therapeutic range by which self‐care interventions are successful in AF.

    Heart Failure

    For more than a decade, there has been evidence from meta‐analyses of randomized, controlled trials that interventions focused on enhancing self‐care in HF are efficacious versus usual care in improving outcomes. McAlister et al168 first provided evidence across 4 trials that interventions focused on enhancing HF self‐care reduced all‐cause hospitalizations and HF hospitalizations. A subsequent meta‐analysis of trials focused on HF self‐care provided evidence that interventions focused on improving self‐care (5 trials) reduced all‐cause readmission and HF readmissions (3 trials).169 Grady170 reviewed trials focused on self‐care interventions and quality of life in HF. Nine of the 17 trials included in her review showed an improvement in quality of life compared to usual care; but, methodological heterogeneity across the trials reviewed interfered considerably with the ability to draw any strong conclusions.170 Ditewig et al171 completed a systematic review without a meta‐analysis on effectiveness of self‐management programs in HF and concluded that self‐care interventions generally had a positive effect on all‐cause readmission, mortality, and quality of life. These researchers also highlighted significant methodological heterogeneity across studies that interfered with any strong summative conclusions. Another meta‐analysis focused on structured telephone support interventions to improve HF self‐care.172 In this meta‐analysis, self‐care focused structured telephone support interventions reduced all‐cause mortality (22 trials; HR, 0.87 [95% CI, 0.77–0.98]) and HF hospitalizations (16 trials; HR, 0.85 [95% CI, 0.77–0.93]). Recently, Jonkman et al173 completed an individual patient data meta‐analysis of HF self‐management interventions. Self‐management interventions (20 trials) reduced the composite risk of HF hospitalization or all‐cause death (HR, 0.80 [95% CI, 0.71–0.89]), the risk of HF hospitalization (HR, 0.80 [95% CI, 0.69–0.92]), and improved 12‐month HF‐related quality of life (standardized mean difference, 0.15 [95% CI, 0.00–0.30]).173 In subsequent analyses, it was concluded that no specific intervention characteristics other than longer intervention duration were associated with better self‐management intervention efficacy.174 In summary, there is sufficient information across many trials that interventions targeting HF self‐care are associated with better clinical outcomes and improvements in quality of life, suggesting readiness for implementation studies.

    Mechanisms of Effectiveness

    Healthy self‐care behaviors lower the risk of incident disease, and the mechanisms underlying these effects can be broadly summarized as cardioprotection.175 This is particularly true of the self‐care maintenance behaviors of smoking cessation, maintaining normal BMI, routine physical activity, reducing dietary sodium intake, decreasing alcohol use, and maintaining a healthy diet, a low cholesterol, a normal BP, and a normal fasting plasma glucose. Other self‐care maintenance behaviors help reduce inflammation attributed to infection in CVD such as routine preventive dental care and annual influenza vaccination.176, 177 Medication adherence is perhaps the most obvious cardioprotective self‐care behavior. For example, it has been proposed that HF patients who are adherent to prescribed therapies may avoid the escalation of loop diuretics,178, 179, 180 avoid the need for inotropic therapy,181, 182 and potentially avoid having efficacious therapies discontinued during hospitalization,183 all of which are associated with worse outcomes. When the emphasis of self‐care changes from preventing to managing CVD and secondary prevention, these health behaviors become no less important because they address common cardiovascular pathophysiological mechanisms such as inflammation.184

    Section Summary

    • In hypertension, there is evidence across multiple trials demonstrating that self‐care monitoring of BP is an efficacious means of reducing BP and improving medication adherence.

    • In coronary heart disease, self‐care interventions that focus on lifestyle modifications improve clinical outcomes.

    • In peripheral artery disease, there is evidence across multiple trials that home‐based exercise programs improve walking ability.

    • In stroke, there is evidence across multiple systematic reviews and multiple trials to support self‐care as a means of improving activities of daily living, quality of life, and self‐efficacy, as well as reducing dependency and premature death.

    • In AF, self‐care monitoring of oral anticoagulation reduces the risk of thromboembolic events, but more research is needed into mechanisms of self‐care for AF other than time in therapeutic range.

    • In HF, there is sufficient information across many trials that interventions targeting HF self‐care are associated with better clinical outcomes and improved quality of life, suggesting the need for implementation studies.

    • Further research is needed to fully understand the mechanisms by which self‐care exerts benefit.

    Factors Influencing Self‐Care

    To effectively perform the self‐care behaviors needed to prevent or manage CVD requires self‐care knowledge, skills, confidence, and motivation to engage in these activities routinely, while contending with multiple individual barriers.185, 186, 187 These individual barriers can be compounded by family and community influences. Some of these major barriers are described below.

    Individual Factors Influencing Self‐Care


    Depression and depressive symptoms are substantially more common in people with CVD that in the general population. The highest rates are observed in patients with HF (20%–30% depending on assessment used),188 but rates with other cardiac conditions do not lag far behind.189 Depression and depressive symptoms are independently and strongly associated with death, recurrent major cardiac events, and cardiac and all‐cause hospitalizations in the short and long term.188, 190, 191, 192

    Depression and depressive symptoms are associated with poor self‐care in those with CVD or risk factors for CVD.193, 194, 195, 196, 197 Self‐care areas negatively affected include diet and medication adherence, exercise adherence, self‐monitoring, and appropriate response to symptoms.198, 199, 200, 201, 202, 203

    Poor self‐efficacy.

    Self‐efficacy, or confidence in one's ability to perform a particular behavior, is a powerful and consistent influence on performance of self‐care across patient populations, various self‐care behaviors, and developmental levels.204, 205, 206 The effect of self‐efficacy on self‐care behaviors has been demonstrated for both self‐care maintenance and management behaviors in multiple chronic CVD populations.

    A systematic review of evidence to identify intrapersonal, social, and physical environmental determinants of moderate‐to‐vigorous intensity physical activity among working‐age women found that enhanced self‐efficacy was a consistent positive influence on activity.207 Among patients with coronary artery disease, self‐efficacy is a significant predictor of physical activity, especially in the early phase of initiating physical activity behavior.208 Increased levels of self‐efficacy promote greater antihypertensive and HF medication adherence209, 210 and dietary adherence.211 Smoking cessation is clearly linked with confidence to quit and confidence to quit in the face of obstacles.212 When examining self‐efficacy in persons with HF categorized on their level of self‐care performance, higher self‐care confidence increased the odds of being an expert in self‐care.213, 214

    Self‐efficacy is a key influencer of enhanced self‐care in populations with cardiac and comorbid conditions215 and specifically complex self‐care in those with concomitant HF and diabetes melliitus.216 In a secondary analysis of data from symptomatic older HF patients, level of comorbidity moderated the relationship between self‐efficacy and self‐care maintenance, but not self‐care management.217 Specifically, in patients with less comorbidity, the relationship between self‐efficacy and self‐care was significantly stronger than in patients with more comorbidity. It appears that the additional conditions make it difficult for patients to identify the self‐care needed in specific situations, thereby decreasing self‐efficacy. Self‐care confidence appears to be an important factor influencing HF self‐care even in patients with impaired cognition, and interventions addressing confidence were suggested as a way to improve self‐care in this population.218

    Self‐efficacy is reinforced by the actual performance of the self‐care behavior; with successful performance, self‐efficacy is enhanced, whereas failure undermines self‐efficacy.219 Self‐efficacy enhancement empowerment early in a lifestyle intervention program improves self‐efficacy and adherence to healthy lifestyle behaviors.220 A study testing an intervention to improve HF symptom monitoring and response found greater improvements in self‐care confidence in the intervention group compared with a control group.221 A study of behavioral factors influencing physical activity in patients with stable coronary artery disease found that perceived social support explained 12% of the variance in physical activity, with self‐efficacy partially mediating the relationship between perceived social support and physical activity.222

    Cognitive decline.

    CVD is a major and increasingly recognized cause of dementia.223 The incidence of cognitive decline increases with increasing age because of pathological changes in the cardiovascular and cerebrovascular systems commonly observed in those who have not maintained cardioprotective lifestyle habits through the life course.52, 223 CVD in many of its manifestations (eg, coronary artery disease, AF, hypertension, and myocardial infarction) and most CVD risk factors (eg, smoking, obesity, sedentary lifestyle) are independently associated with cognitive dysfunction along the continuum from mild cognitive impairment to dementia.224 For example, cognitive impairment is common among those with HF,225, 226 with a prevalence ranging from 25% to 50%.227, 228, 229

    In the cardiac population, cognitive decline commonly includes deterioration in memory and learning, attention, executive function, psychomotor speed, and visuospatial recall.226, 230, 231, 232, 233 Such impairment can have a profoundly negative effect on an individual's ability to engage in effective self‐care.234 Interestingly, engagement in intensive CVD risk reduction self‐care activities can improve cognitive impairment. For example, in older adults with HF, increased daily physical activity was associated with better cognition.235 Optimal medical management,236, 237 cardiac rehabilitation,238 and computer‐based cognitive training programs239, 240 can improve cognitive function. Improvement in self‐care behaviors was observed when patients used a computerized board game that stimulated daily activities.239


    The rise in prevalence of multimorbidity is a function of the aging of the population.241 By 2030, ≥170 million people in the United States are expected to be performing self‐care for ≥1 chronic condition.242 Self‐care is crucial for complex patients with multimorbidity, yet the presence of more than 1 condition is a significant barrier to self‐care and is associated with poor outcomes such as mortality and increased healthcare use.243, 244 Currently, the most commonly co‐occurring conditions are arthritis, hypertension, and diabetes mellitus.245 When only CVD index populations are considered, the top 5 conditions are hypertension, hyperlipidemia, diabetes mellitus, arthritis, and anemia.246

    Multimorbidity creates a web of co‐occurring, interacting conditions, each of which require self‐care. A recent review and analysis of multiple CVD data sets identified a complex network of 10 conditions (musculoskeletal disorders, thyroid disease, anemia, renal impairment, arrhythmias, cognitive impairment, depression/anxiety, respiratory disease, sleep disorders, and diabetes mellitus/metabolic disorders) that complicate self‐care and increase mortality.247 The higher the number of chronic illnesses, the lower the adequacy of self‐care.215 The complexity of multimorbidity has a direct effect on individual self‐care decisions, particularly in the higher‐level decision‐making tasks of symptom recognition and management.215 For example, someone with dyspnea must detect a change in the subjective perception of shortness of breath, interpret its meaning, label it correctly as associated with a particular condition, and then respond appropriately with the correct symptom management protocol.35 Complicating this task is the overlap in symptom patterns coupled with distinct and sometimes conflicting self‐care requirements of each individual chronic condition.248 Symptom monitoring and differentiating the cause of a particular symptom are the most challenging skills when more than 1 chronic illness is present.215 Key barriers to patient self‐care arises from the confusing or contradictory information provided by multiple healthcare providers.249 Management strategies must take into account the multiple and complex issues created by the web of multimorbidity in order to develop efficacious and efficient self‐care support programs.

    Multimorbidity is significantly higher in non‐Hispanic white and black women than in any other demographic groups.245 Because these populations have been historically understudied, little is known about outcomes of patients who are older, female, or racially diverse. These issues have a direct impact on the development of clinical practice guidelines and guideline‐directed care.246 Prevalence, poor outcomes, and understudied populations all point to a critical need for research to delineate the impact of multimorbidity on self‐care.

    Family‐Level Factors Influencing Self‐Care

    A growing body of research points to a significant impact of social relationships on all‐cause and disease‐specific morbidity and mortality.250, 251, 252 Both the size and diversity of one's social network or capital (informal connections available for support, help, and information) are strongly and prospectively linked with CVD morbidity and mortality. In particular, epidemiological data suggest that people who have larger, more integrated social networks are at reduced risk for mortality and ischemic heart disease253 and stroke254 and have a better prognosis after myocardial infarction,255 compared with more socially isolated individuals.

    Community‐Level Factors Influencing Self‐Care

    Social networks simultaneously reflect both the individual's social capital and the community's social structures. People who are connected to a network or community rich in support, social trust, information, and healthy norms may have more access to resources that can help them achieve health goals.256, 257 Studies of urban versus rural communities suggest that rural residents have a higher burden of risk factors and encounter more challenges with self‐care than their urban counterparts because of inequalities related to socioeconomic resources and access to care.258, 259, 260, 261 However, other studies in lower‐ and upper‐middle‐income countries report a greater prevalence of CVD risk factors attributed to urbanization,262, 263, 264, 265 so this issue remains unclear. It is not yet sufficiently clear what factors at the community level are proportionately more important to CVD risk management. Several study protocols have been designed to identify these critical elements, but results are not yet available.187, 266, 267

    Section Summary

    • Knowledge, skills, confidence, and motivation are required to effectively perform self‐care.

    • Common individual barriers that impede self‐care include depression, poor self‐efficacy, cognitive decline, and multimorbidity.

    • Lack of social support is the most important family‐level factor influencing self‐care.

    • Lack of social capital at the community‐level limits access and affordability of care.

    Interventions to Enhance Self‐Care

    Literally hundreds of studies have been conducted testing approaches to promoting self‐care in CVD populations. Interventions based on self‐care decision making268, 269 focus on the importance of building experiential skill in specific behaviors, sociocultural resources (ie, social support), and personal values. Innovative CVD self‐care decision aids support complex decision making by assisting those with CVD and stroke to recognize decision situations (ie, those occasions with an opportunity to choose between courses of action) and by providing aids to facilitate effective responses based on evidence270 and promote adherence to effective prevention strategies.271 One particular complex decision that has captured the attention of investigators is the response to symptoms. Numerous studies have demonstrated that the time from initial symptom onset to seeking urgent treatment is prolonged in individuals with acute coronary syndrome and stroke.272, 273 Even in patients with extant CVD, the delay in onset of worsening symptoms to urgent healthcare utilization is often measured in days.274

    Individual‐Level Self‐Care Interventions

    A consistent theme among successful studies addressing symptom response and a variety of other self‐care behaviors is the use of motivational approaches to promote self‐care. For example, a systematic review and meta‐analysis of interventions to improve medication adherence in adults with hypertension included 112 eligible treatment‐versus‐control group outcome comparisons of 34 272 subjects.275 The most promising intervention components were those linking adherence behavior with habits, giving adherence feedback to patients, self‐monitoring of BP, using pill boxes and other special packaging, and motivational interviewing. The most effective interventions used multiple components and were delivered over many days.

    Knowledge and skill.

    Interventions to improve self‐care in persons with CVD have generally included an educational component intended to improve knowledge and understanding about CVD and associated self‐care practices.276, 277 Strategies to increase patient knowledge include didactic sessions, use of written materials and/or technology (eg, DVDs, iPADs), mHealth applications, and telemonitoring.278 The effects of such strategies are mixed. Although some investigators report increased knowledge, results have been inconclusive as to the improvement in self‐care behaviors.279, 280, 281, 282 A recent systematic review of 33 HF interventions examined the main mechanism of program effectiveness on clinical outcomes and reported that when interventions focused on improving knowledge alone, the effects were diminished.283 The general consensus is that knowledge is necessary for effective self‐care, but not sufficient to improve behavior or influence outcomes.186, 284, 285

    In addition to knowledge, adequate self‐care requires skill in performing routine preventive behaviors and skill in making decisions about signs and symptoms of a worsening condition.286 Skill refers to the ability to use information and apply it in a specific context,277 that is, carry out a task with a predetermined result. Much of what we know about how to improve skill in self‐care comes from the HF self‐care literature.213, 221, 287, 288 Patients report needing tactical self‐care skills (“how to”; eg, preparing meals, monitoring weight) as well as situational skill (“what to do when”).286, 289 Skill development has been studied in numerous populations with HF and shown to be important in improving self‐care in ethnically diverse samples268 as well as persons with both HF and diabetes mellitus.290

    There is a growing body of literature examining the effects of interventions to improve self‐care in persons with other CVDs, including hypertension,291, 292 AF,167 coronary heart disease,293, 294 and stroke,165 with a shift in focus to skill development as essential to improving self‐care and related outcomes. For example, the “Keep Moving toward Healthy Heart and Healthy Brain” trial compared the effects of an exercise intervention that incorporated skills training to promote physical activity on clinical outcomes of BP, heart attack, and stroke.295 Participants in the intervention group compared with those in the usual care group had reduced incidence of heart attack and stroke and a moderate reduction in BP (−3.72 mm Hg in systolic BP and −2.92 mm Hg in diastolic BP) 6 months after the intervention. The intervention group also demonstrated significant increases in physical activity at 3 and 6 months postintervention. Another example of the emerging emphasis across CVD self‐care trials on skill development is the ACT (Achieving Blood Pressure Control Together) study,292 a planned trial testing the effects of an intervention that includes skills training to enhance performance of hypertension self‐care behaviors to improve BP in black adults with uncontrolled hypertension.

