Open-source automated insulin delivery systems for the management of type 1 diabetes during pregnancy

A 40-year-old woman used an open-source automated insulin delivery system to manage her type 1 diabetes (T1D) prior to conception. The code for building the iPhone application called ‘Loop’ that carried the software for the hybrid closed-loop controller was available online. Her glycated hemoglobin before conception was 6.4%. Between 6 and 12 weeks gestation, she spent 66% time-in-range (TIR), 28% time-above-range (TAR) and 6% time-below-range (TBR). Between 18 and 24 weeks gestation, she spent 68% TIR, 27% TAR and 5% TBR. During her third trimester, she spent 72% TIR, 21% TAR and 7% TBR. She delivered a healthy infant with no neonatal complications. Clinicians should be aware of this technology as it gains traction in the T1D community and seeks Food and Drug Administration approval.

Accessing the ECG Data of the Apple Watch and Accomplishing Interoperability Through FHIR

Medical data generated by wearables and smartphones can add value to health care and medical research. This also applies to the ECG data that is created with Apple Watch 4 or later. However, Apple currently does not provide an efficient solution for accessing and sharing ECG raw data in a standardized data format. Our method aims to provide a solution that enables patients to share their Apple Watch’s ECG data with any health care institution via an iPhone application. We achieved this by implementing a parser in Swift that converts the Apple Watch’s raw ECG data into a FHIR observation. Furthermore, we added the capability of transmitting these observations to a specified server and equipping it with the patient’s reference number. The result is a user-friendly iPhone application, enabling patients to share their Apple Watch’s ECG data in a widely known health data standard with minimal effort. This allows the personnel involved in the patient’s treatment to use data that was previously difficult to access for further analyses and processing. Our solution can facilitate research for new treatment methods, for example, utilizing the Apple Watch for continuous monitoring of heart activity and early detection of heart conditions.

Applications and barriers to use of an mHealth iPhone application for self-management of chronic recurrent medical conditions: A Pilot Study

Management of chronic recurrent medical conditions (CRMC), such as migraine headaches, chronic pain and anxiety/depression, is a major challenge for modern providers. The fact that often the most effective treatments and/or preventative measures for CRMCs vary from patient to patient lends itself to a platform for self-management by patients. However, to develop such an mHealth app requires an understanding of the various applications, and barriers, to real-world use. In this pilot study with internet-based recruitment, we conducted an assessment of user satisfaction of the iMTracker iOS (iPhone) application for CRMC self-management through a self-administered survey of subjects with CRMCs. From May 15, 2019 until March 27, 2020, we recruited 135 subjects to pilot test the iMTracker application for user-selected CRMCs. The most common age group was 31–45 (48.2%), followed by under 30 (22.2%) and 46–55 (20%). There were no subjects over 75 years old completing the survey. 38.8% of subjects were college graduates, followed by 29.6% with a Master’s degree, and 25.9% with some college. No subjects had not graduated from high school, and only 2 (1.5%) did not attend college after high school. 80.7% of subjects were self-identified as Caucasian, and 90.4% as not Hispanic or Latino. The most common CRMC was pain (other than headaches) in 40% of subjects, followed by mental health in 17.8% and headaches in 15.6%. 39.3% of subjects experienced the condition multiple times in a day, 40.0% experienced the condition daily, and 14.8% experienced the condition weekly, resulting in a total of 94.1% of subjects experiencing the condition at least weekly. Among the concerns about a self-management app, time demands (54.8%) and ineffectiveness (43.7%) were the most prominent, with privacy (24.4%) and data security (25.2%) also noted. In summary, we found internet-based recruitment identified primarily Caucasian population of relatively young patients with CRMCs of relatively high recurrence rate. Future work is needed to examine the use of this application in older, underrepresented minorities, and lower socioeconomic status populations.