Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder

被引:101
作者
Tsanas, A. [1 ,2 ,3 ]
Saunders, K. E. A. [4 ]
Bilderbeck, A. C. [4 ]
Palmius, N. [1 ]
Osipov, M. [1 ]
Clifford, G. D. [5 ,6 ]
Goodwin, G. M. [4 ]
De Vos, M. [1 ,3 ]
机构
[1] Univ Oxford, Dept Engn Sci, Inst Biomed Engn, Old Rd Campus Res Bldg, Oxford OX3 7DQ, England
[2] Univ Oxford, Math Inst, Oxford Ctr Ind & Appl Math, Oxford OX1 2JD, England
[3] Sleep & Circadian Neurosci Inst, Nuffield Dept Clin Neurosci, Oxford, England
[4] Univ Oxford, Dept Psychiat, Oxford OX1 2JD, England
[5] Emory Univ, Dept Biomed Informat, Atlanta, GA 30322 USA
[6] Georgia Inst Technol, Dept Biomed Engn, Atlanta, GA 30332 USA
基金
英国惠康基金;
关键词
Bipolar disorder; Borderline personality disorder; Digital health; Mood assessment; Mood monitoring; Patient reported outcome measures; DEPRESSIVE SYMPTOMATOLOGY; ANXIETY DISORDER; QUICK INVENTORY; INSTABILITY; RECOGNITION; COMORBIDITY; SYSTEM; CARE;
D O I
10.1016/j.jad.2016.06.065
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background: Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. Methods: We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median +/- iqr: 313 +/- 194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. Results: MZ correlated well (vertical bar R vertical bar>0. 5, p < 0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (vertical bar R vertical bar>0. 3, p < 0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. Limitations: All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. Conclusions: MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice. (C) 2016 The Authors. Published by Elsevier B.V.
引用
收藏
页码:225 / 233
页数:9
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