Effective self-management (SM) is an essential component of comprehensive disease management of patients with heart failure (HF),1 which prevents adverse outcomes (eg, mortality, complications),2,3 reduces hospital readmission,4 and improves functioning and independence.5 However, patients with HF often fail to routinely perform SM behaviors,1,6 and those living in rural settings may be less likely to engage in SM behaviors.7
Based on Bandura’s social cognitive theory,8 the common central mechanism of previous SM interventions is self-efficacy. Supported by observational studies,4,9–11 increased self-efficacy results in the improvement in SM behaviors. However, experimental studies have not demonstrated that increased knowledge and self-efficacy lead to increased SM behaviors in HF patients1,7,12
To address these contradictions in the evidence, researchers are examining additional intervention mechanisms, used alone or in combination, that may improve HF patients’ SM behaviors. Recently, investigators have reported the positive association between patient activation and SM behaviors in populations with various chronic conditions (eg, HF,13 heart disease,14 hypertension,15 and diabetes16). Patient activation, the central concept in Wagner’s chronic illness care model,17 is defined by Hibbard as the degree to which the person is ready, willing, and able to engage in health behavior change and manage his/her own health.18 In current literature, however, there is little evidence about the interaction between self-efficacy and patient activation on HF patients’ SM behaviors.
Previously, we found that patient activation mediated the effect of self-efficacy on SM behaviors, using baseline data collected from a randomized controlled trial (RCT).19 This study examined whether the mediation pathway remains at 3 months after the intervention. Moreover, investigators have reported that the factors associated with high levels of activation for SM, both within the general population and for those living with multiple chronic conditions, were female gender,20 younger age,21 higher level of education,21 greater self-care knowledge and support,22 adequate physical functioning,21 and lower disease severity (including both primary disease and comorbidities).23,24 Whether these factors impact the mediation pathway between self-efficacy, patient activation, and SM behaviors in HF patients is unknown. Therefore, we explored moderators of the mediation pathway to establish for whom the mediating effects of patient activation were strongest. Without a clear understanding of the mechanisms influencing SM behavior, it is impossible to develop effective interventions to promote SM behaviors. Therefore, the overall purpose of this study was to examine the moderating effects of SM knowledge and support on the relationships among self-efficacy, patient activation and SM behavior in rural HF patients who participated in an RCT aimed to promote SM behaviors.
This was a secondary analysis, using the data collected from an RCT, entitled “Patient Activated Care at Home (PATCH).” The trial was intended to examine the feasibility and efficacy of a 12-week home-based intervention to improve SM behaviors in HF patients.25 The study was approved by the university institutional review board and the hospital ethical committees. All subjects provided written informed consent. Details regarding intervention content and intervention administration were previously reported.25
Sample and Setting
Subjects were recruited from a rural hospital in Southeast Nebraska. The included subjects (1) were 21 years or older; (2) had a discharge diagnosis of HF; (3) were classified New York Heart Association (NYHA) class I with at least 1 HF-related hospitalization or emergency department visit in the previous year; (4) were classified NYHA class II to IV; (5) were discharged to home; (6) were able to achieve a score of 3 or greater on the Mini-Cog test, a validated screening test for dementia in a population-based sample26; (7) understood and spoke English; and (8) had access to a telephone. We excluded patients who were diagnosed with (1) depression, (2) cirrhosis, (3) chronic renal failure, and (4) other end-stage and/or terminal illness (eg, cancer), which limited the patient’s SM capacity.25 With the exception of the Mini-Cog test, all other clinical diagnoses (eg, NYHA, depression, cirrhosis, chronic renal failure, or terminal illness) were collected and confirmed from patients’ medical records in hospital, primary care, and specialty clinics. We were unable to obtain the effect size from similar studies in comparable populations or from a pilot study. Therefore, a power analysis was conducted based on a medium effect correlation of r = 0.3.27 Using a 2-sided test, 5% significance level (α = .05) with 80% power, the required sample size was approximately 98 (n = 98).28
A battery of measures was administered at 3-month intervals (baseline and 3 and 6 months after intervention).
