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Detecting Early Signs of Depression Using Smartphone and Wearable Data

Development of multidimensional depression detection platform based on digital phenotyping

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0011936
Enrollment
90
Registered
2026-05-07
Start date
2020-10-24
Completion date
Unknown
Last updated
2026-05-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

None listed

Sponsors

Yonsei University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Participants who agreed to the online informed consent and were screened for depressive severity. Individuals were assigned to a depressive-severity group when their PHQ-9 score exceeded the cutoff for that group, prior to the target number for the group being reached, following a first-come, first-served allocation procedure.

Exclusion criteria

Exclusion criteria: Participants who completed the depression screening but could not be assigned to the appropriate severity group because the target number for that group had already been filled. Participants who, at any point after providing informed consent, wish to discontinue their participation in the study.

Design outcomes

Primary

MeasureTime frame
Performance of depression-state classification algorithms (Accuracy, AUC, F1-score)

Secondary

MeasureTime frame
Differences in smartphone behavioral patterns (e.g., mobility, screen-use duration, communication frequency) across PHQ-9–defined depressive severity groups.

Countries

Korea, Republic of

Contacts

Public ContactSeoi Lee

Yonsei University

seoilatte@yonsei.ac.kr+82-2-2123-2448

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: May 16, 2026