Evidence map›Paper›PMID 25143156›Full record

Trial reportJournal of medical Internet research2014

How an online intervention to prevent excessive gestational weight gain is used and by whom: a randomized controlled process evaluation.

Margaret Mochon Demment, Meredith Leigh Graham, Christine Marie Olson

Registry-linked trialAbstract 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. It is linked to trial NCT01331564 (Electronically-Mediated Weight Interventions for Pregnant and Postpartum Women), which is not on this map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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.

NCT01331564 phase3completednot on this map

Electronically-Mediated Weight Interventions for Pregnant and Postpartum Women

TypeinterventionalSponsorUniversity of RochesterRan2011 to 2014Enrolled1,641ConditionsGestational Weight Gain, Postpartum Weight RetentionArmselectronic intervention during pregnancy and postpartum, electronic intervention during pregnancy, Control
3 · Its place in the literature

Who cites it

17 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

3 authors.

Margaret Mochon DemmentCommunity Nutrition, Division of Nutritional Sciences, Cornell University, Ithaca, NY, United States. mmd238@cornell.edu.ORCID http://orcid.org/0000-0003-2377-4343

Funding

Electronically-Mediated Weight Interventions for Pregnant and Postpartum WomenU01HL096760 · NHLBI · CORNELL UNIVERSITY · PI FERNANDEZ, ISABEL DIANA, OLSON, CHRISTINE MARIE · 2009 to 2013
$4.5M
TRAINING IN MATERNAL AND CHILD NUTRITIONT32HD007331 · NICHD · CORNELL UNIVERSITY ITHACA · PI RASMUSSEN, KATHLEEN M · 1987 to 2013
$1.3M
NHLBI NIH HHS HL096760NHLBI NIH HHS U01 HL096760NICHD NIH HHS 5T32HD007331NICHD NIH HHS T32 HD007331
6 · The paper itself

Abstract

backgroundOnline interventions have emerged as a popular strategy to promote healthy behaviors. Currently, there is little agreement about how to capture online intervention engagement. It is also uncertain who engages with weight-related online interventions and how engagement differs by demographic and weight characteristics.

objectiveThe objectives of this study were to (1) characterize how pregnant women engaged with features of an online intervention to prevent excessive gestational weight gain, (2) identify demographic and weight status subgroups of women within the sample, and (3) examine differences in use of intervention features across the demographic and weight status subgroups.

methodsA sample of racially and socioeconomically diverse pregnant women from a northeastern US city was assigned to the intervention group in a randomized controlled trial to prevent excessive gestational weight gain (n=1014). The intervention website included these features: weight-gain tracker, health-related articles, blogs, physical activity and diet goal-setting tools, and local resources. Engagement variables were created to capture the amount, consistency, and patterns of feature use across pregnancy using latent class analysis. Demographic/weight status subgroups were also created using latent class analysis. Differences in engagement across the demographic/weight status subgroups were examined using chi-square analysis.

resultsSix engagement patterns emerged: "super-users" (13.02%, 132/1014), "medium-users" (14.00%, 142/1014), "consistent weight-tracker users" (14.99%, 152/1014); "almost consistent weight-tracker users" (21.99%, 223/1014), "inconsistent weight-tracker users" (15.98%, 162/1014), and "non-users" (20.02%, 203/1014). Four demographic/weight status subgroups emerged: three minority and one white. There were different engagement patterns by demographic/weight status subgroups. Super-users were more likely to be in the white subgroup, while non-users were more likely to be in the minority subgroups. However, around a third of women in minority subgroups were consistently or almost consistently engaging with the weight-tracker (black, young women, 32.2%, 67/208; black, heavier women, 37.9%, 50/132; Hispanic women, 27.4%, 32/117).

conclusionsWhile white and higher income women had higher engagement in general, depending on the measure, there was still considerable engagement by the minority and low-income women.

trial registrationClinicaltrials.gov: NCT01331564; http://clinicaltrials.gov/ct2/show/NCT01331564 (Archived by WebCite at http://www.webcitation.org/6Rw4yKxI5).

Indexed as

Health EducationWeight GainAdultBody Mass IndexDietExerciseFemaleHealth BehaviorHumansInternetOverweightPregnancyPregnancy ComplicationsYoung Adultdemographic subgroupslatent class analysisobesity preventiononline engagementonline interventionprocess evaluationsocioeconomic differences

Identifiers

PMID25143156
PMCPMC4156016

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.