Research

Poster and presentation content from @DanaMLewis at #ADA2020 and #DData20

by Dana Lewis

In previous years (see 2019 and 2018), I mentioned sharing content from ADA Scientific Sessions (this year it’s #ADA2020) with those not physically present at the conference. This year, NO ONE is present at the event, and we’re all virtual! Even more reason to share content from the conference. :)

I contributed to and co-authored two different posters at Scientific Sessions this year:

  • “Multi-Timescale Interactions of Glucose and Insulin in Type 1 Diabetes Reveal Benefits of Hybrid Closed Loop Systems“ (poster 99-LB) along with Azure Grant and Lance Kriegsfeld, PhD.
  • “Do-It-Yourself Artificial Pancreas Systems for Type 1 Diabetes Reduce Hyperglycemia Without Increasing Hypoglycemia” (poster 988-P in category 12-D Clinical Therapeutics/New Technology—Insulin Delivery Systems), alongside Jennifer Zabinsky, MD MEng, Haley Howell, MSHI, Alireza Ghezavati, MD, Andrew Nguyen, PhD, and Jenise Wong, MD PhD.

And, while not a poster at ADA, I also presented the “AID-IRL” study funded by DiabetesMine at #DData20, held in conjunction with Scientific Sessions. A summary of the study is also included in this post.

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First up, the biological rhythms poster, “Multi-Timescale Interactions of Glucose and Insulin in Type 1 Diabetes Reveal Benefits of Hybrid Closed Loop Systems” (poster 99-LB). (Twitter thread summary of this poster here.)

Building off our work as detailed last year, Azure, Lance, and I have been exploring the biological rhythms in individuals living with type 1 diabetes. Why? It’s not been done before, and we now have the capabilities thanks to technology (pumps, CGM, and closed loops) to better understand how glucose and insulin dynamics may be similar or different than those without diabetes.

Background:

Mejean et al., 1988Blood glucose and insulin exhibit coupled biological rhythms at multiple timescales, including hours (ultradian, UR) and the day (circadian, CR) in individuals without diabetes. The presence and stability of these rhythms are associated with healthy glucose control in individuals without diabetes. (See right, adapted from Mejean et al., 1988).

However, biological rhythms in longitudinal (e.g., months to years) data sets of glucose and insulin outputs have not been mapped in a wide population of people with Type 1 Diabetes (PWT1D). It is not known how glucose and insulin rhythms compare between T1D and non-T1D individuals. It is also unknown if rhythms in T1D are affected by type of therapy, such as Sensor Augmented Pump (SAP) vs. Hybrid Closed Loop (HCL). As HCL systems permit feedback from a CGM to automatically adjust insulin delivery, we hypothesized that rhythmicity and glycemia would exhibit improvements in HCL users compared to SAP users. We describe longitudinal temporal structure in glucose and insulin delivery rate of individuals with T1D using SAP or HCL systems in comparison to glucose levels from a subset of individuals without diabetes.

Data collection and analysis:

We assessed stability and amplitude of normalized continuous glucose and insulin rate oscillations using the continuous wavelet transformation and wavelet coherence. Data came from 16 non-T1D individuals (CGM only, >2 weeks per individual) from the Quantified Self CGM dataset and 200 (n = 100 HCL, n = 100 SAP; >3 months per individual) individuals from the Tidepool Big Data Donation Project. Morlet wavelets were used for all analyses. Data were analyzed and plotted using Matlab 2020a and Python 3 in conjunction with in-house code for wavelet decomposition modified from the “Jlab” toolbox, from code developed by Dr. Tanya Leise (Leise 2013), and from the Wavelet Coherence toolkit by Dr. Xu Cui. Linear regression was used to generate correlations, and paired t-tests were used to compare AUC for wavelet and wavelet coherences by group (df=100). Stats used 1 point per individual per day.

Wavelets Assess Glucose and Insulin Rhythms and Interactions

Wavelet Coherence flow for glucose and insulin

Morlet wavelets (A) estimate rhythmic strength in glucose or insulin data at each minute in time (a combination of signal amplitude and oscillation stability) by assessing the fit of a wavelet stretched in window and in the x and y dimensions to a signal (B). The output (C) is a matrix of wavelet power, periodicity, and time (days). Transform of example HCL data illustrate the presence of predominantly circadian power in glucose, and predominantly 1-6 h ultradian power in insulin. Color map indicates wavelet power (synonymous with Y axis height). Wavelet coherence (D) enables assessment of rhythmic interactions between glucose and insulin; here, glucose and insulin rhythms are highly correlated at the 3-6 (ultradian) and 24 (circadian) hour timescales.

Results:

Hybrid Closed Loop Systems Reduce Hyperglycemia

Glucose distribution of SAP, HCL, and nonT1D
  • A) Proportional counts* of glucose distributions of all individuals with T1D using SAP (n=100) and HCL (n=100) systems. SAP system users exhibit a broader, right shifted distribution in comparison to individuals using HCL systems, indicating greater hyperglycemia (>7.8 mmol/L). Hypoglycemic events (<4mmol/L) comprised <5% of all data points for either T1D dataset.
  • B) Proportional counts* of non-T1D glucose distributions. Although limited in number, our dataset from people without diabetes exhibits a tighter blood glucose distribution, with the vast majority of values falling in euglycemic range (n=16 non-T1D individuals).
  • C) Median distributions for each dataset.
  • *Counts are scaled such that each individual contributes the same proportion of total data per bin.

HCL Improves Correlation of Glucose-Insulin Level & Rhythm

Glucose and Insulin rhythms in SAP and HCL

SAP users exhibit uncorrelated glucose and insulin levels (A) (r2 =3.3*10-5; p=0.341) and uncorrelated URs of glucose and insulin (B) (r2 =1.17*10-3; p=0.165). Glucose and its rhythms take a wide spectrum of values for each of the standard doses of insulin rates provided by the pump, leading to the striped appearance (B). By contrast, Hybrid Closed Loop users exhibit correlated glucose and insulin levels (C) (r2 =0.02; p=7.63*10-16), and correlated ultradian rhythms of glucose and insulin (D) (r2 =-0.13; p=5.22*10-38). Overlays (E,F).

HCL Results in Greater Coherence than SAP

Non-T1D individuals have highly coherent glucose and insulin at the circadian and ultradian timescales (see Mejean et al., 1988, Kern et al., 1996, Simon and Brandenberger 2002, Brandenberger et al., 1987), but these relationships had not previously been assessed long-term in T1D.

coherence between glucose and insulin in HCL and SAP, and glucose swings between SAP, HCL, and non-T1DA) Circadian (blue) and 3-6 hour ultradian (maroon) coherence of glucose and insulin in HCL (solid) and SAP (dotted) users. Transparent shading indicates standard deviation. Although both HCL and SAP individuals have lower coherence than would be expected in a non-T1D individual,  HCL CR and UR coherence are significantly greater than SAP CR and UR coherence (paired t-test p= 1.51*10-7 t=-5.77 and p= 5.01*10-14 t=-9.19, respectively). This brings HCL users’ glucose and insulin closer to the canonical non-T1D phenotype than SAP users’.

B) Additionally, the amplitude of HCL users’ glucose CRs and URs (solid) is closer (smaller) to that of non-T1D (dashed) individuals than are SAP glucose rhythms (dotted). SAP CR and UR amplitude is significantly higher than that of HCL or non-T1D (T-test,1,98, p= 47*10-17 and p= 5.95*10-20, respectively), but HCL CR amplitude is not significantly different from non-T1D CR amplitude (p=0.61).

Together, HCL users are more similar than SAP users to the canonical Non-T1D phenotype in A) rhythmic interaction between glucose and insulin and B) glucose rhythmic amplitude.

Conclusions and Future Directions

T1D and non-T1D individuals exhibit different relative stabilities of within-a-day rhythms and daily rhythms in blood glucose, and T1D glucose and insulin delivery rhythmic patterns differ by insulin delivery system.

Hybrid Closed Looping is Associated With:

  • Lower incidence of hyperglycemia
  • Greater correlation between glucose level and insulin delivery rate
  • Greater correlation between ultradian glucose and ultradian insulin delivery rhythms
  • Greater degree of circadian and ultradian coherence between glucose and insulin delivery rate than in SAP system use
  • Lower amplitude swings at the circadian and ultradian timescale

These preliminary results suggest that HCL recapitulates non-diabetes glucose-insulin dynamics to a greater degree than SAP. However, pump model, bolusing data, looping algorithms and insulin type likely all affect rhythmic structure and will need to be further differentiated. Future work will determine if stability of rhythmic structure is associated with greater time in range, which will help determine if bolstering of within-a-day and daily rhythmic structure is truly beneficial to PWT1D.
Acknowledgements:

Thanks to all of the individuals who donated their data as part of the Tidepool Big Data Donation Project, as well as the OpenAPS Data Commons, from which data is also being used in other areas of this study. This study is supported by JDRF (1-SRA-2019-821-S-B).

(You can download a full PDF copy of the poster here.)

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Next is “Do-It-Yourself Artificial Pancreas Systems for Type 1 Diabetes Reduce Hyperglycemia Without Increasing Hypoglycemia” (poster 988-P in category 12-D Clinical Therapeutics/New Technology—Insulin Delivery Systems), which I co-authored alongside Jennifer Zabinsky, MD MEng, Haley Howell, MSHI, Alireza Ghezavati, MD, Andrew Nguyen, PhD, and Jenise Wong, MD PhD. There is a Twitter thread summarizing this poster here.

This was a retrospective double cohort study that evaluated data from the OpenAPS Data Commons (data ranged from 2017-2019) and compared it to conventional sensor-augmented pump (SAP) therapy from the Tidepool Big Data Donation Project.

