SereneMind: Design and Evaluation of a Persuasive Mobile App for Managing Stress Among Adults
Bibliographic record
Abstract
Stress is a global public health problem that leads to severe physical and psychological health risks if not managed. Various stress management interventions have been delivered using mobile health (mHealth) apps. Although there are different existing mobile apps promoting stress-coping strategies, more work is still needed to design and evaluate such apps from a persuasion standpoint. Persuasive mobile apps incorporate persuasive strategies (PS) that work as backbone mechanics to motivate desired behaviour change. Therefore, we design and evaluate a mobile app called SereneMind following a five-step process. First, we deconstruct the PS operationalized in 150 stress management apps from both Google Play and App Store to identify the most implemented PS using the Persuasive Systems Design (PSD) framework. Second, we design a low-fidelity prototype (LFP) depicting various features of the app based on the top ten PS. Third, we conduct a user study involving our target audience to evaluate the perceived persuasiveness/effectiveness of each feature and uncover further improvement based on users' feedback. Fourth, based on our findings from the LFP evaluation, we design a high-fidelity prototype (HFP) using only the features that were perceived as effective by our target audience and applying user suggestions to refine the prototypes. Finally, we run a usability study to evaluate the HFP. Our results show that participants perceived SereneMind as usable and useful for managing stress. Therefore, it is more likely to be adopted and used. Lastly, we offer design recommendations for stress management apps in line with our findings.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".