Understanding Cognitive Trajectories in Middle-Aged and Older Cancer Survivors: An Analysis of the Korean Longitudinal Study of Aging.
Purpose: This study aimed to examine cognitive trajectories and to identify predictors of cognitive decline in middle-aged and older cancer survivors using longitudinal data and machine learning models. Methods: Data from 399 cancer survivors aged 45 years and older were analyzed from the Korean Lon...
| Publicado en: | Journal of Korean Academy of Fundamentals of Nursing Vol. 32; no. 4; pp. 507 - 520 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Korean Academy of Fundamentals of Nursing
Nov2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189737797&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189737797 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: DAEG jtl: Journal of Korean Academy of Fundamentals of Nursing maglogo: N pubinfo: dt: Nov2025 vid: 32 iid: 4 pid: 72189 pub: Korean Academy of Fundamentals of Nursing place: , <Blank> artinfo: ui: 189737797 189737797 189737797 10.7739/jkafn.2025.32.4.507 189737797 ppf: 507 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Understanding Cognitive Trajectories in Middle-Aged and Older Cancer Survivors: An Analysis of the Korean Longitudinal Study of Aging. aug: au: Jung, Mi Sook Park, Munkyung Cha, Kyeongin Cui, Xirong Dlamini, Nondumiso Satiso Lee, Ah Rim affil: Professor, College of Nursing, Chungnam National University, Daejeon, Korea sug: subj: Cognition Evaluation Cancer Survivors In Middle Age Cancer Survivors In Old Age Cognition Disorders Risk Factors Risk Assessment Aging Physiology Prediction Models Human South Korea Male Female Middle Age Aged Aged, 80 and Over Prospective Studies Secondary Analysis Cancer Patients Health Status Body Mass Index Health Behavior Smoking Support, Psychosocial Disease Duration Self Report Descriptive Statistics Data Analysis Software Logistic Regression Renal Insufficiency, Chronic Sensitivity and Specificity Parametric Statistics Age Factors Educational Status Physical Activity Time Factors Disease Progression Psychological Tests Scales Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: This study aimed to examine cognitive trajectories and to identify predictors of cognitive decline in middle-aged and older cancer survivors using longitudinal data and machine learning models. Methods: Data from 399 cancer survivors aged 45 years and older were analyzed from the Korean Longitudinal Study of Aging (KLoSA). Latent class growth analysis was used to identify cognitive trajectories, while logistic regression, random forest, neural network, and support vector machine algorithms were employed to predict trajectory group membership based on sociodemographic, health-related, and cancer-related variables. Results: Two distinct cognitive trajectories were identified: maintenance (85.2%) and decline (14.8%). For predicting the decline trajectory, the random forest model achieved the best performance (accuracy=0.92, AUC=0.93), followed by logistic regression and support vector machine (accuracy=0.86, AUC=0.86), whereas the neural network demonstrated lower performance (accuracy=0.82, AUC=0.78). Key predictors included age, education, physical activity, BMI, time since cancer diagnosis, symptom progression, and functional limitations related to cancer. Conclusion: These findings highlight the importance of proactive cognitive monitoring and the integration of targeted, personalized interventions into survivorship care for aging cancer survivors. Future studies should incorporate comprehensive clinical data and conduct external validation to enhance model reliability and clinical applicability. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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