scholarly journals Association between cardiometabolic health and objectively-measured, free-living sleep parameters in a rural African setting: a pilot study.

2020 ◽  
Author(s):  
Ian Cook ◽  
Matlawa Mohlabe ◽  
Herbert Mabalane Makgopa
Keyword(s):  
Sleep Quality ◽  
Fasting Blood Glucose ◽  
Sleep Time ◽  
Biological Data ◽  
Cardiometabolic Health ◽  
Free Living ◽  
Logistic Regression Models ◽  
Sleep Quantity ◽  
Objectively Measured ◽  
African Setting

Abstract Objectives To investigate the relationship between objectively-measured, free-living sleep quantity and quality, and cardiometabolic health, in a rural African setting in 139 adults (≥40 years, female: n=99, male: n=40). Wrist-mounted, tri-axial accelerometry data was collected over nine days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. Self-reported data included behavioural, health and socio-demographic variables. Biological data included body composition, resting blood pressure and fasting blood glucose, insulin and lipids. Logistic regression models were constructed with insulin resistance (IR) and Cardiometabolic (CM) risk, as dependent variables, adjusting for socio-demographic, behavioural and biological factors. Results Nocturnal sleep time was longer in females (p=0.054) and sleep quality was better in males (p≤0.017). Few participants slept >9 hours/night (4-5%), and 46-50% slept <7 hours/night. IR and CM risk was higher in females (p≤0.006). In adjusted models, sleep variables were independently associated with IR (p<0.05). Sleep quantity was non-linearly associated with CM risk (p≤0.0398), and linearly associated with IR (p≤0.0444). Sleep quality was linearly related with CM risk and IR (p≤0.0201). In several models, sleep quantity and sleep quality measures were concurrently and significantly associated with IR (p≤0.044).

scholarly journals Association between cardiometabolic health and objectively-measured, free-living sleep parameters: a pilot study in a rural African setting

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Ian Cook ◽  
Matlawa Mohlabe ◽  
Herbert Mabalane Makgopa
Keyword(s):  
Sleep Quality ◽  
Fasting Blood Glucose ◽  
Sleep Time ◽  
Biological Data ◽  
Cardiometabolic Health ◽  
Free Living ◽  
Logistic Regression Models ◽  
Sleep Quantity ◽  
Objectively Measured ◽  
African Setting

Abstract Objectives To investigate the relationship between objectively-measured, free-living sleep quantity and quality, and cardiometabolic health, in a rural African setting in 139 adults (≥40 years, female: n = 99, male: n = 40). Wrist-mounted, tri-axial accelerometry data was collected over 9 days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. Self-reported data included behavioural, health and socio-demographic variables. Biological data included body composition, resting blood pressure and fasting blood glucose, insulin and lipids. Logistic regression models were constructed with insulin resistance (IR) and cardiometabolic (CM) risk, as dependent variables, adjusting for socio-demographic, behavioural and biological factors. Results Nocturnal sleep time was longer in females (p = 0.054) and sleep quality was better in males (p ≤ 0.017). Few participants slept > 9 h/night (4–5%), and 46–50% slept < 7 h/night. IR and CM risk was higher in females (p ≤ 0.006). In adjusted models, sleep variables were independently associated with IR (p < 0.05). Sleep quantity was non-linearly associated with CM risk (p ≤ 0.0398), and linearly associated with IR (p ≤ 0.0444). Sleep quality was linearly related with CM risk and IR (p ≤ 0.0201). In several models, sleep quantity and sleep quality measures were concurrently and significantly associated with IR (p ≤ 0.044).

scholarly journals Association between cardiometabolic health and objectively-measured, free-living sleep parameters in a rural African setting: a pilot study.

