MSLife

2021 ◽  
Vol 5 (4) ◽  
pp. 1-35
Author(s):  
Gabriel Guo ◽  
Hanbin Zhang ◽  
Liuyi Yao ◽  
Huining Li ◽  
Chenhan Xu ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Sleep Quality ◽  
Low Cost ◽  
Behavioral Phenotype ◽  
Moderate Physical Activity ◽  
Poor Sleep ◽  
Caffeine Consumption ◽  
Behavioral Phenotyping ◽  
Home Based ◽  
In The Wild

Treatment for multiple sclerosis (MS) focuses on managing its symptoms (e.g., depression, fatigue, poor sleep quality), varying with specific symptoms experienced. Thus, for optimal treatment, there arises the need to track these symptoms. Towards this goal, there is great interest in finding their relevant phenotypes. Prior research suggests links between activities of daily living (ADLs) and MS symptoms; therefore, we hypothesize that the behavioral phenotype (revealed through ADLs) is closely related to MS symptoms. Traditional approaches to finding behavioral phenotypes which rely on human observation or controlled clinical settings are burdensome and cannot account for all genuine ADLs. Here, we present MSLife, an end-to-end, burden-free approach to digital behavioral phenotyping of MS symptoms in the wild using wearables and graph-based statistical analysis. MSLife is built upon (1) low-cost, unobtrusive wearables (i.e., smartwatches) that can track and quantify ADLs among MS patients in the wild; (2) graph-based statistical analysis that can model the relationships between quantified ADLs (i.e., digital behavioral phenotype) and MS symptoms. We design, implement, and deploy MSLife with 30 MS patients across a one-week home-based IRB-approved clinical pilot study. We use the GENEActiv smartwatch to monitor ADLs and clinical behavioral instruments to collect MS symptoms. Then we develop a graph-based statistical analysis framework to model phenotyping relationships between ADLs and MS symptoms, incorporating confounding demographic factors. We discover 102 significant phenotyping relationships (e.g., later rise times are related to increased levels of depression, history of caffeine consumption is associated with lower fatigue levels, higher relative levels of moderate physical activity are linked with decreased sleep quality). We validate their healthcare implications, using them to track MS symptoms in retrospective analysis. To our best knowledge, this is one of the first practices to digital behavioral phenotyping of MS symptoms in the wild.

scholarly journals Sleep Tracking of a Commercially Available Smart Ring and Smartwatch Against Medical-Grade Actigraphy in Everyday Settings: Instrument Validation Study (Preprint)

2020 ◽  
Author(s):  
Milad Asgari Mehrabadi ◽  
Iman Azimi ◽  
Fatemeh Sarhaddi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilén ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Poor Sleep ◽  
Sample Population ◽  
Sleep Monitoring ◽  
Daily Routines ◽  
Home Based ◽  
Significant Difference ◽  
Sleep Parameters ◽  
The Mean ◽  
Mean Differences

BACKGROUND Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. OBJECTIVE This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. METHODS We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired <i>t</i> tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. RESULTS We found significant correlations between the ring’s and actigraphy’s TST (<i>r</i>=0.86; <i>P</i>&lt;.001<i>)</i>, WASO (<i>r</i>=0.41; <i>P</i>&lt;.001), and SE (<i>r</i>=0.47; <i>P</i>&lt;.001). Comparing the watch with actigraphy showed a significant correlation in TST (<i>r</i>=0.59; <i>P</i>&lt;.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (<i>P</i>&lt;.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (<i>P</i>&lt;.001) between female and male groups. CONCLUSIONS In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.

Does Cigarette Smoking Increase Sleep Problems?

