Skip to main content

Chris Taylor's "No, there's nothing wrong with your Fitbit"

Taylor, writing for Mashable, describes what happens when carefully conducted public health research (published in the Journal of the American Medical Association) becomes attention grabbing and poorly represented click bait.

Data published in JAMA (Case, Burwick, Volpp, & Patel, 2015) tested the step-counting reliability of various wearable fitness tracking devices and smart phone apps (see the data below). In addition to checking the reliability of various devices, the article makes an argument that, from a public health perspective, lots of people have smart phones but not nearly as many people have fitness trackers. So, a way to encourage wellness may be to encourage people to use the the fitness capacities within their smart phone (easier and cheaper than buying a fitness tracker). The authors never argue that fitness trackers are bad, just that 1) some are more reliable than others and 2) the easiest way to get people to engage in more mindful walking might be via resources they already possess (in this case, smart phones).

Image from JAMA

But then the media went and got a hold of the research. The resulting headlines?

From Mother Jones:

http://www.motherjones.com/environment/2015/02/science-fitbit-fuelband-fitness-trackers-cellphone-health

Nope. Nope, that isn't exactly it. AND...if anything, the Fitbits did pretty well in the experimental trials.


From Jezebel:

http://jezebel.com/your-fitbit-is-bullshit-says-science-1686024094

Again, "science" never made that declaration. Sure, the Nike Fuelband doesn't seem to be very accurate but the Fitbit performed well on the tests. And, yes, cell phones may be just as accurate, but then you have to take your phone with you everywhere, which may not be ideal if you are wearing a dress without pockets or during your Zumba class.

How to use this in class: 1) Illustrate the gap between science and the reporting of science, 2) the research here is really easy for students to understand, 3) the data is displayed in nice, easy to follow graphs, and 4) be weary of click bait, especially as it applies to research findings.

Popular posts from this blog

Ways to use funny meme scales in your stats classes

Have you ever heard of the theory that there are multiple people worldwide thinking about the same novel thing at the same time? It is the multiple discovery hypothesis of invention . Like, multiple great minds around the world were working on calculus at the same time. Well, I think a bunch of super-duper psychology professors were all thinking about scale memes and pedagogy at the same time. Clearly, this is just as impressive as calculus. Who were some of these great minds? 1) Dr.  Molly Metz maintains a curated list of hilarious "How you doing?" scales.  2) Dr. Esther Lindenström posted about using these scales as student check-ins. 3) I was working on a blog post about using such scales to teach the basics of variables.  So, I decided to create a post about three ways to use these scales in your stats classes:  1) Teaching the basics of variables. 2) Nominal vs. ordinal scales.  3) Daily check-in with your students.  1. Teach your students the basics...

Leo DiCaprio Romantic Age Gap Data: UPDATE

Does anyone else teach correlation and regression together at the end of the semester? Here is a treat for you: Updated data on Leonardo DiCaprio, his age, and his romantic partner's age when they started dating. A few years ago, there was a dust-up when a clever Redditor r/TrustLittleBrother realized that DiCaprio had never dated anyone over 25. I blogged about this when it happened. But the old data was from 2022. Inspired by this sleuthing,  I created a wee data set, including up-to-date information on his current relationship with Vittoria Ceretti, so your students can suss out the patterns that exist in this data.

Tyler Vigen's Spurious Correlations

Tyler Vigen has has created  a long list of easy-to-paste-into-a-powerpoint graphs that illustrate that correlation does not equal causation. For instance, while per capita consumption of cheese and number of people who die by become tangled in their bed sheets may have a strong relationship (r = 0.947091), no one is saying that cheese consumption leads to bed sheet-related death. Although, you could pose The Third Variable question to your students for some of these relationships). Property of Tyler Vigens, http://i.imgur.com/OfQYQW8.png Vigen has also provided a menu of frequently used variables (deaths by tripping, sunlight by state) to help you look for specific examples. This portion is interactive, as you and your students can generate your own graphs. Below, I generated a graph of marriage rates in Pennsylvania and consumption of high fructose corn syrup. Generated at http://www.tylervigen.com/