Patterns Of Movement Behaviours Research Articles

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.

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

Background: At present there are no objective tests or biomarkers in routine use that can help with the diagnosis of major mental disorders. This study aimed to determine the potential value of eye movement behaviour patterns for distinguishing individuals with schizophrenia compared to those with major affective disorders and control groups. Methods: Using a boosted decision tree multiclass classifier in a machine learning framework, we compared eye movement viewing patterns in a training set of UK subjects with Schizophrenia (SCZ) n=120, Bipolar affective disorder (BPAD) n=141, Major depressive disorder (MDD) n=136 and control (CON) n=142 group. A total of 133 individuals with group ratios similar to the training set acted as a hold-out test set for internal validation and a German cohort of SCZ (n=60) and a Scottish cohort of CON subjects (n=184) were used as a semi-independent test set for external validation. Findings: Based on internal validation, estimates of the area under the curve (AUC) for the one-versus-all comparisons for SCZ (0.85), BPAD (0.78), MDD (0.76) and CON (0.85) (with an overall estimate of 0.81) suggested high levels of discriminability between groups. The estimates of AUCs for the one-versus-one comparisons were also reasonably high; SCZ v CON 0.84, SCZ v BPAD 0.82, SCZ v MDD 0.76. The classifier showed good discrimination of BPAD and MDD from CON with AUCs 0.85 and 0.82, respectively; however, the performance was relatively lower between BPAD versus MDD with AUC 0.69. The estimates of AUC based on external validation were comparable: 0.89 for German SCZ and 0.65 for Scottish CON. The best individual discriminators included free viewing, fixation duration and smooth pursuit tasks. Our findings appear robust in the presence of potential confounders such as age, sex, medication or mental state at the time of testing. Interpretation: Eye movement behaviour patterns have good power to discriminate schizophrenia from both the major mood disorders and control subjects. The performance characteristics are within the range required of diagnostic biomarkers. Our findings are consistent with the Kraepelinian dichotomy of the functional psychoses. Funding: Royal Society of London, Chief Scientist Office Scotland (CZB/4/734), NHS Grampian,Tenovus Scotland (G12/31), Miller MacKenzie Trust, EU-FP6 (SGENE) and Health Innovation Challenge Fund, Wellcome Trust and Department of Health (WT-103911/Z/14/Z Declaration of Interest: Philip Benson and David St Clair are co-founders of SACCADE Diagnostics Ltd a spin out company tasked to develop eye movement technology to assist diagnosis of major mental health disorders. The University of Aberdeen has patents pending in Europe (PCT/GB2013/050016) and USA (14/370,611). The data reported in this paper arose solely from the acknowledged UK research bodies and charities none of whom have vested interests in the company. There are otherwise no competing interests. Ethical Approval: The Scottish and German studies obtained full multiregional ethics committee (MREC) and institutional review board (IRB) approvals respectively and were conducted in accordance with the Declaration of Helsinki.

The course of physical functioning in the first two years after stroke depends on peoples' individual movement behavior patterns.

Background and purposeDeterioration of physical functioning after stroke in the long term is regarded as a major problem. Currently, the relationship between “peoples'” movement behavior patterns (the composition of sedentary behavior and physical activity during waking hours) directly after stroke and the development of physical functioning over time is unknown. Therefore, the objectives of this study were to investigate (1) the course of physical functioning within the first two years after returning home after stroke, and (2) the association between physical functioning and baseline movement behavior patterns.MethodIn the longitudinal RISE cohort study, 200 persons with a first-ever stroke discharged to the home-setting were included. Participants’ physical functioning was assessed within three weeks, at six months, and one and two years after discharge using the Stroke Impact Scale (SIS) 3.0 subscale physical and the five-meter walk test (5MWT). Three distinct movement behavior patterns were identified in a previous study at baseline and were used in the current study: (1) sedentary exercisers (sufficiently active and 64% of waking hours sedentary), (2) sedentary movers’ (inactive and 63% of waking hours sedentary), and (3) sedentary prolongers (inactive and >78% of waking hours sedentary accumulated in long prolonged bouts). The association between movement behavior patterns and the course of physical functioning was determined using longitudinal generalized estimating equations analyses.ResultsOverall participants’ physical functioning increased between discharge and six months and declined from six months up to two years. Physical functioning remained stable during the first two years after stroke in sedentary exercisers. Physical functioning improved during the first six months after discharge in sedentary movers and sedentary prolongers and deteriorated in the following six months. Only physical functioning (SIS) of sedentary prolongers further declined from one up to two years. A similar pattern was observed in the 5MWT.ConclusionMovement behavior patterns identified directly after returning home in people with stroke are associated with and are predictive of the course of physical functioning. Highly sedentary and inactive people with stroke have unfavorable outcomes over time than individuals with higher amounts of physical activity.

