Datarizing the Self: Reconstructing Identities Through Self-Quantifying Practices

Undergraduate Thesis

Morgane Suel: 260356817

McGill University

Supervisor: Andre Costopolous

Montreal, 2013Introduction

Timothy Ferriss is a “self-proclaimed” guinea pig andmeticulous data cruncher. He mentions in his New York TimesBestseller, The 4-Hour Body, that he’s recorded almost everyworkout he’s done since he was 18, had more than 1,000 bloodtests performed since 2004 (tracking everything from lipidpanels, insulin, hemoglobin A1c, to IGF-1 freetestosterone), spent more than $250,000 on testing andtweaking in the last decade… and collects pulse oximeters,ultrasound machines and medical devices for measuringeverything from galvanic skin response to REM sleep like mygreat-grandmother collected owl statuettes (read:obsessively) (Ferriss 2010: 4).

Meanwhile, Larry Smarr is an astrophysicist turnedcomputer scientist who’s made it his mission to chart everyone of his bodily functions in minute detail. Hemeticulously measures each serving of food in grams orteaspoons, and breaks his meals down into 6 key categories(protein, carbohydrates, fat, sodium and fiber). Alongsidehis intake, he measures his outtake in calories burned(during workouts) and daily steps. This might sound a tadobsessive, but not necessarily out of the ordinary. However,Smarr takes it a step (or quite a few more) further: hewears an armband that records skin temperature, heat flux,galvanic skin response and acceleration in three dimensionswhen he works out, a headband monitors the patterns of hissleep every 30 seconds at night, and he has his blood drawnabout 8 times per year and regularly tracks 100 separatemarkers. In fact, he is on a first-name basis with hisultrasound and MRI technicians who regularly provide himwith 3D images of his body from head to toe. You can even

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find stool samples in his fridge, which he scrupulouslyanalyzes along with the rest (Bowden 2012).

What do Ferriss and Smarr have in common? It’s not agrave undiagnosed case of narcissistic personality disorder.Both are Self Quantifiers: meaning that they seek self-knowledgethrough numbers. Throughout the following pages, I examinethis ‘Quantified Self’ phenomenon, first by providing abackground on the movement, identifying the various types oftools used and categorizing types of self-quantifiers.Second, I examine the historical and modern contexts thatenabled the emergence and popularity of various quantified-self tracking tools and behaviors. Third, I look at how theadoption of these tools puts forth a preventative healthmodel by promoting health and wellness. Finally, I considerthe implications of the Quantified life and how itactualizes the construction of a quantified Self throughcomplex mathematical algorithms.

1. Background1.1. The Quantified Self

“Self quantifier” is derived from the term Quantified Self,coined in 2007 by Wired editors Gary Wolf and Kevin Kelly(Quantified Self 2012). It represents a movement of thoughtthat encourages the datarization of the individual viatechnology as a means to “replace the guesswork andsupposition presently guiding individual health decisionswith specific guidance tailored to the particular details ofeach person’s body” (Bowden 2012). In a 2010 TED talk, Wolfsuggests that we think of this data as a “window” ontopeoples’ activities, and that the QS movement was founded asa means to think of data “as a mirror” and what kinds ofreflections, learning, and personal insights might emerge(Knafus and Sherman 3: 2013). Since its inception, QS hasspread internationally to include over 135 groups in 34countries. In fact, since 2007, the popularity of themovement enabled it to take a life of its own: QS has becomemuch more than just a “tech and/or health-geek”-centeredmovement, it has become an increasingly common aspect of our

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everyday lifestyles. In the following lines I define severalterms for the sake of clarity:

Quantified Self: A movement initiated by Wolf and Kellyin 2007 that represents a community of life-loggers whouse technology as a means to gather and analyze dataabout themselves in an attempt to improve their lives.Their motto: “self-knowledge through numbers,” drives22,000 members in 135 meet-up groups across 34countries (as of July 2013) to discuss, debate and demoanything related to the phenomenon of self-datarization(Quantified Self). QS is also a hotbed fortechnological innovation and encourages developers andstartups to contribute to self-tracking era technology(ranging from mobile apps to wearable devices) byproviding them with various resources ranging from demoand networking opportunities, workshops and discussionscentered on the value of self-tracking. The QS groupalso hosts a yearly conference for “toolmakersinterested in self-tracking systems” (Quantified Self).

Quantified self-tracking: simply put, quantified self-tracking is the regular collection of any data that canbe measured about the self such as biological,physical, behavioral or environmental (Swan 2009: 509).This behavior is also commonly-known as life-logging.

quantified self: (lowercase) a self that is quantified.Someone who engages in the collection of his or her owndata.

Self-Quantifier: see above. Quantified self-tracking tools: technology that enables

self-tracking behaviors (online tools, applications,devices etc.).

