Rams Have Heart, a Mobile App Tracking Activity and Fruit and Vegetable Consumption to Support the Cardiovascular Health of College Students: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: With the increasing use of mobile devices to access the internet and as the main computing system of apps, there is a growing market for mobile health apps to provide self-care advice. Their effectiveness with regard to diet and fitness tracking, for example, needs to be examined. The majority of American adults fail to meet daily recommendations for healthy behavior. Testing user engagement with an app in a controlled environment can provide insight into what is effective and not effective in an app focused on improving diet and exercise. OBJECTIVE: We developed Rams Have Heart, a mobile app, to support a cardiovascular disease (CVD) intervention course. The app tracks healthy behaviors, including fruit and vegetable consumption and physical activity, throughout the day. This paper aimed to present its functionality and evaluated adherence among the African American college student population. METHODS: We developed the app using the Personal Health Informatics and Intervention Toolkit, a software framework. Rams Have Heart integrates self-reported health screening with health education, diary tracking, and user feedback modules to acquire data and assess progress. The parent study, conducted at a historically black college and university-designated institution in southeastern United States, consisted of a semester-long intervention administered as an academic course in the fall, for 3 consecutive years. Changes were made after the cohort 1 pilot study, so results only include cohorts 2 and 3, comprising a total of 115 students (n=55 intervention participants and n=54 control participants) aged from 17 to 24 years. Data collected over the study period were transferred using the secure Hypertext Transfer Protocol Secure protocol and stored in a secure Structured Query Language server database accessible only to authorized persons. SAS software was used to analyze the overall app usage and the specific results collected. RESULTS: Of the 55 students in the intervention group, 27 (49%) students in cohort 2 and 25 (45%) in cohort 3 used the Rams Have Heart app at least once. Over the course of the fall semester, app participation dropped off gradually until exam week when most students no longer participated. The average fruit and vegetable intake increased slightly, and activity levels decreased over the study period. CONCLUSIONS: Rams Have Heart was developed to allow daily tracking of fruit and vegetable intake and physical activity to support a CVD risk intervention for a student demographic susceptible to obesity, heart disease, and type 2 diabetes. We conducted an analysis of app usage, function, and user results. Although a mobile app provides privacy and flexibility for user participation in a research study, Rams Have Heart did not improve compliance or user outcomes. Health-oriented research studies relying on apps in support of user goals need further evaluation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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 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".