Cerina—Cognitive Behavioral Therapy–Based Mobile App for Managing Generalized Anxiety Disorder Symptoms Among University Students: Results From a Pilot Feasibility Randomized Controlled Trial
Bibliographic record
Abstract
Background Generalized anxiety disorder (GAD) is common among university students due to academic pressure and financial uncertainty, among other challenges. Despite the need, the receipt of available psychological services is often low. Objective This study investigates the feasibility of a digital unguided cognitive behavioral therapy (CBT)–based mobile app, Cerina, and examines the likely effects of this intervention in reducing GAD symptoms compared to the waitlist control group. Methods Eligible students (n=158) with mild to moderate GAD symptoms were self-assessed through web-based questionnaires and were randomly allocated to the intervention group (n=79) or to the waitlist control group (n=79) following their informed consent. The intervention group had direct access to Cerina and followed CBT-based interactive sessions for 6 weeks. The waitlist control group participants had access to optional on-campus well-being services, and they were given access to Cerina 6 weeks after their randomization. Participants completed assessments on anxiety, depression, worry, and usability at three time points. Additionally, upon completing the intervention, they were invited to a web-based interview to understand the implementation of the intervention in more depth. Results On average, 13% (10/79) intervention group participants dropped out, 61% (36/69) completed the core clinical content (2 sessions), and 12% (7/69) completed the desired number of sessions (6 or 7 sessions). Analyses of the completers (2 or more sessions) revealed significant group differences in GAD (mean 8.4, SD 3.7; t42=–2.25; P=.03; d=–0.7) and worry symptoms (mean 42.3, SD 10.8; t42=–2.50; P=.02; d=–0.8), as well as functional impairment (mean 16.7, SD 2.44; t42=–2.12; P=.04; d=–0.6) in favor of the intervention group at posttest with medium to large effect sizes. The intention-to-treat analyses confirmed significant group differences in GAD (mean 8.47, SD 2.7; t156=–2.23; P=.03; d=–0.4), and there were marginally nonsignificant group differences in worry symptoms (mean 41.5, SD 8.40; t156=–1.94; P=.05; d=–0.3) in favor of the intervention group at posttest with medium effect sizes. These results suggest that the intervention had a meaningful impact on reducing GAD symptoms and a modest impact on reducing worry symptoms among participants. Conclusions The Cerina app showed promising results in reducing GAD symptoms among students. This result supports findings from other randomized controlled trials showing that digital CBT-based interventions are effective and feasible for a wide range of age groups and populations experiencing GAD symptoms. The low number of participants completing the recommended number of sessions suggests a usability issue. To address this, the intervention could be refined through an iterative design process informed by user feedback, and the long-term impact of specific engagement features in improving usability and retention could be assessed through extended evaluations. Trial Registration ClinicalTrials.gov NCT06146530; https://clinicaltrials.gov/study/NCT06146530 International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2023-083554
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".