Effectiveness of smartphone-based ambulatory assessment (SBAA-BD) including a predicting system for upcoming episodes in the long-term treatment of patients with bipolar disorders: study protocol for a randomized controlled single-blind trial

Esther Mühlbauer, Michael Bauer, Ulrich Ebner-Priemer, Philipp Ritter, Holger Hill, Fabrice Beier, Nikolaus Kleindienst, Emanuel Severus, Esther Mühlbauer, Michael Bauer, Ulrich Ebner-Priemer, Philipp Ritter, Holger Hill, Fabrice Beier, Nikolaus Kleindienst, Emanuel Severus

Abstract

Background: The detection of early warning signs is essential in the long-term treatment of bipolar disorders. However, in bipolar patients' daily life and outpatient treatment the assessment of upcoming state changes faces several difficulties. In this trial, we examine the effectiveness of a smartphone based automated feedback about ambulatory assessed early warning signs in prolonging states of euthymia and therefore preventing hospitalization. This study aims to assess, whether patients experience longer episodes of euthymia, when their treating psychiatrists receive automated feedback about changes in communication and activity. With this additional information an intervention at an earlier stage in the development of mania or depression could be facilitated. We expect that the amount of time will be longer between affective episodes in the intervention group.

Methods/design: The current study is designed as a randomized, multi-center, observer-blind, active-control, parallel group trial within a nationwide research project on the topic of innovative methods for diagnostics, prevention and interventions of bipolar disorders. One hundred and twenty patients with bipolar disorder will be randomly assigned to (1) the experimental group with included automated feedback or (2) the control group without feedback. During the intervention phase, the psychopathologic state of all participants is assessed every four weeks over 18 months. Kaplan-Meier estimators will be used for estimating the survival functions, a Log-Rank test will be used to formally compare time to a new episode across treatment groups. An intention-to-treat analysis will include data from all randomized patients.

Discussion: This article describes the design of a clinical trial investigating the effectiveness of a smartphone-based feedback loop. This feedback loop is meant to elicit early interventions at the detection of warning signs for the prevention of affective episodes in bipolar patients. This approach will hopefully improve the chances of a timely intervention helping patients to keep a balanced mood for longer periods of time. In detail, if our hypothesis can be confirmed, clinical practice treating psychiatrists will be enabled to react quickly when changes are automatically detected. Therefore, outpatients would receive an even more individually tailored treatment concerning time and frequency of doctor's appointments.

Trial registration: ClinicalTrials.gov : NCT02782910 : Title: "Smartphone-based Ambulatory Assessment of Early Warning Signs (BipoLife_A3)". Registered May 25 2016. Protocol Amendment Number: 03. Issue Date: 26 March 2018. Author(s): ES.

Keywords: Ambulatory assessment; Bipolar disorder; Early warning signs; Prevention.

Conflict of interest statement

Ethics approval and consent to participate

This trial has been approved by the Ethics Committee of the Technical University Dresden (central ethics committee, DE/EKSN38) and the local ethic committees of the participating trial centers (DE/EKBE14, DE/EKHE18, DE/EKHH17, DE/EKNW28). The Ethics Committee will be notified of any amendment to the study protocol.

Aims, procedures and design of the study are comprehensively explained to eligible patients, written informed consent is required prior to enrollment of all participating patients. In case of any amendment to the study protocol, all patients enrolled provide modified written informed consent regarding the amendments. All patients can withdraw previously provided consent at any time without specification of reasons and without any adverse or disadvantageous consequences.

A Data Monitoring (Safety) Committee (DMSC) is not needed. This clinical trial conforms to the requirements of the MDR (Medical Device Regulation). As the medical device itself is expected to have a low safety risk it is exempt from the approval of the higher federal authorities. For the same reason, no interim analysis or stopping guidelines are installed. Collecting, assessing, reporting, and managing solicited and spontaneously reported adverse events and other unintended effects of trial interventions or trial conduct will be performed according to the MDR. If deemed necessary, audits can be arranged by the sponsor. Two different types of insurance are provided to all participants to compensate for those who suffer harm from trial participation. One insurance will cover harms that may happen on the way to study appointments and back home respectively. The other insurance will cover for harms directly associated with study procedures.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Information flow. standard information flow feedback information flow
Fig. 2
Fig. 2
Study flow

References

    1. Suppes T, Dennehy EB, Gibbons EW. The longitudinal course of bipolar disorder. J Clin Psychiatry. 2000;61(Suppl 9):23–30.
    1. Miller S, Dell'Osso B, Ketter TA. The prevalence and burden of bipolar depression. J Affect Disord. 2014;169:3–11. doi: 10.1016/S0165-0327(14)70003-5.
    1. Gutiérrez-Rojas L, Gurpequi M, Ayuso-Mateos JL, Gutiérrez-Ariza JA, Ruiz-Vequilla M, Jurado D. Quality of life in bipolar disorder patients: a comparison with a general population sample. Bipolar Disord. 2008;10(5):625–634. doi: 10.1111/j.1399-5618.2008.00604.x.
    1. Shippee Nathan D, Shah Nilay D, Williams Mark D, Moriarty James P, Frye Mark A, Ziegenfuss Jeanette Y. Differences in demographic composition and in work, social, and functional limitations among the populations with unipolar depression and bipolar disorder: results from a nationally representative sample. Health and Quality of Life Outcomes. 2011;9(1):90. doi: 10.1186/1477-7525-9-90.
    1. Dilsaver SC. An estimate of the minimum economic burden of bipolar I and II disorders in the United States: 2009. J Affect Disord. 2011;129(1–3):79–83. doi: 10.1016/j.jad.2010.08.030.
    1. Kessler RC, Akiskal HS, Ames M, Birnbaum H, Greenberg P, Hirschfeld RM, Jin R, Merikanga KR, Simon GE, Wang PS. Prevalence and effects of mood disorders on work performance in a nationally representative sample of U.S. workers. Am J Psychiatry. 2006;163(9):1561–1568. doi: 10.1176/ajp.2006.163.9.1561.
    1. Morriss RK, Faizal MA, Jones AP, Williamson PR, Bolton C, McCarthy JP. Interventions for helping people recognise early signs of recurrence in bipolar disorder. Cochrane Database Syst Rev. 2007;1(1):CD004854.
    1. Kessler RC, Wittchen HA, Abelson J, Zhao S. Methodological issues in assessing psychiatric disorders with self-reports. In: Stone AA, Turkkan JS, Bachrach CA, Jobe JB, Kurtzman HS, Cain VS, editors. The science of self-report. Mawhah: Lawrence Erlbaum Associates; 2000. pp. 229–255.
    1. Goldberg JF, Chengappa KN. Identifying and treating cognitive impairment in bipolar disorder. Bipolar Disord. 2009;11(Suppl 2):123–137. doi: 10.1111/j.1399-5618.2009.00716.x.
    1. Perich T, Mitchell PB, Loo C, Hadzi-Pavlovic D, Roberst G, Frankland A, Lau P, Wright A. Clinical and demographic features associated with the detection of early warning signs in bipolar disorder. J Affect Disord. 2013;145(3):336–340. doi: 10.1016/j.jad.2012.08.014.
    1. Pfennig A, Bschor T, Baghai T, Bräunig P, Brieger P, Falkai P, et al. S3 guidelines on diagnostics and therapy of bipolar disorders: development process and essential recommendations. Nervenarzt. 2012;83(5):568–586. doi: 10.1007/s00115-011-3415-3.
