Advertisement

Mobile Health pp 411-433 | Cite as

From Markers to Interventions: The Case of Just-in-Time Stress Intervention

  • Hillol Sarker
  • Karen Hovsepian
  • Soujanya Chatterjee
  • Inbal Nahum-Shani
  • Susan A. Murphy
  • Bonnie Spring
  • Emre Ertin
  • Mustafa al’Absi
  • Motohiro Nakajima
  • Santosh Kumar
Chapter

Abstract

The use of sensor-based assessment of stress to trigger the delivery of just-in-time intervention has the potential to help people manage daily stress as it occurs in the person’s natural environment. The challenge is to mine the continuous stream of sensor data and identify those few opportune moments for triggering an intervention—when there is sufficient confidence in the accuracy of the sensor-based stress markers, in order to limit interruptions to the daily lives. In this chapter, we describe the process of developing a real-time method to identify stress episodes, from a time series of stress markers, to inform the triggering of just-in-time stress-management interventions.

Notes

Acknowledgements

The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01CA190329, R01MD010362, and R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative, and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative. We also thank Barbara Burch Kuhn from University of Memphis.

References

  1. 1.
    mCerebrum: An Open Source Software Suite for Mobile Sensor Data. https://md2k.org/software/ (2016)
  2. 2.
    Al’Absi, M.: Stress and addiction: Biological and psychological mechanisms. Academic Press (2011)Google Scholar
  3. 3.
    Al’Absi, M., Bongard, S., Buchanan, T., Pincomb, G.A., Licinio, J., Lovallo, W.R.: Cardiovascular and neuroendocrine adjustment to public speaking and mental arithmetic stressors. Psychophysiology 34(3), 266–275 (1997)CrossRefGoogle Scholar
  4. 4.
    al’Absi, M., Hatsukami, D., Davis, G., Wittmers, L.: Prospective examination of effects of smoking abstinence on cortisol and withdrawal symptoms as predictors of early smoking relapse. Drug and Alcohol Dependence 73(3), 267–278 (2004)Google Scholar
  5. 5.
    Baer, J.S., Lichtenstein, E.: Classification and prediction of smoking relapse episodes: an exploration of individual differences. Journal of consulting and clinical psychology 56(1), 104 (1988)CrossRefGoogle Scholar
  6. 6.
    Bland, J., Altman, D.: Statistics: notes Cronbach’s alpha. BMJ 314(7080), 572–572 (1997)CrossRefGoogle Scholar
  7. 7.
    Bunker, S.J., Colquhoun, D.M., Esler, M.D., Hickie, I.B., Hunt, D., Jelinek, V.M., Oldenburg, B.F., Peach, H.G., Ruth, D., Tennant, C.C., et al.: “stress” and coronary heart disease: psychosocial risk factors. The Medical Journal of Australia 178(6), 272–276 (2003)Google Scholar
  8. 8.
    Cohen, S., Lichtenstein, E.: Perceived stress, quitting smoking, and smoking relapse. Health Psychology 9(4), 466 (1990)CrossRefGoogle Scholar
  9. 9.
    Cummings, K.M., Jaén, C.R., Giovino, G.: Circumstances surrounding relapse in a group of recent exsmokers. Preventive Medicine 14(2), 195–202 (1985)CrossRefGoogle Scholar
  10. 10.
    for Disease Control, C., (CDC, P., et al.: Smoking-attributable mortality, years of potential life lost, and productivity losses–united states, 2000–2004. MMWR. Morbidity and mortality weekly report 57(45), 1226 (2008)Google Scholar
  11. 11.
    Ertin, E., Stohs, N., Kumar, S., Raij, A., al’Absi, M., Shah, S.: Autosense: Unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In: ACM SenSys, pp. 274–287 (2011)Google Scholar
  12. 12.
    Fogarty, J., Hudson, S., Lai, J.: Examining the robustness of sensor-based statistical models of human interruptibility. In: ACM CHI, pp. 207–214 (2004)Google Scholar
