Skip to content

Advertisement

BMC Cardiovascular Disorders

Research article
Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Effects of a home-based activation intervention on self-management adherence and readmission in rural heart failure patients: the PATCH randomized controlled trial

BMC Cardiovascular DisordersBMC series – open, inclusive and trusted201616:176

https://doi.org/10.1186/s12872-016-0339-7

©  The Author(s). 2016

Received: 19 March 2016

Accepted: 6 July 2016

Published: 8 September 2016

Open Peer Review reports

Abstract

Background

Heart failure (HF) patients discharged from rural hospitals have higher 30-day readmission rates. Self-management (SM) reduces readmissions, but adherence to SM guidelines is low in the rural HF population. We tested a home-based intervention to enhance patient activation and lead to improved SM adherence.

Methods

In this two-group, repeated measures randomized control trial, the main outcomes were patient reported and clinical outcomes associated with SM adherence, and all-cause readmission at 30, 90 and 180 days.

Results

The study included 100 HF patients discharged from a rural critical access hospital. The intervention group received a 12-week SM training and coaching program delivered by telephone and tailored on subjects’ activation levels. At α = .10, the PATCH intervention showed significantly greater improvement compared to usual care in patient-reported SM adherence: weighing themselves, following a low-sodium diet, taking prescribed medication, and exercising daily (all p < .0005) at 3 and 6 months after discharge. In contrast, groups did not differ in physical activity assessed by actigraphy or in clinical biomarkers. Contrary to expectation, the 30-day readmission rate was significantly higher (p = .088) in the intervention group (19.6 %) than in the control group (6.1 %), with no differences at 90 or 180 days.

Conclusion

It is feasible to conduct a randomized controlled trial in HF patients discharged from rural critical access hospitals. Significantly higher patient-reported SM adherence was not accompanied by lower clinical biomarkers or readmission rates. Further research is needed to understand mechanisms that influence outcomes and healthcare utilization in this population.

Trial registration

Clinical Trial Registration Information: ClinicalTrials.gov; NCT01964053.

Keywords

Heart Failure PatientEmergency Department VisitHospital ReadmissionPatch InterventionPatch Group

Background

Compared to their urban counterparts, HF patients discharged from rural hospitals, primarily critical access hospitals (CAHs), have higher 30-day readmission [1]. Failing to adhere to self-management (SM) guidelines accounted for 50 % of hospital readmissions in HF patients [2]. SM adherence involves engaging in recommended SM behaviors such as monitoring daily weight, following a restricted sodium diet, taking medication as prescribed, exercising regularly, and keeping follow-up appointments [3].

To date, most strategies promoting SM adherence were developed and tested in urban health centers [4]. Less is known about these interventions’ feasibility and efficacy in rural areas where resources such as HF specialists and multidisciplinary HF management teams are often limited [5]. Further, rural patients may have lower self-management knowledge and health literacy [5], lack of HF-specific SM education and counselling from providers [6], and lack of SM support [7]. Consequently, rural HF patients tend to exhibit low SM adherence [5]. Past research also has been limited by lack of a theoretical framework [8], unclear mechanism of the intervention [9], and lack of objective measures of SM adherence [10].

To address the knowledge gap, we conducted a randomized controlled trial to test an activation-enhancing intervention to improve SM adherence. Patient activation is defined by Hibbard as the person’s readiness, willingness, and ability to manage his/her own health and healthcare [11]. We hypothesized that the HF patient with higher activation will have greater SM adherence. The purpose of this study is to examine the effects of a 12-week patient activation intervention (Patient AcTivated Care at Home [PATCH]) on the improvement of SM adherence and its health outcome (i.e., hospital readmission) in HF patients following discharge from CAHs.

This study had the following specific aims:

  • Aim 1. To evaluate the immediate (3 months) and extended effects (6 months) of the patient activation intervention on SM adherence.

  • Aim 2. To evaluate the effects of the patient activation intervention on hospital readmission and emergency department (ED) visit rates at 30 days, 3 and 6 months.

  • Aim 3. To evaluate the mechanism of the patient activation intervention, comparing the intervention and usual care (UC) groups on SM knowledge, self-efficacy for SM, patient activation, and SM strategies at the end of intervention.

Conceptual framework

To guide the PATCH intervention, we developed a conceptual framework based on components of Lorig’s chronic disease self-management model [12], Hibbard’s patient activation theory [13], and Bandura’s conceptualization of self-efficacy [14]. The mechanism of intervention is to improve HF patients’ SM adherence by helping them advance through four activation levels: 1) starting to take a SM role; 2) building SM knowledge, skills and confidence; 3) taking SM actions; and 4) maintaining SM behaviors. The central hypothesis is that HF patients with higher levels of activation will be more likely to engage in SM behaviors, leading to improved clinical biomarkers and fewer hospital readmissions [15].

Methods

Study design

This was a two-group, repeated measures, randomized controlled trial. The study is registered on the Clinical Trial website (NCT01964053). The study protocol was approved by the University of Nebraska Medical Center Institutional Review Board and rural hospital ethics committee. All participants gave written informed consent. Detailed information has appeared in a previously published study protocol [16].

Study setting

The study was conducted between September 2013 and October 2015 at a rural critical access hospital (CAH). To reduce the financial vulnerability of rural hospitals and improve rural residents’ healthcare access, Centers for Medicare and Medicaid Services (CMS) created a “Critical Access Hospitals (CAHs)” designation based on the 1997 Balanced Budget Act. A certified rural CAH must have less than 25 acute care inpatient beds and be located more than 35 miles from another hospital [17].