2020 ◽  
Author(s):  
Ian Cook ◽  
Matlawa Mohlabe ◽  
Herbert Mabalane Makgopa
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Fasting Blood Glucose ◽  
Sleep Time ◽  
Biological Data ◽  
Cardiometabolic Health ◽  
Free Living ◽  
Sleep Quantity ◽  
Objectively Measured ◽  
African Setting

Abstract Objectives To investigate the relationship between objectively-measured, free-living sleep quantity and quality, and cardiometabolic health, in a rural African setting in 139 adults (≥40 years, female: n=99, male: n=40). Wrist-mounted, tri-axial accelerometry data was collected over nine days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. Self-reported data included behavioural, health and socio-demographic variables. Biological data included body composition, resting blood pressure and fasting blood glucose, insulin and lipids. Regression models were constructed with insulin resistance (IR), Cardiometabolic (CM) risk and a metabolic z-score, as dependent variables, adjusting for socio-demographic, behavioural and biological factors. Results Nocturnal sleep time was longer in females (p=0.054) and sleep quality was better in males (p≤0.017). Few participants slept >9 hours/night (4-5%), and 46-50% slept <7 hours/night. IR and CM risk was higher in females (p≤0.006). In adjusted models, sleep variables were independently associated with IR (p<0.05). Sleep quantity was linearly (p<0.05) and non-linearly (p≤0.0196) associated with IR, and non-linearly (p≤0.0398) associated with CM risk. Sleep quality was linearly related with IR (p<0.05). A number of non-sleep behavioural variables were independently associated with CM risk (alcohol and tobacco use, p≤0.034) and IR (physical activity, sugar-sweetened beverage consumption, p<0.05).

scholarly journals Descriptive epidemiology of objectively-measured, free-living sleep parameters in a rural African setting.

2020 ◽  
Author(s):  
Ian Cook ◽  
Matlawa Mohlabe ◽  
Marianne Alberts
Keyword(s):  
Physical Activity ◽  
Body Mass ◽  
Sleep Onset ◽  
Sleep Time ◽  
Free Living ◽  
Sleep Quantity ◽  
Sleep Parameters ◽  
Reported Data ◽  
Objectively Measured ◽  
African Setting

Abstract Objectives To investigate the descriptive nature of objectively-measured, free-living sleep quantity and quality, and the relationship to adiposity, in a rural African setting in 145 adults (≥40 years, female: n=104, male: n=41). Wrist-mounted, tri-axial accelerometry data was collected over nine days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. From stature, body mass and waist circumference, body-mass-index and conicity index were calculated. Self-reported data included behavioural, health and socio-demographic variables. Community consultation followed the quantitative data analyses, for validation and interpretation of findings. Results Females had more nocturnal sleep than males (7.2 vs. 6.8 hours/night, p=0.0464) while males recorded more diurnal sleep time (p=0.0290). Wake after sleep onset and number of awakenings were higher in females, and sleep efficiency was higher in males (p≤0.0225). Sleep indices were generally similar between weekdays and weekends, except for sleep fragmentation index (p=0.0458). Sleep quantity, but not sleep quality was independently and inversely associated with adiposity (p=0.0453). Physical activity and morbidity measures were significantly and consistently associated with sleep and adiposity measures (p<0.0458). The qualitative data explained some of the unexpected associational directions of the independent variables correlated with sleep variables.

scholarly journals Descriptive epidemiology of objectively-measured, free-living sleep parameters in a rural African setting.

2020 ◽  
Author(s):  
Ian Cook ◽  
Matlawa Mohlabe ◽  
Marianne Alberts
Keyword(s):  
Physical Activity ◽  
Sleep Onset ◽  
Sleep Time ◽  
Free Living ◽  
Cultural Economic ◽  
Sleep Quantity ◽  
Sleep Parameters ◽  
Reported Data ◽  
Objectively Measured ◽  
African Setting

