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Record W4400304314 · doi:10.7759/cureus.63711

Rebound Exercises in Rehabilitation: A Scoping Review

2024· review· en· W4400304314 on OpenAlexaff
Manisha Rathi, Reema Joshi, Pinal Munot, Chaitanya Kulkarni

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsCommunity Based Research Centre
Fundersnot available
KeywordsMedicineRehabilitationPhysical therapyPhysical medicine and rehabilitationObservational study

Abstract

fetched live from OpenAlex

The term "trampoline" was coined in 1969, introducing a dynamic feedback mechanism for exercise. Rebounding exercise on a mini-trampoline utilizes an elastic surface supported by springs and gravity, potentially reducing cumulative trauma from repetitive loading. This type of physical activity provides enjoyable and engaging exercise for adolescents, especially those who are overweight, thereby reducing the likelihood of injuries associated with exercise. Mini-trampoline exercises enhance blood circulation, oxygen delivery, and bone health, impacting lower limb strength, balance, motor performance, blood glucose levels, executive function, physiological markers, and overall quality of life. The study focused on examining the overall impact of rebounding exercises in the field of rehabilitation. Its main goal was to assess how these exercises affect the rehabilitation process and different health measures. By investigating the comprehensive influence of rebounding exercises, the study aimed to determine their effectiveness in aiding physical and functional recovery, targeting specific rehabilitation goals, and enhancing overall health outcomes. We systematically reviewed medical literature databases such as PubMed, MEDLINE, Scopus, Google Scholar, and EBSCO. We included research articles, systematic reviews, meta-analyses, clinical trials, case studies, and observational studies published in English up to 10 years before the review's cutoff in December 2023. We considered participants across all age groups. Articles not in English were excluded from the review. The outcome measures were body composition, waist-hip ratio, Bruininks-Oseretsky test for motor proficiency, reaction time, insulin resistance, lipid profile, blood cholesterol level, forced expiratory volume in one second, and forced vital capacity, bone health indicators, blood lactate level, balance, strength: repetitive maximum, brief pain inventory (short form). A total of 11 reports met these criteria. In conclusion, this review provides a thorough look into the use, challenges, and future potential of rebound exercises in rehabilitation and fitness. Despite their wide-ranging applications, issues such as insufficient research, equipment variability, and safety concerns persist. Advancement requires more research for evidence-based guidelines, improved equipment design and safety measures, and collaboration among researchers, clinicians, and manufacturers. Overcoming challenges and fostering innovation can establish rebound exercises as a valuable tool in rehabilitation and fitness.

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)0.007
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0170.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
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 head0.066
GPT teacher head0.432
Teacher spread0.366 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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