Affective Game Planning for Health Applications: Quantitative Extension of Gerontoludic Design Based on the Appraisal Theory of Stress and Coping (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED User retention is the first challenge in introducing any information and communication technologies (ICT) for health applications, particularly for seniors who are increasingly targeted as beneficiaries of such technologies. Interaction with digital technologies may be too stressful to older adults to guarantee their adoption in their routine selfcare. The second challenge, which also relates to adoption, is to supply empirical evidence that support the expectations of their beneficial outcomes. To address the first challenge, persuasive technologies such as serious games (SGs) are increasingly promoted as ludic approaches to deliver assistive care to older adults. However, there are no standards yet to assess the efficacy of different genres of games across populations, or compare and contrast variations in health outcomes arising from user interface design and user experience. For the past 3 decades, research has focused either on qualitative assessment of the appeal of digital games for seniors (by game designers) or on the quantitative evaluation of their clinical efficacy (by clinical researchers). The consensus is that interindividual differences play a key role in whether games can be useful or not for different individuals. Our challenge is to design SGs that retain their users long enough to sustain beneficial transfer effects. We propose to add a neuropsychological experimental framework (based on the appraisal theory of stress and coping) to a Gerontoludic design framework (that emphasizes designing positive and meaningful gaming experience over benefit-centric ones) in order to capture data to guide SG game development. Affective Game Planning for Health Applications (AGPHA) adds a model-driven mixed-methods experimental stage to a user-centered mechanics-dynamics-aesthetics game-design cycle. This intersectoral framework is inspired by latest trends in the fields of neuroimaging and neuroinformatics that grapple with similar challenges related to the psychobiological context of an individual's behaviors. AGPHA aims to bring users, designers, clinicians, and researchers together to generate a common data repository that consists of 4 components to define, design, evaluate, and document SGs. By unifying efforts under a standard approach, we will accelerate innovations in persuasive and efficacious ICTs for the aging population.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".