Patient inclusion and exclusion

The principal investigator and research assistants who have ethical access at the study site were responsible for identifying potential subjects, screening for eligibility and recruitment. Eligible subjects: 1) were 21 or older; 2) had HF as one of their discharge diagnoses; 3) had New York Heart Association (NYHA) class II to IV symptoms or NYHA class I symptoms and at least one other HF-related hospitalization or ED visit in the previous year; 4) were discharged to home; 5) passed a mini-cog screen;[18] 6) understood English; and 7) had access to a phone. Patients were not eligible if they had: 1) scheduled procedures/surgeries during hospitalization; 2) depressive symptoms indicated by a score ≥ 3 on the Patient Health Questionnaire-2 (PHQ-2);[19] 3) documented diagnostic evidence of liver cirrhosis; or 4) renal failure (serum creatinine greater than 2.0 mg/dl).

Intervention and usual care

Subjects randomized to the Control Group received only usual care, the standard discharge teaching for HF that includes written and verbal information about HF self-care and scheduled follow-up doctor appointments. Subjects randomized to the Intervention Group received both usual care and the 12-week PATCH intervention. The intervention was comprised of two phases: a one-on-one in-hospital SM training session and post-discharge reinforcement sessions (twice a week for the first 2 weeks, once a week for weeks 3–6, and every other week for weeks 7–12) delivered by telephone. Intervention content was presented in a variety of formats (e.g., verbal, written, visual) with interactive ability. Besides SM workbooks, each subject was provided an SM toolkit, including a calendar for weight and salt daily logging, a step-on weight scale with large and bright readings, and an electronic pill organizer reminder alarm. Each intervention session lasted about 45–50 min. Booster sessions were administered to subjects struggling with SM at home. Subjects received the tailored intervention sessions based on activation level, pre-set goals, and specific SM needs. Intervention details were reported in another publication [16].

Outcome measures

Baseline data collection occurred prior to hospital discharge and at 3 and 6 months after discharge. The primary outcomes measured at all three times were: SM adherence (self-reported frequencies of daily weighing, following a low-sodium diet, taking prescribed medications, exercising, and attending follow-up appointments), clinical biomarkers (B-type natriuretic peptide [BNP] and urine sodium/creatinine ratio [Na/Cr]), and all-cause readmissions and ED visits measured at 30, 90 and 180 days. To assess baseline SM adherence, we asked the participants to recall specific SM behaviors in the past 12 months. The healthcare utilization data were collected from both self-report and primary care provider records. In addition to self-report measures, objective measures of physical activity were obtained using an accelerometer that subjects were asked to wear for 7 consecutive days at each assessment period.

The secondary outcomes were measured via questionnaire at baseline and 3 months to test the intervention mechanisms, including SM knowledge, self-efficacy for SM, patient activation, and SM strategies. Details about measures, instruments and their psychometric properties were reported in another publications [16, 20, 21].

Randomization, blinding and allocation concealment

Given the nature of the treatment, blinding of either subject or interventionist was impossible, but the data collector was blinded to treatment assignment. The project statistician used an on-line pseudo-random number generator to create an allocation schedule; random ordering of block sizes four and six was used to maintain even accrual through the study. Group assignments were placed in sealed envelopes and opened sequentially as patients were enrolled.

Sample size and statistical analysis

The required sample size was estimated using two-sided tests and α = .10. A liberal alpha was chosen to minimize the likelihood of overlooking promising effects in this preliminary study. For a moderate effect size (Cohen’s f = .25), 41 per group provided power of .80 for the test of the mean group difference over time. It provided similar power for a z-test of the difference in group proportions of at least .25. A target sample size of 100 patients allowed for up to 20 % attrition. Further details may be found in Young et al. [16].

For the continuous outcomes in Aim 1 (e.g., physical activity outcomes and level of BNP and urine Na/Cr), linear mixed model methods were used to compare the groups across the 6-month period, adjusting for baseline levels on the respective outcome, with tests of the difference in estimated marginal means (Group effect) and whether change from 3 to 6 months differed in the groups (Group X Time effect). These methods allow for inclusion of partial cases (missing either month 3 or month 6 follow-up data) and for flexible specification of the covariance structure of repeated measurements. However, cases missing on covariates or having only covariate (baseline) measures cannot be included.

Distributions of adherence outcomes measured as number of days per week were clearly non-normal, so responses to those questions also were categorized as non-adherent (0 days), partially adherent (1–6 days), or adherent (7 days), and groups compared using χ2. For outcomes having adherence guidelines (e.g., weighing), patients also were classified as being adherent or not and group differences in the proportion of adherent patients at the end of the intervention (3 months) and at 6 months were tested using χ2.

To evaluate immediate and extended effects of the intervention on rehospitalization and ED visits (Aim 2), a χ2 test was used to compare group proportions separately at 30 days, 3, and 6 months.

To evaluate the mechanism of the patient activation intervention (Aim 3), an independent t-test was used to compare the groups on average change in intervention components from baseline to 3 months after hospital discharge. These tests were one-tailed to correspond to the hypothesis that the PATCH intervention would increase SM knowledge, self-efficacy, activation levels, and use of SM strategies.

Effect sizes were also estimated for the estimated marginal mean difference between groups. There is no established method of estimating effect sizes in linear mixed models, so standardization (d = |M1 – M2| / SD) was carried out using the baseline standard deviation of the outcome, pooled across groups. For variables having no baseline measurement, the standard deviation from the control group at 3 months was used. For tests of intervention components, the group difference in mean change was standardized using a pooled standard deviation of the change scores.