Methods:

  • From the OpenAPS Data Commons, one month of CGM data (with more than 70% of the month spent using CGM), as long as they were >1 year of living with T1D, was used. People could be using any type of DIYAPS (OpenAPS, Loop, or AndroidAPS) and there were no age restrictions.
  • A random age-matched sample from the Tidepool Big Data Donation Project of people with type 1 diabetes with SAP was selected.
  • The primary outcome assessed was percent of CGM data <70 mg/dL.
  • The secondary outcomes assessed were # of hypoglycemic events per month (15 minutes or more <70 mg/dL); percent of time in range (70-180mg/dL); percent of time above range (>180mg/dL), mean CGM values, and coefficient of variation.
Methods_DIYAPSvsSAP_ADA2020_DanaMLewis

Demographics:

  • From Table 1, this shows the age of participants was not statistically different between the DIYAPS and SAP cohorts. Similarly, the age at T1D diagnosis or time since T1D diagnosis did not differ.
  • Table 2 shows the additional characteristics of the DIYAPS cohort, which included data shared by a parent/caregiver for their child with T1D. DIYAPS use was an average of 7 months, at the time of the month of CGM used for the study. The self-reported HbA1c in DIYAPS was 6.4%.
Demographics_DIYAPSvsSAP_ADA2020_DanaMLewis DIYAPS_Characteristics_DIYAPSvsSAP_ADA2020_DanaMLewis

Results:

  • Figure 1 shows the comparison in outcomes based on CGM data between the two groups. Asterisks (*) indicate statistical significance.
  • There was no statistically significant difference in % of CGM values below 70mg/dL between the groups in this data set sampled.
  • DIYAPS users had higher percent in target range and lower percent in hyperglycemic range, compared to the SAP users.
  • Table 3 shows the secondary outcomes.
  • There was no statistically significant difference in the average number of hypoglycemic events per month between the 2 groups.
  • The mean CGM glucose value was lower for the DIYAPS group, but the coefficient of variation did not differ between groups.
CGM_Comparison_DIYAPSvsSAP_ADA2020_DanaMLewis SecondaryOutcomes_DIYAPSvsSAP_ADA2020_DanaMLewis

Conclusions:

    • Users of DIYAPS (from this month of sampled data) had a comparable amount of hypoglycemia to those using SAP.
    • Mean CGM glucose and frequency of hyperglycemia were lower in the DIYAPS group.
    • Percent of CGM values in target range (70-180mg/dL) was significantly greater for DIYAPS users.
    • This shows a benefit in DIYAPS in reducing hyperglycemia without compromising a low occurrence of hypoglycemia. 
Conclusions_DIYAPSvsSAP_ADA2020_DanaMLewis

(You can download a PDF of the e-poster here.)

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Finally, my presentation at this year’s D-Data conference (#DData20). The study I presented, called AID-IRL, was funded by Diabetes Mine. You can see a Twitter thread summarizing my AID-IRL presentation here.

AID-IRL-Aim-Methods_DanaMLewis

I did semi-structured phone interviews with 7 users of commercial AID systems in the last few months. The study was funded by DiabetesMine – both for my time in conducting the study, as well as funding for study participants. Study participants received $50 for their participation. I sought a mix of longer-time and newer AID users, using a mix of systems. Control-IQ (4) and 670G (2) users were interviewed; as well as (1) a CamAPS FX user since it was approved in the UK during the time of the study.

Based on the interviews, I coded their feedback for each of the different themes of the study depending on whether they saw improvements (or did not have issues); had no changes but were satisfied, or neutral experiences; or saw negative impact/experience. For each participant, I reviewed their experience and what they were happy with or frustrated by.

Here are some of the details for each participant.

AID-IRL-Participant1-DanaMLewisAID-IRL-Participant1-cont_DanaMLewis1 – A parent of a child using Control-IQ (off-label), with 30% increase in TIR with no increased hypoglycemia. They spend less time correcting than before; less time thinking about diabetes; and “get solid uninterrupted sleep for the first time since diagnosis”. They wish they had remote bolusing, more system information available in remote monitoring on phones. They miss using the system during the 2 hour CGM warmup, and found the system dealt well with growth spurt hormones but not as well with underestimated meals.

AID-IRL-Participant2-DanaMLewis AID-IRL-Participant2-cont-DanaMLewis2 – An adult male with T1D who previously used DIYAPS saw 5-10% decrease in TIR (but it’s on par with other participants’ TIR) with Control-IQ, and is very pleased by the all-in-one convenience of his commercial system.He misses autosensitivity (a short-term learning feature of how insulin needs may very from base settings) from DIYAPS and has stopped eating breakfast, since he found it couldn’t manage that well. He is doing more manual corrections than he was before.

AID-IRL-Participant5-DanaMLewis AID-IRL-Participant5-cont_DanaMLewis5 – An adult female with LADA started, stopped, and started using Control-IQ, getting the same TIR that she had before on Basal-IQ. It took artificially inflating settings to achieve these similar results. She likes peace of mind to sleep while the system prevents hypoglycemia. She is frustrated by ‘too high’ target; not having low prevention if she disables Control-IQ; and how much she had to inflate settings to achieve her outcomes. It’s hard to know how much insulin the system gives each hour (she still produces some of own insulin).

AID-IRL-Participant7-DanaMLewis AID-IRL-Participant7-cont-DanaMLewis7 – An adult female with T1D who frequently has to take steroids for other reasons, causing increased BGs. With Control-IQ, she sees 70% increase in TIR overall and increased TIR overnight, and found it does a ‘decent job keeping up’ with steroid-induced highs. She also wants to run ‘tighter’ and have an adjustable target, and does not ever run in sleep mode so that she can always get the bolus corrections that are more likely to bring her closer to target.

AID-IRL-Participant3-DanaMLewis AID-IRL-Participant3-cont-DanaMLewis3 – An adult male with T1D using 670G for 3 years didn’t observe any changes to A1c or TIR, but is pleased with his outcomes, especially with the ability to handle his activity levels by using the higher activity target.  He is frustrated by the CGM and is woken up 1-2x a week to calibrate overnight. He wishes he could still have low glucose suspend even if he’s kicked out of automode due to calibration issues. He also commented on post-meal highs and more manual interventions.

AID-IRL-Participant6-DanaMLewis AID-IRL-Participant6-contDanaMLewis6 – Another adult male user with 670G was originally diagnosed with T2 (now considered T1) with a very high total daily insulin use that was able to decrease significantly when switching to AID. He’s happy with increased TIR and less hypo, plus decreased TDD. Due to #COVID19, he did virtually training but would have preferred in-person. He has 4-5 alerts/day and is woken up every other night due to BG alarms or calibration. He does not like the time it takes to charge CGM transmitter, in addition to sensor warmup.

AID-IRL-Participant4-DanaMLewis AID-IRL-Participant4-contDanaMLewis4 – The last participant is an adult male with T1 who previously used DIYAPS but was able to test-drive the CamAPS FX. He saw no TIR change to DIYAPS (which pleased him) and thought the learning curve was easy – but he had to learn the system and let it learn him. He experienced ‘too much’ hypoglycemia (~7% <70mg/dL, 2x his previous), and found it challenging to not have visibility of IOB. He also found the in-app CGM alarms annoying. He noted the system may work better for people with regular routines.

You can see a summary of the participants’ experiences via this chart. Overall, most cited increased or same TIR. Some individuals saw reduced hypos, but a few saw increases. Post-meal highs were commonly mentioned.

AID-IRL-UniversalThemes2-DanaMLewis AID-IRL-UniversalThemes-DanaMLewis

Those newer to CGM have a noticeable learning curve and were more likely to comment on number of alarms and system alerts they saw. The 670G users were more likely to describe connection/troubleshooting issues and CGM calibration issues, both of which impacted sleep.

This view highlights those who more recently adopted AID systems. One noted their learning experience was ‘eased’ by “lurking” in the DIY community, and previously participating in an AID study. One felt the learning curve was high. Another struggled with CGM.

AID-IRL-NewAIDUsers-DanaMLewis

Both previous DIYAPS users who were using commercial AID systems referenced the convenience factor of commercial systems. One DIYAPS saw decreased TIR, and has also altered his behaviors accordingly, while the other saw no change to TIR but had increased hypo’s.

AID-IRL-PreviousDIYUsers-DanaMLewis

Companies building AID systems for PWDs should consider that the onboarding and learning curve may vary for individuals, especially those newer to CGM. Many want better displays of IOB and the ability to adjust targets. Remote bolusing and remote monitoring is highly desired by all, regardless of age. Post-prandial was frequently mentioned as the weak point in glycemic control of commercial AID systems. Even with ‘ideal’ TIR, many commercial users still are doing frequent manual corrections outside of mealtimes. This is an area of improvement for commercial AID to further reduce the burden of managing diabetes.

AID-IRL-FeedbackForCompanies-DanaMLewis

Note – all studies have their limitations. This was a small deep-dive study that is not necessarily representative, due to the design and small sample size. Timing of system availability influenced the ability to have new/longer time users.

AID-IRL-Limitations-DanaMLewis

Thank you to all of the participants of the study for sharing their feedback about their experiences with AID-IRL!

(You can download a PDF of my slides from the AID-IRL study here.)

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Have questions about any of my posters or presentations? You can always reach me via email at Dana@OpenAPS.org.

Convening The Center

by Dana Lewis

(Update: see the latest about Convening the Center in 2021 here)

Patients and care partners who want to make a difference in health care are advised to give up our day jobs, create non-profits, or change previously identified career paths to “go work for a healthcare organization.” These formal constructs are not the only ways to achieve change or make a difference.

Those who choose to work outside of traditional pathways often end up with fewer resources and fewer opportunities (not just financial, but also the opportunity of collaborations and connections).

Thinking about these gaps in resources and opportunities has been swimming around my head since the Convening we hosted as part of the Opening Pathways project (more about it here). As a project, we learned so much from the conversations we had when we were able to just bring people together.

The feedback we received from non-traditional healthcare stakeholders was one of the most surprising results of the Convening. These are people who are not necessarily working professionally in healthcare, but doing a lot of work in the nontraditional spaces. In the year since the Convening we’ve repeatedly heard how valuable it was for this group to come together, in person, to connect with others with a similar drive and passion.

Fast forward to early last year. My friend Liz Salmi (of #BTSM) reached out Alicia Staley (of #BCSM) and me to share about an exciting, random conversation and brainstorm she had with Steve Downs from Robert Wood Johnson Foundation (RWJF).  The idea: What if there was an ‘unconference’ to bring together more of these individuals–those working outside of traditional pathways–to learn and collaborate, without the agenda driven by an existing organization, association, established conference, or company?

This concept sounded great to me! It feels like a next logical step to take with Opening Pathways especially if we pair it with a few structured activities similar to what we did at the Convening to create more equitable participation opportunities for patients and care partners to help people feel comfortable engaging together in person.

When Liz said she didn’t have time to lead this project I volunteered to take it on. Liz and Alicia agreed and expressed their full support.

I put together a proposal in partnership with John Harlow who also worked on Opening Pathways, and was instrumental in designing the original Convening. We submitted a proposal to RWJF, did a few rounds of feedback and discussion about the proposal, waited a bit, and found out right around the new year that the proposal was accepted and had been awarded funding! Yay!

We’re calling this project “Convening The Center.” This both picks up on the name of the previous Convening, and emphasizes the people/patients as the center on which all of health and healthcare should be focused.

Convening The Center: What if there was a gathering for individuals working outside of traditional healthcare pathways?

What this means:

  • We have funding to put together a ~2 day meeting for ~25 individuals who are doing both the possible and the impossible to change and improve healthcare.
  • The funding includes travel (ground transportation, flights), lodging (hotel), food during the event, and an honorarium for the participants’ time.
  • The meeting was originally scheduled to be sometime in 2020 (August or September was goal; COVID-19 disrupted this planning, TBD for new dates but looking at 2021 instead).

