The experience of illness is a very personal and specific one. No one can understand what the body is feeling like its owner can, who knows it better than anyone. Resident in my body, I notice the smallest sensations, like the itchy tag on the back of my shirt. But it’s hard to explain this feeling to other people, because it can’t be fully described just by talking about its triggering event. The same shirt probably feels different to someone else. My emotional response to sensation also influences how I experience it. If I’m at the tail end of a bad day, I’ll have a much lower tolerance for scratchy clothes. And when I’m ill, I am the only person who fully understands what my own combination of physical and emotional symptoms feels like. However, expressing all this to my doctor can be complicated. My answer to the question, “How are you feeling?” depends on the day. To help me find the words, I often take inspiration from authors, both old and new, who have taken their turn at the problem.
Even for a symptom common as fever, the words used to describe it depend on each person’s background and experience. Sylvia Plath’s poem “Fever 103°” depicts illness in terms drawn from sources as diverse as Classical myth, botany, and modern dance. An image of a wheezing Cerberus, the three-headed dog of the underworld, appears alongside a giant red flower, evoking the heat and discomfort of fever. “All by myself I am a huge camellia, glowing and coming and going, flush on flush.” A reference to the sudden death of dancer Isadora Duncan rounds out the speaker’s list of symptoms. Even the mundane details of the patient’s life receive attention. Plath describes chicken soup in the most disgusting way possible, calling it “chicken water,” a term that reflects a sick person’s loss of appetite.
The effort to derive some benefit from the suffering caused by illness, however, can encourage a person to dwell on their imagined flaws in a damaging way. In this frame of mind, Plath’s speaker discusses their personal faults, and imagines that by enduring the discomfort of fever, they can become “too pure for you or anyone.” In making this claim, the speaker both separates themselves from their loved ones, and implies there was something wrong with them in the first place. This gives pain a positive effect, and gives meaning to a person’s suffering. It can be tempting to imagine suffering as a sort of character-building exercise in this way. It becomes a strategy to cope with things we can’t change, like the weather. Here in Minnesota, it’s often said that wintertime is good for us.
But scraping the snow off my windshield each morning is easier when I know that spring is coming. What if a future without illness will never arrive? Some diseases are incurable, and become a permanent part of someone’s life. In these cases it’s especially harmful to imagine suffering as a way to fix our shortcomings. Daniel Drubach’s contemporary essay on his experience living with an incurable condition shows a kinder way to live with a permanent state of impaired health. Unlike Plath’s pitiless examination of faults, Drubach’s treatment of himself is gentler. “While I do not hold pity for myself,” he writes, “I do acknowledge a certain degree of compassion.”
If we choose not to see suffering itself as a means of self-improvement, what other benefit can we gain from it? Drubach suggests that one’s personal experience of disease offers a special version of perception that is not accessible to the healthy self. Opening himself to the full experience of his symptoms, Drubach’s condition enables “the discovery of unique visions and sensations, strange emotions, unusual forms of dreaming, and even a bit of magic.” The visual and auditory sensations produced by Drubach’s illness become familiar parts of his life that provide vivid, if not always pleasant, experiences. Drubach accepts the fact that these sensations will occur, though their content cannot be fully predicted or managed. This mindset positions the body’s demands and their influence on one’s life not as limitations, but as a valuable aspect of being human.
It can be frustrating, however, to have to rearrange our lives around our bodies’ imperatives. When I’m sick, I’m sleepy and can’t concentrate. I find myself re-reading the same page over and over through a fog of body aches. As Virginia Woolf writes, “All day, all night the body intervenes.” Like Drubach, Woolf sees the conditions enforced by illness as a chance to experience the world in a different way. This context transforms the sick person’s enforced absence from their daily routine into a chance to see their life from a new perspective. The familiar proportions of one’s priorities and concerns seem to recede. “The whole landscape of life lies remote and fair, like the shore seen from a ship far out at sea.” Viewing my life from this distance, it can be easier to notice what really matters to me. It can even be beneficial to work with the short attention span of illness, when one’s available moments of concentration are “sudden, fitful, intense.” In this state of mind, I notice those moments of pleasure that often escape my attention when I have the energy to multitask. Even a snowplow methodically clearing the street under my window has its own fascination.
I try to be gentle with myself in illness and accept the difficulties I experience, whether it’s the reduced capacity for activity, or the struggle to communicate my experience to others. Illness is not the time to inventory my faults. It’s okay to step back, to do less. Sometimes, just being is enough.
Even if care seems right from a medical perspective, if care doesn’t fit for each individual, we may ‘deliver care’ without actually ‘caring’. The recently published Making Care Fit Manifesto (1) states that for care to fit, care should be maximally responsive to patients’ unique situation and supportive to their priorities. Care should also be minimally disruptive to patients’ lives, loved ones, and social network. Making care fit requires patients (and their caregivers) and clinicians to collaborate, both in content and manner, and it is an ongoing and iterative process where care plans should continuously be evaluated and modified.
This is especially pertinent for young adults living with type 1 diabetes. Previous research showed that young adults with type 1 diabetes have relatively poor biomedical and psychosocial outcomes (2,3). For example, HbA1c levels are higher in younger adults compared to other age groups, and strikingly, they are also higher now than they were a decade ago (3).
At the same time, very little is known about what young adults do to implement diabetes care in their lives, and what price they have to pay in terms of negative effects on themselves and their surroundings. Also, how do we bridge what happens in their personal environment (‘point of life’) and what happens during clinical encounters (‘point of care’)? Because whatever is left undiscussed with their clinicians, is also left unconsidered when designing care plans.
We explored experiences of young adults with type 1 diabetes trying to make care fit into their lives. First, we asked 62 young adults with type 1 diabetes (Median age: 27, IQR 24 to 27, 80% women) from the French ‘Community of Patients for Research’ (ComPaRe) for their experiences with the burden of treatment. They reported a high burden4 of diabetes treatment (76.5 out of 150, IIQR 59 to 94). Importantly, 3 of every 4 young adults (74%) reported that their investment of time, energy, and efforts in healthcare is unsustainable over time. This is about twice as high as for other people with chronic conditions.
Second, the Dutch ééndiabetes foundation (for and by young adults with type 1 diabetes) asked its members for their experiences in making diabetes care fit into their lives. Nine of 25 young adults (36%) indicated that their diabetes care regularly or very often hinders their education, work, hobbies, leisure or social lives. When asked to describe their biggest efforts to fit diabetes care into their daily life, they responded:
“You want to live a fun and spontaneous life, but you have to nonstop keep an eye on your sugars.”
“Food. Everything you have to do then. That’s why I sometimes skip my meals.”
“Planning. Not just the hospital appointments and changing needles, but also planning with the energy I will or will not have.”
“Regulating hypo’s and then compensating for the time I couldn’t function well due to a hypo.”
“On time and constant planning ahead. What do I need? Do I have all my stuff before I leave? When do I have to place new orders to make sure I don’t run out?”
Additionally, we asked young adults what they do to fit diabetes care into their lives, but what they don’t discuss with their clinician. Some indicated they discuss “everything” or “nothing” with their clinician. Others said:
“I use a DIY loop. (I did tell my clinician but he wasn’t interested)”
“I delay my hospital appointments as long as I can.”
“I discuss everything about my diabetes with my clinician. All the better they can help me.”
“I bolus less to prevent hypos.”
Communication is key to bridge efforts of making care fit at the point of life and at the point of care. Our future work will focus on uncovering better ways to improve the quality of diabetes care through improving conversations. And to help young adults with their lifelong, daily and ongoing endeavor of making diabetes care fit.
References
Kunneman M, Griffioen IPM, Labrie NHM, Kristiansen M, Montori VM, van Beusekom MM, Making Care Fit Working G. Making care fit manifesto. BMJ Evid Based Med. 2021.
Johnson B, Elliott J, Scott A, Heller S, Eiser C. Medical and psychological outcomes for young adults with Type 1 diabetes: no improvement despite recent advances in diabetes care. Diabet Med. 2014;31(2):227-231.