Abstract Objectives: To investigate the descriptive nature of objectively-measured, free-living sleep quantity and quality, and the relationship to adiposity, in a rural African setting in 145 adults (≥40 years, female: n=104, male: n=41). Wrist-mounted, triaxial accelerometry data was collected over nine days. Measures of sleep quantity and quality, and physical activity were extracted from valid minute-by-minute data. Adiposity indices were body-mass-index, waist circumference and conicity index. Self-reported data included behavioural, health and socio-demographic variables. Community consultation followed the quantitative data analyses, for validation and interpretation of findings. Results: Females had more nocturnal sleep than males (7.2 vs. 6.8 hours/night, p=0.0464) while males recorded more diurnal sleep time (p=0.0290). Wake after sleep onset and number of awakenings were higher in females, and sleep efficiency was higher in males (p≤0.0225). Sleep indices were generally similar between weekdays and weekends, except for sleep fragmentation index (p=0.0458). Sleep quantity, but not sleep quality was independently and inversely associated with adiposity (p=0.0453). Physical activity and morbidity measures were significantly and consistently associated with sleep and adiposity measures (p<0.0458). The preliminary qualitative data suggests that future studies should include more detailed data around contextual issues of sleep (social, cultural, economic, environment).

scholarly journals 0260 Association Between Free-Living Physical Activity and Sleep in Icelandic Adolescents

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A99-A99
Author(s):  
V Rognvaldsdottir ◽  
E Johannsson ◽  
H M Soffia ◽  
R S Stefansdottir ◽  
S A Arngrimsson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Total Sleep Time ◽  
Linear Regression Analysis ◽  
Sleep Onset ◽  
Sleep Time ◽  
Free Living ◽  
Negatively Associated ◽  
Significant Difference ◽  
Objectively Measured

Abstract Introduction Sleep and physical activity are both important to health, but the demands of our modern schedule often require individuals to choose one over the other. In adolescents, the association between objectively measured sleep and physical activity is not well established in the literature. The aim of current study was to assess associations between free-living and physical activity and sleep among 15-year-old adolescents. Methods Free-living physical activity and sleep were assessed with wrist-worn accelerometers, sleep diary, and questionnaires during a 7-day period including school days and non-school days in 270 (161 girls) adolescents (mean age 15.8±0.3y) in Reykjavik, Iceland. Linear regression analysis was used to explore the associations between objectively measured physical activity and sleep. T-test was used to determine if there is a significant difference in objectively measured sleep between those who reported sports or exercising &lt;6 versus ≥6 h/week. Results Weekly mean physical activity (2040±466 counts/min of wear/day) was negatively associated with total sleep time (6.6±0.64 h/night) (β±SE=-3.5±0.7, p&lt;0.001). However, physical activity was also negatively associated with minutes of wake after sleep onset on non-school days (p=0.047) and standard deviation (i.e. night-to-night variability) of total sleep time over the week (p=0.028). Subjects who reported exercising ≥6 h/week (n=116) had lower night-to-night variability in bedtime (41.2±27.9 min) than those who did not (49.8±37.5 min), p=0.033. Conclusion The negative association between physical activity and sleep duration suggests that in more active individuals’ physical activity may be displacing sleep. However, greater physical activity is also associated with fewer minutes of awakening and a less variable sleep schedule, indicating better sleep quality. These findings suggest that physical activity is important for good sleep quality, but students should more closely consider sleep guidelines when designing an exercise schedule. Future studies should test how change in sleep patterns might influence physical activity. Support Icelandic Centre for Research, National Institute of Diabetes and Digestive and Kidney Diseases.

scholarly journals Relationship between Physical Activity, Screen Time, and Sleep Quantity and Quality in US Adolescents Aged 16–19

2019 ◽  
Vol 16 (9) ◽  
pp. 1524 ◽  
Author(s):  
Furong Xu ◽  
Sue K. Adams ◽  
Steven A. Cohen ◽  
Jacob E. Earp ◽  
Mary L. Greaney
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Screen Time ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Cross Sectional ◽  
Lower Odds ◽  
Logistic Regression Models ◽  
Sleep Quantity ◽  
Objectively Measured

