575834
research-article2015
CNU0010.1177/1474515115575834European Journal of Cardiovascular NursingKessing et al.
EUROPEAN SOCIETY OF CARDIOLOGY ®
Original Article
Fatigue and self-care in patients with chronic heart failure
European Journal of Cardiovascular Nursing 1–8 © The European Society of Cardiology 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1474515115575834 cnu.sagepub.com
Dionne Kessing1, Johan Denollet1, Jos Widdershoven1,2 and Nina Kupper1
Abstract Background: Fatigue is a debilitating and highly prevalent symptom in patients with chronic heart failure (HF) possibly complicating HF self-care behaviour which is crucial for maintaining health. Aims: The purpose of this study was to examine whether general and exertion fatigue are distinctively associated with self-care in patients with chronic HF. Methods: In total, 545 outpatients with chronic HF (mean age=66.2 years; 75% male) completed measures of general fatigue (i.e. a sustained sense of exhaustion including mental efforts), exertion fatigue (i.e. fatigue directly related to physical activity), sleep problems, mood symptoms and HF self-care behaviour at baseline, 12-, and 18-month follow-up. Results: Linear mixed modelling results showed that general and exertion fatigue were significantly associated with poor HF self-care (estimate=0.10, p=0.004; estimate=0.06, p=0.01, respectively) and poor consulting behaviour (estimate=0.05, p=0.04; estimate=0.04, p=0.007, respectively) over time, independent of sleep and mood problems, and other clinical covariates. Exertion fatigue was associated with supplementary determinants. Conclusion: This is the first prospective study demonstrating that general and exertion fatigue were both associated with poor HF self-care, which could not be explained by sleep problems or mood symptoms, and was independent of clinical indicators of disease severity. Treatment of fatigue problems may improve HF self-care skills and ultimately quality of life and cardiovascular outcomes. Further research is needed to examine this potential causal relationship. Keywords Fatigue, compliance, self care, heart failure, sleep, depression Date received: 24 November 2014; revised: 9 February 2015; accepted: 11 February 2015
Introduction Fatigue is highly prevalent in patients with chronic heart failure (HF)1,2 and associated with increased re-hospitalization2 and mortality.3 Fatigue can be explained not only as a direct consequence of cardiac disorder,4 but also by other clinical factors such as aging,1 anaemia,5 poor sleep quality6,7 and psychosocial distress.1,7,8 In chronic HF, fatigue not only affects quality of life4,9–11 and the performance of daily activities12 but also may impede a patient’s ability to adhere to HF self-care13 and lifestyle recommendations to maintain good health.14 Related to fatigue, poor sleep has been related to impaired HF self-care through the effects of cognitive impairment.15 As fatigue has been associated with poor sleep,13,15 it may exert similar effects on self-care. Finally, fatigue has been associated with depressive symptoms, decreased exercise capacity, and dyspnoea;7 all factors assumed to complicate self-care.16
Regarding predictors of fatigue in HF patients, exertion fatigue (i.e. fatigue related to physical activity) was primarily predicted by physical characteristics, while general fatigue (i.e. a sustained sense of (mental) exhaustion) was predicted by physical and psychological characteristics.7
1Department
of Medical and Clinical Psychology, Tilburg University, the Netherlands 2Department of Cardiology, Elisabeth-TweeSteden Hospital, the Netherlands Corresponding author: Nina Kupper, Department of Medical and Clinical Psychology, Tilburg University, Center of Research on Psychology in Somatic diseases (CoRPS), Warandelaan 2, 5037 AB/PO Box 90153 5000 LE, Tilburg, the Netherlands. Email: [email protected]
Downloaded from cnu.sagepub.com at University of Otago Library on October 22, 2015
2
European Journal of Cardiovascular Nursing
General and exertion fatigue reflect qualitatively different manifestations of fatigue7 and are differentially related to HF prognosis17 and, thus, may be hypothesized to be differentially associated with self-care. Therefore, we examined fatigue and self-care in patients with chronic HF. Our main objective was to prospectively examine whether general and exertion fatigue were differentially associated with HF self-care at inclusion, 12- and 18-month follow-up. We also examined whether poor sleep,6,18 anxiety/depression7,8,18,19 or other clinical covariates did explain potential relationships between fatigue and self-care.