    Technology to Support Self‐Care Maintenance, Monitoring, and Management

    Rapidly evolving personal technologies offer the promise of support for many aspects of self‐care. The widespread availability of mobile phones has generated intense interest in mHealth (mobile health)—the use of mobile phones to receive information and coaching to support self‐care. mHealth provides the prospect of increasing our reach to deliver efficient, affordable healthcare services to widespread populations in need of primary and secondary prevention for diverse goals such as smoking cessation, weight loss, and physical activity.296, 297, 298, 299

    Mobile phone text messaging (“mHealth 1.0”300) has been shown to be effective in promoting a wide variety of self‐care behaviors such as physical activity299, 301, 302, 303, 304 and adherence to multiple lifestyle behaviors,305 including BP control, weight control, and smoking cessation.306 The content of successful text messages has included advice, motivational reminders, and support to change lifestyle behaviors. Notably, although the results are promising, they are still limited to a small number of trials, inconsistent outcome measures, and ineffective reporting of intervention characteristics.307 The duration of effects remains to be determined.

    A recent systematic review found that 79% of studies of text messaging, mobile applications, and telemonitoring by mobile phones were effective in improving CVD outcomes.130 Some key factors associated with improved outcomes included personalized messages with tailored advice, greater engagement (2‐way text messaging, higher frequency of messages), and use of multiple modalities. Not every mHealth intervention has been effective, however; the recent BEAT‐HF (Better Effectiveness After Transition—Heart Failure) trial failed to reduce readmissions at 6 months in HF patients receiving telemonitoring and health coaching by telephone.308 Large‐scale, longitudinal studies are warranted to gain a clear understanding of the effects of mHealth on behavior change, CVD risk factors, and clinical outcomes and better identify who will benefit most from these interventions.309

    “mHealth 2.0”300 involves more‐complex approaches than messaging, such as smartphone software applications (apps) and the use of sensors or monitors for self‐care monitoring. Thousands of apps have been created since they first appeared in 2008, with health a particular focus. Systematic reviews on the effectiveness of mobile phone and tablet apps in self‐care of long‐term conditions support their potential for improving management and health outcomes through self‐care. They also note, however, that health apps are largely unregulated and few are evidence based.310, 311, 312

    Wearable devices such as pedometers measure exercise parameters, heart rate, and sleep. Self‐care monitoring helps people become more aware of how their bodies work and what is normal, and enables them to track their progress in real time and stay motivated while making lifestyle changes. Wearable monitoring devices may alert a patient earlier to a change in health that needs medical attention. Even “low‐tech” home‐based equipment such as weight scales and BP monitors remain useful. Monitoring of BP,313 glucose,314 perceived exertion,315 body weight,316, 317 international normalized ratio values,318, 319, 320, 321 calorie intake and energy expenditure,322, 323 postprandial glycemia,323 and HF symptoms221, 324, 325, 326 has been shown to be comparable to provider management to improve outcomes.

    In the future, “mHealth 3.0” may involve smartphone machine‐learning algorithms and body sensors that make accurate patient‐specific assessments and recommendations.300 An example would be an app that recognizes that BP is elevated or detects an alarming arrhythmia through electrocardiograph signal analysis.327, 328 New self‐care behaviors will emerge such as adherence to wearing these sensors and making decisions in response to such alerts.

    There is potential for people with mobile devices to use apps with sensors and machine‐learning algorithms to self‐diagnose and treat. Use of these apps may shift the focus of patients away from CVD prevention to CVD treatment. Of equal concern is that they will also allow people to bypass healthcare providers. Consequently, it is essential that providers play an active role in the development of mHealth technology that supports active patient and healthcare provider engagement in self‐care. In the AHA scientific statement on the use of mHealth‐based strategies for CVD prevention, the authors called for testing of mHealth approaches that provide interaction between patients and healthcare providers using real‐time data for joint decision making.329 This use of mHealth technology has the potential to revolutionize self‐care by allowing patients, family members, and providers to engage in active, real‐time partnerships regarding self‐care.

    Family‐Level Self‐Care Interventions

    Recognition of the positive influence of family engagement on self‐care has supported the design and testing of family‐focused self‐care interventions for CVD patients and their family members. Although some clinical trials testing family‐ or dyad‐level interventions for CVD self‐care hold promise, they are limited in number, tend to have small sample sizes with short evaluation time frames, are based on varying theoretical frameworks, and have yielded conflicting results. The majority of studies have been with HF patients. Promising approaches focus on communication, decision making, reciprocity,330 caregiver self‐care, coping and communication, problem solving, and dealing with stress and negative emotions.331 Factors that may interfere with a family‐focused approach include high levels of anxiety and lack of perceived control among the family members,332, 333 disease severity, and caregiver burden.331

    Multiple intervening and complex factors affect family‐level self‐care. The timing in the trajectory of care, pre‐existing and evolving comorbidities, psychosocial status at baseline, family context and relational variables, as well as health literacy and dyadic interdependence converge to influence self‐care. Family‐focused self‐care approaches needs to incorporate these factors when designing interventions for dyads or families. Furthermore, given that the physical and mental health of caregivers may be affected by the demands of caregiving, family‐focused interventions need to offer support without increasing burden to the family caregiver.

    Community‐Level Self‐Care Interventions

    Community settings such as social or community centers provide an additional option to the local medical facility or the home in helping to build self‐care skills.334 Participants in community‐based self‐care training initiatives showed significant improvement in self‐care maintenance and self‐care management268, 335, 336 and in CVD risk reduction.337 There is, however, a paucity of studies on the direct links between community activities and CVD. One exception is a new and encouraging report from the World Health Organization, which found that 6 in 10 people globally are currently protected by at least 1 tobacco control measure–4 times more than in 2007. These community‐level approaches reflect policies, warnings, advertising bans, and taxes for example.338

    Theoretically, community neighborhoods may encourage self‐care through activities and events that raise awareness and knowledge about healthy lifestyles, encourage collective activity and engagement, and alleviate social isolation. Examples include organized walks and runs, health fairs, farmers’ markets, and support groups in the local community. Some evidence from the United States suggests that adults with access to a community garden consume more fruit and vegetables,339 and community gardens have been used to improve the diet of poor communities, particularly in the developing world. Farmers’ markets and community gardens may promote active lifestyles and mental well‐being,340 but few well‐designed studies have been completed to confirm this assumption.

    Support in the community may come from lay community health workers such as health trainers or coaches.187 Dye et al341 reported a successful initiative designed to improve hypertension self‐care among older rural residents through education and support offered by trained volunteers. Participants received baseline and follow‐up health risk appraisals with blood work, educational materials, and items such as BP monitors and pedometers. These participants demonstrated significant increases in hypertension‐related knowledge that persisted at 16 weeks, as well as significant improvements in stage of readiness to change behaviors and in actual behaviors. Furthermore, clinically significant decreases in all outcome measures were observed, with statistically significant changes in systolic BP, weight, and glucose.

    Section Summary

    • Educational strategies focused on increasing knowledge have been inconclusive as to the improvement in self‐care behaviors. Skill development approaches to improve self‐care are more promising than purely educational approaches.

    • Technology is promising as an approach to improving self‐care behaviors in an efficient and affordable manner. The duration of effect needs further study. Future devices may use sensors and machine‐learning algorithms that allow self‐diagnosis and treatment. It is essential that healthcare providers play an active role in the development of mHealth technology that supports active patient and healthcare provider engagement in self‐care.

    • Promising family‐focused intervention approaches have focused on communication, decision making, reciprocity, caregiver self‐care, coping and communication, problem solving, and dealing with stress and negative emotions. Factors that interfere with family‐focused approaches include high levels of anxiety and lack of perceived control among the family members, disease severity, and caregiver burden.

    • Community‐based interventions encourage self‐care through activities and events that raise awareness and knowledge about healthy lifestyles (eg, AHA Heart Walk), encourage collective activity and engagement, and alleviate social isolation. There are few studies on the direct links between community activities and self‐care outcomes.

    Conclusions and Implications

    In this scientific statement, we have argued the importance of self‐care in the AHA mission and vision of building healthier lives, free of CVD and stroke. The evidence supporting specific self‐care behaviors, the effectiveness of self‐care in improving outcomes, and barriers to self‐care were discussed. Although there are many nuances to relationships between self‐care and outcomes, there are numerous instances of Level A (multiple populations evaluated, data derived from multiple trials or meta‐analyses) and Class I to IIa evidence (benefit ≫ risk with sufficient evidence from multiple trials or meta‐analyses) regarding interventions that promote cardiovascular self‐care. As such, greater emphasis should be placed on self‐care in evidence‐based guidelines.

    Many self‐care behaviors are consistent across cardiovascular conditions, and most CVD risk reduction self‐care activities prevent other diseases as well. Ultimately, promoting wide uptake of the self‐care behaviors outlined in this document is essential to reducing the incidence and prevalence of chronic CVD and other noncommunicable diseases.

    To be maximally effective, self‐care to optimize healthy lifestyle and prevent chronic CVD must become part of the culture of society.342 A theme in the research summarized in this scientific statement is that interventions are more effective for people with known CVD than those without perceived risk. We can build on this knowledge with the design of interventions that focus the attention of individuals with CVD or perceived risk of CVD on self‐care (Table 2). However, to truly control CVD, self‐care must be practiced by all individuals at all ages and should not be relegated to only those who already have CVD. Further research is required to understand the factors that enable and motivate people to make healthy self‐care choices.

    John Wiley & Sons, Ltd

    Table 2. A World View of CVD Risk

    AreaIssues and Comorbid Conditions That Increase CVD RiskWithin Border Ethnic, Racial, and Socioeconomic DisparitiesBetween Border Ethnic, Racial, and Socioeconomic Disparities
    WorldwideWorldwide, 15.5% of all births (20 million) are LBW. LBW infants are more than twice as common in developing countries (16.5%) as in developed countries (7%)343
    United States, 300 million people Changing demographics with increase in diabetes mellitus, obesity, hypertension Age‐adjusted death rates higher for males and strikingly higher for blacks compared with whites344 Risk factors more prevalent in minorities with resultant increase in CVD344 Racial disparity345, 346 LBW infant rate of blacks twice that of whites347CVD death rates highest in the most rural counties348YesNA
    Europe, 53 member states of the WHO European region and >740 million people Alcohol and tobacco highest in the world46, 349 Large imbalance between nations related to economics and race/ethnicity46, 349YesYes
    Asia, >40 countries and >3 billion people Increase in obesity and cholesterol on top of high tobacco and hypertension Increasing conversion of cerebral vascular disease to CVD as primary cause of death350, 351YesYes
    Australia/New Zealand, 29 million people Large disparity in CVD, disproportionate risk factors in indigenous population352 Racial disparity353, 354 In Australia, compared with non‐indigenous people, Aboriginal and Torres Strait Islander peoples were 3 times more likely to have a heart attack and nearly twice as likely to die of heart disease354 The indigenous Maori of New Zealand have higher rates of CVD mortality and morbidity than that for non‐Maori non‐Pacific New Zealanders353YesNA
    Africa, 1.2 billion people Westernization of diet Increase in industrialization355, 356YesYes
    Latin America/Caribbean, 643 million peoplePoorer diets, increased smoking, increased obesity (high end of obesity figures for Organization for Economic Cooperation and Development countries), less exercise, limited access to effective health care and medications357, 358YesYes

    CVD indicates cardiovascular disease; LBW, low birthweight; NA, not applicable; WHO, World Health Organization.


    Writing Group Disclosures

    Writing Group MemberEmploymentResearch GrantOther Research SupportSpeakers’ Bureau/HonorariaExpert WitnessOwnership InterestConsultant/Advisory BoardOther
    Barbara RiegelUniversity of Pennsylvania School of NursingNIH*NoneNoneNoneNoneNoneNone
    Debra K. MoserUniversity of Kentucky College of NursingNIHNoneNoneNoneNoneNoneNone
    Harleah G. BuckUniversity of South Florida NursingNoneNoneNoneNoneNoneNoneNone
    Victoria Vaughan DicksonNew York University College of NursingNoneNoneNoneNoneNoneNoneNone
    Sandra B. DunbarEmory University, Nell Hodgson Woodruff School of NursingNoneNoneNoneNoneNoneNoneNone
    Christopher S. LeeOregon Health & Science University School of Nursing and Knight Cardiovascular InstituteNIH/NINR; NIH/NHLBINoneNoneNoneNoneNoneNone
    Terry A. LennieUniversity of Kentucky College of NursingNIH*NoneNoneNoneNoneNoneNIH*
    JoAnn LindenfeldVanderbilt University Medical CenterNoneNoneNoneNoneNoneNoneNone
    Judith E. MitchellState University of New York Downstate Medical CenterNoneNoneNoneNoneNoneNoneNone
    Diane J. Treat‐JacobsonUniversity of Minnesota School of NursingMargaret A. Cargill FoundationNoneNoneNoneNoneNoneNone
    David E. WebberInternational Self‐Care FoundationNoneNoneNoneNoneNoneNoneNone

    This table represents the relationships of writing group members that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all members of the writing group are required to complete and submit. A relationship is considered to be “significant” if (a) the person receives $10 000 or more during any 12‐month period, or 5% or more of the person's gross income; or (b) the person owns 5% or more of the voting stock or share of the entity, or owns $10 000 or more of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.



    Reviewer Disclosures

    ReviewerEmploymentResearch GrantOther Research SupportSpeakers’ Bureau/HonorariaExpert WitnessOwnership InterestConsultant/Advisory BoardOther
    Janet Prvu BettgerDuke Clinical Research InstituteNoneNoneNoneNoneNoneNoneNone
    Darren A. DeWaltUniversity of North CarolinaNoneNoneNoneNoneNoneNoneNone
    Corrine Y. JurgensStony Brook UniversityNoneNoneNoneNoneNoneNoneNone
    Michael W. RichWashington UniversityNIA (conference grant)*NoneNoneNoneNoneNoneNone

    This table represents the relationships of reviewers that may be perceived as actual or reasonably perceived conflicts of interest as reported on the Disclosure Questionnaire, which all reviewers are required to complete and submit. A relationship is considered to be “significant” if (a) the person receives $10 000 or more during any 12‐month period, or 5% or more of the person's gross income; or (b) the person owns 5% or more of the voting stock or share of the entity, or owns $10 000 or more of the fair market value of the entity. A relationship is considered to be “modest” if it is less than “significant” under the preceding definition.



    The American Heart Association makes every effort to avoid any actual or potential conflicts of interest that may arise as a result of an outside relationship or a personal, professional, or business interest of a member of the writing panel. Specifically, all members of the writing group are required to complete and submit a Disclosure Questionnaire showing all such relationships that might be perceived as real or potential conflicts of interest.

    This statement was approved by the American Heart Association Science Advisory and Coordinating Committee on May 15, 2017, and the American Heart Association Executive Committee on August 21, 2017. A copy of the document is available at by using either “Search for Guidelines & Statements” or the “Browse by Topic” area.

    The American Heart Association requests that this document be cited as follows: Riegel B, Moser DK, Buck HG, Dickson VV, Dunbar SB, Lee CS, Lennie TA, Lindenfeld J, Mitchell JE, Treat‐Jacobson DJ, Webber DE; on behalf of the American Heart Association Council on Cardiovascular and Stroke Nursing; Council on Peripheral Vascular Disease; and Council on Quality of Care and Outcomes Research. Self‐care for the prevention and management of cardiovascular disease and stroke: a scientific statement for healthcare professionals from the American Heart Association. J Am Heart Assoc. 2017;6:e006997. DOI: 10.1161/JAHA.117.006997.

    Expert peer review of AHA Scientific Statements is conducted by the AHA Office of Science Operations. For more on AHA statements and guidelines development, visit Select the “Guidelines & Statements” drop‐down menu, then click “Publication Development.”


