Are You What You Read or Do You Read What You Are?

The environment plays a significant role in our health. We are exposed to multiple physical, chemical and biological challenges, including information  - like news and gossip stories related to health and wellness. How exactly is it affecting us?

University of Pennsylvania researchers surveyed over two thousand US adults 40 to 70 years of age on how they scanned for information about specific health behaviors. The researchers followed up one year later to see how participants' behaviors changed. The result?  Consumption of health information does affect specific behaviors. But the effect is not as straightforward or as strong as one might think.

As was shown earlier, people who seek information about particular health issues are typically in the middle of making a decision, and need information to ease anxiety or reinforce confidence in their already made decision. The recent study also shows that people already motivated to change their behavior may be more motivated to scan information about this change of behavior. But exposure to information might not be helpful if they have not made a decision yet.

For example, women actively scanning information about breast cancer after getting a mammogram are more likely to get another one next year compared to those who consume the same amount of information but have not made up their mind about getting a mammogram yet. People that exercise and eat healthy are more likely to continue doing so one year later than those not adhered to healthy behaviors yet, despite the same amount of health-related information consumed during the past year.


Online content discovery platform Outbrain did their own research and found similar if not more dramatic results. Analysis based on total U.S. page views across Outbrain’s network of 100,000+ publisher sites during the month of June 2013 as well as data from the external sources is captured on the figure. Surprised? Health content consumption actually positively correlates with unhealthy weight.

The more obese people live in the region, the more they read online about health. Reading a lot about jobs does not lower unemployment rates either. Information about relationships does help to avoid divorce though. So reading can be good for you. But not sufficient. After all, it was Albert Einstein who said -  Any man who reads too much and uses his own brain too little falls into lazy habits of thinking.

REFERENCES

Hornik R, Parvanta S, Mello S, Freres D, Kelly B, & Schwartz JS (2013). Effects of Scanning (Routine Health Information Exposure) on Cancer Screening and Prevention Behaviors in the General Population. Journal of health communication PMID: 24083417

Bennett, A. 7 Surprising (or not?) Facts about the Content Americans Consume. Outbrain blog. October 16, 2013

Will you get the flu this season?

Worst of flu season may be over. But you can still catch a chill. If you shake hands with lots of sick people, for example. Or don't keep sufficiently warm. Yes, your mother has told you, and you thought it was just an old wives' tale, but it wasn't. Scientists (Johnson and Eccles, 2005) provide evidence that cold exposure may induce cold symptoms without any contact to sick individuals. As we all carry dormant (sub-clinical) infections in our nose, genitals and other parts of the body, and these viruses may get reactivated. Ever noticed the need to blow nose after spending some time in cold air? Your body might be trying to expel the waking-up microbes.

Emerging health analysis software tools like Aurametrix aim at keeping us healthy by warning about symptoms and diseases. Prolonged exposure to cold means that 4-5 days after the exposure there is 10% probability of developing nasal stuffiness, sneezing, throat irritation of mild fever. 10% if Aurametrix knows nothing else about you. Higher if you are in the most vulnerable age & health conditions group, have a history of more frequent cold infections in prior years or were recently exposed to other stressors. Aurametrix can draw additional conclusions from looking at ingredients in your diet and chemicals in your environment.  Medical records could add another thousand variables. Medical codes given to every documented complaint, prior medications, procedures, information about attending doctors and payments were shown to help predict C.difficile infections in hospitals using machine learning (Wiens, Guttag, Horvitz, 2012).

Social media (in addition to notifications by official sources) keeps us more aware and more afraid of the flu. But what if we are not able to keep away from exposure to a virus, forgot to clean our hands and could not avoid a non-ventilated area with sneezing sick people? The good news is that if we did everything else right we have a fighting chance. As it was shown in a scientific study (Huang et al., 2011), only 9 out of 17 healthy human volunteers exposed to H3N2 virus developed mild to severe symptoms.

So be happy to be healthy, in addition to doing your best to stay flu-free.

Image Credits: Allison Morris, OnlineEducation.net  

REFERENCES

Johnson C, & Eccles R (2005). Acute cooling of the feet and the onset of common cold symptoms. Family practice, 22 (6), 608-13 PMID: 16286463

Jankowski R, Philip G, Togias A, Naclerio R. Demonstration of bilateral cholinergic secretory response after unilateral nasal cold, dry air challenge. Rhinology 1993; 31: 97-100

J. Wiens, J. Guttag, E. Horvitz. Learning Evolving Patient Risk Processes for C. Diff Colonization, Machine Learning for Clinical Data Analysis, ICML 2012, Edinburgh, Scotland, June 2012.

