Personal and Ubiquitous Computing

, Volume 19, Issue 2, pp 335–353 | Cite as

Mobile phones as medical devices in mental disorder treatment: an overview

  • Franz Gravenhorst
  • Amir Muaremi
  • Jakob Bardram
  • Agnes Grünerbl
  • Oscar Mayora
  • Gabriel Wurzer
  • Mads Frost
  • Venet Osmani
  • Bert Arnrich
  • Paul Lukowicz
  • Gerhard Tröster
Original Article


Mental disorders can have a significant, negative impact on sufferers’ lives, as well as on their friends and family, healthcare systems and other parts of society. Approximately 25 % of all people in Europe and the USA experience a mental disorder at least once in their lifetime. Currently, monitoring mental disorders relies on subjective clinical self-reporting rating scales, which were developed more than 50 years ago. In this paper, we discuss how mobile phones can support the treatment of mental disorders by (1) implementing human–computer interfaces to support therapy and (2) collecting relevant data from patients’ daily lives to monitor the current state and development of their mental disorders. Concerning the first point, we review various systems that utilize mobile phones for the treatment of mental disorders. We also evaluate how their core design features and dimensions can be applied in other, similar systems. Concerning the second point, we highlight the feasibility of using mobile phones to collect comprehensive data including voice data, motion and location information. Data mining methods are also reviewed and discussed. Based on the presented studies, we summarize advantages and drawbacks of the most promising mobile phone technologies for detecting mood disorders like depression or bipolar disorder. Finally, we discuss practical implementation details, legal issues and business models for the introduction of mobile phones as medical devices.


Mobile phone Medical device Mental disorder 



Thanks go to Rosa Brown ( for proofreading the manuscript. This Project is sponsored by the European Project MONARCA in the 7th Framework Program under Contract Number: 248545.


  1. 1.
    Alonso J, Angermeyer MC, Bernert S, Bruffaerts R, Brugha T, Bryson H, Girolamo GD, Graaf RD, Demyttenaere K, Gasquet I et al (2004) Prevalence of mental disorders in Europe: results from the european study of the epidemiology of mental disorders (ESEMeD) project. Acta Psychiatr Scand 109(420):21–27Google Scholar
  2. 2.
    Arnrich B, Mayora O, Bardram J, Tröster G (2010) Pervasive healthcare—paving the way for a pervasive, user-centered and preventive healthcare model. J Methods Inf Med 49:67–73Google Scholar
  3. 3.
    Azizyan M, Constandache I, Roy Choudhury R (2009) Surroundsense: mobile phone localization via ambience fingerprinting. In: Proceedings of the 15th annual international conference on mobile computing and networking, ACM, pp 261–272Google Scholar
  4. 4.
    Baltaxe CA (1977) Pragmatic deficits in the language of autistic adolescents. J Pediatr Psychol 2(4):176–180CrossRefGoogle Scholar
  5. 5.
    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. Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’13. NY, USA, ACM, New York, pp 2627–2636Google Scholar
  6. 6.
    Begley CE, Annegers JF, Swann AC, Lewis C, Coan S, Schnapp WB, Bryant-Comstock L (2001) The lifetime cost of bipolar disorder in the US. Pharmacoeconomics 19(5):483–495CrossRefGoogle Scholar
  7. 7.
    Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, Drake RE (2013) Mobile technologies among people with serious mental illness: opportunities for future services. Adm Policy Ment Health Ment Health Serv Res 40(4):340–343CrossRefGoogle Scholar
  8. 8.
    Blazer DG (1982) Social support and mortality in an elderly community population. Am J Epidemiol 115(5):684–694Google Scholar
  9. 9.
    Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC (2011) Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res 13(3):e55Google Scholar
  10. 10.
    Cafazzo AJ, Casselman M, Hamming N, Katzman KD, Palmert RM (2012) Design of an mhealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res 14(3):e70CrossRefGoogle Scholar
  11. 11.
    Cantwell DP, Baker L (1977) Psychiatric disorder in children with speech and language retardation: a critical review. Arch Gen Psychiatry 34(5):583CrossRefGoogle Scholar
  12. 12.
    Cole-Lewis H, Kershaw T (2010) Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev 32(1):56–69CrossRefGoogle Scholar
  13. 13.
    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 the SIGCHI conference on human factors in computing systems, CHI ’08. NY, USA, ACM, New York, pp 1797–1806Google Scholar
  14. 14.
