Wearable Health Devices Research Articles

Low-Cost and Active Control of Radiation of Wearable Medical Health Device for Wireless Body Area Network

Wearable devices, wireless networks and body area networks have become an effective way to solve the problem of human health monitoring and care. However, the radiation problems of wireless devices, the power supply problems of wearable devices and the deployment of body area networks have become obstacles to their wide application in the field of health care. In order to solve the above problems, this paper studies and designs a wearable health medical body area network which is convenient for human health monitoring and medical care, starting from low-cost deployment of wireless wearable devices and active control of wireless radiation. Firstly, in order to avoid replacing equipment batteries, improve the relay and data aggregation capabilities of wireless body area network, and reduce the communication and computing load of edge devices, a deployment scheme of wireless medical health wearable devices is designed based on the optimal segmentation algorithm of Steiner spanning tree. Then, in order to minimize the charging cost and maximize the global charging utility of single source and multiple points in a finite time slot, an approximate algorithm for the optimal charging sequence based on 01 knapsack problem, i.e., the access path of wireless wearable devices, is designed. Then, an active radiation control algorithm for wearable medical health body area network is proposed, which can actively control the transmission power and radiation status of these wireless devices. Finally, simulation results show that the proposed algorithm is better than battery-powered wireless body area network and wireless rechargeable body area network, 16% and 44% reduction of devices, 25%13% reduction of energy consumption, 26% reduction of radiation, and 5.18 and 1.13 times improvement of signal quality.

An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

Co-creating and Assessing Future Wellbeing Technology Using Design Fiction

In this article, we present our research using design fiction to explore future wellbeing technologies for older adults. “For Good Times and Bad Times” is a design fiction representing a range of emotional, qualitative, and functional goals associated with wellbeing health technology. We invited a small cohort of experts to participate in a wearable monitoring device co-creation session, and then invited older-aged participants to review and reflect on a design fiction crafted from the experts’ findings and a set of psychological needs and values that have been associated with wearable health devices. The results offer insight into the future of wellbeing monitoring technology, and demonstrate the power design fiction has to communicate and generate deep discussions on the psychological, cultural, and social implications of using technologies that mediate complex personal experiences. We discuss the challenges associated with creating and using design fiction in research with older adults, and the potential of fiction to shift perspectives and elicit prospective outcomes of using future technologies.

THE IMPORTANCE OF WEARABLE TECHNOLOGY IN HEALTHCARE

Many of the wearable devices such as smartwatch, smartphone and smart clothes have advantages in our everyday lives. For example, smartphones are carried everywhere 24 hours a day. The large screens of smartphones became very helpful in accomplishing many useful and interesting tasks. A limited number of sensors and the location of some of these sensors can restrict the functionality of smartphones. Wearable health devices can help control the health conditions of people. It is can serve as a guide for early diagnosis of a disease or the follow-up after a treatment. The revolution in electronic device technology makes it possible to design more reliable and adaptable wearable devices for health monitoring around the world. Wearable health devices measure the following parameters: heart rate, blood pressure, respiratory rate, blood oxygen saturation, blood sugar, skin sweating, capnography, body temperature, motion evaluation. In this study, the importance of wearable technologies in healthcare, the current and future of these technologies will be discussed.

Threat Analysis for Wearable Health Devices and Environment Monitoring Internet of Things Integration System

With the rapid development of Internet of Things (IoT) applications, heterogeneous device management issues tend to arise in architecture security due to hardware computing power, types of software, data transmission interfaces, and networking protocols. Even during data exchange between devices and systems, traditional IoT devices are prone to the disclosure of personal information, which compromises privacy. Thus, planning an effective information security management strategy has become an essential part of application development. This paper presents a strategy to achieve information security verification and risk assessment for an IoT-based personal health information system. Using several interfaces of IoT devices, including Wi-Fi and Bluetooth, we simulate possible attack hypotheses and define test methods and evaluation methods suitable for each device. In our application systems for information security analysis, we also consider and integrate weaknesses of the system architecture to achieve a more complete information security threat analysis.

