Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change

Transcription

1 Health Mashups: Presentng Statstcal Patterns between Wellbeng Data and Context n Natural Language to Promote Behavor Change FRANK BENTLEY, Motorola Appled Research Center KONRAD TOLLMAR, Royal Insttute of Technology PETER STEPHENSON, Humana LAURA LEVY, BRIAN JONES, SCOTT ROBERTSON, ED PRICE, RICHARD CATRAMBONE, and JEFF WILSON, Georga Insttute of Technology People now have access to many sources of data about ther health and wellbeng. Yet, most people cannot wade through all of ths data to answer basc questons about ther long-term wellbeng: Do I gan weght when I have busy days? Do I walk more when I work n the cty? Do I sleep better on nghts after I work out? We bult the Health Mashups system to dentfy connectons that are sgnfcant over tme between weght, sleep, step count, calendar data, locaton, weather, pan, food ntake, and mood. These sgnfcant observatons are dsplayed n a moble applcaton usng natural language, for example, You are happer on days when you sleep more. We performed a plot study, made mprovements to the system, and then conducted a 90-day tral wth 60 dverse partcpants, learnng that nteractons between wellbeng and context are hghly ndvdual and that our system supported an ncreased self-understandng that lead to focused behavor changes. Categores and Subject Descrptors: H.3.4 [Informaton and Storage Retreval]: Systems and Software User profles and alert servces; H.5.2 [Informaton Interfaces and Presentaton (e.g., HCI)]: User Interfaces Evaluaton/methodology, natural language, user-centered desgn; J.3 [Lfe and Medcal Scences]: Health General Terms: Desgn, Expermentaton, Human Factors Addtonal Key Words and Phrases: Health, context, moble, wellbeng, mashups ACM Reference Format: Bentley, F., Tollmar, K., Stephenson, P., Levy, L., Jones, B., Robertson, S., Prce, E., Catrambone, R., and Wlson, J Health mashups: Presentng statstcal patterns between wellbeng data and context n natural language to promote behavor change. ACM Trans. Comput.-Hum. Interact. 20, 5, Artcle 30 (November 2013), 27 pages. DOI: INTRODUCTION The Merram-Webster Dctonary defnes wellbeng as the state of beng happy, healthy, or prosperous. [Merram-Webster 2011] Wellbeng s an mportant measure Wreless@KTH funded the plot study for ths research and Humana funded the full study. F. Bentley s currently afflated wth Yahoo! Labs. Authors addresses: F. Bentley, Yahoo! Labs, 701 Frst Avenue, Sunnyvale, CA 94089; emal: fbentley@yahoo-nc.com; K. Tollmar, Royal Insttute of Technology, Valhallavägen 79, , Stockholm, Sweden; emal: konrad@kth.se; P. Stephenson, Humana; emal: peterdstephenson@gmal.com; L. Levy, B. Jones, S. Robertson, E. Prce, R. Catrambone, and J. Wlson, Georga Insttute of Technology, North Avenue, Atlanta, GA 30332; emal: {laura.levy, bran.jones, scott.robertson, ed.prce}@mtc.gatech.edu, rchard.catrambone@psych.gatech.edu, jeff.wlson@gatech.edu. Permsson to make dgtal or hard copes of part or all of ths work for personal or classroom use s granted wthout fee provded that copes are not made or dstrbuted for proft or commercal advantage and that copes show ths notce on the frst page or ntal screen of a dsplay along wth the full ctaton. Copyrghts for components of ths work owned by others than ACM must be honored. Abstractng wth credt s permtted. To copy otherwse, to republsh, to post on servers, to redstrbute to lsts, or to use any component of ths work n other works requres pror specfc permsson and/or a fee. Permssons may be requested from Publcatons Dept., ACM, Inc., 2 Penn Plaza, Sute 701, New York, NY USA, fax +1 (212) , or permssons@acm.org. c 2013 ACM /2013/11-ART30 $15.00 DOI:

2 30:2 F. Bentley et al. of the qualty of a person s lfe and s a measure that we, as socal computer scence researchers, hope to ncrease through new computatonal systems that help people understand the patterns n ther lves that mpact ther wellbeng over tme. Much of the world today s experencng a breakdown of general wellbeng at a scale never before seen. In Amerca, over one-thrd of adults are offcally obese as are 17% of chldren. [CDC 2010] Western-European countres are also quckly approachng these numbers. For example, one n sx chldren aged between 2 and 15 are obese n the UK. [BUPA 2011] Major factors contrbutng to obesty nclude a sedentary lfestyle and hgh-calore food choces. Many aspects of people s lves can lead to these lfestyle choces. Some people lve n cultures of fast and fred foods, rch desserts, and peer pressure to eat to excess [Chrstaks and Fowler 2007]. For others, a busy or car-centered lfestyle can lead to less tme for physcal actvty. Ths sedentary lfestyle leads to an ncreased rsk of chronc dseases, the leadng cause of death n the world, ncludng hypertenson, dabetes and obesty. Another challenge for wellbeng s sleep. Mllons of people around the world have trouble sleepng, many to the pont where t severely mpacts ther ablty to functon durng the day. It s often qute dffcult to understand what factors lead to a better nght s sleep. Many mllons also suffer from mood dsorders, and understandng the aspects of lfe that affect mood s often a crtcal, but dffcult, frst step towards mprovement. A varety of contextual factors can work together to mpact a person s overall wellbeng. The weather, food, alcohol or caffene ntake, stress levels, or havng late-nght or early mornng appontments all can affect sleep as well as daly actvty levels and weght. We are nterested n understandng the effects of these contextual varables on wellbeng over tme and how people change ther behavor wth ncreased awareness. All of these aspects of wellbeng are able to be captured farly easly usng devces on the market. However, solutons that combne data from all of these sources are not yet prevalent, much less ones that look for longer-term trends. Therefore, we set out to study these long-term patterns n ndvduals daly lves n order to make people aware of them and reflect upon. Managng an overall state of wellbeng s a game of tradeoffs. A slce of cake may contrbute to your happness today, but not to your health n the longer term. Makng one bad decson of ths magntude wll not mpact overall wellbeng, but, over tme, such decsons add up to create patterns and long-term effects. [Rachln 2004] We wll argue that by provdng a tool to help people understand long-term patterns of behavor, users wll be empowered to see the tradeoffs that they face n daly lfe n new ways that are dffcult to spot on ther own. Understandng these trends helps to focus behavor change to specfc and actonable moments. For example, dscoverng trends n personal nformaton ndcatng that on days when you have many scheduled meetngs you gan weght could cause you to reflect on why that s the case and take acton to change t on your next busy day by optng for the salad over the French fres n the cafetera or explctly reservng tme for exercse. We ntally postulated that these correlatons between context and aspects of wellbeng would be qute dfferent for each ndvdual. Therefore, we have bult a system that analyzes an ndvdual s wellbeng across multple dmensons and dentfes the sgnfcant patterns that emerge over tme wth respect to a varety of automatcally gathered contextual data. It s currently qute dffcult for people to dscover these long-term patterns about themselves. Even for those who use tools lke the Wthngs scale and Ftbt pedometer to track ther daly weght and step count, t s not possble to see trends between the two or to see how they nteract on specfc days of the week, weekends vs. weekdays, month to month, etc. over tme wthout exportng the data nto complex statstcal packages [L et al. 2011]. Users are facng a data overload n whch the complex

