Gambling Prevention Mobile Applications: Understanding the Inclusion and Use of Behaviour Change Techniques
Bibliographic record
Abstract
Online gambling is emerging as a significant health behaviour of concern at a population level. Mobile applications (apps) are a popular tool to target change in health behaviour. Behaviour change techniques (BCTs) can be included within such apps to change relevant psychological mechanisms along established pathways, yet the content of apps targeting gambling problems specifically is not currently known. The purpose of the review was to identify the BCTs included in gambling prevention apps. Apps were downloaded from the Apple App Store and Google Play Store in October 2020. Apps were included if they related to gambling problems, were freely downloadable, and available in English. Once downloaded, two researchers independently coded the apps in November 2020 using the behaviour change technique taxonomy version 1 (Michie et al., 2013). The screening led to forty apps meeting the inclusion criteria (12 Apple App Store, 28 Google Play). The analyses identified 32 BCTs (20 Apple apps, 28 Google Play apps), with apps including between 0 and 9 BCTs (mean = 2.82, median = 2). The BCTs included most frequently were “3.1. Social support (unspecified),” “2.3. Self-monitoring of behaviour,” and “7.4. Remove access to the reward.” The review provides important information on the BCTs used in apps developed to reduce gambling-related problems. A limited number of BCTs were adopted within apps. Developers of apps seeking to develop effective gambling reduction products should draw upon a greater variety of BCTs.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".