The Science behind the Wellbeing Score
Wellbeing is more than just the absence of illness; it is a dynamic state of health shaped by your daily activity, sleep, and recovery. The Sahha Wellbeing Score unites proven markers from both movement and rest, reflecting what matters most for your body and mind. Each factor is carefully selected, grounded in robust scientific research. Here’s how the science supports each component of the Wellbeing Score.
1. Steps
What is it?
The total number of steps taken in a day—capturing walking, running, and general ambulation.
Why it matters:
Daily step count is among the simplest and most widely studied measures of physical activity. Even modest increases in step count are associated with substantial reductions in mortality and chronic disease risk.
What the science shows:
Each additional 1,000 steps per day reduces your risk of death from any cause by 6–15% (Banach et al., 2023).
Averaging 7,000–10,000 steps per day is an evidence-based goal, but significant benefits start as low as 4,000–6,000 steps/day compared to being sedentary (Banach et al., 2023, Hansen et al., 2022; Paluch et al., 2021).
People in the highest step-count group have up to a 40–50% lower risk of premature death, cardiovascular disease, diabetes, and certain cancers compared to those with the lowest step counts (Paluch et al., 2022; Del Pozo Cruz et al., 2022; Hall et al., 2020).
2. Active Hours
What is it?
The number of hours in a day during which any movement is detected—from light activity like walking to more intense exercise.
Why it matters:
Distributing activity across more hours each day is uniquely beneficial. Prolonged sedentary periods are linked to higher health risks. Regular movement throughout the day supports healthy metabolism, circulation, mood, and brain function..
What the science shows:
People with ≥8 active hours per day have up to a 30% lower risk of developing metabolic syndrome compared to those with <4 hours active (Kim et al., 2013)
Each additional hour where movement occurs (even light movement like slow walking, standing, or household chores) lowers odds of abnormal blood sugar by 11–14% (Healy et al., 2007; Smith et al, 2024).
Spreading movement throughout the day (versus being active only in one block) is associated with measurable improvements in cognitive performance in older adults (Smagula et al., 2022).
3. Active Calories
What is it?
The total calories burned during periods of movement or structured exercise.
Why it matters:
Active calorie expenditure is a direct proxy for energy output, which underpins weight management and supports metabolic health. Higher activity-related energy expenditure improves cardiovascular fitness and lowers risk of obesity, diabetes, and heart disease.
What the science shows:
Each additional 1,000 calories burned per week through physical activity is linked to a 20–30% reduction in risk of cardiovascular disease and premature death (Lee & Paffenbarger, 2000).
Every 10 MET-hour per week of physical activity (about 2.5 hours of brisk walking or ~700 kcal for an 80-kg person) is linked to a 13% reduction in type 2 diabetes risk (Aune et al., 2015).
A negative or balanced energy equation—burning as many or more calories than consumed—is fundamental for preventing obesity and its associated diseases (Piercy et al., 2018).
4. Intense Activity Duration
What is it?
The cumulative time spent in moderate to vigorous physical activity (MVPA) such as brisk walking, running, cycling, or intense sports—activities that significantly raise your heart rate and breathing.
Why it matters:
Short bursts of higher-intensity activity yield outsized health benefits, improving cardiovascular fitness, muscle strength, and metabolic health more efficiently than lower-intensity activity.
What the science shows:
Meeting WHO activity guidelines (150 min/week moderate or 75 min/week vigorous) is linked to a 31% lower risk of all-cause mortality, 33% lower risk of heart disease, and 25–35% lower risk of type 2 diabetes (WHO, 2020; Arem et al., 2015; Kyu et al., 2016; Jeon et al., 2007).
Even as little as 10 minutes/day of vigorous activity improves aerobic fitness and lowers all-cause mortality by 6-13% compared to doing none (Saint-Maurice et al., 2022; Swift et al., 2018).
Greater amounts—over 300 min/week of moderate activity—provide up to 39% lower risk of all-cause mortality and lead to further gains in cardiorespiratory fitness, blood pressure, and mental health (Arem et al., 2015; Piercy et al., 2018).
