Evidence map›Paper›PMID 24846909›Full record

Trial reportJournal of medical Internet research2014

Initial outcomes from a 4-week follow-up study of the Text4baby program in the military women's population: randomized controlled trial.

W Douglas Evans, Jasmine Wallace Bihm, Daniel Szekely, Peter Nielsen, Elizabeth Murray, Lorien Abroms, Jeremy Snider

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 5 pooled it
–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

43 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  12. Effects of postpartum mobile phone-based education on maternal and infant health in Ecuador.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2016
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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

7 authors.

W Douglas EvansMilken Institute School of Public Health, The George Washington University, Washington, DC, United States. wdevans@gwu.edu.
Jasmine Wallace Bihm
Daniel Szekely
Peter Nielsen
Elizabeth Murray
Lorien Abroms
Jeremy Snider

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of mobile phone technologies for health promotion and disease prevention has advanced rapidly in recent years. Text4baby is a theory-based mobile health (mHealth) program in which text messages are delivered to pregnant women and new mothers to improve their health care beliefs and behaviors and improve health status and clinical outcomes. Recent evaluations of Text4baby have found that it improves targeted health attitudes and beliefs, but effects on behavior have not yet been determined.

objectiveIn this study, investigators aimed to evaluate Text4baby in the military women's population.

methodsInvestigators conducted a randomized controlled trial at Madigan Army Medical Center in Tacoma, Washington, from December 2011 through September 2013. All participants were pregnant women first presenting for care at Madigan. Investigators conducted a baseline assessment using a 24-item, self-administered online survey of attitudes and behaviors related to Text4baby message content. Participants were randomized to Text4baby plus usual care (intervention) or usual care alone (control). Investigators analyzed treatment effects of Text4baby on short-term targeted outcomes 4 weeks post enrollment.

resultsFor this study, 943 patients were randomized and completed a baseline assessment. The average patient age was 28 years and nearly 70% self-identified as Caucasian. 48.7% of enrollees (459/943) completed the first follow-up assessment. Higher rates of single and working/in-school patients dropped out of the intervention arm of the study, and we adjusted for this finding in subsequent models. However, while investigators were unable to re-survey these participants, only 1.9% of Text4baby enrollees (18/943) dropped the service during the study period. Adjusted and unadjusted logistic generalized estimating equation models were developed to assess intervention effects on measured outcomes. In the model adjusting for age, marital status, having had a previous baby, and race/ethnicity, there was a significant effect of Text4baby intervention exposure on increased agreement with belief in the importance of taking prenatal vitamins (OR 1.91, 95% CI 1.08-3.34, P=.024). All of these attitudes had been targeted by at least one text message during the 4-week evaluation period examined in this study. In unadjusted models, there was a significant effect of intervention exposure on belief in the importance of visiting a health care provider to be a healthy new mother (OR 1.52, 95% CI 1.01-2.31, P=.046) and in the health risks of alcohol during pregnancy (OR 2.06, 95% CI 1.00-4.31, P=.05). No behavioral effects of the intervention were observed in this analysis.

conclusionsText4baby is a promising program that offers lessons for future mHealth activities. This large-scale study demonstrated initial effects of the program on attitudes and beliefs targeted by the messages received by women during the study period. Results confirm previous findings from Text4baby studies and other mHealth research. Future analyses will examine dosage effects of the intervention on behaviors and clinical outcomes.

Indexed as

Health BehaviorMilitary PersonnelTelemedicineText MessagingAdultCell PhoneFemaleFollow-Up StudiesHealth PromotionHumansLogistic ModelsPregnancyPrenatal Carehealth behaviormilitary healthmobile healthprenatal health careText4baby

Identifiers

PMID24846909
PMCPMC4051747

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Registered trials

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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.