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“You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions

“You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple... Background: Consumer health information technologies (HIT) that encourage self-tracking, such as diet and fitness tracking apps and disease journals, are attracting widespread interest among technology-oriented consumers (such as “quantified self” advocates), entrepreneurs, and the health care industry. Such electronic technologies could potentially benefit the growing population of patients with multiple chronic conditions (MCC). However, MCC is predominantly a condition of the elderly and disproportionately affects the less affluent, so it also seems possible that the barriers to use of consumer HIT would be particularly severe for this patient population. Objective: Our aim was to explore the perspectives of individuals with MCC using a semistructured interview study. Our research questions were (1) How do individuals with MCC track their own health and medical data? and (2) How do patients and providers perceive and use patient-tracked data? Methods: We used semistructured interviews with patients with multiple chronic diseases and providers with experience caring for such patients, as well as participation in a diabetes education group to triangulate emerging themes. Data were analyzed using grounded theory and thematic analysis. Recruitment and analysis took place iteratively until thematic saturation was reached. Results: Interviews were conducted with 22 patients and 7 health care providers. The patients had an average of 3.5 chronic conditions, including type 2 diabetes, heart disease, chronic pain, and depression, and had regular relationships with an average of 5 providers. Four major themes arose from the interviews: (1) tracking this data feels like work for many patients, (2) personal medical data for individuals with chronic conditions are not simply objective facts, but instead provoke strong positive and negative emotions, value judgments, and diverse interpretations, (3) patients track for different purposes, ranging from sense-making to self-management to reporting to the doctor, and (4) patients often notice that physicians trust technologically measured data such as lab reports over patients’ self-tracked data. Conclusions: Developers of consumer health information technologies for data tracking (such as diet and exercise apps or blood glucose logs) often assume patients have unlimited enthusiasm for tracking their own health data via technology. However, our findings potentially explain relatively low adoption of consumer HIT, as they suggest that patients with multiple chronic illnesses consider it work to track their own data, that the data can be emotionally charged, and that they may perceive that providers do not welcome it. Similar themes have been found in some individual chronic diseases but appeared more complex because patients http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 1 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al often encountered “illness work” connected to multiple diseases simultaneously and frequently faced additional challenges from aging or difficult comorbidities such as chronic pain, depression, and anxiety. We suggest that to make a public health impact, consumer HIT developers should engage creatively with these pragmatic and emotional issues to reach an audience that is broader than technologically sophisticated early adopters. Novel technologies are likely to be successful only if they clearly reduce patient inconvenience and burden, helping them to accomplish their “illness work” more efficiently and effectively. (J Med Internet Res 2015;17(8):e202) doi: 10.2196/jmir.4209 KEYWORDS medical informatics; consumer health information; health knowledge, attitudes, practices; self-care; chronic disease coordinate more different therapeutic regimens than those with Introduction single diseases [17]. Each additional chronic condition places the individual at higher risk of adverse drug events, Background out-of-pocket expenses, impaired functional status, Consumer health information technology (HIT) is exploding in hospitalization, and mortality [17]. It is estimated that two-thirds popularity, attracting the attention of technology-oriented of health care spending is focused on patients with MCC [17]. consumers, patients, caregivers, and entrepreneurs. Technologies These patients are in need of improved strategies and such as disease management apps and “quantified self” tools technologies to support health and medical care, creating a [1-3] offer the potential to help patients track personal data, number of opportunities that could potentially be filled with learn about their health, and manage chronic care needs [4-7]. health IT, yet the barriers to technology adoption might be Consumer HIT appears poised to help inform, motivate, and particularly problematic for these patients as well. MCC engage patients, all of which are known to improve management disproportionately affects the elderly and the less affluent. The skills and health outcomes [5-8]. prevalence of MCC rises sharply with age, affecting 34% of However, it is not yet known whether such technologies will those aged 45-64 and 62% of those age 65 and over [19]. diffuse broadly beyond tech-savvy early adopters such as Furthermore, the prevalence of MCC is highest among the “quantified self” advocates, and whether the technologies would lowest income brackets, affecting nearly 51% of seniors who produce benefits for people with complex medical conditions. live at or below the federal poverty level but only 39% of seniors To date, the measured impact of consumer HIT is still limited. living at four times the poverty level [18]. Computerized interventions for diabetes self-management have As an initial step to exploring the perspectives of individuals shown only limited efficacy [9,10]. In practice, effects have with multiple chronic conditions, with the goal of understanding generally been limited as a result of low adoption and usage. potential applications of consumer HIT and barriers to its use, One in 5 smartphone users has downloaded a health app [11], we conducted a semistructured interview study. This paper yet most apps are abandoned after a few uses [12]. Studies of focuses on tracking or keeping diaries of personal data, a task the effectiveness of apps and websites to promote health that we will refer to as “personal health information tracking”. outcomes (such as a recent study of a phone app to assist in We focused on personal health information tracking because weight loss [13] or a self-management Web community for (1) it has been recommended for a variety of chronic conditions, diabetes [10]) frequently find that participants stop using the and (2) it is a task potentially supported by consumer health IT. technology after a short period of time. Having a chronic Self-monitoring tasks that have been promoted under different condition increases the chances that a patient will use certain circumstances include blood glucose self-monitoring for certain forms of consumer HIT on average [2,11]. But this increased patients with type 1 and type 2 diabetes [20,21], measuring likelihood is often offset by other sociodemographic factors blood pressure in hypertension and heart disease [22], keeping that decrease the likelihood of using technology. Of particular diet logs or food diaries for weight loss or digestive diseases concern from a public health standpoint, the use of consumer [23], and self-monitoring medication adherence and side effects HIT remains lowest among the groups that might be most likely [24]. Patients also often receive the recommendation that they to benefit from additional forms of low-cost disease management should check and be able to report certain laboratory values, support: people who are elderly, less educated, or less affluent such as CD4 count in human immunodeficiency virus (HIV) or [2,11,14]. These disparities in uptake, as well as the low rate of hemoglobin A1c in diabetes. We therefore considered personal sustained use among adopters, suggest mismatches between health information tracking to be a task that was likely to be current consumer HIT and the goals, desires, or capabilities of encountered by patients with MCC, but we did not a priori many patients [15,16]. assume a position on whether patients should self-track or A population with particularly complex and ongoing health whether it was likely to benefit them. Rather, our research needs is the 90 million Americans who have multiple chronic questions were (1) How do individuals with MCC perform conditions (MCC) [17]. Although any combination of chronic medical data tracking? and (2) How do patients and providers conditions qualifies as MCC, the most common combinations perceive and use patient-tracked data? We asked the questions are diabetes plus hypertension, heart disease plus hypertension, broadly to encompass any sort of tool the patients were currently and cancer plus hypertension [18]. Patients with MCC using, including electronic technologies, paper, or memory. experience the challenges associated with living with chronic disease and also typically consult more different doctors and http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 2 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al providers with experience providing care for patients with MCC. Theoretical Framework: Illness Work and Personal We adopted the Department of Health and Human Services Health Information Management definition of chronic conditions as conditions that last a year or This project was conducted from a human factors perspective more and that either require ongoing medical attention or limit influenced by the sociology of illness. This perspective activities of daily living [17]. Patient participants were recruited recognizes that patients’ management of their health comprises from outpatient clinics in internal medicine and endocrinology a wide variety of different activities both inside and outside the and from the patient information library, using both promotional medical encounter: taking medicines, refilling prescriptions, flyers and individual referrals from physicians and nurse buying and cooking food, exercising or doing physical therapy, practitioners. One researcher (JSA) also attended six 90-minute researching health issues, coping with medical crises, finding sessions of a diabetes education support group as a means of doctors and dentists, organizing and traveling to medical triangulating emerging themes. We chose the diabetes education appointments, and keeping records. As these are all effortful, group because many of the study participants had type 2 directed activities to attain goals, they may be conceptualized diabetes. as work [25-27]. Settings Corbin and Strauss identified “illness work” as activities directly Weill Cornell Physicians is a multispecialty academic medical involved with managing an illness, such as following medication practice in Manhattan, with a mix of privately insured, Medicaid, regimens and using technologies such as glucose meters or sleep and Medicare patients. New York-Presbyterian Hospital is the apnea machines [25,26]. Yet even in illness, “everyday life largest academic medical center in Manhattan. The Institute for work” of shopping, paying bills, nurturing relationships, and Family Health is a federally qualified health center with 18 sites managing a household continues [25,26]. “Articulation work” in and around New York City, providing safety net primary is the planning, coordinating, and managing that allows people care to patients regardless of insurance status. to complete all their other work [25,26]. Those components of illness and articulation work that involve Interview Methods acquiring and managing information can be called personal The researchers developed a semistructured interview instrument health information management [27-30]. A growing body of centered on three topics: personal health information tracking, research on personal health information management has personal health information management, and searching for identified tasks including tracking health events, obtaining health-related information. The current manuscript focuses on information, and organizing information [27]; creating personal the first of these. The first author conducted interviews in histories, making decisions, planning, and structuring