Submitted:
04 February 2026
Posted:
06 February 2026
You are already at the latest version
Abstract
Background: Frailty is highly prevalent among older adults with cardiovascular disease (CVD) and strongly predicts disability and mortality after cardiac events. Although cardiac rehabilitation (CR) improves prognosis, frail elderly patients often face barriers to participate to in-person programs. eHealth-based, home-delivered CR programs incorporating tele-rehabilitation and remote monitoring may improve accessibility, yet evidence regarding their effectiveness on frailty status remains limited. Methods: We designed a multicenter, randomized, parallel-group trial enrolling people ≥65 years recently hospitalized for acute heart failure (AHF) and/or acute coronary syndrome (ACS). Participants were randomized 1:1 to an eHealth home-based tele-rehabilitation program or usual care. The primary endpoint is frailty prevalence at follow-up, defined by an Essential Frailty Toolset (EFT) score ≥3, with co-primary outcomes being between-group differences in the mean levels of EFT and Short Physical Performance Battery (SPPB) scores after 3–6 months. Secondary endpoints include mortality and hospitalization, among others. Results: The full protocol and study procedures are reported. Between May 2024 and December 2025, 589 patients were screened at the two Italian centers involved; 442 met eligibility criteria and 209 were enrolled and randomized. Baseline characteristics were largely comparable between groups. Mean age was 77 ± 9 years, 70% were male, and 55% had ACS. At the time of reporting, follow-up had been completed in 172 individuals, with balanced dropout between groups. Lower-than-expected enrollment was mainly attributable to refusal related to difficulties in using digital devices. Conclusions: This randomized trial will evaluate whether a multidomain, eHealth-based CR intervention can reduce the prevalence or degree of frailty in older people after AHF or ACS. We report the study protocol and baseline characteristics of the enrolled cohort, highlighting the challenge of digital illiteracy in contemporary elderly populations.
Keywords:
cardiovascular diseases
; frailty
; telemedicine
; cardiac rehabilitation
; protocol
Introduction
Older people with cardiovascular disease (CVD) are particularly vulnerable, exhibiting both high mortality [1] and a consistent risk of disability following hospitalization [2]. A major determinant of such poor outcomes is frailty, a clinical syndrome highly prevalent among elderly individuals with CVD [3]. Frailty is characterized by a progressive decline in the physiological reserves of multiple organ systems, which limits the individual’s ability to cope with stressors and predisposes to negative health trajectories. As an independent predictor of autonomy loss, frailty is strongly associated with adverse clinical outcomes, including hospital readmissions, institutionalization, and death [4].
Preliminary evidence suggests that frailty may not represent an irreversible condition, raising the possibility that targeted interventions could improve resilience and potentially alter the course of the syndrome [3]. Cardiac rehabilitation (CR) programs are established effective tools to improve cardiovascular prognosis and quality of life [5]. However, despite their proven efficacy, the uptake of CR remains suboptimal, with elderly and frail patients facing the greatest barriers due to mobility limitations, comorbidities, and thus a limited access to structured programs. To this respect, digital health interventions offer the opportunities to extend the reach and adaptability of traditional models. By facilitating adherence, enhancing accessibility, and supporting individualized care, eHealth-based CR programs, which incorporate tele-rehabilitation (TR) and remote monitoring, have the potential to overcome many of the barriers encountered by older patients [6]. However, few studies have explored the efficacy of eHealth, home-based CR interventions on frailty. Indeed, a recent systematic review concluded that more studies are needed to define its efficacy and usefulness to make definitive recommendations [7].
To fill this gap, we undertook a randomized trial to evaluate whether an eHealth, home-based CR intervention, compared with standard of care, is able to reduce the rate or the degree of frailty in people ≥ 65 years and with acute heart failure (AHF) and/or acute coronary syndrome (ACS). Here we report the protocol of the trial and the baseline clinical data of included participants.
Materials and Methods
Study Design
This is a multicenter, randomized, parallel group study in elderly patients with a recent hospitalization for AHF and/or ACS, conducted at two study sites, IRCCS MultiMedica, Sesto San Giovanni (MI), Italy and Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of Bari Institute, Bari, Italy. People with AHF or ACS and ≥ 65 years old were randomized into two groups receiving: Group A) a multidisciplinary eHealth home-based TR program and Group B) usual care. After a minimum of 3 and up to maximum 6 months, enrolled subjects will undergo a new evaluation for frailty. A summary of study design is presented in Figure 1.
Figure 1. Schematic summary of the design of the trial. This multicenter, randomized, parallel-group study enrolled elderly patients recently hospitalized for acute heart failure (AHF) and/or acute coronary syndrome (ACS) at two Italian sites: IRCCS MultiMedica in Sesto San Giovanni (Milan) and the Istituti Clinici Scientifici Maugeri IRCCS, Cardiac Rehabilitation Unit of the Bari Institute in Bari. Participants aged ≥65 years with AHF or ACS were screened for frailty with multiple tools including Essential Frailty Toolset (EFT) and the Short Physical Performance Battery (SPPB) and then randomly assigned to one of two groups: Group A, receiving an eHealth-supported home-based telerehabilitation (TR) program, or Group B, receiving usual care. After a minimum of 3 and up to a maximum of 6 months, all enrolled subjects will undergo a follow-up assessment of frailty.
