Evaluation of the Clear Fear Smartphone App for Young People Experiencing Anxiety: Uncontrolled Pre– and Post–Follow-Up Study
Bibliographic record
Abstract
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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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".