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Record W3192362453 · doi:10.1123/japa.2020-0412

Pragmatic Evaluation of Older Adults’ Physical Activity in Scale-Up Studies: Is the Single-Item Measure a Reasonable Option?

2021· article· en· W3192362453 on OpenAlexfundno aff
Heather Macdonald, Lindsay Nettlefold, Adrian Bauman, Joanie Sims‐Gould, Heather McKay

Bibliographic record

VenueJournal of Aging and Physical Activity · 2021
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMinistry of Health, British ColumbiaCentre for Hip Health and MobilityMichael Smith Health Research BC
KeywordsPhysical activityScale (ratio)PsychologyBaseline (sea)Measure (data warehouse)Intervention (counseling)GerontologyPhysical therapyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Convergent validity and responsiveness to change of the single-item physical activity measure were assessed in adults aged 60 years and older, at baseline (n = 205) and 6 months (n = 177) of a health promotion program, Choose to Move. Spearman correlations were used to examine associations between physical activity as measured by the single-item measure and the Community Health Activities Model Program for Seniors (CHAMPS) questionnaire at baseline and for 6-month change in all participants and for sex and age (60-74 years, and ≥75 years) subgroups. Effect size assessed responsiveness to change in physical activity for both tools. Baseline physical activity by the single-item measure correlated moderately with physical activity by the CHAMPS questionnaire in all participants and subgroups. Correlations were weaker for change in physical activity. Effect size for physical activity change was larger for the single-item measure than for the CHAMPS questionnaire. The single-item measure is a valid, pragmatic tool for use in intervention and scale-up studies with older adults.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.077
GPT teacher head0.382
Teacher spread0.305 · 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 designOther design
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

Citations14
Published2021
Admission routes1
Has abstractyes

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