scholarly journals Actigraphy measurement of physical activity and energy expenditure in narcolepsy type 1, narcolepsy type 2 and idiopathic hypersomnia: A Sensewear Armband study

2020 ◽  
Author(s):  
Axel Bornstein ◽  
Anders Hedström ◽  
Pontus Wasling
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Sensewear Armband ◽  
Idiopathic Hypersomnia

scholarly journals Cerebrospinal fluid monoamine levels in central hypersomnolence disorders

SLEEP ◽  
2021 ◽  
Author(s):  
Lucie Barateau ◽  
Isabelle Jaussent ◽  
Julien Roeser ◽  
Claudio Ciardiello ◽  
Thomas S Kilduff ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Daytime Sleepiness ◽  
Direct Consequence ◽  
Trace Amines ◽  
Idiopathic Hypersomnia ◽  
National Reference Center ◽  
Drug Free ◽  
Sleep Propensity

Abstract Study objectives Whether the cause of daytime sleepiness in narcolepsy type 1 (NT1) is a direct consequence of the loss of orexin neurons or whether low orexin reduces the efficacy of the monoaminergic systems to promote wakefulness is unclear. The neurobiology underlying sleepiness in other central hypersomnolence disorders, narcolepsy type 2 (NT2) and idiopathic hypersomnia (IH), is currently unknown. Methods Eleven biogenic amines including the monoaminergic neurotransmitters and their metabolites and five trace amines were measured in the cerebrospinal fluid (CSF) of 94 drug-free subjects evaluated at the French National Reference Center for Narcolepsy: 39 NT1(orexin-deficient) patients, 31 patients with objective sleepiness non-orexin deficient (NT2 and IH), and 24 patients without objective sleepiness. Results Three trace amines were undetectable in the sample: Tryptamine, Octopamine, and 3-iodothyronamine. No significant differences were found among the three groups for quantified monoamines and their metabolites in crude and adjusted models; however, CSF 5-HIAA levels tended to increase in NT1 compared to other patients after adjustment. Most of biomarkers were not associated with ORX-A levels, clinical or neurophysiological parameters, but a few biomarkers (e.g., MHPG and norepinephrine) correlated with daytime sleepiness and high REM sleep propensity. Conclusion We found no striking differences among CSF monoamines, their metabolites and trace amine levels, and few associations between them and key clinical or neurophysiological parameters in NT1,NT2/IH and patients without objective sleepiness. Although mostly negative, these findings are a significant contribution to our understanding of the neurobiology of hypersomnolence in these disorders that remain mysterious and deserve further exploration.

Hypersomnolence Disorders

2020 ◽  
pp. 134-157
Author(s):  
Sulaiman Alhifzi ◽  
Nevin Zaki ◽  
Aljohara S. Almeneesier ◽  
Ahmed S. BaHammam
Keyword(s):  
Substance Use ◽  
Psychiatric Disorder ◽  
Treatment Strategies ◽  
Classification Systems ◽  
Common Symptom ◽  
Clinical Investigations ◽  
Idiopathic Hypersomnia ◽  
Multiple Sleep Latency

Despite varied classification systems, hypersomnolence disorders (or central disorders of hypersomnolence) are a group of disorders with a common symptom of excessive daytime sleepiness. In addition to a thorough clinical interview and examination, the assessment of hypersomnolence may require clinical investigations such as polysomnography and the multiple sleep latency test. This chapter examines the disorders of hypersomnolence with an emphasis on their clinical features and treatment strategies. The authors discuss the four main types of hypersomnolence disorders, narcolepsy type 1, narcolepsy type 2, idiopathic hypersomnia and Kleine–Levin syndrome, as well as hypersomnia due to a medical disorder, medication, or substance use; a psychiatric disorder; and insufficient sleep syndrome. It also discusses the relationships between hypersomnolence and psychiatric disorders.

scholarly journals The Use of SenseWear Armband for Assessment of Daily Energy Expenditure and the Relation to Body Fat Distribution and Nutritional Intake in Lean Women with Polycystic Ovary Syndrome

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Gulcan Arusoglu
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Energy Expenditure ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Nutritional Intake ◽  
Control Group ◽  
Ovary Syndrome ◽  
Sensewear Armband ◽  
Segmental Body Composition

