Every Step Counts: Understanding the Success of Implementing The 10,000 Steps Project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 10,000 Steps program originated from a landmark whole-of-community multi-strategy intervention to increase physical activity (PA) in Rockhampton, Australia in 2001-2003. It used a social ecological framework to promote physical activity at the individual, population, environmental and policy level. Two of the fundamental aspects of the original program were goal setting (10,000 steps per day) and self-monitoring (use of a pedometer for daily step counts). A project website (www.10000steps.org.au) allowed registered participants to record their physical activity. Over time the program morphed into an e- & mHealth intervention without face-to-face elements. The program is now delivered via website and smartphone apps and employs activity trackers (pedometers, Fitbit, Garmin). To date the project has signed-up over 425,000 members who have logged 221 billion steps (∼43 million a day) on the website or app. More than 14,000 workplaces and community organisations have been involved with the program. A central element of the program, the 'Workplace Challenge' has been used by ∼65% of 10,000 Steps members, which on average increases physical activity by 159 min/week for those who participate in it. In 2011, the Queensland Government designated the 10,000 Steps program as their key physical activity workplace health promotion strategy. Multiple factors underpin the success of the program. The message is simple and clear: the project name, with its distinctive logo and tagline ('Every Step Counts') provides a clear and prescriptive target for the physical activity 'dose'. Using effective behaviour change techniques: goal setting (the 10,000 Steps concept), self-monitoring (steps are tracked), social support (participants organise as 'teams' to reach certain step goals) and gamification (teams competing against each other creating 'friendly competition'). Ongoing redevelopment: since inception, there have been three complete redesigns of the website (including a branding redesign), and new smartphone apps. More recently, the website was modified to allow syncing of steps using popular activity trackers. Resources to support implementation: the program provides resources (e.g. 'Active Workplace Guide') and has dedicated staff to respond to queries from workplaces and individuals to help overcome implementation barriers. Project staff continuously promote the program via media interviews, attendance at events, social media and marketing, advertising, and networking and collaboration. Ongoing evaluation has contributed to continuous funding: to ensure the program remains successful in a fast-changing technology environment, continuous evaluation has been necessary. These evaluation strategies, the success of the original project and the strong partnership with the program funder (Queensland Health) have all contributed to the long-term (19 years) support for the project.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it