Who will be involved:

Convening The Center project team:

  • Dana Lewis (me), Principal Investigator (PI)
  • John Harlow, Co-Principal Investigator (PI)
  • Convening Advisors: Liz Salmi, Alicia Staley, Nick Dawson

Who can participate?:

  • TBD! Here’s why and how:

Why must we convene the Center?

If you’re reading this, you likely have your own story of doing the “impossible” — you’ve faced barriers and obstacles, but have found a way to innovate, overcome, or steer around. There are a LOT of people doing this “work,” whether it’s their professional work, their personal passion, or a necessity driving them to improve things for themselves or a loved one, building and supporting their communities as unfunded labors of love. But we also know that geography, socioeconomic background, and financial resources, among other reasons, commonly leave some of these individuals siloed, or prevent them and their work from reaching its full potential.

We know there is a lack of connectedness among individual innovators, researchers, and advocates who are not employed in the traditional healthcare system. While there have been a handful of attempts to convene patient advocates to share ideas and connect with opportunities and resources, none have been devoted solely to this type of community. Existing attempts have included ad-hoc social media groups and inclusion at existing conferences and meetings. Both face serious limitations.

Social media is limited by one’s ability to stumble across a network, while conferences or meetings—which are traditionally held by legacy institutions—usually include people who are already “in” a network that invites them to such physical events, and are thus already “doing” the work, but these do not do enough to encourage new participants. Additionally, conferences and meetings prioritize the hosting organization’s agenda rather than facilitating the development of non-traditional innovators. Given the limitations of social media and existing conferences, the status quo leads new “doers” to (unknowingly and repeatedly) duplicate the work of others and fail to effectively share knowledge and scale tools that could help others. Overall, there are not a lot of resources for people who do this outside of a professional job.

Therefore, we aim to do something different to identify participants for this meeting.

Rather than just invite the same individuals who have the resources to participate, or have already succeeded somewhat, even in the face of all the existing barriers, we plan to solicit attendees from a mix of health communities, from a range of experiences, with diverse demographics, including those who are newly working in this space, as well as experienced individuals with established credibility.

How will we reach all of these different communities and individuals? This is where we need your help!

We have a two-phase recruitment process to identify potential attendees.

Phase 1 (right now)

  • Fill out this form! 
    • We’d love for you to nominate yourself, if you’re potentially interested in participating.
    • But a crucial part of this is to ALSO nominate someone else – a friend or someone you know who may not otherwise hear about this opportunity.
  • We’d also love for you to help share this form widely and help us reach people in different networks. If you TikTok, post it on TikTok. If you’re on LinkedIn, share it on your LinkedIn or a group. If you’re part of an offline support group, talk about it there. Or reach out and share the link with your advocacy organization and encourage them to nominate other advocates and ‘doers’ that they know.

Nominate someone you know for Convening The Center!
Phase 2 (in a few weeks):

  • Based on the first wave of nominated folks, we’ll work to make sure we’re striking the balance between people who are longer-timers in this space and people who are newly emerging in this type of work.
  • We’ll reach out to a selection of folks identified in phase 1 and ask for a little bit more information to help determine the final cohort of participants for the in-person meeting. (Goal: ~25 participants).

We’ve learned through Opening Pathways and other work in this space that more — and perhaps different — resources are needed for “doers” in healthcare who are not traditionally employed in this space.

We don’t expect the outcome of this project to solve all problems or identify a one-size-fits-all resource. However, we do hope to help manifest a new, more inclusive, and more effective vision for changing the future of healthcare.

The future we seek augments the existing health efforts of legacy institutions by coordinating the work of individual innovators, researchers, and advocates in a more inclusive community of practice. We do not think this will solve all problems around under-representation and the static network of those already “in” and doing this work, but it’s an important step and one we’re happy to be able to take.

FREQUENTLY ASKED QUESTIONS

  • Who is funding this project? How is it being funded? What organization are you partnering with?Robert Wood Johnson Foundation (RWJF) is a great partner, and I’m proud that they’re willing to fund this meeting. Paul Tarini is our project officer at RWJF. While my co-PI is based at an academic institution, we decided to experiment with using a fiscal sponsorship organization to manage the grant. We identified and selected Trailhead Institute, a 501(c)(3) organization that works with a variety of projects and organizations in the public health space. I’ll write more about this in the future, but so far they have been GREAT administrative partners and have been seamless to work with during the application and kickoff of the grant process. Also, we learned from the past Convening that it would be beneficial to directly fund a meeting planner to do logistics work (rather than me), so we included in our budget a meeting planner that is coming from Trailhead to help with administrative and logistics planning for the meeting. Yay!
  • How will you select participants?Our goal is to gain a diverse slate of people, including diversity in socioeconomic background, ethnicity, gender, education, area of healthcare, type of work, how long they have been doing the work, etc. Before finalizing the list of participants we will collect information from potential participants and make sure they’d be interested and available to participate once the date is selected.
  • What are the outputs?We anticipate one primary output from this meeting to be relationships among attendees. After observing the strength and resilience generated for individuals by participating in our Opening Pathways convening, we see relationships as a powerful support for the efforts of healthcare “doers”. By relationships, we do not mean a community of 25. Community building is long-term labor-intensive work. Rather, we hope that some attendees will find common ground and collaborate in various ways after Convening the Center.We do not expect to produce a particular report or website from this work. However, we do expect to write blog posts about our process of developing the meeting, the experience of facilitating the meeting, and the insights derived from conversations at the meeting. We anticipate those insights to be about the wants and needs of healthcare doers, what they wish they had when they started out, what they’d tell their younger selves, and how to refine and scale various healthcare improvement efforts.
  • What about COVID-19?While we have been planning this meeting for August or September 2020, we are aware that currently (in March 2020) there is a lot of uncertainty about how COVID-19 may impact meetings after the next few months. While we are beginning virtual recruitment of participants, we will work with public health officials to get guidance on whether August/September still makes sense, and if not, work with both participants and public health to determine a suitable alternative timeline for holding the meeting. If that’s not feasible, we may find ways to meet this goal virtually.Update: Obviously, it does not make sense to convene the center physically for an in-person meeting in 2020. We are aiming for a gathering – in-person if safe and appropriate, otherwise adapting to virtual – in 2021. We’ll keep everyone posted!

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(Update: see the latest about Convening the Center in 2021 here)

How the sausage gets made – guest editing and peer reviewing for scientific journals (and advice for future publications)

by Dana Lewis

I’m not an academic, but I have spent a lot of time (especially lately) writing, editing, submitting, and reviewing for “peer-reviewed” scientific publications. As a result, I wanted to share some of my experiences and insights gained that may help others who are planning to write, submit, or review similar peer-reviewed process pieces!

My background in publishing in peer-review journals

In 2016, I presented my first poster at a scientific meeting. This was a big deal, because I’m not an academic, I don’t have an academic degree, and I didn’t “work” my day job in the space I was presenting in. After the conference, I was given an invitation to write an article with the results of the study I had presented the poster on. I was nervous, but accepted, and did it. It turns out, it wasn’t that hard. (Granted, it was a Letter to the Editor, rather than a longer format ‘original research article’, but it still wasn’t as hard as I had perceived it to be). My article was successfully published in a scientific journal.

In the years since, I have subsequently decided to write up more of my research and results of work happening in the open source, do-it-yourself diabetes community. Why? As I wrote in this post, I realize that not all HCPs are willing or able to stay up to date with the bleeding edge of what’s being created and innovated on in the diabetes community. If we want HCPs to get up to speed more quickly, we need to play a role in taking the information to them. Thus, I work to publish in journals (since they’re more likely to read or stumble across those than blog posts). (If you’re interested, most of my publications are listed in Google Scholar if you want to see the types of things I’ve been writing and contributing to.)

My new hat: guest editing for a journal

This year, though, I started having a whole set of new experiences with regards to the process of journal publications. I was asked to serve as Guest Editor for the forthcoming special “DIY” issue in the Journal of Diabetes Science and Technology.

Whoa. Hello, imposter syndrome! Who was I, a non-academic, non-MD, non-PhD, non-all-the-things, to play a role in what goes in the literature?! But I said yes anyway, because I figured it would be a good learning process for my own future efforts to publish. And it has been! (Although it is, like writing your own articles and peer-reviewing other people’s articles, unpaid work.)

Here’s what I do as guest editor:

  • First, I dreamed up a list of people who should write for the special issue and likely had new insights not already in the literature, or had new research that would be a good fit for the issue. I sent the list to the production editor, who sent out official invitations to submit, and got people to commit to writing for the special issue.
  • As manuscripts come in, it’s my job to review the submissions and recommend reviewers (usually 2-3) for each manuscript. Thankfully, I think every peer reviewer I have nominated has been willing to review the manuscripts we’ve sent to them – if you’re one of those folks, a big thank you!  
  • As editor, I then review the reviewer comments and make sure they’re appropriate to send back to the author. They have all been, so far. (This has been a super educational process in and of its own, more on that below.)
  • The authors then revise their article, write a response to the reviewer comments, and send it back. It’s my job to review the revisions and response. I can either, based on reviewer feedback: reject it, accept it as revised, have the reviewers re-review it, or in a few cases, I’ve made a few edits myself (when inaccuracies were introduced in the revision, particularly a new added section) and asked the authors to approve or further revise those edits before I accept it for the journal.

Here’s some of what I’ve learned as a result:

I’ve learned a lot from getting to read the reviewer comments on other manuscripts. It’s been really helpful, because I have my own opinions when reading the manuscript in the first pass for picking reviewers, and then I can compare my own perspective on how it might be improved with what the other reviewers have flagged as needing adjustment before publication.

Also, this is especially helpful because I somehow have started getting a lot of reviewer requests myself (separate from my guest editing role) from both diabetes and non-diabetes publications, and this helps with my deer-in-the-headlights feeling of not knowing how to write reviews, other than the reviews I’ve read on my own previous work. What I’ve learned by observing a lot of these other reviews now is that on the one hand, as an author, it can feel nice to get a short, sweet, and positive review. However, as an author who wants the strongest manuscript out in the world, a longer, detailed review with both thematic comments and specific recommendations for improvements both helps the publication in the short term, and helps me write better future publications as well.

Similarly, seeing the variety of author responses to reviewer commentary have been educational. The best responses both respond in a separate document and describe what adjustments or changes should be made in the manuscript, but also highlight (either using different colored font or tracked changes) in the manuscript what those changes are. It’s a lot harder to review the revisions when the edits are all accepted/not colored to be easily spotted.

