Walking and type 2 diabetes risk using CANRISK scores among older adults
Bibliographic record
Abstract
The objective of this study was to determine the association between pedometer-assessed steps and type 2 diabetes risk using the Public Health Agency of Canada-developed 16-item Canadian Diabetes Risk Questionnaire (CANRISK) among a large population-based sample of older adults across Alberta, Canada. To achieve our study objective, adults without type 2 diabetes (N = 689) aged 55 years and older provided demographic data and CANRISK scores through computer-assisted telephone interviews between September and November 2012. Respondents also wore a step pedometer over 3 consecutive days to estimate average daily steps. Logistic regression was used to assess the association between achieving 7500 steps/day and risk of diabetes (low vs. moderate and high). Overall, 41% were male, average age was 63.4 (SD 5.5) years, body mass index was 26.7 (SD 5.0) kg/m2, and participants averaged 5671 (SD 3529) steps/day. All respondents indicated they were capable of walking for at least 10 min unassisted. CANRISK scores ranged from 13–60, with 18% in the low-risk category (<21). After adjustment, those not achieving 7500 steps/day (n = 507) were more than twice as likely to belong to the higher risk categories for type 2 diabetes compared with those walking ≥7500 steps/day (n = 182) (73.6% vs. 26.4%; odds ratio: 2.37; 95% confidence interval: 1.58 – 3.57). Among older adults without diabetes, daily steps were strongly and inversely associated with diabetes risk using the CANRISK score. Walking remains an important modifiable risk factor target for type 2 diabetes and achieving at least 7500 steps/day may be a reasonable target for older adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".