A Domains Oriented Framework of Recent Machine Learning Applications in Mobile Mental Health

  • Max-Marcel TheiligEmail author
  • Kim Janine Blankenhagel
  • Rüdiger Zarnekow
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 29)


This research illustrates how the interdisciplinary integration of mobile health (mHealth) and Machine Learning (ML) can contribute to implementing mobile care for mental health. 94 articles were identified in a literature review to derive functional domains and composing information items improving the comprehension of ML benefits with mHealth integration. Identified items of each domain were pooled into clusters and information flow was quantified according to prevailing occurrence of included articles. We derive a comprehensive domains oriented framework (DF) and visualize an information flow graph. The DF indicates that the utilization of ML is well established (e.g. stress detection, activity recognition). Because deployment and data acquisition currently relies heavily on mobile phones, only 65% of current applications make fully integrated use of data sources to assert patient’s mental state. Big data integration and a lack of commercially available devices to measure physiological or psychological parameters represent current bottlenecks to leverage synergies.


Machine learning Application Mobile health Mental health Framework 


  1. 1.
    Polanczyk, G.V., Salum, G.A., Sugaya, L.S., Caye, A., Rohde, L.A.: Annual research review. A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. J. Child Psychol. Psychiatry 56, 345–365 (2015)CrossRefGoogle Scholar
  2. 2.
    Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55, 78 (2012)CrossRefGoogle Scholar
  3. 3.
    Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutorials 15, 1192–1209 (2013)CrossRefGoogle Scholar
  4. 4.
    Torous, J., Baker, J.T.: Why psychiatry needs data science and data science needs psychiatry. Connecting with technology. JAMA Psychiatry 73, 3–4 (2016)CrossRefGoogle Scholar
  5. 5.
    Iqbal, M.H., Aydin, A., Brunckhorst, O., Dasgupta, P., Ahmed, K.: A review of wearable technology in medicine. J. R. Soc. Med. 109, 372–380 (2016)CrossRefGoogle Scholar
  6. 6.
    Saeb, S., Zhang, M., Karr, C.J., Schueller, S.M., Corden, M.E., Kording, K.P., Mohr, D.C.: Mobile phone sensor correlates of depressive symptom severity in daily-life behavior. An exploratory study. J. Med. Internet Res. 17, e175 (2015)CrossRefGoogle Scholar
  7. 7.
    Atkins, L., Francis, J., Islam, R., O’Connor, D., Patey, A., Ivers, N., Foy, R., Duncan, E.M., Colquhoun, H., Grimshaw, J.M., et al.: A guide to using the theoretical domains framework of behaviour change to investigate implementation problems. Implementation Sci. 12, 1–18 (2017)CrossRefGoogle Scholar
  8. 8.
    Michie, S., Johnston, M., Abraham, C., Lawton, R., Parker, D., Walker, A.: Making psychological theory useful for implementing evidence based practice. A consensus approach. BMJ Qual. Saf., 14, 26–33 (2005)CrossRefGoogle Scholar
  9. 9.
    Vom Brocke, J., Riedl, R., Léger, P.-M.: Application strategies for neuroscience in information systems design science research. J. Comput. Inf. Syst. 53, 1–13 (2015)Google Scholar
  10. 10.
    Vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the giant. On the importance of rigour in documenting the literature search process. In: ECIS 2009 Proc., 9, 2206–2217 (2009)Google Scholar
  11. 11.
    Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Shepperd, M., Hall, T. (eds.) EASE 2014, pp. 1–10. ACM (2014)Google Scholar
  12. 12.
    Setz, C., Arnrich, B., Schumm, J., La Marca, R., Tröster, G., Ehlert, U.: Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans. Inf Technol. Biomed. 14, 410–417 (2010)CrossRefGoogle Scholar
  13. 13.
    Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Behav. 24, 385–396 (1983)CrossRefGoogle Scholar
  14. 14.
    Parkitny, L., McAule, J.: The depression anxiety stress scale (DASS). J. Physiotherapy 56, 204 (2010)CrossRefGoogle Scholar
  15. 15.
    Farhan, A.A., Lu, J., Bi, J., Russell, A., Wang, B., Bamis, A.: multi-view bi-clustering to identify smartphone sensing features indicative of depression. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies, pp. 264–273. IEEE, Piscataway, NJ (2016)Google Scholar
  16. 16.
    Sioni, R., Chittaro, L.: Stress detection using physiological sensors. Computer 48, 26–33 (2015)CrossRefGoogle Scholar
  17. 17.
    Gravina, R., Fortino, G.: Automatic methods for the detection of accelerative cardiac defense response. IEEE Transac. Affect. Comput. 7, 286–298 (2016)CrossRefGoogle Scholar
