The Relationship Between Health-Related Quality of Life and Frailty in Older Patients Participating in Early Phase II Cardiac Rehabilitation

Abstract

Background: Frailty is a significant prognostic risk factor for cardiovascular disease and it can lead to poor quality of life due to malnutrition, fatigue, and reduced physical activity. However, few studies have investigated how frailty affects older patients participating in cardiac rehabilitation (CR) on health-related quality of life (HRQoL).

Methods and Results: Between November 2015 and December 2016 at Juntendo University Hospital, 217 patients (mean age 74.6±5.8 years; males 67%) participated in CR. Patients completed self-evaluations using the 36-item Short Form Survey (SF-36) and the Kihon Checklist (KCL) at the baseline of CR. The patients were divided into 3 groups: frailty group (n=81; 37%); pre-frailty group (n=71; 33%); and non-frailty group (n=65; 30%). Based on the KCL findings, we compared demographics, clinical measures, and SF-36 scores among the 3 groups. Sex, body mass index, 6-min walking distance, hemoglobin level, and low-density lipoprotein cholesterol differed significantly among the 3 groups. All SF-36 items also showed significant group differences; the frailty group scored lower than the other 2 groups on the physical component summary and mental component summary (MCS). Furthermore, the frailty group had a lower MCS score than the average Japanese age level.

Conclusions: Frail older patients undergoing CR experience significant deterioration in both physical and mental dimensions of HRQoL.

Frailty is expected to become more common as Japan’s population ages rapidly.¹ Clinically, frailty is an early manifestation of a physical, social, or psychological impairment and is an important prognostic factor in patients with cardiovascular disease (CVD).² A decline in functioning across various physiological systems as well as an increased susceptibility to stress characterize the progression of frailty.³ Frailty and pre-frailty increase the risk of CVD and have also become a top priority in CVD.^4,5 The Kihon Checklist (KCL), a self-administered questionnaire, is widely regarded as an effective screening tool for assessing frailty in the elderly.^6,7 Research has shown a positive relationship between overall KCL scores and validated assessments of physical functions, nutritional status, cognitive function, depression, mood, and the number of frailty phenotypes.^6,8

Previous studies have shown that frailty is associated with lower health-related quality of life (HRQoL).^9–12 We use HRQoL assessments to evaluate the various effects of a disease and determine its utility and associated disability.¹³ Frailty is strongly associated with higher depression ratings, a larger number of comorbid conditions, and lower HRQoL.¹⁴ Patients with CVD start cardiac rehabilitation (CR) to get comprehensive secondary prevention services and improve their outcomes.^15,16 Nonetheless, only a few studies have examined the relationship between frailty and HRQoL in older patients with CVD.^17,18 Therefore, we conducted a further study to investigate the relationship between the frailty components and HRQoL.

Methods

Study Population

We performed this study on a group of people aged ≥65 years who were enrolled in the early phase II CR program at Juntendo University Hospital in Tokyo, Japan. The study’s design was cross-sectional. Via a self-interview using clinical self-reported questionnaires, we collected baseline clinical data between November 2015 and December 2016 at the start of CR. Japanese people with CVD can receive CR for a variety of conditions, including acute myocardial infarction, angina pectoris following open heart surgery, chronic heart failure, major vascular disease, and peripheral artery disease.^19,20 CR encompasses patient medical evaluations, exercise therapy, secondary preventive education, and psychosocial factor support. The present study assessed clinical characteristics such as coronary risk factors and physical performance in elderly people who had participated in CR. Of the 255 patients, 38 were excluded due to missing or incomplete data on KCL or 36-item Short Form Survey (SF-36) scores, and 217 patients who participated in the CR program and completed questionnaires that met the criteria while hospitalized were recruited. We informed each patient about the study’s objectives and procedures, and all participants signed informed consent forms, in accordance with the Declaration of Helsinki. The study protocol’s approval number is H13-0058, and it was granted by the ethics committee of the institutional review board at Juntendo University Hospital.

