Prevalence of Metabolic Syndrome Based on Activity Type and Dietary Habits in Extremely Low-Income Individuals.

A high prevalence of metabolic syndrome (MS) and cardiovascular disease among low-income individuals has often been reported. However, there is still a lack of research on the relationship between basic livelihood security (BLS) and MS. This study investigated the prevalence of MS according to activity type, dietary habits, and the nutrient intake characteristics of individuals receiving BLS. Data from 14,803 men and 20,299 women were analyzed to assess the association between receiving BLS and MS. The associations between MS and various factors were analyzed separately in men and women by logistic regression analysis. In this cohort, 5.9% of men and 6.8% of women received BLS; of these, 46.9% and 47.7% had MS, respectively. High caloric intake, low-frequency breakfast consumption, and no nutritional education were associated with MS in both men and women. Among those with a low-frequency walking habit and strength training activity type, MS increased by 1.58 and 1.57 times in men and by 1.47 and 2.16 times in women, respectively. Men who were sedentary for 8 h or more had an increased risk of MS, but there was no association between these in women. BLS nutritional intake characteristics were high in carbohydrates and fat and low in dietary fiber and vitamin C (p < 0.05). In conclusion, establishing a healthy eating pattern through nutritional education and increasing walking and strength training may reduce the risk of MS.

Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose-response associations from the Prospective Physical Activity, Sitting and Sleep Consortium.

AIMS/HYPOTHESIS: The aim of this study was to examine the dose-response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. METHODS: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. RESULTS: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] -0.14 [-0.25, -0.02]) and 5 min/day stair climbing (-0.14 [-0.24, -0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose-response associations for any activity type or standing. There was an inverse dose-response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose-response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. CONCLUSIONS/INTERPRETATION: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification.

A longitudinal study on changes in weekend leisure time by age groups in Korea (1999-2019).

BACKGROUND: Data analysis was conducted on 20 years (1999-2019) of the Korean Time Use Survey (KTUS) to identify differences and characteristics among each types by extracting latent clusters of weekend leisure activities. METHODS: Using data from the KTUS, we classified weekend leisure time activities into 6 distinct categories. To identify the latent clusters for each year's KTUS data, we utilized latent profile analysis (LPA). Furthermore, independent samples t-tests and one-way ANOVA were conducted to investigate the characteristics of each latent cluster. RESULTS: As a result of leisure time analysis by survey period, media use accounted for the largest share in all three age groups. The results of the LPA, which included media, sports, culture, and tourism, revealed that the distribution of leisure time for these activities was lower throughout the entire study period. CONCLUSION: Based on the results of this study, it is recommended to explore constructive leisure activities and to develop policy measures to promote the domestic leisure industry and leisure consumption.

The link between disability and social participation revisited: Heterogeneity by type of social participation and by socioeconomic status.

BACKGROUND: While prior literature explores the impact of disability on social participation, the distinct characteristics of diverse social activities could further complicate this relationship. Furthermore, this relationship may exhibit heterogeneity when considering socioeconomic status (SES). OBJECTIVE: This study aims to investigate whether the relationship between disability and social participation differs depending on the type of social participation, and to what extent this relationship is moderated by SES. METHODS: Data from seven waves of the Korean Longitudinal Study of Ageing were analyzed. Various types of social participation, including socializing, leisure, volunteer, political, and religious activities, were considered. Individual fixed effects models were employed to account for unobserved individual-level heterogeneity. To investigate the potential moderating role of SES, an interaction term between disability and SES was included. RESULTS: Disability was associated with a decrease in social participation (b = -0.088). When differentiating types of social participation, the associations were negative for socializing and leisure activities (b = -0.092 and b = -0.012, respectively) and positive for volunteer activities (b = 0.012). The negative association between disability and social participation was generally stronger among higher-SES groups than lower-SES groups. Specifically, the negative association with leisure activities was more pronounced among the high-education groups. In contrast, the positive association with volunteer activities was more evident among the low-education group. CONCLUSIONS: Disability has a negative association with engagement in socializing and leisure activities and a positive association with engagement in volunteer activities. Policymakers should consider the role of SES in complicating the relationship between disability and social participation.

