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Longitudinal Associations Between Sleep Patterns and Psychiatric Symptom Severity in High-Risk and Community Comparison Youth.

ABSTRACT: OBJECTIVE:Sleep disturbance may be involved in symptom progression across multiple domains of psychopathology and could represent a target for treatment development in youth. Our objective was to identify sleep patterns that longitudinally change in conjunction with psychiatric symptom severity in at-risk youth. METHOD:The study included 484 Pittsburgh Bipolar Offspring Study (BIOS) youth with at least 2 sleep assessments occurring between 10 and 18 years of age: 267 offspring of parents with bipolar I or II disorder and 217 community comparison offspring. Assessments occurred approximately every 2 years (mean number of assessments, 2.8 ± 0.8; mean follow-up duration, 3.8 ± 1.6 years). Offspring had a range of psychiatric diagnoses at baseline. Multivariate lasso regression was implemented to select offspring-reported sleep patterns associated with changes in five psychiatric symptom measures from baseline through last follow-up (mania, depression, mood lability, anxiety, inattention/externalizing). Analyses accounted for parent psychiatric diagnoses and offspring demographics, psychiatric diagnoses, and medications. RESULTS:Follow-up duration, baseline socioeconomic status, parental history of bipolar disorder, offspring attention-deficit/hyperactivity disorder, and disruptive behavior disorder, and five sleep patterns were identified as predictors of change in all five psychiatric symptom measures. Decreasing sleep duration, later sleep timing preference, longer sleep latency, increasing nighttime awakenings, and greater sleepiness over follow-up were associated with increasing severity the five psychiatric symptom outcomes over follow-up. These 10 predictors explained 16% of the variance in longitudinal psychiatric symptom change, 33% of which was accounted for by sleep predictors. CONCLUSION:A constellation of sleep features were associated with psychiatric symptom changes in youth, and may represent viable targets for future interventions.

SUBMITTER: Soehner AM

PROVIDER: S-EPMC6733405 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Longitudinal Associations Between Sleep Patterns and Psychiatric Symptom Severity in High-Risk and Community Comparison Youth.

Soehner Adriane M AM Bertocci Michele A MA Levenson Jessica C JC Goldstein Tina R TR Rooks Brian B Merranko John J Hafeman Danella D Diler Rasim R Axelson David D Goldstein Benjamin I BI Hickey Mary Beth MB Monk Kelly K Phillips Mary L ML Birmaher Boris B

Journal of the American Academy of Child and Adolescent Psychiatry 20190306 6

<h4>Objective</h4>Sleep disturbance may be involved in symptom progression across multiple domains of psychopathology and could represent a target for treatment development in youth. Our objective was to identify sleep patterns that longitudinally change in conjunction with psychiatric symptom severity in at-risk youth.<h4>Method</h4>The study included 484 Pittsburgh Bipolar Offspring Study (BIOS) youth with at least 2 sleep assessments occurring between 10 and 18 years of age: 267 offspring of ...[more]

PMID: 30851396

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Project description:This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c) accounting for the role of negation. The machine learning techniques rely on the assumptions that (1) the severity of a patient's positive valence symptoms exists on a latent continuous spectrum and (2) all the patient's answers and narratives documented in the psychological evaluation records are informed by the patient's latent severity score along this spectrum. These assumptions motivated our two-step machine learning framework for automatically recognizing psychological symptom severity. In the first step, the latent continuous severity score is inferred from each record; in the second step, the severity score is mapped to one of the four discrete severity levels used in the CEGS/N-GRID challenge. We evaluated three methods for inferring the latent severity score associated with each record: (i) pointwise ridge regression; (ii) pairwise comparison-based classification; and (iii) a hybrid approach combining pointwise regression and the pairwise classifier. The second step was implemented using a tree of cascading support vector machine (SVM) classifiers. While the official evaluation results indicate that all three methods are promising, the hybrid approach not only outperformed the pairwise and pointwise methods, but also produced the second highest performance of all submissions to the CEGS/N-GRID challenge with a normalized MAE score of 84.093% (where higher numbers indicate better performance). These evaluation results enabled us to observe that, for this task, considering pairwise information can produce more accurate severity scores than pointwise regression - an approach widely used in other systems for assigning severity scores. Moreover, our analysis indicates that using a cascading SVM tree outperforms traditional SVM classification methods for the purpose of determining discrete severity levels.

Project description:Although evidence is accumulating on the importance of a good night's sleep for healthy eating and activity behaviors, existing research has mainly been conducted in high-income, developed countries with limited sociocultural variability. This study is the first to examine the associations between sleep patterns and lifestyle behaviors in children from 12 countries in five major geographic regions of the world.This observational, multinational cross-sectional study included 5777 children aged 9-11 years from sites in Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the United Kingdom and the United States. Nocturnal sleep duration (hours per night), sleep efficiency (%) and bedtime (h:min) were monitored over 7 consecutive days using an accelerometer. Lifestyle behaviors included moderate-to-vigorous physical activity (MVPA), total sedentary time (SED), self-reported screen time (ST) and healthy/unhealthy diet patterns (HDP/UDP). Multilevel modeling analyses were used to account for the hierarchical nature of the data.Overall, participants averaged 8.8 (s.d. 0.9) hours of sleep with 96.2% (s.d. 1.4) sleep efficiency and a mean bedtime of 2218 hours. After adjustment for age, sex, highest parental education and BMI z-score, results showed that (i) sleep duration was negatively associated with MVPA, SED and UDP score; (ii) sleep efficiency was negatively associated with MVPA and UDP score, and positively associated with SED; and (iii) later bedtime was positively associated with SED, ST and UDP score, and negatively associated with MVPA and HDP score. Results using categories of sleep patterns were consistent with the linear associations. Results also revealed that associations between sleep patterns and MVPA, SED and ST were significantly different between study sites, with stronger associations in high-income countries compared with low/middle-income countries.Sleep characteristics are important correlates of lifestyle behaviors in children. Differences between countries suggest that interventions aimed at improving sleep and lifestyle behaviors should be culturally adapted.

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Dataset Information

Longitudinal Associations Between Sleep Patterns and Psychiatric Symptom Severity in High-Risk and Community Comparison Youth.

Publications

Longitudinal Associations Between Sleep Patterns and Psychiatric Symptom Severity in High-Risk and Community Comparison Youth.

Similar Datasets

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