The aspect of time in online health information behaviour

a longitudinal extensive analysis of the Suomi24 discussion forum

Kirjoittajat

• Jonas Tana Arcada University of Applied Sciences; Åbo Akademi University, https://orcid.org/0000-0002-6821-1571
• Emil Eirola Arcada University of Applied Sciences https://orcid.org/0000-0001-7404-977X
• Kristina Eriksson-Backa Åbo Akademi University https://orcid.org/0000-0001-9758-0467

DOI:

https://doi.org/10.23978/inf.83315

Avainsanat:

health information behavior, health, social media, time, temporality

Abstrakti

Temporal structures and rhythmicity are universal phenomena and affect all aspects of life, including dynamic processes like health and well-being that change over time. Health related issues and threats trigger health information behaviour, a majority of which happens online and thus leaves digital traces behind. This study analyses the temporal variations and rhythmicity of health information behaviour through the action of online posting in a large Finnish discussion forum, Suomi24. The findings in this study show that health information behaviour follow clear and robust rhythmicity on both a seasonal and daily level. This endorses the notion that well-being, health and illness are dynamic processes that change with time and can help provide a more holistic picture of health and well-being, including aspects that fall outside professional healthcare settings. Studying when health information behaviour happens can thus have wide-ranging consequences.

Lähdeviitteet

Abedi, V., Mbaye, M., Tsivgoulis, G., Male, S., Goyal, N., Alexandrov, A.V. and Zand, R. (2015). Internet-based information-seeking behavior for transient ischemic attack. International Journal of Stroke, 10(8), 1212-1216. https://doi.org/10.1111/ijs.12593

Aller, (2016). Suomi24 Media Card. Available at https://www.aller.fi/wp-content/uploads/2016/01/Suomi24-Tuotekortti-1_2016.pdf

Adam, B. (1990). Time and Social Theory. Polity Press, Cambridge.

Anker, A. E, Reinhart, M. R. and Feeley, T. H. (2011). Health information seeking: A review of measures and methods. Patient education and counselling, 82(3), 346-354. https://doi.org/10.1016/j.pec.2010.12.008

Arendt, F. and Scherr, S. (2017). Optimizing online suicide prevention: a search engine-based tailored approach. Health Communication, 32(11), 1403-1408. https://doi.org/10.1080/10410236.2016.1224451

Ayers, J. W., Althouse, B. M., Allem, J. P., Rosenquist, J. N. and Ford, D. E. (2013). Seasonality in seeking mental health information on Google. American Journal of Preventive Medicine, 44(5), 520-525. https://doi.org/10.1016/j.amepre.2013.01.012

Ayers, J. W., Althouse, B. M. and Dredze, M. (2014). Could behavioral medicine lead the web data revolution?. Jama, 311(14), 1399-1400. https://doi.org/10.1001/jama.2014.1505

Basnet, S., Merikanto, I., Lahti, T., Männistö, S., Laatikainen, T., Vartiainen, E. and Partonen, T. (2016). Seasonal variations in mood and behavior associate with common chronic diseases and symptoms in a population-based study. Psychiatry Research, 238, 181-188. https://doi.org/10.1016/j.psychres.2016.02.023

Bragazzi, N. L., Alicino, C., Trucchi, C., Paganino, C., Barberis, I., Martini, M., Sticchi, L., Trinka, E., Brigo, F., Ansaldi, F. and Icardi, G. (2017). Global reaction to the recent outbreaks of Zika virus: insights from a big data analysis. PloS One, 12(9), e0185263. https://doi.org/10.1371/journal.pone.0185263

Brigo, F. and Trinka, E. (2015). Google search behavior for status epilepticus. Epilepsy & Behavior, 49, 146-149. https://doi.org/10.1016/j.yebeh.2015.02.029

Carneiro, H. A. and Mylonakis, E. (2009). Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557-1564. https://doi.org/10.1086/630200

Case, Donald. (2012). Looking for information: A survey of research on information seeking, needs, and behavior. Emerald Group Publishing.

Chen, X., Sykora, M. D., Jackson, T. W. and Elayan, S. (2018). What about mood swings: identifying depression on Twitter with temporal measures of emotions. In Companion of the The Web Conference 2018 on The Web Conference 2018, 1653-1660. https://doi.org/10.1145/3184558.3191624

Chew, C. and Eysenbach, G. (2010). Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS One, 5(11), e14118. https://doi.org/10.1371/journal.pone.0014118

Dai, H., Milkman, K. L. and Riis, J. (2014). The fresh start effect: Temporal landmarks motivate aspirational behavior. Management Science, 60(10), 2563-2582. https://doi.org/10.1287/mnsc.2014.1901

Dervin, B. and Nilan, M. (1986). Information needs and uses. In M. E. Williams (Ed.) Annual review of Information Science and Technology, 21, 3-33. White Plains, New York. Knowledge Indistry Inc.

Dervin, B. (1992). From the mind’s eye of the user: The sense-making qualitative-quantitative methodology. In Glazier, J. D. and Powell, R. P. (eds.) Qualitative research in information management (pp. 61-84). Engelwood, Colorado: Libraries Unlimited.

Dervin, B. (1999). On studying information seeking methodologically: the implications of connecting metatheory to method. Information Processing & Management, 35(6), 727-750. https://doi.org/10.1016/S0306-4573(99)00023-0

Dervin, B. (2005). What methodology does to theory: sense-making methodology as exemplar. In Fisher, K.E., Erdelez S. and McKechnie L. (eds.), Theories of information behavior (pp. 25-30). Medford, New Jersey: Information Today, Inc.

Ek, S. (2013). Gender differences in health information behaviour: a Finnish population-based survey. Health promotion international, 30(3), 736-745. https://doi.org/10.1093/heapro/dat063

Ek, S., Eriksson-Backa, K. and Niemelä, R. (2013). Use of and trust in health information on the Internet: A nationwide eight-year follow-up survey. Informatics for Health and Social Care, 38(3), 236-245. https://doi.org/10.3109/17538157.2013.764305

Eriksson, K. (1993). Hälsans idé. Liber/Almqvist & Wiksell medicin.

Eysenbach, G. (2009). Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of medical Internet research, 11(1). https://dx.doi.org/10.2196%2Fjmir.1157

Eysenbach, G. (2011). Infodemiology and infoveillance: tracking online health information and cyberbehavior for public health. American journal of preventive medicine, 40(5), S154-S158. https://doi.org/10.1016/j.amepre.2011.02.006

FIAM - Finnish Internet Audience Measurement, (2018). Tulokset. Available at: http://fiam.fi/

tulokset/

Gabarron, E., Lau, A. Y. and Wynn, R. (2015). Is there a weekly pattern for health searches on Wikipedia and is the pattern unique to health topics?. Journal of medical Internet research, 17(12). https://dx.doi.org/10.2196%2Fjmir.5038

Guy, S., Ratzki-Leewing, A., Bahati, R. and Gwadry-Sridhar, F. (2011, November). Social media: A systematic review to understand the evidence and application in infodemiology. In International Conference on Electronic Healthcare (pp. 1-8). Springer, Berlin, Heidelberg.

Hakko H., Räsänen P. and Tiihonen J. (1998). Seasonal variation in suicide occurrence in Finland. Acta Psychiatrica Scandinavica, 98(2), 92-97. https://doi.org/10.1111/j.1600-0447.1998.tb10048.x

Halder, K., Poddar, L. and Kan, M. Y. (2017). Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 127-135. http://dx.doi.org/10.18653/v1/W17-5217

Hausner, H., Hajak, G. and Spießl, H. (2008). Gender differences in help-seeking behavior on two internet forums for individuals with self-reported depression. Gender Medicine, 5(2), 181-185. https://doi.org/10.1016/j.genm.2008.05.008

Henly, S. J., Wyman, J. F. and Findorff, M. J. (2011). Health and illness over time: The trajectory perspective in nursing science. Nursing research, 60(3 Suppl), S5. https://doi.org/10.1097/NNR.0b013e318216dfd3

Holopainen J., Helama S., Björkenstam C. and Partonen T. (2013). Variation and seasonal patterns of suicide mortality in Finland and Sweden since the 1750s. Environmental Health and Preventive Medicine, 18(6), 494-501. https://doi.org/10.1007/s12199-013-0348-4

Kim, S. M. and Oh, J. Y. (2011). Health information acquisition online and its influence on intention to visit a medical institution offline. Information Research, 16(4). http://www.informationr.net/ir/16-4/paper505.html

Kraut, R. Y., Snedeker, K. G., Babenko, O. and Honish, L. (2017). Influence of school year on seasonality of norovirus outbreaks in developed countries. Canadian Journal of Infectious Diseases and Medical Microbiology, 2017, 9258140. https://doi.org/10.1155/2017/9258140

