Project Details
Projekt
Combining Surveys and Digital Tracking Data for Mental Health Research from a Computational Social Science Perspective (COSDIMH)
Subject Area
Communication Sciences
Personality Psychology, Clinical and Medical Psychology, Methodology
Personality Psychology, Clinical and Medical Psychology, Methodology
Term
since 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 539521008
Mental health (MH) is receiving increasing attention from both the public and academia, particularly in the context of ubiquitous digital media use (DMU). Surveys are the primary method of MH research but have their limitations in accurately capturing DMU because they rely on subjective memories. Digital behavioral trace data and computational methods are gaining traction among researchers who want to understand the relationship between DMU and MH phenomena, such as various forms of stress and well-being. Digital tracking data, a prominent example of digital behavioral traces, can be merged with survey data and are becoming increasingly common in the field. However, despite the rise of literature integrating these new data sources to examine the relationship between DMU and MH, there are notable methodological challenges. These include a lack of systematic comparisons of surveys and digital data, an insufficient understanding of the dynamics between DMU and MH, and a deficit of experimental evidence clarifying the causal relationship between the two variables. As a result, numerous questions about their interplay remain unresolved, highlighting a relevant and contemporary research domain. In the proposed interdisciplinary project, we aim to foster innovation by focusing on combining different DMU and MH measures in survey data and digital tracking data. We conduct the project in four work packages (WP) and in close cooperation with GESIS. In WP1, we design a survey and integrate various DMU and MH measures. In addition, we collect digital tracking data from the same individuals over the course of four weeks. We compare measures within and across data sources to analyze their validity and reliability. To examine the short-term dynamic interaction between DMU and MH measures, in WP2, we conduct an ambulatory assessment study over two weeks and combine different measures from multiple surveys and digital tracking data. In WP3, we analyze the causality of the relationship between DMU and MH in an experimental setting and implement personalized interventions based on the results of WP1 and 2 to improve users' well-being through a tailored DMU diet. Based on the results of WP1-3, we develop in WP4 recommendations for future studies that aim to combine different data sources for DMU and MH research. We identify best practices, methods, and potential pitfalls for such combinatorial research approaches. The proposed project contributes to Area 3 and 4 of the InfPP "New Data Spaces for the Social Sciences". We consider the simultaneously increasing relevance of MH and digitization in the population, develop and test methods for the social sciences to use innovative digital data sources for this field. In this way, we are helping to enable the social sciences to open new possibilities for the research field beyond previous data sources and methods, to shape associated social changes, and to identify the need for political action.
DFG Programme
Infrastructure Priority Programmes
