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BACKGROUND The use of mobile phone apps and connected wearable devices offers a great opportunity for health research. In order make healthcare data accessible across organisations, physicians and patients, recent platforms like Apple HealthKit, Google Fit or Samsung Health allow to securely store and share health data among smartphone apps with the consent of the user. These mobile health records can be combined with software platforms, like ResearchKit and ResearchStack to simplify the development of research apps by providing ready-made common use cases while being compliant to regulations in data protection and health research. Even though a plethora of such platforms exist, none of them can be considered as a widely established tool yet. OBJECTIVE To provide recommendations for new platforms through the analysis of the limitations posed by existing platforms and by identifying common needs and established practices in mHealth development. METHODS We first analyse the state of the art of mobile-health development in research settings, including existing tools and frameworks to support it. To complement the scant literature, we disseminated a survey among mobile-health researchers and developers to understand best practices, what type of tools are used and which would be desirable. RESULTS Related to current practices and unmet needs in mHealth development we identify the following themes: a) costs and resources, b) usability and context awareness, c) middleware and software architectures, d) multi-platform support, e) mobile connectivity, f) reliability and testing, g) data protection and regulatory compliance and h) interoperability. In relation to existing platforms we identified these common shortcomings: 1) poor regulatory compliance, 2) lack of documentation, 3) low maturity, 4) poor usability (from the developer’s perspective) and intuitiveness. Our survey received 28 very varied responses, which identify a core of concerns shared for most of the reported projects and not entirely met by the current offer of development platforms. Based on these results, we provide recommendations for future mobile-health platforms, particularly focusing on multi-operating system support, integration with existing health records, regulatory compliance, involvement of stakeholders, modularity, extensibility and overall quality of the code base. CONCLUSIONS Our recommendations intend to guide the development of a platform to be used as a common tool for future research in mobile-health.

Introduction Home-based programmes for cardiac rehabilitation play a key role in the recovery of patients with coronary artery disease. However, their necessary educational and motivational components have been rarely implemented with the help of modern mobile technologies. We developed a mobile health system designed for motivating patients to adhere to their rehabilitation programme by providing exercise monitoring, guidance, motivational feedback, and educational content. Methods Our multi-disciplinary approach is based on mapping “desired behaviours” into specific system’s specifications, borrowing concepts from Fogg’s Persuasive Systems Design principles. A randomised controlled trial was conducted to compare mobile-based rehabilitation (55 patients) versus standard care (63 patients). Results Some technical issues related to connectivity, usability and exercise sessions interrupted by safety algorithms affected the trial. For those who completed the rehabilitation (19 of 55), results show high levels of both user acceptance and perceived usefulness. Adherence in terms of started exercise sessions was high, but not in terms of total time of performed exercise or drop-outs. Educational level about heart-related health improved more in the intervention group than the control. Exercise habits at 6 months follow-up also improved, although without statistical significance. Discussion Results indicate that the adopted design methodology is promising for creating applications that help improve education and foster better exercise habits, but further studies would be needed to confirm these indications.

Commercial wearable devices and fitness trackers are commonly sold as black boxes of which little is known about their accuracy. This poses serious issues especially in health-related contexts such as clinical research, where transparency about accuracy and reliability are paramount. We present a validated algorithm for computing step counting that is optimised for use in constrained computing environments. Released as open source, the algorithm is based on the windowed peak detection approach, which has previously shown high accuracy on smartphones. The algorithm is optimised to run on a programmable smartwatch (Pine Time) and tested on 10 subjects in 8 scenarios, with varying varying positions of the wearable and walking paces. Our approach achieves a 89% average accuracy, with the highest average accuracy when walking outdoor (98%) and the lowest in a slow-walk scenario (77%). This result can be compared with the built-in step counter of the smartwatch (Bosch BMA421), which yielded a 94% average accuracy for the same use cases. Our work thus shows that an open-source approach for extracting physical activity data from wearable devices is possible and achieves an accuracy comparable to the one produced by proprietary embedded algorithms.

