Combined Digital Interventions for Enhancing Heart Failure Continuum Care and Their Impact on Care Consumption: A Randomized Controlled Trial

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B. Ribeiro, Stéphanie Béchard, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6629871/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital health solutions are increasingly used in heart failure (HF) management to optimize resources. However, their full effectiveness remains unclear. The objective of this study was to assess if by enhancing remote patient follow-up, optimizing guideline directed medical treatment (GDMT), and positively influencing patient behavior, digital solutions could lead to a significant reduction in overall care consumption. In this 12-week randomized controlled trial, a mobile app-based remote patient monitoring, digital therapeutics, and clinical decision support tools were combined to assess its impact on healthcare consumption. A total of 111 HF patients were randomly assigned (1:1) to either the Continuum intervention or usual care. Intent-to-treat analysis revealed trends favoring the intervention. Per-protocol analysis on cardiac events were in favor of the intervention group and showed significant lower costs (mean CAD $ 1,302 vs. $ 3,635, p = 0.035), number of patients affected (3 vs. 11, p = 0.041), total event days (66 vs. 127, p = 0.036), and time to the first event (95% CI 1.055–13.56, p = 0.028). Highly compliant patients (n = 17, 33%) had no cardiac events and represented 8% of the costs. The mean cost of the intervention was $ 172. These findings warrant further investigation with larger studies to confirm the efficacy and cost-effectiveness of this combined complex digital solution intervention. ClinicalTrials.gov registration: NCT05377190 Cardiac & Cardiovascular Systems Nursing Medical Informatics Health Economics & Outcomes Research Digital health heart failure remote patient monitoring digital therapeutics decision support tools randomized controlled trial Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) is a significant global health issue 1 , ranking third in hospital admissions worldwide 2 . In Canada, the increasing burden of HF 3 is surpassing the capacities of current multidisciplinary management programs. There is a growing interest in digital health solutions to address this issue 4 , 5 . Various technologies have been developed to assist clinicians. The first is remote patient monitoring (RPM), which provides potential advantages in decreasing heart failure-related morbidity and mortality 6 , 7 by enabling earlier detection of heart failure decompensation 8 , 9 . More recently, new tools such as digital therapeutics (DTx), software-based interventions 10 , and clinical decision support tools (DST) 11 have been developed to improve HF management by supporting guideline-directed medical therapy (GDMT) optimization 11 , behavioral changes 12 , and self-care management 13 . While telemedicine has theoretical benefits, real-world results have been inconsistent due to the diversity of tested methods including invasive, non-invasive, phone-based, and hybrid approaches. As a result, current heart failure management guidelines do not equally prioritize non-invasive monitoring methods, let alone more complex digital solutions like digital therapeutics (DTx) 14 – 16 . The Continuum project is a collaborative initiative involving a health care center, a software start-up 17 , and an industrial partner. It brings together a RPM solution, a tool to facilitate GDMT optimization, and an innovative platform to improve patient behavior and self-care management. An initial nonrandomized pilot study demonstrated the seamless integration of Continuum into nursing workflow, mobile app accessibility, and adoption by patients. 18 , 19 The objective of this study was to assess if this digital solution—by enhancing remote patient follow-up, optimizing GDMT, and positively influencing patient behavior—could now lead to a significant reduction in overall care consumption. Methods A randomized controlled trial (NCT05377190) 20 was conducted at the Centre Hospitalier de l’Université de Montréal (CHUM, Quebec, Canada), with patients referred from the CHUM and other centers in the province of Quebec. Recruitment began in June 2022 and follow-up concluded in October 2023. Participants were randomized via REDCap 21 , 22 using permuted blocks to either the Continuum intervention or control, based on the following characteristics: sex, preserved or reduced left ventricular ejection fraction (LVEF ≤ 40% vs > 40%), and estimated glomerular filtration rate (eGFR ≤ vs > 60mL/min/1.73 m²). Follow-up lasted 12 weeks. The project was designed according to the principles of the Montreal Model, focusing on patients’ experiences and knowledge 23 , and the Value-Based Healthcare framework, emphasizing outcomes and health needs 24 . Participants. The main inclusion criteria were: a confirmed diagnosis of heart failure; ability to use a mobile application (either independently or with the assistance of a caregiver); New York Heart Association (NYHA) class ≥ 2; actively followed in a heart failure clinic (or equivalent) in Quebec, with no anticipated discharge within 3 months; and finally with either an emergency room visit or hospitalization for heart failure within the last 6 months, or two visits related to heart failure less than 3 months apart, or within the past 3 months a significant change in diuretic regimen. Main exclusion criteria were: history of non-adherence to medication or refusal of treatment; active alcohol or drug abuse; severe major depression without a caregiver; life expectancy of less than 3 months; under dialysis or awaiting a kidney transplant; history of heart transplantation or mechanical heart implant; severe pulmonary disease; severe liver cirrhosis; scheduled surgical or percutaneous intervention; current pregnancy; and participating in other remote monitoring programs (mobile applications or implanted devices) The full list and the study design are available in supplements (Table S1 and figure S1). Participants' (or their caregivers') ability to use the mobile app was assessed after a brief training session of less than 30 minutes. Standard of care. Patients allocated to both the control and intervention group received the standard of care in terms of HF follow-up. This type of intervention included regular follow-ups at an HF clinic according to national guidelines. 16 This clinical routine care remained unchanged for both groups. It also included a booklet with a reminder at the beginning of the study to record daily vital signs and heart failure symptoms, as recommended by the Quebec Heart Failure Society 25 . This booklet also included reminders about acceptable weight fluctuations, liquid and sodium intake limits, and instructions on when to seek treatment. Intervention. Our Continuum program integrated 3 technologies: 1) a RPM system connecting patients’ mobile app data to a secured web-based platform for healthcare professionals, 2) a DTx through the mobile app for smartphones or tablets, to encourage self-care management, and patients’ education; and 3) a DST for GDMT optimization in the format of reports (Figure S2) aiming to support healthcare professionals. Details and screenshots of the app can be found in the supplements (Figure S3). Remote patient monitoring. A nurse reviewed patient data daily (Monday to Friday) using a secure web-based dashboard. Pre-programmed algorithms generated alerts based on patients’ daily inputs and organized data into graphics. The nurse could contact patients, perform a comprehensive evaluation, and provide heart failure education sessions if needed. Additionally, the nurse could contact the patient’s clinical team or refer the patient to the appropriate medical resource, such as the nearest emergency room, if necessary. Automated messages when nurses revised individual data, for example when automated alerts were checked, were sent to keep patients motivated. Mobile application for patients. Patients randomized for the intervention group (INT) received a link to download the app (Modified version of the commercial TakeCare TM app) 26 with the RPM and DTx technologies. This app was made available on both Android and iPhone platforms by invitation only. It was for research only and not approved by Canada Health nor U.S. Food and Drug Administration. For the RPM part, patients were encouraged to use the app daily to enter their vital signs, weight, HF symptoms, and step counts (optional) for 12 weeks. This data could be inputted manually or automatically if the patient had connected devices, and could be shared through portable document format (PDF) reports. Data was also generated into user-friendly graphics to see daily weight evolution. As for the DTx part, patients received weekly automated reminders to encourage self-management through personalized emails. The app also provided key educational components of HF self-care 27 , heart failure and its symptoms, nutrition, physical activity, and psychosocial health. The app did not offer automatic feedback or alert messages to patients based on their daily input. All patients were well advised that the mobile app did not work as a direct contact with the healthcare team, and therefore advised to contact their HF clinic directly if they had any concerns or questions. Decision support tool with guideline-directed medical therapy suggestions. The decision support tool on the web-based dashboard for professionals generated an automated PDF report. It used clinical data entered via the patient's mobile app or by the research team, including cardiac diagnosis, LVEF, current medication list, vital signs, and lab results. The report provided personalized and automated treatment recommendations to assist in GDMT titration, based on the most recent Canadian HF guidelines 16 , 28 , 29 . Four medication classes and doses for HF with reduced ejection fraction (HFrEF) were suggested 28 : beta-blockers (BB), angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) or angiotensin receptor–neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter-2 inhibitors (SGLT2i). Two were also suggested for HF with preserved ejection fraction (HFpEF): MRA 16 and SGLT2i 29 . Reports also highlighted any abnormal vital signs or lab results. Referring teams with patients randomized to the intervention group received detailed reports with GDMT suggestions at baseline, 6 weeks, and 12 weeks. Each report was generated automatically by the platform and then reviewed and validated by two clinicians from the research team, including physicians, nurse practitioners, or pharmacists. No medication changes were made by the research team. Outcomes The primary endpoint was to evaluate the impact of the Continuum program on healthcare consumption among patients from HF specialized clinics, by analyzing costs of all-cause, cardiac (including HF-related events), and HF-related events and their respective number of days. Secondary endpoints included specific sub analysis on events, readmissions (i.e. a second hospitalization during study time), major adverse cardiovascular events (MACE), other clinical events such as unplanned clinical appointments and emergency consultations, the impact of compliance on healthcare consumption, and GDMT initiation. Following paragraphs define data collection, costs calculations, events and compliance. Definitions of other secondary outcomes are available in supplements (Table S2). Data collection included patients’ assessments at three time points: baseline (T0), 6 weeks (T6), and 12 weeks (T12). This study was designed entirely for virtual follow-ups. Medication lists and events were collected through patient phone questionnaires at these time points and confirmed by reviewing patients’ electronic medical records. Patients’ files data was always collected for all events, including for events happening in other hospitals. Costs included events defined as emergency and hospital visits. Emergency costs were based on the Quebec provincial government fees per emergency visit (CAD $ 387) 30 . Three ways of calculation hospitalization costs are presented: 1) costs based on the main medical condition during hospitalization (categorized by case mix groups) 31 ; 2) costs based one daily hospitalization fees 30 ; and 3) standard hospital stay costs 32 . Case-mix groups were coded by two blind authors (EB and PB) and reviewed with a third reviewer (EMT), if discrepancy existed between the first two authors. A consensus was reached between all reviewers. Intervention costs (2 Canadian dollars per patient and per day) were explained by software license (1 $ ) and human resources training and salaries (1 $ ) (nursing and clerical support to explain and help patients how to install the mobile app). Further details are available in the supplements (Table S2). Events were defined as unplanned hospitalizations, emergency visits or deaths. They were categorized as cardiac if the primary diagnosis at admission or one of the symptoms at presentation was cardiac and required a cardiology specialist (e.g., palpitations, chest pain, dyspnea). The same criteria applied for subcategorization of HF events (primary diagnosis being HF or HF symptoms at admission, such as dyspnea or pitting edema). HF events were included in cardiac events. Compliance was evaluated as the proportion of days when at least one data was entered over the total days of participation in the study, excluding days when patients were hospitalized. Compliance groups were generated according to group tertiles (low, medium, and high). Sample size To detect a reduction in all-cause hospitalizations of 30% in the intervention group with a significance level (α) of 0.05, and power (1-β) of 0.80, this study required 90 patients per group. To account for drop-out rates of 10% and continuity correction, we estimated 114 patients per group. These were based on results from our pilot study 18 . Statistics Descriptive analyses are presented as frequency and proportions, or means and standard deviations, as appropriate. Categorical and descriptive variable comparisons are tested with Fisher or Student T-test when needed. Count data (consultations) were compared using unadjusted negative binomial regression. Highly skewed data (days, NTproBNP, and costs) was analyzed using Mann-Whitney U test. Time-to-event was illustrated by Kaplan-Meir curves and the differences were calculated by the log rank test. Cox regressions were used to estimate hazard ratios. Per protocol analysis excluded loss to follow-up patients. Statistical significance was fixed at 5%. Study data were collected and managed using REDCap electronic data capture tools hosted at CHUM 21 , 22 . REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources. Telemonitoring of vital signs, symptoms and compliance were collected through the mobile app Takecare TM 26 . The dataset was analyzed using IBM’s SPSS Statistics® (version 29.0; SPSS Inc., Chicago, IL, USA). Ethics and inclusion statement Ethics approval for this study was granted by the CHUM Research Center's local ethics committee (21.403 (2022–10426)). The study was conducted following the ethical principles outlined in the Declaration of Helsinki. The Continuum intervention technology was presented to and approved by CHUM's telemedicine committee, and it was declared compliant with the institution's security requirements. Each participant provided informed and signed consent. Results Cardiology consultations’ lists were screened for HF and other inclusion and exclusion criteria. A total of 140 patients followed in specialized heart failure clinics were eligible and approached for this study, and 111 patients were randomized (details in Fig. 1 ). OF those, 94 patients over approximately 400 of the CHUM specialized HF clinic total patients took part in this study (24%). Baseline characteristics. The comparison between groups at randomization (Table 1 ) showed no statistical difference. Only a few patients had severe NYHA class dyspnea in both groups. A numerically higher number of patients with new HF was present in the intervention group. Follow-up by community hospitals accounted for 6% (3/52) vs 17% (9/54) in the intervention and control groups accordingly (p = 0.124). Finally, 12% (6/52) of patients needed help from a caregiver to use the app in the intervention group. Table 1 Baseline Characteristics Intervention (n = 52) Control (n = 54) p value Age (years) 67 ± 12 69 ± 12 0.425 Sex (women) (n) 20 (38) 20 (37) 1 Weight (kg) 82 ± 23 76 ± 21 0.205 NYHA class 3 or 4 7 (13) 10 (19) 0.599 LVEF 40 or below 28 (54) 27 (50) 0.703 HF ischemic cause 20 (38) 25 (46) 0.438 Diabetes (I or II) 19 (37) 18 (33) 0.839 DLP (n) 33 (63) 36 (67) 0.839 HTN (n) 38 (73) 36 (67) 0.529 CKD with eGFR < 60 29 (57) 31 (57) 1 Hb below 120g/L (n)* 16 (42) 13 (34) 0.637 NTproBNP (ng/L)*,** 1616 (903–5559) 1404 (924–5471) 0.332 Abnormal results 30 (70) 27 (57) 0.276 CIED 21 (40) 19 (35) 0.689 New diagnosis of HF (last 3 months) 12 (23) 5 (9) 0.066 HRrEF patients n = 27*** n = 27 - ACEi, ARB or ARNI 23 (85) 25 (93) 0.669 MRA 18 (67) 20 (74) 0.766 Beta-blocker 25 (93) 23 (85) 0.669 SGLT2i 18 (67) 19 (73) 1 All 4 GDMT classes 13 (48) 12 (44) 1 Income brackets (Canadian dollars) (n = 42) (n = 41) 0.812 9 (21) 7 (17) - Education years after primary school* 7.7 ± 3.3 8.2 ± 4.1 0.549 Caregiver support for technology use 6 (12) 7 (13) 1 CIED: Cardiac Implantable Electronic Device, CKD: chronic kidney disease, DLP: dyslipidemia, eGFR: estimated glomerular filtration ratio, Hb: hemoglobin, HF: heart failure, HTN: hypertension, LVEF: left ventricular ejection fraction, n: number, NTproBNP: N-terminal-pro-brain natriuretic peptide, NYHA: New York Heart Association dyspnea class, SD: standard deviation Means ± SD, n (%), or median ± SD for NTproBNP accordingly T-tests for means except Mann Withney test for NTproBNP due to skewness of data, Fisher test for categorical data except income brackets with Chi square test *Missing data for more than 20% of the group **Abnormal results for NTproBNP: >450 for 50 years old, > 900 for 50–75 years old and > 1800 for > 75 years old ***Initial medication results missing for one patient Healthcare consumption and clinical events. The results of the primary outcome show trends in favor of the intervention group, but no statistically significant difference in terms of costs, all-cause events, total events’ days, and the number of patients (Table 2 ). The absolute difference in events costs was - $ 1322 per patient for all-cause events, and - $ 2,106 per patient for cardiac events. The mean cost of the digital intervention was $ 172, excluding the regular standard of care in HF clinic. Additional details are available in supplements, including standard hospital stay costs (Table S3 and Table S4). For the other secondary outcomes, major cardiovascular events (MACE) were also numerically lower in the intervention group (5 vs 8; p = 0.386). Hospitalization length of stay was however higher in the intervention group: 15 in 7 patients vs 11 in 10 patients, p = 0.222. Planned cardiac consultations or visits happening during study follow-ups were similar between groups (109 vs 122, INT vs CTRL, p = 0.611), as well as the cardiac ones that were unplanned or urgent (18 vs 21, INT vs CTRL, p = 0.781). Finally, Kaplan-Meier analyses were performed on all-cause (Figure S4), cardiac (Fig. 2 ), and heart failure events, but no differences were found. Other details are available in supplements (Table S5). Only one death occurred during the study in the control group. A per-protocol analysis was performed for both the primary and secondary outcomes. Cardiac costs, cardiac events days, and the number of patients with cardiac events were significantly lower in the intervention group (Table 2 , Fig. 3 ). Moreover, time to the first cardiac event was additionally in favor of the intervention (Fig. 2 ). Additional details and other results are found in supplements (Table S3, Table S4 and Table S5). Table 2 Healthcare consumption per type of analysis Intervention Control p value Sum Mean ± SD Sum Mean ± SD Costs based on main condition during hospitalization (CAD $)* Intention to treat Total 138,005 2,654 ± 7,229 214,703 3,976 ± 9,477 0.649 Cardiac 61,984 1,192 ± 4,986 178,098 3,298 ± 8,437 0.122 Per Protocol Total 107,910 2,296 ± 6,428 214,703 4,382 ± 9,866 0.332 Cardiac 61,211 1,302 ± 5,237 178,098 3,635 ± 8,795 0.035 Costs based on hospitalization days (CAD $)* Intention to treat Total 274,823 5,285 ± 15,917 356,820 6,608 ± 16,016 0.672 Cardiac 150,562 2,895 ± 13,354 285,618 5,289 ± 13,691 0.124 Per Protocol Total 218,670 4,653 ± 14,925 356,820 7,282 ± 16,680 0.350 Cardiac 149,789 3,187 ± 14,029 285,618 5,829 ± 14,275 0.036 Number of days* Intention to treat All-cause days 126 2 ± 7 168 3 ± 7 0.659 Cardiac events days 69 1 ± 6 127 2 ± 6 0.125 Per protocol All-cause days 99 2 ± 7 168 3 ± 8 0.339 Cardiac events days 66 1 ± 6 127 3 ± 6 0.036 *Mann-Whitney U test for costs and days. Table 3 Events per type of analysis Intervention Control Events Patients with events N (%) Mean Events Patients with events N (%) Mean p value* 95% CI Intention to treat All-cause events 12 10/52 (19) 1.2 22 12/54 (22) 1.83 0.812 0.664–1.823 All-cause readmissions 3 2/10 (20) 1.5 10 5/12 (42) 2 0.381 0.415–19.649 Cardiac events 6 5/52 (10) 1.2 13 11/54 (20) 1.18 0.175 0.787–3.549 Per protocol All-cause events 9 8/47 (17) 1.13 22 12/49 (24) 1.83 0.454 0.581–4.303 All-cause readmissions 1 1/8 (13) 10 5/12 (42) 2 0.325 0.459–54.513 Cardiac events 4 3/47 (6) 1.33 13 11/49 (22) 1.18 0.041 1.102–16.350 *Fisher test for the number of patients having events. 2A) Intention to treat analysis for cardiac events. 2B) Per protocol analysis for cardiac events. Patients’ app compliance. For analysis purposes, the intervention group was split into compliance tertiles (n = 17 each) based on the app use (excluding time while hospitalized (available for 51/52)). Compliance proportions ranged from 0%-56% for low compliance, ≥ 56–92% for medium, and ≥ 92%-100% for high compliance group. No patients in the high compliance groups had cardiac or HF events, but no statistical difference was found between compliance groups. Total all-cause events costs per group are presented in the Fig. 3 ; 3 intervention groups based on the compliances tertiles, the control group, and the total intervention cost are shown to present the proportions. Numerically, total costs are decreased when compliance increases. There were also fewer patients with events, number of events, and costs in the high compliance group (Supplementary Table S6), and the latter accounted for only 6% of the total unplanned costs of the intervention group (n = 17/51). Professional dashboard management. A total average time of 64 minutes per patient (approximately one minute per business day) was required by the clinicians to follow the RPM dashboard during the 12-week follow-up. It included calls to patients, referrals, and contacts to their treating teams. Some of this work time would possibly have been incorporated into the usual workload of the referring clinics. Heart failure medication optimization. The four medication classes in HFrEF patients 28 were analyzed according to GDMT initiation during the 12-week program (i.e. patient not having the GDMT class at the baseline). A total of 26 INT and 22 CTL patients had HFrEF and available medication data. Only 14 and 12 patients did not have four recommended medication classes at study start, i.e. equivalent drug optimization at the baseline. No statistical difference was found between groups in terms of initiation of GDMT. Numerical trends for the number of patients with the recommended classes are however in favor of the intervention: 77% (20/26) vs 64% (14/22). Details are presented in Supplementary Figure S5. Discussion Our RCT explored the effects of a tri-module solution (RPM, DTx, and DST) for HF patients and their healthcare team at a university hospital centre with referrals from different regions in Quebec, Canada. Main results of this study were not statistically significant in the intention to treat approach, but positive results on cardiac-related events’ outcomes were present in the per-protocol analysis for costs, number of patients with events, total events days, and time to the first event. These results may be influenced by the study termination before having reached the sample size due to recruitment issues. Regardless, highly compliant patients did not have any cardiac events and accounted for only 6% of the all-cause unplanned costs. The mean intervention cost was $ 172 per patient and required only 1 hour of clinical time