Evidence map›Paper›PMID 31418428›Full record

ArticleJMIR mHealth and uHealth2019

A Novel Mobile Health Tool for Home-Based Identification of Neonatal Illness in Uganda: Formative Usability Study.

Madison Vanosdoll, Natalie Ng, Anthony Ho, Allison Wallingford, Shicheng Xu, Shababa Binte Matin, Neha Verma, Azadeh Farzin, W Christopher Golden, Youseph Yazdi and 3 more

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.9field-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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Scoping review of health information technology usability methods leveraged in Africa.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  6. Article
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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

13 authors at 2 institutions in 2 countries.

Madison Vanosdoll *Center for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-2349-1178
Natalie Ng *Center for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0001-8949-5588
Anthony Ho *Center for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-3966-4168
Allison WallingfordCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-0228-5567
Shicheng XuCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-9144-5739
Shababa Binte MatinCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-5423-4676
Neha VermaCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-3867-9162
Azadeh FarzinSchool of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-3063-9118
W Christopher GoldenSchool of Medicine, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-4834-6778
Youseph YazdiCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-1816-2190
Peter WaiswaSchool of Public Health, Makerere University, Kampala, Uganda.ORCID 0000-0001-9868-0515
Alain LabriqueBloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-2502-7819
Soumyadipta AcharyaCenter for Bioengineering Innovation and Design, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0003-3772-9738
Johns Hopkins University · USMakerere University · UG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile early identification of neonatal illness can impact neonatal mortality rates and reduce the burden of treatment, identifying subtle clinical signs and symptoms of possible severe illness is especially challenging in neonates. The World Health Organization and the United Nations Children's Fund developed the Integrated Management of Neonatal Childhood Illness guidelines, an evidence-based tool highlighting seven danger signs to assess neonatal health. Currently, many mothers in low-resource settings rely on home visits from community health workers (CHWs) to determine if their baby is sick. However, CHWs visit infrequently, and illness is often detected too late to impact survival. Thus, delays in illness identification pose a significant barrier to providing expedient and effective care. Neonatal Monitoring (NeMo), a novel neonatal assessment tool, seeks to increase the frequency of neonatal screening by task-shifting identification of neonatal danger signs from CHWs to mothers.

objectiveThis study aimed to explore the usability and acceptability of the NeMo system among target users and volunteer CHWs by assessing ease of use and learnability.

methodsSimulated device use and semistructured interviews were conducted with 32 women in the Iganga-Mayuge districts in eastern Uganda to evaluate the usability of the NeMo system, which involves a smartphone app paired with a low cost, wearable band to aid in identification of neonatal illness. Two versions of the app were evaluated using a mixed methods approach, and version II of the app contained modifications based on observations of the first cohort's use of the system. During the posed scenario simulations, participants were offered limited guidance from the study team in order to probe the intuitiveness of the NeMo system. The ability to complete a set of tasks with the system was tested and recorded for each participant and closed- and open-ended questions were used to elicit user feedback. Additionally, focus groups with 12 CHWs were conducted to lend additional context and insight to the usability and feasibility assessment.

resultsA total of 13/22 subjects (59%) using app version I and 9/10 subjects (90%) using app version II were able to use the phone and app with no difficulty, despite varying levels of smartphone experience. Following modifications to the app's audio instructions in version II, participants' ability to accurately answer qualitative questions concerning neonatal danger signs improved by at least 200% for each qualitative danger sign. All participants agreed they would trust and use the NeMo system to assess the health of their babies. Furthermore, CHWs emphasized the importance of community sensitization towards the system to encourage its adoption and regular use, as well as the decision to seek care based on its recommendations.

conclusionsThe NeMo system is an intuitive platform for neonatal assessment in a home setting and was found to be acceptable to women in rural Uganda.

Indexed as

AdultFemaleFocus GroupsHumansInfant HealthInfant, NewbornMaleMobile ApplicationsNeonatal ScreeningPediatricsQualitative ResearchSoftware ValidationSurveys and QuestionnairesUgandacommunity health workersdigital healthmaternalmobile healthneonatalsmartphone apptelemedicineUgandaWorld Health Organization

Identifiers

PMID31418428
PMCPMC6714500
OpenAlexW2967287785

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.