Examining the link between framed physical activity messages and behaviour : an application of the communication behaviour change model
Bibliographic record
Abstract
Physical inactivity is a national issue affecting more than half of all Canadian adults (Colley et al., 2011).Health messaging, including message framing, has been a popular medium for encouraging individuals to adopt recommended health behaviours such as physical activity.Previous research has demonstrated that gain-framed messages, which emphasize the benefits of a behaviour, are more effective at promoting physical activity (PA) than loss-framed messages which emphasize the costs.However, the mechanism through which this facilitating effect occurs is unclear.The current study examined the effects of message framing on attention, attitudes, recall, decision to be active and behaviour as well as the mediating effects of these variables on the framebehaviour relationship in accordance with the communication behaviour change (CBC) model (McGuire, 1989).Sixty moderately active women, aged 18-35 viewed 20 gain-or loss-framed ads and 5 control ads while their eye movements were recorded via eye tracking.Attitudes towards PA, message recall, decision to become active and PA behaviour during an acute bout of exercise were measured immediately following ad exposure.Self-reported PA was measured one week later.Univariate ANOVAs, ANCOVAs and logistic regressions were conducted to examine the effects of message framing on each level of the CBC model.The gain-framed ads attracted greater attention, ps<0.05,produced more positive attitudes, p = .06,were better recalled, p < .001,influenced decisions to be active, p = .07,and had an immediate and delayed impact on behaviour, ps < .05,compared to the loss-framed messages.However, mediation analyses failed to reveal any significant effects suggesting that alternate mechanisms may be influencing framing effects on behaviour.This study demonstrates the effects of framed iii messages on several novel outcomes; however the mechanisms underlying these effects remain unclear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".