    • 1 Negrini S, Padua L, Kiekens C, Michail X, Boldrini P. Current research funding methods dumb down health care and rehabilitation for disabled people and aging population: a call for a change. Eur J Phys Rehabil Med. 2014; 50:601–608.Google Scholar
    • 2 Centers for Disease Control and Prevention . Public health and aging: trends in aging—United States and worldwide. MMWR Morb Mortal Wkly Rep. 2003; 52:101–106.Google Scholar
    • 3 Jung M. Challenges of multimorbidities in the era of an aging population. Health Care Manag (Frederick). 2016; 35:134–143.Google Scholar
    • 4 Held FP, Blyth F, Gnjidic D, Hirani V, Naganathan V, Waite LM, Seibel MJ, Rollo J, Handelsman DJ, Cumming RG, Le Couteur DG. Association rules analysis of comorbidity and multimorbidity: the Concord Health and Aging in Men Project. J Gerontol A Biol Sci Med Sci. 2016; 71:625–631.Google Scholar
    • 5 Dall TM, Gallo PD, Chakrabarti R, West T, Semilla AP, Storm MV. An aging population and growing disease burden will require a large and specialized health care workforce by 2025. Health Aff (Millwood). 2013; 32:2013–2020.Google Scholar
    • 6 Riegel B, Moser DK, Anker SD, Appel LJ, Dunbar SB, Grady KL, Gurvitz MZ, Havranek EP, Lee CS, Lindenfeld J, Peterson PN, Pressler SJ, Schocken DD, Whellan DJ; on behalf of the American Heart Association Council on Cardiovascular Nursing, Council on Clinical Cardiology, Council on Nutrition, Physical Activity, and Metabolism, and Interdisciplinary Council on Quality of Care and Outcomes Research . State of the science: promoting self‐care in persons with heart failure: a scientific statement from the American Heart Association. Circulation. 2009; 120:1141–1163.Google Scholar
    • 7 World Health Organization . Health Education in Self‐Care: Possibilities and Limitations. Geneva, Switzerland: World Health Organization; 1983.Google Scholar
    • 8 Webber D, Guo Z, Mann S. Self‐care in health: we can define it, but should we also measure it?SelfCare. 2013; 4:101–106.Google Scholar
    • 9 Riegel B, Jaarsma T, Stromberg A. A middle‐range theory of self‐care of chronic illness. ANS Adv Nurs Sci. 2012; 35:194–204.Google Scholar
    • 10 James PA, Oparil S, Carter BL, Cushman WC, Dennison‐Himmelfarb C, Handler J, Lackland DT, LeFevre ML, MacKenzie TD, Ogedegbe O, Smith SC, Svetkey LP, Taler SJ, Townsend RR, Wright JT, Narva AS, Ortiz E. 2014 evidence‐based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014; 311:507–520.Google Scholar
    • 11 Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M, Christiaens T, Cifkova R, De Backer G, Dominiczak A, Galderisi M, Grobbee DE, Jaarsma T, Kirchhof P, Kjeldsen SE, Laurent S, Manolis AJ, Nilsson PM, Ruilope LM, Schmieder RE, Sirnes PA, Sleight P, Viigimaa M, Waeber B, Zannad F, Redon J, Dominiczak A, Narkiewicz K, Nilsson PM, Burnier M, Viigimaa M, Ambrosioni E, Caufield M, Coca A, Olsen MH, Schmieder RE, Tsioufis C, van de Borne P, Zamorano JL, Achenbach S, Baumgartner H, Bax JJ, Bueno H, Dean V, Deaton C, Erol C, Fagard R, Ferrari R, Hasdai D, Hoes AW, Kirchhof P, Knuuti J, Kolh P, Lancellotti P, Linhart A, Nihoyannopoulos P, Piepoli MF, Ponikowski P, Sirnes PA, Tamargo JL, Tendera M, Torbicki A, Wijns W, Windecker S, Clement DL, Coca A, Gillebert TC, Tendera M, Rosei EA, Ambrosioni E, Anker SD, Bauersachs J, Hitij JB, Caulfield M, De Buyzere M, De Geest S, Derumeaux GA, Erdine S, Farsang C, Funck‐Brentano C, Gerc V, Germano G, Gielen S, Haller H, Hoes AW, Jordan J, Kahan T, Komajda M, Lovic D, Mahrholdt H, Olsen MH, Ostergren J, Parati G, Perk J, Polonia J, Popescu BA, Reiner Z, Rydén L, Sirenko Y, Stanton A, Struijker‐Boudier H, Tsioufis C, van de Borne P, Vlachopoulos C, Volpe M, Wood DA. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Eur Heart J. 2013; 34:2159–2219.Google Scholar
    • 12 Eckel RH, Jakicic JM, Ard JD, de Jesus JM, Houston Miller N, Hubbard VS, Lee I‐M, Lichtenstein AH, Loria CM, Millen BE, Nonas CA, Sacks FM, Smith SC, Svetkey LP, Wadden TA, Yanovski SZ. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014; 25(suppl 2):S76–S99.Google Scholar
    • 13 Dickson VV, Lee C, Yehle KS, Abel WM, Riegel B. Psychometric testing of the self‐care of hypertension inventory. J Cardiovasc Nurs. 2016 Sep 13. DOI: 10.1097/JCN.0000000000000364. [Epub ahead of print].Google Scholar
    • 14 Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Colvin MM, Drazner MH, Filippatos G, Fonarow GC, Givertz MM, Hollenberg SM, Lindenfeld J, Masoudi FA, McBride PE, Peterson PN, Stevenson LW, Westlake C. 2016 ACC/AHA/HFSA focused update on new pharmacological therapy for heart failure: an update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2016; 134:e282–e293.Google Scholar
    • 15 Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, Johnson MR, Kasper EK, Levy WC, Masoudi FA, McBride PE, McMurray JJ, Mitchell JE, Peterson PN, Riegel B, Sam F, Stevenson LW, Tang WH, Tsai EJ, Wilkoff BL. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2013; 128:e240–e327.Google Scholar
    • 16 Moser DK, Dickson V, Jaarsma T, Lee C, Stromberg A, Riegel B. Role of self‐care in the patient with heart failure. Curr Cardiol Rep. 2012; 14:265–275.Google Scholar
    • 17 Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC, Deruyter F, Eng JJ, Fisher B, Harvey RL, Lang CE, MacKay‐Lyons M, Ottenbacher KJ, Pugh S, Reeves MJ, Richards LG, Stiers W, Zorowitz RD; on behalf of the American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Quality of Care and Outcomes Research . Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published correction appears in Stroke. 2017;48:e78]. Stroke. 2016; 47:e98–e169.Google Scholar
    • 18 Joice S. Self‐management following stroke. Nurs Stand. 2012; 26:39–46.Google Scholar
    • 19 Jones F, Riazi A. Self‐efficacy and self‐management after stroke: a systematic review. Disabil Rehabil. 2011; 33:797–810.Google Scholar
    • 20 January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland JC, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW; ACC/AHA Task Force Members . 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society [published correction appears in Circulation. 2014;130:e272‐e274]. Circulation. 2014; 130:e199–e267.Google Scholar
    • 21 McCabe PJ. Self‐management of atrial fibrillation: a new frontier for nursing research. Prog Cardiovasc Nurs. 2008; 23:37–40.Google Scholar
    • 22 McCabe PJ, Schad S, Hampton A, Holland DE. Knowledge and self‐management behaviors of patients with recently detected atrial fibrillation. Heart Lung. 2008; 37:79–90.Google Scholar
    • 23 Peterson JC, Link AR, Jobe JB, Winston GJ, Marina Klimasiewfski E, Allegrante JP. Developing self‐management education in coronary artery disease. Heart Lung. 2014; 43:133–139.Google Scholar
    • 24 Vaughan Dickson V, Lee CS, Yehle KS, Mola A, Faulkner KM, Riegel B. Psychometric testing of the Self‐Care of Coronary Heart Disease Inventory (SC‐CHDI). Res Nurs Health. 2017; 40:15–22.Google Scholar
    • 25 Hirsch AT, Haskal ZJ, Hertzer NR, Bakal CW, Creager MA, Halperin JL, Hiratzka LF, Murphy WR, Olin JW, Puschett JB, Rosenfield KA, Sacks D, Stanley JC, Taylor LM, White CJ, White J, White RA. ACC/AHA 2005 practice guidelines for the management of patients with peripheral arterial disease (lower extremity, renal, mesenteric, and abdominal aortic): a collaborative report from the American Association for Vascular Surgery/Society for Vascular Surgery, Society for Cardiovascular Angiography and Interventions, Society for Vascular Medicine and Biology, Society of Interventional Radiology, and the ACC/AHA Task Force on Practice Guidelines (Writing Committee to Develop Guidelines for the Management of Patients With Peripheral Arterial Disease). Circulation. 2006; 113:e463–e654.Google Scholar
    • 26 Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, Hong Y, Safford MM, Goyal A, Cushman M. Life's Simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke. 2013; 44:1909–1914.Google Scholar
    • 27 Folsom AR, Shah AM, Lutsey PL, Roetker NS, Alonso A, Avery CL, Miedema MD, Konety S, Chang PP, Solomon SD. American Heart Association's Life's Simple 7: avoiding heart failure and preserving cardiac structure and function. Am J Med. 2015; 128(970–976):e972.Google Scholar
    • 28 Olson NC, Cushman M, Judd SE, McClure LA, Lakoski SG, Folsom AR, Safford MM, Zakai NA. American Heart Association's Life's Simple 7 and risk of venous thromboembolism: the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. J Am Heart Assoc. 2015; 4:e001494. DOI: 10.1161/JAHA.114.001494.Google Scholar
    • 29 Rebholz CM, Anderson CA, Grams ME, Bazzano LA, Crews DC, Chang AR, Coresh J, Appel LJ. Relationship of the American Heart Association's Impact Goals (Life's Simple 7) with risk of chronic kidney disease: results from the Atherosclerosis Risk in Communities (ARIC) cohort study. J Am Heart Assoc. 2016; 5:e003192. DOI: 10.1161/JAHA.116.003192.Google Scholar
    • 30 Thacker EL, Gillett SR, Wadley VG, Unverzagt FW, Judd SE, McClure LA, Howard VJ, Cushman M. The American Heart Association Life's Simple 7 and incident cognitive impairment: the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. J Am Heart Assoc. 2014; 3:e000635. DOI: 10.1161/JAHA.113.000635.Google Scholar
    • 31 Ogunmoroti O, Allen NB, Cushman M, Michos ED, Rundek T, Rana JS, Blankstein R, Blumenthal RS, Blaha MJ, Veledar E, Nasir K. Association between Life's Simple 7 and noncardiovascular disease: the Multi‐Ethnic Study of Atherosclerosis. J Am Heart Assoc. 2016; 5:e003954. DOI: 10.1161/JAHA.116.003954.Google Scholar
    • 32 Paterson B, Russell C, Thorne S. Critical analysis of everyday self‐care decision making in chronic illness. J Adv Nurs. 2001; 35:335–341.Google Scholar
    • 33 Paterson B, Thorne S. Expert decision making in relation to unanticipated blood glucose levels. Res Nurs Health. 2000; 23:147–157.Google Scholar
    • 34 Lippa KD, Klein HA. Portraits of patient cognition: how patients understand diabetes self‐care. Can J Nurs Res. 2008; 40:80–95.Google Scholar
    • 35 Riegel B, Dickson VV, Faulkner KM. The situation‐specific theory of heart failure self‐care: revised and updated. J Cardiovasc Nurs. 2016; 31:226–235.Google Scholar
    • 36 Riegel B, Dickson VV, Topaz M. Qualitative analysis of naturalistic decision making in adults with chronic heart failure. Nurs Res. 2013; 62:91–98.Google Scholar
    • 37 Davidson PM, Inglis SC, Newton PJ. Self‐care in patients with chronic heart failure. Expert Rev Pharmacoecon Outcomes Res. 2013; 13:351–359.Google Scholar
    • 38 Moser DK, Watkins JF. Conceptualizing self‐care in heart failure: a life course model of patient characteristics. J Cardiovasc Nurs. 2008; 23:205–218; quiz, 219–220.Google Scholar
    • 39 Lipshitz R, Klein G, Orasanu J, Salas E. Taking stock of naturalistic decision making. J Behav Decis Mak. 2001; 14:331–352.Google Scholar
    • 40 Orasanu J, Connolly T. The reinvention of decision making. In: Klein G, Orasanu J, Calderwood R, Zsambok C, eds. Decision Making in Action: Models and Methods. Norwood, NJ: Ablex; 1993:3–20.Google Scholar
    • 41 Endsley M. The role of situation awareness in naturalistic decision making. In: Zsambok C, Klein G, eds. Naturalistic Decision Making. New York, NY: Erlbaum; 1996:269–284.Google Scholar
    • 42 Klein G. Naturalistic decision making. Hum Factors. 2008; 50:456–460.Google Scholar
    • 43 United Nations . Report of the Second World Assembly on Aging. Madrid, Spain: United Nations; 2002.Google Scholar
    • 44 Nikolich‐Žugich J, Goldman DP, Cohen PR, Cortese D, Fontana L, Kennedy BK, Mohler MJ, Olshansky SJ, Perls T, Perry D, Richardson A, Ritchie C, Wertheimer AM, Faragher RG, Fain MJ. Preparing for an aging world: engaging biogerontologists, geriatricians, and the society. J Gerontol A Biol Sci Med Sci. 2016; 71:435–444.Google Scholar
    • 45 WHO Regional Office for Europe . Data and statistics. The challenge of cardiovascular disease—quick statistics. Available at: Accessed December 22, 2016.Google Scholar
    • 46 WHO Regional Office for Europe . The European health report 2015. Targets and beyond—reaching new frontiers in evidence. 2015. Available at: Accessed December 16, 2016.Google Scholar
    • 47 Kottke TE, Faith DA, Jordan CO, Pronk NP, Thomas RJ, Capewell S. The comparative effectiveness of heart disease prevention and treatment strategies. Am J Prev Med. 2009; 36:82–88.Google Scholar
    • 48 Goldman DP, Cutler D, Rowe JW, Michaud PC, Sullivan J, Peneva D, Olshansky SJ. Substantial health and economic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood). 2013; 32:1698–1705.Google Scholar
    • 49 Goldman DP, Gaudette E, Cheng WH. Competing risks: investing in sickness rather than health. Am J Prev Med. 2016; 50(5 suppl 1):S45–S50.Google Scholar
    • 50 Manton KG, Liu K. Projecting chronic disease prevalence. Med Care. 1984; 22:511–526.Google Scholar
    • 51 Scuteri A, Lattanzio F, Bernabei R. Life‐course approach to chronic disease: the active and healthy aging perspective. J Am Geriatr Soc. 2016; 64:e59–e61.Google Scholar
    • 52 Hardy R, Lawlor DA, Kuh D. A life course approach to cardiovascular aging. Future Cardiol. 2015; 11:101–113.Google Scholar
    • 53 Godfrey KM, Reynolds RM, Prescott SL, Nyirenda M, Jaddoe VW, Eriksson JG, Broekman BF. Influence of maternal obesity on the long‐term health of offspring. Lancet Diabetes Endocrinol. 2017; 5:53–64.Google Scholar
    • 54 Gluckman PD, Hanson MA. Living with the past: evolution, development, and patterns of disease. Science. 2004; 305:1733–1736.Google Scholar
    • 55 Lucas A. Role of nutritional programming in determining adult morbidity. Arch Dis Child. 1994; 71:288–290.Google Scholar
    • 56 Osmond C, Barker D, Winter P, Fall C, Simmonds S. Early growth and death from cardiovascular disease in women. BMJ. 1993; 307:1519–1524.Google Scholar
    • 57 Rich‐Edwards JW, Stampfer MJ, Manson JE, Rosner B, Hankinson SE, Colditz GA, Willett WC, Hennekens CH. Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976. BMJ. 1997; 315:396–400.Google Scholar
    • 58 Risnes KR, Vatten LJ, Baker JL, Jameson K, Sovio U, Kajantie E, Osler M, Morley R, Jokela M, Painter RC, Sundh V, Jacobsen GW, Eriksson JG, Sørensen TI, Bracken MB. Birthweight and mortality in adulthood: a systematic review and meta‐analysis. Int J Epidemiol. 2011; 40:647–661.Google Scholar
    • 59 Law CM, Shiell AW. Is blood pressure inversely related to birth weight? The strength of evidence from a systematic review of the literature. J Hypertens. 1996; 14:935–942.Google Scholar
    • 60 Davis EF, Lazdam M, Lewandowski AJ, Worton SA, Kelly B, Kenworthy Y, Adwani S, Wilkinson AR, McCormick K, Sargent I, Redman C, Leeson P. Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review. Pediatrics. 2012; 129:e1552–e1561.Google Scholar
