Non-invasive home telemonitoring system for heart failure patients: A randomized clinical trial

Abstract

Introduction

The low quality of life in heart failure patients is related to low self-care and treatment adherence. Consequently, innovative strategies are needed to improve them. The objective of this work is to determine the effectiveness of the use of a home telemonitoring system to improve the self-care and treatment adherence of heart failure patients.

Methods

A randomized clinical trial that compares the efficacy of a home telemonitoring system –intervention group versus usual care control group – among heart failure outpatients over a 90-day monitoring period was carried out. The home telemonitoring system consists of an application that collects measurements of different parameters on a daily basis and provides health education to patients. The home telemonitoring system processes data gathered and generates an alert if a risky situation arises. The outcomes observed were significant changes in patients’ self-care (European Heart Failure Self-care Behaviour Scale), treatment adherence (Morisky Modified Scale) and re-hospitalizations over the follow-up period.

Results

104 heart failure patients were screened; 40 met the inclusion criteria; only 30 completed the study. After the follow-up, intragroup analysis of the control group indicated a decrease in treatment adherence (p = 0.02). The mean European Heart Failure Self-care Behaviour Scale overall score indicated an improved self-care in the intervention group patients (p = 0.03) and a worsened self-care in the control group (p = 0.04) with a p value of 0.004 in the intergroup analysis. Thanks to the home telemonitoring system alerts, two re-hospitalizations were avoided.

Discussion

This study demonstrated that the proposed home telemonitoring system improves patient self-care when compared to usual care and has the potential to avoid re-hospitalizations, even considering patients with low literacy levels.

Trial Registration: Home Telemonitoring System for Patients with Heart Failure. clinicaltrials.gov Identifier: NCT04071093

Keywords

Telemedicine heart failure clinical trial self-care e-health

Introduction

Heart failure (HF) is the leading cause of death in the Western World.¹ It is the most common reason for hospitalizations in adults over 65 years old and, due to the high cost of readmissions, it is a costly burden to any health system.² In Argentina, 30% of deaths are a consequence of cardiovascular diseases and HF is the principal cause of death (e.g. 22,101 deaths in 2017).³

Common reasons for HF re-hospitalizations include delays in symptom recognition, medication and dietary noncompliance, and lack of knowledge and skills for competent self-management.^4–6 Multidisciplinary post-discharge treatment programmes, such as telemonitoring or structured telephone support, have been promoted as a strategy to avoid a large portion of HF readmissions, with benefits related to reducing the risk of all-cause mortality, length of hospital stays and improving patients’ quality of life.⁷ Besides, for many patients, frequent clinic visits may be impeded by different barriers such as transport, cost, or other diseases. In these situations, telemonitoring is an attractive option for the recognition of early signs of HF decompensation.

Several studies were performed with different telemonitoring systems, and several outcomes (positives and negatives) were obtained.^8–14 However, previous related works fail to highlight the importance of establishing the functional requirements that HF telemonitoring systems should have.¹⁵ To this aim, the specialists’ recommendations of the most important guidelines and medical consensus from relevant cardiology associations, such as the American College Cardiology, American Heart Association, European Society of Cardiology, Heart Failure Society of America, Canadian Cardiovascular Society, National Heart Foundation of Australia, Cardiac Society of Australia and New Zealand, Argentine Society of Cardiology among others, have been analysed. From this study, we can conclude that specialists strongly recommend monitoring weight, blood pressure, heart rate and symptoms on a daily basis. In contrast, specialists strongly recommend that HF telemonitoring systems should provide education to HF patients to improve self-care and treatment adherence.¹⁶

On the basis of this study, a non-invasive home telemonitoring system (HTS) for patients with HF was designed and developed by the authors. It consists of an application (app) that collects data and provides health education for HF patients, a web platform for the physicians to monitor patients’ data and an application programming interface that stores data and allows fluid communication between the app and the web platform.

To validate the HTS for the first time in a real setting, its effectiveness to improve self-care and treatment adherence of HF patients was assessed through a randomized controlled clinical trial in Argentina.

