Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis.
• Internet use was associated with dementia among older adults. • A generalized random forest algorithm flexibly uncovered heterogeneity in the association. • Multidimensional heterogeneity was observed across income, education, and population density. • Findings highlight complex heterogeneity miss...
| Publicado en: | Archives of Gerontology & Geriatrics Vol. 136 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2025
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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=185777224&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185777224 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01674943 3HX jtl: Archives of Gerontology & Geriatrics issn: 01674943 maglogo: N pubinfo: dt: Sep2025 vid: 136 pid: 1004 pub: Elsevier B.V. artinfo: ui: 185777224 185777224 185777224 10.1016/j.archger.2025.105912 185777224 ppct: 1 formats: tig: atl: Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis. aug: au: Nakagomi, Atsushi Kondo, Katsunori Shiba, Koichiro affil: Department of Social Preventive Medical Sciences, Center for Preventive Medical Sciences, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba-city, Chiba 263-8522, Japan sug: subj: Internet Utilization Dementia Risk Factors Risk Assessment Machine Learning Methods Human Middle Age Aged Aged, 80 and Over Prospective Studies Japan Random Forest Algorithms Social Class Sociodemographic Factors Educational Status Population Density Media Exposure Survey Research Insurance, Long Term Care Health Status In Old Age Descriptive Statistics Confidence Intervals Physical Activity In Old Age Gerontologic Care Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over ab: • Internet use was associated with dementia among older adults. • A generalized random forest algorithm flexibly uncovered heterogeneity in the association. • Multidimensional heterogeneity was observed across income, education, and population density. • Findings highlight complex heterogeneity missed by traditional analytical methods. Internet use among older adults may reduce the risk of dementia, but it remains unknown how the effects vary across individuals. The aim of this study was to rigorously examine heterogeneity in the association between internet use and dementia among older adults with a machine learning approach. This cohort study used data from the Japan Gerontological Evaluation Study involving functionally independent adults aged 65 or older (n = 5,451). The exposure, internet use a few times a month or more often, was assessed with the 2016 survey (baseline) and covariates (potential confounders and effect modifiers) were assessed with the 2013 survey (pre-baseline). Follow-up continued until 2022, identifying 5.5-year dementia onset (n = 549) using the public long-term care insurance system. Using the generalized random forest algorithm, we estimated how the association between internet use and dementia onset during a 5.5-year follow-up period varies by pre-baseline sociodemographic characteristics and health conditions. Internet use was on average associated with a lower risk of dementia (estimated population average effect = -0.033; 95 % CI: -0.051, -0.016). However, we found evidence of between-individual heterogeneity in this association, where internet use appeared more beneficial among individuals who reported middle income, higher education levels, and were socially and physically inactive at the pre-baseline wave. Internet use may disproportionately benefit people based on socioeconomic status, suggesting equity concerns of universal implementation. Understanding such effect heterogeneity can inform more targeted public health interventions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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