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>

Sleep, Aggression, and Psychosocial Adjustment in Male Prisoners

2014 ◽  
Vol 73 (3) ◽  
pp. 167-176 ◽  
Author(s):  
Nicole Vogler, Shared first authorship ◽  
Nadine Perkinson-Gloor, Shared first autho ◽  
Serge Brand ◽  
Alexander Grob ◽  
Sakari Lemola
Keyword(s):  
Sleep Quality ◽  
Sleep Duration ◽  
Psychosocial Adjustment ◽  
Sleep Hygiene ◽  
Poor Sleep ◽  
Health Complaints ◽  
Prison Inmates ◽  
Poor Sleep Quality ◽  
Sleep Quantity ◽  
Good Sleep

In this study, we investigate sleep quantity and quality and their association with anger-related reactions, symptoms of ADHD, depressive symptoms, physical health complaints, and life satisfaction in male prison inmates. Furthermore, we examine whether good sleep hygiene in the prison context (physical exercise during the day, switching the television off at night, limiting caffeine and cigarette consumption) is related to sleep quantity and quality. Forty-nine prison inmates (mean age = 39.37; SD = 13.95) completed questionnaires assessing sleep quality and quantity, sleep hygiene, and psychosocial and physical functioning. Short sleep duration (6 h or less) and poor sleep quality were related to higher levels of aggressiveness in aggression-provoking social situations and more physical health complaints. In addition, poor sleep quality was related to higher levels of rumination and more symptoms of ADHD. Daily caffeine consumption, a sleep hygiene variable, was related to shorter sleep duration. The results suggest that, among a sample of male prison inmates, adequate sleep duration and good sleep quality were associated with better psychosocial adjustment.

Relationshp Between Sleep Quality, Mood State, and Performance of Elite Air-Rifle Shooters

2021 ◽  
Author(s):  
Jiaojiao Lu ◽  
Yan An ◽  
Jun Qiu
Keyword(s):  
Sleep Quality ◽  
Mood State ◽  
Sleep Latency ◽  
Sleep Time ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Somatic Anxiety ◽  
And Performance ◽  
Air Rifle ◽  
Good Sleep

Abstract Background To evaluate the impact of pre-competition sleep quality on the mood and performance of elite air-rifle shooters. Methods This study included 23 elite air-rifle shooters who participated in an air-rifle shooting-competition from April 2019 to October 2019. Sleep time, sleep efficiency, sleep latency, and wake-up time after sleep onset were monitored using actigraphy. The Pittsburgh sleep quality index and Profile of Mood State were used to assess sleep quality. Competitive State Anxiety Inventory-2 was used to evaluate mood state. Results The average time to fall asleep, sleep time, sleep efficiency, and subjective sleep quality were 20.6 ± 14.9 min, 7.0 ± 0.8 h, 85.9 ± 5.3%, and 5.2 ± 2.2, respectively. Sleep quality decreased as the competition progressed. Pre-competition sleep time in female athletes was significantly higher than that on the competition day (P = 0.05). Pre-competition sleep latency was significantly longer in women than in men (P = 0.021). During training and pre-competition, the tension, fatigue, depression, and emotional disturbance were significantly lower in athletes with good sleep quality than in athletes with poor sleep quality. Athletes with good sleep quality had significantly more energy. The PSQI total score was positively correlated with positive emotion, TMD, cognitive anxiety, and somatic anxiety POMS scores, and negatively correlated with energy and self-confidence scores. Race scores and depression and somatic anxiety scores were negatively correlated. Conclusion Poor sleep quality negatively impacted the mood of athletes; however, sleep indices and competition performance of athletes during competitions were not significantly correlated.

scholarly journals An investigation of the relationship between sleep and fatigue in those with and without insomnia

2021 ◽  
Author(s):  
Andrea L. Harris
Keyword(s):  
Sleep Quality ◽  
Linear Models ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Sleep Diary ◽  
Poor Sleep ◽  
Sleep Quantity ◽  
Cognitive Behavioural ◽  
Multi Method Approach ◽  
The Relationship