Continuous Mobile Wearable Bio-Monitoring of Newly Diagnosed Multiple Myeloma Patients Undergoing Initial Chemotherapy

IntroductionThe current standard to assess chemotherapy tolerability relies on patient self-reporting. However, as the sole mechanism of managing symptom burden, this may be inconsistent and fraught with bias. Mobile wearable health devices have the ability to monitor and aggregate objective activity and sleep data over long periods of time, but have not been systematically used in the oncology clinic. The aim of the study was to assess whether the use of mobile wearable technology establishes patterns of “sleep” and “wake” states in newly diagnosed Multiple Myeloma (NDMM) patients receiving therapy, and whether these patterns differ over time.MethodsPatients presenting to the myeloma clinic at Memorial Sloan Kettering Cancer Center (MSKCC) with a new diagnosis of Multiple Myeloma and smart phone or tablet (iOS or Android) compatible with the Garmin Vivofit device were offered to participate in a mobile wearable bio-monitoring study. All eligible participants were required to receive primary chemotherapy treatment at a MSKCC facility. Treatment was determined by physician. NDMM patients were assigned to one of two cohorts (20 in each; Cohort A - patients <65 years; Cohort B - patients ≥ 65 years). Patients were given Garmin Vivofit devices and asked to download a Garmin Vivofit application and Medidata electronic patient reported outcome (ePRO) application on their phone or tablet. Patients were bio-monitored for physical activity and sleep during baseline period (1-7 days prior to chemotherapy initiation) and continuously up to 6 cycles of chemotherapy. Additionally, patients completed mobile ePRO questionnaires [(EORTC - QLQC30 and MY20) and brief pain inventory scales (BPI)] using the Medidata application at baseline and after each induction cycle. Activity, sleep data, and completed ePRO questionnaire data were automatically synced or transferred to Medidata Rave database through Medidata Sensorlink technology. In this abstract, we report initial results on prospective collection of activity measurements. Additional data from the health-related quality of life questionnaires and clinical outcomes will be presented at later date.ResultsBetween February 2017-March 2018, 37 patients (19 males and 18 females) enrolled onto the study, with 20 in cohort A and 17 in cohort B. The mean age was 55 years (range 41-64) for cohort A and 72 years (range 65-82) for cohort B. Treatment regimens included Carfilzomib/Revlimid/Dexamethasone 14(38%), Velcade/Revlimid/Dexamethasone 10(27%), Daratumumab/Carfilzomib Revlimid/Dexamethasone, 7(19%), Cyclophosphamide/Velcade/Dexamethasone 3(8%), Revlimid/Dexamethasone 2(5%), and Velcade/Revlimid/Dexamethasone-Lite 1(3%). Twenty-four patients have completed the trial, and 7 remain active. Six patients came off-study due to the following reasons: lost devices (n=4), intolerable rash during cycle 3 (n=1), and incompletion of baseline activity (n=1). Three patients were excluded for incomplete data sets with no baseline data collection at the time of analysis. Fifteen patients were available for data review including 10 in cohort A and 5 in cohort B.Mean activity for cohort A was 6,437 steps/24 hr period (1,002 - 12,754) versus for cohort B was 3,218.37 steps/24 hr period (387 - 6,155) (p <0.05). In comparing pre- and post-therapy, overall mean activity for cohort A increased from 5,995 to 6,513 steps/24 hr, 8.6% increase (p=0.78), and for cohort B mean activity increased from 2,249 to 3,420 steps/24 hr, a 52% increase (p=0.2140). We assessed short term effects therapy initiation had on activity for NDMM patients by comparing percent changes in activity (steps/24 hrs) from baseline period to cycle 1 period. We found 3 patients had a >100% increase, 1 patient had 50-100% increase, and 11 patients had within +/- 50% change in activity from baseline.ConclusionElectronic mobile wearable device monitoring in symptomatic NDMM patients may be a useful tool to assess a patient's overall wellness and health as they are receiving chemotherapy. For three patients, we were able to capture a dramatic increase in activity after initiation of treatment. Overall activity in the elderly NDMM patients is decreased compared to younger patients. Mobile wearable monitoring may be an even more useful strategy for tracking elderly and unfit patients that are more prone to side effects, where the balance of response versus quality of life is paramount. DisclosuresMailankody:Physician Education Resource: Honoraria; Janssen: Research Funding; Takeda: Research Funding; Juno: Research Funding. Hassoun:Oncopeptides AB: Research Funding. Lesokhin:Squibb: Consultancy, Honoraria; Serametrix, inc.: Patents & Royalties: Royalties; Janssen: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Takeda: Consultancy, Honoraria. Smith:Celgene: Consultancy, Patents & Royalties: CAR T cell therapies for MM, Research Funding. Shah:Amgen: Research Funding; Janssen: Research Funding. Landgren:Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Research Funding; Pfizer: Consultancy; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy. Korde:Amgen: Research Funding.

