Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Ezgi Dogan, Christian Sander, Xenija Wagner, Ulrich Hegerl, Elisabeth Kohls, Ezgi Dogan, Christian Sander, Xenija Wagner, Ulrich Hegerl, Elisabeth Kohls

Abstract

Background: Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones.

Objective: We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes.

Methods: We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols.

Results: A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage.

Conclusions: The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.

Keywords: ecological momentary assessment; mood disorders; review; smartphone.

Conflict of interest statement

Conflicts of Interest: The author UH declares several potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Within the last three years, UH was: an advisory board member for Lundbeck, Takeda Pharmaceuticals, Servier, and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma. ED, XW, CS and EK declare no competing interests.

©Ezgi Dogan, Christian Sander, Xenija Wagner, Ulrich Hegerl, Elisabeth Kohls. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.07.2017.

Figures

Figure 1
Figure 1
PRISMA Flow chart.

References

    1. World Health Organization . The European Health Report 2009, Health and health systems. Copenhagen: WHO Regional Office for Europe; 2009. [2017-06-30]. .
    1. Eaton WW, Shao H, Nestadt G, Lee HB, Lee BH, Bienvenu OJ, Zandi P. Population-based study of first onset and chronicity in major depressive disorder. Arch Gen Psychiatry. 2008 May;65(5):513–20. doi: 10.1001/archpsyc.65.5.513.
    1. Baumeister H, Härter M. Prevalence of mental disorders based on general population surveys. Soc Psychiatry Psychiatr Epidemiol. 2007 Jul;42(7):537–46. doi: 10.1007/s00127-007-0204-1.
    1. Thomson W. Lifting the shroud on depression and premature mortality: a 49-year follow-up study. J Affect Disord. 2011 Apr;130(1-2):60–5. doi: 10.1016/j.jad.2010.09.028.
    1. Ustün TB, Ayuso-Mateos JL, Chatterji S, Mathers C, Murray CJ. Global burden of depressive disorders in the year 2000. Br J Psychiatry. 2004 May;184:386–92.
    1. Chisholm D, Sweeny K, Sheehan P, Rasmussen B, Smit F, Cuijpers P, Saxena S. Scaling-up treatment of depression and anxiety: a global return on investment analysis. Lancet Psychiatry. 2016 May;3(5):415–24. doi: 10.1016/S2215-0366(16)30024-4.
    1. Hardeveld F, Spijker J, De GR, Nolen WA, Beekman AT. Recurrence of major depressive disorder and its predictors in the general population: results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS) Psychol Med. 2013 Jan;43(1):39–48. doi: 10.1017/S0033291712002395.
    1. Klein DN, Kotov R. Course of depression in a 10-year prospective study: evidence for qualitatively distinct subgroups. J Abnorm Psychol. 2016 Apr;125(3):337–48. doi: 10.1037/abn0000147.
    1. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005 Jun;62(6):593–602. doi: 10.1001/archpsyc.62.6.593.
    1. Merikangas KR, Jin R, He J, Kessler RC, Lee S, Sampson NA, Viana MC, Andrade LH, Hu C, Karam EG, Ladea M, Medina-Mora ME, Ono Y, Posada-Villa J, Sagar R, Wells JE, Zarkov Z. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Gen Psychiatry. 2011 Mar;68(3):241–51. doi: 10.1001/archgenpsychiatry.2011.12.
    1. Grande I, Berk M, Birmaher B, Vieta E. Bipolar disorder. Lancet. 2016 Apr 09;387(10027):1561–72. doi: 10.1016/S0140-6736(15)00241-X.
