The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied

A Framework for Morphological Feature Extraction of Organs from MR Images for Detection and Classification of Abnormalities

In clinical practice, a misdiagnosis can lead to incorrect or delayed treatment, and in some cases, no treatment at all; consequently, the condition of a patient may worsen to varying degrees, in some cases proving fatal. The accurate 3D reconstruction of organs, which is a pioneering tool of medical image computing (MIC) technology, plays a key role in computer aided diagnosis (CADx), thereby enabling medical professionals to perform enhanced analysis on a region of interest. From here, the shape and structure of the organ coupled with measurements of its volume and curvature can provide significant guidance towards establishing the severity of a disorder or abnormality, consequently supporting improved diagnosis and treatment planning. Moreover, the classification and stratification of organ abnormalities is widely utilised within biomedical, forensic and MIC research for exploring and investigating organ deformations following injury, illness or trauma. This paper presents a tool that calculates, classifies and analyses pancreatic volume and curvature following their 3D reconstruction. Magnetic resonance imaging (MRI) volumes of 115 adult patients are evaluated in order to examine a correlation between these two variables. Such a tool can be utilised in the scope of much greater research and investigation. It can also be incorporated into the development of effective medical image analysis software application in the stratification of subjects and targeting of therapies

WEIGHTBIT: An advancement in wearable technology

Wearable devices are becoming an important interface between users and fitness activities. Their capabilities are improving exponentially, and new strategies are being developed to track sports using sensors that are widely used in robotics. These wearable gadgets are normally created in conjunction with smartphone applications enabling the user to visualise the data and share it through social networks, or compete with other users. The technology behind these devices is often simple using sensors that can be found in a smartphone, such as GPS, accelerometer and gyroscope. However, there are currently no devices capable of measuring the gym activity of weight lifting. In this paper, we present WeightBit: a system consisting of technologically enhanced gym gloves, comprised of the aforementioned sensor’s as well as an additional weight sensor to detect weight and arm movements. Using this data in combination with a smartphone application, it will be possible to monitor a new series of sports activities with specific focus on weight training. Furthermore, the data collected by the application will enable broader research by medical researchers or institutions. The goal is to keep users focused and keen to live a healthy life, providing them a great tool to track their progress, and to develop a system that will allow medical institutions access to this data for further study

Predicting Effective Adaptation to Breast Cancer to Help Women BOUNCE Back : Protocol for a Multicenter Clinical Pilot Study

Publisher Copyright: © 2022 Greta Pettini.Background: Despite the continued progress of medicine, dealing with breast cancer is becoming a major socioeconomic challenge, particularly due to its increasing incidence. The ability to better manage and adapt to the entire care process depends not only on the type of cancer but also on the patient's sociodemographic and psychological characteristics as well as on the social environment in which a person lives and interacts. Therefore, it is important to understand which factors may contribute to successful adaptation to breast cancer. To our knowledge, no studies have been performed on the combination effect of multiple psychological, biological, and functional variables in predicting the patient's ability to bounce back from a stressful life event,such as a breast cancer diagnosis. Here we describe the study protocol of a multicenter clinical study entitled "Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back"or, in short, BOUNCE. Objective: The aim of the study is to build a quantitative mathematical model of factors associated with the capacity for optimal adjustment to cancer and to study resilience through the cancer continuum in a population of patients with breast cancer. Methods: A total of 660 women with breast cancer will be recruited from five European cancer centers in Italy, Finland, Israel, and Portugal. Biomedical and psychosocial variables will be collected using the Noona Healthcare platform. Psychosocial, sociodemographic, lifestyle, and clinical variables will be measured every 3 months, starting from presurgery assessment (ie, baseline) to 18 months after surgery. Temporal data mining, time-series prediction, sequence classification methods, clustering time-series data, and temporal association rules will be used to develop the predictive model. Results: The recruitment process stared in January 2019 and ended in November 2021. Preliminary results have been published in a scientific journal and are available for consultation on the BOUNCE project website. Data analysis and dissemination of the study results will be performed in 2022. Conclusions: This study will develop a predictive model that is able to describe individual resilience and identify different resilience trajectories along the care process. The results will allow the implementation of tailored interventions according to patients' needs, supported by eHealth technologies.Peer reviewe

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

Loneliness in Breast Cancer Patients with Early Life Adversity: An Investigation of the Effects of Childhood Trauma and Self-Regulation

Childhood trauma may be prevalent in the general population, and the psychosocial treatment of patients with cancer may require consideration of the effects of such early adversity on the healing and recovery process. In this study, we investigated the long-term effects of childhood trauma in 133 women diagnosed with breast cancer (mean age 51, SD = 9) who had experienced physical, sexual, or emotional abuse or neglect. We examined their experience of loneliness and its associations with the severity of childhood trauma, ambivalence about emotional expression, and changes in self-concept during the cancer experience. In total, 29% reported experiencing physical or sexual abuse, and 86% reported neglect or emotional abuse. In addition, 35% of the sample reported loneliness of moderately high severity. Loneliness was directly influenced by the severity of childhood trauma and was directly and indirectly influenced by discrepancies in self-concept and emotional ambivalence. In conclusion, we found that childhood trauma was common in breast cancer patients, with 42% of female patients reporting childhood trauma, and that these early experiences continued to exert negative effects on social connection during the illness trajectory. Assessment of childhood adversity may be recommended as part of routine oncology care, and trauma-informed treatment approaches may improve the healing process in patients with breast cancer and a history of childhood maltreatment

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas