Depressive symptoms, including anxiety (as a highly common comorbidity) and other mental and/or chronic physical health comorbidities (other than respiratory) Mental and Behavioural Disorders
Conditions
Interventions
This study is an observational, longitudinal, online study recruiting people with and without depression symptoms and participants who don't (as a control group). Note that participants who experience
2. Thymia research questionnaires will be used to gather more context and information to further develop the thymia AI model [not part of the current clinical trial]. These include a tiredness questio
Sponsors
Thymia Limited
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: 1. 18 years old and over 2. Speak English as their first language 3. Willing to be video- and audio-recorded
Exclusion criteria
Exclusion criteria: Control group: 1. Neurological conditions, such as Parkinson’s or seizure disorders like epilepsy 2. Neurodevelopmental conditions like ADHD or Autism 3. Mental health conditions like depression, anxiety or schizophrenia 4. Learning conditions, such as dyslexia or language difficulties 5. Respiratory disease 6. Never had visual problems (glasses are fine) or hearing problems Depression group**: 1. Diagnosis of Major Depression Disorder 2. Neurological conditions, such as Parkinson’s or seizure disorders like epilepsy 3. Neurodevelopmental conditions like ADHD or Autism 4. Mental health conditions other than depression and anxiety (this is due to high comorbidity of MDD and GAD) 5. Learning conditions, such as dyslexia or language difficulties 6. Respiratory disease 7. Never had visual problems (glasses are fine) or hearing problems **To reach target numbers in the depression group, the eligibility criteria may be relaxed to include other mental health diagnoses (e.g. bipolar) and neurodevelopmental conditions (e.g. ADHD, Autism).
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Depression likelihood measured using machine learning across all timepoints (i.e. cross-sectional model, where the timepoints from the same participant are dependent. Importantly, we produce AUC, sensitivity, and specificity as quality metrics. These are standard machine learning evaluation metrics that will help validate and establish the performance and accuracy of a model trained on a set of data in predicting accurately an entirely new set of data collected in a new context (and demographic subsets thereof). | — |
Secondary
| Measure | Time frame |
|---|---|
| Depression scores measured using the Patient Health Questionnaire PHQ-8 every two weeks and for the analysis, the value of the PHQ-8 that is closest to each individual session (three times per week) will be used to label the data at each timepoint | — |
Countries
Australia, Canada, England, Ireland, New Zealand, United Kingdom, United States of America
Contacts
Public ContactAlexandra-Livia Georgescu
Outcome results
None listed