Making Video Games More Inclusive for People Living With Motor Neuron Disease: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that individuals with motor neuron disease (MND), a terminal illness, find enjoyment and social connection through video games. However, MND-related barriers can make gaming challenging, exacerbating feelings of boredom, stress, isolation, and loss of control over daily life. OBJECTIVE: We scoped the evidence to describe relevant research and practice regarding what may help reduce difficulties for people with MND when playing video games. METHODS: A scoping review was conducted using the Arksey and O'Malley framework, recent scoping review guidance, and engaging with people with lived experience of MND. Peer-reviewed studies were sourced from PubMed and the Swinburne University of Technology Library. Gray literature was identified from government, not-for-profit, commercial, and community websites. Data were extracted and summarized from the collected literature. RESULTS: The evidence base, consisting of quantitative and qualitative research, lived experience stories, information resources, reviews, and guidelines, included 85 documents. Only 8 (9%) directly addressed video games and people with MND; however, these studies were limited in depth and quality. The primary technologies examined included customized information and communications technology for communication and control of computing systems (including desktop, laptop, smartphone, tablet, and console systems) and video game software and hardware (including hand controllers and accessibility features, such as difficulty level, speed, and remappable buttons and controls). Common factors in the evidence base highlight barriers and enablers to enjoying video games for people with MND. These include technological, physical, social, and economic challenges. Addressing these requires greater involvement of people with MND in game and technology research and development. Changes to information and communications technology, game software and hardware, policies, and guidelines are needed to better meet their needs. CONCLUSIONS: There is a significant gap in understanding the lived experience of people with MND with video games and what makes playing them easier, including appropriate customization of technology and the social experience of games. This gap perpetuates exclusion from gaming communities and recreation, potentially worsening social isolation. Existing evidence suggesting viable options for future research and practice. Video game and information and communications technology research and development must prioritize qualitative and quantitative research with people with MND at an appropriate scale, with a focus on lived experience, use of improved participant engagement techniques, and user-focused design for more inclusive games. Practical work needs to increase awareness of what can help make games more inclusive, including incorporation of accessibility early in the game production process, early incorporation of accessibility in game production, and affordable options for customized interfaces and other devices to play games.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".