Elsevier

Telematics and Informatics

Volume 34, Issue 8, December 2017, Pages 1419-1432
Telematics and Informatics

In search of computer-aided social support in non-communicable diseases care

https://doi.org/10.1016/j.tele.2017.06.005Get rights and content

Highlights

  • 38 chapters, journals and conferences from 2010 to 2016 were reviewed.

  • Text clustering software was used to optimize the text selection process.

  • Five main categories of papers were identified.

  • Data analytics is becoming a trend for discovering health information.

Abstract

Non-communicable diseases burden is well-known and care for these diseases goes beyond patients’ engagement, extending to their family, friends, and acquaintances. The ability of social relations in alleviating the harmful effects of health risks is known as social support. Computing can be used to promote social support to enhance the care of non-communicable diseases. However, it is unclear how computing obtains such enhancement. This paper presents a systematic review, in the form of a mapping study, aiming to answer how computing enhances non-communicable diseases care by using social data and by promoting social support. It also looks for available computing models focused on social support promotion in non-communicable diseases care. The study was guided by a two-phase process review, resulting in 38 reviewed papers from journals, conferences, and chapters in the period from 2010 to 2016. In general, the reviewed papers focus on controlled trials, frameworks and systems, knowledge discovery, simulation models or social media usage analysis. Knowledge discovery was the predominant subject, followed by social media usage analysis, and frameworks and systems.

Introduction

In 2014, the World Health Organization (WHO) defined the noncommunicable diseases (NCDs) as one of the greatest challenges of the twenty-first century health (WHO, 2014). Among other factors that led WHO to make this decision is the high death rate of those conditions. Only in 2012, the NCDs accounted for 68% of global deaths, and 40% of these deaths are considered premature. That is, deaths of individuals under 70 years old.

Most chronic diseases are caused by habit, such as a sedentary lifestyle, smoking, among others, that result in “metabolic/physiological changes”, such as high blood pressure, over- weight and obesity. Both habits and the results of these habits make up the risk factors that must be controlled in order to prevent cases of those diseases (WHO, 2005).

Treatment of NCDs should be continuous, since most of these diseases have no cure. Thus, patients must be aware of their condition, follow the treatment determined by their physician and learn how to act when necessary. Even so, just the engagement of patients is not enough to cope with the challenges related to their care. Sometimes, patients may not have the confidence to perform certain activities and need someone experienced to aid in their care (Wagner et al., 2001, Wagner and Grove, 2002, Bodenheimer et al., 2002). In this case, the participation of healthcare organizations, family, and community members in the assistance activities of these diseases is fundamental. These entities form the network of social relations of the patient (social network) (Barnes, 1954).

Social network has an important role in health as it regulates access to resources and opportunities to its members, as well as models their behavior, which may be of higher or lower risk. Hence, social support is the ability of that social network in alleviating the harmful effects caused by stress and other health risks through the provision of material, emotional and informational resources, and in the influence of behaviors such as eating, practicing physical activities, drug use and seeking medical follow-up (House et al., 1988).

Researches about the influence of the social environment on health are not new, being already addressed by Emile Durkheim in the nineteenth century. Durkheim contributed significantly with his studies on the weight of the social effect on individuals’ morbidity. In his study on suicide, Durkeheim analyzed particularly the influence that society has on the decision of an individual to commit suicide (Durkheim, 1897, Berkman et al., 2000). More recently, Christakis and Fowler used Framingham Heart Study (Framingham Heart Study, 2016) data to investigate the influence of social networking in individuals health. They found evidences of social network influence in weight gain (Christakis and Fowler, 2007), smoking cessation (Christakis and Fowler, 2008) and in the feeling of happiness (Fowler and Christakis, 2008).

Computing has been applied to support health care for decades. However, it is not clear how computing can aid and improve social support in NCDs care. Hence, the goal of this paper is to clarify this matter by the conduction of a mapping study that aims at understanding the current state of computer aided social support in NCDs care. Thus, this paper is organized as follows: in Section 2 we show how the mapping study was assembled and executed. Results regarding the study are presented in Section 3. In Section 4 we discuss about the obtained results. Finally, in Section 5, we present our final remarks about this work.

