Exposomic determinants of immune-mediated diseases: Special focus on type 1 diabetes, celiac disease, asthma, and allergies: The HEDIMED project approach.

The incidence of immune-mediated diseases (IMDs) is increasing rapidly in the developed countries constituting a huge medical, economic, and societal challenge. The exposome plays an important role since genetic factors cannot explain such a rapid change. In the Human Exposomic Determinants of Immune Mediated Diseases (HEDIMED) project, altogether 22 academic and industrial partners join their multidisciplinary forces to identify exposomic determinants that are driving the IMD epidemic. The project is based on a combination of data and biological samples from large clinical cohorts constituting about 350,000 pregnant women, 30,000 children prospectively followed from birth, and 7,000 children from cross-sectional studies. HEDIMED focuses on common chronic IMDs that cause a significant disease burden, including type 1 diabetes, celiac disease, allergy, and asthma. Exposomic disease determinants and the underlying biological pathways will be identified by an exploratory approach using advanced omics and multiplex technologies combined with cutting-edge data mining technologies. Emphasis is put on fetal and childhood exposome since the IMD disease processes start early. Inclusion of several IMDs makes it possible to identify common exposomic determinants for the diseases, thus facilitating the development of widely operating preventive and curative treatments. HEDIMED includes data and samples from birth cohorts and clinical trials that have used exposomic interventions and cell and organ culture models to identify mechanisms of the observed associations. Importantly, HEDIMED generates a toolbox that offers science-based functional tools for key stakeholders to control the IMD epidemic. Altogether, HEDIMED aims at innovations, which become widely exploited in diagnostic, therapeutic, preventive, and health economic approaches.

Neighbourhood and path-based greenspace in three European countries: associations with objective physical activity.

BACKGROUND: Greenspace has been associated with health benefits in many contexts. An important pathway may be through outdoor physical activity. We use a novel approach to examine the link between greenspace microenvironments and outdoor physical activity levels in the HEALS study conducted in Edinburgh (UK), the Netherlands, and Athens and Thessaloniki (Greece). METHODS: Using physical activity tracker recordings, 118 HEALS participants with young children were classified with regard to daily minutes of moderate to vigorous physical activity (MVPA); 60 were classified with regard to the metabolic equivalent task (MET)-minutes for each of the 1014 active trips they made. Greenspace indicators were generated for Normalised Difference Vegetation Index (NDVI), tree cover density (TCD), and green land use (GLU). We employed linear mixed-effects models to analyse (1) daily MVPA in relation to greenspace within 300 m and 1000 m of residential addresses and (2) trip MET-minutes in relation to average greenspace within a 50 m buffer of walking/cycling routes. Models were adjusted for activity, walkability, bluespace, age, sex, car ownership, dog ownership, season, weekday/weekend day, and local meteorology. RESULTS: There was no clear association between MVPA-minutes and any residential greenspace measure. For example, in fully adjusted models, a 10 percentage point increase in NDVI within 300 m of home was associated with a daily increase of 1.14 (95% CI - 0.41 to 2.70) minutes of MVPA. However, we did find evidence to indicate greenspace markers were positively linked to intensity and duration of activity: in fully adjusted models, 10 percentage point increases in trip NDVI, TCD, and GLU were associated with increases of 10.4 (95% CI: 4.43 to 16.4), 10.6 (95% CI: 4.96 to 16.3), and 3.36 (95% CI: 0.00 to 6.72) MET-minutes, respectively. The magnitude of associations with greenspace tended to be greater for cycling. CONCLUSIONS: More strenuous or longer walking and cycling trips occurred in environments with more greenspace, but levels of residential greenspace did not have a clear link with outdoor MVPA. To build on our research, we suggest future work examine larger, more diverse populations and investigate the influence of greenspace for trip purpose and route preference.

Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort.

BACKGROUND: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer's disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer's disease pathology. OBJECTIVE: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease, and 3) predict cognitive decline. METHODS: Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5) in 58 CN, 58 MCI, and 26 dementia participants, while performing a walk and dual task. Ten gait features were calculated from the vertical L5 accelerations, following principal component analysis clustered in four domains, namely pace, rhythm, time variability, and length variability. Cognitive decline over time was measured using MMSE, and CSF biomarkers were available in a sub-group. RESULTS: Linear mixed models showed that dementia patients had lower pace scores than MCI patients and CN subjects (p < 0.05). In addition, we found associations between the rhythm domain and CSF-tau, especially in the dual task. Gait was not associated with CSF Aß42 levels and cognitive decline over time as measured with the MMSE. CONCLUSION: These findings suggest that gait - particularly measures related to pace and rhythm - are altered in dementia and have a direct link with measures of neurodegeneration.

Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance.

BACKGROUND/AIM: The exposome includes urban greenspace, which may affect health via a complex set of pathways, including reducing exposure to particulate matter (PM) and noise. We assessed these pathways using indoor exposure monitoring data from the HEALS study in four European urban areas (Edinburgh, UK; Utrecht, Netherlands; Athens and Thessaloniki, Greece). METHODS: We quantified three metrics of residential greenspace at 50 m and 100 m buffers: Normalised Difference Vegetation Index (NDVI), annual tree cover density, and surrounding green land use. NDVI values were generated for both summer and the season during which the monitoring took place. Indoor PM2.5 and noise levels were measured by Dylos and Netatmo sensors, respectively, and subjective noise annoyance was collected by questionnaire on an 11-point scale. We used random-effects generalised least squares regression models to assess associations between greenspace and indoor PM2.5 and noise, and an ordinal logistic regression to model the relationship between greenspace and road noise annoyance. RESULTS: We identified a significant inverse relationship between summer NDVI and indoor PM2.5 (-1.27 µg/m3 per 0.1 unit increase [95% CI -2.38 to -0.15]) using a 100 m residential buffer. Reduced (i.e., <1.0) odds ratios (OR) of road noise annoyance were associated with increasing summer (OR = 0.55 [0.31 to 0.98]) and season-specific (OR = 0.55 [0.32 to 0.94]) NDVI levels, and tree cover density (OR = 0.54 [0.31 to 0.93] per 10 percentage point increase), also at a 100 m buffer. In contrast to these findings, we did not identify any significant associations between greenspace and indoor noise in fully adjusted models. CONCLUSIONS: We identified reduced indoor levels of PM2.5 and noise annoyance, but not overall noise, with increasing outdoor levels of certain greenspace indicators. To corroborate our findings, future research should examine the effect of enhanced temporal resolution of greenspace metrics during different seasons, characterise the configuration and composition of green areas, and explore mechanisms through mediation modelling.

Influence of residential land cover on childhood allergic and respiratory symptoms and diseases: Evidence from 9 European cohorts.

INTRODUCTION: Recent research focused on the interaction between land cover and the development of allergic and respiratory disease has provided conflicting results and the underlying mechanisms are not fully understood. In particular, green space, which confers an overall positive impact on general health, may be significantly contributing to adverse respiratory health outcomes. This study evaluates associations between surrounding residential land cover (green, grey, agricultural and blue space), including type of forest cover (deciduous, coniferous and mixed), and childhood allergic and respiratory diseases. METHODS: Data from 8063 children, aged 3-14 years, were obtained from nine European population-based studies participating in the HEALS project. Land-cover exposures within a 500 m buffer centred on each child's residential address were computed using data from the Coordination of Information on the Environment (CORINE) program. The associations of allergic and respiratory symptoms (wheeze, asthma, allergic rhinitis and eczema) with land coverage were estimated for each study using logistic regression models, adjusted for sex, age, body mass index, maternal education, parental smoking, and parental history of allergy. Finally, the pooled effects across studies were estimated using meta-analyses. RESULTS: In the pooled analyses, a 10% increase in green space coverage was significantly associated with a 5.9%-13.0% increase in the odds of wheezing, asthma, and allergic rhinitis, but not eczema. A trend of an inverse relationship between agricultural space and respiratory symptoms was observed, but did not reach statistical significance. In secondary analyses, children living in areas with surrounding coniferous forests had significantly greater odds of reporting wheezing, asthma and allergic rhinitis. CONCLUSION: Our results provide further evidence that exposure to green space is associated with increased respiratory disease in children. Additionally, our findings suggest that coniferous forests might be associated with wheezing, asthma and allergic rhinitis. Additional studies evaluating both the type of green space and its use in relation to respiratory conditions should be conducted in order to clarify the underlying mechanisms behind associated adverse impacts.

Improvement of force-sensor-based heart rate estimation using multichannel data fusion.

The aim of this paper is to present and evaluate algorithms for heartbeat interval estimation from multiple spatially distributed force sensors integrated into a bed. Moreover, the benefit of using multichannel systems as opposed to a single sensor is investigated. While it might seem intuitive that multiple channels are superior to a single channel, the main challenge lies in finding suitable methods to actually leverage this potential. To this end, two algorithms for heart rate estimation from multichannel vibration signals are presented and compared against a single-channel sensing solution. The first method operates by analyzing the cepstrum computed from the average spectra of the individual channels, while the second method applies Bayesian fusion to three interval estimators, such as the autocorrelation, which are applied to each channel. This evaluation is based on 28 night-long sleep lab recordings during which an eight-channel polyvinylidene fluoride-based sensor array was used to acquire cardiac vibration signals. The recruited patients suffered from different sleep disorders of varying severity. From the sensor array data, a virtual single-channel signal was also derived for comparison by averaging the channels. The single-channel results achieved a beat-to-beat interval error of 2.2% with a coverage (i.e., percentage of the recording which could be analyzed) of 68.7%. In comparison, the best multichannel results attained a mean error and coverage of 1.0% and 81.0%, respectively. These results present statistically significant improvements of both metrics over the single-channel results (p < 0.05).

