Using Phone and Wearable Data to Predict Depression Relapse
Studies whether smartphone and wearable data can predict return of depressive episodes in adults.
Plain-language title generated from the registry record
Registry title: Digital Phenotypes for Predicting Depression
Key facts
- Where
- Seoul, South KoreaSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 19 and 75 years old?
Source: NCT07151846, who can take part
Where in South Korea
- Korea University Anam HospitalSeoul, South KoreaNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Korea University Anam Hospital
- Trial code
- NCT07151846
- Record updated
- 3 September 2025
- Original description
This longitudinal study aims to identify and validate digital phenotypes that can predict recurrence of major depressive episodes using passively collected, real-time sensing data from smartphones and wearable devices. Over a 12-month period, 540 participants-including patients with mood disorders and healthy or high-risk controls-will complete five clinical assessments at 3-month intervals, wear a Fitbit device daily, and log daily mood ratings via a mobile app. The study includes the development of AI-based predictive models and the construction of an anonymized wearable big-data repository for mood disorders.Read full original descriptionShow less
Discovery and Validation of Digital Phenotypes for Predicting Recurrence of Depressive Episodes
This longitudinal study aims to identify and validate digital phenotypes that can predict recurrence of major depressive episodes using passively collected, real-time sensing data from smartphones and wearable devices. Over a 12-month period, 540 participants-including patients with mood disorders and healthy or high-risk controls-will complete five clinical assessments at 3-month intervals, wear a Fitbit device daily, and log daily mood ratings via a mobile app. The study includes the development of AI-based predictive models and the construction of an anonymized wearable big-data repository for mood disorders.
Inclusion: Adults aged 19 to 75 years and 11 months; Diagnosed with a major mood disorder (bipolar I or II disorder, or major depressive disorder) based on DSM-5, with a history of psychiatric visits for mood symptoms; Has experienced at least one prior depressive episode (major, minor, or brief); Owns a smartphone with stable internet connectivity, can operate it without difficulty, and agrees to install a digital phenotyping application, wear a Fitbit device daily, and complete daily mood logs throughout the study period; Adults aged 19 to 75 years and 11 months; No current or past diagnosis of bipolar disorder or major depressive disorder; Scores below clinical thresholds on both K-MDQ (<7) and PHQ-9 (<5) at baseline assessment; Owns a smartphone with stable internet connectivity, can operate it without difficulty, and agrees to install a digital phenotyping application, wear a Fitbit device daily, and complete daily mood logs throughout the study period; Adults aged 19 to 75 years and 11 months; No current or past diagnosis of bipolar disorder or major depressive disorder; Meets either of the following:; Scores above the threshold on K-MDQ (≥7) or PHQ-9 (≥5) at baseline. B. Reports persistent mood symptoms during MINI (e.g., ≥2 weeks of depressive mood/anhedonia, or several days of elevated/irritable mood); Owns a smartphone with stable internet connectivity, can operate it without difficulty, and agrees to install a digital phenotyping application, wear a Fitbit device daily, and complete daily mood logs throughout the study period; Exclusion Critera (Overall); Inability to provide informed consent due to cognitive impairment, severe psychiatric symptoms, or language barriers; Presence of any neurological or medical condition that may significantly affect mood, cognition, or behavior (e.g., epilepsy, dementia, traumatic brain injury); Current substance use disorder or dependence (excluding nicotine or caffeine) that is either untreated or clinically unstable; Severe suicidal ideation or risk as determined by clinical judgment at screening; Ongoing participation in another interventional clinical trial, particularly those involving psychotropic medications or digital health interventions; Inability or unwillingness to wear a wearable device (Fitbit) or use the required mobile application daily throughout the study; Lack of stable internet access or a compatible smartphone required for digital phenotyping data collection; Any other condition that, in the investigator's judgment, may compromise the participant's safety, compliance, or the integrity of the study data.