    • 61 Jayet PY, Rimoldi SF, Stuber T, Salmòn CS, Hutter D, Rexhaj E, Thalmann S, Schwab M, Turini P, Sartori‐Cucchia C, Nicod P, Villena M, Allemann Y, Scherrer U, Sartori C. Pulmonary and systemic vascular dysfunction in young offspring of mothers with preeclampsia. Circulation. 2010; 122:488–494.Google Scholar
    • 62 Kajantie E, Eriksson JG, Osmond C, Thornburg K, Barker DJ. Pre‐eclampsia is associated with increased risk of stroke in the adult offspring the Helsinki birth cohort study. Stroke. 2009; 40:1176–1180.Google Scholar
    • 63 Lawlor DA, Macdonald‐Wallis C, Fraser A, Nelson SM, Hingorani A, Davey Smith G, Sattar N, Deanfield J. Cardiovascular biomarkers and vascular function during childhood in the offspring of mothers with hypertensive disorders of pregnancy: findings from the Avon Longitudinal Study of Parents and Children. Eur Heart J. 2012; 33:335–345.Google Scholar
    • 64 Hopkins SA, Cutfield WS. Exercise in pregnancy: weighing up the long‐term impact on the next generation. Exerc Sport Sci Rev. 2011; 39:120–127.Google Scholar
    • 65 Tuovinen S, Aalto‐Viljakainen T, Eriksson J, Kajantie E, Lahti J, Pesonen AK, Heinonen K, Lahti M, Osmond C, Barker DJ, Räikkönen K. Maternal hypertensive disorders during pregnancy: adaptive functioning and psychiatric and psychological problems of the older offspring. BJOG. 2014; 121:1482–1491.Google Scholar
    • 66 Tuovinen S, Räikkönen K, Kajantie E, Pesonen AK, Heinonen K, Osmond C, Barker DJ, Eriksson JG. Depressive symptoms in adulthood and intrauterine exposure to pre‐eclampsia: the Helsinki Birth Cohort Study. BJOG. 2010; 117:1236–1242.Google Scholar
    • 67 Wenger NK, Hayes SN, Pepine CJ, Roberts WC. Cardiovascular care for women: the 10‐Q Report and beyond. Am J Cardiol. 2013; 112:S2.Google Scholar
    • 68 Brown MC, Best KE, Pearce MS, Waugh J, Robson SC, Bell R. Cardiovascular disease risk in women with pre‐eclampsia: systematic review and meta‐analysis. Eur J Epidemiol. 2013; 28:1–19.Google Scholar
    • 69 Gidding SS, McMahan CA, McGill HC, Colangelo LA, Schreiner PJ, Williams OD, Liu K. Prediction of coronary artery calcium in young adults using the Pathobiological Determinants of Atherosclerosis in Youth (PDAY) risk score: the CARDIA study. Arch Intern Med. 2006; 166:2341–2347.Google Scholar
    • 70 Lee CS, Riegel B, Driscoll A, Suwanno J, Moser DK, Lennie TA, Dickson VV, Cameron J, Worrall‐Carter L. Gender differences in heart failure self‐care: a multinational cross‐sectional study. Int J Nurs Stud. 2009; 46:1485–1495.Google Scholar
    • 71 Horowitz CR, Rein SB, Leventhal H. A story of maladies, misconceptions and mishaps: effective management of heart failure. Soc Sci Med. 2004; 58:631–643.Google Scholar
    • 72 Schoenberg NE, Traywick LS, Jacobs‐Lawson J, Kart CS. Diabetes self‐care among a multiethnic sample of older adults. J Cross Cult Gerontol. 2008; 23:361–376.Google Scholar
    • 73 Schoenberg NE, Drungle SC. Barriers to non‐insulin dependent diabetes mellitus (NIDDM) self‐care practices among older women. J Aging Health. 2001; 13:443–466.Google Scholar
    • 74 Kotseva K, Wood D, De Backer G, De Bacquer D, Pyörälä K, Keil U; EUROASPIRE Study Group . Cardiovascular prevention guidelines in daily practice: a comparison of EUROASPIRE I, II, and III surveys in eight European countries. Lancet. 2009; 373:929–940.Google Scholar
    • 75 Pearce KA, Love MM, Shelton BJ, Schoenberg NE, Williamson MA, Barron MA, Houlihan JM; Kentucky Ambulatory Network . Cardiovascular risk education and social support (CaRESS): report of a randomized controlled trial from the Kentucky Ambulatory Network (KAN). J Am Board Fam Med. 2008; 21:269–281.Google Scholar
    • 76 Bardach SH, Schoenberg NE. The content of diet and physical activity consultations with older adults in primary care. Patient Educ Couns. 2014; 95:319–324.Google Scholar
    • 77 Hivert MF, Arena R, Forman DE, Kris‐Etherton PM, McBride PE, Pate RR, Spring B, Trilk J, Van Horn LV, Kraus WE; on behalf of the American Heart Association Physical Activity Committee of the Council on Lifestyle and Cardiometabolic Health; the Behavior Change Committee, a joint committee of the Council on Lifestyle and Cardiometabolic Health and the Council on Epidemiology and Prevention; the Exercise, Cardiac Rehabilitation, and Secondary Prevention Committee of the Council on Clinical Cardiology; and the Council on Cardiovascular and Stroke Nursing . Medical training to achieve competency in lifestyle counseling: an essential foundation for prevention and treatment of cardiovascular diseases and other chronic medical conditions: a scientific statement from the American Heart Association. Circulation. 2016; 134:e308–e327.Google Scholar
    • 78 Dzau VJ, McMClellan M, Burke S, Burke SP, Coye MJ, Diaz A, Daschle TA, Frist WH, Gaines M, Hamburg MA, Henney JE, Kumanyika S, Leavitt MO, Parker RM, Sandy LG, Schaeffer LD, Steele GD, Thompson P, Zerhouni E. Vital Directions for Health and Health Care: Priorities From a National Academy of Medicine Initiative. Washington, DC: National Academy of Medicine Initiative; 2017.Google Scholar
    • 79 Bender BG. Can health care organizations improve health behavior and treatment adherence?Popul Health Manag. 2014; 17:71–78.Google Scholar
    • 80 American Heart Association . Know your numbers? They could just save your life. 2016. Available at: Accessed December 24, 2016.Google Scholar
    • 81 American Heart Association . Know your health numbers. 2016. Available at: Accessed December 24, 2016.Google Scholar
    • 82 Cadilhac DA, Kilkenny MF, Johnson R, Wilkinson B, Amatya B, Lalor E. The Know Your Numbers (KYN) program 2008 to 2010: impact on knowledge and health promotion behavior among participants. Int J Stroke. 2015; 10:110–116.Google Scholar
    • 83 Dalen JE, Devries S. Diets to prevent coronary heart disease 1957–2013: what have we learned?Am J Med. 2014; 127:364–369.Google Scholar
    • 84 United States Department of Agriculture . Dietary Guidelines for Americans 2015–2020. 8th. 2015. Available at: Accessed December 24, 2016.Google Scholar
    • 85 Van Horn L, Carson JA, Appel LJ, Burke LE, Economos C, Karmally W, Lancaster K, Lichtenstein AH, Johnson RK, Thomas RJ, Vos M, Wylie‐Rosett J, Kris‐Etherton P; on behalf of the American Heart Association Nutrition Committee of the Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Stroke Council . Recommended dietary pattern to achieve adherence to the American Heart Association/American College of Cardiology (AHA/ACC) guidelines: a scientific statement from the American Heart Association [published correction appears in Circulation. 2016;134:e534]. Circulation. 2016; 134:e505–e529.Google Scholar
    • 86 Rees K, Dyakova M, Wilson N, Ward K, Thorogood M, Brunner E. Dietary advice for reducing cardiovascular risk. Cochrane Database Syst Rev. 2013;(12):CD002128.Google Scholar
    • 87 Desroches S, Lapointe A, Ratte S, Gravel K, Legare F, Turcotte S. Interventions to enhance adherence to dietary advice for preventing and managing chronic diseases in adults. Cochrane Database Syst Rev. 2013;(2):CD008722.Google Scholar
    • 88 Lesser IA, Gasevic D, Lear SA. The association between acculturation and dietary patterns of South Asian immigrants. PLoS One. 2014; 9:e88495.Google Scholar
    • 89 Raj S, Ganganna P, Bowering J. Dietary habits of Asian Indians in relation to length of residence in the United States. J Am Diet Assoc. 1999; 99:1106–1108.Google Scholar
    • 90 National Heart Lung and Blood Institute . The NHLBI Practical Guide: Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. NIH Publication No. 00‐4084. Bethesda, MD: National Institutes of Health; 2000.Google Scholar
    • 91 Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM, Kushner RF, Loria CM, Millen BE, Nonas CA, Pi‐Sunyer FX, Stevens J, Stevens VJ, Wadden TA, Wolfe BM, Yanovski SZ. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society [published correction appears in Circulation. 2014;129[suppl 2]:S139‐S140]. Circulation. 2014; 129(suppl 2):S102–S138.Google Scholar
    • 92 Wieland LS, Falzon L, Sciamanna CN, Trudeau KJ, Brodney S, Schwartz JE, Davidson KW. Interactive computer‐based interventions for weight loss or weight maintenance in overweight or obese people. Cochrane Database Syst Rev. 2012;(8):CD007675.Google Scholar
    • 93 Agarwal SK. Cardiovascular benefits of exercise. Int J Gen Med. 2012; 5:541–545.Google Scholar
    • 94 Holme I, Anderssen SA. Increases in physical activity is as important as smoking cessation for reduction in total mortality in elderly men: 12 years of follow‐up of the Oslo II study. Br J Sports Med. 2015; 49:743–748.Google Scholar
    • 95 Doukky R, Mangla A, Ibrahim Z, Poulin MF, Avery E, Collado FM, Kaplan J, Richardson D, Powell LH. Impact of physical inactivity on mortality in patients with heart failure. Am J Cardiol. 2016; 117:1135–1143.Google Scholar
    • 96 Loprinzi PD, Sng E. The effects of objectively measured sedentary behavior on all‐cause mortality in a national sample of adults with diabetes. Prev Med. 2016; 86:55–57.Google Scholar
    • 97 Evenson KR, Wen F, Herring AH. Associations of accelerometry‐assessed and self‐reported physical activity and sedentary behavior with all‐cause and cardiovascular mortality among US adults. Am J Epidemiol. 2016; 184:621–632.Google Scholar
    • 98 Fihn SD, Gardin JM, Abrams J, Berra K, Blankenship JC, Dallas AP, Douglas PS, Foody JM, Gerber TC, Hinderliter AL, King SB, Kligfield PD, Krumholz HM, Kwong RY, Lim MJ, Linderbaum JA, Mack MJ, Munger MA, Prager RL, Sabik JF, Shaw LJ, Sikkema JD, Smith CR, Smith SC, Spertus JA, Williams SV. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons [published correction appears in Circulation. 2014;129:e463]. Circulation. 2012; 126:e354–e471.Google Scholar
    • 99 Kernan WN, Ovbiagele B, Black HR, Bravata DM, Chimowitz MI, Ezekowitz MD, Fang MC, Fisher M, Furie KL, Heck DV, Johnston SC, Kasner SE, Kittner SJ, Mitchell PH, Rich MW, Richardson D, Schwamm LH, Wilson JA; on behalf of the American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Peripheral Vascular Disease . Guidelines for the prevention of stroke in patients with stroke and transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/American Stroke Association [published corrections appear in Stroke. 2014;45:e172 and Stroke. 2015;46:e54]. Stroke. 2014; 45:2160–2236.Google Scholar
    • 100 Gerhard‐Herman MD, Gornik HL, Barrett C, Barshes NR, Corriere MA, Drachman DE, Fleisher LA, Fowkes FG, Hamburg NM, Kinlay S, Lookstein R, Misra S, Mureebe L, Olin JW, Patel RA, Regensteiner JG, Schanzer A, Shishehbor MH, Stewart KJ, Treat‐Jacobson D, Walsh ME. 2016 AHA/ACC guideline on the management of patients with lower extremity peripheral artery disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines [published correction appears in Circulation. 2017;135:e791‐e792]. Circulation. 2017; 135:e726–e779.Google Scholar
    • 101 Gardner AW, Parker DE, Montgomery PS, Blevins SM. Step‐monitored home exercise improves ambulation, vascular function, and inflammation in symptomatic patients with peripheral artery disease: a randomized controlled trial. J Am Heart Assoc. 2014; 3:e001107. DOI: 10.1161/JAHA.114.001107.Google Scholar
    • 102 McDermott MM, Guralnik JM, Criqui MH, Ferrucci L, Zhao L, Liu K, Domanchuk K, Spring B, Tian L, Kibbe M, Liao Y, Lloyd Jones D, Rejeski WJ. Home‐based walking exercise in peripheral artery disease: 12‐month follow‐up of the GOALS randomized trial. J Am Heart Assoc. 2014; 3:e000711. DOI:10.1161/JAHA.113.000711.Google Scholar
    • 103 McDermott MM, Liu K, Guralnik JM, Criqui MH, Spring B, Tian L, Domanchuk K, Ferrucci L, Lloyd‐Jones D, Kibbe M, Tao H, Zhao L, Liao Y, Rejeski WJ. Home‐based walking exercise intervention in peripheral artery disease: a randomized clinical trial. JAMA. 2013; 310:57–65.Google Scholar
    • 104 Colella TJ, Gravely S, Marzolini S, Grace SL, Francis JA, Oh P, Scott LB. Sex bias in referral of women to outpatient cardiac rehabilitation? A meta‐analysis. Eur J Prev Cardiol. 2015; 22:423–441.Google Scholar
    • 105 Ades PA, Keteyian SJ, Wright JS, Hamm LF, Lui K, Newlin K, Shepard DS, Thomas RJ. Increasing cardiac rehabilitation participation from 20% to 70%: a road map from the Million Hearts Cardiac Rehabilitation Collaborative. Mayo Clin Proc. 2017; 92:234–242.Google Scholar
    • 106 Prochaska JJ, Benowitz NL. Smoking cessation and the cardiovascular patient. Curr Opin Cardiol. 2015; 30:506–511.Google Scholar
    • 107 Critchley J, Capewell S. Smoking cessation for the secondary prevention of coronary heart disease. Cochrane Database Syst Rev. 2003;(4):CD003041.Google Scholar
    • 108 US Department of Health and Human Services . The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014.Google Scholar
    • 109 Hart C, Gruer L, Bauld L. Does smoking reduction in midlife reduce mortality risk? Results of 2 long‐term prospective cohort studies of men and women in Scotland. Am J Epidemiol. 2013; 178:770–779.Google Scholar
    • 110 Joseph AM, Hecht SS, Murphy SE, Lando H, Carmella SG, Gross M, Bliss R, Le CT, Hatsukami DK. Smoking reduction fails to improve clinical and biological markers of cardiac disease: a randomized controlled trial. Nicotine Tob Res. 2008; 10:471–481.Google Scholar
    • 111 Cahill K, Stevens S, Perera R, Lancaster T. Pharmacological interventions for smoking cessation: an overview and network meta‐analysis. Cochrane Database Syst Rev. 2013;(5):CD009329.Google Scholar
    • 112 Ebbert JO, Hughes JR, West RJ, Rennard SI, Russ C, McRae TD, Treadow J, Yu CR, Dutro MP, Park PW. Effect of varenicline on smoking cessation through smoking reduction: a randomized clinical trial. JAMA. 2015; 313:687–694.Google Scholar
    • 113 Barth J, Jacob T, Daha I, Critchley JA. Psychosocial interventions for smoking cessation in patients with coronary heart disease. Cochrane Database Syst Rev. 2015;(7):CD006886.Google Scholar
    • 114 Stead LF, Lancaster T. Group behaviour therapy programmes for smoking cessation. Cochrane Database Syst Rev. 2005;(2):CD001007.Google Scholar
    • 115 Stead LF, Buitrago D, Preciado N, Sanchez G, Hartmann‐Boyce J, Lancaster T. Physician advice for smoking cessation. Cochrane Database Syst Rev. 2013;(5):CD000165.Google Scholar
    • 116 Rigotti NA, Clair C. Managing tobacco use: the neglected cardiovascular disease risk factor. Eur Heart J. 2013; 34:3259–3267.Google Scholar
    • 117 Patnode CD, Henderson JT, Thompson JH, Senger CA, Fortmann SP, Whitlock EP. Behavioral Counseling and Pharmacotherapy Interventions for Tobacco Cessation in Adults, Including Pregnant Women: A Review of Reviews for the U.S. Preventive Services Task Force. Rockville, MD: Agency for Healthcare Research and Quality (US); 2015.Google Scholar
