There are days when energy does not accompany us at all, and we seem to be more tired. But there are people who believe that they are not just a day, but every day symptoms of exhaustionAnd maybe they are not completely wrong. British scientists have discovered that genetics can generate a predisposition to physical activity in some people, while others do not.
The study, published in nature, and developed by Big Data Index from the University of Oxford, is related, for example, to the time we sit, sleep or move with our genes. Experts programmed and designed "machine training machine"to distinguish a sedentary and active life (and several intermediate levels) in 200 volunteers who took two days camera and wrist that registered their activity every 20 seconds.
Decipher movement, rest or sleep
They then compare this information to that of 91,105 individuals registered in the Biobank UK database, which carry the same type of information. bracelet a week in previous periods.
"How and why we move it does not depend only on the genesBut understanding the role they play will help us improve our knowledge of the causes and effects of physical activity, "says project director Aiden Doherty in a statement," Just by exploring large amounts of data, "he said," you can decipher " the complex genetic basis "of some of the most elemental functions"such as movement, rest or sleep".
Other potential findings in the same study
Scientists observe that "increased physical activity spontaneously reduces blood pressure." Also, genetic analysis reveals the existence of "superposition"between neurodegenerative diseases, mental health and brain structure that demonstrates the important role of the central nervous system in physical activity and sleep.
Physical inactivity, according to experts, is a threat to global public health related to a wide range of diseases a sedentary lifestyle such as obesity, diabetes or cardiovascular problemsSleep changes are also associated with heart and metabolic diseases and psychiatric disorders.
Study specialists emphasized that the use of machine learning machine analyzing large amounts of health data is progressing rapidly and what are the conditions of the type of research that can be developed.
"We have developed these machine learning models to teach machines how to analyze complex functions such as activity," said Carl Smith-Burn, one of the participants in this work. "They could help us, for example, to determine if inactivity is the cause or consequence of obesity"added Michael Holmes of the British Heart Foundation at Oxford University.