activities person, using offices and conference rooms convenient to the (eg, creating medication reminders) [28]; and transferring clinics where patients were recruited. Interviews were audio personal data and records to the physician [31]. In the current recorded and professionally transcribed. The interviewer also project, we focus on the subset of personal health information took field notes, collected samples of artifacts and documents management involved in monitoring and logging personal data for patients such as educational brochures, and photographed (such as symptoms or laboratory values), sometimes called other artifacts or documents such as log sheets used to record personal health information tracking [32]. glucose values. Much of the recent work in personal health information tracking Analysis Methods and management has focused on generally healthy individuals No existing theoretical framework appeared to be appropriate and families [27,28,31,32], on patients with cancer [33-36], or to these data, and therefore we applied methods to develop (in support of information technology design) on meaning inductively from the data. Although this family of computer-literate participants [31]. approaches is sometimes known in the sociology literature as In this project, we sought to apply the insights from this previous development of grounded theory [37], we adopt the newer term work while exploring the perspectives of an economically “inductive thematic analysis” to reflect the fact that our end diverse sample of patients with MCC in more depth. In order product is a series of interrelated themes rather than a fully to develop or adapt technologies for these patients, it is essential formed theory [38]. Qualitative analysis was conducted to understand practices and perspectives of the potential users collaboratively by our multidisciplinary team, which included and the attributes of the tasks they seek to perform, as well as individuals with training in journalism, public health, the social and physical environments in which they will be informatics, psychology, human factors, nursing, and diabetes performing these tasks [15]. Poor fit between individuals, tasks, education. Two of the researchers (HOW and EW) also brought and technologies is likely to be one of the reasons that personal experience of long-standing chronic disease. The self-tracking technologies have not yet spread widely within preliminary version of the codebook was developed by 2 of the populations with multiple diseases. researchers in reading the first three transcripts and was iteratively refined over the coding process. Each transcript and Methods photograph was reviewed by at least 2 team members (the first author and one other team member), who independently coded Participants the transcript and then met to reach consensus on it. For individual interviews, we recruited purposive samples of We followed a staged and iterative approach, first identifying adult English-speaking patients with MCC, and of medical preliminary codes through repeated reading and review of the http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 3 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al data, then identifying relationships between codes and groupings Sjogren syndrome, and cirrhosis. Many described themselves of codes, and finally identifying and refining larger underlying as overweight but none as obese. In addition to their chronic themes. Over the analysis, 47 open codes were developed. These conditions, patients also discussed a wide variety of recently were linked into 6 broad groups: (1) resources, skills, and factors experienced urgent conditions, including diverticulitis, flu, patients need for disease management, (2) the health care system appendicitis, bee stings, and physical injuries. Participants and its components, (3) thinking, feeling, and experiencing sometimes mentioned taking drugs that implied other chronic disease and health, (4) medical data and medical records, (5) conditions that they did not explicitly list: examples included evaluative judgments, and (6) attributions of responsibility. In antidepressants, blood pressure medications, lipid-lowering the final stage, the themes presented in the results section were medications, drugs for prostatic hyperplasia, and anticoagulants. developed. Many of the patients with type 2 diabetes were taking insulin one or more times a day, as was the individual with type 1 To improve internal validity, we conducted member checking diabetes. [39] in two ways. First, several of the emergent groups and themes were presented to new informants during interviews for Half of patients were men and half were women; a third (n=7) their feedback. Second, the resulting themes were presented at were black. Ages ranged from 37-89 (mean 64.1; median 66). a meeting of the diabetes education group, whose members About two-thirds (n=15) were not currently married. Just over validated the themes while also providing additional feedback a third (n=8) used English as a second language. One third (n=7) and nuanced interpretation. were covered by Medicare (US public insurance for those over age 65); one third (n=7) by Medicaid (US public insurance for Analysis and recruitment were conducted simultaneously until those with low income); and the remainder (n=8) by commercial saturation was achieved (ie, no new concepts were arising from insurance. new interviews) [40]. Multiple chronic conditions placed heavy and sometimes This study was approved by the Institutional Review Boards of competing demands on patients. For example, one patient with Weill Cornell Medical College and the Institute for Family diabetes recognized that his morning toast caused increases in Health. All participants gave written informed consent. Members his blood glucose, but on balance had decided not to stop eating of the diabetes education group provided oral consent. toast because his morning medications for other conditions had to be taken with food. Several patients with diabetes or heart Results disease recognized that exercise might help but were prevented because of chronic pain or disability from injury. Patients taking Participants anticoagulants encountered challenges when scheduling surgery Interviews were conducted with 22 patients and 7 health care for other conditions. providers. An additional 3 patient interviews were excluded The diabetes education group was attended by an average of 5 from analysis because the interviewees did not have multiple patients each session (range 4-9). Most patient education group chronic conditions. attendees had type 2 diabetes but a minority had type 1 diabetes The included patients reported having an average of 3.5 chronic or prediabetes. conditions (SD 1.5). The most common conditions mentioned The health care providers were 2 nurse practitioners, 2 internists, were type 2 diabetes, hypertension, heart disease, chronic pain, 2 family medicine physicians, and an emergency medicine and depression. Other conditions included asthma, HIV, hepatitis physician. C, thyroid disorders, rheumatoid arthritis, glaucoma, cataracts, and sleep apnea. Two individuals were in follow-up after cancer Major themes pertaining to personal health information tracking treatment. Conditions reported by only one patient each included are summarized in Table 1 and presented in detail in the results type 1 diabetes, fibromyalgia, post-polio syndrome, sarcoidosis, section. Table 1. Major themes in personal health information tracking. Themes Summary Representative quotes 1. Personal data can carry strong Data are not merely objective facts but prompt strong positive “You get reminded you’re a sick person” and emotional and moral implications and negative emotions as well as value judgments. “I’m not a good patient”. 2. Multiple purposes and uses for Patients use data for a variety of purposes, ranging from active “I’ll [check] it if I’m feeling lightheaded”. personal data self-management to making sense of their condition to report- ing to the doctor. 3. (Un)reliability of personally Patients often notice that physicians do not trust their self- “[The doctors] looked at [my logs] very super- tracked data tracked data. ficially…they seem to rely on your A1c num- bers”. 4. Tracking feels like work Tracking is time-consuming and sometimes emotionally “It’s too cumbersome for me”. draining. http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 4 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al self-monitoring. “I’m tired of sticking myself,” another said. Overview Some patients with diabetes said they were frustrated to see Most patients paid attention to laboratory findings provided by their blood glucose values occasionally spike without a clear their doctors, and a few kept records of selected values. For reason, undermining their confidence that they understood and example, a woman with anemia created a table to track her blood could manage their disease. test results over time, 2 patients with HIV kept records of their CD4 count values over time, and many patients checked on The Moral Valence of Medical Data their cholesterol regularly. Patients and providers frequently described the data with highly However, fewer than half regularly tracked data by self-testing judgmental language, including terms suggesting moral or recording daily activities. The most common example of transgression. For example, one explained a high blood glucose self-tracking was patients with diabetes monitoring their blood value because “I cheated and I had some McDonald’s”. glucose. Among the 16 patients with type 1 or type 2 diabetes, Conversely, patients could feel extremely happy and proud 11 mentioned self-monitoring blood glucose in some fashion when their values were good. Several of the health care (some were fairly regular, some checked values occasionally, providers said it was better to use nonjudgmental language such and some said they used to monitor regularly but had stopped). as “high/low” or “target/nontarget” because patients “get Other examples of tracking mentioned by one or more discouraged because they think they’re being graded or judged”. participants included recording weight or blood pressure (n=7), Yet in the interviews, many providers used more evaluative tracking daily medication administration (n=3), keeping food language such as “good/bad” and “better/worse”. A patient who diaries (n=2, in one case to investigate suspected lactose had altered his diet and was able to lower his doses of intolerance), collecting laboratory reports to manually compare hypertension and hyperlipidemia drugs said he felt satisfied trends over time (n=4), and recording potential side effects with when his doctors said, “Okay, we’re happy with you”. a new medication (n=2). This sort of tracking was conducted The Moral Valence of Tracking on paper or electronically on a spreadsheet, or in one case on a There was also a “good/bad patient” aspect to tracking itself. paper calendar. All the patients interviewed who monitored People with diabetes frequently called themselves a “bad blood glucose used monitors that tracked data electronically. patient” or “not a good patient” when they did not monitor blood In addition, some kept handwritten blood glucose logs. The glucose. One participant explained the fact that she did not track numbers in parentheses above (n=) are provided for perspective, any of her health indicators (including diet and exercise) by but these data were collected through open-ended interview calling herself “lazy”. Although providers most often expressed questions rather than closed-ended survey methods, so the frustration about lack of monitoring, some occasionally interviews may not have captured every instance of tracking. perceived monitoring as excessive. Patients who tracked data Many of the patients older than 65 and most Medicaid patients very diligently (eg, detailed exercise logs, which clinicians saw did not use computers regularly or at all, and many did not have as having little clinical relevance) were sometimes referred to smartphones. as “obsessive and compulsive” or “fastidious”. Theme 1: Personal Data Can Carry Strong Emotional My Interpretation of My Data and Moral Implications Although in some cases patients and physicians were in close agreement about what data values were “good” or “bad”, other Overview patients preferred to interpret their results in light of their own Indicators such as blood glucose, weight, and lab values were unique