for a 12-week period. However, medication changes were similar between groups. The next paragraphs will explore the impact of digital interventions in cardiac and HF settings, our Canadian perspective in terms of costs and outpatient setting, the importance of self-care in these kinds of interventions, reflections on the lack of effect on GDMT, key points to develop and implement such tools in healthcare systems around the world, and our study limitations. The positive effects on cardiac-related events were not observed in heart-failure-related outcomes, those later were in lower numbers than expected, based on our pilot study 18 . Also, cardiac events may be influenced at a higher level by RPM and DTx than HF events alone. More precisely, vital signs and HF symptoms monitoring can lead to the detection of cardiac-related complications such as hypotension or arrhythmias that are not directly related to HF events. This could explain our positive results only in cardiac events. These findings on cardiac events are similar to the larger TELESAT-HF study, where HF hospitalizations outcomes were also neutral, contrary to all-cause time spent in hospital and to number of emergency visits 33 . For HF events, meta-analyses have however showed positive results when pooling different RPM modalities together 6 , 7 . Moreover, a total of 61 days were saved in the intervention group (absolute difference between the groups) in the cardiac hospitalizations or emergency beds. This could also lead to an increased availability of 6 HF hospitalizations based on a mean HF LOS 34 , which is important due to the lack of bed availability in our region and in terms of healthcare efficiency. The time to cardiac event in favor of the intervention group seen in our per-protocol analysis is not frequently explored in the literature, whereas time to death has often been seen in favor of RPM 33 . These results need to be however further explored. The all-cause 30-day readmission rate in our study is also higher than national rates (42% in the control group vs the national rate of 20.6%) 35 , but this may be explained by the subgroup of patients included in the study. Only ill or unstable patients are being followed in HF-specialized clinics 16 , and only the ones at higher risk of hospitalization were included. Our program has been targeting higher-risk patients due to their specific demands and high consumption of specialized resources 36 , and as recently recommended in France 37 . All-cause costs show a difference of $ 1,123 to $ 1,323 per patient (depending on the cost calculation method), resulting in a possible economy of at least $ 6 for each $ 1 invested in the intervention when accounting for the mean intervention cost of $ 172. This intervention is a possible alternative to decrease costs in a public healthcare system. This is particularly important for hospital performance and efficiency. HF has an increasing prevalence and an economic impact in Canada of 2.8 billion dollars projected for 2030 38 , therefore, innovative solutions that avoid hospitalizations or shorten the LOS are needed 38 . Another similar intervention in Canada has previously showed similar results being cost-effective with quality-adjusted life years 39 . Finally, cost calculations based on the main hospitalization cause and using mean costs of these conditions 31 remove outliers. This may give a better perspective on real costs, and be a strength of this study 40 . Outpatient resource consumption such as planned or unplanned cardiac consultations was not increased in the intervention group, similar to other findings 41 . This indicates that creating an extra channel of communication with patients does not increase the flow of appointments in clinics (virtual or in-person). Patients were also well made aware not to replace any clinical decisions or appointments by the ones having for this study – all clinical routines remained intact. Self-care is also essential in chronic HF management on mortality rates 27 , 42 , quality of life and readmission rates 27 . In this study, high compliance of self-care monitoring (vital signs or HF symptoms) had remarkably lower costs, even though not statistically significant (underpowered extra analyses) However, daily weight monitoring adherence > 80% has previously been shown to be a good predictor of HF hospitalizations 43 . This study strengthens the importance of recommending daily self-care monitoring to HF patients, with best results at the higher compliance tertile, 92% or above. Another recent study (TELESAT-HF), reinforcing self-care through personalized education and tele coaching also showed similar results in terms of time spent at the hospital 33 . Selfcare could be reinforced through remote interventions including education sessions by RPM nurses not quantified in regular cardiac appointments. This could have influenced the diminished care consumption results in the intervention group in the per protocol analysis. HF GDMT optimization was similar between groups, but only a few patients in both arms were eligible, therefore our findings are limited. The lack of effect can be caused by multiple factors; GDMT is complex, and digital interventions do not address all the issues related to inertia (i.e. medication changes were at the discretion of the referring medical teams). Digital interventions also do not replace referring healthcare teams nor personal shortages 44 . Previous studies focusing GDMT optimization were positive when 1) educating patients to discuss treatment options with their clinicians 45 , 2) combining frequent remote titration visits and RPM 46 and 3) DST were embedded and interoperable with electronic medical records and done during patients’ visits 11 . Throughout this phase 2 RCT and our pilot study, 18 we learned valuable lessons that could help with the implementation phase of programs such ours in a public healthcare setting. A main point is that patients were not provided with connected health objects; this would have significantly increased both costs and time to educate patients about their use, acting like a barrier to future implementation. Likewise, alerts related to connected objects could have been more complex, as previously reported 47 , and time consuming for clinicians. Moreover, nurses in charge of the RPM in our intervention did not need to be in the same hospital nor in close contact with referring teams, reinforcing the possibility to develop HF RPM specialized expertise in specific centres to improve regional access to specialized HF care, currently lacking in Canada 48 . Similar results have been shown positive with private practice nurses responsible of RPM 33 . This is particularly important when thinking over implementation of digital interventions. Healthcare professionals’ workload must be adapted to promote new innovative solutions, or may bedone through private sectors to increase adoption through specific reimbursements, such as in France 49 . These reimbursements can also be modulated to their proven effectiveness 49 . Another note on financing those interventions, adapted reimbursements must be planned for each step of development, including the testing and adaptation phase, the formal efficiency analysis, implementation, and reevaluations phases 50 . This may lead to faster and more efficient use of those technologies. Specifically on the topic of workload, another key learning is that RPM and GDMT tools may it and therefore should be adapted to improve their efficiency, such as: 1) interoperability with electronic medical records data, optimized algorithms and devices triggering alerts appropriately and at the right time 51 ; and 2) improved time to clinical action with RPM alerts and pre-established trajectories 9 , 51 . Finally, regular phone calls to support RPM with patient education might be important, focusing on low compliant patients, as it recently showed positive results when combined with a mobile app similar to ours 33 . Finally, when comparing this RCT with the latest meta-analysis on the subject, we highlight the added value of RPM through mobile health interventions with clinicians’ feedback 6 and complex telemonitoring combining different modalities, namely non-invasive RPM with DTx and DST 7 . Our study has limitations that need to be acknowledged. First, we did not meet the recruitment goals that were set at 228 patients; therefore, we believe some of our analyses are underpowered and results must be taken with caution. We believe that the extra work with COVID-19 impacted the way we were able to screen, recruit patients and get referrals from other centres. Moreover, limited data interoperability between hospitals, and lack of specific funding may also have contributed to it (i.e. teams with no research personnel support might have faced challenges to refer patients). However, the trends across outcomes in favor of our program are consistent and have been demonstrated before by others 7 , 52 . We also believe that our per protocol analysis is balanced with an approximate dropout rate of 10% in both groups. The setting of application of this study is also limited to the Quebec public healthcare system, which may reduce generalization to other settings. Also, we should be mindful that software development was done during the study, mainly for medication suggestions. In conclusion, our 12-week low-cost, non-invasive trimodal combination of RPM, DTx and DST intervention for heart failure (HF) outpatients per protocol analyses showed positive effects for several key cardiac events outcomes, including reduced costs, event days, patients with events, and time to first event. Our results need further confirmation in a larger sample size implementation trial, but our findings highlight the potential impact of this innovative digital health combination in a Canadian context. As digital health continues to evolve, it may become a standard component of care in the future. Identifying suitable technologies for specific patient populations and optimizing their use will be crucial in managing healthcare costs and alleviating the burden on healthcare professionals. Taking it all together, we believe our findings highlight the importance of further exploring complex telemedicine solutions like ours. Declarations Data availability Deidentified datasets used and/or analyzed in this article can be made available beginning 9 months and ending 36 months following this publication if investigators have been approved by an independent ethics committee and follow legal requirements upon reasonable request. Code availability No software code is made available, as it was done in partnership with a private software developer company. For any inquiry, please reach the corresponding author. Acknowledgments The authors disclose the following financial support for the research, authorship, and/or publication of this article that started in late 2019. This work was supported by a joined grant from Medteq+ (innovation for health) and Prompt (#10-26) including financial contributions from Boehringer Ingelheim, Canada Ltd; Greybox Solutions Inc. and Mitacs, providing resources for both the research team and software development. Emmanuel Marier-Tétrault was also supported by Nursing Grants from 2023-2025 of the CHUM Foundation. Medteq+, Prompt, and the other contributors had no role in the design and conduct of the study; extraction, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. We acknowledge the participation of the CHUM hospital workers, including heart failure clinicians (mainly nurse practitioners Jean-Dominic Rioux and Valérie Fontaine), contributors in the cardiology department, the telehealth department at CHUM (Rudolph de Patureaux), clinicians at Rouyn Noranda Hospital, and the research team (Karim El Kamouni, Zoé Carrier, Priccila Zuchinali, Mouny El Haffaf, Loyda Jean-Charles, and Sarita Sakoto). We also wish to thank our partners at Greybox Solutions Inc. (Pierre Bérubé and Xavier Jodoin) and Boehringer Ingelheim, Canada Ltd. (Suzanne Kimmerle and Eve Blanchet) for their partnership. We finally thank the Canadian Institute for Health Information (CIHI) for their support. Authors contributions E.M.T., P.A.B.R., M.P.P. and F.T. took part in the conception and the design of this the study. E.M.T., P.A.B.R., S.B., E.B., P.B., S.Y. and F.T. contributed to the collection and assembly of the data. All authors contributed to software development suggestions. E.M.T. and S.B. provided remote patient monitoring to patients with other members of the research team. E.M.T. and P.A.B.R. conducted the statistical analyses and prepared the first draft of the paper. P.A.B.R. and F.T. equally supervised the work of E.M.T. The final version of the manuscript was revised and approved by all authors. Competing interests Emmanuel Marier-Tétrault has received speaker fees from Boehringer Ingelheim Canada and is the secretary of the Quebec Heart Failure Society. Dr François Tournoux has received speaker fees from Boehringer Ingelheim Canada and is the past president of the Quebec Heart Failure Society. The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. References Savarese, G. & Lund, L.H. Global Public Health Burden of Heart Failure. Card Fail Rev 3 , 7-11 (2017). Azad, N. & Lemay, G. Management of chronic heart failure in the older population. J Geriatr Cardiol 11 , 329-337 (2014). Massamba, V.K., Rochette, L., Trépanier, P.