Huang Y, Zaas AK, Rao A, Dobigeon N, Woolf PJ, Veldman T, Øien NC, McClain MT, Varkey JB, Nicholson B, Carin L, Kingsmore S, Woods CW, Ginsburg GS, & Hero AO 3rd (2011). Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection. PLoS genetics, 7 (8) PMID: 21901105

More apps, less flu?

Fewer people caught the flu this season compared with  past years. And many more apps tracking the flu have been developed.  Any relationship between these two trends?

Of course, less flu could be just the result of fewer mutations in bugs, warmer weather and more vaccinations. Yet the power of good software - such as google flu trends, twitter-based trackers and numerous apps can not be underestimated. Thanks to these tools, we are now more aware (and more afraid).

The flu is inherently social. "Nip the flu in the bud by spreading information, not germs, through the social network", says Flu Alert app. and lets you sort your friends by their flu exposures. Virtual flu in Fluville is promoting healthy habits by showing how flu can spread. Fluspotter let's you exchange warnings with your facebook friends, Flutracking reads your e-mails, while Influ takes your voice messages and shares it with users around the world. Biodisapora is tracking disease outbreaks by monitoring air travel. Sickweather scans Twitter and Facebook posts, and Germtrax lets you also sync with Foursquare and Google+  to geo-locate your wereabouts while being sick - with one of 6067 sicknesses available in their database.

According to multiple research studies, flu-related Internet searches, use of certain phrases on Twitter and Facebook posts peak 1-2 weeks earlier than the epidemic curve and align reasonably well with CDC data.

Social media is a noisy but powerful adjunct to surveillance systems based on official sources. It gives us an opportunity of contributing to the community's common good. It raises our awareness, but is not sufficient on its own. Many other factors increase our individual risks. Air travel. Or stress (haven't you noticed flu season in Greece was the worst in the world this year?) Age and food, too.

Aurametrix is a personal health analysis system that tackles this problem with an integrative approach. It aligns your medical history and historical CDC information with your food, mood and amount of sleep. It  looks at all environmental predictions for today telling you if pollen, mold spores or air quality are more likely to be the reason for your symptoms, or if it is the rise in infectious diseases in your area. Aurametrix relies on a range of official sources and social media predictions. The data are constantly updated and refined, and causes linked with effects.

REFERENCES

Dugas, A., Hsieh, Y., Levin, S., Pines, J., Mareiniss, D., Mohareb, A., Gaydos, C., Perl, T., & Rothman, R. (2012). Google Flu Trends: Correlation With Emergency Department Influenza Rates and Crowding Metrics Clinical Infectious Diseases, 54 (4), 463-469 DOI: 10.1093/cid/cir883

Manago, Adriana M., Taylor, T., Greenfield, P.M. Me and my 400 friends: The anatomy of college students' Facebook networks, their communication patterns, and well-being. (2012) Developmental Psychology, Jan 30. doi: 10.1037/a0026338

Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One. 2011 May 4;6(5):e19467. PMID: 21573238

Ginsberg J, et al. Detecting influenza epidemics using search engine query data. (2009) Nature 457, 1012–1014.

Ortiz JR, et al. Monitoring influenza activity in the United States: A comparison of traditional surveillance systems with Google Flu Trends. PLoS ONE 6(4):e18687. 2011.

Christakis NA, et al. Social network sensors for early detection of contagious outbreaks. PLoS ONE 5(9):e12948. 2010.

Malik MT, Gumel A, Thompson LH, Strome T, Mahmud SM. "Google flu trends" and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health. 2011 Jul-Aug;102(4):294-7. PMID: 21913587

Collier N, Son NT, Nguyen NM. OMG U got flu? Analysis of shared health messages for bio-surveillance. J Biomed Semantics. 2011 Oct 6;2 Suppl 5:S9. PMID: 22166368

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Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One. 2010 Nov 29;5(11):e14118. PMID: 21124761

Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: twitter and antibiotics. Am J Infect Control. 2010 Apr;38(3):182-8. PMID: 20347636

Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009 Mar 27;11(1):e11. PMID: 19329408