Despite the health benefits associated with physical activity (PA), screen time reduction, and sleep quantity and quality, the relationships between PA, screen time, and sleep quantity and quality remain unclear in adolescents. The present study is a cross-sectional analysis of data from adolescents aged 16–19 years who participated in the 2005–2006 National Health and Nutrition Examination Survey (n = 542). Multivariable logistic regression models, adjusted for confounders, examined the relationship between objectively measured PA, self-reported screen time, and sleep quantity and quality. Respondents who met the current PA recommendation had 50% lower odds of having sufficient sleep (≥8 h) than those not meeting the recommendation (OR = 0.50, 95% CI: 0.26, 0.94). Respondents who met the screen time recommendation (≤2 h/day) had 55% lower odds of reporting poor sleep quality than those whose screen time exceeded the recommendation (OR = 0.45, 95% CI: 0.22, 0.91), with similar patterns observed for females and males. However, males who met both PA and screen time recommendations had 73% lower odds of reporting poor sleep quality than males who met neither recommendation (OR = 0.27, 95% CI: 0.07, 0.99). In conclusion, PA and screen time are associated with sleep quantity or sleep quality in adolescents, and there are differences in these associations by sex.

scholarly journals 106 Self-Reported Sleep and Resilience

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A43-A44
Author(s):  
Michelle Persich ◽  
Sara Cloonan ◽  
Michael Grandner ◽  
William Killgore
Keyword(s):  
Sleep Quality ◽  
Sleep Time ◽  
Self Report ◽  
Subjective Perception ◽  
Psychological Resilience ◽  
Cross Sectional ◽  
Sleep Quantity ◽  
The Past ◽  
Resilience Scale ◽  
Longitudinal Effects

Abstract Introduction Psychological resilience is the ability to withstand setbacks, adapt positively to challenges, and bounce back from the adversities of life. While the construct of resilience is broadly understood, the specific individual factors that contribute to the ability to be resilient and persevere in the face of difficulties remain poorly understood. We recently showed that psychological resilience during the COVID-19 pandemic was associated with a number of factors, including fewer complaints of insomnia, and others have suggested that sleep is an important contributor. We therefore tested the hypothesis that sleep quality and acute sleep quantity would combine to predict measures of psychological resilience and perseverance (i.e. “grit”). Methods We asked 447 adults (18–40 yrs; 72% female) to report the number of hours of sleep obtained the night before their assessment session (SLEEP), and complete several questionnaires, including the Pittsburgh Sleep Quality Index (PSQI), the Connor-Davidson Resilience Scale (CD-RISC), Bartone Dispositional Resilience Scale (Hardiness), and the Grit Scale. Sleep metrics were used to predict resilience, hardiness, and grit using multiple linear regression. Results For resilience, PSQI (β=-.201, p&lt;.00003) and SLEEP (β=.155, p&lt;.001) each contributed uniquely to prediction of CD-RISC (R2=.08, p&lt;.00001). Hardiness was also predicted (R2=.08, p&lt;.00001) by a combination of PSQI (β=-.218, p&lt;.00001) and SLEEP (β=.128, p=.007). Interestingly, worse sleep quality over the past month on the PSQI (β=.13, p=.008) in combination with more SLEEP the night before the assessment (β=.137, p=.005) each contributed uniquely to higher Grit (i.e., perseverance; R2=.03, p=.003). Conclusion Self-reported sleep quality and quantity were both independently associated with greater self-reported resilience, hardiness, and grit. While better sleep quality and more sleep the night before testing each uniquely predicted greater resilience and hardiness, a different pattern emerged for Grit. The combination of lower quality sleep over the past month followed by greater recent sleep duration was associated with increased perseverance. Whereas sleep quality appears to be more important for general resilience/hardiness, recent sleep time appears more important for the subjective perception of perseverance. Because these data are purely self-report and cross sectional, future work will need to determine the longitudinal effects on behavior. Support (if any):

scholarly journals Functional Divergence of Mammalian TFAP2a and TFAP2b Transcription Factors for Bidirectional Sleep Control