    1. Bauer M, Wilson T, Neuhaus K, Sasse J, Pfennig A, Lewitzka U, Grof P, Glenn T, Rasgon N, Bschor T, Whybrow PC. Self-reporting software for bipolar disorder: validation of ChronoRecord by patients with mania. Psychiatry Res. 2008;159(3):359–366. doi: 10.1016/j.psychres.2007.04.013.
    1. Bauer M, Glenn T, Keil M, Bauer R, Marsh W, Grof P, Alda M, Sagduyu K, Murray G, Quiroz D, Baethge C, Whybrow PC. Brief depressive symptoms in patients with bipolar disorder: analysis of long-term self-reported data. Aust N Z J Psychiatry. 2012;46(11):1068–1078. doi: 10.1177/0004867412452017.
    1. Faurholt-Jepsen Maria, Vinberg Maj, Christensen Ellen Margrethe, Frost Mads, Bardram Jakob, Kessing Lars Vedel. Daily electronic self-monitoring of subjective and objective symptoms in bipolar disorder—the MONARCA trial protocol (MONitoring, treAtment and pRediCtion of bipolAr disorder episodes): a randomised controlled single-blind trial. BMJ Open. 2013;3(7):e003353. doi: 10.1136/bmjopen-2013-003353.
    1. Grünerbl A, Muaremi A, Osmani V, Bahle G, Öhler 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;19(1):140–148. doi: 10.1109/JBHI.2014.2343154.
    1. Beiwinkel Till, Kindermann Sally, Maier Andreas, Kerl Christopher, Moock Jörn, Barbian Guido, Rössler Wulf. Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study. JMIR Mental Health. 2016;3(1):e2. doi: 10.2196/mental.4560.
    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;18(5):1625–1635. doi: 10.1109/JBHI.2013.2290382.
    1. Trull TJ, Ebner-Priemer UW. Ambulatory assessment. Annu Rev Clin Psychol. 2013;9:151–176. doi: 10.1146/annurev-clinpsy-050212-185510.
    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;45:2691–2704. doi: 10.1017/S0033291715000410.
    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. Severus E, Seemüller F, Berger M, Dittmann S, Obermeier M, Pfennig A, Riedel M, Frangou S, Möller HJ, Bauer M. Mirroring everyday clinical practice in clinical trial design: a new concept to improve the external validity of randomized double-blind placebo-controlled trials in the pharmacological treatment of major depression. BMC Med. 2012;10:67. doi: 10.1186/1741-7015-10-67.
    1. Faurholt-Jepsen M, Bauer M, Kessing LV. Smartphone-based objective monitoring in bipolar disorder: status and considerations. Int J Bipolar Disord. 2018;6(1):6. doi: 10.1186/s40345-017-0110-8.
    1. Ritter PS, Bermpohl F, Gruber O, Hautzinger M, Jansen A, Juckel G, Kircher T, Lambert M, Mulert C, Pfennig A, Reif A, Rienhoff O, Schulze TG, Severus E, Stamm T, Bauer M. Aims and structure of the German research consortium BipoLife for the study of bipolar disorder. Int J Bipolar Disord. 2016;4(1):26. doi: 10.1186/s40345-016-0066-0.
    1. Forschungsverbund zu Bipolaren Störungen – Bipolife. Accessed 23 Apr 2018.
    1. Wittchen HU, Wunderlich U, Gruschwitz S, Zaudig M. SKID-I Strukturiertes Klinisches Interview für DSM-IV. Achse I: Psychische Störungen. Göttingen: Hogrefe; 1997.
    1. APA American Psychiatric Association . Diagnostic and statistical manual of mental disorders. 5. Arlington: American Psychiatric Publishing; 2013.
    1. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–435. doi: 10.1192/bjp.133.5.429.
    1. Rush AJ, Carmody T, Reimitz PE. The inventory of depressive symptomatology (IDS): clinician (IDS-C) and self-report (IDS-SR) ratings of depressive symptoms. Int J Methods Psychiatr Res. 2000;9:45–59. doi: 10.1002/mpr.79.
    1. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs MM, Suppes T, Crismon ML, Shores-Wilson K, Toprac MG, Dennehy EB, Witte B, Kashner TM. The inventory of depressive symptomatology, clinician rating (IDS-C) and self-report (IDS-SR), and the quick inventory of depressive symptomatology, clinician rating (QIDS-C) and self-report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychol Med. 2004;34(1):73–82. doi: 10.1017/S0033291703001107.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa