The incidence of heart failure (HF) is reaching epidemic proportions in the United States, resulting in an enormous medical and societal burden.1–4 With more than 5 million Americans living with the disease,3 the financial output for HF care will increase in the next few decades due in part to the aging population and treatment progress in pharmacotherapeutics and devices.4 The recent $34 billion dollar total annual cost of HF treatment5 has garnered attention from providers, policy makers, and insurers, yet American Heart Association (AHA) estimates costs to rise to $70 billion by 2030.3 Thirty-day readmission rates for decompensated HF approach 25% in Medicare beneficiaries after hospitalization with HF,6–8 and by 6 months, the rate is almost 50%.1,6,8
Novel interventions are needed to impact escalating costs of hospitalizations for decompensated acute HF, as well as the overall expenses of managing chronic HF patients.9 The complexity of effective self-care at home has been recognized in multiple studies that have failed to clearly demonstrate a successful intervention model.10–13 Numerous barriers exist that hinder patients’ ability to engage in self-care.14,15 Nonadherence to treatment plans for diet, medication regimens, and symptom monitoring contributes to increased resource utilization.16,17 An estimated 60% of adults with HF are nonadherent with medications.17,18 The research to date suggests that people with HF lack knowledge for competent self-care.15,19 Furthermore, cognitive deficits due to decreased cerebral oxygenation, depression, and other etiologies20–23 make it difficult for many patients to learn about their disease and self-care strategies.
The purpose of this randomized control study was to examine the effects of an education-support intervention delivered in the home setting, using strategies to improve health status and self-care in adults/older adults with class I to III HF. At 4 time points over 9 months, we measured the intervention’s effectiveness on health status outcomes (functional status, emotional state/depressive symptoms, and metamemory) and self-care outcomes (knowledge/knowledge retention, self-care ability). Our secondary purpose was to explore participants’ subjective perceptions of the intervention.
This study was based on the health promotion model24 and self-efficacy theory.25,26 According to the health promotion model, levels of health exist along a continuum in interaction with the experience of illness. Health-promoting behaviors contribute to the actualization of potential and typically emphasize self-care rather than expert care. Self-efficacy is a predictor of behavioral change26,27 that provides a basis for health-promoting behaviors, even in the face of disease or treatment-related symptoms.
This was a prospective, randomized control study of adults/older adults living with chronic HF. The design comprised an education-support intervention for 3 months followed by 3 months with telephone and/or e-mail support but no visits and then 3 months without contact with the research team. Data were collected at baseline, 3 months, 6 months, and 9 months.
Sample and Setting
Nonhospitalized participants were recruited from physician/advanced practice registered nurse (APRN) referrals, HF clinics, and media. Inclusion criteria were diagnosed with New York Heart Association (NYHA) class I to III28 systolic or diastolic HF; 45 years or older; willing to participate in a randomized 9-month study; living at home independently; able to speak, read, and write in English; and a score of at least 23 on the Mini-Mental State Examination (MMSE).29 Individuals diagnosed with major cerebrovascular disease (as stroke) or NYHA class IV HF were excluded. Because women have traditionally been underenrolled in studies about HF,30 efforts were made to recruit women as well as ethnically diverse participants.
Fifty participants (25 control and 25 intervention) were randomized, enrolled, and followed for the 9-month intervention. Two participants lost to follow-up were replaced (1 moved out of state, the other moved and could not be located) and their data were eliminated from the analysis. Participants in the control group were wait-listed to receive the intervention at the end of the study. The study was conducted in a southwestern urban area, and the University of Texas at Austin Institutional Review Board approved the study. All participants gave written informed consent for eligibility screening, the study itself, and permission to have their HF medical records reviewed.
The intervention was adapted from Stuifbergen’s health promotion in chronic illness intervention,27,31 which focuses on enhancing self-efficacy and has successfully used an educational and skill-building program with supportive telephone follow-up. The content was delivered individually by APRNs who were adult clinical nurse specialists with master’s or PhD education and expertise in HF and advanced cardiovascular nursing. The educational content (Box 1) was developed and peer reviewed to provide instruction and reinforcement targeting specific areas deemed essential to self-care for people living with chronic HF.32–34 Participants received a loose-leaf notebook with content inserts of approximately 100 pages, divided into 8 modules in large font, with room for note taking. All data were collected during home visits. Support by the APRN to build the participant’s self-efficacy was a significant part of the intervention effect. Specific strategies to build self-efficacy included social persuasion and encouragement, focused feedback, and breaking information down into realistic segments, and skills mastery (eg, reading food labels). Spouses (or significant others) were encouraged to attend.
