Abstract
Background
Heart failure (HF) is a chronic disease with significant impact on quality of life and presents many challenges to those diagnosed with the condition, due to a seemingly complex daily regimen of self‐care which includes medications, monitoring of weight and symptoms, identification of signs of deterioration and follow‐up and interaction with multiple healthcare services. Education is vital for understanding the importance of this regimen, and adhering to it. Traditionally, education has been provided to people with heart failure in a face‐to‐face manner, either in a community or a hospital setting, using paper‐based materials or video/DVD presentations. In an age of rapidly‐evolving technology and uptake of smartphones and tablet devices, mHealth‐based technology (defined by the World Health Organization as mobile and wireless technologies to achieve health objectives) is an innovative way to provide health education which has the benefit of being able to reach people who are unable or unwilling to access traditional heart failure education programmes and services.
Objectives
To systematically review and quantify the potential benefits and harms of mHealth‐delivered education for people with heart failure.
Search methods
We performed an extensive search of bibliographic databases and registries (CENTRAL, MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, ClinicalTrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) Search Portal), using terms to identify HF, education and mHealth. We searched all databases from their inception to October 2019 and imposed no restriction on language of publication.
Selection criteria
We included studies if they were conducted as a randomised controlled trial (RCT), involving adults (≥ 18 years) with a diagnosis of HF. We included trials comparing mHealth‐delivered education such as internet and web‐based education programmes for use on smartphones and tablets (including apps) and other mobile devices, SMS messages and social media‐delivered education programmes, versus usual HF care.
Data collection and analysis
Two review authors independently selected studies, assessed risks of bias, and extracted data from all included studies. We calculated the mean difference (MD) or standardised mean difference (SMD) for continuous data and the odds ratio (OR) for dichotomous data with a 95% confidence interval (CI). We assessed heterogeneity using the I2 statistic and assessed the quality of evidence using GRADE criteria.
Main results
We include five RCTs (971 participants) of mHealth‐delivered education interventions for people with HF in this review. The number of trial participants ranged from 28 to 512 participants. Mean age of participants ranged from 60 years to 75 years, and 63% of participants across the studies were men. Studies originated from Australia, China, Iran, Sweden, and The Netherlands. Most studies included participants with symptomatic HF, NYHA Class II ‐ III.
Three studies addressed HF knowledge, revealing that the use of mHealth‐delivered education programmes showed no evidence of a difference in HF knowledge compared to usual care (MD 0.10, 95% CI −0.2 to 0.40, P = 0.51, I2 = 0%; 3 studies, 411 participants; low‐quality evidence). One study assessing self‐efficacy reported that both study groups had high levels of self‐efficacy at baseline and uncertainty in the evidence for the intervention (MD 0.60, 95% CI −0.57 to 1.77; P = 0.31; 1 study, 29 participants; very low‐quality evidence).Three studies evaluated HF self‐care using different scales. We did not pool the studies due to the heterogenous nature of the outcome measures, and the evidence is uncertain. None of the studies reported adverse events. Four studies examined health‐related quality of life (HRQoL). There was uncertainty in the evidence for the use of mHealth‐delivered education on HRQoL (MD −0.10, 95% CI −2.35 to 2.15; P = 0.93, I2 = 61%; 4 studies, 942 participants; very low‐quality evidence). Three studies reported on HF‐related hospitalisation. The use of mHealth‐delivered education may result in little to no difference in HF‐related hospitalisation (OR 0.74, 95% CI 0.52 to 1.06; P = 0.10, I2 = 0%; 3 studies, 894 participants; low‐quality evidence). We downgraded the quality of the studies due to limitations in study design and execution, heterogeneity, wide confidence intervals and fewer than 500 participants in the analysis.
Authors' conclusions
We found that the use of mHealth‐delivered educational interventions for people with HF shows no evidence of a difference in HF knowledge; uncertainty in the evidence for self‐efficacy, self‐care and health‐related quality of life; and may result in little to no difference in HF‐related hospitalisations. The identification of studies currently underway and those awaiting classification indicate that this is an area of research from which further evidence will emerge in the short and longer term.
Plain language summary
mHealth‐delivered education interventions in heart failure, using smartphone, tablet and internet‐based programmes and apps
Review question
What is the randomised controlled trial evidence for mHealth‐delivered education interventions in heart failure (HF) knowledge, self‐care and self‐efficacy for people with HF when compared to traditional methods of patient education?
Background
Education is vital for self‐care (activities individuals undertake with the intention of improving health, preventing disease, limiting illness and restoring health) in HF. Traditionally, education has been provided to people with heart failure in a face‐to‐face manner, using paper‐based materials or video/DVD presentations. In an age of rapidly‐evolving technology and uptake of smartphones and tablet devices, mHealth‐based technology is a new way to provide health education, with the benefit of being able to reach people who are unable or unwilling to access traditional HF education programmes and services.
Search date
We found studies by searches conducted in October 2019.
Study characteristics
We include in this review five randomised controlled trials (971 participants) of mHealth‐delivered education interventions for people with HF. The average age of participants ranged from 60 years to 75 years and 63% of participants were men. Studies came from Australia, China, Iran, Sweden and The Netherlands.
Key results
Five studies addressed HF knowledge; we found that the use of mHealth‐delivered education programmes showed no evidence of a difference in HF knowledge compared to usual care. One study assessing self‐efficacy reported uncertainty in the evidence for mHealth‐delivered education compared with usual care. Three studies evaluated HF self‐care using different scales. We did not combine the studies, because of the differences between the outcome measures, and the evidence is uncertain. The studies did not report any side effects of the interventions. Four studies examined health‐related quality of life and showed uncertainty in the evidence between mHealth‐delivered education and usual care. Three studies reported on HF‐related hospitalisation rates. The use of mHealth‐delivered education may result in little to no difference in HF‐related hospitalisations.
Quality of the evidence
We rated the quality of the evidence as very low to low, due to limitations in study design and execution and fewer than 500 participants in the analysis.
Conclusion
There is no evidence for a difference in the use of mHealth‐delivered educational intervention for people with HF on HF knowledge. There is uncertainty in the evidence for self‐efficacy, self‐care and health‐related quality of life. There may be little to no difference in HF‐related hospitalisations, compared to usual care. 'Usual care' in this case means enrolment in a heart failure‐management programme (clinic‐ or home‐based). This is an area of HF research from which further evidence will emerge in the short and longer term.
Summary of findings
Summary of findings 1. mHealth education intervention compared to usual care in heart failure.
| mHealth education intervention compared to usual care in heart failure | ||||||||
| Patient or population: Adults with heart failure Setting: Hospital and community Intervention: mHealth education intervention Comparison: Usual care | ||||||||
| Outcomes | Tools | Mean length of follow‐up (months) | Anticipated absolute effects* (95% CI) | Relative effect (95% CI) | № of participants (studies) | Certainty of the evidence (GRADE) | Comments | |
| Risk with usual care | Risk with mHealth education intervention | |||||||
| Heart failure knowledge Analysis 1.1 | Dutch HF Knowledge Questionnaire | 56 (39.6) | The mean heart failure knowledge was 12.7 | MD 0.10 higher (−0.20 lower to 0.40 higher) | ‐ | 411 (3 RCTs) | ⊕⊕⊝⊝ LOWa,d | Higher score equals a better outcome |
| Heart failure self‐efficacy Analysis 1.2 | Self‐efficacy for Managing Chronic Disease Scale | 28 | The mean heart failure self‐efficacy was 7.4 | MD 0.60 (−0.57 lower to 1.77 higher) | ‐ | 29 (1 RCT) | ⊕⊝⊝⊝ VERY LOWc,d,e | Higher score equals a better outcome |
| Heart failure self‐care Analysis 1.3 | European Heart Failure Behaviour Scale (EHFBS) Self‐Care Heart Failure Index (SCHFI) |
56 (39.6) | One study showed an improvement in the SCHFI maintenance subscale in the intervention group (MD 9.9, 95% CI −3.6 to −23.6), whereas the usual‐care group decreased over time (MD −3.5, 95% CI −10.3 to 1.3). Two studies reported improvements on the EHFBS for the intervention groups. |
411 (3 RCTs) | ⊕⊝⊝⊝ VERY LOWa,d,f | Due to the high heterogeneity observed in the analysis, we decided not to pool the studies for this outcome | ||
| Adverse events | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | ‐ | No studies reported this outcome |
| Health‐related quality of life Analysis 1.4 | Kansas City Cardiomyopathy Questionnaire Minnesota Living with HF Questionnaire |
84 | The mean health‐related quality of life was 51.7 | MD −0.10 lower (−2.35 lower to 2.15 higher) | ‐ | 942 (4 RCTs) | ⊕⊝⊝⊝ VERY LOWa,b | Higher score equals a better outcome |
| Heart failure‐related hospitalisations Analysis 1.5 | 84 | Study population | OR 0.74 (0.52 to 1.06) | 894 (3 RCTs) | ⊕⊕⊝⊝ LOW a,e | ‐ | ||
| 275 per 1000 | 219 per 1000 (87 to 454) | |||||||
| *The risk in the intervention group (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI). CI: Confidence interval; MD: mean difference; OR: Odds ratio; RCT: randomised controlled trial. | ||||||||
| GRADE Working Group grades of evidence High certainty: We are very confident that the true effect lies close to that of the estimate of the effect Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different Low certainty: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect | ||||||||
aDowngraded by one level for 'limitations in study design and execution', as the studies were rated at high risk of bias in multiple domains (performance and detection, attrition bias or other bias). bDowngraded by one level for 'inconsistency' due to substantial heterogeneity (I2 = 61%). cDowngraded by one level for 'limitations in study design and execution', as the study was rated at high risk of bias in multiple domains (performance, detection and other bias). dDowngraded by one level for 'imprecision' as there were fewer than 500 participants in the analysis.
eDowngraded by one level for 'imprecision' as the confidence intervals were wide.
fDowngraded by one level for 'inconsistency' due to high heterogeneity that precluded meta‐analysis (I2 = 86%).
Background
Heart failure (HF) is a chronic disease impacting on quality of life and leading to adverse outcomes (Atherton 2018). Living with a life‐limiting, chronic illness such as HF requires significant changes in lifestyle, such as changes to diet, managing and adhering to several medications, caring for oneself through appropriate exercise and energy conservation (Hammash 2017; Jaarsma 2017). Decades of research have underscored that education is necessary to promote a desired health or behavioural outcome. There are many different methods of delivering health education to people with HF, which may include (but are not limited to) one‐on‐one or group consultations with a nurse or allied health professional (Baptiste 2014), internet‐based interventions, smartphone and tablet apps, or printed reading materials (Baker 2011; Seto 2012). The effectiveness of each method of delivering health education, in particular, using mHealth education tools and platforms for people with heart failure, has not yet been examined in an extensive systematic review or meta‐analysis.
The use of eHealth (defined by the World Health Organisation (WHO) as "the use of information and communication technologies for health") (WHO 2011) has the potential to provide innovative solutions to health issues and is a key 'enabling' technology to improve care and the experience of care for those living with chronic conditions. However, there are significant societal and professional constraints associated with its use, including legal, ethical and data protection issues (Cowie 2016). In addition, health professionals may be resistant to such innovations, particularly if the evidence for the impact on quality of care is less than robust (Cowie 2016). Based on their recent position statement on eHealth, the vision of the European Society of Cardiology is to play a proactive role in all aspects of eHealth by helping to develop, assess and implement effective innovations to support cardiovascular health and health‐related activity across Europe (Cowie 2016). This vision is delivered through investment in research, education, training and advocacy (Cowie 2016). The ESC e‐Cardiology Working Group position paper highlighted the importance of patient‐education programmes, digital health workflow design, uniform European‐wide digital health legislation, data standardisation and interoperability assurance and the role of the digital health industry, health‐insurance industry, patient organisations and professional organisations in addressing the challenges in digital health implementation in Europe (Frederix 2019).
Similarly, the American Heart Association has highlighted the importance of integrating the practice of telehealth into traditional healthcare delivery systems and barriers to its effective implementation across broad populations of patients and providers (Schwamm 2017). Their ultimate goal is to increase the access of telehealth technologies to patients with cardiovascular diseases and stroke through partnerships with other organisations to achieve the following objectives:
ensure that coverage mandates exist in all states so that third‐party payers must offer specific, evidence‐based telehealth interventions as covered services;
ensure all properly‐trained providers are deemed eligible providers without restricted networks that would limit reimbursement by the provider;
encourage development of simpler, less expensive technology platforms to keep the patient burden and costs to healthcare systems low;
ensure that large e‐health record systems incorporate telehealth and make it compatible with traditional health records;
encourage development of improved education for providers to increase adoption; and
ensure adoption of telehealth does not sacrifice quality in the name of cost savings (Schwamm 2017).
Description of the condition
Heart failure is defined by the National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand: guidelines for the prevention, detection, and management of heart failure in Australia 2018 as "a complex clinical syndrome with typical symptoms and signs which generally occur on exertion, but can also occur at rest. It is secondary to an abnormality of cardiac structure or function that impairs the ability of the heart to fill with blood at normal pressure or eject blood sufficient to fulfil the needs of the metabolising organs" p. 1136 (Atherton 2018). It is characterised by typical symptoms such as breathlessness, ankle swelling and fatigue which may be accompanied by signs, e.g. elevated jugular venous pressure, pulmonary crackles and peripheral oedema (Ponikowski 2016). Heart‐failure patients often experience many of these symptoms at the same time, which eventually lead to decreased functional status, frequent hospitalisation and a diminished quality of life (Hoekstra 2013). A 2016 update from the American Heart Association estimated that 5.7 million people in America have heart failure and more than 915,000 new diagnoses occur every year (Mozaffarian 2016). Globally, the prevalence of HF is estimated to be 2% to 3% (Ambrosy 2014; Bui 2011; Dunlay 2017), and increases with age. At least 20% of hospital admissions among people older than 65 years are due to HF (Dunlay 2017).
The goal of HF management is to relieve symptom burden, prevent hospitalisation, and improve survival (Ponikowski 2016). Over the last few decades, the use of effective treatment, including pharmacological agents, non‐surgical devices, as well as disease‐management programmes, have reduced the risk of hospitalisation and sudden cardiac death, and improved overall mortality among people with HF (Al‐Khatib 2018; Kristensen 2018; Nochioka 2018; Velazquez 2018). However, the prognosis of HF still remains poor (Al‐Khatib 2018; Schmidt 2016). Approximately 30% of people hospitalised with HF are readmitted within 30 to 90 days (Ambrosy 2014; Pandolfi 2017), and non‐adherence is a common cause of admission. Among 54,322 admissions from 236 hospitals in the USA, non‐adherence to medication or dietary requirements accounted for 10.3% of HF admissions (Ambardekar 2009). There is high‐quality evidence to support HF disease‐management programmes (case management‐type interventions led by a specialist HF nurse), with benefits seen in HF‐related and all‐cause readmissions and all‐cause mortality (Takeda 2019). Patient education is central to all disease‐management programmes (Ambardekar 2009; Angermann 2012; Bekelman 2015; Clark 2015; Jonkman 2016), but effective methods for delivering patient education are yet to be identified.
