Effectiveness of Telehealth Care on Self-care Behaviours in Heart Failure Patients: Systematic Review and Meta-Analysis

Abstract

Background

Heart failure (HF) is a chronic condition requiring ongoing management. Telehealth may improve HF outcomes. This review examines effects on complications, rehospitalizations, costs, and mortality. It aims to inform guidelines and policies. The objective is to evaluate and synthesize evidence about the effectiveness of telehealth interventions in improving self-care behaviours among HF patients.

Method

A systematic review and meta-analysis of randomized controlled trials (RCTs) from PubMed, CINHAL, Web of Science, and manual search through Google Scholar from 2016 until March 2025. Continuous data were pooled using the standardized mean difference. ROB 2 criteria assessed bias risk, while JBI evaluated the evidence level. This review protocol has been registered in PROSPERO.

Results

Ten studies were included, totaling 2,128 patients. A meta-analysis showed improved self-care behaviours across six studies using the European Self-care Scale. The Standardized Mean Difference was significant between intervention and usual care groups (SMD = 1.075, t = 3.637, p < 0.001; 95% CI [0.495, 1.655]). This indicated that interventions were associated with a very large effect size and almost certainly a clinically meaningful improvement in self-care behaviours compared to the control group. However, there was no difference in self-care behaviours across the Management, Maintenance, and Confidence subscales of the Self-care Heart Failure Index between groups.

Discussion

This meta-analysis found that Telehealth care significantly improved self-care in HF patients across 6 studies, as measured by the European Self-care Measure. However, no differences were noted in four studies utilizing the heart failure index. Future studies should have larger sample sizes and longer follow-ups to evaluate telehealth care’s effectiveness on self-care behaviours in HF patients. The findings of this review regarding telehealth interventions would empower nurses’ ongoing health coaching and enhance the quality and accessibility of patient care.

Keywords

effectiveness telehealth intervention self-care heart failure systematic review

Introduction

Heart failure (HF) is a chronic, irreversible disease that reduces function and quality of life (Negarandeh et al., 2021). About 64 million people globally are affected (Groenewegen et al., 2020), representing 1%–3% of adults worldwide (Savarese et al., 2022). HF is common in developed countries with tremendous economic impact on healthcare systems due to high morbidity, mortality, and rehospitalization (Bakogiannis et al., 2021).

HF patients require continuous management (Heidenreich et al., 2022). Goals include better self-care, improved quality of life, fewer complications, and fewer hospitalizations. Managing HF is challenging, especially for patients who must perform confusing and often time-consuming self-care practices (Bakogiannis et al., 2021).

Self-care is defined as the process of taking charge of one’s health by regulating health factors, using strategies such as tracking fluid intake and weighing themselves, increasing physical activity to reduce risk of illness, and facilitating well-being (Riegel et al., 2022). However, sustaining a commitment to the HF lifestyle in the long term will be challenging (Ware et al., 2022). Achieving a commitment to this requires consistent intervention and follow-up from the multidisciplinary health care team.

Telehealth refers to the use of a technology-driven virtual platform to deliver diverse health information, prevention strategies, monitoring processes, and long-distance medical services (Catalyst, 2021). Moreover, it encompasses education for providers and patients, as well as self-care delivered through digital telecommunications technologies, such as mHealth (mobile health), audiovisual communication tools, digital imaging, and remote patient management (RPM) (Catalyst, 2021).

Also, several studies in the literature have shown that remote management is widely used and has yielded promising results in improving self-care behaviours, particularly when patients are involved (Liu et al., 2022; Nick et al., 2021). Effective self-care requires ongoing symptom management and sufficient patient knowledge. Telehealth provides an effective, lower-cost way for patients and healthcare providers to access and deliver high-quality care remotely and to improve self-care behaviours (Gajarawala & Pelkowski, 2021). For example, it can minimise the need for in-person check-up visits and simplifies the monitoring of discharged patients and supports their health education and follow-up (Haleem et al., 2021). Furthermore, following the COVID-19 pandemic, the implementation of telehealth proved beneficial, greatly enhancing the care of HF patients. This transition was prompted by the need to ensure ongoing care while reducing patient exposure (Shaver, 2022).

A growing number of studies have investigated telehealth technologies and the growing interest in understanding how they can improve self-care behaviours among HF patients (Leng Chow et al., 2020; Ware et al., 2022). Considering that much of the literature has been updated, addresses all-cause mortality and hospitalization (Masotta et al., 2024; Wang et al., 2024). However, there is a lack of recent systematic reviews and meta-analyses that evaluate the effectiveness of telehealth care specifically on self-care behaviours in HF patients. Such reviews are crucial for integrating diverse evidence into a unified summary (Martinez et al., 2025).