Because several memory enhancement interventions for healthy elderly have been successfully conducted,35–38 we incorporated innovative strategies for enhancing memory performance into the intervention delivery. Each participant received a copy of Improving Your Memory,39 a book used effectively in previous research36,37 to read before beginning the in-home classes. Review and repetition were built into content in each module. Advance organizers were integrated to provide overview material and concrete examples to enable learners to activate relevant schemas for content association. Teaching targeted internal memory strategies (chunking, categorization, active observation, association, attention, concentration, elaboration, rehearsal, review and visualization), and external strategies (calendars, lists, notes, person, place).32,36,37
An initial telephone screening provided assessment of cognitive status and potential language barriers. At the first meeting in person, the MMSE was administered.29 Demographic, health, and medical data were obtained, and the battery of instruments was completed. The first phase of the 9-month intervention was delivered over the first 3-month period, meeting every 10 to 14 days for 1 to 1.5 hours to present the educational content. At the end of this phase, participants again completed the instruments and were then given the choice of either a weight scale or blood pressure device as an appreciation gift. During the last home visit, the APRN described the second phase of the study and determined how the participant wanted to be contacted. The second 3-month phase of the intervention was delivered by telephone and/or e-mail with the APRN, with no home visits. The APRN contacted the participant at the beginning of this phase in the agreed-upon method. The number of contacts and length of calls varied depending on the interest of the participant—the average being every 3 to 4 weeks from 5 to 15 minutes. There was no prescribed number of contacts and participants were encouraged to call/e-mail at any time. The research team fostered effective decision making about symptom management decisions and health-promoting activities and reinforced content from the modules. At the end of this phase, participants completed instruments and received a $25 cash retention gift. In the final 3-month phase, participants received no home visits, e-mails, or telephone calls and were instructed to communicate with their physician if questions arose. Patients thus returned to status quo medical care so that we could determine if the intervention had a sustained effect in improved outcomes. A final home visit solely for the purpose of data collection was made at 9 months. In addition to completing the instruments, participants evaluated the usefulness of the intervention and their responses were audiotaped. They also rated the value numerically from 1 to 10 and then received a $25 cash appreciation gift.
The control group received a loose-leaf notebook of selected pages containing information on health promotion for adults/older adults obtained from the National Institute of Aging Web site, Centers for Disease Control and Prevention, American Cancer Society, and the American Geriatric Society. Sample topics were fall prevention, crime and older adults, arthritis, and bladder control. No content about HF was included. Meetings were scheduled during the first 3 months depending on the needs and interest of the participant. No telephone or e-mail teaching was done. Instruments were completed at the same 4 time periods (baseline, 3 months, 6 months, and 9 months), and retention gifts were provided ($25 each testing period). Usual medical care was received. All participants were offered the chance to receive the intervention at the end of the study; most received it.
Approximately 275 home visits were done to deliver the intervention and conduct testing for the 25 participants in the intervention group, and approximately 225 visits were done to deliver the non-HF education and conduct testing in the 25 control group participants at the 4 time points.
Baseline demographic data included gender, race, ethnicity, marital status, age, socioeconomic status, and educational level. Baseline health and medical data obtained included time since diagnosis; current prescription medications; over-the-counter and alternative medications currently or commonly taken (including memory improvement pills, sleep aids, antihistamines); concurrent medical diagnoses and reported health problems, consistent with the American College of Cardiology/AHA recommendations for studies of HF patients40; explanatory model for the cause of the HF41; estimated past education about HF and source; and usual dietary modifications. Data from the following instruments were collected at baseline, 3 months, 6 months, and 9 months.
Health Status Outcome: Functional Status
The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a widely used 23-item questionnaire that quantifies several health status domains including physical limitations, symptoms, self-efficacy, social interference, and quality of life.42,43 It is a valid, sensitive, HF disease–specific health status measure with excellent metric properties.44 Cronbach’s α values for the subscales have been reported as follows: physical limitation, .90; symptoms (frequency, severity, and recent changes over time), .88; quality of life, .78; social limitation, .86; self-efficacy, .62; and KCCQ clinical summary, .95.42 Each KCCQ scale is transformed to a score of 1 to 100, with higher scores reflecting better health status.43 The total score and the overall summary score are the same—the author of the instrument recommends using the total score as an overall summary of the impact of an intervention.45 The overall summary includes the scales for physical limitation, the symptom summary, social limitation, and quality of life.43,45 A 5-point change in the KCCQ total store is a clinically important difference.43
Health Status Outcome: Emotional State: Depressive Symptoms
Depressive symptoms, as a major component of a person’s emotional state, were measured with the Geriatric Depression Scale (GDS) Short Form, which contains 15 true/false items.46 The GDS was found to have 92% sensitivity and 89% specificity when evaluated against diagnostic criteria.46 It correlates highly with other depression measures, with an α reliability coefficient of .94.35 Scores higher than 5 are suggestive of depression and scores higher than 10 almost always indicate clinical depression.47
Health Status Outcome: Metamemory
Metamemory is an individual’s knowledge, perceptions, and beliefs about the functioning, development, and capacities of his/her own memory and the human memory system.48 Three subscales of the Metamemory in Adulthood Questionnaire were used: Capacity (17 items), Change (18 items), and External Strategy (9 items). The Metamemory in Adulthood Questionnaire measures memory components of knowledge, beliefs, and affect,48,49 with responses rated on a 5-point Likert scale from strongly agree to strongly disagree. Psychometric characteristics have been examined with community-dwelling, middle-age, and older adults. Cronbach’s α values for these subscales ranged from .73 to .95.36
Self-care Outcome: Knowledge
Knowledge was measured by a new 20-item multiple-choice tool, the HF Knowledge Test (HFKT). Content validity was established by an extensive literature review and critique by 9 experts in HF and adult/older adult health. The HFKT’s 5 subscales measure pathophysiology (3 items), symptom management (5 items), nutrition (4 items), medications (4 items), and health promotion (4 items). Cronbach’s α for the HFKT in the present study was .765.
