MétaCan
Menu
Back to cohort
Record W2950039822 · doi:10.1093/geronb/gbz072

A Coordinated Multi-study Analysis of the Longitudinal Association Between Handgrip Strength and Cognitive Function in Older Adults

2019· review· en· W2950039822 on OpenAlexafffund
Andrea R. Zammit, Andrea M. Piccinin, Emily C. Duggan, Andriy Koval, Sean Clouston, Annie Robitaille, Cassandra Brown, Philipp Handschuh, Chenkai Wu, Valérie Jarry, Deborah Finkel, Raquel Graham, Graciela Muñiz‐Terrera, Marcus Praetorius Björk, David A. Bennett, Dorly J. H. Deeg, Boo Johansson, Mindy J. Katz, Jeffrey Kaye, Richard B. Lipton, Mike Martin, Nancy L. Pederson, Avron Spiro, Daniel Zimprich, Scott M. Hofer

Bibliographic record

VenueThe Journals of Gerontology Series B · 2019
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité du Québec à MontréalUniversité de SherbrookeUniversity of Victoria
FundersNational Center for Advancing Translational SciencesFonds de Recherche du Québec - SantéMichigan Center on the Demography of Aging, University of MichiganClinical Science Research and DevelopmentNational Institutes of HealthNational Institute on AgingForskningsrådet om Hälsa, Arbetsliv och VälfärdJohn D. and Catherine T. MacArthur FoundationKnut och Alice Wallenbergs StiftelseCanadian Institutes of Health ResearchSwedish Brain PowerVrije Universiteit AmsterdamGeorgia Clinical and Translational Science AllianceWenner-Gren StiftelsernaStiftelsen Handlanden Hjalmar SvenssonsGovernment of the United KingdomRush UniversityU.S. Department of Veterans AffairsRéseau québécois de recherche sur le vieillissementVetenskapsrådet
KeywordsBivariate analysisCognitionPsychologyAssociation (psychology)Grip strengthMeta-analysisDementiaHand strengthEffects of sleep deprivation on cognitive performanceLongitudinal studyCognitive declineGerontologyPhysical medicine and rehabilitationMedicinePhysical therapyStatisticsDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Handgrip strength, an indicator of overall muscle strength, has been found to be associated with slower rate of cognitive decline and decreased risk for cognitive impairment and dementia. However, evaluating the replicability of associations between aging-related changes in physical and cognitive functioning is challenging due to differences in study designs and analytical models. A multiple-study coordinated analysis approach was used to generate new longitudinal results based on comparable construct-level measurements and identical statistical models and to facilitate replication and research synthesis. METHODS: We performed coordinated analysis on 9 cohort studies affiliated with the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA) research network. Bivariate linear mixed models were used to examine associations among individual differences in baseline level, rate of change, and occasion-specific variation across grip strength and indicators of cognitive function, including mental status, processing speed, attention and working memory, perceptual reasoning, verbal ability, and learning and memory. Results were summarized using meta-analysis. RESULTS: After adjustment for covariates, we found an overall moderate association between change in grip strength and change in each cognitive domain for both males and females: Average correlation coefficient was 0.55 (95% CI = 0.44-0.56). We also found a high level of heterogeneity in this association across studies. DISCUSSION: Meta-analytic results from nine longitudinal studies showed consistently positive associations between linear rates of change in grip strength and changes in cognitive functioning. Future work will benefit from the examination of individual patterns of change to understand the heterogeneity in rates of aging and health-related changes across physical and cognitive biomarkers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.178
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
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 head0.149
GPT teacher head0.426
Teacher spread0.276 · 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 designObservational
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

Citations80
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series BSame topicNutrition and Health in AgingFrench-language works237,207