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

Smartwatch interventions in healthcare: A systematic review of the literature

OBJECTIVE

The use of smartwatches has attracted considerable interest in developing smart digital health interventions and improving health and well-being during the past few years. This work presents a systematic review of the literature on smartwatch interventions in healthcare. The main characteristics and individual health-related outcomes of smartwatch interventions within research studies are illustrated, in order to acquire evidence of their benefit and value in patient care.

METHODS

A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to identify research studies incorporating smartwatch interventions. The studies were grouped according to the intervention's target disease, main smartwatch features, study design, target age and number of participants, follow-up duration, and outcome measures.

RESULTS

The literature search identified 13 interventions incorporating smartwatches within research studies with people of middle and older age. The interventions targeted different conditions: cardiovascular diseases, diabetes, depression, stress and anxiety, metastatic gastrointestinal cancer and breast cancer, knee arthroplasty, chronic stroke, and allergic rhinitis. The majority of the studies (76%) were randomized controlled trials. The most used smartwatch was the Apple Watch utilized in 4 interventions (31%). Positive outcomes for smartwatch interventions concerned foot ulcer recurrence, severity of symptoms of depression, utilization of healthcare resources, lifestyle changes, functional assessment and shoulder range of motion, medication adherence, unplanned hospital readmissions, atrial fibrillation diagnosis, adherence to self-monitoring, and goal attainment for emotion regulation. Challenges in using smartwatches included frequency of charging, availability of Internet and synchronization with a mobile app, the burden of using a smartphone in addition to a patient's regular phone, and data quality.

CONCLUSION

The results of this review indicate the potential of smartwatches to bring positive health-related outcomes for patients. Considering the low number of studies identified in this review along with their moderate quality, we implore the research community to carry out additional studies in intervention settings to show the utility of smartwatches in clinical contexts.

A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring

Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.

Use of wearables among Multiple Sclerosis patients and healthcare Professionals: A scoping review

INTRODUCTION

Multiple sclerosis (MS) is an increasingly prevalent chronic, autoimmune, and inflammatory central nervous system illness, whose common symptoms undermine the quality of life of patients and their families. Recent technical breakthroughs potentially offer continuous, reliable, sensitive, and objective remote monitoring solutions for healthcare. Wearables can be useful for evaluating falls, fatigue, sedentary behavior, exercise, and sleep quality in people with MS (PwMS).

OBJECTIVE

This scoping review of relevant literature explores studies investigating the perceptions of patients and healthcare professionals (HCPs) about the use of wearable technologies in the management of MS.

METHODS

The Joanna Briggs Institute methodology for scoping reviews was used. The search strategy was applied to the databases, MEDLINE via Ovid, Embase, APA PsycInfo, and CINAHL. Further searches were performed in IEEE, Scopus, and Web of Science. The review considered studies reporting quantitative or qualitative data on perceptions and experiences of PwMS and HCPs concerning wearables' usability, satisfaction, barriers, and facilitators.

RESULTS

10 studies were included in this review. Wearables' usefulness and accessibility, ease of use, awareness, and motivational tool potential were patient-perceived facilitators of use. Barriers related to anxiety and frustration, complexity, and the design of wearables. Perceived usefulness and system requirements are identified as facilitators of using wearables by HCPs, while data security concerns and fears of increased workload and limited effectiveness in the care plan are identified as barriers to use wearables.

CONCLUSIONS

This review contributes to our understanding of the benefits of wearable technologies in MS by exploring perceptions of both PwMS and HCPs. The scoping review provided a broad overview of facilitators and barriers to wearable use in MS. There is a need for further studies underlined with sound theoretical frameworks to provide a robust evidence-base for the optimal use of wearables to empower healthcare users and providers.

Digital gait measures, but not the 400-meter walk time, detect abnormal gait characteristics in people with Prediabetes

BACKGROUND AND AIM

Abnormal gait characteristics have been observed in people with diabetic neuropathy, but it is unclear if subtle changes in gait occur in prediabetic people with impaired fasting glucose (IFG). The aims of this study were: (1) to investigate if digital gait measures discriminate people with prediabetes from healthy control participants (HC) and (2) to investigate the relationship between gait measures and clinical scores (concurrent validity).

