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Marengo D, Elhai JD, Montag C. Predicting Big Five personality traits from smartphone data: A meta-analysis on the potential of digital phenotyping. J Pers 2023; 91:1410-1424. [PMID: 36738137 DOI: 10.1111/jopy.12817] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 01/23/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Since the first study linking recorded smartphone variables to self-reported personality in 2011, many additional studies have been published investigating this association. In the present meta-analyses, we aimed to understand how strongly personality can be predicted via smartphone data. METHOD Meta-analytical calculations were used to assess the association between smartphone data and Big Five traits. Because of the lack of independence of many included studies, analyses were performed using a multilevel approach. RESULTS Based on data collected from 21 distinct studies, extraversion showed the largest association with the digital footprints derived from smartphone data (r = .35), while remaining traits showed smaller associations (ranging from 0.23 to 0.25). For all traits except neuroticism, moderator analyses showed that prediction performance was improved when multiple features were combined together in a single predictive model. Additionally, the strength of the prediction of extraversion was improved when call and text log data were used to perform the prediction, as opposed to other types of smartphone data CONCLUSIONS: Our synthesis reveals small-to-moderate associations between smartphone activity data and Big Five traits. The opportunities, but also dangers of the digital phenotyping of personality traits based on traces of users' activity on a smartphone data are discussed.
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Affiliation(s)
- Davide Marengo
- Department of Psychology, University of Turin, Turin, Italy
| | - Jon D Elhai
- Department of Psychology, The University of Toledo, Toledo, Ohio, USA
- Department of Psychiatry, The University of Toledo, Toledo, Ohio, USA
| | - Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
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Cao Y, Rajendran S, Sundararajan P, Law R, Bacon S, Sumner SA, Masuda N. Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study. J Med Internet Res 2023; 25:e45171. [PMID: 37252791 DOI: 10.2196/45171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/25/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges such as exposure to intimate partner violence and substance use in the home, can have negative impacts on the lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced them. However, how the social networks of those who experienced ACEs differ from the social networks of those who did not is poorly understood. OBJECTIVE In this study, we used Reddit and Twitter data to investigate and compare social networks between individuals with and without ACE exposure. METHODS We first used a neural network classifier to identify the presence or absence of public ACE disclosures in social media posts. We then analyzed egocentric social networks comparing individuals with self-reported ACEs with those with no reported history. RESULTS We found that, although individuals reporting ACEs had fewer total followers in web-based social networks, they had higher reciprocity in following behavior (ie, mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. CONCLUSIONS These results imply that individuals with ACEs may try to actively connect with others who have similar previous traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections on the web for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs.
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Affiliation(s)
- Yiding Cao
- Department of Systems Science and Industrial Engineering, Binghamton University, State University of New York, Binghamton, NY, United States
| | - Suraj Rajendran
- Tri-Institutional Program in Computational Biology and Medicine, New York, NY, United States
| | - Prathic Sundararajan
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Royal Law
- Division of Injury Prevention, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Sarah Bacon
- Office of Strategy and Innovation, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Steven A Sumner
- Office of Strategy and Innovation, National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY, United States
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Shen Q, Feng H, Song R, Song D, Xu H. Federated Meta-Learning with Attention for Diversity-Aware Human Activity Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:1083. [PMID: 36772123 PMCID: PMC9919758 DOI: 10.3390/s23031083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/25/2022] [Accepted: 01/05/2023] [Indexed: 06/18/2023]
Abstract
The ubiquity of smartphones equipped with multiple sensors has provided the possibility of automatically recognizing of human activity, which can benefit intelligent applications such as smart homes, health monitoring, and aging care. However, there are two major barriers to deploying an activity recognition model in real-world scenarios. Firstly, deep learning models for activity recognition use a large amount of sensor data, which are privacy-sensitive and hence cannot be shared or uploaded to a centralized server. Secondly, divergence in the distribution of sensory data exists among multiple individuals due to their diverse behavioral patterns and lifestyles, which contributes to difficulty in recognizing activity for large-scale users or 'cold-starts' for new users. To address these problems, we propose DivAR, a diversity-aware activity recognition framework based on a federated Meta-Learning architecture, which can extract general sensory features shared among individuals by a centralized embedding network and individual-specific features by attention module in each decentralized network. Specifically, we first classify individuals into multiple clusters according to their behavioral patterns and social factors. We then apply meta-learning in the architecture of federated learning, where a centralized meta-model learns common feature representations that can be transferred across all clusters of individuals, and multiple decentralized cluster-specific models are utilized to learn cluster-specific features. For each cluster-specific model, a CNN-based attention module learns cluster-specific features from the global model. In this way, by training with sensory data locally, privacy-sensitive information existing in sensory data can be preserved. To evaluate the model, we conduct two data collection experiments by collecting sensor readings from naturally used smartphones annotated with activity information in the real-life environment and constructing two multi-individual heterogeneous datasets. In addition, social characteristics including personality, mental health state, and behavior patterns are surveyed using questionnaires. Finally, extensive empirical results demonstrate that the proposed diversity-aware activity recognition model has a relatively better generalization ability and achieves competitive performance on multi-individual activity recognition tasks.
