What Do Adults with Type 2 Diabetes Want from the “Perfect” App? Results from the Second Diabetes MILES: Australia (MILES-2) Study

Abstract

Background:

We investigated what Australian adults with type 2 diabetes (T2D) want from the “perfect” diabetes self-management application.

Methods:

Adults with T2D completed a national online survey including an open-ended question: “If you were describing the perfect app to help you manage your diabetes, what would it do?” Qualitative responses were subjected to thematic analysis.

Results:

Of the 339 participants who provided usable responses, 153 (45%) were women, the mean age was 58 ± 10 years, and 139 participants (41%) managed their diabetes with insulin. Two primary themes emerged. First, participants expressed a desire for assistance with practical aspects of diabetes self-management to improve, and reduce the cognitive burden of, self-management; this included tracking and visualizing multiple sources of data, using data to inform automated, personalized coaching, reminders, and alarms, and automating upload and linking of data through connected devices. Second, they desired assistance with psychological and emotional aspects of diabetes self-management; this included ongoing encouragement and motivation, help with stress management or negative emotions, and complementing existing health care by facilitating interconnectivity with health professionals.

Conclusions:

Our findings suggest that the clear desire of people with type 2 diabetes is for the “perfect app” to reduce not only the practical, but also the cognitive and emotional burden of diabetes self-management. They provide further evidence that understanding the desires of people living with diabetes needs to be the first step in app development to ensure that apps provide features, support, and benefits that people with diabetes value.

Introduction

Digital health interventions delivered through smartphone applications (“apps”) have the potential to support daily self-management of type 2 diabetes (T2D) by providing diabetes education, tracking diabetes-related data, and facilitating improved communication with health professionals (HPs), among other functions. Systematic reviews and meta-analyses indicate that diabetes apps can improve diabetes management under controlled trial conditions.^1
–3

People with diabetes are enthusiastic about the idea of self-management apps, but very few use them regularly.^4

–8 Approximately 1800 diabetes apps are available on app stores, but only 1% of people with diabetes use an app for self-management.⁹ It was noted recently (in a large-scale online survey) that just 8% of Australian adults with T2D use apps to support their self-management; these focused on blood glucose monitoring (61%), tracking of weight (46%), and physical activity (42%).⁷ Furthermore, people with T2D are significantly less likely to use a diabetes self-management app than those with type 1 diabetes (T1D).⁷ The primary reason for low uptake is that people with T2D do not believe they will be useful.^5

–8 Reviews have reported several shortcomings of diabetes self-management apps, for example, limited functionality,¹⁰ requiring manual data entry,^11,12 lacking personalized evidence-based educational content,^12,13 low usability,^12,14
–16 and lacking links to HPs.^12,14 Finally, in recent reviews, experts and users alike have rated diabetes apps as having low quality.^15,17 Collectively, the evidence suggests that current apps are not providing what people with T2D need or want.

To promote better uptake, it is essential to better understand the desires of actual or potential users.⁵ However, few studies have examined what diabetes self-management app features people with T2D would find useful, and what role, if any, they perceive apps to play in their self-management. Participants in a pilot study in Canada indicated that the features they were most interested in using in the future were blood glucose monitoring, dietary planning, and the option to communicate with HPs.⁸ However, in addition to the small sample size, a limitation of this study was that participants were only able to choose from a predefined list of diabetes “management areas,” including physical activity, diet, tracking of blood glucose, reminders, and communication with peers and HPs. Similarly, Conway et al. also compiled a list of features currently available and used a questionnaire to identify that password protection (of personal information), diabetes education, and blood glucose monitoring were most popular among 139 adults with T2D.⁴ Although these studies suggest directions for future app development, their reliance on predefined lists limits the conclusions to features that were currently available or envisaged by the researchers.

Focus group interviews with 20 people with T2D revealed that they wanted a consistently accessible “one stop shop application,” addressing all aspects of diabetes, including medical management, management of negative emotions, and psychosocial support.¹⁸ Interactive regularly updated content, peer support, positive messaging, detailed personalized information, and user-friendly design were also important. Finally, it was stated that self-management apps should be easily integrated into routine care, rather than being “stand-alone.” Similarly, another study using a focus group approach where people with diabetes were asked to give them immediate feedback on four apps reported tracking and visualizing multiple sources of data, personalized actionable feedback, realistic goal setting, customized reminders, and the ability to share data with health professionals.¹⁹ Focus groups can be a useful way to identify new ideas, but due to limited sample size, the findings need to be replicated in a larger study, unconfined by the limits of a predefined list of potential features.

