An early phase trial testing the proof of concept for a gamified smartphone app in manipulating automatic evaluations of exercise.
Bibliographic record
Abstract
People who are more physically active tend to have more favorable automatic evaluations of exercise (i.e., nonconscious evaluations based on mental associations between "exercise" and "pleasant" or "unpleasant" that manifest into approach tendencies). Although some interventions have been shown to modify automatic evaluations in lab-based settings, the training regimes may not translate into scalable real-world interventions. The aim of these studies were to (a) test how often people tend to engage with the app in a "real-world" setting, and (b) test whether an app with gamification features and evaluative conditioning strategies change automatic evaluations of exercise versus sedentary behavior. Participants (N = 289, 238 female, M age = 33) were randomly allocated to have access to either Flex Exercise-a game-based app which contained 70% exercise-related content or Flex Control-the same game-based app with no exercise content. Participants completed an Implicit Association Test (IAT) as assessments of automatic evaluations immediately after exposure to Flex and 24 hr later. No significant between-group difference was observed immediately after exposure to Flex for automatic evaluations; however, 1 day following exposure, those in the Flex Exercise condition had significantly more favorable automatic evaluations of exercise than those in the Flex Control condition (d = 0.24). This effect was driven by a change in automatic evaluations, as assessed through the IAT, in the control condition more favorable toward sedentary behavior relative to physical activity and was magnified by user engagement. This mHealth intervention may have inadvertently enhanced sedentary automatic evaluations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".