Longitudinal study of open goals in physical activity promotion: protocol for ‘Open to Move’
Bibliographic record
Abstract
Open goals (eg, 'see how many steps you can reach today') have developed as a promising strategy for increasing physical activity and producing beneficial psychological outcomes such as autonomous motivation, enjoyment and confidence. However, it is not yet clear what the long-term outcomes of open goals are, what factors moderate their use or whether/how individuals transition away from open goals. Therefore, in this study ('Open to Move'), we aim to understand the mechanisms that explain why, when and for whom open goals are beneficial in promoting and maintaining physical activity. 'Open to Move' is a 12-month, exploratory, mixed-methods longitudinal study involving healthy adults aged 18-69 in Australia. Participants will receive a walking programme based on open goals via a mobile app and website, which will also provide feedback on their step counts and fortnightly one-to-one meetings online for the first 6 months. The outcomes will be measured using self-report surveys, interviews, recorded step counts on a mobile phone and process evaluation. The study is ongoing, and 81 participants have commenced thus far, with a target of 210 participants. We expect to conclude recruitment by August 2025 and anticipate that data collection will be completed by August 2026. This study will develop an understanding of the long-term outcomes of open goals, moderating factors and transitions to other goal types-providing important insights for developing a programme theory that can inform full-scale testing and implementation of open goals within physical activity interventions in future.
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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 | 0.033 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.028 |
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".