Original Article
ISSN ONLINE : 1880-2400
[School Health Vol.20, 39-47, 2024]
Developing a Mid-to-Long Term Screen Time Prediction Model Using Metrics from the “Media Control Challenge”: An Exploratory Study in School Education”
Yusuke Arai*, Kazuhiro Suzuki**, Daimei Sasayama***, Yuta Kuraishi*,
Hiroki Yamada*, Takugo Maeda* and Shinsuke Washizuka*
- *Department of Psychiatry, Shinshu University School of Medicine
- 3-1-1 Asahi, Matsumoto, Nagano 390-8621 JAPAN
- **Department of Community Mental Health, Shinshu University School of Medicine
- 3-1-1 Asahi, Matsumoto, Nagano 390-8621 JAPAN
- kazsuzuki@shinshu-u.ac.jp
- ***Department of Child and Adolescent Developmental Psychiatry, Shinshu University School of Medicine
- 3-1-1 Asahi, Matsumoto, Nagano 390-8621 JAPAN
[Received March 5, 2024; Accepted August 13, 2024]
- Keywords:
- screen time, media control challenge, school education, media usage
Abstracts
Background: The Media Control Challenge (MCC) is a ten-day initiative designed to educate and motivate elementary and junior high school students to manage their screen time. However, due to a lack of research on the metrics for interpreting MCC results, it is unclear what points schools should emphasize when providing feedback to students and their parents.
Objective: This study aims to develop a model to predict mid-to-long term screen time using performance metrics from the MCC and to explore how MCC can be effectively utilized in school settings.
Method: A questionnaire survey on current screen time was conducted among families with elementary and junior high school students in a specific region in northern Nagano Prefecture, seven months after the MCC. The survey statistically analyzed the relationship between the average Challenge Level (CL), Achievement Rate (AR), and their product (CL×AR) during the MCC period and the screen time seven months after the MCC.
Results: Among the 92 participants, 40 had screen times exceeding two hours seven months post-MCC. Among the performance metrics, only CL×AR showed a significant negative correlation with screen time after seven months (Spearman's rho = -0.289, P = 0.005, 95% Confidence Interval: -0.471 to -0.083). Even after adjusting for covariates using logistic regression analysis, CL×AR was significantly associated with screen time after seven months (Odds ratio = 0.415, P = 0.003, 95% Confidence Interval: 0.232 to 0.744). The area under the Receiver Operating Characteristic (ROC) curve (AUC) for the predicted probability including CL×AR and covariates was 0.730. The AUC for CL×AR alone was 0.650. The cutoff value that maximizes the Youden index was estimated to be 1.98. At this cutoff, the sensitivity was 46.2% and specificity was 77.5%.
Conclusion: CL×AR remained significantly associated with mid-to-long term screen time even after adjusting for covariates, indicating its potential usefulness as a predictive model. Additionally, based on the ROC curve for CL×AR alone, cutoff values of 1.98 were considered important as a guideline for maintaining appropriate screen time for students. Future studies should develop Japan-specific intervention strategies based on these results and evaluate their effectiveness through practical research.
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