Even when optimally treated by clinicians with evidence-based therapies, patients with heart failure (HF) and their care partners are responsible for the vast majority of HF care.1 Most research on HF care, however, has focused on either individuals performing self-care behaviors1 or, more recently, care partner contributions to self-care.2 As such, we have limited information on the interdependent nature of illness management and various ways in which patient–care partner dyads actually work together to manage HF.3 Without further information on how patient–care partner dyads work together as a team to manage HF with varying degrees of success, our clinical recommendations and interventions assume that all dyads are the same.
According to the new Theory of Dyadic Illness Management, the management of conditions such as HF is a dyadic phenomenon.4 Importantly, the theory proposes that there is a spectrum of collaboration in how dyads work together to manage illness. On one end of the spectrum, 1 member of the dyad is independent in illness management (autonomous care), and on the other end of the spectrum, illness management is a collaborative endeavor between both members of the dyad (collaborative care).4 There is evidence that HF dyads that take a collaborative approach to HF care may have better dyadic outcomes.5 There also is evidence from qualitative research of distinct types of HF dyads that focused largely on the degree of collaboration.6 Distinct archetypes of dyadic management also have been identified in a quantitative analysis of Italian patient–care partner dyads that centered on the degree of collaboration within HF dyads.7 Finally, several factors have been theorized and/or shown to influence how dyads manage illness, including demographics such as age and gender4; specific illness contexts such as metrics of severity including symptoms,8 both functional class and comorbid conditions,9 and affective symptoms10; patient concealment (ie, hiding concerns)11; care-related strain7,9; and relational factors such as relationship quality.7
Building on this important previous research, the primary aims of this article were to (a) identify and (b) compare distinct patterns of dyadic illness management in HF with the patient–care partner dyad as the unit of analysis. This study represents the first attempt to quantitatively identify patterns of dyadic illness management in an English-speaking sample of HF dyads. Moreover, a secondary aim of this study was to provide insight into differences between direct engagement of the care partner in illness management behaviors versus recommending as a management strategy.
This was a secondary analysis of cross-sectional data on HF dyads collected during a descriptive study of 62 community-dwelling adults living with HF and their spousal/partnered care partners.8 In brief, patient participants were eligible if they were willing and able to provide written informed consent, were 21 years or older, had a formal diagnosis of HF (verified by a treating HF cardiologist), were able to read and understand fifth-grade English, had current symptoms of HF (technically New York Heart Association [NYHA] class II–IV HF), and were living with their primary informal care partner who also was willing to participate. Care partners were spouses/partners and had to be 21 years or older, living with the patient for a minimum of 1 year, and also willing and able to provide written informed consent. Recruitment from outpatient HF clinics of an academic medical center in the Pacific Northwest of the United States was completed between June and December of 2015. Patients with HF and their informal care partners completed surveys separately at home or by preference over the phone.
Heart Failure Management
Patterns of dyadic illness management in this study were a function of patient self-care and care partner contributions to care. Patient-level HF management was measured using the Self-Care of HF Index v6.2.12 The Self-Care of HF Index has 6 items that capture self-care management (the primary focus of this study) and 10 items that measure self-care maintenance. Scores were calculated for each scale (range, 0–100), with higher scores indicating better self-care. “Consulting behaviors” were captured by 4 items on the European HF Self-care Behavior Scale.13 Each item addresses the likelihood of contacting a healthcare provider in response to symptoms; the subscale ranges from 4 to 20, with lower scores indicating better consulting behaviors.13 Care partner contributions to HF care were measured using the Caregiver Contribution to Self-Care of HF Index,14 the parallel scale to the SCHFI, and the care partner version of the European HF Self-care Behavior Scale consulting behaviors, the parallel scale to the European HF Self-care Behavior Scale. Validity and reliability of the Caregiver Contribution to Self-Care of Heart Failure Index and care partner version of the EHFScB-9 have been shown by others.14 The Caregiver Contribution to Self-Care of Heart Failure Index is typically presented in a way that care partners indicate that they either do the behavior for the patient or recommend the behavior to the patient without indicating which method they use. For this study, we specifically asked care partners to indicate for each Caregiver Contribution to Self-Care of Heart Failure Index item whether they did the behavior for the patient or recommended that the patient do it themselves.