Methods Patients and procedure Outpatients with chronic HF were consecutively recruited between 2003–2008 from four Dutch hospitals (Amphia Hospital, Breda; St Elisabeth Hospital, Tilburg; TweeSteden Hospital, Tilburg; Zorgsaam Hospital, Terneuzen). Inclusion criteria were HF diagnosis with a reduced left ventricular ejection fraction (LVEF) ⩽40%, age ⩽80 years, and being stable on HF medication. Exclusion criteria were hospital admission one month prior to inclusion, other lifethreatening conditions, psychiatric comorbidity except for mood disorders, severe cognitive impairment, or insufficient Dutch linguistic competence. This is a secondary analysis of prospective data from two separate studies.20,21 Eligible patients were approached for participation by their cardiologist or heart failure nurse during an outpatient clinic visit. If willing to participate, patients were called by an independent investigator to schedule a baseline study appointment in which patients were given additional information about the study and signed informed consent. At baseline, 12- and 18-month follow-up, participants completed questionnaires at home to assess psychosocial variables and HF self-care that were returned by mail. Patients were contacted when either a questionnaire was not returned within two weeks or in the event of missing items. Data originated from two observational prospective studies for which ethics approval was obtained from the medical ethics committees of all hospitals. The investigation conforms with the principles outlined in the Declaration of Helsinki (2013).22
Fatigue At baseline, 12- and 18-month follow-up, general and exertion fatigue were assessed with the Fatigue Assessment Scale (FAS)23 and the Dutch Exertion Fatigue Scale (DEFS)24, respectively. The FAS contains 10 items that are rated on a five-point Likert scale (range from 1=‘never’ to 5=‘always’); examples of items are: ‘I get tired very quickly’, ‘I am bothered by fatigue’, and ‘Mentally, I feel exhausted’. The DEFS
contains nine items that are rated on a five-point Likert scale (range from 0=‘no’ to 4=‘yes’); e.g. ‘Is it fatiguing for you to walk for 30 minutes/to go upstairs and downstairs?’. Higher scores reflect higher levels of fatigue in both scales. In the present study, Cronbach’s α were 0.88 and 0.93, respectively, indicating good internal consistency.
Self-care HF self-care was assessed at all measurement occasions using the nine-item version of the European Heart Failure Self-care Behaviour scale (EHFScB-9).14,25 Items were rated on a five-point Likert scale ranging from 1 (‘I completely agree’) to 5 (‘I do not agree at all’). Higher scores reflect poor performance of HF self-care behaviours. The EHFScB-9 comprises a four-item ‘consulting behaviour’ subdimension20,26 (I contact my doctor or nurse if (i) shortness of breath increases, (ii) my legs/feet are more swollen, (iii) I gain weight, and (iv) I experience fatigue). Cronbach’s α were 0.80 and 0.86 for total self-care and the consulting behaviour subscale, respectively, indicating good internal consistency in the current dataset.
Demographic and clinical characteristics Socio-demographic variables were assessed at baseline using purpose-designed items including educational level (primary school or less vs >8 years of education), current smoking status, marital status (alone vs having a partner) and employment status (yes/no). From patients’ medical records, information was obtained on age, gender, disease characteristics (i.e. aetiology, LVEF, New York Heart Association (NYHA) functional class I–II vs III–IV), cardiac history (i.e. previous myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft surgery), co-morbidities (e.g. diabetes, chronic obstructive pulmonary disease (COPD)) and pharmaceutical treatment (e.g. beta-blockers, angiotensin-converting enzyme inhibitors, diuretics, psychotropic medication).
Sleep and mood problems At all time points, sleep problems were measured using the three-item sleep problem subscale of the Health Complaints Scale (HCS)27 which includes: (i) sleep that is restless and disturbed, (ii) trouble falling asleep, and (iii) feeling you can’t sleep. Items were rated on a five-point Likert scale (range from 0=‘not at all’ to 4=‘extremely’, and this scale showed high internal consistency (Cronbach’s α=0.88) in this study. Symptoms of anxiety/depression were assessed with the four-item Symptoms of Anxiety-Depression index (SAD4) that has been shown to detect an increased risk of depression and anxiety disorder in cardiac patients.28 SAD4 contains two anxiety (i.e. tension and restlessness)
Downloaded from cnu.sagepub.com at University of Otago Library on October 22, 2015
3
Kessing et al. and two depression (i.e. feeling blue and hopelessness) items. Items were answered on a five-point Likert scale (range from 0=‘not at all’ to 4=‘very much’), and the internal consistency was high in this study (Cronbach’s α=0.90).
