Evidence map›Paper›PMID 33627775›Full record

SynthesisInternational journal of obesity (2005)2021

Harnessing technological solutions for childhood obesity prevention and treatment: a systematic review and meta-analysis of current applications.

Lauren A Fowler, Anne Claire Grammer, Amanda E Staiano, Ellen E Fitzsimmons-Craft, Ling Chen, Lauren H Yaeger, Denise E Wilfley

Open access · bronzeAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in International journal of obesity (2005), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 5 pooled it
12.8field-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.

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

40 citing papers in PubMed, 5 syntheses or guidelines pooled it, 61 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Lauren A FowlerDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA. lauren.fowler@wustl.edu.ORCID http://orcid.org/0000-0002-9240-7267
Anne Claire GrammerDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
Amanda E StaianoLSU's Pennington Biomedical Research Center, Baton Rouge, LA, USA.
Ellen E Fitzsimmons-CraftDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
Ling ChenDivision of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
Lauren H YaegerWashington University School of Medicine, St. Louis, MO, USA.
Denise E WilfleyDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
Washington University in St. Louis · USPennington Biomedical Research Center · US

Funding

Tracking & Evaluation CoreU54GM104940 · NIGMS · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Peter Todd Katzmarzyk · 2012 to 2026
$69.1M
Washington University Nutrition Obesity Research CenterP30DK056341 · NIDDK · WASHINGTON UNIVERSITY · PI Nada A. Abumrad · 1999 to 2026
$30.2M
Washington University Center for Diabetes Translation Research P30DK092950 · NIDDK · WASHINGTON UNIVERSITY · PI Ross C Brownson, Debra Haire-Joshu · 2011 to 2026
$11.7M
WUSTL Transdisciplinary Pre- and Postdoctoral Training Program in Obesity and Cardiovascular DiseaseT32HL130357 · NHLBI · WASHINGTON UNIVERSITY · PI WILFLEY, DENISE ELLA · 2016 to 2025
$4.3M
Developing an Optimized Conversational Agent or "Chatbot" to Facilitate Mental Health Services Use in Individuals with Eating DisordersK08MH120341 · NIMH · WASHINGTON UNIVERSITY · PI FITZSIMMONS-CRAFT, ELLEN E. · 2019 to 2023
$824k
A Naturalistic Examination of Dietary Lapses in Low-Income, Treatment-Seeking Families with ObesityF31HL158000 · NHLBI · WASHINGTON UNIVERSITY · PI GRAMMER, ANNE CLAIRE · 2021 to 2022
$92k
NHLBI NIH HHS F31 HL158000NHLBI NIH HHS T32 HL130357NIDDK NIH HHS P30 DK056341NIDDK NIH HHS P30 DK092950NIGMS NIH HHS U54 GM104940NIMH NIH HHS K08 MH120341
6 · The paper itself

Abstract

backgroundTechnology holds promise for delivery of accessible, individualized, and destigmatized obesity prevention and treatment to youth.

objectivesThis review examined the efficacy of recent technology-based interventions on weight outcomes.

methodsSeven databases were searched in April 2020 following PRISMA guidelines. Inclusion criteria were: participants aged 1-18 y, use of technology in a prevention/treatment intervention for overweight/obesity; weight outcome; randomized controlled trial (RCT); and published after January 2014. Random effects models with inverse variance weighting estimated pooled mean effect sizes separately for treatment and prevention interventions. Meta-regressions examined the effect of technology type (telemedicine or technology-based), technology purpose (stand-alone or adjunct), comparator (active or no-contact control), delivery (to parent, child, or both), study type (pilot or not), child age, and intervention duration.

findingsIn total, 3406 records were screened for inclusion; 55 studies representing 54 unique RCTs met inclusion criteria. Most (89%) included articles were of high or moderate quality. Thirty studies relied mostly or solely on technology for intervention delivery. Meta-analyses of the 20 prevention RCTs did not show a significant effect of prevention interventions on weight outcomes (d = 0.05, p = 0.52). The pooled mean effect size of n = 32 treatment RCTs showed a small, significant effect on weight outcomes (d = ‒0.13, p = 0.001), although 27 of 33 treatment studies (79%) did not find significant differences between treatment and comparators. There were significantly greater treatment effects on outcomes for pilot interventions, interventions delivered to the child compared to parent-delivered interventions, and as child age increased and intervention duration decreased. No other subgroup analyses were significant.

conclusionsRecent technology-based interventions for the treatment of pediatric obesity show small effects on weight; however, evidence is inconclusive on the efficacy of technology based prevention interventions. Research is needed to determine the comparative effectiveness of technology-based interventions to gold-standard interventions and elucidate the potential for mHealth/eHealth to increase scalability and reduce costs while maximizing impact.

Indexed as

TechnologyChildHumansOverweightPediatric ObesityRandomized Controlled Trials as TopicTelemedicine

Identifiers

PMID33627775
PMCPMC7904036
OpenAlexW3132221639

What Socratic holds

Textmetadata
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 Socratic graph.