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Record W4387215894 · doi:10.2196/48022

Effectiveness of a 5-Week Virtual Reality Telerehabilitation Program for Children With Duchenne and Becker Muscular Dystrophy: Prospective Quasi-Experimental Study

2023· article· en· W4387215894 on OpenAlexvenueno aff
María Rosa Baeza-Barragán, María Teresa Labajos Manzanares, Mercedes Cristina Amaya-Álvarez, Fabián Morales Vega, J Ruiz, Rocío Martín‐Valero

Bibliographic record

VenueJMIR Serious Games · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersUniversidad de Málaga
KeywordsTelerehabilitationDuchenne muscular dystrophyPhysical therapyMedicinePhysical medicine and rehabilitationIntervention (counseling)Quality of life (healthcare)Muscular dystrophyCerebral palsyProspective cohort studyTelemedicineHealth care

Abstract

fetched live from OpenAlex

BACKGROUND: Duchenne and Becker muscular dystrophy (from now "DMD" and "BMD" respectively) are the neuromuscular diseases with the most significant involvement in children. It affects dystrophin production, reducing the patient's mobility and quality of life. New technologies have become part of physical therapy in DMD and BMD. During the COVID-19 pandemic, telerehabilitation through virtual reality-based games could help these children to keep their abilities. OBJECTIVE: The purpose of this study is to know if the use of the virtual platform in a multimodal intervention program achieves changes in the results obtained in the six-minute walk test in children affected by DMD and BMD. To estimate the difference in mobility in patients with DMD and BMD, as measured with the six-minute walking test (6MWT), between 10 conventional and telerehabilitation treatment sessions. As secondary objectives, measuring other specific motor scales was proposed to see whether these had changed after receiving the 10 defined sessions. METHODS: Descriptive, open, quasi-experimental study with prospective A-B (control-intervention) design. Sample size of twelve participants who fulfilled the control criteria followed the program for five weeks, up to 10 telerehabilitation sessions. During the sessions, the participants used virtual reality glasses to train for the treatment goals. All sessions were in person, and participants were assessed before and after the intervention. Analysis was performed using R (R Core Team (2022) according to the different functional assessments performed for each test. RESULTS: The participants showed a 19.55 m increase in the 6MWT scale. The motor function was also kept stable according to other scales used to assess it. North Start result were kept stable in both treatments (P value = .199). Furthermore, Time up and go test was shorter in 0.1 seconds in telerehabilitation time and Motor Function Measure in all of the 3 dimensions shown no significant differences with a P value = .084. Finally, Infant effort (EPInfant) shown that during the training the fatigue increased in the middle and decreased by the end but the perception throughout the sessions, was lower even though the exercise intensity increased. CONCLUSIONS: There is no difference between a conventional and telerehabilitation treatment, so the telerehabilitation tool could be used without harming this type of children, facilitating their access to therapies and stimulating learning to maintain their functional capacity. Telerehabilitation may helpful maintain motor function in children with DMD and BMD. The learning effect helped to reduce the feeling of fatigue in children during the program. CLINICALTRIAL: This trial has the approval of the Andalucía Ethics Committee with PEIBA code 0107-N-20. The results of the research will be disseminated by the investigators to peer-reviewed journals. Trial registration no. NCT03879304.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.007
GPT teacher head0.291
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations14
Published2023
Admission routes1
Has abstractyes

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