Whole Communities-Whole Health - Published Research
Permanent URI for this collectionhttps://hdl.handle.net/2152/74281
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Item Sensing everyday activity: Parent perceptions and feasibility(2019) Levin, Hannah I.; Egger, Dominique; Andres, Lara; Johnson, Mckensey; de Barbaro, KayaMobile and wearable sensors provide a unique opportunity to capture the daily activities and interactions that shape developmental trajectories, with potential to revolutionize the study of development (de Barbaro, 2019). However, developmental research employing sensors is still in its infancy, and parents’ comfort using these devices is uncertain. This report assesses parent willingness to participate in sensor studies via a nationally representative survey (N=210) and live recruitment of a low-income, minority population for an ongoing study (N=359). The survey allows us to assess how protocol design influences acceptability, including various options for devices and datastream resolution, conditions of data sharing, and feedback. By contrast, our recruitment data provides insight into parents’ true willingness to participate in a sensor study, with a protocol including 72hrs of continuous audio, motion, and physiological data. Our results indicate that parents are relatively conservative when considering participation in sensing studies. However, nearly 41% of surveyed parents report that they would be at least somewhat willing to participate in studies with audio or video recordings, 26% were willing or extremely willing, and 14% reported being extremely willing. These results roughly paralleled our recruitment results, where 58% of parents indicated interest, 29% of parents scheduled to participate, and 10% ultimately participated. Additionally, 70% of caregivers stated their reason for not participating in the study was due to barriers unrelated to sensing while about 25% noted barriers due to either privacy concerns or the physical sensors themselves. Parents’ willingness to collect sensitive datastreams increases if data stay within the household for individual use only, are shared anonymously with researchers, or if parents receive feedback from devices. Overall, our findings suggest that given the correct circumstances, mobile sensors are a feasible and promising tool for characterizing children’s daily interactions and their role in development.Item Whole Communities–Whole Health: A Participatory Team Science Cohort Model(Society for Research in Child Development Biennial Meeting, 2019-03) Maslowsky, Julie; Bearman, Sarah KateItem Can I Health You? Integrating Health and Physical Education the Whole Communities – Whole Health Story(Integrated Public Health-Aligned Physical Education Conference, Columbia, SC, 2019-09) Castelli, DarlaItem Whole Communities–Whole Health: Changing the way science helps society thrive(Interdisciplinary Association for Population Health Science, 2019 Annual Conference, 2019-10) Barczyk, AmandaItem Parental Epigenetic Influences Within and Across Generations(Neuroscience Program Seminar, University of Illinois, Urbana-Champaign, 2019-11) Champagne, FrancesItem Ecologically Valid, Multimodal Data Collection.(BuildSys 2020, Yokohama, Japan, 2019-11) Fritz, Hagen; Kinney, Kerry; Nagy, ZoltanItem BEVO Beacon: A Low-Cost Sensor Platform to Monitor Indoor Environmental Quality.(AAAR 2019, Portland, OR, 2019-11) Fritz, Hagen; Waites, William; Bastami, Sepehr; Kinney, Kerry; Nagy, ZoltanItem Validity of a Dense, Low-Cost Particulate Matter Sensor Network.(BuildSys 2020, Yokohama, Japan, 2019-11) Fritz, Hagen; Lin, Calvin; Kinney, Kerry; Nagy, ZoltanItem Beyond PETE and rePETE: Transdisciplinary team science integrating public health and physical activity into education(American Educational Research Association SIG Catherine Ennis Scholar Award, 2020) Castelli, DarlaItem Epigenetics and Trauma(Berkeley Law, University of California, 2020-01) Champagne, FrancesItem Epigenetics, Environments, and the Dynamic Brain(Department of Pharmacology and Center for Biomedical Neuroscience, UT Health San Antonio, 2020-03) Champagne, FrancesItem Biogenic VOC emissions under drought and temperature stress: implications for climate change and air quality(Environmental and Water Resource Engineering Research Seminar Series, University of Texas at Austin, 2020-04-09) Blomdahl, DanielItem Testing and Evaluation of Ozone Removal Air Cleaning Devices for Improving IAQ(ASHRAE AP-1579, 2020-07-15) Tang, Mengjia; Corsi, Richard; Siegel, J.; Misztal, Pawel; Novoselac, AtilaItem Chemical Exposure to disinfection byproducts interacting on personal face masks(RIG Sensors Seminar Series, University of Texas at Austin, 2020-10-09) Blomdahl, DanielItem Multi-Modal Data Collection for Measuring Health, Behavior, and Living Environment of Large-Scale Participant Cohorts: Conceptual Framework and Findings from Deployments(2020-10-16) Wu, Congyu; Fritz, Hagen; Nagy, Zoltan; Maestre, Juan P.; Thomaz, Edison; Julien, Christine; Castelli, Darla M.; de Barbaro, Kaya; Harari, Gabriella M.; Craddock, R. Cameron; Kinney, Kerry A.; Gosling, Samuel D.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.Item Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data(2020-10-19) Wu, Congyu; Barczyk, Amanda N.; Craddock, R. Cameron; Harari, Gabriella M.; Thomaz, Edison; Shumake, Jason D.; Beevers, Christopher G.; Gosling, Samuel D.Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual's Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features' statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications.Item Future Directions in Understanding Human Volatilome(Indoor Air 2020, Seoul, South Korea, 2020-11) Misztal, PawelItem Contamination of Surfaces and Dust in a Home with COVID-19 Cases(UT COVID-19 Conference, Austin, TX, 2020-11) Jarma, David; Maestre, Juan; Kinney, KerryItem The Protect Texas Together App(UT COVID-19 Conference, Austin, TX, 2020-11) Craddock, CameronItem Emission of Volatile Byproducts from Ozone Removal Filters(Indoor Air 2020, Seoul, South Korea, 2020-11) Tang, Mengjia; Novoselac, Atila; Misztal, Pawel