Thus, the aim of this study was to explore the free-text responses of a large sample of adults with T2D to the open-ended question: “If you were describing the perfect app to help you manage your diabetes, what would it do?”

Methods

This study used data from the second Australian Diabetes Management and Impact for Long-term Empowerment and Success (MILES-2) study. The Deakin University Human Research Ethics Committee (2011–046) approved this study, and written consent was obtained from all participants. The study methods have been described in full elsewhere.²⁰ In brief, MILES-2 was a national cross-sectional survey of Australians with T1D or T2D, aged 18–75 years. The survey focused on the impact of diabetes upon psychological, behavioral, and social aspects of living with diabetes, using validated scales as well as study-specific individual items. The main aim was to provide a longitudinal follow-up of the original MILES cohort²¹ and a new large-scale cross-sectional sample. In March 2015, an invitation to complete an online survey was posted to 20,000 potential participants identified from the National Diabetes Services Scheme (NDSS) database, as well as 2065 respondents of the original MILES (2011) survey. The study was also promoted through social media (Twitter and Facebook), e-newsletters, and advertisements in magazines. The final sample of eligible participants in the MILES-2 study included 2342 unique consenting respondents. Of these, 1264 (54%) had T2D, 43% were women, and the mean age was 61 ± 9 years. This study focuses on data from the respondents with T2D.

Measures

Four study-specific questions were used to assess the use of diabetes-specific apps: those results have been published elsewhere.⁷ The focus of this study is on the free text responses to a single open-ended question: “If you were describing the ‘perfect app’ to help you manage your diabetes, what would it do?” Self-reported demographic data included age, gender, education, employment status, and socioeconomic status (SES). Postcode was used as a proxy for SES, based on the Index of Relative Socioeconomic Advantage and Disadvantage, one of the Socioeconomic Indexes for Areas for Australia.²² For this study, these deciles were collapsed into three SES groups: low (1–3), middle (4–7), and high (8–10). Self-reported clinical data included the duration of diabetes, treatment regimen, and hemoglobin A_1c (HbA_1c).

Participants

Eligible participants for this study were aged 18–75 years, with a self-reported diagnosis of people with T2D, living in Australia and able to complete the survey in English without assistance.

Data analysis

Participants' self-reported demographic and clinical characteristics were summarized using descriptive statistics. Qualitative responses to the open-ended survey question were analyzed using NVivo 11™ following Braun and Clarke's “phases of thematic analysis” method.²³ In the first step, Shaira Baptista read participant responses to familiarize herself with the data. Next, Shaira Baptista developed an initial coding framework to describe the thematic contents of the comments using an inductive data-driven approach. Candidate themes were reviewed and refined iteratively by Shaira Baptista in consultation with Prof. Jane Speight and Dr. Steven Trawley. Dr. Steven Trawley double-coded 20% of the data, and although agreement was high (94%), any discrepancies in coding and interpretation of the data were resolved through mutual consensus.

Results

Respondent sample characteristics

Of the 1264 eligible respondents, 559 (44%) did not answer the open-ended “perfect app” question. Of the 707 who did, 285 (40%) responded “don't know,” 37 (5%) responded “don't need,” 19 (2%) responded “don't use,” and 25 responded with miscellaneous responses that were irrelevant to the aims of this study (e.g., “cure diabetes”). Those who did not respond had a lower SES (P < 0.001) and were older (P < 0.001) than those providing eligible responses, whereas those who responded “don't know” were also significantly older (P < 0.001) than those providing eligible responses. Supplementary Table 1 details the demographic and clinical characteristics by response type.

After ineligible responses were excluded, the free text responses of 339 (27%) eligible participants were analyzed. The mean length of usable responses was 23 words (range: 1–253 words). Forty-eight (14%) of those providing usable responses were “currently” using apps for diabetes self-management. They offered slightly longer responses than those not currently using apps (29 vs. 23 words). Table 1 details participant characteristics, as a total sample, and by treatment type: 153 (45%) were women, mean age was 58 ± 10 years, and mean diabetes duration was 10 ± 7 years. Compared with those managing their T2D with lifestyle/tablets, participants with insulin-treated T2D reported a longer duration of diabetes (P < 0.001), lower SES (P < 0.005), lower education (P < 0.001), and higher HbA_1c (P < 0.001). We did not detect differences by age, gender, duration of diabetes, or any other factors beyond those already noted, and differences were not apparent from repeated readings of the data.

