MétaCan
Menu
Back to cohort
Record W3114243255 · doi:10.1093/ptj/pzaa221

Progressive Resistance Training for Improving Health-Related Outcomes in People at Risk of Fracture: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

2020· review· en· W3114243255 on OpenAlexafffund
Matteo Ponzano, Isabel B. Rodrigues, Zeinab Hosseini, Maureen C. Ashe, Debra A. Butt, Philip D. Chilibeck, Jackie Stapleton, Lehana Thabane, John D. Wark, Lora Giangregorio

Bibliographic record

VenuePhysical Therapy · 2020
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsResearch Institute for AgingMcMaster UniversitySt. Joseph’s Healthcare HamiltonThe Scarborough HospitalUniversity of British ColumbiaUniversity of SaskatchewanUniversity of TorontoUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research FoundationOsteoporosis Canada
KeywordsMedicinePhysical therapyFemoral neckOsteoporosisMeta-analysisRandomized controlled trialBone mineralInternal medicineConfidence intervalStrictly standardized mean differencePopulationHip fractureIncidence (geometry)

Abstract

fetched live from OpenAlex

OBJECTIVE: Osteoporosis clinical practice guidelines recommend exercise to prevent fractures, but the efficacy of exercise depends on the exercise types, population studied, or outcomes of interest. The purpose of this systematic review was to assess the effects of progressive resistance training (PRT) on health-related outcomes in people at risk of fracture. METHODS: Multiple databases were searched in October 2019. Eligible articles were randomized controlled trials of PRT interventions in men and women ≥50 years with low bone mineral density (BMD) or fracture history. Descriptive information and mean difference (MD) and SD were directly extracted for included trials. A total of 53 studies were included. RESULTS: The effects of PRT on the total number of falls (incidence rate ratio [IRR] = 1.05; 95% CI = 0.91 - 1.21; 7 studies) and on the risk of falling (risk ratio [RR] = 1.23; 95% CI = 1.00 - 1.51; 5 studies) are uncertain. PRT improved performance on the Timed "Up and Go" test (MD = -0.89 seconds; 95% CI = -1.01 to -0.78; 13 studies) and health-related quality of life (standardized MD = 0.32; 95% CI = 0.22-0.42; 20 studies). PRT may increase femoral neck (MD = 0.02 g/cm2; 95% CI = 0.01-0.03; 521 participants, 5 studies) but not lumbar spine BMD (MD = 0.02 g/cm2; 95% CI = -0.01-0.05; 4 studies), whereas the effects on total hip BMD are uncertain (MD = 0.00 g/cm2; 95% CI = 0.00-0.01; 435 participants, 4 studies). PRT reduced pain (standardized MD = -0.26; 95% CI = -0.37 to -0.16; 17 studies). Sensitivity analyses, including PRT-only studies, confirmed these findings. CONCLUSION: Individuals at risk of fractures should be encouraged to perform PRT, as it may improve femoral neck BMD, health-related quality of life, and physical functioning. PRT also reduced pain; however, whether PRT increases or decreases the risk of falls, the number of people experiencing a fall, or the risk of fall-related injuries is uncertain. IMPACT: Individuals at risk of fractures should be encouraged to perform PRT, as it may have positive effects on femoral neck BMD, health-related quality of life, physical functioning, and pain, and adverse events are rare. LAY SUMMARY: Exercise is recommended for people at risk of osteoporotic fractures. Our study showed that progressive resistance training improves physical functioning, quality of life, and reduces pain. The effects of progressive resistance training on the risk of falling are unclear. Adverse events are rare, and often minor (eg, soreness, pain, musculoskeletal injury). Considering the benefits and safety, people at risk of fractures should engage in progressive resistance training interventions.

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)0.018
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.1200.017
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.469
Teacher spread0.316 · 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; both teacher heads agree on what is shown here.

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

Citations56
Published2020
Admission routes2
Has abstractyes

Explore more

Same venuePhysical TherapySame topicBone health and osteoporosis researchFrench-language works237,207