Flow and Grit by Design: Exploring Gamification in Facilitating Adherence to Swallowing Therapy
Bibliographic record
Abstract
PURPOSE: Delivery of swallowing therapy is faced with challenges regarding access to in-clinic services and adherence to prescribed home programs. Mobile health (mHealth) technologies are being developed at a rapid pace to address these difficulties. Whereas some benefits to using these modern tools for therapy are obvious (e.g., electronic reminders), other advantages are not as well understood. One example is the potential for mHealth devices and apps to enhance adherence to treatment regimens. METHOD: This article introduces a number of psychological concepts that relate to adherence and that can be leveraged by mHealth. Elements that contribute to flow (optimal experience) during an activity and those that reinforce grit (perseverance to achieve a long-term goal) can be used to engage patients in their own rehabilitation. RESULTS: The experience of flow can be targeted by presenting the rehabilitation exercise as an optimally challenging game, one that offers a match between challenge and ability. Grit can be supported by reinforcing routine and by varying the therapy experience using different games. CONCLUSIONS: A combination of hardware and software design approaches have the potential to transform uninteresting and repetitive activities, such as those that make up swallowing therapy regimens, into engaging ones. The field of gamification, however, is still developing, and gamified mHealth apps will need to withstand scientific testing of their claims and demonstrate effectiveness in all phases of outcome research.
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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 | 0.004 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".