Data Collection and Measurements

Clinicians assessed the patients’ age, gender, body mass index (BMI), underlying illness, coronary risk factors, left ventricular ejection fraction, body composition, muscular strength, and exercise tolerance at the beginning of the CR. All patients were measured for anthropometric parameters, grip strength in both hands and a 6-min walking test, as described previously.^7,15,21,22 Blood samples were analyzed for hemoglobin (Hb), albumin, creatinine, estimated glomerular filtration rate (eGFR), triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), hemoglobin A1c (HbA1c), brain natriuretic peptide, and geriatric nutritional risk index (GNRI).^15,23

Frailty Ascertainment

We used the KCL to comprehensively evaluate frailty, solely to identify individuals who may require new certification for long-term care insurance. This self-administered measure includes 25 items divided into 7 categories of questions designed to assess instrumental activities of daily living, physical function, nutritional status, oral function, social activities, cognitive function, and depressed mood. As such, the KCL is a reliable and comprehensive instrument that focuses on the social, psychological, and physical components for predicting overall frailty and specific frailty-related characteristics in elderly patients. A binary system rates each category as ‘yes’ or ‘no’. The final score ranges from 0 (indicating no frailty) to 25 (indicating severe frailty). A higher score indicates poorer overall performance. Out of a maximum of 25 points, a total score of ≥8 points indicates frailty, while a score between 4 and 7 points indicates pre-frailty, and a score of ≤3 points indicates non-frailty, based on the criteria provided by Satake et al.⁶ For statistical analysis, we divided the patients into frailty, pre-frailty, and non-frailty groups.

HRQoL Instrument

To assess HRQoL, we used the Japanese iteration of the Medical Outcome Study 36-item Short Form Survey version 2 (Fukuhara S., Suzukamo Y. Manual of SF-36v2 Japanese version: Institute for Health Outcomes & Process Evaluation Research, Kyoto, 2004). The SF-36 questionnaire consists of 36 items divided into 8 distinct categories. These dimensions include various aspects of health, such as physical functioning, role-physical, bodily pain, general health, vitality, social functioning, role-emotional, and mental health.²⁴ Five of the 8 dimensions – physical functioning, role-physical, bodily pain, general health, and vitality – are most closely related to the summary of physical health, while the other 5 – general health, vitality, social functioning, role-emotional, and mental health – are more closely related to the summary of mental health.²⁴ The 8-dimensional scores range from 0 to 100, with higher scores indicating better levels of function and lower scores indicating poorer levels of function. These dimensions are then separated into physical and mental component summaries (PCS/MCS). Standardized norm-based scoring, which compares results to normative data from the Japanese population with a mean score of 50 and a standard deviation (SD) of 10, suggests that higher scores indicate a higher HRQoL.

Statistical Analysis

Continuous variables are presented as mean±SD. Following a 1-way analysis of variance (ANOVA), we used Tukey’s honestly significant difference test to compare the multiple comparisons. Comparisons of categorical variables were done using the χ² test. We used Spearman’s correlation coefficient to determine the relationship between the KCL and SF-36 scores. The multiple regression analysis was performed using the stepwise method on factors that showed significant differences among the 3 groups for both PCS and MCS. P values <0.05 indicated significant differences. We performed the statistical analysis using the software JMP12.2pro (SAS Institute Inc., Cary, NC, USA).

Results

Patient’s Clinical Characteristics

Table 1 shows the individual clinical study characteristics. The study involved all 217 patients. With a mean age of 74.6±5.8 years, 146 (67%) patients were male. Based on the Satake et al.⁶ criteria, we divided the patients into 3 groups: a non-frailty group of 65 (30%) patients, a pre-frailty group of 71 (33%) patients, and a frailty group of 81 (37%) patients. The 3 groups did not differ significantly in terms of age, underlying illness, insurance-covered diseases, or prevalence of coronary risk factors. Gender was a significant difference among the 3 groups (P<0.05). The patients were significantly under-represented in both the frailty and pre-frailty groups compared with the non-frailty group, with lower lean body weight, 6-min walking distance (6MWD), and Hb levels (P<0.05 for all metrics). The frailty group had significantly lower BMI and GNRI levels compared with the non-frailty group (P<0.05 for all parameters).

Table 1.