Mobile phones, wearable trackers and Internet of Things devices continuously produce data about our health and lifestyle that can be used for medical research. However, how data is accessed, by whom and for what purpose is not always understood. This lack of transparency undermines citizens trust in the use of such technologies for research purposes. This paper proposes a set of 6 use cases and related mock-up interfaces for citizen science, mobile-based health research: “Curated information about the institution”, “Sequential consent of shared data”, “Updates from the institution”, “Privacy notifications”, “Overview of donated data” and “Personal impact in medical research”. Interviews and Kano analysis of the interfaces with 6 prospective users show that all except “Privacy notifications” are perceived as important and beneficial for increasing users’ trust. The defined use cases can guide the development of future data collection platforms.

Background The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs. Objective This study aimed to develop a mobile phone–based system that allows patients to perform the 6MWT in the community. Methods We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion. Results The mean difference between the mobile phone-estimated distances and the reference values was −2.013 m (SD 7.84 m) for the indoor algorithm and −0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term. Conclusions The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient’s health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value.

Collecting data from smartphones can provide useful information for health research, but developing mobile health (mHealth) apps requires extensive resources and implies often overlooked regulatory and privacy-related requirements. Mobile health "aggregators" like HealthKit or Google Fit and specialised platforms may allow reducing costs in mHealth research. Current platforms, however, do not efficiently exploit mHealth aggregators and provide little attention to regulations. We propose an open-source platform that can be used by health researchers without the need to develop custom apps. The platform has a strong focus on regulatory compliance, patient consent and transparency, and allows collecting data through electronic surveys and querying aggregators. We tested the feasibility of our platform in a pilot study with 18 healthy volunteers. The results show that the participants' app is usable and well-accepted and is able to frequently collect data about physical activity from both phones and connected wearables. Some limitations were identified regarding data loss because of insufficient connectivity and the impossibility of extracting data from wearables that are not compatible with aggregators.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple’s ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study’s launch and the time of the data freeze for this data release (March 10 2015–October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices., Design Type(s)observation design · source-based data analysis objective · data collection and processing objectiveMeasurement Type(s)physical activity · sleepTechnology Type(s)crowd-sourced data generationFactor Type(s)sex · height · weight · age · smoking status measurement · employment statusSample Characteristic(s)Homo sapiens · United States of America Machine-accessible metadata file describing the reported data (ISA-Tab format)

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

BACKGROUND The 6-min walk test (6MWT) is a convenient method for assessing functional capacity in patients with cardiopulmonary conditions. It is usually performed in the context of a hospital clinic and thus requires the involvement of hospital staff and facilities, with their associated costs. OBJECTIVE This study aimed to develop a mobile phone–based system that allows patients to perform the 6MWT in the community. METHODS We developed 2 algorithms to compute the distance walked during a 6MWT using sensors embedded in a mobile phone. One algorithm makes use of the global positioning system to track the location of the phone when outdoors and hence computes the distance travelled. The other algorithm is meant to be used indoors and exploits the inertial sensors built into the phone to detect U-turns when patients walk back and forth along a corridor of fixed length. We included these algorithms in a mobile phone app, integrated with wireless pulse oximeters and a back-end server. We performed Bland-Altman analysis of the difference between the distances estimated by the phone and by a reference trundle wheel on 49 indoor tests and 30 outdoor tests, with 11 different mobile phones (both Apple iOS and Google Android operating systems). We also assessed usability aspects related to the app in a discussion group with patients and clinicians using a technology acceptance model to guide discussion. RESULTS The mean difference between the mobile phone-estimated distances and the reference values was −2.013 m (SD 7.84 m) for the indoor algorithm and −0.80 m (SD 18.56 m) for the outdoor algorithm. The absolute maximum difference was, in both cases, below the clinically significant threshold. A total of 2 pulmonary hypertension patients, 1 cardiologist, 2 physiologists, and 1 nurse took part in the discussion group, where issues arising from the use of the 6MWT in hospital were identified. The app was demonstrated to be usable, and the 2 patients were keen to use it in the long term. CONCLUSIONS The system described in this paper allows patients to perform the 6MWT at a place of their convenience. In addition, the use of pulse oximetry allows more information to be generated about the patient’s health status and, possibly, be more relevant to the real-life impact of their condition. Preliminary assessment has shown that the developed 6MWT app is highly accurate and well accepted by its users. Further tests are needed to assess its clinical value.