Results

Between September 2013 and October 2015, 629 potentially eligible candidates were screened. Of these, 524 subjects were excluded because of 1) failing to meet the screening criteria; 2) declining participation; 3) being transferred to another facility during hospital stay; 4) deteriorated health conditions; or 5) less than 24-h length of stay, which made it impractical to conduct Phase I intervention and baseline data collection. After randomization, three subjects were excluded from the PATCH intervention group and two from the control group as described in the CONSORT study flow diagram (Fig. 1). A sample of 100 HF subjects (PATCH = 51, control = 49) entered the final analysis.
Fig. 1

Study flow diagram

Sample characteristics

The 64 females and 36 males ranged in age from 40 to 93, with a mean of 70.2 (±12.2) years. On average, they had 12.9 (±2.3) years of education and a median annual household income of $20,000–29,000. Most were NYHA level II (49 %) or III (42 %) with preserved ejection fraction (55.7 ± 11.1). On average, patients had eight (±2.6) comorbidities, with the overwhelming majority having hypertension (99 %), coronary artery disease (94 %), arthritis or degenerative joint disease (89 %), and hypercholesterolemia (84 %). Subjects reported having an average of 12.3 (±6.0) prescriptions, requiring 16.2 (±8.8) pills per day (Table 1). At hospital discharge, 50 % of the subjects were prescribed beta blockers and 52 % either ACE inhibitors or angiotensin receptor blockers.
Table 1

Patient demographic and clinical characteristics

 

All (n =100)

Intervention group (n = 51)

Control group (n = 49)

Demographic data

   Age (years)

70.2 ± 12.2

68.7 ± 11.8

71.8 ± 12.6

   Male

36 (36)

24 (47.1)

12 (24.5)

   Education (years)

12.9 ± 2.3

13 ± 2.4

12.8 ± 2.1

   Caucasian

95 (95)

48 (94.1)

47 (95.9)

   Married/living with partner

50 (50)

31 (60.8)

19 (38.8)

   Currently employed outside home

29 (29)

16 (30.8)

13 (26.5)

   Annual family income (< $30,000)

51 (51)

24 (47.10)

27 (55.1)

Risk factor profile

   Body Mass Index (kg/m2)

32.3 ± 7.1

33.4 ± 7.4

31.2 ± 6.8

Clinical data

   Number of comorbidities

8 ± 2.6

7.8 ± 2.5

8.0 ± 2.7

      1.Hypertension

99 (99)

51 (100.0)

48 (98.0)

      2.Coronary artery disease

94 (94)

46 (90.2)

48 (98)

      3.Arthritis degenerative joint disease

89 (89)

43 (84.3)

44 (89.8)

      4.Hypercholesterolemia

84 (84)

43 (84.3)

41 (83.7)

      5.Diabetes mellitus with or without complications

41 (41)

41 (80.4)

33 (67.4)

      6.Dyspepsia

50 (50)

24 (47.1)

26 (53.1)

      7.Peripheral vascular disease or lower extremity edema

45 (45)

22 (43.1)

23 (46.9)

      8.Chronic obstructive pulmonary disease

38 (38)

22 (43.1)

16 (32.7)

      9.Chronic renal disease

23 (23)

12 (23.5)

11 (22.4)

Number of medications taking per day

16.2 (±8.8)

16.4 ± 10.0

15.9 ± 7.4

   Beta blockers

50 (50)

27 (52.9)

23 (46.9)

   ACE inhibitors

35 (35)

16 (31.4)

19 (38.8)

   Angiotensin receptor blocker

18 (18)

8 (15.7)

10 (20.4)

   Previous hospitalizations within 1 year (≥1)

43 (43)

22 (43.2)

21 (42.8)

   Previous ED visits within 1 year (≥1)

60 (60)

30 (58.9)

30 (61.2)

Cardiac function

   Functional class (NYHA)

   

      •II

49 (49)

15 (29.4)

34 (69.4)

      •III

42 (42)

29 (56.9)

13 (26.5)

   Ejection fractiona

55.7 ± 11.1

53.4 ± 12.9

58.3 ± 8.1

   Ejection fraction < 50 %a

16 (16)

12 (23.5)

4 (8.2)

Main outcome: Patient Activation Measure (PAM) level

      1.Starting to take a role

39 (39)

19 (37.3)

20 (40.8)

      2.Building knowledge, skills and confidence

23 (23)

9 (17.6)

14 (28.6)

      3.Taking action

18 (18)

13 (25.5)

5 (10.2)

      4.Maintaining behaviors

19 (19)

10 (19.6)

9 (18.4)

Results are presented as mean ± SD or n (%)

aEjection fraction was available for n = 47 in the intervention group, n = 41 in the usual care group. Percentages include cases with missing data in the denominator

The PATCH intervention and control groups were comparable on all demographic and baseline clinical characteristics with a few exceptions. A detailed breakdown is presented in Table 1. There were more females in the control group (75.5 %) than in the intervention group (52.9 %); only 38.8 % were married or partnered in the control group compared to 60.8 % in the intervention group. Those randomized to the intervention group had lower cardiac function than those in control group. Compared to subjects in the control group (26.5 %), more than twice as many subjects (56.9 %) in the intervention group were NYHA class III with ejection fraction less than 50 (23.5 % in PATCH group vs. 8.2 % in control group).

At baseline, the majority of subjects had low activation in terms of SM adherence. Patient activation measure (PAM) scores placed 39 % of subjects in activation level 1 (starting to take a role), 23 % in level 2 (building knowledge, skills and confidence), 18 % in level 3 (taking action), and 19 % in level 4 (maintaining behaviors).

  • Aim 1. To evaluate the immediate and extended effects of the patient activation intervention on SM adherence.

