SleepFit: A Persuasive Mobile App for Improving Sleep Habits in Young Adults
Bibliographic record
Abstract
Sleep disorders have been associated with mental distress, depressive symptoms, and anxiety. Digital interventions using mobile technology can address sleep difficulties due to the pervasiveness of smartphones, especially among young adults. Therefore, we design a persuasive mobile app, called SleepFit, targeted at young adults with the aim of improving their sleep habits or behaviours which in turn would improve their mental health and wellbeing. To achieve this, we employ the user-centered design approach in five stages. First, we elicit user preferences by conducting two concurrent focus group (FG) sessions to understand participants' current sleep habits, contributing factors, and how a persuasive mobile app (such as SleepFit) can help to improve their situation. Second, we design low-fidelity prototypes (LFP) illustrating various features of SleepFit based on our findings from the FG sessions after data analysis. Third, we conduct a user study to assess perceived persuasiveness of the features illustrated by the LFP. Our findings show that users perceived 7 features (Sleep Analysis, News Feed, Smart Alarm, Chat, Sleep Music, Notification, and Sleep Diary) as significantly persuasive for improving their sleep habits. Fourth, we design high-fidelity prototypes which reflect only the features that are perceived as significantly persuasive, based on the results of the LFP evaluation. Finally, we conduct a usability evaluation of the high-fidelity prototypes (HFP) and then refine the HFP based on qualitative feedback after thematic analysis.
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) | 0.001 | 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) | 0.006 | 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".