Methods

Study design

The randomized controlled trial was designed to compare the HTS versus usual care of HF ambulatory patients to detect if routine use of the telemonitoring app actually improves self-care and treatment adherence. The entire intervention process lasted 90 days, a similar period of time used in other trials performed by different authors.^17–20 The study follows the guidelines of the Declaration of Helsinki.²¹ The Methodological Committee and the Research Ethics Committee from the Provincial Health System (Tucumán, Argentina) reviewed and approved the trial protocol (REC Opinion No. 94). A written informed consent was obtained from each patient before randomization.

Inclusion and exclusion criteria

All outpatients eligible for inclusion in the study were over 18 years old with a primary diagnosis of HF, had been hospitalized at least once as a result of a HF decompensation, had a smartphone and access to WiFi, to a weight scale (accuracy of up to +/− 0.5 kg) and to a blood pressure monitor (accuracy: ± 10 mmHg (pressure)/± 5% (pulse), and measuring range: 20–280 mmHg (pressure)/40–200 beats per minute (pulse)). Patients were not recruited if they were illiterate, had learning difficulties, had a cognitive impairment sufficient to interfere with the use of the telemonitoring system, or had severe depression that could interfere with their quality of life perception and affect their treatment adherence, health status and other outcomes.

After informed consent was obtained, patients were randomly assigned in a 1:1 ratio to the intervention group (IG), telemonitoring, or the control group (CG), usual follow-up, using computer-generated randomised permuted blocks. All patients recruited were evaluated by a team of expert physicians and completed a baseline survey with two questionnaires. These questionnaires collected information about their treatment adherence (Morisky Modified Scale, MMS) and their self-care behaviour (European Heart Failure Self-care Behaviour Scale, EHFScB).^22–24

Intervention

The HTS consists of an app that collects measurements of weight, blood pressure, heart rate and symptoms (checklist of questions about swelling in ankles, legs, shortness of breath etc.) daily. The HTS processes online data and sends an alert to the participating physicians if a risky situation occurs (measurements are outside of normal ranges defined by the specialists in the HF guidelines; Table 1). The app has also an educational functionality that promotes self-learning about lifestyle, self-care and healthy habits of HF patients. To this aim, the app: a) has a frequently asked questions section, b) has a question and answer game about HF, and c) provides health education messages to the patients every day. Also, the HTS has a website through which healthcare professionals can access patients’ data (measurements and alerts).

Table 1.

Normal ranges of parameters, extracted from the study of the heart failure (HF) guidelines.

Parameter	Normal ranges
Blood pressure	Systolic pressure	140/90 mmHg
Blood pressure	Diastolic pressure	>60 mmHg
Heart rate	Heart rate	50/84 beats per minute
Weight	Weight (today) – Weight (3 days ago) <= 2 kg
Symptoms	Answer to the symptom question	0: No; 1: Very little; 2: Little; or 3: Yes

Once enrolled, the IG patients installed the app on their smartphones and received brief training about how to use it. Patients continued receiving usual care from their regular healthcare professionals while they were being telemonitored through the app. Every day the same nurse reviewed patients' data through the website and acted to all alert situations by contacting the patient for assessment and then, if necessary, contacted the participating physicians to receive advice about needed changes in medications or diet. The medical team was composed of four cardiologists specialised in HF and a nurse with extensive experience in cardiology.

Usual care

Patients receive care from their physicians at the hospital. Medical consultations were made either every week, every fortnight, after 3 weeks or on a monthly basis, depending on the patients' health status. All these medical consultations were recorded in the Electronic Medical Record.

Outcome measures

As a first step, physicians collect socio-demographic and other health information (baseline health status, risk factors, comorbidities, among others) from all patients to determine if both groups (CG and IG) were similar.

The primary outcomes, namely changes in self-care and treatment adherence, were measured using the EHFScB and MMS questionnaires, respectively. These questionnaires are considered valid and reliable. The MMS is useful to evaluate treatment adherence and to detect medication noncompliance in patients with chronic diseases.²² The EHFScB was tested and validated in different countries and languages, including Spanish, and is useful to analyse the self-care level in HF patients.^23–25

Baseline survey measures were collected by a nurse or a research assistant in person at the hospital, whereas the survey measures at the end of the follow-up period were collected by phone or in person.

The secondary outcome of interest, re-hospitalization, was measured as the frequency of hospitalizations over the 3-months follow-up period. This information was collected from the Electronic Medical Record at the end of the study.

