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Record W7037213483

DIABETES AND TECHNOLOGY FOR INCREASED ACTIVITY (DaTA)

2010· article· en· W7037213483 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityDiabetes mellitusMetabolic syndromePrimary careDiseaseIntervention (counseling)Type 2 diabetesType 2 Diabetes MellitusPhysical exercise
DOInot available

Abstract

fetched live from OpenAlex

Physical inactivity is a primary target for prevention of cardiovascular disease and type 2 diabetes. Rural Canadians are at increased risk of metabolic syndrome - a clustering of risk factors preceding these conditions. This study investigated feasibility and effectiveness of a stage-matched physical activity intervention using novel self­ monitoring technologies in rural adults with metabolic syndrome. Adherence to self­ monitoring protocols was >94%. Stage of change increased by 1 stage (p=0.001). Physical activity increased from 5579 ± 1964 steps/day at week 1 to 7818 ± 4235 steps/day at week 8 (p=0.02). V02max increased by 17% (p<0.05). BMI decreased from 33.1 to 32.7 (p=0.016). Participants were comfortable using the technology, found it easy- to-use, of low burden, and perceived it positively. This pilot study shows that this stage- matched technology intervention for increased physical activity was feasible and effective in high-risk adults in rural Ontario.

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)0.005
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.212
Threshold uncertainty score0.709

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2120.062

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 head0.081
GPT teacher head0.294
Teacher spread0.213 · 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
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

Citations0
Published2010
Admission routes1
Has abstractyes

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