TY - CHAP

T1 - How to Predict Mood?

T2 - Americas Conference on Information Systems - AMCIS 2016

AU - Becker, Dennis

AU - Bremer, Vincent

AU - Funk, Burkhardt

AU - Asselbergs, Joost

AU - Riper, Heleen

AU - Ruwaard, Jeroen

N1 - Conference code: 22

PY - 2016/8

Y1 - 2016/8

N2 - 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.

AB - 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.

KW - Business informatics

KW - Unobtrusive EMA

KW - E-mental health

KW - Mood Prediction

KW - Smartphone-Based Data

KW - Bayesian Modeling

KW - Digital media

KW - Smartphone

UR - http://aisel.aisnet.org/amcis2016/Health/

M3 - Article in conference proceedings

SN - 978-0-9966831-2-8

T3 - Proceedings of the Americas Conference on Information Systems (AMCIS)

BT - Proceedings of the AMCIS 2016

PB - AIS eLibrary

Y2 - 11 August 2016 through 14 August 2016

ER -