Work

Participant Engagement and Data Quality: Lessons Learned from a Mental Wellness Crowdsensing Study

Year: 2025

Type: article

Abstract: Mental health is a growing concern, especially among young adults, but gathering data from this demographic presents distinct challenges. Crowdsensing is a research approach that has become increasingly popular due to its ability to collect data from many individuals continuously and at scale. However, it is equally important to ensure that the data collected is of high quality, as it depends on many factors. In this paper, we discuss the data quality issues encountered during our crowdsensing study conducted from October 2022 to August 2023, which aimed at collecting data to study college students' emotions and mental wellness. We present our findings on data quality issues related to participant recruitment, device usability, data quantity, compliance, consistency, privacy concerns, and incentive mechanisms. We discuss the strategies to address these challenges and plans for future improvements. Our results and discussion highlight the effectiveness of crowdsensing in data collection for this demographic. Additionally, we identified positive and negative emotional drivers and potential stressors affecting this group's mental wellness. The insights from this work can aid the design of future crowdsensing applications and studies.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3711043

Authors: Enshi Zhang, Rafael M. Trujillo, Christian Poellabauer

Institution: Florida International University

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: National Science Foundation