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Rottstädt F, Becker E, Wilz G, Croy I, Baumeister H, Terhorst Y. Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial. Front Digit Health 2024; 6:1335776. [PMID: 38698889 PMCID: PMC11063245 DOI: 10.3389/fdgth.2024.1335776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Objective Smart sensing has the potential to make psychotherapeutic treatments more effective. It involves the passive analysis and collection of data generated by digital devices. However, acceptance of smart sensing among psychotherapy patients remains unclear. Based on the unified theory of acceptance and use of technology (UTAUT), this study investigated (1) the acceptance toward smart sensing in a sample of psychotherapy patients (2) the effectiveness of an acceptance facilitating intervention (AFI) and (3) the determinants of acceptance. Methods Patients (N = 116) were randomly assigned to a control group (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a control video. An online questionnaire was used to assess acceptance of smart sensing, performance expectancy, effort expectancy, facilitating conditions and social influence. The intervention effects of the AFI on acceptance were investigated. The determinants of acceptance were analyzed with structural equation modeling (SEM). Results The IG showed a moderate level of acceptance (M = 3.16, SD = 0.97), while the CG showed a low level (M = 2.76, SD = 1.0). The increase in acceptance showed a moderate effect in the intervention group (p < .05, d = 0.4). For the IG, performance expectancy (M = 3.92, SD = 0.7), effort expectancy (M = 3.90, SD = 0.98) as well as facilitating conditions (M = 3.91, SD = 0.93) achieved high levels. Performance expectancy (γ = 0.63, p < .001) and effort expectancy (γ = 0.36, p < .001) were identified as the core determinants of acceptance explaining 71.1% of its variance. The fit indices supported the model's validity (CFI = .95, TLI = .93, RMSEA = .08). Discussion The low acceptance in the CG suggests that enhancing the acceptance should be considered, potentially increasing the use and adherence to the technology. The current AFI was effective in doing so and is thus a promising approach. The IG also showed significantly higher performance expectancy and social influence and, in general, a strong expression of the UTAUT factors. The results support the applicability of the UTAUT in the context of smart sensing in a clinical sample, as the included predictors were able to explain a great amount of the variance of acceptance.
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Affiliation(s)
- Fabian Rottstädt
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
- DZPG (German Center for Mental Health), Partner Site Halle-Jena-Magdeburg, Jena, Germany
| | - Eduard Becker
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
| | - Gabriele Wilz
- Department of Clinical-Psychological Intervention, Friedrich Schiller University of Jena, Jena, Germany
| | - Ilona Croy
- Department of Clinical Psychology, Friedrich Schiller University of Jena, Jena, Germany
- DZPG (German Center for Mental Health), Partner Site Halle-Jena-Magdeburg, Jena, Germany
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, University Ulm, Ulm, Germany
- DZPG (German Center for Mental Health), Partner Site Mannheim-Ulm-Heidelberg, Ulm, Germany
| | - Yannik Terhorst
- Department of Clinical Psychology and Psychotherapy, University Ulm, Ulm, Germany
- DZPG (German Center for Mental Health), Partner Site Mannheim-Ulm-Heidelberg, Ulm, Germany
- Department of Psychological Methods and Assessment, Ludwigs-Maximilian University Munich, Munich, Germany
- DZPG (German Center for Mental Health), Partner Site München, Munich, Germany
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Lee K, Cheongho Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas MA, Wright AA, Demiris G, Ritchie CS, Pickering CE, Nicholas Dionne-Odom J. Using Digital phenotyping to understand health-related outcomes: A scoping review. Int J Med Inform 2023; 174:105061. [PMID: 37030145 DOI: 10.1016/j.ijmedinf.2023.105061] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/10/2023] [Accepted: 03/24/2023] [Indexed: 04/01/2023]
Abstract
BACKGROUND Digital phenotyping may detect changes in health outcomes and potentially lead to proactive measures to mitigate health declines and avoid major medical events. While health-related outcomes have traditionally been acquired through self-report measures, those approaches have numerous limitations, such as recall bias, and social desirability bias. Digital phenotyping may offer a potential solution to these limitations. OBJECTIVES The purpose of this scoping review was to identify and summarize how passive smartphone data are processed and evaluated analytically, including the relationship between these data and health-related outcomes. METHODS A search of PubMed, Scopus, Compendex, and HTA databases was conducted for all articles in April 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Review (PRISMA-ScR) guidelines. RESULTS A total of 40 articles were included and went through an analysis based on data collection approaches, feature extraction, data analytics, behavioral markers, and health-related outcomes. This review demonstrated a layer of features derived from raw sensor data that can then be integrated to estimate and predict behaviors, emotions, and health-related outcomes. Most studies collected data from a combination of sensors. GPS was the most used digital phenotyping data. Feature types included physical activity, location, mobility, social activity, sleep, and in-phone activity. Studies involved a broad range of the features used: data preprocessing, analysis approaches, analytic techniques, and algorithms tested. 55% of the studies (n = 22) focused on mental health-related outcomes. CONCLUSION This scoping review catalogued in detail the research to date regarding the approaches to using passive smartphone sensor data to derive behavioral markers to correlate with or predict health-related outcomes. Findings will serve as a central resource for researchers to survey the field of research designs and approaches performed to date and move this emerging domain of research forward towards ultimately providing clinical utility in patient care.
