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Record W4389443808 · doi:10.1016/j.bone.2023.116986

The effects of high velocity resistance training on bone mineral density in older adults: A systematic review

2023· review· en· W4389443808 on OpenAlexfundno aff
Inaya Haque, Thomas Z. Schlacht, Dawn A. Skelton

Bibliographic record

VenueBone · 2023
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
FundersUniversity of Regina
KeywordsMedicineBone mineralFemoral neckPhysical therapyPsychological interventionResistance trainingRandomized controlled trialOsteoporosisConcentricInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the effects of high velocity resistance training (HVRT) on bone mineral density (BMD) in older adults. METHODS: A systematic review was conducted using five databases. Records were screened by two independent reviewers. INCLUSION CRITERIA: adults ≥50 years old, HVRT defined as rapid concentric and slow eccentric phase against an external load, control group and/or other intervention group, BMD measured using dual X-ray absorptiometry, and ≥6 months. RESULTS: 25 studies met the inclusion criteria. 12 were original intervention studies (8 RCTs) with n = 1203 people. 13 papers were follow up studies of these original interventions. Heterogeneity of studies meant no meta-analysis was performed. Moderate evidence suggests a small statistically significant effect of HVRT on BMD in older adults at the lumbar spine, total hip, and femoral neck ranging from 0.9 % to 5.4 %. BMD measurements significantly decreased post-intervention in follow-up studies where the interventions had ceased. Dose-response of HVRT was shown to positively impact BMD when ≥2 sessions per week are completed. CONCLUSIONS: HVRT plays a role in increasing BMD of the lumbar spine, femoral neck, and total hip. Doses of higher intensity exercise performed ≥2 sessions per week will yield the most skeletal benefits, and if exercise is stopped for >6 months, benefits achieved may be lost.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
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 head0.045
GPT teacher head0.362
Teacher spread0.317 · 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.

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

Citations16
Published2023
Admission routes1
Has abstractyes

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