Variables and Measures for Mediation Analysis
The Self-care of HF Index (SCHFI) Subscale C (ie, self-care confidence scale) was used to assess self-efficacy for SM. The conceptual framework supporting our intervention design was built upon Wagner’s Chronic Care Model that emphasizes the impacts of patients’ knowledge, confidence, and activation level on SM.29 The SCHFI was designed to evaluate intervention effects on self-efficacy and activation level, which is congruent with the intervention mechanisms supported by Bandura’s work.8,29,30 The SCHFI self-care confidence scale is a 6-item self-report measure.30 Items are rated on a 4-point Likert scale. Scores are standardized to range from 0 to 100, with higher scores indicating higher self-efficacy. The SCHFI has shown good psychometric properties, with high reliability in the HF population (α = .827).30 There was also a significant correlation between self-efficacy and SM in HF patients (r = 0.42).30
Patient activation was assessed by the Patient Activation Measure (PAM) with 13 items.31 Each item is rated on a 5-point Likert scale. The score ranges from 0 to 100, with higher scores indicating higher activation levels. The PAM demonstrated high internal consistency (Cronbach α = .87) and construct validity, as evidenced by significant associations with levels of physical activity, medication adherence, health status, and quality of healthcare.24,31 The PAM has been tested in various populations living with chronic complex conditions (eg, multiple scoliosis, arthritis, heart disease, and diabetes).24,32
Heart failure SM behaviors were assessed by the 29-item Revised Heart Failure Self-Care Behavior Scale (RHFSCBS).33 Each response was rated on a 5-point Likert scale (0 = none of the time, 5 = all of the time).33 The internal reliability of this questionnaire is demonstrated by a Cronbach α level of .84.33
Variables and Measures for Moderation Analysis
The data on moderators were collected from medical records (eg, gender, age, and education level), the questionnaires (eg, SM knowledge and support) and physiology devices (eg, physical functioning).
Self-management knowledge was measured by a 7-item Heart Failure Management Knowledge questionnaire with a 10-point Likert scale and reported reliability (Cronbach’s α = .75).34 Higher scores indicated greater SM knowledge.34
Self-management support was assessed by Medication Adherence in HF Patients, Section 4 (Subjective Norm) subscale.34 This is a 4-item, 5-point Likert questionnaire with acceptable reliability (item-total correlations, 0.52–0.67).34 This measure assessed the patients’ perceived self-care support from their family members and healthcare professionals. The scores ranged from 4 to 40, and higher scores indicated greater perceived SM support from family and healthcare providers.34
Physical functioning was assessed by a waist-worn accelerometer, an Actigraph GT3X-BT (Actigraph, Pensacola, Florida). The data were collected at baseline and 3 and 6 months the after intervention. Actigraph data included calories expended in activity, activity counts, and minutes in moderate or greater intensity physical activity. Minutes in moderate or greater intensity physical activity were used to assess subjects’ physical functioning.
Renal function (ie, serum creatinine, glomerular filtration rate, and blood urea nitrogen) was used as the proxy measure of HF severity because renal dysfunction is an independent indicator of disease severity and poor prognosis in HF patients with comorbidities.35 Reduced renal function measured by increased serum creatinine, blood urea nitrogen, and reduced glomerular filtration rate indicates worse HF.35 Renal function profiles were assessed at baseline and 3 and 6 months after the intervention.
