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Record W2233961159 · doi:10.1123/japa.2015-0013

Sedentary Behavior and Physical Activity Patterns in Older Adults After Hip Fracture: A Call to Action

2015· article· en· W2233961159 on OpenAlexfundaboutno aff
Lena Fleig, Megan M. McAllister, Penny Brasher, Wendy L. Cook, Pierre Guy, Joseph H. Puyat, Karim M Khan, Heather McKay, Maureen C. Ashe

Bibliographic record

VenueJournal of Aging and Physical Activity · 2015
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMedicinePhysical activityWaistHip fractureSedentary behaviorPhysical therapyPhysical medicine and rehabilitationGaitGerontologyBody mass indexOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To characterize patterns of sedentary behavior and physical activity in older adults recovering from hip fracture and to determine characteristics associated with activity. METHODS: Community-dwelling, Canadian adults (65 years+) who sustained hip fracture wore an accelerometer at the waist for seven days and provided information on quality of life, falls self-efficacy, cognitive functioning, and mobility. RESULTS: There were 53 older adults (mean age [SD] 79.5 [7.8] years) enrolled in the study; 49 had valid data and demonstrated high levels of sedentary time (median [p10, p90] 591.3 [482.2, 707.2] minutes/day), low levels of light activity (186.6 [72.6, 293.7]), and MVPA (2 [0.1, 27.6]), as well as few daily steps (2467.7 [617.1, 6820.4]). Regression analyses showed that age, gender, gait speed, and time since fracture were associated with outcomes. CONCLUSIONS: Older adults have long periods of sedentary time with minimal activity. Results are a call to action to encourage people to sit less and move more.

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)0.002
metaresearch head score (Gemma)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.121
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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
GenreCommentary

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

Citations54
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Aging and Physical ActivitySame topicHip and Femur FracturesFrench-language works237,207