3 Health Mashups 30:3 nteractons among streams of weght, steps, workout data, calore ntake data, locaton, calendar nformaton, and more are just too much to process on one s own, especally through tme-seres graphs. Our work s based on the dea that that seeng cross-sensor nsghts nto one s wellbeng over tme s needed to create an understandng of the underlyng causes of negatve behavors and to start on a path towards contemplatve acton [Prochaska and Velcer 1997]. Smartphones are a useful platform for collectng contextual data from many aspects of a person s lfe and for dscoverng trends that llumnate correlatons between aspects of a person s context (e.g., total tme free/busy n a day, locaton, weather, etc.) and ther wellbeng (e.g., weght change, daly step count, sleep, mood, etc.). People carry smartphones throughout the day and use them frequently each day n small bursts of tme. By dsplayng sgnfcant observatons about a person s wellbeng on the phone, we amed to encourage reflecton on wellbeng n these small breaks, much along the lnes of prevous work wth step counts (e.g., Connelly et al. [2006] and Consolvo et al. [2006, 2008b, 2009]). Ths artcle wll dscuss our system, the frst ndvdually focused platform for automatcally fndng sgnfcant trends n long-term wellness and context data, as well as fndngs from two feld studes, an ntal plot study and a larger 60-partcpant, 90-day feld study wth an mproved verson of the system. We wll dscuss how partcpants were able to reflect on ther wellbeng n new ways through ths moble-based system and how they created focused behavor change strateges based on the observatons that were presented to them n the system. These behavor changes led to sgnfcant mprovements n weght, mood, and WHO-5 wellbeng scores over the course of the study. Ths work can serve to help others n creatng wellbeng systems that cause users to reflect on the mpact of patterns n ther daly lves and make postve changes to ther behavor to mprove mood, weght, actvty levels, sleep, or other aspects of ther general wellbeng. 2. BACKGROUND AND DESIGN MOTIVATION Over the past fve years, there has been a sold stream of work on moble wellbeng systems both n the HCI communty and n the commercal applcatons/devces space. Commercally, devces such as the Ftbt and Nke+ sensors have allowed people to examne ther physcal actvty at a great level of detal. Smlar devces such as Phlps Drect Lfe provde easy ways to understand daly actvty levels and provde smple suggestons on ways to be more actve throughout the day. Internet-connected scales (e.g., the popular Wthngs model) allow people to easly keep track of ther weght and changes over tme wthout the need for manual log-keepng. Whle some of these servces, such as Ftbt, allow users to mport data from multple sensors (e.g., Ftbt and Wthngs) nto a sngle account, these commercal servces currently do not provde any graphs, nsghts, or suggestons to users based on the combnaton of dfferent wellbeng data feeds. Each sensor s devoted to ts own space n the nterface. For example, graphs on the Ftbt webste show nformaton regardng the number of steps the user has walked n one box and a graph of the user s weght n another wth no way to drectly compare them or to easly dscern patterns n the data over tme. In the research communty, L et al. [2011] have developed a system to dsplay contextual nformaton wth related wellbeng data n tme-seres graphs. Ths system allows users to remember the context surroundng specfc sngle scenaros when tryng to nterpret spkes or valleys n data such as step count or weght. Ths work shows the mportance of understandng the connectons between context and wellbeng data, but only showed the potental on a small scale of what happened on a partcular day. We beleve that ths s an mportant start, but that long term trends and correlatons

4 30:4 F. Bentley et al. are stll qute dffcult to dscover n such systems. Ths s why we focus on mnng wellbeng and contextual data streams for correlatons and devatons over months of data. In hs Ph.D. dssertaton [L 2011], L agrees and says that correlaton analyss of ths data s dffcult and another research project. Other research systems such as Salud! [Medynsky and Mynatt 2010] also aggregate data from multple sensor and contextual streams. In Salud!, as n L s work, users are left to nterpret the data on ther own, from tme seres graphs and large data tables. It can be qute hard for users, especally those not able to nterpret graphs, to gan any meanngful nsghts from ths data. Hence, we are nterested n automatcally analyzng smlar data sets usng statstcal methods to dentfy long-term patterns for users. Studes of numeracy and statstcal lteracy have shown that many lack the ablty to understand and apply data from graphs. Galesc and Garca-Retamero [2011] found that 41% of Amercans as well as 44% of Germans had low graph lteracy sklls n understandng very smple bar and pe charts. Ancker and Kaufman [2007] take a broader look at health numeracy and dscuss not only problems n nterpretng graphs, but also problems n understandng statstcal data. These studes motvated us to consder alternate methods to present the complex nteractons between wellbeng data streams over tme and between sensors. Whle there appear to be no clear practces on how to verbally convey statstcal data [Ggerenzer et al. 2007], several suggestons have emerged from the lterature. Lpkus [2007] dscusses usng varatons on the phrase lkely to dscuss rsk or statstcal certanty. We chose to adopt ths n appendng very lkely on the end of any observaton wth hgh statstcal certanty (p < 0.01). For the rest of the formulaton of the natural language sentences that we presented to the user, we were largely on our own. We debated several formats and settled on forms that were qute neutral and dd not convey a partcular need for acton, leavng ths to the user s nterpretaton. Ths resulted n sentences of the form On days when you, you Y or On Wednesdays you more than usual. Consolvo et al. have explored moble systems to encourage people to be more actve n ther daly lves. They have bult and feld-tested several prototypes n ths doman startng wth Houston, a system to track step counts and share them wth frends or famly to create a competton/game around beng actve n daly lfe [Consolvo et al. 2006]. In another system, they used the moble homescreen to dsplay physcal actvty logged by UbFt [Consolvo et al. 2008a], a system that allows people to vsualze ther ndvdual physcal actvty n the form of a garden that grows on the homescreen of the phone as a user performs a wde set of physcal actvtes. They showed that users wth the awareness dsplay were able to better mantan ther level of physcal actvty compared wth those who dd not use the dsplay. These systems showed the promse of the moble platform for wellbeng-related behavor change, even whle focusng only on physcal actvty. Consolvo et al. [2009] have also developed a set of desgn gudelnes for systems that support health behavor change and Klasnja et al. [2011] have explored gudelnes for evaluatng these systems. Whle many of these gudelnes focus on systems for people already makng change, some are relevant to consder for systems that are focused on ntatng change, especally those around beng reflectve, postve, controllable, hstorcal, and comprehensve. We focused on these gudelnes when creatng our system. Fogg [2002, 2003] has created a seres of gudelnes for behavor change and has been explorng the moble devce as a platform to encourage behavor change [Fogg and Allen 2009]. He has explored text messagng as a means to encourage behavor change and the power of trggers n notfyng users of potental change opportuntes n addton to learnng about ther current progress [Fogg and Allen 2009].

