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.

翻译：本文旨在探究是否能通过预训练模型改善由强迫症症状引发的压力预测，并在此呈现我们的统计分析计划。通过本计划所述方法，我们力求避免因数据知识带来的偏差，从而增强假设与发现的可靠性。该分析计划所关注的"手腕天使"研究，纳入了9名年龄在8至17岁之间、确诊患有强迫症的参与者数据。数据通过Empatica E4腕带采集，参与者在清醒状态下连续佩戴该设备8周。研究旨在评估预测此期间记录的真实环境中强迫症事件的可行性。分析中，我们将考察能否改进由强迫症症状引发的压力预测，为此已构建了一个基于四个开源压力预测数据集训练的预训练模型。我们计划通过微调将该预训练模型应用于手腕天使数据，实现迁移学习。该预训练模型采用卷积神经网络架构，以血容量脉搏、心率、皮肤电活动及皮肤温度作为时间序列窗口来预测强迫症事件。此外，利用加速度计数据，另一模型会过滤体力活动以进一步提升性能——因为体力活动在生理特征上与压力具有相似性。通过评估模型的不同应用方式（是否进行微调、是否使用预训练、是否结合活动分类），我们对问题进行了情境化分析，从而判断迁移学习在该领域的可行性。