ArticleFrontiers in digital health2026
Monitoring students' well-being through journal analysis-study protocol of an explorative approach using natural language processing on typed and transcribed entries to monitor and generate personalized feedback.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: More than one-third of German students report high emotional exhaustion. One effective and low-cost method for improving emotional well-being is journaling, and advances in artificial intelligence (AI), particularly in Natural Language Processing (NLP), enable automated analysis of journal content for mental health monitoring. These analyses can be returned to users as personalized feedback, thereby enhancing the positive effects of journaling through increased self-reflection and creating an incentive for continuous use, which, in turn, improves monitoring. Journaling apps offer various input modalities (e.g., typing, speaking), potentially further increasing participation. Despite the promising potential of AI-powered journaling apps, their effects on mental health have so far been scarcely investigated. Research question: This study investigates the performance of NLP models in predicting emotional well-being from journal entries. Specifically, it examines whether typed or spoken entries provide a more suitable input modality for these predictive models. Additionally, we explore whether receiving feedback is positively evaluated and which types of feedback students prefer. Method: In a two-week observational study, Expected results: We expect that emotional well-being can be predicted from journal entries using NLP, with transcribed entries yielding higher accuracy than typed entries. We also expect the feedback to be well-received. Discussion: This study explores an accessible and engaging journaling application for monitoring and providing feedback on emotional well-being. It addresses several challenges in e-health research (e.g., high dropout rates, low user engagement). If this innovative approach yields strong user engagement and predictive performance, future work should evaluate automatically generated NLP-based feedback in journaling interventions to promote mental health. Clinical Trial registration: identifier DRKS00034660.
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