histories or symptoms. For example, several patients not discussed as value-free facts but instead carried strong with diabetes said that they aimed for a blood glucose level or emotional and evaluative connotations. People recognized hemoglobin A1c that was appropriate “for me”. In some cases, tracking as work, judged themselves as “good” or “bad” for these were values that made them feel well, or values that were their data and their diligence in collecting it, and noted that data high enough to minimize the risk of hypoglycemia. In other should be considered within the patient’s personal context. cases, patients wanted their personal history to be taken into Negative Aspects of Illness account in interpreting data. For example, a person with a history of obesity took pride in the number of dress sizes she had gone Medical data often reminded patients of the negative aspects of down, rather than aiming for a particular target weight. One their illness. An individual who did not monitor her blood provider told an anecdote about a patient who had brought her glucose regularly said her values were “depressing”, and another hemoglobin A1c from 13% to below 8% with diet and said they made her “scared”. Discussing tracking sometimes medication. When urged to continue lowering it, the patient raised feelings of anger or injustice not only about the tracking said, “I don’t want to be a poster child for perfect diabetes”. but also about having chronic disease. “I hate to be focused on The doctor recalled saying, “Actually, you’re right. This is good my health in every friggin’ second of the day...I don’t want to for you...I should’ve been jumping up and down because that’s live like that every day”. A patient with HIV, hypertension, and really great”. other chronic illnesses said he avoided looking at his regular test results: “I don’t ask about no numbers. If anything is messing up, then [my doctor] tells me”. The physical experience could also be unpleasant. “Poking my finger, that was irritating to me,” said one person who had abandoned blood glucose http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 5 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al headaches. However, some patients described frustration (or Theme 2: Multiple Purposes and Uses for Personal even abandoning tracking altogether) after failing to see Data connections between their data values and their behavior. Overview Tracking for the Doctor Not all patients closely monitored their own data values. Patients A few patients appeared to perceive self-monitoring as who did track their own data through either self-monitoring or something done not for their own use but partly or largely to laboratory testing described a variety of purposes, which create records for the doctor. A few seemed confused that depended on aspects of their disease and on their own experience doctors rarely reviewed their logs. “They don’t monitor that of their disease. They might use their tracked data for real-time part of it, I don’t know why”. decision making, for medium-term self-assessment, or for making sense of various elements of data, such as physical Theme 3: (Un)reliability of Personally Tracked Data symptoms. Providers often perceived patient-recorded data as unreliable. Tracking for Action The lack of confidence was attributed to perceived lack of diligence, moral valence of the data (with patients unwilling to Some experienced patients with diabetes monitored blood “admit” undesirable numbers), and fear of consequences. The glucose multiple times per day as “working data” [30] that they most striking example, told by a provider, was a woman who would use immediately to adjust their diet or their medication. faked her daughter’s blood glucose log to persuade the doctor For example, one woman described a highly effective routine to delay starting insulin therapy. of using thrice-daily glucose monitoring to adjust sliding-scale medication doses and diet. She had used these techniques to Providers sometimes described lab data as more trustworthy reduce her hemoglobin A1c level to 6.1% for nearly a year. than data from self-tracking. “The hemoglobin A1c don’t lie Most health care providers perceived this active, real-time use [sic], so you can tell me whatever you want, but it’s going to of data for self-management as important for patients who were tell me the truth of what’s going on in your body”. Another struggling to manage conditions in which data values were said: “For the most part a lot of this information I don’t really highly sensitive to behavior (such as a younger patient with [need] because I can check the A1c and know what it’s like”. new-onset diabetes), but less important for others (such as older Current diabetes treatment guidelines recommend attention to patients with stable disease). self-monitored blood glucose for extreme values and trends, in addition to hemoglobin A1c as an indicator of overall control Tracking for Goal-Checking [20,21]. A second approach was to use data periodically to assess These perceptions on the part of providers were evident to many progress toward a goal. Patients with this approach referred to of the patients. “I remember when I used to go to the diabetes the data for a holistic assessment of how “well” they were doing, center up there with [a doctor] and she looked at it very but not necessarily for active, hour-to-hour self-management. superficially too, and they seem to rely on your A1c numbers,” This was also often the approach used by patients who were said a patient who had abandoned logging his daily glucose monitoring indicators that they themselves could not measure, values. Providers also sometimes perceived automated recording such as cholesterol, blood count values in anemia, HIV viral devices as more reliable than patient-recorded information, load levels, and CD4 counts. which was also noticed by some of the patients: “[My doctor] Tracking for Sense-Making is like, ‘Please bring me the machine’”. One provider told an A different approach was to examine data values as part of anecdote about a patient with a dangerous blood pressure trying to make sense of the disease. Several patients with increase; the patient’s spouse used a monitor to print out the diabetes who did not regularly monitor described checking previous week’s blood pressure readings, which were low glucose when they felt symptoms they suspected indicated enough to persuade the doctors to rule out their initial suspicion hypoglycemia: “I’ll do it if I’m feeling lightheaded”. Another of “medication noncompliance”. said he did it when he felt a “hunch”. This approach was In only one case, a highly engaged patient said that her provider sometimes encouraged by physicians for patients who seemed preferred reviewing her blood glucose logs rather than the unlikely to monitor regularly: “Usually I tell them that if they’re glucose monitor because the log made it easier to link the not feeling well, check their blood sugar”. One patient with HIV readings to meals. “It was a lot of confusion with the doctor asked his doctor for explanations whenever his lab values because I was just bringing the machine. So now [with the changed. “I saw this is different [from] last 2-3 months ago, notebook] they know that first one, two, three is breakfast, lunch, and now something is wrong. And he explained to me if it’s and dinner”. something wrong or not [important]”. During visits, health care providers frequently explicitly linked lab values to patient Theme 4: Tracking as Work behavior to encourage them to develop a more biomedical Patients said that tracking was effortful and time-consuming, concept of the disease. For example, one provider used a sometimes explicitly describing it as work. A patient with patient’s headache as a teaching example to discuss the role of diabetes said it was a waste of time to write down her values: salt in her diet. Some also saw it as a useful short-term exercise “I’m not going to sit down and write a paper for the month to for patients seeking an understanding of behavioral triggers for keep track of it”. One woman noted that she kept medical conditions such as asthma, irritable bowel syndrome, or migraine information about her multiple conditions, as well as her http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 6 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al multiple health care providers, in her office rather than her Comparison With Prior Work home. Data tracking sometime was felt to conflict with the work Our work contributes to a growing body of research in personal of everyday living forcing trade-offs when patients did not have health information management and personal health information sufficient time or emotional resources. A diabetes patient who tracking—research that has already identified a range of tasks had given up self-monitoring of blood glucose said, “It’s too frequently performed by patients, ranging from tracking health cumbersome for me”. A patient with heart disease who kept a data to managing medical records to creating personal reminder diet log gave it up after it became “overwhelming”. systems [27-29,31,32]. However, much of the previous work in this field has focused on generally healthy individuals and Discussion families [27,28,31,32], on patients with cancer [33-36], or (in support of HIT design) on computer-literate participants [31]. Principal Findings The current project identifies different perspectives brought by Developers of consumer health information technologies for an economically diverse group of patients with multiple chronic data tracking (such as diet and exercise apps or blood glucose diseases. Our participants each had several chronic diseases, logs) often assume patients have unlimited enthusiasm for including diabetes, HIV, heart disease, depression, and many tracking their own health data via technology, that these data others, and about one third were covered by Medicaid. Their are objective facts with unambiguous interpretations and perspectives were in many cases different from what has been applications, and that health care providers welcome such data found in previous work with healthy families. For example, in their assessment of a patient’s health status. Potential users while healthy consumers in Canada rejected the idea that health are believed to be “willing to assume a more participatory role information management was “work” [32], our patients with in the management of their health, to learn how to use new tools, MCC frequently described managing data as time-consuming and to commit themselves to doing so constantly” [31]. and tiring. There are several potential explanations for this By contrast, the concept of data tracking as patient work was contrast. First, keeping track of even a single chronic disease strongly supported by our interviews with patients with multiple is likely to be more challenging than keeping track of preventive chronic conditions. Furthermore, personal medical data did not care or minor medical events among largely healthy individuals. appear to be objective facts, interpreted in the same way by Second, individuals with multiple chronic conditions are likely patients and their providers. The data provoke strong negative to have “illness work” connected to each of the diseases (our and positive emotional reactions, sometimes overwhelming patients had an average of 3.5 chronic conditions). Third, MCC ones that prevent people from wanting to track or access their is disproportionately a condition of the elderly as well as the data. These data can also make individuals feel judged by their less affluent, meaning that an MCC patient may be conducting health care providers or even by themselves. Patients may resist “illness work” while simultaneously facing challenges related their physician’s interpretation of their data values as to aging and poverty. Finally, the multiple chronic conditions “one-size-fits-all” and may prefer to weight their own personal included physically and emotionally challenging comorbidities history and disease experience. Physicians often trust such as depression, anxiety, and chronic pain—conditions that technologically measured data more than manual self-tracked themselves might make it more difficult to conduct any “illness data; their preference is apparent to patients and may work”. This workload burden may have been particularly evident inadvertently be sending patients mixed messages about the as many of our patients were unmarried and had