There is currently mixed evidence for the relationship between poor sleep and daytime fatigue. It is well documented that retrospective measures of insomnia and fatigue are highly correlated with one another. However, other studies fail to demonstrate a link between objectively less sleep and fatigue; that is, individuals with shorter sleep times do not necessarily report increased fatigue. As such, the relationship between these two constructs remains unclear. The current investigation will help to elucidate the complex relationship between sleep and fatigue among those with and without insomnia by advancing the existing literature in two important ways. First, this study proposed to examine the temporal relationship between sleep and fatigue across two weeks, thereby investigating whether sleep and fatigue occur in accordance with one anotherover time. Second, this study utilized a multi-method approach by collecting subjective (i.e.,sleep diary) and objective (i.e., actigraphy) measures of sleep, as well as retrospective (i.e.,visual analogue scales: VAS) and prospective (i.e., momentary ratings) measures of fatigue. Two separate hierarchical linear models were used to test whether sleep (measured by sleep quality and total sleep time) predicted daytime fatigue on the VAS and actigraph, respectively. The secondary objective asked whether cognitive-behavioural variables (i.e., maladaptive sleep beliefs, fear and avoidance of fatigue, and fatigue-based rumination) may help account for the relationship between sleep and fatigue using mediation. The results of the primary analyses suggested that sleep quality significantly predicted VAS fatigue ratings, whereas total sleep time was a significant predictor of fatigue within- but not between-persons. No significant relationships were found between objective measures of sleep and momentary fatigue ratings. Finally, each of the cognitive-behavioural variables, with the exception of avoidance of fatigue, were significant mediators of the relationship between sleep and fatigue. The results demonstrated that compared to sleep quantity, our perception of sleep may play a more important role in predicting reports of daytime fatigue. These findings could help decrease the burden that individuals with insomnia place on their total sleep times, and instead, treatment could focus on challenging maladaptive sleep-related cognitions, which ultimately could lessen the overall sleep-related anxiety.

scholarly journals Exploration of Sleep Quality and Sleep Hygiene among QU students

2020 ◽  
Author(s):  
Raja Mahamade Ali ◽  
Monica Zolezzi ◽  
Ahmed Awaisu
Keyword(s):  
University Students ◽  
Sleep Quality ◽  
Online Survey ◽  
Negative Impact ◽  
Sleep Hygiene ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Cross Sectional Survey ◽  
Sleeping Problems ◽  
Good Sleep

Sleep is an important component of healthy lifestyles. Worldwide reports suggest that one in every three adults suffers from insomnia. University students are vulnerable to insomnia due to their stressful lifestyle and inconsistent sleeping schedules, which contribute to poor, sleep hygiene. The purpose of this study is to explore the prevalence of sleeping problems among university students in Qatar and to investigate factors contributing to insomnia development. A cross-sectional survey utilizing two validated sleep questionnaires, the Pittsburgh sleep quality index (PSQI) and the sleep hygiene index (SHI), were administered to Qatar University (QU) students in either English or Arabic. An online survey was sent to all QU students through e-mail. Descriptive and inferential statistics were used to analyse and report the findings. A total of 2,062 students responded to this survey. Most of the respondents were females, Qataris, and the majority of them belonged to the colleges of Arts and Sciences, Business and Economics or Engineering. Around 25% of the participating students reported previous use of sleep aids. The findings indicated that the majority of the students had poor sleep quality (69.7%) and poor sleep hygiene (79%). A positive association was found between sleep quality and sleep hygiene (r = 0.39; p < 0.0001). College distribution and marital status were shown to significantly influence sleep quality (p =0.031 and p=0.02 respectively). The regression analysis revealed that sleep hygiene had the greatest effect on sleep quality (accounting for 7% of the variance) and individuals with good sleep hygiene were 4 times more likely to have good sleep quality. The findings of this study suggest that poor sleep quality and inadequate sleep hygiene practices are common among university students in Qatar, both of which may have a negative impact on students’ academic performance which warrants further investigation in future studies.

scholarly journals O033 Predicting subjective sleep quality using multi-day actigraphy data: A machine learning approach

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A14-A15
Author(s):  
C Kao ◽  
A D’Rozario ◽  
N Lovato ◽  
D Bartlett ◽  
S Postnova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Quality ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Poor Sleep ◽  
Healthy Controls ◽  
Subjective Sleep Quality ◽  
Subjective Sleep ◽  
Quality Ratings ◽  
Insomnia Disorder

Abstract Objectives Insomnia is diagnosed using clinical interview but actigraphy is often used as a consecutive multi-day measurement of activity-rest cycles to quantify sleep-wake periods. However, discrepancies between subjective complaints of insomnia and objective actigraphy measurement exist. The aims of the current study were to (i) predict subjective sleep quality using actigraphic data and, (ii) identify features of actigraphy that are associated with poor subjective sleep quality. Methods Actigraphy data were collected for 14-consecutive days with corresponding subjective sleep quality ratings from participants with Insomnia Disorder and healthy controls. We fitted multiple machine learning algorithms to determine the best performing method with the highest accuracy of predicting subjective quality rating using actigraphic data. Results We analysed a total of 1278 days of actigraphy and corresponding subjective sleep quality ratings from 86 insomnia disorder patients and 20 healthy controls. The k-neighbors classifier provided the best performance in predicting subjective sleep quality with an overall accuracy, sensitivity and specificity of 83%, 74% and 87% respectively, and an average AUC-ROC of 0.88. We also found that activity recorded in the early morning (04:00-08:00) and overnight periods (00:00-04:00) had the greatest influence on sleep quality scores, with poor sleep quality related to these periods.. Conclusions A machine learning model based on actigraphy time-series data successfully predicted self-reported sleep quality. This approach could facilitate clinician’s diagnostic capabilities and provide an objective marker of subjective sleep disturbance.

scholarly journals Introducing Sleep Hygiene to the Church of Sidang Jemaat Allah Bethlehem Congregation, Bogor, West Java

2021 ◽  
Vol 5 (1) ◽  
pp. 61-70
Author(s):  
Alya Dwiana ◽  
Triyana Sari ◽  
David Limanan
Keyword(s):  
Sleep Quality ◽  
Cognitive Abilities ◽  
Healthy Lifestyle ◽  
Sleep Hygiene ◽  
Sleep Time ◽  
The Body ◽  
Poor Sleep ◽  
Sleep Patterns ◽  
Lack Of Sleep ◽  
The Individual

Insomnia is one of the most common health problems. Approximately one-third of adults show symptoms related to insomnia. Around 9%-15% of people have sleeping disorders dan suffer the consequences of it in the daytime, and roughly 6% suffer from diagnosed insomnia. Although the prevalence and significant effects of insomnia have been known, sleep disorders are still rarely diagnosed and receive proper treatments. Lack of sleep will create sleep debt that the body will have to compensate for by adding more sleep time in the next day. Should this compensation fail to be fulfilled, the individual will suffer from excessive sleepiness, memory problems, difficulty concentrating, and disturbances in performing daily activities. Chronic lack of sleep can also decrease memory and cognitive abilities, trigger mood disorders and even cause hallucination. The Sidang Jemaat Allah Bethlehem church (GSJA) is one of the biggest churches in Bogor. The people of GSJA’s knowledge of healthy sleep patterns and their sleep quality was unknown. Therefore, it was necessary to provide sleep health education to raise the awareness of the importance of healthy sleep patterns, both the quantity and the quality, in practicing a healthy lifestyle. We also assessed their sleep quality using the Pittsburgh Sleep Quality Index (PSQI) questionnaire. The data show that out of 41 participants, 30 of them (73.11%) have poor sleep quality (score ≥ 5) and most of them were in the 71-80-year-old age group. The participants' level of understanding about sleep hygiene has increased after the event.

scholarly journals An investigation of the relationship between sleep and fatigue in those with and without insomnia

2021 ◽  
Author(s):  
Andrea L. Harris
Keyword(s):  
Sleep Quality ◽  
Linear Models ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Sleep Diary ◽  
Poor Sleep ◽  
Sleep Quantity ◽  
Cognitive Behavioural ◽  
Multi Method Approach ◽  
The Relationship

There is currently mixed evidence for the relationship between poor sleep and daytime fatigue. It is well documented that retrospective measures of insomnia and fatigue are highly correlated with one another. However, other studies fail to demonstrate a link between objectively less sleep and fatigue; that is, individuals with shorter sleep times do not necessarily report increased fatigue. As such, the relationship between these two constructs remains unclear. The current investigation will help to elucidate the complex relationship between sleep and fatigue among those with and without insomnia by advancing the existing literature in two important ways. First, this study proposed to examine the temporal relationship between sleep and fatigue across two weeks, thereby investigating whether sleep and fatigue occur in accordance with one anotherover time. Second, this study utilized a multi-method approach by collecting subjective (i.e.,sleep diary) and objective (i.e., actigraphy) measures of sleep, as well as retrospective (i.e.,visual analogue scales: VAS) and prospective (i.e., momentary ratings) measures of fatigue. Two separate hierarchical linear models were used to test whether sleep (measured by sleep quality and total sleep time) predicted daytime fatigue on the VAS and actigraph, respectively. The secondary objective asked whether cognitive-behavioural variables (i.e., maladaptive sleep beliefs, fear and avoidance of fatigue, and fatigue-based rumination) may help account for the relationship between sleep and fatigue using mediation. The results of the primary analyses suggested that sleep quality significantly predicted VAS fatigue ratings, whereas total sleep time was a significant predictor of fatigue within- but not between-persons. No significant relationships were found between objective measures of sleep and momentary fatigue ratings. Finally, each of the cognitive-behavioural variables, with the exception of avoidance of fatigue, were significant mediators of the relationship between sleep and fatigue. The results demonstrated that compared to sleep quantity, our perception of sleep may play a more important role in predicting reports of daytime fatigue. These findings could help decrease the burden that individuals with insomnia place on their total sleep times, and instead, treatment could focus on challenging maladaptive sleep-related cognitions, which ultimately could lessen the overall sleep-related anxiety.