Statistical analyses Baseline differences in socio-demographic and clinical variables were examined using χ2 tests (Fisher’s exact test when appropriate) for nominal variables and independent samples t-tests for continuous variables between responders vs non-responders and to examine the relationships of categorical baseline characteristics with general and exertion fatigue. Pearson correlations were calculated to explore the strength and direction of the relationships of sleep problems, symptoms of anxiety-depression, and selfcare with general and exertion fatigue at baseline. To assess the unique longitudinal relationship of general fatigue and exertion fatigue with total self-care and consulting behaviour, a sub-dimension of self-care, four separate generalized linear mixed modelling analyses were conducted using maximum likelihood estimation and an unstructured covariance matrix with a two-level structure (i.e. repeated measurement occasions (lower level), participant (higher level)).29 General and exertion fatigue were not examined simultaneously due to multicollinearity. In step 1, general fatigue was examined as a time-varying independent, continuous predictor of total self-care (i.e. all self-care behaviours including consulting behaviour) at baseline, 12-, and 18-month follow-up (Model 1). Hence, the linear mixed modelling results represent the association between general fatigue and total self-care across all time points. It was examined whether self-care scores changed significantly over time and whether there was a fatigue by time interaction effect. In step 2, additional baseline covariates (i.e. not varying over time) were added (Model 2). A priori selected covariates expected to influence fatigue and self-care were: age,1,7 gender,30 NYHA functional class III–IV, LVEF, diabetes mellitus, chronic obstructive pulmonary disease (COPD), kidney disease, partner and employment status, low educational level and body mass index (BMI).6,16 In step 3, we controlled for the potential influence of sleep and mood problems using continuous scores (Model 3). This stepwise procedure was repeated to examine the associations between exertion fatigue and self-care, and lastly, between both fatigue constructs and consulting behaviour (i.e. a subscale of the total self-care scale). Mixed modelling results were presented through estimates (E; i.e. regression coefficients of the dependent variable when the independent variable increases with one unit) with t- and p-values and 95% confidence intervals. It was explored whether the addition of variables in each step contributed significantly to explaining total self-care and consulting behaviour using model fit statistics (χ2 and −2 log
likelihood (–2LL)). In addition, we examined whether participants with complete data at follow-up vs participants with data at one or two time points differed with regard to any of the predictors that were examined in the generalized linear mixed models. A p-value
research-article2015
CNU0010.1177/1474515115575834European Journal of Cardiovascular NursingKessing et al.
EUROPEAN SOCIETY OF CARDIOLOGY ®
Original Article
Fatigue and self-care in patients with chronic heart failure
European Journal of Cardiovascular Nursing 1–8 © The European Society of Cardiology 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1474515115575834 cnu.sagepub.com
Dionne Kessing1, Johan Denollet1, Jos Widdershoven1,2 and Nina Kupper1
Abstract Background: Fatigue is a debilitating and highly prevalent symptom in patients with chronic heart failure (HF) possibly complicating HF self-care behaviour which is crucial for maintaining health. Aims: The purpose of this study was to examine whether general and exertion fatigue are distinctively associated with self-care in patients with chronic HF. Methods: In total, 545 outpatients with chronic HF (mean age=66.2 years; 75% male) completed measures of general fatigue (i.e. a sustained sense of exhaustion including mental efforts), exertion fatigue (i.e. fatigue directly related to physical activity), sleep problems, mood symptoms and HF self-care behaviour at baseline, 12-, and 18-month follow-up. Results: Linear mixed modelling results showed that general and exertion fatigue were significantly associated with poor HF self-care (estimate=0.10, p=0.004; estimate=0.06, p=0.01, respectively) and poor consulting behaviour (estimate=0.05, p=0.04; estimate=0.04, p=0.007, respectively) over time, independent of sleep and mood problems, and other clinical covariates. Exertion fatigue was associated with supplementary determinants. Conclusion: This is the first prospective study demonstrating that general and exertion fatigue were both associated with poor HF self-care, which could not be explained by sleep problems or mood symptoms, and was independent of clinical indicators of disease severity. Treatment of fatigue problems may improve HF self-care skills and ultimately quality of life and cardiovascular outcomes. Further research is needed to examine this potential causal relationship. Keywords Fatigue, compliance, self care, heart failure, sleep, depression Date received: 24 November 2014; revised: 9 February 2015; accepted: 11 February 2015
