## 2 Technologies in DTx
### Data Collection  
In terms of data collection, accurate symptoms and severity must be identified first before providing medical treatment to users. Although there are differences in diagnostic criteria and methods for each disease, physical disorder diagnoses are usually made through medical data collected in hospitals, while various rating scales are widely used for mental illnesses. Medical image data or biosignal data have the advantage of observing the objective state of the patient, but only the data of the specific day can be viewed [12]. In addition, the medical rating scales include questionnaires that can determine past conditions, but it is less objective because the score highly depends on the patient's self-response. However, digital therapeutics can consider objectivity and time series characteristics through various types of data collection. Many digital therapeutic products are currently developed in a mobile application format. One of the valuable aspects of the mobile applications of DTx is that it can bring additional data through the user's wearable device. Various wearable sensor devices, such as smartwatch types, bracelets, rings, necklaces, chest bands, and shoe insoles, have been developed to gather human biosignal data. Photoplethysmogram (PPG) sensors are the most widely utilized body indicators of heart-related data such as heart rate, heart rate variability, and electrocardiogram [13]. In addition, user data can be collected with various sensors of wearable devices, such as user activity detection using a gyroscope and accelerometer, biological fluid calculation with a sweat-rate sensor [14], user motion recognition using pressure sensors, and emotional state prediction using skin conductivity and electromyography [15]. DTx products can optimize these user data to provide a more objective treatment according to their target disorder. In addition to collecting user data using mobile devices, disease-related data using virtual reality (VR) devices have also been actively developed recently. VR devices were adopted as exposure therapy devices provided to patients with mental illness in [16], where they could create specific spaces and situations similar to reality. The treatment and diagnosis of mental disorders are also continuously developing so that they can be provided through VR devices, and as one of the representative examples, there is research to identify and determine attention deficit and hyperactivity disorder (ADHD) symptoms in children based on collected VR data [17]. That study generated a virtual environment to collect data from users in multiple VR scenes, and an ADHD classification was conducted with an AI model created through a DSM-5-based rating scale. Data Preprocessing  
Since the data accumulated from wearable sensors and smart devices cannot be directly applied to algorithm analysis due to their high dimensionality and high noise rate [18],  
Figure 2: DTx technologies from two perspectives: system developer and system user. it must first undergo a noise filtering, feature selection, and extraction process to obtain representative data features from the original dataset. However, when collecting and applying data at both the development stage and the actual service stage, it is essential to check whether the data contain critical personal information that can infer a specific individual or not. Physical and psychological data that determine the user's medical condition are classified as personal health information (PHI), which must be controlled under strict data protection regulations, especially in the United States through the Health Insurance Portability and Accountability Act (HIPAA) [19]. Therefore, user data should be collected without personal identification information or need to be deidentified [20; 21; 22] through separate data preprocessing after the data collection step. For example, [PERSON] et al. [20] developed a pattern-matching deidentification algorithm based on the Perl language, to deidentify patients' critical information from medical records.