Clinical Predictors of Adherence to Exercise Training Among Individuals With Heart Failure: THE HF-ACTION STUDY : Journal of Cardiopulmonary Rehabilitation and Prevention

Secondary Logo

Journal Logo

Heart Failure

Clinical Predictors of Adherence to Exercise Training Among Individuals With Heart Failure


Collins, Katherine A. PhD; Reeves, Gordon R. MD, MPT; Miller, Nancy Houston BSN, RN; Whellan, David J. MD, MHS; O'Connor, Christopher M. MD; Marcus, Bess H. PhD; Kitzman, Dalane W. MD; Kraus, William E. MD

Author Information
Journal of Cardiopulmonary Rehabilitation and Prevention 43(3):p 205-213, May 2023. | DOI: 10.1097/HCR.0000000000000757


What is novel?

  • Few studies have assessed characteristics and predictors of exercise training adherence, especially among individuals with heart failure.
  • Although baseline clinical and demographic characteristics are important to assess when predicting exercise training adherence, they provide very limited information for identifying patients with heart failure who are at risk for poor adherence to exercise interventions.

What are the clinical and/or research implications?

  • The findings from the present analysis suggest that many of the variables influencing exercise adherence are currently unrecognized or poorly understood.
  • Implying, future research is needed to explore a broader range of variables from system level factors and the physical environment to genetic determinants; the complex interactions of multiple variables; the influence of these variables in important subpopulations; and changes over time and in response to exercise training or adherence interventions.

Cardiac rehabilitation (CR) improves exercise capacity and quality of life, while reducing risk of hospitalization in patients with chronic heart failure (HF).1,2 Participation in CR is now recommended by HF guidelines3,4 and supported by the Centers for Medicare & Medicaid Services5 for patients with HF with reduced ejection fraction.

Unfortunately, one of the major limiting factors to successful delivery of CR is poor exercise training adherence, with adherence being especially challenging for individuals with HF.6–8 Among patients with ischemic heart disease, those with concomitant HF participate in CR significantly less than those with no HF.9 Furthermore, exercise training adherence is consistently lower than adherence to other HF-related self-care behaviors.10–12 Even HF patients who believe exercise to be an important health behavior frequently show poor exercise adherence,12 resulting in an increased risk for adverse clinical outcomes.13 Identifying factors associated with nonadherence is essential to helping HF patients realize the full benefits of exercise programs.

Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION) is the largest randomized controlled trial of exercise training to date. As a result, this study is uniquely positioned to offer potentially valuable insights into the clinical and demographic factors influencing exercise intervention adherence. As in other studies of exercise training,14–16 maintaining exercise adherence proved to be challenging during both supervised and home exercise training periods.17 Prior work has examined psychosocial predictors of exercise adherence in the HF-ACTION trial.13 The purpose of the current analysis was to provide a more comprehensive description of patient adherence characteristics and identify clinical and demographic predictors of exercise training adherence.


A detailed description of the HF-ACTION trial design and the results of the primary analysis have been published previously.17 HF-ACTION was a multicenter, randomized controlled trial of exercise training and usual care versus usual care alone in 2331 patients with chronic, stable, New York Heart Association (NYHA) Class II-IV HF and low left ventricular ejection fraction (LVEF ≤35%) in the context of optimal medical therapy. Participants in HF-ACTION were randomized from April 2003 through February 2007 within the United States, Canada, and France. Exclusion criteria included major comorbidities or limitations that could interfere with exercise training, recent (within 6 wk) or planned (within 6 mo) major cardiovascular events or procedures, performance of regular exercise training, or use of devices that limited the ability to achieve target heart rates (HR). The current analysis was conducted using the cohort randomized to the intervention (exercise training) arm only (Figure 1). The HF-ACTION study was approved by all local institutional review boards and all participants provided informed consent. Eligible participants were randomized 1:1 using a permuted block randomization scheme, stratified by clinical center and HF etiology (ischemic vs nonischemic).

Figure 1.:
Participants in the HF-ACTION trial included in the adherence analysis. HF-ACTION indicates Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training.

