A Digital Intervention for Australian Adolescents Above a Healthy Weight (Health Online for Teens): Protocol for an Implementation and User Experience Study
Bibliographic record
Abstract
BACKGROUND: More than one-fourth of Australian adolescents are overweight or obese, with obesity in adolescents strongly persisting into adulthood. Recent evidence suggests that the mid-teen years present a final window of opportunity to prevent irreversible damage to the cardiovascular system. As lifestyle behaviors may change with increased autonomy during adolescence, this life stage is an ideal time to intervene and promote healthy eating and physical activity behaviors, well-being, and self-esteem. As teenagers are prolific users and innate adopters of new technologies, app-based programs may be suitable for the promotion of healthy lifestyle behaviors and goal setting training. OBJECTIVE: This study aims to explore the reach, engagement, user experience, and satisfaction of the new app-based and Web-based Health Online for Teens (HOT) program in a sample of Australian adolescents above a healthy weight (ie, overweight or obese) and their parents. METHODS: HOT is a 14-week program for adolescents and their parents. The program is delivered online through the Moodle app-based and website-based learning environment and aims to promote adolescents' lifestyle behavior change in line with Australian Dietary Guidelines and Australia's Physical Activity and Sedentary Behaviour Guidelines for Young People (aged 13-17 years). HOT aims to build parental and peer support during the program to support adolescents with healthy lifestyle behavior change. RESULTS: Data collection for this study is ongoing. To date, 35 adolescents and their parents have participated in one of 3 groups. CONCLUSIONS: HOT is a new online-only program for Australian adolescents and their parents that aims to reduce cardiovascular disease risk factors. This protocol paper describes the HOT program in detail, along with the methods to measure reach, outcomes, engagement, user experiences, and program satisfaction. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618000465257; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374771. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13340.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".