My Experiences: Leveraging Digital Technologies to Better Understand Mental Health

NCT05753605 | Completed

• 1 other identifier: 4UMYEXP01
• Study Type: observational
• Target: 302
• Locations: 1 country
• Sites: 1

Brief Summary

Mental health disorders are one of the most challenging chronic conditions to identify, treat and manage. This is largely due to the fact that diagnoses are almost entirely based on the patient's recall of current and past subjective experiences of symptoms; and then further interpreted by a healthcare professional introducing multiple layers of information biases in the formulation of a diagnosis. Accordingly, mental health conditions remain prevalent with high rates of misdiagnosis, inappropriate treatment and delayed intervention. In light of the heterogeneity across and within mental health conditions, a personalized interventional approach holds merit, yet the tools to effectively track an individual's day to day objective and subjective experience needed to achieve an individualized care approach have not until recently existed. Digital technologies such as passive and active sensing from smartphones and from wearable devices are shedding light on the capabilities of tracking new objective measures of health that could translate to key symptoms of mental health conditions. 'Multimodal data' approaches are those that attempt to translate a variety of electrical signals from digital devices to relevant health outcomes. The combination of digital devices to detect multimodal measures of mental health symptoms offers a unique opportunity to take a ground up approach in understanding the fluidity of mental health symptoms occurring at the individual level that might lend insight into new phenotypes of mental health illnesses that could have a physiological underpinning. The Study Investigators aim to characterize the multiplexing and fluid nature of mental health symptoms across individuals experiencing mental health symptoms and conditions using digital tools (i.e., wearables and mobile apps) and additional context information collected from virtual study support calls. The Investigators hope to know how objective measures from sensor data translate to core symptoms, episodes and flares across mental health disorders, and develop new (or new applications of) machine learning anomaly detection approaches and determine whether anomalies in expected symptom portraits can be reliably detected and enhanced by the addition of objectively measured data.

Conditions

Mental Health Symptoms; Wearables

Keywords

Mental Health; Depression; Anxiety; Bipolar Disorder; Psychosis; Wearables

Outcome Measures

Primary Outcomes (3)

• Study Adherence (daily app survey)

  Proportion of participants completing the app daily survey 70% of active study time.

  3-9 months

• Study Adherence (weekly symptom survey)

  Proportion of participants completing the app weekly symptom survey 70% of active study time

  3-9 months

• Study Adherence (Wearable device)

  Average Oura ring usage over active study follow-up

  3-9 months

Secondary Outcomes (1)

• Sensor data relationships with active measurements of mental health (exploratory)

  3-9 months

Study Arms (2)

Oura Ring Arm

Participant will be provisioned an Oura smartring.

BYOD (Bring Your Own Device) Arm

Participant will use their personal wearable.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

We will employ a multisite recruitment. The primary recruitment site will include students attending undergraduate and graduate programs from all 3 School of Nursing campuses at the University of Washington (Seattle, Tacoma and Bothell) from a total student body of 1026 students. The advantage of this specific population is its diversity. According to the UW 2021 census, 63% of this student body reflect Black Indigenous People of Color (BIPOC) or Underrepresented Minorities (URM). Additional sites may include other academic sites in addition to recruitment channels through social media campaigns and student organizations.

You may qualify if:

• + years of age
• Experiencing mental health symptoms associated with one or more mental health conditions including major depression, bipolar disorder, anxiety disorder or schizophrenia as defined by cut off scores on validated screening instruments OR a self-reported clinician confirmed diagnosis of a major mood disorder, anxiety disorder or psychotic spectrum disorder at baseline.
• Major depression (Patient Health Questionnaire (PHQ-9)): total score 10+
• Bipolar disorder (Mood Disorder Questionnaire (MDQ)): yes to 6+ items on question 1
• Anxiety disorder (Generalized Anxiety Disorder Scale (GADS-7)): Total score 10+
• Psychotic spectrum (Prodromal Questionnaire - Brief version (PQ-B)): a cut-off of 6+ endorsed positive items and 6+ for the distress subscale total score
• Able to speak, write and read English, given the app will be available only in English
• Able to provide informed consent
• Participants must have a personally owned iPhone 5s or newer (iOS 12 or higher) and use their phone for this study. This includes a willingness to download and use the study applications and sync their phone with the necessary study devices.
• Willingness to continuously wear a personally owned wearable device and permit researcher access to pre-identified streams of data (BYOD arm only) Willingness to continuously wear the Oura smartring and permit research access to its data (Oura arm only)

You may not qualify if:

• Not willing to permit access to wearable device data or use the study smartphone applications
• Currently or attempting to get pregnant
• Willing to extract 50-60 hairs from the back of the head
• Hair at least 6 centimeters long
• Not currently taking glucocorticoid containing medication (e.g., beclomethasone, betamethasone, budesonide, cortisone, dexamethasone, hydrocortisone, methylprednisolone, prednisolone, prednisone, triamcinolone)

Sponsors & Collaborators

• 4YouandMe: lead
• Definium Therapeutics: collaborator
• Vector Institute for Artificial Intelligence: collaborator
• Cambridge Cognition Ltd: collaborator
• University of Washington: collaborator

Study Sites (1)

4YouandMe

Seattle, Washington, 98121, United States

Location

MeSH Terms

Conditions

Psychological Well-Being; Depression; Anxiety Disorders; Bipolar Disorder; Psychotic Disorders

Condition Hierarchy (Ancestors)

Personal Satisfaction; Behavior; Behavioral Symptoms; Mental Disorders; Bipolar and Related Disorders; Mood Disorders; Schizophrenia Spectrum and Other Psychotic Disorders

Study Officials

• Sarah Goodday, PhD

  4YouandMe

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 20, 2022
• First Posted: March 3, 2023
• Study Start: June 26, 2023
• Primary Completion: May 31, 2025
• Study Completion: May 31, 2025
• Last Updated: September 16, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE

Locations

US (1)