This article presents an introduction to the Health Belief Model (HBM). The HBM states that the perception of a personal health behavior threat is influenced by at least three factors: general health values, interest and concern about health; specific beliefs about vulnerability to a particular health threat; and beliefs about the consequences of the health problem. Once an individual perceives a threat to his health and is simultaneously cued to action, if his perceived benefits outweighs his perceived costs, then the individual is most likely to undertake the recommended preventive health action. Key words: health promotion, health belief model, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, self-efficacy. Content available only in Romanian.

Health Belief Model

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

The Internet of Things for Health Care: A Comprehensive Survey

The Internet of Things (IoT) is a methodology or a system that encompasses real-world things to interact and communicate with each other with the assistance of networking technologies. This article describes surveys on advances in IoT-based healthcare methods and reviews the state-of-the-art technologies in detail. Moreover, this review classifies an existing IoT-based healthcare network and represents a summary of all perspective networks. IoT healthcare protocols are analyzed in this context and provide a broad discussion on it. It also initiates a comprehensive survey on IoT healthcare applications and services. Extensive insights into IoT healthcare security, its requirements, challenges, and privacy issues are visualized in IoT surrounding healthcare. In this review, we analyze security and privacy features consisting of data protection, network architecture, Quality of Services (QoS), app development, and continuous monitoring of healthcare that are facing difficulties in many IoT-based healthcare architectures. To mitigate the security problems, an IoT-based security architectural model has been proposed in this review. Furthermore, this review discloses the market opportunity that will enhance the IoT healthcare market development. To conduct the survey, we searched through established journal and conference databases using specific keywords to find scholarly works. We applied a filtering mechanism to collect only papers that were relevant to our research works. The selected papers were then examined carefully to understand their contributions/research focus. Eventually, the paper reviews were analyzed to identify any existing research gaps and untouched areas of research and to discover possible features for sustainable IoT healthcare development.

Internet of Things (IoT): A Review of Its Enabling Technologies in Healthcare Applications, Standards Protocols, Security, and Market Opportunities

The last decade has witnessed extensive research in the field of healthcare services and their technological upgradation. To be more specific, the Internet of Things (IoT) has shown potential application in connecting various medical devices, sensors, and healthcare professionals to provide quality medical services in a remote location. This has improved patient safety, reduced healthcare costs, enhanced the accessibility of healthcare services, and increased operational efficiency in the healthcare industry. The current study gives an up-to-date summary of the potential healthcare applications of IoT- (HIoT-) based technologies. Herein, the advancement of the application of the HIoT has been reported from the perspective of enabling technologies, healthcare services, and applications in solving various healthcare issues. Moreover, potential challenges and issues in the HIoT system are also discussed. In sum, the current study provides a comprehensive source of information regarding the different fields of application of HIoT intending to help future researchers, who have the interest to work and make advancements in the field to gain insight into the topic.

/pdf/iot-based-applications-in-healthcare-devices-3wncwpumo6.pdf

IoT-Based Applications in Healthcare Devices.

Progress in mobile health (mHealth) technology has enabled the design of just-in-time adaptive interventions (JITAIs). We define JITAIs as having three features: behavioural support that directly corresponds to a need in real-time; content or timing of support is adapted or tailored according to input collected by the system since support was initiated; support is system-triggered. We conducted a systematic review of JITAIs for physical activity to identify their features, feasibility, acceptability and effectiveness. We searched Scopus, Medline, Embase, PsycINFO, Web of Science, DBLP, ACM Digital Library, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov
 and the ISRCTN register using terms related to physical activity, mHealth interventions and JITAIs. We included primary studies of any design reporting data about JITAIs, irrespective of population, age and setting. Outcomes included physical activity, engagement, uptake, feasibility and acceptability. Paper screening and data extraction were independently validated. Synthesis was narrative. We used the mHealth Evidence Reporting and Assessment checklist to assess quality of intervention descriptions. We screened 2200 titles, 840 abstracts, 169 full-text papers, and included 19 papers reporting 14 unique JITAIs, including six randomised studies. Five JITAIs targeted both physical activity and sedentary behaviour, five sedentary behaviour only, and four physical activity only. JITAIs prompted breaks following sedentary periods and/or suggested physical activities during opportunistic moments, typically over three to four weeks. Feasibility challenges related to the technology, sensor reliability and timeliness of just-in-time messages. Overall, participants found JITAIs acceptable. We found mixed evidence for intervention effects on behaviour, but no study was sufficiently powered to detect any effects. Common behaviour change techniques were goal setting (behaviour), prompts/cues, feedback on behaviour and action planning. Five studies reported a theory-base. We found lack of evidence about cost-effectiveness, uptake, reach, impact on health inequalities, and sustained engagement. Research into JITAIs to increase physical activity and reduce sedentary behaviour is in its early stages. Consistent use and a shared definition of the term ‘JITAI’ will aid evidence synthesis. We recommend robust evaluation of theory and evidence-based JITAIs in representative populations. Decision makers and health professionals need to be cautious in signposting patients to JITAIs until such evidence is available, although they are unlikely to cause health-related harm. PROSPERO 2017 CRD42017070849.