    1. Bonnín CM, Torrent C, Goikolea JM, Reinares M, Solé B, Valentí M, Sánchez-Moreno J, Hidalgo D, Tabarés-Seisdedos R, Martínez-Arán A, Vieta E. The impact of repeated manic episodes and executive dysfunction on work adjustment in bipolar disorder. Eur Arch Psychiatry Clin Neurosci. 2014 Apr;264(3):247–54. doi: 10.1007/s00406-013-0431-2.
    1. Malhi GS, Bassett D, Boyce P, Bryant R, Fitzgerald PB, Fritz K, Hopwood M, Lyndon B, Mulder R, Murray G, Porter R, Singh AB. Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for mood disorders. Aust N Z J Psychiatry. 2015 Dec;49(12):1087–206. doi: 10.1177/0004867415617657.
    1. Fernández A, Haro JM, Martinez-Alonso M, Demyttenaere K, Brugha TS, Autonell J, de GG, Bernert S, Lépine JP, Alonso J. Treatment adequacy for anxiety and depressive disorders in six European countries. Br J Psychiatry. 2007 Feb;190:172–3. doi: 10.1192/bjp.bp.106.023507.
    1. Kohn R, Saxena S, Levav I, Saraceno B. The treatment gap in mental health care. Bull World Health Organ. 2004 Nov;82(11):858–66.
    1. Trivedi MH, Lin EH, Katon WJ. Consensus recommendations for improving adherence, self-management, and outcomes in patients with depression. CNS Spectr. 2007 Aug;12(8 Suppl 13):1–27.
    1. Vuorilehto MS, Melartin TK, Riihimäki K, Isometsä ET. Pharmacological and psychosocial treatment of depression in primary care: low intensity and poor adherence and continuity. J Affect Disord. 2016 Sep 15;202:145–52. doi: 10.1016/j.jad.2016.05.035.
    1. Clement S, Schauman O, Graham T, Maggioni F, Evans-Lacko S, Bezborodovs N, Morgan C, Rüsch N, Brown JS, Thornicroft G. What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychol Med. 2015 Jan;45(1):11–27. doi: 10.1017/S0033291714000129.
    1. Mohr DC, Ho J, Duffecy J, Baron KG, Lehman KA, Jin L, Reifler D. Perceived barriers to psychological treatments and their relationship to depression. J Clin Psychol. 2010 Apr;66(4):394–409. doi: 10.1002/jclp.20659.
    1. Arensman E, Koburger N, Larkin C, Karwig G, Coffey C, Maxwell M, Harris F, Rummel-Kluge C, van AC, Sisask M, Alexandrova-Karamanova A, Perez V, Purebl G, Cebria A, Palao D, Costa S, Mark L, Tóth MD, Gecheva M, Ibelshäuser A, Gusmão R, Hegerl U. Depression awareness and self-management through the Internet: protocol for an internationally standardized approach. JMIR Res Protoc. 2015 Aug 06;4(3):e99. doi: 10.2196/resprot.4358.
    1. Vallury KD, Jones M, Oosterbroek C. Computerized cognitive behavior therapy for anxiety and depression in rural areas: a systematic review. J Med Internet Res. 2015;17(6):e139. doi: 10.2196/jmir.4145.
    1. Høifødt RS, Lillevoll KR, Griffiths KM, Wilsgaard T, Eisemann M, Waterloo K, Kolstrup N. The clinical effectiveness of web-based cognitive behavioral therapy with face-to-face therapist support for depressed primary care patients: randomized controlled trial. J Med Internet Res. 2013;15(8):e153. doi: 10.2196/jmir.2714.
    1. Hidalgo-Mazzei D, Mateu A, Reinares M, Matic A, Vieta E, Colom F. Internet-based psychological interventions for bipolar disorder: Review of the present and insights into the future. J Affect Disord. 2015 Aug 28;188:1–13. doi: 10.1016/j.jad.2015.08.005.
    1. Trefflich F, Kalckreuth S, Mergl R, Rummel-Kluge C. Psychiatric patients' internet use corresponds to the Internet use of the general public. Psychiatry Res. 2015 Mar 30;226(1):136–41. doi: 10.1016/j.psychres.2014.12.037.