Lambert, S. D. and Loiselle, C. G. (2007). Health information—seeking behavior. Qualitative health research, 17(8), 1006-1019. https://doi.org/10.1177/1049732307305199

Lee, K., Hoti, K., Hughes, J. D. and Emmerton, L. (2014). Dr Google and the consumer: a qualitative study exploring the navigational needs and online health information-seeking behaviors of consumers with chronic health conditions. Journal of Medical Internet Research, 16(12), e262. https://dx.doi.org/10.2196%2Fjmir.3706

Madden, K. M. (2017). The seasonal periodicity of healthy contemplations about exercise and weight loss: ecological correlational study. JMIR Public Health and Surveillance, 3(4), e92. https://dx.doi.org/10.2196%2Fpublichealth.7794

Matsuda, S., Aoki, K., Tomizawa, S., Sone, M., Tanaka, R., Kuriki, H. and Takahashi, Y. (2017). Analysis of patient narratives in disease blogs on the internet: an exploratory study of social pharmacovigilance. JMIR Public Health and Surveillance, 3(1), e10. https://dx.doi.org/10.2196%2Fpublichealth.6872

McGowan, P. (2002). Self-care behavior. In L. Breslow (Ed.), Encyclopedia of Public Health. New York : Macmillan Reference.

McKenzie, P. J. and Davies, E. (2002). Time is of the essence: Social theory of time and its implications for LIS research. In Proceedings of the Annual Conference of CAIS/Actes du congrès annuel de l'ACSI 2002, 1-13. http://publish.uwo.ca/~pmckenzi/McKenzie_Davies_CAIS2002_paper.pdf

Mogilner, C., Hershfield, H. E. and Aaker, J. (2018). Rethinking time: Implications for well-being. Consumer Psychology Review, 1(1), 41-53. https://doi.org/10.1002/arcp.1003

Mustonen, H., Metso, L. and Mäkelä, P. (2010). Milloin suomalaiset juovat. In Mäkelä, P., Mustonen, H. and Tigerstedt, C. (eds.). Suomi juo - Suomalaisten alkoholinkäyttö ja sen muutokset 1968–2008 (pp. 55-69), Terveyden ja hyvinvoinnin laitos, Helsinki. http://urn.fi/URN:NBN:fi-fe201205085402

Nimrod, G. (2013). Online depression communities: members’ interests and perceived benefits. Health Communication 28(5), 425-434. https://doi.org/10.1080/10410236.2012.691068

Oliphant T. (2010). The Information Practices of People Living with Depression: Constructing Credibility and Authority. Faculty of Information & Media Studies University of Western Ontario, London. http://ir.lib.uwo.ca/etd/35

Ortiz-Martínez, Y. and Jiménez-Arcia, L. F. (2017). Yellow fever outbreaks and Twitter: rumors and misinformation. American Journal of Infection Control, 45(7), 816-817. https://doi.org/10.1016/j.ajic.2017.02.027

Osuka, H., Hall, A. J., Wikswo, M. E., Baker, J. M. and Lopman, B. A. (2018). Temporal relationship between healthcare-associated and nonhealthcare-associated norovirus outbreaks and Google Trends data in the United States. Infection Control & Hospital Epidemiology, 39(33), 355-358. https://doi.org/10.1017/ice.2017.322

Paul, M. J., Chisolm, M. S., Johnson, M. W., Vandrey, R. G. and Dredze, M. (2016). Assessing the validity of online drug forums as a source for estimating demographic and temporal trends in drug use. Journal of addiction medicine, 10(5), 324-330. https://doi.org/10.1097/ADM.0000000000000238

Pesälä, S., Virtanen, M. J., Sane, J., Mustonen, P., Kaila, M. and Helve, O. (2017). Health information–seeking patterns of the general public and indications for disease surveillance: register-based study using Lyme disease. JMIR Public Health and Surveillance, 3(4), e86. https://dx.doi.org/10.2196%2Fpublichealth.8306

Poikolainen, K., Leppänen, K. and Vuori, E. (2002). Alcohol sales and fatal alcohol poisonings: a time-series analysis. Addiction, 97(8), 1037-1040. https://doi.org/10.1046/j.1360-0443.2002.00177.x

Reinberg, A. E., Dejardin, L., Smolensky, M. H. and Touitou, Y. (2017). Seven-day human biological rhythms: An expedition in search of their origin, synchronization, functional advantage, adaptive value and clinical relevance. Chronobiology international, 34(2), 162-191. https://doi.org/10.1080/07420528.2016.1236807

Roenneberg, T. (2012). Internal time: Chronotypes, social jet lag, and why you're so tired. Harvard University Press.

