A Feasibility Study of Music and Play to Enhance Health and Developmental Outcomes in Infants From Low-income Families
Phase: NA
Conditions: Obesity, Infant, Language Development, Parent-Child Relations
Sponsor: Children's Mercy Hospital Kansas City

A community-based music enrichment program to address health and developmental disparities 
during early childhood 
NCT05868811 
7-3-2025 
 
Study Protocol 
The study procedures were approved by the Institutional Review Board at Children’s Mercy Hospital. 
Interested parents were screened via an online questionnaire or in-person interview at a hospital-affiliated 
primary care clinic. Eligible families were scheduled for two baseline assessment visits: a home visit (~45 
min) followed by a lab visit (~45 min). When researchers arrived at the home, parents were given a brief 
description of study protocols and completed a consent form for their participation and the infant’s 
participation. During the visit, parents filled out study questionnaires. Parents were then provided with 
instructions to record two days of the infant’s natural language environment using the Language 
Environment Analysis (LENA) devices. Before leaving their home, parents scheduled their second lab 
assessment visit. This visit was scheduled during a time when the parent felt the infant would be awake, 
alert, and willing to do the food/non-food reinforcement task. Parents were instructed to avoid feeding 
their child one hour prior to the visit and to provide the infant’s favorite solid food for the food portion of 
the task. Upon the family’s arrival to the lab, researchers interacted with the infants, establishing rapport 
by using toys and reading books. While infants became familiar with the researchers, parents completed 
study questionnaires. This orientation period lasted 5–10 min, until the infant had acclimated to their 
surroundings, as confirmed by the parent. Then, the child was placed in a highchair next to the parent to 
avoid separation anxiety and stranger anxiety. Parents were informed not to interact with the child during 
any of the research tasks. After the food/non-food reinforcement task, research staff measured the height 
and weight of the parent and infant.  
After completing both baseline assessment appointments, families were randomly assigned to the music 
or control group. Families completed follow-up assessments using the same procedure after their first 
semester (8-week) and second semester (16-week). When possible, the same research staff ran 
appointments so that families remained familiar with the staff, especially the researcher who delivered the 
reinforcers to the infants. Lastly, we sent out a parent satisfaction survey via email to all enrolled families 
to assess the acceptability of the intervention.  
 
Data Analysis Plan 
Group baseline characteristics were compared using ANOVA or Pearson’s chi-square test for frequency 
data. We performed an intention-to-treat analysis using all randomly assigned participants, including 
participants who dropped out of the program. The primary outcomes (RRVfood and home environmental 
enrichment measures) were analyzed with a mixed-model ANOVA, which handles missing data at 
random using maximum likelihood estimation and retains all randomly assigned subjects in the analysis. 
In an effort to choose a reasonably fitted model satisfying the model assumptions, the residual plots and 
the residual-based fit statistics, such as Akaike’s Information Criterion, for various covariance structures 
were examined. The models included group, time (base- line, mid-, and post-intervention), and the group 

× time interaction as class variables using the unstructured (UN) covariance structure. As a sensitivity 
analysis, we also per- formed repeated measures ANOVA for those who had complete data for all 
timepoints (i.e., the completers). Effect sizes were reported as partial eta squared for mixed model 
ANOVA, and eta-squares were converted to Cohen’s f to ease model comparison. Effect sizes for 
repeated measures ANOVA similarly were computed from partial eta square and converted to Cohen’s f 
using G*Power 3.1.9.6. Though p-values can inform whether an effect exists, Cohen’s f can help to assess 
the strength of that effect and contextualize the results. Given the small sample size, we focus on 
interpreting group differences when there are medium (0.25) to large (0.4) effect size estimates. Since our 
study is a pilot study, knowing the expected effect size can inform power analyses for sample size 
determination of a future larger RCT. All models were performed using PROC MIXED in SAS version 
9.4 (©2020, SAS Institute, Cary, NC, USA).