Digital Interventions for Reducing Loneliness and Depression in Korean College Students: Mixed Methods Evaluation
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has exacerbated the prevalence of loneliness and depression among college students. Digital interventions, such as Woebot (Woebot Health, Inc) and Happify (Twill Inc), have shown promise in alleviating these symptoms. OBJECTIVE: This study aims to investigate the effectiveness and acceptability of Woebot and Happify in reducing loneliness and depression among college students after the COVID-19 pandemic. METHODS: A mixed methods approach was used over 4 months. A total of 63 participants aged 18 to 27 years from Sungkyunkwan University in Seoul, South Korea, were initially recruited, with an inclusion criterion of University of California, Los Angeles (UCLA) Loneliness Scale score ≥34. The final sample consisted of 27 participants due to attrition. Participants were randomly assigned to Woebot (15/27, 55%); Happify (9/27, 33%); or a control group using Bondee (Metadream), a metaverse social network messenger app (3/27, 11%). Quantitative measures (UCLA Loneliness Scale and Patient Health Questionnaire-9) and qualitative assessments (user feedback and focused interviews) were used. RESULTS: Although mean decreases in loneliness and depression were observed in the control and intervention groups after the intervention, the differences between the control and intervention groups were not statistically significant (UCLA Loneliness: P=.67; Patient Health Questionnaire-9: P=.35). Qualitative data indicated user satisfaction, with suggestions for improved app effectiveness and personalization. CONCLUSIONS: Despite limitations, this study highlights the potential of well-designed digital interventions in alleviating college students' loneliness and depression. The findings contribute to the growing body of research on accessible digital mental health tools and underscore the importance of comprehensive support systems. Further research with larger and more diverse samples is needed to better understand the effectiveness and optimization of such interventions. TRIAL REGISTRATION: Clinical Research Information Service KCT0009449; https://bit.ly/4d2e4Bu.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".