This review could illuminate what is understood and what remains unclear about telehealth’s effectiveness by assessing whether it minimizes complications in HF patients. Additionally, systematic reviews and meta-analyses help inform clinical guidelines and health policy on telehealth and its relevance to the HF population (Dai et al., 2023). Therefore, this systematic review and meta-analysis aimed to evaluate and combine evidence from published (RCTs) regarding the effectiveness of telehealth care on self-care behaviours in HF patients. In this regard, the main research question is: Does a telehealth intervention effectively improve self-care behaviours in HF patients compared with usual care?

Method

Design

A systematic review with meta-analysis was conducted, and reported followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist (Page et al., 2021). This review has been registered as a protocol in PROSPERO international database (Sallam et al., 2025). The Institutional Review Board (IRB) was not required, as no human data were collected.

Search Strategy

Primarily, a pilot MeSH search identified relevant key terms. The search strategy was based on recent literature on self-care in HF and telehealth to ensure rigor. The following keywords that retrieved were ‘heart failure’ ‘telehealth’ ‘mobile health’ ‘telemedicine’ ‘remote health’ ‘self-care’ ‘self-management’. Then a comprehensive and systematic electronic search was conducted for open access, written in English, available in the full text and abstract and RCTs articles through several databases: PubMed, CINHAL, and Web of Science. The Boolean operators “AND” and “OR” were used to develop search string: (((effect) AND (Telehealth OR phone call OR video calls OR messaging OR mHealth OR wearable device OR telemedicine) AND (self-care OR self-management) AND (heart failure) NOT (telemonitoring). Studies published from 2016 to March 2025 were included, a 10-year time frame was chosen to encompass recent telehealth interventions and the rapid growth of digital health technologies. The sample search is in supplemental file 1. Complementary searches used Google Scholar and the reference lists of the studies, which were managed with EndNote 20.

Eligibility Criteria

Inclusion and exclusion criteria were performed following PICO framework (Table 1). The search strategies targeting studies identifying (a) populations of adult 18+ outpatients with heart failure; (b) telehealth care interventions (telemedicine, telephone-delivered care, smartphone apps, text messages, non-invasive methods e.g., smartwatches, web-based platforms, and video calls) were included, (c) the comparator or control group include usual care or standard care given to the patients with outcomes focused on self-care behaviours improvement, (d) randomised studies published within ten years were included. The exclusion criteria include (a) conference and opinion papers; (b) telerehabilitation, (c) interventions with implemented/wireless devices, (d) studies that focused on telemonitoring only (measuring physiological parameters only without healthcare professionals’ virtual consultation or education), and (e) studies related telehealth but no self-care measurement.

Table 1.

Inclusion and Exclusion Criteria

Inclusion criteria	Exclusion criteria
• Populations of adult 18+ outpatients with heart failure.	• Conference and opinion papers.
• Telehealth care interventions (telemedicine, telephone-delivered care, smartphone apps, text messages, non-invasive methods e.g., smartwatches, web-based platforms, and video calls) were included.	• Telerehabilitation.
• The comparator or control group include usual care or standard care given to the patients with outcomes focused on self-care behaviours improvement.	• Interventions with implemented/wireless devices.
• Randomised studies published within ten years were included.	• Studies that focused on telemonitoring only (measuring physiological parameters only without healthcare professionals’ virtual consultation or education) were excluded.
	• Studies related telehealth but no self-care measurement.

Selection of Studies

All relevant studies were entered into the Rayyan platform (https://new.rayyan.ai), and duplicate studies were removed manually (Ouzzani et al., 2016). Two authors independently evaluated titles and abstracts of all studies obtained from the search. The abstracts were then manually categorized and labelled as “eligible” or “maybe” by two authors. Abstracts that were unclear or potentially eligible underwent additional review. Subsequently, the authors obtained the full texts of the studies and determined which to include in the paper. The first and second authors also documented the reasons for the studies’ ineligibility. Disagreements were resolved by a third author until a consensus was reached.

Data Extraction

The first and second authors independently extracted data using a structured form, collecting details like author, year, country, intervention, design, sample size, duration, follow-up, instruments, outcomes, and findings. Disagreements were resolved through discussion or with a third author.

Evaluation of Methodological Quality

The first and second authors independently assessed study quality and bias using JBI appraisal tools (Barker et al., 2023) and the ROB 2 criteria (Sterne et al., 2019). RCT quality was judged across five domains: the randomization process, deviation from the intended intervention, missing outcome data, outcome measurement, and selection of reported results. The risk of bias was rated as high, moderate, or low, and the level of evidence as low, medium, or high (Table 2).

