Physical activity and the outcome of cognitive trajectory: a machine learning approach
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.
Authors
- Jay Shah (ORCID: https://orcid.org/0000-0002-3617-2395)
- Bettina Barisch‐Fritz (ORCID: https://orcid.org/0000-0003-4801-2310)
- Alexander Wöll (ORCID: https://orcid.org/0000-0002-5736-2980)
- Jelena Krafft (ORCID: https://orcid.org/0000-0003-0631-5890)
- Yonas E. Geda (ORCID: https://orcid.org/0000-0003-1187-7230)
- Teresa Wu (ORCID: https://orcid.org/0000-0002-0529-7048)
- Janina Krell-Roesch
Institutions
- Karlsruhe Institute of Technology (DE)
- Barrow Neurological Institute (US)
- Arizona State University (US)
Publication Details
- Journal
- European Review of Aging and Physical Activity
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1186/s11556-024-00367-2
- Citations
- 1
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.47