Self-management in heart failure: where have we been and where should we go?

Figures & data

Table 1 Selected studies reporting on characteristics that influence self-management in HF patients

Reference	Study design	Results	Comments
Artinian^Citation39	Descriptive correlation study to examine relationship between select characteristics (personal/environmental factors) and self-management behaviors in convenience sample of 110 HF patients.	No significant relationship between select characteristics and the specific HF measure (SCHFI). Trends observed: older age more adherent with taking medications, keeping appointments, and getting flu shot. Younger patients less likely to discuss worsening symptoms. When living alone, less likely to seek help when short of breath or contact physician when symptomatic, and more likely to report being physically active. Low income patients living alone: more likely to eat canned and packaged foods. If perceived poor health, more apt to rest, limit activities, ask for help, and contact their doctor regarding symptoms. Three of top 5 most frequently performed HF behaviors related to taking prescribed medications; 5 least frequently performed behaviors concerned with symptom management/monitoring.	CDSM in HF is varied set of behaviors influenced by diverse factors. Understanding influential effects of characteristic performance of HF behaviors is first step to tailoring interventions to patients’ situation.
Cameron^Citation19	Descriptive correlation study to test conceptual model of determinants of HF self-management; convenience sample of 50 consecutive patients hospitalized with HF in Australia from June to December 2005.	Four variables contributed significantly to variance in HF self-management (self-reported behaviors): 1) gender, 2) moderate to severe comorbidity, 3) depressions, and 4) self-care confidence. Results of study illustrate characteristics associated with low levels of HF self-management in an Australian population.	Further research to investigate the interplay between nonmodifiable characteristics and the application of CDSM selectively directed to those in greatest need of improvement in self-management.
Chriss^Citation37	Non-experimental, correlational, test–retest study (baseline and 3 months) from a sample of 66 chronic HF patients; replicating a model using 7 variables: social support, symptom severity, comorbidity, education, age, gender, and income; in this analysis, the last variable income was not included due to problems with missing data.	Significant predictors of self-management were higher age and male gender. Elderly men and those with fewer comorbid illnesses were most successful with HF self-management.	Extra instructional support/follow-up for elderly female patients with HF and those with several comorbid conditions needed. Further research needed to identify why predictors of HF self-management change over time.
Moser^Citation33	Descriptive study to examine multiple risk factors for rehospitalization in recently discharged HF patients; convenience sample of 202 participants in RCT of a home-based HF disease management intervention. Data reported were collected at baseline prior to interventions.	Results: 70% NYHA III/IV; 80% >1 comorbidity, 48% >2; large percentage (88% NYHA III and 52% NYHA IV) lived alone; high level anxiety (50%) and depression (69%); substantially impaired HRQoL and symptom burden; poor (<31%) symptom recognition, poor adherence with recommended self-management strategies (14% got weights – 9% monitored symptoms for worsening HF). Of the entire sample, 23% had all of the following risk factors: NYHA III/IV lived alone, 1 comorbidity, and depression/anxiety.	HF patients may be deemed medically stable for hospital discharge but exhibit psychosocial and behavioral risk factors that make them vulnerable for rehospitalization. Need to develop strategy to assess risk factors and provide early care to HF patients after discharge to improve chronic care and CDSM support.
Richardson^Citation43	Review of psychological variables (depression, social support, and adherence) in patients with HF.	Summarizes issues that surround depression and social support in HF patients: many reported cases but many not optimally treated; potential for higher rates of readmission in HF patients with depression or lack of social support; NP in optimal position to evaluate psychosocial and adherence issues in HF patients; need for additional studies to assess.	Conclusion that negative effects of depression and lack of support on clinical outcomes in HF patients can be noteworthy and need further research.
Riegel^Citation40	Qualitative study using a convenience sample of 26 patients with chronic HF; structured interviews to examine how HF influences daily life; assess how patients perform self-care; and determined how patients’ life situations facilitate or impede HF self-care.	Physical limitations, debilitating symptoms, difficulties coping with treatment, lack of knowledge, distressed emotions, multiple comorbidities and personal struggles were common findings.	Assess motivation, acknowledge success, and apply a stepped approach to CDSM. Helping patients identify barriers may facilitate self-management.
Riegel^Citation41	Extreme case sampling using mixed-methods design in 29 chronic HF patients; to describe and understand how expertise in HF self-care develops.	Ten percent of sample were expert in HF self-care. Poor HF self-care associated with worse cognition, more sleepiness, depression, and worse family functioning. Factors distinguishing good vs expert self-care were sleepiness and family engagement.	Engaged supporters can foster self-care skill development in patients with HF. More research needed to understand effects of daytime sleepiness on HF self-care.
Rockwell^Citation38	Descriptive correlation study, replicating model of 7 variables derived from MSCCI^Citation36 as predictors of HF self-care in convenience sample of 209 adults (≥18) participating in community CDSM program.	Typical participant: age 73; Class III NYHA; married; grade-school education; earning income <$20,000 per year; equal gender. Educational level and symptom severity were identified as predictors of self-management, explaining 10.3% of the variance.	Better educated persons may be more likely to engage in self-management than those who are poorly educated. Patients with more severe symptoms are more likely to be knowledgeable about importance of HF symptoms.
Sayers^Citation44	Descriptive study to examine the effects of social support in a convenience sample of 74 participants with HF.	Self-management was poor as measured across several self-care domains. Age associated with increased levels of perceived social support, suggesting that those involved with aging HF patients respond to their need for greater support. Perceived support moderately associated with better self-reported medication and dietary adherence and other aspects of self-management (ie, daily weighing).	Higher levels of self-management are important correlates of social support and may explain how social support influences HF outcomes. Support can influence clinical care for HF patients focusing on improving self-management.
Zambroski^Citation31	Descriptive, cross-sectional study in convenience sample of 53 patients (>40) with mild NYHA II–IV HF.	HF patients experience a mean of 15.1 ± 8.0 symptoms. Shortness of breath and lack of energy were most prevalent; difficulty sleeping most burdensome symptom; lower age, worse functional status, total symptom prevalence, and total symptom burden predicted 67% of variance in HRQoL.	Patients with HF experience a high level of symptom burden.