Background: Blood pressure (BP) is a key modifiable risk factor for patients with chronic kidney disease (CKD), with current guidelines recommending strict control to reduce the risk of progression of both CKD and cardiovascular disease. Trials involving BP lowering require multiple visits to achieve target BP, which increases the costs of such trials, and in routine care, BP measured in the clinic may not accurately reflect the usual BP. Objective: We sought to assess whether a telemonitoring system for BP (using a Bluetooth-enabled BP machine that could transmit BP measurements to a tablet device installed with a bespoke app to guide the measurement of BP and collect questionnaire data) was acceptable to patients with CKD and whether patients would provide sufficient BP readings to assess variability and guide treatment. Methods: A total of 25 participants with CKD were trained to use the telemonitoring equipment and asked to record BP daily for 30 days, attend a study visit, and then record BP on alternate days for the next 60 days. They were also offered a wrist-worn applanation tonometry device (BPro) which measures BP every 15 minutes over a 24-hour period. Participants were given questionnaires at the 1- and 3-month time points; the questionnaires were derived from the System Usability Scale and Technology Acceptance Model. All eligible participants completed the study. Results: Mean participant age was 58 (SD 11) years, and mean estimated glomerular filtration rate was 36 (SD 13) mL/min/1.73m2. 13/25 (52%) participants provided >90% of the expected data and 18/25 (72%) provided >80% of the expected data. The usability of the telemonitoring system was rated highly, with mean scores of 84.9/100 (SE 2.8) after 30 days and 84.2/100 (SE 4.1) after 90 days. The coefficient of variation for the variability of systolic BP telemonitoring was 9.4% (95% CI 7.8-10.9) compared with 7.9% (95% CI 6.4-9.5) for the BPro device, P=.05 (and was 9.0% over 1 year in a recently completed trial with identical eligibility criteria), indicating that most variation in BP was short term. Conclusions: Telemonitoring is acceptable for patients with CKD and provides sufficient data to inform titration of antihypertensive therapies in either a randomized trial setting (comparing BP among different targets) or routine clinical practice. Such methods could be employed in both scenarios and reduce costs currently associated with such activities. Trial Registration: International Standard Randomized Controlled Trial Number ISRCTN13725286; http://www.isrctn.com/ISRCTN13725286 (Archived by WebCite at http://www.webcitation.org/74PAX51Ji).

Hypertension affects 1 in 10 pregnancies, often persisting postpartum, when antihypertensive requirements may vary substantially. This unmasked, randomized controlled trial evaluated the feasibility and effects on blood pressure (BP) of self-management of postpartum hypertension. Women with gestational hypertension or preeclampsia, requiring postnatal antihypertensive treatment, were randomized to self-management or usual care. Self-management entailed daily home BP monitoring and automated medication reduction via telemonitoring. Women attended 5 follow-up visits during 6 months. The primary outcome was feasibility: specifically recruitment, retention, and compliance with follow-up rates. Secondary outcomes included BP control and safety, analyzed on an intention-to-treat basis. Forty-nine percent (91/186) of those women approached were randomized (45 intervention, 46 control), and 90% (82/91) finished follow-up. The groups had similar baseline characteristics. After randomization, BP was lower in the intervention group, most markedly at 6 weeks: intervention group mean (SD), systolic 121.6 (8.7)/diastolic 80.5 (6.6) mm Hg; control group, systolic 126.6 (11.0)/diastolic 86.0 (9.7) mm Hg; adjusted differences (95% confidence interval), systolic −5.2 (−9.3 to −1.2)/diastolic −5.8 (−9.1 to −2.5) mm Hg. Diastolic BP remained significantly lower in those self-managing to 6 months: adjusted difference −4.5 (−8.1 to −0.8) mm Hg. This is the first randomized evaluation of BP self-management postpartum and indicates it would be feasible to trial this intervention in larger studies. Self-management resulted in better diastolic BP control to 6 months, even beyond medication cessation. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT02333240.

