Sleep hygiene games and gamification: where are we and where are we heading?
Bibliographic record
Abstract
Sleep hygiene encompasses the habits, behaviors, and environmental adjustments conducive to achieving a restful sleep at night. Practices such as maintaining a consistent bedtime routine, avoiding sleep-disruptive substances such as alcohol and caffeine, and ensuring a dark and quiet bedroom are examples of good sleep hygiene. There is growing evidence that well-designed serious games can facilitate desired healthy behavioral changes, suggesting their potential for sleep hygiene intervention. This paper presents a narrative review of existing serious games and gamified systems designed for sleep hygiene intervention in daily life, providing an overview of the current landscape of Sleep Hygiene Games and Gamified Systems (SHG2S). We searched for peer-reviewed publications using four databases including Web of Science, PubMed, ACM Digital Library, and IEEE Xplore. The analysis focused on the targeted sleep hygiene, game design elements, and system evaluation and validation. We found that Accomplishment (e.g., rewards, level-up, leaderboard), Avoidance (e.g., punishment), and Social Relatedness (e.g., group quest, social prod, friending) were the most frequently employed game designs in SHG2S. Most SHG2S focused on addressing nighttime routine, while optimizing sleep environment has largely been underexplored. Existing SHG2S are also limited in addressing different cultural background and sleep patterns. Overall, empirical evidence is still limited regarding whether, why and how gamification leads to favorable effects on sleep hygiene over various temporal scales. Future work should focus on establishing a comprehensive and standardized evaluation framework to facilitate cross-study comparison and to collect evidence on the effectiveness of SHG2S.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".