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Wu C, Fritz H, Bastami S, Maestre JP, Thomaz E, Julien C, Castelli DM, de Barbaro K, Bearman SK, Harari GM, Cameron Craddock R, Kinney KA, Gosling SD, Schnyer DM, Nagy Z. Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts. Gigascience 2021; 10:giab044. [PMID: 34155505 PMCID: PMC8216865 DOI: 10.1093/gigascience/giab044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/09/2021] [Indexed: 11/15/2022] Open
Abstract
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.
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Affiliation(s)
- Congyu Wu
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Hagen Fritz
- Department of Civil, Environmental, and Architectural Engineering, University of Texas at Austin, 301 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Sepehr Bastami
- Department of Civil, Environmental, and Architectural Engineering, University of Texas at Austin, 301 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Juan P Maestre
- Department of Civil, Environmental, and Architectural Engineering, University of Texas at Austin, 301 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Edison Thomaz
- Department of Electrical and Computer Engineering, University of Texas at Austin, 2501 Speedway, Austin, Texas, 78712, USA
| | - Christine Julien
- Department of Electrical and Computer Engineering, University of Texas at Austin, 2501 Speedway, Austin, Texas, 78712, USA
| | - Darla M Castelli
- Department of Kinesiology and Health Education, University of Texas at Austin, 2109 San Jacinto Blvd, Austin, Texas, 78712, USA
| | - Kaya de Barbaro
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Sarah Kate Bearman
- Department of Educational Psychology, University of Texas at Austin, 1912 Speedway, Austin, Texas, 78712, USA
| | - Gabriella M Harari
- Department of Communication, Stanford University, 450 Serra Mall, Stanford, California, 94305, USA
| | - R Cameron Craddock
- Department of Diagnostic Medicine, University of Texas at Austin, 1601 Trinity St, Austin, Texas, 78712, USA
| | - Kerry A Kinney
- Department of Civil, Environmental, and Architectural Engineering, University of Texas at Austin, 301 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Samuel D Gosling
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, Texas, 78712, USA
- Melbourne School of Psychological Sciences, University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia
| | - David M Schnyer
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, Texas, 78712, USA
| | - Zoltan Nagy
- Department of Civil, Environmental, and Architectural Engineering, University of Texas at Austin, 301 E Dean Keeton St, Austin, Texas, 78712, USA
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de Moura IR, Teles AS, Endler M, Coutinho LR, da Silva e Silva FJ. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. SENSORS (BASEL, SWITZERLAND) 2020; 21:s21010086. [PMID: 33375630 PMCID: PMC7795828 DOI: 10.3390/s21010086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.
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Affiliation(s)
- Ivan Rodrigues de Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, 65080-805 São Luís, Brazil; (A.S.T.); (L.R.C.); (F.J.d.S.e.S.)
| | - Ariel Soares Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, 65080-805 São Luís, Brazil; (A.S.T.); (L.R.C.); (F.J.d.S.e.S.)
- Federal Institute of Maranhão, 65570-000 Araioses, Brazil
| | - Markus Endler
- Department of Informatics, Pontifical Catholic University of Rio de Janeiro, 22453-900 Rio de Janeiro, Brazil;
| | - Luciano Reis Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, 65080-805 São Luís, Brazil; (A.S.T.); (L.R.C.); (F.J.d.S.e.S.)
| | - Francisco José da Silva e Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, 65080-805 São Luís, Brazil; (A.S.T.); (L.R.C.); (F.J.d.S.e.S.)
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Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. J Biomed Inform 2020; 107:103454. [PMID: 32562895 DOI: 10.1016/j.jbi.2020.103454] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 03/23/2020] [Accepted: 05/10/2020] [Indexed: 11/29/2022]
Abstract
Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and various cognitive biases. Today, however, there is a growing amount of studies that have provided methods to objectively monitor social behavior through ubiquitous devices and have used this information to support mental health services. In this paper, we present a Systematic Literature Review (SLR) to identify, analyze and characterize the state of the art about the use of ubiquitous devices to monitor users' social behavior focused on mental health. For this purpose, we performed an exhaustive literature search on the six main digital libraries. A screening process was conducted on 160 peer-reviewed publications by applying suitable selection criteria to define the appropriate studies to the scope of this SLR. Next, 20 selected studies were forwarded to the data extraction phase. From an analysis of the selected studies, we recognized the types of social situations identified, the process of transforming contextual data into social situations, the use of social situation awareness to support mental health monitoring, and the methods used to evaluate proposed solutions. Additionally, we identified the main trends presented by this research area, as well as open questions and perspectives for future research. Results of this SLR showed that social situation-aware ubiquitous systems represent promising assistance tools for patients and mental health professionals. However, studies still present limitations in methodological rigor and restrictions in experiments, and solutions proposed by them have limitations to be overcome.
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Affiliation(s)
- Ivan Moura
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil.
| | - Ariel Teles
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil; Federal Institute of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | | | - Markus Endler
- Pontifical Catholic University of Rio de Janeiro, Brazil
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