Table 2.

Characteristics of the Included Studies

Author/publication date/country	Study design	Aim	Intervention	Measurement	Intervention and control group sample size	Follow-upperiod	Outcomes	Level of the evidence
(Athilingam et al., 2017) (USA)	RCT	To evaluate the feasibility of a new mobile app (HeartMapp) designed to promote self-care behaviors and quality of life of patients with HF, as well as to calculate effect sizes for sample size for a full study.	Mobile App “HeartMapp”	(HFSCI)	HeartMap Intervention Group mobile app (n=7), Heart Failure Control Group (HF) education (n=6)	30 days	Self-care behaviors (management) have changed significantly among intervention group (P=.01), but not significant with confidence.	Medium (62%)
Deckwart et al. (2023)(Germany)	RCT, parallel group, unmasked, multicentre trial.	To examine the effects of remote patient management (RPM) used in the TIM-HF2 trial on heart failure-precise self-care.	Remote Patient Management (RPM)	(EHFSCBS-9).	Usual intervention care (UC) plus Remote Patient Management (RPM) (n=667), HF Usual Control care (UC) only (n=654)	12 months	Self-care behaviors have significantly increased (P < 0.001)	High (77%)
Choi et al. (2023)(South Korea)	RCT un-blinded	To create a mobile self-management app program for HF patients and to recognise the impact of it.	Mobile App “Heart Failure-Smart Life”	(EHFSCBS-12)	Intervention Heart Failure-Smart Life mobile app (n=36), Control Usual care (n=38)	3 months	Self-care behaviors have significantly increased (p < 0.001)	Medium (62%)
Negarandeh et al. (2019)(Iran)	RCT	To evaluate the effects of monitoring over telephone (tele-monitoring) on self-care behaviors and read- mission of patients with HF after discharge	Telephone-Based Interventions	(EHFSCBS-12)	Received intervention tailored tele-monitoring intervention (n=35), Received control usual care (n= 33)	8 weeks	self-care behaviors have improved significantly among intervention group (p < 0.001)	High (77%)
Dorsch et al. (2021)(USA)	RCT	To assess the effectiveness of boosted self-management through a mobile app intervention on health-related quality of life, self-management, and HF readmissions.	Mobile App “ManageHF4Life” with a Fitbit.	Self-Care Heart Failure Index (SCHFI).	Intervention Group (n=36), Control group (n=35)	12 weeks	Self-care behaviors not significant (management, confidence, maintenance)	Medium (62%)
Yanicelli et al. (2021)(Argentina)	RCT	To regulate the efficiency of the use of a home telemonitoring system to advance the self-care and treatment adherence of HF patient	Remote Patient Management (RPM)	European Heart Failure Self-care 12 items	Intervention group arm (n=15), Control group, usual care arm (n=15)	90 days	Remote Patient Management showed enhanced self-care in the intervention group (p= 0.03)	High (69%)
Piotrowicz et al. (2024)(Poland)	Prospective, randomized, open-label, and controlled with two parallel groups	To evaluate the effects of a innovative model of medical telecare on HRQoL and self-care in patients with HF (the AMULET study).	Combining Multiple Methods: Telecare and standard care patient clinical assessments with cardiologist telemedical education.	12-item standardized European Heart Failure Self-care Behavior Scale (EHFScBS-12).	Intervention group (n= 223), Control group (n=211)	12-month follow-up visit	decrease was noted in the telecare group but not significant difference among both groups.	Low (54%)
Clays et al. (2021) (Belgium, Italy)	A randomized controlled proof-of-concept trial	To evaluate the potential effectiveness of Heart Man—a mobile personal health system providing decisional support for the management of congestive heart failure (CHF)—on health-related quality of life (HRQoL), self-management, exercise capacity, illness perception, and mental and sexual health.	A Mobile Application “HeartMan” And Telephone Assistance	The Self-Care of Heart Failure Index (SCHFI) was used to measure self-care maintenance (10 items) and self-care confidence (6 items.	Intervention group (n=34), Control group (n=22)	6 months	Differences were not significant in self-care behaviors among both groups (maintenance, confidence)	High (77%)
Kitsiou et al. (2025)(USA)	Randomized Controlled Trial.	To explore the feasibility, appropriateness, safety, and primary efficacy of a patient-centered mHealth intervention (iCardia4HF) that combined 3 consumer mHealth apps and devices.	Combining Multiple Methods: “iCardia4HF” plus usual care. And mHealth Applications	Self-care Heart Failure Index (SCHFI)	Intervention group (n=13), Control group (n=14)	8 weeks	No significance difference at 8 weeks (maintenance, management). The SCHFI subscale scores favoured the intervention over the control group maintenance (Cohen d=0.19) management (Cohen d=0.25)	High (92%)
Araujo De Oliveira et al. (2017)(Brazil)	RCT	To investigate self-care and knowledge in patients with HF who were supervised telephonically, and to examine the relationship of knowledge with self-care	Telephone-Based Interventions	EHFScB-12	Intervention group (n=19), Control group (n=17)	4 months	Self-care behaviors have significantly improved p=0. 04).	High (77%)