To be fair, it’s not always easy as the author(s) to make the changes in track changes like this. I just participated in a revision of a publication where I’m a co-author: this was a 19 page manuscript with over a dozen co-authors and likely hundreds, if not thousands, of changes. That revision was a LOT of work. But when there are obvious and few changes, and you’re an author, if you don’t already, consider using tracked changes or coloring the edits/additions. It makes it easier for the (guest) editor(s) to review and accept your revision!

How this has influenced my own reviews and future articles:

I also have a better idea of how to do reviews in the future, too. I know now that if there are many flaws that would prevent the publication from getting accepted with only minor edits, I try to stay high level (thanks to Aaron Neinstein for this feedback!) and note the major revision areas, instead of getting stuck in the weeds, because major revisions mean a lot of details will change underneath. I also try to specify where my recommendations go – i.e. make them in order as I read the manuscript, note major section headings or line numbers (although page/line numbers can be hard depending on whether someone is looking at a PDF with the cover page and abstract page and then the article, or just the original article).

Also, I now have a much better sense of the time it takes to do a review. I always try to do a quick skim of the article first. If I only mentally make small, minor or pedantic comments/suggestions, the review itself should only take 15-30 minutes to write and upload/submit the review. However, a manuscript with major flaws and major revision needed should have at least an hour scheduled. I learned this the hard way: a manuscript I procrastinated reviewing because it needed a lot of work took about 45 minutes to provide detailed (but needed) feedback. My review ended up running more than 1,000 words! This has happened several times now, but at least I know to budget an hour for those reviews.

And as a result, the major things I learned from reviewing that will help me with my own articles that I write in the future will be to check for gaps in logic where I assume common understanding that may not exist, and to make sure not to mix commentary in the middle of an article when I’m presenting background or factual information. These are common issues I regularly provide feedback on when reviewing other articles, and so I plan to check my own writing for logical flow and to make sure that discussion points are gathered correctly in the discussion and conclusion sections instead of sprinkled throughout.

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I’m not done learning: I imagine I’ll continue having new insights as to the most effective way to write, provide reviews, and make edits to my own work in the future. But when I mentioned that I didn’t feel equipped to peer review at first, my brother (a professor with a PhD in math) wisely pointed out that academics don’t really get training in peer reviewing, or editing, either – so we’re all in the same boat of learning as we go along!

If you’ve ever guest edited or edited a journal, or served as a peer reviewer, what have you learned in the process that has been helpful for writing and submitting your own articles? What advice would you share? Please do share with us here!

Presentations and poster content from @DanaMLewis at #ADA2019

by Dana Lewis

Like I did last year, I want to share the work being presented at #ADA2019 with those who are not physically there! (And if you’re presenting at #ADA2019 or another conference and would like suggestions on how to share your content in addition to your poster or presentation, check out these tips.) This year, I’m co-author on three posters and an oral presentation.

  • 1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes, co-authored by Dana Lewis and Azure Grant.
    • Come see us at the poster session, 12-1pm on Sunday! Dana & Azure will be presenting this poster.
  • 76-OR, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.
    • Come hear our presentation in room S-157 (South, Upper Mezzanine Level), 2:15-2:30 pm on Saturday!
  • 117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.
    • Come see us at the poster session, 12-1pm on Sunday! Scott will be presenting this poster.
  • 78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.
    • Come see us at the poster session, 12-1pm on Sunday! Bryan Cleal will be presenting this poster.

See below for full written summaries and pictures from each poster and the oral presentation.

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First up: the biological rhythms poster, formally known as 1056-P in category 12-D Clinical Therapeutics/New Technology–Insulin Delivery Systems, Preliminary Characterization of Rhythmic Glucose Variability In Individuals With Type 1 Diabetes!

Lewis_Grant_BiologicalRhythmsT1D_ADA2019

As mentioned in this DiabetesMine interview, Azure Grant & I were thrilled to find out that we have been awarded a JDRF grant to further this research and undertake the first longitudinal study to characterize biological rhythms in T1D, which could also be used to inform improvements and personalize closed loop systems. This poster is part of the preliminary research we did in order to submit for this grant.

There is also a Twitter thread for this poster:

Poster from #ADA2019

Background:

  • Human physiology, including blood glucose, exhibits rhythms at multiple timescales, including hours (ultradian, UR), the day (circadian, CR), and the ~28-day female ovulatory cycle (OR).
  • Individuals with T1D may suffer rhythmic disruption due not only to the loss of insulin, but to injection of insulin that does not mimic natural insulin rhythms, the presence of endocrine-timing disruptive medications, and sleep disruption.
  • However, rhythms at multiple timescales in glucose have not been mapped in a large population of T1D, and the extent to which glucose rhythms differ in temporal structure between T1D and non-T1D individuals is not known.

Data & Methods:

  • The initial data set used for this work leverages the OpenAPS Data Commons. (This data set is available for all researchers  – see www.OpenAPS.org/data-commons)
  • All data was processed in Matlab 2018b with code written by Azure Grant. Frequency decompositions using the continuous morlet wavelet transformation were created to assess change in rhythmic composition of normalized blood glucose data from 5 non-T1D individuals and anonymized, retrospective CGM data from 19 T1D individuals using a DIY closed loop APS. Wavelet algorithms were modified from code made available by Dr. Tanya Leise at Amherst College (see http://bit.ly/LeiseWaveletAnalysis)

Results:

  • Inter and Intra-Individual Variability of Glucose Ultradian and Circadian Rhythms is Greater in T1D
Figure_BiologicalRhythms_Lewis_Grant_ADA2019

Figure 1. Single individual blood glucose over ~ 1 year with A.) High daily rhythm stability and B.) Low daily rhythm stability. Low glucose is shown in blue, high glucose in orange.

Figure 2. T1D individuals (N=19) showed a wide range of rhythmic power at the circadian and long-period ultradian timescales compared to individuals without T1D (N=5).

A). Individuals’ CR and UR power, reflecting amplitude and stability of CRs, varies widely in T1D individuals compared to those without T1D. UR power was of longer periodicity (>= 6 h) in T1D, likely due to DIA effects, whereas UR power was most commonly in the 1-3 hour range in non-T1D individuals (*not shown).  B.) On average, both CR and UR power were significantly higher in T1D (p<.05, Kruskal Wallis). This is most likely due to the higher amplitude of glucose oscillation, shown in two individuals in C.

Conclusions:

  • This is the first longitudinal analysis of the structure and variability of multi-timescale biological rhythms in T1D, compared to non-T1D individuals.
  • Individuals with T1D show a wide range of circadian and ultradian rhythmic amplitudes and stabilities, resulting in higher average and more variable wavelet power than in a smaller sample of non-T1D individuals.
  • Ultradian rhythms of people with T1D are of longer periodicity than individuals without T1D. These analyses constitute the first pass of a subset of these data sets, and will be continued over the next year.

Future work:

  • JDRF has recently funded our exploration of the Tidepool Big Data Donation Project, the OpenAPS Data Commons, and a set of non-T1D control data in order to map biological rhythms of glucose/insulin.
  • We will use signal processing techniques to thoroughly characterize URs, CRs, and ORs in the glucose/insulin for T1D; evaluate if stably rhythmic timing of glucose is associated with improved outcomes (lower HBA1C); and ultimately evaluate if modulation of insulin delivery based on time of day or time of ovulatory cycle could lead to improved outcomes.
  • Mapping population heterogeneity of these rhythms in people with and without T1D will improve understanding of real-world rhythmicity, and may lead to non-linear algorithms for optimizing glucose in T1D.

Acknowledgements:

We thank the OpenAPS community for their generous donation of data, and JDRF for the grant award to further this work, beginning in July 2019.

Contact:

Feel free to contact us at Dana@OpenAPS.org or azuredominique@berkeley.edu.

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Next up, 78-LB, Detailing the Lived Experiences of People with Diabetes Using Do-it-Yourself Artificial Pancreas Systems – Qualitative Analysis of Responses to Open-Ended Items in an International Survey, co-authored by Bryan Cleal, Shane O’Donnell, Katarina Braune, Dana Lewis, Timothy C. Skinner, Bastian Hauck, Klemens Raille, and the OPEN consortium.

78-LB_LivedExperiencesDIYAPS_OPEN_ADA2019

There is also a Twitter thread for this poster:

Poster from OPEN survey on lived experiences

Introduction

There is currently a wave of interest in Do-it-Yourself Artificial Pancreas Systems (DIYAPS), but knowledge about how the use of these systems impacts on the lives of those that build and use them remains limited. Until now, only a select few have been able to give voice to their experiences in a research context. In this study we present data that addresses this shortcoming, detailing the lived experiences of people using DIYAPS in an extensive and diverse way.

Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the Diabetes Online Community (DOC). Participants were posed two open-ended questions in the survey, where personal DIYAPS stories were garnered; including knowledge acquisition, decision-making, support and emotional aspects in the initiation of DIYAPS, perceived changes in clinical and quality of life (QoL) outcomes after initiation and difficulties encountered in the process. All answers were analyzed using thematic content analysis.

Results

In total, 886 adults responded to the survey and there were a combined 656 responses to the two open-ended items. Knowledge of DIYAPS was primarily obtained via exposure to the communication fora that constitute the DOC. The DOC was also a primary source of practical and emotional support (QUOTES A). Dramatic improvements in clinical and QoL outcomes were consistently reported (QUOTES B). The emotional impact was overwhelmingly positive, with participants emphasizing that the persistent presence of diabetes in everyday life was markedly reduced (QUOTES C). Acquisition of the requisite devices to initiate DIYAPS was sometimes problematic and some people did find building the systems to be technically challenging (QUOTE D). Overcoming these challenges did, however, leave people with a sense of accomplishment and, in some cases, improved levels of understanding and engagement with diabetes management (QUOTE E).

QuotesA_OPEN_ADA2019 QuotesB_OPEN_ADA2019 QuotesC_OPEN_ADA2019 QuotesD_OPEN_ADA2019 QuotesE_OPEN_ADA2019

Conclusion

The extensive testimony from users of DIYAPS acquired in this study provides new insights regarding the contours of this evolving phenomenon, highlighting factors inspiring people to adopt such solutions and underlining the transformative impact effective closed-loop systems bring to bear on the everyday lives of people with diabetes. Although DIYAPS is not a viable solution for everyone with type 1 diabetes, there is much to learn from those who have taken this route, and the life-changing results they have achieved should inspire all with an interest in artificial pancreas technology to pursue and dream of a future where all people with type 1 diabetes can reap the benefits that it potentially provides.

Also, see this word cloud generated from 665 responses in the two open-ended questions in the survey:

Wordle_OPEN_ADA2019

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Next up is 117-LB, DIWHY: Factors Influencing Motivation, Barriers and Duration of DIY Artificial Pancreas System Use Among Real-World Users, co-authored by Katarina Braune, Shane O’Donnell, Bryan Cleal, Ingrid Willaing, Adrian Tappe, Dana Lewis, Bastian Hauck, Renza Scibilia, Elizabeth Rowley, Winne Ko, Geraldine Doyle, Tahar Kechadi, Timothy C. Skinner, Klemens Raille, and the OPEN consortium.