  13. 13.
    Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P., Botstein, D.: Imputing missing data for gene expression arrays (1999)Google Scholar
  14. 14.
    Hirshfield, L.M., Solovey, E.T., Girouard, A., Kebinger, J., Jacob, R.J., Sassaroli, A., Fantini, S.: Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy. In: ACM CHI, pp. 2185–2194. ACM (2009)Google Scholar
  15. 15.
    Hobfoll, S.E.: Conservation of resources: A new attempt at conceptualizing stress. American psychologist 44(3), 513 (1989)CrossRefGoogle Scholar
  16. 16.
    Hobfoll, S.E., Vinokur, A.D., Pierce, P.F., Lewandowski-Romps, L.: The combined stress of family life, work, and war in air force men and women: A test of conservation of resources theory. International Journal of Stress Management 19(3), 217 (2012)CrossRefGoogle Scholar
  17. 17.
    Hong, J., Ramos, J., Dey, A.: Understanding physiological responses to stressors during physical activity. In: ACM UbiComp, pp. 270–279 (2012)Google Scholar
  18. 18.
    Hossain, S., Ali, A., Rahman, M., Ertin, E., Epstein, D., Kennedy, A., Preston, K., Umbricht, A., Chen, Y., Kumar, S.: Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity. In: ACM IPSN, pp. 71–82 (2014)Google Scholar
  19. 19.
    Hovsepian, K., al’Absi, M., Ertin, E., Kamarck, T., Nakajima, M., Kumar, S.: cStress: towards a gold standard for continuous stress assessment in the mobile environment. In: ACM UbiComp, pp. 493–504 (2015)Google Scholar
  20. 20.
    Iqbal, S., Zheng, X., Bailey, B.: Task-evoked pupillary response to mental workload in human-computer interaction. In: ACM CHI Extended Abstracts, pp. 1477–1480 (2004)Google Scholar
  21. 21.
    Iqbal, S.T., Adamczyk, P.D., Zheng, X.S., Bailey, B.P.: Towards an index of opportunity: understanding changes in mental workload during task execution. In: ACM CHI, pp. 311–320 (2005)Google Scholar
  22. 22.
    Konrad, A., Bellotti, V., Crenshaw, N., Tucker, S., Nelson, L., Du, H., Pirolli, P., Whittaker, S.: Finding the adaptive sweet spot: Balancing compliance and achievement in automated stress reduction. In: ACM CHI, pp. 3829–3838 (2015)Google Scholar
  23. 23.
    Lyu, Y., Luo, X., Zhou, J., Yu, C., Miao, C., Wang, T., Shi, Y., Kameyama, K.i.: Measuring photoplethysmogram-based stress-induced vascular response index to assess cognitive load and stress. In: ACM CHI, pp. 857–866 (2015)Google Scholar
  24. 24.
    Matthews, M., Snyder, J., Reynolds, L., Chien, J.T., Shih, A., Lee, J.W., Gay, G.: Real-time representation versus response elicitation in biosensor data. In: ACM CHI, pp. 605–608 (2015)Google Scholar
  25. 25.
    McEwen, B.: Stress, adaptation, and disease: Allostasis and allostatic load. Annals of the New York Academy of Sciences 840(1), 33–44 (2006)CrossRefGoogle Scholar
  26. 26.
    McEwen, B.: Physiology and neurobiology of stress and adaptation: Central role of the brain. Physiological Reviews 87(3), 873–904 (2007)CrossRefGoogle Scholar
  27. 27.
    McEwen, B.S.: Protection and damage from acute and chronic stress: allostasis and allostatic overload and relevance to the pathophysiology of psychiatric disorders. Annals of the New York Academy of Sciences 1032(1), 1–7 (2004)CrossRefGoogle Scholar
  28. 28.
    Mokdad, A.H., Marks, J.S., Stroup, D.F., Gerberding, J.L.: Actual causes of death in the united states, 2000. Journal of the American Medical Association (JAMA) 291(10), 1238–1245 (2004)CrossRefGoogle Scholar
  29. 29.
    Nahum-Shani, I., Hekler, E., Spruijt-Metz, D.: Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Health PsychologyGoogle Scholar
  30. 30.
