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Preliminary Evidence That Real World Sleep Timing and Duration are Associated With Laboratory-Assessed Alcohol Response.

ABSTRACT: BACKGROUND:Sleep timing and evening chronotype have been implicated in alcohol use problems but research has yet to study them in relation to theory-driven laboratory-based measures of alcohol use disorder risk. The current study examined (i) whether chronotype, sleep timing, and/or sleep duration are associated with alcohol response (subjective stimulation, sedation, and behavioral disinhibition) and (ii) if sex and race moderate these associations. METHODS:Adult drinkers (N = 144; 46 female participants) completed 2 counterbalanced beverage administration sessions (alcohol and nonalcohol) during which they rated stimulation/sedation and completed a cued go/no-go task. They reported bed and waketimes over 10 days. RESULTS:Later sleep timing was associated with greater increases in alcohol stimulation, but among male and White participants only. Later sleep timing (among male participants) and greater eveningness (examined among White male participants only) were associated with greater overall stimulation on average in the alcohol session relative to the nonalcohol session, irrespective of alcohol consumption. More variable sleep duration was associated with greater increases in sedation. CONCLUSIONS:These findings offer preliminary, but novel evidence that sleep characteristics may relate to the relative stimulating and sedating effects of alcohol, thereby influencing the risk for alcohol problems.

SUBMITTER: Hasler BP

PROVIDER: S-EPMC7493816 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Preliminary Evidence That Real World Sleep Timing and Duration are Associated With Laboratory-Assessed Alcohol Response.

Hasler Brant P BP Wallace Meredith L ML White Sarah J SJ Molina Brooke S G BSG Pedersen Sarah L SL

Alcoholism, clinical and experimental research 20190521 7

<h4>Background</h4>Sleep timing and evening chronotype have been implicated in alcohol use problems but research has yet to study them in relation to theory-driven laboratory-based measures of alcohol use disorder risk. The current study examined (i) whether chronotype, sleep timing, and/or sleep duration are associated with alcohol response (subjective stimulation, sedation, and behavioral disinhibition) and (ii) if sex and race moderate these associations.<h4>Methods</h4>Adult drinkers (N = 14 ...[more]

PMID: 31070238

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Preliminary Evidence That Real World Sleep Timing and Duration are Associated With Laboratory-Assessed Alcohol Response.

Publications

Preliminary Evidence That Real World Sleep Timing and Duration are Associated With Laboratory-Assessed Alcohol Response.

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