Quantified-self knowledge: through technology-enabledobservation and detailed quantified records, the self-tracker is certain of being the expert on him orherself

Tracking oneself isn’t unheard of; it has always beenquite common. The Pew Research Center’s Internet & American

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Life Project released the results of a study determiningthat 69% of adults track health indicators (Duggan and Fox:2013). After all many people keep journals, popular programslike Weight Watchers encourage individuals to track theirintake and outtake, and various wearables like pedometerstrack steps etc. However, born in the ‘digital age,’ theQuantified Self brings something new to the table:technology. Whitney Erin Boesel, a sociology PhD student andwell-known internet persona, suggests that while theQuantified Self may be at the forefront of some new methodsof self-tracking, it did not initiate the growing popularinterest in self-tracking; rather, Quantified Self came toexist because people were already self-tracking (Boesel2012). The high-tech factor introduced by QS facilitatesdata collection while enabling higher accuracy and an easiermanner of keeping records. Instead of keeping a handwrittenjournal of various activities, QS encourages the use oftechnology for self-tracking behaviors. One of theunderlying assumptions for the QS movement is that reliabledata is an objective resource that can bring visibility,information and action to a situation, and that technologyis the most accurate way to measure and map it (Swan 2009:508).

Another aspect of technological monitoring is the varietyof algorithms that make the data accumulated during self-tracking behaviors meaningful. Self-tracking data is onething, but the algorithms found in QS mobile applicationsand devices help self-trackers make something of it; theyenable the graphical display of the collected data,qualitative that can be mapped on a quantitative scale (howis your mood affected by your exercise?) or even relativizedto yesterday (or last year’s) readings. And in some cases,your data can be stacked up against fellow users’ and giveyou advice for improvement. Oftentimes the algorithmicanalysis and presentation of a quantified self’s data givesusers the tools to detect trends, examine and reassesscorrelations and make lifestyle changes in the goal of self-improvement.

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1.2. Categories and types of quantified-self tracking tools

Focusing on the consumer self-tracking market,excluding the highly specialized equipment Tim and Larry hadaccess to, there are a variety of self-tracking tools beingused today. These tools can be separated into fourcategories: websites (Table 1), apps (Table 2), devices(Table 3) and personal genomics/biomarker testing (Table 4).Health-tracking websites and applications are generally un-automated, meaning that data must be entered manually.Wearable devices, however, have automated data collection.Applications, and in most cases, wearable devices, requirethe user to own a smartphone. Most devices come with similarfunctionalities, the most basic being accelerometer-detectedenergy expenditure or calories burned, and in some casessleep measurement (Swan 2009: 510). In some cases, thereare some overlaps: an app operates on a device and links toa website.

Table 1. Popular self-tracking websites today (manual data

input)Service FunctionMoodscopewww.moodscope.com/

A site that allows you to track your moodeach day using an online card game; look atscores over time and share with friends.

CureTogetherwww.curetogether.com

A site where patients come together to sharequantitative data about themselves, theirtreatments and their success. Acquired by23andMe, it is a leader in patient-drivenexperimentation.

PatientsLikeMewww.patientslikeme.com

A health social network site for patients totrack their history and progress, connectwith those who share similar experiences andlearn about living with and treating theircondition.

Stickkwww.stickk.com

A site that enables users to create monetary“commitment contracts” to help you achieveyour goals. Daily tracking helps the userkeep stay updated with his or her progress.

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FitWatchwww.fitwatch.com/

This site offers free fitness and nutritiontracking that allows users to keep track ofyour exercise and diet. A paid version thatoffers more features also exists.

Table 2. Popular self-tracking smartphone applications today

(manual data input).Name Function Connected to:MoodPanda A mood-tracking application

where users rate theirhappiness on a 1-10 scalewith brief notes.

A websitewww.moodpanda.com

RunKeeper Runkeeper enables users totrack the time, distance,speed, pace, calories, andpath on a map of theirfitness activities.

A websitewww.runkeeper.com

Momento Momento is a unique journalwriting application thatprovides a quick and easyway to privately recordmoments throughout the day.

You can sync yoursocial feeds(Instagram,Foursquare, Twitter,RSS, Facebook, Youtubeand Last.Fm) into yourdaily journal. Alldata is exportable.

TickTrac TickTrack is a platformthat ties together all thedata collected by differentsensors and services. Byhaving datapoints in oneplace, enables the user tosearch for correlations intheir measured life.

A websitewww.ticktrac.com

MyFitnessPal A free application thatenables the user to accessdiet plans, count calories

A websitewww.myfitnesspal.com

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(and nutrients) and recordexercise.

