Sedentary behaviors

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Contribution to the surveillance and measurement of physical activity and sedentary behaviors

Contribution to the surveillance and measurement of physical activity and sedentary behaviors

Canada has also a well-developed surveillance system of physical activity and sedentary behaviors relying on many surveys. The Canadian Health Measures Survey (CHMS), launched in 2007, is an ongoing survey collecting physical activity and sedentary behaviors data from a nationally representative sample of the population aged 3 to 79 years (6 to 79 years in cycle 1). CHMS uses both questionnaires and accelerometers and provides information regarding adherence to physical activity guidelines, total moderate-to-vigorous physical activity, total sedentary time, and recreational screen time for adults and youth (Statistics Canada, 2015). In addition, the Canadian Fitness and Lifestyle Research Institute conducts the Physical Activity Monitoring (PAM) surveys, and Statistics Canada conducts the National Population Health Survey - Canadian Community Health Survey (CCHS), providing valuable information for the population surveillance of physical activity in Canada. The NPHS was conducted from 1994 to 1999, and has been replaced by the CCHS. The PAM survey has first been implemented in 1995, and provides information on the percentage of adults intending to be physically active, who report that physical activity is generally pleasant, and who report being confident for engaging in 30 minutes of moderate physical activity three or four times a week (Katzmarzyk and Tremblay, 2007; Canadian Fitness and Lifestyle Research Institute, 2017a). For youth, the PAM survey asks for participation in sports, level of parental support and level of community safety. The CCHS provides information on occupational, household and leisure time physical activity, active travel, and the built environment (for examples the presence of parks and recreation facilities) (Canadian Research Data Center Network, n.d.). In addition, the surveillance system of physical activity and sedentary behaviors in Canada includes the Physical Activity Longitudinal Study (Canadian Fitness and Lifestyle Research Institute, 2017b), the Canada’s Physical Activity Levels among Youth survey (Canadian Fitness and Lifestyle Research Institute, 2017c), and setting-based surveys assessing policies, practices, and opportunities to support physical activity of Canadians in key settings (municipalities, schools and workplaces) (Canadian Fitness and Lifestyle Research Institute, 2017d).
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Device-Assessed Physical Activity and Sedentary Behaviors in Canadians with Chronic Disease(s): Findings from the Canadian Health Measures Survey

Device-Assessed Physical Activity and Sedentary Behaviors in Canadians with Chronic Disease(s): Findings from the Canadian Health Measures Survey

2 Research Center, University Institute of Mental Health at Montréal, Montréal, QC H1N3M5, Canada * Correspondence: bernard.paquito@uqam.ca; Tel.: +1-514-987-3000 (ext. 3606) Received: 13 March 2019; Accepted: 13 May 2019; Published: 16 May 2019    Abstract: Physical activity and sedentary behaviors (SB) are major determinants of quality of life in adults with one or more chronic disease(s). The aim of this study is to compare objectively measured physical activity and SB in a representative sample of Canadian adults with and without chronic disease(s). The Canadian Health Measures Survey (CHMS) (2007–2013) was used in this study. Daily time spent in physical activities and sedentary behaviors were assessed by an accelerometer in Canadians aged 35–79 years. Data are characterized as daily mean time spent in moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), steps accumulated per day and SB. Chronic diseases (chronic obstructive pulmonary disease, diabetes, heart diseases, cancer) were assessed via self-report diagnostic or laboratory data. Weighted multivariable analyses of covariance comparing physical activity and SB variables among adults without and with chronic disease(s) were conducted; 6270 participants were included. Analyses indicated that 23.9%, 4.9% and 0.5% had one, two, and three or more chronic diseases. Adults with two and more chronic diseases had significantly lower daily duration of MVPA and LPA, daily step counts, and higher daily duration of SB compared to adults without chronic diseases. Interventions targeting physical activity improvement and SB reduction might be beneficial for Canadian multimorbid adults.
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Physical Activity, Inactivity, and Sedentary Behaviors: Definitions and Implications in Occupational Health

Physical Activity, Inactivity, and Sedentary Behaviors: Definitions and Implications in Occupational Health

Standing work stations have been proposed to break this sedentary time. Although standing stations remain inactive, according to the framework proposed by the Sedentary Behavior Research Group, passive standing corresponds to 2 METs, which is above the 1.5 METs threshold used to define sedentary behaviors, considered as low physical activity ( 18 ). Although this energetic cost of passive standing rests on a strong body of scientific evidence, a recent study showed that passive standing does not significantly increase heart rate and energy expenditure above rest ( 31 ). According to the authors, the observed rises in heart rate and energy expenditure are due to the transition from sitting to standing before returning to resting values, particularly in “energy saver individuals” ( 31 ). This may explain why some studies failed to find any effects of standing desk allocation vs. classical sitting on metabolic profiles and body composition among tertiary employees ( 32 ). This could also explain why regular sitting breaks have been shown to improve health compared with permanent passive standing positions ( 33 ). In their research, Bailey and Locke showed that only active breaks consisting in brief bouts of light-intensity activities (2-min walk every 20 min) but not passive standing breaks might enhance cardiometabolic health in tertiary employees ( 34 ). Although further research is needed regarding the exact effects of standing desks and regular breaks, active standing ( 18 ) such as walking and cycling desks or walking breaks should be prescribed, regardless of the employees’ physical activity level. Even though new investigations are warranted, some promising results already demonstrate the beneficial effect of walking or bike-desks on overall health, well-being, and work- related cognitive performance among tertiary workers ( 35 ). Some recent findings have also underlined the cardiometabolic benefits obtained by interrupting sitting time by the use of active walking desks compared with prolonged sitting ( 36 ). Future research should consider a potential inter-individual variability in the responses to such strategies, with some people that might adopt compensatory mechanisms leading to increased sedentary time outside of work, for instance.
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France's 2020 Report Card on Physical Activity and Sedentary Behaviors in Children and Youth: Results and Progression

