Visceral Dissemination of Mucocutaneous Leishmaniasis in a Kidney Transplant Recipient.

Intracellular protozoan of the genus Leishmania, endemic in the Mediterranean basin, are the cause of cutaneous (CL), mucocutaneous (MCL), and visceral leishmaniasis (VL). A 75-year-old woman was admitted nine years after a second kidney transplant (KT), due to persistent pancytopenia and fever. She presented edema and erythema of the nose in the last two years and an exophytic nodular lesion located on the left arm, with areas of peripheral necrosis and central ulceration in the last 18 months. A bone marrow biopsy revealed features compatible with Leishmania amastigotes, and polymerase chain reaction test (PCR) for Leishmania infantum was positive. Moreover, biopsy and PCR for L. infantum of the cutaneous lesion on the patient's left arm and nose and PCR from peripheral blood were positive. Thus, a diagnosis of CL, MCL, and VL was made, and liposomal amphotericin B was initiated, but the patient had an unfavorable outcome and died. This is the first report of a KT recipient presenting with the entire spectrum of leishmaniasis. In Portugal, this infection is rare-so a high degree of clinical suspicion is required for its diagnosis, especially in endemic regions, as visceral leishmaniasis is a potentially life-threatening infection.

What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project.

BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.

Exploring the Frequency Domain of Continuous Glucose Monitoring Signals to Improve Characterization of Glucose Variability and of Diabetic Profiles.

BACKGROUND: Continuous glucose monitoring (CGM) devices measure interstitial glucose concentrations (normally every 5 minutes), allowing observation of glucose variability (GV) patterns during the whole day. This information could be used to improve prescription of treatments and of insulin dosages for people suffering diabetes. Previous efforts have been focused on proposing indices of GV either in time or glucose domains, while the frequency domain has been explored only partially. The aim of this work is to explore the CGM signal in the frequency domain to understand if new indexes or features could be identified and contribute to a better characterization of glucose variability. METHODS: The direct fast Fourier transform (FFT) and the Welch method were used to analyze CGM signals from three different profiles: people at risk of developing type 2 diabetes (P@R), T2D patients, and type 1 diabetes (T1D) patients. RESULTS: The results suggests that features extracted from the FFT (ie, the localization and power of the maximum peak of the power spectrum and the bandwidth at 3 dB) are able to provide a characterization for all the three populations under study compared with the Welch approach. CONCLUSIONS: Such preliminary results can represent a good insight for futures investigations with the possibility of building and using new indexes of glucose variability based on the frequency features.

OB CITY-Definition of a Family-Based Intervention for Childhood Obesity Supported by Information and Communication Technologies.

Childhood obesity is becoming one of the 21st century's most important public health problems. Nowadays, the main treatment of childhood obesity is behavior intervention that aims at improve children's lifestyle to arrest the disease. Information and communication technologies (ICTs) have not been widely employed in this intervention, and most of existing ICTs systems are not having a long-term effect. The purpose of this paper is to define a system to support family-based intervention through a state-of-the-art analysis of family-based interventions and related technological solutions first, and then using the analytic hierarchy process to derive a childhood obesity family-based behavior intervention model, and finally to provide a prototype of a system called OB CITY. The system makes use of applied behavior analysis, affective computing technologies, as well as serious game and gamification techniques, to offer long term services in all care dimensions of the family-based behavioral intervention aiming to provide positive effects to the treatment of childhood obesity.

Monitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform.

Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.

Using the Analytic Hierarchy Process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson's disease.

BACKGROUND: The assessment of a new health technology is a multidisciplinary and multidimensional process, which requires a complex analysis and the convergence of different stakeholders into a common decision. This task is even more delicate when the assessment is carried out in early stage of development processes, when the maturity of the technology prevents conducting a large scale trials to evaluate the cost effectiveness through classic health economics methods. This lack of information may limit the future development and deployment in the clinical practice. This work aims to 1) identify the most relevant user needs of a new medical technology for managing and monitoring Parkinson's Disease (PD) patients and to 2) use these user needs for a preliminary assessment of a specific system called PERFORM, as a case study. METHODS: Analytic Hierarchy Process (AHP) was used to design a hierarchy of 17 needs, grouped into 5 categories. A total of 16 experts, 6 of them with a clinical background and the remaining 10 with a technical background, were asked to rank these needs and categories. RESULTS: On/Off fluctuations detection, Increase wearability acceptance, and Increase self-management support have been identified as the most relevant user needs. No significant differences were found between the clinician and technical groups. These results have been used to evaluate the PERFORM system and to identify future areas of improvement. CONCLUSIONS: First of all, the AHP contributed to the elaboration of a unified hierarchy, integrating the needs of a variety of stakeholders, promoting the discussion and the agreement into a common framework of evaluation. Moreover, the AHP effectively supported the user need elicitation as well as the assignment of different weights and priorities to each need and, consequently, it helped to define a framework for the assessment of telehealth systems for PD management and monitoring. This framework can be used to support the decision-making process for the adoption of new technologies in PD.

PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

Wearability assessment of a wearable system for Parkinson's disease remote monitoring based on a body area network of sensors.

