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Trends in adherence to the muscle-strengthening activity guidelines in the US over a 20-year span

2023· article· en· W4382358492 on OpenAlexaff
Joaquín Calatayud, Rubén López‐Bueno, Rodrigo Núñez‐Cortés, Lin Yang, Borja del Pozo Cruz, Lars L. Andersen

Bibliographic record

VenueGeneral Hospital Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersEuropean Commission
KeywordsSpan (engineering)GerontologyMedicinePsychologyPhysical medicine and rehabilitationEngineeringCivil engineering

Abstract

fetched live from OpenAlex

PURPOSE: Purpose to evaluate the prevalence and temporal trends in adherence to muscle-strengthening activity (MSA) guidelines among the US population from 1997 to 2018 (pre-Covid 19). METHODS: We used nationally representative data from the National Health Interview Survey of the US (NHIS; a cross-sectional household interview survey). We pooled data from 22 consecutive cycles (1997 to 2018) and estimated prevalence and trends of adherence to MSA guidelines among adults aged 18-24 years, 25-34 years, 35-44 years, 45-64 years, and ≥ 65 years. RESULTS: A total of 651,682 participants (mean age 47.7 years [SD = 18.0], 55.8% women) were included. The overall prevalence of adherence to MSA guidelines significantly increased (p < .001) from 1997 to 2018 (19.8% to 27.2%, respectively). Adherence levels significantly increased (p < .001) for all age groups from 1997 to 2018. Compared with their white non-Hispanic counterparts, the odds ratio for Hispanic females was 0.5 (95% CI = 0.4-0.6). CONCLUSIONS: It is over a 20-year span, adherence to MSA guidelines increased across all age groups, although the overall prevalence remained below 30%. Future intervention strategies to promote MSA are required with a particular focus on older adults, women, Hispanic women, current smokers, those with low educational levels, and those with functional limitations or chronic conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.272
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.381
Teacher spread0.328 · 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 teacher head, 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

Citations10
Published2023
Admission routes1
Has abstractyes

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