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Record W2737748447 · doi:10.1177/0898264317721554

Instrumented Assessment of Physical Activity Is Associated With Muscle Function but Not With Muscle Mass in a General Population

2017· article· en· W2737748447 on OpenAlexaff
Anna G.M. Rojer, Esmee M. Reijnierse, Marijke C. Trappenburg, Rob C. van Lummel, Martijn Niessen, Kimberley S. van Schooten, Mirjam Pijnappels, Carel G. M. Meskers, Andrea B. Maier

Bibliographic record

VenueJournal of Aging and Health · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsSimon Fraser University
FundersEuropean Commission
KeywordsSarcopeniaMuscle massMedicinePhysical medicine and rehabilitationPopulationSarcopenic obesityPhysical therapyYoung adultTrunkSkeletal muscleAffect (linguistics)Body mass indexInternal medicinePsychologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: Self-reported physical activity has shown to affect muscle-related parameters. As self-report is likely biased, this study aimed to assess the association between instrumented assessment of physical activity (I-PA) and muscle-related parameters in a general population. METHOD: Included were 156 young-to-middle-aged and 80 older community-dwelling adults. Seven days of trunk accelerometry (DynaPort MoveMonitor, McRoberts B.V.) quantified daily physical activity (i.e., active/inactive duration, number and mean duration of active/inactive periods, and number of steps per day). Muscle-related parameters included muscle mass, handgrip strength, and gait speed. RESULTS: I-PA was associated with handgrip strength in young-to-middle-aged adults and with gait speed in older adults. I-PA was not associated with muscle mass in either age group. DISCUSSION: The association between I-PA and muscle-related parameters was age dependent. The lack of an association between I-PA and muscle mass indicates the relevance of muscle function rather than muscle mass.

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)0.004
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.377
Teacher spread0.316 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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