Overview

The secret language of your vital signs and how to read and understand it. From the firing of neurons in a fraction of a second to the monthly cycle of ovulation to a seasonal shift in sleep patterns, the human body runs on rhythms all more knowable now than ever, thanks to wearables. Making sense and making use of these signals is something else, and this is precisely what Daniel Forger explains in Biological Rhythms. Sorting through a plethora of data gathered over the past decade, this practical, user-friendly book gives readers the tools for reading and interpreting the rhythms that regulate physiological processes as varied and critical as sleep, brain activity, heart rate, hormone secretion, metabolism, and temperature. Once translated, the language of biological rhythms can be used to improve health and productivity by athletes, travelers, and shift workers, sufferers of fatigue or sleep disorders, or those wishing to lose weight, monitor infection, or time fertility in short, anyone with an interest in reading and understanding the body s vital signs.

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9780262553148

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Reviews

ENDORSEMENTS “Dr. Forger’s book reveals how wearable technologies and vast streams of digital data are transforming our understanding of the body’s rhythms, from the cadence of our sleep and heartbeats to the cycles that govern mood and health. Blending cutting-edge science with compelling personal stories, this book shows how decoding our daily patterns can illuminate the hidden clocks that shape human life.” —Russ van Gelder, Chairman of the Department of Ophthalmology, University of Washington; and Aziz Sancar, Principle Investigator at Sancar Lab, University of North Carolina, winner of the 2015 Nobel Prize in Chemistry

Author Information

Daniel B. Forger is Robert W. and Lynn H. Browne Professor of Science, Professor of Mathematics, and Research Professor of Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. Hundreds of thousands of people have used his apps and algorithms for scoring sleep and circadian rhythms, predicting mood and fatigue, and analyzing time series data.

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