Self-care Outcome: Self-management/Self-care Ability
Self-care was measured with the 15-item Revised Self-care of Heart Failure Index (SCHFI), version R4.50 This instrument measures 3 main concepts: (1) self-care maintenance, which encompasses monitoring and treatment adherence12 performed to maintain one’s health with positive health practices14; (2) self-care management, a decision-making process of recognizing and evaluating HF symptoms, along with treating and evaluating treatment choices12; and (3) self-care confidence or self-efficacy, which is thought to moderate the relationship between self-care and outcomes.14 The α coefficients calculated for the present sample were .62 for self-care maintenance and .88 for self-care confidence. The management score reliability could not be calculated because an insufficient number of subjects reported symptoms. Scores on each scale range from 0 to 100, with higher scores reflecting better self-care.12 Scores of 70 or higher on each SCHFI scale are thought to indicate individuals with adequate self care (self-care adequacy).12
Descriptive statistics were computed for study variables. Repeated-measures analysis of variance was used to examine the effects of the intervention on health status and self-care outcomes. In addition, a series of analyses was conducted to examine the impact of HF class on the proposed outcomes over time. Repeated-measures analysis of variance was used to determine the main effect of time while controlling for the effect of HF class and gender. Simple contrasts were used to look for differences between 2 populations—intervention and control. For the HFKT, statistical analysis included Mann-Whitney U and Wilcoxon rank sum tests to test the differences between the 2 groups. The α level was set at .05.
The characteristics of the 50 participants are shown in Table 1. Gender was split almost evenly in the sample, with slightly more women. Only 5 participants were younger than 50 years. All 3 inclusion criteria NYHA classes were represented. Half of the participants were married and most were white and well educated, with only 1 subject lacking a high school degree. There were no group differences except for somewhat higher reported socioeconomic status in the intervention group.
Most of the participants had other concomitant diseases, including diabetes mellitus (48%) and obesity (mean body mass index of 34.1% mg/m2). Most participants were prescribed evidence-based HF medications.33,51 At baseline, 73% of participants reported eating foods with salt restrictions, and 33% indicated they ate a low-fat diet. At baseline, participants reported a large number of troublesome symptoms, including fatigue (88%), shortness of breath (76%), edema (78%), dizziness (60%), anxiety, nausea (28%), thirst (66%), and thinking clearly (54%). Sleep and memory problems were also described.
The effects of the education-support intervention are reported by health status outcomes and self-care outcomes.
Health Status Outcome: Functional Status: Kansas City Cardiomyopathy Questionnaire Total Score
No significant main effect for time was found (F2.55, 117.109 = 0.083, P = .953) (Table 2). However, there was a significant interaction effect between time and group (F2.55, 117.109 = 3.142, P = .035). The control group showed no change in their KCCQ scores over time, but the intervention group increased their KCCQ scores over time. Simple contrast results showed that participants in the intervention group showed significantly higher scores in their KCCQ scores at time point 2 (F1, 46 = 6.618, P = .013) and 3 (F1, 46 = 4.773, P = .034) than at time point 1. No significant effect for gender or HF class was found.
Kansas City Cardiomyopathy Questionnaire Self-efficacy and Quality of Life Subscales
The intervention group had a significant time-by-group interaction for self-efficacy (F1.952, 89.807 = 3.774, P = .028) and quality of life (F2.490, 112.028 = 3.790, P = .018) (Table 2). Simple contrast showed that self-efficacy in intervention group participants at time point 2, 3, and 4 were significantly higher than the scores at time 1. Also, quality of life in the intervention group at time point 2 and 4 were significantly higher than scores at time 1.
