The sociability score: App-based social profiling from a healthcare perspective |
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| Affiliation: | 1. Department of Information and Computing Science, Utrecht University, Princetonplein 5, Utrecht, The Netherlands;2. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, The Netherlands;3. Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Universiteitsweg 100, Utrecht, The Netherlands;1. Department of English Language and Translation, King Saud University, Riyadh, Saudi Arabia;2. Information Technology Department, King Saud University, Riyadh, Saudi Arabia;3. College of Languages & Translation, King Saud University, Riyadh, Saudi Arabia;4. College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;1. Department of Psychology, Goethe-University, Theodor-W.-Adorno-Platz 6, D-60629 Frankfurt, Germany;2. School of Psychology, University of Sydney, Brennan MacCallum Bldg, A18, Sydney, NSW, 2006 Australia;3. Institute of Mathematics, University Potsdam, Am Neuen Palais 10, D-14469 Potsdam, Germany;1. Institute of Psychology, University of Wroclaw, Poland;2. Interdisciplinary Center “Smell & Taste”, Department of Otorhinolaryngology, TU Dresden, Germany;3. Creative Education Lab, The Maria Grzegorzewska University, Poland;1. Department of English Language, Mariwan Branch, Islamic Azad University, Mariwan, Iran;2. Shiraz University, Shiraz, Iran |
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| Abstract: | As the smartphone becomes an integral part of our lives, its value as a rich data source reaches an increasing potential. Several previous studies have used smartphone-derived data to discover relationships between user characteristics and different types of smartphone use. However, none tried to use smartphone data to capture an individual's social behavior into one profile, aimed at providing additional information for the diagnostic evaluation of social deficits. This study presents a novel way of combining different modalities of smartphone data for the creation of sociability profiles using a scoring mechanism that allows for easy addition and removal of data sources. Following installation of the smartphone application, data is being sampled in the background to allow for the assessment of spontaneous smartphone use. Sociability scores were based on the integration of social communication and social exploration scores derived from smartphone use and environmental data sampling (e.g., GPS and external Bluetooth signals). Finally, we have applied our Sociability model to create social profiles of ten test subjects as a baseline for future studies. This pilot study provided insight in the usability of the individual sociability scores for future smartphone application to provide longitudinal objective measures of normal and atypical human social behavioral profiles in their natural environment. |
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| Keywords: | Data mining Psychological health Smartphone use Sociability Social deficits Social profiling |
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