Effects of a Structured Physical Activity Program on Lifestyle, Metabolic, and Inflammatory Parameters in Women with Gestational Diabetes Mellitus

Effects of a Structured Physical Activity Program on Lifestyle, Metabolic, and Inflammatory Parameters in Women with Gestational Diabetes Mellitus | 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 a Structured Physical Activity Program on Lifestyle, Metabolic, and Inflammatory Parameters in Women with Gestational Diabetes Mellitus Joao Paulo Batista de Souza, Eric Massao Iwama, Gleisson Alisson Pereira de Brito, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9066411/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Gestational Diabetes Mellitus (GDM) represents a significant challenge in women’s health, as it is associated with an increased risk of obstetric complications and adverse maternal outcomes. Given the limitations of pharmacological therapy alone, non-pharmacological interventions, such as structured physical activity, have shown promise in promoting public health. This study aimed to evaluate the effects of a supervised physical activity program on physiological, metabolic, and inflammatory parameters in pregnant women with GDM. The research was conducted within the context of Primary Health Care (PHC). The intervention protocol consisted of at least 150 minutes per week of moderate-intensity physical activity over a 12-week period, with emphasis on monitored walking, stretching exercises, and educational sessions focused on healthy lifestyle behaviors. Participants used smartwatches to record step counts, completed the FANTÁSTIC lifestyle questionnaire, and underwent laboratory assessments at baseline and at the end of the study. The results demonstrated that, at the end of the intervention, the intervention group showed a significant increase in the total lifestyle score, from 73.70 to 80.57 points (Δ = +6.87), with a statistically significant difference compared with the control group (p = 0.034). As an exception, postprandial glucose showed a mean reduction of − 13.45 mg/dL in the intervention group, with a statistically significant effect compared with the control group (p < 0.001). Smartwatch monitoring revealed an association between a higher number of daily steps and lower final systolic blood pressure, with significant correlation (p < 0.05) and linear regression (F = 39.40; p = 0.024; β = −0.976). These findings indicate that, even over a short intervention period, the program was able to produce clinically relevant benefits, highlighting the innovative use of smartwatches within PHC as a promising tool for the care of pregnant women with GDM. These findings suggest that structured physical activity programs may represent an effective and scalable non-pharmacological strategy for the management of gestational diabetes in primary health care settings. gestational diabetes physical exercise lifestyle primary health care perinatal care Figures Figure 1 Figure 2 Figure 3 Introduction The Gestational Diabetes Mellitus (GDM) is defined as an alteration in glucose levels first diagnosed during pregnancy. Thus, this concept does not exclude the possibility that the glycemic abnormality may have been preexisting prior to pregnancy but remained undetected. Epidemiologically, there has been a global increase in the prevalence of GDM, associated with the rising number of individuals with obesity and type 2 diabetes mellitus [ 1 ]. GDM has an estimated worldwide incidence of approximately 14%, and excessive gestational weight gain is directly related to its development. GDM negatively affects the course of pregnancy and fetal outcomes [ 2 ]. According to the guidelines of the Brazilian Society of Diabetes (SBD), dysglycemia is the most common metabolic alteration during pregnancy. It is estimated that approximately 16% of live births are to women who experienced some form of hyperglycemia during pregnancy. About 8% of these cases involve women with diabetes diagnosed prior to pregnancy. The increasing prevalence of pregnancies in women with pregestational diabetes parallels the rising frequency of type 1 and type 2 diabetes mellitus among women of reproductive age [ 3 ]. Maternal hyperglycemia is one of the most common conditions during pregnancy. In Brazil, it is estimated that 18% of pregnant women assisted by the Unified Health System (SUS) meet the current diagnostic criteria for GDM. Major risk factors include obesity, maternal age over 25 years, positive family and/or personal history, multiple gestation, arterial hypertension, dyslipidemia, smoking, physical inactivity, previous macrosomia, unexplained fetal death, among others [ 4 ]. According to the Mortality Information System (SIM), diabetes mellitus during pregnancy is the third leading cause of maternal death in Brazil, accounting for 15.32% of total maternal deaths between 2014 and 2019 [ 5 ]. Hyperinsulinemia during pregnancy represents a fundamental adaptive physiological response aimed at maintaining maternal glycemic homeostasis in the face of profound metabolic changes imposed by gestation. Initially, there is an increase in insulin sensitivity in early pregnancy, followed by a progressive development of insulin resistance from the second trimester onward, primarily mediated by placental hormones and adipose tissue–derived factors. To compensate for this resistance, there is a marked increase in insulin secretion by pancreatic β-cells, resulting in physiological hyperinsulinemia [ 6 ]. Skeletal muscle is the primary site of glucose utilization in the human body. During the second half of pregnancy, together with adipose tissue, it becomes increasingly resistant to the effects of insulin. In a normal pregnancy, there is an approximate 50% reduction in insulin-mediated glucose disposal, accompanied by a compensatory increase in insulin secretion of up to 200%, in an attempt to maintain normal maternal blood glucose levels [ 7 ]. According to the current guidelines of the Brazilian Ministry of Health, the diagnosis of GDM should be performed using a 75-g oral glucose tolerance test (OGTT). Initial screening includes the measurement of fasting plasma glucose, preferably up to 20 weeks of gestation. Pregnant women with fasting glucose levels below 92 mg/dL are required to undergo the OGTT between 24 and 28 weeks of gestational age. In cases where prenatal care begins late (after 20 weeks of gestation), the 75-g OGTT should be performed as soon as possible to ensure timely diagnosis of both GDM and previously undiagnosed preexisting diabetes [ 3 ]. A number of risk factors increase the likelihood of developing GDM. Ethnicity may play a role in GDM development, as higher incidence rates have been reported in certain ethnic subgroups. In the United States, epidemiological studies have shown a higher prevalence of GDM among African American, Native American, and Hispanic women [ 8 ]. Risk factors for GDM include both modifiable factors, such as diet, overweight, physical inactivity, and hypertension, and non-modifiable factors, such as ethnicity, age, and family history of diabetes. Identification of these factors at the time of diagnosis provides important opportunities to optimize GDM management [ 8 ]. The diagnosis of GDM is established when one or more of the following criteria are present: fasting plasma glucose ≥ 92 mg/dL and ≤ 125 mg/dL prior to the oral glucose tolerance test (OGTT); plasma glucose ≥ 180 mg/dL at 1 hour after the 75 g OGTT; plasma glucose ≥ 153 mg/dL and ≤ 199 mg/dL at 2 hours after the 75 g OGTT. Pregestational diabetes or overt diabetes is diagnosed when fasting plasma glucose ≥ 126 mg/dL and/or 2-hour post-load plasma glucose ≥ 200 mg/dL and/or glycated hemoglobin (HbA1c) > 6.5% [ 9 ]. Women diagnosed with GDM present an approximately threefold higher risk of adverse pregnancy outcomes compared with those without diabetes, including fetal macrosomia, stillbirth, neonatal metabolic disorders, preeclampsia, and higher rates of cesarean delivery. In addition, GDM is associated with an increased maternal risk of developing type 2 diabetes mellitus (T2DM) in the postpartum period, reinforcing the need for continuous monitoring after pregnancy [ 2 , 21 ]. In this context, pregnancy represents an ideal opportunity to adopt a healthy lifestyle, when this has not already been established. Measures such as smoking cessation, reduction of alcohol and caffeine intake, improvement of dietary habits, and engagement in physical activity contribute to better maternal health and optimal fetal development [ 10 ]. Furthermore, regular exercise is known to be beneficial for both maternal and fetal health, preventing excessive maternal adiposity and worsening of GDM [ 11 ]. Nutritional management and regular physical activity are among the most challenging components of treatment strategies and lifestyle modification. The importance of nutritional therapy in the management of GDM has long been emphasized, as well as its central role in the prevention, treatment, and reduction of disease-related complications. Lifestyle modification is considered the cornerstone of diabetes control, whether or not combined with pharmacological therapy, since achieving adequate glycemic control reduces the risk of microvascular complications and may also minimize the likelihood of cardiovascular disease. Food choices directly influence energy balance and, consequently, body weight, glycemic levels, blood pressure, and plasma lipid concentrations [ 12 ]. The benefits of physical activity require engagement in exercise for 30–60 minutes at moderate intensity on five days per week, or an average of 150 minutes of aerobic activity per week, depending on the woman’s prior physical activity level and fitness status before pregnancy. Both the intensity and type of activity should be individualized, always considering the FITT exercise principles (frequency, intensity, time/duration, and type) [ 8 ]. Accordingly, strategies such as brisk walking, resistance exercises, and home-based aerobic activities, when combined with standard prenatal care, have demonstrated positive effects on glycemic control in women with GDM. Clinical studies have shown that regular practice of aerobic exercise, resistance training, or a combination of both is associated with significant reductions in fasting and postprandial glucose levels compared with conventional GDM management alone [ 13 , 14 ]. In a systematic review by Allehdan et al. [ 15 ], which included eight randomized clinical trials, evidence indicated that dietary management combined with aerobic or resistance exercise improved glycemic outcomes and reduced fasting and postprandial glucose levels in women with GDM compared with dietary management alone. Adequate glycemic control through diet and exercise may prevent or delay the need for insulin therapy, with only 20–30% of women requiring insulin. Furthermore, the combination of diet and exercise reduces excessive gestational weight gain and may improve pregnancy outcomes in women at risk of or diagnosed with GDM [ 16 , 20 , 27 ]. The main physiological mechanisms through which physical exercise performed during pregnancy exerts beneficial effects on maternal metabolism, endothelial function, and the placental environment, all of which are directly related to the pathophysiology of GDM. In pregnant women with GDM, increased insulin resistance, low-grade systemic inflammation, oxidative stress, and endothelial dysfunction are commonly observed, contributing both to maternal hyperglycemia and to a higher risk of obstetric complications [ 8 ]. Despite the well-established evidence regarding the benefits of physical activity in the management of GDM, relevant gaps persist in the literature, particularly concerning the effectiveness of short-term structured interventions developed within the context of Primary Health Care (PHC) and monitored using objective measures of physical activity. Furthermore, studies investigating the association between daily volume of monitored physical activity and early clinical, metabolic, and inflammatory outcomes in women with GDM remain scarce. Therefore, the