    1. Ericsson Mobility Report. Rima Qureshi; 2016. [2017-06-30]. On the pulse of the networked society .
    1. Faurholt-Jepsen M, Frost M, Vinberg M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 2014 Jun 30;217(1-2):124–7. doi: 10.1016/j.psychres.2014.03.009.
    1. Tams S, Hill K, Ortiz de Guinea A, Thatcher J, Grover V. NeuroIS-alternative or complement to existing methods? Illustrating the holistic effects of neuroscience and self-reported data in the context of technostress research. ‎J Assoc Inf Syst. 2014;15(10):e1.
    1. Dey A, Wac K, Ferreira D, Tassini K, Hong J, Ramos J. Getting closer: an empirical investigation of the proximity of user to their smart phones. Proceedings of the 13th International Conference on Ubiquitous Computing; 13th International Conference on Ubiquitous Computing; 2011; New York. USA: ACM; 2011. p. A.
    1. Gravenhorst F, Muaremi A, Bardram J, Grünerbl A, Mayora O, Wurzer G, Frost M, Osmani V, Arnrich B, Lukowicz P, Tröster G. Mobile phones as medical devices in mental disorder treatment: an overview. Pers Ubiquit Comput. 2014 Sep 21;19(2):335–353. doi: 10.1007/s00779-014-0829-5.
    1. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009 Oct;62(10):1006–12. doi: 10.1016/j.jclinepi.2009.06.005.
    1. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health. 1998 Jun;52(6):377–84.
    1. Hootman JM, Driban JB, Sitler MR, Harris KP, Cattano NM. Reliability and validity of three quality rating instruments for systematic reviews of observational studies. Res Synth Methods. 2011 Jun;2(2):110–8. doi: 10.1002/jrsm.41.
    1. Mohr DC, Stiles-Shields C, Brenner C, Palac H, Montague E, Kaiser SM, Carty-Fickes E, Duffecy J. MedLink: a mobile intervention to address failure points in the treatment of depression in general medicine. Int Conf Pervasive Comput Technol Healthc. 2015 May;2015:100–107.
    1. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC. Harnessing context sensing to develop a mobile intervention for depression. J Med Internet Res. 2011;13(3):e55. doi: 10.2196/jmir.1838.
    1. Faurholt-Jepsen M, Frost M, Ritz C, Christensen EM, Jacoby AS, Mikkelsen RL, Knorr U, Bardram JE, Vinberg M, Kessing LV. Daily electronic self-monitoring in bipolar disorder using smartphones - the MONARCA I trial: a randomized, placebo-controlled, single-blind, parallel group trial. Psychol Med. 2015 Oct;45(13):2691–704. doi: 10.1017/S0033291715000410.
    1. Kane JM, Perlis RH, DiCarlo LA, Au-Yeung K, Duong J, Petrides G. First experience with a wireless system incorporating physiologic assessments and direct confirmation of digital tablet ingestions in ambulatory patients with schizophrenia or bipolar disorder. J Clin Psychiatry. 2013 Jun;74(6):e533–40. doi: 10.4088/JCP.12m08222.
    1. Naslund JA, Aschbrenner KA, Bartels SJ. Wearable devices and smartphones for activity tracking among people with serious mental illness. Ment Health Phys Act. 2016 Mar;10:10–17. doi: 10.1016/j.mhpa.2016.02.001.
    1. Dickerson R, Gorlin E, Stankovic J. Empath: A continuous remote emotional health monitoring system for depressive illness. Proceedings of the 2nd Conference on Wireless Health 2011; 2nd Conference on Wireless Health; 2011; New York. USA: ACM; 2011. p. A.
    1. Dang M, Mielke C, Diehl A, Haux R. Accompanying depression with FINE - a smartphone-based approach. Stud Health Technol Inform. 2016;228:195–9.
