Qualifying the Quantified: Structural Topic Modeling Analysis of Engagement in Digital Health Platform for Dementia Caregivers

Abstract

Qualitative analysis methods, while crucial for understanding complex factors influencing human behaviors like engagement with digital health technologies, can be conceptually challenging, time-consuming, and resource intensive. To address these challenges, semi-automated text analysis techniques like structural topic modeling (STM) may enhance the efficiency of qualitative analysis. This study investigates how STM can facilitate qualitative data analysis to gain insights into engagement with CareVirtue, a web-based health platform for dementia caregivers. We employed STM on interview data, using the number of CareVirtue journal posts as a covariate to represent caregiver engagement. Our STM model revealed six topics with varying levels of association with engagement. The topics of “recalling positive experiences” and “help from a care network” were associated with high engagement. These topics complement qualitative analysis by providing a deeper understanding of specific topics and demonstrate the potential of quantitative methods, such as STM, to support and augment qualitative analysis.

Keywords

qualitative analysis topic modeling dementia caregiving human factors machine learning

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