Early detection of students’ mental health issues from a traditional daily health observation scheme in Japanese schools and its digitalization - Nishimura, Tomoko; Wakuta, Manabu; Osuka, Yuko; Tsukui, Nobuaki; Hirata, Ikue; Takahashi, Michio; Adachi, Masaki; Katayama, Taiichi; Aizaki, Kyoko; Sumiya, Motofumi; Kawakami, Sayaka; Iwabuchi, Toshiki; et al. (Frontiers in Public Health, 2024)
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 4 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.