Step counting from smart-phones allows a wide range of applications related to fitness and health. Estimating steps from phones' accelerometers is challenging because of the multitude of ways a smart-phone can be carried. We focus our work on the windowed peak detection algorithm, which has previously been shown to be accurate and efficient and thus suitable for mobile devices. We explore and optimise further the algorithm and its parameters making use of data collected by three volunteers holding the phone in six different positions. In order to simplify the analysis of the data, we also built a novel device for the detection of the ground truth steps. Over the collected data set, the algorithm reaches 95% average accuracy. We implemented the algorithm for the Android OS and released it as an open source project. A separate dataset was collected with the algorithm running on the smart-phone for further validation. The validation confirms the accuracy of the algorithm in real-time conditions.

Traditional heart failure markers fail to reliably predict heart-failure related hospitalisations and deaths. Multi-sensor patch data can provide an objective insight into activity and sleep patterns of patients and may therefore improve the performance of current risk-quantification algorithms. This work aimed to establish the feasibility of collecting multi-sensor patch data from heart failure patients and to perform an initial analysis of activity and sleep patterns of heart failure patients in relation to disease severity. 13 heart failure patients from the SUPPORT-HF study were provided with chest-worn multi-sensor patches and asked to wear the devices continuously for up to seven consecutive days. Using a combination of impedance, heart rate and accelerometer data participants' sleep and wakefulness information were extracted and analyzed in relation to self-reported symptom scores. Patch data for eleven patients were of high enough quality to be included in the analysis, accounting for 63 patient days worth of data. The heart failure patients slept for an average of 8.3 hours a night and experienced 2.8 sleep interruptions. Potential differences in sleep angle, heart rate and wake-time activity were found for patients with different heart failure severity. Larger studies are necessary to create a more coherent picture of the potential of activity and sleep as a markers for heart failure deterioration.

Summary Objectives To assess the feasibility in routine primary care consultation and investigate the effect on risk recall and self‐management of a new type of risk communication intervention based on behavioural economics (“nudge‐based”) for people with Type 2 diabetes mellitus (T2DM). Methods Forty adults with poorly controlled T2DM (HbA1c > 7.5%) were randomized to receive a personalized, nudge‐based risk communication intervention (n = 20) or standard care (n = 20). Risk recall and self‐management were evaluated at baseline and 12 weeks after the intervention. Results Both in terms of feasibility and acceptability, this new risk communication intervention was very satisfactory. Study retention rate after 12 weeks was very high (90%) and participants were highly satisfied with the intervention (4.4 out of 5 on the COMRADE scale). Although not powered to identify significant between‐group effects, the intervention significantly improved risk recall after 12 weeks and intentions to make lifestyle changes (dietary behaviour) compared to standard care. Conclusions This pilot study provides the first evidence of the feasibility of implementing in primary care a nudge‐based risk communication intervention for people with T2DM. Based on the promising results observed, an adequately powered trial to determine the effectiveness of the intervention on long‐term self‐management is judged feasible. As a result of this feasibility study, some minor adaptations to the intervention and study methods that would help to facilitate a definitive trial are also reported.

BACKGROUND Blood pressure (BP) is a key modifiable risk factor for patients with chronic kidney disease (CKD), with current guidelines recommending strict control to reduce the risk of progression of both CKD and cardiovascular disease. Trials involving BP lowering require multiple visits to achieve target BP, which increases the costs of such trials, and in routine care, BP measured in the clinic may not accurately reflect the usual BP. OBJECTIVE We sought to assess whether a telemonitoring system for BP (using a Bluetooth-enabled BP machine that could transmit BP measurements to a tablet device installed with a bespoke app to guide the measurement of BP and collect questionnaire data) was acceptable to patients with CKD and whether patients would provide sufficient BP readings to assess variability and guide treatment. METHODS A total of 25 participants with CKD were trained to use the telemonitoring equipment and asked to record BP daily for 30 days, attend a study visit, and then record BP on alternate days for the next 60 days. They were also offered a wrist-worn applanation tonometry device (BPro) which measures BP every 15 minutes over a 24-hour period. Participants were given questionnaires at the 1- and 3-month time points; the questionnaires were derived from the System Usability Scale and Technology Acceptance Model. All eligible participants completed the study. RESULTS Mean participant age was 58 (SD 11) years, and mean estimated glomerular filtration rate was 36 (SD 13) mL/min/1.73m2. 13/25 (52%) participants provided >90% of the expected data and 18/25 (72%) provided >80% of the expected data. The usability of the telemonitoring system was rated highly, with mean scores of 84.9/100 (SE 2.8) after 30 days and 84.2/100 (SE 4.1) after 90 days. The coefficient of variation for the variability of systolic BP telemonitoring was 9.4% (95% CI 7.8-10.9) compared with 7.9% (95% CI 6.4-9.5) for the BPro device, P=.05 (and was 9.0% over 1 year in a recently completed trial with identical eligibility criteria), indicating that most variation in BP was short term. CONCLUSIONS Telemonitoring is acceptable for patients with CKD and provides sufficient data to inform titration of antihypertensive therapies in either a randomized trial setting (comparing BP among different targets) or routine clinical practice. Such methods could be employed in both scenarios and reduce costs currently associated with such activities. CLINICALTRIAL International Standard Randomized Controlled Trial Number ISRCTN13725286; http://www.isrctn.com/ISRCTN13725286 (Archived by WebCite at http://www.webcitation.org/74PAX51Ji).

We describe an evaluation of photoplethysmography (PPG) signals with two wavelengths channels (infrared and red) using a wrist-worn sensor for the estimation of heart rate variability (HRV) and oxygen saturation (SpO2). Five healthy subjects were equipped with a commercial wrist-worn pulse oximeter (Wavelet Health, USA) on the right hand, and both a commercial smart watch for fitness use (Huawei Watch, Series 2) and a clinically-validated transmission-mode pulse oximeter (Creative Medical PC-68B) on their left hand as a reference. Synchronised PPG signals from the Wavelet Health, the Huawei watch, and the PC-68B were recorded for approximately 10 minutes. Subjects were asked to leave the left hand in a resting state, while moving the right hand with two types of movement (periodic and random). A method is proposed to incorporate coupling information between the two wavelengths of PPGs based on the bivariate empirical mode decomposition algorithm. Our method is shown to improve the quality of red PPG allowing improved estimation of SpO2. A comparison of average heart rate (HR), HRV, and SpO2 from all devices is provided.

Ambient Assisted Living (AAL) technologies aim at supporting elderly and disabled people in their environment, with affordable, easy to use and meaningful ICT tools. Given the complexity of AAL, software platforms are needed to abstract and reuse typical functionalities. To this purpose, the universAAL project was established to create an open platform that provides a standardized approach making it technically feasible and economically viable to develop AAL solutions. In order to make AAL technologies widely accepted, high quality must be guaranteed, but measuring quality in AAL is difficult because of the variety of stakeholders, the wide set of needs addressed and the heterogeneity of the employed technologies. This paper proposes a methodological framework for evaluating AAL solutions with the aim of prioritizing evaluation needs, customizing assessment techniques and producing value for the project. The framework is based on empirical methods and on scientific-like theory building mixed with the standard ISO/IEC 25000 for software quality. It can be applied for both technical and non-technical (end-user) evaluations. To support the framework, the results of its application in universAAL are shown, i.e., how it was used at different stages of the project for assessing the results, for taking decisions and for building scientific knowledge.

