Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Recent technological advances in sensors, low-power microelectronics and miniaturization, and wireless networking enabled the design and proliferation of wireless sensor networks capable of autonomously monitoring and controlling environments. One of the most promising applications of sensor networks is for human health monitoring. A number of tiny wireless sensors, strategically placed on the human body, create a wireless body area network that can monitor various vital signs, providing real-time feedback to the user and medical personnel. The wireless body area networks promise to revolutionize health monitoring. However, designers of such systems face a number of challenging tasks, as they need to address often quite conflicting requirements for size, operating time, precision, and reliability. ::: ::: In this paper we present hardware and software architecture of a working wireless sensor network system for ambulatory health status monitoring. The system consists of multiple sensor nodes that monitor body motion and heart activity, a network coordinator, and a personal server running on a personal digital assistant or a personal computer. <s> BIB001 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Designing Web-applications is considerably different for mobile computers (handhelds, Personal Digital Assistants) than for desktop computers. The screen size and system resources are more limited and end-users interact differently. Consequently, detecting handheld-browsers on the server side and delivering pages optimized for a small client form factor is inevitable. The authors discuss their experiences during the design and development of an application for medical research, which was designed for both mobile and personal desktop computers. The investigations presented in this paper highlight some ways in which Web content can be adapted to make it more accessible to mobile computing users. As a result, the authors summarize their experiences in design guidelines and provide an overview of those factors which have to be taken into consideration when designing software for mobile computers. “The old computing is about what computers can do, the new computing is about what people can do” (Leonardo’s laptop: human needs and the new computing technologies, MIT Press, 2002). <s> BIB002 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system. <s> BIB003 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> In this paper, a single-chip-microprocessor-based multi-channel data acquisition and controller for mobile health monitoring system (MHMS) is developed. The presented controller separately interfaces with the high speed downlink packet access (HSDPA), GPS, and a ring-type physiological sensor by a multiplexer. Therefore, its cost is economical and its size is compact, making it suitable for mobility. Moreover, the developed hardware and firmware of the controller can be easily expanded for incorporating additional physiological sensors with the help of the multiplexer. For data transmission, in this proposed MHMS the HSDPA wireless technology is used, and therefore data can be transmitted to the remote server with no distance limit. The GPS further provides the position information of the monitored person. The remote server not only collects physiological measurements but also tracks the position of the monitored person in real time. Based on the experimental test, the proposed MHMS demonstrates satisfactory performance. <s> BIB004 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Remote ambulatory monitoring is widely seen as playing a key part in addressing the impending crisis in health care provision. We describe two mobile health solutions, one developed in the Netherlands and one in Australia. In both cases a patient’s biosignals are measured by means of a body sensor network which communicate wirelessly with a handheld device. Alarms and biosignals can be transmitted over wireless communication links to a remote location, and a remote health professional can view the biosignal data via a web portal. Both systems are undergoing commercialisation. The clinical purposes are similar, however the technological approaches differ in some respects. We compare the two approaches, present a generic architecture for healthcare sensor network systems and discuss the experience gained working with a number of different patient groups and clinical specialties during patient trials in Europe and Australia. <s> BIB005 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements. <s> BIB006 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications. <s> BIB007 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Advances in smartphone technology and data communications facilitate the use of ubiquitous health monitoring and mobile health application as a solution of choice for the overwhelming problems of the healthcare system. In addition to easier management and seamless access to historical records, ubiquitous technology has the potential to motivate users to take an active role and manage their own conditions. In this paper we present capabilities of the current generation of smartphones and possible applications for ubiquitous health monitoring and wellness management. We describe the architecture and organization of ubiquitous health monitoring systems, Body Sensor Networks, and integration of wearable and environmental sensors. We also describe mainstream mobile health related applications in today's mobile marketplaces such as Apple App Store and Google Android Marketplace. Finally, we present the development of UAHealth - our integrated mobile health monitoring