Background: The emergence of smartphones and tablets featuring vastly advancing functionalities (eg, sensors, computing power, interactivity) has transformed the way mHealth interventions support chronic disease management for older adults. Baby boomers have begun to widely adopt smart devices and have expressed their desire to incorporate technologies into their chronic care. Although smart devices are actively used in research, little is known about the extent, characteristics, and range of smart device-based interventions. Objective: We conducted a scoping review to (1) understand the nature, extent, and range of smart device-based research activities, (2) identify the limitations of the current research and knowledge gap, and (3) recommend future research directions. Methods: We used the Arksey and O’Malley framework to conduct a scoping review. We identified relevant studies from MEDLINE, Embase, CINAHL, and Web of Science databases using search terms related to mobile health, chronic disease, and older adults. Selected studies used smart devices, sampled older adults, and were published in 2010 or after. The exclusion criteria were sole reliance on text messaging (short message service, SMS) or interactive voice response, validation of an electronic version of a questionnaire, postoperative monitoring, and evaluation of usability. We reviewed references. We charted quantitative data and analyzed qualitative studies using thematic synthesis. To collate and summarize the data, we used the chronic care model. Results: A total of 51 articles met the eligibility criteria. Research activity increased steeply in 2014 (17/51, 33%) and preexperimental design predominated (16/50, 32%). Diabetes (16/46, 35%) and heart failure management (9/46, 20%) were most frequently studied. We identified diversity and heterogeneity in the collection of biometrics and patient-reported outcome measures within and between chronic diseases. Across studies, we found 8 self-management supporting strategies and 4 distinct communication channels for supporting the decision-making process. In particular, self-monitoring (38/40, 95%), automated feedback (15/40, 38%), and patient education (13/40, 38%) were commonly used as self-management support strategies. Of the 23 studies that implemented decision support strategies, clinical decision making was delegated to patients in 10 studies (43%). The impact on patient outcomes was consistent with studies that used cellular phones. Patients with heart failure and asthma reported improved quality of life. Qualitative analysis yielded 2 themes of facilitating technology adoption for older adults and 3 themes of barriers. Conclusions: Limitations of current research included a lack of gerontological focus, dominance of preexperimental design, narrow research scope, inadequate support for participants, and insufficient evidence for clinical outcome. Recommendations for future research include generating evidence for smart device-based programs, using patient-generated data for advanced data mining techniques, validating patient decision support systems, and expanding mHealth practice through innovative technologies. [JMIR Mhealth Uhealth 2017;5(5):e69]

Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review

In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed for myocardial infarction detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and fuzzy information granulation is adopted for preprocessing. Then, the beats are input into our multilead-CNN (ML-CNN), a novel model that includes sub two-dimensional (2-D) convolutional layers and lead asymmetric pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 and 26.75 ms, respectively, which indicate that this method has good potential for mobile healthcare applications.

Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection

This paper presents a wireless biosignal acquisition system-on-a-chip (WBSA-SoC) specialized for electrocardiogram (ECG) monitoring. The proposed system consists of three subsystems, namely, 1) the ECG acquisition node, 2) the protocol for standard IEEE 802.15.4 ZigBee system, and 3) the RF transmitter circuits. The ZigBee protocol is adopted for wireless communication to achieve high integration, applicability, and portability. A fully integrated CMOS RF front end containing a quadrature voltage-controlled oscillator and a 2.4-GHz low-IF (i.e., zero-IF) transmitter is employed to transmit ECG signals through wireless communication. The low-power WBSA-SoC is implemented by the TSMC 0.18-μm standard CMOS process. An ARM-based displayer with FPGA demodulation and an RF receiver with analog-to-digital mixed-mode circuits are constructed as verification platform to demonstrate the wireless ECG acquisition system. Measurement results on the human body show that the proposed SoC can effectively acquire ECG signals.

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Implementation of a Wireless ECG Acquisition SoC for IEEE 802.15.4 (ZigBee) Applications

In the demand for protecting the increasing aged groups from heart attacks, the improvement of the mobile electrocardiogram (ECG) monitoring systems becomes significant. The limitations of the arrhythmia classification in these systems are the lack of ability to cope with motion state and the low accuracy in new users’ data. This paper proposes a system which applies the impulse radio ultra wideband radar data as additional information to assist the arrhythmia classification of ECG recordings in the slight motion state. Besides, this proposed system employs a cascade convolutional neural network to achieve an integrated analysis of ECG recordings and radar data. The experiments are implemented in the Caffe platform and the result reaches an accuracy of 88.89% in the slight motion state. It turns out that this proposed system keeps a stable accuracy of classification for normal and abnormal heartbeats in the slight motion state.

