Objectives:This study aims to assess sleep disorders among secondary school adolescents and explore the relationship between sociodemographic factors(age,gender,household income,and sleep duration)and the occurrence o...Objectives:This study aims to assess sleep disorders among secondary school adolescents and explore the relationship between sociodemographic factors(age,gender,household income,and sleep duration)and the occurrence of these disorders.Methods:A quantitative,descriptive,cross-sectional study,was conducted from November 20th,2022,to May 25th,2023,involving 200 secondary school students selected through convenience sampling.Data collection utilized a structured questionnaire divided into sociodemographic and sleep disorder sections.Validity was ensured by a panel of ten experts,and reliability was confirmed using Cronbach’s Alpha(0.77).Statistical analysis employed SPSS version 26.Results:Findings revealed that a majority of participants(70.5%)had low-level sleep disorders,followed by moderate disorders represented(29%).Significant associations were found between sleep disorders and gender(P=0.000),economic status for family(P=0.020),and nightly sleep duration(P=0.016).However,no significant relationship was observed between sleep disorders and family structure or age(P>0.05).Conclusions:The study highlights that most secondary school students experience mild sleep disorders,followed by moderate disorders.Notably,gender,income,and sleep duration showed significant correlations with sleep disorders.展开更多
The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly...The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.展开更多
In the past, efforts have been made to determine the influence of sleep quantity and its deprivation, on functioning efficiency of human beings. However, determination of sleeping patterns that could improve intellect...In the past, efforts have been made to determine the influence of sleep quantity and its deprivation, on functioning efficiency of human beings. However, determination of sleeping patterns that could improve intellectual performance has been largely neglected. This study is designed to discover the effects of different sleeping patterns on academic performance among medical students. A descriptive study was carried out in King Edward Medical University in Lahore, Pakistan during a six-month time span from May 11th, 2011 to September 30th, 2011. Of the total population of 1350 students in King Edward Medical University, 591 undergraduates were included in the study. A questionnaire designed on sleeping patterns and academic performance was distributed in May 2011. What was described as outstanding students were greater in number in group 4 (7/19) 36.8% and group 6 (6/19) 31.6%. Above average students with sleeping patterns were in group 4 (13/37) 35.1% and group 6 (10/37) 27%. Average students were shown to have sleeping patterns of group 4 (11/25) 44% and group 6 (7/25) 28%. Below average students were shown to have sleeping patterns of group 4 (3/3) 100%. Most of our students had a reduction in the total amount of sleeping hours throughout the years. Midnight to 6 o’clock in the morning with an afternoon nap was the sleeping pattern that was most commonly seen in all groups. We concluded that different sleeping patterns do not affect the performance of medical students in the academic prospective. Many other factors may be involved in the lack of significant achievement, in order to prove that the sleeping patterns are not related to the academic performance, and more data would need to be collected.展开更多
目的系统总结不同饮食模式在改善成年人睡眠质量中应用的现状。方法计算机检索PubMed、Cochrane Library、Web of Science、Embase、MEDLINE、中国生物医学文献数据库、中国知网、万方数据库中与饮食模式在改善成年人睡眠质量中应用的...目的系统总结不同饮食模式在改善成年人睡眠质量中应用的现状。方法计算机检索PubMed、Cochrane Library、Web of Science、Embase、MEDLINE、中国生物医学文献数据库、中国知网、万方数据库中与饮食模式在改善成年人睡眠质量中应用的相关文献,检索时限为建库至2023年6月25日,对纳入的文献进行归纳总结。结果共纳入29篇文献,其中18篇为横断面研究、7篇为队列研究、4篇为随机对照试验,从干预时间、干预人群、饮食模式的类型、干预的结局指标和干预结果进行了分析和总结。结论得舒饮食、生酮饮食、地中海饮食、北欧饮食和健康植物性饮食对改善睡眠质量具有积极作用,且不同的饮食模式对睡眠障碍具有不同作用,进一步分析发现这些饮食模式的组成成分具有共性特征。未来应该注重探究微量营养素对睡眠质量的影响和使用更客观的评价指标,根据患者的具体情况设计个性化的饮食模式和干预方案。展开更多
Objective: This systematic review examines the impact of lifestyle factors on migraine frequency and severity through a comprehensive analysis of lifestyle factors such as diet, physical activity, sleep patterns, stre...Objective: This systematic review examines the impact of lifestyle factors on migraine frequency and severity through a comprehensive analysis of lifestyle factors such as diet, physical activity, sleep patterns, stress, mental health, and environmental influences. Methods: We thoroughly searched Google Scholar, PUBMED, Scopus, and Web of Science databases using keywords related to migraines and lifestyle factors. Keywords incorporated the Boolean operator “and” to narrow search results. Following the PRISMA guidelines, we identified, screened, and evaluated studies for inclusion, resulting in nine studies meeting the eligibility criteria. Results: A total of 4917 records were initially identified from Scopus (2786), PubMed (854), and Web of Science (1277). Following deduplication, 3657 records underwent title screening, with 382 additionally screened by abstract. Ultimately, 88 full-text articles were assessed, resulting