Evidence mapPaperPMID 37989361Full record

ArticleBMJ open2023

Dietitian-led cluster randomised controlled trial on the effectiveness of mHealth education on health outcomes among pregnant women: a protocol paper.

Ying Ting Er, Yoke Mun Chan, Zalilah Mohd Shariff, Habibah Abdul Hamid, Zulfitri 'Azuan Mat Daud, Heng Yaw Yong

Registry-linked trialOpen access · goldAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05377151 (A Dietitian-led Cluster Randomised Controlled Trial on the Effectiveness of mHealth Education on Health Outcomes Among Pregnant Women), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 21% 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.

NCT05377151 naunknown statusnot on this map

A Dietitian-led Cluster Randomised Controlled Trial on the Effectiveness of mHealth Education on Health Outcomes Among Pregnant Women

TypeinterventionalSponsorUniversiti Putra MalaysiaRan2023 to 2024Enrolled294ConditionsPregnant WomenArmsMobile application
3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

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

6 authors at 2 institutions in 1 country.

Ying Ting ErDepartment of Dietetics, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.ORCID 0009-0002-3579-5467
Yoke Mun ChanDepartment of Dietetics, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia cym@upm.edu.my.
Zalilah Mohd ShariffDepartment of Nutrition, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Habibah Abdul HamidDepartment of Obstetrics & Gynaecology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.
Zulfitri 'Azuan Mat DaudDepartment of Dietetics, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia.
Heng Yaw YongDivision of Nutrition and Dietetics, School of Health Sciences, International Medical University, Bukit Jalil, 57000 Kuala Lumpur, Wilayah Persekutuan, Malaysia.
Universiti Putra Malaysia · MYIMU University · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionNutrition education is the cornerstone to maintain optimal pregnancy outcomes including gestational weight gain (GWG). Nevertheless, default for appointments is common and often lead to suboptimal achievement of GWG, accompanied with unfavourable maternal and child health outcomes. While mobile health (mHealth) usage is increasing and helps minimising barriers to clinic appointments among pregnant mothers, its effectiveness on health outcomes has been inconclusive. Therefore, this study aimed to address the gap between current knowledge and clinical care, by exploring the effectiveness of mHealth on GWG as the primary outcome, hoping to serve as a fundamental work to achieve optimal health outcomes with the improvement of secondary outcomes such as physical activity, psychosocial well-being, dietary intake, quality of life and sleep quality among pregnant mothers. METHODS AND ANALYSIS: A total of 294 eligible participants will be recruited and allocated into 3 groups comprising of mHealth intervention alone, mHealth intervention integrated with personal medical nutrition therapy and a control group. Pretested structured questionnaires are used to obtain the respondents' personal information, anthropometry data, prenatal knowledge, physical activity, psychosocial well-being, dietary intake, quality of life, sleep quality and GWG. There will be at least three time points of data collection, with all participants recruited during their first or second trimester will be followed up prospectively (after 3 months or/and after 6 months) until delivery. Generalised linear mixed models will be used to compare the mean changes of outcome measures over the entire study period between the three groups. ETHICS AND DISSEMINATION: Ethical approvals were obtained from the ethics committee of human subjects research of Universiti Putra Malaysia (JKEUPM-2022-072) and medical research & ethics committee, Ministry of Health Malaysia: NMRR ID-22-00622-EPU(IIR). The results will be disseminated through journals and conferences targeting stakeholders involved in nutrition research. TRIAL REGISTRATION NUMBER: Clinicaltrial.gov ID: NCT05377151.

Indexed as

NutritionistsTelemedicineChildFemaleHumansOutcome Assessment, Health CarePregnancyPregnancy OutcomePregnant PeopleQuality of LifeRandomized Controlled Trials as TopicInformation technologyNUTRITION & DIETETICSOBSTETRICS

Identifiers

PMID37989361
PMCPMC10660825
OpenAlexW4388871653

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.