Study Endpoints
Primary endpoints
To assess whether a home-based TR program significantly reduces the prevalence of frailty in comparison to usual care in elderly individuals hospitalized for ACS and/or AHF. Frailty status is defined as having an Essential Frailty Toolset (EFT) scale (ranging from 0 to 5) ≥ 3 [8].
Since recent literature suggests that frailty is a progressive continuum and not a present/absent condition [9,10] and considering that small improvement in the degree of frailty are associated with tangible benefits [11], the mean degree of frailty was considered as a co-primary endpoint. In particular, mean differences between groups at the end of treatment in the EFT and in the Short Physical Performance Battery (SPPB) scales were set as co-primary outcomes after the start of the enrollment due to the considerations reported above.
Secondary endpoints
- A composite endpoint of all-cause death and all-cause hospitalizations.
- A composite endpoint of cardiovascular death and cardiovascular hospitalizations (ACS, AHF, cardiac arrhythmias, cerebrovascular events, peripheral arterial vascular event).
- Changes in walking distance at 6-min walk test at follow-up vs baseline in the two groups.
- Assessment of the quality of life, comorbidity burden, nutritional and cognitive status, depression, adherence to medical therapy and anthropometric measures. Tools for the evaluation of these end-points are described below.
- Incidence of falls during follow-up.
- Proteomic and miRNOmic analyses to search for biomarkers of frailty.
End of Study Definition
The end of study is defined as the date of the last visit of the last subject in the study. A participant is considered to have completed the study if he/she has completed all steps of the protocol.
Study Population
Inclusion criteria
- Age ≥ 65 yrs. The protocol was modified after the start of the study to lower the previous threshold of 75 years due to the high rate of refusals to participate attributable to the inability of the elderlies to use technological devices (as detailed below).
- Recent (< 30 days) hospitalization for AHF or ACS.
- Signed informed consent
Exclusion criteria
- Judgment by the investigator that the participant is unlikely to comply with study procedures (i.e. ability of patient or caregiver in utilizing E-Health device)
- Other medical conditions determining a ≤ 6-months survival prognosis
- Severe cognitive impairment, assessed through Mini Mental State Examination (MMSE < 15)
- Participation in another clinical study with a study intervention administered in the last 4 week
Study Procedures
Step 1. Patients were screened and recruited during hospitalization in the Cardiovascular Department of IRCCS MultiMedica (Sesto San Giovanni – MI, Italy) or Cardiac rehabilitation Unit of IRCCS ICS Maugeri (Bari-BA, Italy). Main clinical and demographic data were collected for each patient. The day before discharge, patients will perform a 6-min walk test, a blood sample for molecular biomarkers and a frailty evaluation through both the EFT and SPPB scales. Depression, quality-of-life, cognitive status, nutritional status, adherence to medical therapy were evaluated with adequate tools. Blood samples were collected to identify biomarker able to identify frailty and predict its progression.
Step 2. Before discharge, all enrolled patients were randomized with a ratio 1:1 to intervention (Group A) or usual care (Group B). Group A followed a multidisciplinary home-based TR program while Group B was referred to general practitioners (GPs).
Step 3. Home-based TR program was implemented in Group A patients. Group B was referred to GPs according to usual care. During step 3 clinical outcome and adverse event were collected.
Step 4. After at least 3 to maximum 6 months follow-up patients will undergo final evaluation; procedures of step 1 will be repeated. Study procedures are summarized in Supplementary Table 1.
Description of procedures
Measure of frailty: EFT and SPPB
ETF is a brief four-item (chair rise, cognitive status, hemoglobin, and serum albumin) frailty scale [8]. The EFT is scored 0 (least frail) to 5 (most frail). Prevalence of frailty was defined according to the EFT score, specifically EFT ≥3 of 5.
The SPPB is an objective tool measuring the physical performance status with a recognized prognostic values for multiple frailty-related outcomes [22,23,24]. The SPPB is calculated with the time spent or needed to complete multiple tasks: standing balance, usual gait speed, and five times chair tests. The timed results of each subtest are rescaled according to predefined cut-points for obtaining a score ranging from 0 (most physically frail) to 12 (least frail/best performance) [24,25].
Human Biological Sample Biomarkers Collection and Analysis
Collection of samples for biomarker research is part of this study. Plasma was collected from all participants in this study at baseline and will be collected at the end of follow-up.
To identify novel biomarkers able to identify frailty and predict its progression, we will use plasma samples collected at the moment of frailty evaluation to identify miRNAs and inflammatory proteins cross-sectionally associated with frailty. Such candidates will derive from an unbiased miRNOmic analysis and a focused proteomic analysis. Statistical analysis will reveal which markers (and relative combination) identify the frail status. Then, after dosing such candidate molecules in the whole cohort, we will perform statistical analyses to explore if such markers are able also to predict the eventual improvement or worsening of frailty at follow-up, after intervention.
Six-minute walk test (6MWT)
The 6MWT was performed in an indoor 60-m-long corridor, according to the recommendations of the American Thoracic Society [12]. All patients were instructed to walk along the corridor from one end to the other at their own pace, as many times as possible, in the permitted time. After 6 min had elapsed, the patients were instructed to stop walking, and the total distance walked was determined. This test was supervised by a physical therapist who encouraged the patients in a standardized fashion at regular intervals.