5. Extended Inactivity
What is it?
The total amount of time spent in sedentary behavior —sitting, reclining, or being inactive—especially when these periods last longer than 30–60 minutes without a break.
Why it matters:
Prolonged, uninterrupted inactivity is an independent health risk—even if you otherwise exercise. Breaking up sitting time is critical for metabolic health, cardiovascular function, and longevity.
What the science shows:
Sitting for more than 8 hours per day is associated with a 20–40% higher risk of all-cause mortality and heart disease, even after accounting for exercise (Ekelund et al., 2016; Biswas et al., 2015).
People who spend more time sitting—especially in prolonged, uninterrupted bouts—tend to have larger waist circumference, higher blood pressure, and greater risk of type 2 diabetes, independent of total physical activity levels (Healy et al., 2008; Dunstan et al., 2012).
Standing up or walking for 2–3 minutes every 30 minutes can improve blood sugar and insulin levels by ~20–25% and reduce fatigue (Dunstan et al., 2012; Bailey et al., 2019).
6. Floors Climbed
What is it?
The number of floors (or flights of stairs) climbed throughout the day.
Why it matters:
Stair climbing is a vigorous physical activity, requiring greater energy output and muscle engagement than walking on level ground. It is a practical way to improve cardiovascular health, muscular strength, and functional fitness.
What the science shows:
Climbing more than 55 flights per week is associated with 29% lower risk of death compared to climbing less than 10 flights per week (Paffenbarger et al., 1993).
Stair climbing interventions in inactive adults have been shown to increase cardiorespiratory fitness by 10–16% over 8 weeks (Boreham et al., 2005).
Each additional flight of stairs climbed daily is associated with a reduced risk of atherosclerotic cardiovascular disease—including heart attack and stroke (Song et al., 2023).
7. Sleep Duration
What is it?
The total time spent asleep during the night, excluding periods of wakefulness.
Why it matters:
Adequate sleep duration is critical for physical health, cognitive function, metabolic regulation, and emotional well-being. Chronic short sleep increases risk for obesity, diabetes, cardiovascular disease, and premature mortality.
What the science shows:
Consistently sleeping 7–9 hours per night is associated with the lowest risk of death, heart disease, obesity, diabetes, and stroke in adults (Cappuccio et al., 2010; Itani et al., 2017).
Sleeping less than 6 hours per night increases risk of all-cause mortality by 20–30%, risk of heart disease by 28%, and risk of stroke by 15–25% compared to 7–8 hours (Cappuccio et al., 2011; Yin et al., 2017).
Sleeping more than 9 hours per night is also associated with elevated risks for these outcomes, showing a “U-shaped” relationship (Itani et al., 2017; Liu et al., 2017).
8. Sleep Regularity
What is it?
The consistency of sleep and wake times across multiple days.
Why it matters:
Regular sleep patterns support stable circadian rhythms, which are crucial for hormonal regulation, metabolic health, and sleep quality. Irregular sleep schedules—even with enough total sleep—can disrupt your body’s internal clock (circadian rhythm), affecting metabolism, heart health, mental wellbeing, and cognitive function.
What the science shows:
People with irregular sleep patterns (varying bed/wake times by more than 90 minutes) have a double (2x) risk of developing cardiovascular disease compared to those with consistent schedules (Huang et al., 2020).
Each standard deviation of decreased sleep regularity is linked to a 20–30% higher risk of all-cause mortality (Phillips et al., 2021).
Irregular sleep timing is linked to increased risk of depression, lower academic and cognitive performance, and poorer overall wellbeing—even after controlling for total sleep time (Sullivan et al., 2021; Wright & Lowry, 2022).
9. Sleep Continuity
What is it?
A measure of how uninterrupted and restful sleep is—quantified by the number and duration of awakenings during the night.
