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Record W4304756160 · doi:10.1186/s40798-022-00507-x

Total Sedentary Time and Cognitive Function in Middle-Aged and Older Adults: A Systematic Review and Meta-analysis

2022· review· en· W4304756160 on OpenAlexaff
Kirsten Dillon, Anisa Morava, Harry Prapavessis, Lily Grigsby‐Duffy, Adam Nović, Paul A. Gardiner

Bibliographic record

VenueSports Medicine - Open · 2022
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsWestern University
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsCINAHLCognitionGerontologyMeta-analysisPsycINFOScopusSedentary lifestylePopulationCognitive declineMedicineDementiaPsychologyPhysical therapyMEDLINEPsychological interventionPhysical activityDiseasePsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: An estimated 47 million people have dementia globally, and around 10 million new cases are diagnosed each year. Many lifestyle factors have been linked to cognitive impairment; one emerging modifiable lifestyle factor is sedentary time. OBJECTIVE: To conduct a systematic review and meta-analysis of peer-reviewed literature examining the association between total sedentary time with cognitive function in middle-aged and older adults under the moderating conditions of (a) type of sedentary time measurement; (b) the cognitive domain being assessed; (c) looking at sedentary time using categorical variables (i.e., high versus low sedentary time); and (d) the pattern of sedentary time accumulation (e.g., longer versus shorter bouts). We also aimed to examine the prevalence of sedentary time in healthy versus cognitively impaired populations and to explore how experimental studies reducing or breaking up sedentary time affect cognitive function. Lastly, we aimed to conduct a quantitative pooled analysis of all individual studies through meta-analysis procedures to derive conclusions about these relationships. METHODS: Eight electronic databases (EMBASE; Web of Science; PsycINFO; CINAHL; SciELO; SPORTDiscus; PubMed; and Scopus) were searched from inception to February 2021. Our search included terms related to the exposure (i.e., sedentary time), the population (i.e., middle-aged and older adults), and the outcome of interest (i.e., cognitive function). PICOS framework used middle-aged and older adults where there was an intervention or exposure of any sedentary time compared to any or no comparison, where cognitive function and/or cognitive impairment was measured, and all types of quantitative, empirical, observational data published in any year were included that were published in English. Risk of bias was assessed using QualSyst. RESULTS: = 89%). Subgroup analyses demonstrated a significant negative association for studies using a device to capture sedentary time r = -0.035 [95% CI - 0.063, - 0.008], p = 0.012). Specifically, the domains of global cognitive function (r = -0.061 [95% CI - 0.100, - 0.022], p = 0.002) and processing speed (r = -0.067, [95% CI - 0.103, - 0.030], p < 0.001). A significant positive association was found for studies using self-report (r = 0.037 [95% CI - 0.019, 0.054], p < 0.001). Specifically, the domain of processing speed showed a significant positive association (r = 0.057 [95% CI 0.045, 0.069], p < 0.001). For prevalence, populations diagnosed with cognitive impairment spent significantly more time sedentary compared to populations with no known cognitive impairments (standard difference in mean = -0.219 [95% CI - 0.310, - 0.128], p < 0.001). CONCLUSIONS: The association of total sedentary time with cognitive function is weak and varies based on measurement of sedentary time and domain being assessed. Future research is needed to better categorize domains of sedentary behaviour with both a validated self-report and device-based measure in order to improve the strength of this relationship. PROSPERO registration number: CRD42018082384.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0120.000
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)0.0060.000

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 head0.112
GPT teacher head0.374
Teacher spread0.262 · 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

Citations41
Published2022
Admission routes1
Has abstractyes

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