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Record W3129161534 · doi:10.1123/japa.2020-0262

The Effects of Walking or Nordic Walking in Adults 50 Years and Older at Elevated Risk of Fractures: A Systematic Review and Meta-Analysis

2021· review· en· W3129161534 on OpenAlexfundno aff
Isabel B. Rodrigues, Matteo Ponzano, Debra A. Butt, Joan Bartley, Zahra Bardai, Maureen C. Ashe, Philip D. Chilibeck, Lehana Thabane, John D. Wark, Jackie Stapleton, Lora Giangregorio

Bibliographic record

VenueJournal of Aging and Physical Activity · 2021
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsMedicinePhysical therapyMeta-analysisRandomized controlled trialPsychological interventionPhysical medicine and rehabilitationPreferred walking speedBone mineralOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

Walking is a common activity among older adults. However, the effects of walking on health-related outcomes in people with low bone mineral density (BMD) are unknown. The authors included randomized controlled trials comparing walking to control in individuals aged ≥50 years with low BMD and at risk of fractures. The authors identified 13 randomized controlled trials: nine multicomponent interventions including walking, one that was walking only, and three Nordic walking trials. Most studies had a high risk of bias. Nordic walking may improve the Timed Up-and-Go values (1.39 s, 95% CI [1.00, 1.78], very low certainty). Multicomponent interventions including walking improved the 6-min walk test (39.37 m, 95% CI [21.83, 56.91], very low certainty) and lumbar spine BMD (0.01 g/cm2, 95% CI [0.00, 0.03], low certainty evidence). The effects on quality of life or femoral neck BMD were not significant. There were insufficient data on fractures, falls, or mortality. Nordic walking may improve physical functioning. The effects on other outcomes are less certain; one may need to combine walking with other exercises to be of benefit.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.491
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.396
Teacher spread0.365 · 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
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

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

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