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Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. J Biomed Inform 2023; 138:104278. [PMID: 36586498 DOI: 10.1016/j.jbi.2022.104278] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 12/20/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022]
Abstract
Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.
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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
| | - Davi Viana
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Jean Marques
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Luciano Coutinho
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
| | - Francisco Silva
- Laboratory of Intelligent Distributed Systems (LSDi), Federal University of Maranhão, Brazil
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Barthwal A. A Markov chain-based IoT system for monitoring and analysis of urban air quality. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 195:235. [PMID: 36574091 DOI: 10.1007/s10661-022-10857-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Severe deterioration of urban air quality in Asian cities is the cause of a large number of deaths every year. A Markov chain-based IoT system is developed in this study to monitor, analyze, and predict urban air quality. The proposed sensing setup is integrated with an automobile and is used for collecting air quality information. An Android application is used to transfer and store the sensed data in the data cloud. The data stored is used to generate the transition matrix of the AQI states and calculate return periods for each AQI state. The estimated time interval after which an AQI event recurs or is repeated is known as return period. The actual return periods for each AQI state at the test locations in Delhi-NCR are compared with those predicted using discrete time Markov chain (DTMC) models. Average absolute forecast error using our model was found to be 3.38% and 4.06%, respectively, at the selected locations.
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Affiliation(s)
- Anurag Barthwal
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, Uttar Pradesh, India.
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5
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Santillán Cooper M, Armentano MG. Predicting future sedentary behaviour using wearable and mobile devices. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.103104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Comput Sci 2022; 8:e1042. [PMID: 36092018 PMCID: PMC9455148 DOI: 10.7717/peerj-cs.1042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Mental health issues are a serious consequence of the COVID-19 pandemic, influencing about 700 million people worldwide. These physiological issues need to be consistently observed on the people through non-invasive devices such as smartphones, and fitness bands in order to remove the burden of having the conciseness of continuously being monitored. On the other hand, technological improvements have enhanced the abilities and roles of conventional mobile phones from simple communication to observations and improved accessibility in terms of size and price may reflect growing familiarity with the smartphone among a vast number of consumers. As a result of continuous monitoring, together with various embedded sensors in mobile phones, raw data can be converted into useful information about the actions and behaviors of the consumers. Thus, the aim of this comprehensive work concentrates on the literature work done so far in the prediction of mental health issues via passive monitoring data from smartphones. This study also explores the way users interact with such self-monitoring technologies and what challenges they might face. We searched several electronic databases (PubMed, IEEE Xplore, ACM Digital Libraries, Soups, APA PsycInfo, and Mendeley Data) for published studies that are relevant to focus on the topic and English language proficiency from January 2015 to December 2020. We identified 943 articles, of which 115 articles were eligible for this scoping review based on the predetermined inclusion and exclusion criteria carried out manually. These studies provided various works regarding smartphones for health monitoring such as Physical activity (26.0 percent; 30/115), Mental health analysis (27.8 percent; 32/115), Student specific monitoring (15.6 percent; 18/115) are the three analyses carried out predominantly.
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Affiliation(s)
- Abinaya Gopalakrishnan
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Revathi Venkataraman
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Rohan Genrich
- School of Business, University of Southern Queensland, Toowoomba, Australia
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7
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WANG WEICHEN, NEPAL SUBIGYA, HUCKINS JEREMYF, HERNANDEZ LESSLEY, VOJDANOVSKI VLADO, MACK DANTE, PLOMP JANE, PILLAI ARVIND, OBUCHI MIKIO, DASILVA ALEX, MURPHY EILIS, HEDLUND ELIN, ROGERS COURTNEY, MEYER MEGHAN, CAMPBELL ANDREW. First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2022; 6:95. [PMID: 36561350 PMCID: PMC9770714 DOI: 10.1145/3543194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social demands. First-year students need to gain new skills and strategies to cope with these new demands in order to make good decisions, ease their transition to independent living and ultimately succeed. In general, first-generation students are less prepared when they enter college in comparison to non-first-generation students. This presents additional challenges for first-generation students to overcome and be successful during their college years. We study first-year students through the lens of mobile phone sensing across their first year at college, including all academic terms and breaks. We collect longitudinal mobile sensing data for N=180 first-year college students, where 27 of the students are first-generation, representing 15% of the study cohort and representative of the number of first-generation students admitted each year at the study institution, Dartmouth College. We discuss risk factors, behavioral patterns and mental health of first-generation and non-first-generation students. We propose a deep learning model that accurately predicts the mental health of first-generation students by taking into account important distinguishing behavioral factors of first-generation students. Our study, which uses the StudentLife app, offers data-informed insights that could be used to identify struggling students and provide new forms of phone-based interventions with the goal of keeping students on track.
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8
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Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. SENSORS 2022; 22:s22103893. [PMID: 35632301 PMCID: PMC9147201 DOI: 10.3390/s22103893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 12/10/2022]
Abstract
Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.
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Koudela-Hamila S, Smyth J, Santangelo P, Ebner-Priemer U. Examination stress in academic students: a multimodal, real-time, real-life investigation of reported stress, social contact, blood pressure, and cortisol. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2022; 70:1047-1058. [PMID: 32669059 DOI: 10.1080/07448481.2020.1784906] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 03/18/2020] [Accepted: 06/12/2020] [Indexed: 06/11/2023]
Abstract
ObjectiveAcademic examinations are a frequent and significant source of student stress, but multimodal, psychophysiological studies are still missing. Participants & methods: Psychological and physiological variables were assessed on 154 undergraduate students in daily life using e-diaries resp. blood pressure devices at the beginning of the semester, and again before an examination. Results: Multilevel analysis revealed lower calmness, more negative valence, higher task-related stress, higher demands, lower perceived control, lower frequency of social contact, and a higher desire to be alone during the examination period (all p values < .0001), as well as lower ambulatory systolic blood pressure (p = .004), heightened cortisol at awakening (p = .021), and a smaller increase in cortisol (p = .012). Conclusions: Our study revealed empirical evidence that examination periods are not only associated with indicators of dysphoria, stress, and social withdrawal but also by altered physiological processes, which might reflect anticipatory stress and withdrawal effects.
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Affiliation(s)
- Susanne Koudela-Hamila
- Department of Applied Psychology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Joshua Smyth
- Department of Biobehavioral Health, Pennsylvania State University, State College, PA, USA
| | - Philip Santangelo
- Department of Applied Psychology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Ulrich Ebner-Priemer
- Department of Applied Psychology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Vidal Bustamante CM, Coombs G, Rahimi-Eichi H, Mair P, Onnela JP, Baker JT, Buckner RL. Fluctuations in behavior and affect in college students measured using deep phenotyping. Sci Rep 2022; 12:1932. [PMID: 35121741 PMCID: PMC8816914 DOI: 10.1038/s41598-022-05331-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/05/2022] [Indexed: 12/31/2022] Open
Abstract
College students commonly experience psychological distress when faced with intensified academic demands and changes in the social environment. Examining the nature and dynamics of students’ affective and behavioral experiences can help us better characterize the correlates of psychological distress. Here, we leveraged wearables and smartphones to study 49 first-year college students continuously throughout the academic year. Affect and sleep, academic, and social behavior showed substantial changes from school semesters to school breaks and from weekdays to weekends. Three student clusters were identified with behavioral and affective dissociations and varying levels of distress throughout the year. While academics were a common stressor for all, the cluster with highest distress stood out by frequent report of social stress. Moreover, the frequency of reporting social, but not academic, stress predicted subsequent clinical symptoms. Two years later, during the COVID-19 pandemic, the first-year cluster with highest distress again stood out by frequent social stress and elevated clinical symptoms. Focus on sustained interpersonal stress, relative to academic stress, might be especially helpful to identify students at heightened risk for psychopathology.
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Affiliation(s)
- Constanza M Vidal Bustamante
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA. .,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
| | - Garth Coombs
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA
| | - Habiballah Rahimi-Eichi
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Patrick Mair
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Justin T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Randy L Buckner
- Department of Psychology, Harvard University, Northwest Science Building 280.05, 52 Oxford Street, Cambridge, MA, 02138, USA.,Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.,Department of Psychiatry, Harvard Medical School, Boston, MA, 02114, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Charlestown, MA, 02129, USA
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Keusch F, Wenz A, Conrad F. Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2021.107054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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12
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Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps? SENSORS 2021; 21:s21134254. [PMID: 34206167 PMCID: PMC8271573 DOI: 10.3390/s21134254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 11/16/2022]
Abstract
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.
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Affiliation(s)
- Maryam Abo-Tabik
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
| | - Yael Benn
- Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK
- Correspondence: (Y.B.); (N.C.)
| | - Nicholas Costen
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UK;
- Correspondence: (Y.B.); (N.C.)
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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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14
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Video-Based Stress Detection through Deep Learning. SENSORS 2020; 20:s20195552. [PMID: 32998327 PMCID: PMC7582689 DOI: 10.3390/s20195552] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/18/2020] [Accepted: 09/26/2020] [Indexed: 11/27/2022]
Abstract
Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%.
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Tackman AM, Baranski EN, Danvers AF, Sbarra DA, Raison CL, Moseley SA, Polsinelli AJ, Mehl MR. ‘Personality in its Natural Habitat’ Revisited: A Pooled, Multi–sample Examination of the Relationships between the Big Five Personality Traits and Daily Behaviour and Language Use. EUROPEAN JOURNAL OF PERSONALITY 2020. [DOI: 10.1002/per.2283] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Past research using the Electronically Activated Recorder (EAR), an observational ambulatory assessment method for the real–world measurement of daily behaviour, has identified several behavioural manifestations of the Big Five domains in a small college sample ( N = 96). With the use of a larger and more diverse sample of pooled data from N = 462 participants from a total of four community samples who wore the EAR from 2 to 6 days, the primary purpose of the present study was to obtain more precise and generalizable effect estimates of the Big Five–behaviour relationships and to re–examine the degree to which these relationships are gender specific. In an extension of the original article, the secondary purpose of the present study was to examine if the Big Five–behaviour relationships differed across two facets of each Big Five domain. Overall, while several of the behavioural manifestations of the Big Five were generally consistent with the trait definitions (replicating some findings from the original article), we found little evidence of gender differences (not replicating a basic finding from the original article). Unique to the present study, the Big Five–behaviour relationships were not always comparable across the two facets of each Big Five domain. © 2020 European Association of Personality Psychology
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Affiliation(s)
| | | | | | - David A. Sbarra
- Department of Psychology, University of Arizona, Tucson, AZ USAz
| | - Charles L. Raison
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI USA
| | | | | | - Matthias R. Mehl
- Department of Psychology, University of Arizona, Tucson, AZ USAz
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16
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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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17
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Buhrmester MD, Talaifar S, Gosling SD. An Evaluation of Amazon's Mechanical Turk, Its Rapid Rise, and Its Effective Use. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2019; 13:149-154. [PMID: 29928846 DOI: 10.1177/1745691617706516] [Citation(s) in RCA: 237] [Impact Index Per Article: 47.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Over the past 2 decades, many social scientists have expanded their data-collection capabilities by using various online research tools. In the 2011 article "Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data?" in Perspectives on Psychological Science, Buhrmester, Kwang, and Gosling introduced researchers to what was then considered to be a promising but nascent research platform. Since then, thousands of social scientists from seemingly every field have conducted research using the platform. Here, we reflect on the impact of Mechanical Turk on the social sciences and our article's role in its rise, provide the newest data-driven recommendations to help researchers effectively use the platform, and highlight other online research platforms worth consideration.
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Affiliation(s)
| | - Sanaz Talaifar
- 2 Department of Psychology, University of Texas at Austin
| | - Samuel D Gosling
- 2 Department of Psychology, University of Texas at Austin.,3 Melbourne School of Psychological Sciences, University of Melbourne
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18
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Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019; 7:e12649. [PMID: 31444874 PMCID: PMC6729117 DOI: 10.2196/12649] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/24/2019] [Accepted: 05/28/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations. OBJECTIVE This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems. METHODS A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems? RESULTS Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred. CONCLUSIONS One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.
