Background Higher accelerometer-assessed volume and intensity of physical activity(PA)have been associated with a longer life expectancy but can be difficult to translate into recommended doses of PA.We aimed to:(a)im...Background Higher accelerometer-assessed volume and intensity of physical activity(PA)have been associated with a longer life expectancy but can be difficult to translate into recommended doses of PA.We aimed to:(a)improve interpretability by producing UK Biobank age-referenced centiles for PA volume and intensity;(b)inform public-health messaging by examining how adding recommended quantities of moderate and vigorous PA affect PA volume and intensity.Methods 92,480 UK Biobank participants aged 43-80 years with wrist-worn accelerometer data were included.Average acceleration and intensity gradient were derived as proxies for PA volume and intensity.We generated sex-specific centile curves using Generalized Additive Models for Location Scale and Shape(GAMLSS)and modeled the effect of adding moderate(walking)or vigorous(running)activity on the combined change in the volume and intensity centiles(change in PA profile).Results In men,volume was lower as age increased while intensity was lower after age 55;in women,both volume and intensity were lower as age increased.Adding 150 min of moderate PA weekly(5×30 min walking)increased the PA profile by 4 percentage points.Defining moderate PA as brisk walking approximately doubled the increase(9 percentage points)while 75 min of vigorous PA weekly(5×15 min running)trebled the increase(13 percentage points).Conclusion These UK Biobank reference centiles provide a benchmark for interpretation of accelerometer data.Application of our translational methods demonstrate that meeting PA guidelines through shorter duration vigorous activity is more beneficial to the PA profile(volume and intensity)than longer duration moderate activity.展开更多
Background: Based on the transtheoretical model, the current study investigated whether awareness of physical activity(PA) recommendations had an impact on the stages of PA behavior change and levels of PA among Chine...Background: Based on the transtheoretical model, the current study investigated whether awareness of physical activity(PA) recommendations had an impact on the stages of PA behavior change and levels of PA among Chinese college students.Methods: In Study 1, with a cross-sectional study design, 9826 students were recruited, and their knowledge of international PA recommendations,PA stage distribution, and self-reported PA level were surveyed. Pearson's χ2 test was used to test whether those participants who were aware and not aware of PA guidelines were equally distributed across the stages of PA behavior, and independent t test was conducted to test the group difference in the actual levels of PA. In Study 2, 279 students who were not aware of the PA recommendations were randomly allocated to either an intervention group or a control group, and only those in the intervention group were presented with international PA guidelines. In both groups,students' PA stages and PA level were examined before the test and then 4 months post-test. Mc Nemar's test for correlated proportions and repeated-measures analysis of variance were conducted to examine the changes in PA stage membership and PA level after the intervention.Results: Study 1 results revealed that only 4.4% of the surveyed students had correct knowledge of PA recommendations. Those who were aware of the recommendations were in later stages of PA behavior(χ~2(4) = 167.19, p < 0.001). They were also significantly more physically active than those who were not aware of the recommendations(t(443.71) = 9.00, p < 0.001, Cohen's d = 0.53). Study 2 results demonstrated that the intervention group participants who were at the precontemplation and contemplation stages at the pre-test each progressed further in the PA stages in the post-test(χ~2(1) = 112.06, p < 0.001; χ~2(1) = 118.76, p = 0.03, respectively), although no significant change in PA level was observed(t(139) < 1, p = 0.89).Conclusion: The results showed that awareness of the PA recommendations was associated with higher stages and levels of PA behavior, and a brief educational exposure to PA recommendations led to improved stages of PA behavior but no change in the levels of PA among Chinese college students. More effective public health campaign strategies are needed to promote the dissemination of the PA recommendations and to raise the awareness of the Chinese student population.展开更多
The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,...The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.展开更多
With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available fro...With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.展开更多
With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
基金supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre and NIHR Applied Research Collaboration East Midlands (ARC EM, (IS-BRC-1215-20010))funded by the National Institute for Health and Care Research Bristol Biomedical Research Centre (IS-BRC-1215-20011)supported by a UKRI research grant (EP/X042464/1)。
