Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study

Validating the Use of Continuous Glucose Monitors With Nondiabetic Recreational Runners

PURPOSE

Continuous glucose monitors (CGMs) are becoming increasingly popular among endurance athletes despite unconfirmed accuracy. We assessed the concurrent validity of the FreeStyle Libre 2 worn on 2 different sites at rest, during steady-state running, and postprandial.

METHODS

Thirteen nondiabetic, well-trained recreational runners (age = 40 [8] y, maximal aerobic oxygen consumption = 46.1 [6.4] mL·kg-1·min-1) wore a CGM on the upper arm and chest while treadmill running for 30, 60, and 90 minutes at intensities corresponding to 50%, 60%, and 70% of maximal aerobic oxygen consumption, respectively. Glucose was measured by manually scanning CGMs and obtaining a finger-prick capillary blood glucose sample. Mean absolute relative difference, time in range, and continuous glucose Clarke error grid analysis were used to compare paired CGM and blood glucose readings.

RESULTS

Across all intensities of steady-state running, we found a mean absolute relative difference of 13.8 (10.9) for the arm and 11.4 (9.0) for the chest. The coefficient of variation exceeded 70%. Approximately 47% of arm and 50% of chest paired glucose measurements had an absolute difference ≤10%. Continuous glucose Clarke error grid analysis indicated 99.8% (arm) and 99.6% (chest) CGM data fell in clinically acceptable zones A and B. Time-in-range analysis showed reduced accuracy at lower glucose levels. However, CGMs accurately detected trends in mean glucose readings over time.

CONCLUSIONS

CGMs are not valid for point glucose monitoring but appear to be valid for monitoring glucose trends during steady-state exercise. Accuracy is similar for arm and chest. Further research is needed to determine whether CGMs can detect important events such as hypoglycemia during exercise.

Practical Aspects and Exercise Safety Benefits of Automated Insulin Delivery Systems in Type 1 Diabetes

Regular exercise is essential to overall cardiovascular health and well-being in people with type 1 diabetes, but exercise can also lead to increased glycemic disturbances. Automated insulin delivery (AID) technology has been shown to modestly improve glycemic time in range (TIR) in adults with type 1 diabetes and significantly improve TIR in youth with type 1 diabetes. Available AID systems still require some user-initiated changes to the settings and, in some cases, significant pre-planning for exercise. Many exercise recommendations for type 1 diabetes were developed initially for people using multiple daily insulin injections or insulin pump therapy. This article highlights recommendations and practical strategies for using AID around exercise in type 1 diabetes.

Accuracy and Safety of Dexcom G7 Continuous Glucose Monitoring in Adults with Diabetes

Background: We evaluated the accuracy and safety of a seventh generation (G7) Dexcom continuous glucose monitor (CGM) during 10.5 days of use in adults with diabetes. Methods: Adults with either type 1 or type 2 diabetes (on intensive insulin therapy or not) participated at 12 investigational sites in the United States. In-clinic visits were conducted on days 1 or 2, 4 or 7, and on the second half of day 10 or the first half of day 11 for frequent comparisons with comparator blood glucose measurements obtained with the YSI 2300 Stat Plus glucose analyzer. Participants wore sensors concurrently on the upper arm and abdomen. Accuracy evaluation included the proportion of CGM values within 15% of comparator glucose levels >100 mg/dL or within 15 mg/dL of comparator levels ≤100 mg/dL (%15/15), along with the %20/20 and %30/30 agreement rates. The mean absolute relative difference (MARD) between temporally matched CGM and comparator values was also calculated. Results: Data from 316 participants (619 sensors, 77,774 matched pairs) were analyzed. For arm- and abdomen-placed sensors, overall MARDs were 8.2% and 9.1%, respectively. Overall %15/15, %20/20, and %30/30 agreement rates were 89.6%, 95.3%, and 98.8% for arm-placed sensors and were 85.5%, 93.2%, and 98.1% for abdomen-placed sensors. Across days of wear, glucose concentration ranges, and rates of change, %20/20 agreement rates varied by no more than 9% from the overall %20/20. No serious adverse events were reported. Conclusions: The G7 CGM provides accurate glucose readings with single-digit MARD with arm or abdomen placement in adults with diabetes. Clinicaltrials.gov: NCT04794478.

Open-source automated insulin delivery: international consensus statement and practical guidance for health-care professionals

Open-source automated insulin delivery systems, commonly referred to as do-it-yourself automated insulin delivery systems, are examples of user-driven innovations that were co-created and supported by an online community who were directly affected by diabetes. Their uptake continues to increase globally, with current estimates suggesting several thousand active users worldwide. Real-world user-driven evidence is growing and provides insights into safety and effectiveness of these systems. The aim of this consensus statement is two-fold. Firstly, it provides a review of the current evidence, description of the technologies, and discusses the ethics and legal considerations for these systems from an international perspective. Secondly, it provides a much-needed international health-care consensus supporting the implementation of open-source systems in clinical settings, with detailed clinical guidance. This consensus also provides important recommendations for key stakeholders that are involved in diabetes technologies, including developers, regulators, and industry, and provides medico-legal and ethical support for patient-driven, open-source innovations.