Emotional State: Geriatric Depression Scale
No significant main effect was found (F2.882, 132.584 = 0.401, P = .744) for time-by-group interaction effect (F2.882, 132.584 = 1.887, P = .137) for depressive symptoms. Trend data showed that both groups improved in scores. No significant effect for gender or HF class was found.
Memory Subscales of the Metamemory in Adulthood Questionnaire
Metamemory: Capacity Subscale
Results indicate that although overall scores did not differ significantly over time, there was a significant difference across time-by-group status (F3, 138 = 3.853, P = .011) (Table 3). Whereas the control group showed a decrease in their scores, the intervention group showed a significant increase in their scores after the intervention. Results of simple contrast indicated that the scores of participants in the intervention group at time point 2 (F1, 46 = 6.834, P = .012), 3 (F1, 46 = 6.325, P = .015), and 4 (F1, 46 = 5.996, P = .018) were significantly higher than the scores at time point 1. No significant effect for gender or HF class was found.
Metamemory: Change Subscale
Results showed that the main effect of time was not significant. However, a significant interaction of time and group was evident, suggesting that metamemory scores changed in different ways for the 2 groups (F3, 138 = 7.671, P = .000). The control group showed a decrease in their scores over time, whereas the intervention group showed an increase in their scores. Simple contrast showed that the scores of participants in the intervention group differed significantly from those of control group participants at time point 2 (F1, 46 = 14.624, P = .000), 3 (F1, 46 = 10.454, P = .002), and 4 (F1, 46 = 13.555, P = .001). No significant effect for gender or HF class was found.
Metamemory: External Strategies Subscale
No significant main effect for time (F2.848, 131.014 = 1.823, P = .149) nor for time × group interaction effect (F2.848, 131.014 = 1.004, P = .390) was found. Also, HF class and gender did not have a significant effect on the scores.
Knowledge/Knowledge Retention: Heart Failure Knowledge Test
After controlling for class and gender effects, there was a significant change over time in the HFKT scale (F3, 138 = 5.321, P = .005) and there was also a significant difference in the change by group (F3, 138 = 11.885, P = .000). These results suggest that HFKT scores changed significantly across time and there were differences in the patterns of HFKT by group status. Whereas the control group showed little change in scores, the intervention group showed significant increases in their HFKT scores after the intervention. The results of simple contrast indicated that HFKT scores of participants in the intervention group at time point 2 (F1, 46 = 20.682, P = .000), 3 (F1, 46 = 12.651, P = .001), and 4 (F1, 46 = 15.191, P = .000) were significantly higher than the scores at time point 1.
Self-care Ability: Self-care of Heart Failure Index
Self-care maintenance scores improved significantly over time in both groups (F = 7.24, df = 3, 46, P < .001). In addition, even with this small sample, there was a strong trend toward statistical significance in differential group change in maintenance scores (F = 2.59, df = 3, 46, P = .06), with the intervention group improving more than the control group did over time. When this analysis was replicated with self-care confidence, both groups improved over time (F = 7.04, df = 3, 43, P = .001), but the intervention group improved significantly more than the control group did (F = 6.70, df = 3,43, P = .001). α coefficients could be calculated for self-care maintenance (.62) and confidence (.88). The SCHFI instrument asks patients about 2 symptoms—trouble breathing and ankle swelling. The management score reliability could not be calculated because so few subjects said they had these symptoms (8 at time 1; 14 at time 2; 18 at time 3; 12 at time 4).
Group differences in self-care management were analyzed at time 3 (when the most participants provided data on this scale) using nonparametric statistics. The mean rank was almost double in the intervention group compared with the control group (12.22 vs 6.78) and the difference in rank was statistically significant (Mann Whitney U = 16.00, 2-tailed P = .03).
When group differences in self-care adequacy were compared with χ2 analysis, the intervention group was significantly more likely than the control group to be adequate in self-care confidence at time 3 (χ2 = 7.71, df = 1, P = .006). Group differences in self-care maintenance adequacy were not significantly different at the various time periods. When χ2 was used to assess group differences in those who were adequate in management the last time they were symptomatic, significantly more of those in the intervention than the control group were adequate in self-care management (100% vs 58.3%, P = .04).
Participants’ Perceptions of the Intervention
Content analysis of the audiotaped interviews was done to evaluate the participants’ perception of the usefulness of the intervention. All 25 intervention participants completed interviews and reported the intervention to be extremely valuable. On the 1-to-10 numerical scale, 24 of 25 rated the usefulness as a 10 (very helpful); 1 rated it at 8 of 10. It was also evident based on verbal comments from the control group that they also felt supported with the ongoing relationship with someone interested in their well-being.