    Conwell Y, Brent D (1995) Suicide and aging I: patterns of psychiatric diagnosis. Int Psychogeriatr 7(02):149–164 CrossRefGoogle Scholar
  15. 15.
    Dey AK, Wac K, Ferreira D, Tassini K, Hong J-H, Ramos J (2011) Getting closer: an empirical investigation of the proximity of user to their smart phones. In: Proceedings of the ACM international conference on ubiquitous computing, UbiComp ’11. NY, USA, ACM, New York, pp 163–172Google Scholar
  16. 16.
    Ehrenreich B, Righter B, Rocke DA, Dixon L, Himelhoch S (2011) Are mobile phones and handheld computers being used to enhance delivery of psychiatric treatment?: a systematic review. J Nerv Ment Dis 199(11):886–891CrossRefGoogle Scholar
  17. 17.
    Empatica (2014) Empatica e3 wristband. March 2014
  18. 18.
    Ericsson AB (2014) Interim ericsson mobility report. February 2014
  19. 19.
    Eyben F, Wöllmer M, Schuller B (2010) Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the international conference on multimedia, ACM, pp 1459–1462Google Scholar
  20. 20.
  21. 21.
    FitBit (2014) Fitbit flex. March 2014
  22. 22.
    W. Fitness (2014) Wahoo blue hr heart rate strap. March 2014
  23. 23.
    Franko OI, Tirrell TF (2012) Smartphone app use among medical providers in acgme training programs. J Med Syst 36(5):3135–3139CrossRefGoogle Scholar
  24. 24.
    Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, Patel V, Haines A (2013) 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):e1001362CrossRefGoogle Scholar
  25. 25.
    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 ACM international conference on pervasive and ubiquitous computing (UbiComp), pp 133–142Google Scholar
  26. 26.
    Gartner (2013) Market share analysis: mobile phones, worldwide, 2q13.
  27. 27.
    George LK, Blazer DG, Hughes DC, Fowler N (1989) Social support and the outcome of major depression. Br J Psychiatry 154(4):478–485CrossRefGoogle Scholar
  28. 28.
    Granholm E, Ben-Zeev D, Link PC, Bradshaw KR, Holden JL (2012) Mobile assessment and treatment for schizophrenia (mats): a pilot trial of an interactive text-messaging intervention for medication adherence, socialization, and auditory hallucinations. Schizophr Bull 38(3):414–425CrossRefGoogle Scholar
  29. 29.
    Gravenhorst F, Muaremi A, Arnrich B, Tröster G (2012) Unobtrusive electrodermal activity measurement device and voice analysis for supporting bipolar disorder monitoring. In: Workshop presentation at 34st annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2012)Google Scholar
  30. 30.
    Grünerbl A, Oleksy P, Bahle G, Haring C, Weppner J, Lukowicz P (2012) Towards smart phone based monitoring of bipolar disorder. In: Proceedings of the second ACM workshop on mobile systems, applications, and services for healthcare, ACM, p 3Google Scholar
  31. 31.
    Grünerbl A, Osmani V, Bahle G, Carrasco JC, Oehler S, Mayora O, Haring C, Lukowicz P (2013) Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. In: ACM Proceedings of the 5th augmented human international conference, March 2013, DOI: 10.1145/2582051.2582089, Kobe, Japan
  32. 32.
    Harrison V, Proudfoot J, Wee PP, Parker G, Pavlovic DH, Manicavasagar V (2011) Mobile mental health: review of the emerging field and proof of concept study. J Ment Health 20(6):509–524CrossRefGoogle Scholar
  33. 33.
    Heires K (2007) Why it pays to give away the store. CNN business 2.0 rovat.
  34. 34.
    Heron K, Smyth J (2010) Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol 15:1–39CrossRefGoogle Scholar
  35. 35.
    Janney CA, Richardson CR, Holleman RG, Glasheen C, Strath SJ, Conroy MB, Kriska AM (2010) Corrigendum to “gender, mental health service use and objectively measured physical activity: Data from the national health and nutrition examination survey (nhanes 2003–2004)” [Ment Health Phys Act 1 (2008) 9–16]. Ment Health Phys Act 3(2):104 Google Scholar
  36. 36.
    Kappeler-Setz C, Gravenhorst F, Schumm J, Arnrich B, Tröster G (2013) Towards long term monitoring of electrodermal activity in daily life. Pers Ubiquitous Comput 17(2):261–271CrossRefGoogle Scholar
  37. 37.