    1. Bardram J, Frost M, Szántó K, Faurholt-Jepsen M, Vinberg M, Kessing L. 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; SIGCHI Conference on Human Factors in Computing Systems; 2013; New York. USA: ACM; 2013. p. A.
    1. Faurholt-Jepsen M, Busk J, Frost M, Vinberg M, Christensen EM, Winther O, Bardram JE, Kessing LV. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry. 2016 Jul 19;6:e856. doi: 10.1038/tp.2016.123.
    1. Faurholt-Jepsen M, Vinberg M, Frost M, Christensen EM, Bardram JE, Kessing LV. Smartphone data as an electronic biomarker of illness activity in bipolar disorder. Bipolar Disord. 2015 Nov;17(7):715–28. doi: 10.1111/bdi.12332.
    1. Faurholt-Jepsen M, Vinberg M, Frost M, Debel S, Margrethe CE, Bardram JE, Kessing LV. Behavioral activities collected through smartphones and the association with illness activity in bipolar disorder. Int J Methods Psychiatr Res. 2016 Dec;25(4):309–323. doi: 10.1002/mpr.1502.
    1. Frost M, Doryab A, Faurholt-Jepsen M, Kessing L, Bardram J. Supporting disease insight through data analysis: refinements of the MONARCA self-assessment system. Proceedings of the 2013 ACM international joint conference on Pervasiveubiquitous computing; ACM international joint conference on Pervasive and ubiquitous computing; 2013; New York. USA: ACM; 2013. pp. A–142.
    1. Abdullah S, Matthews M, Frank E, Doherty G, Gay G, Choudhury T. Automatic detection of social rhythms in bipolar disorder. J Am Med Inform Assoc. 2016 May;23(3):538–43. doi: 10.1093/jamia/ocv200.
    1. Lanata A, Valenza G, Nardelli M, Gentili C, Scilingo EP. Complexity index from a personalized wearable monitoring system for assessing remission in mental health. IEEE J Biomed Health Inform. 2015 Jan;19(1):132–9. doi: 10.1109/JBHI.2014.2360711.
    1. Guidi A, Salvi S, Ottaviano M, Gentili C, Bertschy G, de RD, Scilingo EP, Vanello N. Smartphone application for the analysis of prosodic features in running speech with a focus on bipolar disorders: system performance evaluation and case study. Sensors (Basel) 2015 Nov 06;15(11):28070–87. doi: 10.3390/s151128070.
    1. Gentili C, Valenza G, Nardelli M, Lanatà A, Bertschy G, Weiner L, Mauri M, Scilingo EP, Pietrini P. Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: a pilot study. J Affect Disord. 2017 Feb;209:30–38. doi: 10.1016/j.jad.2016.11.008.
    1. Beiwinkel T, Kindermann S, Maier A, Kerl C, Moock J, Barbian G, Rössler W. Using smartphones to monitor bipolar disorder symptoms: a pilot study. JMIR Ment Health. 2016;3(1):e2. doi: 10.2196/mental.4560.
    1. Saunders KE, Bilderbeck AC, Panchal P, Atkinson LZ, Geddes JR, Goodwin GM. Experiences of remote mood and activity monitoring in bipolar disorder: a qualitative study. Eur Psychiatry. 2017 Mar;41:115–121. doi: 10.1016/j.eurpsy.2016.11.005.
    1. Prociow P, Wac K, Crowe J. Mobile psychiatry: towards improving the care for bipolar disorder. Int J Ment Health Syst. 2012 May 29;6(1):5. doi: 10.1186/1752-4458-6-5.
    1. Alvarez-Lozano J, Osmani V, Mayora O, Frost M, Bardram J, Faurholt-Jepsen M, Kessing L. Tell me your apps and I will tell you your mood: correlation of apps usage with bipolar disorder state. Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments; 7th International Conference on Pervasive Technologies and Related to Assistive Environments; 2014; New York. USA: ACM; 2014. p. A.
    1. Maxhuni A, Muñoz-Meléndez A, Osmani V, Perez H, Mayora O, Morales EF. Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients. Pervasive Mob Comput. 2016 Sep;31:50–66. doi: 10.1016/j.pmcj.2016.01.008.
