Evidence map›Paper›PMID 40755925›Full record

ReviewmHealth2025

A systematic review and evaluation of mobile health apps designed for parents who are preparing or caring for medically complex infants.

Anne Chevalier McKechnie, Nellie Munn Swanson, Ratchada Jantraporn, Kristin M Elgersma, Taylor Iwaszko Wagner, Suhyun Park, Anna Trebilcock

Abstract readReview
In one paragraph

Review in mHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Anne Chevalier McKechnieChild and Family Health, School of Nursing, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0002-1210-3379
Nellie Munn SwansonChild and Family Health, School of Nursing, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0002-7629-9702
Ratchada JantrapornChild and Family Health, School of Nursing, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0002-6815-7958
Kristin M ElgersmaChild and Family Health, School of Nursing, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0001-6559-0812
Taylor Iwaszko WagnerChild and Family Health, School of Nursing, University of Minnesota, Minneapolis, MN, USA.ORCID https://orcid.org/0000-0003-0079-6283
Suhyun ParkCizik School of Nursing, University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID https://orcid.org/0000-0003-3414-4443
Anna TrebilcockCranial Technologies, Inc., Tempe, AZ, USA.ORCID https://orcid.org/0009-0006-0235-0604

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UM1TR004405 · NCATS · UNIVERSITY OF MINNESOTA · PI Bruce R Blazar, Damien A Fair · 2023 to 2026
$30.8M
CTSA K12 Program at University of MinnesotaK12TR004373 · NCATS · UNIVERSITY OF MINNESOTA · PI David H Ingbar · 2024 to 2026
$3.2M
NCATS NIH HHS K12 TR004373NCATS NIH HHS UM1 TR004405
6 · The paper itself

Abstract

Background: Parents learning about their children with medical complexity often use numerous health-related resources, including mobile health applications (mHealth apps). mHealth apps could provide broad access to key information and support, lower healthcare costs, and improve care. The quality of mHealth apps for this population has been a concern, but is currently unknown. The objective of this systematic review was to identify and evaluate the quality of publicly available mHealth apps designed for parents who are preparing or caring for medically complex infants. Methods: A systematic search strategy was applied to identify mHealth apps in the Apple and Google Play app stores in November 2022 and replicated in August 2024. Apps with no cost, in English, designed for parents of infants with perinatal medical complexity requiring hospitalization were included. Apps for healthy pregnancies, children >1 year, non-parental caregivers or healthcare professionals, primarily for tracking/monitoring, or unrelated activities/products were excluded. Independent raters used the Mobile Application Rating Scale (MARS) subscales of Engagement, Functionality, Aesthetics, and Information to evaluate quality for each app. Mean ratings were calculated by subscale and for overall app quality. Results: From 1,917 identified apps, 32 apps were downloaded and fully screened. The final sample of 15 unique apps were available on the Apple App Store, with six also available on the Google Play Store. Most apps focused on prematurity (n=6), followed by the neonatal intensive care experience (n=4), congenital heart disease (n=4), and hypoxic-ischemic encephalopathy (n=1). MARS ratings of the overall sample (mean =3.61, median =3.58; range, 2.65-4.68) indicated 20% (3/15) were of good quality and 67% (10/15) were of acceptable quality. Apps showed strengths in Functionality and Information and performed worst in Engagement. Conclusions: The poorest quality found in Engagement suggests that most of these apps do not effectively target users' interests or needs. Notably, many suffered from a lack of recent updates or became unavailable. This decline appears to parallel the increasing integration of digital health technologies within healthcare systems, which could prompt testing of mHealth apps on health outcomes. High-quality mHealth apps that are valued by parents and offer evidence-based information and support are needed to extend care.

Indexed as

Infant caremobile applicationsmobile healthneonatal intensive caresystematic review

Identifiers

PMID40755925
PMCPMC12314697

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.