Electronic Food and Exercise Diaries: Knowledge Gaps and Future Research
Bibliographic record
Abstract
Electronic food and exercise diaries are increasingly popular, both on the Internet and mobile devices. These tools offer a potential low-cost solution to help control and manage weight in those who suffer from obesity, as well as reduce the strain of obesity on the Canadian healthcare system. The body of knowledge for electronic food and exercise diaries, however, is lacking as to their effectiveness and related issues. This paper presents several key issues pertaining to the use of these applications, as well as proposed research directions, in which theories integrated from different areas can address these gaps. These theories include those that address acceptance of technology, continuity of use, and ability to produce behavioral change. Preliminary research results indicate that the diaries are effective in weight reduction, but issues associated with initial adoption remain.
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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 | 0.074 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".