This paper is in the following e-collection/theme issue:

Wearables and MHealth Reviews (294) Heart Failure Self-Management (170) Novel Sensors and Data Acquisition Methods in Cardiology (217) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1614) Digital Health Reviews (1585) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (553) Patient Engagement and Empowerment (62)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63409, first published .
Adherence to eHealth Interventions Among Patients With Heart Failure: Scoping Review

Adherence to eHealth Interventions Among Patients With Heart Failure: Scoping Review

Adherence to eHealth Interventions Among Patients With Heart Failure: Scoping Review

Authors of this article:

Arno Joachim Gingele1 Author Orcid Image ;   Bianca Steiner2 Author Orcid Image ;   Bettina Zippel-Schultz2 Author Orcid Image ;   Hans-Peter Brunner-La Rocca1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Cardiology, Maastricht University Medical Center, P Debyelaan 25, 6229 HX, Maastricht, The Netherlands

    2German Foundation for the Chronically Ill, Berlin, Germany

    Corresponding Author:

    Arno Joachim Gingele, MD, PhD


    Background: Heart failure (HF) is a significant global health challenge, requiring innovative management strategies like eHealth. However, the success of eHealth in managing HF heavily relies on patient adherence, an area currently not sufficiently investigated despite its critical role in ensuring the effectiveness of this approach.

    Objective: This review was initiated to gather evidence on adherence to eHealth devices among patients with HF. The goal was to survey the current state of adherence, pinpoint factors that promote successful engagement, and identify gaps needing further research.

    Methods: A scoping review was conducted to gather quantitative data on eHealth engagement from relevant clinical HF studies indexed in PubMed, CINAHL, and PsycINFO up to February 2025. Descriptive characteristics of the publications were extracted, and generalized mixed model analyses were used to identify eHealth characteristics affecting patient adherence.

    Results: Our analysis included 70 studies, primarily using noninvasive eHealth interventions with wearables (n=51), followed by wearables only (n=8), noninvasive eHealth interventions without wearables (n=6), invasive devices (n=3), and telephone support (n=2). The median number of patients per study was 49 (IQR 20‐139), and the median follow-up duration was 180 (IQR 84‐360) days. Variability in reporting and definitions of eHealth adherence was noted. In total, 20 studies assessed adherence trends, with 13 noting a decline, 6 observing no change, and 1 reporting an increase over time. Factors influencing adherence were explored in 29 studies; 7 indicated higher adherence with increasing patient age, 2 showed a negative correlation, and 9 detected no age-related differences. No gender differences were found in the 10 publications that reported on gender, and 9 studies found no association between adherence and the New York Heart Association classification, while 1 noted higher adherence in patients with more severe symptoms. In 35 (50%) studies, adherence was quantified as the percentage of mean days the intervention was used, yielding a median adherence rate of 78% (IQR 61%‐86%; range 31%‐98%). No significant correlations were found between adherence rates and the number of eHealth device users, type of intervention, follow-up duration, number of parameters monitored, or data collection frequency.

    Conclusions: Reporting and definitions of patient adherence in HF studies are incomplete and inconsistent. Trends indicate a decrease in eHealth use over time. Customizing devices to meet patient needs may help mitigate this issue. Future research should offer a more detailed description of adherence to pinpoint factors that enhance patient adherence with eHealth technologies.

    JMIR Mhealth Uhealth 2025;13:e63409

    doi:10.2196/63409

    Keywords

    heart failure; eHealth; patient adherence; review; quantitative data; mixed model; wearables; invasive devices; telephone support 


    Background

    Heart failure (HF) is a critical global health issue marked by high morbidity and mortality, challenging health care systems with its complex management needs and significant resource use [1,2]. The aging population is expected to drive a 46% increase in HF prevalence over the next decade [3], necessitating innovative management strategies to address the growing burden [4].

    eHealth, defined as the use of information and communication technologies to support health services or information, offers the potential to revolutionize HF care by enhancing access to health care services, improving patient outcomes, and reducing the necessity for hospital readmissions [5]. Evidence suggests that eHealth interventions like web-based remote patient management systems or mobile apps can significantly improve morbidity and mortality outcomes in patients with HF, presenting a viable pathway to mitigate the burden of this condition [6]. For instance, the Optimization of the Ambulatory Monitoring for Patients With Heart Failure by Tele-cardiology trial demonstrated that telemonitoring reduced HF hospitalizations in patients with more advanced HF [7].

    However, the effectiveness and potential benefits of eHealth in HF management are contingent upon patient adherence to the technologies used [8]. Adherence in eHealth has been defined by Donkin et al [9] as “the degree to which the user followed the program as it was designed” and is influenced by various technological (eg, front-end design) and individual (eg, digital literacy) factors [10]. Despite the acknowledged importance of adherence, there is a paucity of research exploring this aspect, particularly in the context of the diverse range of eHealth devices available, each with its own set of functionalities [6]. Existing reviews on this topic are scarce and often focus on specific subdomains of eHealth, such as mobile health (mHealth) [11], or examine adherence in chronic diseases more broadly rather than addressing HF specifically [12]. Understanding the factors that influence adherence to eHealth in HF is essential for the development and implementation of effective interventions [13].

    Consequently, our study embarked on a scoping review to collate evidence on adherence to eHealth devices within the patient population with HF. The aim was to provide an overview of the current state of adherence, identify the determinants of successful engagement, and highlight areas requiring further investigation. This endeavor is critical for informing future strategies to enhance the utility and impact of eHealth solutions in managing HF, ultimately contributing to better patient outcomes and more sustainable health care systems.

    Objectives

    The primary objective of our literature analysis was to present an overview of eHealth adherence in HF studies, describing trends in adherence and identifying patient characteristics associated with improved adherence. The secondary objective was to identify eHealth characteristics linked to better adherence by assessing the relationship between adherence and these characteristics. Such insights could inform the design of future eHealth interventions by incorporating characteristics that promote sustained adherence.


    Overview

    The scoping review conducted to synthesize evidence regarding engagement with eHealth followed the guidelines outlined in the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, as detailed in Checklist 1 [14].

    Information Sources

    The search for clinical studies was conducted through the PubMed, CINAHL, and PsycINFO databases, encompassing literature published up to February 2025.

    Search Strategy

    The keywords used in our search were “heart failure,” “adherence,” and “eHealth,” combined with the MeSH (Medical Subject Headings) terms “Heart Failure,” “Patient Compliance,” and “Telemedicine” (see Multimedia Appendix 1 for the exact searching strategy, including search terms used for PubMed, PsycINFO, and CINAHL). Our search strategy was guided by the population, intervention, comparison, and outcome framework.

    • Population: Studies involving patients with HF were included due to the significant impact of HF on health care systems.
    • Intervention: Studies that involved eHealth interventions were included, as they have the potential to improve HF care by providing more accessible and continuous care.
    • Comparison: Clinical studies, with or without a comparison group, were included. This broad inclusion criterion ensured that a wide range of evidence was captured.
    • Outcome: Studies reporting on patient adherence were included, as improved adherence is crucial for the success of eHealth interventions.

