Evidence map›Paper›PMID 31146731›Full record

ArticleBMC medical informatics and decision making2019

Development of a novel mobile application to detect urine protein for nephrotic syndrome disease monitoring.

Chia-Shi Wang, Richard Boyd, Russell Mitchell, W Darryl Wright, Courtney McCracken, Cam Escoffery, Rachel E Patzer, Larry A Greenbaum

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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, 13 citations in OpenAlex.

  1. Article
  2. Smartphone-Based Colorimetric Analysis of Urine Test Strips for At-Home Prenatal Care.IEEE journal of translational engineering in health and medicine · 2022
    Article
  3. Article
  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

8 authors at 3 institutions in 1 country.

Chia-Shi WangDivision of Pediatric Nephrology, Emory University, 2015 Uppergate Drive NE, Atlanta, GA, 30322-1015, USA. chia-shi.wang@emory.edu.ORCID 0000-0001-6941-0057
Richard BoydGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, USA.
Russell MitchellGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, USA.
W Darryl WrightGeorgia Tech Research Institute, Georgia Institute of Technology, Atlanta, GA, USA.
Courtney McCrackenDivision of Pediatric Nephrology, Emory University, 2015 Uppergate Drive NE, Atlanta, GA, 30322-1015, USA.
Cam EscofferyDivision of Pediatric Nephrology, Emory University, 2015 Uppergate Drive NE, Atlanta, GA, 30322-1015, USA.
Rachel E PatzerDivision of Pediatric Nephrology, Emory University, 2015 Uppergate Drive NE, Atlanta, GA, 30322-1015, USA.
Larry A GreenbaumDivision of Pediatric Nephrology, Emory University, 2015 Uppergate Drive NE, Atlanta, GA, 30322-1015, USA.
Emory University · USGeorgia Institute of Technology · USEmory Healthcare · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHome monitoring of urine protein is a critical component of disease management in childhood nephrotic syndrome. We describe the development of a novel mobile application, UrApp - Nephrotic Syndrome Manager, to aid disease monitoring.

methodsUrApp was iteratively developed by a panel of two pediatric nephrologists and three research engineers from May 2017 to October 2018 for Apple iPhones. App features were devised by this expert panel to support urine monitoring and other home care tasks. Each feature and user-app interface element was systematically reviewed by the panel and iteratively redesigned to remove anticipated use issues. The app prototype was then refined based on two rounds of usability testing and semi-structured user interviews with a total of 20 caregivers and adolescent patients. The analytic function of UrApp in providing a camera read of the urine test strip was compared to a standard urinalysis machine using 88 patient urine samples and three iPhones, model versions 6S and 7. Exact agreement and weighted kappa were calculated between the UrApp and urinalysis machine reads.

resultsThe final UrApp features include: camera read of a urine test strip; analysis of urine protein trends and alerts for new disease relapse/remission; transmission of urine protein results to providers; education materials; and medication reminders. During the second round of UrApp usability testing, all users were able to perform each of the functions without error and all perceived UrApp to be helpful and indicated that they would use UrApp. UrApp camera results had 97% exact agreement and an overall weighted kappa value of 0.91 (95% CI, 0.85-0.97) compared with standard urinalysis machine interpretation.

conclusionsUrApp was specifically designed to support patients and families living with nephrotic syndrome by supporting disease monitoring and home management tasks. The technically innovative feature that makes this possible is the use of a smartphone camera to read the urine test strip. This novel tool has the potential to improve disease monitoring and reduce management burden.

Indexed as

Medical Informatics ApplicationsMobile ApplicationsAdolescentHumansImage Processing, Computer-AssistedMonitoring, PhysiologicNephrotic SyndromeProteinsSmartphoneUrinalysisUrineProteinsChildrenMobile applicationsNephrotic syndromeUrinalysis

Identifiers

PMID31146731
PMCPMC6543567
OpenAlexW2947924541

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.