Sansone, R. A. and Sansone, L. A. (2011). The christmas effect on psychopathology. Innovations in clinical neuroscience, 8(12), 10. https://www.ncbi.nlm.nih.gov/pubmed/22247812

Savolainen, R. (2006). Time as a context of information seeking. Library & Information Science Research, 28(1), 110-127. https://doi.org/10.1016/j.lisr.2005.11.001

Savolainen, R. (2018). Information-Seeking Processes as Temporal Developments: Comparison of Stage-based and Cyclic Approaches. Journal of the Association for Information Science and Technology, 69(6), 787-797. https://doi.org/10.1002/asi.24003

Seifter, A., Schwarzwalder, A., Geis, K. and Aucott, J. (2010). The utility of “Google Trends” for epidemiological research: Lyme disease as an example. Geospatial Health, 4(2), 135-137. https://doi.org/10.4081/gh.2010.195

Seo, D. W. and Shin, S. Y. (2017). Methods using social media and search queries to predict infectious disease outbreaks. Healthcare Informatics Research, 23(4), 343-348. https://doi.org/10.4258/hir.2017.23.4.343

Siepmann, M. (2008). Health behavior. In Wilhelm Kirch (Ed.), Encyclopedia of Public Health (pp. 515-521). Dordrecht: Springer Science & Business Media.

Solomon, P. (1997). Discovering information behavior in sense making. I. Time and timing. Journal of the American Society for Information Science, 48(12), 1097-1108. https://doi.org/10.1002/(SICI)1097-4571(199712)48:12<1097::AID-ASI4>3.0.CO;2-P

Svalastog, A. L., Donev, D., Kristoffersen, N. J. and Gajović, S. (2017). Concepts and definitions of health and health-related values in the knowledge landscapes of the digital society. Croatian medical journal, 58(6), 431. https://dx.doi.org/10.3325%2Fcmj.2017.58.431

Tana, J. (2018). An infodemiological study using search engine query data to explore the temporal variations of depression in Finland. Finnish Journal of eHealth and eWelfare, 10(1), 133-142. https://doi.org/10.23996/fjhw.60778

Tana, J. C., Kettunen, J., Eirola, E. and Paakkonen, H. (2018). Diurnal variations of depression-related health information seeking: case study in Finland using Google Trends data. JMIR Mental Health, 5(2), e43. https://dx.doi.org/10.2196%2Fmental.9152

Tilastokeskus. (2017). Väestön tieto- ja viestintätekniikan käyttö 2017. Available at: https://www.stat.fi/til/sutivi/2017/13/sutivi_2017_13_2017-11-22_fi.pdf

Tkachenko, N., Chotvijit, S., Gupta, N., Bradley, E., Gilks, C., Guo, W., Crosby, H., Shore, E., Thiarai, M., Procter, R. and Jarvis, S. (2017). Google trends can improve surveillance of type 2 diabetes. Scientific Reports, 7(1), 4993. https://doi.org/10.1038/s41598-017-05091-9

Velamoor, V. R., Voruganti, L. P. and Nadkarni, N. K. (1999). Feelings about Christmas, as reported by psychiatric emergency patients. Social Behavior and Personality, 27(3), 303–308. https://doi.org/10.2224/sbp.1999.27.3.303

Wilson, T. D. (1999). Models in information behaviour research. Journal of documentation, 55(3), 249-270. https://doi.org/10.1108/EUM0000000007145

Wong, P. W. C., Fu, K. W., Yau, R. S. P., Ma, H. H. M., Law, Y. W., Chang, S. S. and Yip, P. S. F. (2013). Accessing suicide-related information on the internet: a retrospective observational study of search behavior. Journal of Medical Internet Research, 15(1), e3. https://dx.doi.org/10.2196%2Fjmir.2181

Zeraatkar, K. and Ahmadi, M. (2018). Trends of infodemiology studies: a scoping review. Health Information & Libraries Journal, 35(2), 91-120. https://doi.org/10.1111/hir.12216