Data Analysis and Synthesis

This meta-analysis was performed using MedCalc for Windows version 15.0, developed by MedCalc Software, Ostend, Belgium. The results are conveyed in a narrative format, supplemented by tables and forest plots. The meta-analysis was conducted on four continuous variables: the self-care behaviour scores from the European Heart Failure Self-care Behaviour Scale (EHFScBS) (Jaarsma et al., 2003, 2009), as well as three sub-scales (management, maintenance, and confidence) from the Self-Care Heart Failure Index (SCHFI)(Riegel et al., 2019). Since the outcome variables are quantitative, the Standardized Mean Difference (SMD) served as a summary pooled statistic, with the following thresholds indicating effect size as recommended by Cohen (1988). Both fixed and random effect models were applied to derive the pooled estimates of SMD based on the heterogeneity. The SMD was assessed for statistical significance using the student’s t-test for independent samples (Wadhwa & Marappa-Ganeshan, 2023). Heterogeneity among the pooled data was assessed using Cochran’s Q and I² was used to assess the percentage of total variation throughout the studies entered in this analysis (Choi and Kang, 2025). A threshold of I² > 50% is often used to remove studies that were likely to have high levels of heterogeneity in the effect sizes (JPTTJ et al., 2024). Instead, cautious interpretation of the pooled results was used. This approach emphasizes the completeness of the evidence synthesis over the statistical homogeneity of the pooled estimate. Publication bias was assessed using Egger’s and Begg’s tests, funnel plots, and a p-value threshold of 0.05 (Duval & Tweedie, 2000; Egger et al., 1997).

Assessing Certainty in the Findings

The GRADE (Grading of Recommendations, Assessment, Development and Evaluation) tool systematically enhances transparency and rigor in evidence assessment, reliability of evidence, and the strength of healthcare recommendations, using four certainty levels: high, moderate, low, and very low. High certainty means strong studies with consistent results; very low certainty signals major flaws or bias. Also, the Summary of Findings (SoF) table presents evidence quality, absolute and relative risks in both groups, and study limitations, directness, consistency, heterogeneity, precision, and risk of publication bias. Outcomes include self-care in HF patients as measured by EHFScBS and SCHFI (Prasad, 2024).

Results

Search Outcomes

The scientific databases have been searched; 740 potentially relevant records have been identified. After removing duplicates and references lacking abstracts, 122 titles and abstracts were evaluated for eligibility. Subsequently, 23 full texts were screened, and 7 of these were included. The additional three studies from Google scholar and reference list were included. PRISMA flow chart (Figure 1) presents the search outcomes.

Figure 1.

Prisma flowchart

Characteristics of Included Studies

The review includes ten studies (Table 2) that were published from 2017 to 2025 and were conducted in various countries, including the USA (n = 3), Italy and Belgium (n = 1), Germany (n = 1), Poland (n = 1), South Korea (n = 1), Iran (n = 1), Brazil (n = 1), and Argentina (n = 1). All studies were randomized controlled trials (n = 10). A total of 2,128 patients with HF participated across these studies, with sample sizes varying from 6 to 677 participants. Furthermore, two distinct scales were employed to evaluate self-care practices: the EHFScBS, with some studies utilizing 12-item while others used 9-item across 6 studies (Araujo De Oliveira et al., 2017; Choi et al., 2023; Deckwart et al., 2023; Negarandeh et al., 2019; Piotrowicz et al., 2024; Yanicelli et al., 2021), and SCHFI across 4 studies (Athilingam et al., 2017; Clays et al., 2021; Dorsch et al., 2021; Kitsiou et al., 2025).

Most studies had a greater majority of males compared to females consistent with the higher incidence of HF in males. For example, Deckwart et al. (2023) reported a 70% male participation in the intervention group and, Piotrowicz et al. (2024) reported a greater ratio (79 % male). Nonetheless, Athilingam et al. (2017) and Kitsiou et al. (2025), exhibited a more equal gender distribution or even had a slightly higher number of women in particular groups.

