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Chow SM, Nahum-Shani I, Baker JT, Spruijt-Metz D, Allen NB, Auerbach RP, Dunton GF, Friedman NP, Intille SS, Klasnja P, Marlin B, Nock MK, Rauch SL, Pavel M, Vrieze S, Wetter DW, Kleiman EM, Brick TR, Perry H, Wolff-Hughes DL. The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols. Transl Behav Med 2023; 13:7-16. [PMID: 36416389 PMCID: PMC9853092 DOI: 10.1093/tbm/ibac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts. We review the process through which harmonization challenges and opportunities motivated the development of tools and collection of metadata within the ILHBN. A variety of strategies have been adopted within the ILHBN to facilitate harmonization of ecological momentary assessment, location, accelerometer, and participant engagement data while preserving theory-driven heterogeneity and data privacy considerations. Several tools have been developed by the ILHBN to resolve challenges in integrating ILD across multiple data streams and time scales both within and across studies. Harmonization of distinct longitudinal measures, measurement tools, and sampling rates across studies is challenging, but also opens up new opportunities to address cross-cutting scientific themes of interest.
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Affiliation(s)
- Sy-Miin Chow
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
| | - Inbal Nahum-Shani
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Justin T Baker
- Department of Psychiatry, McLean Hospital, Boson, MA, USA
- Department of Psychiatry, Harvard Medical School, Boson, MA, USA
| | - Donna Spruijt-Metz
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | | | - Randy P Auerbach
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Genevieve F Dunton
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Naomi P Friedman
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Stephen S Intille
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, USA
| | - Benjamin Marlin
- College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Matthew K Nock
- Department of Psychology, Harvard University, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Franciscan Children’s, Boston, MA, USA
- Children’s Hospital, Boston, MA, USA
| | - Scott L Rauch
- Department of Psychiatry, McLean Hospital, Boson, MA, USA
- Department of Psychiatry, Harvard Medical School, Boson, MA, USA
| | - Misha Pavel
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - David W Wetter
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Evan M Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Timothy R Brick
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Heather Perry
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802, USA
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Halpin PF, Gates K, Liu S. Guest Editors' Introduction to the Special Issue on Forecasting with Intensive Longitudinal Data. PSYCHOMETRIKA 2022; 87:373-375. [PMID: 35230595 DOI: 10.1007/s11336-022-09850-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/29/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Peter F Halpin
- School of Education, The University of North Carolina at Chapel Hill, 100 E Cameron Ave, Chapel Hill, NC, 27599-3500, USA.
| | - Kathleen Gates
- Department of Psychology & Neuroscience, University of North Carolina at Chapel Hill, 341A Davie Hall, Chapel Hill, NC, 27599-3720, USA
| | - Siwei Liu
- Department of Human Ecology, University of California at Davis, One Shields Avenue, Davis, CA, 95616, USA
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Zhou S, Li Y, Chi G, Yin J, Oravecz Z, Bodovski Y, Friedman NP, Vrieze SI, Chow SM. GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. JOURNAL OF BEHAVIORAL DATA SCIENCE 2021; 1:127-155. [PMID: 35281484 PMCID: PMC8915920 DOI: 10.35566/jbds/v1n2/p5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.
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Affiliation(s)
- Shuai Zhou
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yanling Li
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Guangqing Chi
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Junjun Yin
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Zita Oravecz
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yosef Bodovski
- The Pennsylvania State University, University Park, PA 16801, USA
| | | | | | - Sy-Miin Chow
- The Pennsylvania State University, University Park, PA 16801, USA
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Zhou S, Li Y, Chi G, Yin J, Oravecz Z, Bodovski Y, Friedman NP, Vrieze SI, Chow SM. GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. JOURNAL OF BEHAVIORAL DATA SCIENCE 2021. [PMID: 35281484 DOI: 10.5281/zenodo.4672651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.
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Affiliation(s)
- Shuai Zhou
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yanling Li
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Guangqing Chi
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Junjun Yin
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Zita Oravecz
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yosef Bodovski
- The Pennsylvania State University, University Park, PA 16801, USA
| | | | | | - Sy-Miin Chow
- The Pennsylvania State University, University Park, PA 16801, USA
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