IntroductionSuccessive confidential enquiries into maternal deaths in the UK have identified an urgent need to develop a national early warning score (EWS) specifically for pregnant or recently pregnant women to aid more timely recognition, referral and treatment of women who are developing life-threatening complications in pregnancy or the puerperium. Although many local EWS are in use in obstetrics, most have been developed heuristically. No current obstetric EWS has defined the thresholds at which an alert should be triggered using evidence-based normal ranges, nor do they reflect the changing physiology that occurs with gestation during pregnancy.Methods and analysisAn observational cohort study involving 1000 participants across three UK sites in Oxford, London and Newcastle. Pregnant women will be recruited at approximately 14 weeks’ gestation and have their vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation and temperature) measured at 4 to 6-week intervals during pregnancy. Vital signs recorded during labour and delivery will be extracted from hospital records. After delivery, participants will measure and record their own vital signs daily for 2 weeks. During the antenatal and postnatal periods, vital signs will be recorded on an Android tablet computer through a custom software application and transferred via mobile internet connection to a secure database. The data collected will be used to define reference ranges of vital signs across normal pregnancy, labour and the immediate postnatal period. This will inform the design of an evidence-based obstetric EWS.Ethics and disseminationThe study has been approved by the NRES committee South East Coast–Brighton and Sussex (14/LO/1312) and is registered with the ISRCTN (10838017). All participants will provide written informed consent and can withdraw from the study at any point. All data collected will be managed anonymously. The findings will be disseminated in international peer-reviewed journals and through research conferences.

Low adherence to cardiac rehabilitation (CR) might be improved by remote monitoring systems that can be used to motivate and supervise patients and tailor CR safely and effectively to their needs. The main objective of this study was to evaluate the feasibility of a smartphone-guided training system (GEX) and whether it could improve exercise capacity compared to CR delivered by conventional methods for patients with coronary artery disease (CAD). A prospective, randomized, international, multi-center study comparing CR delivered by conventional means (CG) or by remote monitoring (IG) using a new training steering/feedback tool (GEx System). This consisted of a sensor monitoring breathing rate and the electrocardiogram that transmitted information on training intensity, arrhythmias and adherence to training prescriptions, wirelessly via the internet, to a medical team that provided feedback and adjusted training prescriptions. Exercise capacity was evaluated prior to and 6 months after intervention. 118 patients (58 +/- 10 years, 105 men) with CAD referred for CR were randomized (IG: n = 55, CG: n = 63). However, 15 patients (27 %) in the IG and 18 (29 %) in the CG withdrew participation and technical problems prevented a further 21 patients (38 %) in the IG from participating. No training-related complications occurred. For those who completed the study, peak VO2 improved more (p = 0.005) in the IG (1.76 +/- 4.1 ml/min/kg) compared to CG (-0.4 +/- 2.7 ml/min/kg). A newly designed system for home-based CR appears feasible, safe and improves exercise capacity compared to national CR. Technical problems reflected the complexity of applying remote monitoring solutions at an international level.

In this paper we present an interesting case of what we callbehavioral network engineering, i.e. an approach to optimize network operation by exploiting information about user’s behavior. Behavioral information is needed both to characterize the overall usage context of the network and to describe the specific attitudes of single users. Indeed, in a network which is autonomically managed global knowledge of the current network “situation” is of paramount importance to optimally exploit available network resources. Furthermore, in case the behavior of single individuals can significantly impact network operation, it becomes mandatory to hold more fine-grained information. Distributed Denial of Service (DDoS) attacks represent an interesting example, since attack patterns can definitely be seen as particular (i.e. malicious) behaviors. To better illustrate the above concepts, we present an Intrusion Detection System (Ids) designed around the behavioral network engineering paradigm and relying on a flow monitoring system used for behavior summarization.

Oral cavity cancers are the seventh tumor by diffusion worldwide with more than 90 % being diagnosed as oral squamous cell carcinomas (OSCCs). According to the latest WHO statistics, OSCC accounts for 5 % of the cancer deaths worldwide, being the eighth more lethal cancer entity. Early identification of cancer relapses would have the potentiality to improve the disease control and the patient survival. NeoMark is a European co-funded research project (Seventh Framework Program, Information and Communication Technologies: EU-FP7-ICT-20072-22483-NeoMark) that has the objective to identify relevant biomarkers of OSCC recurrence. It integrates high-throughput gene expression analysis in tumor cells and IT-assisted imaging with traditional staging and follow-up protocols to improve the recurrence risk stratification and to obtain the earlier identification of locoregional relapses. The architecture of the project is based on the following key points: – Creation of a web application tool: a unified interface that helps the storage and management of all information – NeoMark database: the heterogeneous NeoMark data (demographics and risk factors; clinical, pathological, and immunohistochemical parameters; filtered and cleaned genomic and imaging data) are stored in a single database – the M.J.R. da Fonseca Link Consulting – Tecnologias De Informacao S.A., Lisbon, Portugal e-mail: manuel.fonseca@link.pt M. De Fazio STMicroelectronics, Agrate Brianza (MI), Italy e-mail: marco.de-fazio@st.com 730 T. Poli et al.

