Patterns Of Movement Behaviours Research Articles

Association between movement behavior patterns and cardiovascular risk among Chinese adults aged 40–75: a sex-specific latent class analysis

BackgroundCardiovascular disease (CVD) is a major global health threat, particularly in China, contributing to over 40% of deaths. While sleep behaviors, sedentary behaviors, and physical activities are recognized as independent lifestyle risk factors for CVD, there remains limited understanding of specific movement behavior patterns and their CVD risks, especially considering sex-specific differences. This study examines movement behavior patterns among Chinese adults (40–75) and their associations with cardiovascular risk, with a focus on sleep, physical activity (PA), and sedentary behavior (SB).MethodsData pertaining to 13,465 male participants and 15,613 female participants, collected from the Chronic Disease and Risk Factor Surveillance Survey in Nanjing from February 2020 to December 2022. The latent class analysis method was employed to identify underlying movement patterns across sexes. Multinomial logistic regression models assessed CVD risk, and the China-PAR model calculated 10-year risk.ResultsThree male and four female movement patterns emerged. Active Movers (17.10% males, 5.93% females) adhered to PA recommendations but had poorer sleep quality. Moderate Achievers (61.42% males, 45.32% females) demonstrated moderate behavior. Sedentary Sleepers (21.48% males, 10.20% females) exhibited minimal PA but good sleep. Female Moderate Physical Activity (MPA) Dominant Movers demonstrated a prevalent adherence to recommended MPA levels. Active movers had the lowest CVD risk. After adjusting for potential confounders, moderate achievers (OR = 1.462, 95% CI 1.212, 1.764) and sedentary sleepers (OR = 1.504, 95% CI 1.211, 1.868) were both identified as being associated with a high-risk of cardiovascular diseases (CVDs) compared to active movers in males, demonstrating a similar trend for intermediate risk. Such associations were not statistically significant among females.ConclusionsOur study revealed sex-specific movement patterns associated with CVD risks among middle-aged Chinese adults. We suggest that adopting an active movement behavior pattern, characterized by meeting or exceeding recommended levels of vigorous physical activity (VPA) and reducing sedentary behavior, is beneficial for all middle-aged adults, particularly males. An active lifestyle could help counteract the adverse effects of relatively poor sleep quality on the risk of developing CVD in this population. Integrating sleep, PA, and SB information provides a holistic framework for understanding and mitigating CVD risks.

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health

BackgroundTraditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders.MethodsOur method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20–65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003–2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data.ResultsDeep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as “Early-morning movers” and the profile characterized as “Highest activity” both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the “Lowest activity” profile. No significant differences were observed for the “Least sedentary movers” profile compared to the “Lowest activity” profile.ConclusionsDeep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.

Factors related to high-risk movement behaviour in people with stroke who are highly sedentary and inactive

Purpose To identify Capabilities, Opportunities, and Motivational factors influencing movement behaviour throughout the day in people with stroke who are highly sedentary and inactive to enable intervention development. Methods A qualitative study was conducted using semistructured interviews with people with stroke. The interview guide was based on the Capabilities, Opportunities, and Motivation Behavioural model. Results Eleven interviews were conducted. Participants reported a lack of knowledge regarding healthy movement behaviour patterns, a lack of insight into their own movement behaviour, and some physical and cognitive limitations to engage in certain physical activities. Several social and environmental elements affecting movement behaviours were mentioned, their impact on movement behaviour varied among participants. Movement behaviour was mostly driven by habits and daily routine, without conscious regulation. Conclusion Our findings show that people with stroke are unaware of their own movement behaviour or of the consequences of these behaviours on health. Movement behaviour is, for the most part, based on daily routine and personal habits. This indicates the need for a behaviour change intervention. Such interventions will need to include providing information about healthy movement behaviour, feedback on individual’s movement behaviour and individualized support, taking into account the social and environmental context and personal capabilities.