primary value of their data tracking efforts. responsibility for their own personal health information. By contrast, previous research with families often shows that one Our study also suggests that patients who do keep track of their family member takes primary responsibility for the information data require it for different purposes. Some patients examine needs of the household [27,28,31,32]. Such a division of labor their data periodically for a holistic check on their own progress within the family context might offer several advantages, toward goals, and others use their data for real-time decisions including the ability for the information manager to specialize about their behavior. Yet another group of individuals inspect and develop expertise in information management, and might and interpret this data as part of the process of developing an also alleviate the workload burden on more ill members of the understanding of their disease. household. Finally, we encountered many elderly and low-income patients Our findings support previous work in the field of technology who had limited experience with and access to electronic development for elderly patients or others who do not use technologies. As our sample was fairly representative of the electronic technologies regularly. The people we spoke with demographics of those with MCC (with a mean age of 64 and conducted personal health information management and tracking about one third covered by Medicaid), it is plausible that this with a variety of paper and electronic tools, both custom-made reflects the experience of broader MCC populations. and adapted, as has been found by other researchers [27,29,31]. These findings support the proposal that existing self-tracking As others have found, we found that older patients and those technologies such as mobile phone apps may not provide a good with Medicaid were frequently unfamiliar with electronic fit to the needs and abilities of individuals with MCC and the technologies. In addition to lack of access, some have found tasks they are seeking to perform with them [15]. that elderly patients may find usability barriers discouraging them from adopting new technologies [41]. We additionally found that some adults with experience of chronic disease have already solved their own data management problems to their http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 7 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al own satisfaction and did not express much interest in novel noted with some surprise that their health care providers did technologies. Similarly, Grindrod et al found that older patients, not seem very interested in their self-logged data; others have when introduced to new technologies, “struggled to think of a noted that diabetes patients can interpret their providers’ need for the applications in their own lives” [41]. preference for lab-measured hemoglobin A1c as meaning that self-monitoring was not important [48]. Peel et al found that When combined with our finding that patients considered data counterintuitive blood glucose values confused patients and tracking to be “cumbersome”, this suggests that novel could lead to discontinuation of monitoring, as was reported by technologies will succeed only if they are highly intuitive, easy one of our patients [49]. to learn, and unambiguously reduce the burden of work on the patient. Uploadable device data [42] or mining of personal data One highly relevant study reports a trial of an electronic diabetes traces from phones and other technologies [43,44] may be diary and information app, which incorporates some of the effective ways of accomplishing this, especially given the fact concepts we have recommended here [52]. In that trial, blood that both patients and providers in our study recognized the glucose measurements were automatically uploaded via additional perceived credibility of technologically measured Bluetooth from an electronic monitor, although food and data. The gamification trend in the health promotion and disease exercise data had to be manually input. Counseling, including management literature is also potentially relevant [42,45,46]. motivational interviewing, was added in one of the two Games that provide motivation to track learning opportunities, technology arms. Nevertheless, after 4 months the app (with or social support, or emotional coping support for dealing with without supplemental counseling) was not associated with data could potentially be useful for patients with chronic disease. changes in hemoglobin A1c levels [52]. The 18% attrition rate However, designers of games for self-tracking may wish to in this study may have resulted from the relatively heavy work consider our findings that patients often see data tracking as burden of self-tracking the electronic data. work and may perceive the data as having moral meaning that Limitations could be positive or negative. As noted by others, patients can The sample was generally representative of the demographics have strong emotional responses to learning their own numbers of the MCC population. However, type 2 diabetes may have and can feel judged by themselves and others [47-49]. Turning been more prevalent in our sample than in the national MCC information tracking into a game might appear to trivialize population, in which type 2 diabetes occurs in three of the top important tasks, and “losing” in a game might amplify negative nine pairs of chronic conditions and four of the top nine emotions. It might even be that some patients might prefer less condition triads [53]. Interviews were also conducted in a US emotionally charged technologies inspired by office or financial urban area and in English only, limiting the sample to patients management software, which are explicitly designed to make comfortable in that language. These reasons may limit relevance necessary activities efficient and even pleasant while still to other populations, such as individuals in other countries with treating those activities as work. different health care systems, people in rural locations with Our findings also have relevance for the literature on patients’ different challenges in accessing health care, or people of other mental models of disease. As others [50,51] have pointed out, cultures or language groups. individuals work to make sense of their disease and health Conclusions and Implications experiences, seeking a label or name, identifying its cause, establishing its probable timeline and consequences, and Developers of consumer health information technologies for learning the extent to which it is manageable or curable. Over data tracking (such as diet and exercise apps or blood glucose time, people use these insights to construct what have been logs) often assume that a wide variety of patients will have called “common-sense models of disease” or “illness unlimited enthusiasm for tracking their own health data via representations”, that is, explanations of health conditions that technology. However, adoption of new technologies does not are internally coherent but that may or may not coincide with always rapidly spread beyond computer-literate, highly the biomedical model of the disease [50,51]. These illness motivated early adopters. We suggest that to make a public representations can affect risk perceptions, coping behavior, health impact, developers should be prepared to engage management, and disease outcomes. Data tracking clearly offers creatively with a variety of pragmatic and emotional issues to the possibility of demonstrating the link between behavior and reach a broader audience that includes patients with chronic disease indicators (eg, between diet, medication administration, disease. and blood glucose), thereby encouraging patients to develop a One recommendation is to explore ways to engage directly with more biomedical model of their disease. the emotional impact associated with medical data, exploring However, not all patients wanted to examine their data for this ways not only to motivate progress but also cope with negative purpose. Our findings are striking in the degree to which medical feelings. Developers should seek not to exacerbate negative data were shown to have extremely serious emotional feelings or judgments, look at creative ways to support positive implications for patients with MCC, sometimes serious enough feelings, and facilitate personal goal setting rather than imposing to be associated with abandonment of data tracking altogether. external goals. Technologies could integrate techniques such “Bad” data values can be extremely upsetting, especially when as motivational interviewing [54] that have been demonstrated those “bad” values have or are perceived to have some link to to help patients establish personally relevant goals and action behavior. Patients’ language revealed the extent to which they plans, rather than seeking to persuade patients to adopt their use judgmental terms of sin and transgression to describe both providers’ priorities. The behavioral economics literature can their data and themselves. Furthermore, some of our patients http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 8 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al provide valuable guidance in leveraging effects such as framing, It must still be recognized that older generations are not defaults, and behavioral “nudges” to promote engagement and universally comfortable with electronic technologies and that better decision making [55]. many low-income patients still do not have access to them. For the foreseeable future, a significant subset of patients will lack Another suggestion is to provide different formats for different access to information technology. This creates tremendous purposes. Patients who are building a conceptual understanding opportunities for exploring improved paper technologies. For of disease might benefit from data-driven links with explanatory example, scannable paper forms might ease the burden of material or even simulations. Patients who are using data to tracking data on paper and be more widely used than mobile check on goals might benefit from progress bars or visualized apps by some groups. Technologies that benefit only younger target thresholds. A relatively small number of patients (such or more technologically sophisticated patients could have the as those adjusting insulin doses or high blood pressure potential to widen health disparities rather than narrow them. medications [22]) will be using data for self-management; these This issue of equity must be addressed in health information individuals are most likely to be interested in reminders or alerts. technology broadly, but especially in technology intended for Developing systems with the wrong purpose in mind appears personal health information tracking and management. likely to irritate patients rather than support them. For example, patients who have not established personally relevant goals are Finally, the concept of data tracking as yet another piece of unlikely to welcome visualizations that depict their “progress”, patient “work” resonated strongly with the participants. 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Use of psychology and behavioral economics to promote healthy eating. Am J Prev Med 2014 Dec;47(6):832-837. [doi: 10.1016/j.amepre.2014.08.002] [Medline: 25441239] Abbreviations HIT: health information technology HIV: human immunodeficiency virus MCC: multiple chronic disease http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 11 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al Edited by G Eysenbach; submitted 07.01.15; peer-reviewed by K Grindrod, A Moen; comments to author 11.05.15; revised version received 03.06.15; accepted 24.07.15; published 19.08.15 Please cite as: Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E J Med Internet Res 2015;17(8):e202 URL: http://www.jmir.org/2015/8/e202/ doi: 10.2196/jmir.4209 PMID: 26290186 ©Jessica S Ancker, Holly O Witteman, Baria Hafeez, Thierry Provencher, Mary Van de Graaf, Esther Wei. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.08.2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 12 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Medical Internet Research JMIR Publications

“You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions

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1438-8871
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10.2196/jmir.4209
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Abstract

Background: Consumer health information technologies (HIT) that encourage self-tracking, such as diet and fitness tracking apps and disease journals, are attracting widespread interest among technology-oriented consumers (such as “quantified self” advocates), entrepreneurs, and the health care industry. Such electronic technologies could potentially benefit the growing population of patients with multiple chronic conditions (MCC). However, MCC is predominantly a condition of the elderly and disproportionately affects the less affluent, so it also seems possible that the barriers to use of consumer HIT would be particularly severe for this patient population. Objective: Our aim was to explore the perspectives of individuals with MCC using a semistructured interview study. Our research questions were (1) How do individuals with MCC track their own health and medical data? and (2) How do patients and providers perceive and use patient-tracked data? Methods: We used semistructured interviews with patients with multiple chronic diseases and providers with experience caring for such patients, as well as participation in a diabetes education group to triangulate emerging themes. Data were analyzed using grounded theory and thematic analysis. Recruitment and analysis took place iteratively until thematic saturation was reached. Results: Interviews were conducted with 22 patients and 7 health care providers. The patients had an average of 3.5 chronic conditions, including type 2 diabetes, heart disease, chronic pain, and depression, and had regular relationships with an average of 5 providers. Four major themes arose from the interviews: (1) tracking this data feels like work for many patients, (2) personal medical data for individuals with chronic conditions are not simply objective facts, but instead provoke strong positive and negative emotions, value judgments, and diverse interpretations, (3) patients track for different purposes, ranging from sense-making to self-management to reporting to the doctor, and (4) patients often notice that physicians trust technologically measured data such as lab reports over patients’ self-tracked data. Conclusions: Developers of consumer health information technologies for data tracking (such as diet and exercise apps or blood glucose logs) often assume patients have unlimited enthusiasm for tracking their own health data via technology. However, our findings potentially explain relatively low adoption of consumer HIT, as they suggest that patients with multiple chronic illnesses consider it work to track their own data, that the data can be emotionally charged, and that they may perceive that providers do not welcome it. Similar themes have been found in some individual chronic diseases but appeared more complex because patients http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 1 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al often encountered “illness work” connected to multiple diseases simultaneously and frequently faced additional challenges from aging or difficult comorbidities such as chronic pain, depression, and anxiety. We suggest that to make a public health impact, consumer HIT developers should engage creatively with these pragmatic and emotional issues to reach an audience that is broader than technologically sophisticated early adopters. Novel technologies are likely to be successful only if they clearly reduce patient inconvenience and burden, helping them to accomplish their “illness work” more efficiently and effectively. (J Med Internet Res 2015;17(8):e202) doi: 10.2196/jmir.4209 KEYWORDS medical informatics; consumer health information; health knowledge, attitudes, practices; self-care; chronic disease coordinate more different therapeutic regimens than those with Introduction single diseases [17]. Each additional chronic condition places the individual at higher risk of adverse drug events, Background out-of-pocket expenses, impaired functional status, Consumer health information technology (HIT) is exploding in hospitalization, and mortality [17]. It is estimated that two-thirds popularity, attracting the attention of technology-oriented of health care spending is focused on patients with MCC [17]. consumers, patients, caregivers, and entrepreneurs. Technologies These patients are in need of improved strategies and such as disease management apps and “quantified self” tools technologies to support health and medical care, creating a [1-3] offer the potential to help patients track personal data, number of opportunities that could potentially be filled with learn about their health, and manage chronic care needs [4-7]. health IT, yet the barriers to technology adoption might be Consumer HIT appears poised to help inform, motivate, and particularly problematic for these patients as well. MCC engage patients, all of which are known to improve management disproportionately affects the elderly and the less affluent. The skills and health outcomes [5-8]. prevalence of MCC rises sharply with age, affecting 34% of However, it is not yet known whether such technologies will those aged 45-64 and 62% of those age 65 and over [19]. diffuse broadly beyond tech-savvy early adopters such as Furthermore, the prevalence of MCC is highest among the “quantified self” advocates, and whether the technologies would lowest income brackets, affecting nearly 51% of seniors who produce benefits for people with complex medical conditions. live at or below the federal poverty level but only 39% of seniors To date, the measured impact of consumer HIT is still limited. living at four times the poverty level [18]. Computerized interventions for diabetes self-management have As an initial step to exploring the perspectives of individuals shown only limited efficacy [9,10]. In practice, effects have with multiple chronic conditions, with the goal of understanding generally been limited as a result of low adoption and usage. potential applications of consumer HIT and barriers to its use, One in 5 smartphone users has downloaded a health app [11], we conducted a semistructured interview study. This paper yet most apps are abandoned after a few uses [12]. Studies of focuses on tracking or keeping diaries of personal data, a task the effectiveness of apps and websites to promote health that we will refer to as “personal health information tracking”. outcomes (such as a recent study of a phone app to assist in We focused on personal health information tracking because weight loss [13] or a self-management Web community for (1) it has been recommended for a variety of chronic conditions, diabetes [10]) frequently find that participants stop using the and (2) it is a task potentially supported by consumer health IT. technology after a short period of time. Having a chronic Self-monitoring tasks that have been promoted under different condition increases the chances that a patient will use certain circumstances include blood glucose self-monitoring for certain forms of consumer HIT on average [2,11]. But this increased patients with type 1 and type 2 diabetes [20,21], measuring likelihood is often offset by other sociodemographic factors blood pressure in hypertension and heart disease [22], keeping that decrease the likelihood of using technology. Of particular diet logs or food diaries for weight loss or digestive diseases concern from a public health standpoint, the use of consumer [23], and self-monitoring medication adherence and side effects HIT remains lowest among the groups that might be most likely [24]. Patients also often receive the recommendation that they to benefit from additional forms of low-cost disease management should check and be able to report certain laboratory values, support: people who are elderly, less educated, or less affluent such as CD4 count in human immunodeficiency virus (HIV) or [2,11,14]. These disparities in uptake, as well as the low rate of hemoglobin A1c in diabetes. We therefore considered personal sustained use among adopters, suggest mismatches between health information tracking to be a task that was likely to be current consumer HIT and the goals, desires, or capabilities of encountered by patients with MCC, but we did not a priori many patients [15,16]. assume a position on whether patients should self-track or A population with particularly complex and ongoing health whether it was likely to benefit them. Rather, our research needs is the 90 million Americans who have multiple chronic questions were (1) How do individuals with MCC perform conditions (MCC) [17]. Although any combination of chronic medical data tracking? and (2) How do patients and providers conditions qualifies as MCC, the most common combinations perceive and use patient-tracked data? We asked the questions are diabetes plus hypertension, heart disease plus hypertension, broadly to encompass any sort of tool the patients were currently and cancer plus hypertension [18]. Patients with MCC using, including electronic technologies, paper, or memory. experience the challenges associated with living with chronic disease and also typically consult more different doctors and http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 2 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al providers with experience providing care for patients with MCC. Theoretical Framework: Illness Work and Personal We adopted the Department of Health and Human Services Health Information Management definition of chronic conditions as conditions that last a year or This project was conducted from a human factors perspective more and that either require ongoing medical attention or limit influenced by the sociology of illness. This perspective activities of daily living [17]. Patient participants were recruited recognizes that patients’ management of their health comprises from outpatient clinics in internal medicine and endocrinology a wide variety of different activities both inside and outside the and from the patient information library, using both promotional medical encounter: taking medicines, refilling prescriptions, flyers and individual referrals from physicians and nurse buying and cooking food, exercising or doing physical therapy, practitioners. One researcher (JSA) also attended six 90-minute researching health issues, coping with medical crises, finding sessions of a diabetes education support group as a means of doctors and dentists, organizing and traveling to medical triangulating emerging themes. We chose the diabetes education appointments, and keeping records. As these are all effortful, group because many of the study participants had type 2 directed activities to attain goals, they may be conceptualized diabetes. as work [25-27]. Settings Corbin and Strauss identified “illness work” as activities directly Weill Cornell Physicians is a multispecialty academic medical involved with managing an illness, such as following medication practice in Manhattan, with a mix of privately insured, Medicaid, regimens and using technologies such as glucose meters or sleep and Medicare patients. New York-Presbyterian Hospital is the apnea machines [25,26]. Yet even in illness, “everyday life largest academic medical center in Manhattan. The Institute for work” of shopping, paying bills, nurturing relationships, and Family Health is a federally qualified health center with 18 sites managing a household continues [25,26]. “Articulation work” in and around New York City, providing safety net primary is the planning, coordinating, and managing that allows people care to patients regardless of insurance status. to complete all their other work [25,26]. Those components of illness and articulation work that involve Interview Methods acquiring and managing information can be called personal The researchers developed a semistructured interview instrument health information management [27-30]. A growing body of centered on three topics: personal health information tracking, research on personal health information management has personal health information management, and searching for identified tasks including tracking health events, obtaining health-related information. The current manuscript focuses on information, and organizing information [27]; creating personal the first of these. The first author conducted interviews in histories, making decisions, planning, and structuring activities person, using offices and conference rooms convenient to the (eg, creating medication reminders) [28]; and transferring clinics where