#Menopause on Instagram: a mixed-methods study

OBJECTIVE

The aims of this study were to: (1) characterize the menopause transition (MT) on social media and (2) determine if concordance or discordance exists when comparing MT-focused social media posts and biomedical research literature.

METHODS

We analyzed 440 sequential Instagram posts with the hashtag #menopause over 2 weeks from January to February 2019. Posts were composed of 299 unique accounts, resulting in an average of 1.7 posts per account (standard deviation [SD] 1; range 1-9; median 1 and interquartile range [IQR] 1-2). Each account had an average of 2,616 followers (SD 11,271; range 3-129,000; median 421.5 and IQR 177-1,101). Content and thematic analyses were completed for posts, images, and videos to identify codes related to the MT. The top 15 codes were then searched along with the key term "menopause" in PubMed to ascertain the level of concordance between Instagram content and peer-reviewed literature on the MT.

RESULTS

We identified 69 codes in our corpus of Instagram content, resulting in 9 categories: physical health, mental health, complementary and integrative health, advertising, social, advice, self-care, nature, and self-expression (kappa 0.95-1.00). The most prevalent codes were related to weight loss/fitness (20.5%) and hormones (18.4%). The majority of frequent codes identified in Instagram posts were infrequently listed in biomedical literature related to menopause. However, there were two codes, Weight loss/Fitness and Hot flashes, that were frequently discussed in Instagram posts and the biomedical literature.

CONCLUSIONS

The examination of #menopause on Instagram provides novel insights for researchers and clinicians. Our findings provide a better understanding of the experiences and support needs of individuals experiencing menopause. Furthermore, codes related to menopause have low prominence in the biomedical literature, suggesting key topics that could be explored in the future.

Patient-Driven Diabetes Technologies: Sentiment and Personas of the #WeAreNotWaiting and #OpenAPS Movements

BACKGROUND

Patients with diabetes have developed innovative do-it-yourself (DIY) methods for adapting existing medical devices to better fit individual needs.

METHOD

A multiple method study used Symplur Analytics to analyze aggregated Twitter data of #WeAreNotWaiting and #OpenAPS tweets between 2014 and 2017 to examine DIY patient-led innovation. Conversation sentiment was examined between diabetes stakeholders to determine changes over time. Two hundred of the most shared photos were analyzed to understand visual representations of DIY patient-led innovations. Finally, discourse analysis was used to identify the personas who engage in DIY patient-led diabetes technologies activities and conversations on Twitter.

RESULTS

A total of 7886 participants who generated 46 578 tweets were included. Sentiment analysis showed that 82%-85% of interactions around DIY patient-led innovation was positive among patient/caregiver and physician groups. Through photo analysis, five content themes emerged: (1) disseminating media and conference coverage, (2) showcasing devices, (3) celebrating connections, (4) providing instructions, and (5) celebrating accomplishments. Six personas emerged across the overlapping userbase: (1) fearless leaders, (2) loopers living it up, (3) parents on a mission, (4) the tech titans, (5) movement supporters, and (6) healthcare provider advocates. Personas had varying goals and behaviors within the community.

CONCLUSIONS

#WeAreNotWaiting and #OpenAPS on Twitter reveal a fast-moving patient-led movement focused on DIY patient innovation that is further mobilized by an expanding and diverse userbase. Further research is indicated to bring technology savvy persons with diabetes into conversation with healthcare providers and researchers alike.

Social Media in the Diabetes Community: a Novel Way to Assess Psychosocial Needs in People with Diabetes and Their Caregivers

PURPOSE OF REVIEW

Diabetes is a chronic disease that, regardless of type, requires intensive, ongoing self-management. As a result, people with diabetes (PWD) often have complex environmental, social, behavioral, and informational needs, many of which are unmet in healthcare settings and systems. To help meet these needs, many PWD interact with diabetes online communities (DOCs), including platforms such as Facebook, Twitter, and blogs, to share real-life support, problems, and concerns with other PWD, offering a rich source of data on patient-reported outcomes. This article reviews recent psychosocial needs and outcomes identified by studies of DOCs and/or their users.

RECENT FINDINGS

Participation in DOCs appears driven by a need for psychosocial support, unmet by providers and the healthcare system, as well as a sense of duty to provide it to others. The most common activities observed in DOCs are giving and receiving various types of support: psychosocial, technical, informational, and self-management. General and specific challenges (e.g., continuous glucose monitoring) as well as frustrations and worries associated with those challenges are commonly expressed, leading to reciprocal sharing, support, and encouragement, in a judgment-free manner, from other PWD. This leads users to feel more understood, empowered, validated, less alone, and more supported. Negative findings were reported very rarely and focused more on how other participants used social media rather than on the exchange of misplaced or dangerous information or advice. Diabetes online communities have grown from unmet needs for problem-solving and psychosocial support for living with a complex condition and from the availability of a new communications medium (i.e., social media). This has enabled communities of peers to both seek and receive support for living with diabetes, providing an important supplement to what is provided in healthcare settings and offering valuable information about what is most important to PWD and their families, with the potential to improve psychosocial care.