Clinical Characteristics of the Study Participants

	All (n=217)	Non-frailty (n=65)	Pre-frailty (n=71)	Frailty (n=81)	P value among the 3 groups
Age (years)	74.6±5.8	73.4±5.4	74.7±5.9	75.4±5.7	0.09
Sex, male	146 (67)	52 (80)	42 (59)	52 (64)	0.02
BMI (kg/m2)	22.3±3.9	23.5±2.5	22.4±4.0	21.2±4.4a	<0.01
Hypertension	132 (60)	43 (66)	45 (63)	44 (54)	0.3
Dyslipidemia	104 (47)	33 (50)	33 (46)	38 (46)	0.85
Diabetes	56 (25)	16 (24)	14 (19)	26 (32)	0.21
Current smoker	32 (14)	13 (20)	7 (9)	12 (14)	0.25
CVD at the beginning of CR
Ischemic heart disease	115 (52)	32 (49)	39 (54)	44 (54)	0.76
Open heart surgery	133 (61)	36 (55)	42 (59)	55 (67)	0.27
Chronic heart failure	44 (20)	11 (16)	17 (23)	16 (19)	0.58
Insurance-covered diseases
Acute myocardial infarction	13 (6)	8 (12)	3 (4)	2 (2)	0.41
Angina pectoris	12 (6)	4 (6)	5 (7)	3 (3)
Chronic heart failure	49 (23)	12 (18)	18 (25)	19 (23)
Major vessel disease	10 (5)	3 (4)	3 (4)	4 (4)
Post heart surgery	130 (59)	37 (56)	42 (59)	51 (62)
Peripheral artery disease	3 (1)	1 (1)	0 (0)	2 (2)
Anthropometric parameters and physical function
Body fat percentage (%)	22.9±9.7	23.0±7.8	24.2±11.2	21.8±9.8	0.35
Lean body weight (kg)	44.0±9.0	47.6±7.7	42.5±8.9a	42.2±9.3a	<0.01
Right grip strength (kg)	27.4±7.1	28.0±6.8	27.2±7.5	26.8±7.4	0.8
Left grip strength (kg)	26.7±7.3	27.4±7.2	25.9±7.4	26.4±7.7	0.7
6-min walking distance (m)	349±94	389±75	342±91a	312±102a	<0.01
Laboratory data
Hemoglobin (g/dL)	12.6±1.8	13.3±1.8	12.4±1.8a	12.2±1.6a	<0.01
Albumin (g/dL)	3.7±0.4	3.8±0.4	3.7±0.4	3.7±0.4	0.07
Creatinine (mg/dL)	1.19±1.38	1.00±1.10	1.22±1.35	1.31±1.60	0.38
eGFR (mL/min/1.73 m2)	62.5±24.9	67.2±21.1	63.3±27.6	58.1±24.8	0.08
TG (mg/dL)	110±72	112±60	115±101	104±47	0.59
HDL-C (mg/dL)	49±14	49±15	50±14	48±14	0.73
LDL-C (mg/dL)	97±28	101±27	102±29	91±28	0.03
HbA1c (%)	6.1±0.8	6.0±0.6	6.0±0.9	6.2±0.8	0.37
BNP (pg/mL)	278±582	211±435	295±392	317±779	0.53
GNRI	98.3±12.6	102.4±8.1	97.9±15.1	95.5±12.2a	<0.01
LV ejection fraction (%)	57.7±15.5	58.9±15.9	56.5±14.5	57.7±16.2	0.66

Data are presented as n (%), or mean±SD. The P value is presented as a comparison of the non-frailty, pre-frailty and non-frailty groups. ^aP<0.05 vs. non-frailty using Tukey’s honestly significant difference test. BMI, body bass index; BNP, B-type natriuretic peptide; CVD, cardiovascular disease; CR, cardiac rehabilitation; eGFR, estimated glomerular filtration rate; GNRI, geriatric nutritional risk index; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LV, left ventricular; TG, triglycerides.

KCL Score and Frailty Levels

The 3 groups showed significant differences in all 7 frailty components (P<0.01). Table 2 shows that all 7 frailty components, subscore and total score, excluding socialization and activities of daily living, were significantly greater in the pre-frailty and frailty groups than those in the non-frailty group (P<0.01). Also, all 7 frailty components, subscore and total score, with the exception of nutritional status, were significantly higher in the frailty group compared with the pre-frailty group (P<0.05).

Table 2.