Introduction Fatigue is highly prevalent in patients with chronic heart failure (HF)1,2 and associated with increased re-hospitalization2 and mortality.3 Fatigue can be explained not only as a direct consequence of cardiac disorder,4 but also by other clinical factors such as aging,1 anaemia,5 poor sleep quality6,7 and psychosocial distress.1,7,8 In chronic HF, fatigue not only affects quality of life4,9–11 and the performance of daily activities12 but also may impede a patient’s ability to adhere to HF self-care13 and lifestyle recommendations to maintain good health.14 Related to fatigue, poor sleep has been related to impaired HF self-care through the effects of cognitive impairment.15 As fatigue has been associated with poor sleep,13,15 it may exert similar effects on self-care. Finally, fatigue has been associated with depressive symptoms, decreased exercise capacity, and dyspnoea;7 all factors assumed to complicate self-care.16
Regarding predictors of fatigue in HF patients, exertion fatigue (i.e. fatigue related to physical activity) was primarily predicted by physical characteristics, while general fatigue (i.e. a sustained sense of (mental) exhaustion) was predicted by physical and psychological characteristics.7
1Department
of Medical and Clinical Psychology, Tilburg University, the Netherlands 2Department of Cardiology, Elisabeth-TweeSteden Hospital, the Netherlands Corresponding author: Nina Kupper, Department of Medical and Clinical Psychology, Tilburg University, Center of Research on Psychology in Somatic diseases (CoRPS), Warandelaan 2, 5037 AB/PO Box 90153 5000 LE, Tilburg, the Netherlands. Email: [email protected]
Downloaded from cnu.sagepub.com at University of Otago Library on October 22, 2015
2
European Journal of Cardiovascular Nursing
General and exertion fatigue reflect qualitatively different manifestations of fatigue7 and are differentially related to HF prognosis17 and, thus, may be hypothesized to be differentially associated with self-care. Therefore, we examined fatigue and self-care in patients with chronic HF. Our main objective was to prospectively examine whether general and exertion fatigue were differentially associated with HF self-care at inclusion, 12- and 18-month follow-up. We also examined whether poor sleep,6,18 anxiety/depression7,8,18,19 or other clinical covariates did explain potential relationships between fatigue and self-care.
Methods Patients and procedure Outpatients with chronic HF were consecutively recruited between 2003–2008 from four Dutch hospitals (Amphia Hospital, Breda; St Elisabeth Hospital, Tilburg; TweeSteden Hospital, Tilburg; Zorgsaam Hospital, Terneuzen). Inclusion criteria were HF diagnosis with a reduced left ventricular ejection fraction (LVEF) ⩽40%, age ⩽80 years, and being stable on HF medication. Exclusion criteria were hospital admission one month prior to inclusion, other lifethreatening conditions, psychiatric comorbidity except for mood disorders, severe cognitive impairment, or insufficient Dutch linguistic competence. This is a secondary analysis of prospective data from two separate studies.20,21 Eligible patients were approached for participation by their cardiologist or heart failure nurse during an outpatient clinic visit. If willing to participate, patients were called by an independent investigator to schedule a baseline study appointment in which patients were given additional information about the study and signed informed consent. At baseline, 12- and 18-month follow-up, participants completed questionnaires at home to assess psychosocial variables and HF self-care that were returned by mail. Patients were contacted when either a questionnaire was not returned within two weeks or in the event of missing items. Data originated from two observational prospective studies for which ethics approval was obtained from the medical ethics committees of all hospitals. The investigation conforms with the principles outlined in the Declaration of Helsinki (2013).22
Fatigue At baseline, 12- and 18-month follow-up, general and exertion fatigue were assessed with the Fatigue Assessment Scale (FAS)23 and the Dutch Exertion Fatigue Scale (DEFS)24, respectively. The FAS contains 10 items that are rated on a five-point Likert scale (range from 1=‘never’ to 5=‘always’); examples of items are: ‘I get tired very quickly’, ‘I am bothered by fatigue’, and ‘Mentally, I feel exhausted’. The DEFS
contains nine items that are rated on a five-point Likert scale (range from 0=‘no’ to 4=‘yes’); e.g. ‘Is it fatiguing for you to walk for 30 minutes/to go upstairs and downstairs?’. Higher scores reflect higher levels of fatigue in both scales. In the present study, Cronbach’s α were 0.88 and 0.93, respectively, indicating good internal consistency.