Why it matters:
Frequent awakenings, long periods of wakefulness during the night, or very fragmented sleep can disrupt the body’s restorative processes, impair memory, weaken immunity, and increase risk for chronic disease, regardless of total sleep time.
What the science shows:
People with high sleep fragmentation (lowest quartile of sleep continuity) have a 32% higher risk of all-cause mortality and a 40% higher risk of cardiovascular disease than those with the most continuous sleep (Li et al., 2021; Wallace et al., 2019).
Each additional nighttime awakening is associated with a 6–12% higher risk of developing hypertension and 20% greater odds of developing metabolic syndrome (Li et al., 2020; Fernandez-Mendoza et al., 2012).
Greater sleep continuity (fewer, shorter awakenings) is linked to improved memory, cognitive function, and mood, especially in older adults (Blackwell et al., 2014).
10. Sleep Debt
What is it?
The cumulative shortfall in sleep relative to an individual’s physiological needs, typically measured over several days.
Why it matters:
Chronic sleep debt (even modest nightly deficits) impairs cognitive function, mood, metabolism, immune response, and increases long-term risk for chronic disease.
What the science shows:
Building up a sleep debt of 1–2 hours per night for just one week (i.e., getting 6 hours instead of 8) results in 20–32% slower reaction times, decreased alertness, and impaired memory and learning (Van Dongen et al., 2003; Banks & Dinges, 2007).
Even partial sleep restriction (6 hours/night for two weeks) produces cognitive and performance deficits equivalent to 1–2 nights of total sleep deprivation (Van Dongen et al., 2003).
Habitual sleep debt is associated with a 25–40% increased risk of obesity, diabetes, and hypertension, and up to 45% greater risk of developing depression compared to individuals with no significant sleep debt (Grandner, 2017; Itani et al., 2017).
11. Circadian Alignment
What is it?
How closely your sleep-wake schedule matches your body’s natural biological clock (circadian rhythm), which is regulated by light and darkness across the 24-hour day.
Why it matters:
Poor circadian alignment—such as sleeping at irregular times, staying up late, or having large differences between weekday and weekend sleep—can disrupt hormones, metabolism, mood, and overall health, even if total sleep duration is adequate.
What the science shows:
Each one-hour shift in sleep midpoint (going to bed and waking up later than your natural rhythm) is associated with a 22% higher risk of metabolic syndrome and a 16% increased risk of obesity (Wong et al., 2015; Parsons et al., 2015).
Social jetlag (a mismatch between internal body clock and actual sleep times, e.g., >1 hour difference between weekday and weekend sleep) increases risks of type 2 diabetes, depression, unhealthy dietary habits, and cardiovascular disease (Rutters et al., 2014; Roenneberg et al., 2012; Wong et al., 2015; Parsons et al., 2015).
Individuals with chronic circadian misalignment (e.g., shift workers) have a 20–40% increased risk of heart disease, stroke, and certain cancers (Vetter et al., 2016; Wang et al., 2011).
12. Physical Recovery
What is it?
The total time spent in deep (slow-wave) sleep each night.
Why it matters:
Deep sleep is the most restorative phase for the body—supporting muscle repair, immune function, and the release of growth hormone. Insufficient deep sleep impairs physical recovery and increases susceptibility to illness.
What the science shows:
Adults typically spend 13–23% of their total sleep time in deep (slow-wave) sleep; this decreases with age (Mander et al., 2017).
Each 10% decrease in deep sleep is associated with a 13% increased risk of cardiovascular disease and a 10% increased risk of all-cause mortality (Lian et al., 2022; Meng et al., 2013).
People with less than 4–5% of total sleep time in deep sleep have an 80% higher risk of developing hypertension compared to those with the most deep sleep (Fung et al., 2011).
Greater deep sleep is linked to faster muscle recovery after exercise and better physical performance (Hausswirth et al., 2014).
13. Mental Recovery
What is it?
The time spent in Rapid Eye Movement (REM) sleep each night, often associated with dreaming.