    Kauer SD, Reid SC, Crooke AHD, Khor A, Hearps SJC, Jorm AF, Sanci L, Patton G et al (2012) Self-monitoring using mobile phones in the early stages of adolescent depression: randomized controlled trial. J Med Internet Res 14(3):e67Google Scholar
  38. 38.
    Kessler RC, Berglund PA, Bruce ML, Koch JR, Laska EM, Leaf PJ, Manderscheid RW, Rosenheck RA, Walters EE, Wang PS (2001) The prevalence and correlates of untreated serious mental illness. Health Serv Res 36(6 Pt 1):987Google Scholar
  39. 39.
    Kessler RC, Chiu WT, Demler O, Walters EE (2005) Prevalence, severity, and comorbidity of 12-month dsm-iv disorders in the national comorbidity survey replication. Arch Gen Psychiatry 62(6):617–627CrossRefGoogle Scholar
  40. 40.
    Kessler RC, Zhao S, Katz SJ, Kouzis AC, Frank RG, Edlund M, Leaf P (1999) Past-year use of outpatient services for psychiatric problems in the national comorbidity survey. Ame J Psychiatry 156(1):115–123CrossRefGoogle Scholar
  41. 41.
    Knutson JF, Lansing CR (1990) The relationship between communication problems and psychological difficulties in persons with profound acquired hearing loss. J Speech Hearing Disord 55(4):656–664CrossRefGoogle Scholar
  42. 42.
    Krumeich J, Burkhart T, Werth D, Loos P (2012) Towards a component-based description of business models: a state-of-the-art analysis. AMCIS 2012 Proceedings, paper 19Google Scholar
  43. 43.
    Kuhn E, Greene C, Hoffman J, Nguyen T, Wald L, Schmidt J, Ramsey KM, Ruzek J (2014) Preliminary evaluation of ptsd coach, a smartphone app for post-traumatic stress symptoms. Mil Med 179(1):12–18CrossRefGoogle Scholar
  44. 44.
    Lauronen E, Veijola J, Isohanni I, Jones PB, Nieminen P, Isohanni M (2004) Links between creativity and mental disorder. Psychiatry: Interpers Biol Process 67(1):81–98Google Scholar
  45. 45.
    Lin J, Mamykina L, Lindtner S, Delajoux G, Strub H (2006) Fish’n’steps: encouraging physical activity with an interactive computer game. In: Dourish P, Friday A (eds) Proceedings of the ACM International conference on ubiquitous computing, vol 4206., Lecture Notes in Computer Science, Springer, Berlin / Heidelberg, pp 261–278Google Scholar
  46. 46.
    Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig LO (2009) Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. Biomed Eng IEEE Trans 56(4):1015–1022CrossRefGoogle Scholar
  47. 47.
    Lomranz J, Bergman S, Eyal N, Shmotkin D (1988) Indoor and outdoor activities of aged women and men as related to depression and well-being. Int J Aging Hum Dev 26(4):303–314CrossRefGoogle Scholar
  48. 48.
    Lu H, Frauendorfer D, Rabbi M, Mast MS, Chittaranjan GT, Campbell AT, Perez DG, Choudhury T (2012) Stresssense: detecting stress in unconstrained acoustic environments using smartphones. In: Proceedings of ACM UbiCompGoogle Scholar
  49. 49.
    Luxton DD, McCann RA, Bush NE, Mishkind MC, Reger GM (2011) mHealth for mental health: integrating smartphone technology in behavioral healthcare. Prof Psychol: Res Pract 42(6):505CrossRefGoogle Scholar
  50. 50.
    Matthews M, Doherty G (2011) In the mood: engaging teenagers in psychotherapy using mobile phones. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’11. NY, USA, ACM, New York, pp 2947–2956Google Scholar
  51. 51.
    Mayora O, Arnrich B, Bardram J, Dräger C, Finke A, Frost M, Giordano S, Gravenhorst F, Grunerbl A, Haring C, Haux R, Lukowicz P, Muaremi A, Mudda S, Ohler S, Puiatti A, Reichwaldt N, Scharnweber C, Tröster G, Kessing LV, Wurzer G (2013) Personal health systems for bipolar disorder: anecdotes, challenges and lessons learnt from monarca project. In 7th IEEE international conference on pervasive computing technologies for healthcare (PervasiveHealth), pp 424–429Google Scholar
  52. 52.
    Mazilu S, Blanke U, Hardegger M, Tröster G, Gazit E, Hausdorff JM (2014) Gaitassist: a daily-life support and training system for parkinson’s disease patients with freezing of gait. In: ACM SIGCHI conference on human factors in computing systems (CHI)Google Scholar
  53. 53.