    1. Osmani V, Maxhuni A, Grünerbl A, Lukowicz P, Haring C, Mayora O. Monitoring activity of patients with bipolar disorder using smart phones. Proceedings of International Conference on Advances in Mobile Computing & Multimedia; Proceedings of International Conference on Advances in Mobile Computing & Multimedia; 2013; New York. USA: ACM; 2013. p. A.
    1. Gruenerbl A, Osmani V, Bahle G, Carrasco J, Oehler S, Mayora O, Haring C, Lukowicz P. Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients. Proceedings of the 5th Augmented Human International Conference; 5th Augmented Human International Conference; 2014; New York. USA: ACM; 2014. p. A.
    1. Karam ZN, Provost EM, Singh S, Montgomery J, Archer C, Harrington G, Mcinnis MG. Ecologicaly valid long-term mood monitoring of individuals with bipolar disorder using speech. Proc IEEE Int Conf Acoust Speech Signal Process. 2014 May;2014:4858–4862. doi: 10.1109/ICASSP.2014.6854525.
    1. Grünerbl A, Oleksy P, Bahle G, Haring C, Weppner J, Lukowicz P. Towards smart phone based monitoring of bipolar disorder. Proceedings of the Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare; Second ACM Workshop on Mobile Systems, Applications, and Services for HealthCare; 2012; New York. USA: ACM; 2012. p. A.
    1. Matthews M, Abdullah S, Murnane E, Voida S, Choudhury T, Gay G, Frank E. Development and evaluation of a smartphone-based measure of social rhythms for bipolar disorder. Assessment. 2016 Jun 29;:472–483. doi: 10.1177/1073191116656794.
    1. 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. 2015 Jan;19(1):140–8. doi: 10.1109/JBHI.2014.2343154.
    1. Muaremi A, Gravenhorst F, Grünerbl A, Arnrich B, Tröster G. Assessing bipolar episodes using speech cues derived from phone calls. Pervasive Computing Paradigms for Mental Health; 4th International Symposium, MindCare; 2014; Tokyo. Cham: Springer; 2014. pp. 978–3.
    1. Moore RC, Depp CA, Wetherell JL, Lenze EJ. Ecological momentary assessment versus standard assessment instruments for measuring mindfulness, depressed mood, and anxiety among older adults. J Psychiatr Res. 2016 Apr;75:116–23. doi: 10.1016/j.jpsychires.2016.01.011.
    1. Faurholt-Jepsen M, Vinberg M, Frost M, Christensen EM, Bardram J, Kessing LV. Daily electronic monitoring of subjective and objective measures of illness activity in bipolar disorder using smartphones--the MONARCA II trial protocol: a randomized controlled single-blind parallel-group trial. BMC Psychiatry. 2014;14:309. doi: 10.1186/s12888-014-0309-5.
    1. Bauer S, Moessner M. Technology-enhanced monitoring in psychotherapy and e-mental health. J Ment Health. 2012 Aug;21(4):355–63. doi: 10.3109/09638237.2012.667886.
    1. Valenza G, Lanatà A, Paradiso R, Scilingo EP. Advanced technology meets mental health: how smartphones, textile electronics, and signal processing can serve mental health monitoring, diagnosis, and treatment. IEEE Pulse. 2014;5(3):56–9. doi: 10.1109/MPUL.2014.2309582.
    1. Faurholt-Jepsen M, Brage S, Vinberg M, Christensen EM, Knorr U, Jensen HM, Kessing LV. Differences in psychomotor activity in patients suffering from unipolar and bipolar affective disorder in the remitted or mild/moderate depressive state. J Affect Disord. 2012 Dec 10;141(2-3):457–63. doi: 10.1016/j.jad.2012.02.020.
