International Conference on Smart Health

Smart Health pp 43-54 | Cite as

Network Analysis of Ecological Momentary Assessment Data for Monitoring and Understanding Eating Behavior

  • Gerasimos Spanakis
  • Gerhard Weiss
  • Bastiaan Boh
  • Anne Roefs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9545)

Abstract

Ecological Momentary Assessment (EMA) techniques have been blooming during the last years due to the emergence of smart devices (like PDAs and smartphones) that allow the collection of repeated assessments of several measures (predictors) that affect a target variable. Eating behavior studies can benefit from EMA techniques by analysing almost real-time information regarding food intake and the related conditions and circumstances. In this paper, an EMA method protocol to study eating behavior is presented along with the mobile application developed for this purpose. Mixed effects and vector autoregression are utilized for conducting a network analysis of the data collected and lead to inferring knowledge for the connectivity between different conditions and their effect on eating behavior.

Keywords

Ecological momentary assessment Mixed effects Vector autoregression Network analysis 

References

  1. 1.
    Borsboom, D., Cramer, A.O.J.: Network analysis: an integrative approach to the structure of psychopathology. Ann. Rev. Clin. Psychol. 9(1), 91–121 (2013). PMID: 23537483CrossRefGoogle Scholar
  2. 2.
    Bringmann, L.F., Lemmens, L.H.J.M., Huibers, M.J.H., Borsboom, D., Tuerlinckx, F.: Revealing the dynamic network structure of the beck depressioninventory-II. Psychol. Med. 45, 747–757, 3 (2015)Google Scholar
  3. 3.
    Carels, R.A., Douglass, O.M., Cacciapaglia, H.M., O’Brien, W.H.: An ecological momentary assessment of relapse crises in dieting. J. Consult. Clin. Psychol. 72(2), 341–348 (2004)CrossRefGoogle Scholar
  4. 4.
    Ebner-Priemer, U.W., Trull, T.J.: Ecological momentary assessment of mood disorders and mooddys regulation. Psychol. Assess. 21(4), 463 (2009)CrossRefGoogle Scholar
  5. 5.
    Gelman, A.: Analysis of variance - why it is more important than ever. Ann. Statist. 33(1), 1–53, 02 (2005)Google Scholar
  6. 6.
    Gorrostieta, C., Ombao, H., Bdard, P., Sanes, J.N.: Investigating brain connectivity using mixed effects vector autoregressive models. NeuroImage 59(4), 3347–3355 (2012)CrossRefGoogle Scholar
  7. 7.
    Heron, K.E., Smyth, J.M.: Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br. J. Health Psychol. 15(1), 1–39 (2010)CrossRefGoogle Scholar
  8. 8.
    Hofmann, W., Adriaanse, M., Vohs, K.D., Baumeister, R.F.: Dieting and the self-control of eating in everyday environments: an experience sampling study. Br. J. Health Psychol. 19(3), 523–539 (2014)CrossRefGoogle Scholar
  9. 9.
    Kashdan, T.B., Lorraine, R.: Collins. Social anxiety and the experience of positive emotion and anger ineveryday life: an ecological momentary assessment approach. Anxiety Stress Coping 23(3), 259–272 (2010). PMID: 19326272CrossRefGoogle Scholar
  10. 10.
    Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econometrics 54(13), 159–178 (1992)MATHCrossRefGoogle Scholar
  11. 11.
    Lavie, P.: Sleep-wake as a biological rhythm. Ann. Rev. Psychol. 52(1), 277–303 (2001)CrossRefGoogle Scholar
  12. 12.
    McKee, H.C., Ntoumanis, N., Taylor, I.M.: An ecological momentary assessment of lapse occurrences in dieters. Ann. Behav. Med. 48(3), 300–310 (2014)CrossRefGoogle Scholar
  13. 13.
    Moskowitz, D.S., Young, S.N.: Ecological momentary assessment: what it is and why it is a method of the future in clinical psychopharmacology. J. Psychiatry Neurosci. 31, 13–20 (2006)Google Scholar
  14. 14.
    Shiffman, S.: Conceptualizing analyses of ecological momentary assessment data. Nicotine and Tob. Res. (2013)Google Scholar
  15. 15.
    Shiffman, S., Stone, A.A., Hufford, M.R.: Ecological momentary assessment. Ann. Rev. Clin. Psychol. 4(1), 1–32 (2008)CrossRefGoogle Scholar
  16. 16.
    Stone, A.A., Shiffman, S.: Ecological momentary assessment (EMA) in behavorial medicine. Ann. Behav. Med. 16(3), 199–202 (1994)Google Scholar
  17. 17.
    White, D.R., Borgatti, S.P.: Betweenness centrality measures for directed graphs. Soc. Netw. 16(4), 335–346 (1994)CrossRefGoogle Scholar
  18. 18.
    Zellner, A.: An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. J. Am. Stat. Assoc. 57(298), 348–368 (1962)MATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gerasimos Spanakis
    • 1
  • Gerhard Weiss
    • 1
  • Bastiaan Boh
    • 2
  • Anne Roefs
    • 2
  1. 1.Department of Knowledge EngineeringMaastricht UniversityMaastrichtThe Netherlands
  2. 2.Faculty of Psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands

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