@inproceedings{be885ff975974e2aaec5c0ffa86a858c,
title = "Towards Hybrid Profiling: combining digital phenotyping with validated survey questions to balance data entry effort with predictive power",
abstract = "Tailoring apps based on user traits has attracted tremendous interest in developing mHealth apps, and understanding a users' personality is a key challenge in that context. This challenge is typically addressed via classic surveys, which pose a regrettably high burden on app users. This study aims to reduce the response burden of personality tests by introducing a model for predicting the user personality based on digital footprints of app usage. At the same time, skipping surveys completely turns out to undermine prediction accuracy. Therefore, this paper conceptualizes a hybrid framework that utilizes user event data in combination with surveys that have fewer questions than conventionally. The proposed method demonstrates a promising trade-off between the simplicity of using user event data and the accuracy of the validated survey methods: when applying the hybrid method to a retrospective case study, the accuracy is higher than when using the event data exclusively. Also, the number of survey questions needed is significantly lower. Since this is a novel method, we expect that results will strengthen as larger data sets available over time. To facilitate that process, we also present a mathematical model for rationalizing the design process of hybrid profiling systems. This may boost adoption by developers who aim to implement the method in their specific app.",
keywords = "digital phenotype, mHealth, big-five, personality prediction, health interventions, multi-layer perceptron, neural network, hybrid system",
author = "{Hadian Haghighi}, Ehsan and Nuijten, {Raoul C.Y.} and {van Gorp}, {Pieter M.E.}",
year = "2021",
month = feb,
day = "19",
doi = "10.1145/3459104.3459199",
language = "English",
isbn = "978-1-4503-8983-9",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery, Inc",
pages = "585--593",
booktitle = "Proceedings - 2021 International Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2021",
address = "United States",
note = "ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering ; Conference date: 19-02-2021 Through 21-02-2021",
}