Abstract
Background
This study aimed to examine the impact of post-discharge exercise habits on functional decline in older patients with heart failure (HF).
Methods and Results
Sixty-three older patients hospitalized due to HF (mean age 79.7±7.1 years; 46.0% male) were enrolled. Patients were categorized as exercisers if they reported engaging in moderate-intensity exercise for at least 30 min, ≥4 times per week, at 6 months post-discharge. Physical function was measured using the Short Physical Performance Battery (SPPB) at discharge and 6 months thereafter. Functional decline, the primary outcome, was defined as a decrease of ≥1 point in SPPB score over 6 months. The underlying etiologies of HF were arrhythmia (n=37; 58.7%), valvular heart disease (n=37; 58.7%), and ischemic heart disease (n=27; 42.9%). Patients were grouped into exercisers (n=36; 57.1%) and non-exercisers (n=27; 42.9%). Non-exercisers showed a significantly higher incidence of functional decline compared with exercisers (29.6% [n=8] vs. 2.8% [n=1]; P=0.003). Firth’s penalized likelihood logistic regression revealed that non-exercising status independently predicted functional decline (odds ratio 5.98; 95% confidence interval 1.41–35.44; P=0.014) after adjusting for relevant confounders.
Conclusions
Absence of post-discharge exercise habits significantly increased the risk of functional decline in older HF patients.
Key Words: Aged, Cardiovascular disease, Patient compliance, Physical activity, Physical function
Central Figure.
Heart failure (HF) has emerged as a significant global health issue, especially with the rapidly aging population. In Japan, the prevalence of HF is growing sharply, with outpatient cases expected to rise from 979,000 in 2005 to approximately 1.3 million by 2030.1 Additionally, the mean age of Japanese HF patients increased from 78.9 years in 2012 to 80.9 years in 2020.2 As the population continues to age, addressing functional decline in older HF patients has become a critical public health priority.
Preventing or mitigating functional decline in these patients is essential, given that gait speed in older HF patients is approximately 35–40% slower compared with their healthy counterparts, and slower gait speed is associated with a poorer prognosis.3 The Short Physical Performance Battery (SPPB), which evaluates gait speed, balance, and strength, is widely used in older populations, including those with HF.4 A mere 1-point improvement in SPPB score has been linked to significant improvements in mortality among HF patients.5 Thus, the SPPB is a key outcome measure in cardiac rehabilitation for older patients with HF.
Despite clear evidence of the benefits of exercise in enhancing physical function in HF patients,6 the consequences of the absence of post-discharge exercise habits remain inadequately studied. Notably, outpatient cardiac rehabilitation in Japan is very low, around 7.3%, hindering the effectiveness of supervised exercise intervention.7 Understanding the impact of the absence of post-discharge exercise habits on functional decline could highlight the importance of sustained exercise and support the rationale for supervised rehabilitation programs.
This study aimed to investigate the impact of exercise habits on SPPB scores among older HF patients after discharge from acute care hospitalization.
Methods
Study Design and Patient Population
This was a single-center, prospective observational study conducted at the Department of Cardiovascular Medicine of Odawara Municipal Hospital from July 2020 to April 2023. Patients meeting the following inclusion criteria were consecutively recruited: (1) age ≥65 years; (2) diagnosis of HF based on the Framingham criteria; (3) discharge home; (4) able to walk without assistance; and (5) survival without unscheduled readmission for 6 months post-discharge. Exclusion criteria included missing data (SPPB scores or exercise habits), and referral to another hospital post-discharge. The study complied with the Declaration of Helsinki and was approved by the Odawara Municipal Hospital institutional review board (approval no. 2020-23). Patients were allowed to opt out.
Exercise Habits
A physical therapist provided standardized exercise guidance at hospital discharge, recommending moderate-intensity exercise for at least 30 min per session, 3–5 times per week. Moderate-intensity exercise was defined as physical activity that causes a slight increase in breathing or heart rate, such as brisk walking or similar daily activities. Exercise habits were reassessed 6 months after discharge through face-to-face interviews, following the modified 2021 Japanese Circulation Society/Japanese Association of Cardiac Rehabilitation guidelines.8 Patients were classified as exercisers if they reported performing ≥30 min of moderate-intensity exercise at least 4 times per week. Although this threshold is slightly lower than the international recommendation of 150 min per week, it is supported by prior HF studies: the HF-ACTION randomized trial established a maintenance goal of 120 min per week,9 and the HEART Camp trial defined adherence as ≥120 min per week of moderate-intensity exercise.10 This criterion was considered both feasible and clinically meaningful for the present study population.
