Aging in Place and 'The Little Things': Prioritizing Mobile Health and Social Care in Rural Communities.

Older adults are more frequently wanting to age in place. Governments are seeking cost-effective and efficient methods of supporting aging populations. Older adults who want to stay in their homes for as long as possible encounter multiple barriers, including struggling to maintain their homes, inadequate levels of social and healthcare support, and the lack of financial capacity to pay for home support services. The Mobile Seniors' Wellness Network (MSWN), a multi-disciplinary and person-centered mobile health and social support intervention study was designed to investigate and support aging in place for older adults living in rural New Brunswick, Canada. Secondary analysis of case notes and exit interviews using content analysis revealed concerns with the lack of affordable and mobile care services for vulnerable rural older adults. Older adults revealed that their needs include "the little things" rather than grand gestures or sweeping policies to age in place such as assistance with grounds and home maintenance, in addition to relational and person-centered health and social care in the home. Reliance on private service delivery and volunteer organizations can increase the likelihood that older adults will experience a breakdown of social support networks tied together loosely by friends, family, and their communities. When services are unattainable aging in place becomes an unreachable goal.

Mobile applications in gastrointestinal surgery: a systematic review.

BACKGROUND: Mobile applications can facilitate or improve gastrointestinal surgical care by benefiting patients, healthcare providers, or both. The extent to which applications are currently in use in gastrointestinal surgical care is largely unknown, as reported in literature. This systematic review was conducted to provide an overview of the available gastrointestinal surgical applications and evaluate their prospects for surgical care provision. METHODS: The PubMed, EMBASE and Cochrane databases were searched for articles up to October 6th 2022. Articles were considered eligible if they assessed or described mobile applications used in a gastrointestinal surgery setting for healthcare purposes. Two authors independently evaluated selected studies and extracted data for analysis. Descriptive data analysis was conducted. The revised Cochrane risk of bias (RoB-2) tool and ROBINS-I assessment tool were used to determine the methodological quality of studies. RESULTS: Thirty-eight articles describing twenty-nine applications were included. The applications were classified into seven categories: monitoring, weight loss, postoperative recovery, education, communication, prognosis, and clinical decision-making. Most applications were reported for colorectal surgery, half of which focused on monitoring. Overall, a low-quality evidence was found. Most applications have only been evaluated on their usability or feasibility but not on the proposed clinical benefits. Studies with high quality evidence were identified in the areas of colorectal (2), hepatopancreatobiliary (1) and bariatric surgery (1), reporting significantly positive outcomes in terms of postoperative recovery, complications and weight loss. CONCLUSIONS: The interest for applications and their use in gastrointestinal surgery is increasing. From our study, it appears that most studies using applications fail to report adequate clinical evaluation, and do not provide evidence on the effectiveness or safety of applications. Clinical evaluation of objective outcomes is much needed to evaluate the efficacy, quality and safety of applications being used as a medical device across user groups and settings.

Mobile healthcare simulation units: a narrative review.

For healthcare professionals working and living in geographical isolation, there are few opportunities to maintain skills and knowledge, and to update themselves with recent advances in care compared to their citydwelling peers. It is known that within a short period and limited practice, clinical skills erode. A mobile healthcare simulation unit provides high-quality, technologicallyenhanced, convenient, and affordable training for healthcare professionals under expert supervision in any remote setting. The current narrative review was planned to summarise the outcomes and challenges related to developing and effectively utilising mobile healthcare simulation units as experienced globally. A literature search was performed on PubMed, Google Scholar and Cochrane databases for relevant articles published between 2000 and 2020, which resulted in 18 articles that were shortlisted and three major themes. The identification of common strengths, weaknesses and challenges will be a starting point for those engaged in planning and operating such a centre in any location.

Mobile healthcare services in rural areas: an application with periodic location routing problem.