5 Health Mashups 30:5 Anderson et al. [2007] developed a system called Shakra n whch users physcal actvty s montored usng the GSM cell sgnal nformaton. In ther study, they found that makng the user aware of ther daly actvty encouraged reflecton and ncreased motvaton for achevng hgh actvty levels. We were encouraged by ths fndng and also by the possblty of usng the moble phone as a sensor of envronmental context. Based on ths work and the avalablty of consumer-grade sensors to detect varous streams of data, we chose to focus on step count, sleep data, food, exercse, calendar data, and locaton for our frst plot study. These captured a wde range of wellbeng sensors and contextual nformaton and would be suffcent to valdate our hypotheses on fndng sgnfcant patterns n personal wellbeng data and context. The overarchng goal was to have no sensor stream take more than a few seconds of nteracton per day to keep the burden of usng the system as low as possble over tme. Other, harderto-sense, data could of course be added n the future such as stress levels, calorc or salt ntake, etc. The research on these types of data streams ndcated that they were just not ready for wder deployment n sustaned daly use due to the complexty of manual entry [Connolley et al. 2006] or the burden of capture and analyss [van Eck et al. 1996]. Lastly, there exsts a vast amount of related work on behavor change n socal psychology and preventve medcne. For example, Emmons and McCullough [2003] demonstrated n an expermental study that people who kept weekly grattude journals exercsed more regularly, reported fewer physcal symptoms, felt better about ther lves as a whole, and were more optmstc about the upcomng week compared to those who recorded hassles or neutral lfe events. Kahn et al. [2002] revewed and evaluated the effectveness of varous approaches to ncreasng physcal actvty n preventve medcne. Besdes drect pont-of-decson prompts to encourage physcal actvty they descrbe approaches such as behavoral and socal nterventons, ndvdually adapted health behavor change, and envronmental and polcy nterventons. The revew summarzes the actual effectveness of these approaches but also hghlghts the complex dependences among varous nformaton nterventons. Multple sources of nformaton need to come together effectvely to create postve changes and ths s what we are tryng to acheve wth our system. Along the lnes of Mamykna et al. [2008], we sought to support reflecton on personal data, and through ths reflecton our users could be better prepared to make lastng changes to ther behavor. However, as Mamykna s study showed, ndvduals are often not comfortable wth analyzng ther own data. They want physcans or other professonals to reach the conclusons for them. We saw the text-based observatons n our system as a good mddle ground for reflecton that dd not requre nterpretaton of the raw data by the ndvdual user. 3. PILOT SYSTEM To start explorng the concept of personalzed health mashups, we created a plot system that we felded wth ten partcpants to examne whether the system could produce useful observatons wth the types of data that people can log about themselves. Further, we wshed to see f these observatons could lead to behavor change. Fndngs from ths plot study were then used n constructng the complete system that was traled wth 60 partcpants for 90 days. The results of ths larger study wll be dscussed n Sectons 5 and 6. The plot system conssted of a Mashup Server that nterfaced wth the contextual and wellbeng data for each user from multple data sources, performed a statstcal analyss across the data, and presented the user-specfc observatons as a natural language feed to a moble phone wdget (as shown n Fgure 1). Ths wdget then lnked

6 30:6 F. Bentley et al. Fg. 1. The plot system provded mashups observatons n a wdget on the homescreen as well as on a moble webste. Manual loggng of food and exercse was accomplshed va another wdget, whch opened the loggng screen on the rght. Table I. Data Streams that Were Used n the Plot Study and the Full Study Automatcally Sensed Locaton (Cty-level) Weather Calendar Free/Busy Hours Sensor Inputs Step Count (Ftbt) Sleep (Ftbt) Weght (Wthngs scale) Manually Logged Food Exercse Mood Pan Plot Study Full Study to addtonal graphs and data that users could explore to dg deeper nto the detals of ther wellbeng. We collected data from several sources as shown n Table I. Data sources connect to the open APIs of each commercal sensor and use custom REST APIs for data comng from our own context loggng software on the phone. Data from the phone ncluded automatcally capturng hours busy per day from the calendar, locaton at a cty level, and manual loggng of daly food ntake and exercse. Each nght, we performed a statstcal analyss of the data for each user and updated the feed of sgnfcant observatons per user. The feed could nclude observatons across data types such as On days when you sleep more, you get more exercse or observatons from a partcular sensor over tme: You walk sgnfcantly more on