Measure of quality of life: EuroQol (EQ) visual analogue scale (VAS)
The EQ VAS is a measure of self-reported health outcomes, based on a visual analogue graduated scale (0 to 100) for assessing health status, ranging from the worst imaginable health state (0) to the best imaginable health state (100) [13].
Measure of comorbidity: Cumulative Illness Rating Scale (CIRS).
Pre-existing comorbidities were assessed using the CIRS, which gives a severity score and comorbidity score. This index is based on the scoring (from 0 to 4) of disease severity for 14 items corresponding to organs that may be affected by chronic disease. The CIRS severity score can be calculated as the average of each CIRS item score. The CIRS comorbidity score is based on the count of organ systems with moderate-to-severe impairment [14].
Measure of the nutritional status: Mini Nutritional Assessment (MNA)
MNA is a screening tool aimed at assessing nutritional status in elderly patients through 18 questions in 4 areas (basic anthropometrics, dietary intake, global indicators and self-assessed health status). Individuals with a score of 24–30 are considered to have a normal nutritional status, a score of 17–23.5 suggests a risk of malnutrition, and a score of < 17 identifies malnutrition [15].
Cognitive status: Mini Mental Examination (MMSE)
Global cognitive function was assessed by the MMSE. In clinical practice, MMSE is used to detect cognitive impairment, monitor cognitive decline over time, and evaluate the impact of potential treatments on cognition (particularly in older adults). It is brief, easily administered, and quickly scored. The measure assesses orientation, attention and calculation (serial 7 s, spell “world” backward), language (naming, repetition, comprehension, reading, writing, copying), and immediate and delayed recall. Scores > 24 indicate normal cognitive status, while lower scores indicate cognitive impairment [16].
Depression: Geriatric Depression Scale (GDS)
The GDS is a self-administered test developed for a brief screening of depression in elderly persons. The 15-items short form (GDS-15) was used for this study [17]. Scores may range from 0 (no depression) to 15 (severe depression). Answers are dichotomic to facilitate understanding and answering in elderly individuals. The questions can be read to patients. The GDS correlates highly with well-studied measures such as the Beck Depression Inventory, Zung Depression Inventory, and Hamilton Rating Scale for Depression [17].
Functional Independence: Barthel index of Activity daily living (ADL)
The Barthel Index measures functional disability in 10 ADLs by quantifying patient performance. 5-point increments are used in scoring, with a maximal score of 100 indicating full independence whilst a lowest score of 0 indicating a patient with a complete bed-bound state [18].
Adherence to therapy: Morisky medication adherence scale (MMAS-8)
MMAS-8 is an 8-item structured self-measured, self-reported measure that assesses medication adherence [19].
Anthropometric measures
The patients were weighed before breakfast without their shoes, and the body mass index (BMI) were calculated as [weight (kg)]/[height (m)]2.
Laboratory
Complete blood counts and creatinine, total cholesterol, haemoglobin, albumin, and NT-proBNP levels were measured as routine clinical practice. The glomerular filtration rate (eGFR) was estimated by the Cockcroft–Gault formula.
E-Health home-based CR program
A multidisciplinary home-based CR program was implemented in Group A patients. The program last maximum 6 months; it was coordinated by a cardiologist and it was based on a multidisciplinary intervention by different health-care providers: nurse (case manager), physiotherapist, psychologist, and dietician.
In the present study a synchronous model of cardiac TR, based on a real-time interaction between the patient and healthcare provider, was used. The advantages of this approach are that it allows a very close follow-up and a better program personalization.
The core of the TR program was represented by scheduled video-calls with the patient (2 per week, 30-45 min duration each) by the nurse and physiotherapist focused on: education and cognitive training, exercise training session along with physiotherapy counseling once a week, assessment of healthy lifestyle, risk factor control, medication adherence and therapeutic targets. A video-call by psychologist and dietician for psychosocial and nutritional counselling was provided at least 1 fortnight. Social assistant was involved as needed by the nurse case-manager.
Videocall was done by 1) cell phone 2) tablet 3) PC according to patient’s preference.
For monitoring medication adherence and fall risk, one of tools, i.e. text messaging, smartphone applications, or other electronic device, tailored on the patient characteristics, was applied. If applicable, an initial in-home visit to assess fall risk elements was also performed and a digital health alarm system to alert caregivers in case of physical discomfort was provided.
The program included also unscheduled calls from the patient in the case of symptoms in predefined time slots.
A pulse oximeter and a portable one-lead electrocardiograph for telemonitoring of vital signs were available and patients were able to call and to be assisted in the case of urgent need or emergency, according to internal standard operating instruction of each site.
Exercise training sessions were based on a 'low level' and a 'high level' intensity, as detailed in the section below. The 'low level' consisted of 10-30 min of free exercise on a mini-ergometer without load and 30 min of callisthenic exercises, performed 3 times/week and free walking twice a week. The 'high level' consisted of 30-45 min on a free mini-ergometer with incremental load, 30-40 min of muscle reinforcement exercises using weights between 0.5 and 2.5 kg.