Identifying patterns of movement behaviours in relation to depressive symptoms during adolescence: A latent profile analysis approach

Movement behaviour guideline adherence has been associated with lower depressive symptoms during adolescence, yet no studies have used person-centered approaches to examine this relationship. The purpose of the present study was to identify whether unique adolescent movement behaviour profiles exist, evaluate predictors of profile membership, and determine whether profile membership was associated with differences in depressive symptoms cross sectionally and longitudinally. This study involved secondary analysis of the public-use data from Wave 1 and Wave 2 of the National Study of Adolescent Health. Adolescents (N = 6436; 48% male) in grades 7 to 12 (Mage = 16.03 ± 1.75) completed measures to assess moderate-to-vigorous physical activity (MVPA), recreational screen time (ST), and sleep – collectively known as movement behaviours – and depressive symptoms. Latent profile analysis identified four profiles that had similar sleep patterns and were thus characterized by different levels of MVPA and ST: high MVPA/low ST (29%), high MVPA/high ST (4%), low MVPA/low ST (53%), and low MVPA/high ST (14%). Several socio-demographic variables were found to influence profile membership. After adjusting for covariates, findings revealed depressive symptoms were lowest among the high MVPA/low ST profile and this trend was evident one year later. Engaging in high levels of either MVPA or ST alone did not provide additive benefits for depressive symptoms compared to those who engaged in low levels of both MVPA and ST. These findings suggest intervention efforts should take an integrative approach to improve mental health outcomes among adolescents by considering each of the movement behaviours concurrently.

Movement behaviour patterns in patients with hip and/or knee osteoarthritis in the physical therapy setting: a cross-sectional study

BackgroundOsteoarthritis is one of the most common chronic joint diseases, mostly affecting the knee or hip through pain, joint stiffness and decreased physical functioning in daily life. Regular physical activity (PA) can help preserve and improve physical functioning and reduce pain in patients with osteoarthritis. Interventions aiming to improve movement behaviour can be optimized by tailoring them to a patients’ starting point; their current movement behaviour. Movement behaviour needs to be assessed in its full complexity, and therefore a multidimensional description is needed.ObjectivesThe aim of this study was to identify subgroups based on movement behaviour patterns in patients with hip and/or knee osteoarthritis who are eligible for a PA intervention. Second, differences between subgroups regarding Body Mass Index, sex, age, physical functioning, comorbidities, fatigue and pain were determined between subgroups.MethodsBaseline data of the clinical trial ‘e-Exercise Osteoarthritis’, collected in Dutch primary care physical therapy practices were analysed. Movement behaviour was assessed with ActiGraph GT3X and GT3X+ accelerometers. Groups with similar patterns were identified using a hierarchical cluster analysis, including six clustering variables indicating total time in and distribution of PA and sedentary behaviours. Differences in clinical characteristics between groups were assessed via Kruskall Wallis and Chi2 tests.ResultsAccelerometer data, including all daily activities during 3 to 5 subsequent days, of 182 patients (average age 63 years) with hip and/or knee osteoarthritis were analysed. Four patterns were identified: inactive & sedentary, prolonged sedentary, light active and active. Physical functioning was less impaired in the group with the active pattern compared to the inactive & sedentary pattern. The group with the prolonged sedentary pattern experienced lower levels of pain and fatigue and higher levels of physical functioning compared to the light active and compared to the inactive & sedentary.ConclusionsFour subgroups with substantially different movement behaviour patterns and clinical characteristics can be identified in patients with osteoarthritis of the hip and/or knee. Knowledge about these subgroups can be used to personalize future movement behaviour interventions for this population.Trial registrationDutch clinical trial registration number of e-Exercise Osteoarthritis: NTR4224.

소프트볼 타자의 타격 수행 시 숙련성에 따른 시각탐색 전략 및 선구안 능력 차이

Purpose: The purpose of this study is to classify female softball players into two groups (high batting average and low batting average) and to identify characteristics of gaze behavior patterns and predict the pitch of the ball.<BR> Method: For the study, a total of 16 softball players (8 high, 8 low average) participated in the experiment. The experiment challenged the actual pitcher throwing the ball at a distance of 11.44m, with 15 hits per player. After hitting each ball, the task was to verbally express what pitcher threw. Players’ eye movement behavior patterns were measured using Tobii’s wireless eye movement tracking equipment.<BR> Results: In order to examine the difference in the actual blow performance of the athletes, the analysis was conducted by dividing into preparation action-stretch action (1 section), power position-release (2 sections), and pitching-batter swing (3 sections). In the two groups, the number of fixations was statistically different in all three sections, and the gaze fixation duration was found in the first and second sections. In the task of identifying the pitch of the ball, there was a difference between high and low batting averages groups.<BR> Conclusion: According to the skill of softball players, the results will be meaningful in providing basic information on how gaze behavior patterns appear and how visual information is used.

Friendship prediction model based on factor graphs integrating geographical location

With the development of network services and location-based systems, many mobile applications begin to use users’ geographical location to provide better services. In terms of social networks, geographical location is actively shared by users. In some applications with recommendation services, before the geographical location recommendation is provided, the authors have to obtain user's permission. This kind of social network integrated with geographical location information is called location-based social networks (abbreviate for LBSNs). In the LBSN, each user has location information when he or she checked in hotels or feature spots. Based on this information, they can identify user's trajectory of movement behaviour and activity patterns. In general, if there is friendship between two users, their trajectories in reality are likely to be similar. In this study, according to user's geographical location information over a period of time, they explore whether there exists friendly relationship between two users based on trajectory similarity and the structure theory of graphs. In particular, they propose a new factor function and a factor graph model based on user's geographical location to predict the friendship between two users in the real LBSN.

Natural Patterns of Sitting, Standing and Stepping During and Outside Work-Differences between Habitual Users and Non-Users of Sit-Stand Workstations.

Sit–stand workstations have shown to reduce sitting time in office workers on a group level. However, movement behaviour patterns might differ between subgroups of workers. Therefore, the objective of this study was to examine sitting, standing and stepping outcomes between habitual users and non-users of sit–stand workstations. From an international office population based in the Netherlands, 24 users and 25 non-users of sit–stand workstations were included (all had long-term access to these workstations). Using the ActivPAL, sitting, standing and stepping were objectively measured during and outside working hours. Differences in outcomes between users and non-users were analysed using linear regression. During working hours, users sat less (−1.64; 95% IC= −2.27–−1.01 h/8 h workday) and stood more (1.51; 95% IC= 0.92–2.10 h/8 h workday) than non-users. Attenuated but similar differences were also found for total sitting time over the whole week. Furthermore, time in static standing bouts was relatively high for users during working hours (median= 0.56; IQR = 0.19−1.08 h/8 h workday). During non-working hours on workdays and during non-working days, no differences were found between users and non-users. During working hours, habitual users of their sit–stand workstation sat substantially less and stood proportionally more than non-users. No differences were observed outside working hours, leading to attenuated but similar differences in total sitting and standing time between users and non-users for total days. This indicated that the users of sit–stand workstations reduced their sitting time at work, but this seemed not to be accompanied by major carry-over or compensatory effects outside working hours.

Promoting healthy movement behaviours among children during the COVID-19 pandemic

Promoting healthy movement behaviours among children during the COVID-19 pandemic

Ethnic Disparities in Mental Health and Movement Behaviors During Childhood: Examining the Longitudinal Developmental Patterns

The purpose of this study was to examine the longitudinal developmental patterns of mental health (i.e., physical, psychosocial, and executive functioning) and movement behaviors (i.e., sedentary behavior and moderate-to-vigorous physical activity [MVPA]) between Hispanic and non-Hispanic children. We tracked 153 children for 3years from kindergarten (time 1) to third grade (time 2; 91 Hispanic; mean age = 5.36, SD = .48). Physical, psychosocial, and executive functioning was assessed using validated surveys, while school-based sedentary behavior and MVPA were assessed by accelerometers. A 2 × 2 (time × ethnicity) factorial repeated measures MANCOVA (gender and SES as covariates) revealed significant ethnicity effect (p = .05) and time by ethnicity interaction effect (p < .05). Hispanic children reported lower physical (p < .05) and psychosocial functioning (p > .05) but higher executive functioning (p < .05) than non-Hispanic children in kindergarten, but the gaps narrowed in third grade. Results also revealed that children's sedentary behavior increased significantly (p < .05) and MVPA showed a declining trend (p > .05) over time. Ethnic disparities in mental health between Hispanic and non-Hispanic children exist but may reduce from early to middle childhood. The longitudinal changes relative to ethnicity highlight the need for tailored interventions to address health disparities in mental health and movement behaviors between Hispanic and non-Hispanic children.

Movement behaviour patterns in adults with haemophilia.

Background:Joint bleeds are the hallmark of haemophilia, and can lead to disabling haemophilic arthropathy. Consequently, the movement behaviour of adults with haemophilia differs from that of healthy adults. It seems unlikely that a single outcome is able to reflect all relevant information regarding movement behaviour. The aim of the current study was to identify patterns of movement behaviour within persons with haemophilia (PWH) and compare clinical characteristics between patterns of movement behaviour.Methods:A total of 105 PWH [70% severe haemophilia; median age 43 years (30.0–54.0)] were included in the study. Hierarchical cluster analysis was used to identify patterns of movement behaviour. Clustering variables included seven parameters of movement behaviour: sitting, standing, walking, biking, running, frequency of active bouts and length of active bouts. Clinical characteristics included age, severity of haemophilia, joint health, physical functioning and pain. Clinical characteristics were compared between identified clusters by Kruskall–Wallis test. Movement behaviour was assessed with the Activ8 accelerometer, joint health was assessed on the Haemophilia Joint Health Score, physical functioning on the Haemophilia Activity List and the 40 m self-paced walk test and pain on the Numerical Pain Rating Score.Results:Cluster analysis identified three clusters, which were defined as: ‘sedentary’ (57%), ‘bikers and runners’ (22%) and ‘walkers’ (20%). The ‘bikers and runners’ showed better joint health and experienced fewer limitations in activities than the ‘walkers’ and the ‘sedentary’. The ‘walkers’ perceived fewer limitations in activities than the ‘sedentary’, with comparable joint health. We did not identify differences in pain, walking speed and age between the clusters.Conclusions:We identified three patterns of movement behaviour. The majority of PWH was identified as sedentary, whereas less sitting and regular walking during the day seemed to be more beneficial.

Movement Behavior Patterns in People With First-Ever Stroke.

Background and Purpose- Movement behaviors, that is, both physical activity and sedentary behavior, are independently associated with health risks. Although both behaviors have been investigated separately in people after stroke, little is known about the combined movement behavior patterns, differences in these patterns between individuals, or the factors associated with these patterns. Therefore, the objectives of this study are (1) to identify movement behavior patterns in people with first-ever stroke discharged to the home setting and (2) to explore factors associated with the identified patterns. Methods- Cross-sectional design using data from 190 people with first-ever stroke discharged to the home setting. Movement, behavior was measured over 2 weeks using an accelerometer. Ten movement behavior outcomes were calculated and compressed using principal component analysis. Movement behavior patterns were identified using a k-means clustering algorithm. Demographics, stroke, care, physical functioning, and psychological, cognitive and social factors were obtained. Differences between and factors associated with the patterns were investigated. Results- On average, the accelerometer was worn for 13.7 hours per day. The average movement behavior of the participants showed 9.3 sedentary hours, 3.8 hours of light physical activity, and 0.6 hours of moderate-vigorous physical activity. Three patterns and associated factors were identified: (1) sedentary exercisers (22.6%), with a relatively low age, few pack-years, light drinking, and high levels of physical functioning; (2) sedentary movers (45.8%), with less severe stroke symptoms, low physical functioning and high levels of self-efficacy; and (3) sedentary prolongers (31.6%), with more severe stroke symptoms, more pack-years, and low levels of self-efficacy. Conclusions- The majority of people with stroke are inactive and sedentary. Three different movement behavior patterns were identified: sedentary exercisers, sedentary movers, and sedentary prolongers. The identified movement behavior patterns confirm the hypothesis that an individually tailored approach might be warranted with movement behavior coaching by healthcare professionals.

Cross-sectional and longitudinal associations between active commuting and patterns of movement behaviour during discretionary time: A compositional data analysis.

BackgroundActive living approaches seek to promote physical activity and reduce sedentary time across different domains, including through active travel. However, there is little information on how movement behaviours in different domains relate to each other. We used compositional data analysis to explore associations between active commuting and patterns of movement behaviour during discretionary time.Methods and findingsWe analysed cross-sectional and longitudinal data from the UK Biobank study. At baseline (2006–2010) and follow up (2009–2013) participants reported their mode of travel to work, dichotomised as active (walking, cycling or public transport) or inactive (car). Participants also reported activities performed during discretionary time, categorised as (i) screen time; (ii) walking for pleasure; and (iii) sport and do-it-yourself (DIY) activities, summed to produce a total. We applied compositional data analysis to test for associations between active commuting and the composition and total amount of discretionary time, using linear regression models adjusted for covariates. Adverse events were not investigated in this observational analysis. The survey response rate was 5.5%. In the cross-sectional analysis (n = 182,406; mean age = 52 years; 51% female), active commuters engaged in relatively less screen time than those who used inactive modes (coefficient -0.12, 95% confidence interval [CI] -0.13 to -0.11), equating to approximately 60 minutes less screen time per week. Similarly, in the longitudinal analysis (n = 4,323; mean age = 51 years; 49% female) there were relative reductions in screen time in those who used active modes at both time points compared with those who used inactive modes at both time points (coefficient -0.15, 95% confidence interval [CI] -0.24 to -0.06), equating to a difference between these commute groups of approximately 30 minutes per week at follow up. However, as exposures and outcomes were measured concurrently, reverse causation is possible.ConclusionsActive commuting was associated with a more favourable pattern of movement behaviour during discretionary time. Active commuters accumulated 30–60 minutes less screen time per week than those using inactive modes. Though modest, this could have a cumulative effect on health over time.

Which intensities, types, and patterns of movement behaviors are most strongly associated with cardiometabolic risk factors among children?

PurposeThe purpose of this study was to determine which intensities, patterns, and types of 24-h movement behaviors are most strongly associated with cardiometabolic risk factors among children. MethodsA total of 369 children aged 10–13 years were studied. Participants wore an Actical accelerometer and a Garmin Forerunner 220 Global Positioning System logger and completed an activity and sleep log for 7 days. Data from these instruments were combined to estimate average minute per day spent in 14 intensities, 11 types, and 14 patterns of movement. Body mass index, resting heart rate, and systolic blood pressure values were combined to create a cardiometabolic risk factor score. Partial least squares regression analysis was used to examine associations between the 39 movement behavior characteristics and the cardiometabolic risk factor score. The variable importance in projection (VIP) values were used to determine and rank important movement behavior characteristics. There was evidence of interaction by biological maturity, and the analyses were conducted separately in the 50% least mature and 50% most mature participants. ResultsFor the least biologically mature participants, fifteen of the 39 movement behavior characteristics had important VIP value scores; eight of these reflected movement intensities (particularly moderate and vigorous intensities), six reflected movement patterns, and one reflected a movement type. For the most mature participants, thirteen of the 39 movement behavior characteristics had important VIP value scores, with five reflecting intensities (particularly moderate and vigorous intensities), five reflecting patterns, and three reflecting types of movement. ConclusionMore than 12 movement behavior characteristics were associated with cardiometabolic risk factors within both the most and least mature participants. Movement intensities within the moderate and vigorous intensity ranges were the most consistent correlates of these risk factors.

Space-use patterns highlight behavioural differences linked to lameness, parity, and days in milk in barn-housed dairy cows.

Lameness is a key health and welfare issue affecting commercial herds of dairy cattle, with potentially significant economic impacts due to the expense of treatment and lost milk production. Existing lameness detection methods can be time-intensive, and under-detection remains a significant problem leading to delayed or missed treatment. Hence, there is a need for automated monitoring systems that can quickly and accurately detect lameness in individual cows within commercial dairy herds. Recent advances in sensor tracking technology have made it possible to observe the movement, behaviour and space-use of a range of animal species over extended time-scales. However, little is known about how observed movement behaviour and space-use patterns in individual dairy cattle relate to lameness, or to other possible confounding factors such as parity or number of days in milk. In this cross-sectional study, ten lame and ten non-lame barn-housed dairy cows were classified through mobility scoring and subsequently tracked using a wireless local positioning system. Nearly 900,000 spatial locations were recorded in total, allowing a range of movement and space-use measures to be determined for each individual cow. Using linear models, we highlight where lameness, parity, and the number of days in milk have a significant effect on the observed space-use patterns. Non-lame cows spent more time, and had higher site fidelity (on a day-to-day basis they were more likely to revisit areas they had visited previously), in the feeding area. Non-lame cows also had a larger full range size within the barn. In contrast, lame cows spent more time, and had a higher site-fidelity, in the cubicle (resting) areas of the barn than non-lame cows. Higher parity cows were found to spend more time in the right-hand-side area of the barn, closer to the passageway to the milking parlour. The number of days in milk was found to positively affect the core range size, but with a negative interaction effect with lameness. Using a simple predictive model, we demonstrate how it is possible to accurately determine the lameness status of all individual cows within the study using only two observed space-use measures, the proportion of time spent in the feeding area and the full range size. Our findings suggest that differences in individual movement and space-use behaviour could be used as indicators of health status for automated monitoring within a Precision Livestock Farming approach, potentially leading to faster diagnosis and treatment, and improved animal welfare for dairy cattle and other managed animal species.

Levels and correlates of 24-hour movement behaviors among South Koreans: Results from the Korea National Health and Nutrition Examination Surveys, 2014 and 2015

PurposeThis study aimed to examine the levels and correlates of 24-h movement behaviors (i.e., physical activity, sedentary time, and sleep), and different patterns of these behaviors in a nationally representative sample of South Koreans aged 12 years and older. This study also aimed to examine the sociodemographic correlates of 24-h movement behaviors stratified by age groups. MethodsSelf-reported, repeated cross-sectional data from 10,708 participants in the 2014 and 2015 Korea National Health and Nutrition Examination surveys were used. Key variables included moderate-to-vigorous intensity physical activity, muscular strengthening exercises, walking, active transportation, sedentary time, and sleep. Sociodemographic variables included age, sex, household income, area of residence, and education level. Descriptive statistics by sex and age as well as general linear models by age group were performed. ResultsThe proportions of individuals meeting the moderate-to-vigorous intensity physical activity, muscular strengthening exercises, and sleep guidelines were, respectively, 21.6%, 22.1%, and 32.5% in male youth; 6.9%, 4.5%, and 22.8% in female youth; 55.5%, 30.8%, and 54.0% in male adults; 48.8%, 14.4%, and 57.6% in female adults; 44.0%, 30.6%, and 45.5% in male older adults; and 29.5%, 8.9%, and 37.3% in female older adults. The proportions of individuals showing the most ideal combinations of 24-h movement behaviors were only 3.2% in youth, 0.4% in adults, and 0 in older adults. Universally, older age, female sex, or living in metro Seoul were associated with unfavorable patterns of 24-h movement behaviors across different age groups. However, the associations of income and education with movement behaviors were mixed across age groups. ConclusionOverall, the proportion of South Koreans with a healthy 24-h movement behavior pattern is low. The sociodemographic correlates of different types of 24-h movement behaviors should be considered when designing targeted interventions for the promotion of healthy active living for South Koreans.

Spatial configuration becomes more important with increasing habitat loss: a simulation study of environmentally-transmitted parasites

