Dynamic Modeling of Activity Happiness: An Investigation of the Intra-activity Hedonic Treadmill

  • Isabel Viegas de Lima
  • Maya Abou-Zeid
  • Ronny Kutadinata
  • Zahra Navidi
  • Stephan Winter
  • Fang Zhao
  • Moshe Ben-Akiva
Part of the Applying Quality of Life Research book series (BEPR)


While travel has traditionally been considered a means to reach activities, researchers have begun to investigate the effect it has on well-being. Improved surveying methods enabled by mobile phone applications, leveraging GPS, GSM, accelerometer, and WiFi, allow researchers to collect more complete data and test hypotheses related to individuals’ happiness with travel and activities. This chapter describes a data collection effort that took place in Melbourne, Australia using Future Mobility Sensing, a mobile phone application and web-based platform. Throughout the study, users were asked twice daily to report on happiness for a single activity, including travel. The chapter develops a dynamic Ordinal Logit Model based on the collected data and discusses the estimation results in the context of Hedonic Theory. The deviation of the reported happiness for an activity observation and an individual Set Point, defined as the median reported happiness of a user, is modeled as a function of covariates. The results show how different activity types (work, education, personal, discretionary, travel, staying at home, and other) affect individuals’ experienced happiness. It is found that educational activities, followed by work and travel, are the most disliked. Discretionary actives—which include social activities, meals, recreation, etc.—and other activities are seen to lead to more positive experiences of happiness. The model is used to test for the presence of an intra-activity Hedonic Treadmill Effect. It is found that people remember their activities as more neutral in later reports of happiness. The implications for the measurement of happiness data are discussed.


Hedonic treadmill Real-time happiness Retrospective happiness Duration neglect Smartphone data Future mobility sensing Dynamic ordinal logit model 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Isabel Viegas de Lima
    • 1
  • Maya Abou-Zeid
    • 2
  • Ronny Kutadinata
    • 3
  • Zahra Navidi
    • 3
  • Stephan Winter
    • 3
  • Fang Zhao
    • 4
  • Moshe Ben-Akiva
    • 1
  1. 1.Massachusetts Institute of TechnologyCambridgeUSA
  2. 2.American University of BeirutBeirutLebanon
  3. 3.The University of MelbourneMelbourneAustralia
  4. 4.Singapore-MIT Alliance for Research and TechnologySingaporeSingapore

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