Redondo MJ, Libman I, Maahs DM, Lyons SK, SaracoM, Reusch J, Rodriguez H, DiMeglio LA. The Evolution of Hemoglobin A1c Targets for Youth With Type 1 Diabetes: Rationale and Supporting Evidence. Diabetes Care. 2021;44(2):301-312.
Tran VT, Harrington M, Montori VM, Barnes C, Wicks P, Ravaud P. Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Med. 2014;12:109.
Cardiovascular disease (CVD) continues to be a leading cause of mortality and disease burden worldwide. There are several approaches to prevent CVD and new ones continue to emerge.[1] The American College of Cardiology/American Heart Association guidelines for primary cardiovascular (CV) prevention recommend shared decision making (SDM) to co-create individualized plans for preventive care.[2]
As part of an NIH-funded project which aims to identify implementation approaches that promote high-quality SDM about CV prevention for patients in primary care settings, designers and researchers at the KER Unit created Heart Health: A CV Prevention Choice Tool, an SDM tool for use by patients and clinicians during the clinical encounter to co-create a plan of care. The tool:
Offers an individualized 10-year estimate of the patient’s atherosclerotic cardiovascular disease (ASCVD) risk.
Offers lifestyle interventions – Mediterranean diet, physical activity, smoking cessation – and medication – e.g., statins, aspirin, other lipid, blood pressure, glucose lowering drugs – options and demonstrates how starting/continuing/stopping them can affect jointly and cumulatively the patient’s ASCVD risk.
Supports a conversation by which patient and clinician create an evidence-based plan that is desirable, useful, and feasible for both.
This document describes the development process we followed to produce Heart Health.
Steps for the design, evaluation, and prototyping of the tool
A. Literature review
Clinicians and researchers thoroughly searched the literature for relative risk reduction estimates of major adverse cardiovascular events for the interventions of interest. The relative risk (RR) is the probability of an outcome in the exposed group compared to the probability of an outcome in the unexposed group.[3] The RR estimates for smoking cessation and exercise come from observational cohort studies. All other estimates of the effect of these interventions on CV risk come from randomized trials. When possible, we sought high quality systematic reviews of these primary studies. For each intervention we also sought evidence on other (nonCV) benefits, medication administration routine, adverse effects, and patient out-of-pocket costs.
Among the interventions of interest were nutrition and diet, antiplatelet medication, glucose-lowering medications, lipid-lowering therapy, blood pressure lowering medications, smoking cessation, and weight loss.
B. Summarizing the evidence
What to include?
After completing this extensive review, and discussing our findings with preventive cardiologists, we decided to include in the final tool:
Activities:
Mediterranean diet
Exercise
Smoking cessation
Medications
Statins- medium and high dose
Ezetimibe
PCSK-9 inhibitors
Aspirin
Blood pressure lowering medications
GLP-1 agonists
SGLT2- inhibitors
Decisions made on content
In addition to the factors needed to compute a 10-year ASCVD risk, we also included 2 additional parameters: lipoprotein (a) and coronary calcium score. We assumed these could be helpful in patients who were unsure as to how intensely to pursue preventive care (e.g., patients at so-called intermediate CV risk). When evaluating the use of the tool in clinic, it was evident that these parameters were rarely available in primary care settings and rarely used in discussions. Thus, we decided to exclude them from the current version.
Another parameter that was added after discussing with experts was a question on family history of premature (males <55 years, females <65 years) myocardial infarction, stroke, or sudden death in a first degree relative. This question did not affect the risk calculations per se but, if selected, a disclaimer would be displayed when calculating risk: “Your family history of heart disease means that your risk may be higher than shown. Consider further discussion with a preventive cardiologist.”
For the activities included (smoking cessation, Mediterranean diet, and exercise), it was decided to include links to patient education websites created by the Mayo Clinic for each activity, where patients and clinicians could obtain more detailed information and suggestions on how to make these changes to their lifestyle.
