Personalized support for well-being at work: an overview of the SWELL project

by | Jun 20, 2019 | Research Publications

Author(s):

  • Kraaij, Wessel
  • Verberne, Suzan
  • Koldijk, Saskia
  • de Korte, Elsbeth
  • van Dantzig, Saskia
  • Sappelli, Maya
  • Shoaib, Muhammad
  • Bosems, Steven
  • Achterkamp, Reinoud
  • Bonomi, Alberto
  • Schavemaker, John
  • Hulsebosch, Bob
  • Wabeke, Thymen
  • Vollenbroek-Hutten, Miriam
  • Neerincx, Mark
  • Sinderen, Marten van

Abstract:

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

Documentation:

https://doi.org/10.1007/s11257-019-09238-3

References:
  1. Achterkamp, R., Hermens, H., Vollenbroek-Hutten, M.: The influence of success experience on self-efficacy when providing feedback through technology. Comput. Hum. Behav. 52, 419–423 (2015)Google Scholar 
  2. Achterkamp, R., Dekker-Van Weering, M.G., Evering, R.M., Tabak, M., Timmerman, J.G., Hermens, H.J., Vollenbroek-Hutten, M.M.: Strategies to improve effectiveness of physical activity coaching systems: development of personas for providing tailored feedback. Health Inf. J. 24, 92–102 (2018)Google Scholar 
  3. Alberdi, A., Aztiria, A., Basarab, A., Cook, D.J.: Using smart offices to predict occupational stress. Int. J. Ind. Ergon. 67, 13–26 (2018a)Google Scholar 
  4. Alberdi, A., Aztiria, A., Basarab, A., Cook, D.J.: Using smart offices to predict occupational stress. Int. J. Ind. Ergon. 67, 13–26 (2018b)Google Scholar 
  5. Albrecht, D.W., Zukerman, I., Nicholson, A.E., Bud, A.: User modeling. In: Proceedings of the Sixth International Conference, UM97, Chapter Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains, pp. 365–376. Springer, New York (1997)
  6. Alharthi, R., Alharthi, R., Guthier, B., El Saddik, A.: Casp: context-aware stress prediction system. Multimed. Tools Appl. 78, 9011–9031 (2019)Google Scholar 
  7. Ardissono, L., Bosio, G.: Context-dependent awareness support in open collaboration environments. User Model. User Adapt. Interact. 22(3), 223–254 (2012)Google Scholar 
  8. Bailey, J.E., Pearson, S.W.: Development of a tool for measuring and analyzing computer user satisfaction. Manag. Sci. 29(5), 530–545 (1983)Google Scholar 
  9. Bakker, J., Holenderski, L., Kocielnik, R., Pechenizkiy, M., Sidorova, N.: Stress work: from measuring stress to its understanding, prediction and handling with personalized coaching. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 673–678. ACM (2012)
  10. Bawden, D., Robinson, L.: The dark side of information: overload, anxiety and other paradoxes and pathologies. J. Inf. Sci. 35(2), 180–191 (2009)Google Scholar 
  11. Betti, S., Lova, R.M., Rovini, E., Acerbi, G., Santarelli, L., Cabiati, M., Del Ry, S., Cavallo, F.: Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers. IEEE Trans. Biomed. Eng. 65(8), 1748–1758 (2018)Google Scholar 
  12. Bokhove, W., Hulsebosch, B., Schoonhoven, B.V., Sappelli, M., Wouters, K.: User privacy in applications for well-being and well-working: requirements and approaches for user controlled privacy. In: Weyn, M. (ed) AMBIENT 2012: Proceedings of a Meeting Held 23–28 September 2012, Barcelona, Spain, pp. 53–59. Curran Associates, New York (2012)
  13. Bonomi, A.G.: Towards valid estimates of activity energy expenditure using an accelerometer: searching for a proper analytical strategy and big data. J. Appl. Physiol. 115(9), 1227–1228 (2013)Google Scholar 
  14. Bosems, S., van Sinderen, M.J.: Model-driven development for user-centric well-being support: from dynamic well-being domain models to context-aware applications. In: 3rd International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2015), Angers, France, pp. 425–432. SCITEPRESS (2015)
  15. Brouwer, A.-M., van de Water, L., Hogervorst, M., Kraaij, W., Schraagen, J.M., Hogenelst, K.: Monitoring mental state during real life office work. In: Ham, J., Spagnolli, A., Blankertz, B., Gamberini, L., Jacucci, G. (eds.) Symbiotic Interaction, pp. 18–29. Springer, Cham (2018)Google Scholar 
  16. Buckley, J.P., Hedge, A., Yates, T., Copeland, R.J., Loosemore, M., Hamer, M., Bradley, G., Dunstan, D.W.: The sedentary office: an expert statement on the growing case for change towards better health and productivity. Br. J. Sports Med. 49(21), 1357–1362 (2015)Google Scholar 
  17. Choe, E.K., Abdullah, S., Rabbi, E., Thomaz Epstein, D.A., Kay, M., Cordeiro, F., Abowd, G.D., Choudhury, T., Fogarty, J., Lee, B., Matthews, M., Kientz, J.A.: Semi-automated tracking: a balanced approach for self-monitoring applications. IEEE Pervasive Comput. 16(1), 74–84 (2017)Google Scholar 
  18. Conner, M., Norman, P.: Predicting Health Behaviour. McGraw-Hill Education, New York (2005)Google Scholar 
  19. de Korte, E., Wiezer, N., Roozeboom, M.B., Vink, P., Kraaij, W.: Behavior change techniques in mhealth applications for mental and physical health of employees. JMIR mMealth uHealth 6(10), e167 (2018a)Google Scholar 
  20. de Korte, M.E., Wiezer, N., Janssen, J.H., Vink, P., Kraaij, W.: Evaluating an mhealth app for health and well-being at work: mixed-method qualitative study. JMIR mHealth uHealth 6(3), e72 (2018b)Google Scholar 
  21. DellaVigna, S., Malmendier, U.: Paying not to go to the gym. Am. Econ. Rev. 96, 694–719 (2006)Google Scholar 
  22. Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B.: The job demands-resources model of burnout. J. Appl. Psychol. 86(3), 499 (2001)Google Scholar 
  23. den Akker, H.O., Jones, V.M., Hermens, H.J.: Tailoring real-time physical activity coaching systems: a literature survey and model. User Model. User Adapt. Interact. 24, 351–392 (2014)Google Scholar 
  24. Edmunds, A., Morris, A.: The problem of information overload in business organisations: a review of the literature. Int. J. Inf. Manag. 20(1), 17–28 (2000)Google Scholar 
  25. Fogg, B.J.: Persuasive technology: using computers to change what we think and do. Ubiquity 2002, 5 (2002)Google Scholar 
  26. Fox, K.R.: The influence of physical activity on mental well-being. Public Health Nutr. 2(3a), 411–418 (1999)Google Scholar 
  27. Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Patel, V., Haines, A.: The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med. 10(1), e1001362 (2013)Google Scholar 
  28. Gaggioli, A., Pallavicini, F., Morganti, L., Serino, S., Scaratti, C., Briguglio, M., Crifaci, G., Vetrano, N., Giulintano, A., Bernava, G., Tartarisco, G., Pioggia, G., Raspelli, S., Cipresso, P., Vigna, C., Grassi, A., Baruffi, M., Wiederhold, B., Riva, G.: Experiential virtual scenarios with real-time monitoring (interreality) for the management of psychological stress: a block randomized controlled trial. J. Med. Internet Res. 16(7), e167 (2014)Google Scholar 
  29. Giakoumis, D., Drosou, A., Cipresso, P., Tzovaras, D., Hassapis, G., Gaggioli, A., Riva, G.: Using activity-related behavioural features towards more effective automatic stress detection. PLoS ONE 7, e43571 (2012)Google Scholar 
  30. Goldman, R.P., Geib, C.W., Miller, C.A.: A new model of plan recognition. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 245–254. Morgan Kaufmann Publishers Inc (1999)
  31. Gomez-Perez, J., Grobelnik, M., Ruiz, C., Tilly, M., Warren, P.: Using task context to achieve effective information delivery. In: Proceedings of the 1st Workshop on Context, Information and Ontologies, p. 3 (2009)
  32. Greer, A.E., Sui, X., Maslow, A.L., Greer, B.K., Blair, S.N.: The effects of sedentary behavior on metabolic syndrome independent of physical activity and cardiorespiratory fitness. J. Phys. Act. Health 12(1), 68–73 (2015)Google Scholar 
  33. Gyllensten, I.C., Bonomi, A.G.: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life. IEEE Trans. Biomed. Eng. 58(9), 2656–2663 (2011)Google Scholar 