Description of the intervention
Traditionally, patient education has been conducted in a face‐to‐face manner using printed or written materials, with perhaps the use of a video to support key messages. Information communication technology is now commonplace in both the developed and developing worlds (WHO 2011) and as healthcare rationalisation reforms reduce or restrict face‐to‐face services, using information communication technology to assess, educate, support and interact with patients is becoming more common. Using technologies such as smartphone and tablet apps, SMS messages and social media‐delivered education programmes will become more commonplace in the future. To date, many patients already expect these innovations to support modern healthcare delivery by facilitating a more personalised person‐centred care (Cowie 2016).
The interventions included in this review fall under the description of mHealth. mHealth is defined by the WHO as " the use of mobile and wireless technologies to support the achievement of health objectives" (WHO 2011).
mHealth‐delivered education interventions provide patients with disease‐specific information through a mobile platform. This mobile platform could be a tablet, a mobile phone or a laptop. Technology‐based mobile patient‐education interventions have been used for people living with many different conditions, such as depression (Pinto 2013), diabetes (Heinrich 2012; Pal 2014), and breast cancer (Jibaja‐Weise 2011). An example of such an intervention in heart failure is the 'Heart Failure Matters' (heartfailurematters.org) by the European Society of Cardiology. The interventions we examine in this review provide education to people with heart failure, with the purpose of increasing their knowledge and understanding of what heart failure is, symptoms of heart failure, and how these can be managed and prevented from exacerbation, and the importance of adhering to any prescribed therapies such as medications, diet or exercise. The intervention are aimed at the patient, but caregivers may also access the information provided to the patient.
Technological innovations are very heterogeneous and lack shared definitions, which limits the possibility of comparing and evaluating different programmes. This review addresses a critical gap by examining the effectiveness of technology‐based HF patient‐education tools and platforms. We examine mHealth education for people with HF, focusing on interventions which are delivered using technologies such as smartphone and tablet apps, SMS messages and social media‐delivered education programmes. We exclude TV programmes, videos (other than those incorporated into the included technologies), telephone calls, interactive voice response systems and paper‐based education materials, where these are used in isolation from one of the listed technologies. We provide a description of the mode, method, and frequency of the intervention, as well as conceptual underpinnings of the intervention, methods of instructional design and processes of promoting intervention fidelity. We identify both the structural and functional mechanisms of effective interventions to facilitate broad translation.
How the intervention might work
Self‐management is “the engagement of a person in activities which protect or promote their own health, manage their symptoms and the impact which their condition has on their life and also adhere to any prescribed therapies” (Gruman 1996). Self‐management can be learned through health‐education interventions, and targets improving knowledge about the disease, treatment, and outcomes (health literacy), so that the person is better informed and capable of engaging in activities which protect or promote their own health (e.g. fluid restriction), manage their symptoms (e.g. breathlessness and fatigue), the impact which heart failure has on their life, and also adherence to any prescribed medications. A range of patient, provider and healthcare system factors influence HF management. As a consequence, self‐care and patient outcomes and technological innovation are promising in promoting access to health education and self‐management strategies (Omura 2017).
Engaging clinicians and patients as partners in care is increasingly recognised as critical, and has led to an increased emphasis on shared decision‐making (Ting 2014). Implicit in this process is engaging the patient as an active partner, and this can challenge traditional paradigms of health education, where the patient is a passive recipient of care. In promoting effective self‐management, the capacity to tailor and target the approaches to an individual's needs is important. Innovative health education using eHealth and mHealth technologies may provide greater opportunities for patient and clinician engagement, as well as being better tailored to patient preferences and needs, be these cognitive, language, or sensory disabilities. These individual factors include understanding how factors such as health literacy, numeracy, cognitive capacity, cultural needs and health‐seeking behaviours contribute to both the learning experience and the capacity to adopt health information and education, and progress to self‐management (Davidson 2013; Dennison 2011; McNaughton 2013). Due to the comorbidity burden in heart failure, and the fact that it is a condition of the elderly (Dunlay 2017), factors such as cognition and psychomotor skills influence the capacity to both engage in and benefit from technological innovation in information delivery and promotion of behaviour change (Holden 2013). Recent research in neuroscience, cognitive and developmental psychology has also increased our understanding of how to actively engage learners in achieving a desired outcome. Cognitive impairment is an important consideration in understanding both the design and the feasibility and acceptability of educational interventions (Cameron 2017; Uchmanowicz 2017). Appreciating the diverse range of physical, social, psychological, economic and cultural factors that contribute to heart failure outcomes is important to promote both the efficacy and effectiveness of education interventions. As drivers of health outcomes are strongly mediated by social determinants of health, it is important to appraise health‐education interventions within this context (Marmot 2017).
In addition, mHealth‐delivered interventions can provide timely access to health information and communication with healthcare professionals to support self‐management and decision‐making (WHO 2011). In this review, we examine whether mHealth‐delivered education interventions work, which is important not only in implementing evidence‐based recommendations, but also in advancing the science of self‐care and self‐management. This is a novel and important focus for this review and is guided by a standardised taxonomy using criteria such as theoretical underpinning, mode of instructional design, and mode of delivery and capacity for interaction. Appreciating how eHealth‐ and mHealth‐based assisted learning incorporate concepts such as grounded cognition, spaced learning, and simulation in educational media is important, and both underscore the complexity and capability of intervention development (Koong 2014). Focusing solely on the medium of technology (such as SMS or social media support) in evaluating the efficacy of interventions is simplistic and less likely to be effective in translating research into practice.
Why it is important to do this review
Disease management has been shown to significantly improve patient outcomes and reduce costs of care for HF (Angermann 2012; Bekelman 2015). Although improvements in care and outcomes have been achieved through disease management, the range of patient, provider and healthcare‐system factors that influence HF management often remain unaddressed (Omura 2017), and disparities in HF outcomes are growing. As a consequence, the overall prognosis of HF remains poor (Schmidt 2016).
Health care is rapidly evolving, and information technology, which is evolving ever more rapidly, offers great promise as a tool to promote effective communication and learning that is highly individualised to patients' circumstances and needs in the developing and developed worlds (WHO 2011). Patient education is central to all disease‐management programmes (Ambardekar 2009; Angermann 2012; Bekelman 2015; Clark 2015; Jonkman 2016; Takeda 2019). It is not clear which of the various methods for delivering patient education are effective. A text messaging intervention to improve HF self‐management in a largely African‐American population has shown a positive impact on outcomes (Nundy 2013). Similarly, an education and coaching programme integrated with telehealth has also shown improved outcomes (Stut 2015). The ever‐increasing role of technology and access to health apps, social media and web pages, not all of which may have been underpinned by evidence or evaluated in any way, warrants the need to conduct this systematic review. The survey of global mHealth use undertaken by the WHO in 2011 identified effectiveness and cost effectiveness as key barriers to mHealth implementation (Cowie 2016).
Implementation of effective, tailored, technology‐driven patient‐education interventions that promote self‐management in heart failure may promote access to heart‐failure care across the trajectory and reduce growing health disparities.
In the current setting of increasing use of information communication technology worldwide, mHealth‐delivered interventions for patient education are becoming more available (Cowie 2016; Schwamm 2017). We do not believe that age is a barrier to the application of these interventions, as smartphones and tablet computers are becoming more common (Cowie 2016; Schwamm 2017). Although limited research exists to fully understand the use of information‐communication technology amongst typically older people with cardiovascular illness, a survey of 123 patients (mean age 51 years) at an outpatient cardiopulmonary clinic in a large tertiary hospital in Australia indicates that for this sampled population, information‐communication devices are becoming a part of everyday life, with most survey respondents engaging regularly with a computer (83%), mobile telephone (97%) and the internet (86%) (Disler 2015). Accessing health information online was also a common activity, with 74% regularly consulting online health information sites (Disler 2015). Although this study was undertaken at a single site, it does provide some insight into the patterns of use amongst people for whom the technologies examined in this review are being designed.
The National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand guidelines strongly recommend educating patients and carers about HF and self‐management to decrease hospitalisation and mortality (Atherton 2018). This should begin soon after diagnosis, must be patient‐centred, appropriate to their level of health literacy, culturally appropriate and revised throughout the person's life (Atherton 2018). Similarly, the European Society of Cardiology guidelines have also emphasised the importance of education for self‐care (Ponikowski 2016). It has recommended that patients must be provided with sufficient up‐to‐date information to make decisions on lifestyle adjustment and self‐care (Ponikowski 2016). The American Heart Association guidelines also highlight that education must be tailored individually and must consider relevant comorbidities that may influence retention of information (Mozaffarian 2016).
Objectives
To systematically review and quantify the potential benefits and harms of mHealth‐delivered education for people with heart failure.
Methods
Criteria for considering studies for this review
Types of studies
We include only randomised controlled trials (RCTs). We include studies reported as full text and those published as abstract, as long as sufficient information was available to determine study protocols and outcomes. We excluded case reports, case‐control and cross‐sectional studies.
Types of participants
We included adults (≥ 18 years) with a diagnosis of chronic heart failure as in the National Heart Foundation of Australia and Cardiac Society of Australia and New Zealand and European Society of Cardiology 2016 guidelines (see also Description of the condition). We excluded studies which target general cardiac disorders rather than HF specifically. We excluded participants with comorbidities such as dementia which impair their ability to engage in education interventions. We excluded studies of people with end‐stage HF or other terminal conditions, or those receiving advanced HF therapies (e.g. left ventricular‐assist devices or heart transplant).
Types of interventions
We include trials comparing mHealth‐delivered education such as internet and web‐based education programmes for use on smartphones and tablets (including apps) and other mobile devices, SMS messages and social media‐delivered education programmes, versus usual HF care, which may include verbal discussion and/or provision of paper‐based education materials about HF, medications and self‐management of HF. Interventions could be one‐off (a single session) or could occur at regular intervals or on demand by the participant. We considered 'usual care' for each study to ensure that this did not include mHealth.
We exclude interventions that do not incorporate the technologies listed above, but rather only provide education by the following methods: TV programmes, videos (other than those incorporated into the included technologies), telephone calls, interactive voice response systems and paper‐based education materials, where these are used in isolation from one of the included technologies.
We exclude interventions where both control and intervention participants received different care other than the provision of education, which may include enrolment in a heart‐failure management programme (clinic‐ or home‐based), telemonitoring or structured telephone support programme (where signs and symptoms of HF and clinical assessments were communicated and responded to) (Inglis 2015a; Inglis 2011), cardiovascular rehabilitation, exercise programme, or enrolment in a drug trial.
Types of outcome measures
Primary outcomes
Heart‐failure knowledge using validated tools
Self‐efficacy using validated tools
Self‐care using validated tools
Adverse events
Secondary outcomes
Health‐related quality of life using validated tools
Heart failure‐related hospitalisations (number of participants hospitalised at least once)
Medication adherence
Cost effectiveness
Since publication of the protocol we have amended the primary study outcomes. We have removed all‐cause mortality as an outcome, and have added self‐care using validated tools.
We include studies that reported at least one of the following primary or secondary outcomes: heart failure knowledge, self‐efficacy or self‐care using validated tools; health‐related quality of life using validated tools; or heart failure‐related hospitalisations; or medication adherence. We have reported participant feedback on the intervention (acceptance and satisfaction) and cost of the intervention where available for studies that met the conditions for inclusion based on the primary and secondary outcomes given above.
We have nominated the primary outcomes (#1, #2, #3) as these are the most direct measures of HF knowledge, self‐care and self‐efficacy (a person’s belief that they can achieve a given task). We selected the secondary outcomes because these are commonly reported in such studies and may be indirect measures of participant knowledge.
Search methods for identification of studies
Electronic searches
We identified trials through systematic searches of the following bibliographic databases on 14 October 2019:
Cochrane Central Register of Controlled Trials (CENTRAL), through the Cochrane Register of Studies (CRS Web);
Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, MEDLINE Daily and MEDLINE (Ovid, 1946 to October 11 2019);
Embase (Ovid, 1980 to 2019 week 41);
CINAHL (EBSCOHost, 1937 to 14 October 2019);
PsycINFO (Ovid, 1806 to week 41 2019).
We adapted the preliminary search strategy for MEDLINE (Ovid) (Appendix 1) for use in the other databases. We applied the Cochrane sensitivity‐maximising RCT filter (Lefebvre 2011) to MEDLINE (Ovid) and adaptations of it to the other databases, except CENTRAL.
We searched all databases from their inception to October 2019, and imposed no restriction by language of publication.
Searching other resources
We checked reference lists of all relevant primary studies and review articles for additional references. We also conducted a search of IEEE Xplore (ieeexplore.ieee.org/Xplore/home.jsp), ClinicalTrials.gov (www.ClinicalTrials.gov) and the WHO ICTRP Search Portal (apps.who.int/trialsearch/) in October 2019. We present the search terms for these in Appendix 2.
Data collection and analysis
Selection of studies
Two review authors (SCI, SA) independently screened titles and abstracts of all the potentially includable studies we identified from the search using Covidence (Mavergames 2013), and coded them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. If there were any disagreements, we asked a third review author (HD) to arbitrate. RP assisted with screening a smaller number of titles. We retrieved the full‐text study reports/publication and two review authors (SCI, SA) independently screened the full text, identified studies for inclusion, and identified and recorded reasons for exclusion of the ineligible studies. We resolved any disagreement through discussion or, if required, we consulted a third review author (HD). We identified and excluded duplicates and collated multiple reports of the same study, so that each study rather than each report is the unit of interest in the review. We recorded the selection process in sufficient detail to complete a PRISMA flow diagram (Moher 2009) and 'Characteristics of excluded studies' table.
Data extraction and management
We used a data‐collection form for study characteristics and outcome data which we had piloted on at least one study in the review. We extracted the following study characteristics.
Methods: study design, duration of study, number of study centres and location, study setting, withdrawals, and date of study.
Participants: N, mean age, age range, sex, severity of condition, diagnostic criteria, baseline left ventricular ejection fraction, inclusion criteria, and exclusion criteria.
Interventions: intervention, comparison.
Outcomes: primary and secondary outcomes specified and collected, and time points reported.
Notes: funding for trial, and notable conflicts of interest of trial authors.
Two review authors (SCI, SA) independently extracted outcome data from included studies. We resolved disagreements by consensus or by involving a third review author (HD). One review author (SA) transferred data into Review Manager 5 (RevMan 2014) file. We double‐checked that data were correctly entered by comparing the data presented in the review with the study reports. A second review author (HD) spot‐checked study characteristics for accuracy against the trial reports.
Assessment of risk of bias in included studies
Two review authors (SCI, SA) independently assessed risks of bias for each study, using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2017). We resolved any disagreements by discussion or by involving another review author (HD). We assessed the risks of bias according to the following domains.