system for wellness management, designed to monitor physical activity, weight, and heart activity. <s> BIB008 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Web apps are cheaper to develop and deploy than native apps, but can they match the native user experience? <s> BIB009 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Mobile health (mHealth), as an important development direction of eHealth, is an innovative application of spatial information technology used in health field. In this paper, the key technology of location based mobile health system is studied and the system prototype is designed and implemented. The mobile monitoring terminal receives data collected by the physiological parameters measuring instrument, obtains location information through GPS/WiFi positioning module, and uploads data to the service center using GPRS communication. The service center, a Silver light based WebGIS system, monitors the location and physiological parameters of mobile monitoring terminal users, makes alarms and reminders through spatial analysis. The mobile health system with real-time monitoring, precise positioning, rapid analysis, visualization display, can be widely used in family sickbeds, geracomiums, empty-nest elders' care, chronic disease patients and other special populations or scenes. <s> BIB010 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> We are in the midst of what some have called a "mobile health revolution". Medical applications ("apps") for mobile phones are proliferating in the marketplace and clinicians are likely encountering patients with questions about the medical value of these apps. We conducted a review of medical apps focused on endocrine disease. We found a higher percentage of relevant apps in our searches of the iPhone app store compared with the Android marketplace. For our diabetes search in the iPhone store, the majority of apps (33%) focused on health tracking (blood sugars, insulin doses, carbohydrates), requiring manual entry of health data. Only two apps directly inputted blood sugars from glucometers attached to the mobile phone. The remainder of diabetes apps were teaching/training apps (22%), food reference databases (8%), social blogs/forums (5%), and physician directed apps (8%). We found a number of insulin dose calculator apps which technically meet criteria for being a medically regulated mobile application, but did not find evidence for FDA-approval despite their availability to consumers. Far fewer apps were focused on other endocrine disease and included medical reference for the field of endocrinology, access to endocrine journals, height predictors, medication trackers, and fertility apps. Although mobile health apps have great potential for improving chronic disease care, they face a number of challenges including lack of evidence of clinical effectiveness, lack of integration with the health care delivery system, the need for formal evaluation and review and organized searching for health apps, and potential threats to safety and privacy. <s> BIB011 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Every month at Google Campus in London, dozens of software developers, clinicians, behavioural scientists and investors get together to discuss new strategies to influence health behaviours. The collective aim of these networking events is to develop digital ‘games with purpose’ that can improve health by integrating software design and game mechanics with public health theory and behavioural insights. Gamification is a purposely-broad umbrella term used to encompass the process of using ‘gaming’ elements to motivate and engage people in non-game contexts.1 Enhanced opportunities now exist to deliver behaviour change interventions through game platforms on new smartphone devices. <s> BIB012 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> The award winning work was published in the Proceedings of the International MultiConference of Engineers and Computer Scientists 2013 Vol II, IMECS 2013, March 13 - 15, 2013, Hong Kong. <s> BIB013 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Abstract Accessibility is a major concern in our society nowadays. However, technology is always one step ahead and new devices appear before we can address the accessibility problems for the older ones. People with disabilities are not the only ones that are being excluded by technologies. Indeed, there is an exponential growth of the elderly population that suffers from age-related disabilities. Accessibility issues should be in mind for developers. Unfortunately, addressing these issues is even harder in new devices like mobile phones and tablets where there is not a proper set of guidelines focusing on this domain. This work provides: (1) a set of guidelines to keep in mind in order to achieve accessibility in mobile interfaces for older people. This checklist is the result of a review study of the literature, standards and best practices that are being performed in this area of knowledge, (2) use of this accessibility checklist aimed at elderly people, a survey of three mobile native Apps on android platform has been carried out, these Apps have as aim to modify the default interface for another more accessible one. <s> BIB014 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Background: Self-management is crucial in the daily management of type 2 diabetes. It has been suggested that mHealth may be an important method for enhancing self-management when delivered in combination with health counseling. Objective: The objective of this study was to test whether the use of a mobile phone–based self-management system used for 1 year, with or without telephone health