Study protocol: Safety and efficacy of smart watch integrated do-it-yourself continuous glucose monitoring in adults with Type 1 diabetes, a randomised controlled trial

Do-it-yourself continuous glucose monitoring in people aged 16 to 69 years with type 1 diabetes: A qualitative study

AIMS

In many countries, real-time continuous glucose monitoring (rt-CGM) is not funded, and cost presents a barrier to access. A do-it-yourself conversion of intermittently scanned CGM (DIY-CGM) is a cheaper alternative. This qualitative study aimed to explore user experiences with DIY-CGM in people aged 16 to 69 years with type 1 diabetes (T1D).

METHODS

Convenience sampling was used to recruit participants for semi-structured virtual interviews exploring experiences of DIY-CGM use. Participants were recruited after completing the intervention arm of a crossover randomised controlled trial that evaluated DIY-CGM versus intermittently scanned CGM (isCGM). Participants were previously naive to DIY-CGM and rt-CGM but not isCGM. The DIY-CGM intervention consisted of a Bluetooth bridge connected to isCGM, adding rt-CGM functionality over 8 weeks. Interviews were transcribed, then thematic analysis was performed.

RESULTS

Interviews were with 12 people aged 16 to 65 years, with T1D: mean age ± SD 43 ± 14 years; baseline mean HbA1c ± SD 60 mmol/mol ± 9.9 (7.6 ± 0.9%) and time in range 59.8% ± 14.8%. Participants perceived that using DIY-CGM improved both glycaemic control and aspects of quality of life. Alarm and trend functionality allowed participants to perceive reduced glycaemic variability overnight and following meals. The addition of a smartwatch increased discrete access to glucose information. There was a high degree of trust in DIY-CGM. Challenges while using DIY-CGM included signal loss during vigorous exercise, alarm fatigue and short battery life.

CONCLUSIONS

This study suggests that for users, DIY-CGM appears to be an acceptable alternative method of rt-CGM.

The Effect of Do-It-Yourself Real-Time Continuous Glucose Monitoring on Glycemic Variables and Participant-Reported Outcomes in Adults With Type 1 Diabetes: A Randomized Crossover Trial

AIM

Real-time continuous glucose monitoring (rtCGM) has several advantages over intermittently scanned continuous glucose monitoring (isCGM) but generally comes at a higher cost. Do-it-yourself rtCGM (DIY-rtCGM) potentially has benefits similar to those of rtCGM. This study compared outcomes in adults with type 1 diabetes using DIY-rtCGM versus isCGM.

METHODS

In this crossover trial, adults with type 1 diabetes were randomized to use isCGM or DIY-rtCGM for eight weeks before crossover to use the other device for eight weeks, after a four-week washout period where participants reverted back to isCGM. The primary endpoint was time in range (TIR; 3.9-10 mmol/L). Secondary endpoints included other glycemic control measures, psychosocial outcomes, and sleep quality.

RESULTS

Sixty participants were recruited, and 52 (87%) completed follow-up. Glucose outcomes were similar in the DIY-rtCGM and isCGM groups, including TIR (53.1% vs 51.3%; mean difference -1.7% P = .593), glycosylated hemoglobin (57.0 ± 17.8 vs 61.4 ± 12.2 mmol/L; P = .593), and time in hypoglycemia <3.9 mmol/L (3.9 ± 3.8% vs 3.8 ± 4.0%; P = .947). Hypoglycemia Fear Survey total score (1.17 ± 0.52 vs 0.97 ± 0.54; P = .02) and fear of hypoglycemia score (1.18 ± 0.64 vs 0.97 ± 0.45; P = .02) were significantly higher during DIY-rtCGM versus isCGM. Diabetes Treatment Satisfaction Questionnaire status (DTSQS) score was also higher with DIY-rtCGM versus isCGM (28.7 ± 5.8 vs 26.0 ± 5.8; P = .04), whereas diabetes-related quality of life was slightly lower (DAWN2 Impact of Diabetes score: 3.11 ± 0.4 vs 3.32 ± 0.51; P = .045); sleep quality did not differ between the two groups.

CONCLUSION

Although the use of DIY-rtCGM did not improve glycemic outcomes compared with isCGM, it positively impacted several patient-reported psychosocial variables. DIY-rtCGM potentially provides an alternative, cost-effective rtCGM option.

Development of an AI based automated analysis of pediatric Apple Watch iECGs

Introduction

The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study.

Methods

A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI.

Results

The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm.

Conclusion

The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.