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Record W3196090512 · doi:10.2196/30672

Active Video Gaming Using an Adapted Gaming Mat in Youth and Adults With Physical Disabilities: Observational Study

2021· article· en· W3196090512 on OpenAlexvenueno aff
Laurie A. Malone, Ganisher Davlyatov, Sangeetha Padalabalanarayanan, Mohanraj Thirumalai

Bibliographic record

VenueJMIR Serious Games · 2021
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersNational Institute on Disability, Independent Living, and Rehabilitation Research
KeywordsObservational studyVideo gamePsychologyPhysical activityApplied psychologySet (abstract data type)MultimediaComputer sciencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: A common leisure-time activity amongst youth and adults in the United States is video gameplay. Playing video games is typically a sedentary endeavor; however, to encourage an increased level of physical activity in an engaging and enjoyable way, active video gaming has become popular. Unfortunately, the accessibility of gaming controllers is often an issue for persons with disabilities. A commercial off-the-shelf (OTS) gaming mat was adapted to facilitate use by individuals with mobility impairments to address this issue. OBJECTIVE: Our study aimed to examine energy expenditure, enjoyment, and gameplay experience in youth and adults with mobility impairment during active video gaming using an OTS and adapted versions of a gaming mat. METHODS: The study used an observational design. During visit 1, physical function was assessed, and participants were given a familiarization period with the gaming system. For visit 2, based on observation during the physical function tests and discussion with the participant, it was decided whether the participant would play in a standing or seated position. For standing gameplay, the mat was placed on the floor, and for seated play, the mat was placed on a height-adjustable and tilt-adjustable tabletop. Metabolic data were collected during a 20-minute baseline and four 10-minute bouts of Wii Fit Plus gameplay, with 2 bouts on each of the mats (adapted and OTS). During gameplay, the research staff observed and rated participants' ability to use the game controller (mat) and the quality of gameplay. At the end of each game set, participants reported their rating of perceived exertion on a scale from 0 to 10. During rest, participants completed the physical activity enjoyment scale. Participants also answered additional questions regarding the system's usability with each controller (adapted mat and OTS mat). Statistical analyses were computed using Stata 16 (version 16.1; StataCorp). Linear mixed-effects maximum likelihood regression was performed separately for individuals who could play standing and for those who played seated. RESULTS: A convenience sample of 78 individuals with mobility impairments between the ages of 12 and 60 years (mean 39.6, SD 15.8) participated in the study. Of the sample, 48 participants played the video games in a seated position, while 30 played the games standing. Energy expenditure and heart rate tended to be higher in the OTS mat condition for seated players, while values were similar for both conditions among standing players. However, seated participants reported greater gameplay experience, and both groups exhibited a higher quality of gameplay during the adapted mat condition. CONCLUSIONS: Active video gaming using an adapted gaming mat provided an enjoyable exercise activity for individuals with mobility impairments. The use of the adapted controller provides a means by which this population can engage in light to moderate intensity active video gaming, thereby reducing sedentary leisure time. TRIAL REGISTRATION: ClinicalTrials.gov NCT02994199; https://clinicaltrials.gov/ct2/show/NCT02994199.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.328
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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