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Record W2857759162 · doi:10.2196/10116

Active Video Games for Rehabilitation in Respiratory Conditions: Systematic Review and Meta-Analysis

2018· article· en· W2857759162 on OpenAlexvenueno aff
Joshua Simmich, Anthony Deacon, Trevor Russell

Bibliographic record

VenueJMIR Serious Games · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyPsychological interventionMeta-analysisVideo gameMedicineRehabilitationRespiratory exchange ratioPhysical medicine and rehabilitationRandomized controlled trialSession (web analytics)Heart ratePsychologyMultimediaBlood pressureInternal medicineComputer science

Abstract

fetched live from OpenAlex

Background: Exercise and physical activity are key components of treatment for chronic respiratory diseases. However, the level of physical activity and adherence to exercise programs are low in people with these diseases. Active video games (AVGs) may provide a more engaging alternative to traditional forms of exercise. Objective: This review examines the effectiveness of game-based interventions on physiological outcome measures, as well as adherence and enjoyment in subjects with chronic respiratory diseases. Methods: A systematic search of the literature was conducted, with full texts and abstracts included where they involved an AVG intervention for participants diagnosed with respiratory conditions. A narrative synthesis of included studies was performed. Additionally, meta-analysis comparing AVGs with traditional exercise was undertaken for 4 outcome measures: mean heart rate (HR) during exercise, peripheral blood oxygen saturation (SpO2) during exercise, dyspnea induced by the exercise, and enjoyment of the exercise. Results: A total of 13 full-text papers corresponding to 12 studies were included in the review. Interventions predominantly used games released for the Nintendo Wii (8 studies) and Microsoft Xbox Kinect (3 studies). There were 5 studies that examined the acute effects of a single session of AVGs and 7 studies that examined the long-term effects after multiple sessions of AVGs. Trials conducted over more than 1 session varied in duration between 3 and 12 weeks. In these, AVG interventions were associated with either similar or slightly greater improvements in outcomes such as exercise capacity when compared with a traditional exercise control, and they also generally demonstrated improvements over baseline or nonintervention comparators. There were a few studies of unsupervised AVG interventions, but the reported adherence was high and maintained throughout the intervention period. Additionally, AVGs were generally reported to be well liked and considered feasible by participants. For outcome measures measured during a single exercise session, there was no significant difference between an AVG and traditional exercise for HR (mean difference 1.44 beats per minute, 95% CI –14.31 to 17.18), SpO2 (mean difference 1.12 percentage points, 95% CI –1.91 to 4.16), and dyspnea (mean difference 0.43 Borg units, 95% CI –0.79 to 1.66), but AVGs were significantly more enjoyable than traditional exercise (Hedges g standardized mean difference 1.36, 95% CI 0.04-2.68). Conclusions: This review provides evidence that AVG interventions, undertaken for several weeks, can provide similar or greater improvements in exercise capacity and other outcomes as traditional exercise. Within a single session of cardiovascular exercise, an AVG can evoke similar physiological responses as traditional exercise modalities but is more enjoyable to subjects with chronic respiratory diseases. However, there is very limited evidence for adherence and effectiveness in long-term unsupervised trials, which should be the focus of future research.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.375
Teacher spread0.341 · 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
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

Citations43
Published2018
Admission routes1
Has abstractyes

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