Descriptive statistics for SM adherence outcomes and the results of the statistical tests from fitting a linear mixed model are presented in Table 2. None of the tests of Group x Time interaction were significant. Those receiving the PATCH intervention had significantly higher self-reported adherence to SM behaviors, including average days per week weighing themselves (p < .0005; d = 1.1), following a low-sodium diet (p < .0005; d = .9), and exercising (p < .0005; d = .6). The intervention group also reported significantly fewer days missing any doses of prescribed medication (p = .030; d = .6) (Table 2). Approximately twice as many patients in the intervention group as in the control group reported that they carried out these behaviors 7 days a week. Details of the frequencies of SM adherence for these outcomes at 3 and 6 months are displayed in Table 3. In the intervention group, approximately 84 % at 3 months and 86 % at 6 months reported not missing any doses in the previous week compared to 68 % at both times in the control group. Nearly all subjects (100 % of the PATCH group, 98 % of the control group) attended the scheduled follow-up appointment with their primary care provider within 30 days of discharge.
Table 2

Self-management adherence outcomes by group and time

Variable

Group

Unadjusted Meana (SD)

Estimatedb Marginal Mean (SE)

Group (p-value)

Group X Time (p-value)

Estimated group difference in marginal means [95 % CI]

3 months

6 months

Adherence to self-management behaviors per self-report

Days per week weigh self

Intervention

4.8 ± 2.7

4.6 ± 2.4

4.7 ± 0.3

<.0005

.687

2.98 [2.10, 3.86]

Control

1.9 ± 2.7

1.5 ± 2.5

1.7 ± 0.3

   

Days per week follow low-sodium diet

Intervention

5.6 ± 2.1

5.1 ± 2.6

5.3 ± 0.3

<.0005

.668

2.62 [1.74, 3.50]

Control

3.1 ± 2.9

2.3 ± 3.0

2.7 ± 0.3

   

Days missed any medication doses in past 7 days

Intervention

.39 ± 1.2

.26 ± 1.0

0.3 ± 0.2

.030

.544

−0.51 [−0.97, −0.05]

Control

.81 ± 1.6

.83 ± 1.4

0.8 ± 0.2

   

Days per week exercise

Intervention

5.4 ± 1.8

4.5 ± 2.5

4.9 ± 0.3

<.0005

.230

1.66 [0.79, 2.53]

Control

3.4 ± 2.9

3.1 ± 2.5

3.3 ± 0.3

   

Physical Activity measured by ActiGraphc

Average daily activity counts

Intervention

285,707 ± 152,860

306,648 ± 165,316

279,160 ± 19,821

.780

.693

8236 [−50,155; 66,628]

Control

251,265 ± 145,744

250,913 ± 275,411

270,924 ± 271,313

   

Average activity kcals/kg/day

Intervention

2.2 ± 1.6

2.3 ± 1.9

2.08 ± 0.2

.773

.664

0.07 [−0.42, 0.56]

Control

1.8 ± 1.7

1.7 ± 1.8

2.01 ± 0.2

   

Average daily mins ≥ moderate intensity activity

Intervention

5.8 ± 8.8

5.9 ± 10.8

4.81 ± 0.9

.897

.860

0.17 [−2.34, 2.71]

Control

3.3 ± 10.5

3.7 ± 7.1

4.65 ± 0.9

   

Adherence to self-management behaviors measured by biomarkers

B-type natriuretic peptide (log-transformed)

Intervention

1.7 ± 0.7

1.7 ± 0.6

1.70 ± 0.1

.282

.512

−0.09 [−0.26, 0.08]

Control

1.8 ± 0.5

1.8 ± 0.4

1.80 ± 0.1

   

Average daily sodium intake (<1500 mg)

Intervention

3607.0 ± 1395.3

3748.7 ± 1586.2

3647.9 ± 157.3

.234

.818

−272.03 [−722.70, 178.65]

Control

3876.9 ± 1097.2

3926.6 ± 1279.3

3919.9 ± 163.5

   

a Observed means for the intervention group were based on n = 51, 51, and 50 cases for self-report measures, biomarkers, and actigraphy variables, respectively, at month 3 and n = 50, 50, and 47 at month 6. For the control group, n = 47, 47, and 46 at month 3 and n = 46–47, 47, and 47 at month 6. The supplemental analysis reported in the text that included baseline as a time point was based on all 99 cases having any actigraphy data at any time

b Estimated marginal means and statistical tests were obtained from a linear mixed model analysis, adjusted for baseline values of the outcome and specifying an unstructured variance/covariance matrix

c Valid time needed to assess physical activity by ActiGraph was a minimum of 8 h of wear time or at least 2 valid days were required for calculation of composites at each time point. The mean (± SD) days worn ranged from 5.9 (±1.4) to 6.3 (±1.2) days. Group mean hours worn ranged from 15.2 (±2.6) to 16.2 (±3.2)

Table 3

Frequencies of self-management adherence by outcomes, group and time

Variable

Group

 

Baseline

3 Months

6 Months

Days missed any medication doses in past 7 daysa

Intervention

0 days

86.3 % (44/51)

84.3 % (43/51)

86.0 % (43/50)

≥1 day

13.7 % (7/51)

15.7 % (8/51)

14.0 (7/50)

Control

0 days

81.6 % (40/49)

68.1 % (32/47)

68.1 % (32/47)

≥1 day

18.4 % (9/49)

31.9 % (15/47)

31.9 % (15/47)

 

χ2 = .130 (p = .719) b

χ2 = 2.74 (p = .098) b

χ2 = 3.47 (p = .060) b

Kept scheduled appointment with primary care provider (within 30 days)

Intervention

 

100 % (51/51)

  

Control

 

97.9 % (46/47)

  
 

Fisher’s exact p = .295c

  

Days/wk follow low-sodium diet

Intervention

0 days

-

7.8 % (4/51)

14.0 % (7/50)

1 – 6 days

-

35.3 % (18/51)

30.0 % (15/50)

7 days

-

56.9 % (29/51)

56.0 % (28/50)

Control

0 days

-

38.3 % (18/47)

55.3 % (26/47)

1 – 6 days

-

40.4 % (19/47)

21.3 % (10/47)

7 days

-

21.3 % (10/47)

23.4 % (11.47)

  

χ2 = 18.06 (p < .0005)

χ2 = 19.28 (p < .0005)

Days/wk exercise

Intervention

0 days

-

2.0 % (1/51)

12.0 % (6/50)

