Research paperDaily mood monitoring of symptoms using smartphones in bipolar disorder: A pilot study assessing the feasibility of ecological momentary assessment
Introduction
Bipolar disorder (BD) is a recurrent, episodic illness affecting 1–4.4% of the population (Merikangas et al., 2007). While BD is defined by episodes of depression, mania and/or hypomania, studies have shown that as many as half of patients have chronic, subsyndromal symptoms (Judd et al., 2002). Core symptoms of BD, such as disturbances in mood state and energy level are solicited by the healthcare provider through the use of clinical questioning, and – in systems using best-practice standards – patient-rated questionnaires and mood diaries between weekly or monthly appointments (Harding et al., 2011, Sachs et al., 2003). This current model is generally successful in detecting large fluctuations in mood, yet is limited by recall bias and the influence of manic or depressive states on recollection of symptoms (Ghaemi, 2007).
There is growing interest in self-monitoring of mood and behavior on a daily basis, allowing for observation of progression into episodic depressive or manic states (Bauer et al., 2007, Bauer et al., 2008, Aan Het Rot et al., 2012). The capacity for frequent self-monitoring of symptomology associated with BD, including prodromal symptoms, would allow clinicians to tailor treatment approaches in a more timely manner, and to better study the effects of early interventions on daily symptoms (Proudfoot et al., 2014, Judd et al., 2002). Additionally, monitoring of medication changes and mood changes related to medication are important for treatment (Bauer et al., 2013b, Bauer et al., 2013a).
Ecological momentary assessment (EMA) is a data-capturing method related to experience sampling that is designed to assess data as events happen in real-time (Csikszentmihalyi and Larson, 1987, Johnstone et al., 1989, Terracciano et al., 2003, Ebner-Priemer and Trull, 2009, Trull and Ebner-Priemer, 2009, Shiffman et al., 2008). Emotions and behaviors can be recorded in the moment they occur within the patient's natural environment (Aan Het Rot et al., 2012, Moskowitz and Young, 2006). EMA has been used in paper-and-pencil forms to explore emotional reactivity to daily life stress among participants with psychosis and affective disorder (Havermans et al., 2010, Havermans et al., 2011, Havermans et al., 2007, Myin-Germeys et al., 2003), and to gather mood data to improve prediction of suicidal ideation in subjects with BD (Thompson et al., 2014). Several studies in BD have also investigated negative or positive affect on a daily basis (Knowles et al., 2007, Myin-Germeys et al., 2003, Husky et al., 2010). Response to negative events have been identified as more stressful for BD than HC (Havermans et al., 2007), and correlated with higher cortisol levels (Havermans et al., 2011).
Advancing technology, including internet services and smartphones, has enhanced the ability to gather data in the field and has been well-accepted. Using at-home computer-based software, Bauer et al. (2004) demonstrated a greater than 80% completion rate for more than 90% of participants over a 3-month period; reliability between self-report and clinician-report (Bauer et al., 2004); and that the use of the computer does not bias data collection based on comparable demographics (Bauer et al., 2005). As part of a trial of a new intervention, (Miklowitz et al., 2012) developed a text or email-based system to report scores of self-rated scales to the study team, and subjects responded to 71% of the daily prompts and 88% of the weekly prompts. (Faurhopsen et al., 2013) developed a system for detecting prodromal symptoms and reported participation using this system in a randomized controlled trial of 66.1%. (Depp et al., 2012) found higher compliance using paper-and-pencil data collection when compared to using mobile phones in 56 participants with BD, however, mobile phones were better able to capture intra-participant variability. Concordance between self- and clinician-ratings has been found to be greater for depressive symptoms than manic symptoms (Faurholt-Jepsen et al., 2014, Depp et al., 2012).
We hypothesized that using smartphones to capture EMA ratings of mood symptoms would be feasible in those with BD, as measured by completion rates. To measure symptomology of BD, we selected a visual analog scale for mood, energy, speed of thoughts and impulsivity. These four areas correspond to independent factors of mania shown through factor analysis: dysphoric mood (mood), psychomotor pressure (energy), psychosis (not conducive to self-report measure), increased hedonic function (impulsivity) and irritable aggression (Cassidy et al., 1998a, Cassidy et al., 1998b). Additionally, mood, energy and intellect (thoughts) are the three areas noted through careful, longitudinal study of phenomenology by Emil Kraepelin to cycle out of sync, producing mixed states (Kraepelin, 1921, Mackinnon and Pies, 2006). It was hypothesized that the BD group would demonstrate lower mood and energy; and higher speed of thoughts, impulsivity and social stress. We hypothesized that greater variability in all symptoms would be present in the BD group.
Section snippets
Design and sample
The study was approved by the institutional review board of the Hershey Medical Center (PSU COM IRB # 00251, approval 3/28/2014). A two-arm, parallel group, observational study was designed to measure a method of capturing information on symptoms of mood, energy, speed of thought, impulsivity and stress in a BD and a HC group. Personal health information collected for this study was stored in Research Electronic Data Capture System (REDCap), a secure data-management system supported by the Penn
Completion rates
Demographic characteristics of the control and BD groups are presented in Table 1. Groups did not differ by age, sex or employment rate. Eighteen out of twenty participants completed the full 14 days of the study; one BD participant dropped out on day 10 due to development of a manic episode, and one HC participant dropped out on day 12 due to a family emergency. Descriptive data on the completion of surveys are presented in Table 1. Median completion rates of surveys were not significantly
Discussion
Completion rates for this study demonstrated that the use of EMA on a smartphone device is a feasible modality for data collection in BD. The majority of the surveys were completed through the auto-generated survey, reflecting true EMA. While technological methods for capturing data are generally well-accepted, some have shown less compliance with smartphones than pencil-and-paper (Depp et al., 2012). However, capitalizing on technological methods quells concerns for factors that may generate
Contributors
S. Schultz and S. Schwartz were involved with the design of the trial, the collection of data, analysis of data and preparation of all drafts of the manuscript. AR was involved with study design, data collection, and reviewing the manuscript. ES was involved with study design, data collection, data analysis and preparation of all drafts of the manuscript.
Institutional review board
The study was approved by the institutional review board of the Hershey Medical Center (PSU COM IRB # 00251, approval 3/28/2014).
Conflict of interest
None.
Financial disclosures
EFHS has been a consultant for Projects In Knowledge, CME; AR, SS, SS – none.
Acknowledgements and role of the funding source
The authors wish to acknowledge the participants in the study who donated time and effort to research. The project described was supported by the National Center for Research Resources, Grant KL2 RR033180 (EFHS), and is now at the National Center for Advancing Translational Sciences, Grant KL2 TR000126. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The sponsors of this research did not have direct influence over the
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These authors contributed equally to this manuscript