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Validation of a Computational Model for Mood and Social Integration

  • Altaf Hussain AbroEmail author
  • Michel C. A. Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)

Abstract

The social environment of people is an important factor for the mental health. However, in many internet interventions for mental health the interaction with the environment has no explicit role. It is known that the social environment can help people to reduce the feelings of loneliness and has a positive impact on mood in particular. Participation in social activities and maintaining social interaction with friends and relatives are frequently seen as indicators of a happy and healthy life. It is also commonly accepted that being integrated in social network has a strong protective effect on health and helps to avoid feelings of loneliness. In this paper we present a computational model that can be used for analyzing and predicating the mood level of individuals by taking into account the social integration, the participation in social activities and the enjoyableness of those activities. In addition to this, we explain the method that we developed to validate the computational model. For the validation, we use real EMA data that was collected from E-COMPARED project. This model allows to make more precise predictions on the effect of social interaction on mood and might be part of future internet interventions.

Keywords

Social network Social integration Mood Social interaction Social activities 

Notes

Acknowledgement

Funding for this research work is provided by the E-COMPARED project. The E-COMPARED project is funded by the European Commission’s Seventh Framework Programme, under grant number 603098.

References

  1. 1.
    Nicholas Jr., N.R.: Social isolation in older adults: an evolutionary concept analysis. J. Adv. Nurs. 65(6), 1342–1352 (2008)Google Scholar
  2. 2.
    Cacioppo, J.T., James, H.F., Nicholas, A.C.: Alone in the crowd: the structure and spread of loneliness in a large social network. J. Pers. Soc. Psychol. 97(6), 977–991 (2009)CrossRefGoogle Scholar
  3. 3.
    Cohen, S., Willis, T.A.: Stress, social support, and the buffering hypothesis. Psychol. Bull. 98, 310–357 (1985)CrossRefGoogle Scholar
  4. 4.
    Zavaleta, D., Samuel, K., Mills, C.: Social isolation: a conceptual and measurement proposal. In: OPHI Working Papers, vol. 67. University of Oxford (2014)Google Scholar
  5. 5.
    Cacioppo, J.T., Patrick, B.: Loneliness: Human Nature and the Need for Social Connection. W.W. Norton & Company, New York (2008)Google Scholar
  6. 6.
    Cohen, S., Gottlieb, B., Underwood, L.: Social relationships and health. In: Cohen, S., Underwood, L., Gottlieb, B. (eds.) Measuring and Intervening in Social Support, pp. 3–25. Oxford University Press, New York (2000)CrossRefGoogle Scholar
  7. 7.
    Uchino, B.N., Cacioppo, J.T., Kiecolt-Glaser, J.K.: The relationship between social support and physiological processes: a review with emphasis on underlying mechanisms and implications for health. Psychol. Bull. 119, 488–531 (1996)CrossRefGoogle Scholar
  8. 8.
    Brissette, I., Cohen, S., Seeman, T.E.: Measuring social integration and social networks. In: Cohen, S., Underwood, L., Gottlieb, B. (eds.) Measuring and Intervening in Social Support, pp. 53–85. Oxford University Press, New York (2000)CrossRefGoogle Scholar
  9. 9.
    Cacioppo, J.T., Hawkley, L.C., Crawford, E., Ernst, J.M., Burleson, M.H., Kowalewski, R.B., et al.: Loneliness and health: potential mechanisms. Psychosom. Med. 64, 407–417 (2002)CrossRefGoogle Scholar
  10. 10.
    Kiecolt-Glaser, J.K., Newton, T.L.: Marriage and health: his and hers. Psychol. Bull. 127, 472–503 (2001)CrossRefGoogle Scholar
  11. 11.
    Steger, M.F., Todd, B.K.: Depression and everyday social activity, belonging, and well-being. J. Couns. Psychol. 56(2), 289–300 (2009)CrossRefGoogle Scholar
  12. 12.