Statistical analyses were conducted using IBM SPSS 22 (IBM Corporation, Armonk, New York), with P < .05 considered significant. The mediation and moderation models were estimated using Mplus Version 7 (Muthén & Muthén, Los Angeles, California) with full information maximum likelihood in which the estimates are not biased by missing data.36 The normality assumption was tested and verified via histograms and Shapiro-Wilk tests before conducting the path analysis.37 Descriptive statistics (mean and standard deviation for continuous data and frequency and percentage for categorical data) were computed for demographic and clinical variables. Pearson correlations and linear regressions were used to identify relationships among the variables of interest. Path analysis with maximum likelihood was used to test for mediation among self-efficacy, patient activation, and SM behaviors. Mediation was assessed following the guidelines provided by Barron and Kenney,38 which require a direct effect to first be present, then an effect of the independent variable on the mediator and the mediator on the outcome. Finally, if mediation exists, the direct effect is no longer significant after accounting for the mediator. For moderation analysis, multiple linear regressions were used first to test the presence of moderating relationships, then the moderating effects were interpreted in the context of the path model by a median split of the sample based on the moderator. Although age was found to be related to self-efficacy, neither age nor gender were related to any of the outcome variables in these analyses, so no covariates were included in the models.
The final sample used for the analysis included 100 subjects (64 women and 36 men). Participant summary demographic and clinical characteristics are presented in Table 1. Participant mean (SD) age was 70.2 (12.21) years. Most subjects were white (95%), retired (71%), and women (64%) and had a mean (SD) of 12.9 (2.3) years of education. A total of 50% of them lived with someone. Clinically, most subjects’ cardiac functioning was classified at NYHA level II (49%) or III (42%) with preserved ejection fraction (55.7 ± 11.1). Most subjects were overweight or obese, with a mean (SD) body mass index of 32.3 (7.1) kg/m2. On average, subjects had 8 (±2.6) comorbidities, including hypertension (99%), coronary artery disease (94%), arthritis or degenerative joint disease (89%), and hypercholesterolemia (84%). Subjects reported taking a mean (SD) of 16.2 (8.8) pills per day. At baseline, the subjects in both intervention and control groups had low scores in self-efficacy, activation, and engaging SM (Table 1).
Results of the Mediation Analysis
Bivariate correlations showed significant relationships among the variables of interest. Among HF patients at 3 months postintervention, self-efficacy for SM was positively related to patient activation (r = 0.712, P < .001) and SM behaviors (r = 0.46, P < .001). Patient activation was significantly associated with SM behaviors (r = 0.528, P < .001) (Table 2). Path analysis showed that patient activation mediated the effect of self-efficacy on SM in HF patients discharged from the rural hospital. Significant mediation effects are represented in Figure. First, patients with greater self-efficacy were more likely to engage in SM behaviors (r = 0.46, P < .001). Second, self-efficacy for SM had a significant association with patient activation (β = .747, P < .001). Third, patient activation was significantly related to the SM behaviors (β = .48, P = .001). In the final step, self-efficacy was no longer directly related to SM behaviors when patient activation was entered into the model (β = .17, P = .248). These findings suggest that self-efficacy for SM led to changes in patient activation which, in turn, led to subsequent changes in SM behaviors at 3 months.
Results of Moderation Analysis
Bivariate correlations showed that the factors related to both baseline and 3-month patient activation scores were education level, physical functioning, SM knowledge, and support. Patients who had less disease severity and received the 12-week activation-enhancing intervention had significantly greater patient activation scores at 3 months. Gender and age were not associated with patient activation (Table 3). Moderation analysis showed that only SM knowledge and support had significant moderating effects on the relationships between self-efficacy, patient activation, and SM behaviors. Self-management knowledge significantly reduced the strength of the relationships between self-efficacy and SM behavior, as well as the relationship between patient activation and SM behavior (Table 4). The follow-up multiple group analysis showed a significant mediating effect of patient activation between self-efficacy and SM behaviors at low levels of SM knowledge (β = .59, P = .006). In patients with high levels of SM knowledge, patient activation did not mediate the effect of self-efficacy on SM behaviors (β = .15, P = .47). In patients with low levels of SM support, less confident patients were less likely to engage in SM behaviors (β = .55, P = .002). In HF patients with high levels of SM support, the impact of self-efficacy on SM behaviors was no longer significant (β = −.02, P = .928). Furthermore, when SM support was entered in the path model, patient activation was not a significant mediator between self-efficacy and SM behavior at high (β = .27, P = .27) or low (β = .27, p = 0.25) levels of SM support (Table 5).
Consistent with our previous findings on baseline data of the parent RCT,19 patient activation remained a mediator of the relationship between self-efficacy and SM behavior in rural HF patients at 3 months of intervention. We further tested several evidence-based moderators20–24 and found that SM knowledge and support were significant moderators of the relationship between self-efficacy, patient activation, and SM behaviors. In HF patients with low levels of SM knowledge, levels of self-efficacy had a positive impact on SM behavior through patient activation. However, in HF patients with high levels of SM knowledge, neither self-efficacy nor patient activation significantly accounted for SM behavioral changes (Figure 1). Studies of the relationship between knowledge, self-efficacy, and SM behaviors have produced inconsistent findings.9,10,39 A number of investigators have reported that improved knowledge or confidence did not necessarily lead to enhanced SM behaviors in HF patients,1,41 whereas others found a significant impact of confidence (self-efficacy) and knowledge on SM behaviors by HF patients.40 Our study results may explain the inconsistency between study findings. The varied level of SM knowledge in participants across different studies might impact the strength of relationship between self-efficacy and SM behaviors.
Similar to others’ findings,41,42 we also found an interaction between SM support and self-efficacy for SM. In patients with low SM support, confident patients (higher self-efficacy) were more likely to engage in SM behaviors than less confident patients were. On the other hand, if the patients perceived greater SM support, they were more likely to engage in SM behaviors, regardless of their levels of confidence (self-efficacy) and activation (Figure 1). Studies showed perceived SM support was significantly affected by patients’ relationships with their healthcare providers.43 A good patient-provider relationship contributed to positive SM support, improved engagement, and higher level of activation in SM behaviors.22,44 We previously reported that the participants in our intervention group had significant improvement in SM behaviors. However, the analysis showed that the mediating effect of patient activation did not contribute to the group difference in SM behavior at 3 months of intervention. We speculate that the improvement in SM behaviors in the intervention group may be the result of effective patient-provider relationships as they are known to (1) empower patients’ engagement in self-care; (2) promote close supervision and feedback related to SM behavior adherence; (3) promote positive feedback and encouragement; (4) boost the effectiveness of counseling and education; and (5) assist in developing effective coping strategies.22
Several limitations exist in this study. First, the use of convenience sampling affects the generalizability of the findings to other HF populations. Second, the small sample size might produce unstable results that need to be verified in a larger sample with a more diverse population before generalizing the results. Third, participant recruitment may have resulted in selection bias. The original study was an RCT aimed to improve SM adherence. It is possible that patients who enrolled in this study could be more confident and more actively engaged in SM behaviors than were patients who declined. However, the moderating and mediating relationships among variables of interest should be consistent in both enrolled and unenrolled patient groups. In addition, the study included more female subjects, which could also contribute to the selection bias. It has been reported that rural elderly women experienced greater social isolation and mental health issues compared to their urban counterparts,45 which may affect the study results. On the other hand, the intervention provided by the original RCT appeared to be feasible and effective to improve SM behaviors and address SM needs in this vulnerable and high-risk population. Lastly, the measures of SM knowledge and support were rudimentary and heterogeneous in content and, therefore, may not adequately capture the complexity of the constructs, which hinders our ability to pinpoint which subdomain of knowledge and support is affecting SM behaviors the most.