counseling by a diabetes specialist nurse for the first 4 months, could improve glycated hemoglobin A1c (HbA1c) level, self-management, and health-related quality of life compared with usual care. Methods: We conducted a 3-arm prospective randomized controlled trial involving 2 intervention groups and 1 control group. Eligible participants were persons with type 2 diabetes with an HbA 1c level ≥7.1% (≥54.1 mmol/mol) and aged ≥18 years. Both intervention groups received the mobile phone–based self-management system Few Touch Application (FTA). The FTA consisted of a blood glucose–measuring system with automatic wireless data transfer, diet manual, physical activity registration, and management of personal goals, all recorded and operated using a diabetes diary app on the mobile phone. In addition, one intervention group received health counseling based on behavior change theory and delivered by a diabetes specialist nurse for the first 4 months after randomization. All groups received usual care by their general practitioner. The primary outcome was HbA1c level. Secondary outcomes were self-management (heiQ), health-related quality of life (SF-36), depressive symptoms (CES-D), and lifestyle changes (dietary habits and physical activity). Data were analyzed using univariate methods (t test, ANOVA) and multivariate linear and logistic regression. Results: A total of 151 participants were randomized: 51 to the FTA group, 50 to the FTA-health counseling (FTA-HC) group, and 50 to the control group. Follow-up data after 1 year were available for 120 participants (79%). HbA1c level decreased in all groups, but did not differ between groups after 1 year. The mean change in the heiQ domain skills and technique acquisition was <s> BIB015 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Background: Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective: The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods: An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. Results: This review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification ( P <.001). Behavioral theory was associated with gamification ( P <.05), but not game elements, and upon further analysis gamification was only associated with composite motivational behavior scores ( P <.001), and not capacity or opportunity/trigger. Conclusions: This research, to our knowledge, represents the first comprehensive review of gamification use in health and fitness apps, and the potential to impact health behavior. The results show that use of gamification in health and fitness apps has become immensely popular, as evidenced by the number of apps found in the Apple App Store containing at least some components of gamification. This shows a lack of integrating important elements of behavioral theory from the app industry, which can potentially impact the efficacy of gamification apps to change behavior. Apps represent a very promising, burgeoning market and landscape in which to disseminate health behavior change interventions. Initial results show an abundant use of gamification in health and fitness apps, which necessitates the in-depth study and evaluation of the potential of gamification to change health behaviors. [JMIR Serious Games 2014;2(2):e9] <s> BIB016 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Advances in mobile technology have led to the emergence of the "smartphone", a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that "count" steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to "close the loop" by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions. <s> BIB017 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Automatic disease inference is of importance to bridge the gap between what online health seekers with unusual symptoms need and what busy human doctors with biased expertise can offer. However, accurately and efficiently inferring diseases is non-trivial, especially for community-based health services due to the vocabulary gap, incomplete information, correlated medical concepts, and limited high quality training samples. In this paper, we first report a user study on the information needs of health seekers in terms of questions and then select those that ask for possible diseases of their manifested symptoms for further analytic. We next propose a novel deep learning scheme to infer the possible diseases given the questions of health seekers. The proposed scheme is comprised of two key components. The first globally mines the discriminant medical signatures from raw features. The second deems the raw features and their signatures as input nodes in one layer and hidden nodes in the subsequent layer, respectively. Meanwhile, it learns the inter-relations between these two layers via pre-training with pseudo-labeled data. Following that, the hidden nodes serve as raw features for the more abstract signature mining. With incremental and alternative repeating of these two components, our scheme builds a sparsely connected deep architecture with three hidden layers. Overall, it well fits specific tasks with fine-tuning. Extensive experiments on a real-world dataset labeled by online doctors show the significant performance gains of our scheme. <s> BIB018 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Sensor-equipped smartphones and wearables are transforming a variety of mobile apps ranging from health monitoring to digital assistants. However, reliably inferring user behavior and context from noisy and complex sensor data collected under mobile device constraints remains an open problem, and a key bottleneck to sensor app development. In recent years, advances in the field of deep learning have resulted in nearly