    Study Selection

    Studies qualified for inclusion if they were peer-reviewed and investigated the impact of eHealth interventions on patients aged 18 years or older diagnosed with HF across its entire spectrum (HF with reduced ejection fraction, HF with mildly reduced ejection fraction, or HF with preserved ejection fraction). Additionally, the publications needed to report quantitative system use data to enable objective measurement of patient adherence to the eHealth device. The paper had to be written in English. Study protocols, systematic reviews, qualitative studies, meta-analyses, and conference abstracts were excluded.

    Data Collection

    Sourced studies were assessed for full-text eligibility by a dual independent review performed by AJG and HPB-LR. In case of disagreement, a third reviewer (BZ-S) was consulted for the final decision. Data extraction was executed by AJG using a structured extraction form to ensure consistency and completeness. For each study, the following study characteristics were extracted: title of the study, name of the first author, year of publication, number of patients included in the eHealth intervention group, type of eHealth intervention (noninvasive eHealth without wearables, noninvasive eHealth with wearables, invasive eHealth, telephone support, and wearables only), description of the eHealth intervention implemented, duration of intervention, overview of adherence metrics, and any factors associated with adherence as reported by the authors. To enhance accuracy, the extracted data were cross-checked for completeness and correctness, and any uncertainties were resolved through discussion with the coauthors.

    Statistical Analysis

    Descriptive statistics were used to describe the study characteristics of included studies, trends in adherence, and patient characteristics associated with adherence. To assess the relationship between adherence and eHealth characteristics (type of eHealth intervention, duration of intervention, number of patients with eHealth, number of parameters collected by eHealth, and frequency of data collection), a generalized mixed model analysis was performed. As it was expected that definitions and reporting of adherence might vary significantly between studies, adherence was defined for this analysis as the mean percentage of days the intervention was used. Studies that did not report adherence in this manner or did not provide sufficient data to calculate it were excluded from the inferential statistics. A P value of .05 or less was considered to be statistically significant. All analyses were performed in SPSS (version 28; IBM Corp).


    Description of Study Characteristics

    A total of 669 titles were initially identified (Figure 1). Subsequently, title and abstract screening was conducted, resulting in 103 records being deemed potentially relevant. Following a detailed assessment of the full texts for eligibility, 70 papers were ultimately included in the review.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart for study selection.

    Among the 70 studies included, the majority used noninvasive eHealth devices with wearables (n=51), followed by wearables only (n=8), noninvasive eHealth devices without wearables (n=6), invasive eHealth devices (n=3), and telephone support (n=2). Among the studies reporting on wearables, the most commonly used devices were weight scales (n=44), blood pressure monitors (n=32), activity trackers (n=11), and electrocardiogram recorders (n=8). The most frequent combination was a weight scale and a blood pressure monitor (n=31). Median number of patients per study using an eHealth device was 49 (IQR 20‐139, range 9‐3449). Median duration of follow-up was 180 (IQR 84‐360, range 7‐1023) days.

    Description of eHealth Adherence Across Studies

    The reporting of eHealth adherence exhibited substantial variation across studies, including differences in the definitions used. For example, Koehler et al [15] reported adherence as the number of patients with at least 70% of daily data transfers and no break for >30 days (except during hospitalizations), whereas Kastner et al [16] described adherence as a number of days a complete set of patient data was sent. Additionally, adherence documentation was frequently concise and lacked detail. The diversity of devices used and the types of data collected (eg, vital parameters and symptoms) complicated the pooling of results (see Multimedia Appendix 2 [7,15-83] for detailed study characteristics of all included studies).

    In total, 20 (29%) studies explored trends in adherence: 13 reported a decline in adherence over the course of the follow-up, 6 found no change, and 1 study noted improved adherence throughout the study period. For example, Ware et al [17] reported a decline in adherence to a noninvasive eHealth intervention with wearables, defined as the percentage of days all patient data were transmitted, from 81.2% in the first month to 63.1% after 12 months.

    Association Between Adherence and Patient Characteristics

    In 29 (41%) studies, factors influencing eHealth adherence were reported (Table 1). Among these, 7 studies identified a positive association between patient age and adherence, indicating that older patients were more likely to adhere to eHealth device protocols. Conversely, 2 studies reported a negative association between patient age and adherence levels, while 9 studies observed no association. Regarding the impact of gender on adherence, all 10 studies examining this variable found no significant association between gender and eHealth adherence. Furthermore, 9 of 10 studies reported no association between the New York Heart Association (NYHA) functional classification and eHealth adherence, and 1 study found increased adherence among patients exhibiting more severe symptoms. Besides age, NYHA classification, and gender, most studies reported on living status (n=7), educational level (n=5), and race (n=4). Additionally, 31 factors were mentioned once, including loss of interest and mental overload.