The average age of participants in the studies typically fell between 53 and 70 years, which is representative of the usual demographic for HF patients. Certain studies, like Athilingam et al. (2017), indicated a lower average age of 53 years, whereas others, such as Deckwart et al. (2023) and Choi et al. (2023), featured older participants with average ages close to 70 years. A few studies, like Yanicelli et al. (2021), mentioned only that participants were over 18 years old, but the majority concentrated on middle-aged and older individuals, consistent with the increased occurrence of HF in the older population.

Methodological Quality

The findings from the risk of bias evaluation ROB 2 are presented in Figure 2. Among the studies included in the meta-analysis, six studies (60%) exhibited some concerns regarding overall bias (Araujo De Oliveira et al., 2017; Choi et al., 2023; Clays et al., 2021; Dorsch et al., 2021; Negarandeh et al., 2019; Yanicelli et al., 2021), while two studies (20%) showed low risk (Deckwart et al., 2023; Kitsiou et al., 2025), and two studies (20%) were categorized as high bias (Athilingam et al., 2017; Piotrowicz et al., 2024). And the level of evidence on critical appraisal among these studies ranged from medium to high quality. Four studies achieved medium quality from 54% to 62% (Athilingam et al., 2017; Choi et al., 2023; Dorsch et al., 2021; Piotrowicz et al., 2024), and six studies between 77% to 92% indicating high quality (Araujo De Oliveira et al., 2017; Clays et al., 2021; Deckwart et al., 2023; Kitsiou et al., 2025; Negarandeh et al., 2019; Yanicelli et al., 2021).

Figure 2.

Quality assessment risk of bias domain for individual included study

Assessing Certainty in the Findings

The evidence was summarized using the GRADEPro methodology (Schünemann et al., 2011), with quality ranging from a very low to moderate. EHFScBS was downgraded by three points due to risk bias, inconsistency, and imprecision. SCHFI maintenance was downgraded by one point for imprecision. SCHFI management dropped by two points due to serious inconsistency and imprecision. Lastly, SCHFI confidence was downgraded by three points for risk bias, inconsistency, and imprecision.

Effect of Intervention on Self-care Behaviours

The meta-analysis assessed self-care by pooling mean differences between telehealth and usual care groups using EHFScBS and SCHFI scales. This meta-analysis focused on SCHFI scores and subgroups “management, maintenance, confidence”, using fixed- and random-effect models to obtain combined estimates that accounted for heterogeneity. Outcome scores reflect changes from baseline to post-intervention (Supplemental file 2). In summary, the analysis revealed that studies used the EHFScBS scale, self-care behaviour scores were significantly higher in the intervention group than in the control group (SMD=1.075, t=3.637, p<0.001) (95% CI [0.495,1.655]). However, studies using SCHFI revealed no statistically significant difference in self-care behaviours among the intervention group across the three-subscale management, Maintenance, and confidence, p= 0.059, p= 0.568, and p= 0.849, respectively.

European Heart Failure Self-care Behaviour Scale

The total number of participants involved across the six studies for this analysis was 1963 (IG=995 & CG=968). The findings revealed a highly statistically significant difference in the pooled SMD. Cochran’s Q value was significant (Q=104.23, DF=5, p<0.0001), with an I² value of 95.20%, indicating substantial and statistically significant heterogeneity. Therefore, the pooled SMD calculated using the random effects model was employed to ascertain the significant difference in mean self-care behaviour scores between both groups (SMD=1.075, t=3.637, p<0.001) (95% CI [0.495,1.655]); Overall, self-care behaviour scores were significantly higher in the intervention group than in the control group. GRADE indicates low-quality evidence.

The forest plot (SMD) shows each study’s effect size and the overall effect size, with 95% confidence intervals based on random effects models. Egger’s test for publication bias yielded a p-value of 0.1448, indicating no significant publication bias. The funnel plot (Figure 3) displays the distribution of studies.

Figure 3.

