This book offers a snapshot of cutting-edge applications of digital phenotyping and mobile sensing for studying human behavior and planning innovative e-healthcare interventions. The respective chapters, written by authoritative researchers, cover both theoretical perspectives and good scientific and professional practices related to the use and development of these technologies. They share novel insights into established applications of mobile sensing, such as predicting personality or mental and behavioral health on the basis of smartphone usage patterns, and highlight emerging trends, such as the use of machine learning, big data and deep learning approaches, and the combination of mobile sensing with AI and expert systems. Important issues relating to privacy and ethics are analyzed, together with selected case studies. This thoroughly revised and extended second edition provides researchers and professionals with extensive information on the latest developments in the field of digital phenotyping and mobile sensing. It gives a special emphasis to trends in diagnostics systems and AI applications, suggesting important future directions for research in public health and social sciences.
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1 Digital Phenotyping and Mobile Sensing in Psychoinformatics--A Rapidly Evolving Interdisciplinary Research Endeavor |
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1 | (12) |
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Part I Privacy and Ethics |
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2 Privacy in Mobile Sensing |
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13 | (12) |
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3 Ethical Considerations of Digital Phenotyping from the Perspective of a Healthcare Practitioner Including Updates |
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25 | (18) |
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Part II Applications in Psycho-Social Sciences |
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4 Computerized Facial Emotion Expression Recognition |
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43 | (14) |
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5 An Overview on Doing Psychodiagnostics in Personality Psychology and Tracking Physical Activity via Smartphones Including Updates |
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57 | (20) |
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6 Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile Sensing |
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77 | (28) |
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7 Digital Brain Biomarkers of Human Cognition and Mood |
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105 | (16) |
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8 Mining Facebook Data for Personality Prediction: An Overview |
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121 | (16) |
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9 Orderliness of Campus Lifestyle Predicts Academic Performance: A Case Study in Chinese University |
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137 | (14) |
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10 From Outside In: Profiling, Persuasion and Political Opinion in the Age of Big Data |
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151 | (20) |
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11 A Practical Guide to WhatsApp Data in Social Science Research |
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171 | (38) |
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Part III Applications in Health Sciences |
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12 Latest Advances in Computational Speech Analysis for Mobile Sensing |
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209 | (20) |
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13 Passive Sensing of Affective and Cognitive Functioning in Mood Disorders by Analyzing Keystroke Kinematics and Speech Dynamics |
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229 | (30) |
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14 Studying Psychopathology in Relation to Smartphone Use: From Self-reports to Objectively Measured Smartphone Use Behavior |
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259 | (18) |
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15 Connecting Domains--Ecological Momentary Assessment in a Mobile Sensing Framework |
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277 | (8) |
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16 Momentary Assessment of Tinnitus--How Smart Mobile Applications Advance Our Understanding of Tinnitus |
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285 | (20) |
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Matheus P. C. G. Lourenco |
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17 Mobile Crowdsensing in Healthcare Scenarios: Taxonomy, Conceptual Pillars, Smart Mobile Crowdsensing Services |
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305 | (16) |
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18 Mhealth Applications: Potentials, Limitations, Current Quality and Future Directions |
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321 | (14) |
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19 Using Chatbots to Support Medical and Psychological Treatment Procedures Challenges, Opportunities, Technologies, Reference Architecture |
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335 | (12) |
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20 Persuasive e-Health Design for Behavior Change |
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347 | (18) |
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21 Optimizing mHealth Interventions with a Bandit |
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365 | (14) |
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22 Parkinsonism and Digital Measurement |
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379 | (16) |
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Chrystalina A. Antoniades |
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23 Smart Sensors for Health Research and Improvement |
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395 | (18) |
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24 Smart Sensing Enhanced Diagnostic Expert Systems |
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413 | (14) |
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25 Ecological Momentary Interventions in Public Mental Health Provision |
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427 | (16) |
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Julia C. C. Schulte-Strathaus |
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443 | (4) |
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27 Defining Ecological Momentary Assessment |
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447 | (4) |
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28 Defining Artificial Intelligence |
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451 | (4) |
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29 Denning Machine Learning |
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455 | (6) |
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461 | (4) |
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31 Defining Digital Biomarkers |
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465 | |
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Christian Montag is Professor for Molecular Psychology at Ulm University, Germany. From 2016 to 2022, he has been a visiting professor at the University of Electronic Science and Technology of China, in Chengdu. He received his diploma and PhD degree in psychology in 2006 and 2009, respectively, and his habilitation in the same field in 2011. Christian Montag's research has been dealing with various topics relating to molecular genetics of personality and emotions, affective neuroscience, neuroeconomics, internet addiction, and psychoinformatics. He has also been involved in developing applications to track human behavior via smartphones.
Harald Baumeister is Professor for Clinical Psychology and Psychotherapy, and Head of the Psychotherapeutic outpatient clinic at Ulm University, Germany. He received his diploma and PhD in psychology in 2001 and 2005, respectively. In 2007, he gained his professional license to work as a psychotherapist, and received his habilitation in psychology in 2012. Harald Baumeister“s research focuses on e-mental- and e-behavioral health. He has developed and evaluated several strategies for Internet- and Mobile-based Interventions (IMI) and Diagnostics for both mental and somatic, and primary and secondary, care settings. More recently, he started to leverage interdisciplinary competencies to level up IMI-and mental health research by bringing together informatics and data science with engineering, biological, psychological/psychotherapeutic and medical expertise. Big data-based machine learning approaches, deep learning- and artificial intelligence-based mental- and behavioral health solutions, as well as adaptive, smart sensing-informed interventional approaches are just some of his current research topics.