There are days when energy does not accompany us and it seems we are more tired. But there are those who believe that they are not just one day, but every day with symptoms of exhaustion. And maybe they are not completely wrong. British scientists have discovered that genetics could predisposition to physical activity in some people, while in others not.
The research published in Natureand developed by Big Data Institute of Oxford University, reports, for example, the time we sit, sleep, or move with our genes. Experts have programmed and designed a "machine learning machine"to differentiate sedentary and active lives (and various intermediate levels) in 200 volunteers who took two days a camera and a bracelet that recorded its activity every 20 seconds.
Decipher movement, rest or sleep
They then compared this information with that of 91,105 individuals registered in the Biobank UK database who had the same type of information. bracelet during a week in previous periods.
"How and why we move does not depend only on genesBut understanding the role they play will help us improve our understanding of the causes and consequences of physical activity, "said project director Aiden Doherty in a statement," Only through studying large amounts of data, "he said. you can decipher "the complex genetic foundations" of some of the most elementary functions "like movement, rest or sleep"
Other potential findings in the same study
The scientists noted that "increased physical activity spontaneously reduces blood pressure." In addition, the genetic analysis revealed the existence of a "overlap"in between neurodegenerative diseases, mental health and brain structure, which 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, with a broad spectrum of associated diseases sedentary lifestyle, such as obesity, diabetes or cardiovascular problems. Also changes in sleep are related to heart disease, metabolic and psychiatric disorders.
The study's experts emphasized that the use of the "machine learning machine" to analyzing large amounts of health data is moving fast and what conditions the type of studies that can develop.
"We designed these models of machine learning to teach machines to analyze complex functions such as activity," explained Karl Smith-Byrne, one of the participants in this paper. "They could help us, for example, to determine if inactivity is a cause or consequence of obesity"added Michael Holmes of the British Heart Foundation at Oxford University.