Table 1.

Demographic and Clinical Characteristics of Total Sample and by Treatment Type

	Total sample (N = 339)	Insulin treated (n = 139)	Noninsulin treated (n = 202)
Gender: female	153 (45%)	63 (45%)	90 (45%)
Age, years	58 ± 10	58 ± 9	59 ± 10
Socioeconomic status
Low	69 (21%)	30 (22%)	39 (20%)
Medium	120 (35%)	48 (35%)	72 (36%)
High	145 (43%)	59 (42%)	87 (43%)
Education, highest level
Year 10	66 (19%)	34 (24%)	32 (16%)
Year 12 or apprentice	140 (41%)	58 (42%)	82 (41%)
Graduate/postgraduate	132 (39%)	46 (33%)	86 (43%)
Diabetes duration, years	10 ± 7	14 ± 6	8 ± 6
HbA_1c: unknown	69 (20%)	29 (20%)	40 (19%)
Self-reported HbA_1c, % (mmol/mol)	7.3 ± 2.0 (56 ± 44)	7.7 ± 1.7 (61 ± 37)	7.0 ± 2.2 (53 ± 50)
Currently using app(s) for diabetes self-management: yes	48 (14%)	19 (14%)	29 (14%)

HbA_1c , hemoglobin A_1c.

Two overarching themes emerged from the data: (1) assistance with practical aspects of diabetes self-management and (2) assistance with psychological and emotional aspects of diabetes self-management. These are described hereunder along with their subthemes.

Theme 1: desire for assistance with practical aspects of diabetes self-management

Theme 1a: more comprehensive and personalized tracking and visualizing of data

Respondents described wanting an app enabling them to track “as much as possible” of their self-care activities [e.g., insulin doses and other medication(s), physical activity, food/meals, and carbohydrates] and health indicators (e.g., glucose levels, mood, sleep, weight, blood pressure, hypoglycemic events, and hyperglycemia). In addition to this daily tracking, participants also wanted to track other aspects of their health and medical history (e.g., sick days and clinical test results, such as HbA_1c). As one participant noted, “There needs to be a single app that tracks EVERYTHING.” The ability to track over the long-term was also requested, “Keep track of blood sugar level readings over a long period of time e.g., 1–2 years. Keep track of my medications, dosage levels and changes” and “provide a full history of BG readings with complementary HbA_1c data to get a full overview of diabetes progress.”

Respondents desired a greater ability to personalize how they track their glucose levels. For example, being able to adjust the timing of readings, “to test my blood sugars just before I eat and then 2 hours after I eat,” or to place glucose readings in context, “variation in meal times against blood glucose levels” and “write notes against each of the (BG) readings to have a record of why levels were high/low” to understand how “unusual activities, stress etc.” can impact glucose levels. There were requests for comprehensiveness, that is an app that could “take blood sugar all day,” allowing for a “continuous picture of blood glucose levels.” Finally, respondents asked for tracked data to be presented in a way that was easy to understand and interpret, for example, “visual graphs to show management within and outside of normal range” and to “present information in a dashboard for overall view” to “provide information about how my diabetes management is going in a visual/graphical manner.”

Theme 1b: automated personalized diabetes management coaching

Respondents suggested that assistance with decision-making based on their historical data would be a useful feature. For example, an app could recommend certain self-care actions based on previous glucose readings: “if the prick test results are trending up, it could prompt for more frequent prick tests” or “suggest remedial action where there is a problem.” Such prompts could also be based on past behaviors, for example, “it would monitor your intake whatever it may be and inform you of what you could eat for the remainder of the day, including taking into account your exercise.”

Although some respondents were looking for an app to provide direct instructions: “exactly what to eat and do according to (my) readings,” others were looking for more general coaching, such as “suggestions on how to improve your diet and exercise,” or real-time feedback: “I could set goals, and the app could give me ideas on how to reach those goals. For example, if I set a goal to burn 8000 kJ each day and I had recorded that I had walked for 40 minutes. The app could give me calculated ideas on how to make up the difference” and encouragement: “instant appraisal of how my management is going against targets set by me or my doctor.”

