Wearable and Interview-based Assessment of Psychological Risk in Alzheimers Caregivers: Machine Learning vs. Large Language Models
Xiao, J.; Zhao, Z.; King, Z. D.; Khalid, M.; Davies, S.; Zanna, K.; Argueta, D. L.; Brice, K. N.; Wu-Chung, E. L.; Lai, V. D.; Paoletti-Hatcher, J.; Denny, B. T.; Henry, S.; Schulz, P. E.; Fagundes, C. P.; Sano, A.
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Spousal caregivers of individuals with Alzheimers disease and related dementias frequently experience elevated perceived stress, caregiver burden, and loneliness, which are associated with adverse health outcomes. Early identification is therefore critical for timely intervention. Existing approaches commonly rely on wearable sensor data and standardized psychological questionnaires, while recent multimodal methods aim to improve prediction by integrating behavioral and linguistic information. In this study, we explored three modality configurations, wearable-derived features, interview-based text, and their combination, to classify caregiver psychological risk using the Perceived Stress Scale (PSS), Zarit Burden Interview, and UCLA Loneliness Scale. We compared traditional machine learning models and large language models (LLMs) (Gemini 2.0, Llama 4, and GPT-4o) under psychometrician-centered and caregiver-centered prompting strategies. Traditional machine learning models performed better under multimodal settings, while LLMs achieved stronger performance with Interview-Only input. We further demonstrate that PSS was the most predictable construct and prompting strategies substantially influenced LLM performance.
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