unprecedented gains in related inference tasks such as speech and object recognition. However, although mobile sensing shares many of the same data modeling challenges, we have yet to see deep learning be systematically studied within the sensing domain. If deep learning could lead to significantly more robust and efficient mobile sensor inference it would revolutionize the field by rapidly expanding the number of sensor apps ready for mainstream usage. In this paper, we provide preliminary answers to this potentially game-changing question by prototyping a low-power Deep Neural Network (DNN) inference engine that exploits both the CPU and DSP of a mobile device SoC. We use this engine to study typical mobile sensing tasks (e.g., activity recognition) using DNNs, and compare results to learning techniques in more common usage. Our early findings provide illustrative examples of DNN usage that do not overburden modern mobile hardware, while also indicating how they can improve inference accuracy. Moreover, we show DNNs can gracefully scale to larger numbers of inference classes and can be flexibly partitioned across mobile and remote resources. Collectively, these results highlight the critical need for further exploration as to how the field of mobile sensing can best make use of advances in deep learning towards robust and efficient sensor inference. <s> BIB019 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Tuberculosis (TB) is a chronic infectious disease worldwide and remains a major cause of death globally. Of the estimated 9 million people who developed TB in 2013, over 80% were in South-East Asia, Western Pacific, and African. The majority of the infected populations was from resource-poor and marginalized communities with weak healthcare infrastructure. Reducing TB diagnosis delay is critical in mitigating disease transmission and minimizing the reproductive rate of the tuberculosis epidemic. The combination of machine learning and mobile computing techniques offers a unique opportunity to accelerate the TB diagnosis among these communities. The ultimate goal of our research is to reduce patient wait times for being diagnosed with this infectious disease by developing new machine learning and mobile health techniques to the TB diagnosis problem. In this paper, we first introduce major technique barriers and proposed system architecture. Then we report two major progresses we recently made. The first activity aims to develop large-scale, real-world and well-annotated X-ray image database dedicated for automated TB screening. The second research activity focus on developing effective and efficient computational models (in particularly, deep convolutional neural networks (CNN)-based models) to classify the image into different category of TB manifestations. Experimental results have demonstrated the effectiveness of our approach. Our future work includes: (1) to further improve the performance of the algorithms, and (2) to deploy our system in the city of Carabayllo in Peru, a densely occupied urban community and high-burden TB. <s> BIB020 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Abstract Background Gamification is increasingly used as a design strategy when developing behavior change support systems in the healthcare domain. It is commonly agreed that understanding the contextual factors is critical for successful gamification, but systematic analyses of the persuasive contexts have been lacking so far within gamified health intervention studies. Objectives and methods Through a persuasion context analysis of the gamified health behavior change support systems (hBCSSs) literature, we inspect how the contextual factors have been addressed in the prior gamified health BCSS studies. The implications of this study are to provide the practitioners and researchers examples of how to conduct a systematic analysis to help guide the design and research on gamified health BCSSs. The ideas derived from the analysis of the included studies will help identify potential pitfalls and shortcomings in both the research and implementations of gamified health behavior change support systems. Results We systematically analyzed the persuasion contexts of 15 gamified health intervention studies. According to our results, gamified hBCSSs are implemented under different facets of lifestyle change and treatments compliance, and use a multitude of technologies and methods. We present a set of ideas and concepts to help improve endeavors in studying gamified health intervention through comprehensive understanding of the persuasive contextual factors. Conclusions Future research on gamified hBCSSs should systematically compare the different combinations of contextual factors, related theories, chosen gamification strategies, and the study of outcomes to help understand how to achieve the most efficient use of gamification on the different aspects of healthcare. Analyzing the persuasion context is essential to achieve this. With the attained knowledge, those planning health interventions can choose the ‘tried-and-tested’ approaches for each particular situation, rather than develop solutions in an ad-hoc manner. <s> BIB021 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Physical activity helps people maintain a healthy weight and reduces the risk for several chronic diseases. Although this knowledge is widely recognized, adults and children in many countries around the world do not get recommended amounts of physical activity. While many interventions are found to be ineffective at increasing physical activity or reaching inactive populations, there have been anecdotal reports of increased physical activity due to novel mobile games that embed game play in the physical world. The most recent and salient example of such a game is Pok\'emon Go, which has reportedly reached tens of millions of users in the US and worldwide. We study the effect of Pok\'emon Go on physical activity through a combination of signals from large-scale corpora of wearable sensor data and search engine logs for 32 thousand users over a period of three months. Pok\'emon Go players are identified through search engine queries and activity is measured through accelerometry. We find that Pok\'emon Go leads to significant increases in physical activity over a period of 30 days, with particularly engaged users (i.e., those making multiple search queries for details about game usage) increasing their activity by 1473 steps a day on average, a more than 25% increase compared to their prior activity level ($p<10^{-15}$). In the short time span of the study, we estimate that Pok\'emon Go has added a total of 144 billion steps to US physical activity. Furthermore, Pok\'emon Go has been able to increase physical activity across men and women of all ages, weight status, and prior activity levels showing this form of game leads to increases in physical activity with significant implications for public health. We find that Pok\'emon Go is able to reach low activity populations while all four leading mobile health apps studied in this work largely draw from an already very active population. <s> BIB022 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Background: Type 2 diabetes is the most expensive chronic disease in the United States. Two-thirds of US adults have prediabetes or are overweight and at risk for type 2 diabetes. Intensive in-person behavioral counseling can help patients lose weight and make healthy behavior changes to improve their health outcomes. However, with the shortage of health care providers and associated costs, such programs do not adequately service all patients who could benefit. The health care system needs effective and cost-effective interventions that can lead to positive health outcomes as scale. This study investigated the ability of conversational artificial intelligence (AI), in the form of a standalone, fully automated text-based mobile coaching service, to promote weight loss and other health behaviors related to diabetes prevention. This study also measured user acceptability of AI coaches as alternatives to live health care professionals. Objective: The objective of this study was to evaluate weight loss, changes in meal quality, and app acceptability among users of the Lark Weight Loss Health Coach AI (HCAI), with the overarching goal of increasing access to compassionate health care via mobile health. Lessons learned in this study can be applied when planning future clinical trials to evaluate HCAI and when designing AI to promote weight loss, healthy behavior change, and prevention and self-management of chronic diseases. Methods: This was a longitudinal observational study among overweight and obese (body mass index ≥25) participants who used HCAI, which encourages weight loss and healthy diet choices through elements of cognitive behavioral therapy. Weight loss, meal quality, physical activity, and sleep data were collected through user input and, for sleep and physical activity, partly through automatic detection by the user’s mobile phone. User engagement was assessed by duration and amount of app use. A 4-question in-app user trust survey assessed app usability and acceptability. Results: Data were analyzed for participants (N=70) who met engagement standards set forth by the Centers for Disease Control and Prevention criteria for Diabetes Prevention Program, a clinically proven weight loss program focused on preventing diabetes. Weight loss (standard error of the mean) was 2.38% (0.69%) of baseline weight. The average duration of app use was 15 (SD 1.0) weeks, and users averaged 103 sessions each. Predictors of weight loss included duration of AI use, number of counseling sessions, and number of meals logged. Percentage of healthy meals increased by 31%. The in-app user trust survey had a 100% response rate and positive results, with a satisfaction score of 87 out of 100 and net promoter score of 47. Conclusions: This study showed that use of an AI health coach is associated with weight loss comparable to in-person lifestyle interventions. It can also encourage behavior changes and have high user acceptability. Research into AI and its application in telemedicine should be pursued, with clinical trials investigating effects on weight, health behaviors, and user engagement and acceptability. [JMIR Diabetes 2017;2(2):e28] <s> BIB023 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform. <s> BIB024 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability. <s> BIB025 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential. <s> BIB026 </s> Towards the Uses of Blockchain in Mobile Health Services and Applications: A Survey <s> M-Health Enabling Technologies <s> Online social networks provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices. <s> BIB027
