## 5 Conclusions
### Limitations and Future Directions  
While this study advanced understanding of mHealth research dynamics of the mHTI scholars, several limitations should be noted: First, topic granularity was determined internally via Silhouette scores, an unsupervised approach that balances cluster coherence but risks over-specificity or over-generalization depending on sample characteristics. As a validation of our proposed method, we compared our results with those generated by NOMIC Atlas ([[https://atlas.nomic.ai/](https://atlas.nomic.ai/)]([https://atlas.nomic.ai/](https://atlas.nomic.ai/))), a state-of-the-art topic modeling and visualization tool. Although NOMIC's clustering and labeling processes are not open-source, it produced 256 topics with a minimum cluster size of 11 when applied to our embedding vectors. This outcome is comparable to our results in terms of topic count and cluster size, validating the robustness of our approach. While minor performance differences may stem from NOMIC's noise handling and topic merging methods, the overall consistency between the two methods reinforces the reliability of our findings. In addition, further validation of the alignment between LLM-generated topic labels and the associated publications would strengthen the credibility of the results. In the current study, this validation was performed by a single human evaluator from the research team. Incorporating multiple independent raters in future work would enhance objectivity and enable formal assessment of inter-rater reliability. Second, our analysis focused on the publications by a curated sample of mHTI-affiliated scholars; although practical for tracking training impacts, it may reflect institutional or selection biases. The resulting semantic space reflects topic configurations within this community rather than the broader mHealth field. Third, our kernel-based trajectory method assumes monotonic proximity gradients in the semantic probability density landscape; however, interdisciplinary scholars often navigate multipeak regions, as shown in Figure 8. When a scholar's research interest shifts from one mHealth-related peak to another, it becomes challenging to determine whether they are moving closer to or away from mHealth. However, we note that in our analysis, scholars identified as moving closer to mHealth (left subplot in Figure 8) are less likely to switch between mHealth-related intersections, as the green arrows are mostly distributed around the peaks, with fewer cases of arrows pointing from one peak to another. In contrast, for scholars identified as moving away from mHealth (right subplot in Figure 8), we observe several scholars migrating between peaks in the upper-right corner, and they might have been misclassified as moving away from mHealth. As a result, it is possible that we might have underestimated the number of scholars moving towards mHealth. If we included scholars switching between peaks, the proportion of scholars moving closer to mHealth would likely be higher. Fourth, this study faces limitations related to data sources. We rely solely on Google Scholar to collect publication data for scholar matching and comprehensive publication coverage. This choice is driven by the interdisciplinary nature of mHealth research, which spans medicine, public health, psychology, computer science, engineering, and the social sciences. We focus on tracking shifts in scholars' research interests rather than evaluating their research productivity. From this perspective, a wide range of academic outputs, including preprints (e.g., arXiv) and conference proceedings, are relevant indicators of evolving interests. Google Scholar offers broad coverage of these outputs and mitigates name ambiguity across data sources. That said, our framework is flexible and can incorporate additional bibliographic sources, ideally with manual verification, to further enhance accuracy. In addition, the availability of mHTI participant data begins in 2015, and our data collection ends in 2023.