(A)Forest plot of European heart failure self-care behavior scale; (B) funnel plot of European heart failure self-care behavior scale

Self-Care Heart Failure Index: Sub-Scale (Management)

The analysis included 111 participants across the three studies (IG=56, CG=55). The (SMD) was positive, indicating that the mean management scores were higher in the intervention group compared to the control group (Athilingam et al., 2017; Dorsch et al., 2021; Kitsiou et al., 2025). The findings showed no statistically significant variation in the pooled (SMD). Cochran’s Q statistic was significant (Q=8.2645, DF=2, p=0.0160). The high I² value (75.80%) indicated substantial heterogeneity among the three studies, which was statistically significant. As a result, the pooled SMD from the random-effects model was used. This led to the conclusion that there was no significant difference in the mean management scores between the two groups (SMD=0.906, t=1.908, p=0.059) (95% CI [-0.0349, 1.846]). However, the overall effect indicated higher mean management scores in the intervention group than in the control group. A GRADE rating showed low-quality evidence. The associated forest plot illustrated the effect size (SMD). Publication bias was evaluated using Egger’s test, which yielded a p-value of 0.4816. This indicated non-statistical significance regarding the absence of publication bias. The funnel plot showed the arrangement of studies within the funnel (Figure 4).

Figure 4.

Forest and funnel plot of self-care heart failure index: Sub-scale (A) management; (B) maintenance and (C) confidence

Self-Care Heart Failure Index: Sub-Scale (Maintenance)

The cumulative sample size for this analysis across the four studies was 167 participants (IG=90, CG=77). The findings showed that there was no statistically significant difference in the pooled (SMD) (Athilingam et al., 2017; Clays et al., 2021; Dorsch et al., 2021; Kitsiou et al., 2025).

Additionally, Cochran’s Q value showed no statistical significance (Q=3.9358, DF=3, p=0.2685), and the I² value (23.78%) was low, suggesting non-heterogeneity among studies and non-statistical significance. Therefore, the pooled SMD calculated using the fixed effect model showed no significant difference in maintenance scores between the two groups (SMD= -0.088, t=-0.572, p=0.568) (95% CI [-0.392, 0.216]). The overall effect suggested that maintenance scores were lower in the intervention group than in the control group. GRADE: moderate quality of evidence. The forest plot illustrating the mean difference values of maintenance scores presented the effect size (SMD). The publication bias using Egger’s test resulted in a p-value of 0.4982, indicating no statistical significance in the absence of publication bias. Additionally, the funnel plot depicted the distribution of studies within the funnel.

Self-Care Heart Failure Index: Sub-Scale (Confidence)

The total sample size across these three studies for this analysis was 129 participants (IG=69 and CG=60). The results exposed no statistically significant difference in the (SMD) values (Athilingam et al., 2017; Clays et al., 2021; Dorsch et al., 2021). Cochran’s Q statistic was significant (Q=46.9013, DF=2, p<0.0001) with an I² of 95.74%, indicating high heterogeneity among the three studies. Therefore, the pooled SMD derived from the random effects model suggested no significant difference in confidence scores between the two groups (SMD= -0.194, t= -0.190, p=0.849) (95% CI [-2.216 to 1.827]). The overall effect showed that the mean confidence scores were higher in the control group compared to the intervention group. GRADE: very low quality of evidence. The associated forest plot displaying the mean difference values of confidence scores illustrated the effect size (SMD). The evaluation of publication bias used Egger’s test (p= 0.6121), suggesting no statistical significance regarding the presence of publication bias. Additionally, the funnel plot represented the distribution of studies within the funnel.

Sensitivity Analysis

A leave-one-out sensitivity analysis assessed each study’s impact on pooled effect size and heterogeneity (Marušić et al., 2020). In EHFScBS studies, five of six showed minimal change in heterogeneity, indicating they are not the primary source of true heterogeneity. But removing Negarandeh et al. (2019) greatly reduced both the effect size (∼0.41) and the heterogeneity (I² ∼ 89.2%) due to its specialised, intensive nurse-led program designed for participants with low foundational health literacy, which proved to be significantly more effective than the alternative programs. SCHFI (management) studies showed high heterogeneity across three studies. Therefore, no sensitivity analysis was performed, as results could be unreliable and potentially lead to an inaccurate pooled estimate; their significance and understanding are greatly restricted and should be handled with caution (JPTTJ et al., 2024).

Statistical Significance Versus Clinical Significance

The intervention using the European self-care scale had a very large effect (i.e., (SMD =1.075, t=3.637, p<0.001) (95% CI [0.495,1.655]) compared to usual care and this effect is highly statistically significant (p<0.001). The statistically significant effect support telehealth as an effective approach for enhancing self-care in HF patients. Nevertheless, the wide confidence interval indicates significant uncertainty and highlights that the effect observed is not consistent across the studies. The true impact could be as low as a moderate 0.49 or reach up to a substantial 1.66. This wide confidence interval (imprecision) stems from the extreme heterogeneity (I² = 95.2%) among included studies, which can be attributed to factors such as differing interventions, sample sizes, countries, and follow-up durations.