Device-measured movement behaviours in over 20,000 China Kadoorie Biobank participants

BackgroundMovement behaviours, including physical activity, sedentary behaviour, and sleep have been shown to be associated with several chronic diseases. However, they have not been objectively measured in large-scale prospective cohort studies in low-and middle-income countries. We aim to describe the patterns of device-measured movement behaviours collected in the China Kadoorie Biobank (CKB) study.MethodsDuring 2020 and 2021, a random subset of 25,087 surviving CKB individuals participated in the 3rd resurvey of the CKB. Among them, 22,511 (89.7%) agreed to wear an Axivity AX3 wrist-worn triaxial accelerometer for seven consecutive days to assess their habitual movement behaviours. We developed a machine-learning model to infer time spent in four movement behaviours [i.e. sleep, sedentary behaviour, light intensity physical activity (LIPA), and moderate-to-vigorous physical activity (MVPA)]. Descriptive analyses were performed for wear-time compliance and patterns of movement behaviours by different participant characteristics.ResultsData from 21,897 participants (aged 65.4 ± 9.1 years; 35.4% men) were received for demographic and wear-time analysis, with a median wear-time of 6.9 days (IQR: 6.1–7.0). Among them, 20,370 eligible participants were included in movement behavior analyses. On average, they had 31.1 mg/day (total acceleration) overall activity level, accumulated 7.7 h/day (32.3%) of sleep time, 8.8 h/day (36.6%) sedentary, 5.7 h/day (23.9%) in light physical activity, and 104.4 min/day (7.2%) in moderate-to-vigorous physical activity. There was an inverse relationship between age and overall acceleration with an observed decline of 5.4 mg/day (17.4%) per additional decade. Women showed a higher activity level than men (32.3 vs 28.8 mg/day) and there was a marked geographical disparity in the overall activity level and time allocation.ConclusionsThis is the first large-scale accelerometer data collected among Chinese adults, which provides rich and comprehensive information about device-measured movement behaviour patterns. This resource will enhance our knowledge about the potential relevance of different movement behaviours for chronic disease in Chinese adults.

Agonism does not covary with territoriality in a gregarious reptile

Natural selection for territoriality is theorised to occur under conditions favouring intra-sexual phenotypic variation in physiology, morphology, and behaviour. In this context, certain suites of behavioural traits associated with territoriality are expected to consistently covary among individuals (sometimes referred to as ‘behavioural syndromes’) within sexes. Agonism (conflict-associated behaviours that may or may not be associated with physical aggression) and movement—for example, ranging, or relocation within or across seasons—are two behavioural components that are associated with territoriality and may be expected to covary in this context. Territorial males are expected to employ agonistic behaviours to actively establish and defend areas and resources and show more stability in their location across the landscape. However, the interaction between agonism and movement especially for wild reptiles has rarely been tested. We investigated whether agonistic and movement behaviours correlate at the individual level both within one year and across multiple years, in a wild population of Australian eastern water dragons, Intellagama lesueurii. Although both types of behaviours exhibited among-individual repeatability over year and multi-year scales, we found no evidence of an agonistic-movement behavioural syndrome. These findings indicate that agonistic and movement behaviours are likely independent traits, and thus, territoriality may not drive shared selective pressures for both. It is possible that other social behaviours and strategies are in place to maintain social structure in this wild population.Significance statementMales of many animals establish and maintain or defend territories. Territory defence is expected to be associated with behaviours associated with conflict or expressions of dominance, to signal territory boundaries to encroaching individuals, or more aggressive approaches for active defence. We investigated whether such agonistic behaviours correlated with the distances individuals ranged or dispersed within and between years, as an indicator of territoriality. Through thousands of observations of a wild population of Australian eastern water dragons, Intellagama lesueurii, we found that although individuals showed repeatable patterns of both agonistic and movement behaviours, we found no evidence that these were correlated. It appears that territory establishment and defence may therefore not rely on agonistic signals in this species in direct contrast to other taxa, indicating that other systems are in place to maintain social structures.