Random sequence generation.
Allocation concealment.
Blinding of participants and personnel.
Blinding of outcome assessment.
Incomplete outcome data.
Selective outcome reporting.
Other potential bias.
In accordance with the Cochrane 'Risk of bias' assessment tool, we graded each potential source of bias as high, low or unclear, and provided a quote from the study report together with a justification for our judgement in the 'Risk of bias in included studies' table (Sterne 2017). We summarised the 'Risk of bias' judgements across different studies for each of the domains listed. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we note this in the 'Risk of bias in included studies' table.
When considering treatment effects, we have taken into account the risks of bias for the studies that contributed to that outcome.
Assessment of bias in conducting the systematic review
We conducted the review according to this published protocol and reported any deviations from it in this review.
Measures of treatment effect
We analysed dichotomous data as odd ratios (ORs) with 95% confidence intervals (CIs), and continuous data as mean differences (MDs) or standardised mean differences (SMDs) with 95% confidence intervals.
We had planned to narratively describe skewed data reported as medians and interquartile ranges, but none of the included studies reported outcomes in this format.
Unit of analysis issues
We did not anticipate any unit‐of‐analysis issues with the included studies. Two of the included studies reported one study time point while one study reported three time points. The first time point was used for analysis. One study had three arms. We used only data from two arms for analysis, as the third arm did not meet the review inclusion criteria. One study used a measurement tool which runs in the opposite direction. We modified the result of this study by multiplying the mean scores by −1 to ensure that both studies run in the same direction.
Dealing with missing data
We contacted investigators in order to verify key study characteristics and to obtain missing numerical outcome data where possible (e.g. when a study is presented as abstract‐only). We used the Revman calculator to compute any missing standard deviations from other information in the publication.
Assessment of heterogeneity
We used the I2 statistic to measure heterogeneity among the trials in each analysis (Deeks 2017). We investigated potential sources of heterogeneity where I2 was greater than 40%. We reported similarities between interventions, participants, design, and outcomes in the Included studies subsection. We checked forest plots visually for signs of heterogeneity.
Assessment of reporting biases
We had planned to creat funnel plots, but due to the small number of included studies these are unlikely to be useful for exploring possible small‐study biases for the primary outcomes.
Data synthesis
We have undertaken meta‐analyses only where this is meaningful, i.e. if the interventions, technologies, participants, and the underlying clinical questions are similar enough for pooling to make sense. We planned to use a random‐effects model, but used a fixed‐effect model due to the population sizes of the included studies. We used inverse variance as the method of analysis.
'Summary of findings' and quality of the evidence
Two of the review authors (SCI, SA) also assessed the quality of evidence according to GRADE (Atkins 2004) by constructing a 'Summary of findings' table for the main outcomes using the GRADEPro tool (GRADEproGDT 2015; Schünemann 2017). We reported the following outcomes in Table 1: heart failure knowledge; self‐efficacy; self‐care; adverse events; health‐related quality of life; heart failure‐related hospitalisations.
Subgroup analysis and investigation of heterogeneity
Owing to the small number of included studies, we have not been able to stratify findings by mode of delivery or intensity of the intervention, nor to perform any subgroup analyses as we had planned.
We had planned to carry out the following subgroup analysis: studies where mean/median participant age is more than 70 years, but we could not undertake this due to the small number of included studies.
Sensitivity analysis
Owing to the small number of included studies we have not performed sensitivity analyses as planned, to include only studies with a low risk of bias.
Reaching conclusions
We based our conclusions only on findings from the quantitative or narrative synthesis of included studies for this review. Our implications for research suggest priorities for future research and outline the remaining uncertainties in the area.
Results
Description of studies
Results of the search
We identified 7896 records in total, including the following from each database:
CENTRAL n = 4762
MEDLINE (OVID) n = 853
EMBASE (OVID) n = 1446
CINAHL Plus with Full text (EBSCO) n = 659
PsycINFO (OVID) n = 176
We found additional references by searching the following:
IEEE Xplore n = 56
ClinicalTrials.gov n = 59
WHO ICTRP Search Portal n = 28
Personal files and contacts = 1
We excluded 515 duplicate references. We screened 7525 titles and abstracts and excluded 7299 irrelevant records. We retrieved full‐text reports for the remaining 226 records. After reading the full texts, we excluded 161 studies (188 references) as they did not meet the review eligibility criteria. We have provided primary reasons for exclusion in the Characteristics of excluded studies table and in Figure 1. We also searched several systematic reviews and meta‐analysis (Flodgren 2015; Konstam 2010; Kotb 2015; Kraai 2011; Krauskopf 2019; Kuijpers 2013; Lazkani 2016; Malik 2014; Mallidi 2011; McDermott 2013; Nakamura 2014; Pandor 2013; Peate 2013; Radhakrishnan 2012; Van Spall 2017; Verheijden Klompstra 2011) found among the full‐text reports for additional references (see Additional references). One study is awaiting classification owing to insufficient information on design, intervention and analysis (see Characteristics of studies awaiting classification). Seventeen studies are classified as ongoing, as they have yet to start recruitment or finalise the analysis (see Characteristics of ongoing studies). Finally, we included five trials (15 references) in the quantitative synthesis.
1.
Study flow diagram.
Included studies
We include five full‐text peer‐reviewed studies of mHealth‐delivered education in the form of web‐based self‐care education programmes for use on smartphones, computer/laptop and tablets, compared to usual care in the meta‐analysis (see Characteristics of included studies).
Trials ranged in size from 28 participants in Bashi 2016 to 512 participants in Li 2016;
Mean age of participants ranged from 60 years to 75 years;
Mean percentage of men was 63%, ranging from 57% to 78%;
The studies originated from Australia (1), China (1), Iran (1), Sweden (1) and The Netherlands (1);
The studies included participants with symptomatic heart failure, NYHA Class II ‐ III.
Bashi 2016 randomised 29 participants (15 to the intervention group and 14 to the control group). One participant in the intervention group was lost to follow‐up, leaving 28 participants included in the analysis. Hagglund 2015 randomised 82 participants. Forty‐two received the mHealth intervention and 40 were allocated to usual care. Ten participants withdrew consent before the system was installed and were therefore not included in the analysis. Seventy‐two participants were included in the final analysis.
Two studies (Hagglund 2015; Wagenaar 2019) reported their funding source. Hagglund 2015 received funding from the Swedish National Quality Registry and Care Ligo which provided the OPTILOGG systems used in the study and paid a small stipend for each included participant. Wagenaar 2019 received funding from a 'CareWithinReach' foundation.
The length of intervention and follow‐up varied between the studies. The duration of intervention in three studies (Bashi 2016; Hagglund 2015; Jalali 2018) was three months, while another (Wagenaar 2019) lasted 12 months. Three studies had one follow‐up time point (Bashi 2016‐ one month; Jalali 2018‐ three months and Li 2016‐ six months) compared to two studies with multiple follow‐up time points (Hagglund 2015‐ three and six months and Wagenaar 2019‐ three, six and 12 months, respectively).
The type of intervention also varied between the studies. Two studies (Bashi 2016; Wagenaar 2019) were web‐based educational interventions to support self‐care which required access to a website, while participants in Hagglund 2015 received a tablet device containing information about HF and lifestyle advice according to the guidelines. Two studies (Jalali 2018; Li 2016) used SMS messages to deliver information about HF, signs and symptoms and lifestyle advice. In terms of usual care, participants in two studies received either an information sheet with advice on HF treatment (Hagglund 2015) or comprehensive educational information on topics such as medication, nutrition, exercise and psychosocial issues from the HF outpatient clinic (Bashi 2016). 'Usual care' in Wagenaar 2019 also received HF education but in conjunction with up‐titration of HF medication, optimising adherence and personalised lifestyle advice by a HF nurse in face‐to‐face consultations. If necessary, additional telephone consultations were also provided.
Excluded studies
We excluded most studies for the following reasons:
Ineligible intervention (not mHealth education): n = 110;
Ineligible study design (not an RCT): n = 40;
Ineligible outcome measures (did not report the primary or secondary outcomes of interest): n = 3
Ineligible comparator (not usual care): n = 2
Opinion piece/review paper: n = 2
Different participant population: n = 5
Ineligible indication n = 1
See also Characteristics of excluded studies tables.
Risk of bias in included studies
We present in Figure 2 a graphical summary of 'Risk of bias' assessments performed by review authors for the five included studies, based on the seven 'Risk of bias' domains. Figure 3 provides a summary of 'Risk of bias' results for each included study. We provided reasons for judgements in the Risk of bias in included studies tables. For clarification, we provided quotes in these tables.
2.
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
3.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Allocation
Using the Cochrane criteria, we rated all the studies as having unclear risk of selection bias and rated no studies as having high risk of selection bias. Three of the studies did not report the method of sequence generation (Bashi 2016; Hagglund 2015; Jalali 2018) and two studies used computerised block randomisation (Li 2016; Wagenaar 2019). Allocation concealment was not clearly reported for any of the included studies (Bashi 2016; Hagglund 2015; Jalali 2018; Li 2016; Wagenaar 2019).
Blinding
We assessed blinding as being at unclear risk of performance and detection bias in two studies (Hagglund 2015; Jalali 2018), as no detail was provided on blinding of participants or the person responsible for outcome assessment. The healthcare professionals delivering the intervention were aware of the allocation schedule in two studies (Li 2016; Wagenaar 2019) and the primary researcher who collected the outcome data was not blinded in one study (Bashi 2016); this contributed to our assessment of a high risk of bias. Although an independent committee assessed one outcome in one of the studies (Wagenaar 2019), it is unclear whether the remaining outcomes were assessed by a blinded outcome assessor.
Incomplete outcome data
We assessed one study as having unclear risk of bias for outcome reporting (Bashi 2016). Two studies were at high risk owing to dropouts or incomplete data (Hagglund 2015; Li 2016). We rated studies as having high risk of bias if dropout rates were uneven between groups and if we suspected that the reason for dropout was related to group allocation. We also rated studies as having high risk of bias if investigators did not report how they dealt with the dropouts (e.g. ITT analysis, last observation carried forward). We rated two studies (Jalali 2018; Wagenaar 2019) as low risk, as the final analysis included all participants i.e. an ITT analysis was presented.
Selective reporting
We rated three studies at unclear risk of bias for selective reporting (Bashi 2016; Hagglund 2015; Li 2016), as no published protocol or pre‐registered trial registry entry was available for these studies to compare with the published results. A published protocol was available for two studies (Jalali 2018; Wagenaar 2019) and all prespecified outcomes in the published protocol were reported in the published results. We therefore rated them at low risk of bias.
Other potential sources of bias
We rated other potential sources of bias as low risk for three studies (Hagglund 2015; Li 2016; Wagenaar 2019), as we detected no other biases. We rated one study (Bashi 2016) at high risk, as it was a pilot trial and a full trial is not being done. We judged the remaining study to be at unclear risk (Jalali 2018).
Effects of interventions
See: Table 1
A 'Summary of findings' table for mHealth‐delivered education intervention compared to usual care is presented in the Table 1.
Primary outcomes
Heart failure knowledge
Three studies addressed HF knowledge in the short term (up to three months) (Bashi 2016; Hagglund 2015; Wagenaar 2019), revealing that the use of mHealth‐delivered education programmes demonstrate no evidence of a difference in HF knowledge compared to usual care (mean difference (MD) 0.10, 95% confidence interval (CI) −0.20 to 0.40; 411 participants; low‐quality evidence; Analysis 1.1). Heterogeneity was low (I2 = 0%). See Table 1.
1.1. Analysis.
Comparison 1: mHealth education intervention vs. usual care, Outcome 1: Heart failure knowledge
Heart failure self‐efficacy
One study (Bashi 2016) assessed self‐efficacy, and reported that both study groups reported high levels of self‐efficacy at baseline, but found uncertainty in the evidence between the intervention (MD 0.60, 95% CI −0.57 to 1.77; 1 trial, 29 participants; very low‐quality evidence; Analysis 1.2) and control groups. See Table 1.
1.2. Analysis.
Comparison 1: mHealth education intervention vs. usual care, Outcome 2: Self‐efficacy
Heart failure self‐care
Due to the heterogeneity (I2= 86%) observed in the analysis, we decided not to pool the studies for this outcome (Analysis 1.3). It is difficult to explain the reasons for this heterogeneity. It may be due to the use of two different measures to assess this outcome. We therefore decided to narratively describe the outcomes of each study. The certainty of evidence is very low for this outcome.Three studies evaluated HF self‐care. Two studies used the European Heart Failure Behaviour Scale (Hagglund 2015; Wagenaar 2019).
1.3. Analysis.
Comparison 1: mHealth education intervention vs. usual care, Outcome 3: Heart failure self‐care
Bashi 2016 used the Self‐Care Heart Failure Index (SCHFI). Bashi 2016 showed an improvement in the SCHFI maintenance subscale in the intervention group (MD 9.9, 95% CI −3.6 to −23.6), whereas the usual‐care group decreased over time (MD −3.5, 95% CI −10.3 to 1.3). No significant between‐group differences were found (P = 0.94).
In Hagglund 2015, the intervention group demonstrated an increase in self‐care compared to the usual‐care group (median 17, IQR 13 to 22 versus median 21, IQR 17 to 25; P < 0.05). The mean score of the usual care‐plus‐HFM group (intervention) also showed higher self‐care scores compared to usual care (73.5 versus 70.8 (difference 2.7, 95% CI 0.6 to 6.2) (Wagenaar 2019).
Adverse events
No studies measured and reported adverse events.
Secondary outcomes
Health‐related quality of life
Four RCTs provided data for this outcome (Hagglund 2015; Jalali 2018; Li 2016; Wagenaar 2019). The measurement tool used in one study (Hagglund 2015) was the Kansas City Cardiomyopathy Questionnaire (KCCQ), with higher scores indicating better health status. A mean difference over time of 5 points on the overall summary scale reflects a clinically‐significant change in heart failure status. The other studies (Jalali 2018; Li 2016; Wagenaar 2019) used the Minnesota Living with HF Questionnaire (MLWHFQ) to assess quality of life. Lower scores in this tool indicate better quality of life. To ensure that both scales run in the same direction, we modified the mean MLWHFQ score. The result shows uncertainty in the evidence for the use of mHealth in HRQoL compared to usual care (MD −0.10, 95% CI −2.35 to 2.15; 4 studies, 942 participants; very low‐quality evidence; Analysis 1.4). Heterogeneity was 61%. See Table 1.
1.4. Analysis.
Comparison 1: mHealth education intervention vs. usual care, Outcome 4: Health‐related quality of life
Heart failure‐related hospitalisation
Three studies reported on HF‐related hospitalisation (Hagglund 2015; Li 2016; Wagenaar 2019). The use of mHealth delivered‐education may result in little to no difference in HF‐related hospitalisation with 69/444 events in the mHealth group and 89/450 events in the usual care group (odds ratio (OR) 0.74, 95% CI 0.52 to 1.06; 3 studies, 894 participants; low‐quality evidence; Analysis 1.5). Heterogeneity was 0%. See Table 1.
