Measures of Patient-Reported Expectations, Acceptance, and Satisfaction Using Automated Insulin Delivery Systems: A Review

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App

BACKGROUND

Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations.

METHODS

The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226).

RESULTS

On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively.

CONCLUSIONS

The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Future acceptance of automated insulin delivery systems in youths with type 1 diabetes: validation of the Italian artificial pancreas-acceptance measure

AIM

The purpose of this study was to develop a questionnaire to examine the future acceptance of Automatic insulin delivery systems (AIDs), their perceived usefulness, ease of use, and trust in the device in subjects with type 1 diabetes (T1D).

METHODS

A questionnaire in Italian, based on the Technology Acceptance Model, was developed to examine intention to use AIDs, considered as a measure of future acceptance, and its determinants to use the system. A total of 43 questions for children and 46 for parents were included, and a 5-point Likert scale was used.

RESULTS

239 subjects with T1D using multiple daily injections (MDI) or sensor-augmented pump (SAP) and their parents completed the questionnaire. The completion rate was excellent, with almost 100% of items answered. The overall Cronbach's coefficient for children and adolescents was 0.92 and 0.93 for parents, indicating excellent internal consistency in both groups. Parent-youth agreement was 0.699 (95% confidence interval: 0.689-0.709), indicating a good agreement between the two evaluations. Factor analysis identified measurement factors for the "artificial pancreas (AP)-acceptance labeled benefits and hassles of AIDs, and the internal consistency of the total scale was alpha = 0.94 for subjects with T1D and 0.95 for parents. The level of AP acceptance was more than neutral: 3.91 ± 0.47 and 3.99 ± 0.43 (p = 0.07) for youths and parents, respectively (possible score range 1 to 5, neutral score is 3.0). Parents reported higher scores in the benefit items than children-adolescents (p = 0.04).

CONCLUSIONS

We developed a new questionnaire based on the items available in the literature, and we demonstrated that the "AP-acceptance" reveals a meaningful factor structure, good internal reliability, and agreement between parent-young people evaluations. This measure could be a valuable resource for clinicians and researchers to assess AP acceptance in pediatric patients with T1D and their parents. This patient profiling approach could help to enroll candidates for AIDs with proper expectations and who most likely will benefit from the system.

The Path From Awareness to Action: Exploring Diabetic Patients' Awareness and Attitudes and Barriers to Utilization of Artificial Pancreas in the Beheira Governorate, Egypt

Background and objective There is scarce data on diabetic patients' awareness, attitude, and barriers to utilization regarding the artificial pancreas. In light of this, the current study aimed to explore the awareness, attitudes, and perceived barriers to utilization of the artificial pancreas experienced by diabetic patients. Methods A cross-sectional study design was employed to achieve the aim of the study. The study was conducted in Damanhur city, the Beheira Governorate, Egypt. The convenience sampling technique was used to include 385 diabetic patients. The researchers designed an interview questionnaire comprising four parts to collect data about knowledge, attitudes, and barriers to utilization. Results The findings showed that 61% of the participants had a satisfactory level of overall knowledge. Regarding overall attitude, 64.1% of participants exhibited a positive attitude toward the artificial pancreas. The data indicated that 37.7% of participants identified the associated high cost as a significant barrier. Additionally, 23.3% expressed concerns about the lack of healthcare provider support, 21.5% had reservations regarding maintenance, and 17.5% felt limited by their technical skills. Conclusions The study revealed a notable satisfactory level of knowledge and attitudes among about two-thirds of participants regarding the artificial pancreas. Concerns about the high cost emerged as a predominant barrier followed by a lack of healthcare provider support. Empowering both healthcare providers and patients through ongoing educational initiatives can play a pivotal role in fostering a positive attitude and addressing concerns related to artificial pancreas technology.