Angela Cristina Bizzotto Trude1, Bridget Armstrong2, Adriana Kramer Fiala Machado3, Emerson M Wickwire4, Lauren B Covington5, Yan Wang6, Erin Hager7, Maureen M Black8. 1. Growth and Nutrition Division, Department of Pediatrics, University of Maryland School of Medicine, Baltimore, Maryland, USA. Electronic address: angela.trude@nyu.edu. 2. Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA. 3. Post Graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil. 4. Sleep Disorders Center, Division of Pulmonary and Critical Care Medicine, Departments of Psychiatry and Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA. 5. School of Nursing, College of Health Sciences, University of Delaware, Newark, Delaware, USA. 6. Department of Prevention and Community Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia, USA. 7. Growth and Nutrition Division, Department of Pediatrics, University of Maryland School of Medicine, Baltimore, Maryland, USA. 8. Growth and Nutrition Division, Department of Pediatrics, University of Maryland School of Medicine, Baltimore, Maryland, USA; RTI International, Research Triangle Park, North Carolina, USA.
Abstract
OBJECTIVE: To identify longitudinal bidirectional associations between unique sleep trajectories and obesity and hypertension among Black, adolescent girls. DESIGN, SETTING, AND PARTICIPANTS: Longitudinal data were from a randomized controlled trial (2009-2013) implemented in schools serving low-income communities aimed at preventing obesity among adolescent girls (mean age = 12.2 years (standard deviation ± 0.72). MEASURES: Nocturnal sleep data were extracted from accelerometers at T1 (enrollment, n = 470), T2 (6-month, n = 348), and T3 (18-month follow-up, n = 277); height and weight were measured at T1-T3; and systolic/diastolic blood pressure at T1 and T3 using an oscillometric monitor. Multilevel models examined longitudinal associations. Finite mixture models identified sleep trajectory groups. Structural equation models examined whether T1 chronic disease risk predicted sleep profiles, and conversely, if sleep trajectories predicted T3 chronic disease risk. Data were analyzed in 2021. RESULTS: For each additional hour of sleep and 1% increase in efficiency there was a 7% lower risk of overweight/obesity at T1 and 6% lower risk at T2, but not at T3. Four sleep trajectories emerged: Worsened, Irregular, Improved, and Regular, with no demographic or metabolic differences between the trajectories. Improved sleep trajectory predicted lower diastolic percentile at T3 (b = -8.81 [95% confidence interval -16.23, -1.40]). CONCLUSIONS: Group-based trajectories of sleep duration and quality provide information on modifiable factors that can be targeted in interventions to evaluate their impact on reducing chronic diseases and addressing disparities. Additional research is needed on samples beyond those recruited in the context of an intervention study.
OBJECTIVE: To identify longitudinal bidirectional associations between unique sleep trajectories and obesity and hypertension among Black, adolescent girls. DESIGN, SETTING, AND PARTICIPANTS: Longitudinal data were from a randomized controlled trial (2009-2013) implemented in schools serving low-income communities aimed at preventing obesity among adolescent girls (mean age = 12.2 years (standard deviation ± 0.72). MEASURES: Nocturnal sleep data were extracted from accelerometers at T1 (enrollment, n = 470), T2 (6-month, n = 348), and T3 (18-month follow-up, n = 277); height and weight were measured at T1-T3; and systolic/diastolic blood pressure at T1 and T3 using an oscillometric monitor. Multilevel models examined longitudinal associations. Finite mixture models identified sleep trajectory groups. Structural equation models examined whether T1 chronic disease risk predicted sleep profiles, and conversely, if sleep trajectories predicted T3 chronic disease risk. Data were analyzed in 2021. RESULTS: For each additional hour of sleep and 1% increase in efficiency there was a 7% lower risk of overweight/obesity at T1 and 6% lower risk at T2, but not at T3. Four sleep trajectories emerged: Worsened, Irregular, Improved, and Regular, with no demographic or metabolic differences between the trajectories. Improved sleep trajectory predicted lower diastolic percentile at T3 (b = -8.81 [95% confidence interval -16.23, -1.40]). CONCLUSIONS: Group-based trajectories of sleep duration and quality provide information on modifiable factors that can be targeted in interventions to evaluate their impact on reducing chronic diseases and addressing disparities. Additional research is needed on samples beyond those recruited in the context of an intervention study.
Authors: Erin R Hager; Margarita S Treuth; Candice Gormely; LaShawna Epps; Soren Snitker; Maureen M Black Journal: Res Q Exerc Sport Date: 2015-08-19 Impact factor: 2.500
Authors: Adriana Kramer Fiala Machado; Andrea Wendt; Ana Maria Baptista Menezes; Helen Gonçalves; Fernando César Wehrmeister Journal: J Sleep Res Date: 2020-08-17 Impact factor: 3.981