1993 ◽  
Vol 77 (1) ◽  
pp. 16-18 ◽  
Author(s):  
Frances J. Lexcen ◽  
Robert A. Hicks
Keyword(s):  
Cigarette Smoking ◽  
Sleep Quality ◽  
Sleep Problems ◽  
Sleep Onset ◽  
Poor Sleep ◽  
Caffeine Consumption ◽  
Daily Diaries ◽  
Delayed Sleep ◽  
And Gender ◽  
Ethnicity And Gender

Cigarette smoking has been associated with delayed sleep onset and diminished sleep duration, primarily on the bases of responses to one-shot questionnaires. This study used a survey format of daily diaries to observe sleep quality. 29 smokers were matched for age, ethnicity, and gender with 29 nonsmokers. Both groups recorded information on cigarette, alcohol, caffeine consumption, daily stress, and sleep quality. While the results showed that smokers were more likely to experience poor sleep than nonsmokers, these data are difficult to interpret because smokers also used significantly greater amounts of alcohol and caffeine.

scholarly journals The Epidemiology of Sleep Quality, Sleep Patterns, Consumption of Caffeinated Beverages, and Khat Use among Ethiopian College Students

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Seblewengel Lemma ◽  
Sheila V. Patel ◽  
Yared A. Tarekegn ◽  
Mahlet G. Tadesse ◽  
Yemane Berhane ◽  
...  
Keyword(s):  
College Students ◽  
Cigarette Smoking ◽  
Sleep Quality ◽  
Poor Sleep ◽  
Sleep Patterns ◽  
Sleep Habits ◽  
Poor Sleep Quality ◽  
Caffeine Consumption ◽  
Sleep Complaints ◽  
Khat Use

Objective. To evaluate sleep habits, sleep patterns, and sleep quality among Ethiopian college students; and to examine associations of poor sleep quality with consumption of caffeinated beverages and other stimulants.Methods. A total of 2,230 undergraduate students completed a self-administered comprehensive questionnaire which gathered information about sleep complaints, sociodemographic and lifestyle characteristics,and theuse of caffeinated beverages and khat. We used multivariable logistic regression procedures to estimate odds ratios for the associations of poor sleep quality with sociodemographic and behavioral factors.Results. Overall 52.7% of students were classified as having poor sleep quality (51.8% among males and 56.9% among females). In adjusted multivariate analyses, caffeine consumption (OR=1.55; 95% CI: 1.25–1.92), cigarette smoking (OR=1.68; 95% CI: 1.06–2.63), and khat use (OR=1.72, 95% CI: 1.09–2.71) were all associated with increased odds of long-sleep latency (>30 minutes). Cigarette smoking (OR=1.74; 95% CI: 1.11–2.73) and khat consumption (OR=1.91; 95% CI: 1.22–3.00) were also significantly associated with poor sleep efficiency (<85%), as well as with increased use of sleep medicine.Conclusion. Findings from the present study demonstrate the high prevalence of poor sleep quality and its association with stimulant use among college students. Preventive and educational programs for students should include modules that emphasize the importance of sleep and associated risk factors.

scholarly journals Motivations Influencing Caffeine Consumption Behaviors among College Students in Korea: Associations with Sleep Quality

Nutrients ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 953 ◽  
Author(s):  
Jinkyung Choi
Keyword(s):  
College Students ◽  
Sleep Quality ◽  
Smoking Status ◽  
Health Benefits ◽  
Coffee Consumption ◽  
Soft Drinks ◽  
Poor Sleep ◽  
Tea Consumption ◽  
Caffeine Consumption ◽  
Beverage Consumption