  34. Hassard, J., Teoh, K.R., Visockaite, G., Dewe, P., Cox, T.: The cost of work-related stress to society: a systematic review. J. Occup. Health Psychol. 23(1), 1 (2018)Google Scholar 
  35. Hoepman, J.-H.: Privacy design strategies. In: Cuppens-Boulahia, N., Cuppens, F., Jajodia, S., Abou El Kalam, A., Sans, T. (eds.) ICT Systems Security and Privacy Protection, pp. 446–459. Springer, Berlin (2014)Google Scholar 
  36. Hooftman, W., G.M.J. Mars, Janssen, B., de Vroome, E., Pleijers, A., Michiels, J., van den Bossche, S.: Nationale enquete arbeidsomstandigheden. Technical report (2016)
  37. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 256–265. Morgan Kaufmann Publishers Inc (1998)
  38. IJsselsteijn, W., de Kort, Y., Midden, C., Eggen, B., van den Hoven, E.: Persuasive technology for human well-being: setting the scene. In: IJsselsteijn, W.A., de Kort, Y.A.W., Midden, C., Eggen, B., van den Hoven, E. (eds.) Persuasive Technology, pp. 1–5. Springer, Berlin (2006)Google Scholar 
  39. Jaimes, L., Steele, R.: Mobile stress interventions: mechanisms and implications. EAI Endorsed Trans. Pervasive Health Technol. 4, 154343 (2018)Google Scholar 
  40. Kalimo, R.: Knowledge jobs-how to manage without burnout? Scand. J. Work Environ. Health 25(6), 605–609 (1999)Google Scholar 
  41. Karkar, R., Jasmine, Z., Vilardaga, R., Mishra, S.R., Fogarty, J., Munson, S.A., Kientz, J.A.: A framework for self-experimentation in personalized health. J. Am. Med. Inf. Assoc. 23, 440–448 (2016)Google Scholar 
  42. Kokar, M.M., Reveliotis, S.A.: Reinforcement learning: architectures and algorithms. Int. J. Intell. Syst. 8(8), 875–894 (1993)Google Scholar 
  43. Koldijk, S., Sappelli, M., Neerincx, M., Kraaij, W.: Unobtrusive monitoring of knowledge workers for stress self-regulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) User Modeling, Adaptation, and Personalization, pp. 335–337. Springer, Berlin (2013)Google Scholar 
  44. Koldijk, S., Koot, G., Neerincx, M., Kraaij, W.: Privacy and user trust in context-aware systems. In: Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization, pp. 134–145. Springer (2014a)
  45. Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M. A., and Kraaij, W.: The swell knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 291–298. ACM (2014b)
  46. Koldijk, S., Bernard, J., Ruppert, T., Kohlhammer, J., Neerincx, M., Kraaij, W.: Visual analytics of work behavior data—insights on individual differences. In: Proceedings of Eurographics Conference on Visualization (EuroVis) (2015)
  47. Koldijk, S.: Context-aware support for stress self-management: from theory to practice. PhD thesis, Radboud University (2016)
  48. Koldijk, S., Neerincx, M.A., Kraaij, W.: Detecting work stress in offices by combining unobtrusive sensors. IEEE Trans. Affect. Comput. 9, 227–239 (2016)Google Scholar 
  49. Lee, D.-C., Artero, E.G., Sui, X., Blair, S.N.: Review: mortality trends in the general population: the importance of cardiorespiratory fitness. J. Psychopharmacol. 24(4 suppl), 27–35 (2010)Google Scholar 
  50. Maes, P.: Agents that reduce work and information overload. Commun. ACM 37(7), 30–40 (1994)Google Scholar 
  51. Margarito, J., Helaoui, R., Bianchi, A., Sartor, F., Bonomi, A.: User-independent recognition of sports activities from a single wrist-worn accelerometer: a template matching based approach. IEEE Trans. Biomed. Eng. 63, 788–796 (2015)Google Scholar 
  52. Matthews, R., McDonald, N.J., Trejo, L.J.: Psycho-physiological sensor techniques: an overview. In: 11th International Conference on Human Computer Interaction (HCII), pp. 22–27 (2005)
  53. McClelland, J.L., Rumelhart, D.E.: An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychol. Rev. 88(5), 375 (1981)Google Scholar 
  54. Meijman, T.F., Mulder, G., Drenth, P., Thierry, H.: Psychological aspects of workload. In: Drenth, P.J.D., Thierry, H., de Wolff, C.J. (eds.) Handbook of Work and Organizational Psychology, vol. 2. Psychology Press, Hove (1998)Google Scholar 
  55. Michie, S.: Causes and management of stress at work. Occup. Environ. Med. 59(1), 67–72 (2002)Google Scholar 
  56. Mokhayeri, F., Akbarzadeh-T, M.-R., Toosizadeh, S.: Mental stress detection using physiological signals based on soft computing techniques. In: 2011 18th Iranian Conference of Biomedical Engineering (ICBME), pp. 232–237. IEEE (2011)
  57. Natarajan, S., Bui, H.H., Tadepalli, P., Kersting, K., Wong, W.-K.: Logical hierarchical hidden Markov models for modeling user activities. In: Železný, F., Lavrač, N. (eds.) Inductive Logic Programming, pp. 192–209. Springer, Berlin (2008)Google Scholar 
  58. NationaalKompas: Gezondheidsdeterminanten (2015). Retrieved 19 June 2015
  59. Neerincx, M.A.: Situated cognitive engineering for crew support in space. Personal Ubiquitous Comput. 15(5), 445–456 (2011)Google Scholar 
  60. Neerincx, M.A., Lindenberg, J.: Situated cognitive engineering for complex task environments. In: Schraagen, J.M., Militello, L.G., Ormerod, T., Lipshitz, R. (eds.) Naturalistic Decision Making and Macrocognition, pp. 373–390. Ashgate, Burlington (2008)Google Scholar 
  61. Op den Akker, H., Jones, V.M., Hermens, H.J.: Tailoring real-time physical activity coaching systems: a literature survey and model. User Model. User-Adapt. Interact. 24(5), 351–392 (2014)Google Scholar 
  62. Plarre, K., Raij, A., Hossain, S.M., Ali, A.A., Nakajima, M., Al’absi, M., Ertin, E., Kamarck, T., Kumar, S., Scott, M., Siewiorek, D., Smailagic, A., Wittmers, L.E.: Continuous inference of psychological stress from sensory measurements collected in the natural environment. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 97–108 (2011)
  63. Rath, A.S., Devaurs, D., Lindstaedt, S.N.: Uico: an ontology-based user interaction context model for automatic task detection on the computer desktop. In: CIAO ’09: Proceedings of the 1st Workshop on Context, Information and Ontologies, New York, NY, USA, pp. 1–10. ACM (2009)
  64. Rospo, G., Valsecchi, V., Bonomi, A.G., Thomassen, I.W., van Dantzig, S., La Torre, A., Sartor, F.: Cardiorespiratory improvements achieved by American college of sports medicine’s exercise prescription implemented on a mobile app. JMIR mHealth uHealth 4(2), e77 (2016)Google Scholar 
  65. Sappelli, M., Verberne, S., Koldijk, S., Kraaij, W.: Collecting a dataset of information behaviour in context. In: Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation (2014)
  66. Sappelli, M., Verberne, S., Kraaij, W.: Adapting the interactive activation model for context recognition and identification. ACM Trans. Interact. Intell. Syst. 6(3), 22:1–22:30 (2016)Google Scholar 
  67. Sarker, H., Hovsepian, K., Chatterjee, S., Nahum-Shani, I., Murphy, S.A., Spring, B., Ertin, E., Al’Absi, M., Nakajima, M., Kumar, S.: From markers to interventions: the case of just-in-time stress intervention. In: Rehg, J., Murphy, S., Kumar, S. (eds.) Mobile Health, pp. 411–433. Springer, Berlin (2017)Google Scholar 
  68. Sartor, F., Vernillo, G., de Morree, H.M., Bonomi, A.G., La Torre, A., Kubis, H.-P., Veicsteinas, A.: Estimation of maximal oxygen uptake via submaximal exercise testing in sports, clinical, and home settings. Sports Med. 43(9), 865–873 (2013)Google Scholar 
  69. Schavemaker, J., Boertjes, E., Koldijk, S., Wiertz, L., Verberne, S., Sappelli, M., Kaptein, R.: Fishualization: a group feedback display. In: Spink, A., van den Broek, E.L., Loijens, L., Woloszynowska-Fraser, M., Noldus, L. (eds) Measuring Behavior 2014-9th International Conference on Methods and Techniques in Behavioral Research, 27–29 August 2014, Wageningen, The Netherlands (2014)
  70. Schön, D.A.: The Reflective Practitioner: How Professionals Think in Action, vol. 5126. Basic Books, New York (1983)Google Scholar 
