## 4 Implementation
### Discussion  
This study attempts to design, develop, and implement a smartphone overdependence management system for self-control of smart devices. Based on the results of this study, we present the discussions below. In the baseline settlement session, we adapt diverse psychological tools, such as SAPS, BAS/BIS, SAS-SV, SCL-90-R, DFDII, and BSCS, to assess the psychological status of smartphone overdependence. These tools support the system to correctly analyze smartphone usage. Future research may identify other psychological tools to address missing areas. In the assessment session, we employ the CHAID Algorithm and six indicators to assess the smartphone overdependence. However, the shortcoming is that the six indicators were developed only for Internet dependence. Thus, future research may involve the development of new indicators that are more applicable in other fields. In the sensing and monitoring session, the background smartphone app monitors specific overuse stats. In the MindsCare PC app, the data are successfully stored and integrated, which enables the monitoring of general application data, exact usage time and period logs. The limitation is that the proposed app only supports Android phones due to security issues at the stage of development, and because more than 85% of smartphone users in South Korea use Android phones. Considering worldwide users, future research should develop the usage collection app for other operating systems (OSs). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**SUD**} & \multicolumn{2}{c}{**SUC**} & \multirow{2}{*}{**Usage Gap**} \\ \cline{2-2} \cline{4-6}  & **User** & **Usage** & & **User** & **Usage** \\ \hline Finance & 24 & 32.5 & 30 & 4.5 & 28.0 \\ System & 27 & 39.7 & 61 & 13.8 & 25.9 \\ Web & 27 & 61.8 & 57 & 42.8 & 19.0 \\ SNS & 27 & 63.7 & 55 & 45.0 & 18.7 \\ Shopping & 16 & 21.5 & 15 & 11.6 & 9.9 \\ Business & 21 & 8.2 & 28 & 3.2 & 5.0 \\ Tool/Productivity & 26 & 14.4 & 55 & 10.6 & 3.8 \\ Entertainment & 27 & 35.0 & 57 & 31.2 & 3.8 \\ Weather & 19 & 4.5 & 11 & 1.6 & 2.9 \\ Transportation & 22 & 5.2 & 15 & 3.1 & 2.1 \\ Photo & 23 & 7.5 & 38 & 6.9 & 0.6 \\ Lifestyle & 26 & 8.8 & 49 & 9.3 & -0.5 \\ Health/exercise & 6 & 4.1 & 8 & 6.4 & -2.3 \\ Game & 24 & 20.5 & 53 & 23.6 & -3.2 \\ Education & 14 & 1.1 & 14 & 7.8 & -6.7 \\ Decoration & 15 & 92.0 & 55 & 102.7 & -10.7 \\ \hline \hline \end{tabular}  
* Usage: minutes; Usage Gap: SUD \(-\) SUC.  
\end{table} 
Table 3: Daily average usage by category. In the analysis and feedback session, medical treatment recommendations are provided based on six indicators. If the user has an impediment in two of the six indicators, recommendations are provided based on these two impediments. Implementation results of the participants of the system indicate that the usage minutes of SUD were higher than the usage minutes of SUC in 11 categories. With the exception of five categories (lifestyle, health/exercise, game, education, and decoration), the daily-average comparison between the SUD group and the SUC group in the 16 categories that were defined from this study indicate that the usage minutes of SUD were higher than the usage minutes of SUC in all 11 categories. In the \"game\" category, the SUD and SUC groups did not significantly differ (\(\text{SUD}-\text{SUC}=-3.2\)). The smartphone usage time for the SUC group was higher than the smartphone usage for the SUD group. Although games can be easily associated with addiction, and this linkage is sometimes viable [34; 35], the proposed results suggest the larger effect of web usage or SNS usage in the case of smartphones. The results also correspond with recent studies that emphasized the importance of considering SNS as a main factor for smartphone overuse [36; 37]. A brief comparison with the SAMS is discussed because it is almost the first reference of the smartphone overuse monitoring system. Other related solutions were simple apps that were non-systematic or were not studies.