Volume 1 Issue 1
James M Gregory*
It is too costly and impractical to complete sleep measurements for all ages and combinations of sleep amounts and sleep debt. The possibility exists that mathematical equations can be developed and calibrated to simulate the various components of the sleep system. As a start for this development, equations were developed to predict the accumulation of each of the four sleep components (stage 1, stage 2, slow wave [stages 3 and 4] and REM) during sleep. Calibration was achieved using a published data set for people 30 years of age with partial sleep debt. Regression results were as follows: stage 1 (R2 = 0.60, not significant), stage 2 (R2 = 0.99, p < 0.001), slow wave (R2 = 0.95, p < 0.01), REM (R2 = 0.99, p < 0.001). All equations are continuous, smooth, and have stable boundary conditions. Future work is needed to adjust the calibration for various ages and non-debt conditions.
Cite this Article: Gregory JM. Mathematical Nature of Sleep Components for Adults. Int J Sleep Disord. 2017;1(1): 012-017.
Published: 06 December 2017
Research Article: The Phenomenon of Weak, Unawakening afferent Signals Involvement in Synchronization Mechanisms of Neuron activity during delta Sleep
Shakhnarovich V M, Indursky P A, Dementienko V V*, Markelov V V
Changes in sleep characteristics were studied under the non-wake-up stimulation with current pulses of less than 1 μA on average, applied to the palmar surface skin receptors during delta-sleep. A significant increase in duration of the first and second cycles of deep sleep has been found, as well as a shorter latent period before the delta-sleep onset and a longer time of the Rapid Eyes Movement Sleep (REM). The sleep structure improvement was accompanied by the reduced reactive anxiety and depression and an increase in subjective physical efficiency.
Cite this Article: Shakhnarovich VM, Indursky PA, Dementienko VV, Markelov VV. The Phenomenon of Weak, Unawakening afferent Signals Involvement in Synchronization Mechanisms of Neuron activity during delta Sleep. Int J Sleep Disord. 2017;1(1): 007-011.
Published: 04 October 2017
Research Article: Patterns of Decline in Sleep Efficiency over the Adult Lifespan: Clarification via Use of Smoothing Splines
Judith A. Floyd*
Purpose: To further explicate age-related changes in sleep efficiency by pinpointing when change over the adult lifespan occurs in related sleep parameters: Sleep latency, waking after sleep onset, total sleep, and time in bed. Accurate information about sleep-parameter decline with age before the age of electronics is useful as baseline knowledge for understanding modern environmental factors' impact on sleep efficiency.
Methods: A research synthesis approach specifically designed to detect nonlinear developmental changes was used. Specifically, Cubic B smoothing splines were fit to scatter plots generated using descriptive study results. English-language research reports produced over 45 years provided data from thousands of subjects for each sleep parameter. All subjects were described in research reports as normal or healthy. Mean ages of samples used ranged from 18.0 to 91.7 years (SD < 4 years). Two coders extracted information; formal reliability testing showed excellent coder reliability.
Results: No nonlinearity was detected in the relationship between age and sleep efficiency, which decreased 2.2% per decade. Similarly, no nonlinearity was detected in total sleep, which decreased 12.2 minutes per decade. Significant nonlinearity was detected for three relationships: Sleep latency increased more rapidly after age 32; waking duration increased more rapidly after age 49; and the relationship between age and time-in-bed was curvilinear with adults spending the least amount of time in bed around age 50.
Conclusions: Sleep efficiency declines with age, not only because sleep latency and waking duration increase, but also because total sleep decreases while time in bed first decreases and then increases. Increased TIB after age 50 may be an attempt to increase amount of sleep despite increasing sleep latencies and more waking after sleep onset. Ability to detect nonlinearity in rates of change in sleep parameters using smoothing splines improves precision for understanding the decline in sleep efficiency over the adult lifespan.
Cite this Article: Floyd JA. Patterns of Decline in Sleep Efficiency over the Adult Lifespan: Clarification via Use of Smoothing Splines. Int J Sleep Disord. 2017;1(1): 001-006.
Published: 22 September 2017
Authors submit all Proposals and manuscripts via Electronic Form!