Theme 1c: reminders and alarms for optimal self-care and safety

Requests for reminders were related to daily glucose monitoring, medication taking, and other aspects of self-care. For example, “remind me to do the blood sugars, remind me when to inject insulin and the dose” and about “diet and exercise.” Also requested were reminders to attend or make appointments with HPs and for regular clinical tests and follow-ups. Alarms could provide “warnings given if health indicators fall outside a safe range.” It was suggested that the “perfect app” could play the role of a guardian, “warning about [going] too high or too low” or “alert me when I need to take my medication.”

Respondents with insulin-treated T2D requested alarms to alert them to hypoglycemia, whereas those managing T2D with lifestyle and/or oral medications requested alarms alerting them to hyperglycemia, for example, “alert to areas of attention” like high blood glucose levels, “suggest strategies to consider”, and alert to perceived suboptimal management, that is “provide warnings when diabetes management is not in control.”

Theme 1d: Automating upload and linking of data, with the option to share with HPs

The ability to reduce manual data entry by capitalizing on automatic data uploads and connected personal devices was described as a useful feature. For example, respondents requested that an app “interfaces wirelessly to [a] blood glucose meter” so that glucose readings are uploaded automatically and viewable on a smartphone: “it would be able to measure and record [glucose] seamlessly without needed to enter it [manually].” There were also requests for the data to be viewed through a screen other than a smartphone: “it would auto-sync with software on my computer so that I can keep track of what is happening from my computer rather than from my phone.”

Respondents wanted to use apps not only to link, upload, and track their data but also to “have the option to link [the data] with [a] general practitioner, endocrinologist or diabetes educator.” It was also suggested that “having a graphing element to use when talking to HPs” would be useful “so a health professional could give feedback and suggestions on results.”

Beyond glucose readings, other requests for integrated data sources included food diaries, physical activity, weight, stress, mood, blood pressure, and clinical test results. Among respondents with insulin-treated T2D, the desire for automatic uploading of glucose readings, carbohydrate, and insulin doses was expressed, whereas those not using insulin were more likely to want automatic linkage between blood glucose data, food diaries physical activity (e.g., recorded steps), and weight.

Theme 2: desire for assistance with psychological and emotional aspects of diabetes self-management

Theme 2a: provision of ongoing encouragement and motivation

Respondents described a desire for an app to “encourage [them] through the day to exercise and move and choose the right food to make [them] feel [their] best,” to “be supportive and encouraging,” and provide “simple tips to give [them] confidence and reassuring support” and enable them to “take charge of [their] life.” Respondents were seeking extrinsic motivation, with gamification of daily diabetes self-care activities, for example, “some type of visual reward.…a happy face sticker or mini-game to reward you for recording each day… Incentive plus positive reinforcement. Tips or positive affirmations to help you stay positive.”

Others requested “not to be patronised by telling [me I am] doing very well.” They referred to the need to keep diabetes in perspective by acknowledging that perfect diabetes self-care is unattainable, and for an app to reassure them that they are “normal,” and “accept that once in a while I might have a piece of honey toast, which really puts the [glucose] reading up but accept that it is a one-off situation.”

Theme 2b: support for stress management and well-being

Participants wanted an app to “suggest how to improve your wellbeing” and how to be “less dependent on other people.” They were also looking for support in dealing with feelings of guilt, for example, being able to eat “a special treat…with less guilt.” Finally, it was important for an app to “understand and suggest hints during times of stress” and to “alert you when you're experiencing stress and anxiety episodes which are impacting on your general wellbeing.”

Discussion

This is the first large-scale study addressing the question of what adults with T2D want from the “perfect” diabetes management application. Two primary themes emerged from the data: (1) desire for assistance with practical aspects of diabetes self-management and (2) desire for assistance with psychological and emotional aspects of diabetes self-management. For theme 1, respondents wanted apps that could track multiple sources of diabetes-related data and visualize trends, use data for personalized coaching, reminders and alarms, automate data uploads, and connect devices with an option to share data with HPs. For theme 2, respondents wanted apps to provide ongoing encouragement and motivation, as well as support for dealing with negative emotions.