    • 118 Snaterse M, Scholte Op Reimer WJ, Dobber J, Minneboo M, Ter Riet G, Jorstad HT, Boekholdt SM, Peters RJ. Smoking cessation after an acute coronary syndrome: immediate quitters are successful quitters. Neth Heart J. 2015; 23:600–607.Google Scholar
    • 119 Newhall K, Burnette M, Brooke BS, Schanzer A, Tan T, Flocke S, Farber A, Goodney P; VAPOR Investigators . Smoking cessation counseling in vascular surgical practice using the results of interviews and focus groups in the Vascular Surgeon offer and report smoking cessation pilot trial. J Vasc Surg. 2016; 63:1011–1017.e2.Google Scholar
    • 120 Lindschou Hansen J, Tolstrup JS, Jensen MK, Grønbæk M, Tjønneland A, Schmidt EB, Overvad K. Alcohol intake and risk of acute coronary syndrome and mortality in men and women with and without hypertension. Eur J Epidemiol. 2011; 26:439–447.Google Scholar
    • 121 Howard AA, Arnsten JH, Gourevitch MN. Effect of alcohol consumption on diabetes mellitus: a systematic review. Ann Intern Med. 2004; 140:211–219.Google Scholar
    • 122 Gonçalves A, Claggett B, Jhund PS, Rosamond W, Deswal A, Aguilar D, Shah AM, Cheng S, Solomon SD. Alcohol consumption and risk of heart failure: the Atherosclerosis Risk in Communities Study. Eur Heart J. 2015; 36:939–945.Google Scholar
    • 123 Jimenez M, Chiuve SE, Glynn RJ, Stampfer MJ, Camargo CA, Willett WC, Manson JE, Rexrode KM. Alcohol consumption and risk of stroke in women. Stroke. 2012; 43:939–945.Google Scholar
    • 124 Jones SB, Loehr L, Avery CL, Gottesman RF, Wruck L, Shahar E, Rosamond WD. Midlife alcohol consumption and the risk of stroke in the Atherosclerosis Risk in Communities Study. Stroke. 2015; 46:3124–3130.Google Scholar
    • 125 Baroletti S, Dell'Orfano H. Medication adherence in cardiovascular disease. Circulation. 2010; 121:1455–1458.Google Scholar
    • 126 Ho PM, Bryson CL, Rumsfeld JS. Medication adherence: its importance in cardiovascular outcomes. Circulation. 2009; 119:3028–3035.Google Scholar
    • 127 Kolandaivelu K, Leiden BB, O'Gara PT, Bhatt DL. Non‐adherence to cardiovascular medications. Eur Heart J. 2014; 35:3267–3276.Google Scholar
    • 128 Riegel B, Knafl GJ. Electronically monitored medication adherence predicts hospitalization in heart failure patients. Patient Prefer Adherence. 2013; 8:1–13.Google Scholar
    • 129 Anglada‐Martinez H, Riu‐Viladoms G, Martin‐Conde M, Rovira‐Illamola M, Sotoca‐Momblona JM, Codina‐Jane C. Does mHealth increase adherence to medication? Results of a systematic review. Int J Clin Pract. 2015; 69:9–32.Google Scholar
    • 130 Park LG, Beatty A, Stafford Z, Whooley MA. Mobile phone interventions for the secondary prevention of cardiovascular disease. Prog Cardiovasc Dis. 2016; 58:639–650.Google Scholar
    • 131 Patel S, Jacobus‐Kantor L, Marshall L, Ritchie C, Kaplinski M, Khurana PS, Katz RJ. Mobilizing your medications: an automated medication reminder application for mobile phones and hypertension medication adherence in a high‐risk urban population. J Diabetes Sci Technol. 2013; 7:630–639.Google Scholar
    • 132 Pressler SJ, Gradus‐Pizlo I, Chubinski SD, Smith G, Wheeler S, Sloan R. Family caregivers of patients with heart failure: a longitudinal study. J Cardiovasc Nurs. 2013; 28:417–428.Google Scholar
    • 133 Buck HG, Mogle J, Riegel B, McMillan S, Bakitas M. Exploring the relationship of patient and informal caregiver characteristics with heart failure self‐care using the actor‐partner interdependence model: implications for outpatient palliative care. J Palliat Med. 2015; 18:1026–1032.Google Scholar
    • 134 Kaholokula JK, Saito E, Mau MK, Latimer R, Seto TB. Pacific Islanders’ perspectives on heart failure management. Patient Educ Couns. 2008; 70:281–291.Google Scholar
    • 135 Dickson VV, Worrall‐Carter L, Kuhn L, Riegel B. Whose job is it? Gender differences in perceived role in heart failure self‐care. J Nurs Healthc Chronic Illn. 2011; 3:99–108.Google Scholar
    • 136 Riegel B, Dickson VV, Kuhn L, Page K, Worrall‐Carter L. Gender‐specific barriers and facilitators to heart failure self‐care: a mixed methods study. Int J Nurs Stud. 2010; 47:888–895.Google Scholar
    • 137 Clark AM, Spaling M, Harkness K, Spiers J, Strachan PH, Thompson DR, Currie K. Determinants of effective heart failure self‐care: a systematic review of patients’ and caregivers’ perceptions. Heart. 2014; 100:716–721.Google Scholar
    • 138 Strachan PH, Currie K, Harkness K, Spaling M, Clark AM. Context matters in HF self‐care: a qualitative systematic review. J Card Fail. 2014; 20:448–455.Google Scholar
    • 139 Buck HG, Harkness K, Wion R, Carroll SL, Cosman T, Kaasalainen S, Kryworuchko J, McGillion M, O'Keefe‐McCarthy S, Sherifali D, Strachan PH, Arthur HM. Caregivers’ contributions to heart failure self‐care: a systematic review. Eur J Cardiovasc Nurs. 2015; 14:79–89.Google Scholar
    • 140 Bidwell JT, Vellone E, Lyons KS, D'Agostino F, Riegel B, Juárez‐Vela R, Hiatt SO, Alvaro R, Lee CS. Determinants of heart failure self‐care maintenance and management in patients and caregivers: a dyadic analysis. Res Nurs Health2015; 38:392–402.Google Scholar
    • 141 Li Y, Li Z, Chang G, Wang M, Wu R, Wang S, Yao C. Effect of structured home‐based exercise on walking ability in patients with peripheral arterial disease: a meta‐analysis. Ann Vasc Surg.2015; 29:597–606.Google Scholar
    • 142 Lee CS, Vellone E, Lyons KS, Cocchieri A, Bidwell JT, D'Agostino F, Hiatt SO, Alvaro R, Buck HG, Riegel B. Patterns and predictors of patient and caregiver engagement in heart failure care: a multi‐level dyadic study. Int J Nurs Stud. 2015; 52:588–597.Google Scholar
    • 143 Kitko LA, Hupcey JE, Pinto C, Palese M. Patient and caregiver incongruence in advanced heart failure. Clin Nurs Res. 2015; 24:388–400.Google Scholar
    • 144 Retrum JH, Nowels CT, Bekelman DB. Patient and caregiver congruence: the importance of dyads in heart failure care. J Cardiovasc Nurs. 2013; 28:129–136.Google Scholar
    • 145 Retrum JH, Boggs J, Hersh A, Wright L, Main DS, Magid DJ. Patient‐identified factors related to heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2013; 6:171–177.Google Scholar
    • 146 Rich MW, Chyun DA, Skolnick AH, Alexander KP, Forman DE, Kitzman DW, Maurer MS, McClurken JB, Resnick BM, Shen WK, Tirschwell DL; on behalf of the American Heart Association Older Populations Committee of the Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, Council on Cardiovascular Surgery and Anesthesia, and Stroke Council; American College of Cardiology; and American Geriatrics Society. Knowledge gaps in cardiovascular care of the older adult population: a scientific statement from the American Heart Association, American College of Cardiology, and American Geriatrics Society. Circulation. 2016; 133:2103–2122.Google Scholar
    • 147 Barton H, Grant M, Mitcham C, Tsourou C. Healthy urban planning in European cities. Health Promot Int. 2009; 24(suppl 1):i91–i99.Google Scholar
    • 148 Black JL, Macinko J. Neighborhoods and obesity. Nutr Rev. 2008; 66:2–20.Google Scholar
    • 149 Baker PR, Francis DP, Soares J, Weightman AL, Foster C. Community wide interventions for increasing physical activity. Cochrane Database Syst Rev. 2015;( 1):CD008366.Google Scholar
    • 150 Caspi CE, Sorensen G, Subramanian SV, Kawachi I. The local food environment and diet: a systematic review. Health Place. 2012; 18:1172–1187.Google Scholar
    • 151 Walker RE, Keane CR, Burke JG. Disparities and access to healthy food in the United States: a review of food deserts literature. Health Place. 2010; 16:876–884.Google Scholar
    • 152 Budzynska K, West P, Savoy‐Moore RT, Lindsey D, Winter M, Newby PK. A food desert in Detroit: associations with food shopping and eating behaviours, dietary intakes and obesity. Public Health Nutr. 2013; 16:2114–2123.Google Scholar
    • 153 Mejia N, Lightstone AS, Basurto‐Davila R, Morales DM, Sturm R. Neighborhood food environment, diet, and obesity among Los Angeles County adults, 2011. Prev Chronic Dis. 2015; 12:E143.Google Scholar
    • 154 Hardin‐Fanning F, Rayens MK. Food cost disparities in rural communities. Health Promot Pract. 2015; 16:383–391.Google Scholar
    • 155 Smith D, Cummins S, Clark C, Stansfeld S. Does the local food environment around schools affect diet? Longitudinal associations in adolescents attending secondary schools in East London. BMC Public Health. 2013; 13:70.Google Scholar
    • 156 Cetateanu A, Jones A. Understanding the relationship between food environments, deprivation and childhood overweight and obesity: evidence from a cross sectional England‐wide study. Health Place. 2014; 27:68–76.Google Scholar
    • 157 Uhlig K, Balk EM, Patel K, Ip S, Kitsios GD, Obadan NO, Haynes SM, Stefan M, Rao M, Kong Win Chang L, Gaylor J, Iovin RC. Self‐Measured Blood Pressure Monitoring: Comparative Effectiveness. Rockville, MD: Agency for Healthcare Research and Quality (US); 2012.Google Scholar
    • 158 Fletcher BR, Hartmann‐Boyce J, Hinton L, McManus RJ. The effect of self‐monitoring of blood pressure on medication adherence and lifestyle factors: a systematic review and meta‐analysis. Am J Hypertens. 2015; 28:1209–1221.Google Scholar
    • 159 McLean G, Band R, Saunderson K, Hanlon P, Murray E, Little P, McManus RJ, Yardley L, Mair FS; DIPSS co‐investigators . Digital interventions to promote self‐management in adults with hypertension systematic review and meta‐analysis. J Hypertens. 2016; 34:600–612.Google Scholar
    • 160 Qamar N, Bray EP, Glynn LG, Fahey T, Mant J, Holder RL, McManus R. Self‐monitoring for improving control of blood pressure in patients with hypertension (Protocol). Cochrane Database Syst Rev. 2013;(1):CD010311.Google Scholar
    • 161 Karmali KN, Davies P, Taylor F, Beswick A, Martin N, Ebrahim S. Promoting patient uptake and adherence in cardiac rehabilitation. Cochrane Database Syst Rev. 2014;(6):CD007131.Google Scholar
    • 162 Janssen V, De Gucht V, Dusseldorp E, Maes S. Lifestyle modification programmes for patients with coronary heart disease: a systematic review and meta‐analysis of randomized controlled trials. Eur J Prev Cardiol. 2013; 20:620–640.Google Scholar
    • 163 Inglis SC, Du H, Newton PJ, DiGiacomo M, Omari A, Davidson PM. Disease management interventions for improving self‐management in lower‐limb peripheral arterial disease (Protocol). Cochrane Database Syst Rev. 2012;(3):CD009714.Google Scholar
    • 164 Parke HL, Epiphaniou E, Pearce G, Taylor SJ, Sheikh A, Griffiths CJ, Greenhalgh T, Pinnock H. Self‐management support interventions for stroke survivors: a systematic meta‐review. PLoS One. 2015; 10:e0131448.Google Scholar
    • 165 Fryer CE, Luker JA, McDonnell MN, Hillier SL. Self management programmes for quality of life in people with stroke. Cochrane Database Syst Rev. 2016;(8):CD010442.Google Scholar
    • 166 Heneghan C, Ward A, Perera R; Self‐Monitoring Trialist Collaboration , Bankhead C, Fuller A, Stevens R, Bradford K, Tyndel S, Alonso‐Coello P, Ansell J, Beyth R, Bernardo A, Christensen TD, Cromheecke ME, Edson RG, Fitzmaurice D, Gadisseur AP, Garcia‐Alamino JM, Gardiner C, Hasenkam JM, Jacobson A, Kaatz S, Kamali F, Khan TI, Knight E, Körtke H, Levi M, Matchar D, Menéndez‐Jándula B, Rakovac I, Schaefer C, Siebenhofer A, Souto JC, Sunderji R, Gin K, Shalansky K, Völler H, Wagner O, Zittermann A. Self‐monitoring of oral anticoagulation: systematic review and meta‐analysis of individual patient data. Lancet. 2012; 379:322–334.Google Scholar
    • 167 Clarkesmith DE, Pattison HM, Lane DA. Educational and behavioural interventions for anticoagulant therapy in patients with atrial fibrillation. Cochrane Database Syst Rev. 2013;(6):CD008600.Google Scholar
    • 168 McAlister FA, Stewart S, Ferrua S, McMurray JJ. Multidisciplinary strategies for the management of heart failure patients at high risk for admission: a systematic review of randomized trials. J Am Coll Cardiol. 2004; 44:810–819.Google Scholar
    • 169 Jovicic A, Holroyd‐Leduc JM, Straus SE. Effects of self‐management intervention on health outcomes of patients with heart failure: a systematic review of randomized controlled trials. BMC Cardiovasc Disord. 2006; 6:43.Google Scholar
    • 170 Grady KL. Self‐care and quality of life outcomes in heart failure patients. J Cardiovasc Nurs. 2008; 23:285–292.Google Scholar
    • 171 Ditewig JB, Blok H, Havers J, van Veenendaal H. Effectiveness of self‐management interventions on mortality, hospital readmissions, chronic heart failure hospitalization rate and quality of life in patients with chronic heart failure: a systematic review. Patient Educ Couns. 2010; 78:297–315.Google Scholar
    • 172 Inglis SC, Clark RA, Dierckx R, Prieto‐Merino D, Cleland JG. Structured telephone support or non‐invasive telemonitoring for patients with heart failure. Cochrane Database Syst Rev. 2015;(10):CD007228.Google Scholar
    • 173 Jonkman NH, Westland H, Groenwold RH, Ågren S, Atienza F, Blue L, Bruggink‐André de la Porte PW, DeWalt DA, Hebert PL, Heisler M, Jaarsma T, Kempen GI, Leventhal ME, Lok DJ, Mårtensson J, Muñiz J, Otsu H, Peters‐Klimm F, Rich MW, Riegel B, Strömberg A, Tsuyuki RT, van Veldhuisen DJ, Trappenburg JC, Schuurmans MJ, Hoes AW. Do self‐management interventions work in patients with heart failure? An individual patient data meta‐analysis. Circulation. 2016; 133:1189–1198.Google Scholar
    • 174 Jonkman NH, Westland H, Groenwold RH, Ågren S, Anguita M, Blue L, Bruggink‐André de la Porte PW, DeWalt DA, Hebert PL, Heisler M, Jaarsma T, Kempen GI, Leventhal ME, Lok DJ, Mårtensson J, Muñiz J, Otsu H, Peters‐Klimm F, Rich MW, Riegel B, Strömberg A, Tsuyuki RT, Trappenburg JC, Schuurmans MJ, Hoes AW. What are effective program characteristics of self‐management interventions in patients with heart failure? An individual patient data meta‐analysis. J Card Fail. 2016; 22:861–871.Google Scholar
    • 175 Lee CS, Tkacs NC, Riegel B. The influence of heart failure self‐care on health outcomes: hypothetical cardioprotective mechanisms. J Cardiovasc Nurs. 2009; 24:179–187; quiz, 188–179.Google Scholar
    • 176 Lockhart PB, Bolger AF, Papapanou PN, Osinbowale O, Trevisan M, Levison ME, Taubert KA, Newburger JW, Gornik HL, Gewitz MH, Wilson WR, Smith SC, Baddour LM; on behalf of the American Heart Association Rheumatic Fever, Endocarditis, and Kawasaki Disease Committee of the Council on Cardiovascular Disease in the Young, Council on Epidemiology and Prevention, Council on Peripheral Vascular Disease, and Council on Clinical Cardiology . Periodontal disease and atherosclerotic vascular disease: does the evidence support an independent association? A scientific statement from the American Heart Association. Circulation. 2012; 125:2520–2544.Google Scholar
