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Record W4386310975 · doi:10.1123/japa.2022-0425

Physical and Cognitive Effects of High-Intensity Interval or Circuit-Based Strength Training for Community-Dwelling Older Adults: A Systematic Review

2023· review· en· W4386310975 on OpenAlexaff
Ashley Morgan, Kenneth S. Noguchi, Ada Tang, Jennifer J. Heisz, Lehana Thabane, Julie Richardson

Bibliographic record

VenueJournal of Aging and Physical Activity · 2023
Typereview
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsSt. Joseph’s Healthcare HamiltonImpactMcMaster University
Fundersnot available
KeywordsCircuit trainingHigh-intensity interval trainingCognitionTraining (meteorology)PsychologyGerontologyCognitive trainingPhysical medicine and rehabilitationIntensity (physics)MedicinePhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Many older adults do not achieve recommended amounts of aerobic or strengthening exercise and high-intensity interval or circuit-based strengthening may offer a time-efficient solution. This review sought to determine the effects of high-intensity interval/circuit strengthening on physical and cognitive functioning for community-dwelling older adults, and its associated adherence, retention, and adverse events. Six databases were searched to June 2022 and 15 studies (11 for effectiveness) were included. The current certainty of evidence is low to very low; upper body-focused physical functioning measures demonstrated small to large benefits and lower body-focused, self-report, and cardiovascular measures had mixed results. There was insufficient evidence (one study) to determine cognitive effects. The mean adherence rates ranged from 73.5% to 95.8%, overall retention across all studies (n = 812) was 86%, and no serious adverse events were reported, suggesting that this type of exercise is feasible for community-dwelling older adults.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0050.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.094
GPT teacher head0.411
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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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