Bibliographic record
Abstract
Eating nutritious foods and being more physically active prevents significant illnesses such as cardiac disease, stroke, and diabetes. However, leading a healthy lifestyle remains elusive and obesity continues to increase in North America. We investigate how online social networks (OSN) can change health behaviour by blending theories from health behaviour and participation in OSNs, which allow us to design and evaluate an OSN through a user-centred design (UCD) process. We begin this research by reviewing existing theoretical models to obtain the determining factors for participation in OSNs and changing personal health behaviour. Through this review, we develop a conceptual framework, Appeal Belonging Commitment (ABC) Framework, which provides individual determinants (Appeal), social determinants (Belonging), and temporal consideration (Commitment) for participation in OSNs for health behaviour change. The ABC Framework is used in a UCD process to develop an OSN called VivoSpace. The framework is then utilized to evaluate each design to determine if VivoSpace is able to change the determinants for health behaviour change. The UCD process begins with an initial user inquiry using questionnaires to validate the determinants from the framework (n=104). These results are used to develop a paper prototype of VivoSpace, which is evaluated through interviews (N=11). These results are used to design a medium fidelity prototype for VivoSpace, which is tested in a laboratory through both direct and indirect methods (n=36). The final iteration of VivoSpace is a high fidelity prototype, which is evaluated in a field experiment with clinical and non-clinical participants from Canada and USA (n=32). The results reveal positive changes for the participants associated with a clinic in self-efficacy for eating healthy food and leading an active lifestyle, attitudes towards healthy behaviour, and in the stages of change for health behaviour. These results are further validated by evaluating changes in health behaviour, which reveal a positive change for the clinical group in physical activity and an increase in patient activation. The evaluation of the high fidelity prototype allow for a final iteration of the ABC Framework, and the development of design principles for an OSN for positive health behaviour change.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".