Reorganizing after the pandemic: A chance to energize physical activity promotion – comment on Hohberg et al.
Bibliographic record
Abstract
In this commentary on “What is needed to promote physical activity? – Current trends and new perspectives in theory, intervention, and implementation” I discuss my support for the many health, social, and economic benefits of moving more and sitting less as detailed by the authors. I discuss my agreement with the challenges of physical inactivity and sedentary behavior during the COVID-19 pandemic, and that while effective promotion initiatives founded on socioecological whole system approaches seem most logical, the role of individual is still essential for downstream uptake of physical activity. Like the authors, I include my support for the testing, development, and assumptions underlying dual-process theories using real time data-capture, in addition to more sophisticated longitudinal dynamic modeling to translate findings into just-in-time intervention approaches. In addition, however, I highlight it is still important for researchers and practitioners to focus on the role of reflective factors, such as building strong intentions to engage in physical activity, and subsequent self-regulation skills to translate these intentions into action. Furthering our understanding on the distinctions between initiation and maintenance of movement behaviors is important to advance theory and practice and the role of apex-system variables such as self- and social identity may hold considerable utility in physical activity science. I suggest that finding meaning in movement behaviors beyond exercise is critical to reorganizing and reenergizing after the pandemic to promote physical activity.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.063 | 0.091 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".