    Ni, K., Ramanathan, N., Chehade, M., Balzano, L., Nair, S., Zahedi, S., Kohler, E., Pottie, G., Hansen, M., Srivastava, M.: Sensor network data fault types. ACM TOSN 5(3), 25 (2009)Google Scholar
  31. 31.
    Nielsen, P., Le Grice, I., Smaill, B., Hunter, P.: Mathematical model of geometry and fibrous structure of the heart. American Journal of Physiology-Heart and Circulatory Physiology 260(4), H1365–H1378 (1991)Google Scholar
  32. 32.
    Pan, J., Tompkins, W.: A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering 32(3), 230–236 (1985)CrossRefGoogle Scholar
  33. 33.
    Plarre, K., Raij, A., Hossain, S., Ali, A., Nakajima, M., Al’absi, M., Ertin, E., Kamarck, T., Kumar, S., Scott, M., et al.: Continuous inference of psychological stress from sensory measurements collected in the natural environment. In: IEEE/ACM IPSN, pp. 97–108 (2011)Google Scholar
  34. 34.
    Rahman, M., Bari, R., Ali, A., Sharmin, M., Raij, A., Hovsepian, K., Hossain, S., Ertin, E., Kennedy, A., Epstein, D., Preston, K., Jobes, M., Beck, G., Kedia, S., Ward, K., al’Absi, M., Kumar, S.: Are we there yet? feasibility of continuous stress assessment via wireless physiological sensors. In: ACM BCB, pp. 479–488 (2014)Google Scholar
  35. 35.
    Saleheen, N., Ali, A.A., Hossain, S.M., Sarker, H., Chatterjee, S., Marlin, B., Ertin, E., al’Absi, M., Kumar, S.: puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In: ACM UbiComp, pp. 999–1010 (2015)Google Scholar
  36. 36.
    Sarker, H., Tyburski, M., Rahman, M., Hovsepian, K., Sharmin, M., Epstein, D.H., Preston, K.L., Furr-Holden, C.D., Milam, A., Nahum-Shani, I., al’Absi, M., Kumar, S.: Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In: ACM CHI (2016)Google Scholar
  37. 37.
    Sauro, K.M., Becker, W.J.: The stress and migraine interaction. Headache: The Journal of Head and Face Pain 49(9), 1378–1386 (2009)CrossRefGoogle Scholar
  38. 38.
    Sharmin, M., Raij, A., Epstien, D., Nahum-Shani, I., Beck, J.G., Vhaduri, S., Preston, K., Kumar, S.: Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions. In: ACM UbiComp, pp. 505–516 (2015)Google Scholar
  39. 39.
    Shiffman, S.: Relapse following smoking cessation: a situational analysis. Journal of consulting and clinical psychology 50(1), 71 (1982)CrossRefGoogle Scholar
  40. 40.
    Shiffman, S., Stone, A., Hufford, M.: Ecological momentary assessment. Annual Review of Clinical Psychology 4, 1–32 (2008)CrossRefGoogle Scholar
  41. 41.
    Speed, T.: Statistical analysis of gene expression microarray data. CRC Press (2004)Google Scholar
  42. 42.
    Tan, C.S.S., Schöning, J., Luyten, K., Coninx, K.: Investigating the effects of using biofeedback as visual stress indicator during video-mediated collaboration. In: ACM CHI, pp. 71–80 (2014)Google Scholar
  43. 43.
    Torres, S.J., Nowson, C.A.: Relationship between stress, eating behavior, and obesity. Nutrition 23(11), 887–894 (2007)CrossRefGoogle Scholar
  44. 44.
    Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.: Missing value estimation methods for dna microarrays. Bioinformatics 17(6), 520–525 (2001)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hillol Sarker
    • 1
  • Karen Hovsepian
    • 2
  • Soujanya Chatterjee
    • 3
  • Inbal Nahum-Shani
    • 4
  • Susan A. Murphy
    • 4
  • Bonnie Spring
    • 5
  • Emre Ertin
    • 6
  • Mustafa al’Absi
    • 7
  • Motohiro Nakajima
    • 7
  • Santosh Kumar
    • 3
  1. 1.IBM T.J. Watson Research CenterCambridgeUSA
  2. 2.Troy UniversityTroyUSA
  3. 3.University of MemphisMemphisUSA
  4. 4.University of MichiganAnn ArborUSA
  5. 5.Northwestern UniversityChicagoUSA
  6. 6.The Ohio State UniversityColumbusUSA
  7. 7.University of Minnesota Medical SchoolDuluthUSA

Personalised recommendations