Table 3. Popular wearable self-tracking devices (automated

data collection)Product Features Price Connected to:Fitbit One2 inch belt unit

Tracks steps, distance,calories burned, stairsclimbed and sleeppatterns

$99 An app and websitewww.fitbit.com

Nike Fuel BandBracelet

Tracks time, motion,steps, calories and“fuel”

$149 An app www.nikeplus.com

JawboneUpSleek bracelet

Tracks steps, caloriesburned, sleep patterns,mood (if logged manually)and has a vibrationalarm,

$129.99

An appwww.jawbone.com

1Basis BSmartwatch

Tracks time, heart ratepatterns, step-counter,perspiration, skintemperature, sleeppatterns and caloriesburned

$199 An app and websitewww.mybasis.com

MemotoWearable camera

Made for life-logging,the device is clipped toclothes and takespictures every 30seconds. A web-based apphelps deal with the largeamount of pictures.

$279 +monthlycloudstorage fee(forpre-orders)

And app and websitewww.memoto.com

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Table 4. Popular personal genomics and biomarker testing

services.Service Function Features Price 23andMewww.23andme.com

A DNA analysisservice thatprovidesinformation andtools for you tolearn about yourgenetic traits,disease risk andancestry; youmail a salivasample forlaboratorytesting.

From a bucal swab,580,000 SNPs arescanned and mapped to78 conditions and nowhas an ancestrycomposition feature.

$99

Talking20 An at home blood-spot analysisservice whereclients send indried blood spotsfor testing andreceive resultsonline.

Cholesterol (Total,HDL, and LDL),Estradiol (Estrogen),Progesterone,Testosterone,Homocysteine, CRP,HgA1c, Cortisol, andVitamins A, B6, B9,B12, C, D3, and Etests are available.

$99-$4,995

Map My Genomewww.mapmygenome.in

Personal genomicsservice thathelps clientsunderstand theirgeneticvariations andhow they canaffect them. Theyalso take intoaccountenvironmentalfactors, andlifestyle anddietary habits

Screening for 24diseases, 9 drugresponses, 10 traitsand 2 inheritedconditions.

$500

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when analyzingsusceptibility tomedicalconditions.

X Code Life Scienceswww.xcode.in

The serviceoffers genotypingfor 4 lifestylediseases bundledwith a nutritionplan, fitnessplan, "bodymetabolismanalysis", dailytipsand reminders.

Genetic RiskAssessment forDiabetes, Obesity,Heart Disorders andStroke, covers 384Genetic Markers(SNPs).

$185

Personaliswww.personalis.com

Whole genomeservices forsequencing andanalysis.

Several differentplans are offered.The user is given rawdata and can payextra for an expertanalysis if theywant.

Unknown

1.3. Categories of self-quantifiers

Not surprisingly, Quantified Self and the concept ofself-tracking have grown in popularity since theirunofficial inception in 2007. After all, one of the mostinteresting things for an individual to measure is him orherself and the ‘Google era’ is making it possible. In thepast, the cost and expertise required for collecting andworking with large-scale datasets and visualizationgenerally limited access to institutional professionals, butcost decreases, internet accessibility and tool improvementshave made data collection and manipulation more available tothe individual (Swan 2009: 508). Up to this point you cansay that the history of medicine has essentially been thehistory of doctors and the biomedical model, but now thatthe increased accessibility of high-tech facilitated the

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leap of self-tracking tool use from the tech obsessed to themainstream public, we’re seeing the industry explode.

With this explosion, an increased interest in self-tracking technology is happening across the board (the primeexample being the popularity of Google Glass, albeit notnecessarily health-related). In fact, between October 2012and July 2013, the QS group quadrupled in members (Weintraub2013) and it has become a regular occurrence to seeheadlines along the lines of “[QS tool] raises $7M in series Afunding” on popular tech sites such as TechCrunch andMashable. The bottom line is that people are interested, andself-tracking technologies, especially wearables, seem to bethe next big thing. For example: the Pebble watch, awearable self-tracking computing accessory, became thehighest-funded Kickstarter project to date. The projectraised $10 million from an initially sought $100,000 and hasamassed over 275,000 pre-orders as of July 2013. With anestimated $800 million in sales in 2012 (and wearablesmaking up an estimated $1.5B industry in 2014), thisseemingly narcissistic self-tracking trend is shaping up tobe a biological metric of the future (Nelson 2013, Jeswani2013).

As a result of this popularity, quantified self-tracking tools are making their way into an increasingnumber of peoples’ everyday lives practices. However,because these tools are so diversified and offer a varietyof different tracking metrics and features, they are notused in the same way, or by the same types of people, acrossthe board. After meticulously combing through QuantifiedSelf and self-tracking literature ranging from online blogs,testimonials, academic literature and magazine publications,I’ve been able to determine several “categories” of self-trackers.

1. The self proclaimed “freak” or “guinea pig”: these arethe more “extreme” self-quantifiers like Tim and Larry.They generally have access to complex and expensiveequipment that enable them to track unusual metricswith extreme accuracy. They also tend to ritualize

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self-tracking, making self-experimentation andmeticulous logging a central aspect of their dailylives. Certain extreme and professional athletes alsofall into this category.

