## 5 Conclusions  
### Summary and Discussion  
This study presents a hybrid analytical framework that pipelined LLM-derived semantic embeddings, UMAP dimensionality reduction, and HDBSCAN clustering to map the interdisciplinary landscape of mobile health (mHealth) research and the impact of the mHTI on the trained scholars' research trajectories. We identified 329 micro-topics, grouped into 14 high-level themes, which delineate how mHealth receives influence from diverse disciplines, such as technology, clinical medicine, public health, mental health, and social sciences. Our method employed LLM-based topic classification to isolate mHealth-specific topics within the semantic latent space, showing that mHealth research serves as a central nexus across diverse disciplines. This result underscores the importance of creating \"boundary space\" where scholars can reconfigure existing expertise into novel research vectors. Furthermore, kernel density estimation revealed that around 64% of mHTI participants shifted their research focus toward mHealth areas within three years of the program participation, demonstrating that structured training initiatives can effectively stimulate cross-disciplinary research engagement. Our study affirms the value of the targeted training program, mHTI, in guiding scholarly inquiry in emerging fields like mHealth, while providing a scalable and nuanced framework for analyzing the evolution of interdisciplinary research domains.  
Compared with previous approaches that rely on qualitative data, self-reported surveys, or coarse quantitative indicators such as publication or grant counts, our study offers a more objective and scalable framework based on publication content to track long-term shifts in research interests. Unlike earlier work on the mHTI program that emphasizes short-term communication dynamics through network modeling [9], our study focuses on content-level changes using semantic analysis. Together, these contributions demonstrate the potential of computational text analysis to uncover long-term intellectual shifts and provide new insights into how interdisciplinary training programs shape the direction and integration of scholarly work.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Discipline** & **Valid** & **Before** & **After** & **\(\Delta\) (After–Before)** \\  & **N** & **(\%)** & **(\%)** & **(\%)** & \(\Delta\) (After–Before)** \\ \hline CS/Stats/Engineering/Data Science & 48 & 26.8 & 35.4 & +8.6 \\ Medicine/Nursing & 33 & 11.6 & 19.1 & +7.5 \\ Psychology & 59 & 12.9 & 20.1 & +7.2 \\ Public Health & 15 & 3.6 & 6.8 & +3.2 \\ Others & 8 & 28.1 & 28.6 & +0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean of mHealth Publication Ratio by Discipline (After–Before).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cohort** & **Valid N** & **Before (\%)** & **After (\%)** & \(\Delta\) (After–Before)** \\ \hline
2015 & 19 & 13.6 & 27.8 & +14.1 \\
2016 & 23 & 7.5 & 22.3 & +14.8 \\
2017 & 29 & 22.3 & 28.9 & +6.6 \\
2018 & 17 & 20.0 & 21.9 & +1.9 \\
2019 & 24 & 18.2 & 29.5 & +11.3 \\
2021 & 28 & 15.8 & 18.7 & +2.9 \\
2022 & 23 & 18.2 & 16.6 & \(-1.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean of mHealth Publication Ratio by Cohort (After–Before). The second column reports the number of scholars who had at least one publication in both the pretraining [\(-3\), \(0\)) and post-training (\(0\), \(3\)] time windows.