    Mazilu S, Hardegger M, Zhu Z, Roggen D, Tröster G, Plotnik M, Hausdorff JM (2012) Online detection of freezing of gait with smartphones and machine learning techniques. In 6th IEEE international conference on pervasive computing technologies for healthcare (PervasiveHealth)Google Scholar
  54. 54.
    McTavish FM, Chih M-Y, Shah D, Gustafson DH (2012) How patients recovering from alcoholism use a smartphone intervention. J Dual Diagn 8(4):294–304CrossRefGoogle Scholar
  55. 55.
    Miller G (2012) The smartphone psychology manifesto. Perspect Psychol Sci 7(3):221–237CrossRefGoogle Scholar
  56. 56.
    Moore P, Little M, McSharry P, Geddes J, Goodwin G (2012) Forecasting depression in bipolar disorder. IEEE Trans Biomed Eng 59(10):2801–2807CrossRefGoogle Scholar
  57. 57.
    Morris M, Kathawala Q, Leen T, Gorenstein E, Guilak F, Labhard M, Deleeuw W (2010) Mobile therapy: case study evaluations of a cell phone application for emotional self-awareness. J Internet Med Res 12(2):e10:12Google Scholar
  58. 58.
    Muaremi A, Arnrich B, Tröster G (2013) Towards measuring stress with smartphones and wearable devices during workday and sleep. BioNanoScience 3(2):172–183CrossRefGoogle Scholar
  59. 59.
    Muaremi A, Bexheti A, Gravenhorst F, Arnrich B, Tröster G (2014) Monitoring the impact of stress on the sleep patterns of pilgrims using wearable sensors. In: IEEE-EMBS international conference on biomedical and health informatics (BHI)Google Scholar
  60. 60.
    Muaremi A, Gravenhorst F, Grünerbl A, Arnrich B, Tröster G (2014) Assessing bipolar episodes using speech cues derived from phone calls. In: 4th international symposium on pervasive computing paradigms for mental health (MindCare)Google Scholar
  61. 61.
    Muñoz RF, McQuaid JR, González GM, Dimas J, Rosales VA (1999) Depression screening in a women’s clinic: using automated Spanish-and English-language voice recognition. J Consult Clin Psychol 67(4):502CrossRefGoogle Scholar
  62. 62.
    Newman S, Mather VG (1938) Analysis of spoken language of patients with affective disorders. Am J Psychiatry 94(4):913–942CrossRefGoogle Scholar
  63. 63.
    Nike (2014) Nike+ fuelband se. March 2014
  64. 64.
    Osmani V, Maxhuni A, Grünerbl A, Lukowicz P, Haring C, Mayora O (2013) Monitoring activity of patients with bipolar disorder using smart phones. In: ACM Proceedings of international conference on advances in mobile computing and multimedia (MoMM2013), December 2013. doi: 10.1145/2536853.2536882, Vienna, Austria
  65. 65.
    Paradiso R, Bianchi A, Lau K, Scilingo E (2010) Psyche: personalised monitoring systems for care in mental health. In: Engineering in Medicine and Biology Society (EMBC), 2010 annual international conference of the IEEE, pp 3602–3605Google Scholar
  66. 66.
    Patel SN, Truong KN, Abowd GD (2006) Powerline positioning: a practical sub-room-level indoor location system for domestic use. In: UbiComp 2006: Ubiquitous computing, Springer, pp 441–458Google Scholar
  67. 67.
    Pijnenborg G, Withaar F, Brouwer W, Timmerman M, Bosch R, Evans J (2010) The efficacy of sms text messages to compensate for the effects of cognitive impairments in schizophrenia. Br J Clin Psychol 49(2):259–274CrossRefGoogle Scholar
  68. 68.
    Puiatti A, Mudda S, Giordano S, Mayora O (2011) Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder. In: Engineering in Medicine and Biology Society, EMBC, 2011 annual international conference of the IEEE, pp 3644–3647Google Scholar
  69. 69.
    Rizvi SL, Dimeff LA, Skutch J, Carroll D, Linehan MM (2011) A pilot study of the dbt coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder. Beh Ther 42(4):589–600CrossRefGoogle Scholar
  70. 70.
    Rutland JB, Sheets T, Young T (2007) Development of a scale to measure problem use of short message service: the sms problem use diagnostic questionnaire. CyberPsychol Behav 10(6):841–844CrossRefGoogle Scholar
  71. 71.
