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Record W2139524810 · doi:10.1123/jpah.6.s1.s5

Progress and Pitfalls in the Use of the International Physical Activity Questionnaire (IPAQ) for Adult Physical Activity Surveillance

2009· article· en· W2139524810 on OpenAlexaff
Adrian Bauman, Barbara E. Ainsworth, Fiona Bull, Cora L. Craig, María Hagströmer, James F. Sallis, Michael Pratt, Michael Sjöstróm

Bibliographic record

VenueJournal of Physical Activity and Health · 2009
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCanadian Fitness and Lifestyle Research Institute
Fundersnot available
KeywordsPhysical activityPsychologyMedicinePhysical therapyGerontology

Abstract

fetched live from OpenAlex

Before the development of the International Physical Activity Questionnaire (IPAQ), population measures of physical activity were country-specific, noncomparable, and mostly developed to assess leisure-time activity. Given the global increases in noncommunicable disease1 the need for internationally comparable physical activity surveillance measures was identified. An initial meeting at World Health Organization Headquarters in 1998 convened a group of physical activity scientists to plan the development and testing of such a measure, resulting in IPAQ. The purpose of this commentary is to reflect upon the first decade of experience with IPAQ, compare its intended to its actual use, and comment on its strengths and weaknesses as an addition to the armamentarium of self-report physical activity measures. IPAQ development was premised on the need to develop international population measurement to assess ‘total physical activity’ across the domains of work, domestic tasks, active transport, and leisure time, because patterns of activity across domains were expected to vary widely by country.2 IPAQ was developed because of the desire for cross-country comparison and international physical activity surveillance. To enhance use across countries, the measures were

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.082
GPT teacher head0.396
Teacher spread0.314 · 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

Citations185
Published2009
Admission routes1
Has abstractyes

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