Tomoko Nishimura ^1,2,3* Manabu Wakuta ^1,3 Yuko Osuka ^1,3 Nobuaki Tsukui ^1,2,3 Ikue Hirata ^1,2 Michio Takahashi ^3,4 Masaki Adachi ^3,5 Taiichi Katayama ^2,3 Kyoko Aizaki ¹ Motofumi Sumiya ¹ Sayaka Kawakami ¹ Toshiki Iwabuchi ¹ Atsushi Senju ^1*

• ¹ Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan
• ² United Graduate School of Child Development, Osaka University, Suita, Ōsaka, Japan
• ³ Institute of Child Developmental Science Research, Hamamatsu, Shizuoka, Japan
• ⁴ Smart-Aging Research Center, Tohoku University, Sendai, Miyagi, Japan
• ⁵ Department of Psychology, Meiji Gakuin University, Tokyo, Tôkyô, Japan

Objective: The implementation of school-based mental health screening offers promise for early detection of mental health issues in children; however, various barriers hinder its widespread adoption. This study aimed to investigate the predictive value of digital data obtained from an established daily health observation scheme in Japanese schools to identify later mental health issues in children. Methods: Data for the analysis were obtained from 2,433 students enrolled in five public schools. The data acquisition period spanned 76 school days, from September 1, 2022, to December 23, 2022, and student absences were recorded during this period. Depressive and anxiety symptoms were assessed in January 2023. The students’ daily physical and emotional health status was recorded as “daily health issue” scores and group-based trajectory modeling was employed to classify the long-term trends in these scores. Additionally, rolling z-scores were utilized to capture variability in daily health issue scores, with z-scores above +1 considered unusual responses. Results: After four months of daily health observations, students’ response trends were classified into five trajectory groups. The group experiencing the highest number of daily health issues (Group 5; 5.4% of the sample) exhibited more subsequent depressive and anxiety symptoms compared to the group with fewer issues (Group 1; 47.5%) (incident rate ratio [IRR] = 5.17; 95% confidence interval [CI]: 3.82, 6.99). Group 5 also demonstrated significantly more days of absence than Group 1 (IRR = 2.14, 95% CI: 1.19, 3.85). The average daily health issue scores for the entire period were associated with both depressive/anxiety symptoms and the number of days absent from school (IRR = 1.59, 95% CI: 1.45, 1.73; IRR = 1.18, 95% CI: 1.04, 1.35, respectively). Furthermore, a higher number of unusual responses during the entire period was also associated with more depressive/anxiety symptoms (IRR = 1.10, 95% CI: 1.07, 1.12). Conclusion: The current study is the first to demonstrate the predictive capability of a traditional daily health observation scheme to identify mental health issues in children. This study highlights the scheme’s potential to screen and safeguard children’s mental health, emphasizing the importance of digitalization and collaboration with various stakeholders.