    Table 1. Factors associated with adherence.
    Author (year of publication)Study characteristicsInterventionAdherenceFactors reported (direction of association, level of significance)
    Yoon et al (2024) [18]
    • na=39
    • Country: Korea
    • Duration: 1 month
    		App with Bluetooth-enabled wearables
    • App: 80% (mean number of days with logins)
    • Age (−b, sc)
    Kagiyama et al (2024)
    [19]
    • n=77
    • Country: Japan
    • Duration: 6 months (median)
    		Digital tablet with Bluetooth-enabled wearables
    • Overall: 75% (patients with ≥70% of days with self-measurement)
    • Age (+d, s)
    • QoLe measured with MLHFQf (−, s)
    • Genderg, BMI, residence type, past HFh hospitalization, NYHAi, QoL measured with EQ-5Dj
    Brons et al (2023)
    [20]
    • n=150
    • Country: The Netherlands
    • Duration: 12 months
    		e-Vita HF web-based platform with wearables
    • Weight: 74% (patients with 3 or more data transmissions a week for at least 42 weeks within 1 year)
    • Weight, blood pressure, and heart rate combined: 67%
    • HF hospitalizations (−, s)
    • Age, gender, education level, living alone, BMI, smoking, comorbidities, NYHA, LVEFk, duration of HF, self-care
    Prescher et al (2023)
    [21]
    • n=670
    • Country: Germany
    • Duration: 12 months
    		Digital tablet with Bluetooth-enabled wearables
    • Overall: 89.1% (days all parameters collected)
    • Blood pressure monitor: 93.71%; weight scale: 92.4%
    • Electrocardiogram: 92.1%
    • Age (+, s)
    • Cardiovascular hospitalization (−, s)
    • Rural areas (+, s)
    • ICDl (−, s)
    • Gender, DMm, NT-proBNPn, living alone, CRTo, LVEF, NYHA
    Ziacchi et al (2023)
    [22]
    • n=138
    • Country: Italy
    • Duration: 12 months
    		MyTriage app
    • Overall: 49.3% (mean)
    • Age (+, s)
    • NYHA (+, s)
    • School qualification (−, s)
    • Assistance by caregiver in app use (+,s)
    Barbaric et al (2022)
    [23]
    • n=20
    • Country: Canada
    • Duration: 1 month
    		Medly voice app
    • Overall: 73%
    • Age (+)
    • Confidence in using technology (+)
    Sabatier (2022)
    [24]
    • n=659
    • Country: France
    • Duration: 4 months
    		Web-based telehealth platform
    • Overall: 65.7% (mean of anticipated monitoring sessions)
    • Visual analog scale moral score (+, s)
    • Comorbidities, NYHA, LVEF, treatment adherence
    Apergi et al (2021)
    [25]
    • n=47
    • Country: United States
    • Duration: 3 months
    		Amazon’s Alexa+ or Avatar (via tablet)
    • Alexa+: mean use of 35.3 times
    • Avatar: mean use of 37.8 times
    • Age (+, s)
    • Number of HF medications (−, s)
    • Race, household income, or confidence in using technology
    Ploux et al (2021)
    [26]
    • n=51
    • Country: France
    • Duration: 2 months
    		CareLine Solutions app with wearables
    • Before lockdown: 84%
    • During lockdown: 87%
    • Age (+)
    • Confidence in using technology (+)
    Guzman-Clark et al (2021)
    [27]
    • n=3449
    • Country: United States
    • Duration: 12 months
    		Telehealth website with wearables
    • Overall: 57.1% (mean number of days patients logged in)
    • Age (−, s)
    • White race (−, s)
    • Severity of illness (−, s)
    Haynes et al (2021)
    [28]
    • n=292
    • Country: United States
    • Duration: 6 months
    		Monitor with Bluetooth-enabled wearables
    • Overall: 49% (days weight was transmitted)
    • Emergency department visits
    Zisis et al (2021)
    [29]
    • n=10
    • Country: Australia
    • Duration: 2 months
    		HF app (via tablet)
    • Overall: 20% of participants completed ≥70% of the full program, 80% did not engage at all
    • Age, education level, QoL, HF knowledge, self-care, anxiety, cognition
    Radhakrishnan et al (2021)
    [30]
    • n=15
    • Country: United States
    • Duration: 3 months
    		Heart Health Mountain app with digital gaming
    • Weight: 80% (patients transmitted weight data for 5 or more days a week)
    • Use of digital gaming (+, s)
    Sohn et al (2020)
    [31]
    • n=20
    • Country: United States
    • Duration: 6 months
    		FitBit activity tracker with BodyTrace scale and smart pill bottles
    • FitBit: 79.1% (median number of hours activity tracker was worn)
    • Bathroom scale: 59.7% (days scale was used)
    • Smart pill bottle: 2.8% (days bottle was used)
    • SCHFIp confidence subscale scores (−, s) (FitBit)
    • SAQq scores (−, s) (FitBit)
    • SCHFI confidence subscale scores (−, s) (Body scale)
    • Gender, age, race, NYHA, ejection fraction, education, annual income
    Ding et al (2020)
    [32]
    • n=91
    • Country: Australia
    • Duration: 6 months
    		Bluetooth-enabled weight scale
    • Weight: 74% (patients with 4 or more days with data transmission a week)
    • Being away from home (−)
    • Technical issues (−)
    • Hospitalizations and emergency department presentations (−)
    • Being unwell, falling, surgery, and chemotherapy (−)
    Haynes et al (2020)
    [33]
    • n=538
    • Country: United States
    • Duration: 6 months
    		Telehealth device with Bluetooth-enabled wearables
    • Weight: 53.3% (study days with weight transmission)
    • Weekend (−)
    • Winter months (−)
    Haynes et al (2020)
    [34]
    • n=12
    • Country: United States
    • Duration: 34 months (median)
    		CardioMEMS device (sensor in the pulmonary artery), CardioMEMS pillow
    • Overall: 77.6%
    • Participants self-identified as committed, capable, eager to follow instructions, and generally do things well (+)
    Hovland-Tånneryd et al (2019) [35]
    • n=82
    • Country: Sweden
    • Duration: 6 months
    		OPTILOGG device (app with wearables)
    • Overall: 94% (median number of days patients used the eHealth tool)
    • Living in rural areas (+)
    • Age, gender
    Ware et al (2019)
    [17]
    • n=232
    • Country: Canada
    • Duration: 12 months
    		Medly smartphone app with wearables
    • Overall: 73.6% (days patients took all readings)
    • Time device is used (−)
    • Age (+)
    • NYHA, gender
    Rosen et al (2017)
    [36]
    • n=48
    • Country: United States
    • Duration: 6 months
    		Telehealth platform (via tablet) with Bluetooth-enabled wearables
    • Overall: 96% (median number of days, data were transmitted)
    • Gender, race, age, living situation, depression, cognitive ability, risk for readmission
    Siebermair et al (2015)
    [37]
    • n=159
    • Country: Germany
    • Duration: 21 months (mean)
    		CareLink system (handheld telemetry wand for data transmission of implanted devices)
    • Initial data transmission: 76.1% (patients completing transmission)
    • Loss of interest (−)
    • Mental overload (−)
    • Lack of support by the responsible general practitioner (−)
    • Concerns about privacy of relevant health-related device data (−)
    • Doubts regarding the telemonitoring concept (−)
    • Technical problems (−)
    Prescher et al (2014)
    [38]
    • n=354
    • Country: Germany
    • Duration: 26 months (median)
    		App with wearables
    • Overall: 88.9% (patients transmitting at least 1 vital parameter a day)
    • Age, gender, NYHA, LVEF, depression, diabetes, NT-proBNP, BMI, living alone, duration of HF
    Guzman-Clark et al (2013)
    [39]
    • n=248
    • Country: United States
    • Duration: 3 months
    		Health Buddy (portable telemonitoring device)
    • Overall: 54.9 days (mean number of days with response per study days)
    • Primary care (+)
    • Comorbidity burden (−, s)
    • Age, marital status, gender, caregiver available, income, insurance
    Seto et al (2012)
    [84]
    • n=50
    • Country: Canada
    • Duration: 6 months
    		App with Bluetooth-enabled wearables
    • Overall: 70% (patients completing at least 80% of their possible daily readings)
    • Planned absence (+)
    • Equipment failure (+)
    Morak et al (2011)
    [40]
    • n=21
    • Country: Austria
    • Duration: 1 week
    		App with Near Field Communication–enabled wearables
    • Overall: 82.2% (at an expected rate of 13 datasets per patient)
    • Technical problems (−)
    Mortara et al (2009)
    [41]
    • n=301
    • Country: Italy
    • Duration: 12 months
    		Telephone support with wearables
    • Vital signs: 81% (requested data that were transmitted); cardiorespiratory recordings: 92%
    • NYHA, age
    Piette et al (2008)
    [42]
    • n=52
    • Country: United States
    • Duration: 3 months (mean)
    		Touch-tone telephone support
    • Overall: 92% (successful assessment attempts)
    • Age, severity of HF, education
    Clark et al (2007)
    [43]
    • n=79
    • Country: Australia
    • Duration: 12 months
    		Telephone support
    • Overall: 65.8% (patients submitting data once a month or more)
    • Gender, age, living status, weight, NYHA, rural area, comorbidities, HF medication
    de Lusignan et al (2001)
    [44]
    • n=10
    • Country: United Kingdom
    • Duration: 12 months (mean)
    		Video consultation equipment with wearables
    • Weight: 74% (mean number of days data were transmitted)
    • Blood pressure and pulse watch: 90%
    • Planned absence and equipment failure (−) (blood pressure and pulse watch)

    an=number of patients with eHealth intervention.