METHODS

108 people with prediabetes (71.20 ± 5.11 years) and 63 HC subjects (70.40 ± 6.25 years) wore 6 inertial sensors (Opals by APDM, Clario) while performing the 400-meter fast walk test. Fifty-five measures across 5 domains of gait (Lower Body, Upper Body, Turning, and Variability) were averaged. Analysis of Covariance was used to investigate the group differences, with body mass index as a covariate. Pearson's correlation coefficient assessed the association between the gait measures and the Short Physical Performance Battery (SPPB) score.

RESULTS

Nine gait measures were significantly different (p < 10-4) between IFG and HC groups. Step duration, cadence, and turn velocity were the most discriminative measures. In contrast, traditional stop-watch time was not significantly different between groups (p = 0.13), after controlling for BMI. Cadence (r = -0.37, p < 0.001), step duration (r = -0.39, p < 0.001), and turn velocity (r = 0.47, p < 0.001) showed a significant correlation with the SPPB score.

CONCLUSION

Body-worn inertial sensors detected gait impairments in people with prediabetes that related to clinical balance test performance, even when the traditional stop-watch time was not prolonged for the 400-meter walk test.

Identifying and mapping measures of medication safety during transfer of care in a digital era: a scoping literature review

BACKGROUND

Measures to evaluate high-risk medication safety during transfers of care should span different safety dimensions across all components of these transfers and reflect outcomes and opportunities for proactive safety management.

OBJECTIVES

To scope measures currently used to evaluate safety interventions targeting insulin, anticoagulants and other high-risk medications during transfers of care and evaluate their comprehensiveness as a portfolio.

METHODS

Embase, Medline, Cochrane and CINAHL databases were searched using scoping methodology for studies evaluating the safety of insulin, anticoagulants and other high-risk medications during transfer of care. Measures identified were extracted into a spreadsheet, collated and mapped against three frameworks: (1) 'Key Components of an Ideal Transfer of Care', (2) work systems, processes and outcomes and (3) whether measures captured past harms, events in real time or areas of concern. The potential for digital health systems to support proactive measures was explored.

RESULTS

Thirty-five studies were reviewed with 162 measures in use. Once collated, 29 discrete categories of measures were identified. Most were outcome measures such as adverse events. Process measures included communication and issue identification and resolution. Clinic enrolment was the only work system measure. Twenty-four measures captured past harm (eg, adverse events) and six indicated future risk (eg, patient feedback for organisations). Two real-time measures alerted healthcare professionals to risks using digital systems. No measures were of advance care planning or enlisting support.

CONCLUSION

The measures identified are insufficient for a comprehensive portfolio to assess safety of key medications during transfer of care. Further measures are required to reflect all components of transfers of care and capture the work system factors contributing to outcomes in order to support proactive intervention to reduce unwanted variation and prevent adverse outcomes. Advances in digital technology and its employment within integrated care provide opportunities for the development of such measures.

T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. Data scarcity is the main challenge for generating these models, as most works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, an open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257 780 days of measurements spanning four years from 736 T1D patients from the province of Granada, Spain. This dataset advances beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.

Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection

An unusual strategy was designed to fabricate conductive patterns for flexible surfaces, which were utilized for non-enzymatic amperometric glucose sensors. The Ag/AgCl/Ag quasi-reference ink formulation utilized two reducing agents, NaBH[Formula: see text] and ethylene glycol. The parameters of the ink, such as sintering time and temperature, NaBH[Formula: see text] concentration, and layer number of coatings on flexible laser-induced graphene (LIG) electrodes were investigated. The conductive Ag/AgCl/Ag ink was characterized using electrochemical and surface analysis techniques. The electrocatalytic activity of Ag/AgCl/Ag NRs can be attributed to their high surface area, which offer numerous active sites for catalytic reactions. The selectivity and sensitivity of the electrodes for glucose detection were investigated. The XRD analysis showed (200) oriented AgCl on covered Ag NRs, and with the addition of NaBH[Formula: see text], the intensity of the peaks of the Ag NRs increased. The wide linear range of non-enzymatic sensors was attained from 0.003 to 0.18 mM and 0.37 to 5.0 mM, with a low limit of detection of 10 [Formula: see text]M and 20 [Formula: see text]M, respectively.The linear range of enzymatic sensor in real sample was determined from 0.040 to 0.097 mM with a detection limit of 50 [Formula: see text]M. Furthermore, results of the interference studies demonstrated excellent selectivity of the Ag/AgCl/Ag NRs/LIG electrode. The Ag/AgCl/Ag NRs on the flexible LIG electrode exhibited excellent sensitivity,long-time stablity,and reproducibility. The efficient electroactivity were deemed suitable for various electrochemical applications and biosensors.

Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.

Role of Caregivers in Remote Management of Patients With Type 2 Diabetes Mellitus: Systematic Review of Literature

BACKGROUND

With the growing use of remote monitoring technologies in the management of patients with type 2 diabetes mellitus (T2DM), caregivers are becoming important resources that can be tapped into to improve patient care.

OBJECTIVE

This review aims to summarize the role of caregivers in the remote monitoring of patients with T2DM.

METHODS

We performed a systematic review in MEDLINE, Embase, Scopus, PsycINFO, and Web of Science up to 2022. Studies that evaluated the role of caregivers in remote management of adult patients with T2DM were included. Outcomes such as diabetes control, adherence to medication, quality of life, frequency of home glucose monitoring, and health care use were evaluated.

RESULTS

Of the 1198 identified citations, 11 articles were included. The majority of studies were conducted in North America (7/11, 64%) and South America (2/11, 18%). The main types of caregivers studied were family or friends (10/11, 91%), while the most common remote monitoring modalities evaluated were interactive voice response (5/11, 45%) and phone consultations (4/11, 36%). With regard to diabetes control, 3 of 6 studies showed improvement in diabetes-related laboratory parameters. A total of 2 studies showed improvements in patients' medication adherence rates and frequency of home glucose monitoring. Studies that evaluated patients' quality of life showed mixed evidence. In 1 study, increased hospitalization rates were noted in the intervention group.

CONCLUSIONS

Caregivers may play a role in improving clinical outcomes among patients with T2DM under remote monitoring. Studies on mobile health technologies are lacking to understand their impact on Asian populations and long-term patient outcomes.

The Role of Natural Antioxidant Products That Optimize Redox Status in the Prevention and Management of Type 2 Diabetes

The worldwide prevalence of type 2 diabetes (T2D) and prediabetes is rapidly increasing, particularly in children, adolescents, and young adults. Oxidative stress (OxS) has emerged as a likely initiating factor in T2D. Natural antioxidant products may act to slow or prevent T2D by multiple mechanisms, i.e., (1) reducing mitochondrial oxidative stress, (2) preventing the damaging effects of lipid peroxidation, and (3) acting as essential cofactors for antioxidant enzymes. Natural antioxidant products should also be evaluated in the context of the complex physiological processes that modulate T2D-OxS such as glycemic control, postprandial OxS, the polyol pathway, high-calorie, high-fat diets, exercise, and sleep. Minimizing processes that induce chronic damaging OxS and maximizing the intake of natural antioxidant products may provide a means of preventing or slowing T2D progression. This "optimal redox" (OptRedox) approach also provides a framework in which to discuss the potential benefits of natural antioxidant products such as vitamin E, vitamin C, beta-carotene, selenium, and manganese. Although there is a consensus that early effective intervention is critical for preventing or reversing T2D progression, most research has focused on adults. It is critical, therefore, that future research include pediatric populations.

Developments in the design and delivery of self-management support for children and young people with diabetes: A narrative synthesis of systematic reviews

AIMS

Facilitated self-management support programmes have become central to the treatment of chronic diseases including diabetes. For many children and young people with diabetes (CYPD), the impact on glycated haemoglobin (HbA_1c ) and a range of self-management behaviours promised by these programmes remain unrealised. This warrants an appraisal of current thinking and the existing evidence to guide the development of programmes better targeted at this age group.

METHODS

Create a narrative review of systematic reviews produced in the last 3 years that have explored the impact on CYPD of the four key elements of self-management support programmes: education, instruction and advice including peer support; psychological counselling via a range of therapies; self-monitoring, including diaries and telemetric devices; and telecare, the technology-enabled follow-up and support by healthcare providers.

RESULTS

Games and gamification appear to offer a promising means of engaging and educating CYPD. Psychological interventions when delivered by trained practitioners, appear to improve HbA_1c and quality of life although effect sizes were small. Technology-enabled interactive diaries can increase the frequency of self-monitoring and reduce levels of HbA_1c . Telecare provided synchronously via telephone produced significant improvements in HbA_1c .

CONCLUSIONS

The cost-effective flexibility of increasing the reliance on technology is an attractive proposition; however, there are resource implications for digital connectivity in underserved populations. The need remains to improve the understanding of which elements of each component are most effective in a particular context, and how to optimise the influence and input of families, caregivers and peers.