Exercise training protocol
All included individuals were subjected to the Short Physical Performance Battery (SPPB) to also evaluate balance and fall risk; based on the result obtained, they will subsequently carry out a Six minute walk test (6MWT) to evaluate their functional capacity. The subjects were thus categorized into two groups based on the test results: low level and high level (Supplementary Figure 1). Subjects unable to perform a 6MWT for clinical and/or functional reasons performed only SPPB.
REMOTE PHYSIOTHERAPY TREATMENT
1. Connect online with the patient via the platform;
2. Wear the monitoring devices the patient has previously been trained to use;
3. Take the basal parameters with the patient sitting at rest for at least 5 minutes: detect heart rate, peripheral saturation, basal pressure and subjective sensation of effort using the Borg CR10 scale and enter them in a specific database;
4. Remote training with exercises based on FITT principles: frequency, intensity, time, type and progression of the exercise according to the patient's functional level;
5. Take vital signs at the end of the session with the patient seated.
LOW LEVEL
Patients started training sessions through session of 30 minutes of callisthenic exercises. Subsequently a free effort reconditioning with the use of a seated pedal set without load for 10/15/20/30 minutes based on the patient's clinical condition. In addition, a suggested free walk session of at least 10 maximum 30 minutes per day was set and monitored via a pedometer.
HIGH LEVEL
The sessions included 30 minutes of fatigue-tolerant resistance exercises with dumbbells and ankle cuffs with 2 sets of 15 repetitions. In addition, approximately 30 minutes of free endurance training on a cycle ergometer trying to achieve a Borg CR10 between 3/10 and 5/10 with 5 minutes of warm-up, 20 minutes of activity and 5 minutes of cool-down was performed. The adequate workload was achieved gradually according to FITT method (Supplementary Table 2).
Sample size estimation
From literature, frailty at discharge is expected to be about 40% in the overall study population [13,14]. We expect frailty reversal in Groups A and B of 48% and 15%, respectively [8], so that frailty at the end of follow-up is hypothesized to be 21% in Group A and 34% in Group B. To achieve 80% power in detecting a 13% difference of frailty prevalence between group A and B after the intervention, with alpha error of 5%, a sample size of 370 subjects is needed. Considering about 22% dropout rate a total of 450 patients are estimated.
Considering that recent trials do not consider frailty as a binary outcome, but as a continuous variable [20], and that an improvement of 1 unit in the EFT scale is associated with a 28% lower mortality incidence and thus represent a clinically meaningful phenomenon [11], we also calculated the sample size needed to observe a difference in the mean level of EFT between the two groups. Considering that: 1- using real data collected in our units in other similar studies, the mean EFT at baseline is 2.25±1.25; 2- assuming an improvement of 1 unit (minimal clinically important difference, i.e., the smallest difference in score that benefits the patient) and therefore a mean score at the end of the study of 1.25±1.25 [11]; 3- assuming a power of 90% and alpha of 5%; 4- assuming a 30% improvement also in the control group treated with the standard of care (and thus expecting a mean EFT of 1.95 in this group) and maintaining the same SD for all, it results that 136 individuals are needed to observe a difference in the mean level of EFT score at the end of the trial. Considering also a 22% possible rate of drop-outs, a sample size of 166 people is needed for this analysis. The same calculation using SPPB as the metric of interest provided similar results.
Randomization
Patients were randomly assigned in a 1:1 ratio to intervention and control group through permuted-block randomization with varying block sizes. Randomization lists were center specific and they were created using SAS® Proc Plan Permuted-block randomization.
Data collection and statistical analysis
Study data were collected in an eCRF of an electronic web-based database, where each patient is identified by a randomly assigned alphanumeric code, center specific.
Baseline data were analyzed through descriptive analyses. Results for the overall population and for both Groups A and B are presented. Categorical variable were presented as frequencies and percentages and compared between the two groups by Chi-square test of Fisher’s exact test. Continuous variables were summarized by mean ± standard deviation and tested for normality distribution by Kolmogorv Smirnov test. Since all variables were not normally distributed, the non-parametric Wilcoxon-Mann Whitney U test will be used to compare baseline variables between groups. The prevalence of frail patients in the two groups will be indicated as EFT score ≥3.
All analyses were performed by using SAS Software version 9.4. Tests will be considered significant for a p value < 0.05.
Withdrawal from the Study (or Modified Follow-up)
A participant may withdraw from the study at any time at his/her own request, or may be withdrawn at any time at the discretion of the investigator for safety, behavioral, compliance, or administrative reasons.
Lost to follow up
A participant was considered lost to follow-up if he/she repeatedly was unable to be contacted by the study site.
Ethical Considerations
This study was conducted in accordance with consensus ethical principles derived from international guidelines including the Declaration of Helsinki and Council for International Organizations of Medical Sciences International Ethical Guidelines, applicable ICH Good Clinical Practice Guidelines, and applicable laws and regulations. The study and its amendments were approved by the Ethic Committee of IRCCS MultiMedica and CET Lombardia 5, Prot.nr.509/24 and Prot.nr.353/25.
Results
From May 2024 to December 2025, 589 individuals were assessed for eligibility at the two centers. Among them, 147 patients were excluded because they met the exclusion criteria: more than one third (39%) were unable to use electronic devices, 19% did not have a caregiver, and 21% were clinically unstable. Among the minor reasons for exclusion were MMSE <15 (6%) age <65 years (4%). Therefore, we screened 442 individuals with recent ACS and AHF who fulfilled inclusion criteria and were considered eligible for the trial. Among those eligible, 233 declined to participate to the study while 2 individuals could not be enrolled because they died during hospitalization. The most common reason to decline the participation to the trial was the perceived complexity of being compliant with a demanding program or the willingness to learn how to use the dedicated eHealth platform, a motivation provided by 46% of the eligible individuals approached. The remaining 209 people were enrolled in the study and assigned to one of the two groups through randomization. Two patients assigned to the control group dropped out before terminating exams or questionnaires and one subject in the intervention group died before discharge (Figure 2). We compared included subjects with a subgroup of subjects who declined to participate with complete data about age, sex and type of cardiac event. The included population was representative of the eligible individuals in terms of age and sex (data not shown) but patients who declined were more likely to have a diagnosis of AHF compared with included individuals (58% vs 42%, p=0.0078).
Figure 2. CONSORT flowchart of people screened, excluded and included in the trial.
Among people undergoing randomization, 103 were assigned to the eHealth intervention group and 103 to the standard of care, control group. The baseline demographic and clinical characteristics of the two groups are presented in Table 1 while the overall characteristics of the population with the relative number of observations for each variable are shown in Table 2. Clinical characteristics were comparable in the two groups, with the exception of sex and SPPB (Table 1). Overall, the mean (±SD) age of the patients was 77±9 years, and 145 subjects (70.4%) were male. The mean BMI was 25.8. More than half of the patients had ACS (55.3%). As expected, most of the people were receiving lipid-lowering medications (77.5%), antiplatelet drugs (56.4%), and one or more anti-hypertensive medications.
At the moment of writing, follow-up was terminated for 172 individuals, 90 in the active group and 82 in the control group. Among those enrolled, 3 persons in the active group and 4 in the control group discontinued the intervention due to early mortality. Drop out was registered for 46 subjects, 23 in each group. The last follow-up visit for the last subject enrolled is planned in March 2026.
Discussion
The efficacy of eHealth-based, home-delivered CR interventions on frailty is debated [7]. To address this gap, we designed a randomized trial evaluating whether an eHealth CR program, compared with standard care, can reduce the prevalence or degree of frailty in patients >65 years hospitalized for AHF or ACS. Here we outlined the trial protocol and reported the baseline characteristics of the enrolled participants.
The number of enrolled individuals is lower that the planned sample size for the primary outcome, but sufficient for the co-primary outcomes. Thus, the study will be powered to detect a difference in the mean level of frailty but not to observe a difference in the prevalence of frailty considered as a binary variable, unless a higher than expected efficacy of the intervention emerges.
The lower than expected enrollment capacity is attributable to the high rate of refusal to participate due to the reported inability or fear of using technological devices such as a tablet. These phenomena commonly referred to as “digital illiteracy” and “technophobia”, are common in the oldest old and particularly pervasive in Italy [21]. As a result, we had to amend the protocol after the start of the study to lower the cutoff of age for inclusion in the study. We believe that this not represent an issue since, as discussed, frailty represents a continuum with a broad spectrum of conditions that should be addressed as early as possible and not only when severe comorbidities are present [9,10]. To this respect, this consideration also emphasize the relevance of the results, which will demonstrate whether an eHealth-based, multi-domain intervention is able to affect the degree of frailty when considered as a continuous, and not binary, variable.
Independently of the results that we will observe, it is important to note that digital incompetence represents a key limit for a wide diffusion and implementation of such programs in real-life scenarios. Here, we observed that roughly 20% of the screened individuals could not be enrolled due to the observed inability to use simple technological devices, while almost half of those screened refused to participate due to the lack of willingness to engage in a home-based program or for the refusal of learning how to use the telemedicine software. Of note, the idea of using remote intervention based on telemedicine and telemonitoring was conceived exactly to provide an alternative to elderlies with a high degree of disability and thus struggling to participate to in-person programs and visits. Thus, even if the eHealth approach is effective, it will remain of limited usefulness if it cannot be implemented in a wide range of individuals. Even though common sense suggests that future generations of elderlies, grown in the technological era, will not face such issues, elderly subjects living with these limitations at present should be helped to learn basic informatics skills to have access to such programs.
Relatively to the composition of the groups, these are well balances, as expected by the randomization process. The only variables not balanced are sex and SPPB. Thus, the analyses will be adjusted for such covariates, given their known relationship with the primary outcomes [26].
Conclusions
Here we describe the full protocol of a trial testing whether a multidomain eHealth-based intervention is able to revert or limit frailty progression in people with CVD and more than 65 years. Baseline data of the 206 included individuals are also reported.
Supplementary Materials
The following supporting information can be downloaded at the website of this paper posted on Preprints.org
Author Contributions
Conceptualization: GC, RFEP, FP, FO, AP; methodology, RFEP, MM, GM, EP, ET; formal analysis, ET; investigation, GC, SSB, GB, TS, SM, LE, VP, RLG, DB, EP, GM, AG, RC, AF, MV, CO, LQ, MG, ID, LP, AC, LS; writing—original draft preparation, RFEP, ET, FP; writing—review and editing, GC, RFEP, FO, ET, FP, AP; supervision and administration, GC, RFEP, MM, FO, ET, FP, AP; funding acquisition, GC, RFEP, FO, ET, FP, AP. All authors have read and agreed to the published version of the manuscript.
