## 5 Conclusions  
This study showed the development of prediction models to predict wearing-off for PD patients. A commercially available fitness tracker (Garmin vivosmart4) was used to collect data in a real-world environment. Moreover, the participants recorded their wearing-off periods and drug intake with a customized smartphone application (FonLog). Then, the datasets were combined to build our prediction models. Predictive models were built for each participant as there were significant differences between the participants' data, except for one feature. Developing predictive models for each participant in a challenging data collection environment resulted in a balanced accuracy of 70.0-71.7% for participant 1 and 76.1-76.9% for participant 2 on the test set. Both models utilized a class imbalanced transformer in their model development pipeline. Finally, only the time that elapsed after taking anti-PD medicine was a common predictor between the participants. Heart rates and stress scores contributed to the prediction model of participant 1, who presented more reports of wearing-off over the 30 days. In the future, more participants will be part of the study to assess the differences among demographic groups. Moreover, these baseline models will be deployed to an early warning system for the participants. The reported feedback will be used to improve the prediction models and develop a symptom-based prediction model. We envision the utilization of these baseline models while collecting more data using a smartwatch. The following are available online at [[https://www.mdpi.com/article/10.3390/app11167354/s1](https://www.mdpi.com/article/10.3390/app11167354/s1)]([https://www.mdpi.com/article/10.3390/app11167354/s1](https://www.mdpi.com/article/10.3390/app11167354/s1)): \"data/garmin\" contains the available raw Garmin dataset from Garmin Health API, \"data/fonlog/records.xlsx\" contains the raw FonLog dataset, \"data/combined_data/combined_data_participant*xlsx\" contains data for the processed and combined dataset for each participant in different re-sampled time interval, \"Data Processing.ipynb\" contains the data processing source code, \"Model Development\" contains the source code for model pipelines, and the \"About Supplementary Files\" contains the guide to the Supplementary Materials. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Wearing-Off (1)** & **Predicted Actual On (0)** \\ \hline \multirow{2}{*}{**Actual Wearing-Off (1)**} & 955 & 472 \\  & True positive (TP) & False negative (FN) \\ \hline \multirow{2}{*}{**Actual On (0)**} & 341 & 1112 \\  & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Confusion matrix for participant 2’s best model on the 15 min test set. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Wearing-Off (1)** & **Predicted Actual On (0)** \\ \hline \multirow{2}{*}{**Actual Wearing-Off (1)**} & 955 & 472 \\  & True positive (TP) & False negative (FN) \\ \hline \multirow{2}{*}{**Actual On (0)**} & 341 & 1112 \\  & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Confusion matrix for participant 1’s best model on the 15 min test set. **Author Contributions:** Conceptualization, T.S. and S.I. ; Data curation, J.N.V. and S.I. ; Formal analysis, J.N.V. ; Funding acquisition, T.S. ; Investigation, J.N.V. and Y.S. ; Methodology, J.N.V., T.S. and S.I. ; Project administration, T.S. and J.N.V. ; Resources, T.S. and S.I. ; Software, J.N.V. and S.I. ; Supervision, T.S. and S.I. ; Validation, T.S. and Y.S. ; Visualization, J.N.V. ; Writing--original draft, J.N.V. ; Writing--review and editing, Y.S., S.I. and T.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the JSPS KAKENHI Grant Number JP16H06534. **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki.