Adapting an Evidence-Based e-Learning Cognitive Behavioral Therapy Program Into a Mobile App for People Experiencing Gambling-Related Problems: Formative Study
Bibliographic record
Abstract
BACKGROUND: Many people who experience harm and problems from gambling do not seek treatment from gambling treatment services because of personal and resource barriers. Mobile health (mHealth) interventions are widely used across diverse health care areas and populations. However, there are few in the gambling harm field, despite their potential as an additional modality for delivering treatment and support. OBJECTIVE: This study aims to understand the needs, preferences, and priorities of people experiencing gambling harms and who are potential end users of a cognitive behavioral therapy mHealth intervention to inform design, features, and functions. METHODS: Drawing on a mixed methods approach, we used creators and domain experts to review the GAMBLINGLESS web-based program and convert it into an mHealth prototype. Each module was reviewed against the original evidence base to maintain its intended fidelity and conceptual integrity. Early wireframes, design ideas (look, feel, and function), and content examples were developed to initiate discussions with end users. Using a cocreation process with a young adult, a Māori, and a Pasifika peoples group, all with experiences of problem or harmful gambling, we undertook 6 focus groups: 2 cycles per group. In each focus group, participants identified preferences, features, and functions for inclusion in the final design and content of the mHealth intervention. RESULTS: Over 3 months, the GAMBLINGLESS web-based intervention was reviewed and remapped from 4 modules to 6. This revised program is based on the principles underpinning the transtheoretical model, in which it is recognized that some end users will be more ready to change than others. Change is a process that unfolds over time, and a nonlinear progression is common. Different intervention pathways were identified to reflect the end users' stage of change. In all, 2 cycles of focus groups were then conducted, with 30 unique participants (13 Māori, 9 Pasifika, and 8 young adults) in the first session and 18 participants (7 Māori, 6 Pasifika, and 5 young adults) in the second session. Prototype examples demonstrably reflected the focus group discussions and ideas, and the features, functions, and designs of the Manaaki app were finalized. Attributes such as personalization, cultural relevance, and positive framing were identified as the key. Congruence of the final app attributes with the conceptual frameworks of the original program was also confirmed. CONCLUSIONS: Those who experience gambling harms may not seek help. Developing and demonstrating the effectiveness of new modalities to provide treatment and support are required. mHealth has the potential to deliver interventions directly to the end user. Weaving the underpinning theory and existing evidence of effective treatment with end-user input into the design and development of mHealth interventions does not guarantee success. However, it provides a foundation for framing the intervention's mechanism, context, and content, and arguably provides a greater chance of demonstrating effectiveness.
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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 | 0.053 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".