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Digital Phenotyping and Mobile Sensing

New Developments in Psychoinformatics

  • Book
  • © 2019

Overview

  • Describes powerful digital technologies for applications in the study of human behavior
  • Describes ways to deal with related privacy, ethical and social concerns
  • With a foreword by Thomas R. Insel, former director of the National Institute of Mental Health (NIMH)

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Table of contents (18 chapters)

  1. Digital Phenotyping and Mobile Sensing: Privacy and Ethics

  2. Digital Phenotyping and Mobile Sensing in Psycho-Social Sciences

  3. Digital Phenotyping and Mobile Sensing in Health Sciences

Keywords

About this book

This book offers a snapshot of cutting-edge applications of mobile sensing for digital phenotyping in the field of Psychoinformatics. The respective chapters, written by authoritative researchers, cover various aspects related to the use of these technologies in health, education, and cognitive science research. They share insights both into established applications of mobile sensing (such as predicting personality or mental and behavioral health on the basis of smartphone usage patterns) and emerging trends. Machine learning and deep learning approaches are discussed, and important considerations regarding privacy risks and ethical issues are assessed.

In addition to essential background information on various technologies and theoretical methods, the book also presents relevant case studies and good scientific practices, thus addressing researchers and professionals alike. To cite Thomas R. Insel, who wrote the foreword to this book: “Patients will only use digital phenotyping if it solves a problem, perhaps a digital smoke alarm that can prevent a crisis. Providers will only use digital phenotyping if it fits seamlessly into their crowded workflow. If we can earn public trust, there is every reason to be excited about this new field. Suddenly, studying human behavior at scale, over months and years, is feasible.”


Editors and Affiliations

  • Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, Ulm University, Ulm, Germany

    Harald Baumeister

  • Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany

    Christian Montag

About the editors

Christian Montag received his diploma in psychology in September 2006. In 2009 he achieved his PhD degree on his psychobiological works testing Gray’s revised reinforcement sensitivity theory. In 2011 he got the venia legendi for psychology. Since September 2014 he is Professor for Molecular Psychology at Ulm University, Germany. Since 2016 he is also Visiting Professor at the NeuScan-Lab/Key Laboratory for Neuroinformation, UESTC in Chengdu, China.  
Research Interests: Christian Montag is interested in the molecular genetics of personality and emotions / affective neuroscience. He combines molecular genetics with brain imaging techniques such as structural/functional MRI to better understand individual differences in human nature. Adding to this he conducts research in the fields of Neuroeconomics and (Internet) addiction including new approaches from Psychoinformatics.  

Harald Baumeister received his diploma inpsychology in March 2001. In 2005 he achieved his PhD degree, in 2007 his license as a psychotherapist and in 2012 his venia legendi for psychology. Since September 2015 he is Professor for Clinical Psychology and Psychotherapy and head of the psychotherapeutic outpatient clinic at Ulm University, Germany.
Research Interests: Harald Baumeister´s research focusses on e-mental- and e-behavioral health. Over the last decade he developed and evaluated several Internet- and Mobile-based diagnostics- and intervention (IMI) solutions for mental and somatic primary and secondary care settings. Recent research aims to combine interdisciplinary competencies to level up IMI-and mental health research by bringing together informatics, engineering, biological, data science, psychological, psychotherapeutically and medical expertise. Big data based machine learning approaches, deep-learning based artificial intelligence mental- and behavioral health solutions and adaptive, smart sensing informed interventional approaches are some of his current fields of interest. 


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