Psycho-informatics: Big Data shaping modern psychometrics.

For the first time in history, it is possible to study human behavior on great scale and in fine detail simultaneously. Online services and ubiquitous computational devices, such as smartphones and modern cars, record our everyday activity. The resulting Big Data offers unprecedented opportunities for tracking and analyzing behavior. This paper hypothesizes the applicability and impact of Big Data technologies in the context of psychometrics both for research and clinical applications. It first outlines the state of the art, including the severe shortcomings with respect to quality and quantity of the resulting data. It then presents a technological vision, comprised of (i) numerous data sources such as mobile devices and sensors, (ii) a central data store, and (iii) an analytical platform, employing techniques from data mining and machine learning. To further illustrate the dramatic benefits of the proposed methodologies, the paper then outlines two current projects, logging and analyzing smartphone usage. One such study attempts to thereby quantify severity of major depression dynamically; the other investigates (mobile) Internet Addiction. Finally, the paper addresses some of the ethical issues inherent to Big Data technologies. In summary, the proposed approach is about to induce the single biggest methodological shift since the beginning of psychology or psychiatry. The resulting range of applications will dramatically shape the daily routines of researches and medical practitioners alike. Indeed, transferring techniques from computer science to psychiatry and psychology is about to establish Psycho-Informatics, an entire research direction of its own.

[1]  Kimberly Young,et al.  Internet Addiction: The Emergence of a New Clinical Disorder , 1998, Cyberpsychology Behav. Soc. Netw..

[2]  Christian Montag,et al.  Low self-directedness is a better predictor for problematic internet use than high neuroticism , 2010, Comput. Hum. Behav..

[3]  A. Sadeh The role and validity of actigraphy in sleep medicine: an update. , 2011, Sleep medicine reviews.

[4]  Charles Duhigg,et al.  How Companies Learn Your Secrets , 2012 .

[5]  M. Hamilton A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.

[6]  J. C. E. Silva Personalized medicine in psychiatry: New technologies and approaches , 2013 .

[7]  R. McCrae,et al.  An introduction to the five-factor model and its applications. , 1992, Journal of personality.

[8]  Neil Stanley,et al.  Actigraphy in human psychopharmacology: a review , 2003, Human psychopharmacology.

[9]  James G. Phillips,et al.  Psychological Predictors of Problem Mobile Phone Use , 2005, Cyberpsychology Behav. Soc. Netw..

[10]  Bradford C. Dickerson,et al.  Amygdala Volume and Social Network Size in Humans , 2010, Nature Neuroscience.

[11]  M. Åsberg,et al.  A New Depression Scale Designed to be Sensitive to Change , 1979, British Journal of Psychiatry.

[12]  D. Black,et al.  Internet Addiction , 2008, CNS drugs.

[13]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[14]  A. Takahashi Rating scale for depression , 1998 .

[15]  C. Pollak,et al.  The role of actigraphy in the study of sleep and circadian rhythms. , 2003, Sleep.

[16]  D. Nutt,et al.  The hidden third: improving outcome in treatment-resistant depression , 2012, Journal of psychopharmacology.

[17]  C. Ko,et al.  The association between Internet addiction and psychiatric disorder: A review of the literature , 2012, European Psychiatry.

[18]  A. Sadeh,et al.  The role of actigraphy in sleep medicine. , 2002, Sleep medicine reviews.

[19]  J. A. Costa e Silva,et al.  Personalized medicine in psychiatry: new technologies and approaches. , 2013, Metabolism: clinical and experimental.

[20]  H. Katschnig Quality of life in mental disorders: challenges for research and clinical practice. , 2006, World psychiatry : official journal of the World Psychiatric Association.

[21]  M. Danhof,et al.  The missing link between clinical endpoints and drug targets in depression. , 2010, Trends in pharmacological sciences.