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Record W4384339163 · doi:10.1186/s40798-023-00602-7

Immediate and Delayed Effects of Joint Loading Activities on Knee and Hip Cartilage: A Systematic Review and Meta-analysis

2023· review· en· W4384339163 on OpenAlexaboutno aff
S. Coburn, Kay M. Crossley, Joanne L. Kemp, Stuart J. Warden, T. West, Andrea M Bruder, Benjamin F. Mentiplay, Adam G Culvenor

Bibliographic record

VenueSports Medicine - Open · 2023
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of Health
KeywordsMeta-analysisKnee JointJoint (building)CartilagePhysical medicine and rehabilitationMedicineArticular cartilageOsteoarthritisSurgeryAnatomyEngineeringInternal medicinePathologyStructural engineeringAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Background The impact of activity-related joint loading on cartilage is not clear. Abnormal loading is considered to be a mechanical driver of osteoarthritis (OA), yet moderate amounts of physical activity and rehabilitation exercise can have positive effects on articular cartilage. Our aim was to investigate the immediate effects of joint loading activities on knee and hip cartilage in healthy adults, as assessed using magnetic resonance imaging. We also investigated delayed effects of activities on healthy cartilage and the effects of activities on cartilage in adults with, or at risk of, OA. We explored the association of sex, age and loading duration with cartilage changes. Methods A systematic review of six databases identified studies assessing change in adult hip and knee cartilage using MRI within 48 h before and after application of a joint loading intervention/activity. Studies included adults with healthy cartilage or those with, or at risk of, OA. Joint loading activities included walking, hopping, cycling, weightbearing knee bends and simulated standing within the scanner. Risk of bias was assessed using the Newcastle–Ottawa Scale. Random-effects meta-analysis estimated the percentage change in compartment-specific cartilage thickness or volume and composition (T2 relaxation time) outcomes. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system evaluated certainty of evidence. Results Forty studies of 653 participants were included after screening 5159 retrieved studies. Knee cartilage thickness or volume decreased immediately following all loading activities investigating healthy adults; however, GRADE assessment indicated very low certainty evidence. Patellar cartilage thickness and volume reduced 5.0% (95% CI 3.5, 6.4, I 2 = 89.3%) after body weight knee bends, and tibial cartilage composition (T2 relaxation time) decreased 5.1% (95% CI 3.7, 6.5, I 2 = 0.0%) after simulated standing within the scanner. Hip cartilage data were insufficient for pooling. Secondary outcomes synthesised narratively suggest knee cartilage recovers within 30 min of walking and 90 min of 100 knee bends. We found contrasting effects of simulated standing and walking in adults with, or at risk of, OA. An increase of 10 knee bend repetitions was associated with 2% greater reduction in patellar thickness or volume. Conclusion There is very low certainty evidence that minimal knee cartilage thickness and volume and composition (T2 relaxation time) reductions (0–5%) occur after weightbearing knee bends, simulated standing, walking, hopping/jumping and cycling, and the impact of knee bends may be dose dependent. Our findings provide a framework of cartilage responses to loading in healthy adults which may have utility for clinicians when designing and prescribing rehabilitation programs and providing exercise advice.

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)0.013
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.031
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.080
GPT teacher head0.353
Teacher spread0.273 · 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 designMeta-analysis
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

Citations13
Published2023
Admission routes1
Has abstractyes

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