1.5. Analysis.
Comparison 1: mHealth education intervention vs. usual care, Outcome 5: Heart failure‐related hospitalisations
Medication adherence
No studies measured and reported medication adherence.
Cost
Only one study (Wagenaar 2019) reported cost: "The mean costs per patient were €4,865 and €5,741 per quality‐adjusted life years for HFM website + usual care and usual care, respectively. The net‐monetary benefit was positive (larger than 0) for HFM versus usual care".
Discussion
Summary of main results
This review included five studies with a total of 971 participants. We were unable to reach any conclusions on the applications of mHealth education interventions in clinical practice for people with heart failure due to the limited number of included studies and the methodological limitations within the studies. Nevertheless, the review has some important implications for future research and for the application of mHealth‐delivered intervention in clinical practice. First, as it would be unethical to deny control‐group participants education of any sort, mHealth‐delivered interventions were compared to traditional or usual care education. While neither study showed evidence of a difference in HF knowledge, this finding might be explained as a ceiling effect, as participants in two of the studies had high baseline scores on the Dutch HF knowledge scale (Bashi 2016; Wagenaar 2019). There was uncertainty in the evidence for mHealth‐delivered interventions for self‐efficacy, self‐care and health‐related quality of life, due to the limited number of studies included in the meta‐analysis and inconsistencies in outcomes, measurement tools and scales used across the study populations. We found little to no difference in HF‐related hospitalisation. In situations where regular face‐to‐face interventions are not possible, as in the case of people living in rural and remote areas, mHealth‐delivered education interventions offer a good alternative for HF patient self‐care.
Secondly, the versatility of the technology means that the mHealth approach to patient education is complex. All of the interventions reported in the reviewed studies included multiple components, related to education, self‐care and self‐monitoring tools, and most required Internet connectivity. While technology offers many advantages, it is of no benefit to patients unless they actually use it. One means of assessing whether an intervention is acceptable to participants is by reviewing treatment adherence, i.e. the extent to which participants actually use the learning system. Treatment adherence was addressed by Hagglund 2015, who reported an adherence rate of 88% (IQR 78% to 96%) of the days the participants were equipped with the system, while Bashi 2016 reported that only 50% of the participants accessed the system and 28% had no record of access at all.
Hagglund 2015 included the contact details of local HF nurses and doctors at the end of the web‐based resources. In Wagenaar 2019, emails were sent to encourage participants to use the web‐based resources. Bashi 2016 also used email to motivate participants, and suggested that future studies should use face‐to‐face interaction for this purpose. Because learning is a human interaction, patient‐educator interaction is important in facilitating behaviour changes and self‐care, and maintaining motivation for self‐monitoring (Abraham 2008; Suhling 2014; Treskes 2018). The intrinsic, limited interaction between human and computer must be considered in clinical application of mHealth interventions.
Patient education is about transferring HF knowledge to patients and helping them to develop self‐care skills. Knowledge is only one of the many factors influencing self‐efficacy (Bandura 2011). Beyond information‐giving, this also involves confidence‐building. The intervention itself must be pedagogically sound and not just a different way of delivering traditional printed materials. To build participants’ self‐efficacy, Bashi 2016 used avatars as role models. The use of an avatar can mimic natural and innate human interaction through facial expressions, body language and speech (An 2013; Anam 2016). Hagglund 2015 explained how telemonitoring and technology could support patient self‐care, while Wagenaar 2019 only had a protocol and abstract available, so there were no discussions of any theoretical underpinnings of their intervention to support learning and self‐care. In clinical practice, nurses and clinicians must realise that patients still need support and encouragement to build confidence if they are to translate the knowledge acquired from various resources into day‐to‐day decision‐making and self‐care activities.
There is a high prevalence of cognitive impairment in older people living with heart failure (Ambrosy 2014). It is crucial that future educational interventions include screening for cognitive impairment and focus on optimising care and outcomes in individuals living with both HF and cognitive impairment (Cameron 2017). Research and validation assessment scales demonstrate the feasibility of undertaking research in HF populations with cognitive impairment. Despite this, the system lacks the information required both to optimise care for this population group and to support caregivers to continue this care using mhealth interventions.
Overall completeness and applicability of evidence
The accuracy of the findings of this review and meta‐analysis is based on the studies which met our inclusion criteria. Future updates of this review will incorporate new data along with the findings of studies which are currently underway but not yet completed, or are only available as a conference abstract or awaiting classification.
Quality of the evidence
Review authors rated the certainty of evidence for all comparisons using the five GRADE considerations (study limitations, consistency of effect, indirectness, imprecision and publication bias (Schünemann 2017). We created a 'Summary of findings' table. Certainty assessment ranged from very low to low.
Limitations in study design or execution (risk of bias)
For the comparison of mHealth‐delivered education intervention versus usual care for heart failure knowledge, self‐efficacy,self‐care, health‐related quality of life and heart failure‐related hospitalisations, we downgraded the certainty of evidence by one level for strong suspicions of performance and detection, attrition and other bias. The strong suspicion of detection bias is associated with the primary researcher collecting the data unblinded. Suspicion of attrition bias was due to uneven dropout rates between groups, with the reason for dropout suspected to be related to group allocation. We suspected other bias, as one study was only a pilot trial and the full trial is not being performed.
Inconsistency of results
We downgraded the certainty of evidence for health‐related quality of life by one level for inconsistency of results (I2 = 61%). We also downgraded the certainty of evidence for self‐care by one level for inconsistency, because the very high unexplained heterogeneity precluded meta‐analysis (I2 = 86%).
Indirectness of evidence
All included trials addressed the main review question (PICO): use of mHealth in the form of web‐based education programmes on smartphones and tablets compared to usual care in men and women with HF, compared to usual care. We therefore did not downgrade any outcome in any comparisons for indirectness of evidence.
Imprecision
We downgraded the certainty of evidence by one level for heart failure knowledge and self‐efficacy, as there were fewer than 500 participants included in the analysis. We also downgraded the certainty of evidence for self‐efficacy and heart failure‐related hospitalisation as the confidence intervals were wide.
Publication bias
For all outcomes, we did not downgrade the certainty of evidence for publication bias, as we did not detect it, although the small number of included studies may have prevented this.
Potential biases in the review process
Our review has adhered to Cochrane methodology, and all review authors and personnel have at all times tried to avoid or minimise any biases in the review process. We undertook an extensive search of databases and additional sources and applied no restrictions on language within the search process. We therefore believe that we have identified and included in this review all potentially relevant studies. We translated possibly relevant and non‐relevant non‐English full‐text study reports into English, to finalise the eligibility process. Furthermore, at least two review authors systematically extracted and managed trial data.
The searches captured one study currently awaiting classification and 17 currently ongoing studies. The currently ongoing studies have not yet reported any findings and only a study protocol or conference abstract was available. We contacted authors of studies available as a conference abstract or study protocol, in order to identify a full peer‐reviewed publication for the study. We received no response from some studies which we classify as ongoing or awaiting classification, despite multiple attempts to contact the authors.
Agreements and disagreements with other studies or reviews
One systematic review has been published on this topic (Cajita 2016). Although this review appears similar, there are important differences in the inclusion criteria from our review, particularly the inclusion of a monitoring system, which typically included a blood‐pressure measuring device, weighing scale, and an electrocardiogram recorder, and also accepting quasi‐experimental trials. Our review focuses solely on mHealth education such as internet‐ and web‐based education programmes for use on smartphones and tablets (including apps) and other mobile devices, SMS messages and social media‐delivered education programmes. Differences in the inclusion criteria of Cajita 2016 on this topic limit the possibility of directly comparing our findings with theirs. However, the outcomes of our review along with Cajita 2016 highlight the uniqueness and importance of our findings. Cajita 2016 includes 10 studies (nine randomised controlled trials and one quasi‐experimental trial) comparing mobile health technology as part of a monitoring system versus usual care, while our systematic review only includes five randomised controlled trials. Nonetheless, the overall findings have been consistent in confirming that the impact of mHealth‐based education interventions on HF knowledge, self‐care and health‐related quality of life was mixed, and highlighting the need for further research. The identification of studies currently underway and those awaiting classification indicate that this is an area of HF research for which further evidence will emerge in the short and longer term.
Strengths and weaknesses of this review
Weaknesses of this review are due to inadequate reporting by some studies, which has precluded classification of risks of bias as either low or high risk, leading to some of the studies being rated at unclear risk of bias.
Authors' conclusions
Implications for practice.
In a real‐world setting there are notable factors to consider when implementing patient education interventions. User preferences and the acceptability of the intervention are paramount, along with consideration of patient factors such as cognition, which will have a significant impact on the use and effect of the intervention. Provision of information alone, in any format, without consideration of the ability of the patient to access, read and interpret that information will be insufficient.
Cognitive impairment is common in older HF patients and presents complex challenges for education (Cameron 2017). Prevalence of cognitive impairment is estimated to be between 50% to 80% in the heart‐failure population depending on age (Vogels 2007). Evidence supports the need for tailoring of treatment, strengthened with clear communication to reduce readmission rates, mortality and functional decline of older people (Hickman 2015). mHealth education interventions could improve communication and tailoring of care which can be customised to cognition, language and healthcare settings for each patient or group of patients.
At this point in time, there is no evidence of a difference in HF knowledge and little to no difference in HF‐related hospitalisation. There is uncertainty in the currently available evidence on self‐efficacy, self‐care and health‐related quality of life which will need to be confirmed with further research.
Implications for research.
There are substantial implications for future research into the use of technology to facilitate patient education and support for people with heart failure. We experienced some challenges synthesising and meta‐analysing data from the included studies, due to inconsistencies in outcomes, measurement tools and scales used across the study populations. Future studies should look to measure heart‐failure education, self‐efficacy, self‐care and health‐related quality of life using validated tools and scales. Heart‐failure hospitalisation, medication adherence and patient acceptance of the intervention and adherence to it would also be valuable outcomes to assess. Taxonomies, such as those by Krumholz 2006 provide a clear guide. It would also be beneficial if future studies could report details of the theoretical underpinnings of their education intervention and relationship to behaviour change, as this will allow for better comparisons between studies and consideration of factors leading to heterogeneity (Michie 2011). As education should be provided in the context of integrated disease management, it will be valuable to consider the setting and care delivered alongside the provision of education.
History
Protocol first published: Issue 8, 2015 Review first published: Issue 7, 2020
Acknowledgements
Sally C Inglis is a Cardiovascular Life Sciences Fellow, supported by the New South Wales Cardiovascular Research Network, which is supported by the Heart Foundation of Australia and the NSW Office for Health and Medical Research (CR 11S 6226).
We wish to acknowledge the contributions of Professor Cheryl Dennison‐Himmelfarb for her contribution to the writing of the protocol for this review.
Appendices
Appendix 1. Search strategies for databases
CENTRAL
#1 MeSH descriptor: [Heart Failure] explode all trees
#2 ((heart or cardiac or myocardial) next (failure or insufficien* or decompensation))
#3 #1 or #2
#4 MeSH descriptor: [Text Messaging] this term only
#5 ((mms or sms) and (text* or messag*))
#6 (multimedia messag* service* or short messag* service*)
#7 (text messag* or texting)
#8 MeSH descriptor: [Cell Phones] explode all trees
#9 ((car or cell* or smart or mobile) near/3 phone*)
#10 (carphone* or cellphone* or smartphone* or mobilephone*)
#11 (iphone* or ipod* or podcast* or ipad* or android* or blackberr* or palm pilot*)
#12 MeSH descriptor: [Computers, Handheld] explode all trees
#13 (pda* or personal digital assistant*)
#14 (tablet near/6 (computer or pc))
#15 ((wireless or handheld) near/3 (device* or technolog*))
#16 MeSH descriptor: [Telemedicine] this term only
#17 telemedicine
#18 telehealth
#19 telemonitor*
#20 ehealth
#21 e‐health
#22 (mobile near/3 health*)
#23 mhealth
#24 m‐health
#25 MeSH descriptor: [Computer‐Assisted Instruction] this term only
#26 ((computer or online or internet or web) near/3 (learn* or educat* or instruct*))
#27 (elearning or e‐learning)
#28 MeSH descriptor: [Electronic Mail] this term only
#29 (electronic mail or email* or e‐mail*)
#30 MeSH descriptor: [Internet] explode all trees
#31 (web or website* or internet)
#32 (social near/3 (media or network*))
#33 chat not (choline or acetylcholine)
#34 #4 or #5 or #6 or #7 or #8 or #9 or #10 or #11 or #12 or #13 or #14 or #15 or #16 or #17 or #18 or #19 or #20 or #21 or #22 or #23 or #24 or #25 or #26 or #27 or #28 or #29 or #30 or #31 or #32 or #33
#35 MeSH descriptor: [Health Education] explode all trees
#36 ((health or patient) near/3 (educat* or teach* or learn* or literate or literacy))
#37 MeSH descriptor: [Health Promotion] explode all trees
#38 ((health or wellness) near/3 (promot* or program* or campaign*))
#39 MeSH descriptor: [Self Care] explode all trees
#40 (self near/3 (care or manage*))
#41 #35 or #36 or #37 or #38 or #39 or #40
#42 MeSH descriptor: [Technology] explode all trees
#43 MeSH descriptor: [Telecommunications] explode all trees
#44 (technolog* or wireless or text messag*)
#45 ((car or cell* or smart or mobile) near/3 phone*)
#46 #42 or #43 or #44 or #45
#47 #41 and #46
#48 #34 or #47
#49 #3 and #48
MEDLINE Ovid
1. exp Heart Failure/
2. ((heart or cardiac or myocardial) adj (failure or insufficien* or decompensation)).tw.
3. 1 or 2
4. Text Messaging/
5. ((mms or sms) and (text* or messag*)).tw.
6. (multimedia messag* service* or short messag* service*).tw.
7. (text messag* or texting).tw.
8. exp Cellular Phone/
9. ((car or cell* or smart or mobile) adj3 phone*).tw.
10. (carphone* or cellphone* or smartphone* or mobilephone*).tw.
11. (iphone* or ipod* or podcast* or ipad* or android* or blackberr* or palm pilot*).tw.
12. exp Computers, Handheld/
13. (pda* or personal digital assistant*).tw.
14. (tablet adj6 (computer or pc)).tw.
15. ((wireless or handheld) adj3 (device* or technolog*)).tw.
16. Telemedicine/
17. telemedicine.tw.
18. telehealth.tw.
19. telemonitor*.tw.
20. ehealth.tw.
21. e‐health.tw.
22. (mobile adj3 health*).tw.
23. mhealth.tw.
24. m‐health.tw.
25. Computer‐Assisted Instruction/
26. ((computer or online or internet or web) adj3 (learn* or educat* or instruct*)).tw.
27. (elearning or e‐learning).tw.
