Heart failure (HF) and diabetes mellitus (DM) account for substantial morbidity and mortality worldwide, with over 500,000 and 2.3 million afflicted with HF and DM in Canada alone, corresponding, respectively, to an annual incidence of over 50,000 and 200,000 [1, 2]. Moreover, these chronic illnesses can occur and modulate each other: among diabetic adults (> 64 years), the prevalence of HF is reported to be 22% [3, 4]. Historically, many advances in medical and/or device therapies for HF and DM care have focused on improving mortality or major adverse cardiovascular events [5,6,7]. In fact, the negative impact of certain anti-diabetic therapies on cardiovascular health prompted the requirement for cardiovascular outcomes trials to be undertaken for regulatory approval [8]. Similarly in HF, poor adherence, adverse clinical outcomes, and readmission burden suggest the need for robust evaluation in this population [9]. It is therefore crucial to consider interventions to improve the multidimensional aspects of patient health and quality-of-life (QoL) metrics.

The growth of digital health technologies (DHT) has been accelerated by the coronavirus pandemic, transforming the delivery of medical care along with highlighting the positive impact and need for persistent advancement in this field. DHT consist of the use of information and communication technologies (e.g., wearables, mobile applications, telehealth, and text messaging platforms) to support health and play an emerging role in managing patients with chronic diseases [10, 11]. However, some barriers to the use of DHT include lack of access, financial burden, loss of patient follow-ups or medication adherence, device failure, and end-point validity [11]. DHT in clinical studies has become more prevalent in evaluating different aspects of care and evaluation of patient-related outcomes in their normal daily routines. Despite the high prevalence of multiple chronic diseases or multimorbidity, most DHT platforms focus on managing a single disease [12]. In fact, the prevalence and effectiveness of DHT use among patients with HF and DM is unknown. Therefore, in patients with HF and DM, we sought to investigate the impact of DHTs on clinical disease variables such as QoL, medication adherence, health behaviors, and clinical biomarkers.


Sources, Study Selection, Risk of Bias, and Data Extraction

We conducted a systematic search of relevant databases (Ovid MEDLINE®, Embase, Cochrane Central Register and Clinical Trials.Org) from 1946 to February 2021 incorporating Medical Subject Headings or “MeSH” terms and integrated validated search filters [13]. Appendix 1 provides the search strategies and search terms applied to the database evaluations [14]. Additional randomized controlled trials (RCTs) were manually identified by reviewing individual citations and Identified studies underwent title and abstract (C.G. and V.K.R.) and full-text (D.K., C.G., L.B., and V.K.R.) screening for study inclusion by at least two independent reviewers. All English RCTs reporting on the use of DHT in adult patients (≥ 18 years) with HF and DM were included, with the intent that this could potentially identify the highest level of evidence and between study standardization. We excluded duplicate studies, abstracts, non-randomized studies, case series with ≤ 5 cases, and/or non-human studies. Studies were managed using the Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). Studies were categorized into two groups based on RCT status: published or “registered” (i.e., active but unpublished or planned) RCTs. The risk of bias (RoB) of complete RCTs were assessed by two independent reviewers (D.K and N.S.) using the Cochrane Collaboration’s Tool for Assessing Risk of Bias 2 (RoB2, version 9, August 2019) [15]. The risk of bias for studies was assessed based on five RoB domains and can be given a bias score of “low,” “some concerns,” or “high.” Any discrepancies related to study inclusion and risk of bias or certainty of evidence assessments were resolved by consensus or by a third reviewer where required. Study design, inclusion criteria, patient characteristics, DHT utilized, and clinical outcomes were extracted and assessed where applicable. Sex, age, type of DHT utilized, and efficacy of DHT intervention were sought.


Our search strategy identified 1,913 unique records after removal of citations that did not adhere to our inclusion criteria and duplicate records (Fig. 1). After title and abstract screening, 26 underwent full-text review and 7 were included in the qualitative synthesis. Of these, one was a published RCT, and 6 were “registered” RCTs. Clinical results of each published and registered trial are reported in Tables 1 and 2, respectively.

Fig. 1
figure 1

Study Selection PRISMA flow diagram. The electronic database search identified 1,939 eligible studies for title and abstract screening after removing 749 duplicate studies from 2,688 initially identified studies. Of these, 1,913 studies were excluded, and 26 studies underwent further full-text review. Of these, 7 studies met the inclusion criteria

Table 1 Characteristics of the published randomized controlled trial using a digital health technology
Table 2 Characteristics of registered randomized controlled trials of digital health technologies

