Safety, Acceptability, and Use of a Smartphone App, BlueIce, for Young People Who Self-Harm: Protocol for an Open Phase I Trial
Bibliographic record
Abstract
BACKGROUND: Up to 18% of adolescents will engage in an act of self-harm before young adulthood, with the majority of acts occurring in private. Mobile apps may offer a way of providing support for young people at times of distress to prevent self-harm. OBJECTIVE: This is a proof-of-concept study designed to explore the safety, acceptability, feasibility, and usability of a smartphone app, BlueIce, with young people who are self-harming. METHODS: In this phase I open trial we will evaluate BlueIce, a smartphone app developed and coproduced with young people with lived experience of self-harm. BlueIce includes a mood-monitoring diary, selection of mood-lifting techniques based on cognitive behavior therapy and dialectical behavior therapy, and direct access to emergency telephone numbers. We will recruit young people (n=50) attending specialist child and adolescent mental health services with a current or past history of self-harm to trial BlueIce as an adjunct to their usual care. Questionnaires and interviews will be completed at baseline, postfamiliarization (2 weeks), and at follow-up (12 weeks after baseline) to assess safety, app use, and acceptability. Interviews will be undertaken with clinicians to assess the feasibility of BlueIce within a clinical setting. RESULTS: Recruitment occurred between May and November 2016. The recruitment target was 50, and by the beginning of November 54 young people had been referred. CONCLUSIONS: This study is the first to evaluate an app specifically developed with young people for young people (under the age of 18 years) who self-harm. It will determine whether BlueIce is acceptable, how often it is used, and whether it is safe and does not have any unintentional adverse effects. This information will determine whether a feasibility trial to test recruitment, randomization, retention, and appropriate outcome measures should be pursued.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".