JMIR Publications

Background:

Depression is a predominant feature of many psychological problems leading to extreme behaviors and, in some cases, suicide. Campus information systems keep detailed and reliable student behavioral data; however, whether these data can reflect depression and we know the differences in behavior between depressive and nondepressive students are still research problems.

Objective:

The purpose of this paper is to investigate the behavioral patterns of depressed students by using multisource campus data and exploring the link between behavioral preferences and depressive symptoms. The campus data described in this paper include basic personal information, academic performance, poverty subsidy, consumption habit, daily routine, library behavior, and meal habit, totaling 121 features.

Methods:

To identify potentially depressive students, we developed an online questionnaire system based on a standard psychometric instrument, the Zung Self-Rating Depression Scale (SDS). To explore the differences in behavior of depressive and nondepressive students, the Mann-Whitney U test was applied. In order to investigate the behavioral features of different depressive symptoms, factor analysis was used to divide the questionnaire items into different symptom groups and then correlation analysis was employed to study the extrinsic characteristics of each depressive symptom.

Results:

The correlation between these factors and the features were computed. The results indicated that there were 25 features correlated with either 4 factors or SDS score. The statistical results indicated that depressive students were more likely to fail exams, have poor meal habits, have increased night activities and decreased morning activities, and engage less in social activities (eg, avoiding meal times with friends). Correlation analysis showed that the somatic factor 2 (F4) was negatively correlated with the number of library visits (r=–.179, P<.001), and, compared with other factors, had the greatest impact on students’ daily schedule, eating and social habits. The biggest influencing factor to poor academic performance was cognitive factor F1, and its score was found to be significantly positively correlated with fail rate (r=.185, P=.02).

Conclusions:

The results presented in this study indicate that campus data can reflect depression and its symptoms. By collecting a large amount of questionnaire data and combining machine learning algorithms, it is possible to realize an identification method of depression and depressive symptoms based on campus data.