The data tracking subtheme highlights the importance of personalization, comprehensiveness, and ease-of-use in how data are tracked and visualized to enable “sense-making,” that is, using data from multiple sources to understand how glucose levels interact with other factors in either the details of daily self-management or in stepping back to see and track the “bigger picture.” Achieving glucose targets on a day-to-day basis can be difficult because of the interaction of numerous factors that are difficult to track and visualize in one place. Research has shown that monitoring and associating glucose levels with other behaviors that might affect them, such as physical activity, may help people to develop the skills, confidence, and motivation needed to improve self-care.^24,25 In addition, apps that enable analysis and interpretation, resulting in explicit and specific actionable recommendations and feedback, are more beneficial than those that do not.^19,26 For example, people with diabetes have found specific advice about how a particular meal can be improved (e.g., nutritional content comparisons); meal suggestions and food preparation tips are more useful than nonspecific general nutritional education and recommendations.^19,25

Although some participants desired autonomy consistent with a “quantified self-management style”²⁷, others desired more explicit decision support or coaching based on their personal data. These differences likely reflect individual differences in self-management styles, confidence in knowledge/skills, and motivation. For example, those who requested numbers may be intrinsically motivated and perceive themselves to be “experts” in their own care. In contrast, those who ask for explicit coaching may be less confident in their self-care knowledge and skills and/or may be more extrinsically motivated. Understanding the role that these factors play in the type of support an individual requires of diabetes self-management apps is a compelling avenue for further research.

Automatically uploading data collected through linked devices can eliminate the need to track data manually, a significant deterrent to the uptake of diabetes apps.²⁸ Alerts and alarms can improve (perceived and actual) safety by providing warnings of hypoglycemia or hyperglycemia and improve self-management outcomes by improving the regularity of medication taking, physical activity, and clinical tests and appointments with HPs. A recent finding that nearly a quarter of people with T1D and T2D report recently forgetting their diabetes medication highlights the importance of reminders.²⁹ Including the option to share data with HPs has been shown to improve efficacy of diabetes apps.^30,31 Our study corroborates findings from previous research that interconnectivity with HPs is desired by people with diabetes and highlights the need for high-quality studies that assess the impact of this function.^8,18,31 It is also important to address the significant barriers to integration at an individual level (e.g., insufficient time to review and interpret large volumes of patient-collected data, the perception that data are unreliable)³² and at a systems level (data incompatibility, security and privacy, and reimbursement models).³³ For diabetes self-management data to be integrated into routine care, these issues will need to be addressed.

Previous research highlights the importance of psychosocial support for people with diabetes.^5,18 Apps that provide support with stress management and problem solving have increased diabetes-related self-efficacy, and reduced diabetes-related distress as well as HbA_1c (average glucose for the past 8–12 weeks).³⁴ However, support with the emotional aspects of diabetes (e.g., specifically focused on diabetes distress or fear of hypoglycemia) is the least common feature among diabetes apps,³⁵ and our findings suggest that this gap in the provision of support needs to be addressed.

The landscape of diabetes apps is evolving rapidly, and there are now apps available that offer some of the features desired by study participants. For example, the BlueStar^® Diabetes app by WellDoc,³⁶ Tactio Health, and My Connected Health Logbook^16,17 offer advanced features, such as personalized real-time feedback, interconnectivity with blood glucose meters, fitness trackers, and the ability to share data with HPs. The One Drop app³⁷ delivers diabetes self-management education, psychosocial support, and access to a certified diabetes educator for additional support and encouragement. Sugar.IQ,³⁸ a collaboration between Medtronic and IBM Watson, will track and integrate glucose levels and meal content and recommend future meal adjustments or other behaviors to improve their glucose readings. Although predictive analytics are promising, if rule-based algorithms are to be used to interpret data and make recommendations such as insulin dosages, care must be taken not to trigger incorrect models, which could do more harm than good,³⁹ and to ensure that the “advice” is provided in a constructive and encouraging way, so that does not berate the person for past “poor choices.”⁴⁰ Several organizations (including the U.S. Food and Drug Administration, the UK's National Health Service, and the American Diabetes Association) are working to develop regulations that assess “safety and effectiveness without inhibiting access.”⁴¹ In addition, user groups and communities are also developing their own lists of preferred and recommended apps (e.g., DiaDigital⁴² and My Health Apps⁴³). Regardless of recent advances, most available apps have limited functionality, are not evidence based, and are not integrated into routine care.

Most previous research of this nature has used small samples for laboratory-based usability testing or in-depth interviews. Thus, the sample of >300 achieved in this study is a strength, although the valid response rate to the open-ended question (27%) is a limitation. One possible reason for the low response rate is that within the MILES-2 survey sample, most people with T2D had primary education or less (67%) and reported a residential postcode from a lower SES area (61%).⁷ It is possible that the lower response rate from those with a low SES may reflect barriers to app use, and this needs to be explored further. With a mean age of 58 ± 10 years, the sample is reasonably representative (45% of Australians with T2D are aged 50–69 years).⁴⁴ However, 39% of Australians with T2D are aged 70+ years, and we cannot generalize our results to this group or to the increasing population of younger people with T2D. Different generations of people with T2D are likely to have different expectations with diabetes self-management apps. Thus, more research on the acceptability, usability, and feasibility of app content and features as a function of age and familiarity with apps is warranted. With 40% managing their diabetes with insulin, the views of this growing cohort are well represented.⁴⁴ However, differences in support desired by treatment type warrant further investigation. In our study, respondents with insulin-treated T2D varied from those not using insulin in the type of data they preferred to track, the advice they wanted, and the types of alarms and reminders they would find useful.

A key strength of this study is the open-ended nature of the question, which gave participants the freedom to interpret the question and respond to it without restriction. This was a critical aspect of the research because it created the potential to explore previously unknown aspects of app features for diabetes self-management. The survey methodology, although enabling a large sample for qualitative research, was also a limitation because some responses lacked detail or were incomplete. Furthermore, analysis and interpretation were limited by ambiguities in the meaning of participant responses, as well as the lack of opportunity to explore themes further with participants and clarify their responses.

Providing what users need and want does not guarantee engagement with an app. An in-depth exploration of the lived experiences of people with T2D using self-management apps in the real world (within the context of their everyday lives and using their own phones) is needed to understand the context within which they are used, the content and delivery formats that are considered to be useful, and how user characteristics such as existing diabetes knowledge, diabetes management style, and familiarity with apps interact to influence actual use and engagement. We also recommend that developers take user perspectives into consideration when they examine important issues of app quality, security, and relevance of educational content, rather than relying on professional/technical input alone.^12,15,17

In summary, diabetes self-management applications have the potential to support self-management and to reduce the burden of living with diabetes. Our data suggest that this means that different features will appeal to different users, but overall, the clear desire is for the “perfect app” to reduce the practical, cognitive, and emotional burden of diabetes self-management. Although academic research demonstrates that diabetes-self management apps need to be theoretically informed and evidence based,^1,2 this study provides compelling evidence that fully understanding the needs and desires of people with diabetes through their lived experiences is a critical starting point to provide features, support, and benefits that they value most.

Footnotes

Acknowledgments

The authors thank Dr. Jessica Browne, Dr. Elizabeth Holmes-Truscott, Dr. Christel Hendrieckx, and Dr. Adriana Ventura (all currently or formerly employed at the Australian Centre for Behavioural Research in Diabetes) for their work on the Diabetes MILES-2 study. The authors thank all the study participants for volunteering their time, insights, and experiences. S.B. is supported by a postgraduate scholarship from the National Health and Medical Research Council, Australia, and Diabetes Australia. Recruitment for the MILES-2 study and the development of the MILES-2 study website were funded through an unrestricted educational grant (paid to The Australian Centre for Behavioural Research in Diabetes) from Sanofi ANZ. Sanofi ANZ was not involved in the study design, data collection, or data analysis, and had no input on the preparation of this article. J.S. is supported by core funding to The Australian Centre for Behavioural Research in Diabetes derived from the collaboration between Diabetes Victoria and Deakin University.

Authors' Contributions

J.S. conceived The Diabetes MILES Study and, together with F.P., developed The Diabetes MILES Study International Collaborative. J.S., S.T., and F.P. were involved in the design and survey content of the MILES-2 study. S.B. set up the online survey, analyzed the data, and prepared the first draft and subsequent revisions of the article. S.T. also analyzed some of the data. All authors reviewed/edited the article for critical content and approved the final version.

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

Supplementary Table S1

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