In this study, we focus on the delivery of mobile healthcare services in rural areas, where doctors visit remote villages which do not have a healthcare facility nearby. The aim is to increase the accessibility of healthcare services for such population centers. We aim to determine the village assignments of the doctors, their monthly visit schedules and base hospitals where they start and end their tours. We model this as a periodic location routing problem and use the policies of Ministry of Health of Turkey as a basis for our mathematical formulation. These policies include the essential components of mobile healthcare services, namely, continuity of care and determining evenly distributed periodic visits. We determine the visit schedules, i.e. routes, of doctors endogenously while satisfying these policies. We also develop a heuristic algorithm based on a cluster first-route second approach and solve larger instances more effectively. The computational experiments support that this solution methodology can effectively find optimal or near-optimal solutions and improve the computational times significantly.

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson's Disease.

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog-a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual-spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment.

The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as 'good' and 'poor' classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a 'poor' class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.

Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device.

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification.

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

Arrhythmia Diagnosis by Using Level-Crossing ECG Sampling and Sub-Bands Features Extraction for Mobile Healthcare.

Mobile healthcare is an emerging technique for clinical applications. It is usually based on cloud-connected biomedical implants. In this context, a novel solution is presented for the detection of arrhythmia by using electrocardiogram (ECG) signals. The aim is to achieve an effective solution by using real-time compression, efficient signal processing, and data transmission. The system utilizes level-crossing-based ECG signal sampling, adaptive-rate denoising, and wavelet-based sub-band decomposition. Statistical features are extracted from the sub-bands and used for automated arrhythmia classification. The performance of the system was studied by using five classes of arrhythmia, obtained from the MIT-BIH dataset. Experimental results showed a three-fold decrease in the number of collected samples compared to conventional counterparts. This resulted in a significant reduction of the computational cost of the post denoising, features extraction, and classification. Moreover, a seven-fold reduction was achieved in the amount of data that needed to be transmitted to the cloud. This resulted in a notable reduction in the transmitter power consumption, bandwidth usage, and cloud application processing load. Finally, the performance of the system was also assessed in terms of the arrhythmia classification, achieving an accuracy of 97%.

An Efficient Mobile-Healthcare Emergency Framework.

Mobile technologies are capable of offering individual level health care services to users. Mobile Healthcare (m-Healthcare) frameworks, which feature smartphone (SP) utilizations of ubiquitous computing made possible by applying wireless Body Sensor Networks (BSNs), have been introduced recently to provide SP clients with health condition monitoring and access to medical attention when necessary. However, in a vulnerable m-Healthcare framework, clients' personal info and sensitive data can easily be poached by intruders or any malicious party, causing serious security problems and confidentiality issues. In 2013, Lu et al. proposed a mobile-Healthcare emergency framework based on privacy-preserving opportunistic computing (SPOC), claiming that their splendid SPOC construction can opportunistically gather SP resources such as computing power and energy to handle computing-intensive Personal Health Information (PHI) with minimal privacy disclosure during an emergency. To balance between the risk of personal health information exposure and the essential PHI processing and transmission, Lu et al. presented a patient-centric privacy ingress control framework based on an attribute-based ingress control mechanism and a Privacy-Preserving Scalar Product Computation (PPSPC) technique. In spite of the ingenious design, however, Lu et al.'s framework still has some security flaws in such aspects as client anonymity and mutual authentication. In this article, we shall offer an improved version of Lu et al.'s framework with the security weaknesses mended and the computation efficiency further boosted. In addition, we shall also present an enhanced mobile-Healthcare emergency framework using Partial Discrete Logarithm (PDL) which does not only achieve flawless mutual authentication as well as client anonymity but also reduce the computation cost.

Collaborative and secure transmission of medical data applied to mobile healthcare.