At the baseline visit, prior to randomization, demographics, past medical history, current medications, physical examination, and the most recent laboratory tests were obtained. All participants underwent baseline cardiopulmonary exercise testing (CPX). The HF-ACTION testing protocol was uniform and rigorous. A CPX was completed using commercially available metabolic carts and motor-driven treadmills employing a modified Naughton protocol.18 Peak oxygen uptake (V˙o2peak) was determined as the highest V˙o2 normalized to body weight (mL/kg/min) for a given 15- or 20-sec interval within the last 90 sec of exercise or the first 30 sec of recovery, whichever was greater. Test results were reviewed by investigators to identify significant arrhythmias or ischemia that would prevent safe exercise training, to determine an appropriate exercise prescription, and to establish training HR ranges. In addition, at baseline, the participants completed the 6-min walk test (6MWT). Each 6MWT was conducted in a standardized format, with specific instructions provided in the HF-ACTION manual of operations, modeled after prior studies.19,20 Each HF-ACTION site was instructed to measure a 20-25 m indoor course and to position a chair at either end, providing participants a place to rest, if needed.


Participants randomized to the exercise training arm initially participated in a structured, group-based, supervised exercise program, with a goal of three sessions/wk for a total of 36 sessions in 3 mo. During the supervised training phase, the participants performed walking on a treadmill, or stationary cycling as their primary training mode. Exercise was initially prescribed at 15-30 min/session at a HR corresponding to 60% of HR reserve (maximal HR on CPX minus resting HR), three sessions/wk. After six sessions, the duration of the exercise was increased to 30-35 min, and intensity was increased to 70% of HR reserve. Participants began home-based exercise after completing 18 supervised sessions and fully transitioned to home exercise after 36 supervised sessions. Participants were provided home exercise equipment (cycle or treadmill [ICON]) and HR monitors (Polar USA, Inc). The target training regimen for home exercise was five sessions/wk for 40 min at a HR of 60-70% of HR reserve. Occasional “booster” sessions of supervised exercise were offered to participants with poor adherence during the period of home-based training.


The Adherence Core Laboratory was established for HF-ACTION to develop adherence strategies and related materials, support the implementation of adherence strategies at enrolling sites, and monitor exercise adherence at the individual and site levels.21 Adherence data were regularly monitored by the Adherence Core Laboratory. When participants were not meeting their exercise prescription, as defined previously, additional adherence measures were implemented, including targeted phone calls and periodic supervised “booster” sessions during the home-based training period. Sites with low exercise adherence were also provided with additional training, including site visits.

Adherence strategies included print reminders (calendars, newsletters, cards), phone calls to reinforce exercise training goals and identify barriers to adherence, involvement of family members and friends for support, logistical assistance including funds to assist with transportation and child care needs, and other incentives (eg, t-shirts, mugs). To further promote adherence to home-based training, HR monitors were provided to participants.

Adherence was assessed using an assortment of measures including attendance at facility-based exercise sessions, completion of physical activity logs for home-based exercise, telephone follow-up logs, HR monitoring data, and self-reported percentage of time at or greater than the prescribed training range. For this analysis, total duration of exercise (min/wk) versus prescribed min/wk was the primary outcome of adherence. Target exercise for 1-3 mo was 90 min/wk, and for 10-12 mo was 120 min/wk.


For descriptive purposes, adherence categories were established a priori based upon the total duration of exercise (min/wk). Over mo 1-3, the participants were considered fully adherent if they achieved an average of >90 min/wk of exercise training, partially adherent if 45-89 min/wk were achieved, poorly adherent if 15-44 min/wk were achieved, and nonadherent if <15 min/wk were achieved. Baseline characteristics in each of these categories were described. Participants who dropped out of the study were considered separately.

The primary analysis focused on adherence to the exercise intervention for 1-3 mo (predominantly supervised exercise). All assumptions required for regression analysis were assessed prior to modeling. A model of the min/wk (adherence) as a continuous variable was developed by considering >50 clinical, demographic, and exercise testing variables—most of the collected clinical variables of the study—as potential predictors (see Supplemental Digital Content Table 1, available at: A bootstrapped backward selection algorithm using >1000 bootstrap samples in total was performed for selection of the final model. A secondary analysis of exercise training adherence in 10-12 mo (predominantly home-based exercise) was also performed, however, not included in the primary results of this analysis (see Supplemental Digital Content Table 2, available at: Variables for the two time periods were identical, except min/wk in 1-3 mo was added as a potential predictor in the model of adherence in 10-12 mo. As a sensitivity analysis, variables from the 1-3 mo and 10-12 mo models were used to predict dropout using multivariable logistic regression; however, these results are not included in the primary results of this analysis (see Supplemental Digital Content Table 3, available at:, and Supplemental Digital Content Table 4, available at: Missing data among the covariates were filled in using a multiple imputation procedure.


