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Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts
BACKGROUND: 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 and ecological validity. A number of human-subject studies have been conducted to examine t...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216865/ https://www.ncbi.nlm.nih.gov/pubmed/34155505 http://dx.doi.org/10.1093/gigascience/giab044 |
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author | Wu, Congyu Fritz, Hagen Bastami, Sepehr Maestre, Juan P Thomaz, Edison Julien, Christine Castelli, Darla M de Barbaro, Kaya Bearman, Sarah Kate Harari, Gabriella M Cameron Craddock, R Kinney, Kerry A Gosling, Samuel D Schnyer, David M Nagy, Zoltan |
author_facet | Wu, Congyu Fritz, Hagen Bastami, Sepehr Maestre, Juan P Thomaz, Edison Julien, Christine Castelli, Darla M de Barbaro, Kaya Bearman, Sarah Kate Harari, Gabriella M Cameron Craddock, R Kinney, Kerry A Gosling, Samuel D Schnyer, David M Nagy, Zoltan |
author_sort | Wu, Congyu |
collection | PubMed |
description | BACKGROUND: 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 and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample 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. RESULTS: To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. CONCLUSIONS: We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies. |
format | Online Article Text |
id | pubmed-8216865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-82168652021-06-22 Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts Wu, Congyu Fritz, Hagen Bastami, Sepehr Maestre, Juan P Thomaz, Edison Julien, Christine Castelli, Darla M de Barbaro, Kaya Bearman, Sarah Kate Harari, Gabriella M Cameron Craddock, R Kinney, Kerry A Gosling, Samuel D Schnyer, David M Nagy, Zoltan Gigascience Data Note BACKGROUND: 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 and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample 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. RESULTS: To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. CONCLUSIONS: We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies. Oxford University Press 2021-06-21 /pmc/articles/PMC8216865/ /pubmed/34155505 http://dx.doi.org/10.1093/gigascience/giab044 Text en © The Author(s) 2021. Published by Oxford University Press GigaScience. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Data Note Wu, Congyu Fritz, Hagen Bastami, Sepehr Maestre, Juan P Thomaz, Edison Julien, Christine Castelli, Darla M de Barbaro, Kaya Bearman, Sarah Kate Harari, Gabriella M Cameron Craddock, R Kinney, Kerry A Gosling, Samuel D Schnyer, David M Nagy, Zoltan Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title | Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title_full | Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title_fullStr | Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title_full_unstemmed | Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title_short | Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
title_sort | multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8216865/ https://www.ncbi.nlm.nih.gov/pubmed/34155505 http://dx.doi.org/10.1093/gigascience/giab044 |
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