Evaluation of the Clear Fear Smartphone App for Young People Experiencing Anxiety: Uncontrolled Pre– and Post–Follow-Up Study
Notice bibliographique
Résumé
Background Mobile health apps are proving to be an important tool for increasing access to psychological therapies early on, particularly with rising rates of anxiety and depression in young people. Objective We aimed to assess the usability, acceptability, safety, and effectiveness of a new app, Clear Fear, developed to help young people manage symptoms of anxiety using the principles of cognitive behavioral therapy. Methods The Clear Fear app was developed to provide cognitive behavioral strategies to suit anxiety disorders. An uncontrolled pre– and post–follow-up design over a 9-week period was used to assess the app and its effects. This study comprised 3 phases: baseline (stage 1), post–app familiarization phase (stage 2), and follow-up (stage 3). Eligible participants were aged between 16 and 25 years with mild to moderate anxiety but not currently receiving treatment or in contact with specialist mental health services or using other interventions or apps to help monitor or manage their mental health. A community sample was recruited via advertisements, relevant websites, and social media networks. Eligible participants completed standardized self-report tools and questionnaires at each study stage. These measured probable symptoms of anxiety (7-item Generalized Anxiety Disorder scale) and depression (Mood and Feelings Questionnaire); emotional and behavioral difficulties (Strengths and Difficulties Questionnaire); and feedback on the usability, accessibility, and safety of the app. Mean scores at baseline and follow-up were compared using paired 2-tailed t tests or Wilcoxon signed rank tests. Qualitative data derived from open-ended questions were coded and entered into NVivo (version 10) for analysis. Results A total of 48 young people entered the study at baseline, with 37 (77%) completing all outcome measures at follow-up. The sample was mostly female (37/48, 77%). The mean age was 20.1 (SD 2.1) years. In total, 48% (23/48) of the participants reached the threshold for probable anxiety disorder, 56% (27/48) had positive scores for probable depression, and 75% (36/48) obtained a total score of “very high” on the Strengths and Difficulties Questionnaire for emotional and behavioral difficulties. The app was well received, offering reassurance, practical and immediate help to manage symptoms, and encouragement to seek help, and was generally found easy to use. A small minority (3/48, 6%) found the app difficult to navigate. The Clear Fear app resulted in statistically significant reductions in probable symptoms of anxiety (t36=2.6, 95% CI 0.41-3.53; P=.01) and depression (z=2.3; P=.02) and behavioral and emotional difficulties (t47=4.5, 95% CI 3.67-9.65; P<.001), representing mostly medium to large standardized effect sizes. Conclusions The Clear Fear app was found to be usable, acceptable, safe, and effective in helping manage symptoms of anxiety and depression and emotional and behavioral difficulties.
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Comment cette classification a été obtenuedéplier
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 | 0,007 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».