2. Self-tracker for health reasons: these self-trackersgenerally use devices to track aspects of a particularmedical condition (eg: high blood pressure, diabetes,insomnia, allergies). They self-track to supplementexisting treatments and wish to gather data not only tokeep track of and understand their condition, but alsoto detect trends that can then be presented to andanalyzed by their physicians. Self-trackers seeking togather data with a diagnostic goal also fall in thiscategory.

3. Self-tracker for improvement: these self-trackers aremore experiment-focused and attempt to address aparticular issue. They tend to define a clear goal inthe beginning of their self-tracking behaviors (ex:improved sleep, improved mile running time etc.), andwish the desired outcome to be some form ofimprovement. They try different interventions whilekeeping track of the effectiveness of each, as a meansto address and improve the particular issue they wishto examine. These trackers also tend to generally trackduring more short-term periods.

4. Self-tracker for motivation: these self-trackers useself-tracking tools as a means to motivate them to leada “healthier lifestyle.” For example, they buy a FitbitOne to motivate them to take more steps and to engagein a more active lifestyle. Now that QS-trackingdevices have become popular and are marketed to themainstream public, this seems to be the most populartype of self-tracker. They are especially interested inweight-control through nutrition and activity andinstilling healthier habits. Tools that have anincorporated social network tend to be especiallypopular for this category (ex: JawboneUp enables you tocreate “teams” and encourage one another if teammembers haven’t met their ‘daily steps).

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5. Self-tracker out of curiosity: these self-trackers doso out of curiosity: they are simply curious abouttheir own data and what it could potentially give them.These self-trackers are usually more device-oriented,as it doesn’t ask for meticulous data-entry orparticular effort. An example of a “curious” self-tracker is one who uses the JawboneUp because it was agift, and is interested in seeing the kind of data ityields. These self-trackers can eventually join anothertracker category, depending on their interest andinteraction with QS-tracking tools. This category alsoincludes people who have the habit of following thelatest trends in recent technology, seeing as self-tracking trough trendy gadgets can now be qualified asa trend.

2. Context for emergence2.1. Self-tracking and self-experimentation for preventive care: a historical practice

While the Quantified Self Movement is a relatively newidea, the concept of self-tracking and self-experimentationfor preventive care is not. The movement encouragesindividuals and patients to take responsibility of theirhealth, and does so in the context of modern medicine wherephysicians traditionally have the responsibility of managingpatient health. For this reason we might assume that thesudden popularity of self-tracking and self-experimentationis disrupting the current biomedical healthcare model ofmodern medicine, whereby health is primarily in the hands ofhealth experts. However, when looking at this phenomenonfrom a historical perspective, we see that self-tracking,self-experimentation and preventive trends have existed forcenturies, and what is in fact “new” is the concept that ourhealth should lie almost exclusively in the hands ofphysicians.

Back before women could control (and track) theirmenstrual cycles with various forms of contraceptivehormones, they would often refer to the moon. Seeing as the

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moon seemed to bring menstruation, many ancient culturescalled it their “moon time,” and would track and predicttheir menstruation according to the lunar cycle with whichthey were synched (Habiger 1998). In fact, fertility andmenstruation were early attributed to the moon. The actualword “menstruation” comes from the Greek root word “men,”which means month, and “menus,” which means both moon andpower (Habiger 1998). Similarly, before sleep-trackingdevices and alarm clocks existed, individuals used to trackand control their sleep patterns (and everyday activities)according to the sun’s position in the sky.

Historically, self-experimentation has been animportant means of making new discoveries, and has only beenincreasingly frowned upon in the past century. In fact,prior to the eighteenth century, human research using onlyone or small numbers of subjects was the mainstay ofscientists. However, as our scientific knowledge developed,so did our higher ethical standards and public demands forstudies with statistical significance increased. As aresult, study sizes increased and methodology changed toaccommodate the demands of the time (Ng: 2005). Despitethis, individuals were not deterred from performingexperiments similar to those undergone by Ferriss and Smarr.

The most well documented case of self-tracking andself-experimentation is from the 1600s, with Sanctorius andhis “weighing chair.” The Italian physiologist and professorweighed himself, everything he ate, everything he drank, andevery one of his excretions (both urine and feces) for 30years to study metabolism. In his studies he compared theweight of his intake to that of his waste products, andconcluded that the latter was considerably smaller: forevery 8lbs of food he ate, only 3lbs were excreted (Voytek2010). To account for this difference, he formulated thetheory of “insensible perspiration,” a process wherebyperspiration sneaks its way out of the body via pores andevaporates before we can perceive it as moisture by the skin(Greenslit 2011).