All 1159 participants randomized to the exercise intervention in HF-ACTION were included in this analysis. After accounting for death, dropout, and missing adherence data, 1142 participants were available for the primary 3-mo analysis and 974 participants were available for the 12-mo analysis (Figure 1). For the 3-mo analysis, the median participant age was 59 yr, 30% were women, median LVEF was 25%, and 51% had HF from ischemic etiology. Among those randomized to the exercise intervention, most were either “very satisfied” (79%) or “somewhat satisfied” (13%) with their study arm assignment.

There was a wide range of adherence throughout the study, with the majority of participants continuing to exercise to some extent (Figure 2). Median exercise duration was 77 min/wk during 1-3 mo (goal ≥90 min/wk) and peaked at 95 min/wk (goal ≥120 min/wk) during the transition to the home-based exercise training (4-6 mo). After this, duration gradually decreased over the first 12 mo and then became relatively stable at approximately 50-60 min/wk—approximately two training sessions/wk—for the remainder of the study (Figure 2).

Figure 2.:
Median min/wk of exercise. The bar indicates exercise adherence goal for supervised (1-3 mo; 90 min) and home-based (4-36 mo; 120 min) training. Median exercise time was closest to goal during supervised training (1-3 mo; 77 min). Exercise time was greatest when participants first transitioned to home-based training (4-6 mo) and then gradually decreased over the first year of follow-up and continued across the 36-mo intervention. Missing data were assumed to be no exercise. This figure is available in color online (

By adherence category, the distribution of key clinical and demographic variables for supervised training is displayed in Table 1. Participants who were older, of White race, and not taking a loop diuretic tended to have greater adherence. Full and partial adherers also tended to have better functional performance, including lower NYHA class, greater 6MWT distance, longer CPX duration, and greater V˙o2peak. Variables that appeared similar across adherence categories included LVEF, presence or absence of atrial fibrillation, device therapy, and medical therapy other than loop diuretics.

Table 1 - Baseline Characteristics by Adherence Category for 1-3 moa
Full Adherersb (n = 482) Partial Adherersc (n = 333) Poor Adherersd (n = 176) Nonadhererse (n = 151)
Age, yr 61 (53, 69) 59 (52, 68) 57 (48, 67) 55 (46, 66)
Female (341) 125 (37) 109 (32) 58 (17) 49 (14)
Male (801) 357 (45) 224 (28) 118 (15) 102 (13)
Black/African-American (373) 117 (31) 106 (28) 66 (18) 84 (23)
White (688) 335 (48) 201 (29) 94 (14) 58 (8)
LVEF, % 25 (21, 30) 24 (20, 30) 24 (20, 29) 25 (19, 31)
Baseline NYHA class
II (712) 334 (47) 211 (30) 91 (13) 76 (11)
III or IV (430) 148 (34) 122 (28) 85 (20) 75 (17)
Etiology of HF
Ischemic (587) 260 (44) 178 (30) 84 (14) 65 (11)
Nonischemic (555) 222 (40) 155 (28) 92 (17) 86 (16)
Diabetes mellitus
Yes (370) 133 (36) 117 (32) 61 (16) 59 (16)
No (772) 349 (45) 216 (28) 115 (15) 92 (12)
Serum creatinine 1.1 (1.0, 1.4) 1.2 (1.0, 1.5) 1.2 (1.0, 1.5) 1.2 (1.0, 1.5)
Atrial fibrillation/flutter
Yes (244) 108 (44) 70 (29) 39 (16) 27 (11)
No (898) 374 (42) 263 (29) 137 (15) 124 (14)
ACE inhibitor
Yes (860) 366 (43) 251 (29) 133 (15) 110 (13)
No (282) 116 (41) 82 (29) 43 (15) 41 (15)
Angiotensin II receptor blocker
Yes (283) 117 (41) 88 (31) 40 (14) 38 (13)
No (859) 365 (42) 245 (29) 136 (16) 113 (13)
Yes (1075) 455 (42) 315 (29) 165 (15) 140 (13)
No (67) 27 (40) 18 (27) 11 (16) 11 (16)
Aldosterone receptor antagonist
Yes (518) 204 (39) 160 (31) 78 (15) 76 (15)
No (624) 278 (45) 173 (28) 98 (16) 75 (12)
Loop diuretic
Yes (883) 351 (40) 265 (30) 139 (16) 128 (14)
No (259) 131 (51) 68 (26) 37 (14) 23 (9)
Yes (485) 210 (43) 145 (30) 73 (15) 57 (12)
No (657) 272 (41) 188 (29) 103 (16) 94 (14)
Biventricular pacemaker
Yes (212) 92 (43) 62 (29) 36 (17) 22 (10)
No (930) 390 (42) 271 (29) 140 (15) 129 (14)
6-min walk test, m 389 (320, 450) 366 (287, 433) 343 (287, 427) 332 (259, 391)
CPX duration, min 10.2 (7.9, 12.3) 9.5 (6.9, 12.0) 8.2 (6.2, 11.0) 8.0 (5.5, 10.6)
V˙O2peak, mL/kg/min 15.1 (12.4, 18.1) 14.0 (11.1, 17.6) 13.1 (10.8, 16.9) 13.5 (10.7, 17.0)
Abbreviations: ACE, angiotensin converting enzyme; CPX, cardiopulmonary exercise test; HF, heart failure; ICD, implantable cardioverter defibrillator; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association; V˙O2peak, oxygen uptake.
aData presented as n (%) or median (IQR).
bFull adherers, ≥ 90 min/wk.
cPartial adherers, 45-89 min/wk.
dPoor adherers, 15-44 min/wk.
eNonadherer, <15 min/wk.

