Daily steroid pills are often necessary for severe cases of asthma, but they raise the risk of several serious conditions. Now, scientists have shown that a monthly antibody injection can eliminate the...

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. artificial intelligence (#AI) has demonstrated its utility in summarizing and identifying patterns in qualitative research and may support or augment human coding efforts. However, there is pause literature to support this assertion. Objective: The objective of this study is to begin to explore the medication management 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 aimed to understand the comparative outcome of human versus multiple Ai platforms in performing thematic analysis. Methods: This study purposefully sampled young adults between the ages of 18 and 29 years old 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 with four participants via a teleconferencing application to elicit young adults’ opinions on the topic. Interviews were recorded and transcribed verbatim using Otter.ai. Investigators listened to the recording to confirm the accuracy of transcriptions and to make corrections when necessary. After performing a second round of line-by-line coding, the codes were reviewed by investigators and grouped into broader, overarching themes. 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: Human analysis yielded four themes: Support from clinicians, social support, digital self-management support, and educational support. The Ai-based analysis also generated similar themes with different labels. The level of overlap on the underlying concept between humans, Gemini, Copilot, and ChatGPT was high, accounting for the fact that, although the specific labels differed, they referred to the same concept. Conclusions: Findings from our pilot exploratory study offer insights into the necessity for a holistic approach in supporting young adults with asthma. Based on the health belief model, if the identified multi-faceted needs are addressed, healthcare systems may support medication adherence and improve health outcomes for this understudied patient population. Our pilot study also offers preliminary findings that artificial intelligence (#AI) may be leveraged upon for successful thematic analysis of qualitative data with appropriate caution.

Background: Adolescents with asthma are vulnerable to poor asthma outcomes due to inadequate self-management skills and non-adherence to medications. Mobile health (mHealth) applications have shown promise in improving asthma control, medication adherence, and self-efficacy. However, existing mHealth asthma applications lack personalization and real-time feedback, and are not tailored for at-risk adolescents. Objective: This study aimed to design, develop, and test a smartphone-based mHealth Asthma Action Plan (AAP) for adolescents, called Pulmonary Education and Knowledge Mobile Asthma Action Plan (PEAK-mAAP), in preparation for a large-scale randomized controlled trial (RCT). Methods: We employed user-centered design principles to develop our application, leveraging our prior work and following guidelines from the National Heart, Lung, and Blood Institute. The application consists of a patient-facing mobile application and a provider-facing portal. A convenience sample of 13 adolescents (aged 12–20 years) was recruited from the Arkansas Children’s Research Institute (ACRI) database or direct healthcare provider referrals. Participants underwent a task-based #usability assessment followed by the System #usability Scale (SUS) assessment to measure user satisfaction, interface effectiveness, and overall system #usability. Results: PEAK-mAAP integrates seven core modules supporting personalized asthma self-management, symptom monitoring, medication tracking, and real-time feedback. The mean SUS score was 83/100, indicating high user satisfaction and system #usability. Notably, older adolescents (>17 years) reported higher #usability scores (87.5) than younger users (77.5), suggesting potential age-related differences in-app navigation and engagement. Conclusions: The results demonstrate that PEAK-mAAP is a feasible and user-friendly mHealth intervention for adolescent asthma self-management. While the high #usability score reflects a positive user experience, some participants encountered initial #usability challenges, highlighting the need for minor refinements and user training materials. The integration of personalized self-management tools and real-time feedback distinguishes PEAK-mAAP from existing asthma applications, addressing key barriers to adherence and engagement. Moving forward, an ongoing RCT will assess its clinical effectiveness, long-term engagement, and impact on asthma outcomes, providing further insights into its potential as a scalable solution for adolescent asthma care.

Every breath tells a story, and for those living with asthma, understanding oxygen levels is key. We start by exploring what normal oxygen levels mean for asthm