Open AccessUsing Text Messaging, Social Media, and Interviews to Understand What Pregnant Youth Think About Weight Gain During Pregnancy
Author(s) -
Melissa DeJonckheere,
Lauren P. Nichols,
V. G. Vinod Vydiswaran,
Xinyan Zhao,
Kevyn Collins-Thompson,
Ken Resnicow,
Tammy Chang
Publication year - 2019
Publication title -
jmir formative research
Language(s) - English
Resource type - Journals
ISSN - 2561-326X
DOI - 10.2196/11397
Subject(s) - weight gain , pregnancy , social media , medicine , psychology , obesity , obstetrics , text messaging , family medicine , developmental psychology , body weight , computer science , internet privacy , world wide web , biology , genetics
Background The majority of pregnant youth gain more weight than recommended by the National Academy of Medicine guidelines. Excess weight gain during pregnancy increases the risk of dangerous complications during delivery, including operative delivery and stillbirth, and contributes to the risk of long-term obesity in both mother and child. Little is known regarding youth’s perceptions of and knowledge about weight gain during pregnancy. Objective The aim of this study was to describe the feasibility and acceptability of 3 novel data collection and analysis strategies for use with youth (social media posts, text message surveys, and semistructured interviews) to explore their experiences during pregnancy. The mixed-methods analysis included natural language processing and thematic analysis. Methods To demonstrate the feasibility and acceptability of this novel approach, we used descriptive statistics and thematic qualitative analysis to characterize participation and engagement in the study. Results Recruitment of 54 pregnant women aged between 16 and 24 years occurred from April 2016 to September 2016. All participants completed at least 1 phase of the study. Semistructured interviews had the highest rate of completion, yet all 3 strategies were feasible and acceptable to pregnant youth. Conclusions This study has described a novel youth-centered strategy of triangulating 3 sources of mixed-methods data to gain a deeper understanding of a health behavior phenomenon among an at-risk population of youth.