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women.

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

The Effects of Leisure Activities on Self-Efficacy and Social Adjustment: A Study of Immigrants in South Korea.

This study classified leisure activity types into active, passive, and social leisure activities based on theory, and focused on determining the type that has a significant influence on the self-efficacy and social adjustment of immigrants staying in South Korea. The results of multivariate analysis of variance (MANOVA), including post-hoc analysis using SPSS 23.0, were as follows: in principle, immigrants who participate in active or social leisure activities perceive their self-efficacy and social adjustment to be high. Differing slightly from this, the passive leisure activity type, which includes activities such as reading alone, listening to music, and surfing the web, may relieve their stress or provide them with psychological stability, but it was not found to be helpful in their adjustment to the new culture. The significance of this study lies in the finding that leisure activities help immigrants with social adjustment, in addition to physical and psychological aids that are already well known. We hope that the findings of the present study can be used as basic data for helping immigrants with smooth social adjustment and increasing their quality of life.

Simple Method for the Objective Activity Type Assessment with Preschoolers, Children and Adolescents.

Background: The objective and accurate assessment of children's sedentary and physical behavior is important for investigating their relation to health. The purpose of this study is to validate a simple and robust method for the identification of sitting, standing, walking, running and biking performed by preschool children, children and adolescents in the age from 3 to 16 years from a single thigh-worn accelerometer. Method: A total of 96 children were included in the study and all subjects followed a structured activity protocol performed in the subject's normal kindergarten or school environment. Thigh acceleration was measured using the Axivity AX3 (Axivity, Newcastle, UK) device. Method development and accuracy was evaluated by equally dividing the subjects into a development and test group. Results: The sensitivity and specificity for identifying sitting and standing was above 99.3% and for walking and running above 82.6% for all age groups. The sensitivity and specificity for identifying biking was above 85.8% for children and adolescents and above 64.8% for the preschool group using running bikes. Conclusion: The accurate assessment of sitting, standing, walking, running and biking from thigh acceleration and with children in the age range of 3 to 16 is valid, although not with preschool children using running bikes.

Using Accelerometer and GPS Data for Real-Life Physical Activity Type Detection.

This paper aims to examine the role of global positioning system (GPS) sensor data in real-life physical activity (PA) type detection. Thirty-three young participants wore devices including GPS and accelerometer sensors on five body positions and performed daily PAs in two protocols, namely semi-structured and real-life. One general random forest (RF) model integrating data from all sensors and five individual RF models using data from each sensor position were trained using semi-structured (Scenario 1) and combined (semi-structured + real-life) data (Scenario 2). The results showed that in general, adding GPS features (speed and elevation difference) to accelerometer data improves classification performance particularly for detecting non-level and level walking. Assessing the transferability of the models on real-life data showed that models from Scenario 2 are strongly transferable, particularly when adding GPS data to the training data. Comparing individual models indicated that knee-models provide comparable classification performance (above 80%) to general models in both scenarios. In conclusion, adding GPS data improves real-life PA type classification performance if combined data are used for training the model. Moreover, the knee-model provides the minimal device configuration with reliable accuracy for detecting real-life PA types.

The Key Factors in Physical Activity Type Detection Using Real-Life Data: A Systematic Review.

