Smart-phone application design for lasting behavioral changes
Bibliographic record
Abstract
The Smart-Condo interdisciplinary team (including computing science, industrial design, health psychology and occupational therapy) conducts research on putting ICT in the service of health care, focusing on empowering individuals to take control of their health management. This past year, the focus of our activities has been the development of a framework for the design and development of mobile apps to encourage behavioral changes. Grounding our framework the Theory of Planned Behavior and the Intention-Behavior Gap Theory, we explicitly designed all the functions in our applications to influence behavior. From a technical perspective, the applications support personalization, easy data recording and interactive reviewing, and subtle interventions (reminders and personalized information) to help behavior change. The user interface, informed by design theory, is conceived to make the applications engaging and easy to navigate. Bringing these three areas of knowledge together in the design of our apps will enable the systematic construction of a family of applications that together will have the potential to affect significant behavior change. In this paper we discuss the framework and the first two applications we have developed with it.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".