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Affiliation(s)
- Qiang Shen
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Haotian Feng
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Rui Song
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Donglei Song
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Hao Xu
- College of Computer Science and Technology, Jilin University, Changchun 130012, China
- Chongqing Research Institute, Jilin University, Chongqing 401123, China
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Thomas BL, Holder LB, Cook DJ. Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data. Methods Inf Med 2022; 61:99-110. [PMID: 36220111 PMCID: PMC9847015 DOI: 10.1055/s-0042-1756649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation. OBJECTIVE The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures. METHODS We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures. RESULTS We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.
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Affiliation(s)
- Brian L. Thomas
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
| | - Lawrence B. Holder
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
| | - Diane J. Cook
- School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
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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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COOK DIANEJ, STRICKLAND MIRANDA, SCHMITTER-EDGECOMBE MAUREEN. Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2022; 3:33. [PMID: 35815157 PMCID: PMC9268550 DOI: 10.1145/3508020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 12/01/2021] [Indexed: 06/15/2023]
Abstract
In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.
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Affiliation(s)
- DIANE J. COOK
- School of Electrical Engineering and Computer Science.
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Hart A, Reis D, Prestele E, Jacobson NC. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment. J Med Internet Res 2022; 24:e34015. [PMID: 35482397 PMCID: PMC9100543 DOI: 10.2196/34015] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/02/2022] [Accepted: 03/13/2022] [Indexed: 01/26/2023] Open
Abstract
Background Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference.
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Affiliation(s)
- Alexander Hart
- Research Group Applied Statistical Modeling, Department of Psychology, Saarland University, Saarbrücken, Germany
| | - Dorota Reis
- Research Group Applied Statistical Modeling, Department of Psychology, Saarland University, Saarbrücken, Germany
| | - Elisabeth Prestele
- Research Group Diagnostics, Differential and Personality Psychology, Methods and Evaluation, Department of Psychology, University of Koblenz-Landau, Landau, Germany
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Departments of Biomedical Data Science and Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States
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Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. FUNDAMENTAL RESEARCH 2022. [DOI: 10.1016/j.fmre.2021.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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9
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Martinez GJ, Mattingly SM, Robles-Granda P, Saha K, Sirigiri A, Young J, Chawla N, De Choudhury M, D'Mello S, Mark G, Striegel A. Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study. JMIR Mhealth Uhealth 2021; 9:e22218. [PMID: 34766911 PMCID: PMC8663716 DOI: 10.2196/22218] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 04/23/2021] [Accepted: 09/24/2021] [Indexed: 01/27/2023] Open
Abstract
Background Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies. Objective This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance. Methods We instrumented 757 information workers with fitness trackers for 1 year and conducted EMAs in the first 56 days of study participation as part of an observational study. Their compliance with the EMA and fitness tracker wearing protocols was analyzed. Overall, 31 individual characteristics (eg, demographics and personalities) and behavioral variables (eg, early compliance and study portal use) were considered, and 14 variables were selected to create beta regression models for predicting compliance with EMAs 56 days out and wearable compliance 1 year out. We surveyed study participation and correlated the results with compliance. Results Our modeling indicates that 16% and 25% of the variance in EMA compliance and wearable compliance, respectively, could be explained through a survey of demographics and personality in a held-out sample. The likelihood of higher EMA and wearable compliance was associated with being older (EMA: odds ratio [OR] 1.02, 95% CI 1.00-1.03; wearable: OR 1.02, 95% CI 1.01-1.04), speaking English as a first language (EMA: OR 1.38, 95% CI 1.05-1.80; wearable: OR 1.39, 95% CI 1.05-1.85), having had a wearable before joining the study (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.50, 95% CI 1.23-1.83), and exhibiting conscientiousness (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.34, 95% CI 1.14-1.58). Compliance was negatively associated with exhibiting extraversion (EMA: OR 0.74, 95% CI 0.64-0.85; wearable: OR 0.67, 95% CI 0.57-0.78) and having a supervisory role (EMA: OR 0.65, 95% CI 0.54-0.79; wearable: OR 0.66, 95% CI 0.54-0.81). Furthermore, higher wearable compliance was negatively associated with agreeableness (OR 0.68, 95% CI 0.56-0.83) and neuroticism (OR 0.85, 95% CI 0.73-0.98). Compliance in the second week of the study could help explain more variance; 62% and 66% of the variance in EMA compliance and wearable compliance, respectively, was explained. Finally, compliance correlated with participants’ self-reflection on the ease of participation, usefulness of our compliance portal, timely resolution of issues, and compensation adequacy, suggesting that these are avenues for improving compliance. Conclusions We recommend conducting an initial 2-week pilot to measure trait-like compliance and identify participants at risk of long-term noncompliance, performing oversampling based on participants’ individual characteristics to avoid introducing bias in the sample when excluding data based on noncompliance, using an issue tracking portal, and providing special care in troubleshooting to help participants maintain compliance.
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Affiliation(s)
- Gonzalo J Martinez
- Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Stephen M Mattingly
- Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Pablo Robles-Granda
- Thomas M Siebel Center for Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, United States
| | - Koustuv Saha
- Microsoft Research, Montreal, QC, Canada.,School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Anusha Sirigiri
- Indian School of Business Gachibowli, Hyderabad Telangana, India
| | - Jessica Young
- Center for Research Computing, University of Notre Dame, Notre Dame, IN, United States
| | - Nitesh Chawla
- Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States
| | - Sidney D'Mello
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, United States
| | - Gloria Mark
- Informatics Department, University of California, Irvine, Irvine, CA, United States
| | - Aaron Striegel
- Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States
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Wang W, Wu J, Nepal S, daSilva A, Hedlund E, Murphy E, Rogers C, Huckins J. On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ICMI (CONFERENCE) 2021; 2021:425-434. [PMID: 36519953 PMCID: PMC9747327 DOI: 10.1145/3462244.3479888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Pandemics significantly impact human daily life. People throughout the world adhere to safety protocols (e.g., social distancing and self-quarantining). As a result, they willingly keep distance from workplace, friends and even family. In such circumstances, in-person social interactions may be substituted with virtual ones via online channels, such as, Instagram and Snapchat. To get insights into this phenomenon, we study a group of undergraduate students before and after the start of COVID-19 pandemic. Specifically, we track N=102 undergraduate students on a small college campus prior to the pandemic using mobile sensing from phones and assign semantic labels to each location they visit on campus where they study, socialize and live. By leveraging their colocation network at these various semantically labeled places on campus, we find that colocations at certain places that possibly proxy higher in-person social interactions (e.g., dormitories, gyms and Greek houses) show significant predictive capability in identifying the individuals' change in social media usage during the pandemic period. We show that we can predict student's change in social media usage during COVID-19 with an F1 score of 0.73 purely from the in-person colocation data generated prior to the pandemic.
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Daniel KE, Mendu S, Baglione A, Cai L, Teachman BA, Barnes LE, Boukhechba M. Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life. ANXIETY STRESS AND COPING 2021; 35:298-312. [PMID: 34338086 PMCID: PMC8801546 DOI: 10.1080/10615806.2021.1959916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Social anxiety disorder is associated with distinct mobility patterns (e.g., increased time spent at home compared to non-anxious individuals), but we know little about if these patterns change following interventions. The ubiquity of GPS-enabled smartphones offers new opportunities to assess the benefits of mental health interventions beyond self-reported data. OBJECTIVES This pre-registered study (https://osf.io/em4vn/?view_only=b97da9ef22df41189f1302870fdc9dfe) assesses the impact of a brief, online cognitive training intervention for threat interpretations using passively-collected mobile sensing data. DESIGN Ninety-eight participants scoring high on a measure of trait social anxiety completed five weeks of mobile phone monitoring, with 49 participants randomly assigned to receive the intervention halfway through the monitoring period. RESULTS The brief intervention was not reliably associated with changes to participant mobility patterns. CONCLUSIONS Despite the lack of significant findings, this paper offers a framework within which to test future intervention effects using GPS data. We present a template for combining clinical theory and empirical GPS findings to derive testable hypotheses, outline data processing steps, and provide human-readable data processing scripts to guide future research. This manuscript illustrates how data processing steps common in engineering can be harnessed to extend our understanding of the impact of mental health interventions in daily life.
