Sensa Mobile App for Managing Stress, Anxiety, and Depression Symptoms: Pilot Cohort Study
Bibliographic record
Abstract
BACKGROUND: An increase in depression, anxiety, and stress symptoms worldwide, attributed to the COVID-19 pandemic, has been reported. If not treated, it may negatively affect a person's everyday life by altering physical and social well-being and productivity and increasing expenditure on health care. Cognitive behavioral therapy (CBT)-based interventions are gaining popularity as a means to reduce stress and alleviate anxiety and depression symptoms. Moreover, CBT delivered through a mobile app has the same elements as traditional CBT training (eg, guided discovery). However, unlike conventional training, users of mobile apps are allowed to tailor their own experience at their own speed and schedule. OBJECTIVE: This study aims to analyze Sensa users' retrospective data and explore the dose-duration effect to find the optimal usage time when the user showed results. METHODS: The study cohort comprised 381 consecutive community-based nonclinical users who started using Sensa between October 2021 and March 2022. All users included in the study took the Depression Anxiety Stress Scale-21 (DASS-21) assessment at least 2 times. Other parameters from the database containing all self-reported data were gender, number of active days, total time of use, and age. The primary outcome of the study was a change in the DASS-21 score. Statistical analyses were performed using GraphPad Prism (version 9, GraphPad Software). In addition, a logistic regression model was created to predict how the obtained independent parameters influenced the DASS-21 score. RESULTS: The main finding of our study was that the majority of participants who started using Sensa were experiencing depression, anxiety, and stress symptoms (92.13%, 80.05%, and 87.93%, respectively). There was a statistically significant decrease of the DASS-21 subdomain scores after the use of the application (anxiety: mean 7.25, SD 4.03 vs mean 6.12, SD 4.00; P=.001; depression: mean 11.05, SD 4.26 vs mean 9.01, SD 4.77; P=.001; stress: mean 11.42, SD 3.44 vs mean 9.96, SD 3.65; P<.001). Finally, the logistic regression model showed that users who were using the app for more than 24 days and had at least 12 active days during that time had 3.463 (95% CI 1.142-11.93) and 2.644 (95% CI 1.024-7.127) times higher chances to reduce their DASS-21 subdomain scores of depression and anxiety, respectively. CONCLUSIONS: Using the Sensa mobile app was related to decreased depression, anxiety, and stress symptoms.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".