Our study is 1 of the first randomized control trials to integrate a memory-enhancing component into an education-support intervention. The results indicate that the intervention led to significant improvements in subjective memory evaluation of capacity and change, functional status, self-efficacy, quality of life, self-care knowledge, and self-care abilities. Telephone and e-mail support follow-up appears to have helped participants maintain gains, which is consistent with the findings of McAlister et al52 and Hansen et al.53 Efforts to recruit more women and a diverse sample were successful, with more than 50% of the sample female and 18% of African American and 24% of Hispanic or Latino ethnicity. Blacks have been found to have an excess risk of HF compared with whites.54 Possible reasons for our lack of attrition may have been the ongoing support in the home setting and the retention gifts.
Although cognitive impairment has been reported in both HF and aging,41,55 our sample had an average MMSE score of 29, indicating no cognitive impairment. However, the MMSE can show a ceiling effect, allowing individuals with cognitive impairment to have a perfect score, especially those with more education.56 Intervention participants perceived increased memory capacity and change toward stability.48,49 There was no change in the use of external strategies. No gender differences were found, unlike Pressler and colleagues,57 who reported finding poorer memory in men.
Both men and women in the intervention group significantly increased their HF knowledge that persisted over the 3 time periods. Our sample was well educated, which likely affected the baseline knowledge of both groups. However, even though both groups were equivalent in education, the intervention group still achieved higher success.
Functional and cognitive limitations can be affected by other comorbidities in addition to HF.15 Nearly half of the participants had concomitant diabetes mellitus (type 1 = 6%; type 2 = 42%). Diabetes and HF share several risk factors, including obesity.58 A future study should target both diseases to synergistically aim to improve outcomes and reduce the adverse effects of diabetes in HF,58 consistent with the recommendations by McCauley and colleagues.69 The degree of obesity in participants was an unexpected finding. Obesity can affect a patient’s interpretation of HF symptoms as dyspnea and fatigue.
A constellation of symptoms can accompany HF and its pharmacologic treatment. The symptom categories were high at baseline. Albert and colleagues60 found that dyspnea was reported by 100% of ambulatory patients (n = 89) as their most common symptom, similar to the 76% in our sample at baseline. In a recent narrative review about symptom onset and treatment, researchers found delays of 2 hours to 7 days from the symptom onset until hospital admission.61 The intervention emphasized symptom recognition of worsening HF to build critically important self-care skills. Previous research62 found that 42% of patients seeking emergency care were sent by relatives or healthcare providers and were uncertain about the seriousness of worsening HF.
Baseline self-care scores in the sample were high. Incorporating knowledge about symptom recognition and management, medications, nutrition, and healthy behaviors was effective in increasing self-care confidence and self-care maintenance measures on the SCHFI instrument. The decision making needed for effective self-care management is complex and more difficult to achieve than maintenance.50 Self-care confidence is important in generating and maintaining behavior change. The emphasis on teaching metacognition strategies to improve may have contributed to the improved self-care scores.50
Both HF and depression conditions share biological processes, including increased neurohormone production and autonomic nervous system dysregulation.63 In our study, participants in both groups improved in depressive symptoms. The intervention group began with higher depressive symptom scores on the GDS and trended down at all 3 time points after baseline, as did the control group. We attribute this to the effect of the APRN support felt by those in both groups. No differences in GDS scores were seen between the NYHA classes across both groups. Some studies have reported more depressive symptoms with higher NYHA functional classes, especially class III and IV.63,64
The percentage of participants who self-reported restricting sodium in their diet (73%) is consistent with Lennie and colleagues,65 who reported that 75% of participants (N = 246) said they followed a low-sodium diet all or most of the time, yet a 24-hour urine sodium excretion level revealed that only 25% of the participants were accurately self-reporting. Nutrition self-care behaviors are unique from most other activities in that they necessitate modifying existing behaviors and habits.66 Teaching diet skills, as label reading for sodium content, was incorporated, as was an interactive “pantry analysis” to specifically evaluate foods and sodium content actually present in the home. We recommended a 2000 to 2400 mg sodium diet, consistent with the teaching in HF Clinics in the city and the Nutrition Committee of the AHA.66,67
The improvement in functional status scores in the intervention group reflects a composite of items on the instrument. The results indicate that the intervention had a positive impact on participants’ health status, including a major improvement in self-efficacy. Self-efficacy scores increased 20 points, indicating a new level of confidence in the intervention group’s perception of their self-care ability. Some studies have noted worsening KCCQ responses over time with more severe cases of HF.43
Using APRNs who were HF experts to deliver the intervention was consistent with recommendations of using specially trained HF nurses from a systematic review of 29 randomized trials of multidisciplinary programs to improve HF care.52 Recent attention to transitional care for Medicare patients with high-risk conditions focuses on the transition from hospital to home. The seminal work of Naylor and colleagues59,68,69 with coordination of care by APRNs for HF patients provided much of the evidence to lay the groundwork. Menefee et al70 found that 50% of HF patients improved KCCQ scores after 3 months of care in an APRN-led HF clinic.
Several limitations were present. Data were collected in 1 southwestern city. Although randomized, the sample size was only 50 patients. The education level of participants was high but could not have been prevented unless some educational limit had been set. Using a more comprehensive battery of neuropsychological tests, similar to the work of Pressler et al,22,57 would improve measurement of cognitive impairment. The sample may not be reflective of many HF patients who are older, with cognitive impairment, and do not receive support in the home setting. Intervention fidelity and consistency in delivery of the intervention may have been affected by having several different APRNs, although we tried to standardize it with the modules and training sessions. Bias may have been present for the data about subjective perceptions of the intervention because the same APRN who conducted the intervention also collected that data, although we tried to mitigate that by having the audiotapes analyzed by a different person and by using a numerical scale rating. Duplicating the in-home intervention could be time consuming and costly. Only literate people were eligible for the study and the study design used multisession education.
This randomized control study examined the outcomes from an education-support intervention infused with a dose of memory enhancement, which we believe to be an innovative approach to this population. Positive outcomes were seen in several patient-reported health status variables, including metamemory, self-efficacy, quality of life, self-care status, and HF knowledge. The AHA says multiple studies have shown that patient-reported health status measures are strong, independent predictors of subsequent mortality.71 Thorne72 suggests that providers need to support competency building for chronically ill patients and assume that most will be able to gain fairly high levels of expertise in self-care. A state-of-the-science paper on self-care in HF contends that it is impossible for community-dwelling patients with chronic HF to avoid self-care.73 The challenge before us is to continue to test models and interventions to improve self-care competencies and prevent costly readmissions.
What’s New and Important
- Building in memory techniques to strengthen knowledge and self-care abilities is essential to gain improvements that last over time.
- In-home education and support by an APRN provide opportunities for ongoing assessment and teaching to increase patient self-care skills to prevent costly hospitalizations.
The authors acknowledge the statistical expertise of Adama Brown, PhD, statistician, Cain Research Center at The University of Texas at Austin School of Nursing; Lynn Chen, PhD, Eunjin Seo, MS; and Hsing-Mei Chen, PhD, RN.
1. Butler J, Kalogeropoulos A. Worsening heart failure
hospitalization epidemic we do not know how to prevent and we do not know how to treat! Am Coll Cardiol. 2008; 52 (6): 435–437.
2. Fang J, Mensah GA, Croft JB, Keenan NL. Heart failure
-related hospitalizations in the U.S. 1979 to 2004. J Am Coll Cardiol. 2008; 52 (6): 428–434.
3. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation. 2014; 129: e242–e247.
4. Neubauer S. The failing heart—an engine out of fuel. N Engl J Med. 2007; 356 (11): 1140–1151.
6. Desai AS. Home monitoring heart failure
care does not improve patient outcomes: looking beyond telephone-based disease management. Circulation. 2012; 125 (6): 828–836.
7. Dharmarajan K, Hsieh AF, Lin Z, et al. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure
, acute myocardial infarction, or pneumonia. JAMA. 2013; 309 (4): 355–363.
8. Ross JS, Chen J, Lin Z, et al. Recent national trends in readmission rates after heart failure
hospitalization. Circ Heart Fail. 2010; 3 (1): 97–103.
9. Lewis EF. Still at the drawing board: improving quality of life in heart failure
. Circ Heart Fail. 2012; 5 (2): 137–139.
10. Artinian NT, Magnan M, Sloan M, Lange MP. Self-care
behaviors among patients with heart failure
. Heart Lung. 2002; 31 (3): 161–172.
11. Buck H, Lee CS, Moser D, et al. Relationship between self-care
and health-related quality of life in older adults with moderate to advanced heart failure
. J Cardiovasc Nurs. 2012; 27 (1): 8–15.
12. Riegel B, Lee CS, Dickson VV, Carlson B. An update on the Self-care
of Heart Failure
Index. J Cardiovasc Nurs. 2009; 24 (6): 485–497.
13. Rockwell JM, Riegel B. Predictors of self-care
in persons with heart failure
. Heart Lung. 2001; 30 (1): 18–25.
14. Shively MJ, Gardetto NJ, Kodiath MF, et al. Effect of patient activation on self-management in patients with heart failure
. J Cardiovasc Nurs. 2013; 28 (1): 20–34.
15. Strömberg A. The crucial role of patient education in heart failure
. Eur J Heart Fail. 2005; 7 (3): 363–369.
16. DiDomenico RJ, Kondos GT, Dickens C. Letter by DiDomenico regarding article, “Recent national trends in readmission rates after heart failure
hospitalization.” Circ Heart Fail. 2010; 3 (3): e13.
17. Wu J-R, Moser DK, Lennie TA, Burkhart PV. Medication adherence in patients who have heart failure
: a review of the literature. Nurs Clin North Am. 2008; 43 (1): 133–153.
18. Riegel B, Lee CS, Ratcliff SJ, et al. Predictors of objectively measured medication nonadherence in adults with heart failure
. Circ Heart Fail. 2012; 5 (4): 430–436.
19. Roncalli J, Perez L, Pathak A, et al. Improvement of young and elderly patient’s knowledge of heart failure
after an educational session. Clin Med Cardiol. 2009; 3: 45–52.
20. Cacciatore F, Abee P, Ferrara N, et al. Congestive heart failure
and cognitive impairment in an older population. J Am Geriatr Soc. 1998; 46: 1343–1348.
21. Clark AP, McDougall G. Cognitive impairment in heart failure
. Dimens Crit Care Nurs. 2006; 25 (3): 93–100.
22. Pressler SJ, Subramanian U, Kareken D, et al. Cognitive deficits and health-related quality of life in chronic heart failure
. J Cardiovasc Nurs. 2010; 25 (3): 189–198.
23. Riegel B, Bennett JA, Davis A, et al. Cognitive impairment in heart failure
: issues of measurement and etiology. Am J Crit Care. 2002; 11 (6): 520–528.
24. Pender NJ, Murdaugh CL, Parsons MA. Health Promotion in Nursing Practice. 4th ed. Upper Saddle River, NJ: Prentice Hall; 2002.
25. Bandura A. Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall; 1977.
26. Bandura A. Self-efficacy: The Exercise of Control. New York, NY: WH Freeman; 1997.
27. Stuifbergen AK, Rogers S. Health promotion: an essential component of rehabilitation for persons with chronic disabling conditions. Adv Nurs Sci. 1997; 19 (4): 1–20.
28. Criteria Committee of the New York Heart Association. Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Blood Vessels. 9th ed. Boston, MA: Little, Brown; 1994.
29. Tombaugh TN, McIntyre JJ. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc. 1992; 40 (9): 922–935.
30. Wenger NK. Women, heart failure
, and heart failure
therapies. Circulation. 2002; 105: 1526–1528.
31. Stuifbergen AK, Becker H, Blozis S, Timmerman G, Kullberg V. A randomized clinical trial of a wellness intervention for women with multiple sclerosis. Arch Phys Med Rehabil. 2003; 84 (4): 467–476.
32. Bartholomew LK, Parcel GS, Kok G, Gottlieb NH. Intervention Mapping: Designing Theory and Evidence-based Health Promotion Programs. Mountain View, CA: Mayfield; 2001.
33. Hunt SA, Abraham WT, Chin MH, et al. ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure
in the adult: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure
. Published 2005. Accessed March 25, 2006.
34. Lainscak M, Blue L, Clark AL, et al. Self-care
management of heart failure
: practical recommendations from the Patient Care Committee of the Heart Failure
Association of the European Society of Cardiology. Eur J Heart Fail. 2011; 13 (2): 115–126.
35. McDougall GJ. Memory
improvement in octogenarians. Appl Nurs Res. 2002; 15 (1): 2–10.
36. McDougall GJ. Memory
improvement in assisted living elders. Issues Ment Health Nurs. 2000; 21 (2): 217–233.
37. McDougall GJ. Cognitive interventions among older adults. In Fitzpatrick JJ, ed. Annual Review of Nursing Research. New York, NY: Springer; 1999; 17: 219–240.
38. McDougall GJ. Memory
improvement program for elderly cancer survivors. Geriatr Nurs. 2001; 22 (4): 185–190.
39. Fogler J, Stern L. Improving Your Memory
What You’re Starting to Forget. 3rd ed. Baltimore, MD: Johns Hopkins University Press; 2005.
40. Radford MJ. ACC/AHA key data elements and definitions for measuring the clinical management and outcomes of patients with chronic heart failure
: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Heart Failure
Clinical Data Standards). J Am Coll Cardiol. 2005; 46 (6): 1179–1207.
41. Clark AP, McDougall GJ, Joiner-Rogers G, et al. Explanatory models of heart failure
etiology. Dimens Crit Care Nurs. 2012; 31 (1): 46–52.
42. Green CP, Porter CB, Bresnahan DR, Spertus JA. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure
. J Am Coll Cardiol. 2000; 25 (5): 1245–1255.
43. Kosiborod M, Soto GE, Jones PG, et al. Identifying heart failure
patients at high risk for near-term cardiovascular events with serial health status assessments. Circulation. 2007; 115 (15): 1975–1981.
44. Garin O, Ferrer M, Pont A, et al. Disease-specific health-related quality of life questionnaires for heart failure
: a systematic review with meta-analyses. Qual Life Res. 2009; 18 (1): 71–85.