-L. & Blais, C. Surveillance de l ’insuffisance cardiaque au Québec : prévalence , incidence et mortalité de 2005-2006 à 2015-2016. INSPQ (2019). Jankowska, E.A. , et al. Optimizing outcomes in heart failure: 2022 and beyond. ESC Heart Fail 10 , 2159-2169 (2023). Tang, M. , et al. Investigating the Association Between Telemedicine Use and Timely Follow-Up Care After Acute Cardiovascular Hospital Encounters. JACC Adv 1 , 100156 (2022). Kitsiou, S. , et al. Effectiveness of Mobile Health Technology Interventions for Patients With Heart Failure: Systematic Review and Meta-analysis. Can. J. Cardiol. 37 , 1248-1259 (2021). Scholte, N.T.B. , et al. Telemonitoring for heart failure: a meta-analysis. Eur. Heart J. 44 , 2911-2926 (2023). Stehlik, J. , et al. Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization. Circ. Heart Fail. 13 (2020). Stevenson, L.W., Ross, H.J., Rathman, L.D. & Boehmer, J.P. Remote Monitoring for Heart Failure Management at Home. J. Am. Coll. Cardiol. 81 , 2272-2291 (2023). Digital Therapeutics Alliance. Digital Therapeutics in the United States. DTA https://dtxalliance.org/wp-content/uploads/2021/06/DTA_DTx-Overview_US.pdf (2021). Mukhopadhyay, A. , et al. Cluster-Randomized Trial Comparing Ambulatory Decision Support Tools to Improve Heart Failure Care. J. Am. Coll. Cardiol. 81 , 1303-1316 (2023). Wang, C., Lee, C. & Shin, H. Digital therapeutics from bench to bedside. NPJ Digit Med 6 , 38 (2023). Liu, S. , et al. Effectiveness of eHealth Self-management Interventions in Patients With Heart Failure: Systematic Review and Meta-analysis. J. Med. Internet Res. 24 , e38697 (2022). McDonagh, T.A. , et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 42 , 3599-3726 (2021). Heidenreich, P.A. , et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. 145 , E895-E1032 (2022). Ezekowitz, J.A. , et al. 2017 Comprehensive Update of the Canadian Cardiovascular Society Guidelines for the Management of Heart Failure. Can. J. Cardiol. 33 , 1342-1433 (2017). Greybox. TAKECARE: Remote Patient Monitoring For Cardiovascular Disease. Greybox https://www.greybox.ca/takecare/ (nd). Marier-Tetrault, E. , et al. Remote Patient Monitoring and Digital Therapeutics Enhancing the Continuum of Care in Heart Failure: Nonrandomized Pilot Study. JMIR Form Res 8 , e53444 (2024). Vo, T.X.H. , et al. Patients’ experience using an app for home remote monitoring of heart failure for a university hospital in Quebec, Canada. BMC Digital Health 2 (2024). ClinicalTrials.gov. Identifier: NCT05377190, Continuum: Digital Health to Manage Heart Failure Outpatients. National Library of Medicine (US) https://clinicaltrials.gov/ct2/show/NCT05377190 (2024). Harris, P.A. , et al. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42 , 377-381 (2009). Harris, P.A. , et al. The REDCap consortium: Building an international community of software platform partners. J. Biomed. Inform. 95 , 103208 (2019). Pomey, M.-P. , et al. Le « Montreal model » : enjeux du partenariat relationnel entre patients et professionnels de la santé. Sante Publique (Bucur.) S1 , 41-50 (2015). Teisberg, E., Wallace, S. & O'Hara, S. Defining and Implementing Value-Based Health Care: A Strategic Framework. Acad. Med. 95 , 682-685 (2020). Société Québécoise d'Insuffisance Cardiaque. Carnet du patient. SQIC https://sqic.org/le-carnet-du-patient/ (2018). Greybox Solutions. Revolutionizing Patient Care [online]. Greybox Solutions https://www.greybox.ca/ (nd). Jaarsma, T. , et al. Self-care of heart failure patients: practical management recommendations from the Heart Failure Association of the European Society of Cardiology. Eur. J. Heart Fail. 23 , 157-174 (2021). McDonald, M. , et al. CCS/CHFS Heart Failure Guidelines Update: Defining a New Pharmacologic Standard of Care for Heart Failure With Reduced Ejection Fraction. Can. J. Cardiol. 37 , 531-546 (2021). Mancini, G.B.J. , et al. 2022 Canadian Cardiovascular Society Guideline for Use of GLP-1 Receptor Agonists and SGLT2 Inhibitors for Cardiorenal Risk Reduction in Adults. Can. J. Cardiol. 38 , 1153-1167 (2022). Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l'Île-de-Montréal. Frais de soins et services pour les personnes non assurées par la RAMQ. CIUSSS Centre-Sud-de-l'Île-de-Montréal https://ciusss-centresudmtl.gouv.qc.ca/informations-pratiques/frais-de-soins-et-services-pour-les-personnes-non-assurees-par-la-ramq (2024). Canadian Institute for Health Information. Patient Cost Estimator. CIHI https://www.cihi.ca/en/patient-cost-estimator (nd). Canadian Institute for Health Information. Cost of a Standard Hospital Stay. CIHI https://www.cihi.ca/en/indicators/cost-of-a-standard-hospital-stay (2023). Girerd, N. , et al. Association of a remote monitoring programme with all-cause mortality and hospitalizations in patients with heart failure: National-scale, real-world evidence from a 3-year propensity score analysis of the TELESAT-HF study. Eur. J. Heart Fail. (2025). Canadian Institute for Health Information. Hospital stays in Canada. CIHI https://www.cihi.ca/en/hospital-stays-in-canada (2023). Poon, S. , et al. The State of Heart Failure Care in Canada: Minimal Improvement in Readmissions Over Time Despite an Increased Number of Evidence-Based Therapies. CJC Open 4 , 667-675 (2022). Umeh, C.A. , et al. Home telemonitoring in heart failure patients and the effect of study design on outcome: A literature review. J. Telemed. Telecare , 1357633X211037197 (2021). Haute autorité de santé. Télésurveillance médicale du patient insuffisant cardiaque chronique. Inscription d’une activité de télésurveillance médicale sous forme générique sur la liste mentionnée à l’article L.162-52 du code de la sécurité sociale. HAS https://www.has-sante.fr/upload/docs/application/pdf/2023-03/tls_lg_insuffisance_cardiaque_chronique_dm_eval-320_avis_du_21_03_2023.pdf (2023). Fondation des maladies du coeur et de l'AVC. Des efforts insuffisants. Les mesures insuffisantes au pays pour les personnes vivant avec une insuffisance cardiaque et la façon de changer les choses. Pleins feux sur l’insuffisance cardiaque 2022. Coeur+AVC https://www.coeuretavc.ca/-/media/pdf-files/canada/2022-heart-month/cavc-rapport-insuffisance-cardiaque-2022-final.pdf?rev=e8266d7bbddc47fe81ac8ec5c2111db6 (2022). Boodoo, C. , et al. Evaluation of a Heart Failure Telemonitoring Program Through a Microsimulation Model: Cost-Utility Analysis. J. Med. Internet Res. 22 , e18917 (2020). Herold, R., Hoffmann, W. & Van Den Berg, N. Telemedical monitoring of patients with chronic heart failure has a positive effect on total health costs. BMC Health Serv. Res. 18 (2018). Zaman, S. , et al. Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs. JMIR Cardio 7 , e45611 (2023). Yang, M. , et al. Knowledge about self-efficacy and outcomes in patients with heart failure and reduced ejection fraction. Eur. J. Heart Fail. 25 , 1831-1839 (2023). Jones, C.D. , et al. Self-reported recall and daily diary-recorded measures of weight monitoring adherence: associations with heart failure-related hospitalization. BMC Cardiovasc. Disord. 14 , 12 (2014). Verhestraeten, C., Heggermont, W.A. & Maris, M. Clinical inertia in the treatment of heart failure: a major issue to tackle. Heart Fail. Rev. 26 , 1359-1370 (2021). Allen, L.A. , et al. An Electronically Delivered Patient-Activation Tool for Intensification of Medications for Chronic Heart Failure With Reduced Ejection Fraction: The EPIC-HF Trial. Circulation 143 , 427-437 (2021). Brahmbhatt, D.H. , et al. The Effect of Using a Remote Patient Management Platform in Optimizing Guideline-Directed Medical Therapy in Heart Failure Patients: A Randomized Controlled Trial. JACC Heart Fail 12 , 678-690 (2024). Koehler, F. , et al. Efficacy of telemedical interventional management in patients with heart failure (TIM-HF2): a randomised, controlled, parallel-group, unmasked trial. Lancet 392 , 1047-1057 (2018). Moghaddam, N. , et al. Access to Heart Failure Services in Canada: Findings of the Heart and Stroke National Heart Failure Resources and Services Inventory. Can. J. Cardiol. (2023). Guichet national de l’innovation du numérique en santé. Remboursement de la Télésurveillance. G_NIUS https://gnius.esante.gouv.fr/fr/financements/fiches-remboursement/remboursement-de-la-telesurveillance (nd). Institut national d'excellence en santé et en services sociaux. Bulletin #12. L’accès aux innovations en santé et en services sociaux: financement et prise de décision. INESSS https://www.inesss.qc.ca/fileadmin/doc/INESSS/DocuMetho/Bulletins_veille/Bulletin_12_Financement_decision_vfinale.pdf (2024). Bhatia, A. & Maddox, T.M. Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy. Int J Heart Fail 3 , 31-50 (2021). Masotta, V. , et al. Telehealth care and remote monitoring strategies in heart failure patients: A systematic review and meta-analysis. Heart Lung 64 , 149-167 (2024). Additional Declarations The authors declare potential competing interests as follows: • Emmanuel Marier-Tétrault has received speaker fees from Boehringer Ingelheim Canada and is the secretary of the Quebec Heart Failure Society. • Dr François Tournoux has received speaker fees from Boehringer Ingelheim Canada and is the past president of the Quebec Heart Failure Society. • The other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Supplementary Files Supplementaryfilev4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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In Canada, the increasing burden of HF\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e is surpassing the capacities of current multidisciplinary management programs. There is a growing interest in digital health solutions to address this issue\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Various technologies have been developed to assist clinicians. The first is remote patient monitoring (RPM), which provides potential advantages in decreasing heart failure-related morbidity and mortality\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e by enabling earlier detection of heart failure decompensation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. More recently, new tools such as digital therapeutics (DTx), software-based interventions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and clinical decision support tools (DST)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e have been developed to improve HF management by supporting guideline-directed medical therapy (GDMT) optimization\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, behavioral changes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and self-care management\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile telemedicine has theoretical benefits, real-world results have been inconsistent due to the diversity of tested methods including invasive, non-invasive, phone-based, and hybrid approaches. As a result, current heart failure management guidelines do not equally prioritize non-invasive monitoring methods, let alone more complex digital solutions like digital therapeutics (DTx)\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The Continuum project is a collaborative initiative involving a health care center, a software start-up\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and an industrial partner. It brings together a RPM solution, a tool to facilitate GDMT optimization, and an innovative platform to improve patient behavior and self-care management. An initial nonrandomized pilot study demonstrated the seamless integration of Continuum into nursing workflow, mobile app accessibility, and adoption by patients. \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe objective of this study was to assess if this digital solution\u0026mdash;by enhancing remote patient follow-up, optimizing GDMT, and positively influencing patient behavior\u0026mdash;could now lead to a significant reduction in overall care consumption.