Effects of Different Combinations of Intensity Categories on Self-Reported Exercise
Bibliographic record
Abstract
Self-reports of exercise are used extensively in behavioral, social psychological, and epidemiological research (Ainsworth, Montoye, & Leon, 1994; Caspersen, 1997). Schwarz (1999) noted that many characteristics strongly influence self-reports of behavior, including question wording, format, and context. Of particular interest in the present study is the possible effect of providing different combinations of intensity categories (i.e., light/mild, moderate, and vigorous/strenuous) on self-reported exercise. A review of the exercise measurement literature indicates that researcher-developed and published questionnaires have varied in the number of exercise intensity categories they present to respondents. For example, researcher-developed questionnaires have often used only one category of exercise intensity, such as moderate (e.g., Miller, Trost, & Brown, 2002; Wallace, Buckworth, Kirby, & Sherman, 2000) or vigorous (e.g., Owen, Sedgwick, & Davies, 1988; Washburn, Goldfield, Smith, & McKinlay, 1990). Conversely, published questionnaires have typically used multiple intensity categories, such as moderate and vigorous/strenuous (e.g., Blair et al., 1985; Heath, Pate, & Pratt, 1993) or light/mild, moderate, and vigorous/strenuous (e.g., Baecke, Burema, & Frijters 1982; Godin & Shephard, 1985; Myers, Bader, Madhavan, & Froelicher, 2001). It is unknown, however, if providing different combinations of exercise intensity categories has any effect on the amount of exercise reported in a given intensity category or in total.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".