28. Electronic Mail/
29. (electronic mail or email* or e‐mail*).tw.
30. exp Internet/
31. (web or website* or internet).tw.
32. (social adj3 (media or network*)).tw.
33. chat.tw. not (choline or acetylcholine).mp.
34. 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33
35. exp Health Education/
36. ((health or patient) adj3 (educat* or teach* or learn* or literate or literacy)).tw.
37. exp Health Promotion/
38. ((health or wellness) adj3 (promot* or program* or campaign*)).tw.
39. exp Self Care/
40. (self adj3 (care or manage*)).tw.
41. 35 or 36 or 37 or 38 or 39 or 40
42. exp Technology/
43. exp Telecommunications/
44. (technolog* or wireless or text messag*).tw.
45. ((car or cell* or smart or mobile) adj3 phone*).tw.
46. 42 or 43 or 44 or 45
47. randomized controlled trial.pt.
48. controlled clinical trial.pt.
49. randomized.ab.
50. placebo.ab.
51. drug therapy.fs.
52. randomly.ab.
53. trial.ab.
54. groups.ab.
55. 47 or 48 or 49 or 50 or 51 or 52 or 53 or 54
56. exp animals/ not humans.sh.
57. 55 not 56
58. 41 and 46
59. 34 or 58
60. 3 and 59
61. 57 and 60
Embase Ovid
1. exp heart failure/
2. ((heart or cardiac or myocardial) adj (failure or insufficien* or decompensation)).tw.
3. 1 or 2
4. text messaging/
5. ((mms or sms) and (text* or messag*)).tw.
6. (multimedia messag* service* or short messag* service*).tw.
7. (text messag* or texting).tw.
8. mobile phone/
9. ((car or cell* or smart or mobile) adj3 phone*).tw.
10. (carphone* or cellphone* or smartphone* or mobilephone*).tw.
11. (iphone* or ipod* or podcast* or ipad* or android* or blackberr* or palm pilot*).tw.
12. personal digital assistant/ or microcomputer/
13. (pda* or personal digital assistant*).tw.
14. (tablet adj6 (computer or pc)).tw.
15. ((wireless or handheld) adj3 (device* or technolog*)).tw.
16. telemedicine/
17. telemedicine.tw.
18. telehealth.tw.
19. telemonitor*.tw.
20. ehealth.tw.
21. e‐health.tw.
22. (mobile adj3 health*).tw.
23. mhealth.tw.
24. m‐health.tw.
25. ((computer or online or internet or web) adj3 (learn* or educat* or instruct*)).tw.
26. (elearning or e‐learning).tw.
27. e‐mail/
28. (electronic mail or email* or e‐mail*).tw.
29. internet/
30. (web or website* or internet).tw.
31. (social adj3 (media or network*)).tw.
32. chat.tw. not (choline or acetylcholine).mp.
33. 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32
34. exp health education/
35. ((health or patient) adj3 (educat* or teach* or learn* or literate or literacy)).tw.
36. ((health or wellness) adj3 (promot* or program* or campaign*)).tw.
37. exp self care/
38. (self adj3 (care or manage*)).tw.
39. 34 or 35 or 36 or 37 or 38
40. exp technology/
41. exp telecommunication/
42. (technolog* or wireless or text messag*).tw.
43. ((car or cell* or smart or mobile) adj3 phone*).tw.
44. 40 or 41 or 42 or 43
45. random$.tw.
46. factorial$.tw.
47. crossover$.tw.
48. cross over$.tw.
49. cross‐over$.tw.
50. cross‐over$.tw.
51. (doubl$ adj blind$).tw.
52. (singl$ adj blind$).tw.
53. assign$.tw.
54. allocat$.tw.
55. volunteer$.tw.
56. crossover procedure/
57. double blind procedure/
58. randomized controlled trial/
59. single blind procedure/
60. 45 or 46 or 47 or 48 or 49 or 50 or 51 or 52 or 53 or 54 or 55 or 56 or 57 or 58 or 59
61. (animal/ or nonhuman/) not human/
62. 60 not 61
63. 39 and 44
64. 33 or 63
65. 3 and 64
66. 62 and 65
CINAHL
S62 S58 AND S61
S61 S3 AND S60
S60 S34 OR S59
S59 S41 AND S46
S58 S47 OR S48 OR S49 OR S50 OR S51 OR S52 OR S53 OR S54 OR S55 OR S56 OR S57
S57 TX allocat* random*
S56 (MH "Quantitative Studies")
S55 (MH "Placebos")
S54 TX placebo*
S53 TX random* allocat*
S52 (MH "Random Assignment")
S51 TX randomi* control* trial*
S50 TX ( (singl* n1 blind*) or (singl* n1 mask*) ) or TX ( (doubl* n1 blind*) or (doubl* n1 mask*) ) or TX ( (tripl* n1 blind*) or (tripl* n1 mask*) ) or TX ( (trebl* n1 blind*) or (trebl* n1 mask*) )
S49 TX clinic* n1 trial*
S48 PT Clinical trial
S47 (MH "Clinical Trials+")
S46 S42 OR S43 OR S44 OR S45
S45 TX ((car or cell* or smart or mobile) N3 phone*)
S44 TX (technolog* or wireless or text messag*)
S43 (MH "Telecommunications+")
S42 (MH "Technology+")
S41 S35 OR S36 OR S37 OR S38 OR S39 OR S40
S40 TX (self N3 (care or manage*))
S39 (MH "Self Care+")
S38 TX ((health or wellness) N3 (promot* or program* or campaign*))
S37 (MH "Health Promotion")
S36 TX ((health or patient) N3 (educat* or teach* or learn* or literate or literacy))
S35 (MH "Health Education")
S34 S4 OR S5 OR S6 OR S7 OR S8 OR S9 OR S10 OR S11 OR S12 OR S13 OR S14 OR S15 OR S16 OR S17 OR S18 OR S19 OR S20 OR S21 OR S22 OR S23 OR S24 OR S25 OR S26 OR S27 OR S28 OR S29 OR S30 OR S31 OR S32 OR S33
S33 TX chat NOT (choline or acetylcholine)
S32 TX (social N3 (media or network*))
S31 TX (web or website* or internet)
S30 (MH "Internet+")
S29 TX (electronic mail or email* or e‐mail*)
S28 (MH "Electronic Mail")
S27 TX (elearning or e‐learning)
S26 TX ((computer or online or internet or web) N3 (learn* or educat* or instruct*))
S25 (MH "Computer Assisted Instruction")
S24 TX m‐health
S23 TX mhealth
S22 TX (mobile N3 health*)
S21 TX e‐health
S20 TX ehealth
S19 TX telemonitor*
S18 TX telehealth
S17 TX telemedicine
S16 (MH "Telemedicine")
S15 TX ((wireless or handheld) N3 (device* or technolog*))
S14 TX (tablet N6 (computer or pc))
S13 TX (pda* or personal digital assistant*)
S12 (MH "Computers, Hand‐Held+")
S11 TX (iphone* or ipod* or podcast* or ipad* or android* or blackberr* or palm pilot*)
S10 TX (carphone* or cellphone* or smartphone* or mobilephone*)
S9 TX ((car or cell* or smart or mobile) N3 phone*)
S8 (MH "Cellular Phone+")
S7 TX (text messag* or texting)
S6 TX (multimedia messag* service* or short messag* service*)
S5 TX ((mms or sms) and (text* or messag*))
S4 (MH "Text Messaging")
S3 S1 OR S2
S2 TX ((heart or cardiac or myocardial) N (failure or insufficien* or decompensation))
S1 (MH "Heart Failure+")
PsycINFO
1. exp heart disorders/
2. ((heart or cardiac or myocardial) adj (failure or insufficien* or decompensation)).tw.
3. 1 or 2
4. text messaging/
5. ((mms or sms) and (text* or messag*)).tw.
6. (multimedia messag* service* or short messag* service*).tw.
7. (text messag* or texting).tw.
8. ((car or cell* or smart or mobile) adj3 phone*).tw.
9. (carphone* or cellphone* or smartphone* or mobilephone*).tw.
10. (iphone* or ipod* or podcast* or ipad* or android* or blackberr* or palm pilot*).tw.
11. exp mobile devices/
12. (pda* or personal digital assistant*).tw.
13. (tablet adj6 (computer or pc)).tw.
14. ((wireless or handheld) adj3 (device* or technolog*)).tw.
15. telemedicine/
16. telemedicine.tw.
17. telehealth.tw.
18. telemonitor*.tw.
19. ehealth.tw.
20. e‐health.tw.
21. (mobile adj3 health*).tw.
22. mhealth.tw.
23. m‐health.tw.
24. computer assisted instruction/
25. ((computer or online or internet or web) adj3 (learn* or educat* or instruct*)).tw.
26. (elearning or e‐learning).tw.
27. computer mediated communication/
28. (electronic mail or email* or e‐mail*).tw.
29. exp internet/
30. (web or website* or internet).tw.
31. (social adj3 (media or network*)).tw.
32. chat.tw. not (choline or acetylcholine).mp.
33. 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32
34. exp health education/
35. ((health or patient) adj3 (educat* or teach* or learn* or literate or literacy)).tw.
36. health promotion/
37. ((health or wellness) adj3 (promot* or program* or campaign*)).tw.
38. health behavior/
39. (self adj3 (care or manage*)).tw.
40. 34 or 35 or 36 or 37 or 38 or 39
41. exp technology/
42. exp telephone systems/
43. (technolog* or wireless or text messag*).tw.
44. ((car or cell* or smart or mobile) adj3 phone*).tw.
45. 41 or 42 or 43 or 44
46. random$.tw.
47. factorial$.tw.
48. crossover$.tw.
49. cross‐over$.tw.
50. placebo$.tw.
51. (doubl$ adj blind$).tw.
52. (singl$ adj blind$).tw.
53. assign$.tw.
54. allocat$.tw.
55. volunteer$.tw.
56. control*.tw.
57. "2000".md.
58. 46 or 47 or 48 or 49 or 50 or 51 or 52 or 53 or 54 or 55 or 56 or 57
59. 40 and 45
60. 33 or 59
61. 3 and 60
62. 58 and 61
Appendix 2. Other search terms
Any of the following keywords were used per column for IEEE Xplore search, ClinicalTrials.gov and WHO ICTRP search:
| A | B | C | |||||
| Heart failure | mhealth | mobile app | technology | App | Text | SMS | education |
| Cardiac failure | mhealth | mobile app | technology | App | Text | SMS | education |
Data and analyses
Comparison 1. mHealth education intervention vs. usual care.
| Outcome or subgroup title | No. of studies | No. of participants | Statistical method | Effect size |
|---|---|---|---|---|
| 1.1 Heart failure knowledge | 3 | 411 | Mean Difference (IV, Fixed, 95% CI) | 0.10 [‐0.20, 0.40] |
| 1.1.1 Dutch Heart Failure Knowledge Scale (higher score = complete HF knowledge) | 3 | 411 | Mean Difference (IV, Fixed, 95% CI) | 0.10 [‐0.20, 0.40] |
| 1.2 Self‐efficacy | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 1.2.1 Self‐Efficacy for Managing Chronic Disease Scale (high score= more confident in self‐care) | 1 | Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 1.3 Heart failure self‐care | 3 | Std. Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 1.3.1 European Heart Failure Self‐care Behaviour Scale (high score= better self‐care) | 2 | Std. Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 1.3.2 Self‐care Heart Failure Index (high score= adequate self‐care) | 1 | Std. Mean Difference (IV, Fixed, 95% CI) | Totals not selected | |
| 1.4 Health‐related quality of life | 4 | 942 | Mean Difference (IV, Fixed, 95% CI) | ‐0.10 [‐2.35, 2.15] |
| 1.4.1 Kansas City Cardiomyopathy Questionnaire (high score= better health status) | 1 | 82 | Mean Difference (IV, Fixed, 95% CI) | 12.10 [3.08, 21.12] |
| 1.4.2 Minnesota Living with Heart Failure Questionnaire (high score= better quality of life) | 3 | 860 | Mean Difference (IV, Fixed, 95% CI) | ‐0.91 [‐3.23, 1.42] |
| 1.5 Heart failure‐related hospitalisations | 3 | 894 | Odds Ratio (M‐H, Fixed, 95% CI) | 0.74 [0.52, 1.06] |
Characteristics of studies
Characteristics of included studies [ordered by study ID]
Bashi 2016.
| Study characteristics | ||
| Methods | Study design: Randomised parallel controlled trial Aim of study: To evaluate the feasibility of a web‐based intervention in improving HF patients’ knowledge, self‐care, and self‐efficacy. Number of arms: 2 Intervention arm: web‐based self‐care intervention Control arm: usual care Follow‐up: 1 month |
|
| Participants | Geographical location: Australia Setting: Tertiary hospital HF service Date conducted: December 2013‐ May 2014 Inclusion criteria: (a) English‐speaking adults, (b) diagnosed by a cardiologist as class I – III HF according to the NYHA classification and (c) had a left ventricle ejection fraction < 40% Exclusion criteria: (a) nursing‐home residents, (b) with severe cognitive impairment, and (c) with critical illness Age, range, mean (standard deviation): mean age 60.8 (11.9) years Total numbers randomised in this trial: 29 Numbers randomised to intervention group: 15; mean age 61.7 (9.9) years, mean ejection fraction 33.4 (8.51), duration of HF 4.9 (8.5) years Numbers randomised to control group: 14; mean age 60.0 (14.0) years, mean ejection fraction 33.7 (8.9), duration of HF 3.0 (4.8) years Total numbers included in the final analysis: 28 Numbers included in intervention group for final analysis: 14 Numbers included in intervention group for final analysis: 14 |
|
| Interventions |
Treatment arm Name of intervention: web‐based self‐care intervention Method involved: the web‐based educational materials were based on the National Heart Foundation of Australia and the CSANZ Chronic Heart Failure Guidelines. The website was password‐protected, and both HF participants and their healthcare professionals had access to content. The content included interactive HF teaching tools, self‐care tools, a chart for recording daily measures, and self‐care questionnaires Intensity of session: not reported Duration of session: not reported Duration of treatment: 12 weeks Delivered number of session: not reported Control arm Name of intervention: usual care Method: usual care from the HF service or clinic, which included comprehensive educational information consisting of topics such as medication, nutrition, exercise, and psychosocial issues Intensity of session: not reported Duration of session: not reported Duration of treatment: 12 weeks Delivered number of session: not reported |
|
| Outcomes | Primary outcomes:
|
|
| Notes | Funding for trial: not reported | |
| Risk of bias | ||
| Bias | Authors' judgement | Support for judgement |
| Random sequence generation (selection bias) | Unclear risk | Quote: "....participants completed a baseline questionnaire and were randomly assigned to the intervention or control group." Comments: Participants were randomly allocated to groups but the method of sequence generation was not detailed |
| Allocation concealment (selection bias) | Unclear risk | Not detailed |
| Blinding of participants and personnel (performance bias) All outcomes | High risk | Primary researcher collected all study data, and publication states blinding was not possible |
| Blinding of outcome assessment (detection bias) All outcomes | High risk | Collection of outcome data was performed by primary researcher who was not blinded. Study states blinding was not possible |
| Incomplete outcome data (attrition bias) All outcomes | Unclear risk | Only 1 participant lost to follow‐up |
| Selective reporting (reporting bias) | Unclear risk | No trial protocol available to compare with the published results of this trial |
| Other bias | High risk | This is a pilot trial and no full trial will be conducted |
Hagglund 2015.
