Development of Smartphone Applications for Nutrition and Physical Activity Behavior Change
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Young adults (aged 18 to 35) are a population group at high risk for weight gain, yet we know little about how to intervene in this group. Easy access to treatment and support with self-monitoring of their behaviors may be important. Smartphones are gaining in popularity with this population group and software applications ("apps") used on these mobile devices are a novel technology that can be used to deliver brief health behavior change interventions directly to individuals en masse, with potentially favorable cost-utility. However, existing apps for modifying nutrition or physical activity behaviors may not always reflect best practice guidelines for weight management. OBJECTIVE: This paper describes the process of developing four apps aimed at modifying key lifestyle behaviors associated with weight gain during young adulthood, including physical activity, and consumption of take-out foods (fast food), fruit and vegetables, and sugar-sweetened drinks. METHODS: The development process involved: (1) deciding on the behavior change strategies, relevant guidelines, graphic design, and potential data collection; (2) selecting the platform (Web-based versus native); (3) creating the design, which required decisions about the user interface, architecture of the relational database, and programming code; and (4) testing the prototype versions with the target audience (young adults aged 18 to 35). RESULTS: The four apps took 18 months to develop, involving the fields of marketing, nutrition and dietetics, physical activity, and information technology. Ten subjects provided qualitative feedback about using the apps. The slow running speed of the apps (due to a reliance on an active Internet connection) was the primary issue identified by this group, as well as the requirement to log in to the apps. CONCLUSIONS: Smartphone apps may be an innovative medium for delivering individual health behavior change intervention en masse, but researchers must give consideration to the target population, available technologies, existing commercial apps, and the possibility that their use will be irregular and short-lived.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it