    1. Luik AI, Zuurbier LA, Direk N, Hofman A, Van Someren EJ, Tiemeier H. 24-hour activity rhythm and sleep disturbances in depression and anxiety: a population-based study of middle-aged and older persons. Depress Anxiety. 2015 Sep;32(9):684–92. doi: 10.1002/da.22355.
    1. Gonzalez R. The relationship between bipolar disorder and biological rhythms. J Clin Psychiatry. 2014 Apr;75(4):e323–31. doi: 10.4088/JCP.13r08507.
    1. Beyer KM, Szabo A, Nattinger AB. Time spent outdoors, depressive symptoms, and variation by race and ethnicity. Am J Prev Med. 2016 Sep;51(3):281–90. doi: 10.1016/j.amepre.2016.05.004.
    1. Valenza G, Gentili C, Lanatà A, Scilingo EP. Mood recognition in bipolar patients through the PSYCHE platform: preliminary evaluations and perspectives. Artif Intell Med. 2013 Jan;57(1):49–58. doi: 10.1016/j.artmed.2012.12.001.
    1. Cummins N, Scherer S, Krajewski J, Schnieder S, Epps J, Quatieri TF. A review of depression and suicide risk assessment using speech analysis. Speech Communication. 2015 Jul;71:10–49. doi: 10.1016/j.specom.2015.03.004.
    1. Vanello N, Guidi A, Gentili C, Werner S, Bertschy G, Valenza G, Lanata A, Scilingo EP. Speech analysis for mood state characterization in bipolar patients. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:2104–7. doi: 10.1109/EMBC.2012.6346375.
    1. Gideon J, Provost EM, McInnis M. Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder. Proc IEEE Int Conf Acoust Speech Signal Process. 2016 Mar;2016:2359–2363. doi: 10.1109/ICASSP.2016.7472099.
    1. Hidalgo-Mazzei D, Mateu A, Reinares M, Murru A, Del Mar Bonnín C, Varo C, Valentí M, Undurraga J, Strejilevich S, Sánchez-Moreno J, Vieta E, Colom F. Psychoeducation in bipolar disorder with a SIMPLe smartphone application: feasibility, acceptability and satisfaction. J Affect Disord. 2016 Aug;200:58–66. doi: 10.1016/j.jad.2016.04.042.
    1. Faurholt-Jepsen M, Ritz C, Frost M, Mikkelsen RL, Margrethe CE, Bardram J, Vinberg M, Kessing LV. Mood instability in bipolar disorder type I versus type II-continuous daily electronic self-monitoring of illness activity using smartphones. J Affect Disord. 2015 Nov 01;186:342–9. doi: 10.1016/j.jad.2015.06.026.
    1. Valenza G, Nardelli M, Lanata A, Gentili C, Bertschy G, Kosel M, Scilingo EP. Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics. IEEE J Biomed Health Inform. 2016 Apr 20;:1. doi: 10.1109/JBHI.2016.2554546.
    1. Valenza G, Nardelli M, Lanatà A, Gentili C, Bertschy G, Paradiso R, Scilingo EP. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J Biomed Health Inform. 2014 Sep;18(5):1625–35. doi: 10.1109/JBHI.2013.2290382.
    1. Burton C, Szentagotai TA, McKinstry B, Matheson C, Matu S, Moldovan R, Macnab M, Farrow E, David D, Pagliari C, Serrano BA, Wolters M, Help4Mood Consortium Pilot randomised controlled trial of Help4Mood, an embodied virtual agent-based system to support treatment of depression. J Telemed Telecare. 2016 Sep;22(6):348–55. doi: 10.1177/1357633X15609793.
    1. Anguera JA, Jordan JT, Castaneda D, Gazzaley A, Areán PA. Conducting a fully mobile and randomised clinical trial for depression: access, engagement and expense. BMJ Innov. 2016 Jan;2(1):14–21. doi: 10.1136/bmjinnov-2015-000098.
    1. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR Mhealth Uhealth. 2016 Sep 21;4(3):e111. doi: 10.2196/mhealth.5960.
    1. Arean PA, Hallgren KA, Jordan JT, Gazzaley A, Atkins DC, Heagerty PJ, Anguera JA. The use and effectiveness of mobile apps for depression: results from a fully remote clinical trial. J Med Internet Res. 2016 Dec 20;18(12):e330. doi: 10.2196/jmir.6482.
