Promoting physical activity: A reversal theory perspective
Bibliographic record
Abstract
Research suggests tailored messages promoting physical activity are not always effective. In an attempt to explain the mixed results, the present study investigated the effects of message tailoring from a reversal theory (RT) perspective University students (N = 189) were exposed to two video messages promoting exercise that were tailored to the interests of telic (i.e., goal-oriented) and paratelic (i.e., playful) state defined by RT. Participants' recall, involvement, attitude, intentions and behaviour toward the subject of exercise were recorded. Among participants who watched the telic message, those in the telic state had better attitudes toward exercise than those in the paratelic state. Among participants who watched the paratelic message, those in the paratelic state reported higher involvement with and intentions to exercise. Metamotivational dominance had no influence on responses to either message. In general, tailored groups responded more favourably than non-tailored groups on a few variables. The results suggest that tailoring messages to recipient's metamotivational state may be an effective strategy to promote physical activity. Public health campaigns should take into account the state recipients are likely to be in when receiving a message.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".