Genetics ◽  
2020 ◽  
Vol 216 (3) ◽  
pp. 735-752
Author(s):  
Yang Hu ◽  
Alejandra Korovaichuk ◽  
Mariana Astiz ◽  
Henning Schroeder ◽  
Rezaul Islam ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Sleep Quality ◽  
Functional Divergence ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Dark Phase ◽  
Quiet Sleep ◽  
Sleep Behavior ◽  
Sleep Quantity ◽  
Sleep Control

Sleep is a conserved behavioral state. Invertebrates typically show quiet sleep, whereas in mammals, sleep consists of periods of nonrapid-eye-movement sleep (NREMS) and REM sleep (REMS). We previously found that the transcription factor AP-2 promotes sleep in Caenorhabditiselegans and Drosophila. In mammals, several paralogous AP-2 transcription factors exist. Sleep-controlling genes are often conserved. However, little is known about how sleep genes evolved from controlling simpler types of sleep to govern complex mammalian sleep. Here, we studied the roles of Tfap2a and Tfap2b in sleep control in mice. Consistent with our results from C. elegans and Drosophila, the AP-2 transcription factors Tfap2a and Tfap2b also control sleep in mice. Surprisingly, however, the two AP-2 paralogs play contrary roles in sleep control. Tfap2a reduction of function causes stronger delta and theta power in both baseline and homeostasis analysis, thus indicating increased sleep quality, but did not affect sleep quantity. By contrast, Tfap2b reduction of function decreased NREM sleep time specifically during the dark phase, reduced NREMS and REMS power, and caused a weaker response to sleep deprivation. Consistent with the observed signatures of decreased sleep quality, stress resistance and memory were impaired in Tfap2b mutant animals. Also, the circadian period was slightly shortened. Taken together, AP-2 transcription factors control sleep behavior also in mice, but the role of the AP-2 genes functionally diversified to allow for a bidirectional control of sleep quality. Divergence of AP-2 transcription factors might perhaps have supported the evolution of more complex types of sleep.

scholarly journals Development of an e-Coaching Framework to Promote Sleep Hygiene Using Machine Learning

2021 ◽  
Vol 17 (03) ◽  
pp. 98
Author(s):  
Arturo Laflor ◽  
Mabel Vazquez-Briseno ◽  
Fernanda Murillo-Munoz
Keyword(s):  
Machine Learning ◽  
Sleep Quality ◽  
Sleep Hygiene ◽  
Sleep Time ◽  
Machine Learning Algorithms ◽  
Poor Sleep ◽  
Way Of Life ◽  
Sleep Quantity ◽  
Computational Sciences ◽  
Good Sleep

<p class="Abstract">Computational sciences have gradually allowed scientists to develop novel technological projects to promote a healthy way of life. Most efforts have focus in promoting healthy diets and physical activity. Sleeping is also a crucial activity for humans. Poor sleep quality has adverse effects on health and might lead to physical and mental deterioration. Many computer systems have been used to measure sleep quantity and quality; however, there are few efforts to guide users about aspects that can influence sleeping. Sleep hygiene is a concept that allows controlling sleep-related habits and promoting good sleep quality; unfortunately, modern lifestyles can cause people to adopt wrong habits without being aware of their impact on sleep quality. This work describes a framework developed to guide user’s during the day in order to achieve good sleep quality during sleep time. A set of sleep hygiene factors (SHFs) intended to control hours before going to sleep was defined. The framework identifies personal SHFs using machine learning algorithms; furthermore, a new algorithm was designed to improve results. The framework also includes a mobile persuasive system to encourage users to control personal SHFs.</p>

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