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We aim to investigate if we can improve predictions of stress caused by OCDsymptoms using pre-trained models, and present our statistical analysis plan inthis paper. With the methods presented in this plan, we aim to avoid bias fromdata knowledge and thereby strengthen our hypotheses and findings. The WristAngel study, which this statistical analysis plan concerns, contains data fromnine participants, between 8 and 17 years old, diagnosed withobsessive-compulsive disorder (OCD). The data was obtained by an Empatica E4wristband, which the participants wore during waking hours for 8 weeks. Thepurpose of the study is to assess the feasibility of predicting the in-the-wildOCD events captured during this period. In our analysis, we aim to investigateif we can improve predictions of stress caused by OCD symptoms, and to do thiswe have created a pre-trained model, trained on four open-source data forstress prediction. We intend to apply this pre-trained model to the Wrist Angeldata by fine-tuning, thereby utilizing transfer learning. The pre-trained modelis a convolutional neural network that uses blood volume pulse, heart rate,electrodermal activity, and skin temperature as time series windows to predictOCD events. Furthermore, using accelerometer data, another model filtersphysical activity to further improve performance, given that physical activityis physiologically similar to stress. By evaluating various ways of applyingour model (fine-tuned, non-fine-tuned, pre-trained, non-pre-trained, and withor without activity classification), we contextualize the problem such that itcan be assessed if transfer learning is a viable strategy in this domain.

Applying Pre-Trained Deep-Learning Model on Wrist Angel Data -- An Analysis Plan