24-hour movement guideline adherence and mental health: A cross-sectional study of emerging adults with chronic health conditions and disabilities

BackgroundRecent work has shown that individuals with chronic health conditions and disabilities (CCD) meet the 24-h movement guidelines at lower rates than population norms; however, the evidence base remains limited across different stages of the lifespan and very few studies have examined associations with mental health outcomes. ObjectiveThis study examined 24-h movement guideline adherence among emerging adults with CCD compared to those without and associations between guideline adherence and indicators of mental health. MethodsThis cross-sectional study used data from the 2020 cycle of the Canadian Campus Wellbeing Survey. A total of 17,874 emerging adults enrolled at 20 post-secondary institutions (mean age = 21.6 ± 2.94 years; 65.2% female), including 3336 who identified with a CCD, self-reported their movement behaviors (physical activity, sedentary behaviors, sleep) and completed measures of psychological distress and mental well-being. Logistic regressions models were computed to examine differences in guideline adherence. Propensity score weighted linear regression models were computed to examine associations between guideline adherence and indicators of mental health. ResultsEmerging adults with CCD had significantly lower odds of meeting the 24-h movement guidelines than their peers, and disparities in guideline adherence were most pronounced among those with multimorbidity, developmental, and physical disabilities. Guideline adherence was associated with significantly more favorable scores for psychological distress and mental well-being among those with and without CCD. ConclusionsFindings suggest emerging adults with CCD engage in less healthy movement behavior patterns than their peers, yet they appear to experience similar mental health benefits when they do meet the 24-h movement guidelines.

Changes in Pediatric Movement Behaviors During the COVID-19 Pandemic by Stages of Lockdown in Ontario, Canada: A Longitudinal Cohort Study.

Children's movement behaviors have been affected by the COVID-19 pandemic; however, little is known regarding movement behavior patterns over time by government-issued lockdowns. Our primary objective was to evaluate how children's movement behaviors changed by stages of lockdown/reopening in Ontario, Canada, from 2020 to 2021. A longitudinal cohort study with repeated measures of exposure and outcomes was conducted. The exposure variables were dates from before and during COVID-19 when child movement behavior questionnaires were completed. Lockdown/reopening dates were included as knot locations in the spline model. The outcomes were daily screen, physical activity, outdoor, and sleep time. A total of 589 children with 4805 observations were included (53.1% boys, 5.9 [2.6]y). On average, screen time increased during the first and second lockdowns and decreased during the second reopening. Physical activity and outdoor time increased during the first lockdown, decreased during the first reopening, and increased during the second reopening. Younger children (<5y) had greater increases in screen time and lower increases in physical activity and outdoor time than older children (≥5y). Policy makers should consider the impact of lockdowns on child movement behaviors, especially in younger children.

Cluster analysis of microscopic spatio-temporal patterns of tourists’ movement behaviors in mountainous scenic areas using open GPS-trajectory data

Understanding spatio-temporal patterns of tourist movement behaviors is vital for Destination Management Departments (DMDs) on destination planning, marketing, and resource management. This study uses open GPS-trajectory data to analyze the microscopic spatio-temporal patterns of tourists' movement behaviors in Mount Huashan in China. Two major measures, Markov chains and cluster analysis, are used to cluster tourists into groups to show their spatio-temporal movement behaviors within the study site. The Markov chain analysis unveiled three kinds of spatial patterns on microscopic tourists’ movement: “proximity transfer”, “span transfer” and “hub transfer”, while the cluster analysis further demonstrated three kinds of microscopic spatio-temporal patterns: “day-climbing”, “night-climbing”, and “full-day sightseeing”. These results provide vital implications for tourism management of the mountainous scenic areas in general and in Mount Huashan in particular.

Movement guidelines for young children: Engaging stakeholders to design dissemination strategies in the Hong Kong early childhood education context.

Early childhood is a critical period during which patterns of movement behaviors are formed. The World Health Organization had endorsed guidelines for physical activity, sedentary behavior and sleep over a 24-h time period, which had been adopted by the Center for Health Protection of Hong Kong. This paper reports on stakeholder engagements that were conducted to inform the design of strategies to disseminate the guidelines in early childhood education (ECE) settings. Using a mixed-methods study design, we sought to (a) assess the stakeholders' levels of awareness and knowledge of the Hong Kong movement guidelines for young children and (b) identify the factors that influence the uptake of the said guidelines. We conducted an online survey of early childhood education teachers (N =314), twelve focus groups involving teachers (N = 18) and parents (N = 18), and individual interviews of key informants (N = 7) and domestic workers who provide care for preschool-aged children (N = 7). Descriptive statistics were used for the quantitative data, and thematic analysis was performed on the qualitative data using an inductive and semantic approach following a realist framework. Our findings show that teachers were aware of the movement guidelines for young children, but their knowledge of the specific guidelines was deficient; parents and domestic workers had limited awareness and knowledge of the guidelines. Uptake of the movement guidelines is enabled by parent engagement, activities in the ECE centers, home-school cooperation, and community activities for children. The challenges include the time poverty of parents, local curriculum requirements, limited physical spaces, social values, and pandemic-related restrictions. We recommend that dissemination strategies in the ECE context should deliver knowledge content and support stakeholders in mitigating the challenges associated with time, space, and social conditions.

Daily Patterns of Accelerometer-Measured Movement Behaviors in Glaucoma Patients: Insights From UK Biobank Participants.

The purpose of this study is to compare daily patterns of accelerometer-measured movement behaviors between glaucoma patients and those without glaucoma. From 2013 to 2015, 106,053 UK Biobank participants took part in a 7-day accelerometer test. Based on established algorithms, continuous accelerometer data were classified into 4 movement behaviors: moderate to vigorous physical activity (MVPA), light physical activity, sedentary behaviors, and sleep. Glaucoma and other covariates were defined according to baseline assessments and inpatient diagnosis records. Negative binomial regression models were used to compare daily patterns of movement behaviors between glaucoma patients and those without glaucoma. Accelerometer data from 1262 glaucoma patients and 81,551 participants without glaucoma were included. Compared with participants without glaucoma, glaucoma patients spent 4.7% less time on MVPA in multivariable models [mean=28.3 vs 31.4min/d; incidence-rate ratio (IRR) 0.953, 95% confidence interval (CI): 0.910-0.998; P=0.044], which was mainly attributed to the decreased MVPA time during 18:00-23:59 (IRR=0.863, Bonferroni-corrected 95% CI: 0.768-0.970; P=0.002). Subgroup analyses indicated that compared with those with normal body mass index, the decreased MVPA time was more pronounced in participants with overweight and obesity (IRR=0.912, Bonferroni-corrected 95% CI: 0.851-0.978; P for interaction=0.007). No significant association was found between glaucoma and time spent on other movement behaviors including light physical activity, sedentary behaviors, and sleep. Daily patterns of movement behaviors were significantly changed in glaucoma patients. Compared with those without glaucoma, glaucoma patients spent less time on MVPA, especially in the evening.

Cross-sectional associations between 24-h movement guideline adherence and suicidal thoughts among Canadian post-secondary students

Suicide is one of the leading causes of death among post-secondary students. It is, therefore, imperative that we identify behavioral risk factors that can have protective effects in preventing against suicide. This study examined whether different combinations of adherence to the Canadian 24-Hour Movement Guidelines for Adults (i.e., physical activity, sedentary behavior, sleep) were associated with reduced odds of suicidal ideation and planning among emerging adults attending post-secondary education. This cross-sectional observational study used data from the first cycle of the Canadian Campus Wellbeing Survey, which included a total of 17,633 students (Mean age = 21.7 ± 2.92 years; 67.1% female, 31.5% male, 1.4% other) enrolled in 20 Canadian post-secondary institutions. Logistic regression models with covariate balanced propensity score weighting were computed. Suicidal ideation and suicidal planning were reported by 14.4% and 4.9% of students, respectively. Adherence to the sleep (OR = 0.71; 95% CI: 0.58–0.86), physical activity and sleep (OR = 0.74; 95% CI: 0.62–0.88), sedentary behavior and sleep guidelines (OR = 0.64; 95% CI: 0.45–0.90) as well as concurrent adherence to all three guidelines (OR = 0.58; 95% CI: 0.44–0.75) were associated with significantly reduced odds of suicidal ideation. Among students who reported suicidal ideation, only adherence to the physical activity and sleep guidelines (OR = 0.67; 95% CI: 0.48–0.96) was associated with suicidal planning. Collectively, findings suggest the promotion of healthy movement behavior patterns may be a promising avenue for broad suicide and mental health prevention efforts on campus, although the potency of these effects may not extend to more severe suicidal thoughts.