Although the patterns identified in this study were a function of dyadic illness management, other ways in which the dyads were different (ie, demographics, clinical characteristics, relationship characteristics) were of interest for comparisons. Demographic information (eg, age, gender) was collected via survey, and clinical data (eg, years of HF, NYHA functional class, ejection fraction) were extracted via a review of patient medical records. Patient anxiety was measured using the Brief Symptom Inventory.15 The Brief Symptom Inventory asks about feelings during the past 7 days and provides 5 response options ranging from 0 (not at all) to 4 (extremely). Subscale scores (ranging from 0 to 4) were calculated, with higher scores indicating higher anxiety. Patient and care partner depressive symptoms were measured using the 9-item Patient Health Questionnaire.16 The 9-item Patient Health Questionnaire scores each of the 9 related Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria providing 4 response options ranging from 0 (not at all) to 3 (nearly every day). Higher scores indicate more depressive symptoms. Patient concealment (ie, protective buffering or hiding fears and worries) was measured using the Emotional-Intimacy Disruptive Behavior Scale.17 Patients report the extent to which they engage in 8 behaviors using a scale from 1 (none of the time) to 4 (most or all of the time); high scores indicate greater concealment. Patient and care partner comorbid conditions were measured using the Charlson Index.18 The patient's and care partner's perceptions of the quality of their relationship were measured with the Archbold Mutuality Scale.19 Respondents are asked to rate 15 items on reciprocity, love, shared pleasurable activities, and shared values from 0 (not at all) to 4 (a great deal), and response options are averaged (α = .91). The Archbold Mutuality Scale has been validated in both patients with HF and their care partners.20 The Multidimensional Caregiver Strain Index21 was used to capture self-reported physical, social, and interpersonal strain, as well as time constraints and care receiver demands.21
Descriptive statistics were used to describe the sample. Dyadic incongruence models were generated for each measure of HF management (ie, management and consulting behaviors reported by both patients and their care partners). Empirical Bayes estimates of the dyadic average and incongruence were generated in separate dyad models for each behavior and corrected for measurement error.22 The product of this step is information on how engaged the members of the dyad are in illness management (ie, dyadic average) and on the magnitude of difference in behaviors between members of the dyad (ie, incongruence). Latent class mixture modeling (LCMM) was then used to identify distinct patterns of dyadic illness management based on empirical Bayes estimates of both dyadic average and dyadic incongruence. Our approach to LCMM is based on common model selection criteria including model entropy, classification probabilities, and class size as well as the significance of the Lo-Mendell-Rubin likelihood ratio test and parametric bootstrap likelihood ratio test.23 Comparative statistics (t tests and χ2) were used to make comparisons between observed patterns on measures of illness management and also other demographic, clinical, and relational characteristics thought to be relevant based on previous research and the Theory of Dyadic Illness Management (as cited in the Introduction). Finally, the proportion of care partners engaging in behaviors for the patient versus recommending that the patients engaged in a behavior was calculated at the level of the behaviors (across all possible questions) and the care partner to help characterize dyadic illness management. Dyadic incongruence modeling was performed using HLM v7 (Skokie, Illinois), LCMM was performed using Mplus v8 (Los Angeles, California), and all other analyses were performed using Stata MP v16 (College Station, Texas). With 62 dyads, an α of .05, a power of 0.80, and unequal group sizes (up to 50% difference between group sizes), small differences in dyadic incongruence between groups (as low as 1.5 on management and consulting behavior scores) would be statistically significant.