patients were recruited. Interviews were audio personal data and records to the physician [31]. In the current recorded and professionally transcribed. The interviewer also project, we focus on the subset of personal health information took field notes, collected samples of artifacts and documents management involved in monitoring and logging personal data for patients such as educational brochures, and photographed (such as symptoms or laboratory values), sometimes called other artifacts or documents such as log sheets used to record personal health information tracking [32]. glucose values. Much of the recent work in personal health information tracking Analysis Methods and management has focused on generally healthy individuals No existing theoretical framework appeared to be appropriate and families [27,28,31,32], on patients with cancer [33-36], or to these data, and therefore we applied methods to develop (in support of information technology design) on meaning inductively from the data. Although this family of computer-literate participants [31]. approaches is sometimes known in the sociology literature as In this project, we sought to apply the insights from this previous development of grounded theory [37], we adopt the newer term work while exploring the perspectives of an economically “inductive thematic analysis” to reflect the fact that our end diverse sample of patients with MCC in more depth. In order product is a series of interrelated themes rather than a fully to develop or adapt technologies for these patients, it is essential formed theory [38]. Qualitative analysis was conducted to understand practices and perspectives of the potential users collaboratively by our multidisciplinary team, which included and the attributes of the tasks they seek to perform, as well as individuals with training in journalism, public health, the social and physical environments in which they will be informatics, psychology, human factors, nursing, and diabetes performing these tasks [15]. Poor fit between individuals, tasks, education. Two of the researchers (HOW and EW) also brought and technologies is likely to be one of the reasons that personal experience of long-standing chronic disease. The self-tracking technologies have not yet spread widely within preliminary version of the codebook was developed by 2 of the populations with multiple diseases. researchers in reading the first three transcripts and was iteratively refined over the coding process. Each transcript and Methods photograph was reviewed by at least 2 team members (the first author and one other team member), who independently coded Participants the transcript and then met to reach consensus on it. For individual interviews, we recruited purposive samples of We followed a staged and iterative approach, first identifying adult English-speaking patients with MCC, and of medical preliminary codes through repeated reading and review of the http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 3 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al data, then identifying relationships between codes and groupings Sjogren syndrome, and cirrhosis. Many described themselves of codes, and finally identifying and refining larger underlying as overweight but none as obese. In addition to their chronic themes. Over the analysis, 47 open codes were developed. These conditions, patients also discussed a wide variety of recently were linked into 6 broad groups: (1) resources, skills, and factors experienced urgent conditions, including diverticulitis, flu, patients need for disease management, (2) the health care system appendicitis, bee stings, and physical injuries. Participants and its components, (3) thinking, feeling, and experiencing sometimes mentioned taking drugs that implied other chronic disease and health, (4) medical data and medical records, (5) conditions that they did not explicitly list: examples included evaluative judgments, and (6) attributions of responsibility. In antidepressants, blood pressure medications, lipid-lowering the final stage, the themes presented in the results section were medications, drugs for prostatic hyperplasia, and anticoagulants. developed. Many of the patients with type 2 diabetes were taking insulin one or more times a day, as was the individual with type 1 To improve internal validity, we conducted member checking diabetes. [39] in two ways. First, several of the emergent groups and themes were presented to new informants during interviews for Half of patients were men and half were women; a third (n=7) their feedback. Second, the resulting themes were presented at were black. Ages ranged from 37-89 (mean 64.1; median 66). a meeting of the diabetes education group, whose members About two-thirds (n=15) were not currently married. Just over validated the themes while also providing additional feedback a third (n=8) used English as a second language. One third (n=7) and nuanced interpretation. were covered by Medicare (US public insurance for those over age 65); one third (n=7) by Medicaid (US public insurance for Analysis and recruitment were conducted simultaneously until those with low income); and the remainder (n=8) by commercial saturation was achieved (ie, no new concepts were arising from insurance. new interviews) [40]. Multiple chronic conditions placed heavy and sometimes This study was approved by the Institutional Review Boards of competing demands on patients. For example, one patient with Weill Cornell Medical College and the Institute for Family diabetes recognized that his morning toast caused increases in Health. All participants gave written informed consent. Members his blood glucose, but on balance had decided not to stop eating of the diabetes education group provided oral consent. toast because his morning medications for other conditions had to be taken with food. Several patients with diabetes or heart Results disease recognized that exercise might help but were prevented because of chronic pain or disability from injury. Patients taking Participants anticoagulants encountered challenges when scheduling surgery Interviews were conducted with 22 patients and 7 health care for other conditions. providers. An additional 3 patient interviews were excluded The diabetes education group was attended by an average of 5 from analysis because the interviewees did not have multiple patients each session (range 4-9). Most patient education group chronic conditions. attendees had type 2 diabetes but a minority had type 1 diabetes The included patients reported having an average of 3.5 chronic or prediabetes. conditions (SD 1.5). The most common conditions mentioned The health care providers were 2 nurse practitioners, 2 internists, were type 2 diabetes, hypertension, heart disease, chronic pain, 2 family medicine physicians, and an emergency medicine and depression. Other conditions included asthma, HIV, hepatitis physician. C, thyroid disorders, rheumatoid arthritis, glaucoma, cataracts, and sleep apnea. Two individuals were in follow-up after cancer Major themes pertaining to personal health information tracking treatment. Conditions reported by only one patient each included are summarized in Table 1 and presented in detail in the results type 1 diabetes, fibromyalgia, post-polio syndrome, sarcoidosis, section. Table 1. Major themes in personal health information tracking. Themes Summary Representative quotes 1. Personal data can carry strong Data are not merely objective facts but prompt strong positive “You get reminded you’re a sick person” and emotional and moral implications and negative emotions as well as value judgments. “I’m not a good patient”. 2. Multiple purposes and uses for Patients use data for a variety of purposes, ranging from active “I’ll [check] it if I’m feeling lightheaded”. personal data self-management to making sense of their condition to report- ing to the doctor. 3. (Un)reliability of personally Patients often notice that physicians do not trust their self- “[The doctors] looked at [my logs] very super- tracked data tracked data. ficially…they seem to rely on your A1c num- bers”. 4. Tracking feels like work Tracking is time-consuming and sometimes emotionally “It’s too cumbersome for me”. draining. http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 4 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al self-monitoring. “I’m tired of sticking myself,” another said. Overview Some patients with diabetes said they were frustrated to see Most patients paid attention to laboratory findings provided by their blood glucose values occasionally spike without a clear their doctors, and a few kept records of selected values. For reason, undermining their confidence that they understood and example, a woman with anemia created a table to track her blood could manage their disease. test results over time, 2 patients with HIV kept records of their CD4 count values over time, and many patients checked on The Moral Valence of Medical Data their cholesterol regularly. Patients and providers frequently described the data with highly However, fewer than half regularly tracked data by self-testing judgmental language, including terms suggesting moral or recording daily activities. The most common example of transgression. For example, one explained a high blood glucose self-tracking was patients with diabetes monitoring their blood value because “I cheated and I had some McDonald’s”. glucose. Among the 16 patients with type 1 or type 2 diabetes, Conversely, patients could feel extremely happy and proud 11 mentioned self-monitoring blood glucose in some fashion when their values were good. Several of the health care (some were fairly regular, some checked values occasionally, providers said it was better to use nonjudgmental language such and some said they used to monitor regularly but had stopped). as “high/low” or “target/nontarget” because patients “get Other examples of tracking mentioned by one or more discouraged because they think they’re being graded or judged”. participants included recording weight or blood pressure (n=7), Yet in the interviews, many providers used more evaluative tracking daily medication administration (n=3), keeping food language such as “good/bad” and “better/worse”. A patient who diaries (n=2, in one case to investigate suspected lactose had altered his diet and was able to lower his doses of intolerance), collecting laboratory reports to manually compare hypertension and hyperlipidemia drugs said he felt satisfied trends over time (n=4), and recording potential side effects with when his doctors said, “Okay, we’re happy with you”. a new medication (n=2). This sort of tracking was conducted The Moral Valence of Tracking on paper or electronically on a spreadsheet, or in one case on a There was also a “good/bad patient” aspect to tracking itself. paper calendar. All the patients interviewed who monitored People with diabetes frequently called themselves a “bad blood glucose used monitors that tracked data electronically. patient” or “not a good patient” when they did not monitor blood In addition, some kept handwritten blood glucose logs. The glucose. One participant explained the fact that she did not track numbers in parentheses above (n=) are provided for perspective, any of her health indicators (including diet and exercise) by but these data were collected through open-ended interview calling herself “lazy”. Although providers most often expressed questions rather than closed-ended survey methods, so the frustration about lack of monitoring, some occasionally interviews may not have captured every instance of tracking. perceived monitoring as excessive. Patients who tracked data Many of the patients older than 65 and most Medicaid patients very diligently (eg, detailed exercise logs, which clinicians saw did not use computers regularly or at all, and many did not have as having little clinical relevance) were sometimes referred to smartphones. as “obsessive and compulsive” or “fastidious”. Theme 1: Personal Data Can Carry Strong Emotional My Interpretation of My Data and Moral Implications Although in some cases patients and physicians were in close agreement about what data values were “good” or “bad”, other Overview patients preferred to interpret their results in light of their own Indicators such as blood glucose, weight, and lab values were unique histories or symptoms. For example, several patients not discussed as value-free facts but instead carried strong with diabetes said that they aimed for