The multivariable model utilizing clinical, demographic, and exercise testing variables demonstrated limited ability to predict exercise adherence for the supervised exercise period (1-3 mo) with an overall adjusted R2 of 0.143 (selected variables shown in Table 2). Variables (partial R2) associated with worse adherence in the model included younger age (0.017), more severe mitral regurgitation (0.015), lower income (0.015), and not enrolled in France (0.021). Other variables associated with worse adherence but explaining <1% of the variance in min/wk of exercise included Black or African American race (0.004), lower LVEF (0.005), shorter CPX duration (0.004), shorter 6MWT distance (0.008), and hospitalizations within the last 6 mo (0.006). Baseline variables evaluated and not associated with adherence included sex, etiology of HF, NYHA class, use or dose of HF-related medications (including β-blocker, angiotensin converting enzyme inhibitors, or diuretic), body mass index, smoking status, comorbid disease (including atrial fibrillation, diabetes mellitus, peripheral vascular disease, and chronic obstructive pulmonary disease), resting HR, resting blood pressure, and education level. A scatterplot of actual min/wk of exercise training and the min/wk of exercise predicted by the model for 1-3 mo illustrates the limited predictive power of the multivariable model (Figure 3). A summary of these findings is displayed in Figure 4.

Figure 3.:
Predicted versus actual min/wk of exercise training in months 1-3. A scatterplot of predicted compared with actual min/wk of exercise in 1-3 mo is widely distributed around the identity line. The prediction model was developed by considering >50 clinical, demographic, and exercise testing variables measured prior to initiation of exercise training. This figure is available in color online (
Figure 4.:
Variance in exercise adherence explained and unexplained by study variables. Multivariate and univariate analyses combined considered >50 baseline variables as predictors of exercise adherence (min/wk of exercise). These variables accounted for only approximately 14% of the variance in adherence, leaving 86% of the variance unexplained. This figure is available in color online (
Table 2 - Multivariable Model of Baseline Demographic and Clinical Variables as Predictors of Adherence to Exercise Training in 1-3 mo
Parameter Estimate Standard Error t P Value Partial R 2
Region = France (vs non-France) 47.0 9.5 4.9 <.0001 0.021
Income (vs <$15 K)
$15-$25 K 13.4 5.5 2.5 .014 0.015
$25-$35 K 20.3 5.9 3.5 .0005
$35-$50 K 16.2 6.4 2.5 .012
$50-$75 K 17.5 6.4 2.8 .006
$75-$100 K 22.4 7.3 3.1 .002
$100 K + 9.2 7.9 1.2 .24
Age 0.87 0.19 4.5 <.0001 0.017
Race (vs White)
Black or African American −8.7 3.9 −2.2 .027 0.004
Other race (not Black or White) −3.4 7.3 −0.5 .65
Marital status (vs married)
Widowed −1.0 6.2 −0.2 .87 0.012
Divorced −15.7 5.2 −3.0 .003
Separated 6.2 8.8 0.7 .48
Single (never married) −3.3 5.9 −0.6 .57
Living with a partner 7.7 9.2 0.8 .41
Decline to answer −14.2 22.7 −0.6 .53
Employment status (vs full-time)
Part-time −5.3 7.8 −0.7 .49 0.008
Student 16.4 32.0 0.5 .61
Homemaker −4.1 11.1 −0.4 .71
Volunteer −28.7 32.1 −0.9 .37
Disabled 10.1 5.3 1.9 .057
Unemployed 7.5 8.1 0.9 .35
Retired 8.2 5.6 1.5 .14
LVEF 2.6 1.1 2.5 .014 0.005
Mitral regurgitation grade (vs none)
Trivial 0.23 6.7 0.03 .97 0.015
Mild −9.2 5.3 −1.73 .084
Mild to moderate −10.8 7.8 −1.4 .17
Moderate −11.3 6.0 −1.9 .058
Moderate to severe −23.2 8.2 −2.8 .005
Severe −16.5 8.1 −2.0 .042
Season at randomization (vs winter)
Spring −6.3 4.6 −1.4 .17 0.009
Summer −3.5 4.6 −0.8 .45
Autumn −13.6 4.5 −3.0 .002
6-min walk test 0.057 0.020 2.9 .004 0.008
CPX duration 4.1 2.0 2.0 .042 0.004
BUN −0.18 0.092 −2.0 .046 0.003