in 9 studies meeting eligibility for qualitative synthesis: 7 prospective and 2 retrospective studies. Our findings highlight the multifaceted role of lifestyle factors in migraine pathophysiology and management. Dietary habits, such as high-calorie, high-fat, and gluten-containing diets were linked to migraine triggers. Moderate physical activity showed beneficial effects on migraine management, while intense exercise could exacerbate symptoms. Poor sleep hygiene and insomnia were strongly associated with increased migraine frequency and severity. Chronic stress and poor mental health significantly contributed to migraine exacerbation, with stress management techniques proving beneficial. Environmental factors, including light, sound, weather changes, and allergens, were also identified as significant migraine triggers. Conclusions: Personalized lifestyle modifications, tailored to individual patient profiles, are crucial in managing migraines. Evidence-based recommendations include balanced diets, moderate physical activity, improved sleep hygiene, stress management techniques, and environmental adaptations.展开更多
人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良...人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。展开更多
文摘Objectives:This study aims to assess sleep disorders among secondary school adolescents and explore the relationship between sociodemographic factors(age,gender,household income,and sleep duration)and the occurrence of these disorders.Methods:A quantitative,descriptive,cross-sectional study,was conducted from November 20th,2022,to May 25th,2023,involving 200 secondary school students selected through convenience sampling.Data collection utilized a structured questionnaire divided into sociodemographic and sleep disorder sections.Validity was ensured by a panel of ten experts,and reliability was confirmed using Cronbach’s Alpha(0.77).Statistical analysis employed SPSS version 26.Results:Findings revealed that a majority of participants(70.5%)had low-level sleep disorders,followed by moderate disorders represented(29%).Significant associations were found between sleep disorders and gender(P=0.000),economic status for family(P=0.020),and nightly sleep duration(P=0.016).However,no significant relationship was observed between sleep disorders and family structure or age(P>0.05).Conclusions:The study highlights that most secondary school students experience mild sleep disorders,followed by moderate disorders.Notably,gender,income,and sleep duration showed significant correlations with sleep disorders.
文摘The quality of sleep may be a reflection of an el- derly individual＇s health state, and sleep pattern is an im- portant measurement. Recognition of sleep pattern by itself is a challenge issue, especially for elderly-care community, due to both privacy concerns and technical limitations. We propose a novel multi-parametric sensing system called sleep pattern recognition system （SPRS）. This system, equipped with a combination of various non-invasive sensors, can mon- itor an elderly user＇s sleep behavior. It accumulates the de- tecting data from a pressure sensor matrix and ultra wide band （UWB） tags. Based on these two types of complemen- tary sensing data, SPRS can assess the user＇s sleep pattern automatically via machine learning algorithms. Compared to existing systems, SPRS operates without disrupting the users＇ sleep. It can be used in normal households with minimal deployment. Results of tests in our real assistive apartment at the Smart Elder-care Lab are also presented in this paper.
文摘In the past, efforts have been made to determine the influence of sleep quantity and its deprivation, on functioning efficiency of human beings. However, determination of sleeping patterns that could improve intellectual performance has been largely neglected. This study is designed to discover the effects of different sleeping patterns on academic performance among medical students. A descriptive study was carried out in King Edward Medical University in Lahore, Pakistan during a six-month time span from May 11th, 2011 to September 30th, 2011. Of the total population of 1350 students in King Edward Medical University, 591 undergraduates were included in the study. A questionnaire designed on sleeping patterns and academic performance was distributed in May 2011. What was described as outstanding students were greater in number in group 4 (7/19) 36.8% and group 6 (6/19) 31.6%. Above average students with sleeping patterns were in group 4 (13/37) 35.1% and group 6 (10/37) 27%. Average students were shown to have sleeping patterns of group 4 (11/25) 44% and group 6 (7/25) 28%. Below average students were shown to have sleeping patterns of group 4 (3/3) 100%. Most of our students had a reduction in the total amount of sleeping hours throughout the years. Midnight to 6 o’clock in the morning with an afternoon nap was the sleeping pattern that was most commonly seen in all groups. We concluded that different sleeping patterns do not affect the performance of medical students in the academic prospective. Many other factors may be involved in the lack of significant achievement, in order to prove that the sleeping patterns are not related to the academic performance, and more data would need to be collected.
