Cited by 172Telemedicine and Telehealth ImplementationHeart Failure Treatment and ManagementMobile Health and mHealth Applications

Effectiveness of telemedicine systems for adults with heart failure: a meta-analysis of randomized controlled trials

Open access
Zhu, Ye, Gu, Xiang, Xu, Chao·2020·Heart Failure Reviews

Abstract

Despite favorable effects from telemedicine (TM) on cardiovascular diseases, outcome and comparative impact of TM on heart failure (HF) adults remain controversial. A meta-analysis was conducted to summarize the evidence from existing randomized controlled trials (RCTs) which compared potential impact of TM on HF with conventional healthcare. TM mainly included structure telephone support (STS), involving interactive vocal response monitoring and telemonitoring. PubMed, MEDLINE, EMBASE, and the Cochrane Library were searched to identify RCTs to fit our analysis (1999 to 2018). Odds ratio (OR) with its 95% confidence interval (CI) was used. Sensitivity analysis, subgroup analysis, and tests for publication bias were conducted. Heterogeneities were also evaluated by I 2 tests. A total of 29 RCTs consisting of 10,981 HF adults were selected for meta-level synthesis, with follow-up range of 1–36 months. Telemonitoring is associated with the reduction in total number of all-cause hospitalization (OR 0.82, 95% CI 0.73–0.91, P = 0.0004) and cardiac hospitalization (OR 0.83, 95% CI 0.72–0.95, P = 0.007). Telemonitoring resulted in statistically significant risk reduction of all-cause mortality (OR 0.75, 95% CI 0.62–0.90, P = 0.003). However, the OR of HF-related mortality (OR 0.84, 95% CI 0.61–1.16, P = 0.28) is not significantly distinguishable from that of conventional healthcare. Receiving STS interventions is likely to reduce the hospitalization for all causes (OR 0.86, 95% CI 0.78–0.96, P = 0.006, I 2 = 6%) and the hospitalization due to HF (OR 0.74, 95% CI 0.65–0.85, P < 0.0001, I 2 = 0%), compared with interventions from conventional healthcare. OR of all-cause STS mortality (OR 0.96, 95% CI 0.83–1.11, P = 0.55) was identified in meta-analyses of eight cases. OR of STS cardiac mortality (OR 0.54, 95% CI 0.34–0.86, P = 0.009) was identified in meta-analyses of three cases. This work represents the comprehensive application of network meta-analysis to examine the comparative effectiveness of telemedicine interventions in improving HF patient outcomes. Compared with conventional healthcare, telemedicine systems with medical support prove to be more effective for HF adults, particularly in reducing all-cause hospitalization, cardiac hospitalization, all-cause mortality, cardiac mortality, and length of stay. While further research is required to confirm these observational findings and identify optimal telemedicine strategies and the duration of follow-up for which it confers benefits.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s10741-019-09801-5

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Previous literature is inconsistent on the role of telemonitoring in reducing rehospitalization, with some studies reporting no differences and others concluding it reduces all-cause hospitalization in adults with heart failure.

    “There is inconsistency in the previous literature on the role that telemonitoring plays in reducing the risk of rehospitalization, with some studies reporting no differences compared with usual care [171] and others concluding that telemonitoring is an effective tool to reduce all-cause hospitalization in adults with heart failure [172].”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 42 · Cited by 172

Authors
Zhu, Y., Gu, X., & Xu, C.
Affiliations
  • Northern Jiangsu People's Hospital (CN)
  • University of Oklahoma Health Sciences Center (US)
Corresponding author
Gu, Xiang
Venue
Heart Failure Reviews
Published
2019-05-24
Volume
25
Issue
2
Pages
231-243
ISSN
  • 1382-4147 (ISSN-L)
  • 1573-7322
OpenAlex
W2952943844
Cited by
172
References
42
Citations by year
2026: 11 · 2025: 26 · 2024: 29 · 2023: 22 · 2022: 34 · 2021: 27 · 2020: 22 · 2019: 1
FWCI
3.43
Type
journal article
Language
en
Open access
Locations
  • Heart Failure Reviews (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Science and Technology Support Program of Jiangsu Province (2013022)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords
telemedicine, heart failure, remote patient monitoring, telephone support, hospitalization, all-cause mortality, adults, cardiovascular care, quality of life, health-related quality of life, patient satisfaction, health care costs, patient empowerment, health care access
MeSH terms
Adult, Equipment Design, Heart Failure, Humans, Randomized Controlled Trials as Topic, Telemedicine, Disease Management

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review