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.

Inclusion Criteria:

• 18+ 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)

Exclusion Criteria:

• Not willing to permit access to wearable device data or use the study smartphone applications
• Currently or attempting to get pregnant

Cortisol Sub-arm Inclusion Criteria:

For participants interested in the hair cortisol sub-arm, they will have to meet the following inclusion criteria:

• 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)

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.

Under the 4YouandMe open source model, we will make all data, findings, and algorithms available in the public domain. Accordingly, coded data produced from this project will be shared broadly with qualified researchers through Sage Bionetworks Synapse. Only data from consenting participants will be shared through Sage Bionetworks Synapse. During study follow-up, internal researchers (4YouandMe, MindMed) will have access to all coded study data for interim analyses. The study app will be owned by MindMed and will not be placed into the public domain, but will be available to be licensed.

During study follow-up, internal researchers (4YouandMe, MindMed) will have access to coded data for interim analyses. Coalition partners (Vector Institute, CamCog) will have access to coded study data on a x (TBD) basis during study follow-up. MindMed will provide a compute-space where coded data can be securely accessed by internal researchers and coalition partners (TBD).