7 Health Mashups 30:7 Frdays. For the frst type of correlaton, we surfaced sgnfcant observatons based on statstcally sgnfcant Pearson correlatons between sensors (p < 0.05). For the devatons, we surfaced dfferences greater than a standard devaton from the mean. For day-of-week dfferences, we performed a t-test and dsplayed sgnfcant results (p < 0.05) n the feed. We performed the analyss based on dfferent tme scales: daly, weekly, monthly and by day of the week. As such, t could nclude observatons such as Last week, you slept sgnfcantly less than usual. Addtonal techncal detal on the statstcal methods used can be found n Tollmar et al. [2012]. Whle Pearson correlatons are relatvely smple, we beleved that they would be an understandable and meanngful measure of how aspects of a person s lfe were related. Along wth the devatons between days and for ndvdual days, they also could easly translate nto a natural language sentence. Other statstcal methods, for example, the ntra-day modelng n Albers et al. [2010], could certanly be appled, however we wanted to start wth smple technques that had the hghest chances of beng understood n natural language. By performng our analyss over weeks and months, n addton to daly, we were able to fnd some effects that had some delay, such as weeks wth hgher step counts leadng to weght loss for some partcpants where ths effect would not be vsble on a daly bass. Data from the feed was presented n a wdget on each user s phone. Only statstcally sgnfcant observatons were dsplayed n the moble wdget and all tems contaned a plan text confdence (e.g., possbly, very lkely, etc.) based on the correspondng p-value. The content on ths wdget was updated each evenng after the server computed the newly sgnfcant observatons. The wdget can be seen n on the bottom-left n Fgure 1. Clckng on a feed tem n the wdget launched our moble webste on whch users could vew a graph detalng that partcular tem. From ths moble webste, users could also navgate to other graphs such as a day-by-day weght graph, average step count by day of week, other correlatons to elements of ther context, or to other streams of wellbeng data. If desred, more techncal users could spend tme explorng these graphs n order to better understand the data behnd the observatons. Addtonally, users could mark observatons to reflect on later n a favortes lst accessble from the moble webste. Beyond the moble webste, users could vst the webste from ther computers, where a larger vew of the feed was vsble and t was a bt easer to navgate between graphs and data from dfferent sensors. On the phone, we developed a smple applcaton for reportng general food and exercse behavors for the day as shown on the rght n Fgure 1. Users were presented wth a 5-star scale and asked to rate ther food ntake and exercse for the day. We suggested that partcpants log ths actvty at the end of each day as a way to reflect on the day s behavor. Whle we are aware of the problems wth users often not supplyng regular and sustaned data wth manual loggng [Burke et al. 2008; Patrck et al. 2009], we felt that these components were mportant to provde for users who were motvated to log and understand these elements of ther lves and that the smple, 2-clck loggng would be a smaller burden than n the prevous studes. The system was ntended to be used daly, where users step on a scale, wear the Ftbt, have ther phones upload contextual data n the background, and log food and exercse habts each evenng. These are all farly low-effort actvtes that we hoped could easly become a part of our partcpants daly lves. The moble phone wdget allowed users to be frequently remnded of what s most sgnfcantly affectng aspects of ther wellbeng whenever they looked at ther devce. We speculated that ths system would then encourage postve behavor changes based on personal reflecton on ths data, and desgned a plot study to nvestgate how ths system would be used over

8 30:8 F. Bentley et al. tme n the daly lves of partcpants n two major ctes and to learn how the concept could be mproved for a larger study. 4. PILOT STUDY 4.1. Methods We recruted ten dverse partcpants for a two-month feld evaluaton of the plot system n the summer of Four users lved n Chcago (from here on referred to as C1-C4) and sx were from Stockholm (S1-S6). The Chcago partcpants were recruted through a professonal recrutng agency to reflect a dversty of age, occupatons (e.g., polce offcer, real estate agent, watress, chemst, etc.), cultural backgrounds, and educaton. The Stockholm partcpants were recruted through extended socal networks of the research team and also ncluded a varety of ages and occupatons. The study lasted for two months. We began the study wth a sem-structured ntervew n the partcpants homes. We explored ther current wellbeng practces as well as any goals they mght have around ther health or general wellness. We helped the partcpants set up the W-F scale and Ftbt devces and nstructed them on ther use. Partcpants also completed a demographc survey. For the frst four weeks of the study, partcpants used the scale and Ftbt n ther daly lves and could use the webstes provded by the devce manufacturers to follow ther progress. However, they dd not have our moble applcaton nstalled or access to the observaton feed generated by the Mashup server. We dd ths n order to get an understandng of what people can nfer from the exstng solutons wthout seeng the mashed up wellbeng data as well as to get a few weeks of background data from whch to create ntal observatons for our feed n the second part of the tral. Durng these frst four weeks, partcpants were nstructed to call a vocemal system or send an emal whenever they had an nsght about ther own wellbeng. We were nterested f they would make any nsghts that nvolved multple sensors or patterns over specfc days of the week, but dd not nform partcpants of ths fact, or what the moble app n the second month would ental. We saw ths frst month as our control as we could see how partcpants would use exstng tools n ther daly lfe and what they could nfer from the data presented n these servces. After the frst month, we met wth the partcpants agan n ther homes and asked follow-up questons based on ther vocemal messages and any addtonal nsghts they were able to make. At ths tme, we nstalled our moble wdget/app on ther phones and demonstrated ts use. We also nstalled the contextual loggng servces ncludng calendar free/busy data upload, cty-level locaton sensng, and the manual food/exercse loggng wdget. Partcpants had the ablty to turn off the background contextual loggng at any tme and some partcpants dd not provde data for all context attrbutes (e.g., f they dd not have ther Androd calendar populated). For the fnal four weeks of the study, partcpants were free to contnue usng the webstes from the ndvdual sensors but also had access to our wdget, moble webste, and full webste to further explore the connectons between the dfferent aspects of ther wellbeng. They were also nstructed to contnue to call nto the vocemal system or send an e-mal whenever they had a new nsght about ther wellbeng. Partcpants were told to use the system as f they just downloaded the app from the market and that no compensaton would be ted to ther use (or non-use) of the system. At the end of the second month, we conducted fnal ntervews (over the phone n Chcago and n-person n Stockholm) revewng nsghts that partcpants were able to make and general comments on the system tself. Partcpants were able to keep the scale and Ftbt as a thank you for partcpatng n the study and were able to keep the wdget on ther phone f they wanted. Most partcpants chose to keep the wdget.

