Internet-Based Cognitive-Behavioral Therapy for College Students With Anxiety, Depression, Social Anxiety, or Insomnia: Four Single-Group Longitudinal Studies of Archival Commercial Data and Replication of Employee User Study
Bibliographic record
Abstract
Background The growing behavioral health needs of college students have resulted in counseling centers reporting difficulties in meeting student demand. Objective This study aims to test the real-world voluntary use by college students of 4 digital, self-directed mental health modules based on a cognitive behavioral therapy clinical model. The findings were also compared with those of employee users. Methods Archival operational data from Learn to Live were extracted for student users at 4 colleges and universities in the Midwest region of the United States (N=951). The inclusion criteria were having clinical symptoms at established levels of moderate or higher severity and the use of 2 or more of the 8 lessons of a program within a 6-month period. Unique users in each program included 347 for depression; 325 for stress, anxiety, and worry; 203 for social anxiety; and 76 for insomnia. Paired t tests (two-tailed) compared the average level of change over time on a standardized measure of clinical symptoms appropriate to each program. Cohen d statistical effect sizes were calculated for each program. Potential moderator factors (age, gender, preliminary comprehensive assessment, number of lessons, duration, live coach support, and live teammate support) were tested together in repeated measures analysis of variance models with covariates in the full sample. Follow-up survey data (n=136) were also collected to explore user satisfaction and outcomes. Select data from another study of the same 4 programs by employee users meeting the same criteria (N=707) were examined for comparison. Results The percentage of users who improved to a clinical status of no longer being at risk after program use was as follows: stress, anxiety, and worry program (149/325, 45.8%); insomnia program (33/76, 43.4%), depression program (124/347, 35.7%); and social anxiety program (45/203, 22.2%). Significant improvements (all P<.001) over time were found in the mean scores for the clinical measures for each program: stress, anxiety, and worry (t324=16.21; d=1.25); insomnia (t75=6.85; d=1.10); depression (t346=12.71; d=0.91); and social anxiety (t202=8.33; d=0.80). Tests of the moderating factors across programs indicated that greater improvement was strongly associated with the use of more lessons and it also differed by program, by gender (males demonstrated more improvement than females), and by the use of live support (particularly coaching). Analyses of survey data found high satisfaction, improved academic outcomes, and successful integration into the university counseling ecosystem. The operational profile and outcomes of the college students were also similar to those of employee users of the same programs from our other study of employee users. Thus, this study provides a replication. Conclusions Self-directed internet-based cognitive behavioral therapy mental health modules are promising as a supplement to traditional in-person counseling services provided by college counseling centers.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".