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Record W2969854238 · doi:10.1177/0733464819868060

Linked Lives: Exploring Gender and Sedentary Behaviors in Older Adult Couples

2019· article· en· W2969854238 on OpenAlexafffund
Maureen C. Ashe, Victoria I. Michalowski, Anna M. Chudyk, Denis Gerstorf, Kenneth Madden, Christiane A. Hoppmann

Bibliographic record

VenueJournal of Applied Gerontology · 2019
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of ManitobaManitoba Harm Reduction NetworkUniversity of British Columbia
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsMichael Smith Health Research BC
KeywordsPsychologyGerontologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Objectives: We explored associations between co-habiting partners for sedentary behavior (type and time, via accelerometry and self-report), gender, and a surrogate health measure (inflammatory biomarker: C-reactive protein, CRP). Methods: Participants completed activity questionnaires and the Timed Up and Go (mobility), wore an accelerometer for 7 days, and provided samples for high-sensitivity (hs) CRP. We used multilevel modeling (partners within couples) to investigate associations between independent variables and (a) sedentary behavior and (b) hsCRP. Results: 112 couples (50% women) provided sedentary data and hsCRP. Sedentary behavior was significantly correlated ( r = .440, p < .001) between women and men, but there were significant differences in sedentary time (women < men) and light activity (women > men). Gender, moderate to vigorous physical activity (MVPA), and mobility estimated 37% of the modeled variance in sedentary time, while body mass index (BMI) and MVPA estimated 10% of the modeled variance in hsCRP. Discussion: Despite differences in how activity was accumulated, there were no significant differences between women’s and men’s health biomarker.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.324
Teacher spread0.245 · 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
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".

Quick stats

Citations12
Published2019
Admission routes2
Has abstractyes

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