## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Data Source** & **Detection Approach** & **Advantage** & **Limitation** \\ \hline \multirow{4}{*}{B, C, D, E, H} & \multirow{4}{*}{Physiological parameters} & Measuring certain physiological properties and comparing it with particular criteria & The human biosignals provide more accurate and real-time cues for detecting addiction & It requires a particular device to record the biosignals that may be impractical to implement. \\  & & & Measuring certain physiological properties and comparing it with particular criteria & The detection based on usage behavior needs some period to extract the data. \\  & & & & \\  & & & & \\  & & & & \\  & & Examining user usage behavior and analyze & It is relatively simple to extract & The usage behavior derived from usage time may not represent the strongest indicator of smartphone addiction. \\  & & & & \\  & & & The data collection is relatively easy to do. The strength of this approach may rely on the strength of the processing & Assessment is based on the participant’s perception. \\  & & & & \\  & & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The advantage and limitation of the detection system approach. a peer-group approach to digital addiction intervention. Peer-groups are classified as support therapy that involves other individuals (counselor, family, and friends) to give social and emotional support to an individual with digital addiction [48]. This approach is grounded in some psychological theories, including self-psychology, cognitive consistency theory, helper therapy principle, social learning theory, and group psychotherapy. The second study in digital addiction intervention (study M) utilizes the advantages of AI to closely monitor and analyze user's internet usage behavior for recommending possible staged intervention. The use of the internet in internet addiction intervention is challenging since the internet itself is considered as the means of addiction. In study M, the AI approach is suggested to tackle some challenges in the development of internet-based intervention systems, namely the complexity of internet addiction diagnosis, the extensive use of ubiquitous devices, and workaholic behaviors. The framework consists of self-assessment, monitoring and AI learning, and various intervention strategies and responses features. However, this study does not provide the theoretical background of the intervention strategy proposed. Because the two studies in the intervention are only focused on the development of the framework, the effectiveness of the intervention strategies proposed cannot be measured yet. Based on the analysis of the extracted studies, two main research directions in the use of IT to combat digital addiction are proposed. First, the utilization of IT for digital addiction prevention and intervention with various approaches may be further investigated in the future. As seen in Figure 5, we find fewer studies in the prevention and intervention efforts. Most studies found are still focused on the detection effort using various approaches. Accurate detection results are the significant input required to determine the proper intervention strategy. Thus, researchers may still seek the most effective approach in digital addiction detection. Therefore, we find most studies are done in this domain. However, up to this date, no study presents what is the best approach in digital addiction detection. In the prevention domain, another review study also finds that the literature is scarce [61]. Most of the previous studies [62; 63; 64; 65] on the prevention of digital addiction are done in the school environment with the various psychoeducational programs. However, there is a lack of study that utilizes IT to carry out those programs. Therefore, future research on prevention may wish to focus on IT utilization in the implementation of psychoeducational programs.