Usage and Acceptability of the iBobbly App: Pilot Trial for Suicide Prevention in Aboriginal and Torres Strait Islander Youth
Bibliographic record
Abstract
BACKGROUND: The proliferation of mental health apps purporting to target and improve psychological wellbeing is ever-growing and also concerning: Few apps have been rigorously evaluated, and, indeed, the safety of the vast majority of them has not been determined. Over 10,000 self-help apps exist but most are not used much after being downloaded. Gathering and analyzing usage data and the acceptability of apps are critical to inform consumers, researchers, and app developers. OBJECTIVE: This paper presents pilot usage and acceptability data from the iBobbly suicide prevention app, an app distributed through a randomized controlled trial. METHODS: Aboriginal and Torres Strait Islander participants from the Kimberley region of Western Australia completed a survey measuring their technology use in general (n=13), and data on their experiences with and views of the iBobbly app were also collected in semistructured interviews (n=13) and thematically analyzed. Finally, engagement with the app, such as the number of sessions completed and time spent on various acceptance-based therapeutic activities, was analyzed (n=18). Both groups were participants in the iBobbly app pilot randomized controlled trial (n=61) completed in 2015. RESULTS: Regression analysis indicated that app use improved psychological outcomes, although only minimally, and effects were not significant. However, results of the thematic analysis indicated that the iBobbly app was deemed effective, acceptable, and culturally appropriate by those interviewed. CONCLUSIONS: There is a scarcity of randomized controlled trials and eHealth interventions in Indigenous communities, while extremely high rates of psychological distress and suicide persist. In this environment, studies that can add evidence from mixed-methods approaches are important. While the regression analysis in this study did not indicate a significant effect of app use on psychological wellbeing, this was predictable considering the small sample size (n=18) and typically brief app use. The results on engagement with the iBobbly app were however positive. This study showed that Indigenous youth are early and frequent users of technology in general, and they regarded the iBobbly app to be culturally safe and of therapeutic value. Qualitative analyses demonstrated that iBobbly app use was associated with self-reported improvements in psychological wellbeing, mental health literacy, and reductions in shame. Importantly, participants reported that they would recommend other similar apps if available to their peers.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".