Evidence map›Paper›PMID 23429637›Full record

Trial reportJournal of the American Medical Informatics Association : JAMIA2013

Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program.

Gabrielle M Turner-McGrievy, Michael W Beets, Justin B Moore, Andrew T Kaczynski, Daheia J Barr-Anderson, Deborah F Tate

3 registry-linked trialsOpen access · bronzeAbstract readComparative StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 174 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
174citing papers in PubMed, 12 pooled it
58.2field-weighted citation impact, top 1% 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.

NCT03961061 nacompletednot on this mapstarted 2020, after this paper: background citation

Increased Monitoring of Physical Activity and Calories With Technology

TypeinterventionalSponsorWake Forest University Health SciencesRan2020 to 2022Enrolled28ConditionsWeight Change, Body, Behavior, Health, Obesity, Childhood, Parent-Child RelationsArmsBrenner mFIT (standard care), Brenner mFIT (standard care plus mobile health components)
NCT04166955 nacompletednot on this mapstarted 2019, after this paper: background citation

WhatsApp-Based Intervention for Promoting Physical Activity Among Female College Students, Saudi Arabia: A Randomized Controlled Trial

TypeinterventionalSponsorKing Khalid UniversityRan2019 to 2020Enrolled103ConditionsPhysical ActivityArmsPromoting Physical Activity through mobile application
NCT05394779 nacompletednot on this mapstarted 2022, after this paper: background citation

Evaluation of Performance and Safety of DTXO App, an Innovative Digital Therapy, in Improving Weight Loss and Weight-Loss Maintenance of Patients With Obesity by Increasing Their Adherence to Dietary, Exercise Regimens and Psycho-Behavioral Program

TypeinterventionalSponsorAdvice Pharma Group srlRan2022 to 2024Enrolled246ConditionsObesity, Health Behavior, Life Style, Dietary HabitsArmsDTXO APP, PLACEBO APP - Control group
3 · Its place in the literature

Who cites it

174 citing papers in PubMed, 12 syntheses or guidelines pooled it, 397 citations in OpenAlex.

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  2. Digital behaviour change interventions to increase vegetable intake in adults: a systematic review.The international journal of behavioral nutrition and physical activity · 2023
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  9. Mobile App-Based Health Promotion Programs: A Systematic Review of the Literature.International journal of environmental research and public health · 2018
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114 more citing papers are in PubMed but not listed here.

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

6 authors at 3 institutions in 1 country.

Gabrielle M Turner-McGrievyDepartment of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA. brie@sc.edu
Michael W Beets
Justin B Moore
Andrew T Kaczynski
Daheia J Barr-Anderson
Deborah F Tate
University of South Carolina · USDepartment of Health Services · USUniversity of North Carolina at Chapel Hill · US

Funding

UNC Interdisciplinary Obesity Training (IDOT)(RMI)T32MH075854 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI POPKIN, BARRY M · 2005 to 2009
$1.2M
NIMH NIH HHS T32 MH075854
6 · The paper itself

Abstract

objectiveSelf-monitoring of physical activity (PA) and diet are key components of behavioral weight loss programs. The purpose of this study was to assess the relationship between diet (mobile app, website, or paper journal) and PA (mobile app vs no mobile app) self-monitoring and dietary and PA behaviors. MATERIALS AND

methodsThis study is a post hoc analysis of a 6-month randomized weight loss trial among 96 overweight men and women (body mass index (BMI) 25-45 kg/m(2)) conducted from 2010 to 2011. Participants in both randomized groups were collapsed and categorized by their chosen self-monitoring method for diet and PA. All participants received a behavioral weight loss intervention delivered via podcast and were encouraged to self-monitor dietary intake and PA.

resultsAdjusting for randomized group and demographics, PA app users self-monitored exercise more frequently over the 6-month study (2.6±0.5 days/week) and reported greater intentional PA (196.4±45.9 kcal/day) than non-app users (1.2±0.5 days/week PA self-monitoring, p<0.01; 100.9±45.1 kcal/day intentional PA, p=0.02). PA app users also had a significantly lower BMI at 6 months (31.5±0.5 kg/m(2)) than non-users (32.5±0.5 kg/m(2); p=0.02). Frequency of self-monitoring did not differ by diet self-monitoring method (p=0.63); however, app users consumed less energy (1437±188 kcal/day) than paper journal users (2049±175 kcal/day; p=0.01) at 6 months. BMI did not differ among the three diet monitoring methods (p=0.20).

conclusionsThese findings point to potential benefits of mobile monitoring methods during behavioral weight loss trials. Future studies should examine ways to predict which self-monitoring method works best for an individual to increase adherence.

Indexed as

DietExerciseOverweightSelf CareWeight Reduction ProgramsAdolescentAdultCell PhoneDiet RecordsEnergy IntakeFemaleHumansInternetMaleMedical Informatics ApplicationsMiddle Aged

Identifiers

PMID23429637
PMCPMC3628067
OpenAlexW205954197

What OpenQuestion holds

Textmetadata
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.