For Diabetes medications, we included GLP-1 agonists and SGLT-2 inhibitors. If the patient has diabetes, these two medications are part of the medications table. If the patient does not have diabetes, they are not initially included. However, the option of adding them to the table is available.
Risk calculators
This tool uses the ASCVD risk calculator to estimate the patient’s 10-year ASCVD risk and a 100-person pictograph to display this risk. Then we use best estimates of risk reduction against this risk estimation to propose a revised ASCVD risk given the interventions chosen, assuming independence. This is based on the approach used in the highly popular and effective Statin Choice tool.
To calculate the current risk of having a coronary event (described as “heart attack” in the tool) in the next 10 years, the tool uses the ASCVD risk calculator equation and data such as: age, sex at birth (M/F), African American (Y/N), smoker (Y/N), Diabetes (Y/N), treated blood pressure (Y/N), total cholesterol (100-350 mg/dL), HDL cholesterol (10-120 mg/dL), and systolic blood pressure (90-250 mmHg) that has to be completed by the clinician or auto-populated from the electronic health record. For further detail on how to calculate the current risk using the ASCVD risk calculator, please refer to pages 32-34 on the 2013 Report on the Assessment of Cardiovascular Risk: Full Work Group Report Supplement. [4]
For each intervention, we used their RR estimates to calculate the future risk of having a coronary event in the next 10 years if the patient started, or stopped using, that intervention. The future risk is calculated by multiplying the current risk by the RR overall. The RR overall is calculated using the RR estimates shown in the following table, which are different depending on the use status of the intervention (i.e. if a medicine and/or an activity is started or stopped). A patient’s RR overall is calculated by multiplying together the RRs of each intervention that the patient chooses to start using, and then further multiplying by the RRs of each intervention that the patient is currently using that they and their clinician elect to stop using. The RR of an intervention that is stopped is the inverse of the RR of an intervention that is started
RR overall = RR interventions started * RR interventions stopped
This means for example, that if a patient switches from medium to high dose statins, then: RR overall = RRstatins high * 1/RRstatins medium
Any interventions that the patient is currently using and will continue to use are not included in the calculation of RR overall and therefore do not contribute to the estimate of future risk. This reflects the difficulty of determining any further risk reduction that may be achieved by continuing current interventions beyond that which is reflected in the patient’s current risk estimate. (For example, for a patient currently taking a blood pressure lowering medicine, the benefits of the intervention are reflected in the estimate of the patient’s current risk and it is unknown to what extent continued use of the medication will lower that risk further.)
Future risk= current risk * RR overall.
Activity or medicine option
RR activity/med started
RR activity/med stopped
Not smoking
0.61[5]
1.64 (1/0.61)
Heart-Healthy Diet
0.7[6]
1.42 (1/0.7)
Exercise
0.75[7]
1.33 (1/0.75)
Statins medium
0.75[8]
1.33 (1/0.75)
Statins high
0.6[8]
1.66 (1/0.6)
Ezetimibe
0.94[9]
1.06 (1/0.94)
Aspirin
0.91[10]
1.09 (1/0.91)
Blood Pressure Lowering Medications
0.88[11]
1.13 (1/0.88)
PCSK-9 inhibitors
0.86[12]
1.16 (1/0.86)
GLP-1 Agonists
0.88[13]
1.13 (1/0.88)
SGLT-2 Inhibitors
0.86[14]
1.16 (1/0.86)
As described above, in order to overcome evidence limitations, a number of methodological compromises were made in calculating future risk. This reflects principles that are used in developing all our shared decision-making tools:
Patients and clinicians need support in making decisions even when optimal evidence does not exist.
Risk is only a device that in some circumstances may be helpful in decision making.
It is more important that any risk presented is a useful approximation that can help people make reasonable decisions than that it is precise, particularly when imprecision is unlikely to affect the final decision.
The most appropriate method for calculating risk should be based on the quality of the reasonably applicable evidence and its ability to contribute usefully and feasibly to patient and clinician decision making.