Functional Decline
Physical function was measured using the SPPB at discharge and 6 months post-discharge. The SPPB assesses balance, gait speed, and lower-extremity strength, with scores ranging from 0–12 (lower scores indicate poorer function).11 Functional decline was defined as a reduction of ≥1 point, reflecting the minimal clinically important difference for cardiovascular patients.12
Possible Worsening of HF
Possible worsening of HF, the secondary outcome, was defined as a ≥30% increase in N-terminal pro B-type natriuretic peptide (NT-proBNP) levels at 6 months post-discharge, in line with recommendations from the Japanese Heart Failure Society.13
Other Variables
Baseline demographic, cardiac, and clinical variables, including age, sex, body mass index (BMI), medical history, smoking, Geriatric Nutritional Risk Index (GNRI),14 biochemical data, Barthel Index (BI), New York Heart Association (NYHA) classification, left ventricular ejection fraction (LVEF), NT-proBNP, and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score, were extracted from medical records.15 Rehabilitation service utilization post-discharge was also recorded.
Sample Size Calculation
Sample size calculation was based on detecting an effect size of 0.50 in SPPB scores, using G*Power ver. 3.1.9.7 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). With an alpha of 0.05 and power of 0.80, the required sample size was 67 patients.
Statistical Analysis
Data are expressed as means±SD, or medians (quartiles). Group comparisons between included and excluded patients, and between exercisers and non-exercisers, were performed using the unpaired t-test, Mann-Whitney U test, or Chi-square test, as appropriate. Firth’s penalized likelihood logistic regression analysis was used to assess the impact of absence of post-discharge exercise habits on functional decline and possible worsening of HF, adjusting for relevant confounders. Two multivariable models were used for each outcome. For functional decline, the first model was based on covariates from the Rehabilitation Therapy in Older Acute Heart Failure Patients (REHAB-HF)6 trial and adjusted for age, sex, BMI, SPPB score ≤9 at discharge, and LVEF ≥45%, while the second model was adjusted for the MAGGIC risk score and SPPB score ≤9 at discharge. As a supplementary analysis, to evaluate the consistency of the predefined cut-off for exercise habits, a receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cut-off value of exercise frequency for predicting functional decline. The optimal cut-off was determined based on the Youden index. For possible worsening of HF, 1 model was adjusted for age, sex, BMI, LVEF ≥45%, and NT-proBNP at discharge, and the other for the MAGGIC risk score and NT-proBNP at discharge. Furthermore, a sensitivity analysis, excluding patients who participated in outpatient cardiac rehabilitation (n=44), was performed to evaluate the association between absence of post-discharge exercise habits and functional decline in this restricted cohort. The same analyses as in the main analysis were conducted.
Statistical analyses, except for Firth’s penalized likelihood logistic regression, were performed using SPSS software version 28.0 (IBM Corp., Armonk, NY, USA). Firth’s penalized likelihood logistic regression analysis was conducted using R version 4.5.1 for Windows (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was set at P<0.05.
Results
Figure 1 illustrates the patient enrollment flowchart. Of 143 patients meeting initial inclusion criteria, 46 patients referred to other hospitals and 34 with missing data were excluded, resulting in 63 patients included in the final analysis. Among the 63 patients, 11 (17.5%) were rehospitalized for planned procedures or examinations. Baseline characteristics of included and excluded patients are presented in Table 1. The included patients had significantly lower age, LVEF, and NT-proBNP levels compared with the excluded patients (P<0.05), whereas there were no significant differences in the MAGGIC risk score or BI between the 2 groups.
Figure 1.
Flowchart of the study.
Table 1.
