Effects of Three Aerobic Exercise Protocols on Acute Glucose Response and Continuous Glucose Monitoring Trends in Patients with Type 2 Diabetes Mellitus and Stroke: A Randomized Controlled Trial

Effects of Three Aerobic Exercise Protocols on Acute Glucose Response and Continuous Glucose Monitoring Trends in Patients with Type 2 Diabetes Mellitus and Stroke: A Randomized Controlled Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Three Aerobic Exercise Protocols on Acute Glucose Response and Continuous Glucose Monitoring Trends in Patients with Type 2 Diabetes Mellitus and Stroke: A Randomized Controlled Trial Meili ng Huang, Kangcheng Chen, Haifeng Ma, Yulong Wang, Dongxia Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6390697/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 13 You are reading this latest preprint version Abstract Objective This study aims to assess the acute and chronic effects of three aerobic exercise protocols, namely, Moderate-Intensity Interval Training (MIIT), Low-to-Moderate Intensity Continuous Training (LMICT), and Reduced-Exertion High-Intensity Training (REHIT), on glycemic control in patients with Type 2 Diabetes Mellitus (T2DM) and stroke. Methods Forty-nine patients diagnosed with both T2DM and stroke were randomly assigned to LMICT, MIIT, REHIT, or the control group. The intervention comprised two phases: from day 3 to day 14 and from day 15 to day 28, with days 1 and 2 designated as a baseline control period. Throughout the intervention, blood glucose levels were continuously monitored and recorded using a Continuous Glucose Monitoring (CGM) system. Results All exercise intervention groups exhibited significant immediate reductions in blood glucose levels following exercise ( t = 30.68, p < 0.001). Repeated measures ANOVA demonstrated significant main effects of group and time, as well as a significant interaction, on mean glucose (MG) and time above range (TAR) ( p < 0.05). Continuous glucose monitoring indicated progressive improvements in MG, time in range (TIR), TAR, peak blood glucose, glucose standard deviation (SD-glucose), and mean amplitude of glycemic excursion (MAGE) in the MIIT group. The REHIT group exhibited significant improvements in peak blood glucose, TIR, TAR, MAGE, SD-glucose, and coefficient of variation (CV) (all p < 0.01). These trends were not evident in the LMICT group. Notably, the MIIT and REHIT groups exhibited early, significant improvements in MG, peak blood glucose, TIR, and TAR, which preceded subsequent changes in SD-glucose and MAGE relative to controls. Conclusions While all exercise regimens resulted in acute reductions in blood glucose, sustained improvements in overall glycemic control and variability were observed exclusively following the four-week MIIT and REHIT interventions. Specifically, REHIT significantly reduced glucose variability, as reflected by decreases in the CV, whereas MIIT was more effective in lowering MG levels. Conversely, the lower-intensity LMICT regimen (51.23% ± 6.94% heart rate reserve) exerted minimal long-term effects. These findings underscore the potential of moderate- to high-intensity intermittent aerobic training in managing glycemic fluctuations in individuals with T2DM and stroke, thereby emphasizing their clinical relevance. Trial registration: The study was registered with the Chinese Clinical Trial Registry (ChiCTR2200065677, http://www.chictr.org.cn/ ) on 11/11/2022. Type 2 Diabetes Mellitus Stroke Aerobic Exercise training Continuous Glucose Monitoring Glycemic variability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction In medicine and public health, T2DM and stroke constitute significant global health challenges. According to data published in 2021 by the International Diabetes Federation, approximately 141 million adults in China are affected by diabetes [ 1 ]. A study involving 18,013 patients with T2DM reported a stroke incidence rate of 9.5% [ 2 ]. Epidemiological studies have demonstrated that diabetes significantly affects the long-term prognosis of stroke survivors [ 3 ]. Recent meta-analyses have indicated that diabetic patients face approximately a 50% increased risk of stroke recurrence [ 4 ]. Diabetes is a critical determinant of stroke prognosis in the general population [ 5 ], and effective glycemic control can improve overall health and markedly reduce the risk of subsequent cerebral injury [ 6 ]. Hemoglobin A1c (HbA1c) is a crucial biomarker for managing blood glucose in patients with T2DM. It reflects mean blood glucose levels over the prior two to three months. However, the association between HbA1c and stroke recurrence risk is relatively weak. The effectiveness of intensive glucose control—specifically, maintaining HbA1c levels at or below 7%—in preventing recurrent strokes during the acute phase in patients with diabetes has not been conclusively established [ 7 ]. This uncertainty may be attributed to the robust relationship between elevated average blood glucose levels and diabetic microvascular complications, whereas the relationship between HbA1c and macrovascular complications appears to be more indirect[ 8 ]. HbA1c cannot capture glucose fluctuations or provide detailed insights into glucose dynamics. With advancements in CGM, high-precision CGM technology now enables more accurate monitoring of glucose fluctuations, hyperglycemia, hypoglycemia, and variability, offering essential metrics for diabetes management [ 9 – 11 ]. Studies have demonstrated a clear association between TIR, TBR, and the risk of cardiovascular and cerebrovascular diseases in diabetic patients [ 12 , 13 ]. Additionally, both prospective and retrospective cohort studies have identified a dose-response relationship between glucose variability and stroke risk, further underscoring the practical significance of employing CGM-derived metrics for assessing glycemic control in stroke patients [ 14 ]. Aerobic exercise is a crucial approach to secondary stroke prevention, as it significantly reduces cardiovascular risk and enhances diabetes management [ 14 – 17 ]. Although previous studies have investigated the effects of aerobic exercise on HbA1c, fasting blood glucose (FBG), and acute post-exercise glucose variability, the impact of long-term exercise (greater than two weeks) on glucose variability remains insufficiently explored [ 18 – 20 ]. Furthermore, owing to the lack of continuous blood glucose monitoring during the intervention period, it remains challenging to assess the long-term impact of exercise on key CGM-derived glucose control indices, including MG, MAGE, SD-glucose, and CV. The American Heart Association (AHA) and American Stroke Association (ASA) recommend exercise for physically active stroke survivors three to five times per week, with each session lasting between 20 and 60 minutes at an intensity ranging from 40–70% of the heart rate reserve (HRR) [ 21 ]. Additionally, the 10-minute REHIT protocol comprises two to three 20- to 30-second sprints, each performed at maximum effort. Research indicates that REHIT may result in beneficial metabolic adaptations, such as improved insulin sensitivity, without inducing excessive fatigue [ 22 – 24 ]. This protocol has shown promise in improving insulin sensitivity and reducing blood glucose levels among individuals without stroke [ 22 , 25 , 26 ]; however, its effects in patients with T2DM who have experienced stroke remain inadequately investigated. In conclusion, this study focused on patients with T2DM and stroke, examining the acute and chronic impacts of MIIT and LMICT—both involving the same duration but differing in intensity—as well as REHIT, which consists of extremely short high-intensity intervals. Special attention was given to key metrics derived from CGM, including TIR, MAGE, SD-glucose, and the CV. These findings aim to provide scientific evidence for the design of personalized clinical exercise interventions. 2. Methods and Analysis 2.1 Participants and Recruitment This study included 49 patients with T2DM who had experienced a stroke and were hospitalized in the rehabilitation department of Shenzhen Second People's Hospital, Guangdong Province, between October 2022 and February 2024. The study was conducted in accordance with the Declaration of Helsinki, adhered to CONSORT guidelines, received approval from the Ethics Committee of Shenzhen Second People's Hospital (approval number 20220901005-FS01), and was registered with the Chinese Clinical Trial Registry (ChiCTR2200065677). All patients or their authorized representatives provided written informed consent before enrollment. Participants were selected according to the criteria outlined in the Diagnostic Essentials of Cerebrovascular Diseases in China (2019), with diagnosis confirmed by imaging, and the Chinese Guidelines for Type 2 Diabetes Mellitus (2020). The inclusion criteria were as follows: (1) aged between 18 and 80 years; (2) stroke occurrence within 15 days to 1 year prior to enrollment; (3) ability to walk independently for at least 10 meters (with or without assistance); and (4) sufficient cognitive ability to participate in study activities. Patients were excluded based on the following criteria: (1) unstable vital signs; (2) progressive or acute stroke; (3) transient ischemic attack (TIA); (4) history of brain injury or other central nervous system disorders; (5) severe cardiovascular or pulmonary diseases; (6) severe hepatic or renal dysfunction; (7) significant musculoskeletal limitations; (8) untreated deep vein thrombosis (DVT); (9) severe diabetic complications; (10) other severe comorbidities; and (11) participation in other ongoing clinical trials. 2.2 Study design This study is a prospective randomized controlled trial, and the rationale, study design, and methodological details have been described previously[ 27 ]. Building on the previously reported protocol, patient recruitment was continued, and daily glucose monitoring data were systematically recorded throughout the study duration, along with acute glucose responses measured immediately pre- and post-exercise sessions. Briefly, a total of 49 participants were randomly assigned to one of three aerobic exercise intervention groups or a control group, with each group undergoing a 28-day intervention period. Participants in the control group received routine care, including bed mobility training, joint mobility training, and balance exercises. Participants in the aerobic exercise intervention groups received routine care in combination with structured aerobic exercise training. The detailed study design is illustrated in Fig. 1 . 2.3 Sample Size Calculation Based on the study by Metcalfe et al.[ 28 ], REHIT significantly reduced MG levels in patients with T2DM compared to the CON group, which did not engage in exercise, with a Cohen's d value of 0.55. This study employed a repeated measures design, consisting of three phases: baseline (days 1–2), Cycle 1 (days 3–14), and Cycle 2 (days 15–28). Using the conversion formula F = d /22, the Cohen's f value was calculated as 0.275[ 29 ]. To ensure an adequate sample size and account for data variability, we set the effect size at the medium range ( F = 0.25). The significance level was set at α = 0.05, with a statistical power of 0.80, and a parallel control design with a 1:1:1:1 allocation ratio. Using G*Power 3.1.9.7 software (Heinrich Heine Universität Düsseldorf, Düsseldorf, Germany), the total sample size was determined to be 40 participants, who were randomly assigned to four study groups, ensuring sufficient statistical power to detect the effects of each intervention on glucose control. Given the small sample size, strategies will be implemented to minimize participant dropout. 2.4 Randomization and Blinding Randomization was conducted using computer-generated random numbers, which were recorded on carbonless copy paper and sealed in uniformly sized opaque envelopes to maintain allocation concealment and ensure randomness. Due to the nature of the intervention, blinding of participants and researchers delivering exercise instruction regarding group allocation was not feasible. However, researchers conducting follow-up assessments and statistical analyses were blinded to group allocation. 2.5 Intervention 2.5.1 Exercise Stress Test On the basis of the relevant literature and clinical practice, this study utilized an extremity cycle ergometer integrated with a cardiopulmonary exercise testing system (Masterscreen CPX; ERGOLINE GMBH, Germany) and a 5-lead electrocardiogram (ECG) to perform a symptom-limited maximal incremental cardiopulmonary exercise test[ 30 – 32 ]. Before testing, equipment calibration was performed in accordance with standard protocols, and participants were fitted with a 5-lead ECG, a gas analyzer mask, and a safety harness. Assistive devices were used as necessary to stabilize the affected limbs of participants, ensuring the accuracy and safety of the test. The testing protocol was individualized according to the gender, age, height, weight, functional status, and exercise habits of each participant, with resistance increasing incrementally by one to two levels every two minutes. The detailed testing protocol comprised four phases: (1) Resting phase (1 min): recording resting heart rate and blood pressure; (2) Unloaded warm-up phase (3 min): participants performed familiarization exercises; (3) Incremental resistance exercise phase (8–12 min): resistance was increased progressively according to a predefined protocol until participants reached maximal exertion or termination criteria were met; (4) Recovery phase (6 min): consisting of three minutes of unloaded pedaling followed by three minutes of rest, during which heart rate and blood pressure recovery were monitored. Throughout the test, each resistance stage had a duration of two minutes, and participants maintained a pedaling cadence of 80 revolutions per minute (rpm) [ 31 ]. The criteria for test termination were as follows: (1) a plateau in oxygen consumption (VO₂) [ 33 ] ; (2) heart rate exceeding 90% of the age-predicted maximum (adjusted to 85% for participants taking beta-blockers) [ 34 ] ; (3) respiratory exchange ratio (RER) greater than 1.00 [ 35 ]; (4) Borg rating of perceived exertion (6–20 scale) greater than 17 [ 32 ]; and (5) participants reaching volitional fatigue and inability to maintain the required pedaling cadence. 2.5.2 Exercise intervention program All exercise interventions were conducted under the supervision of qualified physiotherapists, who had completed the study-specific training, thereby ensuring the safety and effectiveness of the intervention. The experimental group utilized a standardized four-limb coordinated rehabilitation training device. Based on the results of the exercise stress test, trained therapists adjusted the resistance levels and pedal speeds of the apparatus to accommodate each participant’s specific needs. Continuous heart rate monitoring was implemented to ensure that exercise intensity remained appropriate, with participants required to immediately report their perceived exertion levels after each session using the Borg scale (range 6–20). The exercise intervention program commenced on day three and continued until day 24. To prevent overtraining, the program incorporated two rest days after every five consecutive exercise days, resulting in a total of 20 sessions throughout the study. These sessions were divided into two phases, each consisting of 10 sessions. Participants in the CON group were instructed to adhere to their usual treatment plans and did not participate in any structured exercise intervention (Table 1 ). 2.6 Collection of treatment results Upon admission, demographic and clinical data were collected, including age, gender, height, weight, duration of diabetes, time of stroke onset, lifestyle habits (smoking, alcohol consumption), medication history, and past medical history. Baseline data were collected through fasting blood samples drawn from the antecubital vein. The primary measurements included fasting blood glucose (FBG), HbA1c, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Blood glucose was measured via the hexokinase method, HbA1c was assessed using a turbidimetric immunoassay, and all samples were tested in duplicate for verification. 2.6.1 The primary outcomes The primary outcomes of this study were glucose control metrics derived from the CGM system (GS1, SIBIONICS, Shenzhen, Guangdong, China). On study days 0 and 14, trained nurses applied a glucose sensor to the non-dominant upper arm of each participant. Each sensor continuously recorded interstitial glucose concentrations over a 14-day period at 5-minute intervals and automatically transmitted the data to the hospital’s cloud system via Bluetooth connectivity. The following CGM-derived metrics were assessed to evaluate glycemic control: (1) TIR: The percentage of time that glucose levels were within the target range of 3.9–10.0 mmol/L, serving as a key indicator of glycemic stability. (2) TBR: The percentage of time that glucose levels were 10.0 mmol/L, indicating the burden of hyperglycemia. (4) MG: The arithmetic mean of all CGM-derived glucose values, providing an overall profile of glycemic control. (5) SD-glucose: The square root of the mean of squared deviations from MG, reflecting absolute glycemic variability. (6) CV: The ratio of SD-glucose to MG, used to quantify the relative degree of glucose fluctuation. (7) MAGE: The average amplitude of significant glycemic fluctuations (defined as changes exceeding 1 SD-glucose), used to characterize within-day glycemic variability. (8) Peak Blood Glucose: The highest glucose level recorded during each 24-hour period. (9) Nadir Blood Glucose: The lowest glucose level recorded during each 24-hour period. 2.6.2 Secondary outcomes The secondary outcomes focused on the acute glycemic response to exercise, assessed using CGM-derived glucose concentrations. Glucose values were extracted during the 5-minute period immediately preceding the start of each exercise session and the 5-minute period immediately following its completion. These measurements were obtained from all three experimental groups throughout the intervention period. 