Understanding The Final Hours

[Lorin Searing]

Sleepis important to a number of brain functions, including how nerve cells (neurons) communicate with each other. In fact, your brain and body stay remarkably active while you sleep. Recent findings suggest that sleep plays a housekeeping role that removes toxins in your brain that build up while you are awake.

Sleep is a complex and dynamic process that affects how you function in ways scientists are now beginning to understand. This booklet describes how your need for sleep is regulated and what happens in the brain during sleep.

The thalamus acts as a relay for information from the senses to the cerebral cortex (the covering of the brain that interprets and processes information from short- to long-term memory). During most stages of sleep, the thalamus becomes quiet, letting you tune out the external world. But during REM sleep, the thalamus is active, sending the cortex images, sounds, and other sensations that fill our dreams.

The basal forebrain, near the front and bottom of the brain, also promotes sleep and wakefulness, while part of the midbrain acts as an arousal system. Release of adenosine (a chemical by-product of cellular energy consumption) from cells in the basal forebrain and probably other regions supports your sleep drive. Caffeine counteracts sleepiness by blocking the actions of adenosine.

There are two basic types of sleep: rapid eye movement (REM) sleep and non-REM sleep (which has three different stages). Each is linked to specific brain waves and neuronal activity. You cycle through all stages of non-REM and REM sleep several times during a typical night, with increasingly longer, deeper REM periods occurring toward morning.

Stage 1 non-REM sleep is the changeover from wakefulness to sleep. During this short period (lasting several minutes) of relatively light sleep, your heartbeat, breathing, and eye movements slow, and your muscles relax with occasional twitches. Your brain waves begin to slow from their daytime wakefulness patterns.

Stage 2 non-REM sleep is a period of light sleep before you enter deeper sleep. Your heartbeat and breathing slow, and muscles relax even further. Your body temperature drops and eye movements stop. Brain wave activity slows but is marked by brief bursts of electrical activity. You spend more of your repeated sleep cycles in stage 2 sleep than in other sleep stages.

Stage 3 non-REM sleep is the period of deep sleep that you need to feel refreshed in the morning. It occurs in longer periods during the first half of the night. Your heartbeat and breathing slow to their lowest levels during sleep. Your muscles are relaxed and it may be difficult to awaken you. Brain waves become even slower.

REM sleep first occurs about 90 minutes after falling asleep. Your eyes move rapidly from side to side behind closed eyelids. Mixed frequency brain wave activity becomes closer to that seen in wakefulness. Your breathing becomes faster and irregular, and your heart rate and blood pressure increase to near waking levels. Most of your dreaming occurs during REM sleep, although some can also occur in non-REM sleep. Your arm and leg muscles become temporarily paralyzed, which prevents you from acting out your dreams. As you age, you sleep less of your time in REM sleep. Memory consolidation most likely requires both non-REM and REM sleep.

Sleep-wake homeostasis keeps track of your need for sleep. The homeostatic sleep drive reminds the body to sleep after a certain time and regulates sleep intensity. This sleep drive gets stronger every hour you are awake and causes you to sleep longer and more deeply after a period of sleep deprivation.

Factors that influence your sleep-wake needs include medical conditions, medications, stress, sleep environment, and what you eat and drink. Perhaps the greatest influence is the exposure to light. Specialized cells in the retinas of your eyes process light and tell the brain whether it is day or night and can advance or delay our sleep-wake cycle. Exposure to light can make it difficult to fall asleep and return to sleep when awakened.

Night shift workers often have trouble falling asleep when they go to bed, and also have trouble staying awake at work because their natural circadian rhythm and sleep-wake cycle is disrupted. In the case of jet lag, circadian rhythms become out of sync with the time of day when people fly to a different time zone, creating a mismatch between their internal clock and the actual clock.

Millions of people are using smartphone apps, bedside monitors, and wearable items (including bracelets, smart watches, and headbands) to informally collect and analyze data about their sleep. Smart technology can record sounds and movement during sleep, journal hours slept, and monitor heart beat and respiration. Using a companion app, data from some devices can be synced to a smartphone or tablet, or uploaded to a PC. Other apps and devices make white noise, produce light that stimulates melatonin production, and use gentle vibrations to help us sleep and wake.

