Rhythmicity of health information behaviour. Utilizing the infodemiology approach to study temporal patterns and variations
| DOI | https://doi.org/10.1108/AJIM-01-2019-0029 |
| Date | 18 November 2019 |
| Published date | 18 November 2019 |
| Pages | 773-788 |
| Author | Jonas Tana,Emil Eirola,Kristina Eriksson-Backa |
| Subject Matter | Library & information science |
Rhythmicity of health
information behaviour
Utilizing the infodemiology approach to study
temporal patterns and variations
Jonas Tana
Department of Healthcare,
Arcada University of Applied Sciences, Helsinki, Finland and
Department of Information Studies, Faculty of Social Sciences,
Business and Economics, Åbo Akademi University,
Turku, Finland
Emil Eirola
Department of Business Management and Analytics,
Arcada University of Applied Sciences, Helsinki, Finland, and
Kristina Eriksson-Backa
Department of Information Studies, Faculty of Social Sciences,
Business and Economics, Åbo Akademi University,
Turku, Finland
Abstract
Purpose –This paper brings focus and attention to the aspect of time within health information behaviour.
The purpose of this paper is to critically assess and present strengths and weaknesses of utilising the
infodemiology approach and metrics as a novel way to examine temporal variations and patterns of online
health information behaviour. The approach is shortly exemplified by presenting empirical evidence for
temporal patterns of health information behaviour on different time-scales.
Design/methodology/approach –A short review of online health information behaviour is presented and
methodological barriers to studying the temporal nature of this behaviour are emphasised. To exemplify how
the infodemiology approach and metrics can be utilised to examine temporal patterns, and to test the
hypothesis of existing rhythmicity of health information behaviour, a brief analysis of longitudinal data from
a large discussion forum is analysed.
Findings –Clear evidence of robust temporal patterns and variations of online health information behaviour
are shown. The paper highlights that focussing on time and the question of when people engage in health
information behaviour can have significant consequences.
Practical implications –Studying temporal patterns and trends for health information behaviour can help
in creating optimal interventions and health promotion campaigns at optimal times. This can be highly
beneficial for positive health outcomes.
Originality/value –A new methodological approach to study online health information behaviour from a
temporal perspective, a phenomenon that has previously been neglected, is presented. Providing evidence for
rhythmicity can complement existing epidemiological data for a more holistic picture of health and diseases,
and their behavioural aspects.
Keywords Methodology, Social media, Time, Infodemiology, Health information behaviour,
Temporal variations
Paper type Research paper
Aslib Journal of Information
Management
Vol. 71 No. 6, 2019
pp. 773-788
Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-01-2019-0029
Received 30 January 2019
Revised 27 February 2019
4 March 2019
13 March 2019
Accepted 25 March 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
© Jonas Tana, Emil Eirola and Kristina Eriksson-Backa. Published by Emerald Publishing Limited.
This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may
reproduce, distribute, translate and create derivative works of this article (for both commercial & non-
commercial purposes), subject to full attribution to the original publication and authors. The full terms of
this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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Introduction
Time touches every dimension of our being, it is everywhere in human life and is constitutive of
life in nature as well as society (Bender and Wellbery, 1991; Luckmann, 1991; Roenneberg,
2012). All organisms, from single cells to human beings, follow rhythmic behaviour that is the
key to the time-world of nature (Adam, 1990). The rhythmicity or temporal structures of hours,
days, weeks, months, seasons and years deeply affect the earth’s environment and governs and
contextualises many, if not all, aspects of life, including health and illness (Mogilner et al., 2018;
Ayers et al., 2014). This rhythmicity is a universal phenomenon (Adam, 1990). However, in spite
of time beingso omnipresent andpervasive in everydaylife, as well as important to our health
and well-being and behaviours related to them, time has in many aspects been treated as a
phenomenon of secondary importance and even thoroughly neglected (Adam, 1990; Davies and
McKenzie, 2002; Giddens, 1979; Luckmann, 1991; Roenneberg, 2012).
These temporal structures can be divided into circadian, circaseptan, circa-monthly and
circannual. Circadian rhythms are associated with the 24-h cycle arising from the rotation of
the earth around its axis, circaseptan rhythms describes the cyclic seven-day phenomena,
whereas circa-monthly rhythms are associated by the complete lunar orbit around earth
every 30 (29.53) days (Reinberg et al., 2017). Seasonal, or circannual time-periods, again, are
entrained by seasonal and annual changes caused by rotation of the earth around its sun
(Reinberg et al., 2017). All these rhythms have been found to affect health, making them
significant to study in relation to the temporal variations of health-related behaviours
(Ayers et al., 2014; Reinberg et al., 2017). Many health behaviours, issues and disorders, both
somatic and psychological, follow different temporal patterns on circannual, circa-monthly,
circaseptan, as well as circadian, levels. Variations in temporal structures have been
reported for a myriad of symptoms and diseases as well as health-related behaviours, from
sleeping disorders and depression to risk behaviours and diseases like, for example,
diabetes, cardiovascular diseases and cancer (Basnet et al., 2016; Gabarron et al., 2015;
Madden, 2017; Reinberg et al., 2017). This is the case for health information behaviour, or
how people seek, obtain, evaluate, categorise and use health-related information, as well
(Ek, 2013). Health information behaviour can be conceptualised as a process initiated by a
health-related stimuli, an information need or a knowledge gap (Lambert and Loiselle, 2007;
Ormandy, 2011). These gaps can be seen as a discontinuity condition for the individual, and
are a fundamental aspect of reality (Savolainen, 1993, 2006). They are also temporal, affected
and changing over time (Ormandy, 2011).
Research onthe temporal patterns and variationsof health informationbehaviour is scarce
(Davies and McKenzie, 2002; Savolainen, 2006; Savolainen, 2018). Reasons for thishave been
suggested to be methodological. Traditional health-relateddata are usually gathered through
surveys or interviews on an annual or semi-annual level, leaving temporal variations and
rhythms, except some annual or seasonal, outside the scope of data collection and only
providing snapshots of health behaviour in relation to time (Anker et al., 2011; Ayers et al.,
2014). Research on health-related behaviour and conditions are, furthermore, often based on
clinical data, and there is limited use of preclinical data on behavioural patterns, including
health information behaviour, in a population. While traditional data gathering methods
remain invaluable, new data and novel methods such as infodemiology, that is, aggregation
and analysis of health-related information especially on the internet (Eysenbach, 2009), can
complement this data and help shed some light on the temporal rhythms related to health
information behaviour. More and more people turn to the internetin health-related matters to
be better informed, seekpeer-support, find alternative treatments, take charge of their health
and manage minor conditions they perceived they had (Eysenbach, 2009; Eysenbach, 2011;
Lee et al., 2014). Utilising novel methodsand metrics, in this case the infodemiologyapproach,
and mining data from this type of online health information behaviour can complement and
broaden our understanding of health-related knowledge gaps, and provide newinsights into
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