Dataset Information

Impaired neurobehavioral alertness quantified by the psychomotor vigilance task is associated with depression in the Wisconsin Sleep Cohort study.

ABSTRACT:

Background

Excessive daytime sleepiness plays an important role in the presentation and course of mood disorders. Standard objective measures of daytime sleep propensity are of little to no value in depressive illness. This study examined the psychomotor vigilance task (PVT), an objective measure of neurobehavioral alertness, and its cross-sectional and longitudinal associations with depressive symptomatology in the Wisconsin Sleep Cohort Study.

Methods

The sample consisted of 1569 separate 10-min PVT assessments conducted in 942 unique individuals. Cross-sectional and longitudinal conditional logistic regression models were used to estimate associations between the primary outcome of depression symptomatology (adjusted Zung scale?50) and six separate PVT variables: mean reciprocal reaction time (1/RT); total lapses (RTs?500 msec; LAPSE); total false responses (FALSE); reciprocal of the mean of the 10% fastest (FAST) and 10% slowest (SLOW) RTs; and slope of the linear regression line for all transformed 1/RTs (SLOPE).

Results

In fully-adjusted cross-sectional models, 1/RT, LAPSE, FAST, and SLOW were each significantly associated with depression, such that worse neurobehavioral alertness was associated with higher odds of depressive symptomatology. Similar, though attenuated, findings were observed in fully-adjusted conditional longitudinal models that examined within-subject changes in depression status in the subset of participants with repeated PVT assessments. FALSE and SLOPE were not associated with depression in either cross-sectional or conditional longitudinal models.

Conclusions

These findings suggest components of the PVT are associated with depressive symptomatology. Further research is indicated to clarify the role of the PVT in the assessment of hypersomnolence in mood disorders.

SUBMITTER: Plante DT

PROVIDER: S-EPMC7754193 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Impaired neurobehavioral alertness quantified by the psychomotor vigilance task is associated with depression in the Wisconsin Sleep Cohort study.

Plante David T DT Hagen Erika W EW Ravelo Laurel A LA Peppard Paul E PE

Sleep medicine 20191204

<h4>Background</h4>Excessive daytime sleepiness plays an important role in the presentation and course of mood disorders. Standard objective measures of daytime sleep propensity are of little to no value in depressive illness. This study examined the psychomotor vigilance task (PVT), an objective measure of neurobehavioral alertness, and its cross-sectional and longitudinal associations with depressive symptomatology in the Wisconsin Sleep Cohort Study.<h4>Methods</h4>The sample consisted of 156 ...[more]

PMID: 31918119

Similar Datasets

Project description:To identify measures derived from baseline psychomotor vigilance task (PVT) performance that can reliably predict vulnerability to sleep deprivation.Subjects underwent total sleep deprivation and completed a 10-min PVT every 1-2 h in a controlled laboratory setting. Participants were categorized as vulnerable or resistant to sleep deprivation, based on a median split of lapses that occurred following sleep deprivation. Standard reaction time, drift diffusion model (DDM), and wavelet metrics were derived from PVT response times collected at baseline. A support vector machine model that incorporated maximum relevance and minimum redundancy feature selection and wrapper-based heuristics was used to classify subjects as vulnerable or resistant using rested data.Two academic sleep laboratories.Independent samples of 135 (69 women, age 18 to 25 y), and 45 (3 women, age 22 to 32 y) healthy adults.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.In both datasets, DDM measures, number of consecutive reaction times that differ by more than 250 ms, and two wavelet features were selected by the model as features predictive of vulnerability to sleep deprivation. Using the best set of features selected in each dataset, classification accuracy was 77% and 82% using fivefold stratified cross-validation, respectively.Despite differences in experimental conditions across studies, drift diffusion model parameters associated reliably with individual differences in performance during total sleep deprivation. These results demonstrate the utility of drift diffusion modeling of baseline performance in estimating vulnerability to psychomotor vigilance decline following sleep deprivation.

Dataset Information

Impaired neurobehavioral alertness quantified by the psychomotor vigilance task is associated with depression in the Wisconsin Sleep Cohort study.

Background

Methods

Results

Conclusions

Publications

Impaired neurobehavioral alertness quantified by the psychomotor vigilance task is associated with depression in the Wisconsin Sleep Cohort study.

Similar Datasets

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