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Use of ecological momentary assessment to detect variability in mood, sleep and stress in bipolar disorder.

ABSTRACT: OBJECTIVE:Our aim was to study within-person variability in mood, cognition, energy, and impulsivity measured in an Ecological Momentary Assessment paradigm in bipolar disorder by using modern statistical techniques. Exploratory analyses tested the relationship between bipolar disorder symptoms and hours of sleep, and levels of pain, social and task-based stress. We report an analysis of data from a two-arm, parallel group study (bipolar disorder group N?=?10 and healthy control group N?=?10, with 70% completion rate of 14-day surveys). Surveys of bipolar disorder symptoms, social stressors and sleep hours were completed on a smartphone at unexpected times in an Ecological Momentary Assessment paradigm twice a day. Multi-level models adjusted for potential subject heterogeneity were adopted to test the difference between the bipolar disorder and health control groups. RESULTS:Within-person variability of mood, energy, speed of thoughts, impulsivity, pain and perception of skill of tasks was significantly higher in the bipolar disorder group compared to health controls. Elevated bipolar disorder symptom domains in the evening were associated with reduced sleep time that night. Stressors were associated with worsening of bipolar disorder symptoms. Detection of symptoms when an individual is experiencing difficulty allows personalized, focused interventions.

SUBMITTER: Li H

PROVIDER: S-EPMC6894147 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Use of ecological momentary assessment to detect variability in mood, sleep and stress in bipolar disorder.

Li Han H Mukherjee Dahlia D Krishnamurthy Venkatesh Basappa VB Millett Caitlin C Ryan Kelly A KA Zhang Lijun L Saunders Erika F H EFH Wang Ming M

BMC research notes 20191204 1

<h4>Objective</h4>Our aim was to study within-person variability in mood, cognition, energy, and impulsivity measured in an Ecological Momentary Assessment paradigm in bipolar disorder by using modern statistical techniques. Exploratory analyses tested the relationship between bipolar disorder symptoms and hours of sleep, and levels of pain, social and task-based stress. We report an analysis of data from a two-arm, parallel group study (bipolar disorder group N = 10 and healthy control group N ...[more]

PMID: 31801608

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Project description:Changes in motor activity are core symptoms of mood episodes in bipolar disorder. The manic state is characterized by increased variance, augmented complexity and irregular circadian rhythmicity when compared to healthy controls. No previous studies have compared mania to euthymia intra-individually in motor activity. The aim of this study was to characterize differences in motor activity when comparing manic patients to their euthymic selves. Motor activity was collected from 16 bipolar inpatients in mania and remission. 24-h recordings and 2-h time series in the morning and evening were analyzed for mean activity, variability and complexity. Lastly, the recordings were analyzed with the similarity graph algorithm and graph theory concepts such as edges, bridges, connected components and cliques. The similarity graph measures fluctuations in activity reasonably comparable to both variability and complexity measures. However, direct comparisons are difficult as most graph measures reveal variability in constricted time windows. Compared to sample entropy, the similarity graph is less sensitive to outliers. The little-understood estimate Bridges is possibly revealing underlying dynamics in the time series. When compared to euthymia, over the duration of approximately one circadian cycle, the manic state presented reduced variability, displayed by decreased standard deviation (p = 0.013) and augmented complexity shown by increased sample entropy (p = 0.025). During mania there were also fewer edges (p = 0.039) and more bridges (p = 0.026). Similar significant changes in variability and complexity were observed in the 2-h morning and evening sequences, mainly in the estimates of the similarity graph algorithm. Finally, augmented complexity was present in morning samples during mania, displayed by increased sample entropy (p = 0.015). In conclusion, the motor activity of mania is characterized by altered complexity and variability when compared within-subject to euthymia.

Project description:BACKGROUND:Mobile technologies have the potential to be used as innovative tools for conducting research on the mental health and well-being of young people. In particular, they have utility for carrying out ecological momentary assessment (EMA) research by capturing data from participants in real time as they go about their daily lives. OBJECTIVE:The aim of this study was to explore the utility of a mobile phone app as a means of collecting EMA data pertaining to mood, problems, and coping efficacy in a school-based sample of Irish young people. METHODS:The study included a total of 208 participants who were aged 15-18 years, 64% female (113/208), recruited from second-level schools in Ireland, and who downloaded the CopeSmart mobile phone app as part of a randomized controlled trial. On the app, participants initially responded to 5 single-item measures of key protective factors in youth mental health (formal help-seeking, informal help-seeking, sleep, exercise, and sense of belonging). They were then encouraged to use the app daily to input data relating to mood states (happiness, sadness, anger, stress, and worry), daily problems, and coping self-efficacy. The app automatically collected data pertaining to user engagement over the course of the 28-day intervention period. Students also completed pen and paper questionnaires containing standardized measures of emotional distress (Depression, Anxiety, and Stress Scale; DASS-21), well-being (World Health Organization Well-Being Index; WHO-5), and coping (Coping Strategies Inventory; CSI). RESULTS:On average the participants completed 18% (5/28) of daily ratings, and engagement levels did not differ across gender, age, school, socioeconomic status, ethnicity, or nationality. On a scale of 1 to 10, happiness was consistently the highest rated mood state (overall mean 6.56), and anger was consistently the lowest (overall mean 2.11). Pearson correlations revealed that average daily ratings of emotional states were associated with standardized measures of emotional distress (rhappiness=-.45, rsadness=.51, ranger=.32, rstress=.41, rworry=.48) and well-being (rhappiness=.39, rsadness =-.43, ranger=-.27, rstress=-.35, rworry=-.33). Inferential statistics indicated that single-item indicators of key protective factors were related to emotional distress, well-being, and average daily mood states, as measured by EMA ratings. Hierarchical regressions revealed that greater daily problems were associated with more negative daily mood ratings (all at the P<.001 level); however, when coping efficacy was taken into account, the relationship between problems and happiness, sadness, and anger became negligible. CONCLUSIONS:While engagement with the app was low, overall the EMA data collected in this exploratory study appeared valid and provided useful insights into the relationships between daily problems, coping efficacy, and mood states. Future research should explore ways to increase engagement with EMA mobile phone apps in adolescent populations to maximize the amount of data captured by these tools. TRIAL REGISTRATION:Clinicaltrials.gov NCT02265978; http://clinicaltrials.gov/ct2/show/NCT02265978 (Archived by WebCite at http://www.webcitation.org/6mMeYqseA).

Dataset Information

Use of ecological momentary assessment to detect variability in mood, sleep and stress in bipolar disorder.

Publications

Use of ecological momentary assessment to detect variability in mood, sleep and stress in bipolar disorder.

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