Viability study of a personalized and adaptive knowledge-generation telehealthcare system for nephrology (NEFROTEL)

Telehealth strategies in the care of people with chronic kidney disease: integrative review

OBJECTIVE

to evaluate the evidence about telehealth strategies in caring for people with chronic kidney disease.

METHOD

integrative literature review. The search for primary studies was carried out in six databases: PubMed/MEDLINE, Web of Science, EMBASE, CINAHL, LILACS, and Scopus. The sample consisted of 48 articles published between 2000 and 2021. The telehealth strategy was applied by a multidisciplinary team of doctors, nurses, pharmacists, nutritionis, and social workers. The type of study, country, strategy applied, setting, population, and professional were extracted from the articles. The studies were selected by reading the title and abstract (phase 1) and then reading them in full (phase 2), categorizing them by telehealth strategy. The results were summarized descriptively and the studies were classified according to their level of evidence.

RESULTS

the home was the most representative in dialysis and conservative treatment. Six categories of telehealth strategies were identified: remote monitoring devices, teleconsultation, digital platforms, apps, multimodality strategies, and telephone contact.

CONCLUSION

using these strategies for the care of people with chronic kidney disease presents different forms and implementations, being feasible for the renal population at any stage of the disease and applicable by different health professionals with an emphasis on the home environment. The evidence shows that telehealth favors lower cost, accessibility to remote locations, and better monitoring of dialysis with positive resul in symptom control, risk reduction, and patient training.

BACKGROUND

Telehealth in chronic kidney disease care is feasible and promising. (2) Telehealth is feasible for people at all stages of CKD. (3) Health promotion and monitoring were the most applied by telehealth. (4) Remote care can reduce costs, emergencies, and contac with the clinic. (5) Nurses mainly used telephone contact and teleconferencing.

Detection of human impacts by an adaptive energy-based anisotropic algorithm

Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results.

Personalization and adaptation to the medium and context in a fall detection system

The main objective of this paper is to present a distributed processing architecture that explicitly integrates capabilities for its continuous adaptation to the medium, the context, and the user. This architecture is applied to a falling detection system through: 1) an optimization module that finds the optimal operation parameters for the detection algorithms of the system devices; 2) a distributed processing architecture that provides capabilities for remote firmware update of the smart sensors. The smart sensor also provides an estimation of activities of daily living (ADL), which results very useful in monitoring of the elderly and patients with chronic diseases. The developed experiments have demonstrated the feasibility of the system and specifically, the accuracy of the proposed algorithms and procedures (100% success for impact detection, 100% sensitivity and 95.68% specificity rates for fall detection, and 100% success for ADL level classification). Although the experiments have been developed with a cohort of young volunteers, the personalization and adaption mechanisms of the proposed architecture related to the concepts of "design for all" and "design space" will significantly ease the adaptation of the system for its application to the elderly.

Smart sensors and virtual physiology human approach as a basis of personalized therapies in diabetes mellitus

Diabetes mellitus (DM) has a growing incidence and prevalence in modern societies, pushed by the aging and change of life styles. Despite the huge resources dedicated to improve their quality of life, mortality and morbidity rates, these are still very poor. In this work, DM pathology is revised from clinical and metabolic points of view, as well as mathematical models related to DM, with the aim of justifying an evolution of DM therapies towards the correction of the physiological metabolic loops involved. We analyze the reliability of mathematical models, under the perspective of virtual physiological human (VPH) initiatives, for generating and integrating customized knowledge about patients, which is needed for that evolution. Wearable smart sensors play a key role in this frame, as they provide patient's information to the models.A telehealthcare computational architecture based on distributed smart sensors (first processing layer) and personalized physiological mathematical models integrated in Human Physiological Images (HPI) computational components (second processing layer), is presented. This technology was designed for a renal disease telehealthcare in earlier works and promotes crossroads between smart sensors and the VPH initiative. We suggest that it is able to support a truly personalized, preventive, and predictive healthcare model for the delivery of evolved DM therapies.

Development of a health management support system for patients with diabetes mellitus at home

Recently, a patient with diabetes mellitus (DM) type 2 has been increasing in Japan. The patient should be managed not only by a specialist but also by himself focusing his attention on the improvement of his lifestyle at home. In the present study, we tried to develop a health management support system by which a diabetic patient in early stage can easily enter his daily life information, i.e. the biological information such as the data of blood sugar levels and blood pressure levels etc., the information of exercise and diet and send the information to the medical institution with a personal digital assistant (PDA). Afterwards, the patient can receive health instruction information by the physician in charge for self-care at his home with a PDA. The daily life information sent from the patient is stored in a server installed at the medical institution and analyzed. The physician can obtain the results of analysis by using a PC and send the instruction information necessary for patient management to the patient at home by using e-mail after diagnosing the patient's condition by the system. To evaluate usability of the developed patient information input system with a PDA, an experiment was conducted by corporation of 20 volunteers who were possible self management and whose age's range from 20s to 60s by questionnaire survey. As a result, almost examinees answered that lifestyle information could be easily entered by the sense like a mobile-phone and lots of positive opinions were obtained.

Design and implementation of a distributed fall detection system--personal server

In this paper, the main results related to a fall detection system are shown by means of a personal server for the control and processing of the data acquired from multiple intelligent biomedical sensors. This server is designed in the context of a telehealthcare system for the elderly, to whom falls represent a high-risk cause of serious injuries, and its architecture can be extended to patients suffering from chronic diseases. The main design issues and developments in terms of the server hardware and software are presented with the aim of providing a real-time analysis of the processed biosignals. As a result, the evaluation study of the implemented algorithm for fall detection through a set of laboratory experiments is presented, together with some important issues in terms of the device's consumption. The proposed algorithm exhibits excellent outcomes in fall detection.

The inescapable smart impact detection system (ISIS): An ubiquitous and personalized fall detector based on a distributed 'divide and conquer strategy'

Despite the intense research in the last decade with the aim of developing a reliable solution for fall detection in the elderly and other risk populations, it can be asserted that the diffusion of fall detectors in the geriatric practice is near null. This scenario is similar to the very scarce use of telemedicine in healthcare. The present work begins analyzing why fall detectors have not achieved to permeate the industry. That road is used to know the drawbacks of current devices and systems, besides to allow studying several important concepts underlying the principles of fall detection. A novel smart detection system based on that survey is finally briefly presented. The design of this device is founded on the experience and results obtained by an earlier device that was designed in the framework of the thesis of one of the authors.