Depressive and anxiety symptoms screening in an elderly Brazilian population (service users of a private health care provider). They may have multiple physical health comorbidities. Mental and Behavioural Disorders
Conditions
Interventions
Sponsors
Eligibility
Inclusion criteria
Inclusion criteria: 1. Over 50 years of age 2. Receiving ongoing health support via a private healthcare route at Lacos Saude 3. Normal or corrected-to-normal vision 4. Normal or corrected hearing 5. Able to read, understand, and sign the Informed Consent Form 6. Access to a laptop, smartphone, tablet, or other device and able to use them 7. Willingness to be recorded
Exclusion criteria
Exclusion criteria: 1. Under 50 years of age 2. Not receiving ongoing health support via a private healthcare route at Lacos Saude 3. No normal or corrected-to-normal vision 4. No normal or corrected hearing 5. Not able to read, understand, and sign the Informed Consent Form 6. No access to a laptop, smartphone, tablet, or other device and/or not able to use them 7. No willingness to be recorded
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Measured using the thymia platform via the participant’s laptop or smart device twice a week for 6 months: 1. Area Under the Curve (AUC), specificity and sensitivity of the AI models (depression and anxiety). These are standard machine learning evaluation metrics that will help establish the performance and accuracy of a model trained on a set of data can predict accurately in an entirely new set of data collected in a clinical and completely new context. 2. PHQ-8 and GAD-7 to measure anxiety and depression | — |
Secondary
| Measure | Time frame |
|---|---|
| Measured using the thymia platform via the participant’s laptop or smart device twice a week for 6 months: 1. Average reaction time 2. Average omission errors 3. Average false positive rate (commission errors) 4. Average precision 5. Average recall from the n-Back game (1-Back and 2-Back) | — |
Countries
Brazil