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Affiliation(s)
- Alina Trifan
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - Maryse Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - José Luís Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
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19
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Kroencke L, Harari GM, Katana M, Gosling SD. Personality trait predictors and mental well-being correlates of exercise frequency across the academic semester. Soc Sci Med 2019; 236:112400. [PMID: 31336217 DOI: 10.1016/j.socscimed.2019.112400] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/30/2019] [Accepted: 07/03/2019] [Indexed: 12/27/2022]
Abstract
RATIONALE Regular exercise is frequently recommended as a means of combating the negative effects of stress on mental health. But, among college students, exercise frequency remains below recommended levels. OBJECTIVE To better understand exercising behaviors in college students, we examined how exercise patterns change across an academic semester and how these changes relate to personality traits and mental well-being. METHOD We conducted two longitudinal experience sampling studies, using data from four cohorts of students, spanning four semesters (Fall 2015 - Spring 2017). In Study 1, a large sample of United States college students (cohort 1; N = 1126) reported the number of days they exercised and their levels of happiness, stress, sadness, and anxiety each week over the course of one academic semester (13 weeks). Study 2 (cohorts 2-4; N = 1973) was conducted to replicate our exploratory results from Study 1. RESULTS Using latent growth curve modeling, we observed the same normative pattern of change across both studies: The average student exercised twice during the first week of the semester and showed consistent decreases in exercise frequency in following weeks. Across both studies, higher initial levels of exercise frequency at the start of the semester were consistently related to higher extraversion, higher conscientiousness, and lower neuroticism. Furthermore, exercise frequency and mental well-being fluctuated together after controlling for time trends in the data: In weeks during which students exercised more than predicted, they also reported being happier and less anxious. CONCLUSIONS We contextualize the findings with regard to past research and discuss how they can be applied in behavior change interventions to promote students' well-being.
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Affiliation(s)
- Lara Kroencke
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA; Department of Psychology, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany.
| | - Gabriella M Harari
- Department of Communication, Stanford University, 450 Serra Mall McClatchy Hall, Stanford, CA 94305, USA.
| | - Marko Katana
- Department of Psychology, University of Zurich, Binzmühlestrasse 14, 8050 Zurich, Switzerland.
| | - Samuel D Gosling
- Department of Psychology, University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA; School of Psychological Sciences, University of Melbourne, Parkville, Melbourne VIC 3010, Australia.
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20
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Abstract
University students engage in risky patterns of alcohol consumption, which may affect their health and performance at university. This study provides a novel analysis which tracked students' interaction with online course materials over time, and examined associations between online activity and alcohol related harm (as indicated by the Alcohol Use Disorders Identification Test). Study 1 tracked 63 undergraduate psychology students in the second half of a semester and found risky drinking behaviors were marginally related to reductions in online study activity. Study 2 tracked 88 undergraduate psychology students in the first half of a semester. Risky drinking behaviors were associated with less online activity after midday. Students reporting more alcohol related harm were less likely to login between 7 pm and midnight, and between 1 am and 6 am. This study demonstrates a potential sensitivity of online activity levels to alcohol use.
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Affiliation(s)
- James G Phillips
- a Psychology Department , Auckland University of Technology, North Shore Campus, Northcote , Auckland , New Zealand
| | - C Erik Landhuis
- b School of Social Sciences and Public Policy, Auckland University of Technology, Wellesley Campus , Auckland , New Zealand
| | - Rowan P Ogeil
- c Eastern Health Clinical School, Monash University and Turning Point , Melbourne , Victoria , Australia
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21
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“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2018. [DOI: 10.1016/j.ijinfomgt.2018.07.003] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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22
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Wang W, Harari GM, Wang R, Müller SR, Mirjafari S, Masaba K, Campbell AT. Sensing Behavioral Change over Time. ACTA ACUST UNITED AC 2018. [DOI: 10.1145/3264951] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Personality traits describe individual differences in patterns of thinking, feeling, and behaving ("between-person" variability). But individuals also show changes in their own patterns over time ("within-person" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.
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Affiliation(s)
- Weichen Wang
- Dartmouth College, Computer Science, Hanover, NH, USA
| | | | - Rui Wang
- Dartmouth College, Computer Science, Hanover, NH, USA
| | - Sandrine R. Müller
- University of Cambridge, Department of Psychology, Cambridge, United Kingdom
| | | | - Kizito Masaba
- Dartmouth College, Computer Science, Hanover, NH, USA
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