    b“−” indicates negative association with adherence.

    cs=association statistically significant.

    d“+” indicates positive association with adherence.

    eQoL: quality of life.

    fMLHFQ: Minnesota Living with Heart Failure Questionnaire.

    gFactors in italics format indicate no association with adherence.

    hHF: heart failure.

    iNYHA: New York Heart Association.

    jEQ-5D: EuroQol 5 dimensions questionnaire.

    kLVEF: left ventricular ejection fraction.

    lICD: implantable cardioverter defibrillator.

    mDM: diabetes mellitus.

    nNT-proBNP: N-terminal pro-brain natriuretic peptide.

    oCRT: cardiac resynchronization therapy.

    pSCHFI: Self-Care of Heart Failure Index.

    qSAQ: Seattle Angina Questionnaire.

    Association Between Adherence and eHealth Characteristics

    In 35 (50%) studies, adherence was either reported directly or could be inferred as the mean percentage of days the intervention was used (see Multimedia Appendix 2 for detailed study characteristics of all included studies). For these studies, median adherence was 78% (IQR 61%‐86%; range 31%‐98%). No significant correlations were found between adherence rates and predefined eHealth characteristics (Table 2). Telephone support was not detailed in the included studies, and therefore, was not incorporated into the analysis. In the other 35 (50%) studies, adherence was reported in a different manner. For instance, these studies might report the number of patients using the application or solely providing a percentage without further definitions. Therefore, they were excluded from this analysis.

    Table 2. Correlation of eHealth characteristics and adherence.
    eHealth characteristicsWald chi-square (df=1)P value
    Frequency of data collection1.057.30
    Number of patients with eHealth0.070.79
    Duration of intervention0.002.97
    Number of parameters collected by eHealth0.306.58
    Type of eHealth intervention (reference category: noninvasive eHealth with wearables)
    Noninvasive eHealth without wearables0.332.57
    Invasive eHealth0.053.82
    Wearables only0.376.54

    Principal Findings

    The reporting of adherence of patients with HF with eHealth interventions across the included studies was limited and lacked uniformity, which hindered the identification of influencing factors. Despite this, adherence to eHealth interventions was generally high, though there was a large variation between studies. In addition, it tended to decrease over time.

    As HF is a chronic disease, its treatment requires lifelong therapy. We observed a trend toward decreased eHealth use over time, a finding corroborated by Nelson et al [85], who documented a similar decline in eHealth use among patients with diabetes mellitus. This pattern is also evident in pharmacological HF therapy, where patient adherence decreases over time [86]. A potential explanation is that patient interest may wane, as the duration of an intervention extends. This issue deserves more attention in general, as our results indicated that loss of interest was reported as a factor influencing eHealth adherence in only one of the included studies. To bolster long-term adherence, it might be crucial to tailor eHealth devices to meet the specific needs of patients [87]. The information provided by the device should be suitable for the patient’s cognitive abilities and cultural background. Additionally, the design and user interface should align with the patient’s digital experience and interests. As patients will be using the device for years, updating patient education and self-care information in various formats could enhance patient engagement as well. Moreover, adherence to self-care behaviors might vary depending on the specific task. Physical activity is likely the most feasible to track, as it can be measured passively using a smartwatch or activity tracker, whereas behaviors such as weight monitoring or symptom reporting require active patient input, potentially leading to lower adherence. As evidence suggests that changes in functional status can serve as an early indicator of impending decompensation [88], integrating this parameter more routinely into HF care could enhance disease management.

    A significant obstacle to the implementation of eHealth solutions in health care is the concern that patients may be overwhelmed by the additional workload imposed by the devices, potentially leading to the absence or discontinuation of use [89]. Still, no clear trends indicating lower adherence for devices requiring more interaction (such as those monitoring more parameters or collecting data more frequently) were observed in our study. Moreover, none of the other predefined eHealth characteristics were associated with adherence. This could suggest that the eHealth device itself is not the primary driver of nonadherence. However, our findings must be interpreted with caution, as the analysis lacks statistical power due to the small sample size of included studies. Additionally, the heterogeneity of the included devices further limits the robustness of our conclusions. This needs further clarification, as our results indicated that mental overload was reported in only one of the included studies as a factor influencing patient adherence. Previous studies have highlighted the ease of use of technology as a crucial facilitator for eHealth implementation [90]. Thus, patient adherence may depend less on the workload and more on the device’s accessibility [91], and future devices should focus on an appropriate front-end design to optimize patient engagement. Clearly, these findings must be interpreted with caution, given the limited evidence available.

    In addition to patients’ ability to use eHealth devices, it is essential that health care professionals also trust these technologies. A major concern among health care providers regarding the implementation of eHealth is the apprehension that older patients or those with more severe diseases may not be able to use the technology effectively [92]. However, this perception is not corroborated by our findings, as most studies that reported on these parameters did not find a negative association between adherence and either age or NYHA classification. In fact, adherence even improved with increasing age in a substantial number of studies. Health care professionals play a crucial role in providing the necessary clinical information to tailor the device to the patient’s needs and should be actively involved in the implementation of eHealth devices in HF management. Additionally, these findings might also encourage health care policy makers to extend eHealth implementation to broader patient populations, rather than limiting it to specific patient characteristics, while emphasizing the need to improve long-term commitment to these devices.

    Effective patient engagement is essential for the successful implementation of eHealth solutions in HF management and alleviating the HF burden on health care systems. Yet, clinical studies exploring the impact of eHealth frequently provide minimal information on patient engagement, underscoring a gap in current research methodologies. In addition, the identification of factors influencing adherence is crucial for successful implementation. This should include patients who refuse to use eHealth devices. Future studies should provide a more comprehensive description of patient adherence, including information on the frequency and intensity of eHealth use, as described in the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist [93], or the World Health Organization mHealth evidence reporting and assessment checklist [94]. This will ensure better comparability across studies and enhance understanding of adherence behaviors. Furthermore, more research has to be conducted to study suited strategies for enhancing patient engagement. For example, financial incentives have been shown to improve patient adherence to eHealth devices in HF [45]. A similar effect is hypothesized for gamification, the use of game design elements in nongaming settings, although evidence supporting this remains limited [46]. To advance research in this field, HF specialists should be more actively involved in the development and implementation of eHealth solutions. This requires greater interdisciplinary collaboration, including engagement with patients to assess their needs and with software developers to ensure that eHealth tools align with both patient and clinical requirements. Additionally, educating health care professionals about eHealth possibilities can be highly valuable in fostering adoption and effective use.