2.7 Medication Therapy and Dietary Plan All participants adhered to the endocrinologists' recommendations regarding the use of antidiabetic medications. Throughout the entire study period, participants were instructed to maintain their baseline antidiabetic medication regimens, ensuring consistent daily timing for the administration of oral medications or insulin [ 36 ]. Dietary intake was accurately documented during the two days preceding the intervention (defined as baseline days), including daily caloric intake and macronutrient distribution (carbohydrates, proteins, and fats) for each meal. This measure provided a dietary baseline for continuous nutritional monitoring throughout the study. During the subsequent 26-day intervention period, participants maintained consistent dietary patterns under the direct supervision and professional guidance of registered dietitians. To ensure maximum dietary consistency and strict control of dietary factors influencing glycemic control, registered dietitians carefully documented and supervised the composition and quantity of food consumed by participants at each meal. 2.8 Potential Adverse Events in the Exercise Intervention Adverse Events (AEs) refer to any unfavorable health occurrences following aerobic exercise rehabilitation, irrespective of their causal relationship to the treatment. Serious Adverse Events (SAEs) include events necessitating hospitalization, causing disability, impairing work capacity, endangering life, or resulting in death or congenital deformities. These events are classified by severity: mild (tolerable, with no significant impact on treatment or recovery), moderate (difficult to tolerate, necessitating special treatment, and adversely affecting recovery), and severe (life-threatening, causing death or disability, requiring immediate intervention). Expected AEs include fatigue, abdominal discomfort, muscle cramps, soreness, stiffness, and swelling, whereas unexpected AEs involve severe reactions, such as life-threatening events or death, not anticipated in the study protocol. Serious AEs may include conditions such as bradycardia, sudden death, myocardial infarction, heart failure, hypoglycemia, ketoacidosis, and musculoskeletal injuries. Risk management involves permitting participants to rest for up to two minutes if required, halting exercise in the event of adverse events, monitoring the participant, and administering oxygen. Serious events are reported within 24 hours to the sponsor, ethics committee, and relevant regulatory authorities, with follow-up treatment provided until the condition resolves or stabilizes. 2.9 Statistics Data were analyzed using SPSS software (version 23.0; IBM Corporation, Armonk, NY, USA). Categorical variables were expressed as percentages, while continuous variables were presented as means ± standard deviations (mean ± SD). Differences in baseline characteristics between study groups were assessed using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Levene's test was initially performed to assess the homogeneity of variance; if this assumption was violated, Welch’s ANOVA was applied. Fisher’s exact test was used for categorical variables when the expected frequency was less than five. The effects of time periods and exercise protocols on CGM indices were evaluated using two-way repeated-measures ANOVA. When the sphericity assumption was violated, a Greenhouse–Geisser correction was applied. Additionally, generalized estimating equations (GEE) were employed to analyze daily mean values of CGM indices across study groups. Subject ID was treated as a between-subjects factor, and intervention date as a within-subjects factor, with an unstructured covariance matrix employed to model the correlation structure within the data. Bonferroni correction and polynomial trend analysis were performed to further evaluate between-group differences in CGM indices throughout the intervention period, as well as linear trends within each group. Furthermore, a general linear model (GLM) was used to assess acute changes in blood glucose concentrations among different exercise protocols. The CGM blood glucose measurements obtained within the 5-minute period immediately preceding each intervention were used as covariates, and corresponding post-intervention measurements obtained within the first 5 minutes were treated as the dependent variable to control for the influence of baseline glucose levels. Model parameters were estimated using the least squares method, and adjusted within-group effects (least squares means) were calculated. Post hoc multiple comparisons using least squares mean differences were conducted to compare effects among study groups; adjusted means, standard errors, and 95% confidence intervals (95% CI) were reported for each group. The significance level was set at p < 0.05. 3 Results In total, 119 individuals volunteered for participation in this study. After screening for predefined inclusion and exclusion criteria, 70 participants were excluded due to factors including restricted lower-extremity passive joint range of motion, severe comorbid conditions, or an inability to fully commit to the study requirements. Consequently, 49 eligible participants (mean age, 59.06 ± 10.44 years) were enrolled and randomly allocated to four groups: CON (n = 12), LMICT (n = 11), MIIT (n = 12), and REHIT (n = 14). During the study, four participants withdrew: one from the CON group, one from the MIIT group, and two from the REHIT group. Additionally, one participant from the LMICT group was excluded due to fever symptoms that affected adherence to the training protocol. To ensure compliance with the prescribed dietary and exercise protocols, rigorous professional supervision was implemented throughout the study period. While some mild, expected exercise-related adverse events, such as fatigue and muscle soreness, were observed during the intervention period, no serious adverse events were reported. Overall, the intervention was well-tolerated. Except for participants who withdrew mid-study, all remaining participants successfully completed the prescribed training, achieving a completion rate of 90%. 3.1 Participant characteristics The baseline characteristics of the participants in each of the four study groups are summarized in Table 2 . No significant differences were observed among the four groups regarding age, gender, height, weight, diabetes duration, time since stroke onset, stroke type (ischemic stroke), hypertension, dyslipidemia, coronary artery disease, smoking history, alcohol consumption, or concomitant medications (all p > 0.05). Additionally, baseline clinical indicators—including HbA1c, FBG, Barthel Index (BI), and Functional Movement Assessment - Lower Extremity (FMA-LE) scores—showed no significant differences among the groups, demonstrating baseline homogeneity. Table 2 Demographic and baseline clinical characteristics CON (n = 12) LMICT (n = 11) MIIT (n = 12) REHIT (n = 14) Withdraw halfway, n (%) 1 (8.3%) 1(9.1%) 1(8.3%) 2 (14.3%) Male/female, n (%) 3 (25.0%) 1 (9.1%) 5 (41.7%) 3 (21.4%) Age (years) 58.25 ± 12.70 61.09 ± 8.44 59.75 ± 11.47 57.57 ± 9.64 Height (cm) 167.92 ± 6.78 167.00 ± 4.27 163.83 ± 7.80 165.21 ± 7.64 Weight (kg) 65.00 ± 10.18 62.64 ± 9.87 59.42 ± 8.61 65.36 ± 9.58 Duration of diabetes > 10 years, n (%) 5 (41.7%) 5 (45.5%) 7 (58.3%) 5 (35.7%) HbA1c(%) 8.14 ± 1.50 7.46 ± 1.12 8.25 ± 1.37 7.64 ± 1.25 FBG( mmol/L) 6.53 ± 1.43 6.10 ± 1.08 7.13 ± 1.72 6.32 ± 1.08 Days since stroke onset 70.67 ± 65.11 101.09 ± 73.08 98.58 ± 87.32 124.21 ± 85.58 Ischemic stroke, n (%) 11 (91.7%) 8 (72.7%) 10(83.3%) 10(71.4%) History of stroke, n (%) 2 (16.7%) 1 (9.1%) 3(25.0%) 2(14.3%) With hypertension, n (%) 11 (91.7%) 10 (90.9%) 11(91.7%) 13(92.9%) With dyslipidemia, n (%) 2 (16.7%) 1 (9.1%) 2(16.7%) 3(21.4%) With coronary artery disease, n (%) 1 (8.3%) 2 (18.2%) 3(25.0%) 3(21.4%) History of smoking, n (%) 5 (41.7%) 6 (54.5%) 4(33.3%) 6(42.9%) History of alcohol consumption, n (%) 3 (25.0%) 5 (45.5%) 3(25.5%) 7(50%) Bl 56.67 ± 32.15 51.36 ± 17.19 52.50 ± 20.50 58.21 ± 20.25 FMA-LE TG (mmHg) TC (mmHg) HDL-C (mmHg) LDL-C (mmHg) 17.42 ± 7.61 1.31 ± 0.47 3.54 ± 1.53 0.99 ± 0.45 2.00 ± 0.80 17.00 ± 7.71 1.54 ± 0.92 3.45 ± 1.27 1.02 ± 0.22 1.95 ± 0.65 16.50 ± 6.43 1.47 ± 0.56 4.21 ± 1.18 1.12 ± 0.29 2.55 ± 0.90 18.79 ± 7.40 1.20 ± 0.48 3.97 ± 1.07 1.21 ± 0.15 2.17 ± 0.68 Oral blood-glucose-lowering medication Biguanides, n (%) 6 (50.0%) 5 (45.5%) 6(50.0%) 9(64.3%) DPP-4 Inhibitors, n (%) 2 (16.7%) 3 (27.3%) 4(33.3%) 4(28.6%) SGLT2 Inhibitors, n (%) 2 (16.7%) 3 (27.3%) 3(25.0%) 3(21.4%) Sulfonylureas, n (%) 4 (33.3%) 5 (45.5%) 4(33.3%) 6(42.9%) Insulin treatment n (%) Basal Insulin, n (%) 3 (25.0%) 2 (18.2%) 3(25.0%) 3(21.4%) Multiple Daily Injections, n (%) 3 (25.0%) 2 (18.2%) 3(25.0%) 2(14.3%) Biphasic Insulin, n (%) 1 (8.3%) 1 (9.1%) 0(0.0%) 2(14.3%) Statins, n (%) 11 (91.7%) 6 (54.5%) 8(66.7%) 10(71.4%) β1-blocker, n (%) 3 (25.0%) 4 (36.4%) 3(25.0%) 5(35.7%) 3.2 Exercise Intensity Monitoring Results Table 3 summarizes the exercise intensity monitoring data and RPE recorded immediately after each exercise session for each experimental group. Participants in the LMICT group attained an average peak intensity of 51.23% ± 6.94% HRR and reported an average post-exercise RPE score of 12.65 ± 1.36. In the MIIT group, the average peak intensity achieved was 69.75% ± 5.29% HRR, with a corresponding post-exercise RPE of 13.56 ± 1.97. These observed intensities slightly deviated from the predefined target ranges, possibly due to factors such as individual physiological variability, adaptations to training, psychological factors, or potential measurement errors. The REHIT group achieved an average peak intensity of 78.19% ± 5.77% HRR, with a post-exercise RPE of 14.28 ± 1.78. Collectively, these findings confirm successful implementation of the exercise interventions, in accordance with the intended experimental protocols. Table 3 Exercise Intensity Monitoring Results LMICT (n = 10) MIIT (n = 11) REHIT (n = 12) Pre-exercise Resting Heart Rate (bpm) 80.67 ± 11.38 73.40 ± 10.26 78.44 ± 11.38 Exercise HRmax (bpm) 102.79 ± 9.87 116.23 ± 14.39 116.40 ± 15.08 % HRmax Achieved During Exercise (relative to HRpeak) 82.98 ± 5.96 86.43 ± 3.52 91.79 ± 2.43 % HRR Utilization at Exercise HRmax 51.23 ± 6.94 69.75 ± 5.29 78.19 ± 5.77 RPE Score (Post-exercise) 12.65 ± 1.36 13.56 ± 1.97 14.28 ± 1.78 Note : HRpeak: Defined as the maximum heart rate attained by participants during maximal exertion in the cardiopulmonary exercise test; %HRR was calculated using the formula: [(HRmax – HRrest) / (HRpeak – HRres t )] × 100%; HR max : The highest heart rate recorded during an exercise session; HRrest: Resting heart rate measured prior to exercise; RPE: assessed using the Borg scale (range 6–20). 3.3 Acute Glycemic Response Pre- and Post-Exercise As shown in Table 4 , a total of 660 paired acute glucose measurements were recorded across all exercise sessions, including the LMICT, MIIT, and REHIT groups. Acute changes in glucose concentration ranged from a maximum decrease of 3.3 mmol/L to a maximum increase of 1.1 mmol/L. Paired-sample t-tests demonstrated that acute glucose changes were statistically significant in all three exercise groups: LMICT ( p < 0.001), MIIT ( p < 0.001), and REHIT ( p < 0.001). After controlling for baseline glucose levels, the adjusted least squares mean differences in glucose concentration immediately following exercise were − 0.79 mmol/L (LMICT), − 1.00 mmol/L (MIIT), and − 0.64 mmol/L (REHIT), respectively (Fig. 2). Table 4 Acute Glycemic Response Pre- and Post-Exercise n Blood Glucose Concentration (mmol/L) t p Pre-exercise Post-exercise All experimental groups LMICT MIIT REHIT 660 200 220 240 9.26 ± 2.38 9.20 ± 2.30 9.48 ± 2.59 9.11 ± 2.24 8.45 ± 2.06 8.41 ± 1.96 8.44 ± 2.23 8.49 ± 2.00 30.68 15.98 21.76 16.98 < 0.001 < 0.001 < 0.001 < 0.001 3.4 Changes in CGM-derived Glucose Indices Across Study Periods Table 5 summarizes the mean changes in CGM-derived glucose metrics (MG, Peak Blood Glucose, Nadir Blood Glucose, TIR, TAR, TBR, MAGE, SD, and CV) across Baseline, Period 1, and Period 2 for the LMICT, MIIT, REHIT, and CON groups. Repeated-measures ANOVA showed that: (1) MG: There was a significant main effect of group ( F = 3.039; p = 0.040), a significant within-subject effect of time ( F = 5.956; p = 0.010), and a significant group-by-time interaction ( F = 3.743; p = 0.009). (2) Peak Blood Glucose: No significant main effect of group was observed ( F = 1.632; p = 0.197); however, a significant within-subject effect of time ( F = 14.841; p < 0.001) and a significant group-by-time interaction ( F = 4.978; p = 0.001) were identified. (3) Nadir Blood Glucose: No significant main effect of group was found ( F = 1.808; p = 0.161); however, a significant within-subject effect of time ( F = 6.147; p = 0.006) was observed, with no significant group-by-time interaction ( F = 0.519; p = 0.757). (4) TIR: No significant main effect of group was identified ( F = 2.651; p = 0.062); however, a significant within-subject effect of time ( F = 13.762; p < 0.001) and a significant group-by-time interaction ( F = 3.934; p = 0.005) were observed. (5) TAR: There were significant main effects of group ( F = 3.228; p = 0.032), significant within-subject effects of time ( F = 9.409; p = 0.001), and a significant group-by-time interaction ( F = 3.283; p = 0.014). (6) TBR: No significant main effect of group was detected ( F = 0.748; p = 0.748); however, a significant within-subject effect of time ( F = 5.912; p = 0.007) was observed, with no significant group-by-time interaction ( F = 0.519; p = 0.757). (7) MAGE: No significant main effect of group was observed ( F = 0.469; p = 0.705); however, a significant within-subject effect of time ( F = 13.716; p < 0.001) and a significant group-by-time interaction ( F = 2.913; p = 0.025) were found. (8) SD-glucose: No significant main effect of group was observed ( F = 0.469; p = 0.705); however, a significant within-subject effect of time ( F = 13.716; p < 0.001) and a significant group-by-time interaction ( F = 4.813; p = 0.001) were identified. (9) CV: No significant main effect of group was detected ( F = 0.120; p = 0.948); however, a significant within-subject effect of time ( F = 12.446; p < 0.001) was observed, with no significant group-by-time interaction ( F = 1.329; p = 0.125). Table 5 CGM Glycemic Indices from Baseline Days to Cycle 1 and Cycle 2 MG (mmol/L) Peak Blood Glucose (mmol/L) Nadir Blood Glucose (mmol/L) TIR (%) TAR (%) TBR (%) MAGE (mmol/L) SD-glucose (mmol/L) CV (%) CON Baseline Days 8.49 ± 1.37 14.16 ± 2.89 4.21 ± 0.92 70.61 ± 20.20 27.34 ± 19.46 2.05 ± 3.81 6.16 ± 2.69 2.36 ± 0.92 27.43 ± 9.41 Cycle 1 8.51 ± 1.50 13.80 ± 2.20 4.87 ± 1.03 72.36 ± 19.34 26.71 ± 19.49 0.93 ± 1.02 5.68 ± 1.44 2.17 ± 0.65 25.48 ± 6.36 Cycle 2 8.60 ± 1.45 14.37 ± 2.53 4.71 ± 0.74 72.47 ± 19.98 26.55 ± 19.95 0.98 ± 1.45 6.17 ± 1.75 2.41 ± 0.73 27.70 ± 6.00 LMICT Baseline Days 7.78 ± 1.13 13.81 ± 2.25 4.28 ± 0.99 79.88 ± 11.96 18.85 ± 11.80 1.28 ± 1.89 6.12 ± 1.95 2.32 ± 0.64 29.77 ± 7.00 Cycle 1 7.83 ± 1.06 13.70 ± 1.43 4.61 ± 0.85 79.09 ± 12.27 19.72 ± 12.56 1.20 ± 1.38 5.63 ± 1.32 2.14 ± 0.48 27.63 ± 6.23 Cycle 2 7.79 ± 0.98 13.30 ± 1.90 4.49 ± 0.60 81.18 ± 10.69 17.03 ± 10.86 1.79 ± 2.76 5.44 ± 1.56 2.11 ± 0.57 27.04 ± 6.45 MIIT Baseline Days 8.46 ± 2.09 14.44 ± 3.15 3.99 ± 1.03 69.15 ± 23.63 27.16 ± 24.46 3.70 ± 3.45 6.85 ± 2.74 2.67 ± 1.00 31.54 ± 9.96 Cycle 1 7.71 ± 1.00 12.90 ± 2.20 4.20 ± 0.55 81.31 ± 10.64 16.88 ± 10.61 1.87 ± 1.80 5.34 ± 1.43 2.10 ± 0.53 27.05 ± 5.16 Cycle 2 7.04 ± 0.61 11.85 ± 1.63 3.97 ± 0.41 88.59 ± 6.00 9.07 ± 5.70 2.34 ± 1.70 4.74 ± 1.41 1.85 ± 0.43 26.16 ± 5.19 REHIT Baseline Days 7.31 ± 1.23 13.02 ± 2.05 3.88 ± 1.05 82.38 ± 10.41 12.97 ± 9.73 4.65 ± 8.17 5.86 ± 1.32 2.19 ± 0.44 30.34 ± 6.82 Cycle1 7.23 ± 0.85 12.57 ± 1.44 4.31 ± 0.69 87.57 ± 8.16 10.48 ± 7.34 1.95 ± 4.00 5.36 ± 1.42 1.94 ± 0.39 26.95 ± 5.77 Cycle 2 6.84 ± 0.65 11.42 ± 1.44 4.08 ± 0.37 91.89 ± 5.77 6.51 ± 4.73 1.60 ± 2.05 4.50 ± 1.22 1.71 ± 0.38 25.05 ± 4.93 Between-group comparison F, p 3.039, 0.040 1.632, 0.197 1.808, 0.161 2.651, 0.062 3.228, 0.032 0.748, 0.530 0.469, 0.705 0.843, 0.478 0.120, 0.948 Within-subject comparison F, p 5.956, 0.010 14.841, <0.001 6.147,0.006 13.762, <0.001 9.409, 0.001 5.226, 0.020 13.716,<0.001 19.803,<0.001 12.446,<0.001 Interaction effect F, p 3.743, 0.009 4.978, 0.001 0.519, 0.757 3.934, 0.005 3.283, 0.014 1.281, 0.290 2.913, 0.025 4.813, 0.001 1.858, 0.125 3.5 Trends in CGM-derived glucose metrics during the intervention Tables S1 to S9 present the daily mean values of CGM-derived indicators and depict the overall intervention trends over the 28-day period for the LMICT, MIIT, REHIT, and CON groups. Polynomial trend analyses revealed that the MIIT group exhibited significant linear improvements in MG ( p < 0.001), TIR ( p = 0.004), TAR ( p = 0.004), peak blood glucose ( p < 0.001), SD-glucose ( p < 0.001), and MAGE ( p = 0.008). The REHIT group likewise demonstrated significant improvements in peak blood glucose ( p < 0.001), TIR ( p < 0.001), TAR ( p = 0.007), MAGE ( p < 0.001), SD-glucose ( p < 0.001), and CV ( p = 0.001). By contrast, the LMICT group demonstrated no comparable linear improvement (see Figs. 3 – 5 ). Daily CGM comparisons with the CON group across the 28-day intervention yielded the following outcomes (see Table S1 to S9): (1) MG: Over 28 days, the MIIT group first exhibited a significant difference from the CON group on Day 11 ( p = 0.033), with significant differences noted on 11 days ( p < 0.05). The REHIT group first differed on Day 8 ( p = 0.041), with 16 days showing significant differences. The LMICT group displayed a difference on Day 26 ( p = 0.009), as detailed in Table S1 . (2) Peak Blood Glucose levels: During the study, the MIIT group first differed from the CON group on Day 20 ( p = 0.044), with significant differences observed on 6 days ( p 0.05), as shown in Table S2 . (3) Nadir Blood Glucose levels: The MIIT group first demonstrated a difference from the CON group on Day 25 ( p = 0.008), with three days of differences during the intervention. The REHIT group differed on Day 27 ( p = 0.009), the only day with a significant difference. The LMICT group showed no differences throughout the intervention ( p > 0.05), as detailed in Table S3. (4) TIR: The MIIT group first showed significant differences from the CON group on Day 18 ( p = 0.027), with five days of differences noted during the intervention. The REHIT group first differed on Day 12 ( p = 0.018), with 11 days of differences. The LMICT group showed no differences throughout the intervention ( p > 0.05), as seen in Table S4. (5) TAR: The MIIT group first showed differences on Day 11 ( p = 0.049), with seven days of differences noted. The REHIT group first differed on Day 10 ( p = 0.043), with 16 days of differences. The LMICT group showed no differences throughout the intervention ( p > 0.05), as seen in Table S5. (6) TBR: The MIIT group showed differences on Day 21 only ( p = 0.007), detailed in Table S6. (7) MAGE: The MIIT group first differed from the CON group on Day 22 ( p = 0.029), with two days of differences. Additionally, on Day 26, the REHIT group first differed significantly from the CON group ( p = 0.006), with two days of significance. The LMICT group showed no differences throughout the intervention ( p > 0.05), as detailed in Table S7. (8) SD-glucose: The MIIT group showed significant differences on Day 28 only (p = 0.043). The REHIT group first differed on Day 25 ( p = 0.035), with three days of significance noted. Details are provided in Table S8. (9) CV: Throughout the 28-day study period, none of the groups—LMICT, MIIT, or REHIT—showed significant differences when compared to the CON grou p ( p > 0.05), as detailed in Table S9. 