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Record W3162753783 · doi:10.1097/md.0000000000025940

Exercise interventions for older people at risk for frailty

2021· article· en· W3162753783 on OpenAlexaboutno aff
Jianna Zhang, Zhixi Liu, Yi Liu, Lei Ye

Bibliographic record

VenueMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychological interventionGerontologyMEDLINEPhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Frailty is a state of age-related reduced physiological reserve characterized by an increased risk of adverse clinical outcomes. Studies have shown that exercise can improve frailty in older people. However, it remains to be seen which exercises will most improve the fitness of older people with frailty or those at the risk for frailty.Objective: This protocol aims to determine whether physical exercise can improve frailty in older people, and if which methods are most effective. METHODS: We searched the following databases for relevant articles published between January 1, 2012 and January 1, 2021: PubMed, EMBASE, the Cochrane Library, Wanfang, the China National Knowledge Infrastructure, Clinical Trials Database, and the Science Network. Two independent reviewers will carry out data extraction, discuss and resolve differences, and obtain consensus from the third author. We will select randomized control trials (RCTs) according to the preformulated inclusion criteria. The main outcomes in this study are scores from Fried Frailty Phenotype Criteria; the Frailty Trait Scale-short form; the SHARE Frailty Instrument; the FRAIL scale; the Gérontopôle Frailty Screening Tool; the Clinical Frailty Scale, the Rockwood and Mitnitsky Frailty Index; the Study of Osteoporotic Fractures Index; the Edmonton Frailty Scale; the Fatigue, Resistance, Ambulation, Illness and Loss of Weight Index; the Multidimensional Prognostic Index; the Tilburg Frailty Indicator; PRISMA-7; the Groningen Frailty Indicator; the Sherbrooke Postal Questionnaire; and the Kihon Checklist. Secondary outcomes are muscle strength, gait velocity, stair-climbing power, and level of spontaneous physical activity. If the heterogeneity test shows slight or no statistical heterogeneity, a fixed effects model will be used for data synthesis; otherwise, a random effects model will be used. We will develop a unified data extraction table that includes a number of parameters. The Cochrane Cooperative Bias Risk Tool will be used to evaluate the methodological quality of the selected RCTs. RevMan Manager 5.3 and STATA 14.0 will be used for data analysis if enough RCTs (more than 10) are identified and selected. RESULT: The final results will provide information on the effectiveness of intervention programs for frail older adul and further demonstrate which exercise programs are more effective and which methods can significantly improve frailty. CONCLUSION: This protocol will contribute to the development of more effective interventions for elderly individuals with frailty. ETHICS AND DISSEMINATION: This study applies existing literature references; therefore, ethical approval is not required. INPLASY REGISTRATION NUMBER: INPLASY202130107.

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)0.004
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.050
GPT teacher head0.356
Teacher spread0.306 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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