How to Predict Mood? Delving into Features of Smartphone-Based Data

Dennis Becker, Vincent Bremer, Burkhardt Funk, Joost Asselbergs, Heleen Riper, Jeroen Ruwaard

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalTwenty-second Americas Conference on Information Systems
Publication statusPublished - 2016

Keywords

  • bayesian modeling
  • e-mental-health
  • mood prediction
  • smartphone-based data
  • unobtrusive ema

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