Interventions to reduce sedentary behaviour in adults with type 2 diabetes: A systematic review and meta-analysis
Bibliographic record
Abstract
Treatment and management of Type 2 Diabetes (T2D) includes physical activity, nutrition, and pharmacological management. Recently, the importance of reducing and breaking up sedentary behaviour has become recognized. This review aimed to summarize and synthesize the effectiveness of interventions in reducing and/or breaking up sedentary behaviour and cardiometabolic biomarkers in adults with T2D. A study protocol was preregistered on PROSPERO (CRD42022357281) and a database search (PubMed, EMBASE, Scopus, Web of Science, PsycINFO, SPORTDiscus, CINAHL, and Cochrane Library) was conducted on 16/09/2022 and updated on 03/01/2024. This review followed PRISMA guidelines and study quality was assessed with the Cochrane risk of Bias Tools. Twenty-eight articles were included in the review. The meta-analysis of short-term (Range: 3 hours- 4 days) sedentary behaviour interventions found significant improvement in continuous interstitial glucose measured for 24 hours after the sedentary behaviour intervention compared to control (SMD:-0.819,95%CI:-1.255,-0.383,p<0.001). Similarly, there was a significant improvement in postprandial interstitial glucose after the sedentary behaviour intervention compared to control (SMD:-0.347,95%CI:-0.584,-0.110,p = 0.004). Ten out of eleven longer-term (Range: 5 weeks- 3 years) sedentary behaviour interventions improved at least one measure of sedentary behaviour compared to control. Eight out of eight longer-term sedentary behaviour interventions improved at least one cardiometabolic biomarker compared to control. Reducing sedentary behaviour, independent of physical activity, can improve glycemic control in adults with T2D. Further, sedentary behaviour may be a feasible/ sustainable behaviour change.
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 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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".