4 Results 4.1 The Impact of Different Aerobic Exercise Protocols on Acute Glycemic Changes in Patients with Type 2 Diabetes Mellitus Following Stroke Long-term glycemic improvements achieved through exercise are thought to be cumulative effects resulting from repeated acute reductions in glucose concentrations following individual exercise sessions [37].Numerous studies have evaluated acute blood glucose responses to aerobic exercise in diabetic patients and have generally concurred that moderate-intensity exercise effectively lowers glucose levels [38, 39]. However, findings concerning acute glucose responses to high-intensity exercise are inconsistent. Some studies have reported greater glucose reductions following high-intensity exercise compared to moderate-intensity exercise [38, 40]. whereas others have observed significant acute increases in glucose levels after high-intensity exercise [41]. Furthermore, other evidence suggests that acute reductions in blood glucose may depend more on total exercise volume than on exercise intensity [42, 43]. In this study, we investigated the acute glucose responses to three distinct aerobic exercise protocols—LMICT, MIIT, and REHIT—among stroke patients with type 2 diabetes mellitus (T2DM). All aerobic exercise protocols elicited significant acute reductions in blood glucose ( p < 0.001). After adjusting for baseline blood glucose concentrations, the MIIT group exhibited the largest reduction (−1.00 mmol/L), followed by the LMICT (−0.79 mmol/L) and REHIT (−0.64 mmol/L) groups. Although total energy expenditure per session was not directly measured, MIIT—performed at a higher exercise intensity (69.75% ± 5.29% HRR) than LMICT (51.23% ± 6.94% HRR) over the same duration (30 min)—resulted in greater acute glucose reductions. Despite evidence suggesting that a 10-minute REHIT session can significantly deplete glycogen stores by approximately 20% [44], our findings indicate a comparatively smaller acute glucose-lowering effect following REHIT. This discrepancy could be attributed to the increased oxidative stress associated with higher-intensity exercise [45]. Additionally, brief and intense aerobic exercise may markedly elevate plasma catecholamine levels, subsequently increasing glucose production for 1 to 2 hours post-exercise [46], thus limiting immediate reductions in glucose concentration after REHIT. Overall, MIIT demonstrated a more pronounced acute glucose-lowering effect than LMICT and REHIT in stroke patients with T2DM. 4.2 The Impact of Different Aerobic Exercise Protocols on MG Trends During the Intervention Period in Patients with Type 2 Diabetes Mellitus and Stroke MG levels are directly correlated with HbA1c [9]; however, considering individual differences in hemoglobin glycation rates and other physiological factors [9, 47], MG is deemed a more sensitive indicator than HbA1c for evaluating short-term intervention effects [48]. In the present study, repeated-measures ANOVA identified significant within-subject (time) effects ( F = 5.956, p = 0.010) and group-by-time interaction effects ( F = 3.743, p = 0.009) for MG, indicating that specific exercise interventions could lead to progressive improvements in MG over time. Further analysis showed that only the MIIT group exhibited a significant linear decline in MG throughout the intervention period ( p < 0.001). This result underscores the importance of both the interaction between aerobic exercise intensity/type and cumulative exercise duration, as well as the adherence to individualized exercise prescriptions for effectively reducing MG. Consistent with previous research, HbA1c levels are influenced not only by the duration of exercise—with weekly reductions ranging from 0.009% to 0.043% as exercise continues [49] —but also by its intensity and modality (interval vs. continuous). For example, a study comparing 40 minutes of continuous moderate exercise (60% VO2peak) with 40 minutes of interval training (four minutes at 50% VO2peak interspersed with one minute at 80% VO2peak) found significant HbA1c reductions only in the interval exercise group [50]. Although several mechanisms have been proposed, MG is generally considered the primary determinant of hemoglobin and protein glycation, and a key factor in the pathogenesis of chronic diabetic complications [51, 52]. Our findings indicate that MIIT, when performed regularly at sufficient intensity and duration, is highly effective in improving glucose concentrations in stroke patients with T2DM. 4.3 The Impact of Different Aerobic Exercise Protocols on 'Time in Range' in Patients with Type 2 Diabetes Mellitus and Stroke During the Intervention Period At the 2019 Advanced Technologies & Treatments for Diabetes Conference, experts recommended that interpreting CGM data should include assessing the percentage of time within the target range (TIR), above it (TAR), and below it (TBR) to effectively evaluate glycemic control. Prior research demonstrates a strong association between TIR and both all-cause and cardiovascular mortality risks, with an 8% and 5% increase in mortality risks, respectively, for each 10% reduction in TIR [13]. Additionally, TIR is significantly correlated with peripheral nerve function in T2DM patients, a correlation that persists even after adjusting for HbA1c levels. Notably, higher TIR percentiles are independently correlated with improved peripheral nerve function, with a 10% reduction in TIR corresponding to a 25% increase in the risk of developing peripheral neuropathy [53]. Peripheral neuropathy, a frequent complication of diabetes, affects approximately 50% of those diagnosed, profoundly impacting their quality of life [54]. Associated symptoms, including pain and discomfort, can complicate the stroke recovery process. Systematic reviews have demonstrated that physical activity interventions can significantly enhance TIR in T2DM patients, with an average improvement of 4.21% (95% CI: 0.95 to 7.46, p < 0.01) [55]. This study also examines the impact of various aerobic exercise regimens on TIR, revealing significant intragroup and interactive effects ( F = 13.762, p < 0.001; F = 3.934, p =0.005), which underscore the importance of the intervention's duration on TIR management. Trend analyses further verified significant improvements in TIR throughout the interventions with both MIIT and REHIT programs, with notable enhancements observed ( p = 0.004 and p < 0.001, respectively). TAR is a crucial metric for evaluating the hyperglycemia risk in patients. Prolonged or chronic hyperglycemic conditions cause cellular damage and are closely linked to diabetes-related complications [56]. These conditions can also lead to various adverse outcomes in stroke patients, including increased mortality, disease progression, expanded infarct size, and impaired neurological recovery, applicable even to non-diabetic individuals [57-60]. In our study, significant decreases in TAR were observed in both the MIIT and REHIT groups as the interventions progressed ( p = 0.004 and p = 0.007, respectively). This alignment with the observed improvements in TIR suggests a strong correlation between TIR and TAR, where enhancements in TIR lead to reductions in TAR [61]. Inappropriate exercise regimens can trigger adverse reactions, such as hypoglycemia. Diabetic patients often experience autonomic neuropathy, which impairs their ability to manage hypoglycemic episodes, thereby heightening the risk of severe hypoglycemic events [62]. For individuals with T2DM who have experienced a stroke, maintaining normal glucose levels and preventing hypoglycemia is crucial. Hypoglycemic conditions are closely linked to increased risks of cardiovascular incidents, brain injuries, and deteriorating retinopathy, and are considered potential accelerators of atherosclerosis [63-66]. Furthermore, hypoglycemia disrupts normal brain energy metabolism and exacerbates neuronal injury [67]. Although evidence suggests that exercise significantly reduces autonomic and metabolic reactions to subsequent hypoglycemia, potentially raising the risk of hypoglycemia hours after exercising [68]. our findings show no significant differences in TBR among groups ( F = 0.748, p = 0.530). Additionally, no linear trends in TAR were observed across the study periods in the LMICT, MIIT, and REHIT groups (all p = 1.000). Similarly, no significant group differences in Nadir Blood Glucose were found during the cycles, nor were any linear trends observed over time ( F = 1.808, p = 0.161). These results suggest that LMICT, MIIT, and REHIT do not increase the likelihood of hypoglycemic events in terms of overall trends in glucose management. In conclusion, this study demonstrates that MIIT and REHIT significantly reduce the risk of hyperglycemia and effectively shorten the time required to achieve target blood glucose levels. In contrast, LMICT demonstrates limited effectiveness in improving both TIR and TAR. Moreover, closely monitored LMICT, MIIT, and REHIT protocols do not increase TBR in patients with T2DM who have experienced a stroke, providing new insights into safe and effective glucose management. 4.4 The Impact of Different Aerobic Exercise Protocols on Glycemic Variability in Patients with Type 2 Diabetes Mellitus and Stroke During the Intervention Period Increased glucose variability significantly contributes to inflammatory responses and oxidative stress, initiating multiple pathophysiological mechanisms, including the accumulation of advanced glycation end-products (AGEs), elevation of reactive oxygen species, and enhanced expression of their corresponding receptors, thus exacerbating inflammatory and oxidative damage[69-71]. Heightened glucose variability can be particularly detrimental in stroke patients, whose impaired homeostatic mechanisms increase susceptibility to thrombotic events and plaque instability [72, 73]. Prior studies have shown that acute exercise enhances daily blood glucose levels, responses, and variability [20]. However, the long-term effects of regular exercise on glucose variability are less well-documented. This study identified no significant differences between groups in MAGE and SD-glucose (MAGE: F = 0.469, p = 0.705; SD-glucose: F = 0.843, p = 0.478). Nonetheless, significant intragroup differences were observed across various cycles (MAGE: F = 13.716, p < 0.001; SD-glucose: F = 19.803, p < 0.001), with notable interactions between groups and cycles (MAGE: F = 2.913, p = 0.025; SD-glucose: F = 4.813, p = 0.001). These results suggest that the effects of different aerobic exercises on glucose management for patients with T2DM who have experienced a stroke vary over time. Further analysis revealed significant decreases in the daily averages of SD-glucose and MAGE in the MIIT and REHIT groups as the study progressed, with no comparable trends in the LMICT and CON groups. Despite the non-significant main effect on the CV across groups ( F = 0.120, p = 0.948), significant variations were observed within groups across different cycles ( F = 12.446, p <0.001), emphasizing the substantial influence of time on CV. However, the interaction between groups and cycles was not significant ( F = 1.858, p = 0.125), suggesting similar trends across study groups over time. Only the REHIT group exhibited a significant downward trend in CV during the study ( p = 0.001). These findings underscore that MIIT and REHIT significantly enhance glucose variability, with the duration of the intervention and the intensity of exercise being critical factors. Additionally supported by external studies, high-intensity or intermittent exercise formats are particularly effective at improving glucose variability [74, 75]. This aligns with broader research indicating that exercise intensity correlates more strongly with improved glucose control than exercise volume, potentially including enhancements in glucose variability [18, 76]. In summary, our findings confirm that MIIT and REHIT protocols effectively reduce glycemic variability in stroke patients with T2DM, with REHIT offering additional benefits for CV control. Conversely, LMICT demonstrated comparatively limited effectiveness in improving glucose variability. These outcomes emphasize the importance of incorporating appropriate exercise intensity into exercise prescriptions for glycemic control in clinical practice. 4.5 Temporal Analysis of Significant Differences in Key CGM Indicators Between Experimental and Control Groups During the Intervention Period A recent meta-analysis demonstrated that short-term exercise interventions (less than two weeks) significantly reduce MG by approximately 0.5 mmol/L (95% CI: -0.7 to -0.3; p < 0.001), shorten the duration of hyperglycemia, and enhance the MAGE compared to control conditions [19]. However, a separate 2024 study observed improvements in glucose variability indicators, including significant reductions in SD-glucose (from 1.35 mmol/L to 1.10 mmol/L, p = 0.006) and CV (from 20.25% to 17.20%, p = 0.027), but contended that achieving substantial reductions in MG necessitated greater exercise volume or extended intervention periods [77]. Discrepancies between studies may be attributed to variations in glycemic management on non-exercise days, differences in exercise intensity and duration, and participant demographics such as gender. This study's findings reveal that during the intervention, participants in the MIIT and REHIT groups exhibited significant improvements in MG, Peak Blood Glucose, TIR, and TAR earlier than those in the CON group, thus differing from changes observed in the SD-glucose and MAGE. This contrasts with research from 2024, which indicated that significant reductions in MG might necessitate more prolonged exercise compared to adjustments in CV and SD-glucose [19]. Furthermore, recent studies demonstrate that reducing MG from 12 mmol/L to 8 mmol/L—a 33% decrease—is associated with a 20% reduction in SD-glucose, reflecting a significant improvement within the 95% confidence interval of 14.9 to 24.9% [78]. The metrics MG, TIR, and TAR are closely linked to hyperglycemic states, evidenced by a high correlation coefficient of 0.90 [48], suggesting that improvements in TIR directly result in reductions in TAR [61]. Although MAGE offers insights into hypoglycemia during fasting states [79], both SD-glucose and MAGE generally reflect elevated glucose conditions more accurately [80]. In conclusion, this study demonstrates that MIIT and REHIT can significantly mitigate glucose fluctuations. This is achieved by lowering average glucose concentrations, extending the periods during which glucose levels remain within the targeted range, and reducing the duration of high glucose episodes. Consequently, these interventions constitute effective strategies for enhancing overall glucose management in individuals with diabetes. 4.6 Limitations of the study Although numerous studies have investigated the effects of exercise interventions on glycemic variability in patients with T2DM, systematic analyses of prolonged exercise interventions (≥2 weeks) are scarce[18-20]. This study yielded novel insights into the effects of different aerobic exercise protocols (LMICT, MIIT, and REHIT) on acute glycemic responses and longer-term (up to 4 weeks) glycemic variability indicators in patients with T2DM complicated by stroke. Notably, this investigation comprehensively analyzed daily trends of glycemic parameters throughout the intervention period, closely monitored medication usage and dietary intake, and carefully documented the intensity and duration of each exercise session. However, several limitations necessitate additional scrutiny and refinement. Firstly, the relatively small sample size and limited diversity of the study population (e.g., restricted ranges of age, sex distribution, disease duration, and levels of diabetes control) may affect the external validity and generalizability of the results. Future studies should expand the sample size and include more heterogeneous patient populations to enhance the representativeness and applicability of the findings. Secondly, the lack of long-term follow-up after the intervention period precludes assessment of the durability and stability of exercise-induced improvements in glycemic control. Future research designs are recommended to incorporate extended follow-up periods to better elucidate the long-term efficacy and temporal dynamics of glycemic management resulting from various exercise interventions. Lastly, although this study preliminarily indicated a potential advantage of the REHIT protocol in improving glycemic variability indices (e.g., coefficient of variation, CV), the physiological and molecular mechanisms underlying these beneficial effects were not thoroughly investigated. Future studies should utilize advanced approaches, such as metabolomics, genomics, and detailed physiological analyses, to systematically explore the underlying biological mechanisms and signaling pathways involved in exercise-induced improvements of glycemic variability. This will provide clearer theoretical insights and facilitate evidence-based clinical practices. In summary, despite these limitations, the current study provides preliminary scientific evidence supporting individualized aerobic exercise rehabilitation strategies for patients with T2DM complicated by stroke. This study addresses an existing research gap regarding the comparative efficacy of aerobic exercise protocols in this specific population, emphasizing the need for larger-scale studies, longer follow-up durations, and deeper mechanistic explorations in future research. 