Mobile App Intervention to Reduce Substance Use, Gambling, and Digital Media Use in Vocational School Students: Exploratory Analysis of the Intervention Arm of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: During adolescence, substance use and digital media exposure usually peak and can become major health risks. Prevention activities are mainly implemented in the regular school setting, and youth outside this system are not reached. A mobile app ("Meine Zeit ohne") has been developed specifically for vocational students and encourages participants to voluntarily reduce or abstain from a self-chosen addictive behavior including the use of a substance, gambling, or a media-related habit such as gaming or social media use for 2 weeks. Results from a randomized study indicate a significant impact on health-promoting behavior change after using the app. This exploratory study focuses on the intervention arm of this study, focusing on acceptance and differential effectiveness. OBJECTIVE: The aims of this study were (1) to examine the characteristics of participants who used the app, (2) to explore the effectiveness of the mobile intervention depending on how the app was used and depending on participants' characteristics, and (3) to study how variations in app use were related to participants' baseline characteristics. METHODS: Log data from study participants in the intervention group were analyzed including the frequency of app use (in days), selection of a specific challenge, and personal relevance (ie, the user was above a predefined risk score for a certain addictive behavior) of challenge selection ("congruent use": eg, a smoker selected a challenge related to reducing or quitting smoking). Dichotomous outcomes (change vs no change) referred to past-month substance use, gambling, and media-related behaviors. The relationship between these variables was analyzed using binary, multilevel, mixed-effects logistic regression models. RESULTS: The intervention group consisted of 2367 vocational students, and 1458 (61.6%; mean age 19.0, SD 3.5 years; 830/1458, 56.9% male) of them provided full data. Of these 1458 students, 894 (61.3%) started a challenge and could be included in the analysis (mean 18.7, SD 3.5 years; 363/894, 40.6% female). Of these 894 students, 466 (52.1%) were considered frequent app users with more than 4 days of active use over the 2-week period. The challenge area most often chosen in the analyzed sample was related to social media use (332/894, 37.1%). A total of 407 (45.5%) of the 894 students selected a challenge in a behavioral domain of personal relevance. The effects of app use on outcomes were higher when the area of individual challenge choice was equal to the area of behavior change, challenge choice was related to a behavior of personal relevance, and the individual risk of engaging in different addictive behaviors was high. CONCLUSIONS: The domain-specific effectiveness of the program was confirmed with no spillover between behavioral domains. Effectiveness appeared to be dependent on app use and users' characteristics. TRIAL REGISTRATION: German Clinical Trials Register DRKS00023788; https://tinyurl.com/4pzpjkmj. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s13063-022-06231-x.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".