A personalized approach for predicting the effect of aerobic exercise on blood pressure using a Fuzzy Inference System.

Regular aerobic exercise is a recommended treatment for elevated blood pressure (BP). However, making permanent lifestyle changes is not easy. Having personally relevant information about the treatment, about its effects and importance, is a precondition for motivation. Thus, the first step towards a successful lifestyle change is appropriate education. This paper describes a Sugeno-type Fuzzy Inference System (FIS) that predicts the effect of regular aerobic exercise on blood pressure based on the exercise dose variables, exercise frequency and intensity, as well as demographics (age, gender, ethnicity), and the baseline BP of a person. Since BP response to exercise varies largely between individuals, the system takes an initial step towards personalized prediction. Hence, the system can be used to educate a person about the benefits of exercise on BP in a personally relevant way, providing more accurate information than traditional education materials. Furthermore, preliminary validation results of the performance of the FIS are promising. The predictions comply with the findings of medical research for populations, though the individual-level validation remains still to be done.

Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree.

Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

Use of a mobile phone diary for observing weight management and related behaviours.

We studied self-observations related to weight management recorded with a Wellness Diary application on a mobile phone. The data were recorded by 27 participants in a 12-week study, which included a short weight management lecture followed by independent usage of the Wellness Diary. We studied the validity of self-observed weight, and behavioural changes and weight patterns related to weight management success. Self-observed weight data tended to underestimate pre- and poststudy measurements, but there were high correlations between the measures (r >or= 0.80). The amount of physical activity correlated significantly with weight loss (r = 0.44) as did different measures representing healthy changes in dietary behaviours (r >or= 0.45). Weight changes and the weekly rhythms of weight indicated a strong tendency to compensate for high-risk periods among successful weight-losers compared to unsuccessful ones. These preliminary results suggest that the mobile phone diary is a valid tool for observing weight management and related behaviours.

Empowering citizens for well-being and chronic disease management with wellness diary.

Chronic conditions closely related to lifestyles are the major cause of disability and death in the developed world. Behavior change is the key to managing well-being and preventing and managing chronic diseases. Wellness diary (WD) is a mobile application designed to support citizens in learning about their behavior, and both making and maintaining behavior changes. WD has been found acceptable, useful, and suitable for long-term use as a part of an intervention. When used independently, however, it does not seem to have enough engaging and motivating features to support adoption and long-term commitment. The main improvement needs identified based on a review of WD-related studies were: personalization of the application to individual needs, increasing motivation during early use, maintaining motivation, and aiding in relapse recovery in long-term use. We present concepts to improve the personalization of WD as well as improvements to the feedback and interpretation of the self-observation data. We also present usage models on how this type of mobile application could be utilized.

Compliance and technical feasibility of long-term health monitoring with wearable and ambient technologies.

We developed a system consisting of both wearable and ambient technologies designed to monitor personal wellbeing for several months during daily life. The variables monitored included bodyweight, blood pressure, heart-rate variability and air temperature. Two different user groups were studied: there were 17 working-age subjects participating in a vocational rehabilitation programme and 19 elderly people living in an assisted living facility. The working-age subjects collected data for a total of 1406 days; the average participation period was 83 days (range 43-99). The elderly subjects collected data for a total of 1593 days; the average participation period was 84 days (range 19-107). Usage, technical feasibility and usability of the system were also studied. Some technical and practical problems appeared which we had not expected such as thunder storm damage to equipment in homes and scheduling differences between staff and the subjects. The users gave positive feedback in almost all their responses in a questionnaire. The study suggests that the data-collection rate is likely be 70-90% for typical health monitoring data.

Relationship of psychological and physiological variables in long-term self-monitored data during work ability rehabilitation program.

Individual wellness comprises both psychological and physiological wellbeing, which are interrelated. In long-term monitoring of wellness, both components should be included. Work-related stress and burnout are persistent problems in industrial countries. Early identification of work-related stress symptoms and early intervention could reduce individual suffering and improve the working productivity and creativity. The goal of this study was to explore the relationship between physiological and psychological variables measured at home by the users themselves or automatically. In all, 17 (3 males and 14 females, age 40-62) people participating in a work ability rehabilitation program (due to work overload) were monitored for three months. Physiological and behavioral variables (activity, bed occupancy, heart rate (HR) and respiration during night, HR during day, blood pressure, steps, weight, room illumination, and temperature) were measured with different unobtrusive wireless sensors. Daily self-assessment of stress, mood, and behaviors (exercise, sleep) were collected using a mobile phone diary. The daily self-assessment of stress and the Derogatis stress profile questionnaire were used as reference for stress status. Results show modest, but significant pooled overall correlations between self-assessed stress level, and physiological and behavioral variables (e.g., sleep length measured with wrist-worn activity monitor: rho = -0.22, p < 0.001, and variance of nightly bedroom illumination: rho = 0.13, p < 0.001). Strong, but sometimes conflicting correlations can be found at individual level, suggesting individual reactions to stress in daily life.

