A Smartphone Attention Bias Intervention for Individuals With Addictive Disorders: Protocol for a Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Substance use disorders are highly prevalent globally. Relapse rates following conventional psychological interventions for substance use disorders remain high. Recent reviews have highlighted attentional and approach or avoidance biases to be responsible for multiple relapses. Other studies have reported the efficacy of interventions to modify biases. With advances in technologies, there are now mobile versions of conventional bias modification interventions. However, to date, no study has evaluated bias modification in a substance-using, non-Western sample. Existing evaluations of mobile technologies for the delivery of bias interventions are also limited to alcohol or tobacco use disorders. OBJECTIVE: This study aims to examine the feasibility of mobile-based attention bias modification intervention among treatment-seeking individuals with substance use and alcohol use disorders. METHODS: This is a feasibility study, in which inpatients who are in their rehabilitation phase of clinical management will be recruited. On each day that they are in the study, they will be required to complete a craving visual analogue scale and undertake both a visual probe-based assessment and and modification task in a smartphone app . Reaction time data will be collated for the computation of baseline attentional biases and to determine whether there is a reduction of attentional bias across the interventions. Feasibility will be determined by the number of participants recruited and participants' adherence to the planned interventions up until the completion of their rehabilitation program and by the ability of the app in detecting baseline biases and changes in biases. Acceptability of the intervention will be assessed by a short questionnaire of users' perceptions of the intervention. Statistical analyses will be performed using SPSS version 22.0, while qualitative analysis of the perspectives will be performed using NVivo version 10.0. RESULTS: This study was approved by the National Healthcare Group Domain Specific Research Board, with approval number (2018/00316). Results will be disseminated by means of conferences and publications.Currently, we are in the process of recruitment for this study. CONCLUSIONS: To the best of our knowledge, this is the first study to evaluate the feasibility and acceptability of a mobile attention bias modification intervention for individuals with substance use disorders. The data pertaining to the feasibility and acceptability are undoubtedly crucial because they imply the potential use of mobile technologies in retraining attentional biases among inpatients admitted for medical-assisted detoxification and rehabilitation. Participants' feedback pertaining to the ease of use, interactivity, and motivation to continue using the app is crucial because it will determine whether a codesign approach might be warranted to design an app that is acceptable for participants and that participants themselves would be motivated to use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/11822.
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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".