Statistical analysis

According to Marrugat et al., a minimum of 30 patients are required between the control group and the intervention group to assess whether there is a statistically significant difference between them.²⁶

The sample size was calculated using Epidat version 4.1 and was based on the differences of the EHFScB scores between a CG and an IG reported in previous studies recently published.^27,28 These studies reported standardised mean differences of 10 and 22 points, respectively. Then, we assume the average of these data, and we determine a mean difference of 16 points. More specifically, the data to calculate the sample size were: means difference to detect (16), standard deviation of CG (14), standard deviation of IG (16), confidence level (95%) and power (80%). Accordingly, the estimated sample size was 15 patients for each group.

Intergroup and intragroup differences were analysed using hypothesis tests; differences with a p value less than 0.05 were considered statistically significant.

Baseline characteristics of patients were assessed using either Fisher's exact test or a chi-square test for categorical variables, depending on whether they were dichotomous variables. For quantitative variables, the normal distribution assumption was tested using the Shapiro–Wilk test. Whenever variables did not follow a normal distribution, the non-parametric tests were applied.

Due to the characteristics of the study, if the normal distribution assumption in all data is met, then a mixed analysis of variance (ANOVA) of repeated measures was performed for the intergroup and intragroup analysis. Alternatively, based on independent samples, a t-test or a Mann–Whitney U test was used for intergroup analysis. For intragroup analysis, a paired t-test or a Wilcoxon signed-rank test was performed. All Statistical analyses made were performed using the Package for Social Sciences (SPSS) for Windows® version 25.0 (SPSS Inc., Chicago, IL).

Results

In total, 104 HF patients in the Zenón Santillán Health Center Hospital in the city of San Miguel de Tucumán, Tucumán, Argentina between August and December 2018 were screened; only 40 of them met the inclusion criteria (Figure 1). They were randomly assigned, in a 1:1 ratio, to the IG (telemonitoring) or the CG (usual care). During the follow-up period, 10 patients were excluded: six dropped out from the study, one lost her/his smartphone, two were misdiagnosed and one died, hence the study concluded with 30 patients (minimum statistical sample size)²⁶ (15 in the IG and 15 in the CG). Both groups were similar at the baseline (Table 2). The average age was 52 years old; 80% were male; 60% were unemployed; and 75% had no high school education. Most patients were in functional class II or III (defined by the New York Heart Association Classification), and approximately 50% were diabetics and had a sedentary lifestyle.

Figure 1.

Flow diagram showing the progress of patients throughout the trial.

Table 2.

Baseline characteristics of 30 heart failure (HF) patients enrolled in the trial.

Characteristics	Usual care arm (n=15)	Intervention arm (n=15)	p Value
Sociodemographics
Male sex, n (%)	14 (93)	10 (66)	0.2
Education level, n (%)			0.1
Uncompleted elementary school	7 (46)	2 (13)
Elementary school	4 (26)	4 (26)
High school	2 (13)	5 (33)
Unemployed, n (%)	11 (73)	7 (46)	0.3
Without health insurance, n (%)	8 (53)	13 (86)	0.1
Live with company, n (%)	14 (93)	12 (80)	0.6
Medical characteristic, n (%)
New York Heart Association classification, n (%)			0.7
I	3 (20)	2 (13)
II	8 (53)	8 (53)
III	4 (26)	4 (26)
IV	0 (0)	1 (6)
Hypertension, n (%)	10 (66)	8 (53)	0.7
Valve disease, n (%)	1 (6)	1 (6)	1
Dilated cardiomyopathy, n (%)	7 (46)	6 (40)	1
Restrictive cardiomyopathy, n(%)	1 (6)	1 (6)	1
Ischemia, n (%)	1 (6)	1 (6)	1
Diabetes mellitus, n (%)	6 (40)	8 (53)	0.7
Obesity, n (%)	5 (33)	7 (46)	0.7
Dyslipidemia, n (%)	1 (6)	3 (20)	0.6
Sedentary, n (%)	11 (73)	6 (40)	0.1
Smoking, n (%)	8 (53)	5 (33)	0.5
Hospitalisations since diagnosis, mean ± SD	1.8 ± 1.01	1.4 ± 0.74	0.3
Ejection fraction, mean ± SD	29.8 ± 7.23	35.93 ± 12.13	0.1
Time with HF in years, mean ± SD	2.4 ± 3.46	0.93 ± 0.96	0.1
Treatment adherence and self-care, mean ± SD
Morisky Modified Scale Questionnaire score	5.06 ± 1.22	4.20 ± 1.56	0.1
European Heart Failure Self-care Behavior Scale Questionnaire score	78.44 ± 14.68	68.54 ± 21.00	0.3