central research question of this study was whether a structured physical activity program, delivered with the volume and intensity recommended for pregnant women with GDM, is capable of improving maternal lifestyle, metabolic control, and inflammatory parameters when implemented within the context of public PHC. Accordingly, this study aimed to evaluate the effects of such a program on maternal metabolic and inflammatory outcomes, as well as to assess changes in lifestyle before and after the intervention. By investigating a feasible, low-cost strategy integrated into longitudinal PHC, this research seeks to contribute evidence on interventions that can generate clinically meaningful benefits even over relatively short follow-up periods. Methodology This was a quasi-experimental study conducted at the Institute of Diabetics of Foz do Iguaçu (ADIFI). The project included pregnant women diagnosed with GDM who were receiving care through Brazil’s Unified Health System (SUS) within the context of PHC. The study was conducted in accordance with established ethical guidelines and was approved under substantiated opinion no. 6.933.345 by the Centro Universitário Dinâmica das Cataratas (UDC). A total of 35 pregnant women with GDM were included in the final analysis, using a convenience sampling method and according to predefined inclusion and exclusion criteria. Initially, 42 participants were screened; however, seven cases were not retained in the final dataset because of follow-up losses and missing information in the medical records system of the municipality of Foz do Iguaçu. The final sample therefore comprised 23 pregnant women in the control group and 12 in the intervention group, with a maximum of four participants followed per month. The partnership with the high-risk prenatal care service of the PHC network in Foz do Iguaçu was essential to the success of the study, as was the infrastructure and healthcare team provided by the Institute of Diabetics of Foz do Iguaçu (ADIFI). This collaboration demonstrated the feasibility of implementing lifestyle modification follow-up programs in settings dedicated to the care of patients with diabetes. The inclusion criteria were defined as follows: pregnant women with a confirmed diagnosis of GDM; gestational age between 20 and 34 weeks at the time of screening; age between 18 and 50 years; body mass index ranging from 18 to 45 kg/m²; an active electronic medical record in the municipality of Foz do Iguaçu; an established care link with the Association of Diabetics of Foz do Iguaçu (ADIFI); agreement to undergo anthropometric, biochemical, and fetal ultrasonographic assessments; and willingness to participate voluntarily in the study, committing to a 12-week lifestyle follow-up, including three blood sample collections, upon prior signing of the Written Informed Consent Form. Exclusion criteria comprised: the presence of other relevant obstetric complications; a history of major surgical procedures within the previous five years; autoimmune diseases, moderate to severe anemia, or advanced chronic kidney disease; lack of active registration in a PHC Unit; severe psychiatric disorders that could compromise adherence to the intervention; illicit drug use, active smoking, or abusive alcohol consumption; use of medications capable of interfering with the biochemical parameters evaluated, such as corticosteroids; clinical contraindications to physical activity; and significant use of nutritional supplements that could influence metabolic outcomes. The identification of any condition limiting the practice of physical exercise or adherence to the proposed follow-up was considered sufficient grounds for participant exclusion. For diagnostic criteria, the study adopted the presence of one or more of the following findings in the participants’ medical records: fasting plasma glucose ≥ 92 mg/dL and ≤ 125 mg/dL prior to the oral glucose tolerance test (OGTT); 1-hour plasma glucose ≥ 180 mg/dL following a 75-g OGTT; or 2-hour plasma glucose ≥ 153 mg/dL and ≤ 199 mg/dL. Pregestational or overt diabetes was defined as fasting plasma glucose ≥ 126 mg/dL and/or 2-hour plasma glucose ≥ 200 mg/dL and/or glycated hemoglobin > 6.5%, in accordance with the Pan American Health Organization criteria (2017) [ 9 ]. Lifestyle assessment was performed using the FANTASTIC questionnaire, a validated instrument in Brazil designed to evaluate multiple dimensions of lifestyle behaviors among participants [ 17 ]. The exercise protocol was structured in a combined format, including home-based activities and supervised sessions conducted at ADIFI. Aerobic activity consisted of walking performed in the home environment, with daily frequency or a minimum of three sessions per week, lasting 15 to 30 minutes per session, and conducted at moderate intensity according to individual tolerance and previously provided guidance [ 18 ]. Step monitoring was performed using the Xiaomi Mi Band 9 device, which incorporates motion sensors (triaxial accelerometer and gyroscope) embedded in the wristband. These sensors detect rhythmic arm movements during walking, and the internal algorithm processes the signals to identify step patterns while distinguishing them from non-walking arm movements, thereby recording the total number of steps accumulated throughout the day [ 19 ]. In-person sessions were offered twice weekly at ADIFI and consisted of monitored and supervised exercises, with a total duration of approximately 45 to 60 minutes per session. A minimum attendance rate of 75% was required to characterize adherence to the intervention protocol. Resistance training lasted 20 to 25 minutes and comprised six exercises performed in three sets of 10 to 15 repetitions, primarily using body weight and/or elastic resistance bands. Exercise selection and progression were individualized according to each participant’s clinical, functional, and gestational profile [ 8 ]. Additionally, specific exercises targeting pelvic floor strengthening and mobility were included, along with global stretching exercises. At the end of each session, approximately 10 minutes were dedicated to relaxation and recovery techniques aimed at enhancing comfort, reducing muscle tension, and promoting overall well-being. Statistical analyses were conducted using JASP software (version 0.18.0.3, University of Amsterdam, October 15, 2025), while part of the graphical visualizations was generated using R software (version 4.3.2). For qualitative–quantitative variables, parametric tests (such as Student’s t test) or nonparametric tests (such as the Mann–Whitney test) were applied according to data distribution. Statistical significance was defined as a p value < 0.05, indicating a statistically significant difference between groups. Results The baseline characteristics of the participants were analyzed by comparing the control group (n = 23) and the intervention group (n = 12), using appropriate statistical tests according to the distribution of the variables. Mean maternal age was similar between groups, with an average of 31.43 years in the control group and 30.00 years in the intervention group, showing no statistically significant difference (p = 0.238). Regarding gestational age at baseline, the control group presented a significantly higher mean gestational age (27.17 weeks) compared to the intervention group (22.83 weeks), with a statistically significant difference (p < 0.001). Baseline body weight was also higher in the control group (88.28 kg) than in the intervention group (77.18 kg), with a statistically significant difference between groups (p = 0.013). (Table 1 here) Table 1 – Baseline characteristics of participants at the beginning of the program Age GA Weight Height BMI C I C I C I C I C I Mean 31.43 30.00 27.17 22.83 88.28 77.18 161.8 158.4 33.52 30.59 Standard Deviation 5.599 5.560 3.810 2.823 19.10 25.29 8.055 5.648 5.769 8.800 Minimum 20.00 19.00 20.00 20.00 46.50 56.00 145.0 150.0 22.21 23.43 Maximum 39.00 38.00 34.00 28.00 121.0 152.0 181.0 167.0 43.64 55.88 Valid 23 12 23 12 23 12 23 12 23 12 Missing 0 0 0 0 0 0 0 0 0 0 Legend age in years; gestational age (GA) in weeks; weight in kilograms (kg); height in centimeters (cm); body mass index (BMI) in kg/m². C = control group; I = intervention group. At the initial application of the FANTASTIC questionnaire, participants presented high lifestyle scores, predominantly classified as “good” to “very good.” The mean score was 77.3 in the control group and 73.7 in the intervention group, with no statistically significant difference between groups (p = 0.324). A significant increase in the total lifestyle score was observed in the intervention group at the end of the study, rising from 73.70 at baseline to 80.57 at the final assessment (Δ = +6.87; p = 0.034), indicating an overall improvement in lifestyle profile. In the baseline postprandial glycemia assessment, the intervention group showed a numerically higher mean than the control group (114.9 mg/dL vs. 102.6 mg/dL); however, this difference was not statistically significant (p = 0.149). It should be noted that both groups were within the recommended standards for postprandial glycemic monitoring, in which measurements may be performed 1 hour or 2 hours after meals at the clinician’s discretion, without the need to assess both time points. (Table 2 here) Table 2 – Postprandial Glycemia: Comparison Between Groups Baseline Glycemia Follow-up Glycemia Glycemia Difference Control Intervention Control Intervention Control Intervention Mean 102.6 114.9 129.9 101.4 31.68 -13.45 Standard Deviation 22.53 33.13 25.40 22.49 32.44 17.71 Minimum 74.00 88.00 92.00 82.00 -63.00 -40.20 Maximum 172.0 200.1 176.0 162.8 83.00 14.40 Valid 23 12 18 12 18 12 Missing 0 0 5 0 5 0 p-value 0.149 0.004 < 0.001 Legend Baseline glycemia is expressed in mg/dL and refers to the initial measurement, while final glycemia corresponds to the measurement at the end of the study. Glycemia difference represents the change between final and baseline values (Δ = final − baseline). p-values refer to between-group comparisons, obtained using the most appropriate statistical test according to data normality (Student’s t-test or Mann–Whitney test). p-values < 0.05 indicate statistically significant differences between groups. At the end of the study, distinct patterns were observed between groups. Final postprandial glycemia was significantly higher in the control group compared with the intervention group (129.9 mg/dL vs. 101.4 mg/dL), with a statistically significant between-group difference (p = 0.004). In the analysis of postprandial glycemia variation (difference between final and baseline values), the control group showed a mean increase of + 31.68 mg/dL, whereas the intervention group showed a mean reduction of − 13.45 mg/dL, indicating a clear divergence in glycemic behavior over the evaluated period. This between-group difference was statistically significant (p < 0.001), indicating a favorable effect of the intervention on postprandial glycemia. Figure 1 . Box plot comparing baseline postprandial glucose levels between the control and intervention groups. Boxes represent the interquartile range, the horizontal line indicates the median, whiskers represent the minimum and maximum values, and dots indicate outliers. To further support this analysis, a one-way ANOVA was conducted, which also demonstrated a statistically significant difference between groups in post-intervention glycemia (F = 10.05; p = 0.004), confirming the effect of the intervention on glycemic reduction. The homogeneity of variances and the normality of residuals support the adequacy of the ANOVA model for this comparison, as summarized in the figure below: Figure 2 . Raincloud plot illustrating the distribution of final postprandial glucose levels in the intervention and control groups. Individual data points represent each participant. Box plots indicate the median and interquartile range, while the violin plots depict the kernel density distribution of glucose values. Monitoring of daily step counts using the Xiaomi Mi Band 9 smartwatch was performed in only five pregnant