DIWHY_117-LB_OPEN_ADA2019

There is also a Twitter thread for this poster:

DIWHY Poster at ADA2019

Background

Until recently, digital innovations in healthcare have typically followed a ‘top-down’ pathway, with manufacturers leading the design and production of technology-enabled solutions and patients involved only as users of the end-product. However, this is now being disrupted by the increasing influence and popularity of more ‘bottom-up’ and patient-led open source initiatives. A primary example is the growing movement of people with diabetes (PwD) who create their own “Do-it-Yourself” Artificial Pancreas Systems (DIY APS) through remote-control of medical devices employing an open source algorithm.

Objective

Little is known about why PwD leave traditional care pathways and turn to DIY technology. This study aims to examine the motivations of current DIYAPS users and their caregivers.

Research Design and Methods

An online survey with 34 items was distributed to DIYAPS users recruited through the Facebook groups “Looped” (and regional sub-groups) and Twitter pages of the “DOC” (Diabetes Online Community). Self-reported data was collected, managed and analyzed using the secure REDCap electronic data capture tools hosted at Charité – Universitaetsmedizin Berlin.

Results

1058 participants from 34 countries (81.3 % Europe, 14.7 % North America, 6.0 % Australia/WP, 3.1 % Asia, 0.1 % Africa), responded to the survey, of which the majority were adults (80.2 %) with type 1 diabetes (98.9 %) using a DIY APS themselves (43.0 % female, 56.8 % male, 0.3 % other) with a median age of 41 y and an average diabetes duration of 25.2y ±13.3. 19.8 % of the participants were parents and/or caregivers of children with type 1 diabetes (99.4 %) using a DIY APS (47.4 % female, 52.6 % male) with a median age of 10 y and an average diabetes duration of 5.1y ± 3.8. People used various DIYAPS (58.2 % AndroidAPS, 28.5 % Loop, 18.8 % OpenAPS, 5.7 % other) on average for a duration of 10.1 months ±17.6 and reported an overall HbA1c-improvement of -0.83 % (from 7.07 % ±1.07 to 6.24 % ±0.68 %) and an overall Time in Range improvement of +19.86 % (from 63.21 % ±16.27 to 83.07 % ±10.11). Participants indicated that DIY APS use required them to pay out-of-pocket costs in addition to their standard healthcare expenses with an average amount of 712 USD spent per year.

Primary motivations for building a DIYAPS were to improve the overall glycaemic control, reduce acute and long-term complication risk, increase life expectancy and to put diabetes on ‘auto-pilot’ and interact less frequently with the system. Lack of commercially available closed loop systems and improvement of sleep quality was a motivation for some. For caregivers, improvement of their own sleep quality was the leading motivation. For adults, curiosity (medical or technical interest) had a higher impact on their motivation compared to caregivers. Some people feel that commercial systems do not suit their individual needs and prefer to use a customizable system, which is only available to them as a DIY solution. Other reasons, like costs of commercially available systems and unachieved therapy goals played a subordinate role. Lack of medical or psychosocial support was less likely to be motivating factors for both groups.

Figure_OPEN_DIWHY_ADA2019

Conclusions

Our findings suggest that people using Do-it-Yourself Artificial Pancreas systems and their caregivers are highly motivated to improve their/their children’s diabetes management through the use of this novel technology. They are also able to access and afford the tools needed to use these systems. Currently approved and available commercial therapy options may not be sufficiently flexible or customizable enough to fulfill their individual needs. As part of the project “OPEN”, the results of the DIWHY survey may contribute to a better understanding of the unmet needs of PwD and current challenges to uptake, which will, in turn, facilitate dialogue and collaboration to strengthen the involvement of open source approaches in healthcare.

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This is a written version of the oral presentation, In-Depth Review of Glycemic Control and Glycemic Variability in People with Type 1 Diabetes Using Open Source Artificial Pancreas Systems, co-authored by Andreas Melmer, Thomas Züger, Dana Lewis, Scott Leibrand, Christoph Stettler, and Markus Laimer.

APSComponents_Melmer_ADA2019

Artificial Pancreas Systems (APS) now exist, leveraging a CGM sensor, pump, and control algorithm. Faster insulin can play a role, too.  Traditionally, APS is developed by commercial industry, tested by clinicians, regulated, and then patients can access it. However, DIYAPS is designed by patients for individual use.

There are now multiple different kinds of DIYAPS systems in use: #OpenAPS, Loop, and AndroidAPS. There are differences in hardware, pump, and software configurations. The main algorithm for OpenAPS is also used in AndroidAPS.  DIYAPS can work offline; and also leverage the cloud for accessing or displaying data, including for remote monitoring.OnlineOffline_Melmer_ADA2019

This study analyzed data from the OpenAPS Data Commons (see more here). At the time this data set was used, there were n=80 anonymized data donors from the #OpenAPS community, with a combined 53+ years worth of CGM data.

TIR_PostLooping_Melmer_ADA2019Looking at results for #OpenAPS data donors post-looping initiation, CV was 35.5±5.9, while eA1c was 6.4±0.7. TIR (3.9-10mmol/L) was 77.5%. Time spent >10 was 18.2%; time <3.9 was 4.3%.

SubcohortData_Melmer_ADA2019We selected a subcohort of n=34 who had data available from before DIY closed looping initiation (6.5 years combined of CGM records), as well as data from after (12.5 years of CGM records).

For these next set of graphs, blue is BEFORE initiation (when just on a traditional pump); red is AFTER, when they were using DIYAPS.

TIR_PrePost_Melmer_ADA2019Time in a range significantly increased for both wider (3.9-10 mmol/L) and tighter (3.9-7.8 mmol/L) ranges.

TOR_PrePost_Melmer_ADA2019Time spent out of range decreased. % time spent >10 mmol/L decreased -8.3±8.6 (p<0.001); >13 mmol/L decreased -3.3±5.0 (p<0.001). Change in % time spent <3.9 mmol/L (-1.1±3.8 (p=0.153)), and <3.0 mmol/L (-0.7±2.2 (p=0.017)) was not significant.

We also analyzed daytime and nightime (the above was reflecting all 24hr combined; these graphs shows the increase in TIR and decrease in time out of range for both day and night).

TIR_TOR_DayAndNight_Melmer_ADA2019

Hypoglemic_event_reduction_Melmer_ADA2019There were less CGM records in the hypoglycemic range after initiating DIYAPS.

Conclusion: this was a descriptive study analyzing available CGM data from  #OpenAPS Data Commons. This study shows OpenAPS has potential to support glycemic control. However, DIYAPS are currently not regulated/approved technology. Further research is recommended.

Conclusion_Melmer_ADA2019

(Note: a version of this study has been submitted and accepted for publication in the Journal of Diabetes. Obesity, and Metabolism.)

Presentations and poster content from @DanaMLewis at #2018ADA

by Dana Lewis

DanaMLewis_ADA2018As I mentioned, I am honored to have two presentations and a co-authored poster being presented at #2018ADA. As per my usual, I plan to post all content and make it fully available online as the embargo lifts. There will be three sets of content:

  • Poster 79-LB in Category 12-A ‘Detecting Insulin Sensitivity Changes for Individuals with Type 1 Diabetes using “Autosensitivity” from OpenAPS’ poster, co-authored by Dana Lewis, Tim Street, Scott Leibrand, and Sayali Phatak.
  • Content from my presentation Saturday, ‘The Data behind DIY Diabetes—Opportunities for Collaboration and Ongoing Research’, which is part of the “The Diabetes Do-It-Yourself (DIY) Revolution” Symposium!
  • Content from my presentation Monday, ‘Improvements in A1c and Time-in-Range in DIY Closed-Loop (OpenAPS) Users’, co-authored by Dana Lewis, Scott Swain, and Tom Donner.

First up: the autosensitivity poster!

Dana_Scott_ADA2018_autosens_posterYou can find the full write up and content of the autosensitivity poster in a post over on OpenAPS.org. There’s also a twitter thread if you’d like to share this poster with others on Twitter or elsewhere.

Summary: we ran autosensitivity retrospectively on the command line to assess patterns of sensitivity changes for 16 individuals who had donated data in the OpenAPS Data Commons. Many had normal distributions of sensitivity, but we found a few people who trended sensitive or resistant, indicating underlying pump settings could likely benefit from a change.
2018 ADA poster on Autosensitivity from OpenAPS by DanaMLewis

 

Presentation:
‘The Data behind DIY Diabetes—Opportunities for Collaboration and Ongoing Research’

This presentation was a big deal to me, as it was flanked by 3 other excellent presentations on the topic of DIY and diabetes. Jason Wittmer gave a great overview and context setting of DIY diabetes, ranging from DIY remote monitoring and CGM tools all the way to DIY closed loops like OpenAPS. Jason is a dad who created OpenAPS rigs for his son with T1D. Lorenzo Sandini spoke about the clinician’s perspective for when patients come into the office with DIY tools. He knows it from both sides – he’s using OpenAPS rigs, and also has patients who use OpenAPS. And after my presentation, Joyce Lee also spoke about the overarching landscape of diabetes and the role DIY plays in this emerging technology space.

Why did I present as part of this group today? One of the roles I’ve taken on in the last few years in the OpenAPS community (among others) is a collaborator and facilitator of research with and about the community. I put together the first outcomes study (see here in JDST or here in a blog post form on OpenAPS.org) in 2016. We presented a poster on Autotune last year at ADA (see here in a blog post form on OpenAPS.org). I’ve also worked to create and manage the OpenAPS Data Commons, as well as build tools for researchers to use this data, so individuals can easily and anonymously donate their DIY closed loop data for other research projects, lowering the friction and barriers for both patients and researchers. And, I’ve co-led or led several research projects with the community’s data as a result.

My presentation was therefore about setting the stage with background on OpenAPS & how we ended up creating the OpenAPS Data Commons; presenting a selection of research projects that have utilized data from the community; highlighting other research projects working with the OpenAPS community; announcing a new international collaboration (OPEN – more coming on that in the future!) for research with the DIY community; and hopefully encouraging other diabetes researchers to think about sharing their work, data, methods, tools, and insights as openly possible to help us all move forward with improving the lives of people with diabetes.

That is, of course, quite an abbreviated summary! I’ve shared a thread on Twitter that goes into detail on each of the key points as part of the presentation, or there’s a version of this Twitter/presentation content also written below.

If you’re someone who wants to do research with retrospective data from the OpenAPS Data Commons, you can find out more about it here (including instructions on how to request data). And if you’re interested in prospective research, please do reach out as well!