The longitudinal association between movement behavior patterns and the course of participation up to one year after stroke

Purpose (1) To investigate the differences in the course of participation up to one year after stroke between distinct movement behavior patterns identified directly after discharge to the home setting, and (2) to investigate the longitudinal association between the development of movement behavior patterns over time and participation after stroke. Materials and methods 200 individuals with a first-ever stroke were assessed directly after discharge to the home setting, at six months and at one year. The Participation domain of the Stroke Impact Scale 3.0 was used to measure participation. Movement behavior was objectified using accelerometry for 14 days. Participants were categorized into three distinct movement behavior patterns: sedentary exercisers, sedentary movers and sedentary prolongers. Generalized estimating equations (GEE) were performed. Results People who were classified as sedentary prolongers directly after discharge was associated with a worse course of participation up to one year after stroke. The development of sedentary prolongers over time was also associated with worse participation compared to sedentary exercisers. Conclusions The course of participation after stroke differs across distinct movement behavior patterns after discharge to the home setting. Highly sedentary and inactive people with stroke are at risk for restrictions in participation over time. Implications for rehabilitation The course of participation in people with a first-ever stroke up to one year after discharge to the home setting differed based on three distinct movement behavior patterns, i.e., sedentary exercisers, sedentary movers and sedentary prolongers. Early identification of highly sedentary and inactive people with stroke after discharge to the home setting is important, as sedentary prolongers are at risk for restrictions in participation over time. Supporting people with stroke to adapt and maintain a healthy movement behavior after discharge to the home setting could prevent potential long-term restrictions in participation.

There Is a Clinical Need to Consider the Physical Activity: Sedentary Pattern in Children with Obesity – Position Paper of the European Childhood Obesity Group

Introduction: While international prevention guidelines recently advocated, in addition to moderate and vigorous physical activity (MVPA) guidelines, for a minimization of sedentary (SED) time, recommendations remain to be developed for youths with obesity. Methods: A literature search was conducted in PubMed, the Cochrane Library, plus the reference lists of selected articles for relevant publications in English, including original papers, systematic reviews, and meta-analyses, with search terms “sedentary behaviors” or “sedentary time” or “screen time” AND “children” or “adolescents” AND “obesity” or “adiposity” or “cardiometabolic risk” or “cardiometabolic disease.” The results were summarized as a narrative review and presented to the scientific board of the European Childhood Obesity Group (ECOG), who then discussed their implication in clinical practice and proposed the position outlined in this paper. Results: SED and screen times are associated with adiposity and cardiometabolic risks, independently of youths’ physical activity (PA) level. Besides considering MVPA and SED times as separate variables, comprehensive studies have questioned the impact of different patterns of MVPA and SED levels. Although lower body adiposity and better cardiometabolic health are achieved among those with desirable movement behavior patterns (i.e., more MVPA/less SED or active/not SED), youths with intermediate patterns (i.e., high MVPA/high SED and low MVPA/low SED, or active/SED and inactive/not SED) have been found to be associated with intermediate risks. Conclusion: There is a need to decrease SED behaviors irrespective of MVPA and to consider PA-SED patterns in youth with obesity. The ECOG encourages anti-obesity strategies targeting both PA and SED behaviors to support the shift from long periods of SED time, especially screen time, to daily routines incorporating bouts of PA. Stepwise or sequential approaches to movement behavior counseling might start with targeting SED at first to decrease cardiometabolic risks when implementing MVPA is not yet possible.