The mean age of the 62 patients and their 62 care partners was 59.7 ± 11.8 and 58.1 ± 11.9 years, respectively. A majority of patients (71.0%) had NYHA class III/IV HF, and a majority of the couples (95.2%) were married.
Patterns of Dyadic Illness Management
Two patterns of HF dyadic illness management were observed and are presented in the Table and Figure: model entropy, 0.942; classification probabilities, 0.972 and 0.993; the Lo-Mendell-Rubin likelihood ratio test = 44.48, P = .038; and parametric bootstrap likelihood ratio test, P < .0001—higher numbers of patterns resulted in worse model fit and insufficient numbers of dyads in each pattern. On the basis of levels of incongruence in HF management observed between patterns, one (n = 42, 67.7%) was labeled as “collaborative dyadic illness management” (ie, congruence in engagement within dyads), and the other (n = 20, 32.3%) was labeled as “autonomous illness management” (ie, incongruence within dyads with patients more independent in HF care). Although not specifically modeled as an indicator of dyadic illness management, there also were differences in dyadic maintenance between the 2 dyadic illness management patterns.
Comparisons Between Dyadic Illness Management Patterns
The 2 patterns of HF dyadic illness management also differed in several other ways (Table). Patients in the autonomous illness management pattern reported worse anxiety and more depressive symptoms compared with patients in collaborative illness management dyads. More dyads in the autonomous pattern of illness management were composed of female patients and their male care partners compared with collaborative dyads. Collaborative dyads were therefore primarily composed of male patients and female care partners. Finally, relationship quality (as reported by patients but not care partners) was worse in the autonomous pattern of illness management compared with collaborative dyads. Importantly, dyads with the collaborative and autonomous styles of illness management did not differ by age, metrics of HF severity or comorbidity, patient concealment, or care-related strain.
Doing vs Recommending
The vast majority of care partners engaged in recommending management and maintenance behaviors rather than directly engaging in these behaviors on behalf of patients. In fact, only 9 care partners (6.9%) reported doing at least 1 behavior (range, 1–4 out of 14 possible behaviors) for patients with HF directly, whereas the other 93.1% reported recommending behaviors as their only engagement in dyadic illness management. There also was no difference in the number of care partners who actually engaged in HF management behaviors as opposed to those who used recommending as the single HF management strategy between the 2 dyadic illness management patterns.
In this US sample of 62 patients with HF and their family care partners, we observed 2 distinct patterns of dyadic illness management (ie, the ways in which they work together to manage HF). The first pattern was characterized by collaborative dyadic illness management with similar contributions to care within dyads, and the second pattern was characterized by autonomous illness management with patients being more independent in HF care within dyads. We also observed that autonomous illness management dyads were more typically composed of female patients and their male care partners and also that patients in autonomous illness management dyads also had worse anxiety, depression, and relationship quality compared with patients in collaborative illness management dyads. Finally, although we observed that the vast majority of care partner involvement in HF care involved recommending as the main strategy and not direct engagement in behaviors, there were no significant differences between dyad illness management patterns in recommending versus direct engagement in HF management.
In their qualitative research, Buck and colleagues6 identified 4 relationally oriented or individually oriented archetypes of HF dyads. The pattern we identified as being collaborative was most closely matched with Buck et al's collaborative dyads, and the pattern we identified as being autonomous in dyadic illness management was most closely matching Buck et al's patient-oriented dyads. Lee and colleagues7 previously identified 3 archetypes of HF dyads. Our collaborative pattern of dyadic illness management was most closely matched with Lee et al's expert and collaborative dyads, and our autonomous dyads were most closely matched with Lee et al's novice and complementary dyads. Our findings build upon this important previous work by identifying that dyadic patterns of illness management also differ considerably by gender and by patient-rated relationship quality and affective symptoms.
Engendered roles may help explain our key findings. Specifically, the greatest difference between autonomous (predominantly female patients/male care partners) versus collaborative (predominantly male patients/female care partners) illness management dyads was not patient self-care but instead was how engaged care partners were in the management of HF. It is well known that the role of women in relationships is frequently aligned with the care of others over the care of oneself,24 so it is not surprising that illness management dyads in which a female is the care partner and is more engaged in illness management are more collaborative in nature. It also is known that women may have difficulty getting the support they need to help them cope with HF. Women report less family involvement and less psychosocial support with HF care compared with men.25 Moreover, when women receive emotional support from their male care partners (such as the preponderance of recommending observed in this study), both members of the dyad feel closer, but women oftentimes feel more distressed.26 In other illness contexts, male care partners struggle to understand how to help their female counterparts in the dyad and often resort to guessing.27 A similar phenomenon may have contributed to the development of the autonomous illness management pattern in this study. Women with HF may not be getting the support they need from male care partners, leaving the female patients to manage the condition independently. Although patients in autonomous dyads did not conceal more than patients in autonomous dyads in this study, it may also be that they are not effectively communicating what it is they need for help from their care partners.
Differences in affective symptoms and relationship quality between patients in autonomous versus collaborative illness management dyads also may be a function of gender. American women commonly hold an interdependent view of themselves wherein their partners are considered part of the self,28 which may help explain why there is more collaboration in illness management when care partners are women. Similarly, worse patient-reported affective symptoms and relationship quality in autonomous dyads may reflect that women are more relational in how they want to manage HF with their care partners and/or that they may be more emotionally impacted by the quality of their relationship, as has been shown by others.29 It is impossible to know from this research which came first, the rating of poor relationship quality or worse affective symptoms. Nonetheless, there is room for improvement in the narrative of women living with HF.
There are important limitations to our work that should be considered when interpreting the results. First, and like most studies of HF dyads, our research involved a relatively small sample that may not be representative across all important subpopulations of patients with HF and their care partners. Although not by design, in our study, we included only heterosexual couples; therefore, our findings may not be generalizable across all relationship types. Finally, and like most studies of HF dyads, these data were cross-sectional; hence, how the trajectory of illness and changes in relationships over time play out in this research is difficult to speculate.
To overcome the limitations of this work and the previous research upon which our findings build, focusing on what HF dyads need to be more collaborative over time and working within relational aspects of care that, in many instances, preceded HF would help move forward the science of HF dyads. In particular, our theoretical framework conceptualizes dyadic management as more than only the management of the patient's illness; the specific needs of the care partner are also an important component of dyadic collaboration not to be overlooked. Hence, having a greater focus on incorporating the needs of both dyad members and in both research and clinical practice and facilitating and supporting the interdependent, relational strength of the family care dyad will move us closer to optimizing dyadic health in HF.
Things that can be done in clinical practice to help dyads focus on becoming more collaborative in illness management over time are as follows: (a) use 3-way communication that is inclusive of both members of the dyad, (b) encourage teamwork, and (c) encourage both members of the dyad to communicate frequently about their needs.30 Importantly, women with HF may be particularly at risk for illness management that is not collaborative. Hence, we cannot continue to treat women the same as men in clinical practice.
We observed 2 distinct patterns of dyadic illness management that reflect different degrees of collaboration and also that differed significantly by gender, relationship quality, and affective symptoms. Gender roles and uncertainty about how to be helpful may have contributed to the different illness management patterns that were observed. Clearly, not all dyads work together in the same way to manage HF. Understanding the dynamics involved in illness management dyads may help researchers develop interventions that improve the effectiveness of illness management dyads.
1. 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
2. Buck HG, Harkness K, Wion R, et al. Caregivers
' contributions to heart failure
self-care: a systematic review. Eur J Cardiovasc Nurs
3. Buck HG, Zambroski CH, Hupcey JE. Assessing the response of patient and spousal/partner caregivers
to a new instrument measuring dyadic heart failure
care types. Eur J Cardiovasc Nurs
4. Lyons KS, Lee CS. The theory of dyadic illness management
. J Fam Nurs
5. Retrum JH, Nowels CT, Bekelman DB. Patient and caregiver congruence: the importance of dyads
in heart failure
care. Journal of Cardiovascular Nursing
6. Buck HG, Kitko L, Hupcey JE. Dyadic heart failure
care types: qualitative evidence for a novel typology. J Cardiovasc Nurs
7. Lee CS, Vellone E, Lyons KS, et al. Patterns and predictors of patient and caregiver engagement in heart failure
care: a multi-level dyadic study. Int J Nurs Stud
8. Lee CS, Mudd JO, Auld J, et al. Patterns, relevance and predictors of heart failure
dyadic symptom appraisal. Eur J Cardiovasc Nurs
9. Bidwell JT, Vellone E, Lyons KS, et al. Determinants of heart failure
self-care maintenance and management in patients and caregivers
: a dyadic analysis. Res Nurs Health
10. Lyons KS, Hiatt SO, Gelow JM, et al. Depressive symptoms in couples living with heart failure
: the role of congruent engagement in heart failure
management. Aging Ment Health
11. Lyons KS, Sadowski T, Lee CS. The role of concealment and relationship quality on patient hospitalizations, care strain and depressive symptoms in heart failure dyads
. Eur J Cardiovasc Nurs
12. Riegel B, Lee CS, Dickson VV, Carlson B. An update on the self-care of heart failure
index. J Cardiovasc Nurs
13. Jaarsma T, Arestedt KF, Martensson J, Dracup K, Stromberg A. The European Heart Failure
Self-Care Behaviour scale revised into a nine-item scale (EHFScB-9): a reliable and valid international instrument. Eur J Heart Fail
14. Vellone E, Riegel B, Cocchieri A, et al. Validity and reliability of the caregiver contribution to self-care of heart failure
index. J Cardiovasc Nurs
15. Derogatis LR, Melisaratos N. The Brief Symptom Inventory: an introductory report. Psychol Med
16. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med
17. Druley JA, Stephens MAP, Coyne JC. Emotional and physical intimacy in coping with lupus: women's dilemmas of disclosure and approach. Health Psychol
18. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis
19. Archbold PG, Stewart BJ, Greenlick MR, Harvath T. Mutuality and preparedness as predictors of caregiver role strain. Res Nurs Health
20. Dellafiore F, Buck HG, Pucciarelli G, et al. Psychometric characteristics of the mutuality scale in heart failure
patients and caregivers
. Heart Lung
21. Stull DE. The multidimensional caregiver index (MCSI): its measurement and structure. J Clin Geropsychol
22. Sayer AG, Klute MM. Analyzing couples and families: multilevel methods. In: Bengtson VL, Acock AC, Allen KR, Dilworth-Anderson P, Klein DM, eds. Sourcebook of Family Theory & Research
. Thousand Oaks, CA: Sage Publications, Inc; 2005:289–313.
23. Ram N, Grimm KJ. Methods and measures: growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev
24. Digiacomo M, Davidson PM, Zecchin R, Lamb K, Daly J. Caring for others, but not themselves: implications for health care interventions in women with cardiovascular disease. Nurs Res Pract
25. Thomas JR, Clark AM. Women with heart failure
are at high psychosocial risk: a systematic review of how sex and gender
influence heart failure
self-care. Cardiol Res Pract
26. Monin JK, Manigault A, Levy BR, et al. Gender
differences in short-term cardiovascular effects of giving and receiving support for health concerns in marriage. Health Psychol
27. Zahlis EH, Lewis FM. Coming to grips with breast cancer: the spouse's experience with his wife's first six months. J Psychosoc Oncol
28. Cross SE, Hardin EE, Gercek-Swing B. The what, how, why, and where of self-construal. Pers Soc Psychol Rev
29. Beam CR, Marcus K, Turkheimer E, Emery RE. Gender
differences in the structure of marital quality. Behav Genet
30. Northouse LL. Helping patients and their family caregivers
cope with cancer. Oncol Nurs Forum