Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

Digital health technologies/interventions in smart ward development for elderly patients with diabetes: A perspective from China and beyond

Diabetes is highly prevalent among the elderly worldwide, with the highest number of diabetes cases in China. Yet, the management of diabetes remains unsatisfactory. Recent advances in digital health technologies have facilitated the establishment of smart wards for diabetes patients. There is a lack of smart wards tailored specifically for older diabetes patients who encounter unique challenges in glycemic control and diabetes management, including an increased vulnerability to hypoglycemia, the presence of multiple chronic diseases, and cognitive decline. In this review, studies on digital health technologies for diabetes in China and beyond were summarized to elucidate how the adoption of digital health technologies, such as real-time continuous glucose monitoring, sensor-augmented pump technology, and their integration with 5th generation networks, big data cloud storage, and hospital information systems, can address issues specifically related to elderly diabetes patients in hospital wards. Furthermore, the challenges and future directions for establishing and implementing smart wards for elderly diabetes patients are discussed, and these challenges may also be applicable to other countries worldwide, not just in China. Taken together, the smart wards may enhance clinical outcomes, address specific issues, and eventually improve patient-centered hospital care for elderly patients with diabetes.

A Review of Patient Bed Sensors for Monitoring of Vital Signs

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Diabetes management in the era of artificial intelligence

Artificial intelligence is growing quickly, and its application in the global diabetes pandemic has the potential to completely change the way this chronic illness is identified and treated. Machine learning methods have been used to construct algorithms supporting predictive models for the risk of getting diabetes or its complications. Social media and Internet forums also increase patient participation in diabetes care. Diabetes resource usage optimisation has benefited from technological improvements. As a lifestyle therapy intervention, digital therapies have made a name for themselves in the treatment of diabetes. Artificial intelligence will cause a paradigm shift in diabetes care, moving away from current methods and toward the creation of focused, data-driven precision treatment.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

OBJECTIVES

The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

METHODS

We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

RESULTS

Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

CONCLUSION

In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Insight into continuous glucose monitoring: from medical basics to commercialized devices

According to the latest statistics, more than 537 million people around the world struggle with diabetes and its adverse consequences. As well as acute risks of hypo- or hyper- glycemia, long-term vascular complications may occur, including coronary heart disease or stroke, as well as diabetic nephropathy leading to end-stage disease, neuropathy or retinopathy. Therefore, there is an urgent need to improve diabetes management to reduce the risk of complications but also to improve patient's quality life. The impact of continuous glucose monitoring (CGM) is well recognized, in this regard. The current review aims at introducing the basic principles of glucose sensing, including electrochemical and optical detection, summarizing CGM technology, its requirements, advantages, and disadvantages. The role of CGM systems in the clinical diagnostics/personal testing, difficulties in their utilization, and recommendations are also discussed. In the end, challenges and prospects in future CGM systems are discussed and non-invasive, wearable glucose biosensors are introduced. Though the scope of this review is CGMs and provides information about medical issues and analytical principles, consideration of broader use will be critical in future if the right systems are to be selected for effective diabetes management.

Continuous Glucose Monitoring and Other Wearable Devices to Assess Hypoglycemia among Older Adult Outpatients with Diabetes Mellitus

BACKGROUND

Hypoglycemia (HG) causes symptoms that can be fatal, and confers risk of dementia. Wearable devices can improve measurement and feedback to patients and clinicians about HG events and risk.

OBJECTIVES

The aim of the study is to determine whether vulnerable older adults could use wearables, and explore HG frequency over 2 weeks.

METHODS

First, 10 participants with diabetes mellitus piloted a continuous glucometer, physical activity monitor, electronic medication bottles, and smartphones facilitating prompts about medications, behaviors, and symptoms. They reviewed graphs of glucose values, and were asked about the monitoring experience. Next, a larger sample (N = 70) wore glucometers and activity monitors, and used the smartphone and bottles, for 2 weeks. Participants provided feedback about the devices. Descriptive statistics summarized demographics, baseline experiences, behaviors, and HG.

RESULTS

In the initial pilot, 10 patients aged 50 to 85 participated. Problems addressed included failure of the glucometer adhesive. Patients sought understanding of graphs, often requiring some assistance with interpretation. Among 70 patients in subsequent testing, 67% were African-American, 59% were women. Nearly one-fourth (23%) indicated that they never check their blood sugars. Previous HG was reported by 67%. In 2 weeks of monitoring, 73% had HG (glucose ≤70 mg/dL), and 42% had serious, clinically significant HG (glucose under 54 mg/dL). Eight patients with HG also had HG by home-based blood glucometry. Nearly a third of daytime prompts were unanswered. In 24% of participants, continuous glucometers became detached.

CONCLUSION

Continuous glucometry occurred for 2 weeks in an older vulnerable population, but devices posed wearability challenges. Most patients experienced HG, often serious in magnitude. This suggests important opportunities to improve wearability and decrease HG frequency among this population.

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

PURPOSE

Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases.

MATERIALS AND METHODS

The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.

RESULTS

A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983).

CONCLUSION

This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:• To promote trust, the science must be understandable.• To enhance uptake, the workflow should not be impacted greatly.• To maximize buy-in, engagement at all levels is important.• To ensure relevance, the education must be tailored to the clinical role and hospital culture.• To lead to clinical action, the information must integrate into clinical care.• To promote sustainability, there should be periodic support interactions after formal implementation.