5 Conclusions This study demonstrated that MIIT and REHIT significantly enhanced glycemic control in post-stroke patients with T2DM. Specifically, MIIT sessions (30 minutes at 69.75% ± 5.29% HRR) and REHIT sessions (10 minutes at 78.19% ± 5.77% HRR) effectively increased the duration within the normal glucose range and decreased glucose variability. REHIT offered a time-efficient approach, yielding benefits comparable to those of MIIT, with superior reductions in the CV. Conversely, MIIT demonstrated greater efficacy in reducing MG levels. Acute glucose response analyses revealed that MIIT facilitated more rapid glucose reductions than both LMICT (30 minutes at 51.23% ± 6.94% HRR) and REHIT. These findings substantiate the clinical utility of structured aerobic exercise regimens for managing glycemic levels in patients with type 2 diabetes mellitus and a history of stroke. Ongoing research, particularly longitudinal studies, is essential to fully ascertain the long-term effects of these interventions. Declarations Data availability statement The trial protocol, statistical analysis plan, and individual de-identified participant data are available upon reasonable request from the corresponding author. Data sharing will be in accordance with ethical guidelines and privacy considerations. Acknowledgements We are grateful to all staff professionals and participants. Funding This study receives its principal funding from the following grants: Sanming Project of Medicine in Shenzhen (No. SZSM202111010). Author information Authors and Affiliations School of Athletic Performance, Shanghai University of Sport, Shanghai, China Kangcheng Chen, Haifeng Ma, Jun Li Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, China Yulong Wang, Dongxia Li, Yong Huang, Meiling Huang Author Contributions Conceptualization, Kangcheng Chen, Haifeng Ma, Meiling Huang, and Yulong Wang; Data curation, Kangcheng Chen and Yong Huang; Formal analysis, Kangcheng Chen, Haifeng Ma, and Meiling Huang; Investigation, Kangcheng Chen, Dongxia Li and Yong Huang; Methodology, Kangcheng Chen, Haifeng Ma, Meiling Huang and Yulong Wang; Validation, Haifeng Ma and Meiling Huang; Writing – original draft, Kangcheng Chen, Yong Huang and Jun Li; Writing – review & editing, Kangcheng Chen, Haifeng Ma, Meiling Huang, Yulong Wang and Dongxia Li. Ethics declarations Ethics approval and consent to participate The study has been approved by the Ethics Committee of Shenzhen Second People's Hospital (approval number: 20220901005-FS01). Written informed consent is obtained from all patients or their approved proxies before enrolment. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC et al : IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045 . Diabetes research and clinical practice 2022, 183 :109119. He C, Wang W, Chen Q, Shen Z, Pan E, Sun Z, Lou P, Zhang X: Factors associated with stroke among patients with type 2 diabetes mellitus in China: a propensity score matched study . Acta Diabetol 2021, 58 (11):1513-1523. Cha J-K: Epidemiology of Stroke Patients with Diabetes . 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Li F, Zhang Y, Li H, Lu J, Jiang L, Vigersky RA, Zhou J, Wang C, Bao Y, Jia W: TIR generated by continuous glucose monitoring is associated with peripheral nerve function in type 2 diabetes . Diabetes research and clinical practice 2020, 166 :108289. Argoff CE, Cole BE, Fishbain DA, Irving GA: Diabetic peripheral neuropathic pain: clinical and quality-of-life issues . Mayo Clin Proc 2006, 81 (4 Suppl):S3-11. Zhu X, Zhao L, Chen J, Lin C, Lv F, Hu S, Cai X, Zhang L, Ji L: The Effect of Physical Activity on Glycemic Variability in Patients With Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials . Frontiers in endocrinology 2021, 12 :767152. Giri B, Dey S, Das T, Sarkar M, Banerjee J, Dash SK: Chronic hyperglycemia mediated physiological alteration and metabolic distortion leads to organ dysfunction, infection, cancer progression and other pathophysiological consequences: An update on glucose toxicity . Biomedicine & Pharmacotherapy 2018, 107 :306-328. Capes SE, Hunt D, Malmberg K, Pathak P, Gerstein HC: Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: a systematic overview . Stroke 2001, 32 (10):2426-2432. Levine SR, Welch KM, Helpern JA, Chopp M, Bruce R, Selwa J, Smith MB: Prolonged deterioration of ischemic brain energy metabolism and acidosis associated with hyperglycemia: human cerebral infarction studied by serial 31P NMR spectroscopy . Ann Neurol 1988, 23 (4):416-418. Baird TA, Parsons MW, Phan T, Butcher KS, Desmond PM, Tress BM, Colman PG, Chambers BR, Davis SM: Persistent poststroke hyperglycemia is independently associated with infarct expansion and worse clinical outcome . Stroke 2003, 34 (9):2208-2214. Zhang Z, Yan J, Shi H: Role of Hypoxia Inducible Factor 1 in Hyperglycemia-Exacerbated Blood-Brain Barrier Disruption in Ischemic Stroke . Neurobiol Dis 2016, 95 :82-92. Dai DJ, Lu JY, Zhang L, Shen Y, Mo YF, Lu W, Zhu W, Bao YQ, Zhou J: [The appropriate cut-off point of time in range (TIR) for evaluating glucose control in type 2 diabetes mellitus] . Zhonghua yi xue za zhi 2020, 100 (38):2990-2996. Kaze AD, Yuyun MF, Ahima RS, Rickels MR, Echouffo-Tcheugui JB: Autonomic dysfunction and risk of severe hypoglycemia among individuals with type 2 diabetes . JCI insight 2022, 7 (22):e156334. Yakubovich N, Gerstein HC: Serious cardiovascular outcomes in diabetes: the role of hypoglycemia . Circulation 2011, 123 (3):342-348. Khunti K, Davies M, Majeed A, Thorsted BL, Wolden ML, Paul SK: Hypoglycemia and Risk of Cardiovascular Disease and All-Cause Mortality in Insulin-Treated People With Type 1 and Type 2 Diabetes: A Cohort Study . Diabetes care 2014, 38 (2):316-322. Wright RJ, Frier BM: Vascular disease and diabetes: is hypoglycaemia an aggravating factor? Diabetes/metabolism research and reviews 2008, 24 (5):353-363. Hanefeld M, Frier BM, Pistrosch F: Hypoglycemia and Cardiovascular Risk: Is There a Major Link? Diabetes care 2016, 39 (Supplement_2):S205-S209. Auer RN: Hypoglycemic brain damage . Metab Brain Dis 2004, 19 (3-4):169-175. Sandoval DA, Guy DL, Richardson MA, Ertl AC, Davis SN: Acute, same-day effects of antecedent exercise on counterregulatory responses to subsequent hypoglycemia in type 1 diabetes mellitus . American journal of physiology Endocrinology and metabolism 2006, 290 (6):E1331-1338. Ren X, Wang Z, Guo C: Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis . Diabetology & metabolic syndrome 2022, 14 (1):6. Lee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, Yoo HJ, Kim SG, Choi KM, Baik SH et al : Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study . Cardiovascular diabetology 2020, 19 (1):144. Lee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, Yoo HJ, Kim SG, Choi KM, Baik SH et al : Glucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study . Cardiovascular Diabetology 2020, 19 (1):144. Azevedo JRA, Azevedo RP, Miranda MA, Costa NNR, Araujo LO: Management of hyperglycemia in patients with acute ischemic stroke: comparison of two strategies . Critical Care 2009, 13 (3):P48. Ciplak S, Adiguzel A, Ozturk U, Akalin Y: Prognostic value of glucose fluctuation in patients undergoing thrombolysis or thrombectomy due to acute ischemic stroke . The Egyptian Journal of Neurology, Psychiatry and Neurosurgery 2021, 57 (1):159. Terada T, Wilson BJ, Myette-Côté E, Kuzik N, Bell GJ, McCargar LJ, Boulé NG: Targeting specific interstitial glycemic parameters with high-intensity interval exercise and fasted-state exercise in type 2 diabetes . Metabolism: clinical and experimental 2016, 65 (5):599-608. Karstoft K, Clark MA, Jakobsen I, Müller IA, Pedersen BK, Solomon TP, Ried-Larsen M: The effects of 2 weeks of interval vs continuous walking training on glycaemic control and whole-body oxidative stress in individuals with type 2 diabetes: a controlled, randomised, crossover trial . Diabetologia 2017, 60 (3):508-517. Boulé NG, Kenny GP, Haddad E, Wells GA, Sigal RJ: Meta-analysis of the effect of structured exercise training on cardiorespiratory fitness in Type 2 diabetes mellitus . Diabetologia 2003, 46 (8):1071-1081. Liu D, Zhang Y, Wu Q, Han R, Cheng D, Wu L, Guo J, Yu X, Ge W, Ni J et al : Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes . Journal of diabetes 2024, 16 (4):e13549. FATULLA PJ, IMBERG H, HIRSCH IB, HEISE T, LIND M: 967-P: Evaluation of CV and SD as Glucose Variability Metrics Based on Data from the GOLD and SILVER Trials . Diabetes 2023, 72 (Supplement_1). Rodbard D: Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control . Diabetes Technol Ther 2009, 11 Suppl 1 :S55-67. Kovatchev B, Cobelli C: Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes . Diabetes care 2016, 39 (4):502-510. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files TableS19.docx CONSORT2025editablechecklist.docx Table1Exerciseinterventionprogram.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Editor invited by journal 28 Apr, 2025 Submission checks completed at journal 27 Apr, 2025 First submitted to journal 27 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6390697","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449800279,"identity":"4c207b5a-6601-48e2-a17d-8725b80a4a11","order_by":0,"name":"Meili ng 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2","display":"","copyAsset":false,"role":"figure","size":8142321,"visible":true,"origin":"","legend":"\u003cp\u003eAcute Changes in Blood Glucose Concentration Pre- and Post-Exercise.\u003c/p\u003e\n\u003cp\u003e‘I’ represents the standard error\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/e8ec1e5691f4d7f6c4e4547c.png"},{"id":82143007,"identity":"ee1d09bf-75af-47e6-86ba-ce4cd0bca4dd","added_by":"auto","created_at":"2025-05-07 06:38:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30939232,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Daily Mean MG, Peak, and Nadir Blood Glucose Levels Across Experimental Groups During the Intervention Period.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/594f87043d1fd9b27f19c3ef.png"},{"id":82143011,"identity":"d2430d34-0201-4d60-9dff-ddd333ac9b54","added_by":"auto","created_at":"2025-05-07 06:38:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31383784,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Daily Mean TIR, TAR, and TBR Values Across Experimental Groups During the Intervention Period.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/a09021e41555e9104dee49e4.png"},{"id":82143010,"identity":"dc3a6af1-789f-445e-971b-dbb5099df49f","added_by":"auto","created_at":"2025-05-07 06:38:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30020720,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Daily Mean SD-glucose, MAGE, and CV Values Across Experimental Groups During the Intervention Period.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/e63fbe9d2164a369e362e6b6.png"},{"id":82146166,"identity":"01c34bbc-7eed-4064-af46-a6d8813b48c5","added_by":"auto","created_at":"2025-05-07 06:54:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":78720,"visible":true,"origin":"","legend":"","description":"","filename":"TableS19.docx","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/84f6638a00755610806ad9bc.docx"},{"id":82142984,"identity":"42c24c6c-06a9-4ab9-9725-6e22cf3fc89d","added_by":"auto","created_at":"2025-05-07 06:38:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36478,"visible":true,"origin":"","legend":"","description":"","filename":"CONSORT2025editablechecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/5f069afc0f7987b6b2008bae.docx"},{"id":82144575,"identity":"99ee2342-12a1-468d-ad08-faff46786803","added_by":"auto","created_at":"2025-05-07 06:46:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16258,"visible":true,"origin":"","legend":"","description":"","filename":"Table1Exerciseinterventionprogram.docx","url":"https://assets-eu.researchsquare.com/files/rs-6390697/v1/b67b857a362812c405bec639.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Three Aerobic Exercise Protocols on Acute Glucose Response and Continuous Glucose Monitoring Trends in Patients with Type 2 Diabetes Mellitus and Stroke: A Randomized Controlled Trial","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn medicine and public health, T2DM and stroke constitute significant global health challenges. According to data published in 2021 by the International Diabetes Federation, approximately 141\u0026nbsp;million adults in China are affected by diabetes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A study involving 18,013 patients with T2DM reported a stroke incidence rate of 9.5% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epidemiological studies have demonstrated that diabetes significantly affects the long-term prognosis of stroke survivors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent meta-analyses have indicated that diabetic patients face approximately a 50% increased risk of stroke recurrence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Diabetes is a critical determinant of stroke prognosis in the general population [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and effective glycemic control can improve overall health and markedly reduce the risk of subsequent cerebral injury [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHemoglobin A1c (HbA1c) is a crucial biomarker for managing blood glucose in patients with T2DM. It reflects mean blood glucose levels over the prior two to three months. However, the association between HbA1c and stroke recurrence risk is relatively weak. The effectiveness of intensive glucose control\u0026mdash;specifically, maintaining HbA1c levels at or below 7%\u0026mdash;in preventing recurrent strokes during the acute phase in patients with diabetes has not been conclusively established [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This uncertainty may be attributed to the robust relationship between elevated average blood glucose levels and diabetic microvascular complications, whereas the relationship between HbA1c and macrovascular complications appears to be more indirect[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. HbA1c cannot capture glucose fluctuations or provide detailed insights into glucose dynamics. With advancements in CGM, high-precision CGM technology now enables more accurate monitoring of glucose fluctuations, hyperglycemia, hypoglycemia, and variability, offering essential metrics for diabetes management [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies have demonstrated a clear association between TIR, TBR, and the risk of cardiovascular and cerebrovascular diseases in diabetic patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, both prospective and retrospective cohort studies have identified a dose-response relationship between glucose variability and stroke risk, further underscoring the practical significance of employing CGM-derived metrics for assessing glycemic control in stroke patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAerobic exercise is a crucial approach to secondary stroke prevention, as it significantly reduces cardiovascular risk and enhances diabetes management [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although previous studies have investigated the effects of aerobic exercise on HbA1c, fasting blood glucose (FBG), and acute post-exercise glucose variability, the impact of long-term exercise (greater than two weeks) on glucose variability remains insufficiently explored [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, owing to the lack of continuous blood glucose monitoring during the intervention period, it remains challenging to assess the long-term impact of exercise on key CGM-derived glucose control indices, including MG, MAGE, SD-glucose, and CV.\u003c/p\u003e \u003cp\u003eThe American Heart Association (AHA) and American Stroke Association (ASA) recommend exercise for physically active stroke survivors three to five times per week, with each session lasting between 20 and 60 minutes at an intensity ranging from 40\u0026ndash;70% of the heart rate reserve (HRR) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the 10-minute REHIT protocol comprises two to three 20- to 30-second sprints, each performed at maximum effort. Research indicates that REHIT may result in beneficial metabolic adaptations, such as improved insulin sensitivity, without inducing excessive fatigue [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This protocol has shown promise in improving insulin sensitivity and reducing blood glucose levels among individuals without stroke [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; however, its effects in patients with T2DM who have experienced stroke remain inadequately investigated.\u003c/p\u003e \u003cp\u003eIn conclusion, this study focused on patients with T2DM and stroke, examining the acute and chronic impacts of MIIT and LMICT\u0026mdash;both involving the same duration but differing in intensity\u0026mdash;as well as REHIT, which consists of extremely short high-intensity intervals. Special attention was given to key metrics derived from CGM, including TIR, MAGE, SD-glucose, and the CV. These findings aim to provide scientific evidence for the design of personalized clinical exercise interventions.\u003c/p\u003e"},{"header":"2. Methods and Analysis","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Participants and Recruitment\u003c/h2\u003e\n \u003cp\u003eThis study included 49 patients with T2DM who had experienced a stroke and were hospitalized in the rehabilitation department of Shenzhen Second People\u0026apos;s Hospital, Guangdong Province, between October 2022 and February 2024. The study was conducted in accordance with the Declaration of Helsinki, adhered to CONSORT guidelines, received approval from the Ethics Committee of Shenzhen Second People\u0026apos;s Hospital (approval number 20220901005-FS01), and was registered with the Chinese Clinical Trial Registry (ChiCTR2200065677). All patients or their authorized representatives provided written informed consent before enrollment. Participants were selected according to the criteria outlined in the Diagnostic Essentials of Cerebrovascular Diseases in China (2019), with diagnosis confirmed by imaging, and the Chinese Guidelines for Type 2 Diabetes Mellitus (2020). The inclusion criteria were as follows: (1) aged between 18 and 80 years; (2) stroke occurrence within 15 days to 1 year prior to enrollment; (3) ability to walk independently for at least 10 meters (with or without assistance); and (4) sufficient cognitive ability to participate in study activities. Patients were excluded based on the following criteria: (1) unstable vital signs; (2) progressive or acute stroke; (3) transient ischemic attack (TIA); (4) history of brain injury or other central nervous system disorders; (5) severe cardiovascular or pulmonary diseases; (6) severe hepatic or renal dysfunction; (7) significant musculoskeletal limitations; (8) untreated deep vein thrombosis (DVT); (9) severe diabetic complications; (10) other severe comorbidities; and (11) participation in other ongoing clinical trials.