| Study characteristics | ||
| Methods | Study design: Randomised parallel controlled trial Aim of study: To evaluate whether a new home intervention system (HIS) consisting of a tablet computer connected to a patient scale had an effect on self‐care behaviour Number of arms: 2 Intervention arm: home intervention system, OPTILOGG Control arm: standard HF information only Follow‐up: 3 months |
|
| Participants | Geographical location: Sweden Setting: University hospitals Date conducted: February‐ December 2013 Inclusion criteria: (a) had to be hospitalised and diagnosed with HF with HFrEF or HFpEF or both, according to guidelines and (b) with NYHAclass II – IV Exclusion criteria: (a) had other serious conditions with a life expectancy of < 6 months and (b) diagnosed dementia or cognitive impairment of such severity as it would make the patient unable to understand instructions provided Age, range, mean (standard deviation): mean age 75 ± 8 years Total numbers randomised in this trial: 82 Numbers randomised to intervention group: 42; mean age 75 (8), mean ejection fraction and duration of HF not reported Numbers randomised to control group: 40; mean age 76 (7), mean ejection fraction and duration of HF not reported Total numbers included in the final analysis: 72 Numbers included in intervention group for final analysis: 32 Numbers included in control group for final analysis: 40 |
|
| Interventions |
Treatment arm Name of intervention: home intervention system, OPTILOGG Method involved: participants randomised to IG received a basic information sheet and got the HIS installed in their home by a non‐healthcare professional. The tablet was prepared in advance with self‐care advice according to guidelines of flexible doses of participant's diuretic medication in case of an upward trend with a weight increase of 2 kg in 3 days Intensity of session: not reported Duration of session: not reported Duration of treatment: 3 months Delivered number of sessions: not reported Control arm Name of intervention: standard HF information only Method involved: participants received the same basic information sheet as intervention group with advice on HF treatment and with a priority number to call in case of need Intensity of session: not reported Duration of session: not reported Duration of treatment: 3 months Delivered number of sessions: not reported |
|
| Outcomes | Primary outcomes:
|
|
| Notes | Funding for trial: Swedish National Quality registry of HF (RiksSvikt) and Care Ligo provided the OPTILOGG systems used in the study and paid a small stipend for each included participant The study authors provided the mean (SD) for HF knowledge, health‐related quality of life and self‐care |
|
| Risk of bias | ||
| Bias | Authors' judgement | Support for judgement |
| Random sequence generation (selection bias) | Unclear risk | Not detailed |
| Allocation concealment (selection bias) | Unclear risk | Not detailed |
| Blinding of participants and personnel (performance bias) All outcomes | Unclear risk | Not detailed |
| Blinding of outcome assessment (detection bias) All outcomes | Unclear risk | Not detailed |
| Incomplete outcome data (attrition bias) All outcomes | High risk | 10 participants excluded from analysis due to withdrawing consent before system installed at participant's home. |
| Selective reporting (reporting bias) | Unclear risk | No trial protocol available to compare with the published results of this trial. |
| Other bias | Low risk | Only significant difference between 2 groups was prevalence of atrial fibrillation, which was more common in the control group |
Jalali 2018.
| Study characteristics | ||
| Methods | Study design: Randomised parallel controlled trial Aim of study: To compare the efficacy of 3 methods of follow‐up (SMS, telephone and regular follow‐up) Number of arms: 2 Intervention arm: SMS Control arm: Usual care Follow‐up: 3 months |
|
| Participants | Geographical location: Iran Setting: Emam Ali and Emam Reza hospitals Date conducted: July‐ December 2013 Inclusion criteria: (a) Functional level of 2 or 3 in AHA category, (b) diagnosis of heart failure, (c) no work experience in healthcare centres and (d) have a mobile phone Exclusion criteria: (a) unable to continue, (b) readmission and (c) death Age, range, mean (standard deviation): Not reported Total numbers randomised in this trial: 48 Numbers randomised to intervention group: 23; mean age 59.7 (6.4), mean ejection fraction and duration of HF not reported Numbers randomised to control group: 25; mean age 67.9 (11), mean ejection fraction and duration of HF not reported Total numbers included in the final analysis: 48 Numbers included in intervention group for final analysis: 23 Numbers included in control group for final analysis: 25 |
|
| Interventions |
Treatment arm Name of intervention: SMS follow‐up service Method involved: Text messages (100 characters) were sent daily, 6 days a week for the first month and twice a week in the last month Intensity of session: not reported Duration of session: not reported Duration of treatment: 3 months Delivered number of sessions: Text messages (100 characters) were sent daily, 6 days a week for the first month and twice a week in the last month. The messages had information about HF, sign and symptom and treatment Control arm Name of intervention: Usual care Method involved: Routine discharge procedure Intensity of session: not reported Duration of session: not reported Duration of treatment: 3 months Delivered number of sessions: not reported |
|
| Outcomes | Primary outcome:
|
|
| Notes | Funding for trial: Not reported. This trial had 3 arms (telephone, SMS and usual care). Only data from the SMS and usual care arms are reported. The telephone arm did not meet the review inclusion criteria |
|
| Risk of bias | ||
| Bias | Authors' judgement | Support for judgement |
| Random sequence generation (selection bias) | Unclear risk | Method of sequence generation not reported |
| Allocation concealment (selection bias) | Unclear risk | Method of allocation concealment not reported |
| Blinding of participants and personnel (performance bias) All outcomes | Unclear risk | Blinding of participants and personnel not reported |
| Blinding of outcome assessment (detection bias) All outcomes | Unclear risk | Blinding of outcome assessment not reported |
| Incomplete outcome data (attrition bias) All outcomes | Low risk | ITT analysis reported. All participants were included in the final analysis |
| Selective reporting (reporting bias) | Low risk | All prespecified outcomes in the trial registry record were reported in the final publication |
| Other bias | Unclear risk | It is unclear if baseline characteristics between the IG and CG were balanced |
Li 2016.
| Study characteristics | ||
| Methods | Study design: Randomised parallel controlled trial Aim of study: To investigate whether SMS would help to reduce death or readmission‐free survival and self‐care behaviour in people with CHF Number of arms: 2 Intervention arm: SMS Control arm: Standard care and patient education Follow‐up: 6 months |
|
| Participants | Geographical location: China Setting: Tertiary hospital Date conducted: December 2011‐ September 2015 Inclusion criteria: (a) Individuals diagnosed with decompensated CHF Exclusion criteria: (a) were deceased in hospital (b) were unwilling to participate (c) were < 18 years (d) were unable to read in Chinese (e) did not have a phone (f) were discharged to a long‐term care facility (g) were planning to receive cardiac surgery within 6 months (h) were waiting for heart transplantation (i) have malignancy or other critical illness with a life expectancy of < 1 year (j) were unable to participate owing to severe mental disorders (k) and were participating in other research Age, range, mean (standard deviation): mean age 61 ± 15 years Total numbers randomised in this trial: 512 Numbers randomised to intervention group 1: 252; mean age 60 (15), mean ejection fraction 44 (17) and duration of HF not reported Numbers randomised to control group: 260; mean age 61 (15), mean ejection fraction 45 (17) and duration of HF not reported Total numbers included in the final analysis: 512 Numbers included in intervention group for final analysis: 252 Numbers included in control group for final analysis: 260 |
|
| Interventions |
Treatment arm Name of intervention: SMS Method involved: Participants in SMS group as well as their caregivers received standardised messages from text‐messaging platform operated by research nurses. There were 2 kinds of messages: educational SMS and reminder SMS. The educational SMS were condensed messages with knowledge of HF (e.g. symptoms of HF decompensation), while the reminder SMSs were brief messages that prompted participants to do things (e.g. taking medicine or weighing). All educational messages were sent within the first 10 days after discharge, and then the reminder messages were repeated weekly for 1 month. The messages were scheduled to send out automatically. Participants were informed not to reply to the messages Intensity of session: not reported Duration of session: not reported Duration of treatment: 6 months Delivered number of sessions: not reported Control arm Name of intervention: standard care Method involved: Participants received standard care and patient education before discharge, which covered the education contents in both interventions. They were not contacted in any form after discharge, until the 6‐month follow‐up Intensity of session: not reported Duration of session: not reported Duration of treatment: 6 months Delivered number of sessions: not reported |
|
| Outcomes | Primary outcomes:
Secondary outcomes:
|
|
| Notes | Funding for trial: not reported This trial had 3 arms (SMS, structured telephone support and usual care). Only data from the SMS and usual‐care arms are reported. The structured telephone‐support arm did not meet the review inclusion criteria |
|
| Risk of bias | ||
| Bias | Authors' judgement | Support for judgement |
| Random sequence generation (selection bias) | Low risk | Quote: "The random sequence list was generated and encrypted with Excel 2010 and kept by a statistician who had no access to patient information during the trial." p. 165 |
| Allocation concealment (selection bias) | Unclear risk | Quote: "When a new patient was included, the statistician put his or her number on the list in discharge order and informed the research nurse with the allocation result." p. 165 Comment: Method of allocation concealment not reported |
| Blinding of participants and personnel (performance bias) All outcomes | High risk | Quote: "Blinding was not applicable for this study" p. 165 |
| Blinding of outcome assessment (detection bias) All outcomes | Unclear risk | Blinding of outcome assessment not reported |
| Incomplete outcome data (attrition bias) All outcomes | High risk | Based on the CONSORT flow diagram, all participants were analysed for the primary outcome according to ITT principle. However, 103/512 were not included in the analysis for self‐care and 130/512 for quality of life |
| Selective reporting (reporting bias) | Unclear risk | All prespecified outcomes in the protocol are reported in the primary publication |
| Other bias | Low risk | Quote: "All baseline characteristics were balanced among the three groups, except that more patients received cardiac resynchronization therapy (CRT) or CRT with defibrillator in the STS group than in others (P = 0.007)." p. 168 |
Wagenaar 2019.
| Study characteristics | ||
| Methods | Study design: Randomised parallel controlled trial Aim of study: To assess the effects of the HFM website platform, with a link to the HFM website, which replaces routine consultations with HF nurses at the outpatient clinic in people with HF Number of arms: 2 Intervention arm: Heart Failure Matters website Control arm: usual care Follow‐up: 3 months, 6 months and 12 months |
|
| Participants | Geographical location: The Netherlands Setting: Outpatient HF clinic Date conducted: October 2013‐ December 2014 Inclusion criteria: (a) patients referred to the nearest participating HF outpatient clinic to allow for baseline measurements and randomisation, and when necessary to confirm the diagnosis of HF and provide additional and essential education Exclusion criteria: (a) Non‐availability of internet and email (b) inability of the patient or his/her family to work with internet and email and (c) inability of the patient or his/her family or caregivers to read and understand Dutch Age, range, mean (standard deviation): mean age 66.8 ± 11.0 years Total numbers randomised in this trial: 300 Numbers randomised to intervention group: 150; mean age 66.7 (10.4), mean ejection fraction 35.2 (11.1), duration of HF 45.3 (42.4) months Numbers randomised to control group: 150; mean age 66.9 (11.6), mean ejection fraction 36.2 (10), duration of HF 40.6 (36) months Total numbers included in the final analysis: 300 Numbers included in intervention group for final analysis: 150 Numbers included in control group for final analysis: 150 |
|
| Interventions |
Treatment arm Name of intervention: Heart Failure Matters website Method involved: participants received information about the HFM website from the HF nurse and were instructed on how to use it. The content included interactive HF teaching tools, self‐care tools, a chart for recording daily measures, and self‐care questionnaires Intensity of session: not reported Duration of session: not reported Duration of treatment: 12 months Delivered number of sessions: not reported Control arm Name of intervention: Usual care Method involved: participants received usual care from the cardiologist, HF nurse, and other healthcare workers at the HF outpatient clinic, and/or the GP and practice nurse in the primary‐care setting Intensity of session: not reported Duration of session: not reported Duration of treatment: 12 months Delivered number of sessions: not reported |
|
| Outcomes | Primary outcomes:
|
|
| Notes | Funding for trial: Foundation ‘CareWithin Reach’ (Stichting Zorg Binnen Bereik) We calculated missing standard deviation data for self‐care scores using the Revman calculator. The mean and standard deviation data for HF knowledge and health‐related quality of life were provided by the study authors This trial had 3 arms (usual care, HFM website and e‐health adjusted care pathway). We report only data from the usual care and HFM website arms. The e‐health adjusted care pathway arm did not meet the review inclusion criteria. This study reported 3 time points. We used the first time point (3 months) for analysis |
|
| Risk of bias | ||
| Bias | Authors' judgement | Support for judgement |
| Random sequence generation (selection bias) | Low risk | Quote: "Patients were individually randomized by computerized block randomization (maximum of nine patients per block) to one of the three groups." pp. 239 of the primary publication |
| Allocation concealment (selection bias) | Unclear risk | Not detailed |
| Blinding of participants and personnel (performance bias) All outcomes | High risk | Quote: "....a potential pitfall may be that because the healthcare professionals are aware of the allocation of the patient, they could influence healthcare use (e.g. face‐to‐face contacts, hospitalizations) related to a specific study arm. We, however, expect this potential bias to be limited because the healthcare professionals are not involved in development of the platform and do not receive any financial incentive to take part in the study." p. 1315 of the protocol |
