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Record W2329358963 · doi:10.1155/2016/1931590

Gender Differences in Pain-Physical Activity Linkages among Older Adults: Lessons Learned from Daily Life Approaches

2016· article· en· W2329358963 on OpenAlexafffund
Amy Ho, Maureen C. Ashe, Anita DeLongis, Peter Graf, Karim M. Khan, Christiane A. Hoppmann

Bibliographic record

VenuePain Research and Management · 2016
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchCanada Research ChairsMichael Smith Health Research BC
KeywordsPhysical activityMedicineActivities of daily livingGerontologyPhysical therapy

Abstract

fetched live from OpenAlex

Background. Many older adults know about the health benefits of an active lifestyle, but, frequently, pain prevents them from engaging in physical activity. The majority of older adults experience pain, a complex experience that can vary across time and is shaped by sociocultural factors like gender. Objectives. To describe the time-varying associations between daily pain and physical activity and to explore differences in these associations between women and men. Methods. One hundred and twenty-eight community-dwelling older adults aged 65 years and older were asked to report their pain levels three times daily over a 10-day period and wear an accelerometer to objectively capture their daily physical activity (step counts and minutes of moderate to vigorous physical activity). Results. Increased daily step counts and minutes of moderate to vigorous physical activity were associated with increased daily pain, especially among women. Confirming past literature and contrasting findings for daily pain reports, overall pain levels across the study period were negatively associated with minutes of moderate to vigorous physical activity. Conclusions. Findings highlight that pain is significantly associated with physical activity in old age. The nature of this association depends on the time scale that is considered and differs between women and men.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.158
GPT teacher head0.362
Teacher spread0.204 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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