Presently, the technology that is generally used in m-Health services and applications is evolving and becoming increasingly more accessible to the public. Devices, such as, smartphones or tablets and additional accessories are already equipped with sensors that allow measurements of different metrics and vital signs that can be used to extrapolate healthrelated information BIB017 . In fact, metrics such as blood pressure, glucose, oxygen concentration in the blood, heart rhythm, lung function, and mood can be easily monitored and used to infer problems and conditions of a patients health BIB001 , BIB005 , BIB008 . Moreover, a wide range of wearable sensors have become available to the public, providing even more biometric measurement mechanisms and possible sources of information to an already evolved network of sensors BIB006 . Body sensor networks (BSNs) are developed not only in terms of wireless and wearable technology BIB007 , but also on how they interact with other devices and user. The advent of locationbased technologies also had a large impact on the field of m-Health. These usually implement methods to add context and location meaning to health data previously collected, adding a new layer of significance to information BIB004 , BIB010 . In addition to location-based monitoring, it is also possible to combine different aspects of a mobile device and an application, working as a tool to further user engagement. Examples of positive interactions that mobile applications can have in the lifestyle of the user can be seen in BIB015 , with [33] as a good example of an application that uses GPS to promote the users health. However, the promise that many mobile applications make in terms of improving user health should not be taken as guaranteed BIB011 . The availability of a patient health information is a major concern and one of utmost importance in m-Health systems. General and main issues regarding this information availability are related to its secure storage and retrieval mechanisms. Due to this, storing information in mobile devices is not an appropriate model, since it can be accessed through irregular behaviors, its recovery is not guaranteed and the availability is bound to only one device. Hence, a recently introduced solution is to migrate the data storage to cloud services. The advantages of this approach are discussed in detail in section 2.2. The introduction and implementation of machinelearning algorithms in the analysis of health data, in order to provide predictive information and counseling to the user, is one big step in the evolution of technological implementations in healthcare services. However, implementations of such technology are yet to be fully applicable to reliable diagnostics or other important information inferring. Using different metrics gathered from biometric sensors, evolving algorithms can gather statistics from a user's behavior and adapt universal baselines to better suit user's needs. This results in solutions that have clear advantages over more general proposals, that can range not only in terms of efficiency but also practicality towards one specific user. In a smaller and non-critical scale, studies and pilot tests that use these mechanisms are typical applicable to fields such as behavior analysis, correction, prevention, and diagnostics BIB023 , BIB025 , BIB026 , BIB024 , BIB018 , BIB027 , BIB020 . These studies allow users to have a much better understanding of their conditions, as well as have personalized solutions, contributing towards their self-care BIB019 . Although these health prediction systems are still relatively undeveloped, its unexpected interest has the potential of becoming a major source of useful and personalized information from which multiple patterns can be extracted. The growth of m-Health applications usage can also be attributed to its demystification and evolution in design elements, especially in the ways in which they engage and interface with users. Applications that suffer from user interface problems are not able to effectively reach out to audiences such as youth, elderly, special needs and low technological literacy groups  , BIB002 . Each of these groups has different needs regarding the design and functionality of the application, so different and tailored platforms can offer better responses to different kinds of user. Fortunately, technology has evolved allowing rapid development of different user interfaces that have similar root functionalities and characteristics. Hybrid web development frameworks, transformed the development of applications to different demographics in a much agile way and maintainable BIB003 , BIB009 with some notable examples, such as  ,  . Gamification is another popular approach to improve the engagement of users towards health-related applications and commonly applied in mobile applications. This concept introduces entertainment factors into the serious field of health care BIB012 . Therefore, by using this method, applications can be much more appealing to young audiences, as they usually respond better to playful incentives, rewards and positive reinforcements BIB016 . Several examples of games that may encourage positive and healthy behaviors already exist, however, these are not m-Health applications at their core BIB021 . The introduction of health principles into the core of an entertaining applications enables a better user engagement while focusing on health concerns. This is an optimal way of raising awareness to personal health on younger demographics  , as we can see with BIB022 . On older demographic groups, a reliable influx of dayto-day monitoring data is crucial for any type of healthcare service. However, the real-time data collections present considerable challenges. The stems from low technological literacy that this demographic group possesses, increases the difficulty of designing simple but informative interfaces BIB013 . However, the evolution of mobile technologies and user experience methodologies, have improved the overall usability of applications. This enables elderly users to interact with their smart devices and collect useful information about their health condition and act upon this information in a faster and more efficient manner. BIB014 .