Effectiveness of an Integrated Engagement Support System to Facilitate Patient Use of Digital Diabetes Prevention Programs: Protocol for a Randomized Controlled Trial

Digital therapeutics as an emerging new therapy for diabetes mellitus: potentials and concerns

The global burden of controlling and managing diabetes mellitus (DM) is a significant challenge. Despite the advancements in conventional DM therapy, there remain hurdles to overcome, such as enhancing medication adherence and improving patient prognosis. Digital therapeutics (DTx), an innovative digital application, has been proposed to augment the traditional disease management workflow, particularly in managing chronic diseases like DM. Several studies have explored DTx, yielding promising results. However, certain concerns about this innovation persist. In this review, we aim to encapsulate the potential of DTx and its applications in DM management, thereby providing a comprehensive overview of this technique for public health policymakers.

Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study

BACKGROUND

Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting.

OBJECTIVE

This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program.

METHODS

We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency.

RESULTS

Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies.

CONCLUSIONS

ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: Evaluation Study

BACKGROUND

Digital diabetes prevention programs (dDPPs) are effective "digital prescriptions" but have high attrition rates and program noncompletion. To address this, we developed a personalized automatic messaging system (PAMS) that leverages SMS text messaging and data integration into clinical workflows to increase dDPP engagement via enhanced patient-provider communication. Preliminary data showed positive results. However, further investigation is needed to determine how to optimize the tailoring of support technology such as PAMS based on a user's preferences to boost their dDPP engagement.

OBJECTIVE

This study evaluates leveraging machine learning (ML) to develop digital engagement phenotypes of dDPP users and assess ML's accuracy in predicting engagement with dDPP activities. This research will be used in a PAMS optimization process to improve PAMS personalization by incorporating engagement prediction and digital phenotyping. This study aims (1) to prove the feasibility of using dDPP user-collected data to build an ML model that predicts engagement and contributes to identifying digital engagement phenotypes, (2) to describe methods for developing ML models with dDPP data sets and present preliminary results, and (3) to present preliminary data on user profiling based on ML model outputs.

METHODS

Using the gradient-boosted forest model, we predicted engagement in 4 dDPP individual activities (physical activity, lessons, social activity, and weigh-ins) and general activity (engagement in any activity) based on previous short- and long-term activity in the app. The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Brier score metrics determined the performance of the model. Shapley values reflected the feature importance of the models and determined what variables informed user profiling through latent profile analysis.

RESULTS

We developed 2 models using weekly and daily DPP data sets (328,821 and 704,242 records, respectively), which yielded predictive accuracies above 90%. Although both models were highly accurate, the daily model better fitted our research plan because it predicted daily changes in individual activities, which was crucial for creating the "digital phenotypes." To better understand the variables contributing to the model predictor, we calculated the Shapley values for both models to identify the features with the highest contribution to model fit; engagement with any activity in the dDPP in the last 7 days had the most predictive power. We profiled users with latent profile analysis after 2 weeks of engagement (Bayesian information criterion=-3222.46) with the dDPP and identified 6 profiles of users, including those with high engagement, minimal engagement, and attrition.

CONCLUSIONS

Preliminary results demonstrate that applying ML methods with predicting power is an acceptable mechanism to tailor and optimize messaging interventions to support patient engagement and adherence to digital prescriptions. The results enable future optimization of our existing messaging platform and expansion of this methodology to other clinical domains.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04773834; https://www.clinicaltrials.gov/ct2/show/NCT04773834.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/26750.

Issues and Challenges in the Primary Prevention of Type 2 Diabetes Mellitus: A Systematic Review

BACKGROUND

Primary prevention of type 2 diabetes mellitus (T2DM) is possible in at-risk populations, and prevention programmes have been shown to be effective in real-world scenarios. Despite this evidence, diabetes prevalence has tripled in recent decades and is expected to reach 700 million patients by 2045, making it one of the leading causes of death globally. This review is aimed at identifying the issues and challenges in the primary prevention of T2DM.

METHODS

Scopus, Web of Science, PubMed and Ovid MEDLINE were systematically searched for published articles. Articles were screened based of inclusion and exclusion criteria. The inclusion criteria were: (1) published in 2010-2020, (2) full original article, (3) written in English, (4) qualitative, mixed-methods article, observational or interventional study. The exclusion criteria were: (1) animal study, (2) in vivo/in vitro study, (3) type 1 diabetes or gestational DM and (4) conference abstract, book chapter, report, and systematic review. Eligible articles were assessed using Mixed Methods Appraisal Tool (MMAT) by three assessors.

RESULTS

A total of 11 articles were selected for qualitative synthesis from the initial 620 articles. The issues and challenges seen in T2DM primary prevention followed three themes: healthcare program (sub-themes: lack of resources, community partnership, participation, health literacy), health provider (sub-themes: lack of implementation, health care staff, collaboration, availability), individual (sub-themes: awareness, communication, misbehaviour, family conflict).

CONCLUSION

Factors relating to healthcare programmes, health providers, and individual issues are the main challenges in T2DM primary prevention. By establishing sustainable preventative initiatives that address these issues and challenges in the primary prevention of T2DM, a reduction in T2DM prevalence could be achievable.

Development of a computer-aided text message platform for user engagement with a digital Diabetes Prevention Program: a case study

Digital Diabetes Prevention Programs (dDPP) are novel mHealth applications that leverage digital features such as tracking and messaging to support behavior change for diabetes prevention. Despite their clinical effectiveness, long-term engagement to these programs remains a challenge, creating barriers to adherence and meaningful health outcomes. We partnered with a dDPP vendor to develop a personalized automatic message system (PAMS) to promote user engagement to the dDPP platform by sending messages on behalf of their primary care provider. PAMS innovates by integrating into clinical workflows. User-centered design (UCD) methodologies in the form of iterative cycles of focus groups, user interviews, design workshops, and other core UCD activities were utilized to defined PAMS requirements. PAMS uses computational tools to deliver theory-based, automated, tailored messages, and content to support patient use of dDPP. In this article, we discuss the design and development of our system, including key requirements and features, the technical architecture and build, and preliminary user testing.