Barriers to and Facilitators of a Blended Cognitive Behavioral Therapy Program for Depression and Anxiety Based on Experiences of University Students: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Blended cognitive behavioral therapy (bCBT) programs have been proposed to increase the acceptance and adoption of digital therapeutics (DTx) such as digital health apps. These programs allow for more personalized care by combining regular face-to-face therapy sessions with DTx. However, facilitators of and barriers to the use of DTx in bCBT programs have rarely been examined among students, who are particularly at risk for developing symptoms of depression and anxiety disorders. OBJECTIVE: This study aimed to evaluate the facilitators of and barriers to the use of a bCBT program with the elona therapy app among university students with mild to moderate depression or anxiety symptoms. METHODS: Semistructured interviews were conducted via videoconference between January 2022 and April 2022 with 102 students (mean age 23.93, SD 3.63 years; 89/102, 87.2% female) from universities in North Rhine-Westphalia, Germany, after they had completed weekly individual cognitive behavioral therapy sessions (25 minutes each) via videoconference for 6 weeks and regularly used the depression (n=67, 65.7%) or anxiety (n=35, 34.3%) module of the app. The interviews were coded based on grounded theory. RESULTS: Many participants highlighted the intuitive handling of the app and indicated that they perceived it as a supportive tool between face-to-face sessions. Participants listed other benefits, such as increased self-reflection and disorder-specific knowledge as well as the transfer of the content of therapy sessions into their daily lives. Some stated that they would have benefited from more personalized and interactive tasks. In general, participants mentioned the time requirement, increased use of the smartphone, and the feeling of being left alone with potentially arising emotions while working on tasks for the next therapy session as possible barriers to the use of the app. Data security was not considered a major concern. CONCLUSIONS: Students mostly had positive attitudes toward elona therapy as part of the bCBT program. Our study shows that DTx complementing face-to-face therapy sessions can be perceived as a helpful tool for university students with mild to moderate anxiety or depression symptoms in their daily lives. Future research could elaborate on whether bCBT programs might also be suitable for students with more severe symptoms of mental disorders. In addition, the methods by which such bCBT programs could be incorporated into the university context to reach students in need of psychological support should be explored.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".