The normal distribution assumption for the available data was tested with the Shapiro–Wilk test. The MMS scores of CG were, at baseline D(15) = 0.766, p = 0.001, and at the end of the follow-up period D(15) = 0.791, p = 0.003, which reveals a significant deviation from normality. The EHFScB scores of IG at baseline D(15) = 0.891, p = 0.07, which supports the normal distribution assumption, but at the end of the follow-up period D(15) = 0.839, p = 0.008 was significantly non-normal. Therefore, non-parametric tests should be performed for these data. In contrast, the differences between groups at baseline EHFScB scores and at the end of follow-up EHFScB scores of CG, D(15) = 0.967, p = 0.8, and of IG, D(15) = 0.983, p = 0.9, were both significantly normal, then an independent sample t-test was applied.

At the baseline condition, the mean MMS overall scores were 4.20 in the intervention group and 5.06 in the usual care group (p = 0.1), which indicates a similar level of treatment adherence in both groups. After the 3-months follow-up period, the mean MMS overall score had increased by 0.53 points in the IG and decreased by 0.33 in the CG. From these modifications, significant differences were found in the intragroup analysis of the CG (p = 0.02), which highlights a decreased treatment adherence at the end of the study in this group. Regarding intergroup analysis, there were no significant differences in treatment adherence between groups (p = 0.8) (Table 3).

Table 3.

Morisky Modified Scale questionnaire scores at baseline and at the end of follow-up.

	Usual care arm (n=15)	Intervention arm (n=15)
Baseline score, mean ± SD	5.06 ± 1,22	4.20 ± 1.56
End of follow-up, mean ± SD	4.73 ± 1,33	4.73 ±1.48
End of follow-up, p value
Intergroup analysis, Mann-Whitney U test	0.8
Intragroup analysis, Wilcoxon signed-rank test	0.02	0.06

From the self-care viewpoint, at the baseline study the EHFScB scores for CG and IG were similar, with 78.44 and 68.54 points, respectively (p = 0.3). After the 3-months follow-up period, the mean EHFScB overall score had decreased by 9.01 points in the CG and increased by 11.48 points in the IG (69.43 vs 80.03; p = 0.004), which indicates an improvement in self-care for patients from the IG and a decrease in self-care for patients from the CG. Similarly, the intragroup analysis pointed out to significant differences in self-care within both groups, p = 0.04 for the CG and p = 0.03 for the IG (Table 4).

Table 4.

European Heart Failure Self-care Behaviour Scale questionnaire scores at baseline and at the end of follow-up.

	Usual care arm (n=15)	Intervention arm (n=15)
Baseline score, mean ± SD	78.44 ± 14.68	68.54 ± 21.00
End of follow-up score, mean ± SD	69.43 ± 17.94	80.03 ± 18.54
Differences between baseline and end of follow-up, mean ± SD	−9.11 ± 15.55	11.48 ± 19.44
End of follow-up, p-value
Intergroup analysis, Mann-Whitney U test	0.1
Intragroup analysis, Wilcoxon signed-rank test	0.04	0.03
Intergroup analysis, (differences between baseline and end of follow-up) independent sample t-test	0.004

About re-hospitalizations, during the follow-up period two patients from the control group (13%) had decompensations and were hospitalized during 2 and 8 days, respectively. Ankle and leg swelling, progressive dyspnoea and weight gain were the main signs that caused the readmissions. Physicians concluded that patients were not complying with hygienic and dietary indications.

There were no re-hospitalizations in the IG (0%), but the HTS detected two risky conditions for patients in this group. Both situations consisted of a weight increase of more than 2 kg in a few days, which triggered an alert in the HTS, and the nurse contacted the patients to ask them to attend the hospital for a medical interview. After these consultations, physicians reminded the concerned patients of the importance of healthy habits and treatment adherence to avoid re-hospitalization.

There was no significant difference in re-hospitalizations between CG and IG (p = 0.5).

Discussion

In this work, the results of a randomized clinical trial to assess the importance of telemonitoring have been presented. The trial is intended to compare the ability of a HTS to improve self-care and treatment adherence of HF patients (n = 30, during a 90-day follow-up period) against usual care.

Due to the non-normality of the dataset, non-parametric tests were performed. However, due to the robustness of the mixed ANOVA test, it was performed to test the hypothesis stated in the foregoing paragraph with non-parametric tests, and we arrive at similar results.

On one hand, with non-parametric tests, we found the IG patients improved self-care with a significant difference at the end of the follow-up period, whereas self-care decreased in the CG patients. Both in the intragroup and intergroup analysis on self-care, significant differences were observed. With the mixed ANOVA test, the contrast for the interaction at baseline compared to the end of follow-up of the EHFScB score, comparing CG and IG was highly significant, F(1, 28) = 10.16, p = 0.004. This result highlights that the difference between scores found for IG at baseline compared to those at the end of the follow-up period is significantly higher than for CG. Therefore, we can conclude that the telemonitoring system improved the self-care of patients compared with usual care.

On the other hand, both non-parametric and mixed ANOVA tests show that CG patients had decreased their treatment adherence significantly whereas IG patients improved treatment adherence score, although without significant difference, perhaps due to the small sample size. Usually, after discharge patients comply with all medical recommendations (habits, hygienic and dietary indications and symptoms observation), but over time, they tend to relax, forget and not comply with that advice. For these reasons, the CG patients worsen their self-care, whereas the IG patients (who used the HTS) did not. This is because they received educational messages about their disease every day, they monitor their weight themselves every day, measured their heart rate and blood pressure and were aware of their HF symptoms. This information allowed patients to have an active role in their care, resulting in an improvement of the management of their disease, similar to conclusions found in previous studies involving both educational interventions²⁹ and telemonitoring interventions.^{8,13,17–19,30} As a result, the primary outcomes of our clinical study highlight that using HTS increase the chance of successfully improving self-care in HF patients and have the potential to increase treatment adherence.

It is important to remark that these outcomes were obtained in a low-literacy population (75% have no high school education). Several studies demonstrate that this population is more vulnerable to worsen its health status,^31–35 nevertheless, these patients that routinely use the HTS could improve it.

About the secondary outcomes, there were two re-hospitalizations in the CG versus none in the IG (however, no statistically significant differences were calculated). Physicians considered that these two re-hospitalizations could have been avoided using the HTS. It is because the reasons for re-hospitalizations (swelling ankles and legs, progressive dyspnoea and weight gain) are signs the HTS collects and analyses on a daily basis. Also, it can be considered that the two alerts of the HTS during the follow-up period would certainly be used to avoid these re-hospitalizations events. Accordingly, it can be said that the HTS has the potential to prevent re-hospitalizations. Therefore, our evidence supports the finding of other studies, that the use of HTS improve self-care^17,19 has the potential to increase treatment adherence³⁰ and to prevent re-hospitalizations.^8,30

Patients were satisfied with the system and recognized that by the randomized clinical trial they could know the importance of taking an active role in their disease management. In contrast, physicians highlighted that the educational functionality of the system is the most important aspect of it because they observed more compliance with the treatment and a progressive understanding by the patients of its health condition.

The HTS was designed based on the specialists' recommendations derived from the HF consensuses and guidelines.¹⁶ The educational functionality is a characteristic highly recommended by the specialists for any HF telemonitoring systems;¹⁴ however, only a few systems have it.¹⁵ As an example, in the systematic review of telemonitoring technologies in HF by Maric et al., only 10 of 56 articles (<20%) provide education, that is, most systems do not have an educational functionality.³⁶ In addition, prior studies that evaluated the effect of different telemonitoring systems yielded to contradictory results. Some of them did not find satisfactory outcomes.^9–11,14 In several cases, these systems did not comply with the minimum requirements that most specialists recommend, such as providing health education to patients.

This study suggests that the use of systems that meet the HF specialists’ recommendations has a significant probability of generating good outcomes in patients and improves the disease management. For these reasons, we argue that it is necessary to disseminate in a clear way the minimum requirements that HF telemonitoring systems must have to avoid the great efforts involved in conducting a clinical trial with systems that do not have the basic characteristics to improve disease management, which is unfortunately often the case.³⁶

As a limitation of our study, it may be argued that the number of patients involved is rather low so the study can be considered a pilot approach. Although only one hospital participated in this trial, the population heterogeneity is representative enough because there is a wide range of ages between 31 and 68 years, there are patients with many years coping with the disease and there are recently diagnosed patients. Also, there are patients of all functional classes and patients with various comorbidities and risk factors, so the results are generalisable. Our current goal is to increase the sample of patients and extending the follow-up period.

In conclusion, the reported randomized clinical trial demonstrates that the HTS actually improves patient self-care when compared with usual care. Also, telemonitoring using an app has the potential to avoid re-hospitalizations and increase treatment adherence. Finally, the study argues that a system with the characteristics recommended by the specialists could actually benefit patients, even in a small sample of patients with a low literacy rate.

Footnotes

Acknowledgements

We thank physicians from the Heart Failure Office of the Zenón Santillán Health Center Hospital for their assistance with the HF patients during the clinical trial.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Lucía M Yanicelli

References

Members

Benjamin

Blaha

et al. Heart disease and stroke statistics—2017 update: A report from the American Heart Association. Circulat 2017; 135(10):e146.

Benjamin

Virani

Callaway

et al. Heart disease and stroke statistics—2018 update: A report from the American Heart Association. Circulat 2018; 137(12):e67–e492.

Ministerio de Salud de la Nación. Estadísticas vitales. Información básica Argentina. Año 2017, http://www.deis.msal.gov.ar/index.php/estadisticas-vitales (2018, accessed 20 February 2018).

Finkelstein

Wood

Cha

et al. Feasibility of congestive heart failure telemanagement using a wii-based telecare platform. In: 32nd annual international conference of the IEEE engineering in medicine and biology, IEEE, Buenos Aires, Argentina, 31 Aug–4 Sept 2010, pp. 2211–2214.

Perna

Coronel

Címbaro Canella

et al. Revisión de insuficiencia cardíaca en Argentina Avances y retrocesos luego de dos décadas de registros y más de 19000 pacientes incluidos. Insuficiencia Cardíaca 2015; 10(1): 2–10.

Arocha Rodulfo JI. Resumen comentado de las nuevas guías de la Sociedad Europea de Cardiología, Münich 25–29 de Agosto de 2018. Med Interna (Caracas) 2018; 34 (4): 197–206.

Inglis

Clark

Dierckx

et al. Structured telephone support or non-invasive telemonitoring for patients with heart failure. Cochrane Database Systematic Rev. 2016; (10): 1–205.

Dansky

Vasey

Managing heart failure patients after formal homecare. Telemed J E-Health 2009; 15(10): 983–991.

Chaudhry

Mattera

Curtis

et al. Telemonitoring in patients with heart failure. New Eng J Med 2010; 363: 2301–2309.

10.

Boyne

Vrijhoef

Crijns

et al. Tailored telemonitoring in patients with heart failure: Results of a multicentre randomized controlled trial. Eur J Heart Failure 2012; 14(7): 791–801.

11.

Bardsley

Steventon

Doll

Impact of telehealth on general practice contacts: Findings from the whole systems demonstrator cluster randomized trial. BMC Health Serv Res 2013; 13(1): 395.

12.

Bekelman

Plomondon

Carey

et al. Primary results of the patient-centered disease management (PCDM) for heart failure study: A randomized clinical trial. JAMA Intern Med 2015; 175(5): 725–732

13.

Kalter-Leibovici

Freimark

Freedman

et al. Disease management in the treatment of patients with chronic heart failure who have universal access to health care: A randomized controlled trial. BMC Med 2017; 15(1):90.

14.

Kotooka

Kitakaze

Nagashima

et al. The first multicenter, randomized, controlled trial of home telemonitoring for Japanese patients with heart failure: Home telemonitoring study for patients with heart failure (HOMES-HF). Heart Vessels 2018; 33(8): 866–876.

15.

Yanicelli

Parodi

Goy

et al. Heart failure management: Comparative study of telemonitoring systems and the medical consensuses. IFMBE Proc 2015; 49: 821–824.

16.

Yanicelli

Goy

López

et al. Noninvasive home telemonitoring for patients with heart failure: A review of medical consensuses. J Phys Conf Ser 2013; 477. pii: 012007.

17.

Evangelista

Lee

Moore

et al. Examining the effects of remote monitoring systems on activation, self-care, and quality of life in older patients with chronic heart failure. J Cardiovasc Nurs 2015; 30(1): 51.

18.

Gellis

Kenaley

McGinty

et al. Outcomes of a telehealth intervention for homebound older adults with heart or chronic respiratory failure: A randomized controlled trial. Gerontolog 2012; 52(4): 541–552.

19.

Hägglund

Lyngå

Frie

et al. Patient-centred home-based management of heart failure: Findings from a randomised clinical trial evaluating a tablet computer for self-care, quality of life and effects on knowledge. Scand Cardiovasc J 2015; 49(4): 193–199.

20.

Schwarz

Mion

Hudock

et al. Telemonitoring of heart failure patients and their caregivers: A pilot randomized controlled trial. Prog Cardiovasc Nurs 2008; 23(1): 18–26.

21.

World Medical Association. Declaration of Helsinki: Ethical principle for medical research involving human subjects, https://www.wma.net/wp-content/uploads/2018/07/DoH-Oct2008.pdf (2008, accessed 15 October 2019)

22.

Case Management Society of America. Case management adherence guidelines version 2.0, http://docplayer.es/528564-Cmagpautas-de-adherencia-en-la-gestion-de-casos-case-management-adherenceguidelines-version-2-0.html (2006, accessed 21 January 2019).

23.

Jaarsma

Strömberg

Martensson

et al. Development and testing of the European Heart Failure Self-Care Behaviour Scale. Eur J Heart Fail 2003; 5: 363–370.

24.

Jaarsma

Årestedt

Mårtensson

et al. The European Heart Failure Self-care Behaviour scale revised into a nine-item scale (EHFScB-9): A reliable and valid international instrument. Eur J Heart Fail 2009; 11(1): 99–105.

25.

González

Lupon

Parajón

et al. Aplicación de la escala europea de autocuidado en insuficiencia cardiaca (EHFScBS) en una unidad de insuficiencia cardiaca en España. Rev Esp Cardiol 2006; 59(2): 166–170.

26.

Marrugat

Vila

Pavesi

et al. Estimación del tamaño de la muestra en la investigación clínica y epidemiológica. Med Clin (Barc) 1998; 111(7): 267–276.

27.

Wagenaar

Broekhuizen

Jaarsma

et al. Effectiveness of the European Society of Cardiology/Heart Failure Association website ‘heartfailurematters. org’ and an e-health adjusted care pathway in patients with stable heart failure: Results of the ‘e-Vita HF’ randomized controlled trial. Eur J Heart Fail 2019; 21(2): 238–246.

28.

Hovland-Tånneryd

Melin

Hägglund

et al. From randomised controlled trial to real world implementation of a novel home-based heart failure tool: Pooled and comparative analyses of two clinical controlled trials. Open Heart 2019; 6(1): e000954.

29.

Jaarsma

Halfens

Tan

et al. Self-care and quality of life in patients with advanced heart failure: The effect of a supportive educational intervention. Heart Lung 2000; 29(5): 319–330.

30.

Villani

Malfatto

Compare

et al. Clinical and psychological telemonitoring and telecare of high-risk heart failure patients. J Telemed Telecare 2014; 20(8): 468–475.

31.

Berkman

DeWalt

Pignone

et al. Literacy and health outcomes: A systematic review of the literature. J Gen Intern Med 2004; 19: 1228–1239.

32.

Williams

Baker

Honig

et al. Inadequate literacy is a barrier to asthma knowledge and self-care. Chest 1998; 114: 1008–1015.

33.

Williams

Baker

Parker

et al. Relationship of functional health literacy to patients’ knowledge of their chronic disease. A study of patients with hypertension and diabetes. Arch Intern Med 1998; 158:166–172.

34.

Baker

Gazmararian

Williams

et al. Functional health literacy and the risk of hospital admission among Medicare managed care enrollees. Am J Public Health 2002; 92: 1278–1283.

35.

Davis

Wolf

Bass

PF III

et al. Low literacy impairs comprehension of prescription drug warning labels. J Gen Intern Med 2006; 21: 847–851.

36.

Maric

Kaan

Ignaszewski

et al. A systematic review of telemonitoring technologies in heart failure. Eur J Heart Fail 2009; 11(5): 506–517.