Evidence map›Paper›PMID 42265623›Full record

ArticleBMC geriatrics2026

Dementia transition in early cognitive decline trajectories (DETECT) study: a prospective longitudinal study protocol.

Hyunju Ji, Aeyoung Cho, Harim Lee, Gyeoul Jeong, Eun Hye Hong, Kyung Hee Lee

Abstract read
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hyunju JiMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.ORCID 0000-0002-8427-939X
Aeyoung ChoMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.ORCID 0009-0006-2977-5397
Harim LeeMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.ORCID 0000-0002-6926-4544
Gyeoul JeongYonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun- gu, Seoul, 03722, Republic of Korea.ORCID 0009-0001-2454-4761
Eun Hye HongMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.ORCID 0000-0002-6114-5460
Kyung Hee LeeMo-Im Kim Nursing Research Institute, Yonsei University College of Nursing, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. kyungheelee@yuhs.ac.ORCID 0000-0003-2964-8356

Funding

National Research Foundation of Korea RS-2025-23323084
6 · The paper itself

Abstract

backgroundMild cognitive impairment is widely recognized as a high-risk state associated with a progression to dementia. Although previous studies have reported several predictors of the transition from MCI to dementia, many of these predictive factors are non-modifiable, and evidence on modifiable factors remains limited and inconsistent. Moreover, to date, social factors and their role in this transition have not been sufficiently examined. Guided by the Biopsychosocial Model of Dementia, this protocol describes a study which will examine the transition to dementia as a multidimensional process shaped by biological, psychological, and social factors. This study aims to identify the determinants associated with the transition to dementia and to develop machine learning-based prediction models for this transition among older adults with mild cognitive impairment.

methodsThis is a 3-year prospective longitudinal study which will be conducted among older adults with mild cognitive impairment in South Korea. The participants will be recruited from community-based settings, and annual home visits will be conducted to collect data on biological, psychological, and social factors. This data will be obtained through self-reported questionnaires, physical measurements, wrist-worn actigraphy, sweat patch sampling, indoor environmental sensing, and public records. The outcome will be transition to dementia, defined by clinical diagnosis or cognitive screening criteria. Machine-learning algorithms will be used to develop the prediction models, and model performance will be evaluated using classification metrics and area under the receiver operating characteristic curve analysis. DISCUSSION: This study is expected to provide evidence on the determinants associated with the transition to dementia among older adults with mild cognitive impairment. The use of home-based assessments and multiple data sources may offer a broader understanding of factors related to this transition. The findings may be useful for early risk identification and for developing community-based interventions and policies to support dementia prevention.

Indexed as

Cognitive DysfunctionDementiaAgedDisease ProgressionFemaleHumansLongitudinal StudiesMachine LearningMalePredictive Learning ModelsProspective StudiesRepublic of KoreaAgedBiopsychosocial model of dementiaCognitionDementiaLongitudinal studiesPredictive learning models

Identifiers

PMID42265623
PMCPMC13474596

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.