Statistical significance (p-value < 0.05) indicates that the observed effect is unlikely due to random variability, suggesting a difference between the intervention and control group. Clinical significance assesses whether study findings lead to meaningful real-world impacts on a patient’s health or well-being (Carpenter et al., 2021). The result represented almost certainty clinically improvement in daily self-care, which are clinically meaningful actions that can prevent complications. Healthcare professionals and decision-makers should not treat “telehealth” as a unified intervention for HF patients; they should develop interventions with proven clinical significance.

Discussion

The aim of this review was to evaluate and synthesize evidence concerning the effectiveness of telehealth on HF patients’ self-care. This review included 10 RCTs with very low to moderate evidence quality. Six studies reported significant improvements in self-care after telehealth intervention (SMD=1.075, t=3.637, p<0.001; 95% CI [0.495, 1.655]). However, no differences were found between groups in self-care behaviours across the Management, Maintenance, and Confidence subscales of the SCHFI. The nature of the interventions, follow-up methods, and measurement tools varied, as did the duration of the studies, which ranged from four weeks to twelve months. Four categories of interventions were identified: Mobile Health Applications (Apps), RPM, Telephone-Based Interventions, and Combining Multiple Methods. Only two studies included more than 100 participants (Deckwart et al., 2023; Piotrowicz et al., 2024).

Three studies on the Mobile Health Application “HeartMapp” showed mixed results. For instance, Dorsch et al. (2021) found no significant improvement in self-care across the three SCHFI subscales, highlighting the need for standardised baseline scores. In contrast, Athilingam et al. (2017) reported improvements in confidence and management, but not in maintenance, which may be due to inconsistent use of the app’s features. This variability in compliance, coupled with the 30-day study period and small sample sizes, makes it more challenging to detect changes in maintenance. Similarly, Choi et al. (2023) found no notable improvement with the EHFScBS-12 item and noted similar limitations. Overall, the literature suggests that mHealth tools like “HeartMapp” need further development, a larger sample size, and longer follow-up to better measure their effectiveness (Deniz-Garcia et al., 2023; Sivasamy, 2023).

Two studies compared self-care with RPM and the EHFScBS, each comprising 12 and 9 items, respectively. Both resulted in positive changes (Deckwart et al., 2023; Yanicelli et al., 2021). Yanicelli et al. (2021) noted that participants mostly had low literacy, as more than 75% did not complete high school, and were satisfied with the RPM system. This outcome suggests that this type of intervention is reasonably accurate in adherence to HF guidelines, acceptable to end-users, and has utility. Similarly, Deckwart et al. (2023) described that continuous nurse feedback encouraged participants to manage their HF and enhanced self-care behaviours. This conclusion is consistent with the findings of Burgess et al. (2020), who indicated that constructive feedback reinforces behaviours and commitment to health goals.

Furthermore, two studies utilised Telephone-Based Interventions (Araujo De Oliveira et al., 2017; Negarandeh et al., 2019), demonstrating significant improvements in self-care behaviours (P < 0.01; P < 0.05). Structured phone calls (one to two per week) were coupled with tailored educational resources, enhancing patients’ self-care skills despite the small sample size in both studies. Negarandeh et al. (2019) conducted two 20-minute calls each week for two months, while Araujo De Oliveira et al. (2017) made eight total calls over two months, followed by four calls in the subsequent two months. These findings support other research indicating that telephone interventions effectively improve self-care in HF patients (Son et al., 2020). Regular phone communication can boost patient motivation and engagement in their health (Abedi, 2024).

Additionally, three studies have used Combining Multiple Methods (Clays et al., 2021; Kitsiou et al., 2025; Piotrowicz et al., 2024). Piotrowicz et al. (2024) examined self-care using the EHFScBS12-item and found that the intervention was not superior to the control group, despite a large sample size (IG = 223, CG =211). The lack of effectiveness was due to methodological and contextual challenges. Also, AMULET was not intended for cardiac patients, and measuring behavioural change can be complex when comparing pre- and post-intervention (Moradi et al., 2020). Additionally, it should be noted that the study by Piotrowicz et al. (2024) was conducted during the COVID-19 pandemic, which also led to substantial data loss during follow-up assessments. This loss of data reduced the statistical power necessary to detect significant differences between the experimental and control groups. Also, several other intervention studies were disrupted by the pandemic, further inhibiting the ability to draw conclusions (Hawila & Berg, 2021).

