Using a mobile application to reduce anxiety levels, stress levels, and panic severity in a sample of the general population
Bibliographic record
Abstract
During the COVID-19 pandemic, people are feeling more anxious and stressed than ever. As such, mobile solutions that minimize face-to-face contact and can reduce anxiety and stress in the public are needed. The mobile application “Rootd” could be one such solution and we sought to investigate this mobile health application further. In the present work, we recruited 41 participants from the community and had them use the Rootd app on a regular basis. To assess whether Rootd decreased anxiety levels, stress levels, and panic incidence and severity, we had participants complete assessments before using the application (the pre-application period) and after four to six weeks of using the application (the post-application period). All participants were collected during the COVID-19 pandemic. We found that the use of the application led to a decrease in both anxiety and stress levels on two well-validated questionnaires and per the participants’ self-report. As well, we found that Rootd did not decrease panic attack incidence but did decrease panic attack severity. These findings build upon previous investigations that have adopted mobile-health approaches and mindfulness to help treat anxiety, stress, and panic. Moreover, given our generalized sample and the short intervention timeframe, these findings provide evidence that mobile health solutions are invaluable to help people manage anxiety, stress, and panic, during times when face-to-face interaction is reduced – such as during the COVID-19 pandemic.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".