Seasonality of birth of patients with juvenile idiopathic arthritis.

OBJECTIVES: The aim of this study was to determine the seasonality of month of birth (MOB) in children with juvenile idiopathic arthritis (JIA) as compared to the general population. METHODS: Cosinor analysis was used to analyse MOB rhythmicity in 558 children with JIA from a simple rheumatology clinic compared with the MOB pattern of the general population in Israel (n=1.040558). Statistical differences between groups were also analysed by non-parametrical tests. RESULTS: Patients with JIA showed different patterns from that of the general population. A rhythmic pattern of 12 months was found in the MOB patterns of JIA patients. This rhythm with a peak between November to March and a nadir in summer was a mirror image of the rhythmic pattern observed for MOB of the healthy population. Males showed a pattern with combined rhythm of 8 and 6 months with peaks in winter, while females' MOB pattern showed no rhythmicity. Testing different JIA subtypes, only the patients with the enthesitis-related arthritis (ERA) subtype showed rhythmicity in MOB. Rhythmicity patterns were different for males and females, and differed according to several disease characteristics. CONCLUSIONS: The observed pattern of MOB in JIA patients is distinctive and different from that in the healthy population supporting the hypothesis that autoimmune process may begin in utero or in the perinatal period due to seasonal environmental pathogenic agents.

Interpretation of Heart and Lungs Sounds Acquired via Remote, Digital Auscultation Reached Fair-to-Substantial Levels of Consensus among Specialist Physicians.

BACKGROUND: Technological advancement may bridge gaps between long-practiced medical competencies and modern technologies. Such a domain is the application of digital stethoscopes used for physical examination in telemedicine. This study aimed to validate the level of consensus among physicians regarding the interpretation of remote, digital auscultation of heart and lung sounds. METHODS: Seven specialist physicians considered both the technical quality and clinical interpretation of auscultation findings of pre-recorded heart and lung sounds of patients hospitalized in their homes. TytoCareTM system was used as a remote, digital stethoscope. RESULTS: In total, 140 sounds (70 heart and 70 lungs) were presented to seven specialists. The level of agreement was measured using Fleiss' Kappa (FK) variable. Agreement relating to heart sounds reached low-to-moderate consensus: the overall technical quality (FK = 0.199), rhythm regularity (FK = 0.328), presence of murmurs (FK = 0.469), appreciation of sounds as remote (FK = 0.011), and an overall diagnosis as normal or pathologic (FK = 0.304). The interpretation of some of the lung sounds reached a higher consensus: the overall technical quality (FK = 0.169), crepitus (FK = 0.514), wheezing (FK = 0.704), bronchial sounds (FK = 0.034), and an overall diagnosis as normal or pathological (FK = 0.386). Most Fleiss' Kappa values were in the range of "fare consensus", while in the domains of diagnosing lung crepitus and wheezing, the values increased to the "substantial" level. CONCLUSIONS: Bio signals, as recorded auscultations of the heart and lung sounds serving the process of clinical assessment of remotely situated patients, do not achieve a high enough level of agreement between specialized physicians. These findings should serve as a catalyzer for improving the process of telemedicine-attained bio-signals and their clinical interpretation.

International Comparison of Six Basic eHealth Indicators Across 14 Countries: An eHealth Benchmarking Study.

BACKGROUND: Many countries adopt eHealth applications to support patient-centered care. Through information exchange, these eHealth applications may overcome institutional data silos and support holistic and ubiquitous (regional or national) information logistics. Available eHealth indicators mostly describe usage and acceptance of eHealth in a country. The eHealth indicators focusing on the cross-institutional availability of patient-related information for health care professionals, patients, and care givers are rare. OBJECTIVES: This study aims to present eHealth indicators on cross-institutional availability of relevant patient data for health care professionals, as well as for patients and their caregivers across 14 countries (Argentina, Australia, Austria, Finland, Germany, Hong Kong as a special administrative region of China, Israel, Japan, Jordan, Kenya, South Korea, Sweden, Turkey, and the United States) to compare our indicators and the resulting data for the examined countries with other eHealth benchmarks and to extend and explore changes to a comparable survey in 2017. We defined "availability of patient data" as the ability to access data in and to add data to the patient record in the respective country. METHODS: The invited experts from each of the 14 countries provided the indicator data for their country to reflect the situation on August 1, 2019, as date of reference. Overall, 60 items were aggregated to six eHealth indicators. RESULTS: Availability of patient-related information varies strongly by country. Health care professionals can access patients' most relevant cross-institutional health record data fully in only four countries. Patients and their caregivers can access their health record data fully in only two countries. Patients are able to fully add relevant data only in one country. Finland showed the best outcome of all eHealth indicators, followed by South Korea, Japan, and Sweden. CONCLUSION: Advancement in eHealth depends on contextual factors such as health care organization, national health politics, privacy laws, and health care financing. Improvements in eHealth indicators are thus often slow. However, our survey shows that some countries were able to improve on at least some indicators between 2017 and 2019. We anticipate further improvements in the future.

Seasonality of birth month of children with celiac disease differs from that in the general population and between sexes and is linked to family history and environmental factors.

BACKGROUND: Patients with autoimmune diseases, such as type 1 diabetes mellitus, atopic dermatitis, autoimmune thyroid diseases, and multiple sclerosis have a different seasonality of month of birth (MOB) from the general population. This study was undertaken to determine the seasonality of MOB in children with celiac disease (CD), an autoimmune-mediated enteropathy. PATIENTS AND METHODS: The medical records of 431 children with CD (239 girls, 192 boys) were included in the study; 138 girls and 81 boys were under the age of 24 months. Data were analyzed by the cosinor method, which, in addition to statistical significance, provides parameters of rhythms. Statistical differences between groups were also analyzed by the chi test. RESULTS: Patients with CD showed different patterns from that in the general population, which peaks in September. Boys and girls with CD were found to have a different seasonality of MOB (P < 0.02). Girls whose CD was diagnosed before age 24 months (peak July-August) had a different seasonality of MOB from those whose CD was diagnosed after age 24 months (no rhythm; P < 0.005) and showed a different seasonality from boys whose CD was diagnosed above 24 months (peak July; P < 0.02). In addition, we found a different seasonality of MOB in children with a family history of CD compared with those with no family history (P < 0.001). CONCLUSIONS: Girls with the diagnosis of CD and patients of both sexes with a family history of CD have a different pattern of seasonality of birth from the general population. This is suggestive of a perinatal virus infection as a plausible candidate for the primary trigger.

Personalized Health Systems-Past, Present, and Future of Research Development and Implementation in Real-Life Environment.

Personal health systems (PHS) are designed to provide the individual with tailored care while enabling the healthcare system to deliver high-quality care to large populations and maintain a sustainable system. Solutions using electronic health records (EHRs) that include predictive models for the risk of disease onset and deterioration enable the care provider to better identify and treat patients with chronic disease and provide personalized prevention. These tools are well-accepted by doctors and have been proven to improve health outcomes and reduce costs. Integrated telecare programs were implemented for comorbid patients showing improved clinical outcomes self-management and quality of life (QoL). However, different patient populations benefit in different ways from these care plans, and thus, continuous evaluation, service adaptation in a real-life environment set with clear outcome measures, is required for best results. The challenge of the PHS today is to acquire patient-generated data (PGD) and behavioral and patient-reported outcomes (PROs) for PHS development that can be combined with existing clinical data. Some initiatives of healthcare organizations [health maintenance organizations (HMOs)] in Israel demonstrate how this goal can be achieved with relatively small efforts by using a stepwise and agile approach to service implementation that improve service by enabling adoption and adaptation of the service in the short term while collecting data for advanced PHS development in the long term. This approach, combined with programs and incentive payments at the national level, creates an environment and infrastructure for collaboration between healthcare, academia, and industry for research, development, and implementation of future PHS. This article presents examples of PHS development and implementation from the Israeli healthcare system. We discuss the lessons learned and suggest new approaches for research, development implementation, and evaluation of PHS that will address the needs of future healthcare.

Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study.

Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson's disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson's patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient's activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician's assessment and the sensor output was analyzed with the Spearman's correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33-0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76-0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.

A "HOLTER" for Parkinson's disease: Validation of the ability to detect on-off states using the REMPARK system.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. OBJECTIVE: To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. METHODS: Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3days and completed a diary of their motor state once every hour. RESULTS: The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). CONCLUSION: The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Seasonality of month of birth of patients with Graves' and Hashimoto's diseases differ from that in the general population.

OBJECTIVE: We aimed to test the viral hypothesis in the pathogenesis of autoimmune thyroid disease (AITD). DESIGN: We determined the pattern of month of birth (MOB) distribution in patients with AITD and in the general population and searched for differences between them. METHODS: A total of 1023 patients were included in this study; 359 patients had Graves' hyperthyroidism (GrH) and 664 had Hashimoto's hypothyroidism (HH). We divided the patients with HH into three subgroups according to their thyroid peroxidase (TPO) antibody titers at diagnosis: low levels (<500 IU/ml), high levels (500-1000 IU/ml), and extremely high levels (>1000 IU/ml). We used cosinor analysis to analyze the data. RESULTS: Overall, patients with GrH and HH had a different pattern of MOB distribution when compared with the general population and between groups. Furthermore, among both patients with GrH and HH, both genders had a different pattern of MOB distribution when compared with the general population and this pattern was also different between genders. Finally, only women with extremely high titers of TPO antibodies at diagnosis and men with low or extremely high TPO antibody levels showed rhythmicity in MOB, with a pattern of MOB distribution different from that in controls. CONCLUSIONS: The different MOB seasonality in both GrH and HH points towards a similar maybe even common etiology with type 1 diabetes mellitus and multiple sclerosis, namely a seasonal viral infection as the initial trigger in the perinatal period, the clinical disease resulting from further specific damage over time.

Possible linkage between the ability to change the period (tau) of the prolactin and cortisol rhythms in women and breast cancer risk.

The 24 h profiles of plasma hormone concentrations are rhythmic. The circadian period (tau) changes in development, with seasons, and in women with different stages of the menstrual cycle. It is known that the rhythms of prolactin and cortisol are sensitive to environmental time cues, such as changes in day length and phase; however, the importance of these changes is not yet understood. This study investigates whether there is a relation between the ability of a subject to respond to external cues that are associated with seasonal changes causing alteration of the rhythm's periods in cortisol and prolactin and the epidemiologically determined susceptibility to breast cancer. It is shown that the rhythmic output pattern of prolactin and cortisol in vivo is generated by more than one oscillator and structured by more than one rhythmic component. Each cohort of American women, classified on an epidemiologic basis as high risk (HR) or low risk (LR) to develop breast cancer, expresses different rhythmic output patterns of both variables, suggesting that the genetic background as defined by the risk state is related to differences in the circadian time structure, including the ability of the subject to change the rhythm's tau. The LR cohort exhibited a statistically significant change between seasons in the rhythm's tau of both the prolactin and cortisol patterns. In contrast, the HR cohort showed no change in the rhythm's tau between seasons for prolactin and cortisol patterns. These results show that in human beings, the presence of a circannual rhythm in the circadian time structure or the ability to adapt the circadian rhythmic pattern of these variables to external cues, such as seasons, is related to the partly genetically determined risk state to develop breast cancer and may be of importance for human health.

Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

BACKGROUND: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson's (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III). METHOD: Seventy-five patients suffering from Parkinson's disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient's home. Convergence between the algorithm and the scale was evaluated by using the Spearman's correlation coefficient. RESULTS: Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: "axial function, balance, and gait." The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). CONCLUSION: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson's disease and motor fluctuations.

Corrigendum: Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer.

Among Parkinson's disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient's treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

Data-as-a-Service Platform for Delivering Healthy Lifestyle and Preventive Medicine: Concept and Structure of the DAPHNE Project.

BACKGROUND: Overweight and obesity is related to many health problems and diseases. The current obesity epidemic, which is a major health problem, is closely related to a lack of physical activity, high levels of sedentary behavior, and increased energy intake; with evidence to show increasing incidence of these issues in the younger population. Tackling obesity and its comorbid conditions requires a holistic approach encompassing attention on physical activity, healthy diet, and behavioral activation in order to enable and maintain meaningful and long-term weight loss and weight maintenance. OBJECTIVE: The objective of the Data-as-a-Service Platform for Healthy Lifestyle and Preventive Medicine (DAPHNE) project is to develop a breakthrough information communications technology (ICT) platform for tracking health, weight, physical activity, diet, lifestyle, and psychological components within health care systems, whereby the platform and clinical support is linked. METHODS: The DAPHNE platform aims to deliver personalized guidance services for lifestyle management to the citizen/patient by means of (1) advanced sensors and mobile phone apps to acquire and store continuous/real-time data on lifestyle aspects, behavior, and surrounding environment; (2) individual models to monitor their health and fitness status; (3) intelligent data processing for the recognition of behavioral trends; and (4) specific services for personalized guidance on healthy lifestyle and disease prevention. It is well known that weight loss and maintenance of weight loss are particularly difficult. This tool will address some of the issues found with conventional treatment/advice in that it will collect data in real time, thereby reducing reliability issues known with recalling events once they have passed and will also allow adjustment of behavior through timely support and recommendations sent through the platform without the necessity of formal one-to-one visits between patient and clinician. Patient motivation/compliance is a particular issue with conventional weight loss regimes; DAPHNE aims to increase the individuals' awareness of their own behavior and fosters their accountability. RESULTS: The project has been funded and the research work has started. Results for the validation of the different components is due imminently. CONCLUSIONS: In contrast with previous existing solutions, the DAPHNE project tackles the obesity problem from a clinical point of view, designing the different interfaces for its use by patients (adults and children), physicians, and caregivers. A specific design for children and adolescent patients treated for obesity has been followed, guided by pediatric physicians at hospitals in Europe. The final clinical validation of the DAPHNE platform will be carried out in different European hospitals, testing the platform in both adolescents and adults.

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer.

BACKGROUND: After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE: To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS: Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS: Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION: The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

Seasonality of month of birth of children and adolescents with type 1 diabetes mellitus in homogenous and heterogeneous populations.

BACKGROUND: Type 1 childhood-onset diabetes mellitus has a multifactorial origin involving an interplay between genetic and environmental factors. We have previously shown that many children who subsequently develop T1DM have a different seasonality of birth than the total live births of the same population, supporting the hypothesis that perinatal viral infection is a trigger for the autoimmune process of T1DM. OBJECTIVES: To compare the seasonality of children with T1DM in different populations around the world for which data were available. METHODS: We analyzed large cohorts of T1DM patients with a clinical disease onset before age 14 or 18 years. RESULTS: We found a seasonality pattern only in ethnically homogenous populations (such as Ashkenazi Jews, Israeli Arabs, individuals in Sardinia and Canterbury, New Zealand, and Afro-Americans) but not in heterogeneous populations (such as in Sydney, Pittsburgh and Denver). CONCLUSIONS: Our findings attempt to explain the controversial data in the literature by showing that ethnically heterogeneous populations comprising a mixture of patients with various genetic backgrounds and environmental exposures mask the different seasonality pattern of month of birth that many children with diabetes present when compared to the general population.

Wearable technologies - future challenges for implementation in healthcare services.