Mobile diary for wellness management--results on usage and usability in two user studies.

The prevalence of lifestyle-related health problems is increasing rapidly. Many of the diseases and health risks could be prevented or alleviated by making changes toward healthier lifestyles. We have developed the Wellness Diary (WD), a concept for personal and mobile wellness management based on Cognitive-Behavioral Therapy (CBT). Two implementations of the concept were made for the Symbian Series 60 (S60) mobile phone platform, and their usability, usage, and acceptance were studied in two 3-month user studies. Study I was related to weight management and study II to general wellness management. In both the studies, the concept and its implementations were well accepted and considered as easy to use and useful in wellness management. The usage rate of the WD was high and sustained at a high level throughout the study. The average number of entries made per day was 5.32 (SD = 2.59, range = 0-14) in study I, and 5.48 (SD = 2.60, range = 0-17) in study II. The results indicate that the WD is well suited for supporting CBT-based wellness management.

A computationally light classification method for mobile wellness platforms.

The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.

Advancing from offline to online activity recognition with wearable sensors.

Activity recognition with wearable sensors could motivate people to perform a variety of different sports and other physical exercises. We have earlier developed algorithms for offline analysis of activity data collected with wearable sensors. In this paper, we present our current progress in advancing the platform for the existing algorithms to an online version, onto a PDA. Acceleration data are obtained from wireless motion bands which send the 3D raw acceleration signals via a Bluetooth link to the PDA which then performs the data collection, feature extraction and activity classification. As a proof-of-concept, the online activity system was tested with three subjects. All of them performed at least 5 minutes of each of the following activities: lying, sitting, standing, walking, running and cycling with an exercise bike. The average second-by-second classification accuracies for the subjects were 99%, 97%, and 82 %. These results suggest that earlier developed offline analysis methods for the acceleration data obtained from wearable sensors can be successfully implemented in an online activity recognition application.

Long-term subjective and objective sleep analysis of total sleep time and sleep quality in real life settings.

Sleep quality is one of the key elements of the human health status. By observing sleep patterns we can gain information about personal wellbeing. Consumer electronic sleep analysis solutions are now available for use in long-term conditions. In this study we compare different measures for total sleep time and sleep quality. We analyzed visually long- term sleep data collected with actigraphy, sleep logs and ambient sensors to gain more reliable results and compared these results to each single method's output. Correlations of visually analyzed total sleep time between actigraphy total sleep time (correlation coefficient (r) = 0.662, p <0.01) and sleep log total sleep time (r = 0.787, p <0.01) were high. Also comparison between subjective and objective sleep quality was analyzed and small, but significant correlation was found (r = 0.270, p < 0.01).

Activity classification using realistic data from wearable sensors.

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

Application of near field communication for health monitoring in daily life.

We study the possibility of applying an emerging RFID-based communication technology, NFC (Near Field Communication), to health monitoring. We suggest that NFC is, compared to other competing technologies, a high-potential technology for short-range connectivity between health monitoring devices and mobile terminals. We propose practices to apply NFC to some health monitoring applications and study the benefits that are attainable with NFC. We compare NFC to other short-range communication technologies such as Bluetooth and IrDA, and study the possibility of improving the usability of health monitoring devices with NFC. We also introduce a research platform for technical evaluation, applicability study and application demonstrations of NFC.

Long-term self-monitoring of weight: a case study.

The aim of this study was to examine the self-monitoring of weight on a daily basis over a long period of time in order to understand the process of weight regain. An obese female client measured her weight every morning over a period of 10 years. The subject made a total of 2081 weight measurements. Thus, her weight was measured on 67% of all possible days. After the initial weight loss a pattern of gradual weight gain was observed. The subject gained weight during August and September, and at the end of December, in particular. Furthermore, her weight increased slightly at the weekends. This case study highlights the advantage of self-monitoring of weight on a daily basis. Individual patterns of weight change possibly associated with season and weekly variation may be crucial when obese subjects try to maintain their weight after weight loss. However, it may take several months or even years to detect the weekly and yearly rhythms or other patterns in the data. Thus, self-control of weight is problematic, since patterns in the weight regain process are difficult to detect. This may be one reason why self-control of weight is so difficult.