\\No Comment - Influence of Pokémon Go on Physical Activity & The Statistical Mechanics of Human Weight Change
Written by Alex Armstrong
Monday, 31 October 2016
• Influence of Pokémon Go on Physical Activity
• The Statistical Mechanics of Human Weight Change
Sometimes the news is reported well enough elsewhere and we have little to add other than to bring it to your attention.
No Comment is a format where we present original source information, lightly edited, so that you can decide if you want to follow it up.
We normally think about computing as being something that is detrimental to health because it promotes a sedentary lifestyle. Programmers and gamers are perhaps the worst cases but a new paper suggest that the latest hit VR game might actually make people get some exercise:
Physical activity helps people maintain a healthy weight and reduces the risk for several chronic diseases. Although this knowledge is widely recognized, adults and children in many countries around the world do not get recommended amounts of physical activity. While many interventions are found to be ineffective at increasing physical activity or reaching inactive populations, there have been anecdotal reports of increased physical activity due to novel mobile games that embed game play in the physical world.
The most recent and salient example of such a game is Pokemon Go, which has reportedly reached tens of millions of users in the US and worldwide. We study the effect of Pokemon Go on physical activity through a combination of signals from large-scale corpora of wearable sensor data and search engine logs for 32 thousand users over a period of three months. Pokemon Go players are identified through search engine queries and activity is measured through accelerometry. We find that Pokemon Go leads to significant increases in physical activity over a period of 30 days, with particularly engaged users (i.e., those making multiple search queries for details about game usage) increasing their activity by 1473 steps a day on average, a more than 25% increase compared to their prior activity level (p<10^-15).
In the short time span of the study, we estimate that Pokemon Go has added a total of 144 billion steps to US physical activity. Furthermore, Pokemon Go has been able to increase physical activity across men and women of all ages, weight status, and prior activity levels showing this form of game leads to increases in physical activity with significant implications for public health. We find that Pokemon Go is able to reach low activity populations while all four leading mobile health apps studied in this work largely draw from an already very active population.
Physical activity is obviously related to weight gain but we don't really know very much about what the actual mechanics of weight gain are. A new theoretical model might help frame the questions we need to answer.
In the context of the global obesity epidemic, it is important to know who becomes obese and why. However, the processes that determine the changing shape of Body Mass Index (BMI) distributions in high-income societies are not well-understood.
Here we establish the statistical mechanics of human weight change, providing a fundamental new understanding of human weight distributions. By compiling and analysing the largest data set so far of year-over-year BMI changes, we find, strikingly, that heavy people on average strongly decrease their weight year-over-year, and light people increase their weight. This drift towards the centre of the BMI distribution is balanced by diffusion resulting from random fluctuations in diet and physical activity that are, notably, proportional in size to BMI.
We formulate a stochastic mathematical model for BMI dynamics, deriving a theoretical shape for the BMI distribution and offering a mechanism to explain the ongoing right-skewed broadening of BMI distributions over time. The model also provides new quantitative support for the hypothesis that peer-to-peer social influence plays a measurable role in BMI dynamics. More broadly, our results demonstrate a remarkable analogy with drift-diffusion mechanisms that are well-known from the physical sciences and finance.
A few months ago the OpenAI .NET library was released as a beta. It has now reached version 2.0.0 and the time has come to leave beta and, with a few amendments enter production readiness.
JetBrains has improved its IDEs with features to suggest the logical structure of code, to streamline the debugging experience for Kubernetes applications, and provide comprehensive cluster-wide Kuber [ ... ]