    • 177 Nichol KL, Nordin J, Mullooly J, Lask R, Fillbrandt K, Iwane M. Influenza vaccination and reduction in hospitalizations for cardiac disease and stroke among the elderly. N Engl J Med. 2003; 348:1322–1332.Google Scholar
    • 178 Ahmed A, Husain A, Love TE, Gambassi G, Dell'Italia LJ, Francis GS, Gheorghiade M, Allman RM, Meleth S, Bourge RC. Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods. Eur Heart J. 2006; 27:1431–1439.Google Scholar
    • 179 Eshaghian S, Horwich TB, Fonarow GC. Relation of loop diuretic dose to mortality in advanced heart failure. Am J Cardiol. 2006; 97:1759–1764.Google Scholar
    • 180 Hasselblad V, Gattis Stough W, Shah MR, Lokhnygina Y, O'Connor CM, Califf RM, Adams KF. Relation between dose of loop diuretics and outcomes in a heart failure population: results of the ESCAPE trial. Eur J Heart Fail. 2007; 9:1064–1069.Google Scholar
    • 181 Felker GM, Benza RL, Chandler AB, Leimberger JD, Cuffe MS, Califf RM, Gheorghiade M, O'Connor CM; OPTIME‐CHF Investigators . Heart failure etiology and response to milrinone in decompensated heart failure: results from the OPTIME‐CHF study. J Am Coll Cardiol. 2003; 41:997–1003.Google Scholar
    • 182 O'Connor CM, Gattis WA, Uretsky BF, Adams KF, McNulty SE, Grossman SH, McKenna WJ, Zannad F, Swedberg K, Gheorghiade M, Califf RM. Continuous intravenous dobutamine is associated with an increased risk of death in patients with advanced heart failure: insights from the Flolan International Randomized Survival Trial (FIRST). Am Heart J. 1999; 138(1 Pt 1):78–86.Google Scholar
    • 183 Metra M, Torp‐Pedersen C, Cleland JG, Di Lenarda A, Komajda M, Remme WJ, Dei Cas L, Spark P, Swedberg K, Poole‐Wilson PA; COMET investigators . Should beta‐blocker therapy be reduced or withdrawn after an episode of decompensated heart failure? Results from COMET. Eur J Heart Fail. 2007; 9:901–909.Google Scholar
    • 184 Pearson TA, Mensah GA, Alexander RW, Anderson JL, Cannon RO, Criqui M, Fadl YY, Fortmann SP, Hong Y, Myers GL, Rifai N, Smith SC, Taubert K, Tracy RP, Vinicor F. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003; 107:499–511.Google Scholar
    • 185 Cameron J, Worrall‐Carter L, Riegel B, Lo SK, Stewart S. Testing a model of patient characteristics, psychologic status, and cognitive function as predictors of self‐care in persons with chronic heart failure. Heart Lung. 2009; 38:410–418.Google Scholar
    • 186 Hwang B, Moser DK, Dracup K. Knowledge is insufficient for self‐care among heart failure patients with psychological distress. Health Psychol. 2014; 33:588–596.Google Scholar
    • 187 Rogers A, Vassilev I, Sanders C, Kirk S, Chew‐Graham C, Kennedy A, Protheroe J, Bower P, Blickem C, Reeves D, Kapadia D, Brooks H, Fullwood C, Richardson G. Social networks, work and network‐based resources for the management of long‐term conditions: a framework and study protocol for developing self‐care support. Implement Sci. 2011; 6:56.Google Scholar
    • 188 Rutledge T, Reis VA, Linke SE, Greenberg BH, Mills PJ. Depression in heart failure a meta‐analytic review of prevalence, intervention effects, and associations with clinical outcomes. J Am Coll Cardiol. 2006; 48:1527–1537.Google Scholar
    • 189 Moser DK, Dracup K, Evangelista LS, Zambroski CH, Lennie TA, Chung ML, Doering LV, Westlake C, Heo S. Comparison of prevalence of symptoms of depression, anxiety, and hostility in elderly patients with heart failure, myocardial infarction, and a coronary artery bypass graft. Heart Lung. 2010; 39:378–385.Google Scholar
    • 190 Freedland KE, Hesseler MJ, Carney RM, Steinmeyer BC, Skala JA, Dávila‐Román VG, Rich MW. Major depression and long‐term survival of patients with heart failure. Psychosom Med. 2016; 78:896–903.Google Scholar
    • 191 Song EK, Moser DK, Kang SM, Lennie TA. Association of depressive symptoms and micronutrient deficiency with cardiac event‐free survival in patients with heart failure. J Card Fail. 2015; 21:945–951.Google Scholar
    • 192 Freedland KE, Carney RM, Rich MW, Steinmeyer BC, Skala JA, Davila‐Roman VG. Depression and multiple rehospitalizations in patients with heart failure. Clin Cardiol. 2016; 39:257–262.Google Scholar
    • 193 Morgan AL, Masoudi FA, Havranek EP, Jones PG, Peterson PN, Krumholz HM, Spertus JA, Rumsfeld JS; Cardiovascular Outcomes Research Consortium (CORC) . Difficulty taking medications, depression, and health status in heart failure patients. J Card Fail. 2006; 12:54–60.Google Scholar
    • 194 Carney RM, Freedland KE, Eisen SA, Rich MW, Jaffe AS. Major depression and medication adherence in elderly patients with coronary artery disease. Health Psychol. 1995; 14:88–90.Google Scholar
    • 195 Ziegelstein RC, Bush DE, Fauerbach JA. Depression, adherence behavior, and coronary disease outcomes. Arch Intern Med. 1998; 158:808–809.Google Scholar
    • 196 Ziegelstein RC, Fauerbach JA, Stevens SS, Romanelli J, Richter DP, Bush DE. Patients with depression are less likely to follow recommendations to reduce cardiac risk during recovery from a myocardial infarction. Arch Intern Med. 2000; 160:1818–1823.Google Scholar
    • 197 DiMatteo MR, Lepper HS, Croghan TW. Depression is a risk factor for noncompliance with medical treatment: meta‐analysis of the effects of anxiety and depression on patient adherence. Arch Intern Med. 2000; 160:2101–2107.Google Scholar
    • 198 Evangelista LS, Berg J, Dracup K. Relationship between psychosocial variables and compliance in patients with heart failure. Heart Lung. 2001; 30:294–301.Google Scholar
    • 199 Dickson VV, McCarthy MM, Katz SM. How do depressive symptoms influence self‐care among an ethnic minority population with heart failure?Ethn Dis. 2013; 23:22–28.Google Scholar
    • 200 Freedland KE, Carney RM, Rich MW, Steinmeyer BC, Rubin EH. Cognitive behavior therapy for depression and self‐care in heart failure patients: a randomized clinical trial. JAMA Intern Med. 2015; 175:1773–1782.Google Scholar
    • 201 Lee KS, Lennie TA, Yoon JY, Wu JR, Moser DK. Living arrangements modify the relationship between depressive symptoms and self‐care in patients with heart failure. J Cardiovasc Nurs. 2017; 32:171–179.Google Scholar
    • 202 Smith KJ, Pedneault M, Schmitz N. Investigation of anxiety and depression symptom co‐morbidity in a community sample with type 2 diabetes: associations with indicators of self‐care. Can J Public Health. 2016; 106:e496–e501.Google Scholar
    • 203 Wu JR, Lee KS, Dekker RD, Welsh JD, Song EK, Abshire DA, Lennie TA, Moser DK. Prehospital delay, precipitants of admission, and length of stay in patients with exacerbation of heart failure. Am J Crit Care. 2016; 26:62–69.Google Scholar
    • 204 Bandura A. Self‐efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977; 84:191–215.Google Scholar
    • 205 Bandura A, Adams NE, Beyer J. Cognitive processes mediating behavioral change. J Pers Soc Psychol. 1977; 35:125–139.Google Scholar
    • 206 Riegel B, Dickson VV. A situation‐specific theory of heart failure self‐care. J Cardiovasc Nurs. 2008; 23:190–196.Google Scholar
    • 207 Prince SA, Reed JL, Martinello N, Adamo KB, Fodor JG, Hiremath S, Kristjansson EA, Mullen KA, Nerenberg KA, Tulloch HE, Reid RD. Why are adult women physically active? A systematic review of prospective cohort studies to identify intrapersonal, social environmental and physical environmental determinants. Obes Rev. 2016; 17:919–944.Google Scholar
    • 208 Slovinec D'Angelo ME, Pelletier LG, Reid RD, Huta V. The roles of self‐efficacy and motivation in the prediction of short‐ and long‐term adherence to exercise among patients with coronary heart disease. Health Psychol. 2014; 33:1344–1353.Google Scholar
    • 209 Rimando M. Factors influencing medication compliance among hypertensive older African American adults. Ethn Dis. 2013; 23:469–473.Google Scholar
    • 210 Schweitzer RD, Head K, Dwyer JW. Psychological factors and treatment adherence behavior in patients with chronic heart failure. J Cardiovasc Nurs. 2007; 22:76–83.Google Scholar
    • 211 Cha E, Kim KH, Lerner HM, Dawkins CR, Bello MK, Umpierrez G, Dunbar SB. Health literacy, self‐efficacy, food label use, and diet in young adults. Am J Health Behav. 2014; 38:331–339.Google Scholar
    • 212 Clyde M, Tulloch H, Reid R, Els C, Pipe A. Task and barrier self‐efficacy among treatment‐seeking smokers with current, past or no psychiatric diagnosis. Addict Behav. 2015; 46:65–69.Google Scholar
    • 213 Dickson VV, Deatrick JA, Riegel B. A typology of heart failure self‐care management in non‐elders. Eur J Cardiovasc Nurs. 2008; 7:171–181.Google Scholar
    • 214 Riegel B, Lee CS, Albert N, Lennie T, Chung M, Song EK, Bentley B, Heo S, Worrall‐Carter L, Moser DK. From novice to expert: confidence and activity status determine heart failure self‐care performance. Nurs Res. 2011; 60:132–138.Google Scholar
    • 215 Dickson VV, Buck H, Riegel B. Multiple comorbid conditions challenge heart failure self‐care by decreasing self‐efficacy. Nurs Res. 2013; 62:2–9.Google Scholar
    • 216 Ausili D, Rebora P, Di Mauro S, Riegel B, Valsecchi MG, Paturzo M, Alvaro R, Vellone E. Clinical and socio‐demographic determinants of self‐care behaviours in patients with heart failure and diabetes mellitus: a multicentre cross‐sectional study. Int J Nurs Stud. 2016; 63:18–27.Google Scholar
    • 217 Buck HG, Dickson VV, Fida R, Riegel B, D'Agostino F, Alvaro R, Vellone E. Predictors of hospitalization and quality of life in heart failure: a model of comorbidity, self‐efficacy and self‐care. Int J Nurs Stud. 2015; 52:1714–1722.Google Scholar
    • 218 Vellone E, Pancani L, Greco A, Steca P, Riegel B. Self‐care confidence may be more important than cognition to influence self‐care behaviors in adults with heart failure: testing a mediation model. Int J Nurs Stud. 2016; 60:191–199.Google Scholar
    • 219 Bandura A. Self‐Efficacy: The Exercise of Control. New York, NY: Freeman; 1997.Google Scholar
    • 220 Kashani M, Eliasson AH, Walizer EM, Fuller CE, Engler RJ, Villines TC, Vernalis MN. Early empowerment strategies boost self‐efficacy to improve cardiovascular health behaviors. Glob J Health Sci. 2016; 8:55119.Google Scholar
    • 221 Jurgens CY, Lee CS, Reitano JM, Riegel B. Heart failure symptom monitoring and response training. Heart Lung. 2013; 42:273–280.Google Scholar
    • 222 Won MH, Son YJ. Perceived social support and physical activity among patients with coronary artery disease. West J Nurs Res. 2016 Nov 1. DOI: 10.1177/0193945916678374. [Epub ahead of print].Google Scholar
    • 223 Gorelick PB, Scuteri A, Black SE, Decarli C, Greenberg SM, Iadecola C, Launer LJ, Laurent S, Lopez OL, Nyenhuis D, Petersen RC, Schneider JA, Tzourio C, Arnett DK, Bennett DA, Chui HC, Higashida RT, Lindquist R, Nilsson PM, Roman GC, Sellke FW, Seshadri S; on behalf of the American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia . Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2011; 42:2672–2713.Google Scholar
    • 224 Ng JB, Turek M, Hakim AM. Heart disease as a risk factor for dementia. Clin Epidemiol. 2013; 5:135–145.Google Scholar
    • 225 Bauer LC, Johnson JK. BJ P. Cognition in heart failure: an overview of the concepts and their measures. J Am Acad Nurse Pract. 2011; 23:577–586.Google Scholar
    • 226 Dardiotis E, Giamouzis G, Mastrogiannis D, Vogiatzi C, Skoularigis J, Triposkiadis F, Hadjigeorgiou GM. Cognitive impairment in heart failure. Cardiol Res Pract. 2012; 2012:595821.Google Scholar
    • 227 Pressler SJ. Cognitive functioning and chronic heart failure. J Cardiovasc Nurs. 2008; 23:239–249.Google Scholar
    • 228 Dodson JA, Truong TT, Towle VR, Kerins G, Chaudhry SI. Cognitive impairment in older adults with heart failure: prevalence, documenttion and impact on outcomes. Am J Med. 2013; 126:120–126.Google Scholar
    • 229 Gure TR, Blaum CS, Giordani B, Koelling TM, Galecki A, Pressler SJ, Hummel SL, Langa KM. Prevalence of cognitive impairment in older adults with heart failure. J Am Geriatr Soc. 2012; 60:1724–1729.Google Scholar
    • 230 Hanon O, Vidal JS, deGroote P, Galinier M, Isnard R, Logeart D, Komajda M. Prevalence of memory disorders in ambulatory patients aged ≥70 years with chronic heart failure (from the EFICARE Study). Am J Cardiol. 2014; 113:1205–1210.Google Scholar
    • 231 Pressler SJ, Subramanian U, Kareken D, Perkins SM, Gradus‐Pizlo I, Sauvé MJ, Ding Y, Kim J, Sloan R, Jaynes H, Shaw RM. Cognitive deficits in chronic heart failure. Nurs Res. 2010; 59:127–139.Google Scholar
    • 232 Vogels RL, Scheltens P, Schroeder‐Tanka JM, Weinstein HC. Cognitive impairment in heart failure: a systematic review of the literature. Eur J Heart Fail. 2007; 9:440–449.Google Scholar
    • 233 Centers for Disease Control and Prevention . Cognitive impairment: a call for action, now! 2009. Available at: Accessed December 22, 2016.Google Scholar
    • 234 Currie K, Rideout A, Lindsay G, Harkness K. The association between mild cognitive impairment and self‐care in adults with chronic heart failure: a systematic review and narrative synthesis. J Cardiovasc Nurs. 2015; 30:382–393.Google Scholar
    • 235 Alosco ML, Brickman AM, Spitznagel MB, Sweet LH, Josephson R, Griffith EY, Narkhede A, Hughes J, Gunstad J. Daily physical activity is associated with subcortical brain volume and cognition in heart failure. J Int Neuropsychol Soc. 2015; 21:851–860.Google Scholar
    • 236 Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE, Drazner MH, Fonarow GC, Geraci SA, Horwich T, Januzzi JL, Johnson MR, Kasper EK, Levy WC, Masoudi FA, McBride PE, McMurray JJ, Mitchell JE, Peterson PN, Riegel B, Sam F, Stevenson LW, Tang WH, Tsai EJ, Wilkoff BL. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2013; 128:1810–1852.Google Scholar
    • 237 Hoth KF, Poppas A, Ellison KE, Paul RH, Sokobin A, Cho Y, Cohen RA. Link between change in cognition and left ventricular function following cardiac resynchronization therapy. J Cardiopulm Rehabil Prev. 2010; 30:410–418.Google Scholar
    • 238 Alosco ML, Spitznagel MB, Cohen R, Sweet LH, Josephson R, Hughes J, Rosneck J, Gunstad J. Cardiac rehabilitation is associated with lasting improvements in cognitive function in older adults with heart failure. Acta Cardiol. 2014; 69:407–414.Google Scholar
    • 239 Cameron J, Rendell PG, Ski CF, Kure CE, McLennan SN, Rose NS, Prior DL, Thompson DR. PROspective MEmory Training to improve HEart failUre Self‐care (PROMETHEUS): study protocol for a randomised controlled trial. Trials. 2015; 16:196.Google Scholar
    • 240 Pressler SJ, Martineau A, Grossi J, Giordani B, Koelling TM, Ronis DL, Riley PL, Chou CC, Sullivan BJ, Smith DG. Healthcare resource use among heart failure patients in a randomized pilot study of a cognitive training intervention. Heart Lung. 2013; 42:332–338.Google Scholar