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eA randomized controlled trial (NCT05377190)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e was conducted at the Centre Hospitalier de l\u0026rsquo;Universit\u0026eacute; de Montr\u0026eacute;al (CHUM, Quebec, Canada), with patients referred from the CHUM and other centers in the province of Quebec. Recruitment began in June 2022 and follow-up concluded in October 2023. Participants were randomized via REDCap\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e using permuted blocks to either the Continuum intervention or control, based on the following characteristics: sex, preserved or reduced left ventricular ejection fraction (LVEF\u0026thinsp;\u0026le;\u0026thinsp;40% vs\u0026thinsp;\u0026gt;\u0026thinsp;40%), and estimated glomerular filtration rate (eGFR\u0026thinsp;\u0026le;\u0026thinsp;vs\u0026thinsp;\u0026gt;\u0026thinsp;60mL/min/1.73 m\u0026sup2;). Follow-up lasted 12 weeks. The project was designed according to the principles of the Montreal Model, focusing on patients\u0026rsquo; experiences and knowledge\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and the Value-Based Healthcare framework, emphasizing outcomes and health needs\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants.\u003c/b\u003e The main inclusion criteria were: a confirmed diagnosis of heart failure; ability to use a mobile application (either independently or with the assistance of a caregiver); New York Heart Association (NYHA) class\u0026thinsp;\u0026ge;\u0026thinsp;2; actively followed in a heart failure clinic (or equivalent) in Quebec, with no anticipated discharge within 3 months; and finally with either an emergency room visit or hospitalization for heart failure within the last 6 months, or two visits related to heart failure less than 3 months apart, or within the past 3 months a significant change in diuretic regimen.\u003c/p\u003e \u003cp\u003eMain exclusion criteria were: history of non-adherence to medication or refusal of treatment; active alcohol or drug abuse; severe major depression without a caregiver; life expectancy of less than 3 months; under dialysis or awaiting a kidney transplant; history of heart transplantation or mechanical heart implant; severe pulmonary disease; severe liver cirrhosis; scheduled surgical or percutaneous intervention; current pregnancy; and participating in other remote monitoring programs (mobile applications or implanted devices)\u003c/p\u003e \u003cp\u003eThe full list and the study design are available in supplements (Table S1 and figure S1). Participants' (or their caregivers') ability to use the mobile app was assessed after a brief training session of less than 30 minutes.\u003c/p\u003e \u003cp\u003eStandard of care.\u003c/p\u003e \u003cp\u003ePatients allocated to both the control and intervention group received the standard of care in terms of HF follow-up. This type of intervention included regular follow-ups at an HF clinic according to national guidelines.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e This clinical routine care remained unchanged for both groups. It also included a booklet with a reminder at the beginning of the study to record daily vital signs and heart failure symptoms, as recommended by the Quebec Heart Failure Society\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This booklet also included reminders about acceptable weight fluctuations, liquid and sodium intake limits, and instructions on when to seek treatment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIntervention.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur Continuum program integrated 3 technologies: 1) a RPM system connecting patients\u0026rsquo; mobile app data to a secured web-based platform for healthcare professionals, 2) a DTx through the mobile app for smartphones or tablets, to encourage self-care management, and patients\u0026rsquo; education; and 3) a DST for GDMT optimization in the format of reports (Figure S2) aiming to support healthcare professionals. Details and screenshots of the app can be found in the supplements (Figure S3).\u003c/p\u003e \u003cp\u003eRemote patient monitoring.\u003c/p\u003e \u003cp\u003eA nurse reviewed patient data daily (Monday to Friday) using a secure web-based dashboard. Pre-programmed algorithms generated alerts based on patients\u0026rsquo; daily inputs and organized data into graphics. The nurse could contact patients, perform a comprehensive evaluation, and provide heart failure education sessions if needed. Additionally, the nurse could contact the patient\u0026rsquo;s clinical team or refer the patient to the appropriate medical resource, such as the nearest emergency room, if necessary. Automated messages when nurses revised individual data, for example when automated alerts were checked, were sent to keep patients motivated.\u003c/p\u003e \u003cp\u003eMobile application for patients.\u003c/p\u003e \u003cp\u003ePatients randomized for the intervention group (INT) received a link to download the app (Modified version of the commercial TakeCare \u003csup\u003eTM\u003c/sup\u003e app)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e with the RPM and DTx technologies. This app was made available on both Android and iPhone platforms by invitation only. It was for research only and not approved by Canada Health nor U.S. Food and Drug Administration. For the RPM part, patients were encouraged to use the app daily to enter their vital signs, weight, HF symptoms, and step counts (optional) for 12 weeks. This data could be inputted manually or automatically if the patient had connected devices, and could be shared through portable document format (PDF) reports. Data was also generated into user-friendly graphics to see daily weight evolution. As for the DTx part, patients received weekly automated reminders to encourage self-management through personalized emails. The app also provided key educational components of HF self-care\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, heart failure and its symptoms, nutrition, physical activity, and psychosocial health. The app did not offer automatic feedback or alert messages to patients based on their daily input. All patients were well advised that the mobile app did not work as a direct contact with the healthcare team, and therefore advised to contact their HF clinic directly if they had any concerns or questions.\u003c/p\u003e \u003cp\u003e\u003cb\u003e Decision support tool with guideline-directed medical therapy suggestions.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe decision support tool on the web-based dashboard for professionals generated an automated PDF report. It used clinical data entered via the patient's mobile app or by the research team, including cardiac diagnosis, LVEF, current medication list, vital signs, and lab results. The report provided personalized and automated treatment recommendations to assist in GDMT titration, based on the most recent Canadian HF guidelines\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Four medication classes and doses for HF with reduced ejection fraction (HFrEF) were suggested\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e: beta-blockers (BB), angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) or angiotensin receptor\u0026ndash;neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter-2 inhibitors (SGLT2i). Two were also suggested for HF with preserved ejection fraction (HFpEF): MRA\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and SGLT2i\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Reports also highlighted any abnormal vital signs or lab results. Referring teams with patients randomized to the intervention group received detailed reports with GDMT suggestions at baseline, 6 weeks, and 12 weeks. Each report was generated automatically by the platform and then reviewed and validated by two clinicians from the research team, including physicians, nurse practitioners, or pharmacists. No medication changes were made by the research team.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary endpoint was to evaluate the impact of the Continuum program on healthcare consumption among patients from HF specialized clinics, by analyzing costs of all-cause, cardiac (including HF-related events), and HF-related events and their respective number of days.\u003c/p\u003e \u003cp\u003eSecondary endpoints included specific sub analysis on events, readmissions (i.e. a second hospitalization during study time), major adverse cardiovascular events (MACE), other clinical events such as unplanned clinical appointments and emergency consultations, the impact of compliance on healthcare consumption, and GDMT initiation. Following paragraphs define data collection, costs calculations, events and compliance. Definitions of other secondary outcomes are available in supplements (Table S2).\u003c/p\u003e \u003cp\u003eData collection included patients\u0026rsquo; assessments at three time points: baseline (T0), 6 weeks (T6), and 12 weeks (T12). This study was designed entirely for virtual follow-ups. Medication lists and events were collected through patient phone questionnaires at these time points and confirmed by reviewing patients\u0026rsquo; electronic medical records. Patients\u0026rsquo; files data was always collected for all events, including for events happening in other hospitals.\u003c/p\u003e \u003cp\u003eCosts included events defined as emergency and hospital visits. Emergency costs were based on the Quebec provincial government fees per emergency visit (CAD \u003cspan\u003e$\u003c/span\u003e 387)\u003csup\u003e \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e \u003c/sup\u003e. Three ways of calculation hospitalization costs are presented: 1) costs based on the main medical condition during hospitalization (categorized by case mix groups)\u003csup\u003e \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e \u003c/sup\u003e; 2) costs based one daily hospitalization fees\u003csup\u003e \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e \u003c/sup\u003e; and 3) standard hospital stay costs\u003csup\u003e \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e \u003c/sup\u003e. Case-mix groups were coded by two blind authors (EB and PB) and reviewed with a third reviewer (EMT), if discrepancy existed between the first two authors. A consensus was reached between all reviewers. Intervention costs (2 Canadian dollars per patient and per day) were explained by software license (1\u003cspan\u003e$\u003c/span\u003e) and human resources training and salaries (1\u003cspan\u003e$\u003c/span\u003e) (nursing and clerical support to explain and help patients how to install the mobile app). Further details are available in the supplements (Table S2).\u003c/p\u003e \u003cp\u003eEvents were defined as unplanned hospitalizations, emergency visits or deaths. They were categorized as cardiac if the primary diagnosis at admission or one of the symptoms at presentation was cardiac and required a cardiology specialist (e.g., palpitations, chest pain, dyspnea). The same criteria applied for subcategorization of HF events (primary diagnosis being HF or HF symptoms at admission, such as dyspnea or pitting edema). HF events were included in cardiac events.\u003c/p\u003e \u003cp\u003eCompliance was evaluated as the proportion of days when at least one data was entered over the total days of participation in the study, excluding days when patients were hospitalized. Compliance groups were generated according to group tertiles (low, medium, and high).