Reporting of other outcomes
For each medication stated above, we created a table with key information, the most common adverse effects, and other benefits of the medication. We selected the most discussed in practice and the most relevant to the clinical context of primary prevention. Also, we gathered information on average cost of these medications per month according to the GoodRx service, recognizing that these estimates vary greatly depending on the patient’s insurance.
Other benefits
Side effects
Statins medium dose
Prevents strokes by 29%[15]
Muscle aches (0-5 in 100)[8]
Statins high dose
Prevents strokes up to 48%[16]
Muscle aches (0-10 in 100)[8]
Ezetimibe
Prevents strokes up to 14% alone or in combination with statins.[17]
Muscle/joint aches, flu-like symptoms.[18]
Aspirin
Prevents colorectal cancer by 20%.[19]
Easy bruising, bleeding (3 in 1000)[10]
Blood pressure medications
Prevents strokes up to 40%; other benefits depend on medicine used.[11]
Depends on medicine used
PCSK9-inhibitors
Prevents strokes up to 20%. [20]
Flu-like symptoms.[12]
GLP1-agonists
Prevents death by 11%, loss of >5% of body weight, prevents kidney failure by 20%. [21, 22]
Nausea-vomiting (2-3 in 10), diarrhea (1 in 10).[23]
SGLT2-inhibitors
Prevents death by 20%, loss of 2% of body weight, prevents kidney failure by 30%.[14]
Urinary and genital infections (200 in 1000 over 5 years), DKA (4 in 1000 over 5 years).[24, 25]
Design process: Prototyping and refinement of the tool
Designers at the KER Unit start the process of designing the tool by reviewing how relevant conversations take place in medical encounters in usual practice. After obtaining patient and clinician informed consent, we video recorded 5 preventive cardiology visits which were then reviewed by members of the team. These observations were fundamental to creating the first version of the Heart Health tool.
We invited 4 clinicians working in preventive cardiology, consultative internal medicine, and primary care at Mayo Clinic to test this tool prototype with their patients. We tested two versions of the tool with a total of 8 patients. All encounters were video recorded and reviewed. Each version of the tool was improved considering user experience, observed misuses, and clinician recommendations for adding, removing, or modifying the tool’s content.
One example of the kind of refinements done to the tool (that came from the observation of its use in clinical practice and from expert feedback) is the change to the order of display of the intervention screens. Initially, our tool showed the medications tab first and the activities tab second. The order of these seemed to imply that the best approach to discussing CV risk prevention was by focusing on medications first.
However, when observing the videos, we saw that the conversation always started with changes in lifestyle given their large impact on CV health. We also recognized that the issues presented in the tool that pertained to medicines weren’t particularly informative when considering lifestyle changes (e.g. side effects). We wanted our tool to support as much as possible the usual conversation that patients have with their clinicians. For this reason, we made the very simple but relevant decision to order the tool so that activities were available for discussion before medicines.
The third version of the prototype tool was considered the final one and was sent to a software development company that later released the tool to be implemented within the electronic workflow and online as a standalone webapp. The team at KER Unit was in constant communication with the software development team, and together we worked on improving the tool’s visual display while making sure the purpose and logic of each screen was maintained.
1. Roth Gregory, A., et al., Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019. Journal of the American College of Cardiology, 2020. 76(25): p. 2982-3021.
2. Arnett, D.K., et al., 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol, 2019. 74(10): p. e177-e232.
3. Tenny S, H.M., Relative Risk. [Updated 2021 Mar 30]. 2021: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.
4. Goff David, C., et al., 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk. Journal of the American College of Cardiology, 2014. 63(25_Part_B): p. 2935-2959.
5. Duncan, M.S., et al., Association of Smoking Cessation With Subsequent Risk of Cardiovascular Disease. Jama, 2019. 322(7): p. 642-650.
6. Martínez-González, M.A., A. Gea, and M. Ruiz-Canela, The Mediterranean Diet and Cardiovascular Health. Circulation Research, 2019. 124(5): p. 779-798.
7. Wahid, A., et al., Quantifying the Association Between Physical Activity and Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis. Journal of the American Heart Association, 2016. 5(9): p. e002495.