This paper offers a portrayal of how affective computing and persuasive technologies can converge into an effective tool for interfacing biomedical engineering with behavioral sciences and medicine. We describe the characteristics, features, applications, present state of the art, perspectives, and trends of both streams of research. In particular, these streams are analyzed in light of the potential contribution of their convergence for improving computer-mediated health-care systems, by facilitating the modification of patients’ attitudes and behaviors, such as engagement and compliance. We propose a framework for future research in this emerging area, highlighting how key constructs and intervening variables should be considered. Some specific implications and challenges posed by the convergence of these two technologies in health care, such as paradigm change, multimodality, patients’ attitude improvement, and cost reduction, are also briefly addressed and discussed. peerReviewed

Mobile health systems aiming to promote adherence may cost-effectively improve the self-management of chronic diseases like diabetes, enhancing the compliance to the medical prescription, encouraging and stimulating patients to adopt healthy life styles and promoting empowerment. This paper presents a strategy for m-health applications in diabetes self-management that is based on automatic generation of feedback messages. A feedback assistant, representing the core of architecture, delivers dynamic and automatically updated text messages set up on clinical guideline and patient’s lifestyle. Based on this strategy, an m-health adherence system was designed, developed and tested in a small-scale exploratory study with T1DM and T2DM patients. The results indicate that the system could be feasible and well accepted and that its usage increased along with adherence to prescriptions during the 4 weeks of the study. A more extensive research is pending to corroborate these outcomes and to establish a clear benefit of the proposed solution.

Cardiovascular Diseases are the most prevalent and serious chronic conditions existing nowadays. They are the primary cause of death in the world and generate enormous expenditures to the health systems. Tele-monitoring and personal health systems have proven to be good options for tackling this situation; however they are still lacking many functionalities. It is necessary to find solutions that allow health professionals to follow up patients more closely and efficiently, while reducing the non-adherence of patients to the treatment regime. HeartCycle research project (partially funded by the European Commission) has developed a personal health system for cardiovascular diseases management with the aim to address this problem. This paper describes the Patient Loop of this solution, including the different components, the adopted user interaction, and the implemented patients' education and coaching strategy.

Owing to the complexity of Ambient Assisted Living (AAL) systems and platforms, the evaluation of AAL solutions is a complex task that will challenge researchers for years to come. However, the analysis and comparison of proposed solutions is paramount to enable us to assess research results in this area. We have thus organized an international contest called EvAAL: Evaluating AAL Systems through Competitive Benchmarking. Its aims are to raise interest within the research and developer communities in the multidisciplinary research fields enabling AAL, and to create benchmarks for the evaluation and comparison of AAL systems.

One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side effects. A way to optimize treatments is to predict the recurrence probability by analyzing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging, and clinical evidence. This heterogeneous set of data needs a proper representation in order to be stored, computed, and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot in which data will populate the ontology and will be made public for further research.

The Ambient Assisted Living domain is a fast growing area with many new technological artefacts and services developed. Most of the systems developed address end-users' needs. Yet, they have not achieved a large market penetration. The work presented here argues that this is primarily due to not sufficiently addressing the quality requirements posed by the health care organizations. Satisfying quality requirements requires a standardized and easily accessible framework for measuring quality. We present the initial steps towards such a framework by building on relevant ISO standards.

Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases.

