Evidence map›Paper›PMID 29695373›Full record

ArticleJMIR mHealth and uHealth2018

Impact of the Growing Healthy mHealth Program on Maternal Feeding Practices, Infant Food Preferences, and Satiety Responsiveness: Quasi-Experimental Study.

Catherine Georgina Russell, Elizabeth Denney-Wilson, Rachel A Laws, Gavin Abbott, Miaobing Zheng, Sharyn J Lymer, Sarah Taki, Eloise-Kate V Litterbach, Kok-Leong Ong, Karen J Campbell

Open access · goldAbstract 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 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
8.5field-weighted citation impact, top 3% 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.

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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.

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  15. Children's eating behaviours and related constructs: conceptual and theoretical foundations and their implications.The international journal of behavioral nutrition and physical activity · 2023
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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

10 authors at 3 institutions in 1 country.

Catherine Georgina RussellCentre for Advanced Sensory Science, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, Burwood, Australia.ORCID http://orcid.org/0000-0002-0848-2724
Elizabeth Denney-WilsonCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0001-9879-4969
Rachel A LawsCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0003-4328-1116
Gavin AbbottInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia.ORCID http://orcid.org/0000-0003-4014-0705
Miaobing ZhengInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia.ORCID http://orcid.org/0000-0002-4151-3502
Sharyn J LymerCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-7876-6722
Sarah TakiCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-7228-8993
Eloise-Kate V LitterbachCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-4618-1068
Kok-Leong OngCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0003-4688-7674
Karen J CampbellCentre for Obesity Management and Prevention Research Excellence in Primary Health Care, Sydney, Australia.ORCID http://orcid.org/0000-0002-4499-3396
Deakin University · AUThe University of Sydney · AULa Trobe University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfancy is an important life stage for obesity prevention efforts. Parents' infant feeding practices influence the development of infants' food preferences and eating behaviors and subsequently diet and weight. Mobile health (mHealth) may provide a feasible medium through which to deliver programs to promote healthy infant feeding as it allows low cost and easy access to tailored content.

objectiveThe objective of this study was to describe the effects of an mHealth intervention on parental feeding practices, infant food preferences, and infant satiety responsiveness.

methodsA quasi-experimental study was conducted with an mHealth intervention group (Growing Healthy) and a nonrandomized comparison group ("Baby's First Food"). The intervention group received access to a free app with age-appropriate push notifications, a website, and an online forum that provided them with evidence-based advice on infant feeding for healthy growth from birth until 9 months of age. Behavior change techniques were selected using the Behaviour Change Wheel framework. Participants in both groups completed three Web-based surveys, first when their infants were less than 3 months old (baseline, T1), then at 6 months (time 2, T2), and 9 months of age (time 3, T3). Surveys included questions on infant feeding practices and beliefs (Infant Feeding Questionnaire, IFQ), satiety responsiveness (Baby Eating Behaviour Questionnaire), and infant's food exposure and liking. Multivariate linear regression models, estimated using maximum likelihood with bootstrapped standard errors, were fitted to compare continuous outcomes between the intervention groups, with adjustment for relevant covariates. Multivariate logistic regression adjusting for the same covariates was performed for categorical outcomes.

resultsA total of 645 parents (Growing Healthy: n=301, Baby's First Food: n=344) met the eligibility criteria and were included in the study, reducing to a sample size of 546 (Growing Healthy: n=234, Baby's First Food: n=312) at T2 and a sample size of 518 (Growing Healthy: n=225, Baby's First Food: n=293) at T3. There were approximately equal numbers of boy and girl infants, and infants were aged less than 3 months at baseline (Growing Healthy: mean 7.0, SD 3.7 weeks; Baby's First Food: mean 7.9, SD 3.8 weeks), with Growing Healthy infants being slightly younger than Baby's First Food infants (P=.001). All but one (IFQ subscale "concerns about infant overeating or becoming overweight" at T2) of the measured outcomes did not differ between Growing Healthy and Baby's First Food.

conclusionsAlthough mHealth can be effective in promoting some health behaviors and offers many advantages in health promotion, the results of this study suggest that design and delivery characteristics needed to maximize the impact of mHealth interventions on infant feeding are uncertain. The sensitivity of available measurement tools and differences in baseline characteristics of participants may have also affected the results.

Indexed as

appetiteeatingfeeding behaviorfood preferenceshealth promotioninfantmHealthobesityoverweightparentspediatric obesity

Identifiers

PMID29695373
PMCPMC5943630
OpenAlexW2800737354

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.