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Affiliation(s)
- Katharine E Daniel
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Sanjana Mendu
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
| | - Anna Baglione
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
| | - Lihua Cai
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
| | - Bethany A Teachman
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Laura E Barnes
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
| | - Mehdi Boukhechba
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA
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Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR Mhealth Uhealth 2021; 9:e26540. [PMID: 34255713 PMCID: PMC8314163 DOI: 10.2196/26540] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/15/2021] [Accepted: 05/14/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Depression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression. OBJECTIVE The objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression. METHODS Smartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression. RESULTS Of the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants' age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status-normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status-normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression. CONCLUSIONS Our findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors' data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.
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Affiliation(s)
| | - Yannik Terhorst
- Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany
| | - Julio Vega
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ella Peltonen
- Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
| | - Eemil Lagerspetz
- Department of Computer Science, University of Helsinki, Helsinki, Finland
| | - Denzil Ferreira
- Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
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Phan LV, Rauthmann JF. Personality computing: New frontiers in personality assessment. SOCIAL AND PERSONALITY PSYCHOLOGY COMPASS 2021. [DOI: 10.1111/spc3.12624] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Lai J, Rahmani A, Yunusova A, Rivera AP, Labbaf S, Hu S, Dutt N, Jain R, Borelli JL. Using Multimodal Assessments to Capture Personalized Contexts of College Student Well-being in 2020: Case Study. JMIR Form Res 2021; 5:e26186. [PMID: 33882022 PMCID: PMC8115397 DOI: 10.2196/26186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/01/2021] [Accepted: 04/13/2021] [Indexed: 01/27/2023] Open
Abstract
Background The year 2020 has been challenging for many, particularly for young adults who have been adversely affected by the COVID-19 pandemic. Emerging adulthood is a developmental phase with significant changes in the patterns of daily living; it is a risky phase for the onset of major mental illness. College students during the pandemic face significant risk, potentially losing several protective factors (eg, housing, routine, social support, job, and financial security) that are stabilizing for mental health and physical well-being. Individualized multiple assessments of mental health, referred to as multimodal personal chronicles, present an opportunity to examine indicators of health in an ongoing and personalized way using mobile sensing devices and wearable internet of things. Objective To assess the feasibility and provide an in-depth examination of the impact of the COVID-19 pandemic on college students through multimodal personal chronicles, we present a case study of an individual monitored using a longitudinal subjective and objective assessment approach over a 9-month period throughout 2020, spanning the prepandemic period of January through September. Methods The individual, referred to as Lee, completed psychological assessments measuring depression, anxiety, and loneliness across 4 time points in January, April, June, and September. We used the data emerging from the multimodal personal chronicles (ie, heart rate, sleep, physical activity, affect, behaviors) in relation to psychological assessments to understand patterns that help to explicate changes in the individual’s psychological well-being across the pandemic. Results Over the course of the pandemic, Lee’s depression severity was highest in April, shortly after shelter-in-place orders were mandated. His depression severity remained mildly severe throughout the rest of the months. Associations in positive and negative affect, physiology, sleep, and physical activity patterns varied across time periods. Lee’s positive affect and negative affect were positively correlated in April (r=0.53, P=.04) whereas they were negatively correlated in September (r=–0.57, P=.03). Only in the month of January was sleep negatively associated with negative affect (r=–0.58, P=.03) and diurnal beats per minute (r=–0.54, P=.04), and then positively associated with heart rate variability (resting root mean square of successive differences between normal heartbeats) (r=0.54, P=.04). When looking at his available contextual data, Lee noted certain situations as supportive coping factors and other situations as potential stressors. Conclusions We observed more pandemic concerns in April and noticed other contextual events relating to this individual’s well-being, reflecting how college students continue to experience life events during the pandemic. The rich monitoring data alongside contextual data may be beneficial for clinicians to understand client experiences and offer personalized treatment plans. We discuss benefits as well as future directions of this system, and the conclusions we can draw regarding the links between the COVID-19 pandemic and college student mental health.
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Affiliation(s)
- Jocelyn Lai
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Amir Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,School of Nursing, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Asal Yunusova
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Alexander P Rivera
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Sina Labbaf
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Sirui Hu
- Department of Statistics, University of California, Irvine, Irvine, CA, United States.,Department of Economics, University of California, Irvine, Irvine, CA, United States
| | - Nikil Dutt
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States.,Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States.,Department of Cognitive Science, Irvine, CA, United States
| | - Ramesh Jain
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
| | - Jessica L Borelli
- UCI THRIVE Lab, Department of Psychological Science, University of California, Irvine, Irvine, CA, United States.,Institute for Future Health, University of California, Irvine, Irvine, CA, United States
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15
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Lücke AJ, Quintus M, Egloff B, Wrzus C. You can’t always get what you want: The role of change goal importance, goal feasibility and momentary experiences for volitional personality development. EUROPEAN JOURNAL OF PERSONALITY 2020. [DOI: 10.1177/0890207020962332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Most adults want to change aspects of their personality. However, previous studies have provided mixed evidence on whether such change goals can be successfully implemented, perhaps partly due to neglecting the goals’ importance and feasibility as well as the experience of trait-relevant situations and states. This study examined associations between change goals and changes in self-reported Big Five traits assessed four times across two years in an age-heterogeneous sample of 382 adults (255 younger adults, Mage = 21.6 years; 127 older adults, Mage = 67.8 years). We assessed trait-relevant momentary situations and states in multiple waves of daily diaries over the first year ( M = 43.9 days). Perceived importance and feasibility of change goals were analysed as potentially moderating factors. Contrary to our hypotheses, the results demonstrated that neither change goals nor goal importance or feasibility were consistently associated with trait change, likely due to inconsistent associations with momentary situations and behaviours. The results suggest that wanting to change one’s traits does not necessarily lead to changes without engaging in trait-relevant situations and behaviours. These findings provide novel insights into the boundary conditions of volitional personality development.
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Affiliation(s)
- Anna J Lücke
- Institute of Psychology, Ruprecht Karls University, Germany
| | - Martin Quintus
- Department of Psychology, Johannes Gutenberg University Mainz, Germany
| | - Boris Egloff
- Department of Psychology, Johannes Gutenberg University Mainz, Germany
| | - Cornelia Wrzus
- Institute of Psychology, Ruprecht Karls University, Germany
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16
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Dotti D, Popa M, Asteriadis S. Being the Center of Attention. ACM T INTERACT INTEL 2020. [DOI: 10.1145/3338245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
This article proposes a novel study on personality recognition using video data from different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. Therefore, we build a novel multi-stream Convolutional Neural Network (CNN) framework, which considers multiple sources of information. From a given scenario, we extract spatio-temporal motion descriptors from every individual in the scene, spatio-temporal motion descriptors encoding social group dynamics, and proxemics descriptors to encode the interaction with the surrounding context. All the proposed descriptors are mapped to the same feature space facilitating the overall learning effort. Experiments on two public datasets demonstrate the effectiveness of jointly modeling the mutual Person-Context information, outperforming the state-of-the art-results for personality recognition in two different scenarios. Last, we present CNN class activation maps for each personality trait, shedding light on behavioral patterns linked with personality attributes.
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17
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Danvers AF, Sbarra DA, Mehl MR. Understanding Personality through Patterns of Daily Socializing: Applying Recurrence Quantification Analysis to Naturalistically Observed Intensive Longitudinal Social Interaction Data. EUROPEAN JOURNAL OF PERSONALITY 2020. [DOI: 10.1002/per.2282] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Ambulatory assessment methods provide a rich approach for studying daily behaviour. Too often, however, these data are analysed in terms of averages, neglecting patterning of this behaviour over time. This paper describes recurrence quantification analysis (RQA), a non–linear time series technique for analysing dynamic systems, as a method for analysing patterns of categorical, intensive longitudinal ambulatory assessment data. We apply RQA to objectively assessed social behaviour (e.g. talking to another person) coded from the Electronically Activated Recorder. Conceptual interpretations of RQA parameters, and an analysis of Electronically Activated Recorder data in adults going through a marital separation, are provided. Using machine learning techniques to avoid model overfitting, we find that adding RQA parameters to models that include just average amount of time spent talking (a static measure) improves prediction of four Big Five personality traits: extraversion, neuroticism, conscientiousness, and openness. Our strongest results suggest that a combination of average amount of time spent talking and four RQA parameters yield an R2 = .09 for neuroticism. Neuroticism is shown to be associated with shorter periods of extended conversation (periods of at least 12 minutes), demonstrating the utility of RQA to identify new relationships between personality and patterns of daily behaviour. Materials: https://osf.io/5nkr9/ . © 2020 European Association of Personality Psychology
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Affiliation(s)
| | - David A. Sbarra
- Department of Psychology, University of Arizona, Tucson, AZ USA
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18
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Abstract
Smartphones are sensor-rich computers that can easily be used to collect extensive records of behaviors, posing serious threats to individuals’ privacy. This study examines the extent to which individuals’ personality dimensions (assessed at broad domain and narrow facet levels) can be predicted from six classes of behavior: 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity, in a large sample. The cross-validated results show which Big Five personality dimensions are predictable and which specific patterns of behavior are indicative of which dimensions, revealing communication and social behavior as most predictive overall. Our results highlight the benefits and dangers posed by the widespread collection of smartphone data. Smartphones enjoy high adoption rates around the globe. Rarely more than an arm’s length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users’ behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals’ Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain (rmedian = 0.37) and narrow facet levels (rmedian = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals’ private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
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19
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Obuchi M, Huckins JF, Wang W, Dasilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. PROCEEDINGS OF THE ACM ON INTERACTIVE, MOBILE, WEARABLE AND UBIQUITOUS TECHNOLOGIES 2020; 4:23. [PMID: 36540188 PMCID: PMC9762691 DOI: 10.1145/3381001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Brain circuit functioning and connectivity between specific regions allow us to learn, remember, recognize and think as humans. In this paper, we ask the question if mobile sensing from phones can predict brain functional connectivity. We study the brain resting-state functional connectivity (RSFC) between the ventromedial prefrontal cortex (vmPFC) and the amygdala, which has been shown by neuroscientists to be associated with mental illness such as anxiety and depression. We discuss initial results and insights from the NeuroSence study, an exploratory study of 105 first year college students using neuroimaging and mobile sensing across one semester. We observe correlations between several behavioral features from students' mobile phones and connectivity between vmPFC and amygdala, including conversation duration (r=0.365, p<0.001), sleep onset time (r=0.299, p<0.001) and the number of phone unlocks (r=0.253, p=0.029). We use a support vector classifier and 10-fold cross validation and show that we can classify whether students have higher (i.e., stronger) or lower (i.e., weaker) vmPFC-amygdala RSFC purely based on mobile sensing data with an F1 score of 0.793. To the best of our knowledge, this is the first paper to report that resting-state brain functional connectivity can be predicted using passive sensing data from mobile phones.
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Affiliation(s)
- Mikio Obuchi
- Dartmouth College, Computer Science, Hanover, NH, 03755, USA
| | - Jeremy F Huckins
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, 03755, USA
| | - Weichen Wang
- Dartmouth College, Computer Science, Hanover, NH, 03755, USA
| | - Alex Dasilva
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, 03755, USA
| | - Courtney Rogers
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, 03755, USA
| | - Eilis Murphy
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, 03755, USA
| | - Elin Hedlund
- Dartmouth College, Psychological and Brain Sciences, Hanover, NH, 03755, USA
| | - Paul Holtzheimer
- National Center for PTSD, White River Junction, VT, 05009, USA, Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03766, USA
| | | | - Andrew Campbell
- Dartmouth College, Computer Science, Hanover, NH, 03755, USA
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20
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Abstract
The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially specific to each individual, and it seeks to develop formal idiographic statistical models to represent these individual processes. Although the personalized approach draws on long-standing influences in clinical psychology, there has been an explosion of research in recent years following the development of intensive longitudinal data capture and statistical techniques that facilitate modeling of the dynamic processes of each individual's pathology. Advances are also making idiographic analyses scalable and generalizable. We review emerging research using the personalized approach in descriptive psychopathology, precision assessment, and treatment selection and tailoring, and we identify future challenges and areas in need of additional research. The personalized approach to psychopathology holds promise to resolve thorny diagnostic issues, generate novel insights, and improve the timing and efficacy of interventions.
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Affiliation(s)
- Aidan G C Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
| | - William C Woods
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
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