France's 2020 Report Card on Physical Activity and Sedentary Behaviors in Children and Youth: Results and Progression

Gaining in interest and popularity, 38 countries took part in 58 the Global Matrix 2.0 in 2016 14 , wherein France presented its first Report Card under the umbrella of 59 the Frenc[r]

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Taxonomy-based content analysis of sedentary behavior questionnaires: A systematic review

Taxonomy-based content analysis of sedentary behavior questionnaires: A systematic review

Introduction Sedentary behaviors (SB) are defined as “as any waking behavior characterized by an energy expenditure 1.5 METs while in a sitting or reclining posture” [ 1 ]. Health effects of sedentary time have been studied over the past decade with most studies showing negative associations between sedentary time and health outcomes in both adults and youth [ 2 – 4 ]. Much of the evi- dence for these results has been provided by self-report [ 2 ] with the majority of the studies measuring television (TV) viewing or total sitting time derived from a single question [ 4 , 5 ]. However, measuring only total sedentary time may not provide enough information to under- stand the health effects of SB. For example, an individual can engage in different types of SB including TV viewing, using a computer, reading, writing, and eating which have several pur- poses including work, transportation, and leisure time. The types and purposes of SB will differ for each person studied. Some studies have shown that the associations between SB and health-related outcomes vary by the characteristics of the SB measured and in the manner in which sedentary time is accumulated [ 6 – 8 ]. A systematic review of the effects of SB on health outcomes showed that TV viewing had a different impact than reading on cognitive develop- ment in early childhood [ 9 ]. The investigators showed detrimental associations between the total duration and frequency of watching TV and videos and using computers and/or overall screen time with cognitive development, whereas beneficial associations were found between the total duration and frequency of reading or being read to and cognitive development. How- ever, the associations were complex as positive associations were shown for some TV content (educational channel viewing) while negative associations were observed for other content (cartoons). These findings are supported by another systematic review examining the relation- ships between SB and health indicators in children and youth [ 10 ] that showed negative associ- ations between screen-related behaviors with body composition and cardiometabolic status (TV viewing), behavioral conduct and pro-social behavior (TV viewing and video game use), physical fitness (screen time), and self-esteem (screen time and computer use). Conversely, increased duration of reading and doing homework were associated with higher academic achievement. Such relationships imply the association between SB and health outcomes is complex and that multiple characteristics of SB should be taken into consideration in research studies. Therefore, measuring the characteristics of SB is important as it may allow researchers to understand factors mediating the relationships between sedentary time and various health- outcomes, reveal insights into an individual’s behavior, relationships between various determi- nants and correlates of health outcomes, and implement efficient interventions to reduce SB.
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Sedentary behavior at work and cognitive functioning: A systematic review

Sedentary behavior at work and cognitive functioning: A systematic review

Accordingly, studies on the effects of sedentarity, outside the context of work, have shown potential negative consequences ( 16 , 31 ). For instance, time spent watching television is associated with poorer episodic memory capacity (immediate and delayed recall) ( 4 ), verbal fluency ( 4 ), executive functioning ( 32 ), working memory ( 33 ), cognitive inhibition ( 34 ), and information- processing speed ( 34 ) over the long term. These results are extended to children ( 35 ) and elderly adults ( 36 ). In addition, the amount of objective sedentary behaviors (as measured by the use of accelerometers) and cognitive abilities ( 37 ) was found in a longitudinal study (over 2 years) in elderly adults ( 38 ). A large cohort study comprising adults aged 37–73 years ( 31 ) found a negative association between the amount of self- reported sedentary behaviors vs. working memory and speed of information processing. However, the potential detrimental effects of sedentarity on cognition is not always found [see meta- analysis ( 39 )]. Moreover, it is important to take into account the type of sedentary activity, since time spent watching television and time spent reading (or listening to reading) causes different cognitive effects in young children ( 40 ). These correlational studies, outside the context of work, provide initial evidence in favor of the hypothesis that sedentarity has deleterious effect on cognition. Yet, these results are observed for long term sedentary behaviors. It is thus impossible to make a causal link between the production of sedentary behaviors and cognitive alterations since many other lifestyle habits may be involved.
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Comparison of Active and Sedentary Bout Lengths in Normal and Overweight Adults using eMouverecherche

