Evidence map›Paper›PMID 39325254›Full record

ReviewUrolithiasis2024

Non-invasive approaches to hydration assessment: a literature review.

Achraf Tahar, Hadil Zrour, Stéphane Dupont, Agnieszka Pozdzik

Abstract readReview
PubMed Publisher
In one paragraph

Review in Urolithiasis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Achraf TaharDepartment of Research, Development and Innovation, Renal Care and Research Srl, Rue Saint Martin 35, 1457, Walhain, Nil Saint Vicent, Belgium. achraf.tahar.ai@gmail.com.
Hadil ZrourDepartment of Research, Development and Innovation, Renal Care and Research Srl, Rue Saint Martin 35, 1457, Walhain, Nil Saint Vicent, Belgium.
Stéphane DupontArtificial Intelligence Research Unit (MAIA), Department of Computer Science, University of Mons, Avenue Maistriau15, 7000, Mons, Belgium.
Agnieszka PozdzikKidney Stone Clinic, University Hospital Brugmann, Place A. Van Gehuchtenplein 4, 1020, Brussels, Belgium. agapozdzik@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional hydration assessment methods, while accurate, are often invasive and impractical for routine monitoring. In response, innovative non-invasive techniques such as bioelectrical impedance analysis (BIA), electrodermal activity (EDA), electrocardiogram (ECG) monitoring, and urine color charts have emerged, offering greater comfort and accessibility for patients. These methods use various types of sensors to capture a range of bio-signals, followed by machine learning-based classification or regression methods, providing real-time feedback on hydration status, which is crucial for effective management and prevention of urinary stones. This review explores the principles, applications, and efficacy of these non-invasive techniques, highlighting their potential to transform hydration monitoring in clinical and everyday settings. By facilitating improved patient compliance and enabling proactive hydration management, these approaches align with contemporary trends in personalized healthcare. This article presents a literature review on non-invasive approaches to hydration assessment, focusing on their significance in preventing kidney stone disease and enhancing kidney health.

Indexed as

Electric ImpedanceElectrocardiographyGalvanic Skin ResponseHumansKidney CalculiMachine LearningKidney stonesMobile applicationSmart bottleWater intake

Identifiers

What OpenQuestion holds

Textmetadata
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.