Engagement and Effectiveness of a Healthy-Coping Intervention via Chatbot for University Students During the COVID-19 Pandemic: Mixed Methods Proof-of-Concept Study
Bibliographic record
Abstract
Background University students are increasingly reporting common mental health problems, such as stress, anxiety, and depression, and they frequently face barriers to seeking psychological support because of stigma, cost, and availability of mental health services. This issue is even more critical in the challenging time of the COVID-19 pandemic. Digital mental health interventions, such as those delivered via chatbots on mobile devices, offer the potential to achieve scalability of healthy-coping interventions by lowering cost and supporting prevention. Objective The goal of this study was to conduct a proof-of-concept evaluation measuring the engagement and effectiveness of Atena, a psychoeducational chatbot supporting healthy coping with stress and anxiety, among a population of university students. Methods In a proof-of-concept study, 71 university students were recruited during the COVID-19 pandemic; 68% (48/71) were female, they were all in their first year of university, and their mean age was 20.6 years (SD 2.4). Enrolled students were asked to use the Atena psychoeducational chatbot for 4 weeks (eight sessions; two per week), which provided healthy-coping strategies based on cognitive behavioral therapy, positive psychology, and mindfulness techniques. The intervention program consisted of conversations combined with audiovisual clips delivered via the Atena chatbot. Participants were asked to complete web-based versions of the 7-item Generalized Anxiety Disorder scale (GAD-7), the 10-item Perceived Stress Scale (PSS-10), and the Five-Facet Mindfulness Questionnaire (FFMQ) at baseline and postintervention to assess effectiveness. They were also asked to complete the User Engagement Scale–Short Form at week 2 to assess engagement with the chatbot and to provide qualitative comments on their overall experience with Atena postintervention. Results Participants engaged with the Atena chatbot an average of 78 (SD 24.8) times over the study period. A total of 61 out of 71 (86%) participants completed the first 2 weeks of the intervention and provided data on engagement (10/71, 14% attrition). A total of 41 participants out of 71 (58%) completed the full intervention and the postintervention questionnaires (30/71, 42% attrition). Results from the completer analysis showed a significant decrease in anxiety symptoms for participants in more extreme GAD-7 score ranges (t39=0.94; P=.009) and a decrease in stress symptoms as measured by the PSS-10 (t39=2.00; P=.05) for all participants postintervention. Participants also improved significantly in the describing and nonjudging facets, based on their FFMQ subscale scores, and asked for some improvements in the user experience with the chatbot. Conclusions This study shows the benefit of deploying a digital healthy-coping intervention via a chatbot to support university students experiencing higher levels of distress. While findings collected during the COVID-19 pandemic show promise, further research is required to confirm conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".