Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data
| Autor: | Gabriella M. Harari, Amanda N. Barczyk, R. Cameron Craddock, Samuel D. Gosling, Edison Thomaz, Christopher G. Beevers, David M. Schnyer, Jason Shumake, Congyu Wu |
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| Rok vydání: | 2021 |
| Předmět: |
020205 medical informatics
Applied psychology Medicine (miscellaneous) Health Informatics 02 engineering and technology 01 natural sciences law.invention Bluetooth Interpersonal relationship Health Information Management law 0202 electrical engineering electronic engineering information engineering medicine Predictability business.industry 010401 analytical chemistry Loneliness Mental health 0104 chemical sciences Computer Science Applications Predictive power Global Positioning System medicine.symptom Psychology business Statistical correlation Information Systems |
| Zdroj: | Smart Health. 20:100180 |
| ISSN: | 2352-6483 |
| DOI: | 10.1016/j.smhl.2021.100180 |
| Popis: | Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual's Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features' statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications. |
| Databáze: | OpenAIRE |
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