9 Health Mashups 30:9 In addton to the qualtatve data from the ntervews, vocemals and e-mals, we logged accesses to the webste (both desktop and moble) to better understand the use of the graphs and feeds of sgnfcant observatons. In addton, we also have a log of all data uploaded from the sensors themselves ncludng weght, steps per day, hours sleepng, tmes awoken durng the nght, cty-level locaton per day, calendar free/busy data per day, and any manually logged food and exercse data. Unfortunately, as the wdget was on the home screen of the devce, we have no way of knowng how many tmes each user looked at the wdget f they dd not clck through to the moble webste for addtonal nformaton. After the study, all qualtatve data was analyzed usng a grounded theory based affnty. The tems of analyss represented exact quotes from users (or Englsh translatons for Swedsh partcpants) wth groups and themes formed based on these drect statements. All themes dscussed here have support from multple study partcpants. Quanttatve data from usage logs and the demographc survey were also analyzed usng statstcal tools and wll be dscussed n more detal here Fndngs Use of the System. From the webste logs and ntal survey data, we performed a quanttatve analyss to better understand the use of the system. There was a sgnfcant dfference n the amount of use of the webste between the two countres. Over the second month, when they had access to the applcaton, partcpants n Sweden accessed an average of 70 pages whle partcpants n Chcago only accessed an average of only 10 pages (t(8) = 3.0, p < 0.03). Partcpants n Sweden also walked more than twce as much each day compared to partcpants n Chcago (10792 steps vs steps, t(8) = 3.5, p < 0.01). Other wellbeng data dd not dffer sgnfcantly between the two locatons. Startng weght, total weght lost/ganed, varance n daly weghts, and varance n weekly step counts dd not sgnfcantly dffer between the locatons. Eght of the 10 partcpants lost some amount of weght durng the study, averagng 1.6 kg. There were no sgnfcant dfferences by country, gender, or startng weght. Ths data s nterestng because the Chcago and Stockholm groups used the webste qute dfferently but had smlar outcomes n terms of weght loss. If we had more complete step count and sleep data (see 4.2.3), t would also be nterestng to analyze mprovements n these aspects of wellness across ctes. Overall, the use was less than expected, especally the frequency of manual loggng as descrbed n Therefore, we began to develop ways to mprove user engagement wth the system so that the qualty of the observatons could be ncreased. These deas wll be presented as desgn recommendatons n the followng sectons and the fnal mplementaton of these recommendatons wll be dscussed n Secton 5 on the full system Learnng from Observatons. Despte the lmted data that was provded to create the observatons, partcpants found certan entres n ther feeds to be nterestng and they were able to learn more about themselves through the wdget and moble webste. User S5 was able to pece together two observatons that told her that when she eats more she sleeps more but also that when she sleeps more she exercses more. Thus, for her, eatng more (.e., enough) could lead to healther sleep and thus a desre to be more actve the next day and feel better overall. She found ths qute nterestng. The day of the week observatons were also qute useful to better understand trends over tme. Partcpant C1 understood a feed tem that told her that she tends to gan weght on Mondays: It s absolutely true! Cause on the weekends, lke last Sunday, I went to my mom s house and she made blackberry cobbler and I ate some of t.

10 30:10 F. Bentley et al. C2 lked that the correlatons were made explct n the daly feed: Lke f you eat too much or booze too much your weght s gong to go up. But t s one thng to know t and another to actually see the results of what you dd. Many ndvdual correlatons made sense to users such as C3 who sad: I eat less on days when I walk more, whch I thnk s nterestng. I thnk t means that just when I m more actve I tend to eat less and I thnk t s probably accurate because when I eat more t s probably because I m bored or snackng. She mentoned ths correlaton several other tmes durng the study and t was clear to us that ths was somethng she reflected on perodcally, lkely due to t appearng as a sgnfcant correlaton for much of the study. These nsghts from the observatons came n contrast to nsghts reported durng the frst half of the study when partcpants dd not have the mashup data avalable. Before gettng the wdget, partcpants had nsghts about partcular entres (e.g., a lower step count than expected on a certan day) or about the tme seres of one sensor (e.g., not losng weght as quckly as they had hoped they would). However, no partcpants had nsghts across sensors, days of the week over tme, or wth other aspects of ther context, such as the ones enabled by the system n the second half of the study. Therefore, we see the system as a success n provdng new and deeper ways for people to understand themselves and what contrbutes to ther wellbeng. The scentfc nature of the system also appealed to partcpants. C1 lked that the system s very truthful. It doesn t hde or anythng lke that.... It makes me know that I m not reachng t and that I need to be dong physcal actvty. S2 also lked the objectve data and havng metrcs about hs wellbeng. Overall, most partcpants found nterestng correlatons and enjoyed ths new way to learn more about ther own wellbeng and apprecated the easy-to-understand, natural language presentaton. We saw ths as qute postve for the concept overall, f we could address the large ssues of engagement n the next teraton Lack of Data Rchness. In order for the system to operate well and provde accurate observatons to our users, t s necessary to have as much data as possble from multple nputs on the same day. We then need examples of good, bad, and average days n order to fnd patterns. However, many of our study partcpants dd not use the devces wth ths regularty, especally the manual loggng, makng the overall feeds less detaled and relable. Ths sparseness of data was shown for several sensors as shown n Fgure 2. Partcpants, especally those n Chcago, dd not always wear ther Ftbts all day and sometmes just took them out for tmes of exercse. Across all partcpants, manual loggng dd not occur frequently. Food and Exercse were logged on average only 5 tmes by each partcpant over the 30 days of the second half of the study. Ths s consstent wth prevous work on self-loggng [Burke et al. 2008; Patrck et al. 2009] and a man focus of the desgn mprovements for the full study would be on mprovng ths number. At tmes, some partcpants forgot about the wdget or the sensors. Most of these problems were due to these users placng the wdget on a secondary home screen that was not often dsplayed due to the wdget s sze and ther already customzed home screen setups. Thus, they needed to swpe the screen to fnd the wdget and t was sometmes forgotten. As C2 sad, I m never over on that screen. But f t was smaller then t could be on the man page or the next man page. One way to deal wth ths ssue could have been to buld a smaller wdget wth fewer observatons that could more easly ft wth the other content on the man screen. Because of the lack of data provded, we often could not calculate specfc correlatons for users as only a small handful of days had readngs from partcular combnatons of

11 Health Mashups 30:11 Fg. 2. Average number of data ponts recorded for each sensor n the plot study. The study lasted approxmately 60 days (wth Food, Exercse, and Locaton loggng only the fnal 30 days). Note that some users took ther weght or recorded sleep multple tmes per day. Manually-entered streams of food and exercse were not frequently logged, and ths would become a focus for our desgn teraton. sensors. We realzed that n order to have a system that produced meanngful results, we would need to have a way to engage users to provde more data over tme Contradctory Informaton. Because of the lack of consstent use of each of the sensors, at tmes the system provded contradctory nformaton over the course of a week. A correlaton that mght have been postve one day could swng negatve wth a few extreme data ponts on the other sde n the followng days. Our orgnal goal was to use the four weeks of data from the frst half of the study to ensure that the hstorcal data would be more complete by the tme of the second month. However, for several partcpants some data was only present for a few days (e.g., sleep) or only on partcular days of the week makng daly correlatons or devatons based on days of the week qute varable wth the addton of new data. In hs analyss of persuasve systems, Fogg [2003, p.127] warns that systems that produce questonable data are lkely not to be trusted and that ther value n promotng behavor change wll be reduced. Several of our users notced contradctng feed tems over tme and t led some not to trust the system and the observatons n ther feeds. C3 told us that n the feed you ll have three thngs there and maybe two of the three thngs contradct each other lke n my mnd. So t seems lke because t s so vague t sounds lke t all contradcts each other but maybe f t were a lttle more n the weekly correlaton or somethng lke on Mondays, ths has been the case on every Monday for the last four weeks, that just makes more sense to me than randomly assertng that n general I m eatng more when I walk more. Havng more specfc detals than the vague correlatons was a common theme from our users. We had hoped that the graphs would have provded ths addtonal data, but few users explored them n detal when encounterng a feed tem that they dd not understand perhaps due to a general lack of graph lteracy as noted by Galesc and Garca-Retamero [2011]. S2 found that nformaton s repeatng [n the feed] and I don t really understand how all the nformaton s collected and how the correlatons are computed. For some, ths lack of understandng led to a lack of trust n the data. That observatons often repeated on a daly bass dscouraged frequent use because most of the same

12 30:12 F. Bentley et al. observatons remaned sgnfcant from day to day. Many partcpants reported wantng somethng new Desgn Recommendaton: Need for Remnders. Some partcpants suggested usng the moble phone notfcaton system to alert users when new observatons were avalable. C2 ponted out, Wth the Androd you get lttle notfcatons up top when you get somethng new lke a new vocemal or text. Maybe somethng lke that f there s a new correlaton. C4 agreed: The notfcaton bar would work also. I thnk that would be good too. Somethng vsual that would just be a remnder that you would see. Other partcpants needed remnders for usng the sensors. C4 sad, I ve notced that when I get text messages for remnders for dong thngs lke I ll always do t. So f I was to get lke a text message on a daly bass... n the mornng sayng hey, go on your scale or dd you put your pedometer on, I thnk that would be extremely helpful. Our partcpants have clearly demonstrated the need for remnders and Fogg s early work usng text messagng as trggers for health remnders [Fogg and Allen 2009] should be appled even when there s a wdget and applcaton present wth deeper nteractons. Our system requred motvated users who were nterested n dggng deeper nto data about ther wellbeng. But our partcpants needed extra encouragement and ncludng remnders could have dramatcally changed the ways that they engaged wth the system on a daly bass. Ths observaton s n lne wth the Behavor Model from B.J. Fogg where changng a behavor requres the motvaton, the ablty or knowledge to change, and trggers to remnd the user about t [Fogg 2003]. Whle our users were motvated to mprove ther wellbeng and knew how to do t (more actvty, eatng less, etc.), we were hopng that our observatons would be ths trgger. However, they proved to be too passve especally when the tems were on secondary home screens and users were lackng the explct trggers they needed to engage wth the servce. By addng remnders to mprove loggng frequency, we hoped to mprove the data qualty n the system so that contradctng observatons would become much rarer n the full study. 5. FULL SYSTEM Based on our experences wth the plot study, we made several enhancements to the system for the larger study. The two prmary weaknesses n the plot were the nfrequent use of manual loggng and the contradctory nature of some of the observatons over tme as new data ponts were added. We hoped that by makng changes to the system we could ncrease engagement, and thus the qualty of the data and observatons that were presented to users. The frst major change ncluded addng remnders, n the form of status bar notfcatons on the phone. The frst tme that the Mashups applcaton was opened users were presented wth a lst of the data that they could manually log (e.g., Food, Mood, Pan) and were able to set remnders that would appear each day at the same tme. These tmes could be modfed later by gong nto the applcaton s settngs screen. When the tme of a remnder came due, a notfcaton was slently placed nto the status bar on the phone. We dd not want the system to be nterruptng, so no sound or vbraton was used. However, the status lght on the phone would blnk to catch the user s attenton on the next occason that they had tme to nteract wth the phone. We hoped that ths would be enough to catch users attenton and encourage them to log each day when they mght have otherwse forgotten. Ths was confrmed by the data as dscussed n Secton 7. We also ncluded status bar notfcatons that appeared whenever a new

13 Health Mashups 30:13 Fg. 3. The moble applcaton for the man study. (3A) The user selects types of data to collect and sets the tme for the remnder to appear n the status bar. (3B) Recevng a remnder notfcaton for food loggng, (3C) Clckng on the notfcaton led to the smple food-loggng screen n the mddle. (3D) The observatons could be revewed at anytme but the system also added a remnder when a new observaton has been found, below the observatons s a lst of each sensor wth sparklnes used to see recent changes. (3E) Clckng on a sensor wll brng more detals regardng ths partcular nput, n ths case the mood trend and related observatons. observaton was found for a user. These notfcatons led straght to the observatons lst when clcked. The second change nvolved the addton of new data streams to the system to better reflect aspects of context and wellbeng that would be most mportant to users based on the plot study and based on dscussons wth medcal and nsurance professonals. We added the ablty to manually log Mood and Pan as well as added Weather data as an addtonal contextual sensor based on the current cty-level locaton. Ths brought the total number of data streams to nne as shown n Table I. The Ftbt and WThngs scale could automatcally upload step count, weght, and sleep data. The phone automatcally captured locaton, weather, and calendar free/busy data. And users could manually log Food, Mood, and Pan each day. Loggng mood conssted of four 7-pont slders correspondng to standard measures of mood: happy/sad, tred/awake, unwell/well, and tense/relaxed. Loggng food conssted of three 7-pont slders of eatng a lttle/a lot, healthy/unhealthy, and eatng mostly at home/mostly out. These measures were created to be very quck to log n just a second or two, whle stll capturng a varety of the aspects of eatng that could be nfluenced n varous ways by one s context. We also wanted to make t easer for users to access all observatons about a partcular sensor, so that they could better understand what was affectng each specfc area of ther lfe. Partcpants n the plot often had specfc goals for behavor change and we wanted to support someone easly understandng what affected ther sleep or weght wthout havng to wade through other observatons that were unrelated. The updated applcaton provded a lst of each sensor, a sparklne for the recent changes n that sensor s value, and the date t was last logged wth the correspondng value as shown n Fgure 3. Clckng on a sensor tem allowed the user to see all observatons related to that sensor as well as a bgger graph of that sensor s recent values Study Method Sxty dverse partcpants were recruted usng a professonal recrutng agency to use the system n ther daly lves for 90 days. Partcpants represented a dverse array of ages, occupatons, educaton levels, and general wellbeng. Twenty partcpants lved n Chcago or surroundng suburbs whle 40 were from the Atlanta regon. We wanted a broad set of partcpants n order to understand how awareness of one s wellbeng

