Physical activity and the outcome of cognitive trajectory: a machine learning approach.
Background: Physical activity (PA) may have an impact on cognitive function. Machine learning (ML) techniques are increasingly used in dementia research, e.g., for diagnosis and risk stratification. Less is known about the value of ML for predicting cognitive decline in people with dementia (PwD). T...
| Publicado en: | European Review of Aging & Physical Activity Vol. 22; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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BioMed Central
1/10/2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=182191346&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 182191346 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 18137253 3RAQ jtl: European Review of Aging & Physical Activity issn: 18137253 maglogo: N pubinfo: dt: 1/10/2025 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 182191346 10.1186/s11556-024-00367-2 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2MB tig: atl: Physical activity and the outcome of cognitive trajectory: a machine learning approach. aug: au: Barisch-Fritz, Bettina Shah, Jay Krafft, Jelena Geda, Yonas E. Wu, Teresa Woll, Alexander Krell-Roesch, Janina affil: https://ror.org/04t3en479 Karlsruhe Institute of Technology, Karlsruhe, Germany https://ror.org/03efmqc40 Arizona State University, Tempe, USA https://ror.org/01fwrsq33 Barrow Neurological Institute, Phoenix, USA su: Medical sciences Artificial intelligence Mental health Machine learning Cognitive psychology sug: subj: Medical sciences Artificial intelligence Mental health Offices of Mental Health Practitioners (except Physicians) Machine learning Cognitive psychology keyword: Alzheimer's disease Artificial intelligence analysis Cognitive deterioration Neurodegenerative diseases Physical activity interventions Psychology and Cognitive Sciences Psychology Medical and Health Sciences Public Health and Health Services Alzheimer's disease Artificial intelligence analysis Cognitive deterioration Neurodegenerative diseases Physical activity interventions Psychology and Cognitive Sciences Psychology Medical and Health Sciences Public Health and Health Services ab: Background: Physical activity (PA) may have an impact on cognitive function. Machine learning (ML) techniques are increasingly used in dementia research, e.g., for diagnosis and risk stratification. Less is known about the value of ML for predicting cognitive decline in people with dementia (PwD). The aim of this study was to use an ML approach to identify variables associated with a multimodal PA intervention that may impact cognitive changes in PwD, i.e., by distinguishing between cognitive decliners and non-decliners. Methods: This is a secondary, exploratory analysis using data from a Randomized Controlled Trial that included a 16-week multimodal PA intervention for the intervention group (IG) and treatment as usual for the control group (CG) in nursing homes. Predictors included in the ML models were related to the intervention (e.g., adherence), physical performance (e.g., mobility, balance), and pertinent health-related variables (e.g., health status, dementia form and severity). Primary outcomes were global and domain-specific cognitive performance (i.e., attention/ executive function, language, visuospatial skills, memory) assessed by standardized tests. A Support Vector Machine model was used to perform the classification of each primary outcome into the two classes of decline and non-decline. GridSearchCV with fivefold cross-validation was used for model training, and area under the ROC curve (AUC) and accuracy were calculated to assess model performance. Results: The study sample consisted of 319 PwD (IG, N = 161; CG, N = 158). The proportion of PwD experiencing cognitive decline, in the different domains measured, ranged from 27–48% in CG, and from 23–49% in IG, with no statistically significant differences and no time*group effects. ML models showed accuracy and AUC values ranging from 40.6–75.6. The strongest predictors of cognitive decline or non-decline were performance of activities of daily living in IG and CG, and adherence and mobility in IG. Conclusions: ML models showed moderate performance, suggesting that the selected variables only had limited value for classification, with adherence and performance of activities of daily living appearing to be predictors of cognitive decline. While the study provides preliminary evidence of the potential use of ML approaches, larger studies are needed to confirm our observations and to include other variables in the prediction of cognitive decline, such as emotional health or biomarker abnormalities. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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