## 5 Discussion
Some particular respiratory instantaneous frequencies were suggested as addiction indicators (study H). The involvement of physiological parameters from the human eye in digital addiction, such as eye blinks and eye movements, was also investigated. The number of eye blinks and the distance of the saccadic eye movements were found significantly decreased during the gameplay (study D). These conditions may occur due to increased attention during gaming [41]. The applications found in study A and study I are used to collect the user activities to be saved in a log file. These mobile applications are operated in the background. The user behavior data from a log file are then analyzed with various techniques to assess or detect smartphone addiction. In study A, the log data are represented by tensor factorization to later be used to predict smartphone addiction. Study I use log data to record user interaction events in using a smartphone to predict user's smartphone addiction. Different from previous studies, study J uses a mobile application not to create a log file but to stimulate user imagination and reflection on their smartphone usage behavior. The character generated from a mobile application can respond to user usage behavior that reflects the consequences of excessive smartphone use. Three studies develop a framework to guide the development of systems to detect or intervene in digital addiction. Study B proposes a framework to design a wearable mobile addiction sensing system that aimed to detect game addiction. The system proposed may be used as a user personal assistant or as a resource to suggest better rehabilitation by any specialist. Study K proposes a reference model and a process model to design interactive online platforms to host peer groups for a digital addiction intervention. Study M proposed an AI-based recovery framework for internet addiction. Those three studies guide future research in developing digital addiction intervention solutions. The IT used/proposed and its function are shown in Table 2.  
The use of IT in digital addiction has three main purposes, namely to detect/measure digital addiction, to prevent digital addiction, and to intervene in digital addiction. In the detection effort, the detection approaches used can be classified into three groups. The first approach is by examining physiological parameters from various human biosignals to find the particular physiological parameter that may be the indicator of addiction. This approach is conducted to overcome the authenticity problem of questionnaire-based digital addiction detection instruments (i.e., the Internet Addiction Scale (IAS), CIAS, and SAS). Biosignals carry information about the state of the biological system [59]. Therefore, the strength of this approach relies on the reliability of human biosignal data that manifests as a response to a particular stimulus. However, the assessment utilizing biosignals requires a particular recording device that may be impractical to implement, particularly if it will be implemented as a personal monitoring device for an individual with digital addiction. EEG or fMRI devices, for instance, have been typically used to record the brain signals, but it may be impractical or improper to use it for assisting an individual in his daily life. The second approach is to detect digital addiction symptoms by analyzing user's usage behavior. The advantage of this approach lies in the easiness of collecting the data. The user behavior data can be obtained from a log file, temporary internet file, or application programming interface (API). However, this approach may take some time to obtain the intended usage behavior. The amount of data obtained may also influence the findings. Thus, this approach may not be suitable for real-time detection. Furthermore, the usage behavior derived from usage time may not represent the strongest indicator of smartphone addiction [46].