Comparison of Kihon Checklist Score

	Non-frailty (n=65)	Pre-frailty (n=71)	Frailty (n=81)	P value
Kihon Checklist
Activities of daily living (1–5)	0.1±0.4	0.3±0.7	1.3±1.6a,b	<0.01
Physical strength (6–10)	0.3±0.7	1.2±1.0a	2.4±1.3a,b	<0.01
Nutrition status (11–12)	0.2±0.4	0.6±0.6a	0.8±0.6a	<0.01
Oral function (13–15)	0.2±0.4	0.9±0.8a	1.5±0.8a,b	<0.01
Socialization (16–17)	0.1±0.3	0.3±0.4	0.7±0.7a,b	<0.01
Cognitive function (18–20)	0.1±0.3	0.5±0.6a	0.8±0.8a,b	<0.01
Depressive mood (21–25)	0.3±0.5	1.3±1.1a	3.0±1.4a,b	<0.01
Subscore (1–20)	1.2±0.8	4.0±1.3a	7.6±2.5a,b	<0.01
Total score (1–25)	1.6±1.0	5.3±1.1a	10.6±2.7a,b	<0.01

Data are presented as mean±SD. ^aP<0.05 vs. non-frailty, and ^bP<0.05 vs. pre-frailty, using Tukey’s honestly significant difference test.

Comparison of SF-36 Scores Among the 3 Groups

Figure 1 depicts a comparison of SF-36 scores among the 3 groups. In contrast to the non-frailty group, the total subitems of SF-36 components were lower in the pre-frailty and frailty groups (P<0.01). Compared with the non-frailty group and pre-frailty group, the subitems other than bodily pain, social functioning, and role-emotional were lower in the frailty groups (P<0.01). The frailty group scored lower on the MCS and PCS than the other 2 groups. In each group, the PCS score was lower than the average age’s level for Japanese people (value=42.6). Furthermore, the frailty group had a lower MCS score than the average age’s level for Japanese people (value=51.5).

Figure 1.

Comparison of 36-item Short Form Survey (SF-36) components among the 3 groups using an average score from Japanese patients aged 70–79 years. *P<0.05 vs. non-frailty using Tukey’s honestly significant difference test. HRQoL, health-related quality of life.

Correlations of PCS and MCS With 6MWD Among the 3 Groups

Figure 2 shows the correlations of PCS and MCS across the 6MWD. In the pre-frailty group (r=0.52; P=0.002) and the non-frailty group (r=0.38; P=0.006), a significant correlation between PCS score and 6MWD was found, but not in the frailty group (r=0.22; P=0.14). In contrast, no significant association was found between MCS and 6MWD in any group.

Figure 2.

Correlations between the 6-min walking distance (6MWD) and health-related quality of life (HRQoL) summary scores among the 3 groups. MCS, mental health component summary; PCS, physical component summary.

Correlations Between the KCL Score and HRQoL Summary Scores Among the 3 Groups

Figure 3 depicts the correlations between the KCL score and the HRQoL summary scores for the 3 groups. We found significant correlations between the KCL score (1–20) and PCS scores in the frailty group. However, the MCS and KCL scores (1–20) did not show any correlations among the 3 groups. We also found that the MCS score and the KCL depression score (21–25) in the pre-frailty and frailty groups had a significantly negative correlation. Conversely, the MCS score and the KCL depression score (21–25) in the non-frailty group had no significant correlations.

Figure 3.

Correlations between the Kihon Checklist (KCL) Score and health-related quality of life (HRQoL) summary scores among the 3 groups. MCS, mental health component summary; PCS, physical component summary.

Multivariate Analysis of PCS and MCS

Table 3A demonstrates that, in all patients, the multiple regression analysis with clinical parameters correlated with PCS scores showed that both 6MWD and pre-frailty or frailty were significantly associated with PCS scores (β=0.43 and 0.16, 0.43 and 0.21, all P<0.05). Table 3B shows that, for all patients, the multiple regression analysis using clinical parameters correlated with MCS scores, revealing that both LDL-C and pre-frailty or frailty were significantly associated with MCS scores (β=−0.25 and 0.35, −0.30 and 0.36, all P<0.05).

Table 3.

Multivariate Regression Analysis With Clinical Parameters Correlated (A) PCS Levels and (B) MCS Levels

(A)	β	P value	β	P value	β	P value	β	P value	β	P value
PCS
Age	−0.04	0.54	−0.01	0.80	0.11	0.09	0.05	0.49	0.05	0.51
Male	0.14	0.04	0.11	0.11	0.006	0.92	0.01	0.87	0.02	0.81
BMI	0.12	0.09	0.08	0.21	−0.04	0.49	−0.05	0.51	−0.08	0.32
Hemoglobin	0.11	0.14	0.08	0.26	−0.004	0.95	0.05	0.52	0.10	0.26
LDL-C	−0.003	0.95	−0.02	0.72	−0.21	<0.01	0.02	0.76	−0.01	0.87
Albumin	−0.04	0.59	−0.05	0.48	0.096	0.20	−0.07	0.43	−0.09	0.31
eGFR	0.09	0.19	0.08	0.24	0.07	0.31	0.11	0.17	0.08	0.33
6MWD							0.43	<0.01	0.43	<0.01
Pre-frailty			0.26	<0.01			0.16	0.04
Frailty					0.32	<0.01			0.21	0.01
Adjusted R2	0.05		0.11		0.14		0.22		0.23
(B)	β	P value	β	P value	β	P value	β	P value	β	P value
MCS
Age	0.08	0.21	0.12	0.07	0.11	0.09	0.16	0.07	0.15	0.09
Male	0.01	0.81	−0.02	0.73	0.006	0.92	0.04	0.62	0.05	0.52
BMI	0.008	0.90	−0.03	0.62	−0.04	0.49	0.02	0.73	−0.007	0.93
Hemoglobin	0.01	0.90	−0.02	0.70	−0.004	0.95	−0.04	0.61	0.02	0.77
LDL-C	−0.15	0.03	−0.17	0.01	−0.21	<0.01	−0.25	<0.01	−0.30	<0.01
Albumin	0.08	0.26	0.07	0.31	0.09	0.20	0.17	0.06	0.14	0.11
eGFR	0.10	0.18	0.08	0.23	0.07	0.31	0.10	0.27	0.04	0.64
6MWD							−0.04	0.63	−0.03	0.74
Pre-frailty			0.33	<0.01			0.35	<0.01
Frailty					0.32	<0.01			0.36	<0.01
Adjusted R2	0.01		0.11		0.10		0.14		0.13

6MWD, 6-min walking distance; MCS, mental health component summary; PCS, physical component summary. Other abbreviations as in Table 1.

Discussion

The present study sought to determine the relationship between frailty and HRQoL in elderly patients with early phase II CR. We discovered that the frailty group had lower PCS and MCS scores than the non-frailty and pre-frailty groups. The frailty group had significantly lower SF-36 PCS and MCS scores across all 8 domains among the 3 groups. To the best of our knowledge, we are the first to investigate the relationship between frailty and HRQoL outcomes in Japanese older patients undergoing early phase II CR.

According to estimates of the general older adult population in Japan, frailty is estimated to affect 6.9%, with pre-frailty accounting for 49.6%.²⁵ This study found a prevalence of 32% pre-frailty and 37% frailty, which is higher than that of community-dwelling people, as shown in our previous paper.²⁶ Older women with a higher frailty rate (8.8%) experience more impairment, depression, and a lower quality of life after a CR program than men (5.4%).^27,28 Further research found that women, those with less education, and those who were overweight had a lower quality of life.²⁹ Unfortunately, we did not investigate the educational backgrounds of our patients.

Some parameters, such as BMI, lean body weight, 6MWD, Hb, LDL-C levels, and GNRI, differed significantly between the 3 groups. Frail patients with CVD frequently had low levels of physical activity, poor HRQoL, and low self-efficacy.^30–32 Previous reports have demonstrated that the pre-frail state, the performance of a physical activity, and frailty-upper limb dysfunction status all had a significant impact on HRQoL.³³ Our findings revealed that there was a significant difference in Hb levels among the 3 groups. Anemia can cause functional, mobility, falls, and HRQoL impairments.³⁴ The GNRI is a screening tool for assessing an elderly patient’s nutritional status and identifying signs of frailty.³⁵ In older people with CVD, a substantial increase in the risk score for undernutrition (malnutrition) is associated with poor HRQoL.³⁶ The frailty group includes factors such as female anemia and nutritional status, both of which can cause low HRQoL and may be responsible for the current findings.

This study showed that there are differences in HRQoL by the presence or absence of frailty, which was evaluated using the KCL. All 3 groups showed significant differences in the 8 subscales of SF-36 (P<0.05). Furthermore, we investigated the relationship between physical function parameters and PCS and discovered significant negative correlations between PCS score and 6MWD in the non-frailty and pre-frailty groups, but no correlation in the frailty group. In contrast, the KCL score (1–20) and the PCS score in the frailty group had a significant difference. These findings indicate that assessing physical function based on daily activities is more important for PCS in the frail group than 6MWD. The MCS score and KCL score (1–20), as well as the MCS score and 6MWD, showed no significant correlation in any of the 3 groups. We also found a significant relationship between the MCS score and the KCL depression score (21–25) in the pre-frailty and frailty groups. In contrast, the MCS score and the KCL depression score (21–25) in the non-frailty group had no significant difference in our study. Depression may be linked to mental HRQoL domains in pre-frailty and frailty populations.

The SF-36 score was positively correlated with grip strength and walking speed while negatively correlated with frailty in both inpatients and community-dwelling patients.³⁷ We found a statistically significant difference (r=0.38; P=0.04) between left grip strength and PCS in the frailty group. Weight loss (P<0.001) and fatigue (P<0.001) were found to have an independent negative relationship with the MCS of QoL for 496 community-dwelling persons aged ≥70 years. Additionally, gait speed (P<0.001) and grip strength (P<0.001) were demonstrated to have an inverse relationship with the PCS score. According to the report, frailty is independently associated with lower scores on the MCS and PCS of HRQoL. These findings support some of our findings from this study.^12,38,39 However, previous studies found no significant interaction between frailty and HRQoL, indicating the need for additional research.⁴⁰ In the present study, there were differences in quality of life with and without frailty depending on the underlying cardiac disease (Supplementary Figure). Patients with ischemic heart disease and those post open-heart surgery were thought to be related to the effects of pain related to the disease or procedure (Supplementary Tables 1,2). Interestingly, some items did not differ significantly between patients with and without frailty in heart failure (Supplementary Table 3). Even in the non-frail group, there were indications that the quality of life was low due to heart failure and medical conditions.^41,42

Study Limitations

Several limitations apply to this study. First, this study is limited by the inclusion of older participants from Japan, recruited from a single center and small sample, which may not fully represent the diversity within the broader older CVD population. Second, due to its cross-sectional design, the present study was unable to establish a causal relationship between frailty and HRQoL. We will collect data at the end of CR and report on the impact of phase II CR on HRQoL and physical function in elderly frail patients in the future. Third, it was not possible to assess the patients’ educational history or cognitive function, and the questionnaire’s consistency could be limited. Last, for the present study, we enrolled Japanese patients who had only participated in CR. Despite the translation of the KCL into English and other languages, additional research is needed to determine its applicability to patients who participated in CR abroad. Furthermore, the 6MWD was better than in general frailty patients, and grip strength was not significant among the 3 groups. These may be applicable in more under-represented cases. Thus, these findings call for additional research into the connections and relationships between frailty and HRQoL using more screening tools, as well as an increase in the number of CVD patients and the communities in which they live.

Conclusions

HRQOL, as well as physical and mental domains, declined in frail older patients undergoing CR. This suggested that the need for a comprehensive intervention that includes both physical and mental support considering social life and daily roles in the frailty group is necessary.

Acknowledgments

This work was supported by JSPS KAKENHI grant number JP22K11428. The authors thank all of the study participants, as well as Akio Honzawa and Yuki Someya (Cardiovascular Rehabilitation and Fitness), who helped collect data for this study.

Sources of Funding

JSPS KAKENHI (Grant no. 19K11374 and 22K11428) and the High Technology Research Center Grant from the Ministry of Education, Culture, Science, and Technology, Japan, partially supported this study.

Disclosures

H.D. is a member of Circulation Reports’ Editorial Team. All authors declare that there is no conflict of interest.

IRB Information

We conducted the present investigation following the ethical guidelines of the Declaration of Helsinki. According to the ethics committee of our institution review board at Juntendo University Hospital (approval no. H13-0058), every participant provided informed consent. This study’s registration number is UMIN000021647 with the Clinical Trials Registry of the University Hospital Medical Information Network (UMIN).

Data Availability

The deidentified participant data will not be shared.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-24-0180

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