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Experiences of Remote Mood and Activity Monitoring in Bipolar Disorder: A Qualitative Study

Published online by Cambridge University Press:  27 January 2017

K.E.A. Saunders ,
A.C. Bilderbeck ,
P. Panchal ,
L.Z. Atkinson ,
J.R. Geddes  and
G.M. Goodwin
K.E.A. Saunders
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK Oxford Health NHS Foundation Trust, Warneford Hospital, OX3 7JXOxford, UK
A.C. Bilderbeck
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK
P. Panchal
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK
L.Z. Atkinson
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK
J.R. Geddes
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK Oxford Health NHS Foundation Trust, Warneford Hospital, OX3 7JXOxford, UK
G.M. Goodwin*
Affiliation:
Department of Psychiatry, University of Oxford, Warneford hospital, OX3 7JXOxford, UK Oxford Health NHS Foundation Trust, Warneford Hospital, OX3 7JXOxford, UK
*
⁎Corresponding author at: University Department of Psychiatry, Warneford hospital, OX3 7JX Oxford, UK. E-mail address:guy.goodwin@psych.ox.ac.uk (G.M. Goodwin).
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Abstract

Background

Mobile technology enables high frequency mood monitoring and automated passive collection of data (e.g. actigraphy) from patients more efficiently and less intrusively than has previously been possible. Such techniques are increasingly being deployed in research and clinical settings however little is known about how such approaches are experienced by patients. Here, we explored the experiences of individuals with bipolar disorder engaging in a study involving mood and activity monitoring with a range of portable and wearable technologies.

Method

Patients were recruited from a wider sample of 50 individuals with Bipolar Disorder taking part in the Automated Monitoring of Symptom Severity (AMoSS) study in Oxford. A sub-set of 21 patients participated in a qualitative interview that followed a semi-structured approach.

Results

Monitoring was associated with benefits including increased illness insight, behavioural change. Concerns were raised about the potential preoccupation with, and paranoia about, monitoring. Patients emphasized the need for personalization, flexibility, and the importance of context, when monitoring mood.

Conclusions

Mobile and electronic health approaches have potential to lend new insights into mental health and transform healthcare. Capitalizing on the perceived utility of these approaches from the patients’ perspective, while addressing their concerns, will be essential for the promise of new technologies to be realised.

Keywords

Bipolar disorderAmbulatory monitoringActivityMood monitoringWearable devices
Type
Original article
Information
European Psychiatry , Volume 41 , Issue 1 , March 2017 , pp. 115 - 121
Copyright
Copyright © European Psychiatric Association 2017

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References

Bilderbeck, A.C., et al.Psychiatric assessment of mood instability: qualitative study of patient experience. Br J Psychiatry 2014;204(3):236–241.10.1192/bjp.bp.113.128348CrossRefGoogle ScholarPubMed
Depp, C.A., et al.A prospective study of the trajectories of clinical insight, affective symptoms, and cognitive ability in bipolar disorder. J Affect Disord 2014;152–154:250–255.10.1016/j.jad.2013.09.020CrossRefGoogle ScholarPubMed
Malik, A.Goodwin, G.M.Holmes, E.A.Contemporary approaches to frequent mood monitoring in bipolar disorder. J Exp Psychopathol 2012;3(4):572–581.10.5127/jep.014311CrossRefGoogle ScholarPubMed
Bopp, J.M., et al.The longitudinal course of bipolar disorder as revealed through weekly text messaging: a feasibility study. Bipolar Disord 2010;12(3):327–334.10.1111/j.1399-5618.2010.00807.xCrossRefGoogle ScholarPubMed
Parker, G.Brotchie, H.Psychomotor change as a feature of depressive-disorders – an historical overview. Aust N Z J Psychiatry 1992;26(2):146–155.10.3109/00048679209072021CrossRefGoogle ScholarPubMed
Teicher, M.H.Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry 1995;3(1):18–35.CrossRefGoogle ScholarPubMed
Kupfer, D.J., et al.PSychomotor activity in affective states. Arch Gen Psychiatry 1974;30(6):765–768.10.1001/archpsyc.1974.01760120029005CrossRefGoogle ScholarPubMed
Kuhs, H.Reschke, D.Psychomotor activity in unipolar and bipolar depressive patients. Psychopathology 1992;25(2):109–116.10.1159/000284760CrossRefGoogle ScholarPubMed
Faurholt-Jepsen, M., et al.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;141(2–3):457–463.10.1016/j.jad.2012.02.020CrossRefGoogle ScholarPubMed
Weinstock, L.M.Miller, I.W.Functional impairment as a predictor of short-term symptom course in bipolar I disorder. Bipolar Disord 2008;10(3):437–442.10.1111/j.1399-5618.2007.00551.xCrossRefGoogle ScholarPubMed
Valenza, G., et al.Mood recognition in bipolar patients through the PSYCHE platform: Preliminary evaluations and perspectives. Artif Intell Med 2013;57(1):49–58.CrossRefGoogle Scholar
Faurholt-Jepsen, M., et al.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;14:309.10.1186/s12888-014-0309-5CrossRefGoogle ScholarPubMed
Beiwinkel, T., et al.Using smartphones to monitor bipolar disorder symptoms: a pilot study. JMIR Ment Health 2016;3(1):e2.10.2196/mental.4560CrossRefGoogle ScholarPubMed
Faurholt-Jepsen, M., et al.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):e003353.10.1136/bmjopen-2013-003353CrossRefGoogle ScholarPubMed
Faurholt-Jepsen, M., et al.Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res 2014;217(1–2):124–127.10.1016/j.psychres.2014.03.009CrossRefGoogle ScholarPubMed
Grunerbl, A., et al.Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform 2015;19(1):140–148.10.1109/JBHI.2014.2343154CrossRefGoogle ScholarPubMed
Bardram, J.E., et al.Designing mobile health technology for bipolar disorder: a field trial of the monarca system. In: Proceedings of the SIGCHI conference on human factors in computing systems; 2013. p. 2627–2636.10.1145/2470654.2481364CrossRefGoogle Scholar
Frost, M., et al.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; 2013. p. 133–142.10.1145/2493432.2493507CrossRefGoogle Scholar
Faurholt-Jepsen, M., et al.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(13):2691–2704.10.1017/S0033291715000410CrossRefGoogle ScholarPubMed
QSR International Pty Ltd, NVivo qualitative data analysis software; 2010.Google Scholar
Ritchie, J.Spencer, L.Qualitative data analysis for applied policy research. In: Bryman, A.Burgess, R., editors. Analyzing qualitative data. Routlage; 1994. p. 173–194.10.4324/9780203413081_chapter_9CrossRefGoogle Scholar
Naslund, J.A., et al.Emerging mHealth and eHealth interventions for serious mental illness: a review of the literature. J Ment Health 2015;24(5):321–332.10.3109/09638237.2015.1019054CrossRefGoogle ScholarPubMed
Wenze, S.J.Armey, M.F.Miller, I.W.Feasibility and acceptability of a mobile intervention to improve treatment adherence in bipolar disorder: a pilot study. Behav Modif 2014;38(4):497–515.10.1177/0145445513518421CrossRefGoogle ScholarPubMed
Naslund, J.A., et al.Feasibility of popular m-health technologies for activity tracking among individuals with serious mental illness. Telemed J E Health 2014;21(3):213–216.10.1089/tmj.2014.0105CrossRefGoogle ScholarPubMed
Nicholas, J., et al.Mobile apps for bipolar disorder: a systematic review of features and content quality. J Med Internet Res 2015;17(8):e198.10.2196/jmir.4581CrossRefGoogle ScholarPubMed
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