Characteristics of Included and Excluded Patients
| Included patients (n=63) |
Excluded patients (n=80) |
P value | |
|---|---|---|---|
| Age (years) | 79.7±7.1 | 82.3±7.3 | 0.033 |
| Sex, male | 29 (46.0) | 42 (52.5) | 0.442 |
| Living alone | 15 (23.8) | 27 (33.8) | 0.246 |
| BMI (kg/m2) | 21.1±4.3 | 21.9±4.1 | 0.240 |
| GNRI | 93.6±12.4 | 92.0±10.8 | 0.432 |
| Etiology | |||
| Ischemic | 27 (42.9) | 29 (36.3) | 0.422 |
| Arrhythmia | 37 (58.7) | 39 (48.8) | 0.235 |
| Cardiomyopathy | 13 (20.6) | 9 (11.3) | 0.123 |
| Valvular heart disease | 37 (58.7) | 28 (35.0) | 0.005 |
| Comorbidities | |||
| Prior history of heart failure | 29 (46.0) | 37 (46.3) | 0.979 |
| Diabetes | 31 (49.2) | 27 (33.8) | 0.062 |
| Hypertension | 53 (84.1) | 63 (78.8) | 0.415 |
| Dyslipidemia | 30 (47.6) | 23 (28.8) | 0.020 |
| Chronic kidney disease | 23 (36.5) | 26 (32.5) | 0.616 |
| COPD | 4 (6.3) | 5 (6.3) | 0.981 |
| History of cancer | 12 (19.0) | 21 (26.3) | 0.310 |
| Peripheral vascular disease | 2 (3.2) | 2 (2.5) | 0.808 |
| Smoking | 24 (38.1) | 19 (23.8) | 0.063 |
| NYHA class | 0.188 | ||
| I | 5 (7.9) | 3 (3.8) | |
| II | 30 (47.6) | 30 (37.5) | |
| III | 28 (44.4) | 47 (58.8) | |
| <III | 35 (55.6) | 77 (96.3) | |
| LVEF (%) | 45.6±16.7 | 51.8±16.4 | 0.020 |
| Laboratory data | |||
| Cre (mg/dL) | 1.34±0.89 | 1.63±1.47 | 0.146 |
| BUN (mg/dL) | 27.3±14.3 | 29.4±18.5 | 0.468 |
| Alb (g/dL) | 3.7 [3.4–3.9] | 3.3 [3.1–3.7] | 0.003 |
| Hb (g/dL) | 11.8±2.1 | 11.7±2.2 | 0.787 |
| NT-proBNP (pg/mL) | 2,045 [1,028–3,561] | 4,043 [1,349–8,140] | 0.015 |
| Medication | |||
| ACE-I | 9 (14.3) | 15 (18.8) | 0.478 |
| ARB | 27 (42.9) | 38 (47.5) | 0.580 |
| ARNI | 14 (22.2) | 13 (16.3) | 0.365 |
| MRA | 18 (28.6) | 19 (23.8) | 0.513 |
| SGLT2 | 27 (42.9) | 19 (23.8) | 0.015 |
| β-blocker | 48 (76.2) | 54 (67.5) | 0.254 |
| Loop diuretic | 55 (87.3) | 65 (81.3) | 0.328 |
| MAGGIC risk score (points) | 29 [24–33] | 30 [26–34] | 0.334 |
| Barthel Index score | 95 [90–100] | 95 [85–100] | 0.054 |
Data are presented as n (%), median [IQR], or mean±SD. ACE-I, angiotensin converting enzyme inhibitor; Alb, albumin; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor neprilysin inhibitors; BMI, body mass index; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; Cre, creatinine; GNRI, Geriatric Nutritional Risk Index; Hb, hemoglobin; LVEF, left ventricular ejection fraction; MAGGIC, Meta-Analysis Global Group in Chronic Heart Failure; MRA, mineralocorticoid receptor antagonist; NT-proBNP, N-terminal pro B-type natriuretic peptide; NYHA, New York Heart Association; SGLT2, sodium-glucose co-transporter-2.
Table 2 summarizes the baseline characteristics of exercisers and non-exercisers at hospital discharge. Participants had a mean age of 79.7±7.1 years, with 46.0% male, mean BMI of 21.1±4.3 kg/m2, and mean LVEF of 45.6±16.7%. The most common underlying etiologies of HF were arrhythmia (n=37; 58.7%), valvular heart disease (n=37; 58.7%), and ischemic heart disease (n=27; 42.9%). At discharge, 35 (55.6%) patients had NYHA classification <III. Patients were divided into non-exercisers (n=27; 42.9%) and exercisers (n=36; 57.1%). Outpatient cardiac rehabilitation participation was significantly lower among non-exercisers compared with exercisers (14.8% [n=4] vs. 41.7% [n=15]; P=0.020), while the use of day care services or home visit rehabilitation was significantly higher among non-exercisers (25.9% [n=7] vs. 0.0% [n=0]; P=0.002). Dyslipidemia prevalence was significantly higher in non-exercisers. No significant differences were observed for other variables.
Table 2.