Section snippets

Material and methods

As a way to investigate how computing can aid social support on NCDs care, this paper will use systematic mapping study as methodology for its literature review (Budgen et al., 2008, Petersen et al., 2015, Cooper, 2016). Systematic mapping study, as systematic literature review (SLR), are types of systematic review. Even though systematic reviews are not frequently used in computing, they are widely recognized and applied in other areas such as medicine (Cooper, 2016) and social sciences (

Results

After the reviewing process, each paper was categorized as controlled trials, frameworks and systems, knowledge discovery, social media usage analysis or simulation models, according to its perceived characteristics.

Table 4 abstracts the reviewed papers and shows their title, where they were published, type of publication (e.g. journal, conference, or chapter), year of publication and how they were classified. Table 4 also indicates if papers promote social support, use social data, or present

Discussion

In this section, we discuss how the three research questions are accomplished according to the knowledge acquired through the conduction of this study.

Conclusions

The proposal of this paper was to present a mapping study aiming to enlighten future works in the field of computer aided social support for NCDs care. For this, we started by elaborating three research questions to guide the study. Afterwards, the questions were transformed in queries to be used in scientific repositories. The results were then clustered with suffix three method for selecting papers more closely related to the research questions. After that, the first pass of the three-pass

Acknowledgements

The authors wish to acknowledge that this work was supported by CNPq/Brazil (National Council for Scientific and Technological Developmenthttp://www.cnpq.br) and CAPES/Brazil (Coordination for the Improvement of Higher Education Personnel—http://www.capes.gov.br). We are also grateful to Unisinos (http://www.unisinos.br) for embracing this research.

References (65)

  • Barnes, J., 1954. Class and Committees in a Norwegian Island Parish. Human relations. [Offprint]....
  • K.L. Becker

    Cyberhugs: creating a voice for chronic pain sufferers through technology

    Cyberpsychology Behav. Social Networking

    (2012)
  • Bhadoria, R.S., 2015. Security Architecture for Cloud...
  • T. Bodenheimer et al.

    Improving primary care for patients with chronic illness

    JAMA

    (2002)
  • G.E. Bond et al.

    The effects of a web-based intervention on psychosocial well-being among adults aged 60 and older with diabetes

    Diabetes Educ.

    (2010)
  • D. Budgen et al.

    Using mapping studies in software engineering

  • J.-C. Chiêm et al.

    Rule-based modeling of chronic disease epidemiology: elderly depression as an illustration

    PLoS One

    (2012)
  • N.A. Christakis et al.

    The collective dynamics of smoking in a large social network

    N. Engl. J. Med.

    (2008)
  • N.A.A. Christakis et al.

    The spread of obesity in a large social network over 32 years

    N. Engl. J. Med.

    (2007)
  • I.D. Cooper

    What is a mapping study?

    J. Med. Lib. Assoc.: JMLA

    (2016)
  • A. Culotta

    Estimating county health statistics with twitter

  • A. D’Ambrosio et al.

    Web-based surveillance of public information needs for informing preconception interventions

    PLoS One

    (2015)
  • Dhillon, J.S., Wünsche, B.C., Lutteroth, C., 2013. Accessible telehealth – leveraging consumer-level technologies and...
  • E. Durkheim

    Suicide: a study in sociology

    (2005)
  • M. van der Eijk et al.

    Using online health communities to deliver patient-centered care to people with chronic conditions

    J. Med. Internet Res.

    (2013)
  • J.H. Fowler et al.

    Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the framingham heart study

    Br. Med. J.

    (2008)
  • Framingham Heart Study, 2016. History of the framingham heart study....
  • Gomez-Galvez, P., Mejías, C.S., Fernandez-Luque, L., 2015. Social media for empowering people with diabetes: Current...
  • Google Trends, 2016. Google Trends Search on Big Data and Machine Learning From 2010 to 2016. (Online; accessed...
  • J.A. Hartigan

    Clustering Algorithms

    (1975)
  • B. Holtz et al.

    Comparison of veteran experiences of low-cost, home-based diet and exercise interventions

    J. Rehabil. Res. Dev.

    (2014)
  • J. House et al.

    Social relationships and health

    Science

    (1988)
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