WhatsApp-Based Intervention for Diabetes Prevention and Care in Argentina: Implementation and Process Evaluation

Type 2 diabetes (T2D) represents a major public health challenge in Poland. This study investigated geographic heterogeneity and municipality‑level ecological predictors of a high T2D risk in the Świętokrzyskie Voivodeship (Poland), a region undergoing rapid socioeconomic transition. We aimed to identify associations between the T2D risk, assessed by the Finnish Diabetes Risk Score (FINDRISC), and socioeconomic and environmental factors. This cross‑sectional study analyzed data from 252 427 residents of the Świętokrzyskie Voivodeship aged 45-64 years, without diagnosed diabetes, who participated in a regional prevention program (2019-2022). Individual risk, assessed via the FINDRISC questionnaire, was linked with municipality‑level ecological data. We used a logistic regression model with municipality‑clustered standard errors to identify predictors of a high T2D risk (FINDRISC ≥15). Overall, 13.6% of the participants were at a high risk of diabetes (FINDRISC ≥15), with significant geographic variation (P <0.001). Among municipality‑level predictors, higher unemployment rate was significantly associated with increased odds of a high T2D risk (odds ratio [OR], 1.14 per 1% increase; P <0.001), whereas higher proportions of legally disabled residents and higher annual mean concentrations of particulate matter with aerodynamic diameter of 2.5 μm or smaller were inversely associated with the prevalence of a high T2D risk (OR, 0.98; P = 0.03 and OR, 0.92 per 1 μg/m3; P = 0.001, respectively). Results were robust across sensitivity analyses using alternative FINDRISC cutoffs. Unemployment was a key population‑level determinant of a high T2D risk, whereas other socioeconomic and environmental factors showed null or inverse associations. These findings underscore the importance of considering local context when assessing population‑level health determinants.

The incidence of type 2 diabetes mellitus (DM) has been predicted to increase until 2045 in the world. Furthermore, long-term treatment and lifestyle factors affect the quality of life. This study aims to determine the models that can be used to predict the quality-of-life domains in prediabetes patients by using Artificial Intelligent (AI) devices. This is a cross-sectional design in which the inclusion criteria were individuals of age above 18 years and has never been diagnosed with diabetes mellitus (both type 1 DM and type 2 DM), fasted for at least 8 hours, and are willing to sign an informed consent after having received an explanation. Participants were asked to fill out two questionnaires, namely the Indonesian version of the Finnish Diabetes Risk Score (FINDRISC) and the EuroQoL-5 Dimensions-5 Level (EQ-5D-5L). The AI application uses Orange® machine learning with three models used in predictive analysis, such as Logistic Regression, Neural Network, and SVM. In addition, the model was evaluated using the sensitivity, precision, and accuracy of the AU-ROC parameters. The results showed that the neural network model based on the AUC value, precision, accuracy, and also the ROC analysis, was the best for predicting the utility index of domains in the EQ-5D-5L questionnaire, based on demographic data and the FINDRISC questionnaire.

Background: Diabetes mellitus is a real public health problem worldwide and is growing in developing countries. Our aim was to determine the prevalence of undiagnosed type 2 diabetes mellitus (T2DM) and to evaluate the performance of the Finnish Diabetes Risk Score (FINDRISC) questionnaire to identify type 2 diabetics in the urban population of Ouagadougou, Burkina Faso. Methodology: This was a cross-sectional study among volunteers aged 18 years and older recruited systematically from November 11 to 16, 2019, in the city of Ouagadougou, Burkina Faso, FINDRISC score was used. Analysis of the Receiver Operating Characteristic (ROC) curve was used to study the diagnostic performance of FINDRISC for the identification of type 2 diabetics. The optimal threshold was determined by the sum of the highest sensitivity and specificity. Multivariate logistic regression was used to analyze the association of each variable used in the calculation of the FINDRISC score. Results: A total of 1276 individuals were included in the analyses, of which 667 (52.27%) were women. The average age was 34.16 years (SD: ±12.42). The prevalence of T2DM was 10.74%. The mean FINDRISC score was 5.85 (SD: ±4.31). The majority (58.54%) of individuals had a low risk of diabetes according to the FINDRISC score 0 - 7, while 3.61% had a score ≥ 15. The FINDRISC score showed good performance (AUC = 0.70) in identifying undiagnosed type 2 diabetics. However, the variables in the score that best predicted the likelihood of being diabetic were age (p < 0.001), daily physical activity (p = 0.004), use of antihypertensive medication (p = 0.007) and waist circumference (p < 0.001). The optimal cut-off score ≥ 7 was the best predictor of the likelihood of having T2DM. Conclusion: The Finnish Risk Score (FINDRISC) is a good predictor of the risk of dysglycemia in Burkina Faso. It is a score to be promoted in daily clinical practice because it is easy to use, affordable and non-invasive. Further studies are needed to make modifications to the FINDRISC questionnaire in case it is applied to other ethnic groups.