An Energy-Efficient and Reconfigurable Sensor IC for Bio-Impedance Spectroscopy and ECG Recording

This paper presents a low power reconfigurable IC for wearable health devices that perform bio-impedance (BioZ) spectroscopy, respiration, and ECG signals measurement. The IC consists of a broadband excitation current source (I-DAC) and a power efficient readout circuit (RO). Both are highly scalable to mitigate the tradeoff among noise, dynamic range, speed, power, and safe current limits. On circuit level, the I-DAC supports two broadband excitation modes based on maximum length sequence (MLS), a flexible selection of frequency points, bandwidth, and current magnitude. The RO employs a low noise instrumentation amplifier and a reconfigurable 8-to-12 bit ADC for flexible sensitivity, dynamic range and speed with an automatic scaling of the power consumption. On system level, calibration of the entire circuit chain, oversampling and MLS averaging further improve the bandwidth, accuracy and sensitivity of BioZ spectroscopy beyond the intrinsic circuit performance. As a result, the BioZ sensor IC achieves 2 $\text{m}\Omega $ sensitivity within 3.3 $\text{m}\Omega $ – $100~\Omega $ range and $0.6~\Omega $ sensitivity within $3.3~\Omega $ –100 $\text{k}\Omega $ range. The BioZ spectroscopy spans up to 125 kHz while consuming $155~\mu \text{W}$ . Furthermore, this IC can measure respiration and ECG simultaneously at a lower sampling rate of 20 kS/s while consuming $31~\mu \text{W}$ . Compared to prior art, to the best of our knowledge, this IC is the only work that can measure BioZ and ECG simultaneously with a single readout channel while consuming the lowest power and offering the highest flexibility in performance, application range, and power consumption.

Young People’s Perspectives on and Experiences of Health-Related Social Media, Apps, and Wearable Health Devices

It has been reported from numerous international and socio-economic contexts that young people are becoming increasingly interested in and/or using social media, apps, and wearable devices for their health. Yet, there are few robust empirical accounts on the types of health-related information young people find, select, and use, the reasons for their choices, and how young people use these technologies in a way that influences their health-related knowledge and behaviors. This paper synthesizes findings from three separate projects that investigated over 1600 young people’s (age 13–19) perspectives on and experiences of health-related social media, apps, and wearable health devices. The findings show that young people are both critical and vulnerable users and generators of digital health technologies. Many young people experience a range of positive benefits for their physical activity, diet/nutritional, and body image related behaviors. Yet there are a number of risks, and young people report on the power of digital health technologies to shape, influence, and change their health-related behaviors. The paper concludes by providing new and evidence-based direction and guidance on how relevant adults (including teachers, parents/guardians, health professionals/practitioners, policy-makers, and researchers) can better understand and support young people’s engagement with digital health technologies.

Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies.

Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

Strong linear-piezoresistive-response of carbon nanostructures reinforced hyperelastic polymer nanocomposites

Here, we report highly strain-tolerant and sensitive strain sensors based on carbon nanostructures (CNS)-polydimethylsiloxane (PDMS) nanocomposites. CNS consist of clusters of aligned multiwall carbon-nanotubes (MWCNT) with high degree of entanglement and wall sharing between nanotubes. The unique features of CNS result in nanocomposites with very low electrical percolation threshold (0.05 wt% CNS), strong linear-piezoresistive-response up to 110% strain, and high sensitivity with gauge factor ranging from 8 to 47. We also present a simple analytical model for predicting resistivity evolution as a function of stretch considering incompressible hyperelastic behavior of CNS/PDMS nanocomposites. CNS/PDMS nanocomposites also show good hysteresis performance and stability up to 100 repetitive stretch/release cycles for 30% maximum strain. Tunable sensitivity and linear piezoresistivity coupled with high stretchability of CNS/PDMS nanocomposites demonstrated here suggest their potential for applications in wearable health and fitness monitoring devices.