1 – 6 days

-

52.9 % (27/51)

50.0 % (25/50)

7 days

-

45.1 % (23/51)

38.0 % (19/50)

Control

0 days

-

23.4 % (11/47)

23.9 % (11/46)

1 – 6 days

-

42.6 % (20/47)

58.7 % (27/46)

7 days

-

34.0 % (16/47)

17.4 % (8/46)

  

χ2 = 10.49 (p = .005)

χ2 = 5.87 (p = .053)

Days/wk weigh self

Intervention

0 days

-

13.7 % (7/51)

6.0 % (3/50)

1 – 6 days

-

41.2 % (21/51)

56.0 % (28/50)

7 days

-

45.1 % (23/51)

38.0 % (19/50)

Control

0 days

-

46.8 % (22/47)

53.2 % (25/47)

1 – 6 days

-

34.0 % (16/47)

31.9 % (15/47)

7 days

-

19.1 % (9/47)

14.9 % (7/47)

  

χ2 = 14.42 (p = .001)

χ2 = 26.69 (p < .0005)

≤1500 mg Na daily

Intervention

Not met

96.1 % (49/51)

98.0 % (49/50)

98.0 % (49/50)

Met

3.9 % (2/51)

2.0 % (1/51)

2.0 % (1/50)

Control

Not met

100 % (49/49)

100 % (47/47)

100 % (47/47)

Met

0 % (0/49)

0 % (0/47)

0 % (0/47)

 

Fisher’s exact p = .495c

Fisher’s exact p = 1.00c

Fisher’s exact p = 1.00c

Exercise 7 days/wk (self-report)

Intervention

Not met

-

54.9 % (28/51)

62.0 % (31/50)

Met

-

45.1 % (23/51)

38.0 % (19/50)

Control

Not met

-

66.0 % (31/47)

82.6 % (38/46)

Met

-

34.0 % (16/47)

17.4 % (8/46)

  

χ2 = 1.25 (p = .264)

χ2 = 5.03 (p = .025)

≥120 mins/wk of moderate or more intense activityd

Intervention

Not met

91.8 % (45/49)

88.0 % (44/50)

91.5 % (43/47)

Met

8.2 % (4/49)

12.0 % (6/50)

8.5 % (4/47)

Control

Not met

100 % (42/42)

95.7 % (44/46)

95.7 % (45/47)

Met

0 % (0/42)

4.3 % (2/46)

4.3 % (2/47)

 

Fisher’s exact p = .121c

Fisher’s exact p = .271c

Fisher’s exact p = .677c

aWith three categories, days with missed medication doses did not meet the assumptions of the χ2 test. Because very few patients missed 7 days at any time point (1 in the intervention group at 3 months and at 6 months and 1 in the control group at 6 months), data from 1 to 6 days and 7 days were combined

bWith continuity correction for 2 × 2 table

cFisher’s exact test was used for 2 × 2 tables when assumptions of χ2 test were not met

dCriteria of recommended 150 min/wk considered met if 80 % (120 min) of target achieved

In contrast to the self-report measures, there were no significant group differences in the estimated mean activity counts, energy expenditure, or minutes in moderate or more intense activity, with small effect sizes (d = .03 to d = .05). Results from a supplemental analysis of the actigraphy variables that treated baseline as a time point in order to include all partial cases were consistent with these findings, with no significant differences between groups in the pattern of change across time on any of the actigraphy variables (p-values ranged from .589 to .858 for the tests of Group X Time interactions). B-type natriuretic peptide (BNP) was log-transformed due to strong positive skewness. Neither log- transformed BNP nor estimated daily sodium intake based on the urine test differed significantly in the two groups. There were also no significant group differences in the proportion meeting SM adherence criteria of ≤ 1500 mg of sodium daily and of ≥ 120 min per week of moderate or higher intensity activity (adherence defined as 80 % of the recommended 150 min per week), either at the end of the intervention (3 months) or at the 6-month follow up. Few patients in either group met these criteria at any point in the study (Table 3).

  • Aim 2. To evaluate the effects of the patient activation intervention on hospital readmission and ED rates at 30 days, 3 and 6 months.

Contrary to expectation, the 30-day hospital readmission rate was significantly higher (χ2 with continuity correction = 2.914, p = .088) in the PATCH group (n = 10, 19.6 %) than in the control group (n = 3, 6.1 %). At 90 days and at 180 days, the groups were not significantly different (n = 12, 23.5 % and n = 12, 24.5 %, respectively, at 90 days; n = 18, 35.3 % and n = 20, 40.8 % at 180 days). A similar pattern was seen with ED visits at 30 days (n = 6, 11.8 % and n = 1, 2.0 %); 90 days (n = 9, 17.6 %; and n = 5, 10.2 %); and 180 days (n = 12, 23.5 % and n = 11, 22.4 %), but none of the differences were significant.

  • Aim 3. To evaluate the mechanism of the patient activation intervention, comparing the intervention and UC groups on SM knowledge, self-efficacy for self-management, patient activation, and SM strategies at the end of intervention (3 months).