women. Seven participants reported performing the exercise protocol on a weekly basis. Among the monitored participants, the mean number of steps per day was 3,381, with a standard deviation of 326.1 steps, indicating relative homogeneity in daily activity patterns. The minimum recorded value was 2,916 steps, while the maximum reached 3,789 steps, demonstrating moderate variability among the women who adhered to the use of the device. Although only a subset of the group was monitored, these data enabled important additional analyses regarding the relationship between physical activity level and clinical indicators. Correlation analysis between the mean daily step count recorded by the smartwatch and final systolic blood pressure (SBP F) demonstrated a significant association both at baseline and at the end of the intervention. Among the five participants who used the smartwatch, those with higher step counts exhibited lower SBP levels. This inverse relationship was confirmed by both Pearson and Spearman correlation tests, which were applied due to the small sample size (n < 30), thereby reinforcing the robustness of the finding. Specifically, final SBP showed a significant correlation with total step count (Pearson: r = − 0.976; p = 0.012 | Spearman: ρ = −1.000; p = 0.042), while the change in SBP also demonstrated a significant correlation (Spearman: ρ = −0.949; p = 0.026), indicating that a higher number of steps was associated with greater blood pressure reduction. Linear regression analysis further supported this pattern, revealing a strong association between final SBP and the number of steps monitored by the smartwatch. The adjusted model demonstrated excellent performance, with R = 0.976, R² = 0.952, and adjusted R² = 0.928, indicating that approximately 95% of the variance in final SBP could be explained by step count. The regression was statistically significant (F = 39.40; p = 0.024), demonstrating that increased step count was consistently associated with lower final SBP values. The standardized coefficient indicated a robust negative relationship (β = −0.976; p = 0.024), reinforcing that participants who walked more exhibited lower systolic blood pressure at the end of the intervention. Figure 3 illustrates that the downward trend line indicates decreasing estimated final SBP values as step counts increase, while the confidence band demonstrates good precision of the estimates despite the small sample size. This graphical pattern corroborates the statistical findings, clearly showing that a higher volume of daily physical activity, measured by step count, was associated with improved systolic blood pressure control at the end of the intervention. Figure 3 . Marginal effect plot showing the association between daily step count and final systolic blood pressure. The solid line represents the predicted values from the linear regression model, the shaded area indicates the 95% confidence interval, and the dashed lines represent the upper and lower confidence bounds. In addition, a binary logistic regression was performed to evaluate the association between final systolic blood pressure (final SBP) and improvement in glycemia at the end of the intervention, considering a dichotomous outcome variable (glycemic improvement: yes = 1; no = 0). The model showed a statistically significant fit, as indicated by the Wald test. Final SBP was significantly associated with the probability of glycemic improvement, with a negative coefficient (β = −0.916; standard error = 0.418), indicating that higher final SBP values reduced the likelihood of glycemic improvement. The odds ratio (OR) was 0.40 (p = 0.028), suggesting that for each unit increase in final SBP, there was an approximate 60% reduction in the odds of glycemic improvement. Despite the statistical significance of this association, the pseudo R² indices (McFadden, Nagelkerke, and Tjur) were low or null, indicating that although final SBP contributes significantly to explaining glycemic improvement, the model has low overall explanatory power, suggesting that other factors not included in the model also influence this outcome. Discussion The present study showed that a structured physical activity program carried out within Primary Health Care was able to generate meaningful changes in women with gestational diabetes mellitus, even over a relatively short follow-up period. Beyond the statistical significance observed in some outcomes, what draws attention here is the practical relevance of these findings in a real-world care setting. In routine prenatal care, especially in public services, interventions that are simple, feasible, and low cost tend to have greater chances of being sustained. In this sense, the program evaluated in our study appears to offer a realistic strategy for improving maternal care without depending exclusively on pharmacological measures. A relevant finding was the improvement in lifestyle among participants in the intervention group, reflected by the increase in the total FANTASTIC questionnaire score at the end of follow-up. This result suggests that the intervention was not limited to isolated exercise sessions, but may also have influenced daily habits and health-related decisions during pregnancy. In women with GDM, this point is especially important, because metabolic control does not depend only on one component of care, but rather on the combination of movement, food choices, adherence, understanding of the disease, and engagement with prenatal recommendations. Seen from this perspective, the observed change in lifestyle score helps to reinforce that the intervention had a broader effect than a purely physiological response. The behavior of postprandial glycemia was one of the clearest findings in the study. While the control group showed a worsening pattern over time, the intervention group presented a reduction in mean postprandial glucose levels. In clinical terms, this difference is relevant because postprandial glycemia has direct implications for maternal metabolic control and for fetal exposure to hyperglycemia. The result strengthens what has already been suggested in previous studies: regular physical activity, when adequately prescribed and followed, can improve glucose utilization and reduce glycemic excursions after meals. In the context of GDM, this is particularly valuable because postprandial control is often one of the most challenging targets in daily care, and treatment of even mild GDM is associated with better maternal and neonatal outcomes [ 14 , 22 , 23 ]. From a physiological standpoint, this effect is plausible. Skeletal muscle contraction increases glucose uptake through insulin-independent pathways and, when repeated over time, contributes to better peripheral insulin sensitivity. During pregnancy, when insulin resistance tends to increase progressively, especially from the second trimester onward, any intervention capable of partially counterbalancing this process may have an important clinical role. Our findings therefore support the idea that structured exercise should not be viewed only as an accessory recommendation, but as an active component of treatment in women with GDM. Another point that deserves emphasis is the use of smartwatch-based monitoring. Although only part of the sample used the device, the data obtained added an innovative dimension to the study. The inverse association between the number of daily steps and final systolic blood pressure suggests that objective monitoring of movement may help reveal clinically relevant patterns that are not always captured by self-report alone. In practical terms, this is one of the most interesting aspects of the manuscript, because it connects a common wearable technology to prenatal care in a population that is usually monitored with more traditional tools. In Brazil, and even internationally, there is still limited evidence on the incorporation of this type of device into the follow-up of women with GDM in public health settings. For this reason, the study opens an interesting path for future investigations, especially those focused on adherence, remote monitoring, and individualized counseling, in line with current evidence linking physical activity to maternal cardiovascular function during pregnancy [ 24 , 26 ]. At the same time, the smartwatch results should be interpreted with caution. The subgroup monitored with the device was small, which limits broader inferences. Even so, the direction of the findings is coherent and biologically plausible. More importantly, it shows that the use of wearables in this field is not merely a technological embellishment. When well integrated into care, these devices may become practical allies for both patients and professionals, helping transform abstract recommendations such as “walk more” into measurable, monitorable, and clinically interpretable information. Some limitations need to be acknowledged. The final sample size was modest, which reduces statistical power for some analyses and limits external generalization. In addition, the study experienced losses during follow-up and part of the initial data could not be recovered from the medical records system, which is a common challenge in real-life health services but still an important methodological constraint. The follow-up period was also short, which means the findings should be interpreted as early effects of the intervention rather than as evidence of long-term maternal or neonatal benefit. Another point is that not all participants used the smartwatch, making the analyses involving step count more exploratory in nature. On the other hand, the strengths of the study should not be overlooked. It was conducted in a real care context, with women followed within the public health system, and tested an intervention that is feasible outside highly controlled research environments. This gives the findings practical value. Many studies demonstrate benefit under ideal conditions; fewer show what can actually be done in everyday prenatal care with the resources that services truly have. In this regard, our results support the idea that physical activity programs linked to PHC, even when simple, can contribute to better metabolic control, healthier behavior, and potentially improved obstetric outcomes in pregnant women with GDM [ 20 , 25 ]. Taken together, the findings suggest that the relevance of the intervention lies not only in glycemic improvement, but also in the fact that it represents an accessible model of care. A structured exercise program, associated with lifestyle guidance and supported, when possible, by objective monitoring tools such as smartwatches, may help qualify the management of GDM in public services. Future studies with larger samples, longer follow-up, and broader use of wearable monitoring may clarify the magnitude of these effects and expand the role of digital tools in maternal care. Conclusion This study demonstrates that participation in a structured, supervised physical activity program within the Primary Health Care setting can produce clinically meaningful benefits in pregnant women with gestational diabetes mellitus, even over a relatively short intervention period. Significant improvements were observed in lifestyle quality, postprandial glycemic control, and cardiovascular parameters, particularly the association between objectively measured daily steps and lower final systolic blood pressure. These findings support physical activity as a safe, effective, and low-cost non-pharmacological strategy for the management of gestational diabetes and highlight the innovative role of wearable technologies in enhancing monitoring and promoting adherence in public health settings. Limitations This study has some limitations that should be considered when interpreting the findings. The relatively small sample size and short duration of the intervention may limit the generalizability of the results and the ability to detect changes in inflammatory and long-term metabolic markers. Additionally, objective monitoring of physical activity using smartwatches was available for only a subset of participants, which may have reduced the statistical power of analyses involving step count. Finally, the study was conducted in a real-world Primary Health Care context, where logistical constraints and participant adherence challenges may have influenced the intervention exposure. Abbreviations • ADIFI Institute of Diabetics of Foz do Iguaçu • BMI body mass index • GDM gestational diabetes mellitus • OGTT oral glucose tolerance test • PHC Primary Health Care • SBP systolic blood pressure • SUS Unified Health System • T2DM type 2 diabetes mellitus Declarations Acknowledgements The authors would like to acknowledge the Federal University of Latin American Integration (UNILA) for providing the research scholarship and the equipment required for the development of this study. We also express our sincere gratitude to the Institute of Diabetics of Iguassu Falls (ADIFI) for offering the physical facilities, institutional support, and continuous collaboration throughout the research process. Special thanks are extended to the healthcare professionals and students involved in the extension project, whose multidisciplinary engagement was essential for the implementation of the intervention and participant follow-up. Finally, we would like to thank all the pregnant women who participated in this study for their commitment, trust, and valuable contribution, which made this research possible. Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the Centro Universitário Dinâmica das Cataratas (UDC), Brazil, via Plataforma Brasil (CAAE: 79348624.7.0000.8527; approval number: 6.933.345). Written informed consent was obtained from all participants prior to inclusion in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding This study received no external funding. Author contributions J.P.B.S. conceived and designed the study, conducted participant recruitment, data collection, statistical analysis, and drafted the manuscript. E.M.I. contributed to data collection and data tabulation. G.A.P.B. supervised the research, provided methodological guidance, and critically revised the manuscript. A.C.A.F. contributed to the scientific structuring of the manuscript, critical revision, and technical preparation for publication. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. References Bolognani CV, Souza SS, Paranhos Calderon IM. Gestational diabetes mellitus: focus on new diagnostic criteria. Comunicação em Ciências da Saúde . 2011;22(1):31–42. Laredo-Aguilera JA, Gallardo-Bravo M, Rabanales-Sotos JA, Cobo-Cuenca AI, Carmona-Torres JM. Physical Activity Programs during Pregnancy Are Effective for the Control of Gestational Diabetes Mellitus. Int J Environ Res Public Health . 2020 Aug 24;17(17):6151. doi: 10.3390/ijerph17176151. PMID: 32847106; PMCID: PMC7503359. Zajdenverg L, Façanha C, Dualib P, Golbert A, Moisés E, Calderon I, Mattar R, Francisco R, Negrato C, Bertoluci M. Rastreamento e diagnóstico da hiperglicemia na gestação. Diretriz Oficial da Sociedade Brasileira de Diabetes (2023) . DOI: 10.29327/557753.2022-11, ISBN: 978-85-5722-906-8. Brasil . Ministério da Saúde. Secretaria de Atenção Primária à Saúde. Departamento de Ações Programáticas. (2022). Manual de gestação de alto risco [recurso eletrônico]. Brasília: Ministério da Saúde. https://bvsms.saude.gov.br/bvs/publicacoes/gestacao_alto_risco.pdf PERIVOLARIS, Ekaterini Cruz et al. Complicações na gravidez e diabetes mellitus na gestação: dados de morbidade e mortalidade no Brasil. Research, Society and Development , v. 10, n. 11, p. e142101119335-e142101119335, 2021. MITTAL, R.; PRASAD, K.; LEMOS, J. R. N.; AREVALO, G.; HIRANI, K. Unveiling gestational diabetes: an overview of pathophysiology and management. International Journal of Molecular Sciences, [S.l.], v. 26, n. 5, p. 2320, 5 mar. 2025. DOI: 10.3390/ijms26052320. Disponível em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11900321/. Acesso em: 7 jul. 2025. GARCÍA, G. C. Diabetes mellitus gestacional. Medicina Interna de México , Ciudad de México, v. 24 , n. 2 , p. 148–156 , 2008. Disponível em: https://www.medigraphic.com/pdfs/medintmex/mim-2008/mim082g.pdf. Acesso em: 18 dez. 2025 . Padayachee C, Coombes JS. Exercise guidelines for gestational diabetes mellitus. World J Diabetes . 2015 Jul 25;6(8):1033-44. doi: 10.4239/wjd.v6.i8.1033. PMID: 26240700; PMCID: PMC4515443. Organização Pan-americana da Saúde. Ministério da Saúde. Federação Brasileira das Associações de Ginecologia e Obstetrícia. Sociedade Brasileira de Diabetes. Rastreamento e Diagnóstico de Diabetes Mellitus Gestacional no Brasil. Brasília: Opas; 2017. Doi: 10.1080/09638280600756372. JOY, Elizabeth A.; MOTTOLA, Michelle F.; CHAMBLISS, Heather. Integrating exercise is medicine® into the care of pregnant women. Current sports medicine reports , v. 12, n. 4, p. 245-247, 2013. Savvaki D, Taousani E, Goulis DG, et al. Diretrizes para exercícios durante a gravidez normal e diabetes gestacional: uma revisão das recomendações internacionais. Hormônios 17 , 521–529 (2018). https://doi.org/10.1007/s42000-018-0085-6. SOCIEDADE BRASILEIRA DE DIABETES . Diretrizes da Sociedade Brasileira de Diabetes 2019-2020. Disponível em: https://www.saude.ba.gov.br/wp-content/uploads/2020/02/Diretrizes-Sociedade-Brasileira-de-Diabetes-2019-2020.pdf. Acesso em: 22 de janeiro de 2024. BROWN, J., CEYSENS, G., BOULVAIN, M . Exercise for pregnant women with gestational diabetes for improving maternal and fetal outcomes. Cochrane Database Syst Rev . 2017 Jun 22;6(6):CD012202. doi: 10.1002/14651858.CD012202.pub2. PMID: 28639706; PMCID: PMC6481507. Cremona A, O'Gorman C, Cotter A, Saunders J, Donnelly A . Effect of exercise modality on markers of insulin sensitivity and blood glucose control in pregnancies complicated with gestational diabetes mellitus: a systematic review. Obes Sci Pract . 2018 Sep 4;4(5):455-467. doi: 10.1002/osp4.283. PMID: 30338116; PMCID: PMC6180709. Sabika S. Allehdan, Asma S. Basha, Fida F. Asali, Reema F. Tayyem. Dietary and exercise interventions and glycemic control and maternal and newborn outcomes in women diagnosed with gestational diabetes: Systematic review, Diabetes & Metabolic Syndrome: Clinical Research & Reviews , Volume 13, Issue 4, 2019, Pages 2775-2784, ISSN 1871-4021, https://doi.org/10.1016/j.dsx.2019.07.040. O'Malley EG, Reynolds CME, Killalea A., O'Kelly R., Sheehan SR, Turner MJ Obesidade materna e dislipidemia associada ao diabetes mellitus gestacional (DMG). Eur. J. Obsteto. Ginecol. Reprodução. Biol. 2020; 246 :67–71. doi: 10.1016/j.ejogrb.2020.01.007. DRUMMOND, Adriano; ALVES, Elioenai Dornelles. Perfil socioeconômico e demográfico e a capacidade funcional de idosos atendidos pela Estratégia Saúde da Família de Paranoá, Distrito Federal. Revista Brasileira de Geriatria e Gerontologia , v. 16, p. 727-738, 2013. KANALEY, J. A. et al. Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine. Medicine & Science in Sports & Exercise , Philadelphia, v. 54, n. 2, p. 353–368, 2022. DOI: 10.1249/MSS.0000000000002800. Disponível em: https://journals.lww.com/acsm-msse. Acesso em: 19 dez. 2025. DE LA CASA PÉREZ, A.; LATORRE ROMÁN, P. Á.; MUÑOZ JIMÉNEZ, M.; LUCENA ZURITA, M.; LAREDO AGUILERA, J. A.; PÁRRAGA MONTILLA, J. A.; CABRERA LINARES, J. C. Is the Xiaomi Mi Band 4 an accuracy tool for measuring health-related parameters in adults and older people? An original validation study. International Journal of Environmental Research and Public Health , v. 19, n. 3, p. 1593, 30 jan. 2022. DOI: 10.3390/ijerph19031593.Disponível em: https://pubmed.ncbi.nlm.nih.gov/35162615/ . Acesso em: 16 nov. 2025 Teede HJ, Bailey C, Moran LJ, Bahri Khomami M, Enticott J, Ranasinha S, Rogozinska E, Skouteris H, Boyle JA, Thangaratinam S, Harrison CL. Association of Antenatal Diet and Physical Activity-Based Interventions With Gestational Weight Gain and Pregnancy Outcomes: A Systematic Review and Meta-analysis. JAMA Intern Med. 2022 Feb 1;182(2):106-114. doi: 10.1001/jamainternmed.2021.6373. Erratum in: JAMA Intern Med. 2022 Oct 1;182(10):1108. doi: 10.1001/jamainternmed.2022.4410. PMID: 34928300; PMCID: PMC8689430. YE, W.; LUO, C.; HUANG, J.; LI, C.; LIU, Z. Gestational diabetes mellitus and adverse pregnancy outcomes: a systematic review and meta-analysis. BMJ, London, v. 377, e067946, 2022. DOI: 10.1136/bmj-2021-067946. Disponível em: https://doi.org/10.1136/bmj-2021-067946 . Acesso em: 18 dez. 2025. LANDON, M. B.; SPONG, C. Y.; THOM, E.; CARPENTER, M. W.; RAMIN, S. M.; CASEY, B.; WAPNER, R. J.; VARNER, M. W.; ROUSE, D. J.; THORP, J. M. Jr.; SCISCIONE, A.; CATALANO, P.; HARPER, M.; SAADE, G.; LAIN, K. Y.; SOROKIN, Y.; PEACEMAN, A. M.; TOLOSA, J. E.; ANDERSON, G. B.; EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT MATERNAL-FETAL MEDICINE UNITS NETWORK. A multicenter, randomized trial of treatment for mild gestational diabetes. The New England Journal of Medicine , Boston, v. 361, n. 14, p. 1339–1348, 2009. DOI: 10.1056/NEJMoa0902430. Disponível em: https://doi.org/10.1056/NEJMoa0902430. Acesso em: 18 dez. 2025. AMERICAN DIABETES ASSOCIATION. 15. Management of diabetes in pregnancy: Standards of care in diabetes—2025. Diabetes Care , Arlington, v. 48, supl. 1, p. S306–S320, 2025. DOI: 10.2337/dc25-S015. Disponível em: https://diabetesjournals.org/care/article/48/Supplement_1/S306/157565/15-Management-of-Diabetes-in-Pregnancy . Acesso em: 17 dez. 2025. ZHU, Z.; XIE, H.; LIU, S.; et al. Effects of physical exercise on blood pressure during pregnancy. BMC Public Health , Londres, v. 22, n. 1, p. 1733, 2022. DOI: 10.1186/s12889-022-14074-z. Disponível em: https://doi.org/10.1186/s12889-022-14074-z. Acesso em: 18 dez. 2025. DAVENPORT, M. H.; et al. Prenatal exercise for the prevention of gestational diabetes mellitus and hypertensive disorders of pregnancy: a systematic review and meta-analysis. British Journal of Sports Medicine , Londres, v. 52, n. 21, p. 1367–1375, 2018. DOI: 10.1136/bjsports-2018-099355. Disponível em: https://doi.org/10.1136/bjsports-2018-099355 . Acesso em: 18 dez. 2025. NUCKOLS, V. R.; DAVIS, K. G.; PIERCE, G. L.; GIBBS, B. B.; WHITAKER, K. M. Associations of physical activity and sedentary time with aortic stiffness and autonomic function in early pregnancy. Journal of Applied Physiology, Bethesda , v. 138, n. 3, p. 774–782, 2025. DOI: 10.1152/japplphysiol.00889.2024. Disponível em: https://doi.org/10.1152/japplphysiol.00889.2024. Acesso em: 18 dez. 2025. ADAMCZAK, L.; MANTAJ, U.; SIBIAK, R.; GUTAJ, P.; WENDER-OZEGOWSKA, E. Physical activity, gestational weight gain in obese patients with early gestational diabetes and the perinatal outcome: a randomised-controlled trial. BMC Pregnancy and Childbirth , Londres, v. 24, n. 1, p. 104, 2024. DOI: 10.1186/s12884-024-06296-3. Disponível em: https://doi.org/10.1186/s12884-024-06296-3. Acesso em: 18 dez. 2025. Additional Declarations No competing interests reported. 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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-9066411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621178940,"identity":"84c4f804-d92c-46dd-a27a-2c506c2431f8","order_by":0,"name":"Joao Paulo Batista de Souza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACxgYeCN0GZDJ8ALLY2EnRwjgDpIWZoD1QLQ1AghnMJqSFeXbv4Q+MOTayfeyH2x7b/Nomz8fMwPjhYw4eh805lybBuC3NuI0nsd04t++2YRszA7PkzG14tMzIMWNg3HY4sU2CsU06t+c2I1ALGzMvfi3GHxi3/Ydosey5bU+MFgOgww5AtDD8uJ1IWAvIL4nbkkF+aZPsbbid3MbM2IzXL4agEPu4zU52fvvxZxI//ty2nd/efBAogkcLKPYS4Ha2gckG3OqBQF4ChfsHr+JRMApGwSgYoQAAQbdOjCO505QAAAAASUVORK5CYII=","orcid":"","institution":"Federal University for Latin American Integration","correspondingAuthor":true,"prefix":"","firstName":"Joao","middleName":"Paulo Batista","lastName":"de Souza","suffix":""},{"id":621178943,"identity":"8714bd9a-8e37-448c-8d55-55dceae2b84e","order_by":1,"name":"Eric Massao Iwama","email":"","orcid":"","institution":"Federal University for Latin American Integration","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"Massao","lastName":"Iwama","suffix":""},{"id":621178947,"identity":"82e29475-45a0-4d65-b366-60abf0765c0a","order_by":2,"name":"Gleisson Alisson Pereira de Brito","email":"","orcid":"","institution":"Federal University for Latin American Integration","correspondingAuthor":false,"prefix":"","firstName":"Gleisson","middleName":"Alisson Pereira","lastName":"de Brito","suffix":""},{"id":621178948,"identity":"98b1b42e-28bb-4233-8a3f-8a027672d44d","order_by":3,"name":"André de Casemiro e Almeida Filho","email":"","orcid":"","institution":"Federal University for Latin American Integration","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"de Casemiro e Almeida","lastName":"Filho","suffix":""}],"badges":[],"createdAt":"2026-03-08 20:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9066411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9066411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107245452,"identity":"0a6ed338-b019-42cf-b213-e7e4a853b7c7","added_by":"auto","created_at":"2026-04-19 08:05:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65807,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot comparing baseline postprandial glycemia between the control and intervention groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBox plot comparing baseline postprandial glucose levels between the control and intervention groups. Boxes represent the interquartile range, the horizontal line indicates the median, whiskers represent the minimum and maximum values, and dots indicate outliers.