Use of a Guided Imagery Mobile App (See Me Serene) to Reduce COVID-19–Related Stress: Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 pandemic has led to concerns about mental health resulting from regional and national lockdowns, social isolation, job loss, and concern about disease exposure. OBJECTIVE: We describe results of the pilot feasibility study of the See Me Serene mHealth app. The app provides users with immersive, vivid, nature experiences to reduce stress and anxiety related to COVID-19 and other isolation. The goals of the study were to develop the See Me Serene app and test the feasibility and acceptability of study procedures, and explore the potential impact of the app on stress and anxiety. METHODS: We developed and tested the See Me Serene app and our study procedures for feasibility, and gathered preliminary data with a goal of 100 participants. The research was conducted in 2 phases: (1) development and internal testing of the app; and (2) feasibility and pilot testing with participants recruited online through earned media (eg, news stories), presentations at a university campus, and social media (eg, online sharing of earned media and presentations). The feasibility study employed a mixed methods, within-subjects, pre-/posttest design. At baseline and 30-day follow-up, we assessed stress-related variables via validated self-report measures and saliva samples for determination of cortisol concentrations. RESULTS: We met or surpassed all our feasibility benchmarks for recruitment (101 participants recruited), retention (91% [90/99] of 30-day assessment completed), and data collection (99 participants completed all baseline data; 85% [84/99] of salivary cortisol samples returned). Participants adhered to the intervention. On average, participants listened to 48.2 audio files over 30 days or approximately 1.6 audio files per day. Participants were satisfied with the app, with 87% (78/90) rating the app as helpful in dealing with stress and anxiety. The app showed the potential to reduce stress, anxiety, loneliness, and worry. We did not find significant differences (P=.41) in cortisol levels over time. Our findings suggest that future research is warranted to test the efficacy of the See Me Serene app with a representative, diverse sample. CONCLUSIONS: There is a need for evidence-based and easily disseminable stress-reduction interventions. See Me Serene is a feasible intervention and has the potential to reduce stress related to COVID-19 and other forms of social isolation. More research on See Me Serene is warranted.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".