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.

Heuristics used for evaluating the usability of mobile health applications: A systematic literature review

Objective

Mobile health applications hold immense potential for enhancing health outcomes. Usability is one of the main factors for the adoption and use of mobile health applications. However, despite the growing importance of mHealth applications, clear standards for their evaluation remain elusive. The present study aimed to determine heuristics for the usability evaluation of health-related applications.

Methods

We systematically searched multiple databases for relevant papers published between January 2008 and April 2021. Articles were reviewed, and data were extracted and categorized from those meeting inclusion criteria by two authors independently. Heuristics were identified based on statements, words, and concepts expressed in the studies. These heuristics were first mapped to Nielsen's heuristics based on their differences or similarities. The remaining heuristics that were very important for mobile applications were categorized into new heuristics.

Results

Seventeen studies met the eligibility criteria. Seventy-nine heuristics were extracted from the papers. After combining the items with the same concepts and removing irrelevant items based on the exclusion criteria, 20 heuristics remained. Common heuristics such as "Visibility of system status" and "Flexibility and efficiency of use" were categorized into 10 previously established heuristics and new heuristics like "Navigation" and "User engagement" were recognized as new ones.

Conclusions

In our study, we have meticulously identified 20 heuristics that hold promise for evaluating and designing mHealth applications. These heuristics can be used by the researchers for the development of robust tools for heuristic evaluation. These tools, when adapted or tailored for health domain applications, have the potential to significantly enhance the quality of mHealth applications. Ultimately, this improvement in quality translates to enhanced patient safety.

Protocol Registration

(10.17605/OSF.IO/PZJ7H).

Investigating Popular Mental Health Mobile Application Downloads and Activity During the COVID-19 Pandemic

OBJECTIVE

This article analyzes the changes in downloads and activity of users of select popular mental health mobile applications (mHealth apps) during coronavirus disease 2019 (COVID-19).

BACKGROUND

The outbreak of the COVID-19 crisis has shown a negative impact on public mental health. Mobile health has the potential to help address the psychological needs of existing and new patients during the pandemic and beyond.

METHOD

Downloads data of 16 widely used apps were analyzed. The quality of apps was reviewed using the Mobile Application Rating Scale (MARS) framework. Correlation analysis was conducted to investigate the relationship between app quality and app popularity.

RESULTS

Among the 16 apps, 10 were meditational in nature, 13 showed increased downloads, with 11 apps showing above 10% increase in the downloads after the pandemic started. The popular apps were satisfactory in terms of functionality and esthetics but lacked clinical grounding and evidence base. There exists a gap between app quality and app popularity.

CONCLUSION

This study provided evidence for increased downloads of mental mHealth apps (primarily meditation apps) during the COVID-19 pandemic but revealed several gaps and opportunities to address deficiencies in evidence-based design, usability and effective assessment, and integration into current workflows.

APPLICATION

The COVID-19 pandemic is a potential turning point for mHealth applications for mental health care. Whereas the evidence suggests a need for alternative delivery of care, human factors and ergonomics methods should be utilized to ensure these tools are user-centered, easy to use, evidence-based, well-integrated with professional care, and used sustainably.

Usability and clinical efficacy of diabetes mobile applications for adults with type 2 diabetes: A systematic review

OBJECTIVES

To assess the usability and clinical effectiveness of diabetes mobile applications (diabetes apps) developed for adults with type 2 diabetes.

METHOD

A systematic review of the usability and effectiveness of diabetes apps was conducted. Searches were performed using MEDLINE, EMBASE, COMPENDEX, and IEEE XPLORE for articles published from January 1, 2011, to January 17, 2017. Search terms included: diabetes, mobile apps, and mobile health (mHealth).

RESULTS

The search yielded 723 abstracts of which seven usability studies and ten clinical effectiveness studies met the inclusion criteria from 20 publications. Usability, as measured by satisfaction ratings from experts and patients, ranged from 38% to 80%. Usability problem ratings ranged from moderate to catastrophic. Top usability problems are multi-steps task, limited functionality and interaction, and difficult system navigation. Clinical effectiveness, measured by reductions in HbA1c, ranged from 0.15% to 1.9%.

CONCLUSION

Despite meager satisfaction ratings and major usability problems, there is some limited evidence supporting the effectiveness of diabetes apps to improve glycemic control for adults with type 2 diabetes. Findings strongly suggest that efforts to improve user satisfaction, incorporate established principles of health behavior change, and match apps to user characteristics will increase the therapeutic impact of diabetes apps.

A Modified User-Oriented Heuristic Evaluation of a Mobile Health System for Diabetes Self-management Support

Mobile health platforms offer significant opportunities for improving diabetic self-care, but only if adequate usability exists. Expert evaluations such as heuristic evaluation can provide distinct usability information about systems. The purpose of this study was to complete a usability evaluation of a mobile health system for diabetes patients using a modified heuristic evaluation technique of (1) dual-domain experts (healthcare professionals, usability experts), (2) validated scenarios and user tasks related to patients' self-care, and (3) in-depth severity factor ratings. Experts identified 129 usability problems with 274 heuristic violations for the system. The categories Consistency and Standards dominated at 24.1% (n = 66), followed by Match Between System and Real World at 22.3% (n = 61). Average severity ratings across system views were 2.8 (of 4), with 9.3% (n = 12) rated as catastrophic and 53.5% (n = 69) as major. The large volume of violations with severe ratings indicated clear priorities for redesign. The modified heuristic approach allowed evaluators to identify unique and important issues, including ones related to self-management and patient safety. This article provides a template for one type of expert evaluation adding to the informaticists' toolbox when needing to conduct a fast, resource-efficient and user-oriented heuristic evaluation.