8. Taylor, F., et al., Statins for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev, 2013. 2013(1): p. Cd004816.
9. Zhan, S., et al., Ezetimibe for the prevention of cardiovascular disease and all‐cause mortality events. Cochrane Database of Systematic Reviews, 2018(11).
10. Gelbenegger, G., et al., Aspirin for primary prevention of cardiovascular disease: a meta-analysis with a particular focus on subgroups. BMC Medicine, 2019. 17(1): p. 198.
11. Sakima, A., et al., Optimal blood pressure targets for patients with hypertension: a systematic review and meta-analysis. Hypertens Res, 2019. 42(4): p. 483-495.
12. Schmidt, A.F., et al., PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease. Cochrane Database of Systematic Reviews, 2017(4).
13. Jia, X., et al., GLP-1 Receptor Agonists and Cardiovascular Disease: a Meta-Analysis of Recent Cardiac Outcome Trials. Cardiovasc Drugs Ther, 2018. 32(1): p. 65-72.
14. Zou, C.-Y., et al., Effects of SGLT2 inhibitors on cardiovascular outcomes and mortality in type 2 diabetes: A meta-analysis. Medicine, 2019. 98(49): p. e18245.
15. Chou, R., et al., Statins for Prevention of Cardiovascular Disease in Adults: Evidence Report and Systematic Review for the US Preventive Services Task Force. Jama, 2016. 316(19): p. 2008-2024.
16. Watson, K.E., The JUPITER trial: How will it change clinical practice? Rev Cardiovasc Med, 2009. 10(2): p. 91-6.
17. Ouchi, Y., et al., Ezetimibe Lipid-Lowering Trial on Prevention of Atherosclerotic Cardiovascular Disease in 75 or Older (EWTOPIA 75). Circulation, 2019. 140(12): p. 992-1003.
18. Brar, K.S., Ezetimibe (Zetia). Medical journal, Armed Forces India, 2004. 60(4): p. 388-389.
19. Cao, Y., et al., Population-wide Impact of Long-term Use of Aspirin and the Risk for Cancer. JAMA oncology, 2016. 2(6): p. 762-769.
20. Sabatine, M.S., et al., Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. N Engl J Med, 2017. 376(18): p. 1713-1722.
21. Kristensen, S.L., et al., Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of cardiovascular outcome trials. Lancet Diabetes Endocrinol, 2019. 7(10): p. 776-785.
22. Khera, R., et al., Association of Pharmacological Treatments for Obesity With Weight Loss and Adverse Events: A Systematic Review and Meta-analysis. Jama, 2016. 315(22): p. 2424-34.
23. Filippatos, T.D., T.V. Panagiotopoulou, and M.S. Elisaf, Adverse Effects of GLP-1 Receptor Agonists. The review of diabetic studies : RDS, 2014. 11(3-4): p. 202-230.
24. Halimi, S. and B. Vergès, Adverse effects and safety of SGLT-2 inhibitors. Diabetes & Metabolism, 2014. 40(6, Supplement 1): p. S28-S34.
25. Musso, G., et al., Diabetic ketoacidosis with SGLT2 inhibitors. BMJ, 2020. 371: p. m4147.
“So what happened here,” and my doctor would point to a single blood sugar from three Thursdays ago, a 243 mg/dL at 3 am.
And I wracked my brain trying to remember what happened that night. Had I changed my pump site before bed? Sometimes that causes a high afterwards. Did I eat a snack and miscalculate the insulin dose to cover it? Did I over-treat a low? I couldn’t remember specifically what happened. Do I make something up? Do I lie?
I shrugged with frustration. “I think diabetes happened there.”
Type 1 diabetes doesn’t exist outside of the context of life. I wish it did; I wish diabetes was something that existed independent of everything else in my life, making it absent the influence of variables like exercise, eating, and emotions. But diabetes is a pervasive, persistent thread that weaves its way around every aspect of my life, from breakfast to the last thought before falling asleep at night. It’s the preposition that dangles off of every thought – “with diabetes” – and makes my disease a constant and necessary priority.
Minimally Disruptive Medicine makes sense as an approach for chronic illness because it flies in the face of what chronic illness attempts to do, which is to disrupt. Diabetes is very disruptive and intrusive, so making my care approach towards the disease more streamlined and integrated creates a culture of hope, motivation, and effort.
When it comes to building a care plan with my medical team, my personalized variables need to take center stage. Ask me what my goals are, instead of building treatment recommendations around what you think my goals should be. Do I want an A1C that’s within ADA guidelines? Of course. But am I willing to achieve that goal by way of several low blood sugar events per week? No way. My doctor’s goal may be to improve my fasting blood sugars, while my goal might be to overcome my fear of overnight hypoglycemia. How do we take medical guidelines and best practices and balance those within the context of my real life?
Diabetes maps differently in every single life, so personal preferences take precedence. You recommend that I wear an insulin pump to help best control my blood sugars? Prescribing the device is one thing, but I also need training on how to integrate this technology into my real life. Connect me with peers who wear their insulin pumps safely and confidently at the beach, or while running, or while tending to the needs of their small child.
Show me “how” instead of telling me “why.”
Talk to me about my preferences, my goals, and my life, because that’s where my diabetes exists. Diabetes exists around my life, not the other way around. I don’t build my life around diabetes. It’s not a hole in me or the whole of me. There’s life to be found after diagnosis, and my focus remains on making the most of that life.
Kerri Sparling has been living with type 1 diabetes for over 29 years, diagnosed in 1986. She manages her diabetes and lives her life by the mantra “Diabetes doesn’t define me, but it helps explain me.”
Kerri is a passionate advocate for all-things diabetes. She is the creator and author of Six Until Me, one of the first and most widely-read diabetes patient blogs, reaching a global audience of patients, caregivers, and industry. Well-versed in social media and its influence on patients, Kerri presents regularly at conferences and works full-time as a writer and consultant. Her first book, Balancing Diabetes (Spry Publishing), was released in the Spring of 2014.
Kerri and her husband, Chris, live in Rhode Island, USA with their daughter.
By Ian Hargraves, Maggie Breslin, Nassim Jafarinaimi
Healthcare, like any care, is the product of what people can do and who they can be for each other in the midst of suffering. The relationship of people attending to suffering finds its most direct expression in contemporary healthcare in the relationship of patient and clinician. The ways in which these two come together lies at the heart of how we conceive of and organize the healthcare enterprise. If we conceive of the meeting of patient and clinician as rooted in the knowledge and expertise of the medical expert then we may establish paternalistic and patriarchal structures and relationships by which to deploy that knowledge. Beyond this, we may seek to improve and innovate healthcare by heightening the knowledge, technology, and efficiency of the medical expert. Alternatively if, in the coming together of patient and clinician, we focus attention on the demands of the patient who is commissioning and paying for care we may set the suffering person in the role of consumer. Let the buyer beware then becomes the organizing principle, a principle that calls for an empowered patient equipped with authority, information, choice, and control in the face of illness. This is a situation in which we think that if the suffering person would and could only be more—more knowledgeable, more assertive, more discriminating as a purchaser—then illness would be less. There is a third possibility in the coming together of patient and clinician. In this way, the joining of people is called for by the situation of suffering. The reason for healthcare is not the deployment of technical expertise, or the exercise of choice. The reason for healthcare is to attend to the challenges of suffering. This is the reason that in clinic rooms throughout the country and world patients and clinicians sit together, talk, and together take action in attending to suffering or the threat of suffering. In the KER unit, we explore the hypothesis that the medium in which this relationship is made productive and caring is conversation
Shared decision making (SDM) is a patient-centered approach in which clinicians and patients work together to find and choose the best course of action for each patient’s particular situation [1]. This approach is pertinent to the care of patients with chronic conditions [2]. Six key elements of shared decision making can be identified [1-4]:
situation diagnosis (understanding the patient’s situation and establishing the aspects require action)
choice awareness (indicating that multiple options are available and highlighting the
importance of the patient’s preferences in deciding on the course of action)
option clarification (explaining the available options)
discussion of harms and benefits (explaining the harms and benefits of each option)
deliberation of patient preferences (discussing the preferences of the patient)
making the decision (clinician and patient making together the decision)
Decision aids SDM can be facilitated by decision aids that have been developed for use by clinicians and patients, either during or in preparation of the clinical encounter [5-7]. Decision aids can help patients choose an option that is congruent with their values, reduce the proportion of patients remaining undecided and/or who play a passive role in the decision-making process, and improve patient knowledge, decisional conflict, and patient-clinician communication [7-11].