Caffeinated beverages are a part of daily life. Caffeinated beverages such as coffee, tea, energy drinks, and soft drinks are easy to purchase and are frequently consumed by young college students. Moreover, smoking influences the consumption of caffeinated beverages. The concentration of caffeine in these products is an attractive factor for individuals that desire the effects of caffeine; however, abusing such products may lead to poor sleep quality. The motivations that drive caffeinated beverage consumption were investigated in this study through a survey. Self-reported questionnaires were distributed on campus to students enrolled at a university in Korea. The motivations of the students for consuming each caffeinated beverage and their sleep quality were investigated. The results of exploratory factor analysis showed the motivations for caffeinated beverage consumption were alertness, taste, mood, socialization, health benefits, and habit. The motivations for consuming each caffeinated beverage product were different. For instance, coffee consumption was motivated by a desire for alertness (B = 0.107, SE = 0.049, t = 2.181, p < 0.05) and by habit (B = 0.345, SE = 0.046, t = 7.428, p < 0.001), whereas tea consumption was influenced by socialization (B = 0.142, SE = 0.060, t = 2.357, p < 0.05). Energy drink consumption was motivated by a desire for alertness (B = 0.100, SE = 0.034, t = 2.966, p < 0.01) and health benefits (B = 0.120, SE = 0.051, t = 2.345, p < 0.05), while the consumption of soft drinks was not motivated by any specific factors. Caffeinated beverage consumption did not show a significant relationship with sleep quality, although the general sleep quality of the respondents was poor. Smoking status showed significant differences in coffee and tea consumption as well as sleep quality. Smokers had a higher intake of coffee and a lower intake of tea than non-smokers. No interaction effect between smoking and coffee on sleep quality was found. Labeling detailing the amount of caffeine in products is necessary and a cautionary statement informing consumers that smoking cigarettes enhances the effects of caffeine should be included.

scholarly journals PERBEDAAN KUALITAS TIDUR SETELAH MENGONSUMSI BERBAGAI JENIS MINUMAN KOPI PADA USIA DEWASA

2017 ◽  
Vol 6 (4) ◽  
pp. 379
Author(s):  
Adhitya Nugraha Triantara ◽  
Hartanti Sandi Widyastuti
Keyword(s):  
Sleep Quality ◽  
Coffee Consumption ◽  
Bivariate Analysis ◽  
Poor Sleep ◽  
Caffeine Consumption ◽  
Cross Sectional ◽  
Caffeine Content ◽  
Significant Difference ◽  
Espresso Coffee ◽  
The Difference

Background: Coffee is categorized as psychostimulant drinks. The content of caffeine in coffee affects the increase in dopamine levels in the brain and one of the caffeine functions was a stimulant for the central nervous system. Caffeine consumption can also increased adrenaline and blood pressure. This can cause negative effects when consumed in excessive amount (> 400mg / day) such as anxiety, fatigue when woke up the morning. Sleep disorders and poor sleep quality.Objectives : Found the difference of caffeine content in various types of coffee drink and found the difference in sleep quality after consumed various types of coffee drinks..Method: The study design was Cross-Sectional with 54 adults around age 18-25 years as subjects. Subjects were divided into three groups, those who consumed espresso coffee, cappuccino, and café latte. The sleep quality of subjects was measured using the Pittsburg Sleep Quality Index. The caffeine content of espresso coffee, cappuccino, and café latte is tested using UV-Vis Spectrophotometry. Normality test using Kolmogorov Smirnov test (n> 50). Bivariate analysis using the Wilcoxon and Kruskal-Wallis tests.Results: Espresso coffee drinks has a caffeine content of 182.4 mg/cup, cappuccino coffee drinks has a caffeine content of 54 mg/cup, and café latte coffee drinks has a caffeine content of 41.25 mg/cup. The result of bivariate test showed that there was a significant difference in sleep quality after coffee consumption in the three subject groups. Subjects who consumed espresso coffee drink had a sleep quality that tends to be worse compared to subjects who consumed cappuccino and café latte.Conclusion: The study found differences in caffeine levels in espresso, cappuccino and café latte and the study also found a significant difference in sleep quality after consumed various types of coffee drinks. Subjects in the espresso coffee drink group had the worst sleep quality.

scholarly journals EVALUATION OF A CORRELATION BETWEEN SLEEP HOURS, SLEEP QUALITY AND SALIVARY LEVELS OF 8-HYDROXY-2'-DEOXYGUANOSINE IN CHRONIC PERIODONTITIS PATIENTS- AN OBSERVATIONAL STUDY

2020 ◽  
pp. 1-4
Author(s):  
Ayswaria B ◽  
Mathew John ◽  
Sabari Chandramohan ◽  
Bindu Rachel Thomas ◽  
Aswathy S ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Sleep Quality ◽  
Chronic Periodontitis ◽  
Pearson Correlation ◽  
Tertiary Care ◽  
Poor Quality ◽  
Poor Sleep ◽  
Pocket Depth ◽  
Clinical Attachment Loss ◽  
Validated Questionnaire

