title: Seasonal weight variation patterns in seven countries located in northern and southern hemispheres.
abstract: The digital revolution of information and technology in late 20th century has led to emergence of devices that help people monitor their weight in a long-term manner. Investigation of population-level variations of body mass using smart connected weight scales enabled the health coaches acquire deeper insights about the models of people's behavior as a function of time. Typically, body mass varies when the seasons change. That is, during the warmer seasons people's body mass tend to decrease while in colder seasons it usually moves up. In this paper we study the seasonal variations of body mass in seven countries by utilization of linear regression. Deviation of monthly weight values from the starting point of astronomical years (beginning of spring) were modeled by fitting orthogonal polynomials in each country. The distinction of weight variations in southern and northern hemispheres were then investigated. The studied population involves 6429 anonymous weight scale users from:(1) Australia, (2) Brazil, (3) France, (4) Germany, (5) Great Britain, (6) Japan, and (7) United States of America. The results suggest that there are statistically significant differences between the models of weight variation in southern and northern hemispheres. In both northern and southern hemispheres the lowest weight values were observed in the summer. However, the highest weight values were noticed in the winter and in the spring for northern and southern hemispheres, respectively.
authors.forename: Saeed
authors.initials: S
authors.lastname: Mehrang
authors.forename: Elina
authors.initials: E
authors.lastname: Helander
authors.forename: Angela
authors.initials: A
authors.lastname: Chieh
authors.forename: Ilkka
authors.initials: I
authors.lastname: Korhonen
mesh_terms: D000328:Adult; D001315:Australia / Q000453:epidemiology; D001835:Body Weight; D001938:Brazil; D005602:France; D005858:Germany; D006801:Humans; D007564:Japan; D016014:Linear Models; D012621:Seasons; D006113:United Kingdom; D014481:United States