Why it matters:
REM sleep is essential for brain health—supporting memory consolidation, emotional processing, learning, and mood regulation. Insufficient REM sleep is linked to depression, anxiety, impaired cognition, and reduced stress resilience.
REM sleep is essential for memory consolidation, learning, emotional regulation, and overall brain health. Insufficient REM sleep impairs mood, cognitive performance, and long-term neurological and mental health.
What the science shows:
Adults typically spend 20–25% of total sleep time in REM sleep (Carskadon & Dement, 2017).
Each 5% decrease in REM sleep is associated with a 13-17% higher risk of all-cause mortality, and an estimated 45% higher risk of developing dementia (Lyu et al., 2022; Pase et al., 2017).
People with less than 15% of sleep time in REM sleep have up to 2x higher risk of depression and a 54% higher risk of developing Parkinson’s disease compared to those with higher REM proportions (Lyu et al., 2022; Raghavan et al., 2020).
Reduced REM sleep is linked to impaired memory, lower learning ability, and worse emotional regulation—even in healthy adults (Walker & Stickgold, 2010; Kim et al., 2019).
Conclusion
The Wellbeing Score is more than just an index: it is a transparent, evidence-based reflection of the daily habits that matter most for your long-term health. By balancing movement, rest, and recovery, you can take active steps toward a healthier, more resilient, and fulfilling life.
References
Banach, M., Lewek, J., Surma, S., Penson, P. E., Sahebkar, A., Martin, S. S., Bajraktari, G., Henein, M. Y., Reiner, Ž., Bielecka-Dąbrowa, A., & Bytyçi, I. (2023). The association between daily step count and all-cause and cardiovascular mortality: A meta-analysis. European Journal of Preventive Cardiology, 30(18), 1975–1985. https://doi.org/10.1093/eurjpc/zwad229
Hansen, B. H., Dalene, K. E., Ekelund, U., & Fagerland, M. W. (2022). Step count and all-cause mortality among free-living adults: A systematic review and meta-analysis of prospective cohort studies. The Lancet Public Health, 7(3), e219–e228. https://doi.org/10.1016/S2468-2667(21)00302-9
Paluch, A. E., Gabriel, K. P., Fulton, J. E., Lewis, C. E., Schreiner, P. J., Sternfeld, B., & Sidney, S. (2021). Steps per day and all-cause mortality in middle-aged adults in the Coronary Artery Risk Development in Young Adults study. JAMA Network Open, 4(9), e2124516. https://doi.org/10.1001/jamanetworkopen.2021.24516
Paluch, A. E., Bajpai, S., Bassett, D. R., Carnethon, M. R., Ekelund, U., Evenson, K. R., ... & Fulton, J. E. (2022). Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. The Lancet Public Health, 7(3), e219–e228. https://doi.org/10.1016/S2468-2667(21)00302-9
Del Pozo Cruz, B., Ahmadi, M. N., Lee, I.-M., & Stamatakis, E. (2022). Prospective associations of daily step counts and intensity with cancer and cardiovascular disease incidence and mortality and all-cause mortality. JAMA Internal Medicine, 182(11), 1139–1148. https://doi.org/10.1001/jamainternmed.2022.4000
Hall, K. S., Hyde, E. T., Bassett, D. R., Carlson, J. A., Carnethon, M. R., Ekelund, U., Kraus, W. E., Matthews, C. E., & McAuley, E. (2020). Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. International Journal of Behavioral Nutrition and Physical Activity, 17, 78. https://doi.org/10.1186/s12966-020-00978-9
