There are days when energy does not accompany us, and we seem to be more tired. But there are those who think they are not just one day but every day with symptoms of fatigue. And maybe they are not entirely wrong. British researchers discovered that genetics could generate one disposition for physical activity in some people while others do not.
The research published in nature, and developed by Big Data Institute from Oxford University, for example, concerns the time we sit, sleep or move with our genes. The experts programmed and designed a "machine learning machine"to differentiate sedentary and active lives (and more intermediate levels) in 200 volunteers who took two days a camera and a bracelet that recorded their activity every 20 seconds.
Decreases movement, rest or sleep
Then they compared this information with the 91,105 people registered in the biobank UK database that had the same type of information. bracelet for a week in earlier periods.
"How and why do we move It depends not only on the genesBut the understanding of the role they play will help us improve our knowledge about the causes and consequences of physical activity, says the director of this project, Aiden Doherty, in a statement "Only through the study of large amounts of data" , he said. you can decipher "the complex genetic fundamentals" of some of the most elementary features "like motion, rest or sleep".
Other potential findings in the same study
The researchers noted that "increased physical activity reduces blood pressure spontaneously." The genetic analysis also revealed the existence of a "superposition"between neurodegenerative diseasesmental health and brain structure that show the central 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 wide range of diseases associated with sedentary lifestyle, such as obesity, diabetes or cardiovascular problems. Changes in sleep are also related to cardiovascular and metabolic diseases and psychiatric disorders.
The survey experts emphasized the use of the "machine learning machine" for analyzing large amounts of health data is growing fast and what conditions the kind of studies that can develop.
"We have designed these machine learning models to teach machines how to analyze complex features, such as activity," explained Karl Smith-Byrne, one of the participants in this work. "They could help us, for example, to decide whose inactivity is the cause or consequence of obesity"added Michael Holmes, from the British Heart Foundation from Oxford University.