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Study design\u003c/h2\u003e\n \u003cp\u003eThis study is a prospective randomized controlled trial, and the rationale, study design, and methodological details have been described previously[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Building on the previously reported protocol, patient recruitment was continued, and daily glucose monitoring data were systematically recorded throughout the study duration, along with acute glucose responses measured immediately pre- and post-exercise sessions. Briefly, a total of 49 participants were randomly assigned to one of three aerobic exercise intervention groups or a control group, with each group undergoing a 28-day intervention period. Participants in the control group received routine care, including bed mobility training, joint mobility training, and balance exercises. Participants in the aerobic exercise intervention groups received routine care in combination with structured aerobic exercise training. The detailed study design is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Sample Size Calculation\u003c/h2\u003e\n \u003cp\u003eBased on the study by Metcalfe et al.[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], REHIT significantly reduced MG levels in patients with T2DM compared to the CON group, which did not engage in exercise, with a Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e value of 0.55. This study employed a repeated measures design, consisting of three phases: baseline (days 1\u0026ndash;2), Cycle 1 (days 3\u0026ndash;14), and Cycle 2 (days 15\u0026ndash;28). Using the conversion formula \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ed\u003c/em\u003e/22, the Cohen\u0026apos;s f value was calculated as 0.275[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. To ensure an adequate sample size and account for data variability, we set the effect size at the medium range (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25). The significance level was set at \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05, with a statistical power of 0.80, and a parallel control design with a 1:1:1:1 allocation ratio. Using G*Power 3.1.9.7 software (Heinrich Heine Universit\u0026auml;t D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany), the total sample size was determined to be 40 participants, who were randomly assigned to four study groups, ensuring sufficient statistical power to detect the effects of each intervention on glucose control. Given the small sample size, strategies will be implemented to minimize participant dropout.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Randomization and Blinding\u003c/h2\u003e\n \u003cp\u003eRandomization was conducted using computer-generated random numbers, which were recorded on carbonless copy paper and sealed in uniformly sized opaque envelopes to maintain allocation concealment and ensure randomness. Due to the nature of the intervention, blinding of participants and researchers delivering exercise instruction regarding group allocation was not feasible. However, researchers conducting follow-up assessments and statistical analyses were blinded to group allocation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Intervention\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.5.1 Exercise Stress Test\u003c/h2\u003e\n \u003cp\u003eOn the basis of the relevant literature and clinical practice, this study utilized an extremity cycle ergometer integrated with a cardiopulmonary exercise testing system (Masterscreen CPX; ERGOLINE GMBH, Germany) and a 5-lead electrocardiogram (ECG) to perform a symptom-limited maximal incremental cardiopulmonary exercise test[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Before testing, equipment calibration was performed in accordance with standard protocols, and participants were fitted with a 5-lead ECG, a gas analyzer mask, and a safety harness. Assistive devices were used as necessary to stabilize the affected limbs of participants, ensuring the accuracy and safety of the test.\u003c/p\u003e\n \u003cp\u003eThe testing protocol was individualized according to the gender, age, height, weight, functional status, and exercise habits of each participant, with resistance increasing incrementally by one to two levels every two minutes. The detailed testing protocol comprised four phases: (1) Resting phase (1 min): recording resting heart rate and blood pressure; (2) Unloaded warm-up phase (3 min): participants performed familiarization exercises; (3) Incremental resistance exercise phase (8\u0026ndash;12 min): resistance was increased progressively according to a predefined protocol until participants reached maximal exertion or termination criteria were met; (4) Recovery phase (6 min): consisting of three minutes of unloaded pedaling followed by three minutes of rest, during which heart rate and blood pressure recovery were monitored. Throughout the test, each resistance stage had a duration of two minutes, and participants maintained a pedaling cadence of 80 revolutions per minute (rpm) [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe criteria for test termination were as follows: (1) a plateau in oxygen consumption (VO₂) [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] ; (2) heart rate exceeding 90% of the age-predicted maximum (adjusted to 85% for participants taking beta-blockers) [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] ; (3) respiratory exchange ratio (RER) greater than 1.00 [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]; (4) Borg rating of perceived exertion (6\u0026ndash;20 scale) greater than 17 [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]; and (5) participants reaching volitional fatigue and inability to maintain the required pedaling cadence.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.5.2 Exercise intervention program\u003c/h2\u003e\n \u003cp\u003eAll exercise interventions were conducted under the supervision of qualified physiotherapists, who had completed the study-specific training, thereby ensuring the safety and effectiveness of the intervention. The experimental group utilized a standardized four-limb coordinated rehabilitation training device. Based on the results of the exercise stress test, trained therapists adjusted the resistance levels and pedal speeds of the apparatus to accommodate each participant\u0026rsquo;s specific needs. Continuous heart rate monitoring was implemented to ensure that exercise intensity remained appropriate, with participants required to immediately report their perceived exertion levels after each session using the Borg scale (range 6\u0026ndash;20).\u003c/p\u003e\n \u003cp\u003eThe exercise intervention program commenced on day three and continued until day 24. To prevent overtraining, the program incorporated two rest days after every five consecutive exercise days, resulting in a total of 20 sessions throughout the study. These sessions were divided into two phases, each consisting of 10 sessions. Participants in the CON group were instructed to adhere to their usual treatment plans and did not participate in any structured exercise intervention (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Collection of treatment results\u003c/h2\u003e\n \u003cp\u003eUpon admission, demographic and clinical data were collected, including age, gender, height, weight, duration of diabetes, time of stroke onset, lifestyle habits (smoking, alcohol consumption), medication history, and past medical history. Baseline data were collected through fasting blood samples drawn from the antecubital vein. The primary measurements included fasting blood glucose (FBG), HbA1c, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Blood glucose was measured via the hexokinase method, HbA1c was assessed using a turbidimetric immunoassay, and all samples were tested in duplicate for verification.\u003c/p\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.1 The primary outcomes\u003c/h2\u003e\n \u003cp\u003eThe primary outcomes of this study were glucose control metrics derived from the CGM system (GS1, SIBIONICS, Shenzhen, Guangdong, China). On study days 0 and 14, trained nurses applied a glucose sensor to the non-dominant upper arm of each participant. Each sensor continuously recorded interstitial glucose concentrations over a 14-day period at 5-minute intervals and automatically transmitted the data to the hospital\u0026rsquo;s cloud system via Bluetooth connectivity.\u003c/p\u003e\n \u003cp\u003eThe following CGM-derived metrics were assessed to evaluate glycemic control:\u003c/p\u003e\n \u003cp\u003e(1) TIR: The percentage of time that glucose levels were within the target range of 3.9\u0026ndash;10.0 mmol/L, serving as a key indicator of glycemic stability.\u003c/p\u003e\n \u003cp\u003e(2) TBR: The percentage of time that glucose levels were \u0026lt;\u0026thinsp;3.9 mmol/L, used to assess the risk of hypoglycemia.\u003c/p\u003e\n \u003cp\u003e(3) TAR: The percentage of time that glucose levels were \u0026gt;\u0026thinsp;10.0 mmol/L, indicating the burden of hyperglycemia.\u003c/p\u003e\n \u003cp\u003e(4) MG: The arithmetic mean of all CGM-derived glucose values, providing an overall profile of glycemic control.\u003c/p\u003e\n \u003cp\u003e(5) SD-glucose: The square root of the mean of squared deviations from MG, reflecting absolute glycemic variability.\u003c/p\u003e\n \u003cp\u003e(6) CV: The ratio of SD-glucose to MG, used to quantify the relative degree of glucose fluctuation.\u003c/p\u003e\n \u003cp\u003e(7) MAGE: The average amplitude of significant glycemic fluctuations (defined as changes exceeding 1 SD-glucose), used to characterize within-day glycemic variability.\u003c/p\u003e\n \u003cp\u003e(8) Peak Blood Glucose: The highest glucose level recorded during each 24-hour period.\u003c/p\u003e\n \u003cp\u003e(9) Nadir Blood Glucose: The lowest glucose level recorded during each 24-hour period.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e2.6.2 Secondary outcomes\u003c/h2\u003e\n \u003cp\u003eThe secondary outcomes focused on the acute glycemic response to exercise, assessed using CGM-derived glucose concentrations. Glucose values were extracted during the 5-minute period immediately preceding the start of each exercise session and the 5-minute period immediately following its completion. These measurements were obtained from all three experimental groups throughout the intervention period.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Medication Therapy and Dietary Plan\u003c/h2\u003e\n \u003cp\u003eAll participants adhered to the endocrinologists\u0026apos; recommendations regarding the use of antidiabetic medications. Throughout the entire study period, participants were instructed to maintain their baseline antidiabetic medication regimens, ensuring consistent daily timing for the administration of oral medications or insulin [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eDietary intake was accurately documented during the two days preceding the intervention (defined as baseline days), including daily caloric intake and macronutrient distribution (carbohydrates, proteins, and fats) for each meal. This measure provided a dietary baseline for continuous nutritional monitoring throughout the study. During the subsequent 26-day intervention period, participants maintained consistent dietary patterns under the direct supervision and professional guidance of registered dietitians. To ensure maximum dietary consistency and strict control of dietary factors influencing glycemic control, registered dietitians carefully documented and supervised the composition and quantity of food consumed by participants at each meal.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Potential Adverse Events in the Exercise Intervention\u003c/h2\u003e\n \u003cp\u003eAdverse Events (AEs) refer to any unfavorable health occurrences following aerobic exercise rehabilitation, irrespective of their causal relationship to the treatment. Serious Adverse Events (SAEs) include events necessitating hospitalization, causing disability, impairing work capacity, endangering life, or resulting in death or congenital deformities. These events are classified by severity: mild (tolerable, with no significant impact on treatment or recovery), moderate (difficult to tolerate, necessitating special treatment, and adversely affecting recovery), and severe (life-threatening, causing death or disability, requiring immediate intervention). Expected AEs include fatigue, abdominal discomfort, muscle cramps, soreness, stiffness, and swelling, whereas unexpected AEs involve severe reactions, such as life-threatening events or death, not anticipated in the study protocol. Serious AEs may include conditions such as bradycardia, sudden death, myocardial infarction, heart failure, hypoglycemia, ketoacidosis, and musculoskeletal injuries. Risk management involves permitting participants to rest for up to two minutes if required, halting exercise in the event of adverse events, monitoring the participant, and administering oxygen. Serious events are reported within 24 hours to the sponsor, ethics committee, and relevant regulatory authorities, with follow-up treatment provided until the condition resolves or stabilizes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9 Statistics\u003c/h2\u003e\n \u003cp\u003eData were analyzed using SPSS software (version 23.0; IBM Corporation, Armonk, NY, USA). Categorical variables were expressed as percentages, while continuous variables were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Differences in baseline characteristics between study groups were assessed using one-way analysis of variance (ANOVA) for continuous variables and chi-square tests for categorical variables. Levene\u0026apos;s test was initially performed to assess the homogeneity of variance; if this assumption was violated, Welch\u0026rsquo;s ANOVA was applied. Fisher\u0026rsquo;s exact test was used for categorical variables when the expected frequency was less than five. The effects of time periods and exercise protocols on CGM indices were evaluated using two-way repeated-measures ANOVA. When the sphericity assumption was violated, a Greenhouse\u0026ndash;Geisser correction was applied. Additionally, generalized estimating equations (GEE) were employed to analyze daily mean values of CGM indices across study groups. Subject ID was treated as a between-subjects factor, and intervention date as a within-subjects factor, with an unstructured covariance matrix employed to model the correlation structure within the data. Bonferroni correction and polynomial trend analysis were performed to further evaluate between-group differences in CGM indices throughout the intervention period, as well as linear trends within each group.\u003c/p\u003e\n \u003cp\u003eFurthermore, a general linear model (GLM) was used to assess acute changes in blood glucose concentrations among different exercise protocols. The CGM blood glucose measurements obtained within the 5-minute period immediately preceding each intervention were used as covariates, and corresponding post-intervention measurements obtained within the first 5 minutes were treated as the dependent variable to control for the influence of baseline glucose levels. Model parameters were estimated using the least squares method, and adjusted within-group effects (least squares means) were calculated. Post hoc multiple comparisons using least squares mean differences were conducted to compare effects among study groups; adjusted means, standard errors, and 95% confidence intervals (95% CI) were reported for each group. The significance level was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eIn total, 119 individuals volunteered for participation in this study. After screening for predefined inclusion and exclusion criteria, 70 participants were excluded due to factors including restricted lower-extremity passive joint range of motion, severe comorbid conditions, or an inability to fully commit to the study requirements. Consequently, 49 eligible participants (mean age, 59.06\u0026thinsp;\u0026plusmn;\u0026thinsp;10.44 years) were enrolled and randomly allocated to four groups: CON (n\u0026thinsp;=\u0026thinsp;12), LMICT (n\u0026thinsp;=\u0026thinsp;11), MIIT (n\u0026thinsp;=\u0026thinsp;12), and REHIT (n\u0026thinsp;=\u0026thinsp;14). During the study, four participants withdrew: one from the CON group, one from the MIIT group, and two from the REHIT group. Additionally, one participant from the LMICT group was excluded due to fever symptoms that affected adherence to the training protocol.\u003c/p\u003e \u003cp\u003eTo ensure compliance with the prescribed dietary and exercise protocols, rigorous professional supervision was implemented throughout the study period. While some mild, expected exercise-related adverse events, such as fatigue and muscle soreness, were observed during the intervention period, no serious adverse events were reported. Overall, the intervention was well-tolerated. Except for participants who withdrew mid-study, all remaining participants successfully completed the prescribed training, achieving a completion rate of 90%.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the participants in each of the four study groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No significant differences were observed among the four groups regarding age, gender, height, weight, diabetes duration, time since stroke onset, stroke type (ischemic stroke), hypertension, dyslipidemia, coronary artery disease, smoking history, alcohol consumption, or concomitant medications (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, baseline clinical indicators\u0026mdash;including HbA1c, FBG, Barthel Index (BI), and Functional Movement Assessment - Lower Extremity (FMA-LE) scores\u0026mdash;showed no significant differences among the groups, demonstrating baseline homogeneity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and baseline clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMICT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIIT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eREHIT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithdraw halfway, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale/female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.57\u0026thinsp;\u0026plusmn;\u0026thinsp;9.