An IT-based Health Behaviour Change Program To Increase Physical Activity: Evaluation Of Successes And Challenges
Bibliographic record
Abstract
IT interventions initially used to promote health used static platforms, often only as a repository of health-based educational material. Such Web 1.0 technologies failed to promote longer-term user engagement, and rarely allowed the interactivity required for more effective health promotion impact. With more interactive Web 2.0 technologies, greater engagement and retention is often evident, with the participation architecture encouraging interactive user-focused tools and interfaces that allow individuals to determine how information is generated, modified, and shared collaboratively. PURPOSE: To identify successes and challenges of an RCT and real-world trial of an IT-based physical activity (PA) promotion intervention. METHODS: The WALK 2.0 study used a Web 2.0-based platform to engage and retain participants in health behaviour change to increase PA. The program included 2 trials: (1) an RCT comparing a Web 2.0 intervention with a less interactive Web 1.0 intervention, and (2) a real-world randomised ecological trial (RET) comparing a Web 2.0 and Web 1.0 intervention. RESULTS: The RCT showed that, compared to the Web 1.0 group, the Web 2.0 group improved PA in the short-term (p=.02), but that the effect diminished over time, despite higher engagement of the Web 2.0 group. The RET showed that the Web 2.0 intervention was more effective in improving PA (p=.005), and that while the Web 2.0 website was visited significantly more (p=.002), both groups displayed high non-usage attrition and low intervention engagement. Whilst the RCT and RET showed that using a more interactive Web 2.0-based approach was more effective in improving PA, several challenges were identified in designing, implementing, and evaluating such interventions. These include IT-based intervention development in a research context, the ability to establish a self-sustaining online community, the rapid pace of change in web-based technology and implications for trial design, the selection of best outcome measures for ecological trials, and managing engagement, non-usage and study attrition in real-world trials. CONCLUSIONS: Future research must look to broader research designs that allow for the ever changing IT-user landscape and behaviour, and greater reliance on development and testing in real-world settings.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".