Principal Investigator: Dr Alexandra Hanlon
Department: School of Nursing
University of Pennsylvania, School of Nursing, 418 Curie Boulevard, 4L
Claire M. Fagin Hall, Philadelphia, 19104-6096, Pennsylvania, United States
Body composition changes with age. These changes include decreases in fatfree
mass and increases in fat mass and central fat accumulation. Improving
body composition will augment efforts to lower obesity rates and cardiometabolic
diseases (e.g. type 2 diabetes). Sleep may be a determinant of body
composition, whereby shortened sleep duration predicts weight gain.
However, evidence is inconsistent. Differences in chronotype (the extent to
which an individual is a “morning” or an “evening” person) may explain these
inconsistent findings. This study will examine the extent to which chronotype
modifies the association between sleep duration, fat mass, and fat-free mass in
Obesity is associated with leading causes of death and chronic disease (e.g.
cardiovascular disease). To elucidate the relationship between obesity and
sleep, this study will explore sleep duration, with an emphasis on chronotypespecific
differences in eating habits, physical activity, fat mass, and fat free
mass. This is aligned with the UK Biobank’s purpose because if chronotype
modifies the relationship between sleep and obesity, it may be used to predict
who is at greatest risk for obesity onset. These findings may also provide
insight into chronotype based weight management interventions.
We will examine the hypothesis that chronotype modifies the effect between
sleep duration and obesity. Obesity will be regressed on the indicator for
whether or not someone is a “morning” person or an “evening” person and
sleep duration to determine this relationship. Then to determine potential
causal pathways through which sleep duration impacts body composition,
chronotype-specific differences in eating habits, physical activity, and body
composition will be quantified using structural equation modeling. This
approach will allow us to quantify the relationship of other potentially
influential variables such as person, demographic, and environmental factors.
Data from the full UK Biobank cohort, stratified and balanced on sex, will be
used to elucidate the relationship between chronotype, sleep duration and