Objective: Low- and middle-income countries (LMIC) are increasingly experiencing the double burden of malnutrition. Studies to identify 'double-duty' actions that address both undernutrition and overweight in sub-Saharan Africa are needed. We aimed to identify acceptable behaviours to achieve more optimal feeding and physical activity practices among both under- and overweight children in Rwanda, a sub-Saharan LMIC with one of the largest recent increases in child overweight.
Design: We used the Trials of Improved Practices (TIPs) method. During three household visits over 1·5 weeks, we used structured interviews and unstructured observations to collect data on infant and young child feeding practices and caregivers' experiences with testing recommended practices.
Setting: An urban district and a rural district in Rwanda.
Participants: Caregivers with an under- or overweight child from 6 to 59 months of age (n 136).
Results: We identified twenty-five specific recommended practices that caregivers of both under- and overweight children agreed to try. The most frequently recommended practices were related to dietary diversity, food quantity, and hygiene and food handling. The most commonly cited reason for trying a new practice was its benefits to the child's health and growth. Financial constraints and limited food availability were common barriers. Nearly all caregivers said they were willing to continue the practices and recommend them to others.
Conclusions: These practices show potential for addressing the double burden as part of a broader intervention. Still, further research is needed to determine whether caregivers can maintain the behaviours and their direct impact on both under- and overweight.
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http://dx.doi.org/10.1017/S1368980019001551 | DOI Listing |
Ther Adv Musculoskelet Dis
December 2024
Division of Rheumatology, Department of Internal Medicine, Chonnam National University Medical School and Hospital, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea.
Background: Recent studies have shown the impact of obesity on achieving low disease activity or remission in rheumatoid arthritis (RA) patients treated with tumor necrosis factor inhibitors. However, there is limited research on the effects of obesity on clinical responses to non-TNF-targeted treatments.
Objectives: This study investigated the influence of body mass index (BMI) on clinical response to non-TNF-targeted treatments in RA patients.
Sci Rep
December 2024
Department of Statistical Science, Duke University, Durham, 27708-0251, USA.
The article is motivated by an application to the EarlyBird cohort study aiming to explore how anthropometrics and clinical and metabolic processes are associated with obesity and glucose control during childhood. There is interest in inferring the relationship between dynamically changing and high-dimensional metabolites and a longitudinal response. Important aspects of the analysis include the selection of the important set of metabolites and the accommodation of missing data in both response and covariate values.
View Article and Find Full Text PDFLipids Health Dis
December 2024
Department of Oncology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
Background: Cardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.
View Article and Find Full Text PDFSci Rep
December 2024
School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou, 350202, China.
The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features.
View Article and Find Full Text PDFTomography
December 2024
Hospital Regional de Alta Especialidad de la Peninsula de Yucatan, Servicios de Salud del IMSS-BIENESTAR, Merida 97130, Yucatan, Mexico.
Background: Femoroacetabular impingement (FAI) is a condition caused by abnormal contact between the femur head and the acetabulum, which damages the labrum and articular cartilage. While the prevalence and the type of impingement may vary across human groups, the variability among populations with short height or with a high prevalence of overweight has not yet been explored. Latin American studies have rarely been conducted in reference to this condition, including the Mayan and mestizo populations from the Yucatan Peninsula.
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