Objective. To evaluate nutritional intake, energy expenditure, and segmental body composition in lean women with polycystic ovary syndrome (PCOS) and compare them with age- and body mass index- (BMI-) matched control women. Methods. 32 nonobese patients with PCOS and 31 age- and BMI-matched healthy women were included in the study. Energy expenditure and physical activity level were assessed by metabolic Holter equipment (SenseWear Armband, SWA) which was never previously used in lean PCOS population. Food intake is recorded with 24 hours of food record. Segmental body composition analysis was assessed by bioelectrical impedance analyses (BIA). Results. Mean BMI was 22.64 ± 3.64 and 21.55 ± 2.77 kg/m2 (p=0.185) in PCOS and control groups, respectively. Mean age was 22.03 ± 4.21 and 21.71 ± 2.67 year (p=0.720), respectively. No significant differences were found in total energy intake and percentage of carbohydrates, fats, and other micronutrients (p>0.05). Energy percentage of proteins (%12.73 ± 1.98, p=0.008) was statistically lower in subjects versus the control group. The measurements of physical activity duration (PAD) (1.40 ± 0.87/2.18 ± 0.99 hours, p=0.002), active energy expenditure (372.35 ± 198.32/494.10 ± 186.50 kcal, p=0.018), and step counting (9370.03 ± 3587.49/11730.90 ± 3564.31 steps, p=0.013) measurement of the PCOS group were lower than the control group, respectively. Conclusions. New diagnosed women with PCOS had similar distribution and quantity of body fat parameters and nutritional status when compared to healthy women. Control subjects were found more active in energy expenditure.

215: Demographic and Health Factors Associated with Physical Activity in Adults with Type 1 or Type 2 Diabetes

2005 ◽  
Vol 161 (Supplement_1) ◽  
pp. S54-S54
Author(s):  
R Plotnikoff ◽  
L Taylor ◽  
P Wilson ◽  
K Courneya ◽  
R Sigal ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Factors Associated ◽  
Health Factors

scholarly journals Effects of Flash Glucose Monitoring on Dietary Variety, Physical Activity, and Self-Care Behaviors in Patients with Diabetes

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Satoshi Ida ◽  
Ryutaro Kaneko ◽  
Kanako Imataka ◽  
Kaoru Okubo ◽  
Yoshitaka Shirakura ◽  
...  
Keyword(s):  
Physical Activity ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Type 1 Diabetes Mellitus ◽  
Glucose Monitoring ◽  
Flash Glucose Monitoring

The aim of this study was to evaluate the effects of flash glucose monitoring on dietary variety, physical activity, and self-care behavior in patients with diabetes. This study included outpatients with diabetes using insulin who presented at the Department of Diabetes and Metabolism of the Ise Red Cross Hospital. Before initiating flash glucose monitoring and 12 weeks after its initiation, blood glucose-related parameters were assessed and self-administered questionnaires were completed (Dietary Variety Score (DVS), the International Physical Activity Questionnaire (IPAQ), the Summary of Diabetes Self-Care Activities Measure (SDSCA), and the Diabetes Treatment Satisfaction Questionnaire (DTSQ)) and compared between the two time points. We analyzed 42 patients with type 1 diabetes mellitus and 48 patients with type 2 diabetes mellitus. In patients with type 2 diabetes mellitus, but not type 1 diabetes mellitus, there was an increase in moderate/high category scores for IPAQ (P<0.001) and for treatment satisfaction reported via DTSQ. Furthermore, in patients with type 2 diabetes mellitus, the glycemic excursion index improved significantly and HbA1c decreased significantly (from 7.7 (1.2) to 7.4 (0.8), P=0.025). Results showed that standard deviation and mean amplitude of glycemic excursions significantly decreased in patients with type 1 diabetes mellitus (from 71.2 (20.4) to 66.2 (17.5), P=0.033 and from 124.6 (31.9) to 108.1 (28.4), P<0.001, respectively). Flash glucose monitoring is a useful tool to improve physical activity in patients with type 2 diabetes.

Co-morbidity, functionality and time since diagnosis as predictors of physical activity in individuals with type 1 or type 2 diabetes

2007 ◽  
Vol 78 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Ronald C. Plotnikoff ◽  
Sonia Lippke ◽  
Nandini Karunamuni ◽  
Neil Eves ◽  
Kerry S. Courneya ◽  
...  
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Co Morbidity ◽  
Time Since Diagnosis

scholarly journals Can Smart Pens Help Improve Diabetes Management?

2020 ◽  
pp. 193229682096560
Author(s):  
Sarah L. Sy ◽  
Medha M. Munshi ◽  
Elena Toschi
Keyword(s):  
Physical Activity ◽  
Bolus Dose ◽  
Diabetes Management ◽  
Mobile Apps ◽  
The Past ◽  
Continuous Glucose Monitors ◽  
The Relationship ◽  
Insight Into

Smart pen technology has evolved over the past decade with new features such as Bluetooth connectivity, bolus dose calculators, and integration with mobile apps and continuous glucose monitors. While similar in appearance to a traditional insulin pen, smart pens have the ability to record and store data of insulin injections. These devices have the potential to transform diabetes management for clinicians, and patients with type 1 and type 2 diabetes on insulin therapy by improving adherence, glycemic control, and addressing barriers to diabetes management. Smart pens can also highlight the relationship between insulin, food, and physical activity, and provide insight into optimizing insulin regimens. Education of clinicians and patients, and more clinical studies showing the benefits of smart pens and cost-effectiveness, are needed.

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