Background: Physical activity (PA) is paramount for human health and well-being. However, there is a lack of information regarding the types of PA and the way they can exert an influence on functional and mental health as well as quality of life. Studies have measured and classified PA type in controlled conditions, but only provided limited insight into the validity of classifiers under real-life conditions. The advantage of utilizing the type dimension and the significance of real-life study designs for PA monitoring brought us to conduct a systematic literature review on PA type detection (PATD) under real-life conditions focused on three main criteria: methods for detecting PA types, using accelerometer data collected by portable devices, and real-life settings. Method: The search of the databases, Web of Science, Scopus, PsycINFO, and PubMed, identified 1,170 publications. After screening of titles, abstracts and full texts using the above selection criteria, 21 publications were included in this review. Results: This review is organized according to the three key elements constituting the PATD process using real-life datasets, including data collection, preprocessing, and PATD methods. Recommendations regarding these key elements are proposed, particularly regarding two important PA classes, i.e., posture and motion activities. Existing studies generally reported high to near-perfect classification accuracies. However, the data collection protocols and performance reporting schemes used varied significantly between studies, hindering a transparent performance comparison across methods. Conclusion: Generally, considerably less studies focused on PA types, compared to other measures of PA assessment, such as PA intensity, and even less focused on real-life settings. To reliably differentiate the basic postures and motion activities in real life, two 3D accelerometers (thigh and hip) sampling at 20 Hz were found to provide the minimal sensor configuration. Decision trees are the most common classifier used in practical applications with real-life data. Despite the significant progress made over the past year in assessing PA in real-life settings, it remains difficult, if not impossible, to compare the performance of the various proposed methods. Thus, there is an urgent need for labeled, fully documented, and openly available reference datasets including a common evaluation framework.

Emission rates and the personal cloud effect associated with particle release from the perihuman environment.

Inhalation exposure to elevated particulate matter levels is correlated with deleterious health and well-being outcomes. Despite growing evidence that identifies humans as sources of coarse airborne particles, the extent to which personal exposures are influenced by particle releases near occupants is unknown. In a controlled chamber, we monitored airborne total particle levels with high temporal and particle-size resolution for a range of simulated occupant activities. We also sampled directly from the subject's breathing zone to characterize exposures. A material-balance model showed that a sitting occupant released 8 million particles/h in the diameter range 1-10 µm. Elevated emissions were associated with increased intensity of upper body movements and with walking. Emissions were correlated with exposure, but not linearly. The personal PM10 exposure increment above the room-average levels was 1.6-13 µg/m3 during sitting, owing to spatial heterogeneity of particulate matter concentrations, a feature that was absent during walking. The personal cloud was more discernible among larger particles, as would be expected for shedding from skin and clothing. Manipulating papers and clothing fabric was a strong source of airborne particles. An increase in personal exposure was observed owing to particle mass exchange associated with a second room occupant.

The Influence of Acute Physical Activity on Working Memory.

The effect of three types of physical activity on two types of working memory were investigated. Participants were 20 adult males who trained twice a week in volleyball two hours per session. Procedures included two pre and post intervention tests of working memory: the Digit span and Visual Memory Span subtests of the Wechsler Memory Scale-Revised. Interventions included tactical volleyball formation, body-weight resistance exercises, 15 minutes of running, and sub-maximal aerobic activity. Volleyball activity improved memory performance to a greater extent than the other two activities. Results indicate that immediately after acute exercise there is an increase in working memory function, more evident after physical activity in which cognitive functioning is inherent.

Movement prediction using accelerometers in a human population.

We introduce statistical methods for predicting the types of human activity at sub-second resolution using triaxial accelerometry data. The major innovation is that we use labeled activity data from some subjects to predict the activity labels of other subjects. To achieve this, we normalize the data across subjects by matching the standing up and lying down portions of triaxial accelerometry data. This is necessary to account for differences between the variability in the position of the device relative to gravity, which are induced by body shape and size as well as by the ambiguous definition of device placement. We also normalize the data at the device level to ensure that the magnitude of the signal at rest is similar across devices. After normalization we use overlapping movelets (segments of triaxial accelerometry time series) extracted from some of the subjects to predict the movement type of the other subjects. The problem was motivated by and is applied to a laboratory study of 20 older participants who performed different activities while wearing accelerometers at the hip. Prediction results based on other people's labeled dictionaries of activity performed almost as well as those obtained using their own labeled dictionaries. These findings indicate that prediction of activity types for data collected during natural activities of daily living may actually be possible.

Discourse types and (re)distribution of responsibility in simulated emergency team encounters.