    Limitations

    We exclusively used quantitative data to characterize adherence behaviors. While qualitative data from interviews or focus groups could offer deeper insights, our objective was to present trends in adherence as objectively as possible. To enable a meaningful aggregation of the results and quantify the relationships between adherence and eHealth characteristics, outcome measures needed to be comparable. Therefore, we had to exclude nearly 50% (n=35) of the included studies from the inferential statistical analysis, resulting in a small sample size. Due to this small sample size, our study might lack sufficient power to detect a relationship between adherence and eHealth characteristics, thereby increasing the risk of type II errors. The inclusion of studies was restricted to English-language publications to ensure consistency in reporting and interpretation. However, this may have introduced language bias, potentially leading to the exclusion of relevant findings from non-English sources. Nonetheless, as most high-impact papers are published in English, we are confident that the risk of missing substantial data is minimal. Comparing adherence across different device types could have provided valuable guidance for clinical practice. However, this analysis was not feasible due to inconsistencies in adherence reporting, varying adherence definitions across studies, and the small sample sizes within each device category.

    Comparison With Prior Work

    Recent reviews on patient adherence have primarily focused on chronic diseases broadly, with HF often being underrepresented [12]. Furthermore, a recent review by Madujibeya et al [11] specifically addressing adherence to eHealth in HF only included mHealth while neglecting other eHealth categories. Consequently, we offer a more targeted and comprehensive review of adherence data concerning eHealth in HF. Still, their results are comparable to ours, as they also found a lack of consistency in reporting patient adherence and observed trends toward decreased use of eHealth over time.

    Conclusions

    Reporting and definition of patient adherence in HF studies lack completeness and consistency. Overall, trends toward a decrease of eHealth use over time could be identified, and tailoring the devices to meet the patients’ needs might overcome this problem. Future studies should provide a more comprehensive description and definition of adherence to identify factors improving patient compliance with eHealth.

    Acknowledgments

    This work was supported by INTERREG-North-West Europe (grant NWE 702). The funder had no role in the study design, data collection, analysis, interpretation, writing of the manuscript, or the decision to submit for publication.

    Conflicts of Interest

    HPB-LR collaborates with the following digital health companies in European Union–funded projects: Sananet, NL; Orbital Global, UK; Exploris, CH; Nurogames, DE; and Huawei, FI. HPB-LR is the coordinator of the INTERREG-funded Patient Self-Care Using eHealth in Chronic Heart Failure (PASSION-HF) consortium. All other authors declare that they have no conflicts of interest.

    Multimedia Appendix 1

    Searching strategy.

    DOCX File, 12 KB
    Multimedia Appendix 2

    Overview of included studies.

    DOCX File, 37 KB
    Checklist 1

    PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist.

