Evidence map›Paper›PMID 34815242›Full record

Trial reportBMJ global health2021

Effect of mobile application user interface improvements on minimum expected home visit coverage by community health workers in Mali: a randomised controlled trial.

Jane E Yang, Diego Lassala, Jenny X Liu, Caroline Whidden, Isaac Holeman, Youssouf Keita, Yasamba Djiguiba, Sory Ibrahima N'Diaye, Fatou Fall, Kassoum Kayentao and 1 more

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04106921 (Evaluation of a Mobile Application Tool for Increasing Community Health Worker Home Visit Coverage), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 5% 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.

NCT04106921 nacompletednot on this map

Evaluation of a Mobile Application Tool for Increasing Community Health Worker Home Visit Coverage: A Randomized Controlled Study

TypeinterventionalSponsorAri Johnson, MDRan2019 to 2019Enrolled199ConditionsCommunity Health WorkersArmsUniversal Health Coverage Mode
3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Observational
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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

11 authors at 4 institutions in 2 countries.

Jane E YangMuso, San Francisco, California, USA jemilieyang@gmail.com.ORCID 0000-0001-9083-6319
Diego LassalaMuso, Bamako, Mali.ORCID 0000-0002-0976-8417
Jenny X LiuDepartment of Social and Behavioral Sciences, University of California San Francisco, San Francisco, California, USA.ORCID 0000-0002-3929-0135
Caroline WhiddenMuso, Bamako, Mali.ORCID 0000-0003-0570-4632
Isaac HolemanMedic, San Francisco, California, USA.ORCID 0000-0002-9072-4162
Youssouf KeitaMuso, Bamako, Mali.
Yasamba DjiguibaMuso, Bamako, Mali.
Sory Ibrahima N'DiayeMuso, Bamako, Mali.
Fatou FallMedic, Dakar, Senegal.
Kassoum KayentaoMuso, Bamako, Mali.ORCID 0000-0001-6877-0093
Ari D JohnsonMuso, San Francisco, California, USA.ORCID 0000-0002-7048-5467
Mali-Folkecenter · MLUniversity of California, San Francisco · USUniversité des Sciences, des Techniques et des Technologies de Bamako · MLUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProactive community case management (ProCCM) has shown promise to advance goals of universal health coverage (UHC). ProCCM community health workers (CHWs) face operational challenges when pursuing their goal of visiting every household in their service area at least twice monthly to proactively find sick patients. We developed a software extension (UHC Mode) to an existing CHW mobile application featuring user interface design improvements to support CHWs in planning daily home visits. We evaluated the effect of UHC Mode on minimum expected home visit coverage.

methodsWe conducted a parallel-group, two-arm randomised controlled trial of ProCCM CHWs in two separate regions in Mali. CHWs were randomly assigned to UHC Mode or the standard mobile application (control) with a 1:1 allocation. Randomisation was stratified by health catchment area. CHWs and other programme personnel were not masked to arm allocation. CHWs used their assigned intervention for 4 months. Using a difference-in-differences analysis, we estimated the mean change in minimum expected home visit coverage from preintervention to postintervention between arms.

resultsEnrolment occurred in January 2019. Of 199 eligible CHWs randomised to the intervention or control arm, 196 were enrolled and 195 were included in the analysis. Households whose CHW used UHC Mode had 2.41 times higher odds of minimum expected home visit coverage compared with households whose CHW used the control (95% CI 1.68 to 3.47; p<0.0005). Minimum expected home visit coverage in the UHC Mode arm increased 13.6 percentage points (95% CI 8.1 to 19.0) compared with the control arm.

conclusionOur findings suggest UHC Mode is an effective tool that can improve home visit coverage and promote progress towards UHC when implemented in the ProCCM context. User interface design of health information systems that supports health workers' daily practices and meets their requirements can have a positive impact on health worker performance and home visit coverage. TRIAL REGISTRATION NUMBER: NCT04106921.

Indexed as

House CallsMobile ApplicationsCommunity Health WorkersHumansMalihealth services researchother diagnostic or toolprevention strategiespublic healthrandomised control trial

Identifiers

PMID34815242
PMCPMC8609935
OpenAlexW3215426111

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.