The association of objectively and subjectively measured physical activity and sedentary time with prediabetes and type 2 diabetes in adults: a cross-sectional study in Framingham Heart Study cohorts
Bibliographic record
Abstract
The purpose of this study was to examine whether using both objectively (accelerometer) and subjectively (questionnaire) measured moderate- to vigorous-intensity physical activity (MVPA) and sedentary time (SED) improves the prediction of prediabetes and type 2 diabetes (pre/T2D) using data from the Framingham Heart Study ( n = 4200). Logistic regression was used to examine the odds ratio of pre/T2D in groups cross-classified by subjective and objective MVPA and SED. Less than half of participants fell into concordant categories of MVPA and SED using subjective and objective measures, with 7.0%–9.4% of participants in the extreme discordant categories of high-low or low-high subjective–objective MVPA or SED. Low objective MVPA, regardless of subjective MVPA status, was associated with a higher prevalence of pre/T2D ( P < 0.05). When cross-classifying by MVPA and SED, the majority of participants fell into concordant categories of MVPA-SED, with <4% of participants in the extreme discordant categories of MVPA-SED. Low objective MVPA, regardless of objective SED, was associated with a higher prevalence of pre/T2D ( P < 0.05). These findings suggest that low objectively measured MVPA appears more closely associated with pre/T2D risk compared with subjective measures, and there does not appear to be an additive effect of SED on pre/T2D risk after accounting for MVPA.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".