文摘目的系统总结不同饮食模式在改善成年人睡眠质量中应用的现状。方法计算机检索PubMed、Cochrane Library、Web of Science、Embase、MEDLINE、中国生物医学文献数据库、中国知网、万方数据库中与饮食模式在改善成年人睡眠质量中应用的相关文献,检索时限为建库至2023年6月25日,对纳入的文献进行归纳总结。结果共纳入29篇文献,其中18篇为横断面研究、7篇为队列研究、4篇为随机对照试验,从干预时间、干预人群、饮食模式的类型、干预的结局指标和干预结果进行了分析和总结。结论得舒饮食、生酮饮食、地中海饮食、北欧饮食和健康植物性饮食对改善睡眠质量具有积极作用,且不同的饮食模式对睡眠障碍具有不同作用,进一步分析发现这些饮食模式的组成成分具有共性特征。未来应该注重探究微量营养素对睡眠质量的影响和使用更客观的评价指标,根据患者的具体情况设计个性化的饮食模式和干预方案。
文摘Objective: This systematic review examines the impact of lifestyle factors on migraine frequency and severity through a comprehensive analysis of lifestyle factors such as diet, physical activity, sleep patterns, stress, mental health, and environmental influences. Methods: We thoroughly searched Google Scholar, PUBMED, Scopus, and Web of Science databases using keywords related to migraines and lifestyle factors. Keywords incorporated the Boolean operator “and” to narrow search results. Following the PRISMA guidelines, we identified, screened, and evaluated studies for inclusion, resulting in nine studies meeting the eligibility criteria. Results: A total of 4917 records were initially identified from Scopus (2786), PubMed (854), and Web of Science (1277). Following deduplication, 3657 records underwent title screening, with 382 additionally screened by abstract. Ultimately, 88 full-text articles were assessed, resulting in 9 studies meeting eligibility for qualitative synthesis: 7 prospective and 2 retrospective studies. Our findings highlight the multifaceted role of lifestyle factors in migraine pathophysiology and management. Dietary habits, such as high-calorie, high-fat, and gluten-containing diets were linked to migraine triggers. Moderate physical activity showed beneficial effects on migraine management, while intense exercise could exacerbate symptoms. Poor sleep hygiene and insomnia were strongly associated with increased migraine frequency and severity. Chronic stress and poor mental health significantly contributed to migraine exacerbation, with stress management techniques proving beneficial. Environmental factors, including light, sound, weather changes, and allergens, were also identified as significant migraine triggers. Conclusions: Personalized lifestyle modifications, tailored to individual patient profiles, are crucial in managing migraines. Evidence-based recommendations include balanced diets, moderate physical activity, improved sleep hygiene, stress management techniques, and environmental adaptations.
文摘人体呼吸系统相关疾病常常伴随着呼吸深度和节律的异常,因此呼吸信号监测和呼吸模式识别在医疗健康领域中尤其是对于睡眠监测、疾病预断具有重要意义。其中,非接触式的脉冲式超宽带雷达(Impulse Radio Ultra-Wideband,IR-UWB)因具有良好的距离分辨率和穿透能力以及全天候全天时、安全无创的检测优势,正逐步成为睡眠健康监护领域中最关键的感知技术之一。然而受睡眠监测特定的室内场景影响,复杂的测量环境给呼吸模式特征的准确提取带来了限制和挑战,传统的雷达呼吸模式识别算法主要关注一维呼吸时、频域特征,而IR-UWB雷达目标回波信息分散在多个距离门内,使用一维特征识别准确率较低。为此,本文针对IR-UWB雷达中人体呼吸在时间上慢速起伏运动、在距离上是扩展目标的信号模型特点,提出了一种引入时距信息的IR-UWB雷达多域特征融合呼吸模式识别方法。算法在提取一维呼吸信号波形时、频域特征的基础上更进一步挖掘雷达二维时距图像中潜在的呼吸模式形态特征,通过多域特征融合实现呼吸模式的非接触式检测和识别。在图像处理上,针对图像受呼吸异常节律影响呈现局部粘连特性导致呼吸周期提取难的问题,提出一种通过相位矩阵图像处理来检测雷达图像中的呼吸时距条带从而获取图像特征的方法。实验结果表明,利用该算法提取的多域特征对六种呼吸模式进行机器学习的分类识别,可以实现96.3%的识别准确率。