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 The relationship between sleep hygiene and sleep quality among residents of an elderly care institution in DKI Jakarta

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Mutia Annisa ◽  
Dwi Nurviyandari Kusuma Wati
Keyword(s):  
Sleep Quality ◽  
Sleep Problems ◽  
Elderly Care ◽  
Sleep Hygiene ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Cross Sectional ◽  
Cross Sectional Design ◽  
The Relationship ◽  
Purposive Sample

<p class="AbstractContent"><strong>Objective:</strong> Elderly are at risk of poor slepp quality and other health problems due to reduced sleep satisfaction. The objective of this study was to explore the association between sleep hygiene and sleep quality in elderly.</p><p class="AbstractContent"><strong>Methods: </strong>This was a descriptive study with cross sectional design. The study was conducted in four elderly care institutions in Jakarta, Indonesia, involving a purposive sample of 103 elderly aged 60 to 111 years old. Data were collected using Sleep Hygiene Index (SHI) and Pittsburgh Sleep Quality Index (PSQI).</p><p class="AbstractContent"><strong>Results:</strong> Over half of the residents had poor sleep hygiene (51.5%) and more than three quarter (81.6%) had poor sleep quality. The study revealed that there was a highly significant relationship between sleep hygiene and sleep quality (p = 0.001). The study also showed that those with poor sleep hygiene were 7.834 times more likely to have poor sleep quality.<strong></strong></p><p class="AbstractContent"><strong>Conclusion: </strong>Nurses need to include interventions that may address residents’ sleep problems. They also need to promote sleep hygiene and improve residents’ sleep quality.<strong></strong></p><strong>Keywords: </strong>elderly, institution, sleep hygiene, sleep quality

scholarly journals 0342 Self-Silencing within Intimate Relationships, Sleep, and Subclinical Atherosclerosis in Midlife Women

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A129-A130
Author(s):  
K P Jakubowski ◽  
Y Chang ◽  
E Barinas-Mitchell ◽  
K A Matthews ◽  
P M Maki ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Carotid Plaque ◽  
Subclinical Atherosclerosis ◽  
Sleep Onset ◽  
Sleep Time ◽  
Physical Symptoms ◽  
Midlife Women ◽  
Poor Sleep ◽  
Multinomial Regression ◽  
Health Indices

Abstract Introduction Social relationships are important for health. In some relationships, women learn to self-silence, or to inhibit self-expression to avoid conflict or loss. Self-silencing is associated with reported psychiatric and physical symptoms, but no studies have examined whether self-silencing is related to worse sleep or cardiovascular (CV) health. We tested relationships of self-silencing to sleep and carotid plaque in midlife women; secondary analyses examined whether sleep mediated or moderated relationships between self-silencing and plaque. Methods In an ongoing community-based study of nonsmoking women, 304 women aged 40-60 were assessed at baseline; 157 of these women have been assessed 5 years later. At baseline, women reported on self-expression in their current/last intimate relationship via the Silencing the Self Scale. At both visits, women provided self-reports (demographics, medical history, CESD depression, PSQI sleep quality), physical measures, actigraphy (total sleep time [TST], wake after sleep onset [WASO], and efficiency), and carotid artery ultrasound to quantify plaque. Relationships of self-silencing and subscales to sleep (subjective and actigraphic sleep at baseline and averaged across visits) and carotid plaque (0, 1, ≥2) were tested in linear regression and multinomial regression models, respectively, adjusted for demographic and health indices, including depressive symptoms and snoring. Results At baseline, women (72% White) were on average 54 years old; 44% reported poor sleep quality, 46% had plaque (24% score ≥2), and average TST, WASO, and efficiency were 6.2 hrs, 46 min, and 84%, respectively. At baseline, self-silencing (particularly the tendency to judge oneself by external standards) was related to worse sleep quality (p=.001), but better actigraphic WASO (p=.02) and efficiency (p=.02). Self-silencing was related to worse average sleep quality across visits (p=.001). Self-silencing related to higher odds of baseline plaque ≥2 [OR(95% CI)=1.14 (1.02,1.28), p=.02], yet sleep did not explain or moderate this relationship. Conclusion Self-silencing was associated with worse subjective, but better actigraphic sleep at baseline, and with poorer sleep quality over 5 years. Self-silencing related to carotid atherosclerosis, yet sleep did not appear to impact this relationship. Emotional expression is relevant to midlife women’s sleep and CV health. Support R01HL105647, K24123565 (RCT); RF1AG053504 (RCT & PM); T32MH018269 (KPJ)

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