The growing use of wearable technologies increases the ability to have more information from the patient including clinical, behavioural and self-monitored data. The availability and large amounts of data that did not exist before brings an opportunity to develop new tools with intelligent analyses and decision support tools for use in clinical practice. It also opens new possibilities for the patients by providing them with more information and decision support tools specially designed for them, and empowers them in managing their own health conditions, keeping their autonomy. These new developments drive a change in healthcare delivery models and the relationship between patients and healthcare providers. It raises challenges for the healthcare systems in how to implement these new technologies and the growing amount of information in clinical practice, integrate it into the clinical workflows of the various healthcare providers. The future challenge for healthcare will be how to use the developing knowledge in a way that will bring added value to healthcare professionals, healthcare organisations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge is to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints.

Gonadotropin releasing hormone (GnRH) and estradiol (E(2)) regulation of cell cycle in gonadotrophs.

The number of pituitary cells, their size, hormonal content and release and response to external cues varies between day and night and during the estrus cycle. Previous studies have demonstrated that pituitary cells proliferate rhythmically and that estradiol (E(2)) is a mitogen of alpha T3 cells. We, therefore, studied the effect of gonadotropin releasing hormone (GnRH) and E(2), on the cell cycle in primary cultures of mouse pituitary cells and in the gonadotroph cell line L beta T2. We found that GnRH and E(2) modulate the cell cycle in a time dependent manner and induce proliferation in cultures of mouse pituitary and L beta T2 cells. GnRH induces proliferation in cells isolated in the morning of the estrus day and increases the number of cells in G2 stage when isolated in noon and evening. However, the transition into the G1 stage is enabled only by co-addition of E(2) and GnRH. GnRH stimulates LH release from L beta T2 cells after 2 days via exocytosis while after 4 days in culture, the increase in LH release may be accounted for by the increase in cell number. E(2) enhanced the GnRH response after 2 days, and abolished it after 4 days in culture. Furthermore, E(2) has no effect on LH release and cell number after 2 days in culture, however, after 4 days in culture, E(2) had no effect on the total amount of LH released but inhibited LH release per cell due to increase in cell number. Our results show that GnRH and E(2) function to shorten the cell cycle and regulate the cell number of each stage of the cell cycle. The effect of GnRH and E(2) on the cell cycle is dependent on the circadian time. This mechanism may serve to modulate the size and function of the pituitary cell population and consequently the function of pituitary gonadotrophs regulating the surge of LH release before ovulation.

Seasonality of birth and onset of clinical disease in children and adolescents (0-19 years) with type 1 diabetes mellitus in Canterbury, New Zealand.

AIMS: To determine the seasonality of clinical disease onset and month of birth in type 1 diabetes mellitus (DM) in the southern hemisphere. PATIENTS: Two hundred and seventy-five children with type 1 DM in the South Island of New Zealand were studied. The total live births (91,394) of the same period were used as control data. METHODS: Seasonal rhythms were analyzed using the 12 month cosinor method. RESULTS: The month of birth pattern of the patients with DM showed a statistically significant peak (p < 0.01) in summer, whereas the disease onset had a significant peak in winter (p < 0.01), similar to that registered in countries of the northern hemisphere, but in different months of the year. The total live births had no significant rhythm. The different seasonality of birth of the children who subsequently developed type 1 DM from that of the total live births is suggestive of the initiation of the autoimmune process in utero or perinatally.

Differences between males and females in the seasonality of birth and month of clinical onset of disease in children with type 1 diabetes mellitus in Ireland.

The aim was to study the monthly rhythm of birth and clinical onset in 303 children with type 1 diabetes mellitus (DM1) aged 0-15 years (156 males, 147 females) born between 1980 and 1996 in Ireland and compare to 951,717 infants born in the general population during the same period. Analysis was performed using the cosine fit for rhythm and t-test between the seasons of the year. Whereas the males showed a rhythmic pattern of month of birth, peaking in the summer (p < 0.05), similar to that in the general population, the females showed no seasonal differences in either month of birth or month of onset. A mirror image pattern, nadir in spring and summer (p < 0.01), was observed in month of clinical onset, also only in males. If we assume a viral infectious etiology of DMI, females seem to be less susceptible than males to the environmental infectious influences.

A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system.

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.