Wearable technologies for health monitoring have become a reality in the last few years. So far, most research studies have focused on assessments of the technical performance of these systems, as well as the validation of the clinical outcomes. Nevertheless, the success in the acceptance of these solutions depends not only on the technical and clinical effectiveness, but on the final user acceptance. In this work the compliance of a telehealth system for the remote monitoring of Parkinson's disease (PD) patients is presented with testing in 32 PD patients. This system, called PERFORM, is based on a Body Area Network (BAN) of sensors which has already been validated both from the technical and clinical point for view. Diverse methodologies (REBA, Borg and CRS scales in combination with a body map) are employed to study the comfort, biomechanical and physiological effects of the system. The test results allow us to conclude that the acceptance of this system is satisfactory with all the levels of effect on each component scoring in the lowest ranges. This study also provided useful insights and guidelines to lead to redesign of the system to improve patient compliance.

Experience in evaluating AAL solutions in living labs.

Ambient assisted living (AAL) is a complex field, where different technologies are integrated to offer solutions for the benefit of different stakeholders. Several evaluation techniques are commonly applied that tackle specific aspects of AAL; however, holistic evaluation approaches are lacking when addressing the needs of both developers and end-users. Living labs have been often used as real-life test and experimentation environments for co-designing AAL technologies and validating them with relevant stakeholders. During the last five years, we have been evaluating AAL systems and services in the framework of various research projects. This paper presents the lessons learned in this experience and proposes a set of harmonized guidelines to conduct evaluations in living labs.

Feasibility study of a wearable system based on a wireless body area network for gait assessment in Parkinson's disease patients.

Parkinson's disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments.

Proposal of a Kinect(TM)-based system for gait assessment and rehabilitation in Parkinson's disease.

It has been proved that audio and visual cueing can improve the motor performance of Parkinson's disease patients. Specially, gait can benefit from repetitive sessions of exercises using cues. Nevertheless, these effects are not permanent and fade away with time, in that sense, home game systems can be an excellent platform for patients to perform daily exercises, as well as to coach and guide them in a smarter way. Within this work a method to track the walking movement is proposed based on the signals coming from the Kinect sensor of Microsoft. At the same time, different setups have been tested in order to study the feasibility of using this sensor to build a game platform for gait rehabilitation for Parkinson's disease patients.

Designing auditory cues for Parkinson's disease gait rehabilitation.

Recent works have proved that Parkinson's disease (PD) patients can be largely benefit by performing rehabilitation exercises based on audio cueing and music therapy. Specially, gait can benefit from repetitive sessions of exercises using auditory cues. Nevertheless, all the experiments are based on the use of a metronome as auditory stimuli. Within this work, Human-Computer Interaction methodologies have been used to design new cues that could benefit the long-term engagement of PD patients in these repetitive routines. The study has been also extended to commercial music and musical pieces by analyzing features and characteristics that could benefit the engagement of PD patients to rehabilitation tasks.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring.

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7%) and worst (87%) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

A telehealth system for Parkinson's disease remote monitoring. The PERFORM approach.

This paper summarizes the experience and the lessons learned from the European project PERFORM (A sophisticated multi-parametric system FOR the continuous effective assessment and monitoring of motor status in Parkinson's disease and other neurodegenerative diseases). PERFORM is aimed to provide a telehealth system for the remote monitoring of Parkinson's disease patients (PD) at their homes. This paper explains the global experience with PERFORM. It summarizes the technical performance of the system and the feedback received from the patients in terms of usability and wearability.

Leg ulcer and thigh telangiectasia associated with natural killer cell CD56(-) large granular lymphocyte leukemia in a patient with pseudo-Felty syndrome.

Clonal disorders of large granular lymphocytes (LGL) represent a rare spectrum of biologically distinct lymphoproliferative diseases originating either from mature T cells or natural killer cells. Both subtypes can manifest as indolent or aggressive disorders. We report a 77-year-old woman with rheumatoid arthritis, splenomegaly, and neutropenia who developed a painful leg ulcer refractory to treatment and thigh telangiectatic lesions. Because of the association of rheumatoid arthritis, splenomegaly, and nonspecific neutropenia, the diagnosis of Felty syndrome was initially made. Further investigation allowed the diagnosis of a CD56(-) natural killer-cell LGL leukemia and documented skin infiltration by natural killer cells. Cutaneous manifestations of LGL leukemia have been rarely reported. This report of pseudo-Felty syndrome with CD56(-) LGL leukemia, presenting with a leg ulcer and telangiectasia, enhances the role of dermatology in the diagnosis of hematologic neoplasia.

Multi-parametric system for the continuous assessment and monitoring of motor status in Parkinson's disease: an entropy-based gait comparison.

A "Multi-parametric system for the continuous assessment and monitoring of motor status in Parkinson's disease" (PERFORM), is an FP7 project from the European Commission that aims at providing an innovative and reliable tool, able to evaluate, monitor and manage patients suffering from motor neurodegenerative diseases. The current work is related to a module of the project that is in charge of assessing PD patients during locomotion. These initial analyses of gait are based on analyses of Sample Entropy in the acceleration signals. Four PD patients are compared to four healthy using a set of five wireless sensors located in the limbs and in the trunk. A metric to assess the level of symmetry during locomotion, an important clinical feature, is proposed. Results show considerable differences between the patients and the subjects, both for sample entropy (in 3 of the 5 sensors) and in the gait asymmetry index (left vs. right limbs). Future work is proposed including age-matched subjects and a larger sample.