Full content for those who don’t want to read Twitter:

Patients are often seen as passive recipients of care, but many of us PWDs have discovered that problems are opportunities to change things. My journey to DIY began after I was frustrated by my inability to hear CGM alarms at night. 4 years ago, there was no way for me to access my own device data in real time OR retrospectively. Thanks to John Costik for sharing his code, I was able to get my CGM data & send it to the cloud and down to my phone, creating a louder alarm. Scott and I created an algorithm to push notifications to me to take action. This was an ‘open loop’ system we called #DIYPS. With Ben West’s help, we realized could combine our algorithm with small, off-the-shelf hardware & a radio stick to automate insulin delivery. #OpenAPS was thus created, open sourcing all components of DIY closed loop system so others could close the loop, too. An #OpenAPS rig consists of a small computer, radio chip, & battery. The hardware is constantly evolving. Many of us also use Nightscout to visualize our closed loop data, and share with loved ones.

2018ADA_slide12018ADA_slide 42018ADA_slide 32018ADA_Slide 2

 

 

 

 

 

 

I closed the loop in December of 2015. As people learned about it, I got pushback: “It works for you, but how do you know it’s going to work for others?” I didn’t, and I said so. But that didn’t mean I shouldn’t share what was working for me.

Once we had dozens of users of #OpenAPS, we presented a research study at #2016ADA, with 18 individuals sharing outcomes data on A1c, TIR, and QOL improvements. (See that publication here: https://twitter.com/danamlewis/status/763782789070192640 ). I was often asked to share my data for people to analyze, but I’m not representative of entire #OpenAPS community. Plus, the community has kept growing: we estimate there are more than (n=1)*710+ (as of June 2018) people worldwide using different kinds of DIY APs. (Note: if you’d like to keep track of the growing #OpenAPS community, the count of loopers worldwide is updated periodically at  https://openaps.org/outcomes ).  I began to work with Open Humans to build the #OpenAPS Data Commons, enabling individuals to anonymously upload their data and consent to share it with the Data Commons.

2018ADA_Slide 52018ADA_Slide 62018ADA_Slide 72018ADA_Slide 8

 

 

 

 

 

Criteria for using the #OpenAPS Data Commons:

  • 1) share insights back with the community, especially if you find something about an individual’s data set where we should notify them
  • 2) publish in an accessible (and preferably open) manner

I’ve learned that not many are prepared to take advantage of the rich (and complex) data available from #OpenAPS users; and many researchers have varying background and skillsets.  To aid researchers, I created a series of open source tools (described here: http://bit.ly/2l5ypxq, and tools available at https://github.com/danamlewis/OpenHumansDataTools ) to help researchers & patients working with data.

2018ADA_Slide 10 2018ADA_Slide 9

 

 

 

We have a variety of research projects that have leveraged the anonymously donated, DIY closed loop data from the #OpenAPS Data Commons.

  • 2018ADA_Slide 112018ADA_Slide 12One research project, in collaboration with a Stanford team, evaluated published machine learning model predictions & #OpenAPS predictions. Some models (particularly linear regression) = accurate predictions in short term, but less so longer term when insulin peaks. This study is pending publication, but I’d like to note the challenge of more traditional research keeping pace with DIY innovation: the code (and data) studied was from January 2017. #OpenAPS prediction code has been updated 2x since then.
  • In response to the feedback from the #2016ADA #OpenAPS Outcomes study we presented, a follow up study on #OpenAPS outcomes was created in partnership with a team at Johns Hopkins. That study will be presented on Monday, 6-6:15pm (352-OR).
  • 2018ADA_Slide 13Many people share publicly online their outcomes with DIY closed loops. Sulka Haro has shared his script to evaluate the reduction in daily manual diabetes interventions after they began using #OpenAPS. Before: 4.5/day manual corrections; now they treat <1/day.
  • #OpenAPS features such as autosensitivity automatically detect sensitivity changes and insulin needs, improving outcomes. (See above at the top of this post for the full poster content).
  • If you missed it at #2017ADA (see here: http://bit.ly/2rMBFmn) , Autotune is a tool for assessing changes to basal rates, ISF, and carb ratio. Developed for #OpenAPS users but can also be used by traditional pumpers (and some MDI users also utilize it).

I’m also thrilled to share a new tool we’ve created: an #OpenAPS simulator to allow us to more easily back-test and compare settings changes & feature changes in #OpenAPS code.
2018ADA_Slide 14

  • Screen Shot 2018-06-22 at 4.48.06 PM2018ADA_Slide 16  We pulled a recent week of data for n=1 adult PWD who does no-bolus, rough carb entry meal announcements, and ran the simulator to predict what the outcomes would be for no-bolus and no meal-announcement.

 

  • 2018ADA_Slide 172018ADA_Slide 18 We also ran the simulator on n=1 teen PWD who does no-bolus and no-meal-announcement in real life. The simulator tracked closely to his actual outcomes (validated this week with a lab-A1c of 6.1)

 

 

 

The new #OpenAPS simulator will allow us to better test future algorithm changes and features across a diverse data set donated by DIY closed loop users.

There are many other studies & collaborations ongoing with the DIY community.

  • Michelle Litchman, Perry Gee, Lesly Kelly, and myself have a paper pending review analyzing social-media-reported outcomes & themes from DIY community.
  • 2018ADA_Slide 19There are also multiple other posters about DIY outcomes here at #2018ADA:
  • 2018ADA_Slide 20 There are many topics of interest in DIY community we’d like to see studies on, and have data for. These include: “eating soon” (optimal insulin dosing for lesser post-prandial spikes); and variability in sensitivity for various ages, pregnancy, and menstrual cycle.
  • 2018ADA_Slide 21I’m also thrilled to announce funding will be awarded to OPEN (a new collaboration on Outcomes of Patients’ Evidence, with Novel, DIY-AP tech), a 36-month international collaboration assessing outcomes, QOL, further development, access of real-world AP tech, etc. (More to come on this soon!)

In summary: we don’t have a choice in living with diabetes. We *do* have a choice to DIY, and also to research to learn more and improve knowledge and availability of tools for us PWDs, more quickly. We would love to partner and collaborate with anyone interested in working with the DIY community, whether that is utilizing the #OpenAPS Data Commons for retrospective studies or designing prospective studies. If you take away one thing today: let it be the request for us to all openly share our tools, data, and insights so we can all make life with type 1 diabetes better, faster.

2018ADA_Slide 222018ADA_Slide 23

 

 

 

 

A huge thank you as always to the community: those who have donated and shared data; those who have helped develop, test, troubleshoot, and otherwise help power the #OpenAPS and other DIY diabetes communities.

2018ADA_Slide 24

Presentation:
Improvements in A1c and Time-in-Range in DIY Closed-Loop (OpenAPS) Users

(full tweet thread available here; or a description of this presentation below)

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#OpenAPS is an open and transparent effort to make safe and effective Artificial Pancreas System (APS) technology widely available to reduce the burden of Type 1 diabetes. #OpenAPS evolved from my first DIY closed loop system and our desire to openly share what we’ve learned living with DIY closed loops. It takes a small, off-the-shelf computer; a radio; and a battery to communicate with existing insulin pumps and CGMs. As a PWD, I care a lot about safety: the safety reference design is the first thing in #OpenAPS that was shared, in order to help set expectations around what a DIY closed loop can (and cannot) do.

ADA2018_Slide 23ADA2018_Slide 24As I shared about my own DIY experience, people questioned whether it would work for others, or just me. At #2016ADA, we presented an outcomes study with data from 18 of the first 40 DIY closed loop users. Feedback on that study included requests to evaluate CGM data, given concerns around accuracy of self-reported outcomes.

This 2018 #OpenAPS outcomes study was the result. We performed a retrospective cross-over analysis of continuous BG readings recorded during 2-week segments 4-6 weeks before and after initiation of OpenAPS.

ADA2018_Slide 26For this study, n=20 based on the availability of data that met the stringent protocol requirements (and the limited number of people who had both recorded that data and donated it to the #OpenAPS Data Commons in early 2017).  Demographics show that, like the 2016 study, the people choosing to #OpenAPS typically have lower A1C than the average T1D population; have had diabetes for over a decade; and are long-time pump and CGM users. Like the 2016 study, this 2018 study found mean BG and TIR improved across all time categories (overall, day, and nighttime).

ADA2018_Slide 28ADA2018_Slide 29ADA2018_Slide 30ADA2018_Slide 31ADA2018_Slide 32

Overall, mean BG (mg/dl) improved (135.7 to 128.3); mean estimated HbA1c improved (6.4 to 6.1%). TIR (70-180) increased from 75.8 to 82.2%. Overall, time spent high and low were all reduced, in addition to eAG and A1c reduction. Overnight (11pm-7am) had smaller improvement in all categories compared to daytime improvements in these categories.

Notably: although this study primarily focused on a 4-6 week time frame pre-looping vs. 4-6 weeks post-looping, the improvements in all categories are sustained over time by #OpenAPS users.

ADA2018_Slide 33 ADA2018_Slide 34

ADA2018_Slide 35Conclusion: Even with tight initial control, persons with T1D saw meaningful improvements in estimated A1c, TIR, and a reduction in time spent high and low, during the day and at night, after initiating #OpenAPS. Although this study focused on BG data from CGM, do not overlook additional QOL benefits when analyzing benefits of hybrid closed loop therapy or designing future studies! See these examples shared from Sulka Haro and Jason Wittmer as example of quality of life impacts of #OpenAPS.

A huge thank you to the community: those who have donated and shared data; those who have helped develop, test, troubleshoot, and otherwise help power the #OpenAPS and other DIY diabetes communities.

And, special thank you to my co-authors, Scott Swain & Tom Donner, for the collaboration on this study. Lewis_Donner_Swain_ADA2018

Getting ready for #2018ADA (@DanaMLewis) & preparing to encourage photography

by Dana Lewis

We’re a few weeks away from the 78th American Diabetes Scientific Sessions (aka, #2018ADA), and I’m getting excited. Partially because of the research I have the honor of presenting; but also because ADA has made strides to (finally) update their photography policy and allow individual presenters to authorize photography & sharing of their content. Yay!

As a result of preparing to encourage people to take pictures & share any and all content from my presentations, I started putting together my slides for each presentation, including the slide about allowing photography, which I’ll also verbally say at the start of the presentation. Interestingly to me, though, ADA only provided an icon for discouraging photography, saying that if staff notice that icon on any photos, that’s who will be asked to take down photos. I don’t want any confusion (in past years, despite explicit permission, people have been asked to take down photos of my work), so I wanted to include obvious ‘photography is approved’ icons.

