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Record W3173266829 · doi:10.2196/27761

A Telehealth System Incorporating a Serious Game Intervention to Aid Occupational Therapists in Identifying and Treating Children With Difficulty Crossing the Body’s Midline: Key Informant Interviews Among Occupational Therapists

2021· article· en· W3173266829 on OpenAlexvenueno aff
Jonathan Jacobs, Reolyn Heymann, Japie Greeff

Bibliographic record

VenueJMIR Serious Games · 2021
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthIntervention (counseling)PsychologyOccupational therapyPerceptionAction (physics)Applied psychologyMedical educationPhysical medicine and rehabilitationMedicineCognitive psychologyNursingTelemedicinePsychiatryHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: The midline is an imaginary line that isolates the left and right parts of the body. Crossing the midline infers that a body part (eg, hand or foot) can spontaneously move over to the opposite side of the body to perform an action. A child who has difficulty crossing the midline can physically perform actions that cross the center of the body; however, they do not intuitively cross the midline when challenged with a task that requires this movement, as their perceptual components prevent them from engaging on the contralateral side. This requires treatment from an occupational therapist. Owing to the recent COVID-19 pandemic, access to therapeutic sessions was not possible or reduced, putting the responsibility for treatment on caretakers at home. Caretakers do not have the knowledge and skills to provide treatment, and occupational therapists do not receive adequate feedback from caretakers on the child's progress. OBJECTIVE: The first objective is to adapt a simple serious game, or applied game, into a telehealth solution. Children will play the game at home under the supervision of a caretaker, and the results will be stored on the web. Occupational therapists can monitor progress via a web-based dashboard, receive additional valuable feedback about the child's behavior during treatment, and easily adapt the game to target specific needs. The second objective is to evaluate whether the implemented telehealth solution is feasible as a treatment option for midline crossing difficulties and thus fit for purpose. METHODS: To meet the first objective, engineering and game development stakeholders formed a team with an occupational therapist, and through a collaborative design process combined with an agile programming approach, a telehealth solution was designed to assist remote monitoring of the serious gameplay. For the second objective, 6 different occupational therapists were introduced to the game, had the opportunity to play the game, and then provided feedback regarding the feasibility, benefits, and applicability of the system during structured interviews. RESULTS: A telehealth system was designed aimed to address this problem. All results are saved on the web and accessed by occupational therapists via a dashboard. In addition, observed behavioral information is also saved. During the interviews, occupational therapists indicated that the dashboard would support their treatment plan and was indeed a feasible solution. CONCLUSIONS: The feedback from the occupational therapists for this telehealth solution suggests a feasible method to treat midline crossing problems remotely. The therapists commented on the convenience of integrating both assessment and treatment into the same application, as it assists them when grading a child. The therapists collectively agreed that the quantitative aspect the serious game creates by providing measurable and standardized data proves advantageous when compared with traditional methods of assessment and treatment.

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 score0.066
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.307
Teacher spread0.292 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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