A Smartphone App (BlueIce) for Young People Who Self-Harm: Open Phase 1 Pre-Post Trial
Notice bibliographique
Résumé
Background: Recent years have seen a significant increase in the availability of smartphone apps for mental health problems. Despite their proliferation, few apps have been specifically developed for young people, and almost none have been subject to any form of evaluation. Objective: This study aimed to undertake a preliminary evaluation of a smartphone app (BlueIce), coproduced with young people and designed to help young people manage distress and urges to self-harm. We aimed to assess the acceptability, safety, and use of BlueIce and to explore the effects on the primary outcome of self-harm and the secondary outcomes of psychological functioning. Methods: We undertook an open trial where we recruited young people aged 12 to 17 years attending specialist child and adolescent mental health services (CAMHS) who were currently self-harming or had a history of self-harm. Eligible participants were assessed at baseline and then given BlueIce. They were assessed 2 weeks later (post familiarization) and again at 12 weeks (post use). A behavior-screening questionnaire (Strengths and Difficulties Questionnaire) was completed along with standardized measures of depression (Mood and Feelings Questionnaire or MFQ) and anxiety (Revised Child Anxiety and Depression Scale or RCADS), taking into account self-reports of self-harm, app helpfulness, and safety. Results: All core CAMHS professional groups referred at least 1 young person. Out of 40 young people recruited, 37 (93%) elected to use BlueIce after familiarization, with 29 out of 33 (88%) wanting to keep it at the end of the study. No young person called the emergency numbers during the 12-week trial, and no one was withdrawn by his or her clinician due to increased risk of suicide. Almost three-quarters (73%) of those who had recently self-harmed reported reductions in self-harm after using BlueIce for 12 weeks. There was a statistically significant mean difference of 4.91 (t31=2.11; P=.04; 95% CI 0.17-9.64) on postuse symptoms of depression (MFQ) and 13.53 on symptoms of anxiety (RCADS) (t30=3.76; P=.001; 95% CI 6.17-20.90), which was evident across all anxiety subscales. Ratings of app acceptability and usefulness were high. Conclusions: Our study has a number of methodological limitations, particularly the absence of a comparison group and a prospective way of assessing self-harm. Nonetheless, our findings are encouraging and suggest that BlueIce, used alongside a traditional CAMHS face-to-face intervention, can help young people manage their emotional distress and urges to self-harm.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».