A retrospective analysis of real-world use of the eaTracker® My Goals website by adults from Ontario and Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Little is known about use of goal setting and tracking tools within online programs to support nutrition and physical activity behaviour change. In 2011, Dietitians of Canada added "My Goals," a nutrition and physical activity behaviour goal setting and tracking tool to their free publicly available self-monitoring website (eaTracker® ( http://www.eaTracker.ca/ )). My Goals allows users to: a) set "ready-made" SMART (Specific, Measurable, Attainable, Realistic, Time-related) goals (choice of n = 87 goals from n = 13 categories) or "write your own" goals, and b) track progress using the "My Goals Tracker." The purpose of this study was to characterize: a) My Goals user demographics, b) types of goals set, and c) My Goals Tracker use. METHODS: Anonymous data on all goals set using the My Goals feature from December 6/2012-April 28/2014 by users ≥19y from Ontario and Alberta, Canada were obtained. This dataset contained: anonymous self-reported user demographic data, user set goals, and My Goals Tracker use data. Write your own goals were categorized by topic and specificity. Data were summarized using descriptive statistics. Multivariate binary logistic regression was used to determine associations between user demographics and a) goal topic areas and b) My Goals Tracker use. RESULTS: Overall, n = 16,511 goal statements (75.4 % ready-made; 24.6 % write your own) set by n = 8,067 adult users 19-85y (83.3 % female; mean age 41.1 ± 15.0y, mean BMI 28.8 ± 7.6kg/m(2)) were included for analysis. Overall, 33.1 % of ready-made goals were from the "Managing your Weight" category. Of write your own goal entries, 42.3 % were solely distal goals (most related to weight management); 38.6 % addressed nutrition behaviour change (16.6 % had unspecific general eating goals); 18.1 % addressed physical activity behaviour change (47.3 % had goals without information on exercise amount and type). Many write your own goals were poor quality (e.g., non-specific (e.g., missing amounts)), and possibly unrealistic (e.g., no sugar). Few goals were tracked (<10 %). Demographic variables had statistically significant relations with goal topic areas and My Goals Tracker use. CONCLUSIONS: eaTracker® users had high interest in goal setting and the My Goals feature, however, self-written goals were often poor quality and goal tracking was rare. Further research is needed to better support users.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".