Improving Pediatric Obesity Practice Using Prompts (iPOP-UP)

May 13, 2021 updated by: Yale University

Improving Pediatric Obesity Practice Using Prompts (iPOP-UP): Using Electronic Health Records to Support Decision-Making in Pediatric Obesity Care

This study compares the effectiveness of electronic health record (EHR)-based tools to support the management of pediatric obesity in primary care. All clinicians will receive an interruptive "pop-up" alert We will examine the impact -- the added value versus unintended consequences -- of the interruptive alert on the quality of obesity management in pediatric primary care.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

The primary specific aims of this study are to:

  1. To assess the impact of EHR-based tools for pediatric obesity in primary care. Hypotheses: EHR-based clinical decision support tools that interrupt but support clinical workflow will (1) improve measures of pediatric obesity care quality delivered by clinicians (e.g., addition of obesity to the problem list, recommended screening for comorbidities, and follow-up/referral plans) and (2) result in a reduced rate of BMI increase over one year among children with obesity.
  2. To utilize a mixed methods approach with surveys and semi-structured qualitative interviews with clinicians to (1) examine the extent to which the EHR tools positively impact clinicians' awareness, knowledge and adherence to expert guidelines for pediatric obesity management, and (2) to explore the barriers to and facilitators of clinicians' use of the EHR tools and factors that influence adoption.

Study Type

Interventional

Enrollment (Actual)

140

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Massachusetts
      • Boston, Massachusetts, United States, 02115
        • Boston Children's Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • ADULT
  • OLDER_ADULT
  • CHILD

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

All pediatric primary care providers providing well child care for patients ages 2-17 years-old in the Boston Children's Hospital primary care practices will be eligible for the study. There are no exclusion criteria.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: HEALTH_SERVICES_RESEARCH
  • Allocation: NA
  • Interventional Model: SINGLE_GROUP
  • Masking: NONE

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
EXPERIMENTAL: Interruptive Clinical Decision Support

An interruptive, "soft-stop" alert will pop up when a pediatric primary care provider open a child's electronic health record (i.e., a new window in the forefront of the screen interrupting workflow and requiring the clinician to take an action) alerting them that the child meets criteria for obesity based on their age/sex-specific BMI percentile. The pop-up alert includes:

  • One-click addition of elevated BMI to problem list
  • Reminder to utilize Suggested PowerPlan
  • One-click access to a patient handout on evidence-based behavior change goals (screen time, sugary drinks, physical activity, sleep) and link to additional handouts and resources
  • Tables displaying trends in growth measures, blood pressure and relevant laboratory tests
  • Links to existing, evidence-based childhood obesity screening and management guidelines

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in body mass index (BMI)
Time Frame: 1 year pre-intervention, baseline, and 1 year post-intervention
change in BMI, calculated from height and weight measured as part of routine clinical practice during primary care clinic visits and documented in the EHR
1 year pre-intervention, baseline, and 1 year post-intervention
Change in percent BMI above the 95th percentile (%BMIp95)
Time Frame: 1 year pre-intervention, baseline, and 1 year post-intervention
Change in percentage of age/sex-adjusted BMI above the 95th percentile (%BMIp95), calculated from height and weight measured as part of routine clinical practice during primary care clinic visits and documented in the EHR
1 year pre-intervention, baseline, and 1 year post-intervention
Change in documentation of elevated BMI diagnosis
Time Frame: 1-year pre-implementation compared to 1-year post-implementation
Change in proportion of patients with obesity who have elevated BMI documented in the EHR
1-year pre-implementation compared to 1-year post-implementation
Change in proportion of patients with obesity
Time Frame: 1-year pre-implementation compared to 1-year post-implementation
Change in proportion of patients with obesity who receive age-appropriate screening for comorbidities (blood measure measurement and age-appropriate laboratory screening)
1-year pre-implementation compared to 1-year post-implementation
Change in proportion of patients with obesity who have counseling for obesity-related behavior change documented in the EHR
Time Frame: 1-year pre-implementation compared to 1-year post-implementation
1-year pre-implementation compared to 1-year post-implementation
Change in proportion of patients with obesity with follow-up or referral orders
Time Frame: 1-year pre-implementation compared to 1-year post-implementation
1-year pre-implementation compared to 1-year post-implementation

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Change in provider knowledge, attitudes and practice around obesity management in primary care assessed via an electronic surveys and qualitative interviews of clinicians
Time Frame: baseline compared to 6 months post-implementation
baseline compared to 6 months post-implementation
System usability scale (SUS) score
Time Frame: 6 months post-implementation
the system usability scale is a validated 10-item measure of system usability
6 months post-implementation

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Collaborators

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ACTUAL)

September 1, 2018

Primary Completion (ACTUAL)

September 30, 2020

Study Completion (ACTUAL)

December 31, 2020

Study Registration Dates

First Submitted

August 21, 2018

First Submitted That Met QC Criteria

August 24, 2018

First Posted (ACTUAL)

August 27, 2018

Study Record Updates

Last Update Posted (ACTUAL)

May 17, 2021

Last Update Submitted That Met QC Criteria

May 13, 2021

Last Verified

May 1, 2021

More Information

Terms related to this study

Other Study ID Numbers

  • 2000026382
  • 1K08HS024332-01A1 (AHRQ)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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