    PDF File, 562 KB
    1. McMurray JJV, Pfeffer MA. Heart failure. Lancet. 2005;365(9474):1877-1889. [CrossRef] [Medline]
    2. Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. Jan 18, 2023;118(17):3272-3287. [CrossRef] [Medline]
    3. Virani SS, Alonso A, Aparicio HJ, et al. Heart Disease and Stroke Statistics-2021 Update: a report from the American Heart Association. Circulation. Feb 23, 2021;143(8):e254-e743. [CrossRef] [Medline]
    4. Conrad N, Judge A, Tran J, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. Feb 10, 2018;391(10120):572-580. [CrossRef] [Medline]
    5. Liu S, Li J, Qu Z, Li R, Liu J. The effectiveness of eHealth self-management interventions in patients with chronic heart failure: protocol for a systematic review and meta-analysis. PLoS ONE. 2022;17(9):e0268446. [CrossRef]
    6. Scholte NTB, Gürgöze MT, Aydin D, et al. Telemonitoring for heart failure: a meta-analysis. Eur Heart J. Aug 14, 2023;44(31):2911-2926. [CrossRef] [Medline]
    7. Galinier M, Roubille F, Berdague P, et al. Telemonitoring versus standard care in heart failure: a randomised multicentre trial. Eur J Heart Fail. Jun 2020;22(6):985-994. [CrossRef] [Medline]
    8. Berry R, Keeling P. Compliance with telemonitoring in heart failure. Are study findings representative of reality?: A narrative literature review. Telemed J E Health. Apr 2022;28(4):467-480. [CrossRef] [Medline]
    9. Donkin L, Christensen H, Naismith SL, Neal B, Hickie IB, Glozier N. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res. Aug 5, 2011;13(3):e52. [CrossRef] [Medline]
    10. Sieverink F, Kelders SM, van Gemert-Pijnen JE. Clarifying the concept of adherence to eHealth technology: systematic review on when usage becomes adherence. J Med Internet Res. Dec 6, 2017;19(12):e402. [CrossRef] [Medline]
    11. Madujibeya I, Lennie T, Aroh A, Chung ML, Moser D. Measures of engagement with mHealth interventions in patients with heart failure: scoping review. JMIR Mhealth Uhealth. Aug 22, 2022;10(8):e35657. [CrossRef] [Medline]
    12. Pham Q, Graham G, Carrion C, et al. A library of analytic indicators to evaluate effective engagement with consumer mHealth apps for chronic conditions: scoping review. JMIR Mhealth Uhealth. Jan 18, 2019;7(1):e11941. [CrossRef] [Medline]
    13. Maeder A, Poultney N, Morgan G, Lippiatt R. Patient compliance in home-based self-care telehealth projects. J Telemed Telecare. Dec 2015;21(8):439-442. [CrossRef] [Medline]
    14. Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 2, 2018;169(7):467-473. [CrossRef] [Medline]
    15. Koehler F, Winkler S, Schieber M, et al. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: the telemedical interventional monitoring in heart failure study. Circulation. May 3, 2011;123(17):1873-1880. [CrossRef] [Medline]
    16. Kastner P, Morak J, Modre R, et al. Innovative telemonitoring system for cardiology: from science to routine operation. Appl Clin Inform. 2010;1(2):165-176. [CrossRef] [Medline]
    17. Ware P, Dorai M, Ross HJ, et al. Patient adherence to a mobile phone-based heart failure telemonitoring program: a longitudinal mixed-methods study. JMIR Mhealth Uhealth. Feb 26, 2019;7(2):e13259. [CrossRef] [Medline]
    18. Yoon M, Lee S, Choi JY, et al. Effectiveness of a smartphone app-based intervention with Bluetooth-connected monitoring devices and a feedback system in heart failure (SMART-HF Trial): randomized controlled trial. J Med Internet Res. Apr 29, 2024;26:e52075. [CrossRef] [Medline]
    19. Kagiyama N, Kasai T, Murata N, et al. Feasibility of self-measurement telemonitoring using a handheld heart sound recorder in patients with heart failure—SELPH multicenter pilot study. J Cardiol. Oct 2024;84(4):266-273. [CrossRef]
    20. Brons M, Ten Klooster I, van Gemert-Pijnen L, et al. Patterns in the use of heart failure telemonitoring: post hoc analysis of the e-Vita heart failure trial. JMIR Cardio. Jan 31, 2023;7:e41248. [CrossRef] [Medline]
    21. Prescher S, Winkler S, Riehle L, et al. Patient reported experience and adherence to remote patient management in chronic heart failure patients: a posthoc analysis of the TIM-HF2 trial. Eur J Cardiovasc Nurs. Apr 12, 2023;22(3):245-253. [CrossRef] [Medline]
    22. Ziacchi M, Molon G, Giudici V, et al. Integration of a smartphone HF-dedicated app in the remote monitoring of heart failure patients with cardiac implantable electronic devices: patient access, acceptance, and adherence to use. J Clin Med. Aug 25, 2023;12(17):5528. [CrossRef] [Medline]
    23. Barbaric A, Munteanu C, Ross H, Cafazzo JA. A voice app design for heart failure self-management: proof-of-concept implementation study. JMIR Form Res. Dec 21, 2022;6(12):e40021. [CrossRef] [Medline]
    24. Sabatier R, Legallois D, Jodar M, et al. Impact of patient engagement in a French telemonitoring programme for heart failure on hospitalization and mortality. ESC Heart Fail. Oct 2022;9(5):2886-2898. [CrossRef] [Medline]
    25. Apergi LA, Bjarnadottir MV, Baras JS, et al. Voice interface technology adoption by patients with heart failure: pilot comparison study. JMIR Mhealth Uhealth. Apr 1, 2021;9(4):e24646. [CrossRef] [Medline]
    26. Ploux S, Strik M, Abu-Alrub S, et al. Remote monitoring of patients with heart failure during the first national lockdown for COVID-19 in France. Eur Heart J Digit Health. Sep 2021;2(3):487-493. [CrossRef] [Medline]
    27. Guzman-Clark J, Farmer MM, Wakefield BJ, et al. Why patients stop using their home telehealth technologies over time: predictors of discontinuation in Veterans with heart failure. Nurs Outlook. 2021;69(2):159-166. [CrossRef] [Medline]
    28. Haynes SC, Tancredi DJ, Tong K, et al. The effect of rehospitalization and emergency department visits on subsequent adherence to weight telemonitoring. J Cardiovasc Nurs. 2021;36(5):482-488. [CrossRef] [Medline]
    29. Zisis G, Carrington MJ, Oldenburg B, et al. An m-Health intervention to improve education, self-management, and outcomes in patients admitted for acute decompensated heart failure: barriers to effective implementation. Eur Heart J Digit Health. Dec 2021;2(4):649-657. [CrossRef] [Medline]
    30. Radhakrishnan K, Julien C, Baranowski T, et al. Feasibility of a sensor-controlled digital game for heart failure self-management: randomized controlled trial. JMIR Serious Games. Nov 8, 2021;9(4):e29044. [CrossRef] [Medline]
    31. Sohn A, Speier W, Lan E, et al. Integrating remote monitoring into heart failure patients’ care regimen: a pilot study. PLoS ONE. 2020;15(11):e0242210. [CrossRef] [Medline]
    32. Ding H, Jayasena R, Chen SH, et al. The effects of telemonitoring on patient compliance with self-management recommendations and outcomes of the innovative telemonitoring enhanced care program for chronic heart failure: randomized controlled trial. J Med Internet Res. Jul 8, 2020;22(7):e17559. [CrossRef] [Medline]
    33. Haynes SC, Tancredi DJ, Tong K, et al. Association of adherence to weight telemonitoring with health care use and death: a secondary analysis of a randomized clinical trial. JAMA Netw Open. Jul 1, 2020;3(7):e2010174. [CrossRef] [Medline]
    34. Haynes SC, Pallin R, Tong K, Henderson S, Romano PS. Understanding adherence to the CardioMEMS pulmonary artery pressure monitoring system for heart failure: a qualitative study. Heart Lung. 2020;49(3):251-258. [CrossRef] [Medline]
