Call for Papers: Towards Preventive Health Care through Digital Technologies

in Special Issue   Posted on June 12, 2019 

Information for the Special Issue

Submission Deadline: Sat 15 Feb 2020
Journal Impact Factor : 3.526
Journal Name : Journal of Biomedical Informatics
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Special Issue Call for Papers:

Due date for submissions:  February 15, 2020

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) [3].

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 [4] (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 [7] and the use of data gathered through remote monitoring [8] (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 [9]. This requires implementing proactive health-focused tests for each of the prevention levels [10], 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 [11] 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 [12], behavioral [13], nutritional [14], and social factors that can be used in diagnosis [15] (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 [16]. 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.

Figure 1. Preventive healthcare ecosystem.


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.
  • Secondary digital prevention:
  • Long-term patient follow-up through digital technologies to assess disease progression.
  • Real-Time healthcare monitoring and alerting systems of abnormal values.
  • Remote adaptative treatment and smart actuators to mitigate disease complications.
  • Predictive analytics’ role in secondary prevention.
  • Body area sensor networks and mHealth applications for secondary prevention.
  • Fog and cloud computing-based infrastructures for secondary prevention.
  • Tertiary digital prevention:
  • IoT environmental solutions. Technological accommodation of environments to live with a particular disease.
  • Real-time systems for rehabilitation and/or assistive feedback.
  • Management of chronic and non-chronic diseases through digital technologies.
  • Gamification for rehabilitation.
  • Predictive analytics’ role in tertiary prevention.
  • Body area sensor networks and mHealth applications for tertiary prevention.
  • Fog and cloud computing-based infrastructures for tertiary prevention.

Peer Review Process

All submitted papers must be original and will go through a rigorous peer-review process with at least two reviewers. JBI’s editorial policy will be strictly followed by special issue reviewers. Note in particular that JBI emphasizes the publication of papers that introduce innovative and generalizable methods of interest to the informatics community. Specific applications can be described to motivate the methodology being introduced, but papers that focus solely on a specific application are not suitable for JBI.  


Authors must submit their papers via the online Elsevier Editorial System (EES) at by February 15, 2020. Authors should select “Digital Prevention” as their submission category and note in a cover letter that their submission is for the special issue “Towards Preventive Health Care through Digital Technologies.” If the manuscript is not intended as an original research paper, the cover letter should also specify if it is, rather, a Methodological Review, Commentary, or Special Communication. Authors should make sure to place their work in the context of human-focused biomedical research or health care, and to review carefully the relevant literature.

JBI’s editorial policy, and the types of articles that the journal publishes, are outlined under Aims and Scope on the journal home page at (click on “Read more” for full details). All submissions should follow the guidelines for authors at informatics/1532-0464/guide-for-authors. Authors should format and structure their manuscripts according to the guidelines. If the authors speak a first language other than English, editorial assistance by a native English speaker is highly recommended prior to submission. Open-source software code and data should ideally be made available through Internet resources that are enduring. JBI is an international journal and generalizable contributions from throughout the world are highly encouraged.

Questions Regarding the Special Issue

Please direct any questions to Dr. Jose Bravo .


[1]    Clarke EA. What is preventive medicine? Canadian Family Physician 1974; 20(): 65–8. PubMed PMID: 20469128; PubMed Central PMCID: PMC2274388.

[2]    Kresge KJ. Treatment is prevention. IAVIReport 2011; 15(4): 14–6.

[3]    World Health Organization 2015. Self-assessment tool for the evaluation of essential public health operations in the WHO European Region. Chapter: EPHO5. Disease prevention, including early detection of illness. Pp. 64–71. ISBN: 978 92 890 50999.

[4]    Spaulding EM, Marvel FA, Lee MA, et al. Corrie Health Digital Platform for Self-Management in Secondary Prevention After Acute Myocardial Infarction. Circulation: Cardiovascular Quality and Outcomes 2019; 12(5). DOI: 10.1161/CIRCOUTCOMES.119.005509.

[5]    Kelly MP, Digital Technologies and Disease Prevention. American Journal of Preventive Medicine 2016; 51(5): 861–6. DOI: 10.1016/j.amepre.2016.06.012.

[6]    Hlaing PM, Nopparatjamjomras TR, Nopparatjamjomras S. Digital technology for preventative health care in Myanmar. Digital Medicine 2018; 4(3): 117–21. DOI: 10.4103/digm.digm_25_18.

[7]    Cerina L, Notargiacomo S, Paccanit MG, Santambrogio MD. A fog-computing architecture for preventive healthcare and assisted living in smart ambients. In: IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), Modena, 2017, pp. 1-6. DOI: 10.1109/RTSI.2017.8065939

[8]    Dinesen B, Nonnecke B, Lindeman D, et al. Personalized Telehealth in the Future: A Global Research Agenda. Journal of Medical Internet Research 2016; 18(3): e53. DOI: 10.2196/jmir.5257

[9]    St. George’s University. Medical School Programs. What Is Preventive Medicine? A Look at What These Proactive Providers Do.2018;

[10] Grossglauser M, Saner H. Data-driven healthcare: from patterns to actions. European Journal of Preventive Cardiology 2014; 21(2S): 14–17. DOI: 10.1177/2047487314552755

[11] Goodman RA, Bunnel R, Posner SF. What is “community health”? Examining the meaning of an evolving field in public health. Preventive Medicine 2014; 67(Suplement 1): 558–61. DOI: 10.1016/j.ypmed.2014.07.028.

[12] Kim YS, Park YS, Allegrante JP, Marks R, Ok H, Cho KO, Garber CE. Relationship between physical activity and general mental health. Preventive Medicine 2012; 55(5): 458–63. DOI: 10.1016/j.ypmed.2012.08.021.

[13] Shea P, Shern D. Primary Prevention in Behavioral Health: Investing in our Nation’s Future 2011. National Association of State Mental Health Program Directors (NASMHPD)

[14] Anderson JV, Palombo RD, Earl R. Position of the American Dietetic Association: the role of nutrition in health promotion and disease prevention programs. Journal of the American Dietetic Association 1998; 98(2): 205–8. DOI: 10.1016/S0002-8223(98)00052-2

[15] Chang Y-W, Chen W-L, Lin F-G, Fang W-H, Yen M-Y, Hsieh C-C, et al. Frailty and Its Impact on Health-Related Quality of Life: A Cross-Sectional Study on Elder Community-Dwelling Preventive Health Service Users. PLoS ONE 2012; 7(5): e38079. DOI: 10.1371/journal.pone.0038079

[16] Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014; 33(7). DOI: 10.1377/hlthaff.2014.0041

Guest Editors

José Bravo
Email: [email protected]

Iván González
Email: [email protected]

Castilla-La Mancha University (MAmI Research Lab.)
Ciudad Real, Spain

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