Call for Papers: Towards Preventive Health Care through Digital Technologies
• 大类 : 医学 - 3区
• 小类 : 计算机：跨学科应用 - 3区
• 小类 : 医学：信息 - 3区
It is well known that preventive medicine aims to reduce the incidence of diseases, even seeking their eventual eradication or, at least, minimizing their severity and progression (impact on patients) [1,2]. Thus, digital approaches to preventive health care do not necessarily focus solely on the prevention of disease occurrence. Every stage of a disease may be tackled along a spectrum, from primordial prevention (i.e., educate people to practice preventive behaviors and habits to avoid diseases or injuries before they start) to tertiary prevention aimed at rehabilitation following significant illness (i.e., reduce impairment by rehabilitation and through re-education; or limiting severity of disability through non-intrusive continuous monitoring and assistive technologies for pervasive health care) .
There are intermediate stages along the spectrum of digital preventive health care, such as primary prevention (i.e., detect symptoms before the onset of an illness or injury, known also as disease pre-pathogenesis); or secondary prevention  (i.e., increase patient survival by halting or mitigating disease progression, preventing more severe problems and complications through adaptive treatments).
Current technological advances have made significant improvements to deal with each of these prevention stages [5,6]. The landscape for digital prevention has evolved in recent years to include the concepts of big data, cloud and fog capabilities for predictive analytics  and the use of data gathered through remote monitoring  (e.g., mHealth, teleHealth), and real-time patient status follow-up).
The main aim of digital preventive medicine is “to work closely with individual patients", monitoring their particular health conditions . This requires implementing proactive health-focused tests for each of the prevention levels , with special emphasis on primordial and primary prevention stages, i.e., the ones where diseases or injuries have not yet started, even though some symptomatology might already be manifested. These tests should be remote and non-intrusive for people living independently or in communities  and, as far as possible, continuous or in real-time. Vital signs can be monitored, as well as derived markers for specific dimensions, such as functional, cognitive , behavioral , nutritional , and social factors that can be used in diagnosis  (e.g., gait analysis, wandering patterns in home environments, daily energy expenditure, changes in social behaviors, sleep patterns, daily intake, or unintentional weight loss).
Data acquisition typically must rely on wearable sensors, mobile devices, and mHealth apps, but additional inputs can be derived from static embedded sensors arranged in different environments and from knowledge provided by specialists and caregivers. All these heterogeneous data must be preprocessed locally, partially locally (fog), or entirely remotely (cloud) before applying different inference strategies. The latter will determine whether collected data and a patient’s baseline profile match corresponding patterns found in large datasets stored in the cloud. Predictive analytic techniques may be used in each of the prevention levels . The resultant information may then be uploaded to the patient’s history stored in an electronic health record which may, in turn, be used in other large scale analyses.
In this JBI special issue, we solicit contributions presenting novel methods that focus on acquiring, preprocessing, uploading to the cloud, mining, categorizing, summarizing, integrating and analyzing large datasets of heterogeneous information for any level of preventive health care. Furthermore, novel contributions for the acquisition of vital signs and derived health markers through non-intrusive techniques, such as embodied sensing, environmental sensing, or mHealth solutions, are welcome. Figure 1 illustrates the main aspects of the preventive healthcare ecosystem proposed for inclusion in this special issue.
The suggested topics listed below can be discussed in terms of concepts, the state of the art, and standards, but all papers should emphasize the novel methods (and motivating applications) that constitute the paper’s contribution to the science of informatics.
Primordial digital prevention:
New technologies and strategies for preventive healthcare promotion and patient education.
Effects of technology on health promotion and patient education.
Limitations and drawbacks of technology use in preventive healthcare.
The harnessing of social media in improvement of healthcare knowledge.
Assessing the impact of digital campaigns on preventive healthcare.
Real-time health advice and coaching systems.
Gamification for preventive healthcare promotion and patient education.
Electronic health/patient record innovations.
Health data interoperability (standards, security, privacy policies).
Primary digital prevention (detecting symptoms before the onset of the disease):
Long-term & remote monitoring for diagnosis.
Patient similarity in prediction models based on health data.
Gamification for primary prevention.
Early detection and prevention of diseases through predictive analytics.
Real-time healthcare monitoring for early diagnosis.
Environmental healthcare systems (IoT for preventive healthcare).
Body area sensor networks and mHealth applications for primary prevention.
Fog and cloud computing-based infrastructures for primary prevention.