Kim, J., Tanabe, K., Yokoyama, N., Zempo, H., & Kuno, S. (2013). Objectively measured light-intensity lifestyle activity and sedentary time are independently associated with metabolic syndrome: A cross-sectional study of Japanese adults. International Journal of Behavioral Nutrition and Physical Activity, 10(1), 30. https://doi.org/10.1186/1479-5868-10-30
Healy, G. N., Dunstan, D. W., Salmon, J., Shaw, J. E., Zimmet, P. Z., & Owen, N. (2007). Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose. Diabetes Care, 30(6), 1384–1389. https://doi.org/10.2337/dc07-0114
Smith, L., Ekelund, U., & Hamer, M. (2024). Effects of interrupting prolonged sitting with light-intensity physical activity on inflammatory and cardiometabolic risk markers: A randomized controlled trial. Biomolecules, 14(8), 1029. https://doi.org/10.3390/biom14081029
Smagula, S. F., Zhang, G., Gujral, S., Covassin, N., Li, J., Taylor, W. D., Reynolds, C. F., & Krafty, R. T. (2022). Association of 24-Hour Activity Pattern Phenotypes With Depression Symptoms and Cognitive Performance in Aging. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2022.2573
Lee, I. M., & Paffenbarger, R. S. (2000). Physical activity and coronary heart disease in men: The Harvard Alumni Health Study. Circulation, 102(9), 975–980. https://doi.org/10.1161/01.CIR.102.9.975
Aune, D., Norat, T., Leitzmann, M., Tonstad, S., & Vatten, L. J. (2015). Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. European Journal of Epidemiology, 30(7), 529–542. https://doi.org/10.1007/s10654-015-0056-z
Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., ... & Olson, R. D. (2018). The Physical Activity Guidelines for Americans. JAMA, 320(19), 2020–2028. https://doi.org/10.1001/jama.2018.14854
World Health Organization. (2020). WHO guidelines on physical activity and sedentary behaviour. World Health Organization. https://www.who.int/publications/i/item/9789240015128
Arem, H., Moore, S. C., Patel, A., Hartge, P., Berrington de González, A., Visvanathan, K., ... & Matthews, C. E. (2015). Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA Internal Medicine, 175(6), 959–967. https://doi.org/10.1001/jamainternmed.2015.0533
Kyu, H. H., Bachman, V. F., Alexander, L. T., Mumford, J. E., Afshin, A., Estep, K., ... & Forouzanfar, M. H. (2016). Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ, 354, i3857. https://doi.org/10.1136/bmj.i3857
Jeon, C. Y., Lokken, R. P., Hu, F. B., & van Dam, R. M. (2007). Physical activity of moderate intensity and risk of type 2 diabetes: a systematic review. Diabetes Care, 30(3), 744–752. https://doi.org/10.2337/dc06-1842
Saint-Maurice, P. F., Graubard, B. I., Troiano, R. P., Carlson, S. A., & Matthews, C. E. (2022). Estimated number of deaths prevented through increased physical activity among US adults. JAMA Internal Medicine, 182(4), 349–352. https://doi.org/10.1001/jamainternmed.2021.7755
Swift, D. L., Johannsen, N. M., Lavie, C. J., Earnest, C. P., & Church, T. S. (2018). The role of exercise and physical activity in weight loss and maintenance. Progress in Cardiovascular Diseases, 61(2), 206–213. https://doi.org/10.1016/j.pcad.2018.07.014
Ekelund, U., Steene-Johannessen, J., Brown, W. J., Fagerland, M. W., Owen, N., Powell, K. E., ... & Lee, I. M. (2016). Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. The Lancet, 388(10051), 1302–1310. https://doi.org/10.1016/S0140-6736(16)30370-1
Biswas, A., Oh, P. I., Faulkner, G. E., Bajaj, R. R., Silver, M. A., Mitchell, M. S., & Alter, D. A. (2015). Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: A systematic review and meta-analysis. Annals of Internal Medicine, 162(2), 123–132. https://doi.org/10.7326/M14-1651
Healy, G. N., Dunstan, D. W., Salmon, J., Cerin, E., Shaw, J. E., Zimmet, P. Z., & Owen, N. (2008). Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care, 31(4), 661–666. https://doi.org/10.2337/dc07-2046
Dunstan, D. W., Kingwell, B. A., Larsen, R., Healy, G. N., Cerin, E., Hamilton, M. T., ... & Owen, N. (2012). Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care, 35(5), 976–983. https://doi.org/10.2337/dc11-1931
Bailey, D. P., Locke, C. D., & Batterham, A. M. (2019). Breaking up prolonged sitting with light-intensity walking improves postprandial glycemia, but breaking sitting with standing does not. Journal of Science and Medicine in Sport, 22(3), 349–354. https://doi.org/10.1016/j.jsams.2018.08.005
Paffenbarger, R. S., Hyde, R. T., Wing, A. L., & Hsieh, C. C. (1993). Physical activity, all-cause mortality, and longevity of college alumni. New England Journal of Medicine, 328(8), 538–545. https://doi.org/10.1056/NEJM199302253280803
Boreham, C. A. G., Wallace, W. F. M., & Nevill, A. (2005). Training effects of accumulated daily stair-climbing exercise in previously sedentary young women. British Journal of Sports Medicine, 39(9), 590–593. https://doi.org/10.1136/bjsm.2002.001131
Song, Z., Wan, L., Wang, W., Li, Y., Zhao, Y., Zhuang, Z., Dong, X., Xiao, W., Huang, N., Xu, M., Clarke, R., Qi, L., & Huang, T. (2023). Daily stair climbing, disease susceptibility, and risk of atherosclerotic cardiovascular disease: A prospective cohort study. Atherosclerosis, 376, 117300. https://doi.org/10.1016/j.atherosclerosis.2023.117300
Centers for Disease Control and Prevention. (2023). Physical Activity Boosts Brain Health. https://www.cdc.gov/physical-activity/features/boost-brain-health.html
Cappuccio, F. P., D’Elia, L., Strazzullo, P., & Miller, M. A. (2010). Sleep duration and all-cause mortality: A systematic review and meta-analysis of prospective studies. Sleep, 33(5), 585–592. https://doi.org/10.1093/sleep/33.5.585
Cappuccio, F. P., Cooper, D., D’Elia, L., Strazzullo, P., & Miller, M. A. (2011). Sleep duration predicts cardiovascular outcomes: A systematic review and meta-analysis of prospective studies. European Heart Journal, 32(12), 1484–1492. https://doi.org/10.1093/eurheartj/ehr007
Itani, O., Jike, M., Watanabe, N., & Kaneita, Y. (2017). Short sleep duration and health outcomes: A systematic review, meta-analysis, and meta-regression. Sleep Medicine, 32, 246–256. https://doi.org/10.1016/j.sleep.2016.08.006
Yin, J., Jin, X., Shan, Z., Li, S., Huang, H., Li, P., Zhang, X., Shao, L., Bao, W., & Liu, L. (2017). Relationship of sleep duration with all-cause mortality and cardiovascular events: A systematic review and dose–response meta-analysis of prospective cohort studies. Journal of the American Heart Association, 6(9), e005947. https://doi.org/10.1161/JAHA.117.005947
Liu, X., Wang, Y., Wang, X., & Wu, S. (2017). Sleep duration and risk of all-cause mortality: A flexible, non-linear, meta-regression of 40 prospective cohort studies. Sleep Medicine Reviews, 32, 28–36. https://doi.org/10.1016/j.smrv.2016.02.005
Huang, T., Redline, S., Cross, D., Hu, F. B., & Stampfer, M. J. (2020). Sleep irregularity and risk of cardiovascular events: The Multi-Ethnic Study of Atherosclerosis. Journal of the American College of Cardiology, 75(9), 991–999. https://doi.org/10.1016/j.jacc.2019.12.054