As shown in Table 4, the intervention group, on average, showed significantly greater increases in self-efficacy for heart failure SM (p = .034; d = .4), SM strategies (p < .0005; d = 1.0), and patient activation scores (p = .069; d = .3). No group differences were found for SM knowledge, which changed approximately two points in each group.
Table 4

Descriptives and t-tests of mean change in intervention component measures

Variable

Possible score range

Group

Number

Baseline Mean (SD)

Number

Month 3 Mean (SD)

Mean change (SD)

t-test (p-value) a

Self-management knowledgeb

0–27

Intervention

51

21.1 (2.8)

51

23.1 (2.7)

2.0 (3.4)

.337

  

Control

49

19.4 (3.0)

47

21.1 (2.5)

1.7 (2.6)

 

Self-efficacy for HF self-managementc

0–100

Intervention

51

44.9 (24.7)

51

59.7 (17.3)

14.8 (26.0)

.034

  

Control

49

49.2 (22.9)

47

53.8 (24.0)

5.2 (25.5)

 

Patient activation (PAM Rasch scaled scores) d

0–100

Intervention

51

57.3 (19.2)

50

69.1 (16.7)

11.7 (21.3)

.069

  

Control

48

56.6 (18.6)

47

61.4 (18.2)

5.7 (18.5)

 

Self-management strategies (RSCB) e

0–145

Intervention

50

86.8 (19.3)

51

115.7 (19.6)

28.9 (23.0)

<.0005

  

Control

49

91.9 (19.9)

47

97.6 (22.6)

5.6 (22.3)

 

aOne-tailed test of group difference in change from baseline to 3 months, equal variances not assumed

bPossible range for Self-management knowledge: 0-27

cPossible range for self-efficacy for HF self-management: 0-100

dPossible range for patient activation measure: 0-100

ePossible range for self-management strategies: 0-145

Discussion

To our knowledge, this is the first efficacy trial of an activation-enhancing in rural HF patients. Similar to Shively’s and Hibbard’s studies [22, 23], the findings demonstrate that the theory-based activation-enhancing intervention was effective in increasing patient activation level, leading to improved SM behaviors in rural HF patients.

This study contributes to the field of SM in rural HF population by adding the following evidence: 1) understanding the effect of patient activation on SM behaviors, 2) development of a conceptual framework to guide the design and implementation of activation-enhancing interventions to promote life-long SM adherence; 3) using clinical biomarkers (BNP and urine Na/Cr) to assess SM adherence; and 4) feasibility and patients’ acceptance of home- based interventions to improve SM behaviors.

Based on self-reported data, HF patients in the PATCH intervention group had significant improvement in activation scores and SM adherence in weighing themselves daily, following a low-sodium diet and exercising regularly. According to the objective measures (i.e., physical activity measured by accelerometer and daily sodium intake computed from urine sodium), however, the subjects from the intervention group did not reach adherence threshold of sodium restriction and exercise intensity guidelines. Consequently, the reported improvement in SM adherence and behaviors did not improve clinical biomarkers or reduce readmissions. Instead, the 30-day readmission rate was higher in the intervention group than in the control group. Furthermore, the PATCH intervention had no impact on SM knowledge at 3 months.

Similar to the rural HF populations in other studies [24], our sample was predominantly white, retired, and low income. However, our study did have more female participants than have been reported in other studies. During recruitment, we found women more willing than men to participate in the study [15, 25, 26]. Field notes indicated women were more likely to believe SM had positive effects on their sustained independence and HF symptom relief, felt more isolated and more often sought social interactions with the research team, and had greater interest in learning SM strategies. Consistent with Dracup and Powell’s reports [5, 27], improved SM adherence through education and training did not lead to reduced readmissions in rural patients with mild to moderate heart failure.

There are several explanations for this deviation from mainstream belief about the benefit of SM on HF outcomes [28, 29]. First, low SM capability made it difficult and sometimes impossible for some HF patients to perform SM behaviors. SM capability is defined as the capability to manage one’s illness-related symptoms, requiring a competent level of cognition and adequate health literacy [30]. Dracup and others [31] found low health literacy and global cognition impairment were prevalent in rural HF patients [31]. Second, the SM guidelines, especially those related to sodium restriction and exercise intensity, were found unachievable by many participants [25]. Our data analysis showed that only 5 % HF patients fully adhered to the sodium restriction guideline (≤1500 mg per day). BNP and sodium intake are highly correlated [32]. Without fully complying with the sodium restriction guideline, it would be highly unlikely to observe significant changes in clinical biomarkers (BNP or urine sodium/ creatinine ratio). Similarly, only 6 % HF patients followed the physical activity guideline (≥120 min per week moderate or above intense activity). As a result, adherence behaviors didn’t reach the threshold necessary to have affected the clinical outcomes. Third, our patient sample had much higher numbers of comorbidities compared to other HF populations (8 vs. 3) [22], which could create competing SM demands and further strain SM capability [33]. Last, one explanation for the increased 30-day readmission in the intervention group could be the poorer cardiac functioning in the intervention grouppatients, which could potentially increase risk of 30-day readmission [34]. However, even within class III patients only, readmission rates were higher in the intervention group (28 %) than in the usual care group (0 %). Another potential explanation is the HF patients who received the PATCH intervention might have had improved knowledge and skills in recognizing warning signs of HF exacerbation, communicating their health concerns to their providers, leading to appropriate hospitalization to prevent complications. Dracup also reported higher number of healthcare use in the intervention group. She suggested that the HF patients in the intervention group were educated to pay closer attention to their symptoms, leading to subsequent hospitalizations [24]. Whether this patient-initiated hospitalization improved the health outcomes or not was not examined due to short-term follow up (6 months) in our study.

Limitations and challenges

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 preclude the detection of significant benefit brought by the intervention, although the small effect sizes associated with the non-significant effects indicate that this was unlikely. Third, participant recruitment may have resulted in selection bias. Patients’ personal preferences, beliefs and experiences play a major role in their decision whether to participate in a clinical study. Often, those at risk (e.g., those with lower literacy, more comorbidity, poorer health status, lack of support, etc.) are likely to decline to enroll in behavioral intervention trials [35]. If patients who enrolled in this study were more confident and more actively engaged in SM behaviors than patients who declined, the intervention effects could have been diluted. Fourth, the intervention was tailored to patients’ activation levels, but we failed to recognize variations in patient SM capability. Based on recommendations from patients, family, clinicians and our own experience, it is important to develop a reliable and practice tool to assess SM capability, then tailor the interventions to SM capability and patient activation level.

Future research direction

The 30-day readmission has been an important national priority for HF patients given its impact on health outcomes and healthcare cost [36]. However, it may not be an accurate indicator of the effect of SM adherence. In future studies, we plan to extend follow up time to see the impact of patient-initiated hospitalization on long-term health outcomes. In addition, we will also examine the interaction between SM capability and activation level, as well as their impact on SM adherence. The findings may assist clinicians in identifying effective strategies to support SM adherence.

Conclusion

In summary, results of PATCH trial are consistent with previous trials; the activation-enhancing intervention that improved SM behaviors and adherence without reducing readmissions. Given the epidemic of heart failure burdening on the health care system, we urgently need to identify effective but feasible, sustainable strategies to reduce healthcare utilization and improve quality of life in rural HF populations.

Abbreviations

AHFKT-V2: 

Atlanta heart failure knowledge test-version 2

BNP: 

B-type natriuretic peptide

CAH: 

Critical access hospital

ED: 

Emergency department

HF: 

Heart failure

Na/Cr: 

Urine sodium/creatinine ratio

PAM: 

Patient activation measure

PATCH: 

Patient AcTivated Care at Home

RSCB: 

Revised self-care behavior.

SM: 

Self-management

UC: 

Usual care

Declarations

Acknowledgements

As the principle investigator (PI) of the study, Dr. Young contributed the essence part of this manuscript from study’s conception and design; acquisition of data and preparation of the manuscript. Other authors contributed extensively to the work presented in this paper. Dr. Barnason, as co-PI, contributed to the development of conceptual framework, intervention protocol, outcomes and their instrument selection, manuscript revisions, as well as other essential part of manuscript preparation. Dr. Hertzog contributed in analysis and interpretation of data, manuscript preparation, and table and figure editing.

This research was funded by National Institute of Health and National Institute of Nursing Research (NIH/NINR) through grant number 1R15NR 13769-01A1.

Availability of data and materials

The dataset supporting the conclusions of this article is made publicly available in the additional file.

Authors’ contributions

LY the primary investigator of the study, made substantial contributions to every aspect pertinent to the original RCT trial, including development of conception and trial design, patient recruitment, trial implementation, data acquisition, analysis and interpretation. SB and MH as the co-investigators, have participated sufficiently in the preparation of the manuscript. SB contributed to the development of conceptual framework, intervention protocol, outcomes and their instrument selection. MH contributed in analysis and interpretation of data, the preparation of table and figure editing. All three authors participated in manuscript revision critically for important intellectual content. As the corresponding author, LY has full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board at the University of Nebraska Medical Center and ethics committee at clinical site. The written informed consent was obtained from all study participants prior to enrollment. In analyzing data and reporting, all participants will be anonymized.

Grant information

Research reported in this publication was supported by the National Institutes Nursing Research of the National Institutes of Health under award number 1R15NR 13769-01A1. The sponsor had no role in conducting the study, preparing and disseminating the study results. The authors are the recipients of the funding provided by the National Institutes Nursing Research of the National Institutes of Health. She has full access to the study data and takes responsibility for their integrity and the accuracy of the data analysis.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Physiological and Technological Nursing, College of Nursing Augusta University, Augusta, USA
(2)
University of Nebraska Medical Center, College of Nursing, Omaha, USA