14 30:14 F. Bentley et al. Fg. 4. The web nterface that was shown to users at the end of the study. It provded a clearer aggregaton of the observatons than the more constraned nterface on the moble devce. could be useful for a varety of needs and wth varyng techncal lteracy. Some partcpants lved n subsdzed housng whle others lved n large suburban homes. Some had very low educaton levels whle others had advanced degrees n scentfc felds. Some were up to 150 pounds (68 kg) overweght whle others were at deal weghts or even underweght. Importantly, for comparson wth the plot study, we used the same recrutng agency and screener for the full study. Researchers met wth partcpants n ther homes at the start of the study and set up the WThngs scale and Ftbt as well as nstalled the Mashups applcaton onto the partcpants prmary phones. Partcpants were also gven an ntal questonnare about ther wellbeng and goals to complete. The fnal nstructons to the partcpants (as n the plot) ncluded the request that they were to use ths applcaton lke any other applcaton they had downloaded from the market and that use was not requred to partcpate n the study. After 21 days, partcpants were sent a lnk to another onlne questonnare. Ths covered open-ended questons relatng to ther use of the applcaton, observatons that they have found to be useful or useless, and any behavor changes they mght have made based on what they observed n the applcaton. In addton, they completed the WHO-5 wellbeng [WHO 2011] questonnare that was also ncluded n the ntal survey. After 90 days, a fnal questonnare lnk was dstrbuted wth smlar questons to the 21-day survey. Addtonally, partcpants were sent a lnk to a web dashboard vew of the observatons that aggregated fndngs n an easer-to-read way (see Fgure 4). A fnal sem-structured ntervew was conducted to elaborate on statements from the fnal questonnare and to ask addtonal questons about the web dashboard.

15 Health Mashups 30:15 Partcpants who completed the study (completed the 3-week and 3-month questonnares and fnal phone ntervew) could keep the Ftbt and WThngs scale. Partcpants were also compensated wth small gft checks to retal stores for submttng the questonnares. No compensaton was ted to the use of the system, and ths was made clear to all partcpants. In addton to the mostly qualtatve data from the questonnares and ntervews, the moble applcaton was nstrumented to collect usage data of each screen vew n the applcaton and ths was reported to our server. Therefore, we were able to track when users were vewng ther observatons, manually loggng, or vewng more complex features of the applcaton such as the graphs or sensor-specfc vews. Quanttatve analyss was performed to dentfy dfferences n use or wellbeng outcome based on age, gender, cty, educatonal level, and other attrbutes. All qualtatve data was combned nto a large note affnty to dentfy themes across partcpants. Both the quanttatve and qualtatve fndngs wll be dscussed n detal n the next secton. 6. FINDINGS In contrast to the plot, our partcpants demonstrated strong sustaned engagement wth the servce. They consstently logged food and mood at much hgher rates than n the plot study, manly due to the remnders that appeared on the phone. Due to ths added data, the system was able to make more accurate and less contradctory observatons and partcpants were able to both gan a better understandng about ther wellbeng as well as make sgnfcant targeted behavor changes based on the observatons that the system made Sustaned Use Our partcpants showed a much greater engagement wth the system than was observed n our plot study. Ths engagement covered all aspects of the system and dd not show sgnfcant declne throughout the 90 days of the study, as shown n Fgure 5. The dramatc ncrease n loggng was the key to contnung engagement wth the rest of the system, and the remnders lkely contrbuted to ths ncrease. Overall use can be seen n Fgure 6 where each column represents a user and each dash represents an nteracton wth the applcaton. It can be seen that most users that perssted beyond the frst two weeks used the system qute regularly for the remander of the 90 days. Ths s qute encouragng as other self-loggng systems often see a sharp declne n use after days [Burke et al. 2008; Patrck et al. 2009]. Use was consstent wth regards to all demographcs of our partcpants. There were no statstcal dfferences n use between men and women, people n Atlanta and Chcago, by educaton level, or by age. Ths s qute encouragng as all ages and demographcs were smlarly engaged at very hgh levels, ndcatng that the presentaton of these complex statstcal relatonshps n natural language s a useful and understandable way to present wellbeng data to a broad range of people. In our plot study, the manual food loggng was rarely used. In the frst week, a few users tred t out, but after day seven, no more than two out of ten users logged food on the same day. After day 12, only one user sporadcally logged food for the rest of the month as shown n Fgure 7. Ths left us wth an overall food loggng rate of 12%. Ths contrasts wth the larger study wth remnders enabled where 63% of users logged food each day n the frst month. Ths percentage stayed consstent throughout the month, showng the power of smple remnders to promote sustaned loggng. Loggng behavor was sustaned beyond the frst month as well wth numbers between 50 70% each day durng the second and thrd months of the tral. The average loggng frequency for each data type can be seen n Fgure 8.

16 30:16 F. Bentley et al. Fg. 5. Usage of each feature of the moble applcaton over tme, and overall. In the top fgure, each user s a column and tme runs down from day 0 to day 90 n each secton for a feature of the system. The bottom fgure shows the raw count of our users nteractons wth each feature durng the 90 days of the study. Our users reported lkng the remnders as they recognzed how easy t would be to forget to log. A28 stated that the best feature of the app was: the remnder feature because half the tme I forget to logn my nformaton. Also, A37 told us, I lke that t remnds me to add nfo n my notfcaton bar. I would surely forget otherwse. C10 agrees: The onlne remnders are awesome, makes t so much easer to keep track of stuff because I can get absentmnded and lose track of what to do. The questons

17 Health Mashups 30:17 Fg. 6. Use over tme for each user. Each column represents a user and each dash s an nteracton wth the moble applcaton. Tme follows from the top down for 90 days. The bottom graph shows the range n total number of screen vews wthn the applcaton across partcpants over the study perod. Fg. 7. The rate of food loggng behavor per day ncreased by more than 5 n the full study (wth remnders). are easy to answer and make for a quck Q and A. Ths brngs up the mportant pont that the acton requred to enter data after clckng on the remnder should be as quck as possble. In our case, just a few 7-pont scales, n comparson to other foodloggng solutons that requre complex loggng of every food tem eaten that can take ten mnutes or more per day. C20 spoke of the power of loggng to ncrease reflecton each day. I lke that t asks me to log my food and my mood daly. It makes me more conscous of how I m feelng

18 30:18 F. Bentley et al. Fg. 8. Frequency of loggng each data type n the system over the 90 days of the tral. Sleep and pan were the least logged, due to the cumbersome nature of usng the FtBt to log sleep as well as the fact that most partcpants dd not have recurrng pan. The automatc context streams of Calendar, Locaton, and Weather were qute bmodal wth many partcpants not usng these sensors at all, and others who kept them on daly. Overall, engagement s much hgher than what was observed n the plot system (Fgure 2). as well as what I m eatng throughout the day. The more often someone logs, the more opportuntes for reflecton they get. The remnders also encouraged our users to nteract wth the observatons that were created about ther overall wellbeng by clckng on the notfcaton to see newly sgnfcant observatons. Vewng the full lst of observatons ncreased sgnfcantly from the plot to the man study wth ths feature added. Many partcpants commented that the Mashups applcaton became a part of ther daly routne. User 4 from Chcago told us, I learned that ths s an app that s useful enough for me to use on a daly bass lke Facebook. Most apps on my phone I don t use on a daly bass. User 16 from Chcago had prevously been n a health study from Northwestern where she was recordng detaled food and health nformaton nto a smartphone. She descrbed how tme consumng ths was and how quck t was to nteract wth the Mashups system each day. She noted that Health Mashups was much easer to stck wth. She also apprecated vewng the long-term trends n the Mashups system and ths kept her comng back week after week to learn more about herself. Overall, the notfcatons n the system served to contnually engage users and remnd them to enter data nto the system. Ths data then provded more accurate observatons and the notfcatons about new observatons brought users back to the system yet agan. Ths ncreased nteracton greatly mproved both the amount and qualty of data n the system as dscussed n Secton Qualty of Observatons The ncreased loggng not only allowed the users to reflect more on ther own, but provded better data to the analyss engne and thus more accurate observatons were presented to users, combatng the problem of contradctory nformaton seen n the plot. The more days that have data ponts from multple data streams for a gven user, the more accurate correlatons across sensor streams we can provde. For example, n

