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Record W4386968070 · doi:10.3389/fneur.2023.1195694

Physical activity monitoring in Alzheimer’s disease during sport interventions: a multi-methodological perspective

2023· article· en· W4386968070 on OpenAlexaboutno aff
Shari David, Christian Hohenfeld, Luisa Haberl, J. J. Pahl, Ana Sofia Costa, Axel Kilders, Frank Hildebrand, Jörg B. Schulz, Kathrin Reetz, Alexa Haeger

Bibliographic record

VenueFrontiers in Neurology · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersRWTH Aachen University
KeywordsPhysical therapyActivity trackerPhysical fitnessMedicineDiseasePsychologyDementiaPhysical medicine and rehabilitationMontreal Cognitive AssessmentGerontologyPhysical activityInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Assessment methods for physical activity and fitness are of upmost importance due to the possible beneficial effect of physical conditioning on neurodegenerative diseases. The implementation of these methods can be challenging when examining elderly or cognitively impaired participants. In the presented study, we compared three different assessment methods for physical activity from the Dementia-MOVE trial, a 6-months intervention study on physical activity in Alzheimer's disease. The aim was to determine the comparability of physical activity assessments in elderly participants with cognitive impairment due to Alzheimer's disease. Material or methods: 38 participants (mean age 70 ± 7 years) with early-stage Alzheimer's disease (mean MoCA 18.84 ± 4.87) were assessed with (1) fitness trackers for an average of 12 (± 6) days, (2) a written diary on daily activities and (3) a questionnaire on physical activity at three intervention timepoints. For comparison purposes, we present a transformation and harmonization method of the physical assessment output parameters: Metabolic equivalent of task (MET) scores, activity intensity minutes, calorie expenditure and moderate-to-vigorous physical activity (MVPA) scores were derived from all three modalities. The resulting parameters were compared for absolute differences, correlation, and their influence by possible mediating factors such as cognitive state and markers from cerebrospinal fluid. Results: Participants showed high acceptance and compliance to all three assessment methods. MET scores and MVPA from fitness trackers and diaries showed high overlap, whilst results from the questionnaire suggest that participants tended to overestimate their physical activity in the long-term retrospective assessment. All activity parameters were independent of the tested Alzheimer's disease parameters, showing that not only fitness trackers, but also diaries can be successfully applied for physical activity assessment in a sample affected by early-stage Alzheimer's disease. Discussion: Our results show that fitness trackers and physical activity diaries have the highest robustness, leading to a highly comparable estimation of physical activity in people with Alzheimer's disease. As assessed parameters, it is recommendable to focus on MET, MVPA and on accelerometric sensor data such as step count, and less on activity calories and different activity intensities which are dependent on different variables and point to a lower reliability.

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)0.176
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.930

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1760.126
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.443
Teacher spread0.301 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

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