Objective: To examine characteristics and lifestyle behaviours associated with achieving clinically important weight loss (CIWL) in two paediatric weight management interventions (PWMIs).
Methods: We examined 1010 children enrolled in the STAR and Connect for Health trials. We defined achieving CIWL as any participant who had decreased their BMI z-score by ≥0.2 units over 1 year. Using log-binomial regression we examined associations of child and household characteristics and lifestyle behaviours with achieving CIWL.
Results: In multivariable analyses, children with severe obesity had a lower likelihood of achieving CIWL compared to children without severe obesity (RR: 0.68 [95% CI: 0.49, 0.95]). Children who were ≥10 years were less likely to achieve CIWL (RR: 0.56 [95% CI: 0.42, 0.74]) vs those 2-6 years of age. Children who consumed <1 sugary beverage per day at the end of the intervention were more likely to achieve CIWL vs those who did not meet the goal (RR: 1.36 [95% CI 1.09-1.70]).
Conclusion: In this analysis of children enrolled in PWMIs, achieving CIWL was associated with younger age, not having severe obesity and consuming fewer sugary beverages at the end of the intervention. Focusing on intervening earlier in life, when a child is at a lower BMI, and reducing sugary beverages could allow for more effective PWMI's.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355061 | PMC |
http://dx.doi.org/10.1111/ijpo.12784 | DOI Listing |
J Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFIn Vitro Cell Dev Biol Anim
January 2025
College of Traditional Chinese Medicine, Xinjiang Uygur Autonomous Region, Xinjiang Medical University, Urumqi, 830063, China.
The aim of this study is to assess the impact of Tianxiangdan (TXD) on lipophagy in foam cells and its underlying mechanism in treating atherosclerosis, particularly focusing on its efficacy in lowering blood lipids. In vivo, ApoE-/- atherosclerosis mouse models were established for group intervention. Blood lipid levels of the mice were measured, lipid deposition and autophagy levels in atherosclerotic plaques were assessed, and co-localization of lipid droplets and autophagosomes was examined.
View Article and Find Full Text PDFCell Biol Toxicol
January 2025
Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China.
Thyroid cancer (THCA) is an increasingly common malignant tumor of the endocrine system, with its incidence rising steadily in recent years. For patients who experience recurrence or metastasis, treatment options are relatively limited, and the prognosis is poor. Therefore, exploring new therapeutic strategies has become particularly urgent.
View Article and Find Full Text PDFNaunyn Schmiedebergs Arch Pharmacol
January 2025
Solid Tumor Research Center, Cellular and Molecular Medicine Research Institute, Urmia University of Medical Sciences, Urmia, Iran.
Chemotherapy remains the cornerstone of cancer treatment; however, its efficacy is frequently compromised by the development of chemoresistance. Multidrug resistance (MDR), characterized by the refractoriness of cancer cells to a wide array of chemotherapeutic agents, presents a significant barrier to achieving successful and sustained cancer remission. One critical factor contributing to this chemoresistance is the overexpression of ATP-binding cassette (ABC) transporters.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!