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
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.
基金The Biodiversity Survey and Assessment Project of the Ministry of Ecology and EnvironmentChina[grant numbers 2019HJ2096001006]TNC China Program[2002-2004]。
文摘野生灵长类夜宿地的利用方式可以明确地反映一个物种特有的生境利用方式和生存之道。2003年12月至2004年10月,我们利用可自动脱落GPS无线电项圈对云南省丽江市金丝厂的一个滇金丝猴(Rhinopithecus bieti)群体的活动进行了持续跟踪记录。本研究着重于对所研究猴群夜宿树的选择和夜宿地的利用方式的考查,并结合可能影响夜宿地选择和利用的环境因素,比如天气、季节、日均温度等做了系统分析。研究群计有180余只个体,家域面积约27.8 km^(2)。GPS项圈记录到夜宿树的有272个夜晚,由此我们确认了131个夜宿地。其中70个(54.3%)夜宿地仅利用了一次,剩余的则不同程度地多次利用(2~9次)。在这些重复利用的夜宿地中,持续利用同一夜宿点的情形共发生了19次,其中连续3个夜晚在同一夜宿地过夜的现象出现了3次,剩下的16次是连续利用同一夜宿地2次。这种连续利用同一夜宿地的情况占重复利用同一夜宿的7.0%,发生频率不高,而且几乎都出现在冬季(84.0%)。滇金丝猴对于同一夜宿地的重复造访的时间间隔约50 d。一旦发生连续重复利用的情况,猴群当天的移动距离显著缩短(527 m vs.884 m),降低了群体移动所必需的能量消耗。明显地,滇金丝猴夜宿点的选择受其当天下午和第二天早上觅食点的位置的影响。鉴于较大的群体和明显回避夜宿地重复利用的特性,提示这是猴群对家域内食物分布的行为响应,避免对同一地点的过度利用造成食物的不足。采用大量(131个)而分散的夜宿地利用方式会保证猴群能够获得充足的食物供应。此外,相较于其他树种,滇金丝猴更喜欢在云南铁杉(Tsuga dumosa)树上过夜,而且尽量不以夜宿点作为觅食点,可能与卫生和安全有关。
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。