Aims: To determine if there was a correlation between sleep hours, sleep quality with the salivary levels of 8-hydroxy-2’-deoxyguanosine in chronic periodontitis patients, and to decide its relationship with the established clinical periodontal parameters. Settings and Design: Observational analytical study with 100 patients based on the inclusion criteria, who visited the Department of Periodontology of a tertiary care setting. Methods and Material: Bleeding on probing, pocket depth, clinical attachment loss, plaque index score were recorded. Sleep behaviour longer than a month time stretch was surveyed by Pittsburgh Sleep Quality Index, a validated questionnaire. 8-OHdG levels in un-stimulated saliva of all subjects were examined by ELISA. Statistical analysis used: Sleep-hour correlations with sleep quality at salivary 8-OHdG levels as well as clinical periodontal parameters were assessed using the Pearson correlation coefficient. All statistical analysis was performed using SPSS software version 17.0. Results: Salivary 8-OHdG levels and clinical parameters (PPD, CAL, PI) were significantly higher among sleep deprived individuals. On comparison, subjects with a poor quality of sleep (PSQI score>5) showed a significantly higher 8-OHdG levels. Conclusions: Short sleep durations and poor sleep quality can instigate inflammation and oxidative stress and could be a risk factor for periodontitis.

scholarly journals Sleep Tracking of a Commercially Available Smart Ring and Smartwatch Against Medical-Grade Actigraphy in Everyday Settings: Instrument Validation Study

10.2196/20465 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e20465
Author(s):  
Milad Asgari Mehrabadi ◽  
Iman Azimi ◽  
Fatemeh Sarhaddi ◽  
Anna Axelin ◽  
Hannakaisa Niela-Vilén ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Poor Sleep ◽  
Sample Population ◽  
Sleep Monitoring ◽  
Daily Routines ◽  
Home Based ◽  
Significant Difference ◽  
Sleep Parameters ◽  
The Mean ◽  
Mean Differences

Background Assessment of sleep quality is essential to address poor sleep quality and understand changes. Owing to the advances in the Internet of Things and wearable technologies, sleep monitoring under free-living conditions has become feasible and practicable. Smart rings and smartwatches can be employed to perform mid- or long-term home-based sleep monitoring. However, the validity of such wearables should be investigated in terms of sleep parameters. Sleep validation studies are mostly limited to short-term laboratory tests; there is a need for a study to assess the sleep attributes of wearables in everyday settings, where users engage in their daily routines. Objective This study aims to evaluate the sleep parameters of the Oura ring along with the Samsung Gear Sport watch in comparison with a medically approved actigraphy device in a midterm everyday setting, where users engage in their daily routines. Methods We conducted home-based sleep monitoring in which the sleep parameters of 45 healthy individuals (23 women and 22 men) were tracked for 7 days. Total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) of the ring and watch were assessed using paired t tests, Bland-Altman plots, and Pearson correlation. The parameters were also investigated considering the gender of the participants as a dependent variable. Results We found significant correlations between the ring’s and actigraphy’s TST (r=0.86; P<.001), WASO (r=0.41; P<.001), and SE (r=0.47; P<.001). Comparing the watch with actigraphy showed a significant correlation in TST (r=0.59; P<.001). The mean differences in TST, WASO, and SE of the ring and actigraphy were within satisfactory ranges, although there were significant differences between the parameters (P<.001); TST and SE mean differences were also within satisfactory ranges for the watch, and the WASO was slightly higher than the range (31.27, SD 35.15). However, the mean differences of the parameters between the watch and actigraphy were considerably higher than those of the ring. The watch also showed a significant difference in TST (P<.001) between female and male groups. Conclusions In a sample population of healthy adults, the sleep parameters of both the Oura ring and Samsung watch have acceptable mean differences and indicate significant correlations with actigraphy, but the ring outperforms the watch in terms of the nonstaging sleep parameters.

