## 5 Discussion  
Digital addiction is a multifaceted problem. Therefore, it is significant to have a comprehensive understanding of this phenomenon to overcome its adverse impacts. Several factors contribute to the onset of addiction, including IT. We argue that IT as One of the factors that contribute to the development of addiction can also have a contribution to the effort of combating digital addiction. The present study attempts to examine what other researchers have been done so far in exploring the positive role of IT in digital addiction research. This section provides the answer to each question specified in the methodology section. This present study has extracted 15 papers that meet predefined criteria as seen in Table 1. These 15 studies show various IT used for combating digital addiction. A previous review study on e-health intervention for problematic internet use conducted in 2016 only found three papers that meet prespecified criteria [30]. The IT used for intervention in those three papers are an online expert system [53], application [54], and virtual reality [28]. The selected papers from this present study show more various IT use. The IT used in the extracted studies can be classified into four groups. Those are mobile applications, frameworks, AI methods, and biosignal recording systems. The number of studies and the IT used in those studies is shown in Figure 8. This study finds that AI and biosignal recording systems are the two most commonly used IT in digital addiction research. The advantages of AI have also been extensively explored in healthcare, such as to obtain insights for assisting clinical practice, to reduce diagnostic and therapeutic errors in the clinical practice, to predict health outcomes, and to assist in making health risk alerts [55]. Study D, F, G, H, L, N, and O leveraged the advantages of AI to design more reliable digital addiction detection systems. The AI methods used include logistic regression, k-nearest neighbor, naive bayes, decision tree, support vector machine, fuzzy neural network, fuzzy triangular, and extended classifier. Those various methods function as a smart processing machine to find the pattern of addiction or classify the level of addiction from various data sources. Those studies lay the groundwork for the further development of digital addiction detection systems using AI. The biosignal has been extensively used in detecting health problems due to its ability to provide real-time state of the biological system change. While the AI methods act as a processing machine to analyze the data, the biosignal recording systems are used to collect human physiological data for further analysis. The purpose of utilizing physiological parameters is to get real-time and more reliable data for detecting digital addiction that cannot be obtained from traditional assessment tools. Some physiological parameters recorded are brain signal, heart rate, heart rate variability, eye blink, eye movement, respiratory rate, respiratory index, and skin conductance response. Internet and game addiction can lead to neuroadaptation and structural changes in the brain [56]. The marker of game addiction can be observed from particular areas of the brain, such as the occipital, parietal, temporal, and frontal lobes [57; 58]. However, study B found that the distinctive pattern of addiction was found in the occipital region of the brain. Thus, the study suggested that the signals  
Figure 8: The number of studies and the IT used. from the occipital region can provide evidential proof to identify game addiction. The involvement of the cardiorespiratory system has also been studied to find the indicator of addiction. The analysis of pulse transit time, heart rate variability, and skin temperature showed increased sympathetic nerve activities during a computer game, while the parasympathetic nerves became less active (study E). The activation of the sympathetic nervous system was also found when participants were in the craving for gaming conditions (study D).