To Share or Not to Share: Optimal Value of Insurance Rewards for Sharing Data Generated from Wearable Devices for Hypertensive Patients

Wearable devices (smart electronic devices that are worn on the surface of the skin) have the potential for better management of various health conditions, thereby improving health outcomes and reducing health-related costs. Hospitals and clinics are moving towards integration of personal health data from wearable devices with EHR. For example, EPIC, a cloud-based EHR system currently support connecting data from various wearable devices (Dinh-Le et al. 2019). As of October 2018, there were 565 hospitals and 14,427 clinics that support integration of data from wearable devices into EHR. By integrating data from wearable devices into EHR, patients can experience better levels of controls over their health and, therefore, better health outcomes. Wearable devices can be particularly valuable to hypertensive patients by means of monitoring blood pressure without causing major disruptions in patients’ daily life. There currently exist various wearable devices that can help patients monitor their blood pressure: Heart Guide, ViTrack, Vivo Watch BP, and others. Despite the benefits of sharing data from wearable devices, patients are generally unwilling to share their personal data. A recent study indicates that perception of privacy, security, and economic incentives determine patients’ willingness to share their data from wearable devices (Soliño-Fernandez et al. 2019). Some insurance companies do provide patients with incentives in the form of the reward programs for sharing data from wearable devices (e.g., Oscar Health, United HealthCare, Humana, and John Hancock), yet it is not clear if current incentive programs provide enough net value to motivate patients to share their data. The purpose of this study is (1) to explore the current state of net benefits for sharing and accepting data from wearable device for patients and insurers, respectively, and (2) to propose changes to the incentive programs to enhance data sharing among patients. Eventually, the following research question will be answered: What is the optimal value of insurance financial incentives for hypertensive patient’s sharing of data from wearable devices? The first purpose will be explored by means of semi-structured interviews with insurers and extensive literature review. The second purpose will be explored by means of a game theory optimization in which benefits and losses from sharing/accepting, identified in semi-structured interviews and literature review, will be estimated as parameters. The multi-objective function will be used to maximize the net value of sharing (for patients) and accepting (for insurers) data, given the range of parameters and various constraints

A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended

A multi-parametric wearable system to monitor neck movements and respiratory frequency of computer workers

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems