User-Centered Design of Serious Games for Older Adults Following 3 Years of Experience With Exergames for Seniors: A Study Design
Bibliographic record
Abstract
BACKGROUND: Seniors need sufficient balance and strength to manage in daily life, and sufficient physical activity is required to achieve and maintain these abilities. This can be a challenge, but fun and motivational exergames can be of help. However, most commercial games are not suited for this age group for several reasons. Many usability studies and user-centered design (UCD) protocols have been developed and applied, but to the best of our knowledge none of them are focusing on seniors' use of games for physical activity. In GameUp, a European cofunded project, some prototype Kinect exergames to enhance the mobility of seniors were developed in a user-centered approach. OBJECTIVE: In this paper we aim to record lessons learned in 3 years of experience with exergames for seniors, considering both the needs of older adults regarding user-centered development of exergames and participation in UCD. We also provide a UCD protocol for exergames tailored to senior needs. METHODS: An initial UCD protocol was formed based on literature of previous research outcomes. Senior users participated in UCD following the initial protocol. The users formed a steady group that met every second week for 3 years to play exergames and participate in the UCD during the 4 phases of the protocol. Several methods were applied in the 4 different phases of the UCD protocol; the most important methods were structured and semistructured interviews, observations, and group discussions. RESULTS: A total of 16 seniors with an average age above 80 years participated for 3 years in UCD in order to develop the GameUp exergames. As a result of the lessons learned by applying the different methodologies of the UCD protocol, we propose an adjusted UCD protocol providing explanations on how it should be applied for seniors as users. Questionnaires should be turned into semistructured and structured interviews while user consultation sessions should be repeated with the same theme to ensure that the UCD methods produce a valid outcome. By first following the initial and gradually the adjusted UCD protocol, the project resulted in exergame functionalities and interface features for seniors. CONCLUSIONS: The main lessons learned during 3 years of experience with exergames for seniors applying UCD are that devoting time to seniors is a key element of success so that trust can be gained, communication can be established, and users' opinions can be recorded. All different game elements should be taken into consideration during the design of exergames for seniors even if they seem obvious. Despite the limitations of this study, one might argue that it provides a best practice guide to the development of serious games for physical activity targeting seniors.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".