To our knowledge, this is the first study to examine both mediators and moderators of the relationship between self-efficacy and SM behaviors in HF patients.19 Findings help explain not only the interrelationships between patient activation and self-efficacy and their combined effect on SM behaviors but also the impact of between-individual variations (moderators) on the relationships between self-efficacy, patient activation, and SM behaviors. The study suggested that the mediating effect of patient activation on self-efficacy and SM behaviors was significant only in rural HF patients with lower levels of SM knowledge. Thus, SM intervention may need target patients with poor or inadequate SM knowledge. Furthermore, the interaction between SM support and self-efficacy suggested the importance to enhance SM support by improving patient-provider relationship when implementing SM interventions. Engagement of patients’ primary care team may plays a key role to sustain the intervention effect and improve HF patients’ SM behavior.
The study findings showed that patient activation mediated the effect of self-efficacy on SM behaviors in rural HF patients with low levels of SM knowledge. Perceived SM support played an important role in SM behaviors. Targeting the intervention to the subpopulation with low SM knowledge and poor support may enhance the effectiveness and efficiency of future interventions. However, before providing definitive recommendations, additional studies using larger and more diverse samples are needed.
What’s New and Important
- Patient activation mediated the effect of self-efficacy on SM behavior in HF patients with a low level of SM knowledge.
- Neither self-efficacy nor patient activation had a significant impact on SM behavior in patients who received high levels of SM support.
- SM support interventions should target high risk patients with HF with low SM knowledge and support.
1. Riegel B. State of the science: promoting self-care in persons with heart failure
: a scientific statement from the American Heart Association. Circulation
2. White MF, Kirschner J, Hamilton MA. Self-care guide for the heart failure
3. Hernandez AF, Greiner MA, Fonarow GC, et al. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure
4. van der Wal MH. Qualitative examination of compliance in heart failure
patients in The Netherlands. Heart Lung
5. Höllriegel R, Winzer EB, Linke A, et al. Long-term exercise training in patients with advanced chronic heart failure
: sustained benefits on left ventricular performance and exercise capacity. J Cardiopulm Rehabil Prev
6. Macabasco-OConnell A. Relationship between literacy, knowledge
, self-care behaviors, and heart failure
-related quality of life among patients with heart failure
. J Gen Intern Med
7. Dracup K, Moser DK, Pelter MM, et al. Randomized, controlled trial to improve self-care in patients with heart failure
living in rural areas. Circulation
8. Bandura A. Self-efficacy: The Exercise of Control
. New York: WH: Freeman and Company; 1997.
9. Bos-Touwen I, Jonkman N, Westland H, et al. Tailoring of self-management interventions in patients with heart failure
. Curr Heart Fail Rep
10. Peters-Klimm F, Freund T, Kunz CU, et al. Determinants of heart failure
self-care behaviour in community-based patients: a cross-sectional study. Eur J Cardiovasc Nurs
11. Schnell-Hoehn KN, Naimark BJ, Tate RB. Determinants of self-care behaviors in community-dwelling patients with heart failure
. J Cardiovasc Nurs
12. Yehle KS, Plake KS. Self-efficacy and educational interventions in heart failure
: a review of the literature. J Cardiovasc Nurs
13. Bos-Touwen I, Schuurmans M, Monninkhof EM, et al. Patient and disease characteristics associated with activation for self-management in patients with diabetes, chronic obstructive pulmonary disease, chronic heart failure
and chronic renal disease: a cross-sectional survey study. PloS One
14. Wolever RQ, Webber DM, Meunier JP, Greeson JM, Lausier ER, Gaudet TW. Modifiable disease risk, readiness to change, and psychosocial functioning improve with integrative medicine immersion model. Altern Ther Health Med
15. Cooper LA, Roter DL, Carson KA, et al. A randomized trial to improve patient-centered care and hypertension control in underserved primary care patients. J Gen Intern Med
