Assessing Depression and Anxiety in Elderly Brazilians: Transferability of AI Models Trained on a Non-Verbal Working Memory Task (Preprint)

Abstract

Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the elderly population. The challenge of identifying these conditions underscores the necessity for AI-driven, remotely available tools capable of screening and monitoring mental health symptoms. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations.

The current study aims to illustrate the preliminary transferability of two established AI models designed for detecting depression and anxiety. The models were initially trained on data from a non-verbal working memory game (1- and 2-back) in the thymia dataset, encompassing over 6,000 participants from the United Kingdom, United States, Mexico, Spain, and Indonesia. We seek to validate the models’ performance by applying it to a new dataset comprising elderly Brazilian adults, thereby exploring its generalisability across different demographics and cultures.

The 69 Brazilian participants aged 51-92 years old were recruited with the help of Laços Saúde, a company specialising in nurse-led, holistic home care. They received a link to the thymia dashboard every Monday and Thursday for 6 months. The dashboard had a set of activities assigned to them that would take 10-15 minutes to complete, among which a game with two levels of the n-back tasks. Two Random Forest models trained on data to classify depression and anxiety based on thresholds defined by PHQ-8 ≥ 10 and GAD-7 ≥ 10, respectively, were subsequently tested on the Laços Saúde patient cohort.

The depression classification model exhibited robust performance, achieving an AUC of 0.78, specificity of 0.69, and sensitivity of 0.72. The anxiety classification model showed an initial AUC of 0.63, with a specificity of 0.58 and sensitivity of 0.64. This performance surpasses a benchmark model using only age and gender, which had AUCs of 0.47 for PHQ-8 and 0.53 for GAD-7. Re-computing the AUC scores on a cross-sectional subset of the data (the first n-back game session), we found 0.79 for PHQ-8 and 0.76 for GAD-7.

This study successfully demonstrates the preliminary transferability of two AI models trained on a non-verbal working memory task, one for depression and the other for anxiety classification, to a novel demographic of elderly Brazilian adults.