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.42\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes\u0026thinsp;\u0026gt;\u0026thinsp;10 years, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG( mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays since stroke onset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.67\u0026thinsp;\u0026plusmn;\u0026thinsp;65.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101.09\u0026thinsp;\u0026plusmn;\u0026thinsp;73.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.58\u0026thinsp;\u0026plusmn;\u0026thinsp;87.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.21\u0026thinsp;\u0026plusmn;\u0026thinsp;85.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemic stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(71.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of stroke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(92.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith dyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith coronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(42.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of alcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.67\u0026thinsp;\u0026plusmn;\u0026thinsp;32.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.36\u0026thinsp;\u0026plusmn;\u0026thinsp;17.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.50\u0026thinsp;\u0026plusmn;\u0026thinsp;20.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.21\u0026thinsp;\u0026plusmn;\u0026thinsp;20.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMA-LE\u003c/p\u003e \u003cp\u003eTG (mmHg)\u003c/p\u003e \u003cp\u003eTC (mmHg)\u003c/p\u003e \u003cp\u003eHDL-C (mmHg)\u003c/p\u003e \u003cp\u003eLDL-C (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.42\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e \u003cp\u003e1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.79\u0026thinsp;\u0026plusmn;\u0026thinsp;7.40\u003c/p\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOral blood-glucose-lowering medication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiguanides, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(64.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP-4 Inhibitors, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2 Inhibitors, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfonylureas, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(42.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsulin treatment n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal Insulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(21.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple Daily Injections, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiphasic Insulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(14.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatins, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (91.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(71.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1-blocker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(35.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exercise Intensity Monitoring Results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the exercise intensity monitoring data and RPE recorded immediately after each exercise session for each experimental group. Participants in the LMICT group attained an average peak intensity of 51.23% \u0026plusmn; 6.94% HRR and reported an average post-exercise RPE score of 12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36. In the MIIT group, the average peak intensity achieved was 69.75% \u0026plusmn; 5.29% HRR, with a corresponding post-exercise RPE of 13.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97. These observed intensities slightly deviated from the predefined target ranges, possibly due to factors such as individual physiological variability, adaptations to training, psychological factors, or potential measurement errors. The REHIT group achieved an average peak intensity of 78.19% \u0026plusmn; 5.77% HRR, with a post-exercise RPE of 14.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78. Collectively, these findings confirm successful implementation of the exercise interventions, in accordance with the intended experimental protocols.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExercise Intensity Monitoring Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMICT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIIT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eREHIT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-exercise Resting Heart Rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e80.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e73.40\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e78.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise HRmax (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e102.79\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e116.23\u0026thinsp;\u0026plusmn;\u0026thinsp;14.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e116.40\u0026thinsp;\u0026plusmn;\u0026thinsp;15.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% HRmax Achieved During Exercise (relative to HRpeak)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e82.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e86.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e91.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% HRR Utilization at Exercise HRmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e51.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e69.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e78.19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRPE Score (Post-exercise)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e14.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote\u003c/b\u003e: HRpeak: Defined as the maximum heart rate attained by participants during maximal exertion in the cardiopulmonary exercise test; %HRR was calculated using the formula: [(HRmax \u0026ndash; HRrest) / (HRpeak \u0026ndash; HRres\u003csub\u003et\u003c/sub\u003e)] \u0026times; 100%; HR\u003csub\u003emax\u003c/sub\u003e: The highest heart rate recorded during an exercise session; HRrest: Resting heart rate measured prior to exercise; RPE: assessed using the Borg scale (range 6\u0026ndash;20).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Acute Glycemic Response Pre- and Post-Exercise\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a total of 660 paired acute glucose measurements were recorded across all exercise sessions, including the LMICT, MIIT, and REHIT groups. Acute changes in glucose concentration ranged from a maximum decrease of 3.3 mmol/L to a maximum increase of 1.1 mmol/L. Paired-sample t-tests demonstrated that acute glucose changes were statistically significant in all three exercise groups: LMICT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MIIT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and REHIT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After controlling for baseline glucose levels, the adjusted least squares mean differences in glucose concentration immediately following exercise were \u0026minus;\u0026thinsp;0.79 mmol/L (LMICT), \u0026minus;\u0026thinsp;1.00 mmol/L (MIIT), and \u0026minus;\u0026thinsp;0.64 mmol/L (REHIT), respectively (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAcute Glycemic Response Pre- and Post-Exercise\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBlood Glucose Concentration (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-exercise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-exercise\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll experimental groups\u003c/p\u003e \u003cp\u003eLMICT\u003c/p\u003e \u003cp\u003eMIIT\u003c/p\u003e \u003cp\u003eREHIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e660\u003c/p\u003e \u003cp\u003e200\u003c/p\u003e \u003cp\u003e220\u003c/p\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003cp\u003e9.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003cp\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003cp\u003e8.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003cp\u003e8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.68\u003c/p\u003e \u003cp\u003e15.98\u003c/p\u003e \u003cp\u003e21.76\u003c/p\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Changes in CGM-derived Glucose Indices Across Study Periods\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the mean changes in CGM-derived glucose metrics (MG, Peak Blood Glucose, Nadir Blood Glucose, TIR, TAR, TBR, MAGE, SD, and CV) across Baseline, Period 1, and Period 2 for the LMICT, MIIT, REHIT, and CON groups. Repeated-measures ANOVA showed that:\u003c/p\u003e \u003cp\u003e(1) MG: There was a significant main effect of group (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.039; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040), a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.956; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.743; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003e(2) Peak Blood Glucose: No significant main effect of group was observed (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.632; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.197); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.841; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.978; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were identified.\u003c/p\u003e \u003cp\u003e(3) Nadir Blood Glucose: No significant main effect of group was found (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.808; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.161); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.147; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) was observed, with no significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.519; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.757).\u003c/p\u003e \u003cp\u003e(4) TIR: No significant main effect of group was identified (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.651; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.762; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.934; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) were observed.\u003c/p\u003e \u003cp\u003e(5) TAR: There were significant main effects of group (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.228; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), significant within-subject effects of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.409; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.283; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e(6) TBR: No significant main effect of group was detected (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.748; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.748); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.912; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) was observed, with no significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.519; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.757).\u003c/p\u003e \u003cp\u003e(7) MAGE: No significant main effect of group was observed (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.705); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.716; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.913; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) were found.\u003c/p\u003e \u003cp\u003e(8) SD-glucose: No significant main effect of group was observed (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.469; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.705); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.716; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.813; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were identified.\u003c/p\u003e \u003cp\u003e(9) CV: No significant main effect of group was detected (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.120; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.948); however, a significant within-subject effect of time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.446; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was observed, with no significant group-by-time interaction (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.329; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.125).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCGM Glycemic Indices from Baseline Days to Cycle 1 and Cycle 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMG\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeak Blood Glucose\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNadir Blood Glucose\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTIR\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTAR\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTBR\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMAGE\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSD-glucose\u003c/p\u003e \u003cp\u003e(mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBaseline Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.61\u0026thinsp;\u0026plusmn;\u0026thinsp;20.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.36\u0026thinsp;\u0026plusmn;\u0026thinsp;19.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.71\u0026thinsp;\u0026plusmn;\u0026thinsp;19.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72.47\u0026thinsp;\u0026plusmn;\u0026thinsp;19.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.55\u0026thinsp;\u0026plusmn;\u0026thinsp;19.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLMICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBaseline Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.88\u0026thinsp;\u0026plusmn;\u0026thinsp;11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e29.77\u0026thinsp;\u0026plusmn;\u0026thinsp;7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.09\u0026thinsp;\u0026plusmn;\u0026thinsp;12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.72\u0026thinsp;\u0026plusmn;\u0026thinsp;12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.63\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.18\u0026thinsp;\u0026plusmn;\u0026thinsp;10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.03\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.04\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMIIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBaseline Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.15\u0026thinsp;\u0026plusmn;\u0026thinsp;23.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.16\u0026thinsp;\u0026plusmn;\u0026thinsp;24.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.88\u0026thinsp;\u0026plusmn;\u0026thinsp;10.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27.05\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.59\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.07\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eREHIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBaseline Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.38\u0026thinsp;\u0026plusmn;\u0026thinsp;10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e30.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.48\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26.95\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCycle 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBetween-group comparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eF, p\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.039, 0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.632, 0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.808, 0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.651, 0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.228, 0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.748, 0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.469, 0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.843, 0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.120, 0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin-subject comparison\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF, p\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.956, 0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.841, \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.147,0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.762, \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.409, 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.226, 0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.716,\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19.803,\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.446,\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eF, p\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.743, 0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.978, 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.519, 0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.934, 0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.283, 0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.281, 0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.913, 0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.813, 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.858, 0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Trends in CGM-derived glucose metrics during the intervention\u003c/h2\u003e \u003cp\u003eTables S1 to S9 present the daily mean values of CGM-derived indicators and depict the overall intervention trends over the 28-day period for the LMICT, MIIT, REHIT, and CON groups. Polynomial trend analyses revealed that the MIIT group exhibited significant linear improvements in MG (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TIR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), TAR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), peak blood glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SD-glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and MAGE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). The REHIT group likewise demonstrated significant improvements in peak blood glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TIR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TAR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), MAGE (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SD-glucose (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CV (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). By contrast, the LMICT group demonstrated no comparable linear improvement (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDaily CGM comparisons with the CON group across the 28-day intervention yielded the following outcomes (see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e to S9):\u003c/p\u003e \u003cp\u003e(1) MG: Over 28 days, the MIIT group first exhibited a significant difference from the CON group on Day 11 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), with significant differences noted on 11 days (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The REHIT group first differed on Day 8 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041), with 16 days showing significant differences. The LMICT group displayed a difference on Day 26 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), as detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(2) Peak Blood Glucose levels: During the study, the MIIT group first differed from the CON group on Day 20 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), with significant differences observed on 6 days (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The REHIT group first differed on Day 12 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), with 9 days showing differences. The LMICT group showed no significant differences throughout the intervention (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(3) Nadir Blood Glucose levels: The MIIT group first demonstrated a difference from the CON group on Day 25 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), with three days of differences during the intervention. The REHIT group differed on Day 27 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), the only day with a significant difference. The LMICT group showed no differences throughout the intervention (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table S3.