Individual and Household Predictors of Adolescents’ Adherence to a Web-Based Intervention
Bibliographic record
Abstract
BACKGROUND: Adherence to e-health obesity interventions is a significant challenge. PURPOSE: We examined the individual and household predictors of adolescents' adherence to a Web-based lifestyle intervention. METHODS: One hundred sixty overweight/obese adolescents and one of their parents enrolled in the 8-month e-health intervention. Structural equation modeling was used to examine individual factors from the theory of planned behavior and self-determination theory and household factors (food/soda availability, parenting, environment) that predict adolescents' adherence to components of the intervention. RESULTS: We explained 10.8 to 36.9% of the total variance in adherence to components of the intervention. Intrinsic motivation and parenting practices and styles directly predicted adherence. Relatedness and autonomy support indirectly predicted adherence via intrinsic motivation. Finally, household income modulated these effects. CONCLUSION: Taking a self-regulatory perspective (i.e., accounting for intrinsic motivation) contributes to our understanding of intervention adherence, but the household environment may play a greater role in facilitating adolescent behavior change.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".