Evidence map›Paper›PMID 40110225›Full record

ArticleEuropean heart journal. Digital health2025

Efficacy of an Internet of Things-based system for cardiac rehabilitation monitoring: insights from the IntellIoT pivotal trial in heart failure patients.

Maria E Marketou, Ioannis Anastasiou, Alexis Fourlis, Aphrodite Alevizaki, George Kochiadakis

Abstract read
In one paragraph

Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maria E MarketouDepartment of Cardiology, Heraklion University General Hospital, P.O. Box 1352, 71110 Heraklion, Greece.ORCID https://orcid.org/0000-0003-1888-3430
Ioannis AnastasiouDepartment of Cardiology, Heraklion University General Hospital, P.O. Box 1352, 71110 Heraklion, Greece.
Alexis FourlisVidavo S.A., Thessaloniki, Greece.
Aphrodite AlevizakiDepartment of Cardiology, Heraklion University General Hospital, P.O. Box 1352, 71110 Heraklion, Greece.
George KochiadakisDepartment of Cardiology, Heraklion University General Hospital, P.O. Box 1352, 71110 Heraklion, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Digital health solutions targeted to remote clinical monitoring are constantly gaining ground in cardiovascular care. However, evidence regarding their impact on cardiac rehabilitation efficiency in heart failure (HF) patients is relatively limited. In this study, conducted in the context of the IntellIoT project, we evaluated the effect of a purpose-designed Internet of Things (IoT)-based patient monitoring system on cardiac rehabilitation outcomes in a cohort of HF patients. Methods and results: Nineteen clinically stable HF patients were enrolled in the study, which consisted of a 12-month standard-of-care run-in phase and a remote follow-up phase of equal duration, whereby an IoT-based e-health system was provided to study subjects. Device-derived data transmission was facilitated by a mobile phone application, coupled with a web-based platform accessible to study physicians. Study endpoints were (i) patient adherence rates to e-health system use and their associations to key clinical parameters, (ii) the degree of change in physical activity, and (iii) total time dedicated by physicians to enrolled patients' care with and without the aid of the e-health system. Baseline-to-peak increase in daily step count was calculated at 23.34%. System use was associated with a decrease in time dedicated by physicians to enrolled patients' care. A significant negative correlation was observed between age and progressive drop-in adherence rate to system use ( Conclusion: Internet of Things-based healthcare constitutes a promising approach in HF patients' rehabilitation, whereas elderly patients might constitute the population most likely to benefit. However, larger, randomized studies are required to confirm our findings.

Indexed as

Heart failureInternet of ThingsWearables

Identifiers

PMID40110225
PMCPMC11914715

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.