Weber class: (vs A: V˙o 2peak >20)
B (16 < V˙o 2peak ≤20) 8.1 7.3 1.1 .27 0.004
C (10 < V˙o 2peak ≤16) 12.7 7.6 1.7 .097
D (V˙o 2peak ≤ 10), mL/kg/min 14.8 9.7 1.5 .13
Hospitalizations in last 6 mo (vs 0)
1 8.2 3.9 2.1 .03 0.006
≥2 −4.6 5.0 −0.9 .36
Abbreviations: BUN, blood urea nitrogen; CPX, cardiopulmonary exercise test; LVEF, left ventricular ejection fraction.

Baseline demographic and clinical characteristics were similarly limited as predictors of adherence during home-based training. The multivariable model to predict minutes of exercise training during 10-12 mo had an overall R2 value of just 0.207, with exercise min/wk during 1-3 mo being the strongest predictor of adherence.


To identify predictors of exercise adherence, this study examined a wide range of clinical and demographic variables among participants randomized to exercise training in HF-ACTION. Adherence (exercise min/wk) varied and generally declined as the study progressed. However, most participants continued to exercise to some extent throughout the study. Younger age, Black or African American race, lower income, more symptomatic HF, more severe mitral regurgitation, and lower exercise capacity were among the variables associated with decreased adherence. No variable included in our models explained more than approximately 2% of the variance in adherence. Thus, the ability of multivariable models to predict adherence at both 1-3 mo and 10-12 mo was very limited (R2: 0.143 and 0.207, respectively).

Understanding the factors influencing exercise adherence is critical to both the successful delivery of CR6,7 and the successful dissemination of exercise interventions.22 Exercise training adherence is especially challenging for patients with HF,9–12 potentially due to frequent intercurrent illness and hospitalization, HF symptoms, and comorbidities including depression, cognitive impairment, and musculoskeletal disorders.7 Unfortunately, previous methodologies for determining exercise adherence have varied—and at times have not been reported in clinical trials of exercise training in patients with HF.7,21,23 Although we have clearly defined exercise adherence in the present analysis, the inability of such a wide range of baseline clinical and demographic variables to predict more than about 14% of the variability in adherence is striking.

Previous research has similarly noted both limitations in predicting exercise training adherence24,25 among other patient populations, in addition to early adherence proving to be the strongest predictor of subsequent adherence.26,27 In a study of exercise training in 213 community-dwelling older adults, Rejeski and colleagues26 found that baseline demographics, disease burden, symptoms, physical function, cognition, and social status accounted for only 10-13% of the variance in adherence during the initial supervised (1-2 mo), transitional (3-6 mo), and maintenance (7-12 mo) phases of exercise training. When adherence early in the study was combined with baseline variables, the model accounted for 21% and 46% of the variance in exercise training adherence during the transitional (3-6 mo) and maintenance (7-12 mo) phases. The predictive power of prior exercise adherence underscores the importance of closely monitoring and promptly addressing problems with adherence early during the intervention phase26; yet, it does not provide insight into the optimal structure and content of such adherence interventions. In addition, this finding suggests that a ramp-up phase prior to participants achieving the prescribed exercise, combined with an initial period of supervised exercise before prescribing home exercise, may be useful to reinforce the exercise habit and in identifying participants at risk for poor adherence.17