The International Patient Decision Aid Standards (IPDAS) Collaboration developed a minimal set of standards for qualifying a tool as a decision aid, which require that a decision aid support all key elements but making the decision [12].
Systematic review We conducted a systematic review to assess the extent to which decision aids support the six key SDM elements and how this relates to their impact.
We found 24 articles reporting on 23 RCTs of 20 DAs (10 DAs for cardiovascular disease, two DAs for respiratory diseases, and eight DAs for diabetes). With the exception of one, all studies have an unclear or high risk of bias for all outcomes assessed in this review. The option clarification element (included in 20 of 20 DAs; 100%) and the harms and benefits discussion (included in 18 of 20 DAs; 90%; unclear in two DAs) are the elements most commonly clearly included in the DAs. The other elements are less common and more uncertainty is present whether these elements are included, especially with regard to choice awareness (uncertain in 14 out of 20 DAs; 70%). All elements were clearly supported in four DAs (20%). We found no association between the presence of these elements and SDM outcomes.
Conclusion Thus, despite the IPDAS minimal set of qualifying criteria, our systematic review showed that decision aids for cardiovascular diseases, chronic respiratory diseases, and diabetes mostly support the option clarification and the discussion of harms and benefits elements of SDM, while the other SDM elements are less often incorporated.
Future research Possibly, some SDM elements may be left out of decision aids by design. This choice may depend on what features were thought most important by the developers (e.g., patient education, risk communication, preference elicitation, or patient empowerment). The importance of incorporation of SDM elements in decision aids may be situation-dependent, but the way this works is unclear. Therefore, future research should clarify this situation-dependence and eventually inform possible reconsideration of the IPDAS minimum standards for decision aid qualification. The relationship between the extent to which decision aids support SDM elements and outcomes is yet unknown and should be studied in future research as well.
Thomas Wieringa is a post-doc researcher at the department of Epidemiology at the University Medical Center Groningen (UMCG), the Netherlands. He did his PhD, focused on shared decision making and patient-reported outcomes in type 2 diabetes, at the VU University Medical Center. He visited and collaborated with the Knowledge and Evaluation Research (KER) Unit of the Mayo Clinic in the context of his PhD.
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Some methods and equipment allow altering the apparent DOF, and some even allow the DOF to be determined after the image is made. For example, Focus stacking combines multiple images focused on different planes, resulting in an image with a greater (or less, if so desired) apparent depth of field than any of the individual source images. Similarly, in order to reconstruct the 3-dimensional shape of an object, a depth map can be generated from multiple photographs with different depths of field. This method is called “shape from focus.”
Other technologies use a combination of lens design and post-processing: Wavefront coding is a method by which controlled aberrations are added to the optical system so that the focus and depth of field can be improved later in the process.
Butterfly lighting uses only two lights. The key light is placed directly in front of the subject, often above the camera or slightly to one side, and a bit higher than is common for a three-point lighting plan. The second light is a rim light.
Often a reflector is placed below the subject’s face to provide fill light and soften shadows.
This lighting may be recognized by the strong light falling on the forehead, the bridge of the nose, the upper cheeks, and by the distinct shadow below the nose that often looks rather like a butterfly and thus, provides the name for this lighting technique.
Butterfly lighting was a favourite of famed Hollywood portraitist George Hurrell, which is why this style of lighting is often called Paramount lighting.
