Effect of Ice Massage to Abdomen on Blood Glucose Level and Cardiovascular Function in Healthy Individuals: A Single-group Pre-test and Post-test Experimental Study
Bibliographic record
Abstract
Background: Ice massage is commonly employed in the management of non-communicable diseases like hypertension and diabetes. However, there is a paucity of evidence regarding abdominal ice massage on blood glucose level (BGL) and cardiovascular function either in healthy or pathological conditions. Thus, this study was conducted to assess the effects of ice massage to the abdomen on BGL and cardiovascular functions in healthy individuals. Materials and methods: In our single-group pre-test and post-test experimental study, 50 healthy (27 females and 23 males) volunteers aged 24.72 ± 5.48 years were recruited. All the subjects underwent only one session of ice massage to the abdomen for 20 min. Random BGL and cardiovascular functions such as systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), pulse pressure, mean arterial pressure (MAP), rate pressure product (RPP), and double product (Do-P) were assessed before, immediately after, and 20 min after the intervention. Results: The study showed a significant reduction in PR, RPP, and Do-P in the post-test assessments, whereas in the follow-up assessment (i.e., 20 min after the intervention), a significant reduction was found in random blood glucose, SBP, DBP, PR, MAP, RPP, and Do-P compared to the pre-test assessments. No adverse effects were reported by any of the participants during and after the intervention. Conclusion: Twenty minutes of ice massage to the abdomen improves cardiovascular function immediately after the intervention, whereas after 20 min of intervention, it reduces BGL in addition to improving cardiovascular function in healthy individuals. However, long-term randomized controlled trials in patients with diabetes are recommended with a larger sample size to warrant the clinical efficacy of this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".