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As mobile technologies become ever more sensor-rich, portable, andubiquitous, data captured by smart devices are lending rich insights intousers' daily lives with unprecedented comprehensiveness, unobtrusiveness, andecological validity. A number of human-subject studies have been conducted inthe past decade to examine the use of mobile sensing to uncover individualbehavioral patterns and health outcomes. While understanding health andbehavior is the focus for most of these studies, we find that minimal attentionhas been placed on measuring personal environments, especially together withother human-centric data modalities. Moreover, the participant cohort size inmost existing studies falls well below a few hundred, leaving questions openabout the reliability of findings on the relations between mobile sensingsignals and human outcomes. To address these limitations, we developed a homeenvironment sensor kit for continuous indoor air quality tracking and deployedit in conjunction with established mobile sensing and experience samplingtechniques in a cohort study of up to 1584 student participants per data typefor 3 weeks at a major research university in the United States. In this paper,we begin by proposing a conceptual framework that systematically organizeshuman-centric data modalities by their temporal coverage and spatial freedom.Then we report our study design and procedure, technologies and methodsdeployed, descriptive statistics of the collected data, and results from ourextensive exploratory analyses. Our novel data, conceptual development, andanalytical findings provide important guidance for data collection andhypothesis generation in future human-centric sensing studies.

Multi-Modal Data Collection for Measuring Health, Behavior, and Living  Environment of Large-Scale Participant Cohorts: Conceptual Framework and  Findings from Deployments