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Updated: Sep 11, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Preliminary development of a multidomain behavioral intervention for primary prevention of dementia in midlife: a
L E Korthauer1,2, R K Rosen3,4, I Arias2
1Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.
Objectives:
45% of global cases of Alzheimer's disease and related dementias (ADRD) are attributable to 14 risk factors, but population adherence to protective health behaviors is low. The goal of this study was to use qualitative methods to develop a personalized health education intervention (TEACH: Tailored Education for Aging and Cognitive Health), grounded in the Health Belief Model.
Methods:
Guided by a phenomenographic approach, we conducted six focus groups (N = 3-6 participants each) of midlife adults, presenting fictional health information including images conveying ADRD risk and descriptions of personal health belief factors. Participants provided feedback about the images, their understanding of ADRD risk, and interpretation of health belief concepts. Qualitative data were analyzed using framework matrix analysis.
Results:
Groups found the presented risk information to be understandable and relevant to health behaviors. They provided feedback on visual aids and alternative language to improve clarity of the health belief descriptions. Participants were able to identify connections between health beliefs and personal behaviors.
Conclusions:
Results guided the TEACH intervention development project, including creating an explanatory framework for educating participants about ADRD risk factors and health beliefs. The long-term goal is to test a multi-domain intervention to promote sustained health behavior change for primary prevention of ADRD.