| Blinding of outcome assessment (detection bias) All outcomes | Unclear risk | Quote: "Disease‐specific mortality will be assessed by an independent committee of a GP and a cardiologist who are blinded to the study arm." p. 1314 of the protocol Comments: It is unclear whether the rest of the outcomes were assessed by a blinded outcome assessor |
| Incomplete outcome data (attrition bias) All outcomes | Low risk | ITT analysis reported. All participants were included in the final analysis |
| Selective reporting (reporting bias) | Low risk | All prespecified outcomes in the protocol are reported in the primary publication |
| Other bias | Low risk | Quote: "Most baseline characteristics did not differ between the three study groups after randomisation, although there were more smokers in the usual care (19%) than in the heart failure matters website group (12%)." p. 241 of the primary publication |
AHA: American Heart Association; CG: control group; CHF: chronic heart failure; HF: heart failure; HFrEF: reduced ejection fraction; HFpEF: preserved ejection fraction; IG: intervention group; ITT: intention‐to‐treat; NYHA: New York Heart Association; SMS: short message service
Characteristics of excluded studies [ordered by study ID]
| Study | Reason for exclusion |
|---|---|
| ACTRN12618001273279 2018 | Wrong intervention |
| ACTRN12618001547235 2018 | Wrong intervention |
| ACTRN12619000055101 2019 | Wrong intervention |
| Agren 2012 | Wrong intervention |
| Agren 2013 | Wrong intervention |
| Albanese 2001 | Wrong intervention |
| Albert 2007 | Wrong indication |
| Alnosayan 2017 | Wrong study design |
| Anglada‐Martinez 2016 | Wrong study design |
| Artinian 2003 | Wrong intervention |
| Athar 2018 | Wrong intervention |
| Athilingam 2016 | Wrong study design |
| Austin 2012 | Wrong intervention |
| Ball 2019 | No comparator |
| Bekelman 2013 | Wrong intervention |
| Bekelman 2015 | Wrong intervention |
| Bekelman 2016 | Wrong intervention |
| Bennett 2000 | Wrong study design |
| Bennett 2006 | Wrong study design |
| Boyne 2018 | Wrong intervention |
| Breathett 2018 | Wrong outcome measures |
| Casper 2009 | Wrong outcome measures |
| Chen 2018 | Wrong intervention |
| Chittraphorn 2018 | Wrong study design |
| Clark 2015 | Wrong intervention |
| Crengle 2018 | Wrong study design |
| CTRI/2018/06/014603 2018 | Wrong intervention |
| Dang 2017 | Wrong intervention |
| Daniels 2019 | Wrong intervention |
| Dansky 2008 | Wrong intervention |
| Dansky 2009 | Wrong intervention |
| De Vries 2010 | Wrong intervention |
| Deka 2019 | Wrong intervention |
| Dilles 2011 | Wrong study design |
| Evangelista 2006 | Wrong study design |
| Feldman 2005 | Wrong study design |
| Fors 2018 | Wrong intervention |
| Foster 2018 | Wrong study design |
| Frederix 2015 | Wrong intervention |
| Fruhwald 2009 | Wrong intervention |
| Gaikwad 2009 | Wrong study design |
| Gallagher 2016 | Wrong intervention |
| Gellis 2012 | Wrong intervention |
| Goldstein 2014 | Wrong intervention |
| Hale 2016 | Wrong intervention |
| Harter 2016 | Wrong intervention |
| Hernandez‐Pinzon 2017 | Wrong intervention |
| Hernández 2015 | Wrong intervention |
| Hofmann 2015 | Wrong intervention |
| IRCT2017061934647N1 2017 | Wrong study design |
| IRCT20180101038172N1 2018 | Wrong intervention |
| Ishibashi 2018 | Wrong study design |
| Jaana 2019 | Wrong study design |
| Jovicic 2009 | Wrong comparator |
| JPRN‐UMIN000022783 2016 | Wrong intervention |
| Kalowes 2012 | Wrong intervention |
| Karhula 2015 | Wrong intervention |
| Kashem 2006 | Wrong intervention |
| Kastner 2010 | Wrong intervention |
| Kenealy 2015 | Wrong intervention |
| Kessing 2011 | Wrong intervention |
| Khairat 2014 | Wrong intervention |
| Kitsiou 2015 | Wrong study design |
| Klersy 2011 | Wrong study design |
| Klersy 2016 | Wrong intervention |
| Konstam 2012 | Wrong intervention |
| Kraai 2016 | Wrong intervention |
| Krishna 2002 | Wrong study design |
| Kropf 2015 | Wrong intervention |
| Kutzlleb 2006 | Wrong intervention |
| Lambrinou 2013 | Wrong intervention |
| Lawson 2013 | Wrong intervention |
| Lee 2011 | Wrong patient population |
| Lefler 2018 | Wrong intervention |
| Leonard 2014 | Wrong study design |
| Liljeroos 2017 | Wrong intervention |
| Lind 2014 | Wrong intervention |
| Lind 2016 | Wrong intervention |
| Lindsay 2009 | Wrong patient population |
| Linne 1999 | Wrong intervention |
| Linne 2006 | Wrong intervention |
| Liou 2015 | Wrong study design |
| Lloyd 2019 | Wrong study design |
| Loghmani 2018 | Wrong study design |
| Longhurst 2016 | Wrong study design |
| Lopatin 2017 | Wrong study design |
| Lorenzo 2016 | Wrong intervention |
| Luhr 2019 | Wrong intervention |
| Lundgren 2015 | Wrong intervention |
| Luthje 2015 | Wrong intervention |
| Lynga 2013 | Wrong intervention |
| Madigan 2013 | Wrong intervention |
| Mao 2015 | Wrong intervention |
| Martin 2017 | Wrong intervention |
| Masterson‐Creber 2016 | Wrong intervention |
| McKinstry 2014 | Wrong intervention |
| Mealing 2016 | Wrong intervention |
| Mizukawa 2014 | Wrong intervention |
| Morichau‐Beauchant 2014 | Wrong intervention |
| Murtaugh 2005 | Wrong outcome measures |
| Muyiwa‐Ojo 2018 | Wrong study design |
| Nahm 2008 | Wrong study design |
| NCT00878202 | Wrong intervention |
| NCT03334188 | Wrong intervention |
| NCT03529903 | Wrong patient population |
| NCT03734887 | Wrong intervention |
| NCT03820674 | Wrong intervention |
| NCT03988621 | Wrong participants‐ carers |
| Negarandeh 2019 | Wrong intervention |
| Odeh 2014 | Wrong intervention |
| Olson 2015 | Wrong intervention |
| Ong 2016 | Wrong intervention |
| Or 2017 | Wrong study design |
| Pandor 2015 | Wrong intervention |
| Papavasileiou 2016 | Wrong intervention |
| Park 2016 | Wrong study design |
| Patja 2012 | Wrong intervention |
| Pedone 2015 | Wrong intervention |
| Pekmezaris 2012 | Wrong intervention |
| Pekmezaris 2016 | Wrong study design |
| Pekmezaris 2019 | Wrong intervention |
| Persson 2011 | Wrong intervention |
| Piette 2015 | Wrong intervention |
| Piotrowicz 2019 | Wrong intervention |
| Protopapas 2019 | Wrong intervention |
| Radini 2017 | Wrong intervention |
| Ravi 2016 | Wrong intervention |
| Ritchie 2016 | Wrong intervention |
| Rojas 2013 | Wrong intervention |
| Ross 2004 | Wrong intervention |
| Ruffin 2011 | Wrong patient population |
| Scalvini 2016 | Wrong intervention |
| Sebern 2018 | Wrong study design |
| Seto 2012 | Wrong intervention |
| Seto 2017 | Wrong intervention |
| Sherwood 2011 | Wrong intervention |
| Stampehl 2017 | Wrong intervention |
| Strömberg 2006 | Wrong intervention |
| SUPPORT‐HF 2 Investigators and Committees 2019 | Wrong intervention |
| T. E. C. Home Healthcare Innovation Community 2016 | Wrong intervention |
| Thakur 2018 | Wrong study design |
| Tison 2017 | Wrong intervention |
| Tsai 2008 | Wrong study design |
| Tsuyuki 2019 | Wrong intervention |
| Vadlamani 2016 | Wrong intervention |
| Van Spall 2016 | Wrong intervention |
| Vanagas 2012 | Wrong study design |
| Varma 2015 | Wrong intervention |
| Vellone 2017 | Wrong intervention |
| Venter 2012 | Wrong intervention |
| Veroff 2012 | Wrong intervention |
| Villani 2014 | Wrong intervention |
| Vitry 2008 | Wrong study design |
| Wakefield 2017 | Wrong study design |
| Westlake 2007 | Wrong study design |
| White‐Williams 2015 | Wrong intervention |
| Williams 2016 | Wrong study design |
| Winkler 2011 | Wrong study design |
| Yardımcı 2014 | Wrong study design |
| Yeung 2017 | Wrong study design |
| Zhang 2019 | Wrong intervention |
Characteristics of studies awaiting classification [ordered by study ID]
Tomita 2008.
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (web‐based comprehensive HF educational material) and CG |
| Participants | N = 40 (IG = 16 and CG = 24), mean age 76.2 ± 7.4 years, men n = 23 (59%), NYHA class II, n = 31 (79%), NYHA class III, n = 8 (20.8%) |
| Interventions | IG: Participants in the treatment group were provided a standard PC with Internet access as well as basic computer training. Publicly‐accessible and secured websites were created by a multidisciplinary healthcare team. Informational support included online information on: HF, drugs used to treat HF, effects of alcohol and smoking, depression, prescribed home exercise, nutrition, weight management, and exercise in general. Participants were asked to access the website daily to record their vital signs and health behaviours. The site asks questions about blood pressure, pulse, weight, medication use, type and amount of exercise, levels of fatigue, intake of salt, sugar, alcohol, and tobacco, health changes, and HF‐specific questions about swelling CG: Not detailed in the abstract |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Notes | Authors contacted re: outcome assessment tool used to measure HF knowledge on 5 July 2019, no reply received. |
CG: control group; CHF: chronic heart failure; HF: heart failure; NYHA: New York Heart Association; IG: intervention group
Characteristics of ongoing studies [ordered by study ID]
Dorsch 2019.
| Study name | A patient‐centred mobile intervention to promote self‐management and improve patient outcomes in chronic heart failure: The ManageHF Trial |
| Methods | Single‐centre randomised controlled trial, parallel group with 2 arms: IG (mobile application, ManageHF, along with a Fitbit wearable and scale) and CG (usual care) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: The intervention group used a mobile application, ManageHF, along with a Fitbit wearable and scale. The mobile application prompted active self‐monitoring and provided a health status indicator to promote self‐management CG: Usual care |
| Outcomes | Primary outcome:
Secondary outcomes:
|
| Starting date | Unknown |
| Contact information | Michael Dorsch: mdorsch@med.umich.edu |
| Notes | Author contacted re: copy of published results on 10 December 2019. Reply received: no published results |
IRCT20180227038890N1.
| Study name | Effects of a web‐baseline family educational and supportive programme on adherence of treatment and readmission in patients with heart failure after discharge from Shahid Chamran Medical Education Center |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG ('Soroush' messenger app) and CG (usual care) Setting: Iran |
| Participants | Inclusion criteria:
|
| Interventions | IG: The programme consists of 2 parts. In the first part, the researcher will provide educational materials related to the function, anatomy and physiology of the heart, treatment, medications and their complications, diet, activity and rest, the management of signs and symptoms and disease‐related care. The content transmitted through Soroush's messenger will be provided daily to the intervention group in a text and multimedia format. 3 items on the topics listed will be sent to the intervention group daily from 9 am to 9 pm. In the second part, the intervention group will also be followed up weekly by telephone CG: Usual care |
| Outcomes | Primary outcome:
Secondary outcomes:
|
| Starting date | 10 August 2018 |
| Contact information | Mahin Moieni: moeini@nm.mui.ac.ir |
| Notes | Author contacted re: copy of published results on4 January 2020, no reply received |
JPRN‐UMIN000015843.
| Study name | Effects of self‐care support system using a tablet computer on adherence to self‐monitoring in patients with chronic heart failure: A pilot study |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (self‐care support using tablet computer) and CG (usual care) Setting: Japan |
| Participants | Inclusion criteria: 1. People with CHF who have a history of hospital admission due to acute exacerbation of heart failure |
| Interventions | IG: Self‐care support using a tablet computer CG: Usual care using a diary for patients with chronic heart failure |
| Outcomes | Primary Outcome:
Secondary Outcome(s)
|
| Starting date | 12 August 2014 |
| Contact information | Hiroyuki Tsutsui: htsutsui@med.hokudai.ac.jp |
| Notes | Author contacted re: copy of published results on 8 December 2019; no reply received |
JPRN‐UMIN000028156.
| Study name | Educational intervention of heart failure by using pocket computer tablet |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (educational intervention by using the pocket computer tablet) and CG (educational intervention by using the teaching paper pamphlet) Setting: Japan |
| Participants | Inclusion criteria:
|
| Interventions | IG: Educational intervention by using the pocket computer tablet CG: Educational intervention by using the teaching paper pamphlet |
| Outcomes | Primary Outcome:
Secondary Outcome(s):
|
| Starting date | 4 January 2017 |
| Contact information | Yoshiharu Kinugasa: ykinugasa‐circ@umin.ac.jp |
| Notes | Author contacted re: copy of published results on 8 December 2019. Reply received: no published results |
JPRN‐UMIN000032780.
| Study name | Effects of smartphone application for self‐care support on clinical outcomes in patients with chronic heart failure |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (smartphone app) and CG (usual care) Setting: Japan |
| Participants | Inclusion criteria: 1. People with chronic heart failure who have a history of hospital admission due to congestive heart failure diagnosed by the Framingham criteria |
| Interventions | IG: Smartphone app CG: Usual care |
| Outcomes | Primary outcome(s):
Secondary outcome(s):
|
| Starting date | 6 December 2018 |
| Contact information | Takashi Yokota: t‐yokota@med.hokudai.ac.jp |
| Notes | Author contacted re: copy of published results on 8 December 2019. Reply received: trial is underway, no published results |
NCT02632552.
| Study name | A Technology Assisted Care Transition intervention for veterans With CHF or COPD (TACT) |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (technology‐assisted care transition intervention) and CG (active attention control) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: In‐patient virtual nurse on‐screen touch screen and outpatient virtual nurse follow‐up by texting CG: In‐patient brief animated power‐point style didactic onscreen tutorial covering the core pillars of care transitions and brief outpatient texting |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 01 May 2018 |
| Contact information | Timothy Hogan: timothy.hogan@va.gov |
| Notes | Author contacted re: copy of published results on 8 December 2019. Reply received: trial is underway, no published results |
NCT03108235.
| Study name | HOMe‐based HEart failure self‐Management Programme Study (The HOM‐HEMP Study) |
| Methods | Randomised controlled trial, parallel group with 3 arms: IG 1: (Health education booklet), IG 2: (health education booklet + smartphone application) and CG (usual care) Setting: Singapore |
| Participants | Inclusion criteria:
|
| Interventions | IG 1: 6‐week nurse‐led, home‐based self‐management psychosocial education programme comprising a specifically‐developed Heart education toolkit, 1 education session and 6 weekly home visits. IG 2: 6‐week nurse‐led, home‐based self‐management psychosocial education programme (HOM‐HEMP), comprising a specifically‐developed Heart education toolkit, 1 education session and 6 weekly home visits with smartphone app. The smartphone will be used to monitor health data (such as weight and blood pressure). The data will be synchronised to a web‐connected portal on a remote server. The research nurse would be able to access the data through the web‐connected portal and provide consultation through scheduled tele‐ or video‐conference CG: Usual care |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 11 April 2017 |
| Contact information | Wenru Wang: nurww@nus.edu.sg |
| Notes | Author contacted re: copy of published results on 04 January 2020. Reply received: the study is currently under data collection, results will be published in February 2020 |
NCT03149510.
