There is a U-shaped association between sleep duration and risk of metabolic syndrome. The related mechanisms can be grouped into two categories: I. Too little slow-wave sleep; II. Too much REM sleep. Too little slow-wave sleep. 1) Stage 3 is the most crucial sleep stage because growth hormone (GH) and GH releasing hormone (GHRH) are released during this stage. They induce fat burning, bone building, and general repair and regeneration. The greater the volume of GH and GHRH released, the more restorative the sleep. The longest part of stage 3 in sleep takes place before midnight. Delayed sleep onset until midnight or later, would suppress the largest GH pulse. 2) Sleep restriction induces high levels of ghrelin and low levels of leptin. Ghrelin stimulates appetite whereas leptin does the reverse. 3) Advanced glycation end products (AGEs) are significantly increased in chronic sleep insufficiency and are also associated with insulin resistance in males with chronic sleep insufficiency. 4) Sleep insufficiency increases sympathetic activity and pro-inflammatory cytokines, both of which increase insulin resistance. 5) Accumulations of extracellular β amyloid protein plaques and intracellular tau neurofibrillary tangles in brain tissues start immediately after one night of sleep insufficiency. These plaques and tangles are neurotoxins that potentiate each other's destructive effects on the structures and functions of brain cells and cause neuronal death. The consequence is a global decrease in cognition and decision making, manifested in increased consumption of fatty foods and unhealthy snacks in late sleepers. 6) High levels of β amyloid and proteins might lead to sleep fragmentation, worsening of sleep quality and daytime somnolence. Concentration will be more difficult, and performance will be reduced. 7) Astrocytes are special giant cells in brain interstitial fluids that play a major role in β amyloid and tau cleanup. Their activity is increased by growth hormone. As a result, slow-wave sleep insufficiency may lead to impaired peripheral clearance of β amyloid and tau proteins predisposing the brain to Alzheimer's disease. Too much REM sleep. 1) The REM stage is the dreaming stage. The level of cortisol starts to rise in the middle of night based on circadian rhythm, and peaks in the morning. Cortisol is the fight or flight hormone, equipping the individual both to meet the demands of daily life and to handle stressful situations. It stimulates the release of epinephrine and norepinephrine. These hormones increase heart rate and blood pressure as they prepare the body to initiate activity quickly. These reactions occur on top of heart rate variability and a rise in blood pressure induced by REM sleep. If the individual wakes up early in the morning, he will use the cortisol properly to prepare for the new day. If instead he or she remains asleep, there will be no physical activity, and the cortisol levels that are normal for a person who is awake will be high levels for the one who is asleep. Then, the sleeper m...

Background Incorporating trail use into daily activity routines could be an important venue to increase a population’s physical activity. This study presents important health impacts of trail use. Methods A cross-sectional study was conducted on 8 trails throughout the State of Indiana. A mix of urban, suburban, and rural trails were selected. Recruitment sessions were completed during four 1-week periods throughout the study in various locations and at various times of day on each trail between April and October 2017. Data were collected through online and paper surveys. For each type of physical activity, a generalized additive model for self-rated wellness and health was built adjusting for demographics, socioeconomic status, amounts of physical activity on trails, mood status, sleep pattern, diet and smoking habit. The plots of estimated smoothing spline function with 95% confidence band were pictured. All statistical analyses were conducted using R. Results The final sample size included 1299 trail users; 92% were White, 79% aged 18–65 years, 71% were married and 56% were male. Biking, walking and running were the main activities with 52, 29 and 19%, respectively. Female to male ratio was 3:2 in walkers vs. 2:3 in runners and bikers. Runners were significantly younger than the other two groups. Runners also had the highest percentage of college graduates and above, the highest rate of employment, the highest income, and the lowest percentage of being retired among the three groups. They more commonly used the trails alone than the walkers and bikers. Bikers had the highest rate of job satisfaction. They also showed a better mean score of mood than that the walkers and runners. There was a linear association between walking and self-rated wellness and health, and a curved association between running/biking and self-rated wellness and health. Running < 6.5 miles/week and biking > 14 miles/week were associated with steeper rise in self-rated wellness and health. Conclusions Employed educated married middle-aged people had the highest prevalence of walking, running or biking. The higher the walking, the higher self-rated wellness and health. A similar association was observed for running up to 6.5 miles/week or biking > 14 miles/week.

Background: The current study sought to understand whether trail users reported better wellness and health status compared to the non-users, and to recognize the associated factors. Methods: Eight trails from different locations and settings within Indiana were selected to sample trail users for the study. Additionally, areas surrounding these eight trails were included in the study as sample locations for trail non-users. Trail users and non-users were intercepted and asked to participate in a survey including demographics, socioeconomic status, physical activity, mood, smoking, nutrition, and quality of sleep. Information was collected and compared between the trail users and the non-users. Association of self-rated health, age, sex, race, marital status, employment, income, education, smoking, nutrition, sleep, and mood with trail use was evaluated by multivariable linear regression model. Results: The final sample size included 1299 trail users and 228 non-users. Environmental factors (access to nature and scenery) were important incentives for 97% and 95% of trail users, respectively. Age, sex, mood, and sleep quality were significantly associated with using the trail. Mean (SD) self-rated wellness and health out of 10 was 7.6 (1.4) in trail users and 6.5 (1.9) in non-users (p < 0.0001). Importantly, trail users were significantly more physically active outside of the trail compared to the non-users (207 vs. 189 min/week respectively, p = 0.01) and had better sleep qualities and mood scores. Using the trails was significantly associated with higher self-rated wellness and health score. The longer the use of trails, the higher the self-rated wellness and health index (β = 0.016, p = 0.03). Conclusion: Compared to not using the trails, trail use was significantly associated with more physical activity, better sleep quality, and higher self-rated wellness and health.

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