The STRRIDE (Studies of a Targeted Risk Reduction Intervention through Defined Exercise) trials—which examined the differential effects of exercise amount, intensity, and mode on cardiometabolic health among individuals with overweight or obesity and dyslipidemia or prediabetes28–30—conducted an analysis exploring determinants and timing of dropout from the exercise intervention, as well as variation in exercise intervention adherence.31 The STRRIDE trials contained a ramp-up period to allow for gradual adaptation to the exercise prescription; however, a majority of individuals who dropped out from one of the exercise interventions did so during this phase. Yet, if an individual made it past this initial ramp period, adherence to the exercise intervention remained high (>75%) and steady across the intervention period. Thus, although a ramp-up period may be important to increase participant adherence, the progression of the exercise and the duration of the ramp-up period may be critical for increasing adherence and decreasing dropout among individuals with overweight or obesity and dyslipidemia or prediabetes. These findings are important to consider especially among a HF population, which has a high rate of obesity, and which may find exercise prescriptions too lofty to achieve.31

Overall, the findings from the present analysis suggest that many of the variables influencing exercise adherence are currently unrecognized or poorly understood. Further research is needed to explore a broader range of variables from system-level factors and the physical environment to genetic determinants; the complex interactions of multiple variables; the influence of these variables in important subpopulations; and changes over time and in response to exercise training interventions.23,32,33 Alternative adherence strategies and attempts to understand the participant experience during these interventions should also be explored.22 Strategies, such as CR-specific incentive programs, offer the advantage of impacting multiple adherence barriers with a single intervention.34,35 System-level changes, such as innovative delivery models or alternative training methods, also warrant further investigation.6,22 Furthermore, conducting motivational interviewing and health-coaching techniques targeting participant motivation, competing commitments, physical discomfort, self-efficacy, and overall exercise experience could provide insight into what the participant is feeling during the intervention, which in turn may provide information for a more optimal intervention approach for improving adherence among the HF population.36–42

The present analysis has several strengths including a large sample size, a well phenotyped population, randomized controlled trial study design, and a very strong adherence program. Important to note as well are the limitations of the analysis. To allow more consistent and accurate monitoring of adherence rates, the definition of adherence used in this study was developed shortly after the start of the trial. Also, the adherence definition did not incorporate all aspects of the exercise prescription (frequency, duration, intensity, and mode).7 Adherence for 10-12 mo was based largely on self-report, which may not be accurate; thus, there could be differences in predictors of adoption and maintenance of exercise training not captured in this analysis. Given there was an Adherence Core designed to mitigate dropout and improve adherence to the protocol, adherence within this trial may not be generalizable to other interventions. Finally, this study was limited to patients with chronic, stable HF with reduced ejection fraction and who met criteria for participation in a clinical trial. Our ability to assess system-level factors influencing adherence to exercise programs outside the structure of a clinical trial was limited.


Baseline clinical and demographic variables were poor predictors of exercise training adherence. Poorer adherence in 1-3 mo was associated with younger age, lower income, more severe mitral regurgitation, shorter 6MWT distance, lower exercise capacity, and Black or African American race; adherence in 10-12 mo was similarly associated with the same variables. However, the greatest predictor of adherence in 10-12 mo was adherence during 1-3 mo, suggesting that if an individual is able to get through the initial months of exercise, he or she will most likely adhere through ≥12 mo. Nonetheless, additional research is needed to identify stronger predictors of exercise adherence. This research will facilitate the development of strategic interventions that target exercise training adherence both in clinical trials and in clinical practice.


The authors thank all of the HF-ACTION participants and staff members.


