  We aim to investigate if we can improve predictions of stress caused by OCD
symptoms using pre-trained models, and present our statistical analysis plan in
this paper. With the methods presented in this plan, we aim to avoid bias from
data knowledge and thereby strengthen our hypotheses and findings. The Wrist
Angel study, which this statistical analysis plan concerns, contains data from
nine participants, between 8 and 17 years old, diagnosed with
obsessive-compulsive disorder (OCD). The data was obtained by an Empatica E4
wristband, which the participants wore during waking hours for 8 weeks. The
purpose of the study is to assess the feasibility of predicting the in-the-wild
OCD events captured during this period. In our analysis, we aim to investigate
if we can improve predictions of stress caused by OCD symptoms, and to do this
we have created a pre-trained model, trained on four open-source data for
stress prediction. We intend to apply this pre-trained model to the Wrist Angel
data by fine-tuning, thereby utilizing transfer learning. The pre-trained model
is a convolutional neural network that uses blood volume pulse, heart rate,
electrodermal activity, and skin temperature as time series windows to predict
OCD events. Furthermore, using accelerometer data, another model filters
physical activity to further improve performance, given that physical activity
is physiologically similar to stress. By evaluating various ways of applying
our model (fine-tuned, non-fine-tuned, pre-trained, non-pre-trained, and with
or without activity classification), we contextualize the problem such that it
can be assessed if transfer learning is a viable strategy in this domain.