/pdf/a-systematic-review-of-just-in-time-adaptive-interventions-3x2kgnozzu.pdf

A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity

Over one billion smartphones have now been shipped worldwide. These mobile devices feature multi-core CPUs and GPUs, megapixel cameras and an array of sensors. Smartphone sensors can now be processed to diagnose a wide variety of medical conditions including cough detection, irregular heartbeat detection, and lung function analysis. The ability to diagnose ailments in the convenience of patients' homes on smartphones that they already possess could lead to early detection, which could ultimately reduce healthcare costs. This paper reviews state-of-the-art examples, examines the technical issues involved in the use of the smartphone as a medical device, and outlines potential benefits and challenges. A case study of an Android smartphone app for wound detection and healing progress analysis by diabetes patients is also presented.

The smartphone as a medical device: Assessing enablers, benefits and challenges

Sedentary lifestyles have become ubiquitous in modern societies. Sitting, watching television and using the computer are sedentary behaviors that are now common worldwide. Research studies have shown that how often and how long a person is sedentary is linked with an increased risk of obesity, diabetes, cardiovascular disease, and all-cause mortality. Effective strategies for motivating people to become more active are now crucial. In this paper, we present a smartphone application called 'On11', which runs in the background of users' smartphones and monitors their daily physical activity continuously. Unlike traditional pedometers that only passively count steps and estimate burnt calories, On11 also detects sedentary behaviors (sitting, lying down). It presents 'at-a-glance' summaries of what percentage of the user's day have been spent sitting, walking, and running, and total calories burnt thus far that day so that the user can self-reflect. It records the intensity, duration, and type of activities performed and recommends personalized short walks and detours to users' regular routes such as home to workplace. The user can set performance goals, which allows On11 to suggest activities to help them meet their goals. The results of our preliminary user study were encouraging.

On11: an activity recommendation application to mitigate sedentary lifestyle

The wound assessing method and system of the present teachings provide a convenient, quantitative mechanism for diabetic foot ulcer assessment.

System and method for assessing wound

Many people are bored with their current physical activities and would like individualized recommendations of alternatives. Even users who have favorite exercises may seek recommendations if their context (e.g., bad weather, location) changes. Prior work has focused on tracking user activities and goal-setting, but not on recommendations. In this paper, we describe RecFit, which systematically suggests physical activities based on the user's context (e.g. risk tolerance, budget, location, weather). RecFit works from 137 activities selected from the 2011 compendium of physical activities in order to recommend the 5 most suitable recommendations for each user. We describe our filtering criteria, algorithms, prototype and RecFit's activity database, which augments activities with metadata of ideal performance context (popularity, sociability, risk, location, expense, time, and weather).

RecFit: a context-aware system for recommending physical activities

We developed a health and wellness smartphone app to support diabetes self-management. The target users are older, less healthy adults with advanced type 2 diabetes. We illustrate the design implications of a health and wellness smartphone app for such users by presenting the process and results of our usability and user experience lab test with our app.

Design Implications of User Experience Studies: The Case of a Diabetes Wellness App

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