| Study name | The effects of self‐monitoring with a mobile application in heart failure |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (mobile app) and CG (usual care) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: The mobile application will provide the participants with a reminder to perform self‐monitoring, a health status indicator and heart failure education for self‐management CG: Usual care |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 6 March 2017 |
| Contact information | Judith Grossi: jgrossi@med.umich.edu |
| Notes | Author contacted re: copy of published results on 4 January 2020, no reply received |
NCT03539510.
| Study name | Effectiveness of the HF‐ACP website study |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (HF‐ACP website) and CG (usual care) Setting: Canada |
| Participants | Inclusion criteria:
|
| Interventions | IG: The HF‐ACP website leads participants through 4 x e‐learning modules. Each module contains 3 core elements: (1) educational content which provides information and support to help participants complete the module (2) interactive tools for documenting their thoughts and progress and (3) motivational video clips that encourage behaviour change by validating participants' ambivalence, suggesting strategies to help participants complete the task and to encourage and reassure participants that they can do this CG: The standard of care for advance‐care planning at our institution is the "Speak Up" booklet and the Power of Attorney workbook from the Attorney General's Office ‐ Ontario. Patients randomised to the control arm will be asked to register on a separate research portal where participants will have electronic access to both of the booklets and a link to the 'Speak Up' online interactive workbook. There is no specific information on HF or HF treatments. Participants in the control arm will be asked to complete the ACP using the interactive workbook. Participants in the control arm will not receive any additional communication from the research team about their progress |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 28 May 2018 |
| Contact information | Jane Maciver: jane.maciver@uhn.ca |
| Notes | Author contacted re: copy of published results on 04 January 2020. Reply received: currently applying for funding, publication will depend on that |
NCT03642275.
| Study name | Patient‐centered mobile health intervention to improve self‐care in patients with chronic heart failure (iCardia4HF) |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (mobile app, wearable activity‐tracking device, Bluetooth‐enabled blood pressure monitor and weight scale for self‐monitoring, and receive tailored text messages about self‐care) and CG (usual care) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: Participants in the intervention group will receive the Heart Failure Health Storylines mobile app, 3 connected health devices that interface with the app (Fitbit Charge 2 activity tracker, and FDA‐approved Nokia BP monitor and Cardio Body weight scale), and a programme of tailored self‐care text messages, in addition to usual care. Participants will be asked to use the app and devices to record and self‐monitor their daily symptoms, blood
pressure, weight, and physical activity CG: Participants assigned to the usual‐care group will receive standard medical care, which includes nurse‐led patient education about HF self‐care before discharge, and follow‐up visits at the UI Health, outpatient Heart Failure programme |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 21 January 2019 |
| Contact information | Spyros Kitsiou: skitsiou@uic.edu |
| Notes | Author contacted re: copy of published results on 8 December 2019. Reply received: no published results |
NCT03947983.
| Study name | Sensor‐controlled digital game for heart failure self‐management |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (sensor‐controlled digital game, SCDG) and CG (sensor only) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: The SCDG will involve a narrative, the goal of which is to help an avatar in the game avoid rehospitalisation by using game points, earned by the participant's real‐time behaviours, in game tasks that help maintain the avatar's optimal HF health status. Real‐time behaviours of weight‐monitoring and physical activity will be tracked by an off‐the‐shelf sensors and app (Withings). The data from the Withings sensors will then be routed to our SCDG app. The digital game paired with sensors will enable objective tracking of real‐time behaviours such as physical activity and weight monitoring, and provide personalised, contextually‐relevant feedback (e.g. reduce fluid intake or call doctor for weight gain) to motivate engagement in and generate habit formation of heart failure related self‐management behaviours. CG: Real‐time behaviours of weight monitoring and physical activity will be tracked by an off‐the‐shelf sensor and app (Withings). This group will also be provided with standardised evidence‐based HF educational material.However, the data from the Withings sensors will not be routed to the SCDG |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 10 October 2019 |
| Contact information | Kavita Radhakrishnan: kradhakrishnan@mail.nur.utexas.edu |
| Notes | Author contacted re: copy of published results on 8 December 2019. Reply received: no published results |
NCT03982017.
| Study name | Heart Failure Self‐care Mobile Application to Reduce Readmissions Trial (HF‐SMART) |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (mobile health technology platform) and CG (usual care) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: Participants will be provided with a special link to navigate to the online content and resources. When the participant navigates to the programme, they will follow the directions on how to use the programme, sign the terms of agreement, put in a password and begin the welcome page. The participant will receive daily prompts to complete brief questionnaires and review specialised content CG: Consists of routine care at the time of hospital discharge, to be provided at the discretion of the clinicians caring for the participant |
| Outcomes | Primary outcomes:
|
| Starting date | 12 July 2019 |
| Contact information | Amber Johnson: johnsonae2@upmc.edu |
| Notes | Author contacted re: copy of published results on 08 December 2019. Reply received: trial is underway, no published results |
NCT04062461.
| Study name | Evaluate the effectiveness of self‐care multifaceted strategy in heart failure patients (IC‐CBC) |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (multifaceted strategy based on sending text messages (SMS)) and CG (usual care) Setting: Brazil |
| Participants | Inclusion criteria:
|
| Interventions | IG: Press educational content on heart failure for the participant (symptoms of the disease, healthy habits for HF, warning signs for severity). The text messages are about how the participant should take their drugs (even diuretics), measure blood pressure and weight in Kg, and about signals and symptoms (breathlessness during the night), and about how important it is to practice physical exercises and not drink alcohol CG: Educational content on heart failure for the participant (symptoms of the disease, healthy habits for HF, warning signs for severity) |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 08 July 2019 |
| Contact information | Felix Ramires: felix.ramires@incor.usp.br |
| Notes | Author contacted re: copy of published results on 08 December 2019, no reply received |
Nolan 2014.
| Study name | An internet‐based counseling intervention with email reminders that promotes self‐care in adults with chronic heart failure: randomized controlled trial |
| Methods | Randomised controlled trial, multi‐site, double‐blind, parallel group with 2 arms: IG (e‐counselling + usual care) and CG (e‐info control + usual care) Setting: Canada |
| Participants | Inclusion criteria:
|
| Interventions | IG: 28 emails will be sent proactively to each participant in the intervention arm over a 12‐month interval. Each email will link e‐counselling participants to a restricted section of our e‐platform where they will access multimedia materials and interactive e‐tools. As noted above, the clinical method and content of this protocol is consistent with principles of MI. In keeping with I‐START, the e‐counselling messages promote the following: (1) explicit validation of the participant’s stage of 'readiness' for behaviour change via e‐messaging and educational segments; (2) collaborative participation by means of participant‐selected menus and explicit messaging to validate their active participation; and (3) reinforcement of 'change talk' through peer modelling, dramatic vignettes, and self‐help exercises that are designed to help resolve ambivalence to change CG: In addition to usual care, the control group will be provided with e‐messages following the same delivery schedule. The e‐messages will include brief articles that are randomly selected from the Healthy Living section of the Heart and Stroke Foundation of Canada e‐platform. Each e‐support article will provide information tailored for a CHF population, such as appointments with physicians and advice about heart‐healthy guidelines for exercise, diet, smoke‐free living, symptom monitoring, and medications. This intervention will be distinct from the e‐counselling group in 2 ways: (1) information will not be tailored to each participant’s stage of readiness for change, and (2) e‐messages will not include e‐tools and e‐counselling procedures to increase 'readiness' and efficacy to adhere to targeted self‐care behaviours |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | |
| Contact information | Robert Nolan: rnolan@uhnres.utoronto.ca |
| Notes | Study author emailed on 04 January 2020. Reply received: manuscript is under review |
RBR‐9c3ssc 2018.
| Study name | Effect of telephone consultation with short message service (SMS) in patients with heart failure |
| Methods | Randomised controlled trial, parallel groups with 2 arms: IG (SMS + weekly telephone consultations) and CG (usual care) Setting: Brazil |
| Participants | Inclusion criteria:
|
| Interventions | IG: SMS + weekly telephone consultations CG: Usual care |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 01 June 2018 |
| Contact information | Lyvia da Silva Figueiredo: lyviafigueiredo@gmail.com |
| Notes | Author contacted re: copy of published results on 08 December 2019, no reply received |
Sharma 2019.
| Study name | Mobile health behavioral intervention in patients with heart failure and diabetes mellitus (TARGET‐HFDM) |
| Methods | Randomised controlled trial, parallel group with 2 arms: IG (mHealth intervention) and CG (no intervention) Setting: United States of America |
| Participants | Inclusion criteria:
|
| Interventions | IG: Personalised step‐count feedback and medication teaching tool (Duke Pillbox) CG: No intervention |
| Outcomes | Primary outcomes:
Secondary outcomes:
|
| Starting date | 28 September 2016 |
| Contact information | Lori Hudson: lori.hudson@duke.edu |
| Notes | Author contacted re: copy of published results on 04 January 2020. Reply received: no published results, trial is underway |
Wonggom 2018.
| Study name | Evaluation of the effectiveness of an interactive avatar‐based education application for improving heart failure patients’ knowledge and self‐care behaviours |
| Methods | Prospective, multi‐centre, non‐blinded, randomised controlled trial with 2 arms: IG (usual care + avatar‐based education technology) and CG (usual care) Setting: Australia |
| Participants | Inclusion criteria:
|
| Interventions | IG: The intervention group will receive usual care plus the avatar‐based education application during the study period of 6 months. An avatar‐based application will enhance participant learning by using modern computerised tools such as animation (heart and anatomy, links to videos (demonstrating how to self‐care), voice (no need to read) and touch screen response) CG: Usual care |
| Outcomes | Primary outcome:
Secondary outcomes:
|
| Starting date | 01 June 2018 |
| Contact information | A/Prof Robyn Clark: +61 8 8201 3266; Email: robyn.clark@flinders.edu.au |
| Notes | Study author emailed on 04 January 2020. Reply received: trial is underway |
AHA: American Heart Association; BNP: brain natrurietic peptide; CG: control group; CHF: chronic heart failure; HF: heart failure; COPD: chronic obstructive pulmonary disease; HFrEF: reduced ejection fraction; HFpEF: preserved ejection fraction; ICD: Interrnational Classification of Diseases; IG: intervention group; ITT: intention‐to‐treat; LA: left atrial; LVEF: left ventricular ejection fraction; NYHA: New York Heart Association; SMS: short message service; VAS: visual analogue scale
Differences between protocol and review
We removed all‐cause mortality as an outcome and added self‐care as an outcome. We removed all‐cause mortality as our previous research in the area of telemonitoring in heart failure has indicated that there is little potential for interventions supporting disease‐specific self‐care and self‐management to impact on hospitalisations for causes other than disease‐specific hospitalisations (i.e. heart failure‐related hospitalisations). Additionally, it became clear at the stage of data searching/screening that the outcomes we have examined are the ones most consistently examined in studies of education interventions for heart failure.
We also wanted to add self‐care, adverse events and cost as outcomes, and were mindful to not overburden the review with extensive lists of outcomes.
Due to the small number of included studies, we did not conduct any sensitivity analyses, but in future updates of this review we will do this if appropriate.
We planned to use a random‐effects model, but used a fixed‐effect model due to the population sizes of the included studies.
Contributions of authors
SA: Contributed to writing the review. Retrieved studies, completed title screening and inclusion/exclusion review, contacted study authors for additional information. Performed data extraction, analysis, risk of bias and GRADE assessment, created 'Summary of findings' table.
HD: Contributed to writing the review (Discussion). Performed data extraction and checked analysis, 'Summary of findings' table and GRADE assessment.
XX: Checked data entry, extraction and analysis.
RP: Completed title screening and inclusion/exclusion review.
SC: Prepared data for analysis. Checked data extraction.
LDH: Contributed to writing the review (Discussion). Facilitated translation and screening of Spanish‐language publication.
PMD: Contributed to the writing of the protocol. Reviewed versions of the review for intellectual content.
SCI: Responsible for conception and design of the protocol. Responsible for co‐ordinating and completing the protocol and review, including writing the protocol and review. Undertook title screening and inclusion/exclusion review. Performed data extraction and analysis, interpretation of findings and implications for future research and clinical practice.
Sources of support
Internal sources
No sources of support supplied
External sources
Associate Professor Sally C Inglis is supported by a UTS Re‐establishment grant, Australia
This project was supported by the National Institute for Health Research, via Cochrane Infrastructure to the Heart Group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Systematic Reviews Programme, NIHR, NHS or the Department of Health, UK
A/Prof Sally Inglis is currently supported by a Heart Foundation Future Leader Fellowship and has received support from: New South Wales Cardiovascular Research Network Life Sciences Fellowship, supported by the Heart Foundation and the NSW Office for Health and Medical Research, Australia
Declarations of interest
SA: None known. HD: None known. XX: None known. RP: None known. SC: None known. LH: None known. PMD: Patricia M. Davidson has participated in guideline development for the Heart Foundation (Australia) on chronic heart failure and acute coronary syndrome and Cardiac Society of Australia and New Zealand Cardiovascular Nursing Group of COVID‐19. SCI: None known.
New
References
References to studies included in this review
Bashi 2016 {published data only}
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Jalali 2018 {published data only}
- IRCT2013060313568N1. The effect of three methods of follow up on quality of life in heart failure patients. en.irct.ir/trial/13395 (First received 27 July 2013).
- Jalali R, Hoseinpour A, Menati L, Amini M. The effect of three methods of follow-up (Short Message Service SMS, Telephone and Regular) on the quality of life in heart failure patients. Future of Medical Education 2018;Unknown:1-7. [Google Scholar]
Li 2016 {published data only}
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Wagenaar 2019 {published data only}
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ACTRN12618001547235 2018 {published data only}
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ACTRN12619000055101 2019 {published data only}
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Agren 2012 {published data only}
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Clark 2015 {published data only}
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Dang 2017 {published data only}
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NCT03334188 {published data only}
- NCT03334188. Electronic health record-leveraged, patient-centered, intensification of chronic care for HF (EPIC-HF). clinicaltrials.gov/ct2/show/NCT03334188 (First received 7 November 2017).
NCT03529903 {published data only}
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NCT03734887 {published data only}
- NCT03734887. Social engagement strategies to improve medication adherence. clinicaltrials.gov/ct2/show/NCT03734887 (First received 8 November 2018).
NCT03820674 {published data only}
- NCT03820674. A telerehabilitation intervention for people with heart failure and chronic fatigue. clinicaltrials.gov/ct2/show/NCT03820674 (First received 29 January 2019).
NCT03988621 {published data only}
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Negarandeh 2019 {published data only}
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Tison 2017 {published data only}
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Tsuyuki 2019 {published data only}
- Tsuyuki RT, Lockwood EE, Shibata MC, Simpson SH, Tweden EL, Gutierrez R, et al. A randomized trial of video-based education in patients with heart failure: the congestive heart failure outreach program of education (COPE). Canadian Cardiovascular Society Open 2019;1(2):62-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
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References to studies awaiting assessment
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