References

  1. Gamble JM, Eurich DT, Ezekowitz JA, Kaul P, Quan H, McAlister FA. Patterns of care and outcomes differ for urban versus rural patients with newly diagnosed heart failure, even in a universal healthcare system. Circulation: Heart Failure. 2011;4(3):317–323.Google Scholar
  2. van der Wal MH. Qualitative examination of compliance in heart failure patients in The Netherlands. Heart Lung. 2010;39(2):121.View ArticlePubMedGoogle Scholar
  3. Jessup M, Abraham WT, Casey DE, Feldman AM, Francis GS, Ganiats TG, Konstam MA, Mancini DM, Rahko PS, Silver MA, Stevenson LW, Yancy CW. ACCF/AHA guidelines for the diagnosis and management of heart failure in adults. Circulation. 2009;119(14):1977–2016.View ArticlePubMedGoogle Scholar
  4. Giordano A. Multicenter randomised trial on home-based telemanagement to prevent hospital readmission of patients with chronic heart failure. Int J Cardiol. 2009;131(2):192.View ArticlePubMedGoogle Scholar
  5. Dracup K, Moser DK, Pelter MM, Nesbitt TS, Southard J, Paul SM, Robinson S, Cooper LS. Randomized, controlled trial to improve self-care in patients with heart failure living in rural areas. Circulation. 2014;130(3):256–64.View ArticlePubMedGoogle Scholar
  6. Cudney S, Weinert C, Kinion E. Forging partnerships between rural women with chronic conditions and their health care providers. J Holist Nurs. 2011;29(1):53–60.View ArticlePubMedGoogle Scholar
  7. Sanders S. Experiences of rural male caregivers of older adults with their informal support networks. J Gerontol Soc Work. 2007;49(4):97.View ArticlePubMedGoogle Scholar
  8. Lee HJ, Winters CA. Testing rural nursing theory: perceptions and needs of service providers. Online J Rural Nurs Health Care. 2004;4(1):51–63.Google Scholar
  9. Riegel B. State of the science: promoting self-care in persons with heart failure: a scientific statement from the American Heart Association. Circulation. 2009;120(12):1141.View ArticlePubMedGoogle Scholar
  10. Heydari A, Ahrari S, Vaghee S. The relationship between self-concept and adherence to therapeutic regimens in patients with heart failure. J Cardiovasc Nurs. 2011;26(6):475–480.Google Scholar
  11. 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. 2010;21(3):796–808.View ArticlePubMedGoogle Scholar
  12. Lorig K. Self-management of chronic illness: a model for the future. Generations. 1993;XVII:11–4.Google Scholar
  13. Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007;42(4):1443–63.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Bandura A. Self-efficacy: the exercise of control. New York: WH: Freeman and Company; 1997.Google Scholar
  15. Young L, Montgomery M, Barnason S, Schmidt C, Do V. A Conceptual Framework for Barriers to the Recruitment and Retention of Rural CVD Participants in Behavior Intervention Trials. GSTF J Nurs Health Care. 2015;2(2). http://dl6.globalstf.org/index.php/jnhc/article/view/1305.
  16. Young L, Barnason S, Do V. Promoting self-management through adherence among heart failure patients discharged from rural hospitals: a study protocol. F1000Research. 2015;3. doi:10.12688/f1000research.5998.
  17. Dalton K, Slifkin R, Poley S, Fruhbeis M. Choosing to convert to critical access hospital status. Health Care Financ Rev. 2003;25(1):115–32.PubMedPubMed CentralGoogle Scholar
  18. Borson S. The mini-cog: a cognitive vital signs measure for dementia screening in multi-lingual elderly. Int J Geriatr Psychiatry. 2000;15(11):1021.View ArticlePubMedGoogle Scholar
  19. Li C. Validity of the Patient Health Questionnaire 2 (PHQ-2) in identifying major depression in older people. J Am Geriatr Soc. 2007;55(4):596.View ArticlePubMedGoogle Scholar
  20. Young L, Barnason S, Kupzyk K. Mechanism of engaging self-management behavior in rural heart failure patients. Appl Nurs Res. 2016;30:222–227.Google Scholar
  21. Do V, Young L, Barnason S, Tran H. Relationships between activation level, knowledge, self-efficacy, and self-management behavior in heart failure patients discharged from rural hospitals. F1000Res. 2015;4:150.PubMedPubMed CentralGoogle Scholar
  22. Shively MJ, Gardetto NJ, Kodiath MF, Kelly A, Smith TL, Stepnowsky C, Maynard C, Larson CB. Effect of patient activation on self-management in patients with heart failure. J Cardiovasc Nurs. 2013;28(1):20–34.View ArticlePubMedGoogle Scholar
  23. Greene J, Hibbard JH. Why does patient activation matter? An examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med. 2012;27(5):520–6.View ArticlePubMedGoogle Scholar
  24. Dracup K, Moser DK, Pelter MM, Nesbitt T, Southard J, Paul SM, Robinson S, Hemsey JZ, Cooper L. Rural patients' knowledge about heart failure. J Cardiovasc Nurs. 2014;29(5):423–8.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Young L, Barnason S, Do V. Conducting behavioral intervention research in rural communities: barriers and strategies to recruiting and retaining heart failure patients in studies. Nursing and Health Care. 2015;1(1):1–8.Google Scholar
  26. Young L, Barnason S, Do V. Review strategies to recruit and retain rural patient participating self-management behavioral trials. Online Journal of Rural Research & Policy. 2015;10(2):1.View ArticleGoogle Scholar
  27. Powell LH, Calvin Jr JE, Richardson D, Janssen I, de Leon CF M, Flynn KJ, Grady KL, Rucker-Whitaker CS, Eaton C, Avery E, HART Investigators. Self-management counseling in patients with heart failure: the heart failure adherence and retention randomized behavioral trial. JAMA. 2010;304(12):1331–8.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Bos-Touwen I, Jonkman N, Westland H, Schuurmans M, Rutten F, de Wit N, Trappenburg J. Tailoring of self-management interventions in patients with heart failure. Curr Heart Fail Rep. 2015;12(3):223–35.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Fergenbaum J, Bermingham S, Krahn M, Alter D, Demers C. Care in the Home for the Management of Chronic Heart Failure. 2015.Google Scholar
  30. Koetsenruijter J, van Eikelenboom N, van Lieshout J, Vassilev I, Lionis C, Todorova E, Portillo MC, Foss C, Gil MS, Roukova P, Angelaki A. Social support and self-management capabilities in diabetes patients: an international observational study. Patient Educ Couns. 2016;99(4):638–643.Google Scholar
  31. Bratzke LC, Moser DK, Pelter MM, Paul SM, Nesbitt TS, Cooper LS, Dracup KA. Evidence-based heart failure medications and cognition. J Cardiovasc Nurs. 2016;31(1):62–8.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Sadanaga T, Ando K, Hirota S, Mitamura H, Tsuchihashi T, Kohsaka S, Fukuda K, Ogawa S. B‐type natriuretic peptide levels are decreased by reducing dietary salt intake in patients with compensated heart failure with preserved ejection fraction. Intern Med J. 2013;43(6):663–7.View ArticlePubMedGoogle Scholar
  33. Dickson VV, Buck H, Riegel B. Multiple comorbid conditions challenge heart failure self-care by decreasing self-efficacy. Nurs Res. 2013;62(1):2–9.View ArticlePubMedGoogle Scholar
  34. Moretti C, D'Ascenzo F, Omede P, Sciuto F, Presutti DG, Di Cuia M, Colaci C, Giusto F, Ballocca F, Cerrato E, Colombo F, Gonella A, Giordana F, Longo G, Vilardi I, Bertaina M, Orlando A, Andrini R, Ferrando A, DiNicolantonio JJ, Zoccai GB, Sheiban I, Gaita F. Thirty-day readmission rates after PCI in a metropolitan center in Europe: incidence and impact on prognosis. J Cardiovasc Med (Hagerstown). 2015;16(3):238–45.View ArticleGoogle Scholar
  35. Jenkins V, Farewell V, Farewell D, Darmanin J, Wagstaff J, Langridge C, Fallowfield L. Drivers and barriers to patient participation in RCTs. Br J Cancer. 2013;108(7):1402–7.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Axon RN, Williams MV. Hospital readmission as an accountability measure. JAMA. 2011;305(5):504–5.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us