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9066411/v1/f85c6235584e1c570976d037.png"},{"id":107245453,"identity":"c96b8e5b-43ed-4e99-a1e1-91da3eeff174","added_by":"auto","created_at":"2026-04-19 08:05:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69715,"visible":true,"origin":"","legend":"\u003cp\u003eRaincloud plot comparing final glycemia between the control and intervention groups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRaincloud plot illustrating the distribution of final postprandial glucose levels in the intervention and control groups. Individual data points represent each participant. Box plots indicate the median and interquartile range, while the violin plots depict the kernel density distribution of glucose values.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9066411/v1/27f4b8ec3bc2ce9efe2510b7.png"},{"id":107245454,"identity":"86f537d7-b75d-46e5-bbad-941281ba7116","added_by":"auto","created_at":"2026-04-19 08:05:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102516,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal Effect of Monitored Step Count on Final Systolic Blood Pressure.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMarginal effect plot showing the association between daily step count and final systolic blood pressure. The solid line represents the predicted values from the linear regression model, the shaded area indicates the 95% confidence interval, and the dashed lines represent the upper and lower confidence bounds.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9066411/v1/c751924232a33cad0a18ae5b.png"},{"id":108976535,"identity":"9ffe2ed5-dd49-4d5a-8ce9-85f0969be66e","added_by":"auto","created_at":"2026-05-11 11:24:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":558159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9066411/v1/f10968d1-4046-4532-b141-d8deb539b671.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of a Structured Physical Activity Program on Lifestyle, Metabolic, and Inflammatory Parameters in Women with Gestational Diabetes Mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Gestational Diabetes Mellitus (GDM) is defined as an alteration in glucose levels first diagnosed during pregnancy. Thus, this concept does not exclude the possibility that the glycemic abnormality may have been preexisting prior to pregnancy but remained undetected. Epidemiologically, there has been a global increase in the prevalence of GDM, associated with the rising number of individuals with obesity and type 2 diabetes mellitus [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. GDM has an estimated worldwide incidence of approximately 14%, and excessive gestational weight gain is directly related to its development. GDM negatively affects the course of pregnancy and fetal outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e According to the guidelines of the Brazilian Society of Diabetes (SBD), dysglycemia is the most common metabolic alteration during pregnancy. It is estimated that approximately 16% of live births are to women who experienced some form of hyperglycemia during pregnancy. About 8% of these cases involve women with diabetes diagnosed prior to pregnancy. The increasing prevalence of pregnancies in women with pregestational diabetes parallels the rising frequency of type 1 and type 2 diabetes mellitus among women of reproductive age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMaternal hyperglycemia is one of the most common conditions during pregnancy. In Brazil, it is estimated that 18% of pregnant women assisted by the Unified Health System (SUS) meet the current diagnostic criteria for GDM. Major risk factors include obesity, maternal age over 25 years, positive family and/or personal history, multiple gestation, arterial hypertension, dyslipidemia, smoking, physical inactivity, previous macrosomia, unexplained fetal death, among others [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the Mortality Information System (SIM), diabetes mellitus during pregnancy is the third leading cause of maternal death in Brazil, accounting for 15.32% of total maternal deaths between 2014 and 2019 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHyperinsulinemia during pregnancy represents a fundamental adaptive physiological response aimed at maintaining maternal glycemic homeostasis in the face of profound metabolic changes imposed by gestation. Initially, there is an increase in insulin sensitivity in early pregnancy, followed by a progressive development of insulin resistance from the second trimester onward, primarily mediated by placental hormones and adipose tissue\u0026ndash;derived factors. To compensate for this resistance, there is a marked increase in insulin secretion by pancreatic β-cells, resulting in physiological hyperinsulinemia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSkeletal muscle is the primary site of glucose utilization in the human body. During the second half of pregnancy, together with adipose tissue, it becomes increasingly resistant to the effects of insulin. In a normal pregnancy, there is an approximate 50% reduction in insulin-mediated glucose disposal, accompanied by a compensatory increase in insulin secretion of up to 200%, in an attempt to maintain normal maternal blood glucose levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e According to the current guidelines of the Brazilian Ministry of Health, the diagnosis of GDM should be performed using a 75-g oral glucose tolerance test (OGTT). Initial screening includes the measurement of fasting plasma glucose, preferably up to 20 weeks of gestation. Pregnant women with fasting glucose levels below 92 mg/dL are required to undergo the OGTT between 24 and 28 weeks of gestational age. In cases where prenatal care begins late (after 20 weeks of gestation), the 75-g OGTT should be performed as soon as possible to ensure timely diagnosis of both GDM and previously undiagnosed preexisting diabetes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA number of risk factors increase the likelihood of developing GDM. Ethnicity may play a role in GDM development, as higher incidence rates have been reported in certain ethnic subgroups. In the United States, epidemiological studies have shown a higher prevalence of GDM among African American, Native American, and Hispanic women [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRisk factors for GDM include both modifiable factors, such as diet, overweight, physical inactivity, and hypertension, and non-modifiable factors, such as ethnicity, age, and family history of diabetes. Identification of these factors at the time of diagnosis provides important opportunities to optimize GDM management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe diagnosis of GDM is established when one or more of the following criteria are present: fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;92 mg/dL and \u0026le;\u0026thinsp;125 mg/dL prior to the oral glucose tolerance test (OGTT); plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;180 mg/dL at 1 hour after the 75 g OGTT; plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;153 mg/dL and \u0026le;\u0026thinsp;199 mg/dL at 2 hours after the 75 g OGTT. Pregestational diabetes or overt diabetes is diagnosed when fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL and/or 2-hour post-load plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL and/or glycated hemoglobin (HbA1c)\u0026thinsp;\u0026gt;\u0026thinsp;6.5% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen diagnosed with GDM present an approximately threefold higher risk of adverse pregnancy outcomes compared with those without diabetes, including fetal macrosomia, stillbirth, neonatal metabolic disorders, preeclampsia, and higher rates of cesarean delivery. In addition, GDM is associated with an increased maternal risk of developing type 2 diabetes mellitus (T2DM) in the postpartum period, reinforcing the need for continuous monitoring after pregnancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, pregnancy represents an ideal opportunity to adopt a healthy lifestyle, when this has not already been established. Measures such as smoking cessation, reduction of alcohol and caffeine intake, improvement of dietary habits, and engagement in physical activity contribute to better maternal health and optimal fetal development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, regular exercise is known to be beneficial for both maternal and fetal health, preventing excessive maternal adiposity and worsening of GDM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNutritional management and regular physical activity are among the most challenging components of treatment strategies and lifestyle modification. The importance of nutritional therapy in the management of GDM has long been emphasized, as well as its central role in the prevention, treatment, and reduction of disease-related complications. Lifestyle modification is considered the cornerstone of diabetes control, whether or not combined with pharmacological therapy, since achieving adequate glycemic control reduces the risk of microvascular complications and may also minimize the likelihood of cardiovascular disease. Food choices directly influence energy balance and, consequently, body weight, glycemic levels, blood pressure, and plasma lipid concentrations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe benefits of physical activity require engagement in exercise for 30\u0026ndash;60 minutes at moderate intensity on five days per week, or an average of 150 minutes of aerobic activity per week, depending on the woman\u0026rsquo;s prior physical activity level and fitness status before pregnancy. Both the intensity and type of activity should be individualized, always considering the FITT exercise principles (frequency, intensity, time/duration, and type) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccordingly, strategies such as brisk walking, resistance exercises, and home-based aerobic activities, when combined with standard prenatal care, have demonstrated positive effects on glycemic control in women with GDM. Clinical studies have shown that regular practice of aerobic exercise, resistance training, or a combination of both is associated with significant reductions in fasting and postprandial glucose levels compared with conventional GDM management alone [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a systematic review by Allehdan et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which included eight randomized clinical trials, evidence indicated that dietary management combined with aerobic or resistance exercise improved glycemic outcomes and reduced fasting and postprandial glucose levels in women with GDM compared with dietary management alone. Adequate glycemic control through diet and exercise may prevent or delay the need for insulin therapy, with only 20\u0026ndash;30% of women requiring insulin. Furthermore, the combination of diet and exercise reduces excessive gestational weight gain and may improve pregnancy outcomes in women at risk of or diagnosed with GDM [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe main physiological mechanisms through which physical exercise performed during pregnancy exerts beneficial effects on maternal metabolism, endothelial function, and the placental environment, all of which are directly related to the pathophysiology of GDM. In pregnant women with GDM, increased insulin resistance, low-grade systemic inflammation, oxidative stress, and endothelial dysfunction are commonly observed, contributing both to maternal hyperglycemia and to a higher risk of obstetric complications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the well-established evidence regarding the benefits of physical activity in the management of GDM, relevant gaps persist in the literature, particularly concerning the effectiveness of short-term structured interventions developed within the context of Primary Health Care (PHC) and monitored using objective measures of physical activity. Furthermore, studies investigating the association between daily volume of monitored physical activity and early clinical, metabolic, and inflammatory outcomes in women with GDM remain scarce.