Veterans' Perspectives on Fitbit Use in Treatment for Post-Traumatic Stress Disorder: An Interview Study.

BackgroundThe increase in availability of patient data through consumer health wearable devices and mobile phone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues, but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact integration including patients' motivations to use wearables and their subsequent data.ObjectiveThe aim of this study was to gain an understanding of patients’ motivations to use or not to use wearables devices during an intensive treatment program for post-traumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and nonuse of the Fitbit?MethodsWe conducted semistructured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis.ResultsWe identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship.ConclusionsTo integrate wearable data into mental health treatment programs, it is important to understand the patient’s perspectives and motivations in using wearables. We also discuss how the military culture and PTSD may have contributed to our participants' behaviors and attitudes toward Fitbit usage. We conclude with possible approaches for integrating patient-generated data into mental health treatment settings that may address the challenges we identified.

Differential photoplethysmogram sensor with an optical notch filter shows potential for reducing motion artifact signals

Objective: Photoplethysmography (PPG) has become the standard technology used in wearable health and fitness monitoring devices. Although, PPG works well at rest, trying to incorporate it for continuous monitoring is challenging, since the signal is inevitably corrupted by motion artifacts. This paper explores the feasibility of a reflective PPG sensor aiming at reducing the changes caused by motion in the non-pulsatile component of the signal, e.g., changes in venous filling. Approach: The tested sensor consisted of two back-to-back connected photodiodes and a light source with a peak wavelength around 575 nm. Additionally, the second photodiode was covered by a 575 ± 5 nm optical notch filter. Main results: When compared to standard PPG (single detector) on one test subject, the tested sensor provided 4.38 dB of attenuation for a change in venous blood during a gentle lift of upper extremity. Moreover, over 40 dB rejection was measured in the baseline resulting in a very small ‘direct current’ (DC) component. Furthermore, the baseline wander due to respiratory modulation tended to be smaller for the tested sensor. The sensor was not either susceptible for the changes in ambient light or the drift caused by the light source heating up. Significance: We believe that this is the first work that proposes the back-to-back connected photodiodes and a notch filter to reduce in-band noise in PPG. The positive results encourage to further optimize and integrate the sensor. The novelty of the method also brings new perspective to the PPG measurements.

Wearable health information systems intermittent discontinuance

PurposeWearable health information systems (IS) open up a new era for personal health self-management, and bring about disruptive changes to individual lives. However, prior studies on IS post-adoptive behavior primarily focused on either continuance or discontinuance, neglecting the mutual transformation process between IS usage and rejection behavior. This behavior is expressed as intermittent discontinuance in the current study. By revising and extending the expectation-disconfirmation model, the purpose of this paper is to understand the factors affecting information system intermittent discontinuance in the context of wearable eHealth.Design/methodology/approachThis study proposes a revised expectation-disconfirmation model by incorporating four new theoretical constructs, i.e. neutral disconfirmation, attitudinal ambivalence, neutral satisfaction, and intermittent discontinuance. An online survey was used to validate the research model, and 428 wearable health device users were recruited in this study.FindingsEmpirical results demonstrate that neutral disconfirmation exerts positive effects on neutral satisfaction and attitudinal ambivalence, both of which further have positive effects on intermittent discontinuance. In addition, attitudinal ambivalence also has a positive and significant effect on neutral satisfaction.Originality/valueThis study extends the current understanding on IS post-adoption usage behavior by introducing a new concept, i.e., intermittent discontinuance. In particular, post-adoption usage behavior is viewed as a dynamic process within a changing environment in this study. Some unique features specific to intermittent discontinuance are also identified. In this regard, neutral and conflicting constructs further add new values to IS adoption and diffusion literature. In addition, the integration of eHealth and wearable devices provides a fruitful research context for future research, and this study represents one of the first attempts to empirically explore the intermittent usage problems encountered within this emerging phenomenon.

