Background: The joint impact of sedentary behavior and physical activity on obesity has not been assessed in a large cohort followed from adolescence to adulthood.
Methods: Nationally representative longitudinal data from Waves II (1995; mean age: 15.9) and III (2001; mean age: 21.4) of the National Longitudinal Study of Adolescent Health (n = 9,155) were collected. Sex-stratified multivariate logistic regression analysis assessed the odds of obesity associated with Wave II MVPA and screen time, controlling for sociodemographic characteristics and change in MVPA and screen time from Wave II to III. Obesity was defined using body mass index (BMI, kg/m2) International Obesity Task Force cut-points at Wave II and adult cut-points at Wave III (BMI >or= 30).
Results: In males, adjusted odds of prevalent obesity was strongly predicted by MVPA bouts [OR (95% CI): OR6 vs. 1 MVPA bouts = 0.50 (0.40, 0.62); OR4 vs. 40 hrs screen time = 0.83 (0.69, 1.00)]. In females, greater MVPA bouts and lower screen time correlated with lower prevalent obesity [OR (95% CI): OR6 vs. 1 MVPA bouts = 0.67 (0.49, 0.91); OR4 vs. 40 hrs screen time = 0.67 (0.53, 0.85)]. Longitudinally, adolescent screen time hours had a stronger influence on incident obesity in females [OR (95% CI): OR4 vs. 40 hrs = 0.58 (0.43, 0.80)] than males [OR (95% CI): OR4 vs. 40 hrs = 0.78 (0.61, 0.99)]. Longitudinal activity patterns were not predictive of incident obesity.
Conclusion: Reducing screen time during adolescence and into adulthood may be a promising strategy for reducing obesity incidence, especially in females.
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http://dx.doi.org/10.1186/1479-5868-4-26 | DOI Listing |
Anal Methods
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ampere - Laboratório de Plataformas Eletroquímicas - Universidade Federal de Santa Catarina, Departamento de Química, 88040-900 Florianópolis, SC, Brazil.
Terbinafine hydrochloride (TBF) is a broad-spectrum antifungal used to treat various dermatophyte infections affecting the skin, hair, and nails. Accurate, sensitive, and affordable analytical methods are crucial for quantifying this drug. In this study, we report on the use of carbon-based electrodes for the electrochemical determination of TBF in pharmaceutical samples, including raw materials and tablets.
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Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada; Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa. Electronic address:
Purpose: Universal newborn hearing screening (UNHS) programs using audiometric techniques alone are limited in ability to detect non-congenital childhood permanent hearing loss (PHL). In 2019, Ontario launched universal newborn screening (NBS) for PHL risk factors: congenital cytomegalovirus (cCMV) and 22 common variants in GJB2 and SLC26A4. Here we describe our experience with genetic risk factor screening.
View Article and Find Full Text PDFFront Psychol
January 2025
Department of Pediatrics, Geisel School of Medicine at Dartmouth College, Lebanon, NH, United States.
Objective: Problematic media use (PMU) is addiction-like media use. No study has examined if genetic factors for addiction relate to PMU during childhood. This study tested the association between genetic risk factors for addiction and PMU among 9-to-12-year-olds.
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January 2025
Department of Hepatobiliary Surgery, Affiliated Hospital of Chengde Medical University, Chengde, Hebei Province, China.
Background: The choice of surgical methods for common bile duct stones (CBDS) is controversial. The aim of this study was to compare the safety and efficacy of laparoscopic transcystic common bile duct exploration (LTCBDE) and laparoscopic common bile duct exploration (LCBDE).
Methods: Relevant literature published before March 30, 2023 in PubMed, Web of Science, Embase, and Cochrane was searched to screen studies comparing LTCBDE and LCBDE.
Curr Cancer Drug Targets
January 2025
Department of Pharmacology, Sri Shanmugha College of Pharmacy, Sankari, Salem, 637304, Tamil Nadu, India.
Liver metastases from Gastrointestinal (GI) cancers present significant challenges in oncology, often signaling poor prognosis. Traditional detection methods like imaging and tissue biopsies have limitations in sensitivity, specificity, and tumor heterogeneity represen-tation. The advent of artificial intelligence (AI) in healthcare, driven by advancements in ma-chine learning, algorithms, and data science, offers a promising frontier for early detection and management of liver metastases.
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