文摘Background Higher accelerometer-assessed volume and intensity of physical activity(PA)have been associated with a longer life expectancy but can be difficult to translate into recommended doses of PA.We aimed to:(a)improve interpretability by producing UK Biobank age-referenced centiles for PA volume and intensity;(b)inform public-health messaging by examining how adding recommended quantities of moderate and vigorous PA affect PA volume and intensity.Methods 92,480 UK Biobank participants aged 43-80 years with wrist-worn accelerometer data were included.Average acceleration and intensity gradient were derived as proxies for PA volume and intensity.We generated sex-specific centile curves using Generalized Additive Models for Location Scale and Shape(GAMLSS)and modeled the effect of adding moderate(walking)or vigorous(running)activity on the combined change in the volume and intensity centiles(change in PA profile).Results In men,volume was lower as age increased while intensity was lower after age 55;in women,both volume and intensity were lower as age increased.Adding 150 min of moderate PA weekly(5×30 min walking)increased the PA profile by 4 percentage points.Defining moderate PA as brisk walking approximately doubled the increase(9 percentage points)while 75 min of vigorous PA weekly(5×15 min running)trebled the increase(13 percentage points).Conclusion These UK Biobank reference centiles provide a benchmark for interpretation of accelerometer data.Application of our translational methods demonstrate that meeting PA guidelines through shorter duration vigorous activity is more beneficial to the PA profile(volume and intensity)than longer duration moderate activity.
基金partly supported by the China Scholarship Council (No. 201406010330)
文摘Background: Based on the transtheoretical model, the current study investigated whether awareness of physical activity(PA) recommendations had an impact on the stages of PA behavior change and levels of PA among Chinese college students.Methods: In Study 1, with a cross-sectional study design, 9826 students were recruited, and their knowledge of international PA recommendations,PA stage distribution, and self-reported PA level were surveyed. Pearson's χ2 test was used to test whether those participants who were aware and not aware of PA guidelines were equally distributed across the stages of PA behavior, and independent t test was conducted to test the group difference in the actual levels of PA. In Study 2, 279 students who were not aware of the PA recommendations were randomly allocated to either an intervention group or a control group, and only those in the intervention group were presented with international PA guidelines. In both groups,students' PA stages and PA level were examined before the test and then 4 months post-test. Mc Nemar's test for correlated proportions and repeated-measures analysis of variance were conducted to examine the changes in PA stage membership and PA level after the intervention.Results: Study 1 results revealed that only 4.4% of the surveyed students had correct knowledge of PA recommendations. Those who were aware of the recommendations were in later stages of PA behavior(χ~2(4) = 167.19, p < 0.001). They were also significantly more physically active than those who were not aware of the recommendations(t(443.71) = 9.00, p < 0.001, Cohen's d = 0.53). Study 2 results demonstrated that the intervention group participants who were at the precontemplation and contemplation stages at the pre-test each progressed further in the PA stages in the post-test(χ~2(1) = 112.06, p < 0.001; χ~2(1) = 118.76, p = 0.03, respectively), although no significant change in PA level was observed(t(139) < 1, p = 0.89).Conclusion: The results showed that awareness of the PA recommendations was associated with higher stages and levels of PA behavior, and a brief educational exposure to PA recommendations led to improved stages of PA behavior but no change in the levels of PA among Chinese college students. More effective public health campaign strategies are needed to promote the dissemination of the PA recommendations and to raise the awareness of the Chinese student population.
文摘The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global community.Health professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical settings.The Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health care.Healthcare Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have increased.The existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient reports.Patients’health can be continuously monitored using advanced WS that can be fitted or embedded in their bodies.This research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it accordingly.The recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard measures.Through a healthcare DSS,this framework is clever enough to detect and analyze a patient’s data.Experiment results further reveal that the proposed system is efficient and clever enough to deliver health care.The data reported in this study demonstrate the notion.This device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.
基金supported by the National Key Research and Development Program(No.2016YFB0800302)
文摘With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