16. Begum N, Donald M, Ozolins IZ, Dower J. Hospital admissions, emergency department utilisation and patient activation
for self-management among people with diabetes. Diabetes Res Clin Pract
17. Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff (Millwood)
18. Lubetkin EI, Lu WH, Gold MR. Levels and correlates of patient activation
in health center settings: building strategies for improving health outcomes. J Health Care Poor Underserved
19. Young L, Barnason S, Kupzyk K. Mechanism of engaging self-management behavior
in rural heart failure
patients. Appl Nurs Res
20. Smith SG, Pandit A, Rush SR, Wolf MS, Simon CJ. The role of patient activation
in preferences for shared decision making: results from a national survey of US adults. J Health Commun
22. Greene J, Hibbard JH, Alvarez C, Overton V. Supporting patient behavior change: approaches used by primary care clinicians whose patients have an increase in activation levels. Ann Fam Med
23. Hibbard JH, Greene J, Sacks R, Overton V, Parrotta CD. Adding a measure of patient self-management capability to risk assessment can improve prediction of high costs. Health Aff (Millwood)
24. Skolasky RL, Green AF, Scharfstein D, Boult C, Reider L, Wegener ST. Psychometric properties of the Patient Activation
Measure among multimorbid older adults. Health Serv Res
25. Young L, Barnason S, Do V. Promoting self-management through adherence among heart failure
patients discharged from rural hospitals: a study protocol. F1000Research
26. Borson S. The mini-cog: a cognitive vital signs measure for dementia screening in multi-lingual elderly. Int J Geriatr Psychiatry
27. Cohen J. A power primer. Psychol Bull
28. Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Designing Clinical Research
. Lippincott Williams & Wilkins; 2013.
29. Von Korff M, Gruman J, Schaefer J, Curry SJ, Wagner EH. Collaborative management of chronic illness. Ann Intern Med
30. Riegel B. An update on the self-care of heart failure
index. J Cardiovasc Nurs
31. Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the Patient Activation
Measure. Health Serv Res
. 2005;40(6 Pt 1):1918–1930.
32. Hibbard JH, Collins PA, Mahoney E, Baker LH. The development and testing of a measure assessing clinician beliefs about patient self-management. Health Expect
33. Artinian NT. Self-care behaviors among patients with heart failure
. Heart Lung
34. Wu JR. Testing the psychometric properties of the Medication Adherence Scale in patients with heart failure
. Heart Lung
35. Löfman I, Szummer K, Hagerman I, Dahlström U, Lund LH, Jernberg T. Prevalence and prognostic impact of kidney disease on heart failure
patients. Open Heart
36. Enders CK, Bandalos DL. The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Model
37. Kline RB. Principles and Practice of Structural Equation Modeling
. 2nd ed. New York: 2005.
38. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol
39. van Dijk-de Vries A, van Bokhoven MA, Winkens B, et al. Lessons learnt from a cluster-randomised trial evaluating the effectiveness of Self-Management Support (SMS) delivered by practice nurses in routine diabetes care. BMJ Open
40. Dickson VV, Buck H, Riegel B. Multiple comorbid conditions challenge heart failure
self-care by decreasing self-efficacy. Nurs Res
41. Luttik M, Jaarsma T, Stromberg A. The relation between motivational support of partners and self-care behavior in patients with chronic heart failure
. Eur Heart J
. 2013;34(suppl 1):P5133.
42. Dickson VV, McCarthy MM, Howe A, Schipper J, Katz SM. Sociocultural influences on heart failure
self-care among an ethnic minority black population. J Cardiovasc Nurs
43. Doyle C, Lennox L, Bell D. A systematic review of evidence on the links between patient experience and clinical safety and effectiveness. BMJ Open
44. Alexander JA, Hearld LR, Mittler JN, Harvey J. Patient-physician role relationships and patient activation
among individuals with chronic illness. Health Serv Res
. 2012;47(3 pt 1):1201–1223.
45. He C, Ye J. Lonely sunsets: impacts of rural-urban migration on the left-behind elderly in rural China. Popul Space Place
Keywords:Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved
heart failure; knowledge; patient activation; rural health; self-management behavior