Literature DB >> 31329074

Smartphone-Based Analysis of Urine Reagent Strips Is Inaccurate for Assessing Underhydration.

J D Adams1, Catalina Capitan-Jimenez2, Jenna M Burchfield3, Lisa T Jansen3, Stavros A Kavouras4.   

Abstract

Background: Proper hydration is vital for both exercise and general health. Although various methods for hydration assessment exist, many are not valid for either use or never tested. Introduction: The purpose of this study was to determine whether the uChek© smart phone application can be used to diagnose underhydration based on elevated urine specific gravity (USG) assessed by refractometry.
Methods: One hundred forty-seven (n = 147) fresh human urine samples from young and middle-age adults were analyzed for USG with a refractometer and the uChek© application by reading the Siemens Multistix 10G urine reagent strip.
Results: Bland-Altman analysis showed agreement of the two methods of assessment. Overall diagnostic ability of the uChek© to identify underhydration was fair (area under the curve 79%). However, the sensitivity to correctly identify underhydration was poor (60%) as well as the specificity of correctly identifying euhydration (53%).
Conclusion: The uChek© application does not accurately detect underhydration.

Entities:  

Keywords:  hydration; hypohydration; mobile health; telemedicine; urine biomarkers

Mesh:

Substances:

Year:  2019        PMID: 31329074     DOI: 10.1089/tmj.2019.0101

Source DB:  PubMed          Journal:  Telemed J E Health        ISSN: 1530-5627            Impact factor:   3.536


  2 in total

1.  Remote digital urinalysis with smartphone technology as part of remote management of glomerular disease during the SARS-CoV-2 virus pandemic: single-centre experience in 25 patients.

Authors:  Madelena Stauss; Ajay Dhaygude; Arvind Ponnusamy; Martin Myers; Alexander Woywodt
Journal:  Clin Kidney J       Date:  2021-12-21

2.  Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis.

Authors:  Seon-Chil Kim; Young-Sik Cho
Journal:  Sensors (Basel)       Date:  2022-07-21       Impact factor: 3.847

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.