Personal and Ubiquitous Computing

, Volume 19, Issue 2, pp 355–365 | Cite as

Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome

  • Afsaneh DoryabEmail author
  • Mads Frost
  • Maria Faurholt-Jepsen
  • Lars V. Kessing
  • Jakob E. Bardram
Original Article


An increasing number of healthcare systems allow people to monitor behavior and provide feedback on health and wellness. Most applications, however, only offer feedback on behavior in form of visualization and data summaries. This paper presents a different approach—called impact factor analysis—in which machine learning techniques are used to infer the progression of a primary health parameter and then apply parameter ranking to investigate which behavioral data have the highest ‘impact’ on health. We have applied this approach to improve the MONARCA personal health application for patients suffering from bipolar disorder. In the MONARCA system, patients report their daily mood score and by analyzing self-reported and automatically sensed behavioral data with this mood score, the system is able to identify the impact of different behavior on the patient’s mood. We report from a study involving ten bipolar patients, in which we were able to estimate mood values with an average mean absolute error of 0.5. This was used to rank the behavior parameters whose variations indicate changes in the mental state. The rankings acquired from our algorithms correspond to the patients’ rankings, identifying physical activity and sleep as the highest impact parameters. These results revealed the feasibility of identifying behavioral impact factors. This data analysis motivated us to design an impact factor inference engine as part of the MONARCA system. To our knowledge, this is a novel approach in monitoring and control of mental illness, and we argue that the impact factor analysis can be useful in the design of other health and wellness systems.


Health and behavior Machine learning Mental health Bipolar disorder 



This work has been done in close collaboration with a group of clinicians and patients from the Copenhagen Affective Disorder Clinic at the University Hospital of Copenhagen. MONARCA is funded as a STREP project under the FP7 European Framework program. More information can be found at and


  1. 1.
    Bardram JE, Frost M, Szántó K, Faurholt-Jepsen M, Vinberg M, Kessing LV (2013) Designing mobile health technology for bipolar disorder: a field trial of the monarca system, pp 2627–2636. doi: 10.1145/2470654.2481364
  2. 2.
    Bardram JE, Frost M, Szántó K, Marcu G (2012) The monarca self-assessment system: a persuasive personal monitoring system for bipolar patients. In: Proceedings of the 2nd ACM SIGHIT international health informatics symposium, IHI ’12. ACM, New York, pp 21–30. doi: 10.1145/2110363.2110370
  3. 3.
    Basco MR, Rush AJ (2005) Cognitive-behavioral therapy for bipolar disorder, 2nd edn. The Guilford Press, New YorkGoogle Scholar
  4. 4.
    Bentley F, Tollmar K, Stephenson P, Levy L, Jones B, Robertson S, Price E, Catrambone R, Wilson J (2013) Health mashups: presenting statistical patterns between wellbeing data and context in natural language to promote behavior change. ACM Trans Comput Hum Interact 20(5):30:1–30:27. doi: 10.1145/2503823 CrossRefGoogle Scholar
  5. 5.
    Burns M, Begale M, Duffecy J, Gergle D, Karr C, Giangrande E, Mohr D (2011) Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res 13(3). doi: 10.2196/jmir.1838
  6. 6.
    Choe EK, Lee NB, Lee B, Pratt W, Kientz JA (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’14. ACM, New York, pp 1143–1152. doi: 10.1145/2556288.2557372
  7. 7.
    Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R, Smith I, Landay JA (2008) Activity sensing in the wild: a field trial of ubifit garden. In: Proceedings of ACM CHI 2008, CHI ’08. ACM, New York, pp 1797–1806. doi: 10.1145/1357054.1357335
  8. 8.
    Epstein D, Cordeiro F, Bales E, Fogarty J, Munson S (2014) Taming data complexity in lifelogs: exploring visual cuts of personal informatics data. In: Proceedings of the 2014 conference on designing interactive systems, DIS ’14. ACM, New York, pp 667–676. doi: 10.1145/2598510.2598558
  9. 9.
    Frost M, Doryab A, Faurholt-Jepsen M, Kessing LV, Bardram JE (2013) Supporting disease insight through data analysis: refinements of the monarca self-assessment system. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing, UbiComp ’13. ACM, New York, pp 133–142. doi: 10.1145/2493432.2493507
  10. 10.
    Hamilton M (1967) Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol 6(4):278–296CrossRefGoogle Scholar
  11. 11.
    Harrison V, Proudfoot J, Wee P, Parker G, Pavlovic D, Manicavasagar V (2011) Mobile mental health: review of the emerging field and proof of concept study. J Mental Health (Abingdon, England) 20(6). doi: 10.3109/09638237.2011.608746.
  12. 12.
  13. 13.
  14. 14.
    Kasckow J, Zickmund S, Rotondi A, Mrkva A, Gurklis J, Chinman M, Fox L, Loganathan M, Hanusa B, Haas G (2013) Development of telehealth dialogues for monitoring suicidal patients with schizophrenia: consumer feedback. Community Mental Health J, 1–4. doi: 10.1007/s10597-012-9589-8
  15. 15.
    Lane ND, Choudhury T, Campbell A, Mohammod M, Lin M, Yang X, Doryab A, Lu H, Ali S, Berke E (2011) BeWell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of the 5th international ICST conference on pervasive computing technologies for healthcare (Pervasive Health 2011), pervasive health 2011. IEEE PressGoogle Scholar
  16. 16.
    Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’10. ACM, New York, pp 557–566. doi: 10.1145/1753326.1753409
  17. 17.
    Matthews M, Doherty G, Sharry J, Fitzpatrick C (2008) Mobile phone mood charting for adolescents. Br J Guid Counsel 36(2):113–129CrossRefGoogle Scholar
  18. 18.
    Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A (2010) Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on ubiquitous computing, Ubicomp ’10. ACM, New York, pp 281–290. doi: 10.1145/1864349.1864393
  19. 19.
    Rooksby J, Rost M, Morrison A, Chalmers MC (2014) Personal tracking as lived informatics. In: Proceedings of the 32Nd annual ACM conference on human factors in computing systems, CHI ’14. ACM, New York, pp 1163–1172. doi: 10.1145/2556288.2557039
  20. 20.
    Wilde MH, Garvin S (2007) A concept analysis of self-monitoring. J Adv Nurs 57(3):339–350. doi: 10.1111/j.1365-2648.2006.04089.x CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Afsaneh Doryab
    • 1
    Email author
  • Mads Frost
    • 2
  • Maria Faurholt-Jepsen
    • 3
  • Lars V. Kessing
    • 3
  • Jakob E. Bardram
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.Pervasive Interaction Technology Laboratory (PIT Lab)IT University of CopenhagenCopenhagenDenmark
  3. 3.Psychiatric Center Copenhagen, Department O, 6233University Hospital of CopenhagenCopenhagenDenmark

Personalised recommendations