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Record W4406224583 · doi:10.1002/alz.091460

Multimodal Perspective on the Influence of Physical Activity on Alzheimer’s Disease

2024· article· en· W4406224583 on OpenAlexaboutno aff
Alexa Haeger, Sandro Romanzetti, Christian Hohenfeld, Shari David, Ana Sofia Costa, Luisa Haberl, Frank Hildebrand, Jörg B. Schulz, Kathrin Reetz

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Alzheimer's diseaseDiseasePsychologyNeuroscienceMedicineComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Background Physical exercise presents a viable low‐cost, low‐risk, individual, and widely available non‐pharmacological treatment candidate in cognitive decline such as in Alzheimer’s disease (AD). There are even indications that it can reduce the risk of developing dementia in the first place (Livingston et al., The Lancet, 2020). However, the impact of physical activity and fitness on the multimodal facets of AD, encompassing brain function, cognition and metabolism is still poorly understood. Method In the Dementia‐MOVE pilot trial (Multi‐Objective Validation of Exercise), 46 patients diagnosed with Alzheimer’s disease were initially included, randomized to either an intervention arm comprising a 6 months‐supervised sports program or a control condition with a psychoeducational program at the RWTH Aachen University Hospital. Participants underwent a comprehensive multimodal assessment of AD relevant parameters, including multimodal MRI, fitness and activity, neuropsychological assessments, blood examinations and the assessments of neuropsychiatric symptoms (Haeger et al., Alzheimers’s & Dementia TRCI, 2020). Result In the intervention group, cardiorespiratory fitness change, i.e. ΔVO2max, showed a positive association with changes in the Montreal Cognitive Assessment (MoCA), in the executive functions score, and in volumes of the temporal lobe. It also showed a negative correlation with baseline cognitive levels (see also Figure 1 on the multiple regression analysis). High physical activity levels were associated with improved quality of life in the overall sample. In functional MRI using resting state and graph theory (n = 20 subjects), we found prediction by ΔVO2max for changes on the degree, betweenness, triangles, transitivity, hubness, closeness and Katz centrality, with the last showing the most effects in regions in the temporal lobe. Metabolic MRI comprising 23Na‐MRI and 31P‐MRS pointed to possible associations of VO2max and/or group attribution with changes in brain sodium concentrations/cerebral metabolites. Conclusion We show that cardiorespiratory fitness and physical activity examined during an intervention with physical activity can have an impact on different aspects of AD, from cognition, to structural, functional, and metabolic brain alterations. Our results suggest that even patients who are more severely affected by the disease could benefit from interventions in these domains.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.410
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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