Journal of Healthcare Informatics Research最新文献

This systematic review classifies smartwatch-based healthcare applications in the literature according to their application and summarizes what has led to feasible systems. To this end, we conducted a systematic review of peer-reviewed smartwatch studies related to healthcare by searching PubMed, EBSCOHost, Springer, Elsevier, Pro-Quest, IEEE Xplore, and ACM Digital Library databases to find articles between 1998 and 2016. Inclusion criteria were: (1) a smartwatch was used, (2) the study was related to a healthcare application, (3) the study was a randomized controlled trial or pilot study, and (4) the study included human participant testing. Each article was evaluated in terms of its application, population type, setting, study size, study type, and features relevant to the smartwatch technology. After screening 1,119 articles, 27 articles were chosen that were directly related to healthcare. Classified applications included activity monitoring, chronic disease self-management, nursing or home-based care, and healthcare education. All studies were considered feasibility or usability studies, and had limited sample sizes. No randomized clinical trials were found. Also, most studies utilized Android-based smartwatches over Tizen, custom-built, or iOS- based smartwatches, and many relied on the use of the accelerometer and inertial sensors to elucidate physical activities. The results show that most research on smartwatches has been conducted only as feasibility studies for chronic disease self-management. Specifically, these applications targeted various disease conditions whose symptoms can easily be measured by inertial sensors, such as seizures or gait disturbances. In conclusion, although smartwatches show promise in healthcare, significant research on much larger populations is necessary to determine their acceptability and effectiveness in these applications.

The prevalence of physical inactivity and non-communicable diseases is on the rise worldwide. This calls for a systematic approach in addressing the problem, which is almost becoming a global epidemic. Research has shown that theory-driven interventions are more likely to be effective than uninformed interventions. However, research on the determinants of physical activity and the moderating effect of culture is scarce. To bridge this gap, we conducted a large-scale comparative study of the determinants of physical activity among 633 participants from individualist and collectivist cultures. Using the Social Cognitive Theory, a widely applied behavioral theory in health interventions, we modeled the determinants of physical activity for each culture and mapped them to implementable strategies in the application domain. Our structural equation model shows that, in the individualist culture, Self-Efficacy (β_T = 0.55, p < 0.001) and Self-Regulation (β_T = 0.33, p < 0.001) are the strongest determinants of Physical Activity. However, in the collectivist culture, Social Support (β_T = 0.42, p < 0.001) and Outcome Expectation (β_T = 0.11, p < 0.01) are the strongest determinants of Physical Activity. We discussed these findings, mapped the respective behavioral determinants to the corresponding persuasive strategies in the health domain and provided a set of general design guidelines for tailoring the strategies to the respective cultures.

Machine learning-based patient monitoring systems are generally deployed on remote servers for analyzing heterogeneous data. While recent advances in mobile technology provide new opportunities to deploy such systems directly on mobile devices, the development and deployment challenges are not being extensively studied by the research community. In this paper, we systematically investigate challenges associated with each stage of the development and deployment of a machine learning-based patient monitoring system on a mobile device. For each class of challenges, we provide a number of recommendations that can be used by the researchers, system designers, and developers working on mobile-based predictive and monitoring systems. The results of our investigation show that when developers are dealing with mobile platforms, they must evaluate the predictive systems based on its classification and computational performance. Accordingly, we propose a new machine learning training and deployment methodology specifically tailored for mobile platforms that incorporates metrics beyond traditional classifier performance.

Individual performance on complex healthcare tasks can be influenced by acutely stressful situations. Real-time biofeedback using passive physiological monitoring may help to better understand an individual's progression towards acute stress-induced performance decrement. Providing biofeedback at an appropriate time may provide learners within an indicator that their current performance is susceptible to a decrement, and offer the opportunity to intervene. We explored the presentation timing of coping instructions during an acutely stressful task. In this pilot study, we recorded and analyzed electrocardiography data surrounding coping instruction presentation on various time schedules while participants played a first-person shooter computer game. Around times of significantly elevated heart rate, an indicator of acute stress, presenting a coping instruction tended to result in an increase in heart rate variability (HRV) following its presentation, with a more marked effect in high-stress conditions; not presenting a coping instruction at this time tended to result in a decrease in HRV in high-stress conditions, and no change in low-stress conditions. HRV following instruction presentation tended to increase in both high- and low-stress conditions when the instruction was presented at times of elevated heart rate; there was very little change in HRV when instruction presentation was not bound to physiology. Performance data showed that better performance was associated with greater adherence to coping instructions, compared to when zero instructions were followed. Implications for healthcare are significant, as acute stress is constant and it is necessary for providers to maintain a high level of performance.

Consumer sleep tracking technologies offer an unobtrusive and cost-efficient way to monitor sleep in free-living conditions. Technological advances in hardware and software have significantly improved the functionality of the new gadgets that recently appeared in the market. However, whether the latest gadgets can provide valid measurements on overall sleep parameters and sleep structure such as deep and REM sleep has not been examined. In this study, we aimed to investigate the validity of the latest consumer sleep tracking devices including an activity wristband Fitbit Charge 2 and a wearable EEG-based eye mask Neuroon in comparison to a medical sleep monitor. First, we confirmed that Fitbit Charge 2 can automatically detect the onset and offset of sleep with reasonable accuracy. Second, analysis found that both consumer devices produced comparable results in measuring total sleep duration and sleep efficiency compared to the medical device. In addition, Fitbit accurately measured the number of awakenings, while Neuroon with good signal quality had satisfactory performance on total awake time and sleep onset latency. However, measuring sleep structure including light, deep, and REM sleep remains to be challenging for both consumer devices. Third, greater discrepancies were observed between Neuroon and the medical device in nights with more disrupted sleep and when the signal quality was poor, but no trend was observed in Fitbit Charge 2. This study suggests that current consumer sleep tracking technologies may be immature for diagnosing sleep disorders, but they are reasonably satisfactory for general purpose and non-clinical use.

In this introductory paper, we begin by making the case for Computational Health Science, which we define as the interdisciplinary efforts of health scientists, computer scientists, engineers, psychologists, and other social scientists, to conduct innovative research that will inform future practice directed at changing health behavior through improved communication, networking, and social capital. We recognize and discuss some of the main challenges involved with such an enterprise, but also highlight the associated benefits, which, we argue, significantly outweigh them. We then provide a brief summary of the contributions to this first Special Issue on Computational Health Science.