a blood glucose level or emotional and evaluative connotations. People recognized hemoglobin A1c that was appropriate “for me”. In some cases, tracking as work, judged themselves as “good” or “bad” for these were values that made them feel well, or values that were their data and their diligence in collecting it, and noted that data high enough to minimize the risk of hypoglycemia. In other should be considered within the patient’s personal context. cases, patients wanted their personal history to be taken into Negative Aspects of Illness account in interpreting data. For example, a person with a history of obesity took pride in the number of dress sizes she had gone Medical data often reminded patients of the negative aspects of down, rather than aiming for a particular target weight. One their illness. An individual who did not monitor her blood provider told an anecdote about a patient who had brought her glucose regularly said her values were “depressing”, and another hemoglobin A1c from 13% to below 8% with diet and said they made her “scared”. Discussing tracking sometimes medication. When urged to continue lowering it, the patient raised feelings of anger or injustice not only about the tracking said, “I don’t want to be a poster child for perfect diabetes”. but also about having chronic disease. “I hate to be focused on The doctor recalled saying, “Actually, you’re right. This is good my health in every friggin’ second of the day...I don’t want to for you...I should’ve been jumping up and down because that’s live like that every day”. A patient with HIV, hypertension, and really great”. other chronic illnesses said he avoided looking at his regular test results: “I don’t ask about no numbers. If anything is messing up, then [my doctor] tells me”. The physical experience could also be unpleasant. “Poking my finger, that was irritating to me,” said one person who had abandoned blood glucose http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 5 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al headaches. However, some patients described frustration (or Theme 2: Multiple Purposes and Uses for Personal even abandoning tracking altogether) after failing to see Data connections between their data values and their behavior. Overview Tracking for the Doctor Not all patients closely monitored their own data values. Patients A few patients appeared to perceive self-monitoring as who did track their own data through either self-monitoring or something done not for their own use but partly or largely to laboratory testing described a variety of purposes, which create records for the doctor. A few seemed confused that depended on aspects of their disease and on their own experience doctors rarely reviewed their logs. “They don’t monitor that of their disease. They might use their tracked data for real-time part of it, I don’t know why”. decision making, for medium-term self-assessment, or for making sense of various elements of data, such as physical Theme 3: (Un)reliability of Personally Tracked Data symptoms. Providers often perceived patient-recorded data as unreliable. Tracking for Action The lack of confidence was attributed to perceived lack of diligence, moral valence of the data (with patients unwilling to Some experienced patients with diabetes monitored blood “admit” undesirable numbers), and fear of consequences. The glucose multiple times per day as “working data” [30] that they most striking example, told by a provider, was a woman who would use immediately to adjust their diet or their medication. faked her daughter’s blood glucose log to persuade the doctor For example, one woman described a highly effective routine to delay starting insulin therapy. of using thrice-daily glucose monitoring to adjust sliding-scale medication doses and diet. She had used these techniques to Providers sometimes described lab data as more trustworthy reduce her hemoglobin A1c level to 6.1% for nearly a year. than data from self-tracking. “The hemoglobin A1c don’t lie Most health care providers perceived this active, real-time use [sic], so you can tell me whatever you want, but it’s going to of data for self-management as important for patients who were tell me the truth of what’s going on in your body”. Another struggling to manage conditions in which data values were said: “For the most part a lot of this information I don’t really highly sensitive to behavior (such as a younger patient with [need] because I can check the A1c and know what it’s like”. new-onset diabetes), but less important for others (such as older Current diabetes treatment guidelines recommend attention to patients with stable disease). self-monitored blood glucose for extreme values and trends, in addition to hemoglobin A1c as an indicator of overall control Tracking for Goal-Checking [20,21]. A second approach was to use data periodically to assess These perceptions on the part of providers were evident to many progress toward a goal. Patients with this approach referred to of the patients. “I remember when I used to go to the diabetes the data for a holistic assessment of how “well” they were doing, center up there with [a doctor] and she looked at it very but not necessarily for active, hour-to-hour self-management. superficially too, and they seem to rely on your A1c numbers,” This was also often the approach used by patients who were said a patient who had abandoned logging his daily glucose monitoring indicators that they themselves could not measure, values. Providers also sometimes perceived automated recording such as cholesterol, blood count values in anemia, HIV viral devices as more reliable than patient-recorded information, load levels, and CD4 counts. which was also noticed by some of the patients: “[My doctor] Tracking for Sense-Making is like, ‘Please bring me the machine’”. One provider told an A different approach was to examine data values as part of anecdote about a patient with a dangerous blood pressure trying to make sense of the disease. Several patients with increase; the patient’s spouse used a monitor to print out the diabetes who did not regularly monitor described checking previous week’s blood pressure readings, which were low glucose when they felt symptoms they suspected indicated enough to persuade the doctors to rule out their initial suspicion hypoglycemia: “I’ll do it if I’m feeling lightheaded”. Another of “medication noncompliance”. said he did it when he felt a “hunch”. This approach was In only one case, a highly engaged patient said that her provider sometimes encouraged by physicians for patients who seemed preferred reviewing her blood glucose logs rather than the unlikely to monitor regularly: “Usually I tell them that if they’re glucose monitor because the log made it easier to link the not feeling well, check their blood sugar”. One patient with HIV readings to meals. “It was a lot of confusion with the doctor asked his doctor for explanations whenever his lab values because I was just bringing the machine. So now [with the changed. “I saw this is different [from] last 2-3 months ago, notebook] they know that first one, two, three is breakfast, lunch, and now something is wrong. And he explained to me if it’s and dinner”. something wrong or not [important]”. During visits, health care providers frequently explicitly linked lab values to patient Theme 4: Tracking as Work behavior to encourage them to develop a more biomedical Patients said that tracking was effortful and time-consuming, concept of the disease. For example, one provider used a sometimes explicitly describing it as work. A patient with patient’s headache as a teaching example to discuss the role of diabetes said it was a waste of time to write down her values: salt in her diet. Some also saw it as a useful short-term exercise “I’m not going to sit down and write a paper for the month to for patients seeking an understanding of behavioral triggers for keep track of it”. One woman noted that she kept medical conditions such as asthma, irritable bowel syndrome, or migraine information about her multiple conditions, as well as her http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 6 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al multiple health care providers, in her office rather than her Comparison With Prior Work home. Data tracking sometime was felt to conflict with the work Our work contributes to a growing body of research in personal of everyday living forcing trade-offs when patients did not have health information management and personal health information sufficient time or emotional resources. A diabetes patient who tracking—research that has already identified a range of tasks had given up self-monitoring of blood glucose said, “It’s too frequently performed by patients, ranging from tracking health cumbersome for me”. A patient with heart disease who kept a data to managing medical records to creating personal reminder diet log gave it up after it became “overwhelming”. systems [27-29,31,32]. However, much of the previous work in this field has focused on generally healthy individuals and Discussion families [27,28,31,32], on patients with cancer [33-36], or (in support of HIT design) on computer-literate participants [31]. Principal Findings The current project identifies different perspectives brought by Developers of consumer health information technologies for an economically diverse group of patients with multiple chronic data tracking (such as diet and exercise apps or blood glucose diseases. Our participants each had several chronic diseases, logs) often assume patients have unlimited enthusiasm for including diabetes, HIV, heart disease, depression, and many tracking their own health data via technology, that these data others, and about one third were covered by Medicaid. Their are objective facts with unambiguous interpretations and perspectives were in many cases different from what has been applications, and that health care providers welcome such data found in previous work with healthy families. For example, in their assessment of a patient’s health status. Potential users while healthy consumers in Canada rejected the idea that health are believed to be “willing to assume a more participatory role information management was “work” [32], our patients with in the management of their health, to learn how to use new tools, MCC frequently described managing data as time-consuming and to commit themselves to doing so constantly” [31]. and tiring. There are several potential explanations for this By contrast, the concept of data tracking as patient work was contrast. First, keeping track of even a single chronic disease strongly supported by our interviews with patients with multiple is likely to be more challenging than keeping track of preventive chronic conditions. Furthermore, personal medical data did not care or minor medical events among largely healthy individuals. appear to be objective facts, interpreted in the same way by Second, individuals with multiple chronic conditions are likely patients and their providers. The data provoke strong negative to have “illness work” connected to each of the diseases (our and positive emotional reactions, sometimes overwhelming patients had an average of 3.5 chronic conditions). Third, MCC ones that prevent people from wanting to track or access their is disproportionately a condition of the elderly as well as the data. These data can also make individuals feel judged by their less affluent, meaning that an MCC patient may be conducting health care providers or even by themselves. Patients may resist “illness work” while simultaneously facing challenges related their physician’s interpretation of their data values as to aging and poverty. Finally, the multiple chronic conditions “one-size-fits-all” and may prefer to weight their own personal included physically and emotionally challenging comorbidities history and disease experience. Physicians often trust such as depression, anxiety, and chronic pain—conditions that technologically measured data more than manual self-tracked themselves might make it more difficult to conduct any “illness data; their preference is apparent to patients and may work”. This workload burden may have been particularly evident inadvertently be sending patients mixed messages about the as many of our patients were unmarried and had primary value of their data tracking efforts. responsibility for their own personal health information. By contrast, previous research with families often