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Record W4415773286 · doi:10.2196/80729

Effects, Feasibility, and Safety of an Early Mobilization Protocol With Immersive Virtual Reality for Dyspnea in Patients With Acutely Decompensated Heart Failure: The MOVE Randomized Clinical Trial

2025· article· en· W4415773286 on OpenAlexvenueno aff
Iasmin Borges Fraga, Larissa Gussatschenko Caballero, Carlos Eduardo Maciel Tremea, Janaína dos Santos Prates, Gabrielle Perin, Vitor Alves Guedes, Pedro Dal Lago, Eneida Rejane Rabelo

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Virtual realityRandomized controlled trialMobilizationPopulationClinical trial

Abstract

fetched live from OpenAlex

Background: Early mobilization seems to benefit patients with acutely decompensated heart failure (ADHF), but its initiation is challenging due to severe dyspnea, clinical instability, and low adherence to treatment. Understanding whether early mobilization has good acceptance, adherence, and safety is key for rehabilitation in this population. Virtual reality (VR) offers a less stressful environment and may reduce dyspnea, yet its effects on ADHF remain unknown. In addition, the feasibility and safety of combining VR with an early mobilization program in the intensive care unit (ICU) setting are not fully established. Objective: This study aimed to assess the effects, feasibility, and safety of an early mobilization protocol combined with immersive VR for dyspnea in patients with ADHF admitted to an ICU. Methods: The Early Mobilization Protocol with Immersive Virtual Reality (MOVE) study is a single-center, parallel, superiority randomized clinical trial conducted from January 2023 to January 2024 in a teaching hospital in Brazil. Patients with ADHF admitted to the ICU who met the eligibility criteria were invited to participate. After informed consent, participants were electronically randomized into the intervention group (IG) and control group (CG). Both groups underwent up to 3 sessions of the early mobilization protocol supervised by a physiotherapist, including upper- and lower-limb cycle ergometry, standing, and ambulation. Additionally, the IG used VR headsets, headphones, and smartphones displaying 360° videos. The primary outcome was dyspnea measured using the modified Borg scale before and after each session. Secondary outcomes included vital signs before and after each session and the occurrence of adverse events. Physiotherapists were blinded to the primary outcome, and all analyses followed the intention-to-treat principle with a blinded statistician. Results: A total of 58 participants were randomized (IG: n=28, 48%; CG: n=30, 52%), with a mean age of 59 (SD 11.6) years; 42 (72%) were men, the mean left ventricular ejection fraction was 26.6% (SD 12.5%), and 28 (48%) were categorized as New York Heart Association class III. Only 43% (25/58) of the participants completed all 3 protocol sessions (IG: 11/28, 39%; CG: 14/30, 47%). Reasons for noncompletion of the protocol included refusal, clinical instability, discharge from the unit, or scheduled procedures. Changes in the mean dyspnea scores were similar between groups (IG: -0.17, SD 1.68; CG: 0.01, SD 1.73; P=.67). The mean vital signs remained within expected clinical ranges, with no differences between groups (all P>.05). No serious adverse events occurred. Conclusions: Early mobilization with or without VR was feasible and safe in ICU patients with ADHF. VR did not significantly reduce dyspnea, but adherence challenges limited protocol completion. These findings suggest that early mobilization can be safely implemented in this population and that future studies should explore strategies to enhance adherence and evaluate the potential benefits of VR in larger cohorts.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.049
GPT teacher head0.447
Teacher spread0.397 · 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 designRandomized 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

Citations0
Published2025
Admission routes1
Has abstractyes

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