Another notable self-tracker is Benjamin Franklin, whofamously tracked 13 personal virtues in a daily journal to

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push himself to moral perfection. After a rocky first twentyyears, he put together a plan to regulate his life in 1726.Franklin’s plan included 13 virtues, which he felt wereimportant guides for a successful person. He shared hisinsight in his biography where he famously stated, “I wassurprised to find myself so much fuller of faults than I hadimagined, but I had the satisfaction of seeing themdiminish” (Moschel 2013). Franklin tracked his progress in asmall book of 13 charts. The charts had a column for eachday of the week and thirteen rows marked with the 13virtues. Every evening he would review his day and put amark next to each virtue he failed to accomplish for thatday (Roger 2013). Sanctorius and Franklin, like Ferriss andSmarr, were exceptional historical figures of their timebecause they went above and beyond the more common self-tracking practices used by the general public.

With the rise of modern medicine in the 19th century,these early self-tracking practices disappear as anincreasing dependence on doctors, pharmaceutical medicine,the scientific method and a rapidly developing biomedicalmodel takes place. However, there is evidence that self-tracking for preventive care is still valued despite today’sprominent healthcare model. Women are encouraged to performregular at-home breast self-examinations, and shelf-toilets(which facilitate stool-examination) are still popular inGermany and various parts of Europe (Anderson 2003).Although self-tracking and self-experimentation forpreventive care seem to have dwindled and lost relevancesince the dawn of modern medicine, we see a revitalizationof these practices in the context of the technological age.This rekindling, which seems to be gathering practitionersby the masses, is re-appearing in the form of the QuantifiedSelf, through apps, devices etc..

Image 1: General observation of the historical trends ofself-tracking and modern medicine

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2.2. The state of healthcare and the digital age: facilitating the incorporation of QS technologies and philosophies into the everyday

In the past decade, several factors have stacked up tocreate the ideal climate for not only the boom of QS-tracking tools, but also their incorporation into theeveryday lives of the average American. What facilitatedtheir instant popularity can be broken down into twospecific categories: the digital age and the contemporarystate of public healthcare.

With the digital age came the tech boom, which hadseveral implications for the proliferation of quantifiedself-tracking tools. First of all, with the birth of thesmartphone, an increasing number of people have 24/7 accessto powerful hand-held computing devices. In fact, there are5 billion mobile phones in the world, out of which 1.08billion are smartphones. The number of smarphone users inthe USA alone is about 91.4 million, and about 90% of ownersreport to using it throughout the day. Not to mention thatin 2011, an impressive 70% of smartphone owners reported tousing their phones to use downloaded apps (GO-Gulf). When itcomes down to apps, which is QS-tracking tools’ preferredplatform, ABI research estimated that 70 billion apps wouldbe downloaded in 2013—58 billion to smartphones and 14

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billion to tablets (ABI Research). This means that over 10apps are being download per human being on earth! Not tomention that 31,000 of those apps are dedicated to health,fitness and are medically related.

Another factor spurring the popularity of QS behaviorsis the explosion of low-cost sensors and monitoringtechnologies available for several different types offunctionality. The most standard sensors includeaccelerometers, sound, light, electrical potential (viapotentiometer), temperature, moisture, location (via GPS)heart rate and GSR (galvanic skin response of skinconductivity) (Swan 2012: 219). Access to these types ofsensors used to be limited to professional settings(physician office, athletic facilities etc.), but they havebecome widely available due to an increased production atlower cost. Today, popular QS-tracking devices like theFitbit One are offering multi-sensor platforms atincreasingly affordable prices. That being said, thewidespread use of smartphones, paired with the global super-intelligence we have come to know as “the cloud” and thegrowing availability of affordable sensor technologies, arecreating an environment where it is convenient for consumersto incorporate self-tracking behaviors into their everydayroutine.

On one hand, the digital age easily explains theconvenience of using various QS tools to track health.However, what is pushing users to adopt them into theireveryday behaviors? What is the social context of theQuantified Self movement? One of the key undertones of themovement is the empowerment of individuals, or patients, whoare able to regain control of their health, or even theirlives, by being as knowledgeable as they can about theirbodies (seen later in the paper). That being said, with thecritical health challenges of the current era, individualsare motivated to become more cautious of their own healthwith the use of QS-tracking tools.

Americans are used to being told that healthcare willsoon bust the US budget. In fact, the Congressional BudgetOffice projected in 2012 that Medicare, Medicaid and other

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government health programs would eat up 9.6 percent to 10.4percent of the nation’s gross domestic product by 2037,crowding out many other vital programs (Porter 2013). In2010, an estimate $8,402 was being spent per person onhealthcare and it doesn’t seem like costs will drop anytimesoon. Not to mention that 30-40 percent of the money spenton healthcare—more than half a trillion dollars per year—isspent on costs associated with “overuse, under-use, misuse,duplication, system failures, unnecessary repetition, poorcommunication and inefficiency” (Nosta 2013). With the CDCestimating that 42% of American adults will be obese by 2030(34% in 2012), populations aging worldwide, anticipatedphysician and nursing shortages, the high cost of bringing anew drug to market ($1.3 billion in 2012), the fact that 62%of bankruptcies in 2007 were directly medically-related, andthe insecurities regarding the Patient Protection andAffordability Care Act (Obamacare): people are worried notjust about their health, but also about the overwhelmingcost of healthcare which is motivating enough to findalternatives to modern medicine (Swan 2012: 93).