And this is what I landed on for a photography encouraged slide, and the footer of all my other slides:

Encouraging photography in my slides Example encouraging use of photography in content slidesEncouraging photography in the footer of my slides

And, if anyone else plans to encourage (allow) photography and would like to use this slide design, you can find my example slide deck here that you are welcome to use: http://bit.ly/2018ADAexampleslides

I used camera and check mark icons which are licensed to be freely used; and I also licensed this slide deck and all content to be freely used by all! I hope it’s helpful.

—

Where you’ll find me at #2018ADA

And if you’re wondering where and what I’ll be presenting on with these slides…I’ll be sharing new content in a few different times and places!

On Saturday, I’m thrilled there is a full, 2-hour session on DIY-related content, and to get to share the stage with Jason Wittmer, Lorenzo Sandini, and Joyce Lee. That’s 1:45-3:45pm (Eastern), “The Diabetes Do-It-Yourself (DIY) Revolution”, in W415C (Valencia Ballroom). I’ll be discussing some of the data & research in DIY diabetes! A huge thanks to Joshua Miller for championing and moderating this session.

I’m also thrilled that a poster has been accepted on one of the projects from my RWJF grant work, in partnership with Tim Street (as well as Scott Leibrand, and Sayali Phatak who is heading our data science work for Opening Pathways). The embargo lifts on Saturday morning (content will be shared online then), and the poster will be displayed Saturday, Sunday, and Monday. Scott and I will also be present with the poster on Monday during the poster session from 12-1pm.

And last but not least, there is also an oral presentation on Monday evening with a new study on outcomes data from using OpenAPS. I’ll be presenting during the 4:30-6:30pm session (again in W415C (Valencia Ballroom)), likely during the 6-6:15pm slot. I’m thrilled that Scott Swain & Tom Donner, who partnered on this study & work, will also be there to help answer questions about this study!

As we have done in the past (see last year’s poster, for example), we plan to share all of this content online once the embargo lifts, in addition to the in-person presentations and poster discussions.

A huge thanks, as always, goes to the many dozens of people who have contributed to this DIY community in so many ways: development, testing, support, feedback, documentation, data donation, and more! <3

Quantified sickness when you have #OpenAPS and the flu

by Dana Lewis

Getting “real people sick*” is the worst. And it can be terrifying when you have type 1 diabetes, and know the sickness is both likely to send your blood sugars rocketing sky high, as well as leave you exhausted and weak and that much harder to deal with a plummeting low.

*(Scott hates this term because he doesn’t like the implication that PWD’s aren’t real. We’re real, all right. But I like the phrase because it differentiates between feeling bad from blood sugar-related reasons, and the kind of sickness that anyone can get.)

In February 2014, Scott got home from a conference on Friday, and on Saturday complained about being tired with a headache. By Sunday, I started feeling weary with a sore throat. By Monday morning, I had a raging fever, chills, and the bare minimum of energy required to drag myself into the employee health clinic and get diagnosed with the flu. And since they knew I was single and lived by myself, the conversation went from “here’s your prescription for Tamiflu” to “but you can’t be by yourself, maybe we should find a bed for you in the hospital” because of how sick I was. Luckily, I called Scott and asked him to come pick me up and let me stay at his place. And there I stayed in complete misery for several days, the sickest I’d ever been. I remember at one point on the second day, waking up from a fitful doze and seeing Scott standing across the room with his laptop on a dresser, using it as a standing desk because he was so worried about me that he didn’t want to leave the room at that point. It was that bad.

Luckily, I survived. (And good thing, right, given that we went on to build OpenAPS, yes? ;)) This year’s flu experience was different. This year I was real-people sick, but without the diabetes-related fear that I’d so often experienced in the past. My blood sugars were perfectly managed by OpenAPS. I didn’t go low. It didn’t matter if I didn’t eat, or did eat (potato soup, ice cream, and frozen fruit bars were the foods of choice). My BGs stayed almost entirely in range. And because they were so in range that it was odd, I started watching the sensitivity ratio that is calculated by autosensitivity to see how my insulin sensitivity was changing over the course of the sickness. And by day 5, I finally felt good enough to share some of that data (aka, tweet). Here’s what I found from this year’s flu experience:

  • Night 1 was terrible, because I got hardly any deep sleep (45 minutes, whereas 2+h is my usual average per night) and kept waking up coughing. I also was 40% insulin resistant all night long and into Day 2, meaning it took 40% more insulin than usual to keep my BGs at target.
  • Night 2 was even worse – ZERO deep sleep. Ahhhh! It was terrible. Resistance also nudged up to 50%.
  • Night 3 – hallelujah, deep sleep returned. I ended up getting 4h53m of deep sleep, and also was able to sleep for closer to 2 hour blocks at a time, with less coughing. Also, going into night 3 was pretty much the only “high” I had of being sick – up around 180 for a few hours. Then it fell off a cliff and whooshed down to the bottom of my target, marking the drastic end of insulin resistance. After that, insulin sensitivity was fairly normal.
  • Night 4 yielded more deep sleep (>5 hours), and a tad bit of insulin sensitivity (~10%), but it’s unclear whether that’s totally sickness related or more related to the fact that I wasn’t eating much in day 3 and day 4.
  • Night 5 felt like I was going backward – 1h36m of deep sleep, tons of coughing, and interestingly a tad bit of insulin resistance (~20%) again. Night 6 (last night) I supposedly got plenty of deep sleep again (>4h), but didn’t feel like it at all due to coughing. BGs are still perfectly in range, and insulin sensitivity back to usual.

This was all done still with no-bolus, and just carb announcement when I ate whatever it was I was eating. In several cases there was negative IOB on board, but I didn’t have the usual spikes that I would normally see from that. I had 120 carbs of gluten free biscuits and gravy yesterday, and I didn’t go higher than 130mg/dl.

In-range BGs shown on CGM graph thanks to OpenAPS

It’s a weird feeling to have been this sick, and have perfectly normal blood sugars. But that’s why it’s so interesting to be able to look at other data beyond average, time in range, and A1c – we now have the tools and the data to be able to dive in and really understand more about what our bodies are doing in sick situations, whether it’s norovirus or the flu.

I’m thinking if everyone shared their data from when they had the flu, or norovirus, or strep throat, or whatever – we might be able to start to analyze and detect patterns of resistance and otherwise sensitivity changes over the course of typical illness. This way, when someone gets sick with diabetes, we’d know generally “expect around XX% resistance for Days 1-3, and then expect a drop off that looks like this on Day 4”, etc.

That would be way better than the traditional ways of just bracing yourself for sky-high highs and terrible lows with no understanding or ability to make things better during illness. The peace of mind I had during the flu this year was absolutely priceless. Some people will be able to get that with DIY closed loop technology; but as with so many other things we have learned and are learning from this community, I bet we can find ways to help translate these insights to be of benefit for all people with diabetes, regardless of which therapies they have access to or decide to use.

—

Want to help? Been sick? Consider donating your data to my diabetes sick-day analysis project. What you should do:

  1. If you’re using a closed loop, donate your data to the OpenAPS Data Commons. You can do all your data (yay!), or just the time frame you’ve been sick. Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.
  2. Not using a closed loop, but have Nightscout? Donate your data to the Nightscout Data Commons, and do the same thing: Use the “message the project owner” feature to anonymously message and share what kind of illness you had, and the dates of sickness.

As we have more people who identify batches of sick-day data, I’ll look at what insights we can find around sensitivity changes before, during, and after sickness, plus other insights we can learn from the data.

Why Open Humans is an essential part of my work to change the future of healthcare research

by Dana Lewis

I’ve written about Open Humans before; both in terms of how we’re creating Data Commons there for people using Nightscout and DIY closed loops like OpenAPS to donate data for research, as well as building tools to help other researchers on the Open Humans platform. Madeleine Ball asked me to share some more about the background of the community’s work and interactions with Open Humans, along with how it will play into the Opening Pathways grant work, so here it is! This is also posted on the OpenHumans blog. Thanks, Madeleine, and Open Humans!

—

 

So, what do you like about Open Humans?

Health data is important to individuals, including myself, and I think it’s important that we as a society find ways to allow individuals to be able to chose when and how we share our data. Open Humans makes that very easy, and I love being able to work with the Open Humans team to create tools like the Nightscout Data Transfer uploader tool that further anonymizes data  uploads. As an individual, this makes it easy to upload my own diabetes data (continuous glucose monitoring data, insulin dosing data, food info, and other data) and share it with projects that I trust. As a researcher, and as a partner to other researchers, it makes it easy to build Data Commons projects on Open Humans to leverage data from the DIY artificial pancreas community to further healthcare research overall.

Wait, “artificial pancreas”? What’s that?

I helped build a DIY “artificial pancreas” that is really an “automated insulin delivery system”. That means a small computer & radio device that can get data from an insulin pump & continuous glucose monitor, process the data and decide what needs to be done, and send commands to adjust the insulin dosing that the insulin pump is doing. Read, write, read, rinse, repeat!

I got into this because, as a patient, I rely on my medical equipment. I want my equipment to be better, for me and everyone else. Medical equipment often isn’t perfect. “One size fits all” really doesn’t fit all. In 2013, I built a smarter alarm system for my continuous glucose monitor to make louder alarms. In 2014, with the partnership of others like Ben West who is also a passionate advocate for understanding medical devices, I “closed the loop” and built a hybrid closed loop artificial pancreas system for myself. In early 2015, we open sourced it, launching the OpenAPS movement to make this kind of technology more broadly accessible to those who wanted it.

You must be the only one who’s doing something like this

Actually, no. There are more than 400+ people worldwide using various types of DIY closed loop systems – and that’s a low estimate! It’s neat to live during a time when off the shelf hardware, existing medical devices, and open source software can be paired to improve our lives. There’s also half a dozen (or more) other DIY solutions in the diabetes community, and likely other examples (think 3D-printing prosthetics, etc.) in other types of communities, too. And there should be even more than there are – which is what I’m hoping to work on.

So what exactly is your project that’s being funded?

I created the OpenAPS Data Commons to address a few issues. First, to stop researchers from emailing and asking me for my individual data. I by no means represent all other DIY closed loopers or people with diabetes! Second, the Data Commons approach allows people to donate their data anonymously to research; since it’s anonymized, it is often IRB-exempt. It also makes this data available to people (patient researchers) who aren’t affiliated with an organization and don’t need IRB approval or anything fancy, and just need data to test new algorithm features or investigate theories.

But, not everyone implicitly knows how to do research. Many people learn research skills, but not everyone has the wherewithal and time to do so. Or maybe they don’t want to become a data science expert! For a variety of reasons, that’s why we decided to create an on-call data science and research team, that can provide support around forming research questions and working through the process of scientific discovery, as well as provide data science resources to expedite the research process. This portion of the project does focus on the diabetes community, since we have multiple Data Commons and communities of people donating data for research, as well as dozens of citizen scientists and researchers already in action (with more interested in getting involved).

