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Record W7149217046

Mobile Health Interventions for Modifying Indigenous Maternal and Child–Health Related Behaviors: Systematic Review

2025· article· W7149217046 on OpenAlexfundno aff
Sana Ishaque, Ola Ela, Anna Dowling, Chris Rissel, Karla Canuto, Kerry Hall, Niranjan Bidargaddi, Annette Briley, Claire T. Roberts, Billie Bonevski

Bibliographic record

VenueResearchOnline - JCU (James Cook University) · 2025
Typearticle
Language
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersMcGill University
KeywordsmHealthPsychological interventionIndigenousGrey literatureSystematic reviewExcellenceHealth interventionReproductive healthMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Background: Mobile health (mHealth) interventions promoting healthy lifestyle changes offer an adaptable and inexpensive method for accessing health information but require cultural appropriateness and suitability for acceptance and effectiveness in Indigenous populations. No systematic review on effective mHealth interventions for Indigenous women during pregnancy and the early childhood years has been conducted. Objective: This review evaluated the effectiveness of mHealth interventions promoting healthy behaviors for Indigenous mothers and children from conception to 5 years post partum. It also aimed to explore the observed effectiveness differences based on participant engagement, intervention design, and provision of context. Further, the review explored if the interventions were co-designed. Methods: A systematic search of 5 databases was conducted: SCOPUS, MEDLINE, CINAHL, PsycINFO, and ProQuest (Dissertation or Thesis). Studies were included if they were either a randomized controlled trial, pre-post comparison, or a cohort study using mHealth with Indigenous women for maternal and child health following a preregistered PROSPERO protocol (CRD42023395710). HealthInfoNet was searched for gray literature and the reference lists of included studies were hand searched. The initial title and abstract screen for eligibility were performed by 1 reviewer. A full-text screen of eligible studies and a quality appraisal of included studies was performed by 2 reviewers independently. The appraisal tools used were the Mixed Methods Quality Appraisal Tool and the Centre of Excellence in Aboriginal Chronic Disease Knowledge Translation and Exchange (CREATE). A descriptive synthesis of the extracted data was performed. Results: Of the 663 articles screened, only 3 met the eligibility criteria. Each paper evaluated a different mHealth intervention: Remote Prenatal Education; the SMS Parent Action Intervention (two-way text messaging); and the Screening, Brief Intervention and Referral to Treatment (SBIRT) eCHECKUP To Go (web-based screening and intervention). Statistically significant changes were reported in some outcomes, including an increase in the parental participation rate in face-to-face prenatal education; increased rate of breastfeeding initiation and exclusive breastfeeding (2-12 months); improved overall children’s behavior related to sleep, diet, physical activity, screen time, and intake of sugary beverages; improved individual children’s behavior related to physical activity and sleep; and decrease in alcohol drinks per week and binge drinking episodes per 2 weeks due to time effect. However, no study provided a sample size calculation for the reported significant outcomes. Also, due to the small number of included studies and each study evaluating a different intervention, it was not possible to combine results to ascertain if the participant engagement, intervention design, or community context had any impact on the effectiveness. Conclusions: Due to the lack of sample size calculation, it was not possible to establish whether differences in the effectiveness were due to the interventions or a type I statistical error. Therefore, caution is required in the interpretation of these findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.162
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0090.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.449
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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