    • 241 Report of the geriatrics‐hospice and palliative medicine work group: American Geriatrics Society and American Academy of Hospice and Palliative Medicine leadership collaboration. J Am Geriatr Soc. 2012; 60:583–587.Google Scholar
    • 242 Leroy L, Bayliss E, Domino M, Miller BF, Rust G, Gerteis J, Miller T; AHRQ MCC Research Network . The Agency for Healthcare Research and Quality Multiple Chronic Conditions Research Network: overview of research contributions and future priorities. Med Care. 2014; 52(suppl 3):S15–S22.Google Scholar
    • 243 Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance. JAMA. 2005; 294:716–724.Google Scholar
    • 244 Meghani SH, Buck HG, Dickson VV, Hammer MJ, Rabelo‐Silva ER, Clark R, Naylor MD. The conceptualization and measurement of comorbidity: a review of the interprofessional discourse. Nurs Res Pract. 2013; 2013:192782.Google Scholar
    • 245 Ward BW, Schiller JS. Prevalence of multiple chronic conditions among US adults: estimates from the National Health Interview Survey, 2010. Prev Chronic Dis. 2013; 10:E65.Google Scholar
    • 246 Arnett DK, Goodman RA, Halperin JL, Anderson JL, Parekh AK, Zoghbi WA. AHA/ACC/HHS strategies to enhance application of clinical practice guidelines in patients with cardiovascular disease and comorbid conditions: from the American Heart Association, American College of Cardiology, and US Department of Health and Human Services. Circulation. 2014; 130:1662–1667.Google Scholar
    • 247 Stewart S, Riegel B, Thompson DR. Addressing the conundrum of multimorbidity in heart failure: do we need a more strategic approach to improve health outcomes?Eur J Cardiovasc Nurs. 2016; 15:4–7.Google Scholar
    • 248 Bayliss EA, Steiner JF, Fernald DH, Crane LA, Main DS. Descriptions of barriers to self‐care by persons with comorbid chronic diseases. Ann Fam Med. 2003; 1:15–21.Google Scholar
    • 249 Liddy C, Blazkho V, Mill K. Challenges of self‐management when living with multiple chronic conditions systematic review of the qualitative literature. Can Fam Physician. 2014; 60:1123–1133.Google Scholar
    • 250 House JS, Landis KR, Umberson D. Social relationships and health. Science. 1988; 241:540–545.Google Scholar
    • 251 Seeman TE. Health promoting effects of friends and family on health outcomes in older adults. Am J Health Promot. 2000; 14:362–370.Google Scholar
    • 252 Vassilev I, Rogers A, Kennedy A, Wensing M, Koetsenruijter J, Orlando R, Portillo MC, Culliford D. Social network type and long‐term condition management support: a cross‐sectional study in six European countries. PLoS One. 2016; 11:e0161027.Google Scholar
    • 253 Barefoot JC, Gronbaek M, Jensen G, Schnohr P, Prescott E. Social network diversity and risks of ischemic heart disease and total mortality: findings from the Copenhagen City Heart Study. Am J Epidemiol. 2005; 161:960–967.Google Scholar
    • 254 Rutledge T, Linke SE, Olson MB, Francis J, Johnson BD, Bittner V, York K, McClure C, Kelsey SF, Reis SE, Cornell CE, Vaccarino V, Sheps DS, Shaw LJ, Krantz DS, Parashar S, Merz CN. Social networks and incident stroke among women with suspected myocardial ischemia. Psychosom Med. 2008; 70:282–287.Google Scholar
    • 255 Case RB, Moss AJ, Case N, McDermott M, Eberly S. Living alone after myocardial infarction. Impact on prognosis. JAMA. 1992; 267:515–519.Google Scholar
    • 256 Lin N. Building a network theory of social capital. In: Lin N, Cook K, Burt RS, eds. Theory and Research. New York: Aldine de Gruyter; 2001:3–29.Google Scholar
    • 257 Bolin K, Lindgren B, Lindström M, Nystedt P. Investments in social capital—implications of social interactions for the production of health. Soc Sci Med. 2003; 56:2379–2390.Google Scholar
    • 258 Quarells RC, Liu J, Davis SK. Social determinants of cardiovascular disease risk factor presence among rural and urban Black and White men. J Mens Health. 2012; 9:120–126.Google Scholar
    • 259 Lindroth M, Lundqvist R, Lilja M, Eliasson M. Cardiovascular risk factors differ between rural and urban Sweden: the 2009 Northern Sweden MONICA cohort. BMC Public Health. 2014; 14:825.Google Scholar
    • 260 Murphy GK, McAlister FA, Eurich DT. Cardiovascular medication utilization and adherence among heart failure patients in rural and urban areas: a retrospective cohort study. Can J Cardiol. 2015; 31:341–347.Google Scholar
    • 261 Lee KS, Moser DK, Pelter MM, Nesbitt T, Dracup K. Self‐care in rural residents with heart failure: what we are missing. Eur J Cardiovasc Nurs. 2017; 16:326–333.Google Scholar
    • 262 InterASIA Collaborative Group . Cardiovascular risk factor levels in urban and rural Thailand—The International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Eur J Cardiovasc Prev Rehabil. 2003; 10:249–257.Google Scholar
    • 263 Schutte R, Huisman HW, Malan L, Van Rooyen JM, Schutte AE, Malan NT, De Ridder JH. Differences in cardiovascular function of rural and urban African males: the THUSA study. Cardiovasc J S Afr. 2004; 15:161–165.Google Scholar
    • 264 Yamamoto‐Kimura L, Posadas‐Romero C, Posadas‐Sanchez R, Zamora‐Gonzalez J, Cardoso‐Saldana G, Mendez Ramirez I. Prevalence and interrelations of cardiovascular risk factors in urban and rural Mexican adolescents. J Adolesc Health. 2006; 38:591–598.Google Scholar
    • 265 Yajnik CS, Joglekar CV, Chinchwadkar MC, Sayyad MG, Deshpande SS, Naik SS, Bhat DS, Ganpule A, Shetty P, Yudkin JS. Conventional and novel cardiovascular risk factors and markers of vascular damage in rural and urban Indian men. Int J Cardiol. 2013; 165:255–259.Google Scholar
    • 266 Heijmans N, van Lieshout J, Wensing M. Social networks of health care providers and patients in cardiovascular risk management: a study protocol. BMC Health Serv Res. 2014; 14:265.Google Scholar
    • 267 Seguin RA, Eldridge G, Graham ML, Folta SC, Nelson ME, Strogatz D. Strong Hearts, Healthy Communities: a rural community‐based cardiovascular disease prevention program. BMC Public Health. 2016; 16:86.Google Scholar
    • 268 Dickson V, Melkus G, Katz S, Levine‐Wong A, Dillworth J, Cleland CM, Riegel B. Building skill in heart failure self‐care among community dwelling older adults: results of a pilot study. Patient Educ Couns. 2014; 96:188–196.Google Scholar
    • 269 Riegel B, Dickson VV, Garcia LE, Masterson Creber R, Streur M. Mechanisms of change in self‐care in adults with heart failure receiving a tailored, motivational interviewing intervention. Patient Educ Couns. 2017; 100:283–288.Google Scholar
    • 270 Dhukaram AV, Baber C. Modelling elderly cardiac patients decision making using Cognitive Work Analysis: identifying requirements for patient decision aids. Int J Med Inform. 2015; 84:430–443.Google Scholar
    • 271 Sheridan SL, Draeger LB, Pignone MP, Keyserling TC, Simpson RJ, Rimer B, Bangdiwala SI, Cai J, Gizlice Z. A randomized trial of an intervention to improve use and adherence to effective coronary heart disease prevention strategies. BMC Health Serv Res. 2011; 11:331.Google Scholar
    • 272 Luepker RV, Raczynski JM, Osganian S, Goldberg RJ, Finnegan JR, Hedges JR, Goff DC, Eisenberg MS, Zapka JG, Feldman HA, Labarthe DR, McGovern PG, Cornell CE, Proschan MA, Simons‐Morton DG. Effect of a community intervention on patient delay and emergency medical service use in acute coronary heart disease: the Rapid Early Action for Coronary Treatment (REACT) Trial. JAMA. 2000; 284:60–67.Google Scholar
    • 273 Moser DK, Kimble LP, Alberts MJ, Alonzo A, Croft JB, Dracup K, Evenson KR, Go AS, Hand MM, Kothari RU, Mensah GA, Morris DL, Pancioli AM, Riegel B, Zerwic JJ. Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: a scientific statement from the American Heart Association Council on Cardiovascular Nursing and Stroke Council. Circulation. 2006; 114:168–182.Google Scholar
    • 274 Gravely‐Witte S, Jurgens CY, Tamim H, Grace SL. Length of delay in seeking medical care by patients with heart failure symptoms and the role of symptom‐related factors: a narrative review. Eur J Heart Fail. 2010; 12:1122–1129.Google Scholar
    • 275 Conn VS, Ruppar TM, Chase JA, Enriquez M, Cooper PS. Interventions to improve medication adherence in hypertensive patients: systematic review and meta‐analysis. Curr Hypertens Rep. 2015; 17:94.Google Scholar
    • 276 Driscoll A, Davidson P, Clark R, Huang N, Aho Z. Tailoring consumer resources to enhance self‐care in chronic heart failure. Aust Crit Care. 2009; 22:133–140.Google Scholar
    • 277 Jason S, Williamson T. Knowing how. J Philos. 2001; 98:411–444.Google Scholar
    • 278 Dickson V, Nocella J, Yoon HW, Hammer M, Melkus GD, Chyun D. Cardiovascular disease self‐care interventions. Nurs Res Pract. 2013; 2013:407608.Google Scholar
    • 279 Molloy G, O'Carroll R, Witham M, McMurdo M. Interventions to enhance adherence to medications in patients with heart failure: a systematic review. Circ Heart Fail. 2012; 5:126–133.Google Scholar
    • 280 Gwadry‐Sridhar FH, Arnold JM, Zhang Y, Brown JE, Marchiori G, Guyatt G. Pilot study to determine the impact of a multidisciplinary educational intervention in patients hospitalized with heart failure. Am Heart J. 2005; 150:982.Google Scholar
    • 281 Powell L, Calvin J, Mendes de Leon C, Richardson D, Grady KL, Flynn KJ, Rucker‐Whitaker CS, Janssen I, Kravitz G, Eaton C; Heart Failure Adherence and Retention Trial Investigators . The Heart Failure Adherence and Retention Trial (HART): design and rationale. Am Heart J. 2008; 156:452–460.Google Scholar
    • 282 Tsuyuki RT, Fradette M, Johnson JA, Bungard TJ, Eurich DT, Ashton T, Gordon W, Ikuta R, Kornder J, Mackay E, Manyari D, O'Reilly K, Semchuk W. A multicenter disease management program for hospitalized patients with heart failure. J Card Fail. 2004; 10:473–480.Google Scholar
    • 283 Clark AM, Wiens KS, Banner D, Kryworuchko J, Thirsk L, McLean L, Currie K. A systematic review of the main mechanisms of heart failure disease management interventions. Heart. 2016; 102:707–711.Google Scholar
    • 284 Davis KK, Mintzer M, Dennison Himmelfarb CR, Hayat MJ, Rotman S, Allen J. Targeted intervention improves knowledge but not self‐care or readmissions in heart failure patients with mild cognitive impairment. Eur J Heart Fail. 2012; 14:1041–1049.Google Scholar
    • 285 Schroeder K, Fahey T, Ebrahim S. How can we improve adherence to blood pressure–lowering medication in ambulatory care? Systematic review of randomized controlled trials. Arch Intern Med. 2004; 164:722–732.Google Scholar
    • 286 Dickson V, Riegel B. Are we teaching what patients need to know? Building skills in heart failure self‐care. Heart Lung. 2009; 38:253–261.Google Scholar
    • 287 Clark AP, McDougall G, Riegel B, Joiner‐Rogers G, Innerarity S, Meraviglia M, Delville C, Davila A. Health status and self‐care outcomes after an education‐support intervention for people with chronic heart failure. J Cardiovasc Nurs. 2015; 30(4 Suppl 1):S3–S13.Google Scholar
    • 288 Masterson Creber R, Patey M, Lee CS, Kuan A, Jurgens C, Riegel B. Motivational interviewing to improve self‐care for patients with chronic heart failure: MITI‐HF randomized controlled trial. Patient Educ Couns. 2016; 99:256–264.Google Scholar
    • 289 Dickson V, McCarthy M, Howe A, Schipper J, Katz S. Socio‐cultural influences on heart failure self‐care among an ethnic minority black population. J Cardiovasc Nurs. 2013; 28:111–118.Google Scholar
    • 290 Dunbar SB, Butts B, Reilly CM, Gary RA, Higgins MK, Ferranti EP, Culler SD, Butler J. A pilot test of an integrated self‐care intervention for persons with heart failure and concomitant diabetes. Nurs Outlook. 2014; 62:97–111.Google Scholar
    • 291 Glynn LG, Murphy AW, Smith SM, Schroeder K, Fahey T. Interventions used to improve control of blood pressure in patients with hypertension. Cochrane Database Syst Rev. 2010;(3):CD005182.Google Scholar
    • 292 Ephraim PL, Hill‐Briggs F, Roter DL, Bone LR, Wolff JL, Lewis‐Boyer L, Levine DM, Aboumatar HJ, Cooper LA, Fitzpatrick SJ, Gudzune KA, Albert MC, Monroe D, Simmons M, Hickman D, Purnell L, Fisher A, Matens R, Noronha GJ, Fagan PJ, Ramamurthi HC, Ameling JM, Charlston J, Sam TS, Carson KA, Wang NY, Crews DC, Greer RC, Sneed V, Flynn SJ, DePasquale N, Boulware LE. Improving urban African Americans’ blood pressure control through multi‐level interventions in the Achieving Blood Pressure Control Together (ACT) study: a randomized clinical trial. Contemp Clin Trials. 2014; 38:370–382.Google Scholar
    • 293 Murphy BM, Worcester MU, Higgins RO, Elliott PC, Le Grande MR, Mitchell F, Navaratnam H, Turner A, Grigg L, Tatoulis J, Goble AJ. Reduction in 2‐year recurrent risk score and improved behavioral outcomes after participation in the “Beating Heart Problems” self‐management program: results of a randomized controlled trial. J Cardiopulm Rehabil Prev. 2013; 33:220–228.Google Scholar
    • 294 Brown JP, Clark AM, Dalal H, Welch K, Taylor RS. Patient education in the management of coronary heart disease. Cochrane Database Syst Rev. 2011;(12):CD008895.Google Scholar
    • 295 Gong J, Chen X, Li S. Efficacy of a community‐based physical activity program KM2H2 for stroke and heart attack prevention among senior hypertensive patients: a cluster randomized controlled phase‐II trial. PLoS One. 2015; 10:e0139442.Google Scholar
    • 296 Stephens J, Allen J. Mobile phone interventions to increase physical activity and reduce weight: a systematic review. J Cardiovasc Nurs. 2013; 28:320–329.Google Scholar
    • 297 Bort‐Roig J, Gilson ND, Puig‐Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med. 2014; 44:671–686.Google Scholar
    • 298 Dale LP, Whittaker R, Eyles H, Mhurchu CN, Ball K, Smith N, Maddison R. Cardiovascular disease self‐management: pilot testing of an mHealth healthy eating program. J Pers Med. 2014; 4:88–101.Google Scholar
    • 299 Chow CK, Ariyarathna N, Islam SM, Thiagalingam A, Redfern J. mHealth in cardiovascular health care. Heart Lung Circ. 2016; 25:802–807.Google Scholar
    • 300 Kazi DS, Prabhakaran D, Bolger AF. Rising above the rhetoric: mobile applications and the delivery of cost‐effective cardiovascular care in resource‐limited settings. Future Cardiol. 2015; 11:1–4.Google Scholar
    • 301 Maddison R, Pfaeffli L, Whittaker R, Stewart R, Kerr A, Jiang Y, Kira G, Leung W, Dalleck L, Carter K, Rawstorn J. A mobile phone intervention increases physical activity in people with cardiovascular disease: results from the HEART randomized controlled trial. Eur J Prev Cardiol. 2015; 22:701–709.Google Scholar