Successful teamwork, constitutive of team talk, depends largely on shared responsibility in the coordination of tasks in a goal-oriented way. This paper examines how specific modes of talk or 'discourse types' are utilised by a healthcare team in simulated emergency care. The data corpus comprises six video-recorded simulation training sessions in an emergency department at a large Norwegian hospital. Our analysis focuses on the critical moment when the original healthcare team is joined by other specialists in an ad hoc manner, which necessitates the (re)distribution of expert responsibility in the management of the patient's condition. We examine the interactional trajectories and, in particular, the discourse types surrounding the critical moment which marks the incorporation of the new team members. The analysis centres on three discourse types (online commentary, offline commentary and metacommentary) that are utilised in accomplishing the multiple tasks in a collaborative and coordinated fashion. We suggest that team talk overlays and overlaps with distributed medical work in highly charged decision-making contexts such as emergency care. The findings have relevance for how healthcare professionals and students are trained in multidisciplinary team talk and teamwork.

Strain energy in the femoral neck during exercise.

Physical activity is recommended to mitigate the incidence of hip osteoporotic fractures by improving femoral neck strength. However, results from clinical studies are highly variable and unclear about the effects of physical activity on femoral neck strength. We ranked physical activities recommended for promoting bone health based on calculations of strain energy in the femoral neck. According to adaptive bone-remodeling theory, bone formation occurs when the strain energy (S) exceeds its homeostatic value by 75%. The potential effectiveness of activity type was assessed by normalizing strain energy by the applied external load. Tensile strain provided an indication of bone fracture. External force and joint motion data for 15 low- and high-load weight-bearing and resistance-based activities were used. High-load activities included weight-bearing activities generating a ground force above 1 body-weight and maximal resistance exercises about the hip and the knee. Calculations of femoral loads were based on musculoskeletal and finite-element models. Eight of the fifteen activities were likely to trigger bone formation, with isokinetic hip extension (ΔS=722%), one-legged long jump (ΔS=572%), and isokinetic knee flexion (ΔS=418%) inducing the highest strain energy increase. Knee flexion induced approximately ten times the normalized strain energy induced by hip adduction. Strain and strain energy were strongly correlated with the hip-joint reaction force (R(2)=0.90-0.99; p<0.05) for all activities, though the peak load location was activity-dependent. None of the exercises was likely to cause fracture. Femoral neck mechanics is activity-dependent and maximum isokinetic hip-extension and knee-flexion exercises are possible alternative solutions to impact activities for improving femoral neck strength.

Current status of physical activities among preschool children and relationship of activity type with bone mass / 中华健康管理学杂志

Objective To investigate physical activities of preschool children by gender and to explore the effects of activity type on bone indexes.Methods During 2009 and 2010,397 preschool children of 3-5 years old were randomly selected from 4 kindergartens in Chengdu Province of China.Ultrasound bone analyzer was used to assess children's bone mass.A physical activity questionnaire was completed by parents to evaluate physical activities at leisure time.Student's t test and least square regression were used for data analysis.Results Concerning activity types,boys spent more leisure time on running and Wushu than girls did (t values were 1.94 and 2.84,respectively ; both P ＜ 0.05).However,girls spent more time on dancing (0.78 h),jumping rope (0.08 h) and manual labour (0.22 h) each day (t values were-9.50,-3.43 and-1.92,respectively; all P ＜ 0.05).The weekly total exercise time and energy consumption per unit of body weight of girls vs.boys were 7.29 vs.6.51 h and 127.57 vs.113.85 kJ (t values were 2.63 and 2.04,respectively ; both P ＜ 0.05).About per day time on sleeping and per week time on watching television,there were no significant difference between boys and girls (t =0.180,0.520;P ＞0.05).But boys spent more time on electronic game and computer than girls (t =0.760,2.510;P ＜ 0.05).The normalized correlation coefficient for bone mass and moderate physical activities or jumping was 0.184 and 0.275,respectively (both P ＜ 0.05).Conclusions Our data suggest that preschool children's bone volume may be positively correlated with moderate physical activities and jumping activities.Introducing some moderate physical activities or activity appliance,toys and playing fields as well as increasing professional training might be helpful.