Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9672
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Main Title: Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity
Author(s): Theilig, Max-Marcel
Korbel, Jakob J.
Mayer, Gwendolyn
Hoffmann, Christian
Zarnekow, Rüdiger
Type: Article
Language Code: en
Abstract: Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.
URI: https://depositonce.tu-berlin.de/handle/11303/10777
http://dx.doi.org/10.14279/depositonce-9672
Issue Date: 9-Dec-2019
Date Available: 13-Feb-2020
DDC Class: 330 Wirtschaft
610 Medizin und Gesundheit
Subject(s): eHealth
mobile health
digital mental health
quantified self
receptivity
sequential prediction
health information management
Sponsor/Funder: BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASS
TU Berlin, Open-Access-Mittel - 2019
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Volume: 7
Publisher DOI: 10.1109/ACCESS.2019.2958474
Page Start: 179823
Page End: 179841
EISSN: 2169-3536
Appears in Collections:FG Informations- und Kommunikationsmanagement (IKM) » Publications

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