A Stress Analysis Method using Poincaré Plot and Complex Correlation Measures for Wearable Health Devices

A Stress Analysis Method using Poincaré Plot and Complex Correlation Measures for Wearable Health Devices

A 0.6V, 8.4uW AFE circuit for biomedical signal recording

For the detection of biomedical signal in wearable health devices, in this paper a fully-integrated, low-power, low-noise analog front-end (AFE) circuit is proposed. The AFE circuit includes a fully-differential amplifier with high-pass characteristics and a 14bit/500 Hz Sigma-Delta modulator based on cascode inverter. The fully-differential amplifier increases the output swing and improves the signal to noise ratio (SNR). Inverter-based Sigma-Delta modulator using bootstrapped switch completes high accuracy signal sampling in low supply voltage and finally outputs digital code. Two subthreshold bandgaps provide stable bias current and reference voltage for amplifier and Sigma-Delta modulator respectively. The circuit is fabricated with SMIC 0.13 μm CMOS mixed-signal technology occupying area of 1.68 mm2. The measurement results show that when the power supply voltage is 0.6 V, the input referred voltage noise of amplifier is 3.976μVrms and noise efficient factor (NEF) is 3.658. The maximum signal to noise and distortion ratio (SNDR) of AFE is 66.7 dB with only 8.4μw power consumption. Compared with the previous references, it has the maximum SNDR among them.

A CONTENT ANALYSIS OF WIRED MAGAZINE IN TERMS OF SELF-TRACKING DEVICES

In the digital era that we live in now it is possible to gather biological and biographical data of an individual by wearable digital health and activity-tracking devices. The phenomenon is known as self-tracking and it intends to expand individual’s self-knowledge and as a result to improve both the physical and psychological health. By self-tracking applications that digital health and activity-tracking devices provide now we can measure diverse biodata from heart rate, body fat percentage and pain levels to miles run, calories eaten and even to mood and happiness levels. The aim of this paper is the analysis of growing digitalized self-tracking trend through a content analysis of Wired magazine. This well-known design and technology magazine monthly publishes in print and online technology related articles on how emerging technologies affect culture, economy and politics and it reaches online more than 30 million people each month through wired.com, digital edition. This present study, carries out a content analysis of all the issues until December 2016 through the term 'self-tracking' and also two other closely related terms: 'quantified self' and 'lifelogging'. In the analysis, the usage period and popularity of these three terms; their relation network with the main topics and the sub-topics of the magazine are examined. By this analysis, the study presents the scope and relevance of the rising trend of self-tracking technologies from the birth of this trend.

Introduction to the Special Issue: Digital Inequalities and Discrimination in the Big Data Era

Introduction to the Special Issue: Digital Inequalities and Discrimination in the Big Data Era

Use of wearable health devices for early detection of medical disorders

Use of wearable health devices for early detection of medical disorders

PersonalizedHealth MonitoringSystem for Managing Well-Being in Rural Areas.

Rural India lacks easy access to health practitioners and medical centers, depending instead on community health workers. In these areas, common ailments that are easy to manage with medicines, often lead to medical escalations and even fatalities due to lack of awareness and delayed diagnosis. The introduction of wearable health devices has made it easier to monitor health conditions and to connect doctors and patients in urban areas. However, existing initiatives have not succeeded in providing adequate health monitoring to rural and low-literate patients, as current methods are expensive, require consistent connectivity and expect literate users. Our design considerations address these concerns by providing low-cost medical devices connected to a low-cost health platform, along with personalized guidance based on patient physiological parameters in local languages, and alerts to medical practitioners in case of emergencies. This patient-centric integrated healthcare system is designed to manage the overall health of villagers with real-time health monitoring of patients, to offer guidance on preventive care, and to increase health awareness and self-monitoring at an affordable price. This personalized health monitoring system addresses the health-related needs in remote and rural areas by (1) empowering health workers in monitoring of basic health conditions for rural patients in order to prevent escalations, (2) personalized feedback regarding nutrition, exercise, diet, preventive Ayurveda care and yoga postures based on vital parameters and (3) reporting of patient data to the patient's health center with emergency alerts to doctor and patient. The system supports community health workers in the diagnosticprocedure, management, and reporting of rural patients, and functions well even with only intermittent access to Internet.