Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 10, 2024
Date Accepted: Jun 23, 2025
Exploring Young Adults Experiences and Beliefs in Asthma Medication Management: A pilot Qualitative Study Comparing Human and Multiple Ai Thematic Analysis
ABSTRACT
Background:
Background:
Young adults take their asthma maintenance medication 67% of the time or less. Understanding the specific needs and behaviors of young adults with asthma is essential for developing targeted interventions to improve disease self-management. Exploration of these needs and behaviors through qualitative analysis is important but time consuming. Artificial intelligence (AI) could replace human coders in qualitative studies. However, there is a paucity literature to support this claim.
Objective:
The objective of this study is to begin to explore the medication-related needs of young adults with asthma via a pilot feasibility study. We aim to understand how to best assist young adults with asthma self-management and to identify potential areas where digital health interventions can provide support. We further aim to understand the comparative outcome of human versus multiple AI platforms in performing thematic analysis.
Methods:
This study purposefully sampled young adults who had a prescription for an inhaled corticosteroid and were either students or staff of a large metropolitan university in Northeastern USA. Semi-structured interviews lasting 40 minutes on average were conducted to elicit young adults’ opinions on the topic. Interviews were recorded and transcribed verbatim. After performing a second round of line-by-line coding, emergent codes were identified by investigators and categorized as themes using Braun and Clarke’s recommendation for performing a thematic analysis. All investigators reviewed and discussed the final codes. Human qualitative data-analyses were performed using NVivo 14 software (QSR International). After completing human analyses, the investigators performed thematic analysis with multiple AI platforms (Google Gemini, Microsoft Copilot, and ChatGPT) to compare the final themes with investigator derived themes.
Results:
There were four participants in this pilot study. Human analysis yielded four emergent themes: support from clinicians, social support, self-management support, and educational support. The AI-based analysis also generated similar themes with different labels. The percentage agreement between humans, Gemini, Copilot, and ChatGPT was 100%, accounting for the fact that, although the labels differed, they referred to the same concept.
Conclusions:
Findings from our study underscore the necessity for a holistic approach in supporting young adults with asthma. By addressing these multi-faceted needs, healthcare systems can significantly improve medication adherence and health outcomes for this understudied patient population. Our study also indicates that artificial intelligence may be leveraged for successful thematic analysis of qualitative data with appropriate caution. Clinical Trial: Not applicable
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