shows that one Our study also suggests that patients who do keep track of their family member takes primary responsibility for the information data require it for different purposes. Some patients examine needs of the household [27,28,31,32]. Such a division of labor their data periodically for a holistic check on their own progress within the family context might offer several advantages, toward goals, and others use their data for real-time decisions including the ability for the information manager to specialize about their behavior. Yet another group of individuals inspect and develop expertise in information management, and might and interpret this data as part of the process of developing an also alleviate the workload burden on more ill members of the understanding of their disease. household. Finally, we encountered many elderly and low-income patients Our findings support previous work in the field of technology who had limited experience with and access to electronic development for elderly patients or others who do not use technologies. As our sample was fairly representative of the electronic technologies regularly. The people we spoke with demographics of those with MCC (with a mean age of 64 and conducted personal health information management and tracking about one third covered by Medicaid), it is plausible that this with a variety of paper and electronic tools, both custom-made reflects the experience of broader MCC populations. and adapted, as has been found by other researchers [27,29,31]. These findings support the proposal that existing self-tracking As others have found, we found that older patients and those technologies such as mobile phone apps may not provide a good with Medicaid were frequently unfamiliar with electronic fit to the needs and abilities of individuals with MCC and the technologies. In addition to lack of access, some have found tasks they are seeking to perform with them [15]. that elderly patients may find usability barriers discouraging them from adopting new technologies [41]. We additionally found that some adults with experience of chronic disease have already solved their own data management problems to their http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 7 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al own satisfaction and did not express much interest in novel noted with some surprise that their health care providers did technologies. Similarly, Grindrod et al found that older patients, not seem very interested in their self-logged data; others have when introduced to new technologies, “struggled to think of a noted that diabetes patients can interpret their providers’ need for the applications in their own lives” [41]. preference for lab-measured hemoglobin A1c as meaning that self-monitoring was not important [48]. Peel et al found that When combined with our finding that patients considered data counterintuitive blood glucose values confused patients and tracking to be “cumbersome”, this suggests that novel could lead to discontinuation of monitoring, as was reported by technologies will succeed only if they are highly intuitive, easy one of our patients [49]. to learn, and unambiguously reduce the burden of work on the patient. Uploadable device data [42] or mining of personal data One highly relevant study reports a trial of an electronic diabetes traces from phones and other technologies [43,44] may be diary and information app, which incorporates some of the effective ways of accomplishing this, especially given the fact concepts we have recommended here [52]. In that trial, blood that both patients and providers in our study recognized the glucose measurements were automatically uploaded via additional perceived credibility of technologically measured Bluetooth from an electronic monitor, although food and data. The gamification trend in the health promotion and disease exercise data had to be manually input. Counseling, including management literature is also potentially relevant [42,45,46]. motivational interviewing, was added in one of the two Games that provide motivation to track learning opportunities, technology arms. Nevertheless, after 4 months the app (with or social support, or emotional coping support for dealing with without supplemental counseling) was not associated with data could potentially be useful for patients with chronic disease. changes in hemoglobin A1c levels [52]. The 18% attrition rate However, designers of games for self-tracking may wish to in this study may have resulted from the relatively heavy work consider our findings that patients often see data tracking as burden of self-tracking the electronic data. work and may perceive the data as having moral meaning that Limitations could be positive or negative. As noted by others, patients can The sample was generally representative of the demographics have strong emotional responses to learning their own numbers of the MCC population. However, type 2 diabetes may have and can feel judged by themselves and others [47-49]. Turning been more prevalent in our sample than in the national MCC information tracking into a game might appear to trivialize population, in which type 2 diabetes occurs in three of the top important tasks, and “losing” in a game might amplify negative nine pairs of chronic conditions and four of the top nine emotions. It might even be that some patients might prefer less condition triads [53]. Interviews were also conducted in a US emotionally charged technologies inspired by office or financial urban area and in English only, limiting the sample to patients management software, which are explicitly designed to make comfortable in that language. These reasons may limit relevance necessary activities efficient and even pleasant while still to other populations, such as individuals in other countries with treating those activities as work. different health care systems, people in rural locations with Our findings also have relevance for the literature on patients’ different challenges in accessing health care, or people of other mental models of disease. As others [50,51] have pointed out, cultures or language groups. individuals work to make sense of their disease and health Conclusions and Implications experiences, seeking a label or name, identifying its cause, establishing its probable timeline and consequences, and Developers of consumer health information technologies for learning the extent to which it is manageable or curable. Over data tracking (such as diet and exercise apps or blood glucose time, people use these insights to construct what have been logs) often assume that a wide variety of patients will have called “common-sense models of disease” or “illness unlimited enthusiasm for tracking their own health data via representations”, that is, explanations of health conditions that technology. However, adoption of new technologies does not are internally coherent but that may or may not coincide with always rapidly spread beyond computer-literate, highly the biomedical model of the disease [50,51]. These illness motivated early adopters. We suggest that to make a public representations can affect risk perceptions, coping behavior, health impact, developers should be prepared to engage management, and disease outcomes. Data tracking clearly offers creatively with a variety of pragmatic and emotional issues to the possibility of demonstrating the link between behavior and reach a broader audience that includes patients with chronic disease indicators (eg, between diet, medication administration, disease. and blood glucose), thereby encouraging patients to develop a One recommendation is to explore ways to engage directly with more biomedical model of their disease. the emotional impact associated with medical data, exploring However, not all patients wanted to examine their data for this ways not only to motivate progress but also cope with negative purpose. Our findings are striking in the degree to which medical feelings. Developers should seek not to exacerbate negative data were shown to have extremely serious emotional feelings or judgments, look at creative ways to support positive implications for patients with MCC, sometimes serious enough feelings, and facilitate personal goal setting rather than imposing to be associated with abandonment of data tracking altogether. external goals. Technologies could integrate techniques such “Bad” data values can be extremely upsetting, especially when as motivational interviewing [54] that have been demonstrated those “bad” values have or are perceived to have some link to to help patients establish personally relevant goals and action behavior. Patients’ language revealed the extent to which they plans, rather than seeking to persuade patients to adopt their use judgmental terms of sin and transgression to describe both providers’ priorities. The behavioral economics literature can their data and themselves. Furthermore, some of our patients http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 8 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al provide valuable guidance in leveraging effects such as framing, It must still be recognized that older generations are not defaults, and behavioral “nudges” to promote engagement and universally comfortable with electronic technologies and that better decision making [55]. many low-income patients still do not have access to them. For the foreseeable future, a significant subset of patients will lack Another suggestion is to provide different formats for different access to information technology. This creates tremendous purposes. Patients who are building a conceptual understanding opportunities for exploring improved paper technologies. For of disease might benefit from data-driven links with explanatory example, scannable paper forms might ease the burden of material or even simulations. Patients who are using data to tracking data on paper and be more widely used than mobile check on goals might benefit from progress bars or visualized apps by some groups. Technologies that benefit only younger target thresholds. A relatively small number of patients (such or more technologically sophisticated patients could have the as those adjusting insulin doses or high blood pressure potential to widen health disparities rather than narrow them. medications [22]) will be using data for self-management; these This issue of equity must be addressed in health information individuals are most likely to be interested in reminders or alerts. technology broadly, but especially in technology intended for Developing systems with the wrong purpose in mind appears personal health information tracking and management. likely to irritate patients rather than support them. For example, patients who have not established personally relevant goals are Finally, the concept of data tracking as yet another piece of unlikely to welcome visualizations that depict their “progress”, patient “work” resonated strongly with the participants. 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Use of psychology and behavioral economics to promote healthy eating. Am J Prev Med 2014 Dec;47(6):832-837. [doi: 10.1016/j.amepre.2014.08.002] [Medline: 25441239] Abbreviations HIT: health information technology HIV: human immunodeficiency virus MCC: multiple chronic disease http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 11 (page number not for citation purposes) XSL FO RenderX JOURNAL OF MEDICAL INTERNET RESEARCH Ancker et al Edited by G Eysenbach; submitted 07.01.15; peer-reviewed by K Grindrod, A Moen; comments to author 11.05.15; revised version received 03.06.15; accepted 24.07.15; published 19.08.15 Please cite as: Ancker JS, Witteman HO, Hafeez B, Provencher T, Van de Graaf M, Wei E J Med Internet Res 2015;17(8):e202 URL: http://www.jmir.org/2015/8/e202/ doi: 10.2196/jmir.4209 PMID: 26290186 ©Jessica S Ancker, Holly O Witteman, Baria Hafeez, Thierry Provencher, Mary Van de Graaf, Esther Wei. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 19.08.2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. http://www.jmir.org/2015/8/e202/ J Med Internet Res 2015 | vol. 17 | iss. 8 | e202 | p. 12 (page number not for citation purposes) XSL FO RenderX

Journal

Journal of Medical Internet ResearchJMIR Publications

Published: Aug 19, 2015

Keywords: medical informatics; consumer health information; health knowledge, attitudes, practices; self-care; chronic disease

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