The Effect of Cognitive Behaviour Therapy on Sleep and Circadian Rhythm in Young College Students

The present study is to study the effect of cognitive behaviour therapy on various sleep parameters and circadian phase rhythmic in young college going adults. Fifty young college going adults were chosen from the MBBS and BDS students of King George's Medical University Lucknow, their polysomnography was conducted along with it salivary melatonin estimation was conducted to find the time of Dim light melatonin onset (DLMO), the subjects were administered cognitive behaviour therapy (CBT),after completing the sessions of cognitive behaviour therapy another Polysomnographic study and DLMO estimation was conducted, various sleep parameters were compared before and after the CBT. The study showed an improvement in the steep quality, a decrease in daytime sleepiness along with this total sleep time significantly increased, sleep efficiency also improved and there was a decrease in the REM sleep latency. The Dim light melatonin onset advanced for the subjects and the chronotype showed an inclination towards an earlier timings.

Sensor-Based Estimation of Dim Light Melatonin Onset Using Features of Two Time Scales

Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special conditions, and the samples must then be assayed for melatonin. Recently, several computational approaches have been designed for estimating DLMO. These methods collect daily sampled data (e.g., sleep onset/offset times) or frequently sampled data (e.g., light exposure/skin temperature/physical activity collected every minute) to train learning models for estimating DLMO. One limitation of these studies is that they only leverage one time-scale data. We propose a two-step framework for estimating DLMO using data from both time scales. The first step summarizes data from before the current day, whereas the second step combines this summary with frequently sampled data of the current day. We evaluate three moving average models that input sleep timing data as the first step and use recurrent neural network models as the second step. The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.

No Effect of Chronotype on Sleepiness, Alertness, and Sustained Attention during a Single Night Shift

The study’s aim was to examine the effect of chronotype on cognitive performance during a single night shift. Data were collected from 72 (36f) young, healthy adults in a laboratory study. Participants had a 9 h sleep period (03:00–12:00) followed by an 8 h night shift (23:00–07:00). During the night shift, participants completed five test sessions, which included measures of subjective sleepiness, subjective alertness, and sustained attention (i.e., psychomotor vigilance task; PVT). Dim light melatonin onset (DLMO) was derived from saliva samples taken during the evening preceding the night shift. A tertile split of DLMO was used to determine three chronotype categories: earlier (DLMO = 20:22 ± 0:42), intermediate (DLMO = 21:31 ± 0:13), and later (DLMO = 22:54 ± 0:54). There were (a) significant main effects of test session (all p < 0.001); (b) no main effects of chronotype; and (c) no interaction effects between chronotype and test session on sleepiness, alertness, PVT response time, and PVT lapses. The results indicate that under controlled sleeping conditions, chronotype based on dim light melatonin onset did not affect nighttime performance. Differences in performance during night shift between chronotypes reported by field studies may be related to differences in the amount and/or timing of sleep before or between night shifts, rather than circadian timing.

Concordance of Chronotype Categorisations Based on Dim Light Melatonin Onset, the Morningness-Eveningness Questionnaire, and the Munich Chronotype Questionnaire

Chronotype reflects circadian timing and can be determined from biological markers (e.g., dim light melatonin onset; DLMO), or questionnaires (e.g., Morningness-Eveningness Questionnaire; MEQ, or Munich Chronotype Questionnaire; MCTQ). The study’s aim was to quantify concordance between chronotype categorisations based on these measures. A total of 72 (36f) young, healthy adults completed the MEQ and MCTQ and provided saliva samples hourly in dim light during the evening in a laboratory. The corrected midpoint of sleep on free days (MSFsc) was derived from MCTQ, and tertile splits were used to define early, intermediate and late DLMO-CT, MEQ-CT and MSFsc-CT chronotype categories. DLMO correlated with MEQ score (r = −0.25, p = 0.035) and MSFsc (r = 0.32, p = 0.015). For early, intermediate and late DLMO-CT categories, mean(SD) DLMO were 20:25(0:46), 21:33(0:10) and 23:03(0:53). For early, intermediate and late MEQ-CT categories, mean(SD) MEQ scores were 60.5(5.3), 51.4(2.9) and 40.8 (5.0). For early, intermediate and late MSFsc-CT categories, mean(SD) MSFsc were 03:23(0:34), 04:37(0:12) and 05:55(0:48). Low concordance of categorisations between DLMO-CT and MEQ-CT (37%), and between DLMO-CT and MSFsc-CT (37%), suggests chronotype categorisations depend on the measure used. To enable valid comparisons with previous results and reduce the likelihood of misleading conclusions, researchers should select measures and statistical techniques appropriate to the construct of interest and research question.

0039 The Clinical Utility of Dim Light Melatonin Onset in Treatment of Delayed Sleep-Wake Phase Disorder: Preliminary Findings

Introduction Delayed sleep-wake phase disorder (DSWPD) is common, debilitating, and challenging to treat. In an ongoing randomized trial, we are comparing exogenous melatonin treatment outcomes in DSWPD participants for whom dim light melatonin onset (DLMO) is measured objectively vs. estimated. Methods Thus far, 13 participants (27±6 years old, 67% female) have completed a randomized, controlled, double-blind 4-week trial of 0.5 mg of exogenous melatonin timed to either 3 h before measured DLMO (M-DLMO, n = 6) or 3 h before DLMO estimated at 2 h before average sleep onset time based on at least 7 days of wrist actigraphy and sleep diary (E-DLMO, n = 7). All participants met International Classification of Sleep Disorders-3 diagnostic criteria for DSWPD and were otherwise healthy. Participants completed 4 weekly treatment sessions with a blinded psychologist; time of melatonin administration and bed-rise schedule were advanced up to 1 h/week. A validated home saliva collection kit measured DLMO in all participants. Between-group t-tests and Hedges’ g effect sizes (ES) were calculated at post-treatment for the following outcomes: DLMO; Pittsburgh Sleep Quality Index (PSQI) global score; Morningness-Eveningness Questionnaire (MEQ); and the actigraphy parameters sleep efficiency (SE) and clock time of sleep onset and offset. A paired-sample t-test compared the measured vs. estimated DLMO at baseline. Results The M-DLMO group had a 65±88 mins DLMO advance vs. 27±30 mins in the E-DLMO group (ES=0.51 p=.381). PSQI scores were similar between groups (M-DLMO=6.67±2.06, E-DLMO=7.1± 1.57, ES=-0.24, p=.646), as were MEQ scores (M-DLMO=43±4.98, E-DLMO=48±12.72, ES=-0.47, p=.387). Sleep onset time (M-DLMO=0:32±1:02, E-DLMO=0:31±1:38, ES=0.01, p=.98) and offset time (M-DLMO=8:05±1:03, E-DLMO=8:08±2:14, ES=-0.02, p=.968) were similar between the groups, although sleep was more efficient in M-DLMO vs. E-DLMO (84%±3% vs. 76%±10%, ES=0.94, p=.096). On average, baseline measured DLMO occurred 123±83 mins earlier than estimated DLMO (p=.001). Conclusion We are continuing to enroll participants in this trial. Preliminary results suggest some potential benefit of measuring the DLMO, but results will need to be clarified in a larger sample. Support American Sleep Medicine Foundation Strategic Research Award