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

BACKGROUND

In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction.

OBJECTIVE

The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels.

METHODS

We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

RESULTS

From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices.

CONCLUSIONS

This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future.

TRIAL REGISTRATION

PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc.

Effectiveness of Wearable Activity Monitors on Metabolic Outcomes in Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis

OBJECTIVE

Wearable activity monitors are promising tools for improving metabolic outcomes in patients with type 2 diabetes mellitus (T2DM); however, no uniform conclusive evidence is available. This study aimed to evaluate the effects of the intervention using wearable activity monitors on blood glucose, blood pressure, blood lipid, weight, waist circumference, and body mass index (BMI) in individuals with T2DM.

METHODS

Two independent reviewers searched 4 online databases (PubMed, Cochrane Library, Web of Science, and Embase) to identify relevant studies published from January 2000 to October 2022. The primary outcome indicator was hemoglobin A1c (HbA1c), and the secondary outcome indicators included physical activity (steps per day), fasting blood glucose, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, BMI, waist circumference, and weight.

RESULTS

A total of 25 studies were included. The HbA1c level (standardized mean difference [SMD], -0.14; 95% confidence interval [CI], -0.27 to -0.02; P = .02; I² = 48%), BMI (SMD, -0.16; 95% CI, -0.26 to -0.05; P = .002; I² = 0), waist circumference (SMD, -0.21; 95% CI, -0.34 to -0.09; P < .001; I² = 0), and steps/day (SMD, 0.55; 95% CI, 0.36-0.94; P < .001; I² = 77%) significantly improved.

CONCLUSION

Wearable activity monitor-based interventions could facilitate the improvement of the HbA1c level, BMI, and waist circumference and increase in physical activity in individuals with T2DM. Wearable technology appeared to be an effective tool for the self-management of T2DM; however, there is insufficient evidence about its long-term effect.

Knowledge, practice, and challenges of diabetes foot care among patients at the University of Benin Teaching Hospital, Benin City: A cross-sectional study

Background

Foot ulcer is a common complication of diabetes and the most devastating component of diabetes progression that is associated with high morbidity and mortality.

Aims

The aim of this study was to assess the knowledge, practice, and challenges of diabetes foot care among patients with diabetes mellitus.

Materials and methods

This descriptive cross-sectional study assessed knowledge and practice of foot care among type I and type II patients with diabetes attending the University of Benin Teaching Hospital, Benin City. The instrument for data collection was a structured questionnaire with a reliability of 0.880. SPSS version 22 was used to analyze the data.

Results

The findings revealed that there is good knowledge of foot care, among 110 (50.0%) of the diabetic patients, while the practice of foot care was found to be poor among diabetic patients. It also shows the factor that statistically predicts the development of foot ulcers to include combined diet + oral medications + insulin treatment regimen (adjusted odds ratio [AOR] = 0.181, P = 0.016, confidence interval [CI] = 0.045-0.728), history of renal conditions (AOR = 0.115, P = 0.036, CI = 0.015-0.871), not receiving foot care education (AOR = 116.098, P < 0.001, CI = 12.497-1078.554), and receiving foot care education from nurses (AOR = 0.022, P = 0.001, CI = 0.002-0.216). Furthermore, 201 (91.4%) diabetes patients reported fatigue from completing the same task repeatedly, and 198 (90.0%) reported forgetfulness as obstacles to practicing foot care.

Conclusion

When creating DM Patients future care plans, nurses and other health-care administrators must take into account the difficulties and predicting factors related to the practice of diabetes foot care.

Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study

BACKGROUND

Wearable device technology has recently been involved in the healthcare industry substantially. India is the world's third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries.

METHODS

This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above.

RESULTS

A total of 495 responses were obtained. In all, 50.3% were aged between 25-50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032).

CONCLUSIONS

This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.

Wearable Devices in Veterinary Health Care

Wearables are an up-and-coming tool in veterinary health care. This article reviews the current and prospective wearable technology for veterinary patients and the future of wearables in veterinary medicine. These devices allow veterinarians to monitor a patient's vital signs remotely, in addition to other variables, and push the profession away from a reactive health-care system toward a proactive culture that is able to identify diseases earlier. Advances in this technology have the potential to profoundly change the way veterinarians obtain and use patient data to practice medicine.

Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review

Background

Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics.

Objective

This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters.

Methods

We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data.

Results

From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%).

Conclusions

This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review (Preprint).. [DOI: 10.2196/preprints.40259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction.

OBJECTIVE

The purpose of this study is to provide a systematic review complete with a quality assessment looking at diabetes effectiveness of using artificial intelligence (AI) in WDs for forecasting or predicting BG levels.

METHODS

We searched 7 of the most popular bibliographic databases. Two reviewers performed study selection and data extraction independently before cross-checking the extracted data. A narrative approach was used to synthesize the data. Quality assessment was performed using an adapted version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

RESULTS

From the initial 3872 studies, the features from 12 studies were reported after filtering according to our predefined inclusion criteria. The reference standard in all studies overall (n=11, 92%) was classified as low, as all ground truths were easily replicable. Since the data input to AI technology was highly standardized and there was no effect of flow or time frame on the final output, both factors were categorized in a low-risk group (n=11, 92%). It was observed that classical ML approaches were deployed by half of the studies, the most popular being ensemble-boosted trees (random forest). The most common evaluation metric used was Clarke grid error (n=7, 58%), followed by root mean square error (n=5, 42%). The wide usage of photoplethysmogram and near-infrared sensors was observed on wrist-worn devices.

CONCLUSIONS

This review has provided the most extensive work to date summarizing WDs that use ML for diabetic-related BG level forecasting or prediction. Although current studies are few, this study suggests that the general quality of the studies was considered high, as revealed by the QUADAS-2 assessment tool. Further validation is needed for commercially available devices, but we envisage that WDs in general have the potential to remove the need for invasive devices completely for glucose monitoring in the not-too-distant future.

CLINICALTRIAL

PROSPERO CRD42022303175; https://tinyurl.com/3n9jaayc

Effects of Incontro, Alleanza, Responsabilita, Autonomia Intervention Model Combined with Orem Self-Care Model and the Use of Smart Wearable Devices on Perceived Stress and Self-Efficacy in Patients after Total Hip Arthroplasty

Objective

To explore the effects of Incontro, Alleanza, Responsabilita, Autonomia (IARA) combined with Orem self-care model and the use of smart wearable devices on perceived stress and self-efficacy in patients after total hip arthroplasty (THA).

Methods

A total of 60 patients after THA in our hospital were enrolled. Patients were randomly divided into control group (IARA intervention model combined with Orem self-care model) and study group (intelligent wearable device combined conference-IARA and Orem self-care model). Harris hip function score, Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, functional independence measure (FIM) score, social support level, perceived stress, and self-efficacy were compared between the two groups.

Results

Harris hip function score, WOMAC score, FIM score, and the level of social support of the study group were higher compared with the control group after operation (P < 0.05). Additionally, the perceptual pressure in the study group was lower compared with the control group after intervention (P < 0.05). The self-efficacy of the two groups was compared, and the self-efficacy of the study group was higher than that of the control group at 4, 6, 8, and 12 weeks after the intervention, and the difference was statistically significant (P < 0.05).

Conclusion

Patients after THA utilize an intelligent wearable device combined with IARA model and Orem self-care model, which can effectively reduce awareness pressure, improve self-efficacy, and facilitate the improvement of the hip fracture.

Cross-Leg Prediction of Running Kinematics across Various Running Conditions and Drawing from a Minimal Data Set Using a Single Wearable Sensor

The feasibility of prediction of same-limb kinematics using a single inertial measurement unit attached to the same limb has been demonstrated using machine learning. This study was performed to see if a single inertial measurement unit attached to the tibia can predict the opposite leg’s kinematics (cross-leg prediction). It also investigated if there is a minimal or smaller data set in a convolutional neural network model to predict lower extremity running kinematics under other running conditions and with what accuracy for the intra- and inter-participant situations. Ten recreational runners completed running exercises under five conditions, including treadmill running at speeds of 2, 2.5, 3, and 3.5 m/s and level-ground running at their preferred speed. A one-predict-all scheme was adopted to determine which running condition could be used to best predict a participant’s overall running kinematics. Running kinematic predictions were performed for intra- and inter-participant scenarios. Among the tested running conditions, treadmill running at 3 m/s was found to be the optimal condition for accurately predicting running kinematics under other conditions, with R2 values ranging from 0.880 to 0.958 and 0.784 to 0.936 for intra- and inter-participant scenarios, respectively. The feasibility of cross-leg prediction was demonstrated but with significantly lower accuracy than the same leg. The treadmill running condition at 3 m/s showed the highest intra-participant cross-leg prediction accuracy. This study proposes a novel, deep-learning method for predicting running kinematics under different conditions on a small training data set.

