Engagement and Usability of a Cognitive Behavioral Therapy Mobile App Compared With Web-Based Cognitive Behavioral Therapy Among College Students: Randomized Heuristic Trial
Bibliographic record
Abstract
BACKGROUND: Recent evidence in mobile health has demonstrated that, in some cases, apps are an effective way to improve health care delivery. Health care interventions delivered via mobile technology have demonstrated both practicality and affordability. Lately, cognitive behavioral therapy (CBT) interventions delivered over the internet have also shown a meaningful impact on patients with anxiety and depression. OBJECTIVE: Given the growing proliferation of smartphones and the trust in apps to support improved health behaviors and outcomes, we were interested in comparing a mobile app with Web-based methods for the delivery of CBT. This study aimed to compare the usability of a CBT mobile app called MoodTrainer with an evidence-based website called MoodGYM. METHODS: We used convenience sampling to recruit 30 students from a large Midwestern university and randomly assigned them to either the MoodGYM or MoodTrainer user group. The trial period ran for 2 weeks, after which the students completed a self-assessment survey based on Nielsen heuristics. Statistical analysis was performed to compare the survey results from the 2 groups. We also compared the number of modules attempted or completed and the time spent on CBT strategies. RESULTS: The results indicate that the MoodTrainer app received a higher usability score when compared with MoodGYM. Overall, 87% (13/15) of the participants felt that it was easy to navigate through the MoodTrainer app compared with 80% (12/15) of the MoodGYM participants. All MoodTrainer participants agreed that the app was easy to use and did not require any external assistance, whereas only 67% (10/15) had the same opinion for MoodGYM. Furthermore, 67% (10/15) of the MoodTrainer participants found that the navigation controls were easy to locate compared with 80% (12/15) of the MoodGYM participants. MoodTrainer users, on average, completed 2.5 modules compared with 1 module completed by MoodGYM users. CONCLUSIONS: As among the first studies to directly compare the usability of a mobile app-based CBT with smartphone-specific features against a Web-based CBT, there is an opportunity for app-based CBT as, at least in our limited trial, it was more usable and engaging. The study was limited to evaluate usability only and not the clinical effectiveness of the app.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".