Does it matter what we say? Examining the understanding of the terms "physical-activity" and "exercise"
Bibliographic record
Abstract
Despite policy and messaging efforts at both national and local levels, physical inactivity continues to be a public health issue. Surprisingly there is little work examining the impact of the terminology used in these messages. This two-phase, mixed methods study explored the understanding of the terms physical-activity and exercise and how these terms may influence social-cognitions and efforts related to behavior change. In Phase one 184 adults were randomly assigned to complete an online questionnaire measuring social-cognitions and understanding related to either the term physical-activity (n=90) or exercise (n=94). In Phase two interview responses from three experts regarding their interpretation of the two terms, and how this influenced promotional efforts were reviewed for areas of consensus. MANCOVA revealed no overall effect of terminology on social-cognitions (p=.12). Thematic analysis of participants’ definitions of physical-activity and exercise also reflected a lack of differentiation between the terms, and only 31% of respondents felt the two behaviours were different. There was clear consensus among the experts that exercise represented a subset of physical-activity, and that physical-activity was the preferred term due to its inclusiveness. However, there was a strong sense that interventions were having little impact. This initial research suggests that the public does not clearly understand the difference between physical-activity and exercise, presenting us with two possible implications: that the choice of terminology may not be crucial or that educating the public about the differences between these behaviors may be important, particularly within populations who may benefit more from exercise than physical-activity alone.
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 | 0.071 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".