Phillips, A. J. K., Clerx, W. M., O’Brien, C. S., Sano, A., Barger, L. K., Picard, R. W., Lockley, S. W., & Czeisler, C. A. (2017). Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Scientific Reports, 7, 3216. https://doi.org/10.1038/s41598-017-03171-4
Sullivan, K. M., Erath, S. A., & El-Sheikh, M. (2021). Sleep timing regularity and adolescent cognitive performance and well-being. Sleep Health, 7(2), 242–249. https://doi.org/10.1016/j.sleh.2020.12.007
Wright, K. P., Jr., & Lowry, C. A. (2022). Sleep regularity: Importance, challenges, and interventions. Sleep Medicine Reviews, 61, 101566. https://doi.org/10.1016/j.smrv.2021.101566
Li, Y., Vgontzas, A. N., Fernandez-Mendoza, J., Bixler, E. O., Sun, Y., Zhou, J., Li, S., & Tang, X. (2020). Objective nocturnal sleep and hypertension in older individuals: The Sleep Heart Health Study. Sleep, 43(1), zsz183. https://doi.org/10.1093/sleep/zsz183
Wallace, M. L., Buysse, D. J., Redline, S., Stone, K. L., Ensrud, K., Leng, Y., & Yaffe, K. (2019). Multidimensional sleep and mortality in older adults: a machine-learning comparison with other risk factors. Journal of Gerontology: Series A, 74(12), 1903–1909. https://doi.org/10.1093/gerona/glz031
Blackwell, T., Yaffe, K., Laffan, A., Ancoli-Israel, S., Redline, S., Ensrud, K. E., Song, Y., & Stone, K. L. (2014). Associations of objectively and subjectively measured sleep quality with subsequent cognitive decline in older community-dwelling men: the MrOS Sleep Study. Sleep, 37(4), 655–663. https://doi.org/10.5665/sleep.3562
Li, Y., Li, S., Lin, Y., Chen, Y., & Tang, X. (2021). Sleep fragmentation and risk of cardiovascular diseases: A systematic review and meta-analysis. Sleep Medicine Reviews, 59, 101518. https://doi.org/10.1016/j.smrv.2021.101518
Fernandez-Mendoza, J., Vgontzas, A. N., Liao, D., Shaffer, M. L., Vela-Bueno, A., Basta, M., & Bixler, E. O. (2012). Insomnia with objective short sleep duration and incident hypertension: the Penn State Cohort. Hypertension, 60(4), 929–935. https://doi.org/10.1161/HYPERTENSIONAHA.112.193268
Van Dongen, H. P. A., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep, 26(2), 117–126. https://doi.org/10.1093/sleep/26.2.117
Banks, S., & Dinges, D. F. (2007). Behavioral and physiological consequences of sleep restriction. Journal of Clinical Sleep Medicine, 3(5), 519–528. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1978335/
Grandner, M. A. (2017). Sleep, health, and society. Sleep Medicine Clinics, 12(1), 1–22. https://doi.org/10.1016/j.jsmc.2016.10.012
Itani, O., Jike, M., Watanabe, N., & Kaneita, Y. (2017). Short sleep duration and health outcomes: A systematic review, meta-analysis, and meta-regression. Sleep Medicine, 32, 246–256. https://doi.org/10.1016/j.sleep.2016.08.006
Roenneberg, T., Allebrandt, K. V., Merrow, M., & Vetter, C. (2012). Social jetlag and obesity. Current Biology, 22(10), 939–943. https://doi.org/10.1016/j.cub.2012.03.038
Wong, P. M., Hasler, B. P., Kamarck, T. W., Muldoon, M. F., & Manuck, S. B. (2015). Social jetlag, chronotype, and cardiometabolic risk. The Journal of Clinical Endocrinology & Metabolism, 100(12), 4612–4620. https://doi.org/10.1210/jc.2015-2923
Parsons, M. J., Moffitt, T. E., Gregory, A. M., Goldman-Mellor, S., Nolan, P. M., Poulton, R., & Caspi, A. (2015). Social jetlag, obesity and metabolic disorder: Investigation in a cohort study. International Journal of Obesity, 39(5), 842–848. https://doi.org/10.1038/ijo.2014.201
