  As mobile technologies become ever more sensor-rich, portable, and
ubiquitous, data captured by smart devices are lending rich insights into
users' daily lives with unprecedented comprehensiveness, unobtrusiveness, and
ecological validity. A number of human-subject studies have been conducted in
the past decade to examine the use of mobile sensing to uncover individual
behavioral patterns and health outcomes. While understanding health and
behavior is the focus for most of these studies, we find that minimal attention
has been placed on measuring personal environments, especially together with
other human-centric data modalities. Moreover, the participant cohort size in
most existing studies falls well below a few hundred, leaving questions open
about the reliability of findings on the relations between mobile sensing
signals and human outcomes. To address these limitations, we developed a home
environment sensor kit for continuous indoor air quality tracking and deployed
it in conjunction with established mobile sensing and experience sampling
techniques in a cohort study of up to 1584 student participants per data type
for 3 weeks at a major research university in the United States. In this paper,
we begin by proposing a conceptual framework that systematically organizes
human-centric data modalities by their temporal coverage and spatial freedom.
Then we report our study design and procedure, technologies and methods
deployed, descriptive statistics of the collected data, and results from our
extensive exploratory analyses. Our novel data, conceptual development, and
analytical findings provide important guidance for data collection and
hypothesis generation in future human-centric sensing studies.