    • 302 Chow CK, Redfern J, Thiagalingam A, Jan S, Whittaker R, Hackett M, Graves N, Mooney J, Hillis GS. Design and rationale of the tobacco, exercise and diet messages (TEXT ME) trial of a text message‐based intervention for ongoing prevention of cardiovascular disease in people with coronary disease: a randomised controlled trial protocol. BMJ Open. 2012; 2:e000606.Google Scholar
    • 303 Thakkar J, Redfern J, Thiagalingam A, Chow CK. Patterns, predictors and effects of texting intervention on physical activity in CHD—insights from the TEXT ME randomized clinical trial. Eur J Prev Cardiol. 2016; 23:1894–1902.Google Scholar
    • 304 Paul L, Wyke S, Brewster S, Sattar N, Gill JM, Alexander G, Rafferty D, McFadyen AK, Ramsay A, Dybus A. Increasing physical activity in stroke survivors using STARFISH, an interactive mobile phone application: a pilot study. Top Stroke Rehabil. 2016; 23:170–177.Google Scholar
    • 305 Pfaeffli Dale L, Whittaker R, Jiang Y, Stewart R, Rolleston A, Maddison R. Text message and internet support for coronary heart disease self‐management: results from the Text4Heart randomized controlled trial. J Med Internet Res. 2015; 17:e237.Google Scholar
    • 306 Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML, Jan S, Graves N, de Keizer L, Barry T, Bompoint S, Stepien S, Whittaker R, Rodgers A, Thiagalingam A. Effect of lifestyle‐focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial. JAMA. 2015; 314:1255–1263.Google Scholar
    • 307 Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile‐health behaviour change interventions for cardiovascular disease self‐management: a systematic review. Eur J Prev Cardiol. 2016; 23:801–817.Google Scholar
    • 308 Ong MK, Romano PS, Edgington S, Aronow HU, Auerbach AD, Black JT, De Marco T, Escarce JJ, Evangelista LS, Hanna B, Ganiats TG, Greenberg BH, Greenfield S, Kaplan SH, Kimchi A, Liu H, Lombardo D, Mangione CM, Sadeghi B, Sadeghi B, Sarrafzadeh M, Tong K, Fonarow GC; Better Effectiveness After Transition‐Heart Failure (BEAT‐HF) Research Group . Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the Better Effectiveness After Transition—Heart Failure (BEAT‐HF): a randomized clinical trial. JAMA Intern Med. 2016; 176:310–318.Google Scholar
    • 309 Schwamm LH, Chumbler N, Brown E, Fonarow GC, Berube D, Nystrom K, Suter R, Zavala M, Polsky D, Radhakrishnan K, Lacktman N, Horton K, Malcarney MB, Halamka J, Tiner AC; on behalf of the American Heart Association Advocacy Coordinating Committee . Recommendations for the implementation of telehealth in cardiovascular and stroke care: a policy statement from the American Heart Association. Circulation. 2017; 135:e24–e44.Google Scholar
    • 310 Kumar N, Khunger M, Gupta A, Garg N. A content analysis of smartphone‐based applications for hypertension management. J Am Soc Hypertens. 2015; 9:130–136.Google Scholar
    • 311 Neubeck L, Lowres N, Benjamin EJ, Freedman SB, Coorey G, Redfern J. The mobile revolution—using smartphone apps to prevent cardiovascular disease. Nat Rev Cardiol. 2015; 12:350–360.Google Scholar
    • 312 Whitehead L, Seaton P. The effectiveness of self‐management mobile phone and tablet apps in long‐term condition management: a systematic review. J Med Internet Res. 2016; 18:e97.Google Scholar
    • 313 Halme L, Vesalainen R, Kaaja M, Kantola I; HOme MEasuRement of blood pressure study group . Self‐monitoring of blood pressure promotes achievement of blood pressure target in primary health care. Am J Hypertens. 2005; 18:1415–1420.Google Scholar
    • 314 Battista MC, Labonté M, Ménard J, Jean‐Denis F, Houde G, Ardilouze JL, Perron P. Dietitian‐coached management in combination with annual endocrinologist follow up improves global metabolic and cardiovascular health in diabetic participants after 24 months. Appl Physiol Nutr Metab. 2012; 37:610–620.Google Scholar
    • 315 Carvalho VO, Bocchi EA, Guimaraes GV. The Borg scale as an important tool of self‐monitoring and self‐regulation of exercise prescription in heart failure patients during hydrotherapy. A randomized blinded controlled trial. Circ J. 2009; 73:1871–1876.Google Scholar
    • 316 Izawa KP, Watanabe S, Omiya K, Hirano Y, Oka K, Osada N, Iijima S. Effect of the self‐monitoring approach on exercise maintenance during cardiac rehabilitation: a randomized, controlled trial. Am J Phys Med Rehabil. 2005; 84:313–321.Google Scholar
    • 317 Caldwell MA, Peters KJ, Dracup KA. A simplified education program improves knowledge, self‐care behavior, and disease severity in heart failure patients in rural settings. Am Heart J. 2005; 150:983.Google Scholar
    • 318 Menéndez‐Jándula B, Souto JC, Oliver A, Montserrat I, Quintana M, Gich I, Bonfill X, Fontcuberta J. Comparing self‐management of oral anticoagulant therapy with clinic management: a randomized trial. Ann Intern Med. 2005; 142:1–10.Google Scholar
    • 319 Siebenhofer A, Rakovac I, Kleespies C, Piso B, Didjurgeit U; SPOG 60+ Study Group . Self‐management of oral anticoagulation reduces major outcomes in the elderly. A randomized controlled trial. Thromb Haemost. 2008; 100:1089–1098.Google Scholar
    • 320 Sunderji R, Gin K, Shalansky K, Carter C, Chambers K, Davies C, Schwartz L, Fung A. A randomized trial of patient self‐managed versus physician‐managed oral anticoagulation. Can J Cardiol. 2004; 20:1117–1123.Google Scholar
    • 321 Voller H, Glatz J, Taborski U, Bernardo A, Dovifat C, Heidinger K. Self‐management of oral anticoagulation in nonvalvular atrial fibrillation (SMAAF study). Z Kardiol. 2005; 94:182–186.Google Scholar
    • 322 Sevick MA, Korytkowski M, Stone RA, Piraino B, Ren D, Sereika S, Wang Y, Steenkiste A, Burke LE. Biophysiologic outcomes of the Enhancing Adherence in Type 2 Diabetes (ENHANCE) trial. J Acad Nutr Diet. 2012; 112:1147–1157.Google Scholar
    • 323 Zhang DA, Katznelson L, Li M. Postprandial glucose monitoring further improved glycemia, lipids, and weight in persons with type 2 diabetes mellitus who had already reached hemoglobin A1c goal. J Diabetes Sci Technol. 2012; 6:289–293.Google Scholar
    • 324 Lee KS, Lennie TA, Dunbar SB, Pressler SJ, Heo S, Song EK, Biddle MJ, Moser DK. The association between regular symptom monitoring and self‐care management in patients with heart failure. J Cardiovasc Nurs. 2015; 30:145–151.Google Scholar
    • 325 Lee KS, Lennie TA, Warden S, Jacobs‐Lawson JM, Moser DK. A comprehensive symptom diary intervention to improve outcomes in patients with HF: a pilot study. J Card Fail. 2013; 19:647–654.Google Scholar
    • 326 Shao JH, Chang AM, Edwards H, Shyu YI, Chen SH. A randomized controlled trial of self‐management programme improves health‐related outcomes of older people with heart failure. J Adv Nurs. 2013; 69:2458–2469.Google Scholar
    • 327 Lin CT, Chang KC, Lin CL, Chiang CC, Lu SW, Chang SS, Lin BS, Liang HY, Chen RJ, Lee YT, Ko LW. An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. IEEE Trans Inf Technol Biomed. 2010; 14:726–733.Google Scholar
    • 328 Baig MM, Gholamhosseini H, Connolly MJ. A comprehensive survey of wearable and wireless ECG monitoring systems for older adults. Med Biol Eng Comput. 2013; 51:485–495.Google Scholar
    • 329 Burke LE, Ma J, Azar KM, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens J, Shah SH, Suffoletto B, Turan TN, Spring B, Steinberger J, Quinn CC; on behalf of the American Heart Association Publications Committee of the Council on Epidemiology and Prevention, Behavior Change Committee of the Council on Cardiometabolic Health, Council on Cardiovascular and Stroke Nursing, Council on Functional Genomics and Translational Biology, Council on Quality of Care and Outcomes Research, and Stroke Council . Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation. 2015; 132:1157–1213.Google Scholar
    • 330 Sebern MD, Woda A. Shared care dyadic intervention: outcome patterns for heart failure care partners. West J Nurs Res. 2012; 34:289–316.Google Scholar
    • 331 Trivedi R, Slightam C, Fan VS, Rosland AM, Nelson K, Timko C, Asch SM, Zeliadt SB, Heidenreich P, Hebert PL, Piette JD. A couples’ based self‐management program for heart failure: results of a feasibility study. Front Public Health. 2016; 4:171.Google Scholar
    • 332 Lofvenmark C, Karlsson MR, Edner M, Billing E, Mattiasson AC. A group‐based multi‐professional education programme for family members of patients with chronic heart failure: effects on knowledge and patients’ health care utilization. Patient Educ Couns. 2011; 85:e162–e168.Google Scholar
    • 333 Lofvenmark C, Saboonchi F, Edner M, Billing E, Mattiasson AC. Evaluation of an educational programme for family members of patients living with heart failure: a randomised controlled trial. J Clin Nurs. 2013; 22:115–126.Google Scholar
    • 334 Allen JK, Himmelfarb CR, Szanton SL, Bone L, Hill MN, Levine DM. COACH trial: a randomized controlled trial of nurse practitioner/community health worker cardiovascular disease risk reduction in urban community health centers: rationale and design. Contemp Clin Trials. 2011; 32:403–411.Google Scholar
    • 335 Campbell J, Aday RH. Benefits of a nurse‐managed wellness program. A senior center model. Using community‐based sites for older adult intervention and self‐care activities may promote an ability to maintain an independent lifestyle. J Gerontol Nurs. 2001; 27:34–43.Google Scholar
    • 336 South J, Darby F, Bagnall AM, White A. Implementing a community‐based self care training initiative: a process evaluation. Health Soc Care Community. 2010; 18:662–670.Google Scholar
    • 337 Allen JK, Dennison‐Himmelfarb CR, Szanton SL, Bone L, Hill MN, Levine DM, West M, Barlow A, Lewis‐Boyer L, Donnelly‐Strozzo M, Curtis C, Anderson K. Community Outreach and Cardiovascular Health (COACH) Trial: a randomized, controlled trial of nurse practitioner/community health worker cardiovascular disease risk reduction in urban community health centers. Circ Cardiovasc Qual Outcomes. 2011; 4:595–602.Google Scholar
    • 338 WHO report on the global tobacco epidemic, 2017: monitoring tobacco use and prevention policies . Geneva, Switzerland: World Health Organization; 2017. Licence: CC BY‐NC‐SA 3.0 IGO.Google Scholar
    • 339 Alaimo K, Packnett E, Miles RA, Kruger DJ. Fruit and vegetable intake among urban community gardeners. J Nutr Educ Behav. 2008; 40:94–101.Google Scholar
    • 340 McCormack LA, Laska MN, Larson NI, Story M. Review of the nutritional implications of farmers’ markets and community gardens: a call for evaluation and research efforts. J Am Diet Assoc. 2010; 110:399–408.Google Scholar
    • 341 Dye CJ, Williams JE, Evatt JH. Improving hypertension self‐management with community health coaches. Health Promot Pract. 2015; 16:271–281.Google Scholar
    • 342 Lloyd‐Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; on behalf of the American Heart Association Strategic Planning Task Force and Statistics Committee . Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's Strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121:586–613.Google Scholar
    • 343 Wardlaw T, Blanc A, Zupan J, Åhman E. Low birthweight: country, regional and global estimates. 2004. Available at: Accessed July 4, 2017.Google Scholar
    • 344 Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jimenez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Moy C, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JHY, Alger HM, Wong SS, Muntner P; on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee . Heart disease and stroke statistics—2017 update: a report from the American Heart Association [published correction appears in Circulation. 2017;135:e646]. Circulation. 2017; 135:e146–e603.Google Scholar
    • 345 Graham G. Population‐based approaches to understanding disparities in cardiovascular disease risk in the United States. Int J Gen Med. 2014; 7:393.Google Scholar
    • 346 Mensah GA, Brown DW. An overview of cardiovascular disease burden in the United States. Health Aff (Millwood). 2007; 26:38–48.Google Scholar
    • 347 Martin JA, Hamilton BE, Osterman MJ, Curtin SC, Matthews TJ. Births: final data for 2012. Natl Vital Stat Rep. 2013; 62:1–68.Google Scholar
    • 348 Meit M, Knudson A, Gilbert T, Yu AT‐C, Tanenbaum E, Ormson E, TenBroeck S, Bayne A, Popat S. The 2014 Update of the Rural‐Urban Chartbook. 2014. Available at: Accessed July 24, 2017.Google Scholar
    • 349 Townsend N, Nichols M, Scarborough P, Rayner M. Cardiovascular disease in Europe—epidemiological update 2015. Eur Heart J. 2015; 36:2696–2705.Google Scholar
    • 350 Zhang X, Patel A, Horibe H, Wu Z, Barzi F, Rodgers A, MacMahon S, Woodward M; Asia Pacific Cohort Studies Collaboration . Cholesterol, coronary heart disease, and stroke in the Asia Pacific region. Int J Epidemiol. 2003; 32:563–572.Google Scholar
    • 351 Ueshima H, Sekikawa A, Miura K, Turin TC, Takashima N, Kita Y, Watanabe M, Kadota A, Okuda N, Kadowaki T, Nakamura Y, Okamura T. Cardiovascular disease and risk factors in Asia a selected review. Circulation. 2008; 118:2702–2709.Google Scholar
    • 352 Lee‐Koo C, Henry E, Mathur S. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: prevalence and incidence. 2014. Available at: Accessed December 22, 2016.Google Scholar
    • 353 Cameron VA, Faatoese AF, Gillies MW, Robertson PJ, Huria TM, Doughty RN, Whalley GA, Richards MA, Troughton RW, Tikao‐Mason KN, Wells EJ, Sheerin IG, Pitama SG. A cohort study comparing cardiovascular risk factors in rural Maori, urban Maori and non‐Maori communities in New Zealand. BMJ Open. 2012; 2:e000799.Google Scholar
    • 354 Australian Institute of Health and Welfare . Contribution of chronic disease to the gap in adult mortality between Aboriginal and Torres Strait Islander and other Australians. 2011. Available at: Accessed July 24, 2017.Google Scholar
    • 355 Cappuccio FP, Miller MA. Cardiovascular disease and hypertension in sub‐Saharan Africa: burden, risk and interventions. Intern Emerg Med. 2016; 11:299–305.Google Scholar
    • 356 Jamison DT, Feachem RG, Makgoba MW, Bos ER, Baingana FK, Hofman KJ, Rogo KO, eds. Disease and Mortality in Sub‐Saharan Africa. 2nd ed. Washington, DC: The International Bank for Reconstruction and Development/The World Bank; 2006.Google Scholar
    • 357 Alegre‐Díaz J, Herrington W, López‐Cervantes M, Gnatiuc L, Ramirez R, Hill M, Baigent C, McCarthy MI, Lewington S, Collins R, Whitlock G, Tapia‐Conyer R, Peto R, Kuri‐Morales P, Emberson JR. Diabetes and cause‐specific mortality in Mexico City. N Engl J Med. 2016; 375:1961–1971.Google Scholar
    • 358 Glassman A, Gaziano TA, Buendia CP, de Aguiar FC. Confronting the chronic disease burden in Latin America and the Caribbean. Health Aff (Millwood). 2010; 29:2142–2148.Google Scholar


    eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

    Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.