    1. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC. Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res. 2015;17(7):e175. doi: 10.2196/jmir.4273.
    1. Warmerdam L, Riper H, Klein M, van den Ven P, Rocha A, Ricardo HM, Tousset E, Silva H, Andersson G, Cuijpers P. Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project. Stud Health Technol Inform. 2012;181:339–43.
    1. Olbrich S, van DR, Arns M. Personalized medicine: review and perspectives of promising baseline EEG biomarkers in major depressive disorder and attention deficit hyperactivity disorder. Neuropsychobiology. 2015;72(3-4):229–40. doi: 10.1159/000437435.
    1. Young JJ, Silber T, Bruno D, Galatzer-Levy IR, Pomara N, Marmar CR. Is there progress? An overview of selecting biomarker candidates for major depressive disorder. Front Psychiatry. 2016;7:72. doi: 10.3389/fpsyt.2016.00072. doi: 10.3389/fpsyt.2016.00072.
    1. Comello ML, Qian X, Deal AM, Ribisl KM, Linnan LA, Tate DF. Impact of game-inspired infographics on user engagement and information processing in an eHealth program. J Med Internet Res. 2016 Sep 22;18(9):e237. doi: 10.2196/jmir.5976.
    1. Markowetz A, Błaszkiewicz K, Montag C, Switala C, Schlaepfer TE. Psycho-informatics: big data shaping modern psychometrics. Med Hypotheses. 2014 Apr;82(4):405–11. doi: 10.1016/j.mehy.2013.11.030.
    1. Naeem F, Gire N, Xiang S, Yang M, Syed Y, Shokraneh F, Adams C, Farooq S. Reporting and understanding the safety and adverse effect profile of mobile apps for psychosocial interventions: an update. World J Psychiatry. 2016 Jun 22;6(2):187–91. doi: 10.5498/wjp.v6.i2.187.
    1. Torous J, Firth J. The digital placebo effect: mobile mental health meets clinical psychiatry. Lancet Psychiatry. 2016 Feb;3(2):100–2. doi: 10.1016/S2215-0366(15)00565-9.
    1. Anthes E. Mental health: there's an app for that. Nature. 2016 Apr 7;532(7597):20–3. doi: 10.1038/532020a.
    1. Kauer SD, Reid SC, Crooke AH, Khor A, Hearps SJ, Jorm AF, Sanci L, Patton G. Self-monitoring using mobile phones in the early stages of adolescent depression: randomized controlled trial. J Med Internet Res. 2012;14(3):e67. doi: 10.2196/jmir.1858.
    1. Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatry Rep. 2014 Dec;16(12):523. doi: 10.1007/s11920-014-0523-3.
    1. Wenze SJ, Miller IW. Use of ecological momentary assessment in mood disorders research. Clin Psychol Rev. 2010 Aug;30(6):794–804. doi: 10.1016/j.cpr.2010.06.007.
    1. Proudfoot J, Parker G, Hadzi PD, Manicavasagar V, Adler E, Whitton A. Community attitudes to the appropriation of mobile phones for monitoring and managing depression, anxiety, and stress. J Med Internet Res. 2010;12(5):e64. doi: 10.2196/jmir.1475.
    1. Monteith S, Glenn T, Geddes J, Whybrow PC, Bauer M. Big data for bipolar disorder. Int J Bipolar Disord. 2016 Dec;4(1):10. doi: 10.1186/s40345-016-0051-7.
    1. Bussmann JB, Ebner-Priemer UW, Fahrenberg J. Ambulatory activity monitoring. Eur Psychol. 2009 Jan;14(2):142–152. doi: 10.1027/1016-9040.14.2.142.
    1. Dell'Osso L, Pini S, Cassano GB, Mastrocinque C, Seckinger RA, Saettoni M, Papasogli A, Yale SA, Amador XF. Insight into illness in patients with mania, mixed mania, bipolar depression and major depression with psychotic features. Bipolar Disord. 2002 Oct;4(5):315–22.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa