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SmartData Collective > Analytics > The Data Analytics of New Year’s Resolutions
Analytics

The Data Analytics of New Year’s Resolutions

Brett Stupakevich
Brett Stupakevich
5 Min Read
The Data Analytics of New Year’s Resolutions
Photo by eliza28diamonds on Pixabay (https://pixabay.com/photos/year-end-new-calendar-done-over-4720870/)
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Author: Amanda Brandon
Spotfire Blogging Team * 

What’s the one thing a majority of Americans are doing in the early days of 2012? Saying they’ll never eat bad, smoke, or work in dead-end jobs ever again. What’s the real story? They may try the diet thing for a few weeks, the smoking cession program for a few days and spend a little time searching the online job ads. But by fourth week of January, most New Year’s resolutions are things of the past.

In celebration of 2012, we thought it would be interesting to explore the data analytics of why people resolve to get with the program and why so many quit.

all about new years resolutions 526x1024 photo (data analytics)

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Resolutions Are Doomed 

At least for 80% of Americans. According to recent research, the reason we fail to keep our resolve is that the goals are too broad or unrealistic. BJ Fogg (@bjfogg), director of Stanford University’s persuasive technology lab, told Boston.com this week that when you make a sweeping resolution, it’s harder to keep because it’s too abstract. He recommends using tech tools and breaking your resolutions up into “baby steps.”

Quitting Smoking Predictive Analytics

Quitting smoking is number seven on the list of top resolutions according to this information we found at StatisticBrain.com (a nice site devoted to stats and data). This list was developed by researchers at the University of Scranton.

While it’s not the most common resolution, it seems there have been some interesting advances in the predictive analytics of smokers in a 2011 study that actually involved mind reading.

Yes, you read that correctly. The researchers who conducted the study at the UCLA Laboratory of Integrative Neuroimaging Technology took functional MRI brain scans while smokers were watching videos designed to influence nicotine cravings.

The research team then used computerized machine learning methods to identify patterns and the thought processes of the study participants.

By combining these two technologies – the functional MRI scans and the machine learning methods – researchers were able to identify which regions of the subjects’ brains and which neural networks were responsible for resisting nicotine cravings.

“Essentially, we were predicting and detecting what kind of videos people were watching and whether they were resisting their cravings,” says Dr. Ariana Anderson, a postdoctoral fellow in the Integrative Neuroimaging Technology lab and the study’s lead author, in a press release on the study.

The algorithm used in the study was in essence “able to complete or predict the subjects’ mental states and thought processes in much the same way that Internet search engines or texting programs on cellphones anticipate and complete a sentence or request before the user is finished.”

The ultimate goal for this research is to help subjects identify when they are having cravings and the intensity levels so that they can train themselves to control and suppress them, the report says.

“Outsourcing” Resolutions Can Lead to Success

According to a recent report in the Wall Street Journal, having a loved one make your resolutions for you can hold you accountable and point out the “real resolutions” you fail to make for yourself. Dr. David Palmiter, a couples therapist and professor of psychology at Marywood University, said that someone close to you can “provide positive encouragement and hold you accountable when you slip.”

Next Steps: Tweet us with your take on New Year’s resolutions and subscribe to our blog to get an email alert on an upcoming post covering industry expert resolutions for 2012.

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