\u003c/p\u003e \u003cp\u003eTherefore, the central research question of this study was whether a structured physical activity program, delivered with the volume and intensity recommended for pregnant women with GDM, is capable of improving maternal lifestyle, metabolic control, and inflammatory parameters when implemented within the context of public PHC. Accordingly, this study aimed to evaluate the effects of such a program on maternal metabolic and inflammatory outcomes, as well as to assess changes in lifestyle before and after the intervention. By investigating a feasible, low-cost strategy integrated into longitudinal PHC, this research seeks to contribute evidence on interventions that can generate clinically meaningful benefits even over relatively short follow-up periods.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis was a quasi-experimental study conducted at the Institute of Diabetics of Foz do Igua\u0026ccedil;u (ADIFI). The project included pregnant women diagnosed with GDM who were receiving care through Brazil\u0026rsquo;s Unified Health System (SUS) within the context of PHC. The study was conducted in accordance with established ethical guidelines and was approved under substantiated opinion no. 6.933.345 by the Centro Universit\u0026aacute;rio Din\u0026acirc;mica das Cataratas (UDC).\u003c/p\u003e \u003cp\u003eA total of 35 pregnant women with GDM were included in the final analysis, using a convenience sampling method and according to predefined inclusion and exclusion criteria. Initially, 42 participants were screened; however, seven cases were not retained in the final dataset because of follow-up losses and missing information in the medical records system of the municipality of Foz do Igua\u0026ccedil;u. The final sample therefore comprised 23 pregnant women in the control group and 12 in the intervention group, with a maximum of four participants followed per month. The partnership with the high-risk prenatal care service of the PHC network in Foz do Igua\u0026ccedil;u was essential to the success of the study, as was the infrastructure and healthcare team provided by the Institute of Diabetics of Foz do Igua\u0026ccedil;u (ADIFI). This collaboration demonstrated the feasibility of implementing lifestyle modification follow-up programs in settings dedicated to the care of patients with diabetes.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were defined as follows: pregnant women with a confirmed diagnosis of GDM; gestational age between 20 and 34 weeks at the time of screening; age between 18 and 50 years; body mass index ranging from 18 to 45 kg/m\u0026sup2;; an active electronic medical record in the municipality of Foz do Igua\u0026ccedil;u; an established care link with the Association of Diabetics of Foz do Igua\u0026ccedil;u (ADIFI); agreement to undergo anthropometric, biochemical, and fetal ultrasonographic assessments; and willingness to participate voluntarily in the study, committing to a 12-week lifestyle follow-up, including three blood sample collections, upon prior signing of the Written Informed Consent Form.\u003c/p\u003e \u003cp\u003eExclusion criteria comprised: the presence of other relevant obstetric complications; a history of major surgical procedures within the previous five years; autoimmune diseases, moderate to severe anemia, or advanced chronic kidney disease; lack of active registration in a PHC Unit; severe psychiatric disorders that could compromise adherence to the intervention; illicit drug use, active smoking, or abusive alcohol consumption; use of medications capable of interfering with the biochemical parameters evaluated, such as corticosteroids; clinical contraindications to physical activity; and significant use of nutritional supplements that could influence metabolic outcomes. The identification of any condition limiting the practice of physical exercise or adherence to the proposed follow-up was considered sufficient grounds for participant exclusion.\u003c/p\u003e \u003cp\u003eFor diagnostic criteria, the study adopted the presence of one or more of the following findings in the participants\u0026rsquo; medical records: fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;92 mg/dL and \u0026le;\u0026thinsp;125 mg/dL prior to the oral glucose tolerance test (OGTT); 1-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;180 mg/dL following a 75-g OGTT; or 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;153 mg/dL and \u0026le;\u0026thinsp;199 mg/dL. Pregestational or overt diabetes was defined as fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL and/or 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL and/or glycated hemoglobin\u0026thinsp;\u0026gt;\u0026thinsp;6.5%, in accordance with the Pan American Health Organization criteria (2017) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lifestyle assessment was performed using the FANTASTIC questionnaire, a validated instrument in Brazil designed to evaluate multiple dimensions of lifestyle behaviors among participants [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe exercise protocol was structured in a combined format, including home-based activities and supervised sessions conducted at ADIFI. Aerobic activity consisted of walking performed in the home environment, with daily frequency or a minimum of three sessions per week, lasting 15 to 30 minutes per session, and conducted at moderate intensity according to individual tolerance and previously provided guidance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStep monitoring was performed using the Xiaomi Mi Band 9 device, which incorporates motion sensors (triaxial accelerometer and gyroscope) embedded in the wristband. These sensors detect rhythmic arm movements during walking, and the internal algorithm processes the signals to identify step patterns while distinguishing them from non-walking arm movements, thereby recording the total number of steps accumulated throughout the day [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn-person sessions were offered twice weekly at ADIFI and consisted of monitored and supervised exercises, with a total duration of approximately 45 to 60 minutes per session. A minimum attendance rate of 75% was required to characterize adherence to the intervention protocol. Resistance training lasted 20 to 25 minutes and comprised six exercises performed in three sets of 10 to 15 repetitions, primarily using body weight and/or elastic resistance bands. Exercise selection and progression were individualized according to each participant\u0026rsquo;s clinical, functional, and gestational profile [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, specific exercises targeting pelvic floor strengthening and mobility were included, along with global stretching exercises. At the end of each session, approximately 10 minutes were dedicated to relaxation and recovery techniques aimed at enhancing comfort, reducing muscle tension, and promoting overall well-being.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using JASP software (version 0.18.0.3, University of Amsterdam, October 15, 2025), while part of the graphical visualizations was generated using R software (version 4.3.2). For qualitative\u0026ndash;quantitative variables, parametric tests (such as Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test) or nonparametric tests (such as the Mann\u0026ndash;Whitney test) were applied according to data distribution. Statistical significance was defined as a \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating a statistically significant difference between groups.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe baseline characteristics of the participants were analyzed by comparing the control group (n\u0026thinsp;=\u0026thinsp;23) and the intervention group (n\u0026thinsp;=\u0026thinsp;12), using appropriate statistical tests according to the distribution of the variables. Mean maternal age was similar between groups, with an average of 31.43 years in the control group and 30.00 years in the intervention group, showing no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.238).\u003c/p\u003e \u003cp\u003eRegarding gestational age at baseline, the control group presented a significantly higher mean gestational age (27.17 weeks) compared to the intervention group (22.83 weeks), with a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Baseline body weight was also higher in the control group (88.28 kg) than in the intervention group (77.18 kg), with a statistically significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e Baseline characteristics of participants at the beginning of the program\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e88.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e161.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e158.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e30.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e145.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e150.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e152.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e181.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e167.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e43.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\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 \u003cstrong\u003eLegend\u003c/strong\u003e \u003cp\u003eage in years; gestational age (GA) in weeks; weight in kilograms (kg); height in centimeters (cm); body mass index (BMI) in kg/m\u0026sup2;.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eC\u0026thinsp;=\u0026thinsp;control group; I\u0026thinsp;=\u0026thinsp;intervention group.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAt the initial application of the FANTASTIC questionnaire, participants presented high lifestyle scores, predominantly classified as \u0026ldquo;good\u0026rdquo; to \u0026ldquo;very good.\u0026rdquo; The mean score was 77.3 in the control group and 73.7 in the intervention group, with no statistically significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.324).\u003c/p\u003e \u003cp\u003eA significant increase in the total lifestyle score was observed in the intervention group at the end of the study, rising from 73.70 at baseline to 80.57 at the final assessment (Δ = +6.87; p\u0026thinsp;=\u0026thinsp;0.034), indicating an overall improvement in lifestyle profile.