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

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.