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Dementia transition in early cognitive decline trajectories (DETECT) study: a prospective longitudinal study
Hyunju Ji1, Aeyoung Cho1,2, Harim Lee1,2
1Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
BMC Geriatrics
|June 10, 2026
Summary
This study identifies biological, psychological, and social factors predicting dementia progression in older adults with mild cognitive impairment (MCI). Machine learning models will aid early risk identification for targeted dementia prevention strategies.
Area of Science:
- Gerontology
- Neuroscience
- Biomedical Informatics
Background:
- Mild cognitive impairment (MCI) significantly increases dementia risk.
- Predictors of MCI to dementia transition are often non-modifiable, with limited evidence on modifiable and social factors.
- The Biopsychosocial Model guides the examination of multidimensional factors in dementia progression.
Purpose of the Study:
- To identify determinants of dementia transition in older adults with MCI.
- To develop machine learning-based prediction models for dementia transition.
- To explore biological, psychological, and social factors influencing dementia progression.
Main Methods:
- A 3-year prospective longitudinal study in South Korea.
- Data collection via annual home visits using questionnaires, physical measurements, actigraphy, sweat patches, environmental sensing, and public records.
- Machine learning algorithms for prediction model development and evaluation using classification metrics and ROC analysis.
Main Results:
- The study aims to provide evidence on dementia transition determinants.
- Home-based assessments and diverse data sources offer a comprehensive understanding.
- Findings will inform early risk identification and community-based dementia prevention.
Conclusions:
- This research is expected to yield crucial insights into dementia transition.
- The findings will support the development of effective interventions and policies for dementia prevention.
- Early identification of individuals at risk can facilitate timely support and management.
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