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Record W4214742482 · doi:10.2196/31395

The Effectiveness of Virtual Reality–Based Interventions in Rehabilitation Management of Breast Cancer Survivors: Systematic Review and Meta-analysis

2022· review· en· W4214742482 on OpenAlexvenueno aff
Xiaofan Bu, Peter H. F. Ng, Wenjing Xu, Qinqin Cheng, Peter Q. Chen, Andy S. K. Cheng, Xiangyu Liu

Bibliographic record

VenueJMIR Serious Games · 2022
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersCentral South University
KeywordsCINAHLPhysical therapyMedicineBreast cancerPsychological interventionMeta-analysisRandomized controlled trialRehabilitationSystematic reviewCochrane LibraryQuality of life (healthcare)AnxietyMEDLINEPhysical medicine and rehabilitationCancerPsychiatryNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer survivors (BCSs) can present with various physical and psychological symptoms and functional deficits that impact their quality of life. Virtual reality (VR) technology is being used in breast cancer rehabilitation management to improve the emotional, cognitive, and physical well-being of BCSs. OBJECTIVE: This systematic review aimed to examine the effectiveness of VR-based interventions on health-related outcomes in BCSs. A meta-analysis was conducted to evaluate the effectiveness of VR-based interventions in the rehabilitation management of BCSs. METHODS: A systematic search was conducted on PubMed, Web of Science, EMBASE, CINAHL with Full Text, the Cochrane Central Register of Controlled Trials, CNKI, WanFang, VIP, and CBM, from inception to May 25, 2021. The inclusion criteria of the selected studies were as follows: (1) adults diagnosed with breast cancer; (2) any type of VR-based interventions (immersive and nonimmersive virtual environment); (3) comparison of traditional rehabilitation methods; (4) outcomes including pain, depression, anxiety, fatigue, cognitive function, shoulder range of motion (ROM), hand grip strength, lymphedema, cybersickness symptoms, fear of movement, bleeding, effusion, and flap necrosis, both during and after treatment; and (5) randomized controlled trials (RCTs), case-controlled trials, and quasi-experimental studies. The Cochrane Collaboration Tool was used to evaluate the risk of bias. Review Manager version 5.3 (Cochrane Collaboration) was used to conduct the meta-analysis. The mean difference (MD) and SDs with 95% CIs were used to calculate continuous variables. RESULTS: Twelve articles were included in this systematic review, of which 10 contributed information to the meta-analysis. A total of 604 participants were analyzed. The statistical analysis showed significant results for flexion (standard mean difference [SMD] 1.79; 95% CI 0.55 to 3.03; P=.005), extension (SMD 1.54; 95% CI 0.83 to 2.25; P<.001), abduction (MD 17.53; 95% CI 14.33 to 20.72; P<.001), adduction (MD 15.98; 95% CI 14.02 to 17.94; P<.001), internal rotation (MD 7.12; 95% CI 5.54 to 8.70; P<.001), external rotation (SMD 0.96; 95% CI 0.62 to 1.29; P<.001), anxiety (MD -6.47; 95% CI -7.21 to -5.73; P<.001), depression (MD -4.27; 95% CI -4.64 to -3.91; P<.001), pain (MD -1.32; 95% CI -2.56 to -0.09; P=.04), and cognitive function (MD 8.80; 95% CI 8.24 to 9.36; P<.001). The meta-analysis indicated little to no difference in hand grip strength (MD 1.96; 95% CI -0.93 to 4.85; P=.18). CONCLUSIONS: Findings of this review noted a weak but consistent positive association between VR-based interventions and outcomes. However, these results must be interpreted with caution due to the limited number of controlled trials analyzed, small sample sizes, and poor methodological quality. Well-designed, large, high-quality trials may have a significant impact on our confidence in the results. Future studies should identify specific aspects that improve the clinical impact of VR-based interventions on major outcomes in BCSs in the clinical setting. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021250727; https://tinyurl.com/2p89rmnk.

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)0.004
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.685
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0000.002
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 head0.054
GPT teacher head0.390
Teacher spread0.336 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations47
Published2022
Admission routes1
Has abstractyes

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