Baseline Characteristics of Exercisers and Non-Exercisers at Hospital Discharge
| Exercisers (n=36) |
Non-exercisers (n=27) |
P value | |
|---|---|---|---|
| Age (years) | 78.3±7.0 | 81.4±6.9 | 0.081 |
| Sex, male | 19 (52.8) | 10 (37.0) | 0.215 |
| Living alone | 8 (22.2) | 7 (25.9) | 0.733 |
| BMI (kg/m2) | 21.0±4.1 | 21.2±4.7 | 0.841 |
| GNRI | 93.5±12.5 | 93.7±12.5 | 0.943 |
| Etiology | |||
| Ischemic | 14 (38.9) | 13 (48.1) | 0.462 |
| Arrhythmia | 21 (58.3) | 16 (59.3) | 0.941 |
| Cardiomyopathy | 7 (19.4) | 6 (22.2) | 0.787 |
| Valvular heart disease | 22 (61.1) | 15 (55.6) | 0.658 |
| Comorbidities | |||
| Prior history of heart failure | 14 (38.9) | 15 (55.6) | 0.189 |
| Diabetes | 18 (50.0) | 13 (48.1) | 0.884 |
| Hypertension | 29 (80.6) | 24 (88.9) | 0.370 |
| Dyslipidemia | 12 (33.3) | 18 (66.7) | 0.009 |
| Chronic kidney disease | 11 (30.6) | 12 (44.4) | 0.257 |
| COPD | 2 (5.6) | 2 (7.4) | 0.765 |
| History of cancer | 8 (22.2) | 4 (14.8) | 0.459 |
| Peripheral vascular disease | 1 (2.8) | 1 (3.7) | 0.836 |
| Smoking | 14 (38.9) | 10 (37.0) | 0.881 |
| NYHA class | 0.417 | ||
| I | 4 (11.1) | 1 (3.7) | |
| II | 18 (50.0) | 12 (44.4) | |
| III | 14 (38.9) | 14 (51.9) | |
| <III | 22 (61.1) | 13 (48.1) | 0.306 |
| LVEF (%) | 44.8±14.5 | 46.7±19.4 | 0.670 |
| Laboratory data | |||
| Cre (mg/dL) | 1.0 [0.9–1.5] | 1.2 [0.9–1.6] | 0.389 |
| BUN (mg/dL) | 26.6 [18.3–33.4] | 23.2 [16.1–33.6] | 0.697 |
| Alb (g/dL) | 3.7 [3.4–4.0] | 3.6 [3.4–3.8] | 0.653 |
| Hb (g/dL) | 12.2±2.1 | 11.3±2.0 | 0.078 |
| NT-proBNP (pg/mL) | 1,765 [942–3,153] | 2,088 [1,391–6,089] | 0.298 |
| Medication | |||
| ACE-I | 7 (19.4) | 2 (7.4) | 0.177 |
| ARB | 12 (33.3) | 15 (55.6) | 0.078 |
| ARNI | 14 (38.9) | 10 (37.0) | 0.221 |
| MRA | 12 (33.3) | 8 (29.6) | 0.334 |
| SGLT2 | 15 (41.7) | 12 (44.4) | 0.825 |
| β-blocker | 29 (80.6) | 19 (70.4) | 0.348 |
| Loop diuretic | 32 (88.9) | 23 (85.2) | 0.662 |
| MAGGIC risk score (points) | 28 [24–31] | 31 [26–35] | 0.067 |
| Barthel Index score | 100 [90–100] | 95 [90–100] | 0.083 |
Data are presented as n (%), median [IQR], or mean±SD. Abbreviations as in Table 1.
Figure 2 and Table 3 detail changes in total SPPB scores. At discharge, scores were comparable between groups. Six months post-discharge, non-exercisers had significantly lower SPPB scores than exercisers (10 [interquartile range (IQR) 8–12] vs. 12 [ IQR 10–12] points; P=0.040). Functional decline was significantly more common among non-exercisers compared with exercisers (29.6% [n=8] vs. 2.8% [n=1]; P=0.003), although median score changes were not significantly different between groups.
Figure 2.
Distribution of functional decline and changes in Short Physical Performance Battery (SPPB) scores by exercise habits. (A) Proportion of patients with and without functional decline, defined as a decrease of ≥1 point in the SPPB score from discharge to 6 months after discharge. (B) Distribution of changes in total SPPB score from discharge to 6 months post-discharge. Boxplots show the median, interquartile range, and outliers for each group (exercisers vs. non-exercisers).
Table 3.