What else does Open Humans have to do with it?

Since I’ve been administering the Nightscout and OpenAPS Data Commons, I’ve spent a lot of time on the Open Humans site as both a “participant” of research donating my data, as well as a “researcher” who is pulling down and using data for research (and working to get it to other researchers). I’ve been able to work closely with Madeleine and suggest the addition of a few features to make it easier to use for research and downloading large data sets from projects. I’ve also been documenting some tools I’ve created (like a complex json to csv converter; scripts to pull data from multiple OH download files and into a single file for analysis; plus writing up more details about how to work with data files coming from Nightscout into OH), also with the goal of facilitating more researchers to be able to dive in and do research without needing specific tool or technical experience.

It’s also great to work with a platform like Open Humans that allows us to share data or use data for multiple projects simultaneously. There’s no burdensome data collection or study procedures for individuals to be able to contribute to numerous research projects where their data is useful. People consent to share their data with the commons, fill out an optional survey (which will save them from having to repeat basic demographic-type information that every research project is interested in), and are done!

Are you *only* working with the diabetes community?

Not at all. The first part of our project does focus on learning best practices and lessons learned from the DIY diabetes communities, but with an eye toward creating open source toolkit and materials that will be of use to many other patient health communities. My goal is to help as many other patient health communities spark similar #WeAreNotWaiting projects in the areas that are of most use to them, based on their needs.

How can I find out more about this work?
Make sure to read our project announcement blog post if you haven’t already – it’s got some calls to action for people with diabetes; people interested in leading projects in other health communities; as well as other researchers interested in collaborating! Also, follow me on Twitter, for more posts about this work in progress!

Why a non-academic (patient) publishes in academic journals

by Dana Lewis

Today I was able to share that my Letter to the Editor was published in the Journal of Diabetes Science and Technology. It’s on why we need to set expectations to help patients successfully adopt hybrid closed loop/artificial pancreas/automated insulin delivery system technology. (You can read it via image copies in the first link.)

JDST_screenshot_LTE_expectationsI’ve published a few times in academic journals. Last year, Scott and I published another Letter to the Editor in JDST with the OpenAPS outcomes study we had presented at the 2016 ADA Scientific Sessions conference.

But, I’m sure people are wondering why I choose to do so – especially as I am 1) a patient and 2) a non-academic. (Although in case you missed it – I’m now the Principal Investigator on a grant-funded study!)

While there are many healthcare providers, researchers, industry employees, FDA staff, etc. who read blogs like this and are up to speed on the bleeding edge of diabetes technology… there are easily 10x the number that do not.

And if they don’t know about the existence of this world, they won’t know about the valuable lessons we’re learning and won’t be able to share those lessons and knowledge with other healthcare providers and the patients that they treat.

So, in my pursuit to find more ways to share knowledge from our community with the rest of the diabetes community, this is why we submit abstracts for posters and presentations to conferences like ADA’s Scientific Sessions. Our abstracts are evaluated just like the abstracts from traditional healthcare providers (as far as they can tell, I’m just another academic, albeit one with fewer credentials ;)), and I’m proud that they’re evaluated and deemed worthy of poster presentations alongside mainstream researchers. Ditto for our written publications, whether they be letters to the editor or other types of articles submitted to journals and publications.

We need to find more ways to share and distribute knowledge with the “traditional” medical and academic research world. And I’d love to do more – so please share ideas if you have them. And if you’re someone who bridges the gap to the traditional world, I appreciate your help sharing these types of articles and conversations with your colleagues.

Opening pathways for discovery, research, and innovation in health and healthcare

by Dana Lewis

How can we get more patients and other communities to leverage the benefits of the #WeAreNotWaiting mindset for research, development, and innovation in health (and healthcare)?

That’s a question I’ve been asking myself for two years, after seeing the diverse efforts and valuable outpourings from the DIY diabetes community (ranging from amazing remote monitoring solutions for CGM to algorithms, hardware, and other software for automated insulin delivery systems).

But, how to scale? In diabetes, we’re perhaps uniquely positioned given our data-driven disease. However, I believe that the data and innovation approach we’ve taken in diabetes can help many other types of patient communities as well. I just didn’t know how to help scale it… until recently.

Last year when a group of us from the OpenAPS community participated in the Quantified Self Public Health Symposium in 2016, it prompted some follow up conversations with various academic researchers, including Eric Hekler from Arizona State University (ASU).

Eric started a conversation, and kept asking me: What could you do if you partnered with academic researchers? How can traditional researchers help the DIY community, OpenAPS or otherwise?

That also sparked a conversation with Paul Tarini, a senior program officer at the Robert Wood Johnson Foundation (RWJF), about potential funding for a project.

(Important to state here: OpenAPS itself is not a funded project. It has not been, and will not be. It is 100% DIY, non-commercial, and it has been built by a community of volunteers.)

What I wanted to talk to RWJF about was funding a collaboration with academic researchers for studying data and innovation coming out of the community; and to ultimately identify needs and build resources to help scale this type of community effort and empower other patient communities as well.

It took over a year, but we were able to work through initial project proposals and were then invited to submit a full proposal. And on Wednesday (September 6, 2017), I found out that we have been awarded the grant, and this project work will be funded by the Robert Wood Johnson Foundation. The project officially begins on September 15 and will run for 18 months.

So what exactly is this project?

Our project is titled “Learning to not wait: Opening pathways for discovery, research, and innovation in health and healthcare.”

It entails a number of things.

    1. We are creating an on-call data science team to support research in the DIY community. More details will be forthcoming, but essentially this team is there to help do research on the myriad of questions bubbling out of the community. For example – how does sensitivity change during growth spurts, during periods of inactivity, or when changing insulin types? What are some of the most successful mealtime insulin dosing strategies? Etc. People will be able to submit ideas, and get help formulating the idea into a researchable question, and get the research done.
    2. Studying the process of research when done by patients, and the barriers they/their research run into when spreading this scientific knowledge. I personally know there are a lot of barriers, but we need to document them and find solutions. (There are a lot of prejudice and perceived stigmas toward patient researchers doing this type of scientific work, around things like quality of research, methods of distributing knowledge, etc.)
    3. Convening a meeting with patients, traditional researchers, legal experts, and others in this innovative research space to discuss and address some of the known and being-found barriers for this type of research. I envision a white paper type publication to come out of this meeting to document the lay of the land as it is.
    4. Creating toolkit-type resources based on what we’ve learned and are learning in this project for helping patients new to DIY and this type of research take on various levels of research or innovation activity. Part of our project’s scope of work, in #WeAreNotWaiting spirit, includes beta testing with 2-3 other patient communities, so we can get feedback and iterate and roll these out as quickly as possible.

Our project has a couple of principles that I feel strongly about, and am also very proud of in approaching this body of work.

  • I am the scientific Principal Investigator of this project. This is unique in the world of grant-funded research, where a patient is driving the scientific discovery process. (I’m proud and very appreciative to have two amazing co-PI’s who are helping with some of the administrative work since the grant is being administered through Arizona State University Foundation, who is being an awesome partner given the uniqueness of this situation*.) My co-PI’s are Eric Hekler and Erik Johnston. The other members of the team include John Harlow, who’s a MacArthur Foundation Postdoctoral Fellow; Sayali Phatak, a PhD student at ASU; and Keren Hirsch from the ASU Decision Theater.
  • #WeAreNotWaiting is the mantra for this project and our entire team. We plan to be as efficient as possible in doing the project work, which includes being as timely as possible with sharing findings back with the community as soon as they’re ready (a given; there’s no reason to wait) as well as finding ways to publish that are faster than the very traditional academic publishing process, and being thoughtful about the right audiences outside the patient community for communicating about this project’s work.
  • Always asking why. As a brand new PI, I have a lot to learn. But as a non-traditional PI, I also am running into a lot of things that are done the way they’d be done if I was traditionally inside an organization. I plan to explore and challenge as many of these, and try to document the decisions I make in this project as I come to those forks in the road. In some cases, I choose the easier paths because for my project/work/focus, it does not matter. In other cases, based on principle, I choose the harder path-blazing approach.

* About the uniqueness of this project and the administrative details

Since I’m an individual patient researcher, not affiliated with the organization, we decided we would make the official grantee financial organization Arizona State University Foundation, since that’s where my co-PI’s were. But true to the nature of this project, I want to document the challenges and opportunities that come with that, so more to come about all the interesting lessons learned about the process of putting together the proposal and the grant approval process once we heard the grant would be awarded. That way, future patient researchers have a leg up on what is coming when taking on this type of project and are aware of what this approach entailed. The short version is I am a subcontractor to ASU for purpose of the grant; but am not employed or otherwise affiliated with ASU. Props to the many people at ASU who learned about me and this project in the approval process and rolled with it / helped make it happen.

So, what’s next? When do you start? What are you waiting on?!

Coming super soon – a project website (now here) with more details about this project.

For my fellow PWDs:

  • Stay tuned for the project website going live, which will also include more details about how individuals in the diabetes community can pitch ideas/get started working with the on-call data science team.

For patients reading this who are members of other patient disease communities:

  • Ping me if you’re SUPER excited and can’t wait to tell me :), or stay tuned for more info about the process for proposing that your patient community be one of the communities with whom we beta test some of the tools/resources developed toward the latter phases of this project.

If you’re someone else who’s interested in this work (such as a legal expert, other researcher, etc.):

  • Also ping me if you’re interested in hearing more about the meeting we plan to convene with a small multidisciplinary group to discuss and address barriers of patient-driven research. Even if we can’t get everyone interested to attend the in-person meeting, I would still love your input and collaboration for the white paper and/or other publications and intersections with this project.

For everyone else:

  • Please do let me know if there’s a particular aspect of this project that you’re curious to learn more about – whether it’s some of what I’m facing and documenting as a patient PI researcher, or otherwise. That’ll help me prioritize some of the blog posts and articles I’m writing about this process!

Thanks to everyone who managed to read this ginormous blog post.

I am incredibly excited about the project, and having resources to focus on how patients and non-traditional actors in healthcare can drive research, development, innovation, and knowledge sharing in non-traditional methods and from the ground up, plus prioritize and change the healthcare research agenda. Like my work in OpenAPS that stands on the shoulders of so many, I’m hoping this project is the first of many and gets to a place for others to leverage this work and take it beyond the scope of what we’ve all imagined is currently possible.

A huge thanks to the team partnering with me on this work; to ASU for being a great partner as an organization; to the Robert Wood Johnson Foundation for supporting this project (and in particular to our program manager, Paul Tarini, for his ongoing support throughout this entire process); and many extra thanks to Scott and all my family and friends for supporting me throughout the proposal process and being the recipients of some VERY excited and !!! filled texts when I found out we had officially been awarded the grant for this project.