\u003c/p\u003e \u003cp\u003eIn the baseline postprandial glycemia assessment, the intervention group showed a numerically higher mean than the control group (114.9 mg/dL vs. 102.6 mg/dL); however, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.149). It should be noted that both groups were within the recommended standards for postprandial glycemic monitoring, in which measurements may be performed 1 hour or 2 hours after meals at the clinician\u0026rsquo;s discretion, without the need to assess both time points.\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here)\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\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e Postprandial Glycemia: Comparison Between Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBaseline Glycemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFollow-up Glycemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGlycemia Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-13.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-63.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-40.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMissing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.149\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\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 \u003cstrong\u003eLegend\u003c/strong\u003e \u003cp\u003e \u003cem\u003eBaseline glycemia is expressed in mg/dL and refers to the initial measurement, while final glycemia corresponds to the measurement at the end of the study. Glycemia difference represents the change between final and baseline values (Δ\u0026thinsp;=\u0026thinsp;final\u0026thinsp;\u0026minus;\u0026thinsp;baseline). p-values refer to between-group comparisons, obtained using the most appropriate statistical test according to data normality (Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney test). p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate statistically significant differences between groups.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eAt the end of the study, distinct patterns were observed between groups. Final postprandial glycemia was significantly higher in the control group compared with the intervention group (129.9 mg/dL vs. 101.4 mg/dL), with a statistically significant between-group difference (p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003eIn the analysis of postprandial glycemia variation (difference between final and baseline values), the control group showed a mean increase of +\u0026thinsp;31.68 mg/dL, whereas the intervention group showed a mean reduction of \u0026minus;\u0026thinsp;13.45 mg/dL, indicating a clear divergence in glycemic behavior over the evaluated period. This between-group difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a favorable effect of the intervention on postprandial glycemia.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eBox plot comparing baseline postprandial glucose levels between the control and intervention groups. Boxes represent the interquartile range, the horizontal line indicates the median, whiskers represent the minimum and maximum values, and dots indicate outliers.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eTo further support this analysis, a one-way ANOVA was conducted, which also demonstrated a statistically significant difference between groups in post-intervention glycemia (F\u0026thinsp;=\u0026thinsp;10.05; p\u0026thinsp;=\u0026thinsp;0.004), confirming the effect of the intervention on glycemic reduction. The homogeneity of variances and the normality of residuals support the adequacy of the ANOVA model for this comparison, as summarized in the figure below:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eRaincloud plot illustrating the distribution of final postprandial glucose levels in the intervention and control groups. Individual data points represent each participant. Box plots indicate the median and interquartile range, while the violin plots depict the kernel density distribution of glucose values.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eMonitoring of daily step counts using the Xiaomi Mi Band 9 smartwatch was performed in only five pregnant women. Seven participants reported performing the exercise protocol on a weekly basis. Among the monitored participants, the mean number of steps per day was 3,381, with a standard deviation of 326.1 steps, indicating relative homogeneity in daily activity patterns. The minimum recorded value was 2,916 steps, while the maximum reached 3,789 steps, demonstrating moderate variability among the women who adhered to the use of the device. Although only a subset of the group was monitored, these data enabled important additional analyses regarding the relationship between physical activity level and clinical indicators.\u003c/p\u003e \u003cp\u003eCorrelation analysis between the mean daily step count recorded by the smartwatch and final systolic blood pressure (SBP F) demonstrated a significant association both at baseline and at the end of the intervention. Among the five participants who used the smartwatch, those with higher step counts exhibited lower SBP levels. This inverse relationship was confirmed by both Pearson and Spearman correlation tests, which were applied due to the small sample size (n\u0026thinsp;\u0026lt;\u0026thinsp;30), thereby reinforcing the robustness of the finding. Specifically, final SBP showed a significant correlation with total step count (Pearson: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.976; p\u0026thinsp;=\u0026thinsp;0.012 | Spearman: ρ = \u0026minus;1.000; p\u0026thinsp;=\u0026thinsp;0.042), while the change in SBP also demonstrated a significant correlation (Spearman: ρ = \u0026minus;0.949; p\u0026thinsp;=\u0026thinsp;0.026), indicating that a higher number of steps was associated with greater blood pressure reduction.\u003c/p\u003e \u003cp\u003eLinear regression analysis further supported this pattern, revealing a strong association between final SBP and the number of steps monitored by the smartwatch. The adjusted model demonstrated excellent performance, with R\u0026thinsp;=\u0026thinsp;0.976, R\u0026sup2; = 0.952, and adjusted R\u0026sup2; = 0.928, indicating that approximately 95% of the variance in final SBP could be explained by step count. The regression was statistically significant (F\u0026thinsp;=\u0026thinsp;39.40; p\u0026thinsp;=\u0026thinsp;0.024), demonstrating that increased step count was consistently associated with lower final SBP values. The standardized coefficient indicated a robust negative relationship (β = \u0026minus;0.976; p\u0026thinsp;=\u0026thinsp;0.024), reinforcing that participants who walked more exhibited lower systolic blood pressure at the end of the intervention.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates that the downward trend line indicates decreasing estimated final SBP values as step counts increase, while the confidence band demonstrates good precision of the estimates despite the small sample size. This graphical pattern corroborates the statistical findings, clearly showing that a higher volume of daily physical activity, measured by step count, was associated with improved systolic blood pressure control at the end of the intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eMarginal effect plot showing the association between daily step count and final systolic blood pressure. The solid line represents the predicted values from the linear regression model, the shaded area indicates the 95% confidence interval, and the dashed lines represent the upper and lower confidence bounds.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn addition, a binary logistic regression was performed to evaluate the association between final systolic blood pressure (final SBP) and improvement in glycemia at the end of the intervention, considering a dichotomous outcome variable (glycemic improvement: yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0).\u003c/p\u003e \u003cp\u003eThe model showed a statistically significant fit, as indicated by the Wald test. Final SBP was significantly associated with the probability of glycemic improvement, with a negative coefficient (β = \u0026minus;0.916; standard error\u0026thinsp;=\u0026thinsp;0.418), indicating that higher final SBP values reduced the likelihood of glycemic improvement. The odds ratio (OR) was 0.40 (p\u0026thinsp;=\u0026thinsp;0.028), suggesting that for each unit increase in final SBP, there was an approximate 60% reduction in the odds of glycemic improvement.\u003c/p\u003e \u003cp\u003eDespite the statistical significance of this association, the pseudo R\u0026sup2; indices (McFadden, Nagelkerke, and Tjur) were low or null, indicating that although final SBP contributes significantly to explaining glycemic improvement, the model has low overall explanatory power, suggesting that other factors not included in the model also influence this outcome.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study showed that a structured physical activity program carried out within Primary Health Care was able to generate meaningful changes in women with gestational diabetes mellitus, even over a relatively short follow-up period. Beyond the statistical significance observed in some outcomes, what draws attention here is the practical relevance of these findings in a real-world care setting. In routine prenatal care, especially in public services, interventions that are simple, feasible, and low cost tend to have greater chances of being sustained. In this sense, the program evaluated in our study appears to offer a realistic strategy for improving maternal care without depending exclusively on pharmacological measures.\u003c/p\u003e \u003cp\u003eA relevant finding was the improvement in lifestyle among participants in the intervention group, reflected by the increase in the total FANTASTIC questionnaire score at the end of follow-up. This result suggests that the intervention was not limited to isolated exercise sessions, but may also have influenced daily habits and health-related decisions during pregnancy. In women with GDM, this point is especially important, because metabolic control does not depend only on one component of care, but rather on the combination of movement, food choices, adherence, understanding of the disease, and engagement with prenatal recommendations. Seen from this perspective, the observed change in lifestyle score helps to reinforce that the intervention had a broader effect than a purely physiological response.\u003c/p\u003e \u003cp\u003eThe behavior of postprandial glycemia was one of the clearest findings in the study. While the control group showed a worsening pattern over time, the intervention group presented a reduction in mean postprandial glucose levels. In clinical terms, this difference is relevant because postprandial glycemia has direct implications for maternal metabolic control and for fetal exposure to hyperglycemia. The result strengthens what has already been suggested in previous studies: regular physical activity, when adequately prescribed and followed, can improve glucose utilization and reduce glycemic excursions after meals. In the context of GDM, this is particularly valuable because postprandial control is often one of the most challenging targets in daily care, and treatment of even mild GDM is associated with better maternal and neonatal outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a physiological standpoint, this effect is plausible. Skeletal muscle contraction increases glucose uptake through insulin-independent pathways and, when repeated over time, contributes to better peripheral insulin sensitivity. During pregnancy, when insulin resistance tends to increase progressively, especially from the second trimester onward, any intervention capable of partially counterbalancing this process may have an important clinical role. Our findings therefore support the idea that structured exercise should not be viewed only as an accessory recommendation, but as an active component of treatment in women with GDM.