Objective : to clarify the primary incidence of type 2 diabetes mellitus (DM) in patients with rheumatoid arthritis (RA) and to compare the prevalence of traditional risk factors (RFs) in groups of patients with and without carbohydrate metabolic disorders. Patients and methods . A retrospective analysis was carried out in 158 patients with RA (diagnosed at the age of 45 years and older; the disease duration was more than 12 months). The exclusion criteria were concomitant type 1 DM and type 2 DM that was diagnosed before or at the onset of RA. The patients' median age was 62 [57; 68] years. Most RA patients had moderate (41.8%) and high (39.9%) DAS28. New cases of type 2 DM and the presence of hyperglycemia were recorded at the time of the examination. The traditional RFs of type 2 DM were assessed using the Finnish Diabetes Risk Score (FINDRISC). Results and discussion . The incidence rates of type 2 DM was 9.3 per 1000 patient-years. The patients with developed type 2 DM versus those without DM had a larger number of RFs according to the the FINDRISC questionnaire (6 [5; 7] and 5 [4; 5]; p<0.01), had more frequently experienced myocardial infarction and undergone surgery for myocardial revascularization (27.3 and 2.7%; p<0.01), taken beta-adrenoblockers (72.7 and 33.3%; p<0.05) and calcium channel blockers (36.4 and 12.2%; p<0.05). Fasting hyperglycemia was detected in 10.1% of RA patients. The patients with hyperglycemia versus those with normal venous blood glucose levels more often had obesity (50.0 and 29.8%) and a history of hyperglycemic episodes (43.8 and 19.1%) and less frequently used glucocorticoids (18.8 and 47.3%; p<0.05 for all cases). Conclusion. The high incidence of type 2 DM in RA was associated with the presence of a set of traditional RFs and previous cardiovascular disease, while fasting hyperglycemia was with individual RFs for carbohydrate metabolic disorders.

To investigate changes in the prevalence of diabetes and pre-diabetes by shifting from 2-h plasma glucose and/or fasting plasma glucose diagnostic criteria to the proposed new HbA(1c) -based criteria when applied to a Mediterranean population detected to have a high risk of Type 2 diabetes. Individuals without diabetes aged 45-75 years (n = 2287) were screened using the Finnish Diabetes Risk Score questionnaire, a 2-h oral glucose tolerance test plus HbA(1c) test. Prevalence and degree of diagnostic overlap between three sets of criteria (2-h plasma glucose, fasting plasma glucose and HbA(1c) ) and three diagnostic categories (normal, pre-diabetes and diabetes) were calculated. Defining diabetes by a single HbA(1c) measurement resulted in a dramatic decrease in prevalence (1.3%), particularly in comparison with diabetes defined by 2-h plasma glucose (8.6%), but was also significant with regard to fasting plasma glucose (2.8%). A total of 201 screened subjects (8.8%) were classified as having diabetes and 1023 (44.7%) as having pre-diabetes based on at least one of these criteria; among these, the presence of all three criteria simultaneously classified only 21 and 110 individuals respectively, about ten percent of each group. The single overlap index between subjects diagnosed as having diabetes by 2-h plasma glucose/fasting plasma glucose vs. HbA(1c) was 13.9/28%. Similarly, the single overlap index regarding pre-diabetes was 19.2/27.1%. A shift from the glucose-based diagnosis to the HbA(1c) -based diagnosis for diabetes will reduce diabetes prevalence with a low overall or single degree of overlap between diagnostic categories in this high-risk Spanish population.

