Related Experiment Video
Updated: Aug 7, 2026
13:09
Segmentation and Measurement of Fat Volumes in Murine Obesity Models Using X-ray Computed Tomography
Published on: April 4, 2012
16.0K
Improving Understanding of Macrosomia with a Graphics-Based Educational Tool: A Randomized Controlled Trial (MATE)
Kristen A Cagino1, Myra Kurjee1, Emily Hyde1
1Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School at UTHealth Houston, Houston, Texas.
American Journal of Perinatology
|March 25, 2025
Summary
A graphics-based education tool (GBET) significantly improved pregnant individuals' knowledge of macrosomia risk factors and complications. However, the tool did not significantly improve knowledge of management options for macrosomia.
Area of Science:
- Obstetrics and Gynecology
- Medical Education
- Public Health
Background:
- Macrosomia, a condition of excessive fetal growth, poses significant risks to both mother and infant.
- Knowledge gaps regarding macrosomia's risk factors, complications, and management options are prevalent among pregnant individuals.
- Effective patient education tools are crucial for improving understanding and potentially mitigating adverse outcomes associated with macrosomia.
Purpose of the Study:
- To evaluate the effectiveness of a graphics-based education tool (GBET) in enhancing knowledge about macrosomia among pregnant individuals.
- To compare the knowledge gains between participants receiving the GBET and those receiving routine care.
- To assess knowledge specifically related to risk factors/complications (RF/C) and management options (MO) for macrosomia.
Main Methods:
- A randomized controlled trial (NCT06281301) was conducted with pregnant individuals meeting specific gestational age and health criteria.
- Participants were randomized to receive either the GBET or routine care.
- Knowledge was assessed using a 17-question questionnaire focusing on RF/C (11 questions) and MO (6 questions) for macrosomia.
Main Results:
- The GBET group demonstrated significantly higher overall knowledge scores compared to the routine care group (70% vs. 64%, p < 0.001).
- Knowledge scores for risk factors and complications (RF/C) were significantly improved in the GBET group (72% vs. 63%, p = 0.001).
- No significant difference was observed in knowledge scores for management options (MO) between the two groups (65% vs. 68%, p = 0.084).
Conclusions:
- The graphics-based education tool effectively improved pregnant individuals' knowledge of macrosomia's risk factors and complications.
- The GBET did not significantly enhance understanding of macrosomia management options in this study population.
- Further research is warranted to explore if enhanced knowledge translates into improved clinical outcomes for macrosomia.
More Related Videos
12:29Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
Published on: May 3, 2017
9.7K
06:49A Semi-high-throughput Imaging Method and Data Visualization Toolkit to Analyze C. elegans Embryonic Development
Published on: October 29, 2019
6.6K
Related Concept Videos
02:01Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
01:13Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:09Comparing Experimental Results: Student's t-Test
The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...