"Listen to Your Immune System When It's Calling for You": Monitoring Autoimmune Diseases Using the iShU App

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.

Efficacy of Mobile Health Applications to Improve Physical Activity and Sedentary Behavior: A Systematic Review and Meta-Analysis for Physically Inactive Individuals

Physical inactivity and sedentary behavior (SB) have attracted growing attention globally since they relate to noninfectious chronic diseases (NCDs) and could further result in the loss of life. This systematic literature review aimed to identify existing evidence on the efficacy of mobile health (mHealth) technology in inducing physical activity and reducing sedentary behavior for physically inactive people. Studies were included if they used a smartphone app in an intervention to improve physical activity and/or sedentary behavior for physically inactive individuals. Interventions could be stand-alone interventions or multi-component interventions, including an app as one of several intervention components. A total of nine studies were included, and all were randomized controlled trials. Two studies involved interventions delivered solely via a mobile application (stand-alone intervention) and seven studies involved interventions that used apps and other intervention strategies (multi-component intervention). Methodological quality was assessed, and the overall quality of the studies was ensured. The pooled data favored intervention in improving physical activity and reducing sedentary behavior. This review provided evidence that mobile health intervention improved physical activity and reduced sedentary behavior among inactive individuals. More beneficial effects can be guaranteed when interventions include multiple components. Further studies that maintain the effectiveness of such interventions are required to maximize user engagement and intervention efficacy.

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.

Machine Learning and Smart Devices for Diabetes Management: Systematic Review

(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.

Computation of Gait Parameters in Post Stroke and Parkinson's Disease: A Comparative Study Using RGB-D Sensors and Optoelectronic Systems

The accurate and reliable assessment of gait parameters is assuming an important role, especially in the perspective of designing new therapeutic and rehabilitation strategies for the remote follow-up of people affected by disabling neurological diseases, including Parkinson’s disease and post-stroke injuries, in particular considering how gait represents a fundamental motor activity for the autonomy, domestic or otherwise, and the health of neurological patients. To this end, the study presents an easy-to-use and non-invasive solution, based on a single RGB-D sensor, to estimate specific features of gait patterns on a reduced walking path compatible with the available spaces in domestic settings. Traditional spatio-temporal parameters and features linked to dynamic instability during walking are estimated on a cohort of ten parkinsonian and eleven post-stroke subjects using a custom-written software that works on the result of a body-tracking algorithm. Then, they are compared with the “gold standard” 3D instrumented gait analysis system. The statistical analysis confirms no statistical difference between the two systems. Data also indicate that the RGB-D system is able to estimate features of gait patterns in pathological individuals and differences between them in line with other studies. Although they are preliminary, the results suggest that this solution could be clinically helpful in evolutionary disease monitoring, especially in domestic and unsupervised environments where traditional gait analysis is not usable.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities

Physical behaviors (e.g., physical activity and sedentary behavior) have been the focus among many researchers in the biomedical and behavioral science fields. The recent shift from hip- to wrist-worn accelerometers in these fields has signaled the need to develop novel approaches to process raw acceleration data of physical activity and sedentary behavior. However, there is currently no consensus regarding the best practices for analyzing wrist-worn accelerometer data to accurately predict individuals' energy expenditure and the times spent in different intensities of free-living physical activity and sedentary behavior. To this end, accurately analyzing and interpreting wrist-worn accelerometer data has become a major challenge facing many clinicians and researchers. In response, this paper attempts to review different methodologies for analyzing wrist-worn accelerometer data and offer cutting edge, yet appropriate analysis plans for wrist-worn accelerometer data in the assessment of physical behavior. In this paper, we first discuss the fundamentals of wrist-worn accelerometer data, followed by various methods of processing these data (e.g., cut points, steps per minute, machine learning), and then we discuss the opportunities, challenges, and directions for future studies in this area of inquiry. This is the most comprehensive review paper to date regarding the analysis and interpretation of free-living physical activity data derived from wrist-worn accelerometers, aiming to help establish a blueprint for processing wrist-derived accelerometer data.

Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study

Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device.