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Record W4320040035 · doi:10.1016/j.jcjd.2023.02.003

Reducing Sitting Time in Type 1 Diabetes: Considerations and Implications

2023· review· en· W4320040035 on OpenAlexvenueno aff
Anwar M. Alobaid, Paddy C. Dempsey, Monique E. François, Michael A. Zulyniak, Mark Hopkins, Matthew D. Campbell

Bibliographic record

VenueCanadian Journal of Diabetes · 2023
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsnot available
FundersMinistry of Health
KeywordsMedicineSittingType 2 diabetesContext (archaeology)Psychological interventionIntensive care medicineDiabetes mellitusSedentary lifestyleGerontologyPhysical therapyEndocrinologyPhysical activityPsychiatry

Abstract

fetched live from OpenAlex

Sedentary behaviours are ubiquitous in modern society, with Western populations spending approximately 50% of their waking hours in low levels of energy expenditure. This behaviour is associated with cardiometabolic derangements and increased morbidity and mortality. In individuals living with or at risk of developing type 2 diabetes (T2D), "breaking up" sedentariness by interrupting prolonged periods of sitting has been shown to acutely improve glucose management and cardiometabolic risk factors related to diabetes complications. As such, current guidelines recommend interrupting prolonged periods of sitting with short, frequent activity breaks. However, the evidence underpinning these recommendations remains preliminary and is focussed on those with or at risk of developing T2D, with little information regarding whether and how reducing sedentariness may be effective and safe in those living with type 1 diabetes (T1D). In this review, we discuss the potential application of interventions that target prolonged sitting time in T2D within the context of T1D.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.084
GPT teacher head0.351
Teacher spread0.267 · 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 designNot applicable
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

Citations8
Published2023
Admission routes1
Has abstractyes

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