  18. 18.
    Howarth, E., Hoffman, M.S.: A multidimensional approach to the relationship between mood and weather. Br. J. Psychol. 75(Pt 1), 15–23 (1984)CrossRefGoogle Scholar
  19. 19.
    Sanders, J.L., Brizzolara, M.S.: Relationships between weather and mood. J. Gen. Psychol. 107, 155–156 (1982)CrossRefGoogle Scholar
  20. 20.
    LiKamWa, R., Liu, Y., Lane, N.D., Zhong, L.: MoodScope. In: Chu, H.-H. (ed.) MobiSys ‘13 Proceeding of the 11th International Conference on Mobile Systems, Applications, and Services, p. 389. ACM (2013)Google Scholar
  21. 21.
    Ahsan, G.M.T., Addo, I.D., Ahamed, S.I., Petereit, D., Kanekar, S., Burhansstipanov, L., Krebs, L.U.: Toward an mHealth intervention for smoking cessation. In: Proceedings of the Annual International Computer Software and Applications Conference. COMPSAC (2013)Google Scholar
  22. 22.
    Sano, A., Phillips, A.J., Yu, A.Z., Mchill, A., Taylor, S., Jaques, N., Czeisler, C., Klerman, E., Picard, R.: Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones. In: International Conference on Wearable and Implantable Body Sensor Networks, pp. 1–6 (2015)Google Scholar
  23. 23.
    Sanders, C.E., Field, T.M., Diego, M., Kaplan, M.: The relationship of Internet use to depression and social isolation among adolescents. Adolescence 35, 237–242 (2000)Google Scholar
  24. 24.
    Cacioppo, J.T., Hawkley, L.C., Thisted, R.A.: Perceived social isolation makes me sad. 5-year cross-lagged analyses of loneliness and depressive symptomatology in the Chicago health, aging, and social relations study. Psychol. Aging 25, 453–463 (2010)CrossRefGoogle Scholar
  25. 25.
    Valenza, G., Nardelli, M., Lanata’, A., Gentili, C., Bertschy, G., Kosel, M., Scilingo, E.P.: Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics. IEEE J. Biomed. Health Inform. (2016)Google Scholar
  26. 26.
    Zhu, Z., Satizabal, H.F., Blanke, U., Perez-Uribe, A., Troster, G.: Naturalistic recognition of activities and mood using wearable electronics. IEEE Trans. Affect. Comput. 7, 272–285 (2016)CrossRefGoogle Scholar
  27. 27.
    Maxhuni, A., Hernandez-Leal, P., Sucar, E.L., Osmani, V., Morales, E.F., Mayora, O.: Stress modelling and prediction in presence of scarce data. J. Biomed. Inform. 63, 344–356 (2016)CrossRefGoogle Scholar
  28. 28.
    Faurholt-Jepsen, M., Busk, J., Frost, M., Vinberg, M., Christensen, E.M., Winther, O., Bardram, J.E., Kessing, L.V.: Voice analysis as an objective state marker in bipolar disorder. Transl. Psychiatry 6, e856 (2016)CrossRefGoogle Scholar
  29. 29.
    Frost, M., Doryab, A., Bardram, J.: Disease insights through analysis. Using machine learning to provide feedback in the MONARCA system. In: Czerwinski, M., Staff, I. (eds.) 7th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2013). ICST (2013)Google Scholar
  30. 30.
    Katsis, C.D., Katertsidis, N.S., Fotiadis, D.I.: An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomed. Signal Process. Control 6, 261–268 (2011)CrossRefGoogle Scholar
  31. 31.
    Faedda, G.L., Ohashi, K., Hernandez, M., McGreenery, C.E., Grant, M.C., Baroni, A., Polcari, A., Teicher, M.H.: Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. J. Child Psychol. Psychiatry 57, 706–716 (2016)CrossRefGoogle Scholar
  32. 32.
    Grünerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Tröster, G., Mayora, O., Haring, C., Lukowicz, P.: Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J. Biomed. Health Inform. 19, 140–148 (2015)CrossRefGoogle Scholar
  33. 33.
    Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., Pentland, A.: Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits. In: Hua, K.A. (ed.) MM ‘14: Proceedings of the 22nd ACM international conference on Multimedia, pp. 477–486. ACM (2014)Google Scholar
  34. 34.
    Liu, H.-Y., Dunea, D., Oprea, M., Savu, T., Iordache, S.: Improving the protection of children against air pollution threats in Romania—the RokidAIR project approach and future perspectives. Nukleonika -Original Edition- 68, 841–846 (2017)Google Scholar
  35. 35.
    Baig, M.M., GholamHosseini, H., Moqeem, A.A., Mirza, F., Lindé, M.: A systematic review of wearable patient monitoring systems—current challenges and opportunities for clinical adoption. J. Med. Syst. 41, 115 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Max-Marcel Theilig
    • 1
    Email author
  • Kim Janine Blankenhagel
    • 1
  • Rüdiger Zarnekow
    • 1
  1. 1.Technical University Berlin, Chair of Information and Communication ManagementBerlinGermany

Personalised recommendations