Lessons Learned from Gamifying Functional Fitness Training Through Human-Centered Design Methods in Older Adults
Bibliographic record
Abstract
Background: The design of meaningful and enjoyable Exergames for fitness training in older adults possesses critical challenges in matching user's needs and motivators with game elements. These challenges are often due to the lack of knowledge of seniors' game preferences and technology literacy as well as a poor involvement of the target population in the design process. Objective: This research aims at describing a detailed and scrutinized use case of applying human-centered design methodologies in the gamification of fitness training routines and illustrates how to incorporate seniors' feedback in the game design pipeline. Materials and Methods: We focus on how to use the insights from human-centered inquiries to improve in-game elements, such as mechanics or esthetics, and how to iterate the game design process based on playtesting sessions in the field. Results: We present a set of four Exergames created to train the critical functional fitness areas of older adults. We show how through rapid prototyping methods and multidisciplinary research, Exergames can be rigorously designed and developed to match individual physical capabilities. Moreover, we propose a set of guidelines for the design of context-aware Exergames based on the lessons learned. Conclusion: We highlight the process followed; it depicts 19 weeks of various activities delivering particular and actionable items that can be used as a checklist for future games for health design projects.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".