\u003c/p\u003e \u003cp\u003eAnother point that deserves emphasis is the use of smartwatch-based monitoring. Although only part of the sample used the device, the data obtained added an innovative dimension to the study. The inverse association between the number of daily steps and final systolic blood pressure suggests that objective monitoring of movement may help reveal clinically relevant patterns that are not always captured by self-report alone. In practical terms, this is one of the most interesting aspects of the manuscript, because it connects a common wearable technology to prenatal care in a population that is usually monitored with more traditional tools. In Brazil, and even internationally, there is still limited evidence on the incorporation of this type of device into the follow-up of women with GDM in public health settings. For this reason, the study opens an interesting path for future investigations, especially those focused on adherence, remote monitoring, and individualized counseling, in line with current evidence linking physical activity to maternal cardiovascular function during pregnancy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the same time, the smartwatch results should be interpreted with caution. The subgroup monitored with the device was small, which limits broader inferences. Even so, the direction of the findings is coherent and biologically plausible. More importantly, it shows that the use of wearables in this field is not merely a technological embellishment. When well integrated into care, these devices may become practical allies for both patients and professionals, helping transform abstract recommendations such as \u0026ldquo;walk more\u0026rdquo; into measurable, monitorable, and clinically interpretable information.\u003c/p\u003e \u003cp\u003eSome limitations need to be acknowledged. The final sample size was modest, which reduces statistical power for some analyses and limits external generalization. In addition, the study experienced losses during follow-up and part of the initial data could not be recovered from the medical records system, which is a common challenge in real-life health services but still an important methodological constraint. The follow-up period was also short, which means the findings should be interpreted as early effects of the intervention rather than as evidence of long-term maternal or neonatal benefit. Another point is that not all participants used the smartwatch, making the analyses involving step count more exploratory in nature.\u003c/p\u003e \u003cp\u003eOn the other hand, the strengths of the study should not be overlooked. It was conducted in a real care context, with women followed within the public health system, and tested an intervention that is feasible outside highly controlled research environments. This gives the findings practical value. Many studies demonstrate benefit under ideal conditions; fewer show what can actually be done in everyday prenatal care with the resources that services truly have. In this regard, our results support the idea that physical activity programs linked to PHC, even when simple, can contribute to better metabolic control, healthier behavior, and potentially improved obstetric outcomes in pregnant women with GDM [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTaken together, the findings suggest that the relevance of the intervention lies not only in glycemic improvement, but also in the fact that it represents an accessible model of care. A structured exercise program, associated with lifestyle guidance and supported, when possible, by objective monitoring tools such as smartwatches, may help qualify the management of GDM in public services. Future studies with larger samples, longer follow-up, and broader use of wearable monitoring may clarify the magnitude of these effects and expand the role of digital tools in maternal care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that participation in a structured, supervised physical activity program within the Primary Health Care setting can produce clinically meaningful benefits in pregnant women with gestational diabetes mellitus, even over a relatively short intervention period. Significant improvements were observed in lifestyle quality, postprandial glycemic control, and cardiovascular parameters, particularly the association between objectively measured daily steps and lower final systolic blood pressure. These findings support physical activity as a safe, effective, and low-cost non-pharmacological strategy for the management of gestational diabetes and highlight the innovative role of wearable technologies in enhancing monitoring and promoting adherence in public health settings.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has some limitations that should be considered when interpreting the findings. The relatively small sample size and short duration of the intervention may limit the generalizability of the results and the ability to detect changes in inflammatory and long-term metabolic markers. Additionally, objective monitoring of physical activity using smartwatches was available for only a subset of participants, which may have reduced the statistical power of analyses involving step count. Finally, the study was conducted in a real-world Primary Health Care context, where logistical constraints and participant adherence challenges may have influenced the intervention exposure.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ADIFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitute of Diabetics of Foz do Igua\u0026ccedil;u\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; BMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; GDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egestational diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; OGTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoral glucose tolerance test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary Health Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnified Health System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; T2DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etype 2 diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to acknowledge the Federal University of Latin American Integration (UNILA) for providing the research scholarship and the equipment required for the development of this study. We also express our sincere gratitude to the Institute of Diabetics of Iguassu Falls (ADIFI) for offering the physical facilities, institutional support, and continuous collaboration throughout the research process. Special thanks are extended to the healthcare professionals and students involved in the extension project, whose multidisciplinary engagement was essential for the implementation of the intervention and participant follow-up. Finally, we would like to thank all the pregnant women who participated in this study for their commitment, trust, and valuable contribution, which made this research possible.\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of the Centro Universit\u0026aacute;rio Din\u0026acirc;mica das Cataratas (UDC), Brazil, via Plataforma Brasil (CAAE: 79348624.7.0000.8527; approval number: 6.933.345). Written informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eJ.P.B.S. conceived and designed the study, conducted participant recruitment, data collection, statistical analysis, and drafted the manuscript. E.M.I. contributed to data collection and data tabulation. G.A.P.B. supervised the research, provided methodological guidance, and critically revised the manuscript. A.C.A.F. contributed to the scientific structuring of the manuscript, critical revision, and technical preparation for publication. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBolognani CV, Souza SS, Paranhos Calderon IM. 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Associations of physical activity and sedentary time with aortic stiffness and autonomic function in early pregnancy.\u003cstrong\u003e\u0026nbsp;Journal of Applied Physiology, Bethesda\u003c/strong\u003e, v. 138, n. 3, p. 774\u0026ndash;782, 2025. DOI: 10.1152/japplphysiol.00889.2024. Dispon\u0026iacute;vel em: https://doi.org/10.1152/japplphysiol.00889.2024. Acesso em: 18 dez. 2025.\u003c/li\u003e\n \u003cli\u003eADAMCZAK, L.; MANTAJ, U.; SIBIAK, R.; GUTAJ, P.; WENDER-OZEGOWSKA, E. Physical activity, gestational weight gain in obese patients with early gestational diabetes and the perinatal outcome: a randomised-controlled trial.\u003cstrong\u003e\u0026nbsp;BMC Pregnancy and Childbirth\u003c/strong\u003e, Londres, v. 24, n. 1, p. 104, 2024. DOI: 10.1186/s12884-024-06296-3. Dispon\u0026iacute;vel em: https://doi.org/10.1186/s12884-024-06296-3. Acesso em: 18 dez. 2025.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gestational diabetes, physical exercise, lifestyle, primary health care, perinatal care","lastPublishedDoi":"10.21203/rs.3.rs-9066411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9066411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGestational Diabetes Mellitus (GDM) represents a significant challenge in women\u0026rsquo;s health, as it is associated with an increased risk of obstetric complications and adverse maternal outcomes. Given the limitations of pharmacological therapy alone, non-pharmacological interventions, such as structured physical activity, have shown promise in promoting public health. This study aimed to evaluate the effects of a supervised physical activity program on physiological, metabolic, and inflammatory parameters in pregnant women with GDM. The research was conducted within the context of Primary Health Care (PHC). The intervention protocol consisted of at least 150 minutes per week of moderate-intensity physical activity over a 12-week period, with emphasis on monitored walking, stretching exercises, and educational sessions focused on healthy lifestyle behaviors. Participants used smartwatches to record step counts, completed the FANT\u0026Aacute;STIC lifestyle questionnaire, and underwent laboratory assessments at baseline and at the end of the study. The results demonstrated that, at the end of the intervention, the intervention group showed a significant increase in the total lifestyle score, from 73.70 to 80.57 points (Δ = +6.87), with a statistically significant difference compared with the control group (p\u0026thinsp;=\u0026thinsp;0.034). As an exception, postprandial glucose showed a mean reduction of \u0026minus;\u0026thinsp;13.45 mg/dL in the intervention group, with a statistically significant effect compared with the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Smartwatch monitoring revealed an association between a higher number of daily steps and lower final systolic blood pressure, with significant correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and linear regression (F\u0026thinsp;=\u0026thinsp;39.40; p\u0026thinsp;=\u0026thinsp;0.024; β = \u0026minus;0.976). These findings indicate that, even over a short intervention period, the program was able to produce clinically relevant benefits, highlighting the innovative use of smartwatches within PHC as a promising tool for the care of pregnant women with GDM. These findings suggest that structured physical activity programs may represent an effective and scalable non-pharmacological strategy for the management of gestational diabetes in primary health care settings.\u003c/p\u003e","manuscriptTitle":"Effects of a Structured Physical Activity Program on Lifestyle, Metabolic, and Inflammatory Parameters in Women with Gestational Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 08:05:14","doi":"10.21203/rs.3.rs-9066411/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f328716-76ce-4e81-b096-b017b6dd2ae7","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T07:12:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T15:03:10+00:00","index":44,"fulltext":""},{"type":"reviewerAgreed","content":"178394270973109253481154292547953000478","date":"2026-05-02T18:52:09+00:00","index":43,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T07:25:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 08:05:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9066411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9066411","identity":"rs-9066411","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}