VEGFA, PlGF and IGF-1 are three main angiogenic factors which play significant roles in embryo implantation. However, the relationship between serum expressions of VEGFA, PlGF and IGF-1 and pregnancy outcomes has not been fully illustrated. In this study, serum specimens were collected precisely on day 7 after the LH surge in a natural non-conception cycle from 38 infertile patients who underwent frozen embryo transfer (FET) treatment. ELISA was used to determine the concentrations of VEGFA, PlGF and IGF-1. Serum levels of VEGFA, PlGF and IGF-1 were compared between patients who conceived (n=25) and who did not (n=13). Correlation and linear regression analyses were used to investigate the correlations of serum angiogenic factors and β-hCG MoM levels in the pregnant group. The results demonstrated that no significant difference was found in serum VEGFA, PlGF or IGF-1 concentration between pregnant and non-pregnant women. Spearman correlation analysis revealed a positive correlation between IGF-1 concentration and β-hCG level in pregnant participants (rs = 0.490, p = 0.013). In conclusion, serum IGF-1 level correlated positively with β-hCG level in pregnant women, which may provide information on the prognostic value of IGF-1 in this group of women.

Pregnancy Is Characterized by Widespread Deep-Tissue Hypersensitivity Independent of Lumbopelvic Pain Intensity, a Facilitated Response to Manual Orthopedic Tests, and Poorer Self-Reported Health

Translation of an evidence-based virtual reality simulation-based interprofessional education into health education curriculums: An implementation science method

RECENT EMPHASIS ON THE ETHICAL CONDUCT of researchers has resulted in a growing body of literature exploring the impact of trauma-focused research on participants. To date, pregnant women have not been widely included in trauma-focused research, possibly because they are considered a vulnerable population in research. The current research investigated how 41 expectant mothers responded to participation in a trauma-focused study. Overall, the results of this study suggest that trauma-focused research is well tolerated by pregnant women. Specific findings indicated that pregnant women considered more traumatized perceived greater benefits from participation. Best practices for conducting ethical trauma-focused research with pregnant women are provided. As well, recommendations for future research are outlined in an effort to further extend the ethical understanding of the benefits and costs of trauma-focused research with potentially vulnerable populations.

This work presents a dialect-aware and noise-robust Kannada automatic speech recognition (ASR) system that bridges the gap between low-resource linguistic contexts and state-of-the-art deep learning models. We design a two-stage approach: (i) a scratch-built convolutional neural network (CNN)–Transformer hybrid trained on curated Kannada speech data with fast Fourier transform-based noise reduction and (ii) fine-tuning OpenAI’s Whisper-small model on a dialect-diverse corpus. The proposed pipeline integrates adaptive noise suppression, subword tokenization, and beam-search decoding to handle agglutinative morphology, speaker variation, and environmental noise. Extensive experiments were conducted on two datasets: the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) multilingual Kannada subset and a curated 150-sample, Custom-collected Kannada speech dataset covering both formal and conversational speech. On the FLEURS test set (≈ 2.5 hours), our fine-tuned Whisper model achieves a word error rate (WER) of 0.15, a character error rate (CER) of 0.255, and a BLEU score of 0.912, representing a 53% relative reduction in WER and 36% reduction in CER compared to the scratch CNN–Transformer baseline. For the customized dataset, the fine-tuned Whisper model achieves a WER of 0.2311 and a CER of 0.0453, outperforming Google Speech-to-Text Application Programming Interface (API) by 16.8% (relative WER reduction) and surpassing the scratch transformer by over 70% in WER. We further evaluate robustness under dialectal variation and noisy recordings, providing detailed error analysis and computational efficiency metrics. To our knowledge, this is the first comprehensive evaluation of Whisper fine-tuning for Kannada, demonstrating its viability for real-time, edge-deployable applications in education, accessibility, and public administration.

Poor nutrition during the antenatal period can contribute to adverse health outcomes for both mother and baby. Despite the importance of nutrition during pregnancy, there is limited research exploring the attitudes and experiences pregnant women possess towards nutrition care, particularly within the Australian context. Existing literature has indicated that pregnant women lack knowledge of and are not adhering to pregnancy related nutrition recommendations. Furthermore, although pregnant women rely on many health professionals for nutrition care, there are deficiencies in both the quality and quantity of care provided. The aim of this meta-aggregative review was to explore the experiences and attitudes pregnant women in Australia have towards nutrition care. Meta-aggregation was used to synthesise qualitative studies. A search strategy was applied to four electronic databases in May 2023.The findings were extracted and synthesised via the JBI approach. A total of 719 studies were identified in the initial database search with seven eligible studies included in the meta-aggregation. From 11 categories, three synthesised findings were developed: (i) approaches to nutrition care delivery matters, (ii) quality and quantity of nutrition care is insufficient, and (iii) pregnant women value nutrition care. This review highlights pregnant women's desires for better nutrition care in Australia, with current provision of nutrition care lacking in quality and quantity. Improved antenatal dietetics services and increased health professional nutrition training is necessary to address these concerns.