    Sa MD, Carrico L, Antunes P (2007) Ubiquitous psychotherapy. IEEE Pervasive Comput 6(1):20–27CrossRefGoogle Scholar
  72. 72.
    Sewall GK, Jiang J, Ford CN (2006) Clinical evaluation of parkinson’s-related dysphonia. Laryngoscope 116(10):1740–1744CrossRefGoogle Scholar
  73. 73.
    Shapiro JR, Bauer S, Andrews E, Pisetsky E, Bulik-Sullivan B, Hamer RM, Bulik CM (2010) Mobile therapy: use of text-messaging in the treatment of bulimia nervosa. Int J Eat Disord 43(6):513–519CrossRefGoogle Scholar
  74. 74.
    Simmons JQ, Baltaxe C (1975) Language patterns of adolescent autistics. J Autism Child Schizophr 5(4):333–351CrossRefGoogle Scholar
  75. 75.
    Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR (2002) Patient non-compliance with paper diaries. BMJ 324(7347):1193–1194CrossRefGoogle Scholar
  76. 76.
    Szabadi E, Bradshaw C, Besson J (1976) Elongation of pause-time in speech: a simple, objective measure of motor retardation in depression. Br J Psychiatry 129(6):592–597CrossRefGoogle Scholar
  77. 77.
    Tager-Flusberg H (1981) On the nature of linguistic functioning in early infantile autism. J Autism Dev Disord 11(1):45–56CrossRefGoogle Scholar
  78. 78.
    Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: Liwc and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54CrossRefGoogle Scholar
  79. 79.
    Teasdale JD, Fogarty SJ, Williams JMG (1980) Speech rate as a measure of short-term variation in depression. Br J Soc Clin Psychol 19(3):271–278CrossRefGoogle Scholar
  80. 80.
    Z Technology (2014) Zephyr bioharness 3. March 2014
  81. 81.
    Tsanas A, Little M, McSharry P, Spielman J, Ramig L (2012) Novel speech signal processing algorithms for high-accuracy classification of parkinson’s disease. Biomed Eng IEEE Trans 59(5):1264–1271CrossRefGoogle Scholar
  82. 82.
    Vanello N, Guidi A, Gentili C, Werner S, Bertschy G, Valenza G, Lanata A, Scilingo E (2012) Speech analysis for mood state characterization in bipolar patients. In: IEEE Engineering in Medicine and Biology Society (EMBC)Google Scholar
  83. 83.
    Wac K (2013) Smartphone as a personal, pervasive health informatics services platform: literature review. arXiv preprint arXiv:1310.7965
  84. 84.
    Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, Andrews G (2013) Cbt for depression: a pilot rct comparing mobile phone vs. computer. BMC Psychiatry 13(1):49CrossRefGoogle Scholar
  85. 85.
    Weppner J, Lukowicz P (2011) Collaborative crowd density estimation with mobile phones. In: Proceedings of ACM PhoneSenseGoogle Scholar
  86. 86.
    Weppner J, Lukowicz P (2013) Bluetooth based collaborative crowd density estimation with mobile phones. In: Pervasive computing and communications (PerCom), 2013 IEEE international conference on, pp 193–200Google Scholar
  87. 87.
    Westeyn TL, Abowd GD, Starner TE, Johnson JM, Presti PW, Weaver KA (2012) Monitoring children’s developmental progress using augmented toys and activity recognition. Pers Ubiquitous Comput 16(2):169–191CrossRefGoogle Scholar
  88. 88.
    World Health Organisation (2011) mHealth: new horizons for health through mobile technologies.
  89. 89.
    Young R, Biggs J, Ziegler V, Meyer D (1978) A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 133(5):429–435CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Franz Gravenhorst
    • 1
  • Amir Muaremi
    • 1
  • Jakob Bardram
    • 2
  • Agnes Grünerbl
    • 3
  • Oscar Mayora
    • 5
  • Gabriel Wurzer
    • 4
  • Mads Frost
    • 2
  • Venet Osmani
    • 5
  • Bert Arnrich
    • 6
  • Paul Lukowicz
    • 3
  • Gerhard Tröster
    • 1
  1. 1.Wearable Computing LabETH ZurichZurichSwitzerland
  2. 2.ITU CopenhagenCopenhagenDenmark
  3. 3.DFKITU KaiserslauternKaiserslauternGermany
  4. 4.Vienna University of TechnologyViennaAustria
  5. 5.CREATE-NETTrentoItaly
  6. 6.Boğaziçi UniversityIstanbulTurkey

Personalised recommendations