Depression
Conditions
Keywords
depression symptoms
Brief summary
Screening with depression scales alone is subjective, and relying on single-modal data often leads to incomplete identification of symptoms that are easily missed or misdiagnosed. In this study, we first aim to use artificial intelligence to construct a depression symptom recognition model, concatenate multimodal features such as facial expression, audio, text, and postural behavior, and deeply fuse them to construct a multimodal model.
Interventions
Collect the facial expressions, audio, text and postural behavior data of the respondents using electronic devices.
Sponsors
Study design
Eligibility
Inclusion criteria
* Elderly individuals aged 60 years or older. * Residents of the community (with residence time of more than 6 months). * Possessing certain reading, writing and comprehension skills, being able to communicate with researchers without obstacles, and being able to independently complete the measurement of various indicators or, although unable to independently fill out the questionnaire, being able to independently make evaluations of the questionnaire items. * Those who have given informed consent.
Exclusion criteria
* Those who meet the diagnostic criteria for cognitive impairment (dementia) as stipulated in DSM-5, and/or who suffer from severe physical diseases (such as advanced cancer, cardiovascular and cerebrovascular diseases, etc.). * Those who are undergoing antidepressant treatment. * Elderly individuals who have had suicidal thoughts in any psychological assessment, should be referred to the psychological hotline platform in Wuhan.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Use the PHQ-9 (Patient Health Questionnaire - 9 ) to assess whether the respondents have depressive symptoms. The scale score is one point for each item. | 2025.09.01-2026.09.01 | If the PHQ-9 score is 5 or higher, it is determined as a positive sign of depressive symptoms. |
Countries
China