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eTo detect a reduction in all-cause hospitalizations of 30% in the intervention group with a significance level (α) of 0.05, and power (1-β) of 0.80, this study required 90 patients per group. To account for drop-out rates of 10% and continuity correction, we estimated 114 patients per group. These were based on results from our pilot study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eDescriptive analyses are presented as frequency and proportions, or means and standard deviations, as appropriate. Categorical and descriptive variable comparisons are tested with Fisher or Student T-test when needed. Count data (consultations) were compared using unadjusted negative binomial regression. Highly skewed data (days, NTproBNP, and costs) was analyzed using Mann-Whitney U test. Time-to-event was illustrated by Kaplan-Meir curves and the differences were calculated by the log rank test. Cox regressions were used to estimate hazard ratios. Per protocol analysis excluded loss to follow-up patients. Statistical significance was fixed at 5%.\u003c/p\u003e \u003cp\u003eStudy data were collected and managed using REDCap electronic data capture tools hosted at CHUM\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources. Telemonitoring of vital signs, symptoms and compliance were collected through the mobile app Takecare\u003csup\u003eTM\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The dataset was analyzed using IBM\u0026rsquo;s SPSS Statistics\u0026reg; (version 29.0; SPSS Inc., Chicago, IL, USA).\u003c/p\u003e\n\u003ch3\u003eEthics and inclusion statement\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003cp\u003efor this study was granted by the CHUM Research Center's local ethics committee (21.403 (2022\u0026ndash;10426)). The study was conducted following the ethical principles outlined in the Declaration of Helsinki. The Continuum intervention technology was presented to and approved by CHUM's telemedicine committee, and it was declared compliant with the institution's security requirements. Each participant provided informed and signed consent.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eCardiology consultations\u0026rsquo; lists were screened for HF and other inclusion and exclusion criteria. A total of 140 patients followed in specialized heart failure clinics were eligible and approached for this study, and 111 patients were randomized (details in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). OF those, 94 patients over approximately 400 of the CHUM specialized HF clinic total patients took part in this study (24%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline characteristics.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison between groups at randomization (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) showed no statistical difference. Only a few patients had severe NYHA class dyspnea in both groups. A numerically higher number of patients with new HF was present in the intervention group. Follow-up by community hospitals accounted for 6% (3/52) vs 17% (9/54) in the intervention and control groups accordingly (p\u0026thinsp;=\u0026thinsp;0.124). Finally, 12% (6/52) of patients needed help from a caregiver to use the app in the intervention group.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntervention (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (women) (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA class 3 or 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF 40 or below\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHF ischemic cause\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (I or II)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDLP (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHTN (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb below 120g/L (n)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNTproBNP (ng/L)*,**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1616\u003c/p\u003e\n \u003cp\u003e(903\u0026ndash;5559)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1404\u003c/p\u003e\n \u003cp\u003e(924\u0026ndash;5471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbnormal results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNew diagnosis of HF (last 3 months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRrEF patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;27***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACEi, ARB or ARNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeta-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSGLT2i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll 4 GDMT classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome brackets (Canadian dollars)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; \u003cspan\u003e$\u003c/span\u003e44.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e44,545 - \u003cspan\u003e$\u003c/span\u003e66,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e66,000 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation years after primary school*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaregiver support for technology use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eCIED: Cardiac Implantable Electronic Device, CKD: chronic kidney disease, DLP: dyslipidemia, eGFR: estimated glomerular filtration ratio, Hb: hemoglobin, HF: heart failure, HTN: hypertension, LVEF: left ventricular ejection fraction, n: number, NTproBNP: N-terminal-pro-brain natriuretic peptide, NYHA: New York Heart Association dyspnea class, SD: standard deviation\u003c/p\u003e\n\u003cp\u003eMeans\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n (%), or median\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for NTproBNP accordingly\u003c/p\u003e\n\u003cp\u003eT-tests for means except Mann Withney test for NTproBNP due to skewness of data,\u003c/p\u003e\n\u003cp\u003eFisher test for categorical data except income brackets with Chi square test\u003c/p\u003e\n\u003cp\u003e*Missing data for more than 20% of the group\u003c/p\u003e\n\u003cp\u003e**Abnormal results for NTproBNP: \u0026gt;450 for 50 years old, \u0026gt;\u0026thinsp;900 for 50\u0026ndash;75 years old and \u0026gt;\u0026thinsp;1800 for \u0026gt;\u0026thinsp;75 years old\u003c/p\u003e\n\u003cp\u003e***Initial medication results missing for one patient\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealthcare consumption and clinical events.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the primary outcome show trends in favor of the intervention group, but no statistically significant difference in terms of costs, all-cause events, total events\u0026rsquo; days, and the number of patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The absolute difference in events costs was -\u003cspan\u003e$\u003c/span\u003e1322 per patient for all-cause events, and -\u003cspan\u003e$\u003c/span\u003e2,106 per patient for cardiac events. The mean cost of the digital intervention was \u003cspan\u003e$\u003c/span\u003e172, excluding the regular standard of care in HF clinic. Additional details are available in supplements, including standard hospital stay costs (Table S3 and Table S4).\u003c/p\u003e\n\u003cp\u003eFor the other secondary outcomes, major cardiovascular events (MACE) were also numerically lower in the intervention group (5 vs 8; p\u0026thinsp;=\u0026thinsp;0.386). Hospitalization length of stay was however higher in the intervention group: 15 in 7 patients vs 11 in 10 patients, p\u0026thinsp;=\u0026thinsp;0.222. Planned cardiac consultations or visits happening during study follow-ups were similar between groups (109 vs 122, INT vs CTRL, p\u0026thinsp;=\u0026thinsp;0.611), as well as the cardiac ones that were unplanned or urgent (18 vs 21, INT vs CTRL, p\u0026thinsp;=\u0026thinsp;0.781). Finally, Kaplan-Meier analyses were performed on all-cause (Figure S4), cardiac (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and heart failure events, but no differences were found. Other details are available in supplements (Table S5). Only one death occurred during the study in the control group.\u003c/p\u003e\n\u003cp\u003eA per-protocol analysis was performed for both the primary and secondary outcomes. Cardiac costs, cardiac events days, and the number of patients with cardiac events were significantly lower in the intervention group (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, time to the first cardiac event was additionally in favor of the intervention (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Additional details and other results are found in supplements (Table S3, Table S4 and Table S5).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHealthcare consumption per type of analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eCosts based on main condition during hospitalization (CAD $)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention to treat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,654\u0026thinsp;\u0026plusmn;\u0026thinsp;7,229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214,703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,976\u0026thinsp;\u0026plusmn;\u0026thinsp;9,477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61,984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,192\u0026thinsp;\u0026plusmn;\u0026thinsp;4,986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,298\u0026thinsp;\u0026plusmn;\u0026thinsp;8,437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Protocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,296\u0026thinsp;\u0026plusmn;\u0026thinsp;6,428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214,703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,382\u0026thinsp;\u0026plusmn;\u0026thinsp;9,866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61,211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,302\u0026thinsp;\u0026plusmn;\u0026thinsp;5,237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,635\u0026thinsp;\u0026plusmn;\u0026thinsp;8,795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eCosts based on hospitalization days (CAD $)*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention to treat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274,823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,285\u0026thinsp;\u0026plusmn;\u0026thinsp;15,917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,608\u0026thinsp;\u0026plusmn;\u0026thinsp;16,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150,562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,895\u0026thinsp;\u0026plusmn;\u0026thinsp;13,354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,289\u0026thinsp;\u0026plusmn;\u0026thinsp;13,691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer Protocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218,670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,653\u0026thinsp;\u0026plusmn;\u0026thinsp;14,925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e356,820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,282\u0026thinsp;\u0026plusmn;\u0026thinsp;16,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149,789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,187\u0026thinsp;\u0026plusmn;\u0026thinsp;14,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,829\u0026thinsp;\u0026plusmn;\u0026thinsp;14,275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of days*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention to treat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac events days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer protocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac events days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Mann-Whitney U test for costs and days.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvents per type of analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients with events\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEvents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients with events N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep value*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention to treat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10/52 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/54 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.664\u0026ndash;1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause readmissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2/10 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/12 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.415\u0026ndash;19.