%0 Conference Paper %1 9867055 %A Schleicher, Miro %A Hamacher, Sebastian %A Naujoks, Mats %A Günther, Kolja %A Schmidt, Timo %A Pryss, Ruediger %A Schobel, Johannes %A Schlee, Winfried %A Spiliopoulou, Myra %B 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) %D 2022 %K Adherence Informative_missingness Time_series_classification Time_series_with_large_gaps mHealth %P 228-233 %R 10.1109/CBMS55023.2022.00047 %T Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data %U https://ieeexplore.ieee.org/document/9867055/ %X Applications in mobile health (mHealth) empower self-monitoring of chronic conditions of the user and also offer insights to medical experts. The data generated by these apps constitute one time series per user. These time series vary substantially in length and contain ‘gaps’, as users pause or stop interacting with the app. In order to design measures that promote patient engagement with the app, it is necessary to predict and understand decline in engagement. We measured the performance of the algorithms on two real-world datasets from an mHealth app. We show that all approaches outperform the baseline and that shapelet, dictionary and matrix distance approach perform similarly for long-term prediction. This is particularly important because it allows early intervention towards increase of engagement. In this paper, we present an approach that uses the missingness information to process time series with large gaps.

@inproceedings{9867055, abstract = {Applications in mobile health (mHealth) empower self-monitoring of chronic conditions of the user and also offer insights to medical experts. The data generated by these apps constitute one time series per user. These time series vary substantially in length and contain ‘gaps’, as users pause or stop interacting with the app. In order to design measures that promote patient engagement with the app, it is necessary to predict and understand decline in engagement. We measured the performance of the algorithms on two real-world datasets from an mHealth app. We show that all approaches outperform the baseline and that shapelet, dictionary and matrix distance approach perform similarly for long-term prediction. This is particularly important because it allows early intervention towards increase of engagement. In this paper, we present an approach that uses the missingness information to process time series with large gaps.}, added-at = {2023-02-20T01:55:50.000+0100}, author = {Schleicher, Miro and Hamacher, Sebastian and Naujoks, Mats and Günther, Kolja and Schmidt, Timo and Pryss, Ruediger and Schobel, Johannes and Schlee, Winfried and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/2988e4534e79bbbb55601887944713993/kmd-ovgu}, booktitle = {2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)}, doi = {10.1109/CBMS55023.2022.00047}, interhash = {6ffa87e3af1469d5f7fbe61630919459}, intrahash = {988e4534e79bbbb55601887944713993}, issn = {2372-9198}, keywords = {Adherence Informative_missingness Time_series_classification Time_series_with_large_gaps mHealth}, month = {July}, pages = {228-233}, timestamp = {2023-02-20T01:55:50.000+0100}, title = {Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data}, url = {https://ieeexplore.ieee.org/document/9867055/}, year = 2022 }