\u003c/p\u003e \u003cp\u003e(4) TIR: The MIIT group first showed significant differences from the CON group on Day 18 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), with five days of differences noted during the intervention. The REHIT group first differed on Day 12 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), with 11 days of differences. The LMICT group showed no differences throughout the intervention (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as seen in Table S4.\u003c/p\u003e \u003cp\u003e(5) TAR: The MIIT group first showed differences on Day 11 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), with seven days of differences noted. The REHIT group first differed on Day 10 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), with 16 days of differences. The LMICT group showed no differences throughout the intervention (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as seen in Table S5.\u003c/p\u003e \u003cp\u003e(6) TBR: The MIIT group showed differences on Day 21 only (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), detailed in Table S6.\u003c/p\u003e \u003cp\u003e(7) MAGE: The MIIT group first differed from the CON group on Day 22 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), with two days of differences. Additionally, on Day 26, the REHIT group first differed significantly from the CON group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), with two days of significance. The LMICT group showed no differences throughout the intervention (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table S7.\u003c/p\u003e \u003cp\u003e(8) SD-glucose: The MIIT group showed significant differences on Day 28 only (p\u0026thinsp;=\u0026thinsp;0.043). The REHIT group first differed on Day 25 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035), with three days of significance noted. Details are provided in Table S8.\u003c/p\u003e \u003cp\u003e(9) CV: Throughout the 28-day study period, none of the groups\u0026mdash;LMICT, MIIT, or REHIT\u0026mdash;showed significant differences when compared to the CON grou\u003cem\u003ep\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table S9.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003ch2\u003e\u003cstrong\u003e4.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe Impact of Different Aerobic Exercise Protocols on Acute Glycemic Changes in Patients with Type 2 Diabetes Mellitus Following Stroke\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eLong-term glycemic improvements achieved through exercise are thought to be cumulative effects resulting from repeated acute reductions in glucose concentrations following individual exercise sessions [37].Numerous studies have evaluated acute blood glucose responses to aerobic exercise in diabetic patients and have generally concurred that moderate-intensity exercise effectively lowers glucose levels [38, 39]. However, findings concerning acute glucose responses to high-intensity exercise are inconsistent. Some studies have reported greater glucose reductions following high-intensity exercise compared to moderate-intensity exercise [38, 40]. whereas others have observed significant acute increases in glucose levels after high-intensity exercise [41]. Furthermore, other evidence suggests that acute reductions in blood glucose may depend more on total exercise volume than on exercise intensity [42, 43].\u003c/p\u003e\n\u003cp\u003eIn this study, we investigated the acute glucose responses to three distinct aerobic exercise protocols\u0026mdash;LMICT, MIIT, and REHIT\u0026mdash;among stroke patients with type 2 diabetes mellitus (T2DM). All aerobic exercise protocols elicited significant acute reductions in blood glucose (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). After adjusting for baseline blood glucose concentrations, the MIIT group exhibited the largest reduction (\u0026minus;1.00 mmol/L), followed by the LMICT (\u0026minus;0.79 mmol/L) and REHIT (\u0026minus;0.64 mmol/L) groups. Although total energy expenditure per session was not directly measured, MIIT\u0026mdash;performed at a higher exercise intensity (69.75% \u0026plusmn; 5.29% HRR) than LMICT (51.23% \u0026plusmn; 6.94% HRR) over the same duration (30 min)\u0026mdash;resulted in greater acute glucose reductions. Despite evidence suggesting that a 10-minute REHIT session can significantly deplete glycogen stores by approximately 20% [44], our findings indicate a comparatively smaller acute glucose-lowering effect following REHIT. This discrepancy could be attributed to the increased oxidative stress associated with higher-intensity exercise\u0026nbsp;[45]. Additionally, brief and intense aerobic exercise may markedly elevate plasma catecholamine levels, subsequently increasing glucose production for 1 to 2 hours post-exercise [46], thus limiting immediate reductions in glucose concentration after REHIT.\u003c/p\u003e\n\u003cp\u003eOverall, MIIT demonstrated a more pronounced acute glucose-lowering effect than LMICT and REHIT in stroke patients with T2DM.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe Impact of Different Aerobic Exercise Protocols on MG Trends During the Intervention Period in Patients with Type 2 Diabetes Mellitus and Stroke\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMG levels are directly correlated with HbA1c [9]; however, considering individual differences in hemoglobin glycation rates and other physiological factors [9, 47], MG is deemed a more sensitive indicator than HbA1c for evaluating short-term intervention effects [48]. In the present study, repeated-measures ANOVA identified significant within-subject (time) effects (\u003cem\u003eF\u003c/em\u003e = 5.956, \u003cem\u003ep\u003c/em\u003e = 0.010) and group-by-time interaction effects (\u003cem\u003eF\u003c/em\u003e = 3.743, \u003cem\u003ep\u003c/em\u003e = 0.009) for MG, indicating that specific exercise interventions could lead to progressive improvements in MG over time. Further analysis showed that only the MIIT group exhibited a significant linear decline in MG throughout the intervention period (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). This result underscores the importance of both the interaction between aerobic exercise intensity/type and cumulative exercise duration, as well as the adherence to individualized exercise prescriptions for effectively reducing MG.\u003c/p\u003e\n\u003cp\u003eConsistent with previous research, HbA1c levels are influenced not only by the duration of exercise\u0026mdash;with weekly reductions ranging from 0.009% to 0.043% as exercise continues [49] \u0026mdash;but also by its intensity and modality (interval vs. continuous). For example, a study comparing 40 minutes of continuous moderate exercise (60% VO2peak) with 40 minutes of interval training (four minutes at 50% VO2peak interspersed with one minute at 80% VO2peak) found significant HbA1c reductions only in the interval exercise group [50]. Although several mechanisms have been proposed, MG is generally considered the primary determinant of hemoglobin and protein glycation, and a key factor in the pathogenesis of chronic diabetic complications [51, 52].\u003c/p\u003e\n\u003cp\u003eOur findings indicate that MIIT, when performed regularly at sufficient intensity and duration, is highly effective in improving glucose concentrations in stroke patients with T2DM.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe Impact of Different Aerobic Exercise Protocols on \u0026apos;Time in Range\u0026apos; in Patients with Type 2 Diabetes Mellitus and Stroke During the Intervention Period\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAt the 2019 Advanced Technologies \u0026amp; Treatments for Diabetes Conference, experts recommended that interpreting CGM data should include assessing the percentage of time within the target range (TIR), above it (TAR), and below it (TBR) to effectively evaluate glycemic control.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Prior research demonstrates a strong association between TIR and both all-cause and cardiovascular mortality risks, with an 8% and 5% increase in mortality risks, respectively, for each 10% reduction in TIR [13]. Additionally, TIR is significantly correlated with peripheral nerve function in T2DM patients, a correlation that persists even after adjusting for HbA1c levels. Notably, higher TIR percentiles are independently correlated with improved peripheral nerve function, with a 10% reduction in TIR corresponding to a 25% increase in the risk of developing peripheral neuropathy [53]. Peripheral neuropathy, a frequent complication of diabetes, affects approximately 50% of those diagnosed, profoundly impacting their quality of life [54]. Associated symptoms, including pain and discomfort, can complicate the stroke recovery process. Systematic reviews have demonstrated that physical activity interventions can significantly enhance TIR in T2DM patients, with an average improvement of 4.21% (95% CI: 0.95 to 7.46, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) [55]. This study also examines the impact of various aerobic exercise regimens on TIR, revealing significant intragroup and interactive effects (\u003cem\u003eF\u003c/em\u003e = 13.762, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001; \u003cem\u003eF\u003c/em\u003e = 3.934, \u003cem\u003ep\u003c/em\u003e=0.005), which underscore the importance of the intervention\u0026apos;s duration on TIR management. Trend analyses further verified significant improvements in TIR throughout the interventions with both MIIT and REHIT programs, with notable enhancements observed (\u003cem\u003ep\u003c/em\u003e = 0.004 and \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, respectively).\u003c/p\u003e\n\u003cp\u003eTAR is a crucial metric for evaluating the hyperglycemia risk in patients. Prolonged or chronic hyperglycemic conditions cause cellular damage and are closely linked to diabetes-related complications [56]. These conditions can also lead to various adverse outcomes in stroke patients, including increased mortality, disease progression, expanded infarct size, and impaired neurological recovery, applicable even to non-diabetic individuals [57-60]. In our study, significant decreases in TAR were observed in both the MIIT and REHIT groups as the interventions progressed (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004 and \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.007, respectively). This alignment with the observed improvements in TIR suggests a strong correlation between TIR and TAR, where enhancements in TIR lead to reductions in TAR [61].\u003c/p\u003e\n\u003cp\u003eInappropriate exercise regimens can trigger adverse reactions, such as hypoglycemia. Diabetic patients often experience autonomic neuropathy, which impairs their ability to manage hypoglycemic episodes, thereby heightening the risk of severe hypoglycemic events [62]. For individuals with T2DM who have experienced a stroke, maintaining normal glucose levels and preventing hypoglycemia is crucial. Hypoglycemic conditions are closely linked to increased risks of cardiovascular incidents, brain injuries, and deteriorating retinopathy, and are considered potential accelerators of atherosclerosis [63-66]. Furthermore, hypoglycemia disrupts normal brain energy metabolism and exacerbates neuronal injury [67]. Although evidence suggests that exercise significantly reduces autonomic and metabolic reactions to subsequent hypoglycemia, potentially raising the risk of hypoglycemia hours after exercising [68]. our findings show no significant differences in TBR among groups (\u003cem\u003eF\u003c/em\u003e = 0.748, \u003cem\u003ep\u003c/em\u003e = 0.530). Additionally, no linear trends in TAR were observed across the study periods in the LMICT, MIIT, and REHIT groups (all \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 1.000). Similarly, no significant group differences in Nadir Blood Glucose were found during the cycles, nor were any linear trends observed over time (\u003cem\u003eF\u003c/em\u003e = 1.808, \u003cem\u003ep\u003c/em\u003e = 0.161). These results suggest that LMICT, MIIT, and REHIT do not increase the likelihood of hypoglycemic events in terms of overall trends in glucose management.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study demonstrates that MIIT and REHIT significantly reduce the risk of hyperglycemia and effectively shorten the time required to achieve target blood glucose levels. In contrast, LMICT demonstrates limited effectiveness in improving both TIR and TAR. Moreover, closely monitored LMICT, MIIT, and REHIT protocols do not increase TBR in patients with T2DM who have experienced a stroke, providing new insights into safe and effective glucose management.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eThe Impact of Different Aerobic Exercise Protocols on Glycemic Variability in Patients with Type 2 Diabetes Mellitus and Stroke During the Intervention Period\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIncreased glucose variability significantly contributes to inflammatory responses and oxidative stress, initiating multiple pathophysiological mechanisms, including the accumulation of advanced glycation end-products (AGEs), elevation of reactive oxygen species, and enhanced expression of their corresponding receptors, thus exacerbating inflammatory and oxidative damage[69-71]. Heightened glucose variability can be particularly detrimental in stroke patients, whose impaired homeostatic mechanisms increase susceptibility to thrombotic events and plaque instability [72, 73].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior studies have shown that acute exercise enhances daily blood glucose levels, responses, and variability [20]. However, the long-term effects of regular exercise on glucose variability are less well-documented. This study identified no significant differences between groups in MAGE and SD-glucose (MAGE: \u003cem\u003eF\u003c/em\u003e = 0.469, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.705; SD-glucose: \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 0.843, \u003cem\u003ep\u003c/em\u003e = 0.478). Nonetheless, significant intragroup differences were observed across various cycles (MAGE: \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 13.716, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; SD-glucose: \u003cem\u003eF\u003c/em\u003e = 19.803, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), with notable interactions between groups and cycles (MAGE: \u003cem\u003eF\u003c/em\u003e = 2.913, \u003cem\u003ep\u003c/em\u003e = 0.025; SD-glucose: \u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 4.813, \u003cem\u003ep\u003c/em\u003e = 0.001). These results suggest that the effects of different aerobic exercises on glucose management for patients with T2DM who have experienced a stroke vary over time. Further analysis revealed significant decreases in the daily averages of SD-glucose and MAGE in the MIIT and REHIT groups as the study progressed, with no comparable trends in the LMICT and CON groups. Despite the non-significant main effect on the CV across groups (\u003cem\u003eF\u003c/em\u003e = 0.120, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.948), significant variations were observed within groups across different cycles (\u003cem\u003eF\u0026nbsp;\u003c/em\u003e= 12.446, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), emphasizing the substantial influence of time on CV. However, the interaction between groups and cycles was not significant (\u003cem\u003eF\u003c/em\u003e = 1.858, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.125), suggesting similar trends across study groups over time. Only the REHIT group exhibited a significant downward trend in CV during the study (\u003cem\u003ep\u003c/em\u003e = 0.001). These findings underscore that MIIT and REHIT significantly enhance glucose variability, with the duration of the intervention and the intensity of exercise being critical factors. Additionally supported by external studies, high-intensity or intermittent exercise formats are particularly effective at improving glucose variability [74, 75]. This aligns with broader research indicating that exercise intensity correlates more strongly with improved glucose control than exercise volume, potentially including enhancements in glucose variability [18, 76].\u003c/p\u003e\n\u003cp\u003eIn summary, our findings confirm that MIIT and REHIT protocols effectively reduce glycemic variability in stroke patients with T2DM, with REHIT offering additional benefits for CV control. Conversely, LMICT demonstrated comparatively limited effectiveness in improving glucose variability. These outcomes emphasize the importance of incorporating appropriate exercise intensity into exercise prescriptions for glycemic control in clinical practice.