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/52 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11/54 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.787\u0026ndash;3.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003ePer protocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8/47 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12/49 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581\u0026ndash;4.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll-cause readmissions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/8 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5/12 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.459\u0026ndash;54.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/47 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11/49 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.102\u0026ndash;16.350\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Fisher test for the number of patients having events.\u003c/p\u003e\n\u003cp\u003e2A) Intention to treat analysis for cardiac events. 2B) Per protocol analysis for cardiac events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; app compliance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor analysis purposes, the intervention group was split into compliance tertiles (n\u0026thinsp;=\u0026thinsp;17 each) based on the app use (excluding time while hospitalized (available for 51/52)). Compliance proportions ranged from 0%-56% for low compliance, \u0026ge; 56\u0026ndash;92% for medium, and \u0026ge;\u0026thinsp;92%-100% for high compliance group. No patients in the high compliance groups had cardiac or HF events, but no statistical difference was found between compliance groups. Total all-cause events costs per group are presented in the Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e intervention groups based on the compliances tertiles, the control group, and the total intervention cost are shown to present the proportions. Numerically, total costs are decreased when compliance increases. There were also fewer patients with events, number of events, and costs in the high compliance group (Supplementary Table S6), and the latter accounted for only 6% of the total unplanned costs of the intervention group (n\u0026thinsp;=\u0026thinsp;17/51).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProfessional dashboard management.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total average time of 64 minutes per patient (approximately one minute per business day) was required by the clinicians to follow the RPM dashboard during the 12-week follow-up. It included calls to patients, referrals, and contacts to their treating teams. Some of this work time would possibly have been incorporated into the usual workload of the referring clinics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeart failure medication optimization.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe four medication classes in HFrEF patients\u003csup\u003e\u003cstrong\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/strong\u003e\u003c/sup\u003e were analyzed according to GDMT initiation during the 12-week program (i.e. patient not having the GDMT class at the baseline). A total of 26 INT and 22 CTL patients had HFrEF and available medication data. Only 14 and 12 patients did not have four recommended medication classes at study start, i.e. equivalent drug optimization at the baseline. No statistical difference was found between groups in terms of initiation of GDMT. Numerical trends for the number of patients with the recommended classes are however in favor of the intervention: 77% (20/26) vs 64% (14/22). Details are presented in Supplementary Figure S5.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur RCT explored the effects of a tri-module solution (RPM, DTx, and DST) for HF patients and their healthcare team at a university hospital centre with referrals from different regions in Quebec, Canada. Main results of this study were not statistically significant in the intention to treat approach, but positive results on cardiac-related events\u0026rsquo; outcomes were present in the per-protocol analysis for costs, number of patients with events, total events days, and time to the first event. These results may be influenced by the study termination before having reached the sample size due to recruitment issues. Regardless, highly compliant patients did not have any cardiac events and accounted for only 6% of the all-cause unplanned costs. The mean intervention cost was \u003cspan\u003e$\u003c/span\u003e172 per patient and required only 1 hour of clinical time for a 12-week period. However, medication changes were similar between groups. The next paragraphs will explore the impact of digital interventions in cardiac and HF settings, our Canadian perspective in terms of costs and outpatient setting, the importance of self-care in these kinds of interventions, reflections on the lack of effect on GDMT, key points to develop and implement such tools in healthcare systems around the world, and our study limitations.\u003c/p\u003e \u003cp\u003eThe positive effects on cardiac-related events were not observed in heart-failure-related outcomes, those later were in lower numbers than expected, based on our pilot study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Also, cardiac events may be influenced at a higher level by RPM and DTx than HF events alone. More precisely, vital signs and HF symptoms monitoring can lead to the detection of cardiac-related complications such as hypotension or arrhythmias that are not directly related to HF events. This could explain our positive results only in cardiac events. These findings on cardiac events are similar to the larger TELESAT-HF study, where HF hospitalizations outcomes were also neutral, contrary to all-cause time spent in hospital and to number of emergency visits\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For HF events, meta-analyses have however showed positive results when pooling different RPM modalities together\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Moreover, a total of 61 days were saved in the intervention group (absolute difference between the groups) in the cardiac hospitalizations or emergency beds. This could also lead to an increased availability of 6 HF hospitalizations based on a mean HF LOS \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, which is important due to the lack of bed availability in our region and in terms of healthcare efficiency. The time to cardiac event in favor of the intervention group seen in our per-protocol analysis is not frequently explored in the literature, whereas time to death has often been seen in favor of RPM\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These results need to be however further explored. The all-cause 30-day readmission rate in our study is also higher than national rates (42% in the control group vs the national rate of 20.6%) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, but this may be explained by the subgroup of patients included in the study. Only ill or unstable patients are being followed in HF-specialized clinics\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and only the ones at higher risk of hospitalization were included. Our program has been targeting higher-risk patients due to their specific demands and high consumption of specialized resources\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, and as recently recommended in France\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll-cause costs show a difference of \u003cspan\u003e$\u003c/span\u003e1,123 to \u003cspan\u003e$\u003c/span\u003e1,323 per patient (depending on the cost calculation method), resulting in a possible economy of at least \u003cspan\u003e$\u003c/span\u003e6 for each \u003cspan\u003e$\u003c/span\u003e1 invested in the intervention when accounting for the mean intervention cost of \u003cspan\u003e$\u003c/span\u003e172. This intervention is a possible alternative to decrease costs in a public healthcare system. This is particularly important for hospital performance and efficiency. HF has an increasing prevalence and an economic impact in Canada of 2.8\u0026nbsp;billion dollars projected for 2030\u003csup\u003e38\u003c/sup\u003e, therefore, innovative solutions that avoid hospitalizations or shorten the LOS are needed\u003csup\u003e \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e \u003c/sup\u003e. Another similar intervention in Canada has previously showed similar results being cost-effective with quality-adjusted life years\u003csup\u003e \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e \u003c/sup\u003e. Finally, cost calculations based on the main hospitalization cause and using mean costs of these conditions\u003csup\u003e \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e \u003c/sup\u003e remove outliers. This may give a better perspective on real costs, and be a strength of this study\u003csup\u003e \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOutpatient resource consumption such as planned or unplanned cardiac consultations was not increased in the intervention group, similar to other findings\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This indicates that creating an extra channel of communication with patients does not increase the flow of appointments in clinics (virtual or in-person). Patients were also well made aware not to replace any clinical decisions or appointments by the ones having for this study \u0026ndash; all clinical routines remained intact.\u003c/p\u003e \u003cp\u003eSelf-care is also essential in chronic HF management on mortality rates\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, quality of life and readmission rates\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In this study, high compliance of self-care monitoring (vital signs or HF symptoms) had remarkably lower costs, even though not statistically significant (underpowered extra analyses) However, daily weight monitoring adherence\u0026thinsp;\u0026gt;\u0026thinsp;80% has previously been shown to be a good predictor of HF hospitalizations\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This study strengthens the importance of recommending daily self-care monitoring to HF patients, with best results at the higher compliance tertile, 92% or above. Another recent study (TELESAT-HF), reinforcing self-care through personalized education and tele coaching also showed similar results in terms of time spent at the hospital\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Selfcare could be reinforced through remote interventions including education sessions by RPM nurses not quantified in regular cardiac appointments. This could have influenced the diminished care consumption results in the intervention group in the per protocol analysis.