What works best when implementing a physical activity intervention for teenagers? Reflections from the ACTIVE Project: a qualitative study
Bibliographic record
Abstract
OBJECTIVE: This paper explores what aspects of a multicomponent intervention were deemed strengths and weaknesses by teenagers and the local council when promoting physical activity to young people. DESIGN: Qualitative findings at 12 months from a mixed method randomised control trial. METHODS: Active Children Through Incentive Vouchers-Evaluation (ACTIVE) gave teenagers £20 of activity enabling vouchers every month for a year. Peer mentors were also trained and a support worker worked with teenagers to improve knowledge of what was available. Semistructured focus groups took place at 12 months to assess strengths and weaknesses of the intervention. Eight focus groups (n=64 participants) took place with teenagers and one additional focus group was dedicated to the local council's sport development team (n=8 participants). Thematic analysis was used to analyse the data. RESULTS: Teenagers used the vouchers on three main activities: trampolining, laser tag or the water park. These appeal to both genders, are social, fun and require no prior skill or training. Choice and financial support for teenagers in deprived areas was considered a strength by teenagers and the local council. Teenagers did not engage with a trained peer mentor but the support worker was considered helpful. CONCLUSIONS: The ACTIVE Project's delivery had both strengths and weakness that could be used to underpin future physical activity promotion. Future interventions should focus on improving access to low cost, fun, unstructured and social activities rather than structured organised exercise/sport. The lessons learnt from this project can help bridge the gap between what is promoted to teenagers and what they actually want from activity provision. TRIAL REGISTRATION NUMBER: ISRCTN75594310.
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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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".