In addition to being weary of healthcare costs,individuals are seemingly becoming more disenchanted by thetoday’s Western biomedical model. For example, there is aclear rise in the use of alternative medicine in the UnitedStates—and worldwide. In fact, the global traditionalmedicine market is estimated to reach $114 billion by 2015,according to a report published by Global Industry Analysts,Inc. (Global Industry Analysts, Inc. 2012). Global interestin alternative medicine is soaring across the globe as aresult of soaring healthcare costs and the belief thatnature-based products cause less side effects than modernmedicine (Global Industry Analysts, Inc. 2012).

Not to mention that an increasing number of Americanpatients are frustrated by their hospital experiences: theyfeel over-tested, over-prescribed and depersonalized. LeanaWen and Joshua Kosowky, authors of When Doctors Don’t Listen,argue that medicine is becoming progressively depersonalizedas doctors increasingly adopt a “cookbook medicine”approach. And by “cookbook medicine” they mean that patients

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are being reduced to variables: symptoms are popped into anequation and out comes a diagnosis regardless of a patient’spersonal history (Zuger 2013). Furthermore, various studieshave yielded an alarming picture of the medical world: everyyear medical errors cause 98,000 deaths (some studiessuggest twice the number) and one million injuries, medicalerrors kill more people each year than breast cancer, AIDS,or motor vehicles, three out of every 10 tests are reorderedbecause results cannot be found (patient charts can only befound on 30 percent of visits) and one-fifth of medicalerrors are due to the lack of immediate access to patientinformation in the United States (Nosta 2013).

Clearly, and for good reason, Americans are trying todistance themselves from the western medical model currentlyin place in the United States. This context, paired with thetechnological opportunities of the digital age facilitatesand drives the adoption and use of QS-tracking devices bymainstream consumers; convenient options that enableconsumers to avoid the healthcare setting by taking controlof their health are entering the market at affordable pricesand a lot of success.

3. Self-tracking and the promotion of a preventivehealth model: the road to wellness

Increasing shyness regarding traditional healthcarepaired with ever more popular health-oriented self-trackingpractices are key to an important conceptual shift happeningin the way we conceptualize health and healthcare today. Oneof the core principles of Quantified Self philosophyinvolves the empowerment of the individual: by self-tracking, individuals can self-monitor and self-manage theirown health and wellness. In fact, one of the crucialconceptual shifts underway is that one’s health is no longerentirely the responsibility of physicians. Instead, by usingself-tracking tools to monitor and manage their own health,individuals are becoming active participants as opposed tojust patients: they become the nexus of action taking and

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empowerment (Swan 2012: 95). This means that individuals,through QS-tracking and other low-cost newly-availabletools, have the ability to understand their own healthpatterns and baseline measures, and obtain early warnings asto when there is a variance and what to do about it (Swan2012: 95). That being said, healthcare is becomingincreasingly patient-driven.

The increasing incorporation of self-tracking toolsinto our everyday lives broadens our concept of health andhealthcare, making space for an emerging patient-drivepreventive health model. Previously, our prominent heath-model was above all curative, however, more recentdefinitions of health and healthcare are expanding to mean“wellness maintenance and condition prevention” as opposedto just the cure of illness (Swan 2012: 98). In fact, manybusinesses and service providers are starting to make thedistinction between wellness and medicine, all the whileencouraging individuals to live “healthily.” Swan alsomentions that remedy providers are expanding from primarilybased pharmaceutical initiatives to more of a focus onsupplementation, stress reduction and other preventivetreatments (2012: 99).

QS and self-tracking tools neatly find their placewithin society’s increasing awareness and promotion ofwellness. In fact, they even encourage the preventativemodel, adding a “personalized” twist to it. After all, oneof QS’ unofficial mottos is: “do what works for you.” QSencourages individuals to become more conscious of theiroverall wellbeing and “health” not only by self-tracking andmonitoring various biometric data, but also by adoptingbehavior changes that work for them. Thus the advocacy forself-experimentation that lies at the nexus of the movement.Overall, these practices promote a preventative health modelby essentially focusing on and encouraging behavior changesthat promote healthy living. Therefore, the adoption ofself-tracking behaviors into one’s everyday life promulgatesthe aforementioned conceptual shift in health andhealthcare, as it drives individuals to undertakepreventative measures.

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Below is a word frequency visualization map, whichdemonstrates that the discourse adopted by quantified self-tracking apps themselves promotes wellbeing, “healthiness”and preventative behaviors.

Image 2: word frequency visualization of the “about” and/or“mission statement” portion of the “top 20” self-trackingapps and devices according to the Quantified Self website.

4. Implications of the Quantified Life: constructing the quantified Self through complex mathematical algorithms

In his article, “An New Algorithmic Identity: SoftBiopolitics and the Modulation of Control,” John Cheney-Lippold discusses the impact of the implementation ofsophisticated algorithms by marketing and web-analyticscompanies to observe, analyze and categorize online users.Through an analysis of our online behaviors and browsingpractices, a new “algorithmic identity” is emerging—anidentity formation created through code and mathematicalalgorithms to infer categories of identity on otherwise

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anonymous beings (2011: 165). Simply put: as you browseonline, you are identified, your IP addressed is logged andcookies are installed on your computer so that somewhere ina database, you are attributed certain characteristics suchas gender, class and race. Today’s technological age hasgiven rise to an industry of web analytics and marketingfirms that are actively amassing information on individualsand fine-tuning computer algorithms to make sense of thatdata being received. This phenomenon, in turn, enables thenew construction of identity and categories online. Everwonder why your Facebook or Google ads are oddly relevant toyour particular interests? The reality is that online usersare being virtually stalked, tagged, given a profile basedon their web history and then shown relevant ads andinformation to optimize the probability of purchasingaction.

A similar phenomenon is happening in the quantifiedself-tracking world: self-quantifiers are being attributed a‘health identity’ constructed by the code and mathematicalalgorithms embedded in self-tracking tools that analyze userdata. There are essentially two components of self-trackingtechnologies that facilitate the forming of this new digitalidentity: the ability to gather and store raw data, and thecomplex codes and algorithms that render the data meaningfulto the user. That being said, it all boils down to the powerof numbers.

Self-tracking tools, whether web-based, app-based or indevice form, are based on quantifications using complexalgorithms to analyze and display the data collected. Thatis to say that self-tracking tools rarely give users rawdata; data is always processed before being presented in anintelligible manner on a user-friendly interface—anotheralluring aspect of these tools. In this sense, themeaningful ‘body data’ produced by the algorithms embeddedin self-tracking tools, or the ‘number’ that allows users tocompare their biometrics with those generated on previousdays, contribute to a new way of conceptualizing one’ bodyand one’s health (Lupton 2013: 7).

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In their entirety, self-tracking technologies encouragepeople to think about their bodies and selves throughnumbers, suggesting that the more we know about ourbehaviors and our bodies, the more productive, wiser,healthier (etc.), we can become. In fact, one of the mostalluring aspects of quantified self behaviors is thatnumbers are seemingly scientifically neutral and that datacan provide users with objective, quality information aboutthemselves. However, Deborah Lupton argues that numbers areanything but neutral despite the concept of them as devoidof value judgments, assumptions and meanings. Instead sheargues that the ways in which phenomena are quantified andinterpreted, and the purpose to which these measurements areput, are always implicated in social relationships, powerdynamics and ways of seeing (2013: 7).

To demonstrate Lupton’s idea that numbers aren’tneutral, we have to look at the code and algorithms embeddedin quantified self-tracking tools. Essentially, algorithmsattribute “value” to variables, which are then used togenerate a ‘final product,’ or, what is presented to userson the tool’s interface (calories burned, BMI, calorieintake etc.). However, each of these variables is decidedaccording to pre-determined guidelines, like, for example,the Harris-Benedict formula to calculate BMR (basalmetabolic rate). Thus, these pre-determined formulasembedded in self-tracking tools follow various nationalhealth standards developed by physicians and healthcareprofessionals that make up our very own biomedical model.For example, what is the healthy weight range for a 5’5”tall female? What is her BMR? How many calories should sheeat to lose 2lbs per week? How many calories did she burnduring her 35 minute run? Each of these questions can beanswered because specific formulas, and their attributedvariable values according to national health and medicalstandards, are coded into self-tracking tools. And it isthrough these complex mathematical algorithms embeddedinside QS tool software that data is analyzed and a ‘finalproduct’ is generated (Her BMR is X, she burned 400 caloriesetc.). It is in this manner that numbers cannot be

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considered neutral. The numbers that make up our biometricdata are not arbitrary; in fact, our data is loaded withsocial meaning determined by our Western biomedical modelwhich decides their relevance, how they should analyzedand, ultimately, what they mean.

Furthermore, these self-tracking applications, devicesand tools are programmed (and bought) not only to givefeedback in the form of “truths,” but also to givesuggestions to the user, thus creating a power dynamicbetween data and self. Depending on various variables anddata input into self-tracking tools, individuals tend to getlabeled: “healthy,” “unhealthy,” “overweight,”“underweight,” “eating above/below your calorie allocation,”“sleep deprived,” and so on are a few examples that resultfrom QS technology. Not only are these tools programmed toanalyze data, but they are also programmed to contextualizeit in terms of “wellbeing.” For instance, as a part of theirprogram, several QS tools will compare user data toclinically-approved “healthy norms” (ex: the user has ahigh/normal/low BMI), stack it up against the averageusers’, or their own data (ex: personal records), and/orgive suggestions as to how to either achieve goals or “behealthy.” One of the applications mentioned in Table 1,MyFitnessPal, suggests how many calories users should eat(according to the “personal” data they entered) in order toachieve their weight loss goal. By labeling users and givingthem suggestions and ‘feedback’ based on their data, QS-tracking tools are implicated in data v. user powerdynamics: the algorithmic product of the user’s dataanalysis not only contributes to the emerging phenomenon of“health identity” by metaphorically and literally labelingusers, but it also influences user to alter behaviors inorder to achieve their health and fitness goals. This isespecially true when different data sets are mapped (ex: howa user’s coffee intake affects sleep patterns) and oftenresult of ‘body-hacking’ behaviors.

The process of quantified self-tracking, in which ourdata is deconstructed then reconstructed by various codesand algorithms, involves the loss of one identity and the

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re-construction of another. By tracking various biometricsvia self-tracking tools, our ‘whole selves’ are beingdeconstructed and reduced to numbers: to the complexmathematical algorithms analyzing our data, we are thenumber of calories we eat, the number of miles we run, thenumber of hours we sleep and so on. On the other end of thealgorithm, or once our data is processed, contextualized andpresented to the user on an intelligible interface, the‘self’ is re-constructed. Thus, the emergence of a ‘healthidentity’: are you a ‘healthy individual’ according to yourquantified self-tracking tool?

Lupton also argues that this phenomenon is responsiblefor health becoming some form of simulacrum. If thebiometrics recorded by one’s digital device shows that BMIis normal, alcohol isn’t being consumed in excess, and bloodpressure and resting pulse are within a normal range, thedata compromise a simulated “healthy body,” regardless ofhow the individual actually feels (Lupton 2013: 8). Thissimulacrum in turn becomes adopted by the user in theconstruction of a “health identity”: their digital self-tracking device tells them they are within the pre-determined health norms defining health and wellbeing;therefore the user must be “healthy.”

Conclusion

While QS tools seem to be gaining popularity in thewake of the booming digital age and increasingly hostilehealthcare contexts, there are several concerns that shouldbe explored regarding the incorporation of thesetechnologies into our everyday lives.

First of all, quantified self-tracking technologies anddevices are still in fairly early stages of development.Thus their use hasn’t been extensively studied in thecontext of their effect on health. In fact, most of thesetechnologies are in “limbo”—new tools and devices keepemerging but it is unclear as to where and how they shouldbe categorized and how they should be treated. In hindsight,some of these technologies technically qualify as medical

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devices (glucose and blood pressure monitors, urinalysisapps etc.), but haven’t been treated as such because theyoccupy a sort of gray area. QS-tracking tools gather data(in medically-relevant ways), deliver results, advise userson their data and suggest various actions depending on theirresults/’unofficial’ diagnostic. Any tools with medicallyrelated functions should technically go through some form ofapproval, however, most of these technologies remaincompletely unregulated due to the ambivalent space theyoccupy in today’s health realm. This is something thatshould be seriously re-evaluated as any tool advising one’shealth without the consultation of a health-carepractitioner could potentially yield devastatingconsequences if left unregulated. How can we be sure thesedevices and applications are accurate and giving valuableadvice that will be beneficial, and not detrimental, to theindividual using it?

A second worry emerging in the QS world is the issue ofprivacy. Right now, sharing information on our sleepingpatterns, blood pressure and calorie intake seems harmless,but one day this sort of health information is bound tobecome more valuable, and could eventually be used againstus. For example, today, people post genetic information thatreveals things about their future children andgrandchildren. Without thinking, people can, and have been,gathering large amounts of information about themselves andsharing it (via social networks, mostly), without fullyseeing the implications. What would happen if insurers gottheir hands on this information? Or employers? The next stepfor the Quantified Self movement is to address these privacyissues in a way that enables individuals to share their datain a way that maintains privacy, but also helps other peoplewithin the community (Winetraub 2013).

Another possibility that could result from self-tracking habits is that the practice could come to beexperienced as a burden rather than a vital source of self-knowledge and empowerment. Although most self-trackers feela significant sense of fulfillment and success in usingthese technologies, there have been reports that some

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regular self-trackers felt that self-tracking led them tobecome overly focused on their health and experiencedfeelings of failure, anxiety or self hatred as a result(Lupton 9: 2013). That being said, when can self-trackingbehaviors be considered “too much” and who could helpdetermine that, if not a healthcare professional?

All in all, while self-tracking technologies seempromising in helping users address personal health goals andconcerns, it should be kept in mind that these tools arerelatively new and haven’t undergone sufficient examination,and thus, should be incorporated into our everyday liveswith caution.

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