TY  - JOUR
AU  - Georgescu, Alexandra Livia
AU  - Cummins, Nicholas
AU  - Molimpakis, Emilia
AU  - Giacomazzi, Eduardo
AU  - Rodrigues Marczyk, Joana
AU  - Goria, Stefano
PY  - 2024
DA  - 2024/12/12
TI  - Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability
JO  - JMIR Form Res
SP  - e55856
VL  - 8
KW  - depression
KW  - anxiety
KW  - Brazil
KW  - machine learning
KW  - n-back
KW  - working memory
KW  - artificial intelligence
KW  - gerontology
KW  - older adults
KW  - mental health
KW  - AI
KW  - transferability
KW  - detection
KW  - screening
KW  - questionnaire
KW  - longitudinal study
AB  - Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)–driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations. Objective: This study aims to illustrate the preliminary transferability of two established AI models designed to detect high depression and anxiety symptom scores. The models were initially trained on data from a nonverbal working memory game (1- and 2-back tasks) in a dataset by thymia, a company that develops AI solutions for mental health and well-being assessments, encompassing over 6000 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 older Brazilian adults, thereby exploring its transferability and generalizability across different demographics and cultures. Methods: A total of 69 Brazilian participants aged 51-92 years old were recruited with the help of Laços Saúde, a company specializing in nurse-led, holistic home care. Participants 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, which included a 5-minute game with two levels of the n-back tasks. Two Random Forest models trained on thymia data to classify depression and anxiety based on thresholds defined by scores of the Patient Health Questionnaire (8 items) (PHQ-8) ≥10 and those of the Generalized Anxiety Disorder Assessment (7 items) (GAD-7) ≥10, respectively, were subsequently tested on the Laços Saúde patient cohort. Results: The depression classification model exhibited robust performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.78, a specificity of 0.69, and a sensitivity of 0.72. The anxiety classification model showed an initial AUC of 0.63, with a specificity of 0.58 and a sensitivity of 0.64. This performance surpassed a benchmark model using only age and gender, which had AUCs of 0.47 for PHQ-8 and 0.53 for GAD-7. After recomputing the AUC scores on a cross-sectional subset of the data (the first n-back game session), we found AUCs of 0.79 for PHQ-8 and 0.76 for GAD-7. Conclusions: This study successfully demonstrates the preliminary transferability of two AI models trained on a nonverbal working memory task, one for depression and the other for anxiety classification, to a novel sample of older Brazilian adults. Future research could seek to replicate these findings in larger samples and other cultural contexts. Trial Registration: ISRCTN Registry ISRCTN90727704; https://www.isrctn.com/ISRCTN90727704 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e55856
UR  - https://doi.org/10.2196/55856
DO  - 10.2196/55856
ID  - info:doi/10.2196/55856
ER  -