Self-care HF self-care was assessed at all measurement occasions using the nine-item version of the European Heart Failure Self-care Behaviour scale (EHFScB-9).14,25 Items were rated on a five-point Likert scale ranging from 1 (‘I completely agree’) to 5 (‘I do not agree at all’). Higher scores reflect poor performance of HF self-care behaviours. The EHFScB-9 comprises a four-item ‘consulting behaviour’ subdimension20,26 (I contact my doctor or nurse if (i) shortness of breath increases, (ii) my legs/feet are more swollen, (iii) I gain weight, and (iv) I experience fatigue). Cronbach’s α were 0.80 and 0.86 for total self-care and the consulting behaviour subscale, respectively, indicating good internal consistency in the current dataset.
Demographic and clinical characteristics Socio-demographic variables were assessed at baseline using purpose-designed items including educational level (primary school or less vs >8 years of education), current smoking status, marital status (alone vs having a partner) and employment status (yes/no). From patients’ medical records, information was obtained on age, gender, disease characteristics (i.e. aetiology, LVEF, New York Heart Association (NYHA) functional class I–II vs III–IV), cardiac history (i.e. previous myocardial infarction, percutaneous coronary intervention or coronary artery bypass graft surgery), co-morbidities (e.g. diabetes, chronic obstructive pulmonary disease (COPD)) and pharmaceutical treatment (e.g. beta-blockers, angiotensin-converting enzyme inhibitors, diuretics, psychotropic medication).
Sleep and mood problems At all time points, sleep problems were measured using the three-item sleep problem subscale of the Health Complaints Scale (HCS)27 which includes: (i) sleep that is restless and disturbed, (ii) trouble falling asleep, and (iii) feeling you can’t sleep. Items were rated on a five-point Likert scale (range from 0=‘not at all’ to 4=‘extremely’, and this scale showed high internal consistency (Cronbach’s α=0.88) in this study. Symptoms of anxiety/depression were assessed with the four-item Symptoms of Anxiety-Depression index (SAD4) that has been shown to detect an increased risk of depression and anxiety disorder in cardiac patients.28 SAD4 contains two anxiety (i.e. tension and restlessness)
Downloaded from cnu.sagepub.com at University of Otago Library on October 22, 2015
3
Kessing et al. and two depression (i.e. feeling blue and hopelessness) items. Items were answered on a five-point Likert scale (range from 0=‘not at all’ to 4=‘very much’), and the internal consistency was high in this study (Cronbach’s α=0.90).
Statistical analyses Baseline differences in socio-demographic and clinical variables were examined using χ2 tests (Fisher’s exact test when appropriate) for nominal variables and independent samples t-tests for continuous variables between responders vs non-responders and to examine the relationships of categorical baseline characteristics with general and exertion fatigue. Pearson correlations were calculated to explore the strength and direction of the relationships of sleep problems, symptoms of anxiety-depression, and selfcare with general and exertion fatigue at baseline. To assess the unique longitudinal relationship of general fatigue and exertion fatigue with total self-care and consulting behaviour, a sub-dimension of self-care, four separate generalized linear mixed modelling analyses were conducted using maximum likelihood estimation and an unstructured covariance matrix with a two-level structure (i.e. repeated measurement occasions (lower level), participant (higher level)).29 General and exertion fatigue were not examined simultaneously due to multicollinearity. In step 1, general fatigue was examined as a time-varying independent, continuous predictor of total self-care (i.e. all self-care behaviours including consulting behaviour) at baseline, 12-, and 18-month follow-up (Model 1). Hence, the linear mixed modelling results represent the association between general fatigue and total self-care across all time points. It was examined whether self-care scores changed significantly over time and whether there was a fatigue by time interaction effect. In step 2, additional baseline covariates (i.e. not varying over time) were added (Model 2). A priori selected covariates expected to influence fatigue and self-care were: age,1,7 gender,30 NYHA functional class III–IV, LVEF, diabetes mellitus, chronic obstructive pulmonary disease (COPD), kidney disease, partner and employment status, low educational level and body mass index (BMI).6,16 In step 3, we controlled for the potential influence of sleep and mood problems using continuous scores (Model 3). This stepwise procedure was repeated to examine the associations between exertion fatigue and self-care, and lastly, between both fatigue constructs and consulting behaviour (i.e. a subscale of the total self-care scale). Mixed modelling results were presented through estimates (E; i.e. regression coefficients of the dependent variable when the independent variable increases with one unit) with t- and p-values and 95% confidence intervals. It was explored whether the addition of variables in each step contributed significantly to explaining total self-care and consulting behaviour using model fit statistics (χ2 and −2 log
likelihood (–2LL)). In addition, we examined whether participants with complete data at follow-up vs participants with data at one or two time points differed with regard to any of the predictors that were examined in the generalized linear mixed models. A p-value