Funding
Funded by the European Union - Next Generation EU - NRRP M6C2 - Investment 2.1 Enhancement and strengthening of biomedical research in the NHS - PNRR-MAD-2022-12376806 - CUP I43C22000550006.
Institutional Review Board Statement
The Ethic Committees of IRCCS MultiMedica and CET Lombardia 5 (Prot.nr.509/24 and Prot.nr.353/25) approved the study and its amendments.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical restrictions.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Kulmala, J; Nykänen, I; Hartikainen, S. Frailty as a predictor of all-cause mortality in older men and women. Geriatr Gerontol Int. 2014, 14(4), 899–905. [Google Scholar] [CrossRef]
- Buurman, BM; Hoogerduijn, JG; de Haan, RJ; Abu-Hanna, A; Lagaay, AM; Verhaar, HJ; Schuurmans, MJ; Levi, M; de Rooij, SE. Geriatric conditions in acutely hospitalized older patients: prevalence and one-year survival and functional decline. PLoS One 2011, 6(11), e26951. [Google Scholar] [CrossRef] [PubMed]
- Ijaz, N; Buta, B; Xue, QL; Mohess, DT; Bushan, A; Tran, H; Batchelor, W; deFilippi, CR; Walston, JD; Bandeen-Roche, K; et al. Interventions for Frailty Among Older Adults With Cardiovascular Disease: JACC State-of-the-Art Review. J Am Coll Cardiol. 2022, 79(5), 482–503. [Google Scholar] [CrossRef] [PubMed]
- Sirven, N; Dumontet, M; Rapp, T. The dynamics of frailty and change in socio-economic conditions: evidence for the 65+ in Europe. Eur J Public Health 2020, 30(4), 715–719. [Google Scholar] [CrossRef] [PubMed]
- Ambrosetti, M; Abreu, A; Corrà, U; Davos, CH; Hansen, D; Frederix, I; Iliou, MC; Pedretti, RFE; Schmid, JP; Vigorito, C; et al. Secondary prevention through comprehensive cardiovascular rehabilitation: From knowledge to implementation. 2020 update. A position paper from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology. Eur J Prev Cardiol 2021, 28(5), 460–495. [Google Scholar] [CrossRef]
- Scherrenberg, M; Marinus, N; Giallauria, F; Falter, M; Kemps, H; Wilhelm, M; Prescott, E; Vigorito, C; De Kluiver, E; Cipriano, G, Jr.; et al. The need for long-term personalized management of frail CVD patients by rehabilitation and telemonitoring: A framework. Trends Cardiovasc Med. 2023, 33(5), 283–297. [Google Scholar] [CrossRef]
- Esfandiari, E; Miller, WC; Ashe, MC. The Effect of Telehealth Interventions on Function and Quality of Life for Older Adults with Pre-Frailty or Frailty: A Systematic Review and Meta-Analysis. J Appl Gerontol 2021, 40(11), 1649–1658. [Google Scholar] [CrossRef]
- Afilalo, J; Lauck, S; Kim, DH; Lefèvre, T; Piazza, N; Lachapelle, K; Martucci, G; Lamy, A; Labinaz, M; Peterson, MD; et al. Frailty in Older Adults Undergoing Aortic Valve Replacement: The FRAILTY-AVR Study. J Am Coll Cardiol 2017, 70(6), 689–700. [Google Scholar] [CrossRef]
- Howlett, S.E.; Rutenberg, A.D.; Rockwood, K. The degree of frailty as a translational measure of health in aging. Nat Aging 2021, 1, 651–665. [Google Scholar] [CrossRef]
- Newman, AB; Blackwell, TL; Mau, T; Cawthon, PM; Coen, PM; Cummings, SR; Toledo, FGS; Goodpaster, BH; Glynn, NW; Hepple, RT; et al. Vigor to Frailty As a Continuum-A New Approach in the Study of Muscle, Mobility, and Aging Cohort. J Gerontol A Biol Sci Med Sci. 2024, 79(1), glad244. [Google Scholar] [CrossRef]
- Solomon, J; Moss, E; Morin, JF; Langlois, Y; Cecere, R; de Varennes, B; Lachapelle, K; Piazza, N; Martucci, G; Bendayan, M; et al. The Essential Frailty Toolset in Older Adults Undergoing Coronary Artery Bypass Surgery. J Am Heart Assoc. 2021, 10(15), e020219. [Google Scholar] [CrossRef]
- Holland, AE; Spruit, MA; Troosters, T; Puhan, MA; Pepin, V; Saey, D; McCormack, MC; Carlin, BW; Sciurba, FC; Pitta, F; Wanger, J; MacIntyre, N; Kaminsky, DA; Culver, BH; Revill, SM; Hernandes, NA; Andrianopoulos, V; Camillo, CA; Mitchell, KE; Lee, AL; Hill, CJ; Singh, SJ. An official European Respiratory Society/American Thoracic Society technical standard: field walking tests in chronic respiratory disease. Eur Respir J 2014, 44(6), 1428–46. [Google Scholar] [CrossRef]
- Mahoney, F; Barthel, D. Functional evaluation: the Barthel index. Md State Med J 1965, 14, 61–5. [Google Scholar]
- Miller, MD; Paradis, CF; Houck, PR; Mazumdar, S; Stack, JA; Rifai, AH; Mulsant, B; Reynolds, CF, 3rd. Rating chronic medical illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992, 41(3), 237–48. [Google Scholar] [CrossRef]
- Folstei Guigoz, Y; Vallas, BJ; Garry, PJ. Mini Nutritional Assessment: a practical assessment tool for grading the nutritional state of elderly patients. Facts Res Gerontol 1994, 4, s15–59n. [Google Scholar]
- Folstein, S.E.; McHugh, P.R. “Mini Mental State” a practical method for grading the cognitive state of patients for the clinicians. J Psychait Res 1975, 12, 189–198. [Google Scholar] [CrossRef]
- Sheikh, J. I.; Yesavage, J. A. Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clinical Gerontologist: The Journal of Aging and Mental Health 1986, 5(1-2), 165–173. [Google Scholar]
- Mahoney, FI; Barthel, D. Functional evaluation: The Barthel Index. Maryland State Medical Journal 1965, 14, 56–61. [Google Scholar]
- Morisky, DE; Green, LW; Levine, DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986, 24, 67–74. [Google Scholar] [CrossRef] [PubMed]
- Jang, IY; Jung, HW; Lee, HY; Park, H; Lee, E; Kim, DH. Evaluation of Clinically Meaningful Changes in Measures of Frailty. J Gerontol A Biol Sci Med Sci 2020, 75(6), 1143–1147. [Google Scholar] [CrossRef] [PubMed]
- Available online: https://www.istat.it/it/files/2023/06/cs-competenzedigitali.pdf.
- Pavasini, R.; Guralnik, J.; Brown, J.C.; et al. Short Physical Performance Battery and all-cause mortality: systematic review and meta-analysis. BMC Med 2016, 14, 215. [Google Scholar] [CrossRef] [PubMed]
- Guralnik, JM; Ferrucci, L; Simonsick, EM; Salive, ME; Wallace, RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med. 1995, 332(9), 556–61. [Google Scholar] [CrossRef] [PubMed]
- 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(2), M85–94. [Google Scholar] [CrossRef] [PubMed]
- Available online: https://www.nia.nih.gov/research/resource/short-physical-performance-battery-sppb.
- Holmberg, MJ; Andersen, LW. Adjustment for Baseline Characteristics in Randomized Clinical Trials. JAMA 2022, 328(21), 2155–2156. [Google Scholar] [CrossRef]
Table 1.
Baseline characteristics of the included participants allocated to the two groups.
| Variable |
Intervention Group N=103 |
Control Group N=103 |
p-value |
| Acute Coronary Syndrome | 60 (58.25%) | 54 (52.43%) | 0.400 |
| Acute Heart Failure | 43 (41.75%) | 49 (47.57%) | |
| EFT | 1.69 ± 1.19 | 1.89 ± 1.23 | 0.298 |
| Frailty prevalence (EFT ≥3) | 30 (29.13%) | 33 (32.04%) | 0.650 |
| SPPB | 9.11 ± 3.46 | 8.27 ± 3.55 | 0.046 |
| Male sex | 83 (80.58%) | 62 (60.19%) | 0.001 |
| Age | 76.96 ± 7.17 | 77.6 ± 10.38 | 0.220 |
| BMI [kg/m²] | 26.08 ± 4.44 | 25.45 ± 4.82 | 0.392 |
| Systolic Blood Pressure [mmHg] | 115.69 ± 13.62 | 116.67 ± 16.99 | 0.909 |
| Diastolic Blood Pressure [mmHg] | 68.47 ± 8.59 | 69.01 ± 9.16 | 0.829 |
| HR [bpm] | 71.13 ± 12.23 | 70.49 ± 10.93 | 0.962 |
| Hemoglobin [g/dL] | 12.21 ± 1.92 | 12.16 ± 1.63 | 0.973 |
| Albumin [g/dL] | 4.66 ± 5.29 | 3.85 ± 3.07 | 0.460 |
| Total Cholesterol [mg/dL] | 132.69 ± 43.1 | 132.57 ± 37.85 | 0.849 |
| LDL-cholesterol [mg/dL] | 72.69 ± 36.15 | 70.27 ± 31.5 | 0.927 |
| HDL-cholesterol [mg/dL] | 39.16 ± 11.63 | 43.29 ± 31.49 | 0.580 |
| Triglycerides [mg/dL] | 118.81 ± 79.14 | 110.57 ± 43.26 | 0.674 |
| Creatinine [mg/dL] | 1.27 ± 0.52 | 1.36 ± 0.62 | 0.255 |
| eGFR [ml/min] | 71.95 ± 21.22 | 67.91 ± 21.28 | 0.308 |
| Beta-blocker | 83 (83.84%) | 81 (81.82%) | 0.706 |
| ACEi/ARB/ARNI | 67 (68.37%) | 61 (61.62%) | 0.321 |
| MRAs | 70 (70%) | 71 (71%) | 0.877 |
| Ivabradine | 1 (1.05%) | 2 (2.04%) | 1.000* |
| Aspirin | 70 (70%) | 56 (57.14%) | 0.060 |
| Other antiplatelet agents | 58 (59.79%) | 53 (53%) | 0.336 |
| Oral anticoagulant therapy | 28 (29.17%) | 26 (27.37%) | 0.783 |
| Diuretics | 67 (66.34%) | 59 (59%) | 0.282 |
| Metformin | 13 (13.13%) | 10 (10%) | 0.490 |
| Insulin | 12 (12.24%) | 13 (13.27%) | 0.831 |
| SGLT2-i | 51 (51%) | 50 (50.51%) | 0.944 |
| GLP-1RA | 17 (17.89%) | 14 (14.58%) | 0.535 |
| Statins | 80 (79.21%) | 75 (75.76%) | 0.559 |
| Ezetimibe | 65 (65%) | 56 (56%) | 0.193 |
| PCSK9-i | 1 (0.99%) | 0 (0%) | 1.000* |
Data are presented as mean ± SD for continuous variables and as number (percentage) for categorical variables. P values derive from Mann-Whitney U test for continuous variable and from chi-squared test for categorical variables.*Differences between the two groups were analyzed using the Fisher’s exact test. Significant differences are highlighted in bold. EFT: Essential Frailty Toolset; SPPB: Short Physical Performance Battery; BMI: Body Mass Index; HR: Heart Rate; eGFR: estimated Glomerular Filtration Rate; ACEi: Angiotensin-Converting Enzyme inhibitors; ARB: Angiotensin Receptor Blockers; ARNI: Angiotensin Receptor–Neprilysin Inhibitors; MRAs: Mineralocorticoid Receptor Antagonists; SGLT2-inhibitor: Sodium–Glucose Cotransporter 2 inhibitors; GLP-1 RA: Glucagon-Like Peptide-1 Receptor Agonists; PCSK9- i: Proprotein Convertase Subtilisin/Kexin Type 9 inhibitors.
Table 2.
Baseline characteristics of overall population, along with the number (n) of available data for each variable.
Table 2.
Baseline characteristics of overall population, along with the number (n) of available data for each variable.
| Variable | Overall | N |
| Intervention Group | 103 (50%) | 206 |
| Control Group | 103 (50%) | |
| Acute Coronary Syndrome | 114 (55.34%) | 206 |
| Acute Heart Failure | 92 (44.66%) | |
| Frailty prevalence EFT ≥3 | 63 (30.58%) | 206 |
| SPPB | 8.71±3.51 | 168 |
| Males | 145 (70.39%) | 206 |
| Age | 77.28±8.91 | 206 |
| BMI [kg/m²] | 25.77±4.63 | 203 |
| Systolic Blood Pressure [mmHg] | 116.2±15.42 | 150 |
| Diastolic Blood Pressure [mmHg] | 68.75±8.87 | 150 |
| HR [bpm] | 70.79±11.52 | 148 |
| Hemoglobin [g/dL] | 12.19±1.77 | 205 |
| Albumin [g/dL] | 4.26±4.36 | 198 |
| Total Cholesterol [mg/dL] | 132.63±40.44 | 189 |
| LDL-cholesterol [mg/dL] | 71.53±33.92 | 171 |
| HDL-cholesterol [mg/dL] | 41.22±23.71 | 185 |
| Triglycerides [mg/dL] | 114.73±63.92 | 186 |
| Creatinine [mg/dL] | 1.31±0.57 | 202 |
| eGFR [ml/min] | 70.26±21.21 | 81 |
| Beta-blocker | 164 (82.83%) | 198 |
| ACEi/ARB/ARNI | 128 (64.97%) | 197 |
| MRAs | 141 (70.5%) | 200 |
| Ivabradine | 3 (1.55%) | 193 |
| Aspirin | 126 (63.64%) | 198 |
| Other antiplatelet agents | 111 (56.35%) | 197 |
| Oral anticoagulant therapy | 54 (28.27%) | 191 |
| Diuretics | 126 (62.69%) | 201 |
| Metformin | 23 (11.56%) | 199 |
| Insulin | 25 (12.76%) | 196 |
| SGLT2-i | 101 (50.75%) | 199 |
| GLP-1 RA | 31 (16.23%) | 191 |
| Statins | 155 (77.5%) | 200 |
| Ezetimibe | 121 (60.5%) | 200 |
| PCSK9-i | 1 (0.5%) | 201 |
Data are presented as mean ± SD for continuous variables and as number (percentage) for categorical variables. EFT: Essential Frailty Toolset; SPPB: Short Physical Performance Battery; BMI: Body Mass Index; HR: Heart Rate; eGFR: estimated Glomerular Filtration Rate; ACEi: Angiotensin-Converting Enzyme inhibitors; ARB: Angiotensin Receptor Blockers; ARNI: Angiotensin Receptor–Neprilysin Inhibitors; MRAs: Mineralocorticoid Receptor Antagonists; SGLT2-inhibitor: Sodium–Glucose Cotransporter 2 inhibitors; GLP-1 RA: Glucagon-Like Peptide-1 Receptor Agonists; PCSK9- i: Proprotein Convertase Subtilisin/Kexin Type 9 inhibitors.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