Your health care provider may recommend a polysomnogram or other test to diagnose a sleep disorder. A polysomnogram typically involves spending the night at a sleep lab or sleep center. It records your breathing, oxygen levels, eye and limb movements, heart rate, and brain waves throughout the night. Your sleep is also video and audio recorded. The data can help a sleep specialist determine if you are reaching and proceeding properly through the various sleep stages. Results may be used to develop a treatment plan or determine if further tests are needed.

Month view is an excellent way to review your logging activity across several weeks. You'll see your total logged and planned hours, and the money you've made for the month. It's ideal for checking you're on-track to hit your growth goals.

This article documents life-cycle gender differences in labor market outcomes using longitudinal data of a cohort of individuals from the National Longitudinal Survey of Youth 1979. As in other datasets, the gender earnings gap increases with age. We find that hours worked and labor market experience are the most substantial observable variables in explaining the gender pay gap. We also focus on patterns in occupational changes over the life cycle, as a large part of pay growth occurs when workers change jobs. We find that college-educated men, on average, move into occupations with higher task complexity. We further show that women are less likely to change occupations. Moreover, on average, pay grows when workers change occupations, but the growth is smaller for women. Finally, we discuss theories that are consistent with the patterns we document.

This article documents the evolution of the gender earnings gap over the life cycle using data from a cohort of men and women from the National Longitudinal Survey of Youth 1979 (NLSY79). The pattern is similar to the one documented in other datasets: The gender pay gap increases with age. To understand the factors affecting this pattern, we explore the role of occupations, hours worked, and work experience accumulated with age in the observed gender earnings. While previous literature analyzed occupational and labor force participation patterns, we document not only those but also task assignments and changes in task assignments over the life cycle. Previous literature did not analyze the evolution of task assignments and their changes over the life cycle in the context of the gender pay gap. Changes in occupational tasks and occupations are typically important to understanding the wage growth of workers over the life cycle. Our dataset allows us to account for both detailed labor supply history and heterogeneity in test scores and education.

Over time, as the earnings gap increases, the gap in weekly hours worked grows as well. This gap in hours is one of the factors that contributes to the increase in the earnings gap. We then study how occupations change over the life cycle. In particular, we explore whether the increase in the earnings gap is due to changes in occupational assignments and the evolution of the type of tasks workers are assigned. We also examine whether the occupational gap increases with age.

We define occupations along a unidimensional axis, measuring each occupation's demand for complex cognitive tasks. We focus on changes in nonroutine task complexity because over the life cycle workers typically transition into occupations with more cognitive task complexity and fewer motor skills requirements, which cause wages to increase (see Yamaguchi, 2012, for task analysis for men). We follow Antonovics and Golan (2012), who show that this transition is an important source of the increase in wage dispersion among white men, and Golan, Sanders, and James (2019) and Golan and Sanders (2019), who show that the transition is also an important source of the increase in the racial pay gap over the life cycle for men. We document that occupation matches and their pattern over the life cycle vary by education. As expected, workers with a college degree are matched with occupations of higher complexity than workers without a college degree.

We find that among workers with a college degree, men and women start in similar occupations. Over the life cycle, men surpass women in terms of the complexity of tasks performed. Among workers without a college degree, women start in higher-ranked occupations than men. Over the life cycle, these women remain ahead of these men. Thus, unlike the racial gaps and the increasing wage gaps for men, occupational task complexity may not explain much of the earnings gap for workers without a college degree.

The gender gaps above can be partly driven by differences in the observable and unobservable characteristics of men and women. We now analyze the role of differences between men and women based on observable characteristics in our data. A substantial gap in earnings remains after controlling for labor market experience, hours, Armed Forces Qualification Test (AFQT) scores, education, and occupation. We quantify the contribution of the different factors to the pay gap using the Blinder-Oaxca decomposition. Whereas a small difference in the earnings gap is explained by the compositional effects of college-educated men and women, the differences in hours and labor market experience account for the majority of the gender earnings gap for college- and noncollege-educated workers. Moreover, we find that in our sample, the increase in the earnings gap with age is associated with the increase in the labor market experience gap and the breaks in labor force participation for both college- and noncollege-educated workers.6 We discuss our findings in light of different explanations in the literature. The gaps in hours worked and as a result of experience accumulated may be a result of differences in preferences and roles that women play in caring for children. However, discrimination in the labor market and lack of opportunity and promotions may also lead to these choices. Gayle and Golan (2011) find evidence that while there are preference differences, discrimination plays an important role in the choices of hours worked and experience accumulated.

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