Network Analysis Using Markov Chain Applied to Wildlife Habitat Selection

In the present study, behavioral states for habitat selection are examined using a discrete-time Markov chain (DTMC) combined with a network model with wildlife movement data. Four male boars (Sus scrofa Linnaeus) at the Bukhansan National Park in South Korea were continuously tracked with an interval of approximately 2 h to 313 days from June 2018 to May 2019. The time-series movement positions were matched with covariates of environmental factors (leaf types and water) in field conditions. Stationary probabilities were used to quantify the habitat selection preference of wild boars, including maximum probability (0.714) with the “broadleaf without water habitat” where in-degree centrality was at its maximum (0.54), but out-degree centrality was low and even (0.17) for all states. Betweenness was the maximum for the “needleleaf without water habitat”, suggesting its role as a bridging habitat between other habitats. Out-closeness scores presented the highest values in the “broadleaf without water habitat” (0.26). Similarly, the first hitting time to the habitat was shortest at the “broadleaf without water habitat” (3.64–5.16 h) and slightly longer than one day in other examined habitats, including “broadleaf with water,” “needleleaf without water,” and “no-leaf without water”. The network model using the Markov chain provided information on both local movement behavior and general resource-use patterns of wild boars in field conditions.

Movement behavior patterns composition remains stable, but individuals change their movement behavior pattern over time in people with a first-ever stroke

BackgroundMovement behaviors (i.e., physical activity levels, sedentary behavior) in people with stroke are not self-contained but cluster in patterns. Recent research identified three commonly distinct movement behavior patterns in people with stroke. However, it remains unknown if movement behavior patterns remain stable and if individuals change in movement behavior pattern over time.Objectives1) To investigate the stability of the composition of movement behavior patterns over time, and 2) determine if individuals change their movement behavior resulting in allocation to another movement behavior pattern within the first two years after discharge to home in people with a first-ever stroke.MethodsAccelerometer data of 200 people with stroke of the RISE-cohort study were analyzed. Ten movement behavior variables were compressed using Principal Componence Analysis and K-means clustering was used to identify movement behavior patterns at three weeks, six months, one year, and two years after home discharge. The stability of the components within movement behavior patterns was investigated. Frequencies of individuals’ movement behavior pattern and changes in movement behavior pattern allocation were objectified.ResultsThe composition of the movement behavior patterns at discharge did not change over time. At baseline, there were 22% sedentary exercisers (active/sedentary), 45% sedentary movers (inactive/sedentary) and 33% sedentary prolongers (inactive/highly sedentary). Thirty-five percent of the stroke survivors allocated to another movement behavior pattern within the first two years, of whom 63% deteriorated to a movement behavior pattern with higher health risks. After two years there were, 19% sedentary exercisers, 42% sedentary movers, and 39% sedentary prolongers.ConclusionsThe composition of movement behavior patterns remains stable over time. However, individuals change their movement behavior. Significantly more people allocated to a movement behavior pattern with higher health risks. The increase of people allocated to sedentary movers and sedentary prolongers is of great concern. It underlines the importance of improving or maintaining healthy movement behavior to prevent future health risks after stroke.

People with Stroke Are Most Sedentary in the Afternoon and Evening

Questions: What are the daily temporal patterns of movement behaviours (sedentary time, light-intensity physical activity, and moderate-vigorous physical activity) in people with stroke? Do daily temporal patterns of sedentary time differ (a) between subgroups of people with different movement behaviour classifications and (b) over time during the first year after stroke? Design: This study represents secondary exploratory analyses from an observational, longitudinal cohort study (n = 197). Participants: This study included people with first-ever stroke recruited from 4 hospitals in the Netherlands. Outcome Measures: Movement behaviour was objectively measured using the Activ8 activity monitor within 3 weeks after hospital discharge and again at 6 and 12 months later. Results: Participants spent the least time sedentary in the morning with proportionally more sedentary time as the day progressed with maximal sedentary time seen in the evening hours. This pattern did not substantially change over time. Sedentary prolongers spent significantly more absolute time sedentary for each hour of the day, but the daily temporal pattern of sedentary time did not differ between this group and either “sedentary movers” or “sedentary exercisers.” Conclusion: People living at home after stroke are highly sedentary, particularly in the afternoons and evenings, and this pattern does not change during the first year after stroke. Clinicians should encourage people with stroke to find meaningful tasks to do during the day to reduce their sitting time. Researchers developing interventions to encourage people to sit less should include particular focus on the afternoon and evening time periods.

Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects.

Background and hypothesisNo objective tests are currently available to help diagnosis of major psychiatric disorders. This study evaluates the potential of eye movement behavior patterns to predict schizophrenia subjects compared to those with major affective disorders and control groups.Study designEye movements were recorded from a training set of UK subjects with schizophrenia (SCZ; n = 120), bipolar affective disorder (BPAD; n = 141), major depressive disorder (MDD; n = 136), and healthy controls (CON; n = 142), and from a hold-out set of 133 individuals with proportional group sizes. A German cohort of SCZ (n = 60) and a Scottish cohort of CON subjects (n = 184) acted as a second semi-independent test set. All patients met DSMIV and ICD10 criteria for SCZ, BPAD, and MDD. Data from 98 eye movement features were extracted. We employed a gradient boosted (GB) decision tree multiclass classifier to develop a predictive model. We calculated the area under the curve (AUC) as the primary performance metric.Study resultsEstimates of AUC in one-versus-all comparisons were: SCZ (0.85), BPAD (0.78), MDD (0.76), and CON (0.85). Estimates on part-external validation were SCZ (0.89) and CON (0.65). In all cases, there was good specificity but only moderate sensitivity. The best individual discriminators included free viewing, fixation duration, and smooth pursuit tasks. The findings appear robust to potential confounders such as age, sex, medication, or mental state at the time of testing.ConclusionsEye movement patterns can discriminate schizophrenia from major mood disorders and control subjects with around 80% predictive accuracy.

Meeting 24-h Movement Guidelines is Related to Better Academic Achievement: Findings from the YRBS 2019 Cycle

This research is designed to investigate the relationship between the 24-h movement guidelines (24-HMG) and self-reported academic achievement (AA) using nationally representative data derived from the 2019 U.S. National Youth Risk Behaviour Survey. A multiple-stage cluster sampling procedure has been adopted to ensure a representative sample (N = 9127 adolescents; mean age = 15.7 years old; male% = 49.8%). Logistic regression has been adopted to obtain the odds ratio (OR) regarding the associations between adherence to 24-HMG and AA while controlling for ethnicity, body mass index, sex and age. The prevalence of meeting the 24-h movement guidelines in isolation and combination varied greatly (physical activity = 23.3%, screen time = 32.5%, sleep = 22.3%, and 24-HMG = 2.8%), while the percentage of highest-class AA was 42.5%. Compared with the situation when none of 24-HMG is met, the achievement of any of the combined guidelines (except for meeting the physical activity guidelines) was significantly associated with higher odds of achieving first-class AA. Meeting the sleep guideline had 1.42 times increased likelihood to achieve highest-class AA as compared with not meeting the sleep guideline. Meeting screen time guidelines and physical activity guidelines, respectively, were 1.32 and 1.13 times more likely to report first-class AA; but meeting the guidelines of physical activity was not significantly related to AA. Meeting the 24-HMG had the highest odds of achieving first-class AA (OR = 2.01, 95%CI: 1.47– 2.73). In both sexes, adolescents who met 24-HMG self-reported better AA (boys OR = 2.05, 95%CI: 1.34–3.15; girls OR = 2.26, 95%CI: 1.36–3.76). Significant relationships were observed in adolescents from 9–10th grade, but not higher grades. Our research findings suggest that optimal movement behaviours can be seen as an important This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. DOI: 10.32604/IJMHP.2021.017660 ARTICLE Tech Science Press Published Online: 26 October 2021 element to better academic achievement among U.S. adolescents. Future studies can adopt our discoveries to promote adolescents’ academic achievement through implementing optimal 24-h movement behaviour patterns.