Landscape changes can be an important modifier of disease. Habitat fragmentation commonly results in reduced connectivity in host populations and increased use of the remaining habitat. For environmentally transmitted parasites, this presents a possible trade-off between transmission potential at the local and global level. We quantify the effects of fragmentation on the transmission of an environmentally transmitted parasite, teasing apart the relative effects of habitat composition and configuration on both host movement behaviour and subsequent infection patterns. We use a spatially-explicit epidemiological model to simulate the effects of habitat fragmentation, using, as an example, whipworm (Trichuris sp.) within a red colobus monkey population (Procolobus rufomitratus). We found that habitat fragmentation did not always lead to a trade-off between population connectivity and concentration of habitat use in host movement behaviour or in final population infection patterns. However, our simulation results suggest the spatial configuration of the remaining habitat became increasingly influential on behavioural and infection outcomes as habitat was removed. Additionally, we found common fragmentation metrics provided little ability to explain variation in propagation of infections. Our results suggest an interaction between habitat configuration and composition should be considered when assessing disease related impacts of habitat fragmentation on environmentally transmitted parasites, especially in cases where habitat loss is high (≥ 30%). We also propose that spatially-explicit simulations that capture a host’s response to fragmentation could aid in the development of novel landscape metrics targeted towards specific host-parasite-landscape systems.

Individual, Social, and Environmental Correlates of Active Transportation Patterns in French Women.

The objectives were (1) to define physical activity (PA) and sedentary behaviors (SB) patterns in daily life contexts (work, leisure, and transportation) in French working women from NutriNet-Santé web-cohort and (2) to identify pattern(s) of active transportation and their individual, social, and environmental correlates. 23,432 participants completed two questionnaires to evaluate PA and SB in daily life contexts and individual representations of residential neighborhood and transportation modes. Hierarchical cluster analysis was performed which identified 6 distinct movement behavior patterns: (i) active occupation, high sedentary leisure, (ii) sedentary occupation, low leisure, (iii) sedentary transportation, (iv) sedentary occupation and leisure, (v) active transportation, and (vi) active leisure. Multinomial logistic regressions were performed to identify correlates of the “active transportation” cluster. The perceived environmental characteristics positively associated with “active transportation” included “high availability of destinations around home,” “presence of bicycle paths,” and “low traffic.” A “positive image of walking/cycling,” the “individual feeling of being physically active,” and a “high use of active transport modes by relatives/friends” were positively related to “active transportation,” identified as a unique pattern regarding individual and environmental correlates. Identification of PA and SB context-specific patterns will help to understand movement behaviors' complexity and to design interventions to promote active transportation in specific subgroups.

Predicting movement in architectural space

ABSTRACTThis paper argues that the evaluation of architectural spaces as numerical entities can identify seemingly random patterns of movement behaviour. Built upon analytical theories of space syntax that can produce spatially relevant numerical values, it is proposed that, by analysing integration values from space syntax analysis with a Fibonacci mathematical sequence, it is possible to differentiate social importance between a majority of spaces that are neither segregated nor integrated. Numerical analysis is conducted through a finance tool; Fibonacci retracement calculator. For the application of proposed methodology, a restaurant is chosen as a large public architectural space of social activity. The architectural plan is analysed with space syntax tools and the resulting values entered into a Fibonacci calculator; tracking results with the values of space syntax graph revealed several locations with mediocre values. The resulting spaces are re-evaluated using real-life financial data from the restaurant and employee interviews, the findings correlated with the Fibonacci analysis results; indicated spaces were among the most active tables within the restaurant, following those located directly at integration centres. The results indicate that it is in fact possible to produce accurate predictions of increased social activity among architectural spaces of insignificance as informed by space syntax analysis.

Co-speech hand movements during narrations: What is the impact of right vs. left hemisphere brain damage?

Persons with brain damage show deviant patterns of co-speech hand movement behaviour in comparison to healthy speakers. It has been claimed by several authors that gesture and speech rely on a single production mechanism that depends on the same neurological substrate while others claim that both modalities are closely related but separate production channels. Thus, findings so far are contradictory and there is a lack of studies that systematically analyse the full range of hand movements that accompany speech in the condition of brain damage. In the present study, we aimed to fill this gap by comparing hand movement behaviour in persons with unilateral brain damage to the left and the right hemisphere and a matched control group of healthy persons. For hand movement coding, we applied Module I of NEUROGES, an objective and reliable analysis system that enables to analyse the full repertoire of hand movements independent of speech, which makes it specifically suited for the examination of persons with aphasia.The main results of our study show a decreased use of communicative conceptual gestures in persons with damage to the right hemisphere and an increased use of these gestures in persons with left brain damage and aphasia. These results not only suggest that the production of gesture and speech do not rely on the same neurological substrate but also underline the important role of right hemisphere functioning for gesture production.