scholarly journals Investigating sleep quality on an inpatient psychiatry ward

BJPsych Open ◽  
2021 ◽  
Vol 7 (S1) ◽  
pp. S195-S195
Author(s):  
Joshua Hughes ◽  
Rhianne Thomas ◽  
Jonathan Macklin ◽  
Jonathan Owen
Keyword(s):  
Sleep Quality ◽  
Sleep Disturbance ◽  
Psychiatric Symptoms ◽  
Low Cost ◽  
Poor Sleep ◽  
Post Intervention ◽  
Factors Affecting ◽  
Sleep Quantity ◽  
Inpatient Settings ◽  
Sleep Length

AimsSleep is essential for optimal physiological functioning, but often interrupted in hospital settings. Disturbed sleep is associated with relapse of mood disorders and multiple comorbidities including impaired immunological function and increased cardiovascular risk. There are unique environmental challenges on psychiatry wards, such as overnight monitoring. Recent studies highlight the importance of evaluating and managing inpatient sleep disturbance. Aims include exploring the extent to which patients’ sleep is impacted by inpatient admission, elucidating causes of sleep disturbance and determining ways to improve sleep during admission.MethodPatients aged 18–65 years, who consented and were expected to be inpatients for a week, were approached after 72 hours of admission (n = 35). Quantitative and qualitative data, including on pre-hospital and hospital sleep quantity and quality, were gathered, as part of a cohort characterisation. Questionnaires using Pittsburgh Sleep Quality Index elements were used to gather data. Offering earplugs as a sleep-aid intervention was implemented, with sleep quantity and quality reassessed 72 hours post-intervention. In response to feedback, sound monitoring at regular intervals overnight was undertaken using a decibel-metre to determine noise baseline and variation.ResultAll patients approached agreed to participate. Pre-hospital average sleep quantity was 5.2 hours, with restedness score of 4.3, and 71% patients rating their sleep as ‘bad’. After 72 hours post-admission, average sleep length was 6.5 hours and restedness 5.3. Of patients who accepted earplugs (59%), there were improvements to mean sleep quality and quantity (7.6 hours), with 86% patients rating earplugs helpful. All patients surveyed thought that earplugs should be offered routinely on admission. 70% of patients were prescribed benzodiazepines or z-drugs as required. Self-reported factors affecting sleep included noise, psychiatric symptoms and medication side effects, with 13 patients mentioning the former. Sound monitoring recorded an average decibel level with a range of 35–75 dB, with peaks reaching 95 dB.ConclusionPoor sleep in hospital is widespread. There is a need to understand and address modifiable environmental and ward factors implicated in sleep disturbance within inpatient settings. Pharmacological options for sedation are common, but it is important to focus on alternative options of low-cost, non-pharmacological interventions which promote sleep optimisation and enhance inpatient care.

scholarly journals The Relationship of Physical Activity and Sleep Quality Among Elderly Who Are Still Working

2021 ◽  
Vol 6 (2) ◽  
pp. 45
Author(s):  
Anita Tria Purnamasari ◽  
Elida Ulfiana ◽  
Andri Setya Wahyudi
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
The Elderly ◽  
Moderate Physical Activity ◽  
Poor Sleep ◽  
Short Sleep Duration ◽  
Cross Sectional ◽  
Significance Level ◽  
The Relationship ◽  
Good Sleep

Introduction: The elderly tend to have short sleep duration, resulting in poor sleep quality. Physical activity is believed can improved sleep quality. This study was aimed to explain the relationship between physical activity and sleep quality among the elderly who are still working.Method: This study used a correlational research design with a cross-sectional approach. There were 63 elderly who involved as respondents by using the simple random sampling technique. The independent variable of this research was physical activities, while the dependent variable was sleep quality. A questionnaire was used to collect data. Data were then analyzed by using the Chi-Square test with a significance level of p=0.05.Results: The study found that physical activity correlates with sleep quality among the elderly who are still working (p=0.021). The elderly with moderate physical activity has three times chance to have a good sleep quality (OR=3.667).Conclusion: Moderate physical activity correlates with good sleep quality. However, an increase in physical activity will worsen the sleep quality among the elderly. The elderly who are still working can reduce the intensity of their activity in the workplace.

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