Evidence map›Paper›PMID 41374490›Full record

ArticleSensors (Basel, Switzerland)2025

The B-Health Box: A Standards-Based Fog IoT Gateway for Interoperable Health and Wellbeing Data Collection.

Maria Marques, Vasco Delgado-Gomes, Fábio Januário, Carlos Lopes, Ricardo Jardim-Goncalves, Carlos Agostinho

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Maria MarquesCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0001-8371-7557
Vasco Delgado-GomesCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0001-9437-4385
Fábio JanuárioCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0001-8398-7522
Carlos LopesCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0002-0972-7244
Ricardo Jardim-GoncalvesCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0002-3703-6854
Carlos AgostinhoCenter of Technology and Systems (UNINOVA-CTS), Associated Lab of Intelligent Systems (LASI) and Confederation of Laboratories for Artificial Intelligence Research in Europe (CAIRNE), FCT Campus, 2829-516 Caparica, Portugal.ORCID 0000-0002-2884-776X

Funding

European Commission 826117, 857172, 872548, 101016000, 101092043
6 · The paper itself

Abstract

In recent years, healthcare is evolving to meet the needs of a growing and ageing population. To support better and more reliable care, a comprehensive and up-to-date Personal Health Record (PHR) is essential. Ideally, the PHR should contain all health-related information about an individual and be available for sharing with healthcare institutions. However, due to interoperability issues of the medical and fitness devices, most of the times, the PHR only contains the same information as the patient Electronic Health Record (EHR). This results in lack of health-related information (e.g., physical activity, working patterns) essential to address medical conditions, support prescriptions, and treatment follow-up. This paper introduces the B-Health IoT Box, a fog IoT computing framework for eHealth interoperability and data collection that enables seamless, secure integration of health and contextual data into interoperable health records. The system was deployed in real-world settings involving over 4500 users, successfully collecting and transmitting more than 1.5 million datasets. The validation shown that data was collected, harmonized, and properly stored in different eHealth platforms, enriching data from personal EHR with mobile and wearable sensors data. The solution supports real-time and near real-time data collection, fast prototyping, and secure cloud integration, offering a modular, standards-compliant gateway for digital health ecosystems. The health and health-related data is available in FHIR format enabling interoperable eHealth ecosystems, and better equality of access to health and care services.

Indexed as

Data CollectionElectronic Health RecordsHealth Records, PersonalHumansTelemedicineWearable Electronic Devicesdigital healthEHRFHIRfog computingHISinteroperabilityIoTwearablesworkplace

Identifiers

PMID41374490
PMCPMC12693854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.