Evidence map›Paper›PMID 33664477›Full record

ArticlePediatric research2022

The impact of reducing the frequency of night feeding on infant BMI.

Kelly J O'Shea, Marie C Ferguson, Layla Esposito, Lawrence D Hammer, Cameron Avelis, Daniel Hertenstein, Mario Solano Gonzales, Sarah M Bartsch, Patrick T Wedlock, Sheryl S Siegmund and 1 more

Open access · greenAbstract read
In one paragraph

Article in Pediatric research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact, top 97% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Trial
  2. Article
  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

11 authors at 3 institutions in 1 country.

Kelly J O'SheaPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Marie C FergusonPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Layla EspositoChild Development and Behavior, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA.
Lawrence D HammerDepartment of Pediatrics, Stanford University, Stanford, CA, USA.
Cameron AvelisPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Daniel HertensteinPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Mario Solano GonzalesPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Sarah M BartschPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Patrick T WedlockPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Sheryl S SiegmundPublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA.
Bruce Y LeePublic Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA. bruceleemdmba@gmail.com.
The Graduate Center, CUNY · USEunice Kennedy Shriver National Institute of Child Health and Human Development · USStanford University · US

Funding

MULTILEVEL SYSTEMS-ORIENTED CHILDHOOD OBESITY STUDY IN CHINAU54HD070725 · NICHD · JOHNS HOPKINS UNIVERSITY · PI CURRIERO, FRANK · 2011 to 2016
$15.9M
Early Life Determinants of Obesity in U.S. Urban Low Income Minority Birth CohortR01HD086013 · NICHD · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y, WANG, XIAOBIN · 2016 to 2020
$3.3M
Virtual Population Obesity Prevention (VPOP) Labs: Computational, Multi-Scale Models for Obesity SolutionsU01HD086861 · NICHD · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y · 2015 to 2019
$2.8M
Regional Healthcare Ecosystem Analyst for Healthcare Associated InfectionsR01HS023317 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI LEE, BRUCE Y · 2014 to 2014
$2.4M
Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2R01GM127512 · NIGMS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2020 to 2023
$2.1M
MOdeling Nursing homes to Affect Response to COVID-19 (MONARC)R01HS028165 · AHRQ · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2021 to 2022
$993k
AHRQ HHS R01 HS023317AHRQ HHS R01 HS028165NICHD NIH HHS R01 HD086013NICHD NIH HHS U01 HD086861NICHD NIH HHS U54 HD070725NIGMS NIH HHS R01 GM127512
6 · The paper itself

Abstract

backgroundTeaching caregivers to respond to normal infant night awakenings in ways other than feeding is a common obesity prevention effort. Models can simulate caregiver feeding behavior while controlling for variables that are difficult to manipulate or measure in real life.

methodsWe developed a virtual infant model representing an infant with an embedded metabolism and his/her daily sleep, awakenings, and feeds from their caregiver each day as the infant aged from 6 to 12 months (recommended age to introduce solids). We then simulated different night feeding interventions and their impact on infant body mass index (BMI).

resultsReducing the likelihood of feeding during normal night wakings from 79% to 50% to 10% lowered infant BMI from the 84th to the 75th to the 62nd percentile by 12 months, respectively, among caregivers who did not adaptively feed (e.g., adjust portion sizes of solid foods with infant growth). Among caregivers who adaptively feed, all scenarios resulted in relatively stable BMI percentiles, and progressively reducing feeding probability by 10% each month showed the least fluctuations.

conclusionsReducing night feeding has the potential to impact infant BMI, (e.g., 10% lower probability can reduce BMI by 20 percentile points) especially among caregivers who do not adaptively feed. IMPACT: Teaching caregivers to respond to infant night waking with other soothing behaviors besides feeding has the potential to reduce infant BMI. When reducing the likelihood of feeding during night wakings from 79% to 50% to 10%, infants dropped from the 84th BMI percentile to the 75th to the 62nd by 12 months, respectively, among caregivers who do not adaptively feed. Night-feeding interventions have a greater impact when caregivers do not adaptively feed their infant based on their growth compared to caregivers who do adaptively feed. Night-feeding interventions should be one of the several tools in a multi-component intervention for childhood obesity prevention.

Indexed as

Body Mass IndexCircadian RhythmFeeding BehaviorCaregiversHumansInfantModels, Theoretical

Identifiers

PMID33664477
PMCPMC8604149
OpenAlexW3133731663

What OpenQuestion holds

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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 OpenQuestion graph.