1. Keteyian SJ, Michaels A. Heart failure in cardiac rehabilitation: a review and practical considerations. J Cardiopulm Rehab Prev. 2022;42(5):296–303.
2. Taylor RS, Sagar VA, Davies EJ, et al. Exercise-based rehabilitation for heart failure. Cochrane Database Syst Rev. 2014;2014(4):CD003331.
3. Yancy CW, Jessup M, Bozkurt B, et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. Circulation. 2017;136(6):e137–e161.
4. Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation. 2013;128(16):1810–1852.
5. Jacques L JT, Schafer J, Chin J, Issa M. Decision memo for cardiac rehabilitation (CR) programs—chronic heart failure Centers for Medicare & Medicaid Services. Published 2014. Accessed October 20, 2014.
6. Balady GJ, Ades PA, Bittner VA, et al. Referral, enrollment, and delivery of cardiac rehabilitation/secondary prevention programs at clinical centers and beyond: a presidential advisory from the American Heart Association. Circ. 2011;124(25):2951–2960.
7. Conraads VM, Deaton C, Piotrowicz E, et al. Adherence of heart failure patients to exercise: barriers and possible solutions: a position statement of the Study Group on Exercise Training in Heart Failure of the Heart Failure Association of the European Society of Cardiology. Eur J Heart Fail. 2012;14(5):451–458.
8. Keteyian SJ, Jackson SL, Chang A, et al. Tracking cardiac rehabilitation utilization in Medicare beneficiaries: 2017 update. J Cardiopulm Rehab Prev. 2022;42(4):235–245.
9. Suaya JA, Shepard DS, Normand SL, Ades PA, Prottas J, Stason WB. Use of cardiac rehabilitation by Medicare beneficiaries after myocardial infarction or coronary bypass surgery. Circulation. 2007;116(15):1653–1662.
10. Evangelista L, Doering LV, Dracup K, Westlake C, Hamilton M, Fonarow GC. Compliance behaviors of elderly patients with advanced heart failure. J Cardiovasc Nurs. 2003;18(3):197–206; quiz 207-208.
11. Marti CN, Georgiopoulou VV, Giamouzis G, et al. Patient-reported selective adherence to heart failure self-care recommendations: a prospective cohort study: the Atlanta Cardiomyopathy Consortium. Congest Heart Fail. 2013;19(1):16–24.
12. van der Wal MH, Jaarsma T, Moser DK, Veeger NJ, van Gilst WH, van Veldhuisen DJ. Compliance in heart failure patients: the importance of knowledge and beliefs. Eur Heart J. 2006;27(4):434–440.
13. Cooper LB, Mentz RJ, Sun JL, et al. Psychosocial factors, exercise adherence, and outcomes in heart failure patients: insights from Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION). Circ Heart Fail. 2015;8(6):1044–1051.
14. Martin K, Sinden A. Who will stay and who will go? A review of older adults' adherence to randomized controlled trials of exercise. JAPA. 2001;9(2):91–114.
15. McKelvie RS, Teo KK, Roberts R, et al. Effects of exercise training in patients with heart failure: the Exercise Rehabilitation Trial (EXERT). Am Heart J. 2002;144(1):23–30.
16. Pozehl BJ, McGuire R, Duncan K, et al. Effects of the HEART Camp trial on adherence to exercise in patients with heart failure. J Card Fail. 2018;24(10):654–660.
17. O'Connor CM, Whellan DJ, Lee KL, et al. Efficacy and safety of exercise training in patients with chronic heart failure: HF-ACTION randomized controlled trial. JAMA. 2009;301(14):1439–1450.
18. Patterson JA, Naughton J, Pietras RJ, Gunnar RM. Treadmill exercise in assessment of the functional capacity of patients with cardiac disease. Am J Cardiol. 1972;30(7):757–762.
19. Kavanagh T, Myers MG, Baigrie RS, Mertens DJ, Sawyer P, Shephard RJ. Quality of life and cardiorespiratory function in chronic heart failure: effects of 12 months' aerobic training. Heart. 1996;76(1):42–49.
20. Guyatt GH, Sullivan MJ, Thompson PJ, et al. The 6-minute walk: a new measure of exercise capacity in patients with chronic heart failure. Can Med Assoc J. 1985;132(8):919–923.
21. Barbour KA, Miller NH. Adherence to exercise training in heart failure: a review. Heart Fail Rev. 2008;13(1):81–89.
22. Fleg JL, Cooper LS, Borlaug BA, et al. Exercise training as therapy for heart failure: current status and future directions. Circ Heart Fail. 2015;8(1):209–220.
23. Tierney S, Mamas M, Woods S, et al. What strategies are effective for exercise adherence in heart failure? A systematic review of controlled studies. Heart Fail Rev. 2012;17(1):107–115.
24. Evangelista LS, Berg J, Dracup K. Relationship between psychosocial variables and compliance in patients with heart failure. Heart Lung. 2001;30(4):294–301.
25. Ni H, Nauman D, Burgess D, Wise K, Crispell K, Hershberger RE. Factors influencing knowledge of and adherence to self-care among patients with heart failure. Arch Intern Med. 1999;159(14):1613–1619.
26. Rejeski WJ, Miller ME, King AC, et al. Predictors of adherence to physical activity in the Lifestyle Interventions and Independence for Elders pilot study (LIFE-P). Clin Interv Aging. 2007;2(3):485–494.
27. Parashar S, Spertus JA, Tang F, et al. Predictors of early and late enrollment in cardiac rehabilitation, among those referred, after acute myocardial infarction. Circulation. 2012;126(13):1587–1595.
28. Kraus WE, Torgan CE, Duscha BD, et al. Studies of a targeted risk reduction intervention through defined exercise (STRRIDE). Med Sci Sports Exerc. 2001;33(10):1774–1784.
29. Slentz CA, Bateman LA, Willis LH, et al. Effects of aerobic vs. resistance training on visceral and liver fat stores, liver enzymes, and insulin resistance by HOMA in overweight adults from STRRIDE AT/RT. Am J Physiol Endocrinol Metab. 2011;301(5):E1033–E1039.
30. Slentz CA, Bateman LA, Willis LH, et al. Effects of exercise training alone vs a combined exercise and nutritional lifestyle intervention on glucose homeostasis in prediabetic individuals: a randomised controlled trial. Diabetologia. 2016;59(10):2088–2098.
31. Collins KA, Huffman KM, Wolever RQ, et al. Determinants of Dropout from and Variation in Adherence to an Exercise Intervention: The STRRIDE Randomized Trials. Transl J Am Coll Sports Med. 2022;7(1):e000190.
32. Marcus BH, Williams DM, Dubbert PM, et al. Physical activity intervention studies: what we know and what we need to know: a scientific statement from the American Heart Association Council on Nutrition, Physical Activity, and Metabolism (Subcommittee on Physical Activity); Council on Cardiovascular Disease in the Young; and the Interdisciplinary Working Group on Quality of Care and Outcomes Research. Circulation. 2006;114(24):2739–2752.
33. 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. 2009;120(12):1141–1163.
34. Gaalema DE, Elliott RJ, Savage PD, et al. Financial incentives to increase cardiac rehabilitation participation among low-socioeconomic status patients: a randomized clinical trial. JACC Heart Fail. 2019;7(7):537–546.
35. Volpp KG, Troxel AB, Pauly MV, et al. A randomized, controlled trial of financial incentives for smoking cessation. N Engl J Med. 2009;360(7):699–709.
36. Martins RK, McNeil DW. Review of motivational interviewing in promoting health behaviors. Clin Psychol Rev. 2009;29(4):283–293.
37. Oliveira JS, Sherrington C, Amorim AB, Dario AB, Tiedemann A. What is the effect of health coaching on physical activity participation in people aged 60 years and over? A systematic review of randomised controlled trials. Br J Sports Med. 2017;51(19):1425–1432.
38. Olsen JM, Nesbitt BJ. Health coaching to improve healthy lifestyle behaviors: an integrative review. Am J Health Promot. 2010;25(1):e1–e12.
39. Cervone HF. Working through resistance to change by using the “competing commitments model.” OCLC Syst Serv Int Digit Libr Perspect. 2007;23(3):250–253.
40. Kegan R, Kegan LLLR, Lahey LL. Immunity to Change: How to Overcome It and Unlock Potential in Yourself and Your Organization. Boston, MA: Harvard Business Press; 2009.
41. Shaw R, Gillies M, Barber J, et al. Pre-exercise screening and health coaching in CHD secondary prevention: a qualitative study of the patient experience. Health Educ Res. 2012;27(3):424–436.
42. Kinnafick FE, Thøgersen-Ntoumani C, Duda JL. Physical activity adoption to adherence, lapse, and dropout: a self-determination theory perspective. Qual Health Res. 2014;24(5):706–718.

adherence; cardiac rehabilitation; exercise; heart failure

Supplemental Digital Content

© 2022 The Authors. Published by Wolters Kluwer Health, Inc.