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.5\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTemporal Analysis of Significant Differences in Key CGM Indicators Between Experimental and Control Groups During the Intervention Period\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA recent meta-analysis demonstrated that short-term exercise interventions (less than two weeks) significantly reduce MG by approximately 0.5 mmol/L (95% CI: -0.7 to -0.3; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), shorten the duration of hyperglycemia, and enhance the MAGE compared to control conditions\u0026nbsp;[19]. However, a separate 2024 study observed improvements in glucose variability indicators, including significant reductions in SD-glucose (from 1.35 mmol/L to 1.10 mmol/L, \u003cem\u003ep\u003c/em\u003e = 0.006) and CV (from 20.25% to 17.20%, \u003cem\u003ep\u003c/em\u003e = 0.027), but contended that achieving substantial reductions in MG necessitated greater exercise volume or extended intervention periods [77]. Discrepancies between studies may be attributed to variations in glycemic management on non-exercise days, differences in exercise intensity and duration, and participant demographics such as gender.\u003c/p\u003e\n\u003cp\u003eThis study\u0026apos;s findings reveal that during the intervention, participants in the MIIT and REHIT groups exhibited significant improvements in MG, Peak Blood Glucose, TIR, and TAR earlier than those in the CON group, thus differing from changes observed in the SD-glucose and MAGE. This contrasts with research from 2024, which indicated that significant reductions in MG might necessitate more prolonged exercise compared to adjustments in CV and SD-glucose [19]. Furthermore, recent studies demonstrate that reducing MG from 12 mmol/L to 8 mmol/L\u0026mdash;a 33% decrease\u0026mdash;is associated with a 20% reduction in SD-glucose, reflecting a significant improvement within the 95% confidence interval of 14.9 to 24.9% [78]. The metrics MG, TIR, and TAR are closely linked to hyperglycemic states, evidenced by a high correlation coefficient of 0.90 [48], suggesting that improvements in TIR directly result in reductions in TAR [61]. Although MAGE offers insights into hypoglycemia during fasting states [79], both SD-glucose and MAGE generally reflect elevated glucose conditions more accurately [80].\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study demonstrates that MIIT and REHIT can significantly mitigate glucose fluctuations. This is achieved by lowering average glucose concentrations, extending the periods during which glucose levels remain within the targeted range, and reducing the duration of high glucose episodes. Consequently, these interventions constitute effective strategies for enhancing overall glucose management in individuals with diabetes.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e4.6\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLimitations of the study\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAlthough numerous studies have investigated the effects of exercise interventions on glycemic variability in patients with T2DM, systematic analyses of prolonged exercise interventions (\u0026ge;2 weeks) are scarce[18-20]. This study yielded novel insights into the effects of different aerobic exercise protocols (LMICT, MIIT, and REHIT) on acute glycemic responses and longer-term (up to 4 weeks) glycemic variability indicators in patients with T2DM complicated by stroke. Notably, this investigation comprehensively analyzed daily trends of glycemic parameters throughout the intervention period, closely monitored medication usage and dietary intake, and carefully documented the intensity and duration of each exercise session. However, several limitations necessitate additional scrutiny and refinement.\u003c/p\u003e\n\u003cp\u003eFirstly, the relatively small sample size and limited diversity of the study population (e.g., restricted ranges of age, sex distribution, disease duration, and levels of diabetes control) may affect the external validity and generalizability of the results. Future studies should expand the sample size and include more heterogeneous patient populations to enhance the representativeness and applicability of the findings.\u003c/p\u003e\n\u003cp\u003eSecondly, the lack of long-term follow-up after the intervention period precludes assessment of the durability and stability of exercise-induced improvements in glycemic control. Future research designs are recommended to incorporate extended follow-up periods to better elucidate the long-term efficacy and temporal dynamics of glycemic management resulting from various exercise interventions.\u003c/p\u003e\n\u003cp\u003eLastly, although this study preliminarily indicated a potential advantage of the REHIT protocol in improving glycemic variability indices (e.g., coefficient of variation, CV), the physiological and molecular mechanisms underlying these beneficial effects were not thoroughly investigated. Future studies should utilize advanced approaches, such as metabolomics, genomics, and detailed physiological analyses, to systematically explore the underlying biological mechanisms and signaling pathways involved in exercise-induced improvements of glycemic variability. This will provide clearer theoretical insights and facilitate evidence-based clinical practices.\u003c/p\u003e\n\u003cp\u003eIn summary, despite these limitations, the current study provides preliminary scientific evidence supporting individualized aerobic exercise rehabilitation strategies for patients with T2DM complicated by stroke. This study addresses an existing research gap regarding the comparative efficacy of aerobic exercise protocols in this specific population, emphasizing the need for larger-scale studies, longer follow-up durations, and deeper mechanistic explorations in future research.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study demonstrated that MIIT and REHIT significantly enhanced glycemic control in post-stroke patients with T2DM. Specifically, MIIT sessions (30 minutes at 69.75% \u0026plusmn; 5.29% HRR) and REHIT sessions (10 minutes at 78.19% \u0026plusmn; 5.77% HRR) effectively increased the duration within the normal glucose range and decreased glucose variability. REHIT offered a time-efficient approach, yielding benefits comparable to those of MIIT, with superior reductions in the CV. Conversely, MIIT demonstrated greater efficacy in reducing MG levels. Acute glucose response analyses revealed that MIIT facilitated more rapid glucose reductions than both LMICT (30 minutes at 51.23% \u0026plusmn; 6.94% HRR) and REHIT. These findings substantiate the clinical utility of structured aerobic exercise regimens for managing glycemic levels in patients with type 2 diabetes mellitus and a history of stroke. Ongoing research, particularly longitudinal studies, is essential to fully ascertain the long-term effects of these interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe trial protocol, statistical analysis plan, and individual de-identified participant data are available upon reasonable request from the corresponding author. Data sharing will be in accordance with ethical guidelines and privacy considerations.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all staff professionals and participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study receives its principal funding from the following grants: Sanming Project of Medicine in Shenzhen (No. SZSM202111010).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China\u003c/p\u003e\n\u003cp\u003eKangcheng Chen, Haifeng Ma, Jun Li\u003c/p\u003e\n\u003cp\u003eDepartment of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People\u0026rsquo;s Hospital, Shenzhen, China\u003c/p\u003e\n\u003cp\u003eYulong Wang, Dongxia Li, Yong Huang, Meiling Huang\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Kangcheng Chen, Haifeng Ma, Meiling Huang, and Yulong Wang; Data curation, Kangcheng Chen and Yong Huang; Formal analysis, Kangcheng Chen, Haifeng Ma, and Meiling Huang; Investigation, Kangcheng Chen, Dongxia Li and Yong Huang; Methodology, Kangcheng Chen, Haifeng Ma, Meiling Huang and Yulong Wang; Validation, Haifeng Ma and Meiling Huang; Writing \u0026ndash; original draft, Kangcheng Chen, Yong Huang and Jun Li; Writing \u0026ndash; review \u0026amp; editing, Kangcheng Chen, Haifeng Ma, Meiling Huang, Yulong Wang and Dongxia Li.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the Ethics Committee of Shenzhen Second People\u0026apos;s Hospital (approval number: 20220901005-FS01). Written informed consent is obtained from all patients or their approved proxies before enrolment.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045\u003c/strong\u003e. \u003cem\u003eDiabetes research and clinical practice \u003c/em\u003e2022, \u003cstrong\u003e183\u003c/strong\u003e:109119.\u003c/li\u003e\n\u003cli\u003eHe C, Wang W, Chen Q, Shen Z, Pan E, Sun Z, Lou P, Zhang X: \u003cstrong\u003eFactors associated with stroke among patients with type 2 diabetes mellitus in China: a propensity score matched study\u003c/strong\u003e. \u003cem\u003eActa Diabetol \u003c/em\u003e2021, \u003cstrong\u003e58\u003c/strong\u003e(11):1513-1523.\u003c/li\u003e\n\u003cli\u003eCha J-K: \u003cstrong\u003eEpidemiology of Stroke Patients with Diabetes\u003c/strong\u003e. 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\u003cem\u003eCardiovascular diabetology \u003c/em\u003e2020, \u003cstrong\u003e19\u003c/strong\u003e(1):144.\u003c/li\u003e\n\u003cli\u003eLee DY, Han K, Park S, Yu JH, Seo JA, Kim NH, Yoo HJ, Kim SG, Choi KM, Baik SH\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlucose variability and the risks of stroke, myocardial infarction, and all-cause mortality in individuals with diabetes: retrospective cohort study\u003c/strong\u003e. \u003cem\u003eCardiovascular Diabetology \u003c/em\u003e2020, \u003cstrong\u003e19\u003c/strong\u003e(1):144.\u003c/li\u003e\n\u003cli\u003eAzevedo JRA, Azevedo RP, Miranda MA, Costa NNR, Araujo LO: \u003cstrong\u003eManagement of hyperglycemia in patients with acute ischemic stroke: comparison of two strategies\u003c/strong\u003e. \u003cem\u003eCritical Care \u003c/em\u003e2009, \u003cstrong\u003e13\u003c/strong\u003e(3):P48.\u003c/li\u003e\n\u003cli\u003eCiplak S, Adiguzel A, Ozturk U, Akalin Y: \u003cstrong\u003ePrognostic value of glucose fluctuation in patients undergoing thrombolysis or thrombectomy due to acute ischemic stroke\u003c/strong\u003e. \u003cem\u003eThe Egyptian Journal of Neurology, Psychiatry and Neurosurgery \u003c/em\u003e2021, \u003cstrong\u003e57\u003c/strong\u003e(1):159.\u003c/li\u003e\n\u003cli\u003eTerada T, Wilson BJ, Myette-C\u0026ocirc;t\u0026eacute; E, Kuzik N, Bell GJ, McCargar LJ, Boul\u0026eacute; NG: \u003cstrong\u003eTargeting specific interstitial glycemic parameters with high-intensity interval exercise and fasted-state exercise in type 2 diabetes\u003c/strong\u003e. \u003cem\u003eMetabolism: clinical and experimental \u003c/em\u003e2016, \u003cstrong\u003e65\u003c/strong\u003e(5):599-608.\u003c/li\u003e\n\u003cli\u003eKarstoft K, Clark MA, Jakobsen I, M\u0026uuml;ller IA, Pedersen BK, Solomon TP, Ried-Larsen M: \u003cstrong\u003eThe effects of 2 weeks of interval vs continuous walking training on glycaemic control and whole-body oxidative stress in individuals with type 2 diabetes: a controlled, randomised, crossover trial\u003c/strong\u003e. \u003cem\u003eDiabetologia \u003c/em\u003e2017, \u003cstrong\u003e60\u003c/strong\u003e(3):508-517.\u003c/li\u003e\n\u003cli\u003eBoul\u0026eacute; NG, Kenny GP, Haddad E, Wells GA, Sigal RJ: \u003cstrong\u003eMeta-analysis of the effect of structured exercise training on cardiorespiratory fitness in Type 2 diabetes mellitus\u003c/strong\u003e. \u003cem\u003eDiabetologia \u003c/em\u003e2003, \u003cstrong\u003e46\u003c/strong\u003e(8):1071-1081.\u003c/li\u003e\n\u003cli\u003eLiu D, Zhang Y, Wu Q, Han R, Cheng D, Wu L, Guo J, Yu X, Ge W, Ni J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eExercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes\u003c/strong\u003e. \u003cem\u003eJournal of diabetes \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(4):e13549.\u003c/li\u003e\n\u003cli\u003eFATULLA PJ, IMBERG H, HIRSCH IB, HEISE T, LIND M: \u003cstrong\u003e967-P: Evaluation of CV and SD as Glucose Variability Metrics Based on Data from the GOLD and SILVER Trials\u003c/strong\u003e. \u003cem\u003eDiabetes \u003c/em\u003e2023, \u003cstrong\u003e72\u003c/strong\u003e(Supplement_1).\u003c/li\u003e\n\u003cli\u003eRodbard D: \u003cstrong\u003eInterpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control\u003c/strong\u003e. \u003cem\u003eDiabetes Technol Ther \u003c/em\u003e2009, \u003cstrong\u003e11 Suppl 1\u003c/strong\u003e:S55-67.\u003c/li\u003e\n\u003cli\u003eKovatchev B, Cobelli C: \u003cstrong\u003eGlucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes\u003c/strong\u003e. \u003cem\u003eDiabetes care \u003c/em\u003e2016, \u003cstrong\u003e39\u003c/strong\u003e(4):502-510.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus, Stroke, Aerobic Exercise training, Continuous Glucose Monitoring, Glycemic variability","lastPublishedDoi":"10.21203/rs.3.rs-6390697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6390697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to assess the acute and chronic effects of three aerobic exercise protocols, namely, Moderate-Intensity Interval Training (MIIT), Low-to-Moderate Intensity Continuous Training (LMICT), and Reduced-Exertion High-Intensity Training (REHIT), on glycemic control in patients with Type 2 Diabetes Mellitus (T2DM) and stroke.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eForty-nine patients diagnosed with both T2DM and stroke were randomly assigned to LMICT, MIIT, REHIT, or the control group. The intervention comprised two phases: from day 3 to day 14 and from day 15 to day 28, with days 1 and 2 designated as a baseline control period. Throughout the intervention, blood glucose levels were continuously monitored and recorded using a Continuous Glucose Monitoring (CGM) system.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll exercise intervention groups exhibited significant immediate reductions in blood glucose levels following exercise (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Repeated measures ANOVA demonstrated significant main effects of group and time, as well as a significant interaction, on mean glucose (MG) and time above range (TAR) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Continuous glucose monitoring indicated progressive improvements in MG, time in range (TIR), TAR, peak blood glucose, glucose standard deviation (SD-glucose), and mean amplitude of glycemic excursion (MAGE) in the MIIT group. The REHIT group exhibited significant improvements in peak blood glucose, TIR, TAR, MAGE, SD-glucose, and coefficient of variation (CV) (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These trends were not evident in the LMICT group. Notably, the MIIT and REHIT groups exhibited early, significant improvements in MG, peak blood glucose, TIR, and TAR, which preceded subsequent changes in SD-glucose and MAGE relative to controls.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWhile all exercise regimens resulted in acute reductions in blood glucose, sustained improvements in overall glycemic control and variability were observed exclusively following the four-week MIIT and REHIT interventions. Specifically, REHIT significantly reduced glucose variability, as reflected by decreases in the CV, whereas MIIT was more effective in lowering MG levels. Conversely, the lower-intensity LMICT regimen (51.23% \u0026plusmn; 6.94% heart rate reserve) exerted minimal long-term effects. These findings underscore the potential of moderate- to high-intensity intermittent aerobic training in managing glycemic fluctuations in individuals with T2DM and stroke, thereby emphasizing their clinical relevance.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eThe study was registered with the Chinese Clinical Trial Registry (ChiCTR2200065677, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chictr.org.cn/\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on 11/11/2022.\u003c/p\u003e","manuscriptTitle":"Effects of Three Aerobic Exercise Protocols on Acute Glucose Response and Continuous Glucose Monitoring Trends in Patients with Type 2 Diabetes Mellitus and Stroke: A Randomized Controlled Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:38:38","doi":"10.21203/rs.3.rs-6390697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T06:00:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T15:57:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179783335113028910258920674913009199749","date":"2025-08-22T19:16:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T14:02:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227628611505231483515596053987572004579","date":"2025-08-06T15:23:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27879224634509161378185139281387736922","date":"2025-07-28T13:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265506497028217733131469740132867049739","date":"2025-07-15T13:16:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155690699860166978658886117839344145389","date":"2025-04-29T15:46:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T18:29:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T18:22:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T07:23:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-27T15:47:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-04-27T15:46:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a32be022-eedf-4a45-9b52-5cbd6898a1d6","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T15:59:46+00:00","versionOfRecord":{"articleIdentity":"rs-6390697","link":"https://doi.org/10.1186/s13102-025-01402-0","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2025-11-28 15:57:04","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-05-07 06:38:38","video":"","vorDoi":"10.1186/s13102-025-01402-0","vorDoiUrl":"https://doi.org/10.1186/s13102-025-01402-0","workflowStages":[]},"version":"v1","identity":"rs-6390697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6390697","identity":"rs-6390697","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}