Revitalizing wellbeing: App design for stress reduction through artificial intelligence and persuasive technology
Bibliographic record
Abstract
• This paper details the design, development, and evaluation of an mHealth application called “SmileApp,” which aims to promote positive emotions as a way to address stress. • A total of 101 participants used the app for 14 days, completed a questionnaire about their experience, and a subset of participants also participated in interviews to provide additional insights. • SmileApp uses artificial intelligence and persuasive technology to promote positive emotions. • Our findings offer valuable insights into how technology can enhance emotional well-being, with important implications for the design and development of future mHealth applications. Digital health technologies, particularly mobile health (mHealth) applications have been shown to address various needs, such as stress management and the promotion of positive health habits. In this research, we designed, developed, and evaluated an mHealth application called “SmileApp” to promote positive emotions as a means of addressing stress. SmileApp utilizes the advantages of artificial intelligence and persuasive technology and was created by incorporating well-established psychological theories and models. To evaluate the effectiveness of SmileApp , we conducted a 14-day within-subject study involving 101 participants in-the-wild. This is followed by an interview with 23 participants. Our results show that SmileApp can promote positive emotions using artificial intelligence and persuasive technology. Our findings underscore the importance of utilizing technology to support emotional wellbeing and lay the groundwork for further research and development in this area. The implications of this study demonstrate a paradigm shift in mHealth app design by introducing a new approach of promoting desired behaviors by encouraging users to read persuasive messages and play mobile games using their smile.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".