Assessment of Active Video Gaming Using Adapted Controllers by Individuals With Physical Disabilities: A Protocol
Bibliographic record
Abstract
BACKGROUND: Individuals with disabilities are typically more sedentary and less fit compared to their peers without disabilities. Furthermore, engaging in physical activity can be extremely challenging due to physical impairments associated with disability and fewer opportunities to participate. One option for increasing physical activity is playing active video games (AVG), a category of video games that requires much more body movement for successful play than conventional push-button or joystick actions. However, many current AVGs are inaccessible or offer limited play options for individuals who are unable to stand, have balance issues, poor motor control, or cannot use their lower body to perform game activities. Making AVGs accessible to people with disabilities offers an innovative approach to overcoming various barriers to participation in physical activity. OBJECTIVE: Our aim was to compare the effect of off-the-shelf and adapted game controllers on quality of game play, enjoyment, and energy expenditure during active video gaming in persons with physical disabilities, specifically those with mobility impairments (ie, unable to stand, balance issues, poor motor control, unable to use lower extremity for gameplay). The gaming controllers to be evaluated include off-the-shelf and adapted versions of the Wii Fit balance board and gaming mat. METHODS: Participants (10-60 years old) came to the laboratory a total of three times. During the first visit, participants completed a functional assessment and became familiar with the equipment and games to be played. For the functional assessment, participants performed 18 functional movement tasks from the International Classification of Functioning, Disability, and Health. They also answered a series of questions from the Patient Reported Outcomes Measurement Information System and Quality of Life in Neurological Conditions measurement tools, to provide a personal perspective regarding their own functional ability. For Visit 2, metabolic data were collected during an initial 20-minute baseline, followed by 40 minutes of game play. The controller (balance board or gaming mat) played was randomly selected. A set of games was played for 10 minutes, followed by 5 minutes of rest, and then another set of games was played for 10 minutes, followed by rest. Quality of game play was observed and documented for each set. During rest, the participant completed questions regarding enjoyment. Following the same procedures, the participant then played the two sets of games using the other version (off-the-shelf or adapted) of the controller. The entire procedure was repeated during Visit 3 with the controller that was not played. RESULTS: Enrollment began in February 2016 and ended in September 2016. Study results will be reported in late 2017. CONCLUSIONS: We hypothesized that the adapted versions of the Wii Fit balance board and gaming mat would produce greater quality of game play, enjoyment, and energy expenditure in persons with mobility impairments compared to off-the-shelf versions. TRIAL REGISTRATION: ClinicalTrials.gov NCT02994199; https://clinicaltrials.gov/ct2/show/NCT02994199 (Archived by WebCite at http://www.webcitation.org/6qpPszPJ7).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".