In 2015, Maternal Mortality Rate (MMR) was still high especially in Indonesian. It had strong a correlation in Millenium Development Goals to achieve the better quality of human life. Surprisingly, most of maternal mortality case is usually preventable with good quality of knowledge. In this day, the use of social media has been increasing. In 2016, social media has been using by 2,34 billion people worldwide and the sixth highest number of internet user comes from Indonesia. Besides that, pregnant women have a tendency to use social media for seeking out about health information during pregnancy in prenatal, perinatal and postnatal also reproductive health topics. We created Jakpros application, a smart mobile application who designed for women. The purpose of it is for serving better health service and reliable information. Our aim in this study is to analyze how the educational page in Jakpros application can increase pregnant women knowledge. We recruited 126 pregnant women participant from 9 several sub-district hospitals in Jakarta. We detected their knowledge of reproductive health in pre-test questionnaire about basic pregnancy. After that, we guided them to download Jakpros application on their mobile phone. We asked them to use it in 2 weeks, and we were seeing their knowledge in post-test. The observational analytics study was using cross-sectional and analyzed by SPSS 24 to see the differences in knowledge. Their knowledge has been increasing after they used the educational page feature in 2 weeks. Along with this era, Jakpros application is an effective platform for making pregnant women is more educable.

Community Implementation of MI-Enhanced Behavior Therapy for Adolescent ADHD: Linking Fidelity to Effectiveness

Background:Labor is strongly influenced by a mother’s experience during pregnancy, where maternal health plays a crucial role in determining the quality and smoothness of delivery. During pregnancy, many women experience various problems, one of which is reduced physical fitness. Data indicate that 89.9% of pregnant women have low fitness levels, and 71% experience poor labor quality. This study aims to determine the relationship between physical fitness in third-trimester pregnant women and the quality of childbirth. Methods: This study employed a cross-sectional design using the childbirth quality questionnaire developed by Jannah (2024). The questionnaire was administered to each respondent and included demographic data and questions related to the delivery process they underwent. A total of 80 respondents were selected using purposive sampling based on predetermined inclusion and exclusion criteria. Data analysis was conducted using the Chi-Square test and calculation of the Odds Ratio. Results:The Chi-Square analysis revealed a significant relationship between physical fitness in third-trimester pregnant women and childbirth quality, with a p-value of 0.028 (p &lt; 0.05), indicating a statistically significant association between the two variables. Based on the Odds Ratio calculation, pregnant women with poor physical fitness had a 2.756 times higher risk of experiencing negative labor outcomes compared to those with good fitness levels.Conclusion: Pregnant women are strongly recommended to maintain and improve their physical fitness throughout pregnancy to support better childbirth outcomes.

This study evaluates the process of implementation of a longitudinal intervention program to promote oral health in preschool children in Flanders, Belgium. As the program was implemented in an existing preventive health care organization, the study also evaluates this setting as the context for implementation. Qualitative and quantitative methods were used to evaluate implementation fidelity, based on Carroll's theoretical framework of implementation fidelity (Carroll et al., Implementation Science 2:40, 2007). Questionnaire data from participants and health workers were analyzed, and document analyses were performed to compare registrations of the actions with the planning manual. Results were mixed. Whereas more than 88 % of all parents attended all home visits, only 57 % received at least 9 of the 11 planned consultations. Fifty-two percent of the families received all supporting materials, and on average, 73 % of all attending families received all information at a contact as described in the manual. Moderating factors such as the adequate use of facilitators and high participant responsiveness had a positive impact on implementation fidelity, whereas the quality of delivery differed to a great extent between the nurses who were involved during the entire intervention period and those who gave only a few sessions. Implementing an intervention in an existing well-baby program has many advantages, although lack of time presents a challenge to implementation fidelity. The results of this process evaluation allow a better understanding of the contribution of implementation fidelity to the effectiveness of health promotion programs.