45. Spertus J. Which KCCQ score should be used? http://cvoutcomes.org/
. Published June 15, 2008. Accessed August 8, 2008.
46. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982; 17 (1): 37–49.
47. Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. In: Brink TL, ed. Clinical Gerontology: A Guide to Assessment and Intervention. New York, NY: Haworth Press; 1986: 165–173.
48. Dixon R, Hultsch D. Structure and development of metamemory in adulthood. J Gerontol. 1983; 38: 682–688.
49. Dixon R, Hultsch D, Hertzog C. The Metamemory in Adulthood (MIA) questionnaire. Psychopharmacol Bull. 1988; 24: 671–688.
50. Riegel B, Carlson B, Moser DK, Sebern M, Hicks F, Roland V. Psychometric testing of the Self-care
of Heart Failure
Index. J Card Fail. 2004; 10 (4): 350–360.
51. Jessup M, Abraham WT, Casey DE, et al. 2009 focused update: ACCF/AHA guidelines for the diagnosis and management of heart failure
in adults. A report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation. 2009; 119 (14): 1977–2016.
52. McAlister FA, Stewart S, Ferrua S, McMurray J. 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 (4): 810–819.
53. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. Ann Int Med. 2011; 155 (8): 520–528.
54. Eaton CB, Abdulbaki AM, Margolis KL, et al. Racial and ethnic differences in incident hospitalized heart failure
in postmenopausal women: the Women’s Health Initiative. Circulation. 2012; 126: 688–696.
55. Moser DK, Watkins JF. Conceptualizing self-care
in heart failure
: a life course model of patient characteristics. J Cardiovasc Nurs. 2008; 23 (3): 205–218.
56. Holsinger T, Deveau J, Boustani M, Williams JW. Does this patient have dementia? JAMA. 2007; 297: 2391–2404.
57. Pressler SJ, Subramanian U, Kareken D, et al. Cognitive deficits in chronic heart failure
. Nurs Res. 2010; 59 (2): 127–139.
58. Ahmed A, Lloyd-Jones DM. Diabetes-related poor outcomes in chronic heart failure
: complex interactions with sex and age. Cardiol Rev. 2008; 25 (7): 22–28.
59. McCauley KM, Bixby MB, Naylor MD. Advanced practice nurse strategies to improve outcomes and reduce cost in elders with heart failure
. Dis Manag. 2006; 9 (5): 302–310.
60. Albert N, Trochelman K, Li J, Lin S. Signs and symptoms of heart failure
: are you asking the right questions? Am J Crit Care. 2010; 19 (5): 443–452.
61. 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 (10): 1122–1129.
62. Patel H, Shafazand M, Schaufelberger M, Ekman I. Reasons for seeking acute care in chronic heart failure
. Eur J Heart Fail. 2007; 9: 702–708.
63. Kop WJ, Synowski SJ, Gottlieb SS. Depression in heart failure
: biobehavioral mechanisms. Heart Fail Clinic. 2011; 7 (1): 23–28.
64. 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 (8): 1527–1537.
65. Lennie TA, Worrall-Carter L, Hammash M, et al. Relationship of heart failure
patients’ knowledge, perceived barriers, and attitudes regarding low-sodium diet recommendations to adherence. Prog Cardiovasc Nurs. 2008; 23 (1): 6–11.
66. Lennie TA. Nutrition self-care
in heart failure
. J Cardiovasc Nurs. 2008; 23 (3): 197–204.
67. Lichtenstein AH, Appel LJ, Brands M, et al. Diet and lifestyle recommendations revision 2006: a scientific statement from the American Heart Association Nutrition Committee. Circulation. 2006; 114 (1): 82–96.
68. Naylor M, Brooten D, Jones R, Lavizzo-Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. Arch Intern Med. 1994; 120 (12): 999–1006.
69. Naylor MD, Brooten DA, Campbell RL, Maislin G, McCauley KM, Schwartz JS. Transitional care of older adults hospitalized with heart failure
: a randomized, controlled trial. J Am Geriatr Soc. 2004; 52 (5): 675–684.
70. Menefee F, Spertus J, Lipke J, et al. The value of an advanced practice nurse-led heart failure
clinic as demonstrated by improvements in Kansas City Cardiomyopathy Questionnaire scores. J Card Fail. 2007; 13 (6 suppl 2): S185.
71. Rumsfeld JS, Alexander KP, Goff DC, et al. Cardiovascular health: the importance of measuring patient-reported health status. Circulation. 2013; 127: 2233–2249.
72. Thorne S. Patient-provider communication in chronic illness: a health promotion window of opportunity. Fam Community Health. 2006; 29 (15 suppl 1): 4S–11S.
73. Riegel B, Moser DK, Anker SD, et al. 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.