Developing Game-Based Design for eHealth in Practice: 4-Phase Game Design Process
Bibliographic record
Abstract
BACKGROUND: Games are increasingly used in eHealth as a strategy for user engagement. There is an enormous diversity of end users and objectives targeted by eHealth. Hence, identifying game content that drives and sustains engagement is challenging. More openness in the game design process and motivational strategies could aid researchers and designers of future game-based apps. OBJECTIVE: This study aims to provide insights into our approach to develop game-based eHealth in practice with a case study (Personalised ICT Supported Services for Independent Living and Active Ageing [PERSSILAA]). PERSSILAA is a self-management platform that aims to counter frailty by offering training modules to older adults in the domains of healthy nutrition and physical and cognitive training to maintain a healthy lifestyle. We elaborate on the entire game design process and show the motivational strategies applied. METHODS: We introduce four game design phases in the process toward game-based eHealth: (1) end-user research, (2) conceptualization, (3) creative design, and (4) refinement (ie, prototyping and evaluations). RESULTS: First, 168 participants participated in end-user research, resulting in an overview of their preferences for game content and a set of game design recommendations. We found that conventional games popular among older adults do not necessarily translate well into engaging concepts for eHealth. Recommendations include focusing game concepts on thinking, problem-solving, variation, discovery, and achievement and using high-quality aesthetics. Second, stakeholder sessions with development partners resulted in strategies for long-term engagement using indicators of user performance on the platform's training modules. These performance indicators, for example, completed training sessions or exercises, form the basis for game progression. Third, results from prior phases were used in creative design to create the game "Stranded!" The user plays a person who is shipwrecked who must gather parts for a life raft by completing in-game objectives. Finally, iterative prototyping resulted in the final prototype of the game-based app. A total of 35 older adults participated using simulated training modules. End users scored appreciation (74/100), ease of use (73/100), expected effectivity and motivation (62/100), fun and pleasantness of using the app (75/100), and intended future use (66/100), which implies that the app is ready for use by a larger population. CONCLUSIONS: The study resulted in a game-based app for which the entire game design process within eHealth was transparently documented and where engagement strategies were based on extensive user research. Our user evaluations indicate that the strategies for long-term engagement led to game content that was perceived as engaging by older adults. As a next step, research is needed on the user experience and actual engagement with the game to support the self-management of older adults, followed by clinical studies on its added value.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".