    35. Hovland-Tånneryd A, Melin M, Hägglund E, Hagerman I, Persson HE. From randomised controlled trial to real world implementation of a novel home-based heart failure tool: pooled and comparative analyses of two clinical controlled trials. Open Heart. 2019;6(1):e000954. [CrossRef] [Medline]
    36. Rosen D, McCall JD, Primack BA. Telehealth protocol to prevent readmission among high-risk patients with congestive heart failure. Am J Med. Nov 2017;130(11):1326-1330. [CrossRef] [Medline]
    37. Siebermair J, Clauss S, Martens E, et al. Remote monitoring of implantable cardioverter-defibrillators. Problems and implications using a telemonitoring system. Herz. Apr 2015;40 Suppl 2:110-118. [CrossRef] [Medline]
    38. Prescher S, Deckwart O, Koehler K, et al. Will telemonitoring be adopted by patients with chronic heart failure? Dtsch Med Wochenschr. Apr 2014;139(16):829-834. [CrossRef] [Medline]
    39. Guzman-Clark JRS, van Servellen G, Chang B, Mentes J, Hahn TJ. Predictors and outcomes of early adherence to the use of a home telehealth device by older veterans with heart failure. Telemed J E Health. Mar 2013;19(3):217-223. [CrossRef]
    40. Morak J, Kumpusch H, Hayn D, et al. Near Field Communication-based telemonitoring with integrated ECG recordings. Appl Clin Inform. 2011;2(4):481-498. [CrossRef] [Medline]
    41. Mortara A, Pinna GD, Johnson P, et al. Home telemonitoring in heart failure patients: the HHH study (Home or Hospital in Heart Failure). Eur J Heart Fail. Mar 2009;11(3):312-318. [CrossRef] [Medline]
    42. Piette JD, Gregor MA, Share D, et al. Improving heart failure self-management support by actively engaging out-of-home caregivers: results of a feasibility study. Congest Heart Fail. 2008;14(1):12-18. [CrossRef] [Medline]
    43. Clark RA, Yallop JJ, Piterman L, et al. Adherence, adaptation and acceptance of elderly chronic heart failure patients to receiving healthcare via telephone-monitoring. Eur J Heart Fail. Nov 2007;9(11):1104-1111. [CrossRef] [Medline]
    44. de Lusignan S, Wells S, Johnson P, Meredith K, Leatham E. Compliance and effectiveness of 1 year’s home telemonitoring. The report of a pilot study of patients with chronic heart failure. Eur J Heart Fail. Dec 2001;3(6):723-730. [CrossRef] [Medline]
    45. Mohapatra S, Issa M, Ivezic V, et al. Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial. J Am Med Inform Assoc. Jan 1, 2025;32(1):181-192. [CrossRef] [Medline]
    46. Shakoor A, Mohansingh C, van der Boon RMA, Brugts JJ, Schaap J. Gamification and its potential for better engagement in the management of heart failure or quality of care registries: a viewpoint. Curr Heart Fail Rep. Dec 5, 2024;22(1):4. [CrossRef] [Medline]
    47. Athilingam P, Jenkins B, Johansson M, Labrador M. A mobile health intervention to improve self-care in patients with heart failure: pilot randomized control trial. JMIR Cardio. Aug 11, 2017;1(2):e3. [CrossRef] [Medline]
    48. Auton A, Zaman S, Padayachee Y, et al. Smartphone-based remote monitoring for chronic heart failure: mixed methods analysis of user experience from patient and nurse perspectives. JMIR Nurs. Jun 6, 2023;6:e44630. [CrossRef] [Medline]
    49. Blockhaus C, Guelker JE, Feyen L, Bufe A, Seyfarth M, Shin DI. Telemonitoring potential of wearable cardioverter-defibrillators during the follow-up of patients with heart failure. J Cardiovasc Dev Dis. Jun 1, 2022;9(6):175. [CrossRef] [Medline]
    50. Blomqvist A, Bäck M, Klompstra L, Strömberg A, Jaarsma T. Usability and feasibility analysis of an mHealth-tool for supporting physical activity in people with heart failure. BMC Med Inform Decis Mak. Feb 12, 2024;24(1):44. [CrossRef] [Medline]
    51. Blomqvist A, Bäck M, Klompstra L, Strömberg A, Jaarsma T. Testing the recruitment frequency, implementation fidelity, and feasibility of outcomes of the heart failure activity coach study (HEALTHY): pilot randomized controlled trial. JMIR Form Res. Jan 8, 2025;9:e62910. [CrossRef] [Medline]
    52. Boyne JJJ, Vrijhoef HJM, Spreeuwenberg M, et al. Effects of tailored telemonitoring on heart failure patients’ knowledge, self-care, self-efficacy and adherence: a randomized controlled trial. Eur J Cardiovasc Nurs. Jun 2014;13(3):243-252. [CrossRef] [Medline]
    53. Carter J, Swack N, Isselbacher E, Donelan K, Thorndike AN. Feasibility and acceptability of a combined digital platform and community health worker intervention for patients with heart failure: single-arm pilot study. JMIR Cardio. Oct 2, 2023;7:e47818. [CrossRef] [Medline]
    54. Chausiaux O, Williams G, Keyser M, et al. Insights from implementing an AI-enabled peripheral edema monitor in UK-based community settings for therapeutic decision-making. Heart Lung. Nov 2024;68:385. [CrossRef]
    55. Dorsch MP, Farris KB, Rowell BE, Hummel SL, Koelling TM. The effects of the ManageHF4Life mobile app on patients with chronic heart failure: randomized controlled trial. JMIR Mhealth Uhealth. Dec 7, 2021;9(12):e26185. [CrossRef] [Medline]
    56. Eilat-Tsanani S, Golovner M, Marcus O, et al. Evaluation of telehealth service for patients with congestive heart failure in the north of Israel. Eur J Cardiovasc Nurs. Apr 2016;15(3):e78-e84. [CrossRef] [Medline]
    57. Evans J, Papadopoulos A, Silvers CT, et al. Remote health monitoring for older adults and those with heart failure: adherence and system usability. Telemed J E Health. Jun 2016;22(6):480-488. [CrossRef] [Medline]
    58. Gardner CL, Flanagan MC, Franklin C, et al. Electronic physiologic and subjective data acquisition in home-dwelling heart failure patients: an assessment of patient use and perception of usability. Int J Med Inform. Sep 2016;93:42-48. [CrossRef] [Medline]
    59. Guo X, Gu X, Jiang J, et al. A hospital-community-family-based telehealth program for patients with chronic heart failure: single-arm, prospective feasibility study. JMIR Mhealth Uhealth. Dec 13, 2019;7(12):e13229. [CrossRef] [Medline]
    60. Ho K, Novak Lauscher H, Cordeiro J, et al. Testing the feasibility of sensor-based home health monitoring (TEC4Home) to support the convalescence of patients with heart failure: pre-post study. JMIR Form Res. Jun 3, 2021;5(6):e24509. [CrossRef] [Medline]
    61. Johnston W, Keogh A, Dickson J, et al. Human-centered design of a digital health tool to promote effective self-care in patients with heart failure: mixed methods study. JMIR Form Res. May 10, 2022;6(5):e34257. [CrossRef] [Medline]
    62. Keogh A, Brennan C, Johnston W, et al. Six-month pilot testing of a digital health tool to support effective self-care in people with heart failure: mixed methods study. JMIR Form Res. Mar 1, 2024;8:e52442. [CrossRef] [Medline]
    63. Kitsiou S, Gerber BS, Buchholz SW, Kansal MM, Sun J, Pressler SJ. Patient-centered mHealth intervention to improve self-care in patients with chronic heart failure: phase 1 randomized controlled trial. J Med Internet Res. Jan 15, 2025;27:e55586. [CrossRef] [Medline]
    64. Ledwidge MT, O’Hanlon R, Lalor L, et al. Can individualized weight monitoring using the HeartPhone algorithm improve sensitivity for clinical deterioration of heart failure? Eur J Heart Fail. Apr 2013;15(4):447-455. [CrossRef] [Medline]
    65. Lieback A, Proff J, Wessel K, Fleck E, Götze S. Remote monitoring of heart failure patients using implantable cardiac pacing devices and external sensors: results of the Insight-HF study. Clin Res Cardiol. Feb 2012;101(2):101-107. [CrossRef] [Medline]