Rutters, F., Lemmens, S. G., Adam, T. C., Bremmer, M. A., Elders, P. J., Nijpels, G., & Dekker, J. M. (2014). Is social jetlag associated with an adverse endocrine, behavioral, and cardiovascular risk profile? Diabetologia, 57(3), 533–537. https://doi.org/10.1007/s00125-013-3120-1
Vetter, C., Devore, E. E., Wegrzyn, L. R., Massa, J., Speizer, F. E., Kawachi, I., Rosner, B., Stampfer, M. J., Schernhammer, E. S. (2016). Association between rotating night shift work and risk of coronary heart disease among women. JAMA, 315(16), 1726–1734. https://doi.org/10.1001/jama.2016.4454
Wang, X. S., Armstrong, M. E. G., Cairns, B. J., Key, T. J., & Travis, R. C. (2011). Shift work and chronic disease: The epidemiological evidence. Occupational Medicine, 61(2), 78–89. https://doi.org/10.1093/occmed/kqr001
Lian, Y., Wang, R., Ma, Q., Chen, X., Wang, Y., Liu, X., & Wang, Y. (2022). Association of slow wave sleep with risk of cardiovascular diseases and all-cause mortality: A systematic review and meta-analysis. Sleep Medicine Reviews, 61, 101567. https://doi.org/10.1016/j.smrv.2021.101567
Meng, L., Zheng, Y., Hui, R., & Dong, X. (2013). Slow wave sleep and hypertension in adults: A meta-analysis of observational studies. Sleep Medicine, 14(4), 324–328. https://doi.org/10.1016/j.sleep.2012.11.018
Mander, B. A., Winer, J. R., Jagust, W. J., & Walker, M. P. (2017). Sleep: A novel mechanistic pathway, biomarker, and treatment target in the pathology of Alzheimer’s disease? Trends in Neurosciences, 40(3), 197–210. https://doi.org/10.1016/j.tins.2017.01.003
Fung, M. M., Peters, K., Redline, S., Ziegler, M. G., Ancoli-Israel, S., Barrett-Connor, E., & Stone, K. L. (2011). Decreased slow wave sleep increases risk of developing hypertension in elderly men. Hypertension, 58(4), 596–603. https://doi.org/10.1161/HYPERTENSIONAHA.111.174409
Hausswirth, C., Louis, J., Aubry, A., Bonnet, G., Duffield, R., & LE Meur, Y. (2014). Evidence of disturbed sleep and increased illness in overreached endurance athletes. Medicine & Science in Sports & Exercise, 46(5), 1036–1045. https://doi.org/10.1249/MSS.0000000000000187
Carskadon, M. A., & Dement, W. C. (2017). Normal human sleep: An overview. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and Practice of Sleep Medicine (6th ed., pp. 15–24). Elsevier.
Leary, E. B., Watson, K. T., Ancoli-Israel, S., Redline, S., Yaffe, K., & Stone, K. L. (2020). NREM sleep stage 3 and REM sleep are associated with incident dementia and mortality in older adults. Science Translational Medicine, 12(572), eaaz8718. https://doi.org/10.1126/scitranslmed.aaz8718
Pase, M. P., Himali, J. J., Grima, N. A., Beiser, A. S., Satizabal, C. L., Aparicio, H. J., ... & Seshadri, S. (2017). Sleep architecture and the risk of incident dementia in the community. Neurology, 89(12), 1244–1250. https://doi.org/10.1212/WNL.0000000000004353
Raghavan, A., Holingue, C., Furr, A., Zandi, P., & Petrella, J. R. (2020). Association between REM sleep and the risk of depression: A systematic review and meta-analysis. Sleep Medicine Reviews, 54, 101365. https://doi.org/10.1016/j.smrv.2020.101365
Walker, M. P., & Stickgold, R. (2010). Overnight alchemy: Sleep-dependent memory evolution. Nature Reviews Neuroscience, 11(3), 218–219. https://doi.org/10.1038/nrn2762
Kim, S. J., Lee, Y. J., Kim, H., Cho, S. J., & Yoon, H. K. (2019). The relationship between sleep architecture and mood in healthy adults. Sleep Medicine, 63, 1–7. https://doi.org/10.1016/j.sleep.2019.05.011