Comparison of Total SPPB and Each Component Scores
| Overall (n=63) |
Exercisers (n=36) |
Non-exercisers (n=27) |
P value | |
|---|---|---|---|---|
| Total SPPB | ||||
| At discharge | 10 [8–11] | 10 [8–11] | 9 [8–11] | 0.168 |
| At 6 months | 11 [9–12] | 12 [10–12] | 10 [8–12] | 0.040 |
| Functional decline | 9 (14.3) | 1 (2.8) | 8 (29.6) | 0.003 |
| Standing balance | ||||
| At discharge | 4 [3–4] | 4 [3–4] | 4 [2–4] | 0.305 |
| At 6 months | 4 [3–4] | 4 [3–4] | 4 [3–4] | 0.979 |
| Functional decline | 7 (11.1) | 4 (11.1) | 3 (11.1) | 0.651 |
| Gait speed | ||||
| At discharge | 3 [3–4] | 4 [3–4] | 3 [2–4] | 0.053 |
| At 6 months | 4 [3–4] | 4 [4–4] | 4 [2–4] | 0.004 |
| Functional decline | 7 (11.1) | 2 (5.6) | 5 (18.5) | 0.113 |
| Muscle strength | ||||
| At discharge | 3 [2–4] | 3 [2–4] | 3 [2–4] | 0.406 |
| At 6 months | 4 [2–4] | 4 [3–4] | 3 [2–4] | 0.062 |
| Functional decline† | 9 (14.3) | 1 (2.8) | 8 (29.6) | 0.003 |
Data are presented as n (%), or median [IQR]. †Functional decline was defined as a decrease of ≥1 point in the total SPPB, standing balance, gait speed, and muscle strength score at 6 months after discharge. SPPB, short physical performance battery.
Figure 3 and Table 3 present functional decline and individual SPPB component scores. At discharge, gait speed score did not differ significantly between groups. However, gait speed scores were significantly lower for non-exercisers at 6 months post-discharge (4 [IQR 2–4] vs. 4 [IQR 4–4] points; P=0.004). Muscle strength decline was significantly higher among non-exercisers (29.6% [n=8] vs. 2.8% [n=1]; P=0.003). No significant differences were found in standing balance scores or gait speed decline between groups.
Figure 3.
Functional decline in each component of the Short Physical Performance Battery (SPPB). Functional decline was defined as a decrease of ≥1 point in each SPPB component from discharge to 6 months after discharge. (A) Standing balance: no significant difference was observed between exercisers and non-exercisers. (B) Gait speed: no significant difference was observed between exercisers and non-exercisers. (C) Muscle strength: the incidence of decline was significantly higher in non-exercisers compared with exercisers (P=0.003, Chi-square test).
The main analyses used 2 multivariable logistic regression models using Firth’s penalized likelihood method to assess predictors of functional decline 6 months post-discharge (Table 4). The first model was adjusted for age, sex, BMI, SPPB score ≤9 at discharge, and LVEF ≥45%. In this model, non-exercisers independently predicted functional decline (odds ratio [OR] 5.98; 95% confidence interval [CI] 1.41–35.44; P=0.014). In the second model, which included the MAGGIC risk score and SPPB score ≤9 at discharge as covariates, non-exercisers similarly predicted functional decline (OR 5.99; 95% CI 1.40–35.65; P=0.015). In contrast, participation in outpatient cardiac rehabilitation and the use of day care services or home visit rehabilitation did not predict functional decline.
Table 4.
Firth’s Penalized Likelihood Logistic Regression Analysis of Predictors of Functional Decline at 6 Months After Discharge
| Main analysis† | Sensitivity analysis‡ | |||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P value | OR | 95% CI | P value | |
| Non-exerciser (reference: exerciser) | ||||||
| Model 1 | 5.98 | 1.41–35.44 | 0.014 | 5.73 | 1.07–56.43 | 0.041 |
| Model 2 | 5.99 | 1.40–35.65 | 0.015 | 6.12 | 1.11–64.09 | 0.036 |
| Participation in outpatient cardiac rehabilitation | ||||||
| Model 1 | 0.76 | 0.11–4.11 | 0.752 | – | – | – |
| Model 2 | 0.70 | 0.12–2.98 | 0.639 | – | – | – |
| Use of day care service or home visit rehabilitation | ||||||
| Model 1 | 1.07 | 0.09–7.76 | 0.953 | 1.06 | 0.08–9.77 | 0.958 |
| Model 2 | 1.16 | 0.10–8.47 | 0.893 | 1.18 | 0.09–10.30 | 0.886 |
†The main analysis included 63 patients, comprising those who participated in outpatient cardiac rehabilitation. ‡The sensitivity analysis included 44 patients, excluding those who participated in outpatient cardiac rehabilitation. Model 1 was adjusted for age, sex, body mass index, SPPB score ≤9 at discharge, and left ventricular ejection fraction ≥45%. Model 2 was adjusted for the Meta-Analysis Global Group in Chronic Heart Failure risk score and SPPB score ≤9 at discharge. CI, confidence interval; OR, odds ratio; SPPB, short physical performance battery.
Figure 4 shows the ROC curve for predicting functional decline. The ROC curve analysis identified 3.5 days per week of ≥30 min of exercise as the optimal cut-off for predicting functional decline, according to the Youden index (area under the curve 0.74; 95% CI 0.58–0.89; P=0.003; sensitivity 64.2%; specificity 88.9%).
Figure 4.
Optimal cut-off of exercise frequency for predicting functional decline. Receiver operating characteristic curve for exercise frequency (≥30 min per day of moderate-intensity exercise), showing an area under the curve of 0.736 (95% confidence interval 0.579–0.893; P=0.003) and a cut-off of ≥3.5 days per week. Sensitivity and specificity were 64.2% and 88.9%, respectively.
Table 4 presents the results of a sensitivity analysis excluding patients who participated in outpatient cardiac rehabilitation. In this analysis (n=44; 23 non-exercisers and 21 exercisers), non-exercisers had a higher incidence of functional decline compared with exercisers (30.4% [n=7] vs. 4.8% [n=1]; P=0.045). Firth’s penalized logistic regression, adjusted for age, sex, BMI, SPPB score ≤9 at discharge, and LVEF ≥45% in model 1, and for the MAGGIC risk score and SPPB score ≤9 at discharge in model 2, showed that non-exercisers predicted functional decline (model 1: OR 5.73; 95% CI 1.07–56.43; P=0.041; model 2: OR 6.12; 95% CI, 1.11–64.09; P=0.036).
Regarding the secondary outcome, possible worsening of HF incidence did not significantly differ between groups (non-exercisers 18.5% [n=5] vs. exercisers 19.4% [n=7]; P=0.574). Firth’s penalized likelihood logistic regression analyses showed that non-exercisers were not significantly associated with possible worsening of HF. In model 1, adjusted for age, sex, BMI, LVEF ≥45%, and NT-proBNP at discharge, the OR for non-exercisers was 1.23 (95% CI 0.28–5.85; P=0.78). In model 2, adjusted for the MAGGIC risk score and NT-proBNP at discharge, the OR ratio was 0.86 (95% CI 0.21–3.35; P=0.83).
Discussion
We investigated the association between self-reported exercise habits and functional decline in older HF patients. The primary finding was that non-exercisers at 6 months post-discharge predicted a significantly higher incidence of functional decline. However, non-exercisers did not predict a significantly higher incidence of possible worsening of HF. To our knowledge, this study is the first to demonstrate that self-reported absence of regular exercise habits is linked to increased functional decline in older HF patients.
The significantly higher rate of functional decline observed in non-exercisers aligns with previous studies. For instance, disruptions in outpatient cardiac rehabilitation due to the Coronavirus disease 2019 (COVID-19) pandemic worsened frailty indicators such as the Kihon Checklist in older HF patients.16 Additionally, the REHAB-HF trial demonstrated that a comprehensive multi-domain physical rehabilitation program significantly improved SPPB scores among older HF patients.6 Consistent with these studies, our findings indicate that the absence of post-discharge exercise habits predicts functional decline.
Among the SPPB components, the strength test exhibited the greatest proportion of decline among non-exercisers. Similarly, the REHAB-HF trial reported the largest effect size for the strength component of the SPPB,6 and resistance training interventions have demonstrated significant improvements specifically in strength.17 These findings collectively suggest that muscle strength assessments may be the most sensitive indicators of the beneficial effects of habitual exercise, explaining the higher incidence of muscle strength decline in non-exercisers.
Interestingly, we observed that the absence of post-discharge exercise habits did not significantly influence the risk of possible worsening of HF, defined as a ≥30% increase in NT-proBNP levels. A previous meta-analysis has indicated that aerobic exercise improves NT-proBNP levels,18 while another randomized controlled trial aimed at increasing daily physical activity has failed to demonstrate such effect.19 Two potential explanations exist for our finding. First, comprehensive cardiac rehabilitation programs, combining structured exercise with education and psychological support, significantly reduce mortality and readmission.20,21 Second, defined exercise intensity rather than general activity increase might be critical for NT-proBNP reduction. Thus, both comprehensive rehabilitation and clearly defined exercise intensity parameters may be necessary for preventing possible worsening of HF.
Study Limitations
We found that the absence of post-discharge exercise habits predicted significantly higher rates of functional decline. However, this study has several limitations. First, the single-center design limits the generalizability of the findings, and multicenter studies are required for broader applicability. Second, exercise habits were self-reported, potentially introducing recall bias, and specific physical activity metrics, such as daily step counts, sedentary behavior, and exercise intensity, were not assessed objectively. Third, current guidelines recommend at least 150 min of moderate-intensity aerobic exercise per week or 75 min of vigorous-intensity exercise weekly to reduce cardiovascular risks.22 Our definition of regular exercise (≥30 min, ≥4 days per week, totaling ≥120 min weekly) differs slightly from these guidelines, and future studies should evaluate outcomes based on guideline recommendations. Fourth, in the main analyses, the absence of post-discharge exercise habits predicted an increased risk of functional decline, and a sensitivity analysis excluding patients who participated in outpatient cardiac rehabilitation yielded similar results. However, the relatively small sample size and limited number of outcome events resulted in notably wide 95% CIs, indicating that these findings should be interpreted with caution. Future research including a larger number of patients should further investigate the impact of exercise habits on functional decline. Fifth, a large number of patients (n=80) were excluded due to referral to another hospital post-discharge or missing data. Although there were no significant differences in prognostic indicators, such as the MAGGIC risk score, between the included and excluded patients, the possibility of selection bias cannot be ruled out. Last, the interpretation of worsening HF is limited, as patients who were rehospitalized for HF exacerbation were excluded. In this study, worsening HF was assessed as a possible outcome based on NT-proBNP; however, an increase in NT-proBNP alone does not necessarily indicate HF exacerbation. Therefore, findings related to worsening HF should be interpreted with caution.
Conclusions
In older patients with HF, lack of exercise habits post-discharge was associated with a higher incidence of functional decline; however, it did not affect the incidence of possible worsening of HF within 6 months.
Disclosures
The authors have nothing to disclose.
IRB Information
The study protocol underwent review and approval by the ethical review board of Odawara Municipal Hospital (approval no. 2020-23).
Acknowledgments
We thank the staff of Odawara Municipal Hospital for their cooperation. During the preparation of this manuscript, we used DeepL and ChatGPT to assist with English language editing.
Data Availability
The deidentified participant data will not be shared.
References
- 1. Okura Y, Ramadan MM, Ohno Y, Mitsuma W, Tanaka K, Ito M, et al.. Impending epidemic: Future projection of heart failure in Japan to the year 2055. Circ J 2008; 72: 489–491. [DOI] [PubMed] [Google Scholar]
- 2. Kanaoka K, Iwanaga Y, Sumita Y, Nakai M, Miyamoto Y.. Management and outcomes of acute heart failure hospitalizations in Japan. Circ J 2024; 88: 1265–1273. [DOI] [PubMed] [Google Scholar]
- 3. Ozawa T, Yamashita M, Seino S, Kamiya K, Kagiyama N, Konishi M, et al.. Standardized gait speed ratio in elderly patients with heart failure. ESC Heart Fail 2021; 8: 3557–3565. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Pavasini R, Guralnik J, Brown JC, di Bari M, Cesari M, Landi F, et al.. Short Physical Performance Battery and all-cause mortality: Systematic review and meta-analysis. BMC Med 2016; 14: 215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Fuentes-Abolafio IJ, Stubbs B, Pérez-Belmonte LM, Bernal-López MR, Gómez-Huelgas R, Cuesta-Vargas AI.. Physical functional performance and prognosis in patients with heart failure: A systematic review and meta-analysis. BMC Cardiovasc Disord 2020; 20: 512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Kitzman DW, Whellan DJ, Duncan P, Pastva AM, Mentz RJ, Reeves GR, et al.. Physical rehabilitation for older patients hospitalized for heart failure. N Engl J Med 2021; 385: 203–216. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Kamiya K, Yamamoto T, Tsuchihashi-Makaya M, Ikegame T, Takahashi T, Sato Y, et al.. Nationwide survey of multidisciplinary care and cardiac rehabilitation for patients with heart failure in Japan: An analysis of the AMED-CHF Study. Circ J 2019; 83: 1546–1552. [DOI] [PubMed] [Google Scholar]
- 8. Makita S, Yasu T, Akashi YJ, Adachi H, Izawa H, Ishihara S, et al.. JCS/JACR 2021 Guideline on rehabilitation in patients with cardiovascular disease. Circ J 2022; 87: 155–235. [DOI] [PubMed] [Google Scholar]
- 9. O’Connor CM, Whellan DJ, Lee KL, Keteyian SJ, Cooper LS, Ellis SJ, et al.. Efficacy and safety of exercise training in patients with chronic heart failure: HF-ACTION randomized controlled trial. JAMA 2009; 301: 1439–1450. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Alonso WW, Kupzyk KA, Norman JF, Lundgren SW, Fisher A, Lindsey ML, et al.. The HEART Camp exercise intervention improves exercise adherence, physical function, and patient-reported outcomes in adults with preserved ejection fraction heart failure. J Card Fail 2022; 28: 431–442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al.. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol 1994; 49: M85–M94. [DOI] [PubMed] [Google Scholar]
- 12. Rinaldo L, Caligari M, Acquati C, Nicolazzi S, Paracchini G, Sardano D, et al.. Functional capacity assessment and minimal clinically important difference in post-acute cardiac patients: The role of Short Physical Performance Battery. Eur J Prev Cardiol 2022; 29: 1008–1014. [DOI] [PubMed] [Google Scholar]
- 13. Minamisawa M, Anzai T, Inomata T, Kinugawa K, Sakata Y, Sato N, et al.. 2023 update of the Japanese Heart Failure Society scientific statement on BNP and NT-proBNP levels in heart failure practice. J Card Fail 2025; 31: 1453–1459, doi:10.1016/j.cardfail.2025.03.005. [DOI] [PubMed] [Google Scholar]
- 14. Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I, et al.. Geriatric Nutritional Risk Index: A new index for evaluating at-risk elderly medical patients. Am J Clin Nutr 2005; 82: 777–783. [DOI] [PubMed] [Google Scholar]
- 15. Pocock SJ, Ariti CA, McMurray JJ, Maggioni A, Køber L, Squire IB, et al.. Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies. Eur Heart J 2013; 34: 1404–1413. [DOI] [PubMed] [Google Scholar]
- 16. Kato M, Ono S, Seko H, Tsukamoto T, Kurita Y, Kubo A, et al.. Trajectories of frailty, physical function, and physical activity levels in elderly patients with heart failure: Impacts of interruption and resumption of outpatient cardiac rehabilitation due to COVID-19. Int J Rehabil Res 2021; 44: 200–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Vikberg S, Sörlén N, Brandén L, Johansson J, Nordström A, Hult A, et al.. Effects of resistance training on functional strength and muscle mass in 70-year-old individuals with pre-sarcopenia: A randomized controlled trial. J Am Med Dir Assoc 2019; 20: 28–34. [DOI] [PubMed] [Google Scholar]
- 18. Santoso A, Maulana R, Alzahra F, Prameswari HS, Ambari AM, Hartopo AB, et al.. The effects of aerobic exercise on N-terminal pro-B-type natriuretic peptide and cardiopulmonary function in patients with heart failure: A meta-analysis of randomised clinical trials. Heart Lung Circ 2020; 29: 1790–1798. [DOI] [PubMed] [Google Scholar]
- 19. Vetrovsky T, Siranec M, Frybova T, Gant I, Svobodova I, Linhart A, et al.. Lifestyle walking intervention for patients with heart failure with reduced ejection fraction: The WATCHFUL trial. Circulation 2024; 149: 177–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kamiya K, Sato Y, Takahashi T, Tsuchihashi-Makaya M, Kotooka N, Ikegame T, et al.. Multidisciplinary cardiac rehabilitation and long-term prognosis in patients with heart failure. Circ Heart Fail 2020; 13: e006798. [DOI] [PubMed] [Google Scholar]
- 21. Yamamoto S, Okamura M, Akashi YJ, Tanaka S, Shimizu M, Tsuchikawa Y, et al.. Impact of long-term exercise-based cardiac rehabilitation in patients with chronic heart failure: A systematic review and meta-analysis. Circ J 2024; 88: 1360–1371. [DOI] [PubMed] [Google Scholar]
- 22. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al.. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: Executive summary: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2019; 74: 1376–1414. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The deidentified participant data will not be shared.