Evidence mapPaperPMID 41783284Full record

ReviewDigital health

A systematic review on wearable-enabled remote health monitoring.

Rita Ribeiro, Rafael Martins, Hugo Pereira, Vítor Crista, Júlio Souza, Rute Almeida, Diogo Martinho, Luís Conceição, Alberto Freitas, Goreti Marreiros

Abstract readReview
In one paragraph

Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026
    Review
  4. Article
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

10 authors.

Rita RibeiroGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0009-0005-8882-6214
Rafael MartinsGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0003-1222-8136
Hugo PereiraGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0009-0002-2939-0762
Vítor CristaGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0002-8794-6354
Júlio SouzaGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0002-8576-1903
Rute AlmeidaRISE-Health, Department of Community Medicine, Information and Health, Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0001-7755-5002
Diogo MartinhoGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0003-1683-4950
Luís ConceiçãoGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0003-3454-4615
Alberto FreitasRISE-Health, Department of Community Medicine, Information and Health, Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0003-2113-9653
Goreti MarreirosGECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP, Polytechnic of Porto, Porto, Portugal.ORCID https://orcid.org/0000-0003-4417-8401

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This systematic review investigates the application of wearable technologies for remote health monitoring, with a particular focus on their effectiveness in improving patient care and the current state of technological integration. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, a comprehensive search was conducted for studies published between 2020 and 2025. A total of 55 studies were selected, and data were extracted regarding study design, wearable types, feedback mechanisms, and analytical approaches. Results: The analysis reveals a significant reliance on traditional statistical methods, with limited integration of advanced artificial intelligence techniques despite their potential to improve predictive capabilities. The review emphasizes the importance of personalized feedback mechanisms in promoting patient engagement and adherence. Furthermore, a notable gap was identified in research addressing cognitive and psychological well-being compared to physical health monitoring. Conclusion: The findings highlight the need for more rigorous methodologies, including scientific clinical trials, to strengthen the evidence base. Future work should prioritize the integration of machine learning and adopt a more holistic approach to provide a comprehensive understanding of the state-of-the-art and inform future research directions.

Indexed as

artificial intelligencehealth data analyticspatient engagementremote health monitoringWearable technologies

Identifiers

PMID41783284
PMCPMC12954012

What Socratic 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 Socratic graph.