    Beland, F., Zunzunegui, M.V., Alvarado, B., Otero, A., Del Ser, T.: Trajectories of cognitive decline and social relations. J. Gerontol. Ser. B Psychol. Sci. Soc. Sci. 60, 320–330 (2005)CrossRefGoogle Scholar
  13. 13.
    Abro, A.H., Klein, M.C.A., Manzoor, A.R., Tabatabaei, S.A., Treur, T.: Modeling the effect of regulation of negative emotions on mood. Biologically Inspired Cognit. Archit. 13, 35–47 (2015)CrossRefGoogle Scholar
  14. 14.
    Abro, A.H., Klein, M.C.A., Tabatabaei, S.A.: An agent-based model for the role of social support in mood regulation. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Julian, V. (eds.) PAAMS 2015. CCIS, vol. 524, pp. 15–27. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-19033-4_2 CrossRefGoogle Scholar
  15. 15.
    Pitkala, K.H., Routasolo, P., Kautiainen, H., Tilvis, R.S.: Effects of psychosocial group rehabilitation on health, use of health care services, and mortality of older persons suffering from loneliness: a randomised, controlled trial. J. Gerontol. Med. Sci. 64A(7), 792–800 (2009)CrossRefGoogle Scholar
  16. 16.
    Masi, C.M., Chen, H.Y., Hawkley, L.C., Cacioppo, J.T.: A meta-analysis of interventions to reduce loneliness. Pers. Soc. Pyschol. Rev. 15(3), 219–266 (2011)CrossRefGoogle Scholar
  17. 17.
    Allen, N.B., Badcock, P.B.T.: The social risk hypothesis of depressed mood: evolutionary, psychosocial, and neurobiological perspectives. Psychol. Bull. 129, 887–913 (2003)CrossRefGoogle Scholar
  18. 18.
    Eng, P.M., Rimm, E.B., Fitzmaurice, G., Kawachi, I.: Social ties and change in social ties in relation to subsequent total and cause-specific mortality and coronary heart disease incidence in men. Am. J. Epidemiol. 155, 700–709 (2002)CrossRefGoogle Scholar
  19. 19.
    Fratiglioni, L., Paillard-Borg, S., Winblad, B.: An active and socially integrated lifestyle in late life might protect against dementia. Lancet Neurol. 3, 343–353 (2004)CrossRefGoogle Scholar
  20. 20.
    Wang, H.X., Karp, A., Winblad, B., Fratiglioni, L.: Late-life engagement in social and leisure activities is associated with a decreased risk of dementia: a longitudinal study from the Kungsholmen project. Am. J. Epidemiol. 155, 1081–1087 (2002)CrossRefGoogle Scholar
  21. 21.
    Nezlek, J.B., Hampton, C.P., Shean, G.D.: Clinical depression and day-to-day social interactions in a community sample. J. Abnorm. Psychol. 109, 11–19 (2000)CrossRefGoogle Scholar
  22. 22.
    Nezlek, J.B., Imbrie, M., Shean, G.D.: Depression and everyday social interaction. J. Pers. Soc. Psychol. 67, 1101–1111 (1994)CrossRefGoogle Scholar
  23. 23.
    Flatt, J.D., Tiffany, F.H.: Participation in social activities in later life: does enjoyment have important implications for cognitive health? Aging Health 9(2), 149–158 (2013)CrossRefGoogle Scholar
  24. 24.
    Glass, T.A., De Leon, C.M., Marottoli, R.A., Berkman, L.F.: Population based study of social and productive activities as predictors of survival among elderly Americans. BMJ 319(7208), 478–483 (1999)CrossRefGoogle Scholar
  25. 25.
    Rook, K.S.: Emotional health and positive versus negative social exchanges: a daily diary analysis. Appl. Dev. Sci. 5, 86–97 (2001)CrossRefGoogle Scholar
  26. 26.
    Hawkley, L.C., Burleson, M.H., Berntson, G.G., Cacioppo, J.T.: Loneliness in everyday life: cardiovascular activity, psychosocial context, and health behaviors. J. Pers. Soc. Psychol. 85, 105–120 (2003)CrossRefGoogle Scholar
  27. 27.
    Treur, J.: Dynamic modeling based on a temporal–causal network modeling approach. Biologically Inspired Cognit. Archit. 16, 131–168 (2016)CrossRefGoogle Scholar
  28. 28.
    Both, F., Hoogendoorn, M., Klein, M., Treur, J.: Modeling the dynamics of mood and depression. In: Proceedings of the 2008 Conference on ECAI 2008, 18th European Conference on Artificial Intelligenc, pp. 266–270. IOS Press (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Behavioural Informatics GroupVrije Universiteit AmsterdamAmsterdamThe Netherlands

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