Evidence map›Paper›PMID 38758581›Full record

ArticleJMIR formative research2024

The Impact of Behavior Change Counseling Delivered via a Digital Health Tool Versus Routine Care Among Adolescents With Obesity: Pilot Randomized Feasibility Study.

Maura Kepper, Callie Walsh-Bailey, Zoe M Miller, Min Zhao, Kianna Zucker, Angeline Gacad, Cynthia Herrick, Neil H White, Ross C Brownson, Randi E Foraker

Registry-linked trialAbstract read
In one paragraph

Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06121193 (Using Interventional Informatics to Address Social Determinants of Health During Clinical Care Visits to Promote Behavior Change and PREVENT Cardiovascular Disease), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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.

NCT06121193 nacompletednot on this map

Using Interventional Informatics to Address Social Determinants of Health During Clinical Care Visits to Promote Behavior Change and PREVENT Cardiovascular Disease

TypeinterventionalSponsorWashington University School of MedicineRan2020 to 2021Enrolled36ConditionsCardiovascular Diseases, ObesityArmsPREVENT tool, Wait-list Control
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
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

10 authors.

Maura KepperPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-5971-9189
Callie Walsh-BaileyPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-1417-5130
Zoe M MillerPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0001-8836-9301
Min ZhaoInstitute for Informatics, Washington University School of Medicine, St. Louis, MO, United States.ORCID https://orcid.org/0000-0003-1841-4757
Kianna ZuckerPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0009-0002-8232-9098
Angeline GacadPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0009-0006-5479-7272
Cynthia HerrickDivision of Endocrinology, Washington University School of Medicine, St. Louis, MO, United States.ORCID https://orcid.org/0000-0001-9696-6018
Neil H WhiteDivision of Pediatric Endocrinology & Diabetes, Washington University School of Medicine, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-4701-1976
Ross C BrownsonPrevention Research Center, Brown School, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0003-4260-2205
Randi E ForakerInstitute for Informatics, Washington University School of Medicine, St. Louis, MO, United States.ORCID https://orcid.org/0000-0001-9255-9394

Funding

Washington University Nutrition Obesity Research CenterP30DK056341 · NIDDK · WASHINGTON UNIVERSITY · PI Dominic N Reeds · 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
The Retain Study: Recruiting and Engaging with Technology Older Adults to Increase NeurocognitionU48DP006395 · DP · WASHINGTON UNIVERSITY · PI BROWNSON, ROSS C · 2018 to 2023
$4.8M
Washington University K12 Program in T4 Implementation ResearchK12HL137942 · NHLBI · WASHINGTON UNIVERSITY · PI DAVILA-ROMAN, VICTOR G. · 2017 to 2021
$3.1M
ACL HHS U48DP006395NCCDPHP CDC HHS U48 DP006395NHLBI NIH HHS K12 HL137942NIDDK NIH HHS P30 DK056341NIDDK NIH HHS P30 DK092950
6 · The paper itself

Abstract

backgroundYouth overweight and obesity is a public health crisis and increases the risk of poor cardiovascular health (CVH) and chronic disease. Health care providers play a key role in weight management, yet few tools exist to support providers in delivering tailored evidence-based behavior change interventions to patients.

objectiveThe goal of this pilot randomized feasibility study was to determine the feasibility of implementing the Patient-Centered Real-Time Intervention (PREVENT) tool in clinical settings, generate implementation data to inform scale-up, and gather preliminary effectiveness data.

methodsA pilot randomized clinical trial was conducted to examine the feasibility, implementation, and preliminary impact of PREVENT on patient knowledge, motivation, behaviors, and CVH outcomes. The study took place in a multidisciplinary obesity management clinic at a children's hospital within an academic medical center. A total of 36 patients aged 12 to 18 years were randomized to use PREVENT during their routine visit (n=18, 50%) or usual care control (n=18, 50%). PREVENT is a digital health tool designed for use by providers to engage patients in behavior change education and goal setting and provides resources to support change. Patient electronic health record and self-report behavior data were collected at baseline and 3 months after the intervention. Implementation data were collected via PREVENT, direct observation, surveys, and interviews. We conducted quantitative, qualitative, and mixed methods analyses to evaluate pretest-posttest patient changes and implementation data.

resultsPREVENT was feasible, acceptable, easy to understand, and helpful to patients. Although not statistically significant, only PREVENT patients increased their motivation to change their behaviors as well as their knowledge of ways to improve heart health and of resources. Compared to the control group, PREVENT patients significantly improved their overall CVH and blood pressure (P<.05).

conclusionsDigital tools can support the delivery of behavior change counseling in clinical settings to increase knowledge and motivate patients to change their behaviors. An appropriately powered trial is necessary to determine the impact of PREVENT on CVH behaviors and outcomes.

trial registrationClinicalTrials.gov NCT06121193; https://www.clinicaltrials.gov/study/NCT06121193.

Indexed as

adolescentsclinical careclinical trialdietdigital healthobesityphysical activity

Identifiers

PMID38758581
PMCPMC11143394

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.