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Master's Thesis from the year 2022 in the subject Computer Science - Commercial Information Technology, grade: 1,7, University of Hannover (Institut für Wirtschaftsinformatik), language: English, abstract: Wearable devices are frequently used to continuously collect physiological and behavioral data using integrated sensors. The strong correlation between activity levels and psychiatric disorders implies that these data offer potential in the diagnosis of depression. The objective of this master thesis is to answer the question of how activity data from wearables can be used to diagnose depression. To this end, the following research question is posed: How can wearables be applied to automatically detect depression states? To answer the research question, a secondary data analysis of the Depresjon dataset was conducted. The dataset includes motor activity data from 23 unipolar and bipolar depressed subjects and 32 healthy controls. Statistical features were extracted from the motor activity data to subsequently feed a random forest classifier. Using the motor activity signal from the wearable, the results show a sensitivity value of 0.941, indicating that depressed subjects are correctly classified 94.1% of the time, and a specificity value of 0.936, indicating that healthy control subjects are correctly classified 93.6% of the time.