Bibliographic record
Abstract
There are good reasons to conduct international comparisons of physical activity. For example, it is well established that there are large differences in the prevalence of coronary heart disease (CHD) among countries (3). However, the extent to which physical activity contributes to these differences is uncertain because the lack of standardized surveys has hindered progress in this area. The article by Craig et al. (1) in the current issue of Medicine & Science in Sports & Exercise® is both important and timely. It establishes the validity and reliability of the International Physical Activity Questionnaire (IPAQ), a physical activity surveillance instrument suitable for use in modernized and developing countries. Two key features of the IPAQ are that it can be translated into many languages and modified with “culturally relevant” examples of moderate and vigorous physical activity. The authors of the current article, along with the members of the IPAQ consensus group, deserve credit for an impressive collaborative effort. Just as the “seven countries” study of Keys and coworkers (2) taught us about international differences in dietary fat intake and their relationship to CHD, the IPAQ has the potential to do the same for physical activity. In 1995, a group of physical activity epidemiologists and researchers formed the IPAQ Consensus group on Physical Activity Measurement. Michael Pratt of the Centers for Disease Control (CDC) and Michael Booth of the New Children’s Hospital in Sydney, Australia, coordinated the group, which included members from 20 countries. In 1998, they met in Geneva under the auspices of the World Health Organization (WHO) to plan the development of a standardized physical activity instrument. The CDC, the Karolinska Institute in Stockholm, the Canadian Fitness and Lifestyle Research Institute in Ottawa, the University of South Carolina Prevention Research Center, and the Center for Physical Activity and Health in Sydney all supported the efforts of this group. The initial work of this group was to conduct a reliability and validity study of the IPAQ, and the study was subsequently carried out in 14 centers in 12 countries, across a range of nations, cultures, and language groups. Since the development of the IPAQ, the European Union National Physical Activity Surveillance System and the WHO have used it for monitoring and surveillance. There are also other similar international measures being developed and tested for use in cardiovascular surveillance. The IPAQ group has recently begun an international prevalence study using the IPAQ short instrument. This study is currently underway, with initial data expected at the end of 2003 from approximately 15 participating countries. This will enable, for the first time, an international prevalence comparison of physical activity using an instrument developed and validated in multiple countries. The IPAQ is an instrument that is still being tested. It is a first attempt at developing such a surveillance tool, long overdue in physical activity. It may not be the definitive answer to all physical activity epidemiology needs, but for a self-report instrument, it does appear to have acceptable measurement properties across countries and is worthy of further exploration. David R. Bassett Jr.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.404 | 0.191 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".