  71. Seyle, H.: Stress without distress. Vie médicale au Canada fran cais (1975)
  72. Shoaib, M., Scholten, H., Havinga, P.J.: Towards physical activity recognition using smartphone sensors. In: 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 10th International Conference on Autonomic and Trusted Computing (UIC/ATC), pp. 80–87. IEEE (2013)
  73. Shoaib, M., Bosch, S., Incel, O. D., Scholten, H., Havinga, P.J.: Dataset. http://ps.ewi.utwente.nl/Datasets.php (2014a). Accessed 12 June 2019
  74. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.: Fusion of smartphone motion sensors for physical activity recognition. Sensors 14(6), 10146–10176 (2014b)Google Scholar 
  75. Shoaib, M., Bosch, S., Durmaz Incel, O., Scholten, J., Havinga, P.J.: Defining a roadmap towards comparative research in online activity recognition on mobile phones. In: 5th International Conference on Pervasive and Embedded Computing and Communication Systems, PECCS 2015, pp. 154–159. SCITEPRESS–Science and Technology Publications (2015a)
  76. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.: A survey of online activity recognition using mobile phones. Sensors 15(1), 2059–2085 (2015b)Google Scholar 
  77. Shoaib, M., Bosch, S., Scholten, H., Havinga, P.J., Incel, O.D.: Towards detection of bad habits by fusing smartphone and smartwatch sensors. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 591–596. IEEE (2015c)
  78. Slovak, P., Frauenberger, C., Fitzpatrick, G.: Reflective practicum: a framework of sensitising concepts to design for transformative reflection. In: CHI 2017 (2017)
  79. Smets, E., Velazquez, E.R., Schiavone, G., Chakroun, I., D’Hondt, E., De Raedt, W., Cornelis, J., Janssens, O., Van Hoecke, S., Claes, S., et al.: Large-scale wearable data reveal digital phenotypes for daily-life stress detection. npj Digit. Med. 1(1), 67 (2018)Google Scholar 
  80. Thomassen, I.: Collecting heart beats: exploring the effects of progress feedback as a motivational intervention to increase physical activity. Master’s thesis, Technical University Eindhoven, Eindhoven (2013)
  81. Veeningen, M., Chatterjea, S., Horváth, A.Z., Sprindler, G., Boersma, E., van der Spek, P., van der Galiën, O., Gutteling, J., Kraaij, W., Veugen, T.: Enabling analytics on sensitive medical data with secure multi-party computation. Stud. Health Technol. Inform. 247, 76–80 (2018)Google Scholar 
  82. Verberne, S., Sappelli, M., Kraaij, W.: Query term suggestion in academic search. In: Proceedings of the 36th European Conference on IR Research, ECIR 2014, Lecture Notes in Computer Science, vol. 8416, pp. 560–566 (2014)
  83. Vosbergen, S., Mahieu, G.R., Laan, E.K., Kraaijenhagen, R.A., Jaspers, M.W., Peek, N.: Evaluating a web-based health risk assessment with tailored feedback: what does an expert focus group yield compared to a web-based end-user survey? J. Med. Internet Res. 16(1), e1 (2014)Google Scholar 
  84. Wabeke, T.: Recommending tips that support well-being at work to knowledge workers. Master’s thesis, Radboud University, Nijmegen, The Netherlands (2014)
  85. Warren, P.: Personal information management: the case for an evolutionary approach. Interact. Comput. 26, 208–237 (2013)Google Scholar 
  86. WHO: Preamble to the constitution of the World Health Organization as adopted by the international health conference, New York, 19–22 June, 1946; signed on 22 July 1946 by the representatives of 61 states (official records of the World Health Organization, no. 2, p. 100) and entered into force on 7 April 1948 (1948)
  87. Wixom, B.H., Todd, P.A.: A theoretical integration of user satisfaction and technology acceptance. Inf. Syst. Res. 16(1), 85–102 (2005)Google Scholar 
  88. Wright, D.: The state of the art in privacy impact assessment. Comput. Law Secur. Rev. 28(1), 54–61 (2012)Google Scholar