19 Health Mashups 30:19 Table II. 25th Percentle, Medan, and 75th Percentle of Data Ponts per User Between Sensors. These Numbers Represent the Number of Days When a User Logged Both the Sensor from the Row and Column of Each Entry on the Same Day, Thus Creatng a Data Pont for the Correlaton Food Mood Pan Steps Weght Sleep Calendar Locaton Mood 26/48/70 Pan 0/6/21 0/6/22 Steps 11/30/64 11/36/73 0/2/21 Weght 4/18/37 3/18/39 0//1/9 6/18/43 Sleep 1/7/34 1/9/33 0/0/6 1/16/49 0/5/17 Calendar 0/24/51 0/22/56 0/0/14 0/22/73 0/6/23 0/1/24 Locaton 2/30/58 1/34/65 0/0/13 0/36/62 0/13/32 0/4/27 0/33/64 Weather 2/30/56 1/31/64 0/0/13 0/36/61 0/12/31 0/4/26 0/17/62 15/66/77 the month of our plot study, users averaged only a sngle day that had both Food and Weght logged, whle n the full study partcpants averaged 9 such days n the frst month and 21.3 over the full 90 days. For the Steps and Food combnaton, users averaged 2.6 days n the plot study, 14.8 days n the frst month of the full study, and 37.9 days n the full three months. Full results can be seen n Table II. Ths ncreased amount of data lead drectly to the ablty to perform a better statstcal analyss and fnd sgnfcant correlatons between the data streams. Ths was not somethng that could be relably calculated between the manually logged sensors n the plot study and the remnders were the drvng force to encourage ths addtonal loggng. We also were nterested n explorng how unque the sgnfcant observatons would be across partcpants. When we began ths work, our hypothess was that the ways n whch context nfluences wellbeng would be hghly ndvdually dfferent. We assumed that some people mght walk farther on warm days whle others mght walk less. Or some people mght have less pan when they walk more whle others mght have more. In order to see f ths was the case, we analyzed all 450 statstcally sgnfcant sensor-to-sensor correlatons for the 60 partcpants n our study. These observatons were qute stable over tme wth an average duraton of 22 consecutve days where an observaton was statstcally sgnfcant. Ths s qute encouragng, as once an observaton crosses the threshold of sgnfcance, t typcally remans that way for some as new data s added. Even f a user tres to target that behavor for change (e.g., decdng to walk more on hotter days after the system nformed them that they dd not walk as much on these days), t should take several weeks for ths to lose ts statstcal sgnfcance gven the need for a good number of new data ponts to be added to weaken the strong correlaton. Ths s supported by the 22-day average duraton of the observatons. Future work can explore ways to vsually demonstrate how behavor changes are weakenng correlatons over tme or ways to develop goals to remove partcular correlatons that are negatve to one s wellbeng. In order to better understand the types of correlatons that were most common across our user populaton, we looked at the correlaton coeffcents for each combnaton of sensors n each sgnfcant correlaton that exsted. The results can be seen n Fgure 9. It s nterestng to note that nearly all pars of sensors had both postve and negatve correlatons for dfferent partcpants. Ths confrmed our hypothess that the nteractons between wellbeng and context are specfc to ndvduals. In fact, the only correlaton that was unversally postvely correlated was the correlaton between amount of food eaten and mood. The vast majorty of dfferng correlaton drectons valdates the premse of the system, that s, ndvduals need to capture ther own wellbeng data and context to learn about the specfc ways that ther wellbeng s mpacted by the actvtes n ther lves. No smple set of unversal gudelnes exsts.

20 30:20 F. Bentley et al. Fg. 9. The mult-sensor correlatons that were found to be sgnfcant after 90 days plotted aganst ther correlaton coeffcents. Every par of sensors (except for amount of food and mood) that produced sgnfcant results has both postve and negatve correlatons. Each band s a specfc observaton for a user n the study. The darkness of the band represents the p-value for the correlaton (lght = 0.05, dark < 0.01) Increased Self-Understandng Through vewng the observatons that were dsplayed n the system, our partcpants were able to gan a deeper understandng of ther wellbeng and the aspects that affected areas of ther lves as dverse as mood, sleep, food ntake, daly step count, and weght. Partcpants could see relatonshps that they had not expected and became a bt more ntrospectve about ther own wellbeng. A8 broadened her vew of partcular health ssues such as weght loss: It made me realze that I need to stop lookng at health ssues as one entty. A26 learned that everythng s related. Mood, food, weather...all those elements are a factor n my well beng. Partcpants were able to learn ther true actvty and sleep patterns from the data on the sensors themselves and vew trendlnes that were dsplayed n the system over tme. On top of ths, they learned correlatons based on actual data that represented measured fact nstead of just ther pre-exstng hunches and speculaton about how varous aspects of wellbeng and context were related. For some, these observatons served to confrm suspcons that they had, whle others learned totally new nsghts about ther wellbeng. A20 told us, I do fnd [the observatons] useful because t remnds me what my workout habts and eatng habts really are lke nstead of me guessng. Takes a lot of guesswork out and actually makes me reflect on my day. A34 ddn t know that I was not as actve as I thought I was. On the days when I ddn t run or walk I realzed that I ddn t even cover a mle a day and was horrfed! Specfc observatons helped to teach partcpants new patterns about ther lves beyond just the sensor readngs. C18 found that he consstently was less actve on a certan day of the week. A37 learned somethng that was opposte to her expectatons. She was surprsed that for her, walkng more n a day reduced her recurrng pan. She thought t would be the other way around and prevously was reducng her actvty. The nfo on when I walk more I m n less pan really helped wth my back. Made me realze I should exercse more and t dramatcally helps wth my pan levels.