A comparison of children's diet and movement behaviour patterns derived from three unsupervised multivariate methods.

BackgroundBehavioural patterns are typically derived using unsupervised multivariate methods such as principal component analysis (PCA), latent profile analysis (LPA) and cluster analysis (CA). Comparability and congruence between the patterns derived from these methods has not been previously investigated, thus it’s unclear whether patterns from studies using different methods are directly comparable. This study aimed to compare behavioural patterns derived across diet, physical activity, sedentary behaviour and sleep domains, using PCA, LPA and CA in a single dataset.MethodsParent-report and accelerometry data from the second wave (2011/12; child age 6-8y, n = 432) of the HAPPY cohort study (Melbourne, Australia) were used to derive behavioural patterns using PCA, LPA and CA. Standardized variables assessing diet (intake of fruit, vegetable, sweet, and savoury discretionary items), physical activity (moderate- to vigorous-intensity physical activity [MVPA] from accelerometry, organised sport duration and outdoor playtime from parent report), sedentary behaviour (sedentary time from accelerometry, screen time, videogames and quiet playtime from parent report) and sleep (daily sleep duration) were included in the analyses. For each method, commonly used criteria for pattern retention were applied.ResultsPCA produced four patterns whereas LPA and CA each generated three patterns. Despite the number and characterisation of the behavioural patterns derived being non-identical, each method identified a healthy, unhealthy and a mixed pattern. Three common underlying themes emerged across the methods for each type of pattern: (i) High fruit and vegetable intake and high outdoor play (“healthy”); (ii) poor diet (either low fruit and vegetable intake or high discretionary food intake) and high sedentary behaviour (“unhealthy”); and (iii) high MVPA, poor diet (as defined above) and low sedentary time (“mixed”).ConclusionWithin this sample, despite differences in the number of patterns derived by each method, a good degree of concordance across pattern characteristics was seen between the methods. Differences between patterns could be attributable to the underpinning statistical technique of each method. Therefore, acknowledging the differences between the methods and ensuring thorough documentation of the pattern derivation analyses is essential to inform comparison of patterns derived through a range of approaches across studies.

Informing wetland management with waterfowl movement and sanctuary use responses to human-induced disturbance

Long-term environmental management to prevent waterfowl population declines is informed by ecology, movement behavior and habitat use patterns. Extrinsic factors, such as human-induced disturbance, can cause behavioral changes which may influence movement and resource needs, driving variation that affects management efficacy. To better understand the relationship between human-based disturbance and animal movement and habitat use, and their potential effects on management, we GPS tracked 15 dabbling ducks in California over ~4-weeks before, during and after the start of a recreational hunting season in October/November 2018. We recorded locations at 2-min intervals across three separate 24-h tracking phases: Phase 1) two weeks before the start of the hunting season (control (undisturbed) movement); Phase 2) the hunting season opening weekend; and Phase 3) a hunting weekend two weeks after opening weekend. We used GLMM models to analyze variation in movement and habitat use under hunting pressure compared with ‘normal’ observed patterns prior to commencement of hunting. We also compared responses to differing levels of disturbance related to the time of day (high - shooting/~daytime); moderate - non-lethal (~crepuscular); and low - night). During opening weekend flight (% time and distance) more than doubled during moderate and low disturbance and increased by ~50% during high disturbance compared with the pre-season weekend. Sanctuary use tripled during moderate and low disturbance and increased ~50% during high disturbance. Two weeks later flight decreased in all disturbance levels but was only less than the pre-season levels during high disturbance. In contrast, sanctuary use only decreased at night, although not to pre-season levels, while daytime doubled from ~45% to >80%. Birds adjust rapidly to disturbance and our results have implications for energetics models that estimate population food requirements. Management would benefit from reassessing the juxtaposition of essential sanctuary and feeding habitats to optimize wetland management for waterfowl.