\u003c/p\u003e \u003cp\u003eHF GDMT optimization was similar between groups, but only a few patients in both arms were eligible, therefore our findings are limited. The lack of effect can be caused by multiple factors; GDMT is complex, and digital interventions do not address all the issues related to inertia (i.e. medication changes were at the discretion of the referring medical teams). Digital interventions also do not replace referring healthcare teams nor personal shortages \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Previous studies focusing GDMT optimization were positive when 1) educating patients to discuss treatment options with their clinicians\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, 2) combining frequent remote titration visits and RPM\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and 3) DST were embedded and interoperable with electronic medical records and done during patients\u0026rsquo; visits\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThroughout this phase 2 RCT and our pilot study,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e we learned valuable lessons that could help with the implementation phase of programs such ours in a public healthcare setting. A main point is that patients were not provided with connected health objects; this would have significantly increased both costs and time to educate patients about their use, acting like a barrier to future implementation. Likewise, alerts related to connected objects could have been more complex, as previously reported\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and time consuming for clinicians. Moreover, nurses in charge of the RPM in our intervention did not need to be in the same hospital nor in close contact with referring teams, reinforcing the possibility to develop HF RPM specialized expertise in specific centres to improve regional access to specialized HF care, currently lacking in Canada\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Similar results have been shown positive with private practice nurses responsible of RPM\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This is particularly important when thinking over implementation of digital interventions. Healthcare professionals\u0026rsquo; workload must be adapted to promote new innovative solutions, or may bedone through private sectors to increase adoption through specific reimbursements, such as in France\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. These reimbursements can also be modulated to their proven effectiveness\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Another note on financing those interventions, adapted reimbursements must be planned for each step of development, including the testing and adaptation phase, the formal efficiency analysis, implementation, and reevaluations phases\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This may lead to faster and more efficient use of those technologies. Specifically on the topic of workload, another key learning is that RPM and GDMT tools may it and therefore should be adapted to improve their efficiency, such as: 1) interoperability with electronic medical records data, optimized algorithms and devices triggering alerts appropriately and at the right time\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e; and 2) improved time to clinical action with RPM alerts and pre-established trajectories\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Finally, regular phone calls to support RPM with patient education might be important, focusing on low compliant patients, as it recently showed positive results when combined with a mobile app similar to ours\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Finally, when comparing this RCT with the latest meta-analysis on the subject, we highlight the added value of RPM through mobile health interventions with clinicians\u0026rsquo; feedback\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and complex telemonitoring combining different modalities, namely non-invasive RPM with DTx and DST\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has limitations that need to be acknowledged. First, we did not meet the recruitment goals that were set at 228 patients; therefore, we believe some of our analyses are underpowered and results must be taken with caution. We believe that the extra work with COVID-19 impacted the way we were able to screen, recruit patients and get referrals from other centres. Moreover, limited data interoperability between hospitals, and lack of specific funding may also have contributed to it (i.e. teams with no research personnel support might have faced challenges to refer patients). However, the trends across outcomes in favor of our program are consistent and have been demonstrated before by others\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. We also believe that our per protocol analysis is balanced with an approximate dropout rate of 10% in both groups. The setting of application of this study is also limited to the Quebec public healthcare system, which may reduce generalization to other settings. Also, we should be mindful that software development was done during the study, mainly for medication suggestions.\u003c/p\u003e \u003cp\u003eIn conclusion, our 12-week low-cost, non-invasive trimodal combination of RPM, DTx and DST intervention for heart failure (HF) outpatients per protocol analyses showed positive effects for several key cardiac events outcomes, including reduced costs, event days, patients with events, and time to first event. Our results need further confirmation in a larger sample size implementation trial, but our findings highlight the potential impact of this innovative digital health combination in a Canadian context. As digital health continues to evolve, it may become a standard component of care in the future. Identifying suitable technologies for specific patient populations and optimizing their use will be crucial in managing healthcare costs and alleviating the burden on healthcare professionals. Taking it all together, we believe our findings highlight the importance of further exploring complex telemedicine solutions like ours.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eData availability\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eDeidentified datasets used and/or analyzed in this article can be made available beginning 9 months and ending 36 months following this publication if investigators have been approved by an independent ethics committee and follow legal requirements upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eCode availability\u003c/h1\u003e\n\u003cp\u003eNo software code is made available, as it was done in partnership with a private software developer company. For any inquiry, please reach the corresponding author.\u003c/p\u003e\n\u003ch1\u003eAcknowledgments\u003c/h1\u003e\n\u003cp\u003eThe authors disclose the following financial support for the research, authorship, and/or publication of this article that started in late 2019. This work was supported by a joined grant from Medteq+ (innovation for health) and Prompt (#10-26) including financial contributions from Boehringer Ingelheim, Canada Ltd; Greybox Solutions Inc. and Mitacs, providing resources for both the research team and software development. Emmanuel Marier-Tétrault was also supported by Nursing Grants from 2023-2025 of the CHUM Foundation. Medteq+, Prompt, and the other contributors had no role in the design and conduct of the study; extraction, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe acknowledge the participation of the CHUM hospital workers, including heart failure clinicians (mainly nurse practitioners Jean-Dominic Rioux and Valérie Fontaine), contributors in the cardiology department, the telehealth department at CHUM (Rudolph de Patureaux), clinicians at Rouyn Noranda Hospital, and the research team (Karim El Kamouni, Zoé Carrier, Priccila Zuchinali, Mouny El Haffaf, Loyda Jean-Charles, and Sarita Sakoto). We also wish to thank our partners at Greybox Solutions Inc. (Pierre Bérubé and Xavier Jodoin) and Boehringer Ingelheim, Canada Ltd. (Suzanne Kimmerle and Eve Blanchet) for their partnership. We finally thank the Canadian Institute for Health Information (CIHI) for their support.\u003c/p\u003e\n\u003ch1\u003eAuthors contributions\u003c/h1\u003e\n\u003cp\u003eE.M.T., P.A.B.R., M.P.P. and F.T. took part in the conception and the design of this the study. E.M.T., P.A.B.R., S.B., E.B., P.B., S.Y. and F.T. contributed to the collection and assembly of the data. All authors contributed to software development suggestions. E.M.T. and S.B. provided remote patient monitoring to patients with other members of the research team. E.M.T. and P.A.B.R. conducted the statistical analyses and prepared the first draft of the paper. P.A.B.R. and F.T. equally supervised the work of E.M.T.\u0026nbsp;The final version of the manuscript was revised and approved by all authors.\u003c/p\u003e\n\u003ch1\u003eCompeting interests\u003c/h1\u003e\n\u003cul\u003e\n \u003cli\u003eEmmanuel Marier-Tétrault has received speaker fees from Boehringer Ingelheim Canada and is the secretary of the Quebec Heart Failure Society.\u003c/li\u003e\n \u003cli\u003eDr François Tournoux has received speaker fees from Boehringer Ingelheim Canada and is the past president of the Quebec Heart Failure Society.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe other authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSavarese, G. \u0026amp; Lund, L.H. 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J. Cardiol.\u003c/em\u003e (2023).\u003c/li\u003e\n\u003cli\u003eGuichet national de l\u0026rsquo;innovation du num\u0026eacute;rique en sant\u0026eacute;. Remboursement de la T\u0026eacute;l\u0026eacute;surveillance. G_NIUS https://gnius.esante.gouv.fr/fr/financements/fiches-remboursement/remboursement-de-la-telesurveillance (nd).\u003c/li\u003e\n\u003cli\u003eInstitut national d\u0026apos;excellence en sant\u0026eacute; et en services sociaux. Bulletin #12. L\u0026rsquo;acc\u0026egrave;s aux innovations en sant\u0026eacute; et en services sociaux: financement et prise de d\u0026eacute;cision. INESSS https://www.inesss.qc.ca/fileadmin/doc/INESSS/DocuMetho/Bulletins_veille/Bulletin_12_Financement_decision_vfinale.pdf (2024).\u003c/li\u003e\n\u003cli\u003eBhatia, A. \u0026amp; Maddox, T.M. Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy. \u003cem\u003eInt J Heart Fail\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 31-50 (2021).\u003c/li\u003e\n\u003cli\u003eMasotta, V.\u003cem\u003e, et al.\u003c/em\u003e Telehealth care and remote monitoring strategies in heart failure patients: A systematic review and meta-analysis. \u003cem\u003eHeart Lung\u003c/em\u003e\u003cstrong\u003e64\u003c/strong\u003e, 149-167 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Centre Hospitalier de l’Université de Montréal","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital health, heart failure, remote patient monitoring, digital therapeutics, decision support tools, randomized controlled trial","lastPublishedDoi":"10.21203/rs.3.rs-6629871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6629871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Digital health solutions are increasingly used in heart failure (HF) management to optimize resources. However, their full effectiveness remains unclear. The objective of this study was to assess if by enhancing remote patient follow-up, optimizing guideline directed medical treatment (GDMT), and positively influencing patient behavior, digital solutions could lead to a significant reduction in overall care consumption. In this 12-week randomized controlled trial, a mobile app-based remote patient monitoring, digital therapeutics, and clinical decision support tools were combined to assess its impact on healthcare consumption. A total of 111 HF patients were randomly assigned (1:1) to either the Continuum intervention or usual care. Intent-to-treat analysis revealed trends favoring the intervention. Per-protocol analysis on cardiac events were in favor of the intervention group and showed significant lower costs (mean CAD \u003cspan\u003e$\u003c/span\u003e1,302 vs. \u003cspan\u003e$\u003c/span\u003e3,635, p\u0026thinsp;=\u0026thinsp;0.035), number of patients affected (3 vs. 11, p\u0026thinsp;=\u0026thinsp;0.041), total event days (66 vs. 127, p\u0026thinsp;=\u0026thinsp;0.036), and time to the first event (95% CI 1.055\u0026ndash;13.56, p\u0026thinsp;=\u0026thinsp;0.028). Highly compliant patients (n\u0026thinsp;=\u0026thinsp;17, 33%) had no cardiac events and represented 8% of the costs. The mean cost of the intervention was \u003cspan\u003e$\u003c/span\u003e172. These findings warrant further investigation with larger studies to confirm the efficacy and cost-effectiveness of this combined complex digital solution intervention.\u003c/p\u003e \u003cp\u003eClinicalTrials.gov registration: NCT05377190\u003c/p\u003e","manuscriptTitle":"Combined Digital Interventions for Enhancing Heart Failure Continuum Care and Their Impact on Care Consumption: A Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 10:26:48","doi":"10.21203/rs.3.rs-6629871/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f1fe931-2800-4909-b736-92baa402cbb0","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48320956,"name":"Cardiac \u0026 Cardiovascular Systems"},{"id":48320957,"name":"Nursing"},{"id":48320958,"name":"Medical Informatics"},{"id":48320959,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2025-05-13T10:26:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 10:26:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6629871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6629871","identity":"rs-6629871","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}