Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

Telehealth Education via WeChat Improves the Quality of Life of Parents of Children with Type-1 Diabetes Mellitus

OBJECTIVE

The purpose of this study was to explore the effect of telehealth education and care guidance via WeChat (Tencent Ltd., Shenzhen, China; a popular smartphone-based social media application) on improving the quality of life of parents of children with type-1 diabetes mellitus.

METHODS

A prospective randomized controlled study was conducted in our hospital from March 2019 to September 2020 to compare the quality of life of parents of children with type-1 diabetes mellitus in the intervention group and the control group.

RESULTS

Six months after discharge, the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) scores of parents in the intervention group were significantly lower than those in the control group (p < 0.05). Compared with the SAS and SDS scores at discharge time, those scores of parents at 6 months after discharge time in intervention group were significantly lower (p < 0.05), while those scores of parents at 6 months after discharge time in control was similar (p > 0.05). Six months after discharge, the scores of the physiological field, psychological field, social relationship field, and environmental field in the intervention group were significantly higher than those of the control group according to the result of the World Health Organization Quality of Life Brief Scale (WHOQOL-BREF; p < 0.05).

CONCLUSION

Using WeChat to provide telehealth education and home care guidance to the parents of children with type-1 diabetes mellitus can effectively relieve the anxiety and depression of the parents and improve their quality of life.

The Impact of Wearable Technologies in Health Research: Scoping Review

Background

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

Objective

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

Methods

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

Results

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

Conclusions

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review

BACKGROUND

Diabetes mellitus is a metabolic disorder that affects hundreds of millions of people worldwide and causes several million deaths every year. Such a dramatic scenario puts some pressure on administrations, care services, and the scientific community to seek novel solutions that may help control and deal effectively with this condition and its consequences.

OBJECTIVE

This study aims to review the literature on the use of modern mobile and wearable technology for monitoring parameters that condition the development or evolution of diabetes mellitus.

METHODS

A systematic review of articles published between January 2010 and July 2020 was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Manuscripts were identified through searching the databases Web of Science, Scopus, and PubMed as well as through hand searching. Manuscripts were included if they involved the measurement of diabetes-related parameters such as blood glucose level, performed physical activity, or feet condition via wearable or mobile devices. The quality of the included studies was assessed using the Newcastle-Ottawa Scale.

RESULTS

The search yielded 1981 articles. A total of 26 publications met the eligibility criteria and were included in the review. Studies predominantly used wearable devices to monitor diabetes-related parameters. The accelerometer was by far the most used sensor, followed by the glucose monitor and heart rate monitor. Most studies applied some type of processing to the collected data, mainly consisting of statistical analysis or machine learning for activity recognition, finding associations among health outcomes, and diagnosing conditions related to diabetes. Few studies have focused on type 2 diabetes, even when this is the most prevalent type and the only preventable one. None of the studies focused on common diabetes complications. Clinical trials were fairly limited or nonexistent in most of the studies, with a common lack of detail about cohorts and case selection, comparability, and outcomes. Explicit endorsement by ethics committees or review boards was missing in most studies. Privacy or security issues were seldom addressed, and even if they were addressed, they were addressed at a rather insufficient level.

CONCLUSIONS

The use of mobile and wearable devices for the monitoring of diabetes-related parameters shows early promise. Its development can benefit patients with diabetes, health care professionals, and researchers. However, this field is still in its early stages. Future work must pay special attention to privacy and security issues, the use of new emerging sensor technologies, the combination of mobile and clinical data, and the development of validated clinical trials.

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

BACKGROUND

Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

OBJECTIVES

This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

METHODS

We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols.

RESULTS

The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

CONCLUSION

We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

Lessons from Use of Continuous Glucose Monitoring Systems in Digital Healthcare

We live in a digital world where a variety of wearable medical devices are available. These technologies enable us to measure our health in our daily lives. It is increasingly possible to manage our own health directly through data gathered from these wearable devices. Likewise, healthcare professionals have also been able to indirectly monitor patients' health. Healthcare professionals have accepted that digital technologies will play an increasingly important role in healthcare. Wearable technologies allow better collection of personal medical data, which healthcare professionals can use to improve the quality of healthcare provided to the public. The use of continuous glucose monitoring systems (CGMS) is the most representative and desirable case in the adoption of digital technology in healthcare. Using the case of CGMS and examining its use from the perspective of healthcare professionals, this paper discusses the necessary adjustments required in clinical practices. There is a need for various stakeholders, such as medical staff, patients, industry partners, and policy-makers, to utilize and harness the potential of digital technology.