## 5 Conclusions
### Limitations and Future Directions
This time range limits our ability to observe longer-term research interest trajectories. As more longitudinal data become available in the future, our framework can be readily extended to support more comprehensive long-term analyses. Despite these limitations, the present study contributes to the science of science by offering a robust framework for understanding the dynamics of knowledge production in interdisciplinary fields like mHealth. Building on these insights, several avenues remain for future studies. First, the kernel-based method used to track scholars' research trajectories could be enhanced to better account for cross-disciplinary shifts. Second, future studies could expand the scope of analysis to include a broader range of interdisciplinary fields beyond mHealth. Investigating how training programs like mHTI influence participating scholars' research trajectories in other domains could provide comparative insights into the mechanisms of interdisciplinary collaboration and knowledge production. Furthermore, longitudinal studies tracking scholars over extended periods could offer a deeper understanding of the long-term impacts of training programs on research interests and career trajectories. Additionally, the current research focused more on a view of program evaluation, investigating how mHTI impacted the research trajectories of scholars. An important future research question could be how the mHealth field evolves with the infusion of fresh perspectives from scholars across different disciplines. Specifically, how do insights and knowledge from various fields shape mHealth, and what unique research topics emerge as a result? Finally, the proposed workflow, which leverages LLM and semantic trajectory analysis, offers a scalable and interpretable approach to measuring long-term shifts in research interests. While this study focuses on the development and demonstration of a computational pipeline, future work could enhance its analytical depth through the integration of additional methods. Incorporating elements of causal inference, such as introducing a control group, would allow for a more rigorous evaluation of the program's direct impact on research trajectories. At the same time, qualitative methods, including interviews or surveys with program participants, could enrich the analysis by capturing scholars' personal experiences and perspectives on how the training influenced their intellectual focus and collaboration patterns. Together, these extensions would support a more holistic understanding of interdisciplinary research dynamics and the role of training programs in shaping them. By addressing these directions, future research can build on the findings of the current study to further advance our understanding of interdisciplinary research dynamics and the mechanisms underlying knowledge production in fields like mHealth. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15116252/s1](https://www.mdpi.com/article/10.3390/app15116252/s1)]([https://www.mdpi.com/article/10.3390/app15116252/s1](https://www.mdpi.com/article/10.3390/app15116252/s1)). Conceptualization, J.R., [PERSON] and Y.H. ; methodology, [PERSON], [PERSON], Y.H. and M.J.; software, J.R.; validation, J.R., J.L. and Y.H. ; formal analysis, [PERSON]; investigation, J.R., J.L. and Y.H. ; resources, V.S. and M.J.; data curation, J.R., J.L. and Y.H. ; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON], Y.H., V.S. and M.J.; visualization, [PERSON], J.L. and Y.H. ; supervision, M.J.; project administration, J.L. ; funding acquisition, V.S. and M.J. All authors have read and agreed to the published version of the manuscript. This research was funded by National Institutes of Health (NIH) grant number 1R25 DA038167. Not applicable. The publication data analyzed in this study are publicly available through PubMed and Google Scholar.