How can UK public health initiatives support each other to improve the maintenance of physical activity? Evidence from a cross-sectional survey of runners who move from <i>Couch-to-5k</i> to <i>parkrun</i>
Bibliographic record
Abstract
Physical activity improves physical and mental well-being and reduces mortality risk. However, only a quarter of adults globally meet recommended physical activity levels for health. Two common initiatives in the UK are Couch-to-5k (an app-assisted 9-week walk/run programme) and parkrun (a free, weekly, timed 5-km walk/run). It is not known how these initiatives are linked, how Couch-to-5k parkrunners compare to parkrunners, and the extent to which this influences their parkrun performance. The aims were to compare the characteristics and motives and to compare physical activity levels, parkrun performance and the impact of parkrun between Couch-to-5k parkrunners and parkrunners. Three thousand two hundred and ninety six Couch-to-5k parkrunners were compared to 55,923 parkrunners to explore age, sex, ethnicity, employment status, neighbourhood deprivation, motives, physical activity levels, parkrun performance and the impact of parkrun. Couch-to-5k parkrunners were slightly older, more likely to be female and work part-time, but similar in ethnicity, and neighbourhood deprivation compared with other parkrunners. Couch-to-5k parkrunners had different motives for participation and reported high levels of physical activity at registration, which remained to the point of survey completion. This group had slower parkrun times but, when registered for a year, completed a similar number of runs (11) per year. Larger proportions of Couch-to-5k parkrunners perceived positive impacts compared with other parkrunners and 65% of Couch-to-5k parkrunners reported improvements to their lifestyle. parkrun appears to be an effective pathway for those on the Couch-to-5k programme, and the promising positive association between the two initiatives may be effective in assisting previously inactive participants to take part in weekly 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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".