In Clays et al. (2021) and Kitsiou et al. (2025), no significant changes were noted post-intervention. This may be due to the relatively small sample sizes and short intervention periods. More robust sample sizes and longer intervention periods are needed to capture differences (Gardner et al., 2023). There may have been limited effectiveness with “HeartMan” and “iCardia4HF” due to technical problems and adherence issues, as patients had to wear the vests. More RCTs are required to assess these interventions in HF patients. Kitsiou et al. (2025) also noted a high baseline self-care score among participants, which would make it difficult to detect a significant difference due to the intervention.

Moreover, the studies reviewed demonstrate the benefits of integrating telehealth care into nursing workflows. Nurses who use telehealth care effectively can reduce workload and help address staffing shortages. However, nurses require training in telehealth technologies to improve care efficiency (Bulto, 2024).

Strengths and Limitations

This review found that telehealth interventions for self-care benefit HF patients. Notable strengths included a large sample size of 2,128 participants and the use of diverse intervention types, which offered broader insight into telehealth’s effects on HF patient self-care. Adherence to PRISMA guidelines and rigorous quality assessment enhanced the reliability of the results. However, limitations include a focus on English-language, open-access studies, mainly from the U.S. and Europe, with only one from South Korea, and gaps in evidence from Asia, Africa, and the Middle East. Most studies were 7–10 years old, and variation in interventions, durations, tools, sample sizes, and data-collection periods, along with the inclusion of low-quality studies, further limits their relevance to current clinical practice and may have introduced bias. Findings may not be generalizable; caution is advised when interpreting conclusions.

Implications for Practice and Research

Telehealth modalities help patients manage self-care remotely and can be tailored to individual needs and resources. Videoconferencing is ideal for detailed consultations, while messaging is effective for simple follow-ups. RPM with wearables tracks vital signs and enables timely support from healthcare providers, including nurses. Telehealth allows nurses to coach, educate, and support patients, strengthening the workforce and improving care quality and access for at-risk groups. The lack of in-person interaction may also encourage more honest nurse-patient communication (Abedi, 2024). At the policy level, hospital administrators can use these findings to support investing in telehealth tools, nursing units, and standardized nurse training. Integrating telehealth can improve access and lower costs. It also generates data for future research and evidence-based practice (Bulto, 2024). Future studies should focus on who benefits most, in which situations, and which telehealth methods work best.

Conclusion

This meta-analysis found that telehealth care improved self-care in HF patients in studies that used EHFScBS, but not in those that used SCHFI. Robust studies, such as RCTs with embedded qualitative approaches, larger samples, longer follow-ups, and standardized measures, were needed to assess telehealth’s true impact on self-care. The findings highlight the clinical significance of improving self-care interventions for HF patients and might have helped clinicians and nurses develop more effective, patient-centered management strategies. Future research should explore hybrid telehealth and collaborative approaches to achieve stronger, more sustainable outcomes.

Supplemental Material

Supplemental Material - Effectiveness of Telehealth Care on Self-Care Behaviours in Heart Failure Patients: Systematic Review and Meta-Analysis

Supplemental Material for Effectiveness of Telehealth Care on Self-Care Behaviours in Heart Failure Patients: Systematic Review and Meta-Analysis by Lujain Adel Sallam, Nora Ghalib AlOtaibi, Omar Ghazi Baker in Sage Open Nursing

Supplemental Material

Supplemental Material - Effectiveness of Telehealth Care on Self-Care Behaviours in Heart Failure Patients: Systematic Review and Meta-Analysis

Footnotes

Acknowledgment

The authors would like to thanks their universities for their support.

ORCID iDs

Lujain Adel Sallam

Omar Ghazi Baker

Nora Ghalib AlOtaibi

Ethical Considerations

As this study is based on Systematic Review and Meta-analysis (SRMA) methods using existing clinical trial data, ethical approval was not required for this research.

Consent to Participate

As this study is based on SRMA methods using existing clinical trial data, participant consent was not required for this research.

Funding

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

Declaration of Conflicting Interests

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

Data Availability Statement

No data sharing is required for this study as it is an SRMA that relies on existing data from clinical trials rather than generating new data. Data analyzed during the current study is available in supplementary files.*

Contributorship

LAS, NGO, and OGB made significant contributions to the study’s conception and design, data acquisition, or data analysis and interpretation. All authors participated in drafting or critically revising the manuscript for essential intellectual content, and they provided final approval for publication. Each author has engaged sufficiently in the work to take public responsibility for their respective portions of the content. LAS, NGO, and OGB accepted accountabilities for all aspects of the work, ensuring that any concerns regarding the accuracy or integrity of any part are thoroughly investigated and resolved.

Guarantor

LAS.

Supplemental Material

Supplemental Material for this article is available online.

Appendix

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