PURPOSE: We propose a collaborative and secure transmission scheme in order to safely and efficiently transmit medical data and provide telemedicine services, lighten the load on wireless access networks, and improve the quality of medical treatment such as surgery. METHODS: First, the transmission technology based on opportunistic networks is used to upload patient physiological data and share medical information. Second, we propose a trusted transfer scheme based on the circle of friends, which is constructed with historical encounters and social features of nodes. This scheme takes the forwarding policy of each packet by close friends to effectively prevent the participation of strangers, and avoid privacy issues and deal with selfish behaviors. At the same time, the structure of friend circle is beneficial to the improvement of medical data transmission. Third, we present a lossless compression scheme with less computation and higher compression ratio to reduce the amount of medical data and improve the performance of the transmission. RESULTS: The experimental results show that the proposed scheme is effective and has good transmission performance while ensuring the safety and reliability of media data. CONCLUSION: The mobile healthcare faces some challenges such as the vastness of medical data and sensitivity of patient information. Using opportunistic networks to transmit medical data in mobile healthcare is a good solution, which can effectively divert and offload the data traffic of mobile Internet. The structure of friend circles and the technology of data compression are beneficial to safely and efficiently transmit the patient's physiological parameters and medical health information.

Using Health Chatbots for Behavior Change: A Mapping Study.

This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest in the health domain? We identify 30 articles related to health chatbots from 2014 to 2018. We analyze the selected articles qualitatively and extract a triplet for each of them. This data serves to provide a first overview of chatbot-mediated behavior change on the health domain. Main insights include: nutritional disorders and neurological disorders as the main illness areas being tackled; "affect" as the human competence most pursued by chatbots to attain change behavior; and "personalization" and "consumability" as the most appreciated technical enablers. On the other hand, main limitations include lack of adherence to good practices to case-study reporting, and a deeper look at the broader sociological implications brought by this technology.

Cyber Attacks on Healthcare Devices Using Unmanned Aerial Vehicles.

The growing use of wireless technology in healthcare systems and devices makes these systems particularly open to cyber-based attacks, including denial of service and information theft via sniffing (eaves-dropping) and phishing attacks. Evolving technology enables wireless healthcare systems to communicate over longer ranges, which opens them up to greater numbers of possible threats. Unmanned aerial vehicles (UAV) or drones present a new and evolving attack surface for compromising wireless healthcare systems. An enumeration of the types of wireless attacks capable via drones are presented, including two new types of cyber threats: a stepping stone attack and a cloud-enabled attack. A real UAV is developed to test and demonstrate the vulnerabilities of healthcare systems to this new threat vector. The UAV successfully attacked a simulated smart hospital environment and also a small collection of wearable healthcare sensors. Compromise of wearable or implanted medical devices can lead to increased morbidity and mortality.

[Project to Increase the Usage Rate of iCKD Application Mobile Healthcare on Chronic Kidney Disease Patients].

BACKGROUND & PROBLEMS: In recent years, improved mobile capacities and mobile-device computing capabilities as well as a maturing mobile-communications infrastructure have combined to promote the development and widespread use of mobile healthcare applications. Mobile healthcare supports the self-management of chronic diseases, enhances healthcare quality, and reduces medical costs. Due to the low rate of iCKD usage in our hospital, we set up a project team that was tasked to improve the rate of iCKD usage among chronic disease patients. PURPOSE: To improve the iCKD usage rate from the current 5.8% to 9.3%. RESOLUTIONS: Questionnaire-based survey results indicated that the main reasons for the low iCKD usage rate in our hospital were: negative attitudes toward iCKD as a helpful tool in disease management, lack of awareness of the importance of using iCKD, unfamiliarity with how to operate smartphone applications, low numbers of iCKD physician referrals for patients, and slow Internet speeds. The improvement strategy included providing instructions on iCKD use, organizing a practical learning program, designing and printing a mobile healthcare referral form, and holding related events. RESULTS: The rate of iCKD usage increased to 21.3% after the intervention. CONCLUSIONS: The project team successfully increased the iCKD usage rate by getting more patients involved in mobile healthcare, which is expected to have a positive impact on the success of patient self-management.

Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer.

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

SisFall: A Fall and Movement Dataset.

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices.

In the new-generation wearable Electrocardiogram (ECG) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The QRS complex is a combination of three of the graphic deflection seen on a typical ECG. This study proposes a real-time QRS detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation, which also leads to the elimination of baseline wandering. In this study, the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four QRS waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database, where the QRS detected sensitivity (Se) and positive prediction (+P) are 99.82% and 99.81%, respectively. The result reveals the approach's advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.

The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors.

Human-activity recognition (HAR) and energy-expenditure (EE) estimation are major functions in the mobile healthcare system. Both functions have been investigated for a long time; however, several challenges remain unsolved, such as the confusion between activities and the recognition of energy-consuming activities involving little or no movement. To solve these problems, we propose a novel approach using an accelerometer and electrocardiogram (ECG). First, we collected a database of six activities (sitting, standing, walking, ascending, resting and running) of 13 voluntary participants. We compared the HAR performances of three models with respect to the input data type (with none, all, or some of the heart-rate variability (HRV) parameters). The best recognition performance was 96.35%, which was obtained with some selected HRV parameters. EE was also estimated for different choices of the input data type (with or without HRV parameters) and the model type (single and activity-specific). The best estimation performance was found in the case of the activity-specific model with HRV parameters. Our findings indicate that the use of human physiological data, obtained by wearable sensors, has a significant impact on both HAR and EE estimation, which are crucial functions in the mobile healthcare system.

A Priority-Based Adaptive MAC Protocol for Wireless Body Area Networks.

In wireless body area networks (WBANs), various sensors and actuators are placed on/inside the human body and connected wirelessly. WBANs have specific requirements for healthcare and medical applications, hence, standard protocols like the IEEE 802.15.4 cannot fulfill all the requirements. Consequently, many medium access control (MAC) protocols, mostly derived from the IEEE 802.15.4 superframe structure, have been studied. Nevertheless, they do not support a differentiated quality of service (QoS) for the various forms of traffic coexisting in a WBAN. In particular, a QoS-aware MAC protocol is essential for WBANs operating in the unlicensed Industrial, Scientific, and Medical (ISM) bands, because different wireless services like Bluetooth, WiFi, and Zigbee may coexist there and cause severe interference. In this paper, we propose a priority-based adaptive MAC (PA-MAC) protocol for WBANs in unlicensed bands, which allocates time slots dynamically, based on the traffic priority. Further, multiple channels are effectively utilized to reduce access delays in a WBAN, in the presence of coexisting systems. Our performance evaluation results show that the proposed PA-MAC outperforms the IEEE 802.15.4 MAC and the conventional priority-based MAC in terms of the average transmission time, throughput, energy consumption, and data collision ratio.

Analysis of Security Protocols for Mobile Healthcare.

Mobile Healthcare (mHealth) continues to improve because of significant improvements and the decreasing costs of Information Communication Technologies (ICTs). mHealth is a medical and public health practice, which is supported by mobile devices (for example, smartphones) and, patient monitoring devices (for example, various types of wearable sensors, etc.). An mHealth system enables healthcare experts and professionals to have ubiquitous access to a patient's health data along with providing any ongoing medical treatment at any time, any place, and from any device. It also helps the patient requiring continuous medical monitoring to stay in touch with the appropriate medical staff and healthcare experts remotely. Thus, mHealth has become a major driving force in improving the health of citizens today. First, we discuss the security requirements, issues and threats to the mHealth system. We then present a taxonomy of recently proposed security protocols for mHealth system based on features supported and possible attacks, computation cost and communication cost. Our detailed taxonomy demonstrates the strength and weaknesses of recently proposed security protocols for the mHealth system. Finally, we identify some of the challenges in the area of security protocols for mHealth systems that still need to be addressed in the future to enable cost-effective, secure and robust mHealth systems.