Outcome Measures in HF and DM DHT Trials

Focus on Published DHT RCT

The published RCTs in patients with both HF and DM exclusively utilizing a DHT tool were limited to the Renewing Health Project. In this study, the aim was to assess whether a one-year structured telephone-based health coaching program supported by a self-managed remote monitoring system improved QoL and reduced HbA1c over time among patients with type II DM and heart disease (inclusive of HF) versus standard quality of care. Participants in the intervention group received a mobile phone with a personal health record (PHR) app, and a set of medical devices linked the patient’s PHR account via Bluetooth. In addition, patients in the intervention group were also assigned a health coach who called them every 4–6 weeks to evaluate health behaviors and recommend a behavior management plan according to a structured training program (“Pfizer’s health coaching model”). Health-related QoL was assessed via the SF-36 health survey. A total of 519 participants with an age of 18 years or older were recruited and consisted of predominantly males (n = 317/519, 61%). The mean age of patients did not differ between intervention (68.1 years) and the control group (66.9 years) nor between diabetic (66.1 years) or heart disease (68.1 years) patients. Although patients consistently received health coaching calls throughout the study and the majority of the patients (91.6% in heart disease group and 95% in diabetes group) adhered to the home telemonitoring plan, the study found no benefit in their study intervention. This one-year program did not improve QoL or the clinical condition in comparison to patients with standard of care. Nonetheless, beneficial trends in blood pressure (BP) and cholesterol levels for all patients was reported with more improvements in clinical variables (weight, waist circumference, BP, and LDL-cholesterol) being more apparent in the type II DM patient group (weight change =  − 0.9; systolic BP change =  − 6.1; diastolic BP change =  − 2.61; LDL change =  − 0.40) than in the heart disease patient group (weight change = 0.04, systolic BP change =  − 5.43, diastolic BP change =  − 0.27; LDL change =  − 0.34) [17].

The overall risk of bias was considered “high risk” when assessed with the Cochrane RoB tool (Fig. 2). The measurements of the secondary outcomes (BP, weight, activity) were self-reported by participants in the intervention group using remote monitoring tools resulting in more evidence to support a high-risk bias.

Fig. 2
figure 2

Randomized clinical trial risk of bias Cochrane risk assessment summary plot. The overall risk of bias for the Renewing Health Project study is assessed as high

Focus on Registered DHT RCTs identified another 6 “registered” RCTs utilizing DHT in patients with HF and DM along varying time horizons (Table 2). Together, these trials aim to recruit a total of 3,423 participants, with a median recruitment of 423 participants per study. Of these, 5 studies plan to recruit patients ≥ 18 years of age with no further characterization or categorization based on age groups (young adults versus older adults). All studies are to be based out of developed countries: 3 North American (Canada and USA [n = 2]) sites and 3 European (Germany, Sweden, Denmark). The specificity of the inclusion criteria for patient groups differed among these studies: some were restricted to only patients with HF and DM (“Target HF-DM”, NCT02918175), whereas others included patients also diagnosed with other chronic diseases such as ischemic heart disease and obstructive pulmonary disease. One study (“MODEL,” NCT02957513) aimed to recruit African American patients in underserved communities, while others did not restrict inclusion criteria to a specific ethnic cohort.

Most studies (n = 5 of 6) used telemedicine/mobile platforms as their key exposure: mobile applications were used in 3 studies, personalized text messaging in 2, and telemedicine in 2. One study (“Empire HF”, NCT03198585) had a drug intervention with Empagliflozin and an associated sub-study that utilized an accelerometer to categorize patients into those who had either on average a high or low physical activity level [16]. The average duration for monitoring will be 9 months (range 3 to 12 months) among all studies. The primary exposures for the studies are aimed to assess aspects of QoL or self-care surveys (n = 1 each), physical activity (n = 3), and change in serum biomarkers (n = 2, HbA1c and NT-proBNP levels) and clinical endpoints such as weight and BP (n = 2).


HF and DM are two chronic diseases that can modulate each other. Patients with HF are at increased risk of DM, and vice-versa, patients with DM are at higher risk of developing HF [4]. Despite the large incidence of patients with both diseases, a uniform approach to disease management in this population via digital health tools is absent. Among the studies that received full-text review but were excluded, the majority focused on DHT in patients with either DM or HF alone. As a result, this scoping review identified only one published RCT integrating DHTs in patients with both HF and DM, as well as 6 “registered” RCTs that were ongoing. Overall, there was a limited number of studies assessing DHTs exclusively in HF and DM patients. Therefore, 6 of 7 studies included in this review had patients with DM and > 1 cardiovascular comorbidity (but inclusive of HF) in their study population (Tables 1 and 2). In addition, we report significant heterogeneity among these studies with regard to the type of DHT utilized, study design, endpoint evaluations, and clinical outcomes (Fig. 3).

Fig. 3
figure 3

Scope of digital health technologies in heart failure and diabetes mellitus. A summary of the digital health technologies (eHealth), primary outcomes, and findings and limitations of the identified randomized controlled trials

The lack of standardization among studies has potential implications when interpreting, managing, and generalizing study results. For instance, inclusion criteria in some studies were specific but relatively broad in others, which can lead to challenges in generalizing the results. The lack of a defined diagnosis in the inclusion criteria may also lead to varying outcomes as disease severity may not be controlled in a study. Among the registered RCTs (Table 2), half (3 of 6) defined the clinical conditions required for a diagnosis of either HF or DM. In addition, the methodology used to evaluate an outcome differed among the studies despite some studies having similar primary outcomes. For instance, accelerometers, exercise training, and health apps all assessed aspects of physical activity with no clear indication of why each device/intervention was utilized. Moreover, different questionnaires were used to assess QoL (e.g., the “KCCQ” or Kansas City Cardiomyopathy Questionnaire, “EQ-5D-5L” or EuroQoL Health Questionnaire, versus “SF-36” or Short Form 36 Health Survey Questionnaire). Many of the studies included in this review had multiple interventions. The implementation of more than one intervention (telemedicine supported by an app and linked to electronic medical devices; trial NCT01478672) may lead to potential difficulty when determining which intervention contributes to the efficacy of the study outcomes. Overall, standardization of methods across DHT studies will improve the interpretation of outcomes and the ability to adopt digital health interventions into clinical care of patients with HF and DM [18].

The COVID-19 pandemic has expedited the use of DHT and yet has also highlighted the disparities that exist in medical care such as access to digital health devices. Socioeconomic status, education, sex and gender, and race and ethnicity are important baseline characteristics to consider in larger cohort studies of HF and DM patients given the lack of access to DHT that may exist in remote and underprivileged communities. The “Management of Diabetes in Everyday Life” (MODEL) study was the one study in our review that limited their population to a minority group (i.e., African Americans) to assess self-care activities associated to their diagnosis. In the same way, this study design enabled the assessment of a digital health intervention on a minority group, leveraging digital health tools to assess subgroups of patients or even phenotypic profiles of HF and DM which can be immensely beneficial and impactful in optimizing the clinical care for these chronic diseases.

DHTs have shown to be an effective tool in managing chronic diseases and possess great utility if adopted uniformly [19]. Future studies need to evaluate or introduce new DHT platforms that are capable of simultaneously evaluating patients with multiple chronic diseases. This will particularly be beneficial for DM and HF patients given that both these chronic diseases often coexist together. The ability to consolidate care using a single DHT platform may improve the QoL of patients while integrating a proactive model for patient involvement. For example, AliveCor is a portable heart monitor device capable of performing an electrocardiogram using 2 fingers to help detect atrial arrhythmias. While the published AliveCor device study (not included in this scoping review) did not focus on the incidence of atrial arrhythmias in only HF and DM patients, this could be an interesting future study given the validation of this tool over many published studies and the relative propensity of atrial fibrillation in patients with these comorbid conditions [20].

Consensus of which clinical endpoints should be assessed via DHT tools for patients with both HF and DM has yet to be determined. The design of studies can lend themselves to address endpoints such as HF severity or glycemic control. This is particularly important given the impact of certain medications on both endpoints in patients with or without HF and DM [4]: for instance, sodium glucose co-transporter 2 inhibitors (SGLT2i) have been implicated in modulating the recovery of left ventricular ejection fraction and reducing HF readmissions in patients with HF and in improving glycemic control in patients with DM. The safety and efficacy of medications, such as SGLT2 inhibitors, should be investigated in large cohort studies and possibly in clinical trials in the future. Telemonitoring can be used to encourage engagement with self-management (e.g., daily weights, medication adherence, clinical visit follow-ups), improve access to care for remote patients, and help coordinate care which may result in better QoL for patients. In addition, this can facilitate timely clinical visits in accordance with patient needs and disease severity while reducing the burden on healthcare systems. For this reason, leveraging DHT tools to help assess patients with concomitant DM and HF should be prioritized. Lastly, the ongoing studies in this space also provide the opportunity for researchers to consider subgroup analyses of patients that have both DM and HF.


The limited number of studies identified in this scoping may be attributed to the rigor of our search strategy. We were strictly concentrated on studies evaluating DHT in patients with concomitant HF and DM, rather than focusing on global cardiovascular disease and/or risk factors. As such, we were not able to conduct a systematic review which may have encompassed more homogeneity in the field and consideration to sex and gender and race or ethnicity-based analyses. In addition, we restricted our search strategy to RCTs that were published in English. Although this enabled us to review robust standardized studies with the highest level of reported evidence, hypothesis-generating studies, and other published work in this field, some studies may have been overlooked. Majority of studies identified were registered RCTs (yet to be published), and therefore, future analysis of these studies’ data and outcomes are warranted particularly for the HF and DM patient sub-groups. Lastly, we did not register our study protocol in PROSPERO since formal screening was initiated before consideration of protocol registration. This however provides opportunity for future extensive reviews in this space once ongoing studies are completed and published. The limited number of published studies in this space, however, precluded a meta-analysis of currently published data.


Digital health technologies offer a novel way to evaluate patient-centered outcomes and are particularly useful for chronic diseases that modulate each other, such as HF and DM. Given the limited number of studies that include both HF and DM populations, and the significant heterogeneity in existent DHT-based studies, further work should define standardized DHT endpoints and explore the utility, access, and cost-effectiveness in patients with chronic medical conditions.