ECG Monitoring System Integrated With IR-UWB Radar Based on CNN

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

https://www.mdpi.com/1424-8220/20/5/1502/pdf

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis.

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

Heterogeneous nuclear ribonucleoprotein D (HNRNPD) can regulate expression of key proteins in various cancers. However, the prognostic predictive value and biology function of HNRNPD in non-small cell lung cancer (NSCLC) is unknown. First, we used the TCGA and GEO datasets to determine that HNRNPD predicts the prognosis of NSCLC patients. Following that, we knocked down HNRNPD in NSCLC cell lines in vitro and validated its biological function using CCK-8, transwell assays, wound healing tests, and Western blotting. Finally, we constructed tissue microarrays (TMAs) from 174 NSCLC patients and verified our findings using immunohistochemistry staining for HNRNPD from public databases. In both the public datasets, NSCLC tissues with elevated HNRNPD expression had shorter overall survival (OS). In addition, HNRNPD knockdown NSCLC cell lines showed significantly reduced proliferation, invasion, and metastatic capacity via the PI3K-AKT pathway. Finally, elevated HNRNPD expression in NSCLC TMAs was linked to a poorer prognosis and decreased PD-L1 expression levels. HNRNPD is associated with a poorer prognosis in NSCLC and affects tumor growth and metastasis via the PI3K-AKT pathway.

HNRNPD is a prognostic biomarker in non-small cell lung cancer and affects tumor growth and metastasis via the PI3K-AKT pathway.

Background The diagnosis of radioiodine refractory differentiated thyroid cancer (RAIR-DTC) is primarily based on clinical evolution and iodine uptake over the lesions, which is still time-consuming, thus urging a predictive model for timely RAIR-DTC informing. The aim of this study was to develop a nomogram model for RAIR prediction among DTC patients with distant metastases (DM). Methods Data were extracted from the treatment and follow-up databases of Peking Union Medical College Hospital between 2010 and 2021. A total of 124 patients were included and divided into RAIR (n=71) and non-RAIR (n=53) according to 2015 ATA guidelines. All patients underwent total thyroidectomy followed by at least two courses of RAI treatment. Serological markers and various clinical, pathological, genetic status, and imaging factors were integrated into this study. The pre-treatment stimulated Tg and pre- and post-treatment suppressed Tg at the first and second course RAI treatment were defined as s-Tg1, s-Tg2, sup-Tg1, and sup-Tg2, respectively. Δs-Tg denoted s-Tg1/s-Tg2, and Δs-TSH denoted s-TSH1/s-TSH2. Multivariate logistic regression and correlation analysis were utilized to determine the independent predictors of RAIR. The performance of the nomogram was assessed by internal validation and receiver operating characteristic (ROC) curve, and benefit in clinical decision-making was assessed using decision curve. Results In univariate logistic regression, nine possible risk factors were related to RAIR. Correlation analysis showed four of the above factors associated with RAIR. Through multivariate logistic regression, Δs-Tg/Δs-TSH<1.50 and age upon diagnosis were obtained to develop a convenient nomogram model for predicting RAIR. The model was internally validated and had good predictive efficacy with an AUC of 0.830, specificity of 0.830, and sensitivity of 0.755. The decision curve also showed that if the model is used for clinical decision-making when the probability threshold is between 0.23 and 0.97, the net benefit of patients is markedly higher than that of the TreatAll and TreatNone control groups. By using 1.50 as a cut-off ofΔs-Tg/Δs-TSH, differing biochemical progression among the generally so-called RAIR can be further stratified as meaningfully rapidly or slowly progressive patients (P=0.012). Conclusions A convenient user-friendly nomogram model was developed with good predictive efficacy for RAIR. The progression of RAIR can be further stratified as rapidly or slowly progressive by using 1.50 as a cut-off value of Δs-Tg/Δs-TSH.

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Chao Meng

Papers

WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

HNRNPD is a prognostic biomarker in non-small cell lung cancer and affects tumor growth and metastasis via the PI3K-AKT pathway.

A user-friendly nomogram for predicting radioiodine refractory differentiated thyroid cancer