Evidence map›Paper›PMID 26469065›Full record

ArticlePloS one2015

Engagement with eHealth Self-Monitoring in a Primary Care-Based Weight Management Intervention.

Kathleen Y Wolin, Dori M Steinberg, Ilana B Lane, Sandy Askew, Mary L Greaney, Graham A Colditz, Gary G Bennett

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Effective behavior change techniques to promote physical activity in adults with overweight or obesity: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2021
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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.

Kathleen Y WolinCoeus Health, Chicago, IL, United States of America.
Dori M SteinbergDuke University, Durham, NC, United States of America.
Ilana B LaneDuke University, Durham, NC, United States of America.
Sandy AskewDuke University, Durham, NC, United States of America.
Mary L GreaneyUniversity of Rhode Island, Kingston, RI, United States of America.
Graham A ColditzWashington University in St. Louis, St. Louis, MO, United States of America.
Gary G BennettDuke University, Durham, NC, United States of America.

Funding

Integrated Technologies for Weight and Blood Pressure Control in Urban ClinicsU01HL087071 · NHLBI · WASHINGTON UNIVERSITY · PI COLDITZ, GRAHAM A. · 2006 to 2010
$4.9M
Community-based Obesity Prevention among BlacksK22CA126992 · NCI · DUKE UNIVERSITY · PI BENNETT, GARY G · 2007 to 2009
$529k
NCI NIH HHS K22 CA126992NCI NIH HHS K22CA126992NHLBI NIH HHS 5 U01 HL087071NHLBI NIH HHS U01 HL087071
6 · The paper itself

Abstract

backgroundWhile eHealth approaches hold promise for improving the reach and cost-effectiveness of behavior change interventions, they have been challenged by declining participant engagement over time, particularly for self-monitoring behaviors. These are significant concerns in the context of chronic disease prevention and management where durable effects are important for driving meaningful changes. PURPOSE: "Be Fit, Be Well" was an eHealth weight loss intervention that allowed participants to self-select a self-monitoring modality (web or interactive voice response (IVR)). Participants could change their modality. As such, this study provides a unique opportunity to examine the effects of intervention modality choice and changing modalities on intervention engagement and outcomes.

methodsIntervention participants, who were recruited from community health centers, (n = 180) were expected to self-monitor health behaviors weekly over the course of the 24-month intervention. We examined trends in intervention engagement by modality (web, IVR, or changed modality) among participants in the intervention arm.

resultsThe majority (61%) of participants chose IVR self-monitoring, while 39% chose web. 56% of those who selected web monitoring changed to IVR during the study versus no change in those who initially selected IVR. Self-monitoring declined in both modalities, but completion rates were higher in those who selected IVR. There were no associations between self-monitoring modality and weight or blood pressure outcomes.

conclusionsThis is the first study to compare web and IVR self-monitoring in an eHealth intervention where participants could select and change their self-monitoring modality. IVR shows promise for achieving consistent engagement.

Indexed as

Body WeightSelf ReportAdultAgedFemaleHumansInternetMaleMiddle AgedOverweightTelemedicine

Identifiers

PMID26469065
PMCPMC4607302

What OpenQuestion holds

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LicenceCC BY
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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.