Abbreviations: HF, heart failure; CDSM, chronic disease self-management; NYHA, New York Heart Association; HRQoL, health related quality of life.

Table 2 Selected studies reporting on predictive relationships between activation using the Patient Activation Measure (PAM) and health outcomes in chronic care

Reference	Study design	Results	Comments
Hibbard^Citation45	Convergence of findings from national expert consensus panel and patient focus groups to define concept/id domains of activation. Operationalize construct using large item pool, pilot testing, and initial psychometric analysis using Rasch methodology. National probability sample (N = 1515) with or without chronic conditions.	PAM is a valid, highly reliable, unidimensional, probabilistic Guttman-like scale that reflects a developmental model of activation. Original research to delineate the process used to develop a measure for assessing activation and the psychometric properties of that measure. Four stages: 1) Belief that patient role is important. 2) Have the confidence and knowledge to take action. 3) Able to take action to maintain and improve health. 4) Able to stay the course under stress.	Good psychometric properties indicating PAM measure can be used at individual patient level to tailor interventions and to assess changes.
Hibbard^Citation50	Reduce the number of items in the 22-item PAM while maintaining adequate precision.	Analysis used the same data collected in the 2003 via a telephone survey of 1515 randomly selected adults. A 13-item survey scale with psychometric properties similar to original 22-item version. Scores for 13-item measure range in value from 38.6 to 53.0 (on a theoretical 0- to 100-point scale). Range of values unchanged from original 22-item version.	Results of analysis indicate that shortened 13-item version is reliable and valid.
Hibbard^Citation51	RCT with 479 chronic disease patients. Patients randomized either intervention (Lorig’s CDSM program vs usual care). Survey data collected at baseline, 6 weeks, and 6 months.	Significant time effect reported. Activation increased over time for both groups; intervention group had significantly higher scores at 6 weeks but not at 6 months. Positive change in activation is related to positive change in a variety of self-management behaviors. This is true even when behavior in question was not being performed at baseline; increase in activation is related to maintaining higher level of behavior over time. Impact of intervention, however, was less clear, as increase in activation in intervention group was matched by nearly equal increases in control group. Participants who were depressed were less likely to increase activation or improve self-management behaviors.	Results suggest that if activation is increased, improved behaviors will follow. Question remains: what interventions will improve activation?
Mosen^Citation52	A 2004 cross-sectional survey of Kaiser Permanente (KP) medical care program N = 4108. Members with 1 of 6 chronic conditions, including HF, were included. The sample was selected from 7 of KP’s 8 regions.	N = 4108 (61.2%) response rate. Used the 22-item PAM and other instruments/measures. Activation independently associated with likelihood of performing more self-management behaviors, using more services, and reporting higher medication adherence. First study to find independent association between activation and health-related outcome measures; correlation between higher PAM scores and report of higher satisfaction, higher QOL, and higher physical and mental functional status scores, compared to those with lower PAM scores.	Further research is needed to examine the association of PAM with prospective changes in disease specific QOL and utilization measures; impact of incremental changes in PAM scores on key outcomes.
Hibbard^Citation49	Cross-sectional, survey; 843 (61% – relative risk) adults 25–75 yrs; used PAM scores to predict positive and negative emotions; examine relationship between emotion and activation level.	Activation level related to average number of positive and negative emotions: level 1, experience almost equal amounts of +/− emotions; higher activation > greater number of + emotions; activation score is significant determinant of emotions, even after controlling for other factors; PAM level 1 – feeling of being overwhelmed; PAM level 4 – significantly more likely to have specific health goals than those at lower levels.	Activation is a measure of self-management self-concept.
Dixon^Citation53	Qualitative study using semi-structured interviews with stratified convenience sample of 27 people with at least one chronic illness; to describe how people with chronic conditions understand successful self-management, and to explore barriers to self-management and strategies employed to manage chronic conditions and cope with stress.	People lower in activation tended to see successful self-management as compliance, whereas those at higher activation levels saw it as being in control. Lower activators indicated lack of knowledge and confidence as barriers to self-management. Both high and low activated people could be derailed by stress.	Aspects of CDSM support may need to be tailored to people at various levels of activation (PAM scores) to ensure that differences in understanding, knowledge, and confidence are addressed adequately. More research in this area is needed.

Abbreviation: CDSM, chronic disease self-management.

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