Serious Game on a Smartphone for Adolescents Undergoing Hemodialysis: Development and Evaluation
Bibliographic record
Abstract
BACKGROUND: Adolescents with chronic kidney disease have a hard time adhering to hemodialysis as a therapy, indicating a need to establish new alternatives for motivation and adherence to treatment. OBJECTIVE: The objective of this study was to develop and evaluate a serious game to stimulate and motivate adolescents undergoing hemodialysis. METHODS: We describe the technological production followed by a qualitative analysis. We invited 8 adolescents undergoing hemodialysis in the city Goiânia, located in the midwest of Brazil, to participate. The final convenience sample included 7 (87.5% of the target population) adolescents. The process was conducted in 3 phases: creation of a serious game, evaluation of its use, and observation of its motivating effect on behavioral modification with a focus on acquiring the necessary competence for self-care. RESULTS: An app (Bim) in the modality of a serious game was developed to be used during hemodialysis; the player was encouraged to take care of a character with daily actions during his or her treatment. The game was made available to adolescents aged 10-14 years. Mobile devices were offered during the hemodialysis treatment for a period of 30-40 minutes, 3 times a week for 60 days. The usage definitions of the game were freely chosen by the participants. The qualitative evaluation of the use of the Bim app showed that it encompasses scenarios and activities that enable the exercise of daily actions for the treatment of patients. The behavioral evaluation showed that the Bim app worked as a motivating stimulus for behavioral adherence to hemodialysis requirements. CONCLUSIONS: The easy-to-access app interface showed good operability for its users. The description of the character and proposed activities contributed to motivation and ability to cope with hemodialysis care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.000 |
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".