Co-designing a Mobile Gamified Attention Bias Modification Intervention for Substance Use Disorders: Participatory Research Study
Bibliographic record
Abstract
BACKGROUND: Advances in experimental psychology have highlighted the need to modify underlying automatic cognitive biases, such as attentional biases. The effectiveness of bias modification has been well studied for substance use disorders. With recent advances in technology, it is now possible to work outside the laboratory with Web-based and mobile-based attention bias interventions. Gamification technologies might also help diminish the repetitiveness of the task and increase the intrinsic motivation to train. The inconsistent findings of the impact of gaming on the effectiveness of mobile interventions call for further work to better understand the needs of patients (users) and health care professionals. OBJECTIVE: The aim of this study was to involve patients, together with health care professionals, in the design of a gamified mobile attention bias modification intervention for substance use disorders. METHODS: The participatory design research method adopted is that of a user-oriented design approach in the form of a future workshop. In the first phase of the workshop, participants shared their critique of an attention bias modification intervention. In the second phase of the workshop, participants were asked to brainstorm features. Participants were also shown gamification approaches and asked to consider if gaming elements could enhance the existing app. In the last phase, participants were asked to sketch a new prototype. RESULTS: Three co-design workshops were conducted with health care professionals, inpatients, and outpatients. There were 20 participants, consisting of 10 health care professionals and 10 patients. When asked to identify the limitations in the existing app, common issues identified were those of the design, visual probe task, and the included images. Outpatients were also concerned with the safety of administration of the intervention. In the brainstorming sessions, health care professionals made recommendations as to how the stimulus, the mechanism of responding, and the presentation of the scores could be enhanced. Inpatient participants recommended the addition of functionalities, such as information on the harms associated with the substance use, and for there to be enhancements in the design, images, and task. Outpatient participants perceived a need to improve the images and presentation of the results and recommended the inclusion of gaming features. There were differences in opinion on the inclusion of gaming features, as only health care professionals endorsed their inclusion. In the last phase of the workshop, participants were tasked with the conceptualization of prototypes, and the commonality in the design was for a gradual shortening of the interval for stimulus/image presentation. CONCLUSIONS: The results from this research will guide the development of an app that meets the specific needs of patients and is still based on a pre-existing validated task paradigm. .
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".