Evidence map›Paper›PMID 29884601›Full record

ArticleJMIR mHealth and uHealth2018

The Rise and Need for Mobile Apps for Maternal and Child Health Care in China: Survey Based on App Markets.

Puhong Zhang, Le Dong, Huan Chen, Yanling Chai, Jianbo Liu

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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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

5 authors.

Puhong Zhang *Department of Women and Child Health, The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID http://orcid.org/0000-0003-4610-9848
Le Dong *Department of Women and Child Health, The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID http://orcid.org/0000-0002-8322-3554
Huan ChenDepartment of Women and Child Health, The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID http://orcid.org/0000-0003-4125-0666
Yanling ChaiDepartment of Women and Child Health, The George Institute for Global Health at Peking University Health Science Center, Beijing, China.ORCID http://orcid.org/0000-0002-4392-0080
Jianbo Liu *Department of Epidemiology and Statistics, School of Public Health, Hebei Medical University, Shijiazhuang, China.ORCID http://orcid.org/0000-0002-6571-8107

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile health services are thriving in the field of maternal and child health in China due to expansions in the field of electronic health and the introduction of the two-child policy. There are numerous maternal and child health apps in computer stores, but the exact number of apps, number of downloads, and features of these apps is not known.

objectiveThis study aimed to explore the use of maternal and child health apps in Android and iOS app stores and to describe the key functional features of the most popular apps, with the purpose of providing insight into further research and development of maternal and child health mobile health products.

methodsThe researchers conducted a search in the 3 most popular Android app stores (Tencent MyApp, Baidu Mobile Assistant, and 360 Mobile Assistant) and the iTunes App Store in China. All apps regarding family planning (contraception and preparing for pregnancy), pregnancy and perinatal care, neonatal care and health, and development for children under 6 years were included in the initial analysis. Maternal and child health mobile apps with predominant features of product marketing, children's songs, animation, or games were excluded from the study. The 50 most frequently used apps in each of the Android stores as well as the iTunes store (a total of 78 deduplicated apps) were selected and downloaded for an in-depth analysis.

resultsA total of 5276 Android apps and 877 iOS apps developed for maternal and child health care were identified. Of the 78 most frequently used apps, 43 (55%) apps focused on one stage of MCH care, mainly targeting child care (25 apps) and before pregnancy care (11 apps), whereas 35 (45%) of the apps covered 2 or more stages, most of which (32 apps) included both pregnancy and child care services. The app features that were commonly adopted by the popular apps were health education, communication, health status self-monitoring, a diary, reminders, and counseling. Within the app feature of "health status self-monitoring," the researchers found 47 specific tools supporting activities such as pregnancy preparation, fetal heart monitoring, blood glucose and blood pressure monitoring, and doctor visits. A few apps were equipped with external devices (n=3) or sensors. No app with intelligent decision-support features to support disease management for conditions such as gestational diabetes and pregnancy-induced hypertension was found. A small number of apps (n=5) had a Web connection with hospital information systems to support appointment making, payments, hospital service guidance, or checking of laboratory results.

conclusionsThere are thousands of maternal and child health apps in the Chinese market. Child care, pregnancy, and before pregnancy were the mostly covered maternal and child health stages, in that order. Various app features and tools were adopted by maternal and child health apps, but the use of internal or external sensors, intelligent decision support, and tethering with existing hospital information systems was rare and these features need more research and development.

Indexed as

health services, maternal-childmarket researchmHealthmobile apps

Identifiers

PMID29884601
PMCPMC6015267

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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.