The rapid aging of the population worldwide will dramatically increase the number of people affected by chronic diseases in the next years. This social situation makes it necessary a paradigm shift from reactive care to preventive care. New technological paradigms, like Ambient Intelligence and Ubiquitous Computing, allow the development of Personal Health Systems (PHS) that guarantee the continuity of care and a better use of health resources. Therefore, patients should become the centre of the health care process, and PHS should be designed to fulfill the patient's goals and needs. User-centred methodologies provide a good framework for designing general use applications, but they do not usually take into account factors like the context where the interaction is taking place or the medical, social and business contexts that surround the patient. This paper presents a model for designing user's interaction in medical applications. The final goal is to develop highly usable user interfaces and to improve the user experience, aiming to guarantee the patient's adherence to the medical protocols and recommendations.

Cardiovascular diseases (CVDs) account for 45% of all deaths in the western world according to the 2004 World Health Organization statistics report. Heart failure (HF), CVD's primary paradigm, mainly affects people older than 65. The European MyHeart Project's mission is to empower citizens to fight CVD by leading a preventative lifestyle and allowing early diagnosis. This paper presents the iterative design and development of the HF management system, part of MyHeart Project. The system daily measures vital body signals to assess HF. The methodology applied herein has involved stakeholders in an iterative process: concept validation, feasibility, efficiency, patients' experience, and patients' acceptance. The final solution allows patient self-management of their chronic condition.

Cardiovascular diseases (CVD) are the main cause of death in Western world. Furthermore, an aging population will result in increased chronic condition prevalence in the future. Ambient Intelligence (AmI) allows users to benefit from an enormous variety of user-centered services to improve their quality of life and self manage their chronic conditions. Novel collaboration methods are essential with the coming aging society. This paradigm will facilitate user support through novel medical protocol design for chronic disease treatment, based on the healthy lifestyle promotion by allowing intelligent and natural interaction between the context and individuals. This paper presents the iterative validation of the user interaction based on contexts in a Heart Failure Management solution. Heart Failure Management daily monitors vital body signals, using wearable and mobile technologies, to continuously assess this chronic disease. The methodology applied herein has involved stakeholders in an iterative process: Model validation, feasibility, efficiency, user experience, and acceptance. These carefully designed systems play an important role in motivating people to adopt healthier lifestyles by using technical solutions. These solutions allow patient self-management of their chronic condition.

In Western world, the prevalence of chronic diseases is highly increasing due to the increase in the life expectancy. Besides, cardiovascular diseases (CVD) are the leading source of death, causing 45% of all deaths and Heart Failure (HF), the paradigm of CVD, mainly affects people older than 65. This paper focuses on the latest advances in the design and development of the user interaction system to manage Heart Failure Management in a mobile environment, based on daily monitoring of Vital Body Signals, with wearable and information technologies, for the continuous assessment of this chronic disease.

In the Western World, cardiovascular diseases (CVD) are the leading source of death. Only in Europe, they cause 45% of all deaths. Besides heart failure, the paradigm of CVD, affects mainly people older than 65. Facing this reality, the European Union has funded MyHeart Project, whose mission is empowering citizens to fight CVD by means of a preventive lifestyle and an early diagnosis. This paper presents the design and development of the user interaction for a heart failure management system. This system consists on wearable and mobile technologies which monitors the vital body signals in a daily basis, providing a continuous assessment of this chronic disease.

Personalized health devices are the novel paradigm to reduce healthcare costs and to improve the quality of health services. At the same time, health interventions and promotion of self behaviors generate benefits to healthcare and allow citizens to be more involved in their own health management. This paper describes the process followed in HeartCycle project to design education and coaching services to promote self-behaviors in a closed loop monitoring system for patients with coronary heart diseases that suffered a myocardial infarction.

Current trends in healthcare technology include mobile-based applications. Relevant advances in the integration of vital signs monitoring devices with mobile platforms are widely reported nowadays. In this context, conceiving and designing an interoperable application is essential due to the growing necessity of integrating a huge and heterogeneous amount of biomedical data, coming from a wide range of devices and sensors. In this paper the key research issues associated with such integration are presented as well as a specific proposal to solve these problems. It is based on a middleware architecture for the integration of biomedical sensors with mobile devices, derived from the ISO/IEEE 11073 standards family. The application has been developed in the framework of an EU-funded R&D project called METABO.