    66. Lloyd T, Buck H, Foy A, et al. The Penn State Heart Assistant: a pilot study of a web-based intervention to improve self-care of heart failure patients. Health Informatics J. Jun 2019;25(2):292-303. [CrossRef] [Medline]
    67. Marier-Tétrault E, Bebawi E, Béchard S, et al. Remote patient monitoring and digital therapeutics enhancing the continuum of care in heart failure: nonrandomized pilot study. JMIR Form Res. Nov 6, 2024;8:e53444. [CrossRef] [Medline]
    68. Morgan JM, Kitt S, Gill J, et al. Remote management of heart failure using implantable electronic devices. Eur Heart J. Aug 7, 2017;38(30):2352-2360. [CrossRef] [Medline]
    69. Murugappan T, Raut R. Enhancing home monitoring of patients with heart failure: primary care experience with an autonomous foot-measuring device. Home Health Care Manag Pract. Feb 2024;36(1):31-39. [CrossRef]
    70. Nederend M, Zandstra TE, Kiès P, et al. Potential of eHealth smart technology in optimization and monitoring of heart failure treatment in adults with systemic right ventricular failure. Eur Heart J Digit Health. Jun 2021;2(2):215-223. [CrossRef] [Medline]
    71. Nouryan CN, Morahan S, Pecinka K, et al. Home telemonitoring of community-dwelling heart failure patients after home care discharge. Telemed J E Health. Jun 2019;25(6):447-454. [CrossRef] [Medline]
    72. Petrie S, Simard A, Innes E, et al. Bringing care close to home: remote management of heart failure in partnership with Indigenous communities in Northern Ontario, Canada. CJC Open. Dec 2024;6(12):1423-1433. [CrossRef] [Medline]
    73. Rao VN, Kaltenbach LA, Granger BB, et al. The association of digital health application use with heart failure care and outcomes: insights from CONNECT-HF. J Card Fail. Oct 2022;28(10):1487-1496. [CrossRef] [Medline]
    74. Skov Schacksen C, Dyrvig AK, Henneberg NC, et al. Patient-reported outcomes from patients with heart failure participating in the future patient telerehabilitation program: data from the intervention arm of a randomized controlled trial. JMIR Cardio. Jul 2, 2021;5(2):e26544. [CrossRef] [Medline]
    75. Scherrenberg M, Leenen JP, van der Velde AE, et al. Bringing the hospital to home: patient-reported outcome measures of a digital health-supported home hospitalisation platform to support hospital care at home for heart failure patients. Digit Health. 2023;9:20552076231152178. [CrossRef] [Medline]
    76. Schmaderer MS, Struwe L, Loecker C, et al. Feasibility, acceptability, and intervention description of a mobile health intervention in patients with heart failure. J Cardiovasc Nurs. 2023;38(5):481-491. [CrossRef] [Medline]
    77. Smeets CJ, Storms V, Vandervoort PM, et al. A novel intelligent two-way communication system for remote heart failure medication uptitration (the CardioCoach Study): randomized controlled feasibility trial. JMIR Cardio. Apr 4, 2018;2(1):e8. [CrossRef] [Medline]
    78. Triantafyllidis A, Velardo C, Chantler T, et al. A personalised mobile-based home monitoring system for heart failure: the SUPPORT-HF Study. Int J Med Inform. Oct 2015;84(10):743-753. [CrossRef] [Medline]
    79. Vuorinen AL, Leppänen J, Kaijanranta H, et al. Use of home telemonitoring to support multidisciplinary care of heart failure patients in Finland: randomized controlled trial. J Med Internet Res. Dec 11, 2014;16(12):e282. [CrossRef] [Medline]
    80. Wakefield BJ, Holman JE, Ray A, et al. Outcomes of a home telehealth intervention for patients with heart failure. J Telemed Telecare. 2009;15(1):46-50. [CrossRef] [Medline]
    81. Wei KS, Ibrahim NE, Kumar AA, et al. Habits Heart app for patient engagement in heart failure management: pilot feasibility randomized trial. JMIR Mhealth Uhealth. Jan 20, 2021;9(1):e19465. [CrossRef] [Medline]
    82. Yokota T, Fukushima A, Tsuchihashi-Makaya M, et al. The AppCare-HF randomized clinical trial: a feasibility study of a novel self-care support mobile app for individuals with chronic heart failure. Eur Heart J Digit Health. Aug 2023;4(4):325-336. [CrossRef] [Medline]
    83. Zan S, Agboola S, Moore SA, Parks KA, Kvedar JC, Jethwani K. Patient engagement with a mobile web-based telemonitoring system for heart failure self-management: a pilot study. JMIR Mhealth Uhealth. Apr 1, 2015;3(2):e33. [CrossRef] [Medline]
    84. Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Mobile phone-based telemonitoring for heart failure management: a randomized controlled trial. J Med Internet Res. Feb 16, 2012;14(1):e31. [CrossRef] [Medline]
    85. Nelson LA, Coston TD, Cherrington AL, Osborn CY. Patterns of user engagement with mobile- and web-delivered self-care interventions for adults with T2DM: a review of the literature. Curr Diab Rep. Jul 2016;16(7):66. [CrossRef] [Medline]
    86. Juarez DT, Williams AE, Chen C, et al. Factors affecting medication adherence trajectories for patients with heart failure. Am J Manag Care. Mar 1, 2015;21(3):e197-e205. [Medline]
    87. Cotter AP, Durant N, Agne AA, Cherrington AL. Internet interventions to support lifestyle modification for diabetes management: a systematic review of the evidence. J Diabetes Complications. 2014;28(2):243-251. [CrossRef] [Medline]
    88. Gingele AJ, Brandts L, Vossen K, Knackstedt C, Boyne J, Brunner-La Rocca HP. Prognostic value of signs and symptoms in heart failure patients using remote telemonitoring. J Telemed Telecare. Jan 2024;30(1):180-185. [CrossRef] [Medline]
    89. Mileski M, Kruse CS, Catalani J, Haderer T. Adopting telemedicine for the self-management of hypertension: systematic review. JMIR Med Inform. Oct 24, 2017;5(4):e41. [CrossRef] [Medline]
    90. Schreiweis B, Pobiruchin M, Strotbaum V, Suleder J, Wiesner M, Bergh B. Barriers and facilitators to the implementation of eHealth services: systematic literature analysis. J Med Internet Res. Nov 22, 2019;21(11):e14197. [CrossRef] [Medline]
    91. Barbaric A, Munteanu C, Ross H, Cafazzo JA. Design of a patient voice app experience for heart failure management: usability study. JMIR Form Res. Dec 6, 2022;6(12):e41628. [CrossRef] [Medline]
    92. Cohen Rodrigues TR, de Buisonjé DR, Keesman M, et al. Facilitators of and barriers to lifestyle support and eHealth solutions: interview study among health care professionals working in cardiac care. J Med Internet Res. Oct 15, 2021;23(10):e25646. [CrossRef] [Medline]
    93. Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. Dec 31, 2011;13(4):e126. [CrossRef] [Medline]
    94. Agarwal S, LeFevre AE, Lee J, et al. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ. Mar 17, 2016;352:i1174. [CrossRef] [Medline]

    ‎
    CONSORT-EHEALTH: Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth
    HF: heart failure
    MeSH: Medical Subject Headings
    mHealth: mobile health
    NYHA: New York Heart Association
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

    Edited by Michael Dorsch; submitted 19.06.24; peer-reviewed by Adrijana Svenšek, Charles Moss, Guenka Petrova, Kavita Radhakrishnan; final revised version received 25.03.25; accepted 25.04.25; published 27.06.25.

    Copyright

    © Arno Joachim Gingele, Bianca Steiner, Bettina Zippel-Schultz, Hans-Peter Brunner-La Rocca. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 27.6.2025.

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