Mental health is vital to human well-being and its prevention strategies have a huge impact on quality of life. With new developments in sensors, it is now possible to continuously collect behavioral data which can be used to gain insights into mental health states. This has the potential to optimize psychiatric assessment and intervention processes, thereby improving patient experiences and outcomes. However, access to high quality medical data for research purposes is limited due to high costs, time, and privacy concerns, to name a few, and this is especially true for mental health related fields. To this extent, we present the "OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD" dataset which comprises motor activity recordings of patients with bipolar and unipolar depression, schizophrenia, and ADHD (attention deficit hyperactivity disorder). The dataset also contains motor activity data from a clinical and healthy control group, making it suitable for building machine learning predictive models and other analytics. It contains recordings from 162 individuals totaling 1,565 days worth of motor activity data.