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Record W4415546912 · doi:10.1177/07334648251391867

Sedentary Behaviors and Functional Disability Among Chinese Older Adults (2008–2018): Evidence Including Mahjong

2025· article· en· W4415546912 on OpenAlexaff
Yen‐Han Lee, Yen‐Chang Chang, Minzhi Ye, Cai Xu

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsHeritage College
Fundersnot available
KeywordsActivities of daily livingSedentary behaviorSedentary lifestylePhysical activityHealthy agingLongitudinal studyFunctional impairmentRegression analysis

Abstract

fetched live from OpenAlex

PurposeThis study examined how different types of sedentary behavior relate to changes in functional disability, measured by activities of daily living (ADL) and instrumental activities of daily living (IADL), among Chinese older adults.Major findingsUsing data from 17,939 participants in four waves (2008-2018) of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), sedentary behavior was categorized into four types: none, playing mahjong only, combining these with other sedentary activities, and other activities alone (e.g., reading and watching TV). Cox two-state regression models estimated the hazard of transitioning in ADL and IADL disability. Engaging in sedentary behaviors was generally associated with a lower risk of developing ADL and IADL impairments. Playing mahjong alone was significantly associated with reduced risk of developing ADL (HR) = 0.60) and IADL difficulties (HR = 0.82).ConclusionsCognitively and socially engaging sedentary activities, particularly mahjong, may protect against functional decline in later life.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.366
Teacher spread0.329 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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