Comparison of Active and Sedentary Bout Lengths in Normal and Overweight Adults using eMouverecherche

tions that modify time spent in sedentary behavior [ 13 ]. The advantage of using a smartphone com- pared with a research device is the wide availability of smartphones in the general population. This avail- ability makes it possible to consider carrying out ep- idemiological studies without device limitation. Be- cause of objective measurements given by the accel- erometers, the assessments of both the intensity and duration of active and sedentary behaviors are more accurate than the subjective responses collected by physical activity questionnaires. Objective measure- ments avoid errors attributable to reporting bias and difficulties with recall [ 14 ]. In a previous work, we developed the eMouveRecherche application to ac- curately quantify four types of behavior: Immobility, light-, moderate- and vigorous-intensity activity from accelerometry data collected by smartphones. Specif- ic algorithms to quantify time spent in these four cat- egories were developed in two distinct populations and tested in controlled and free-living conditions [ 15 , 16 ]. The algorithms were scientifically validated with less than 5% of error in absolute value against the reference method (indirect calorimetry) or re- search device (Armband®). In free-living conditions, Overweight (OW) individuals spent more time immo- bile (81% vs. 65% of the waking period) and twice less time in light-intensity activities than Normal-Weight (NW) individuals (15% vs. 29%). No difference in moderate or vigorous intensity activity was observed between the two groups [ 17 ]. Such gaps in time spent in immobile or light-intensity activities constitute a behavioral marker of BMI or fat mass. There may be other behavioral markers of excess weight than the cumulative sedentary or lightly active time. The aim of the present work was to compare sedentary and active profiles in the same participants already stud- ied by Rousset, et al. [ 17 ], but this time according to the frequency and the length of sedentary, light, moderate and vigorous physical activity intensity pe- riods. The authors hypothesized that the sedentary and active pattern would be different in NW and OW participants.
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The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study

The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study

During the consensus process it emerged that the framework holds promise as a tool to support the devel- opment of policy and complex interventions to reduce SB via actions on its determinants. In this respect, the framework makes it easier to identify key gaps in know- ledge and to engage stakeholders in the co-creation and development of interventions and policies through help- ing them to identify the most relevant set of factors through which they could play a role in influencing determinants of SB. For example, the framework was used as a basis to develop solutions to reduce sedentary behaviour in stroke survivors at the 2015 UK Stroke Forum. Clinicians, researchers, carers and NHS trust managers used the framework to identify factors they could address in practice within three different settings of acute, rehabilitation and community care. Finally, con- sidering that SB is influenced by such a complex web of factors, interactions and confounders, seeking only to identify relationships between them might be an arduous and lengthy task. Alternatively, the framework could be used in a more solution orientated manner [41], thereby providing a quicker route to developing effective policies and interventions that account for the complexity of SB. It could be used to develop scenarios to identify levers for change and potential strategies to use them at different life stages and in different contexts such as the workplace, school or in care settings [43].
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Scaling behaviors of colloidal aggregates under pressure

Scaling behaviors of colloidal aggregates under pressure

buckles. On Fig.4b is shown the same column after a central particle was removed. This generates a structural defect, and the system prefers to fold down. These two behaviors are indeed expected for the elastic response, if the forces propagate correctly throughout the structure. FIG. 4. Two examples of columns with triangular basis. The bonds between neighboring particles are created in the initial structure, and cannot be destroyed or created during deformation. a) The two left-hand side columns show elastic twisting and buckling. b) The two right-hand side columns show the folding of the column when a defect (one central particle is missing) is created in the initial structure.
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Variety of scaling behaviors in nanocrystalline plasticity

Variety of scaling behaviors in nanocrystalline plasticity

To simulate the size dependence of scaling exponents in small crystals we assumed that, at least in submicron range, the decreasing variance of the quenched disorder can serve as a proxy[r]

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Mediation of Debates with Dynamic Argumentative Behaviors

Mediation of Debates with Dynamic Argumentative Behaviors

Dealing with Non-stationary Behaviors. The decision problem of the mediator can be viewed as a sequential decision-making problem under uncertainty. As debating agents may change their behaviors over time, traditional Markov decision models fail to rep- resent such non-stationary problems. To remedy this, Hidden-Semi-Markov-Mode MDP (HS3MDPs) [6] have been proposed to model problems where the non-stationarity of the environment is modeled as a finite non-controlled Semi-Markov chain, whose states called modes correspond to stationary MDPs. Formally, an HS3MDP is defined by a tuple M, C, H with M a set of modes; C : M × M → [0, 1] a transition function between modes and H : M × M × N → [0, 1] a duration function. Each mode m k ∈ M corresponds to a stationary MDP, characterized by a tuple S, A, T k , R k  where S is a set of states; A a set of actions; T k : S × A × S → [0, 1] a transition function over states
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BeMoRe: a Repository for Handling Models Behaviors

BeMoRe: a Repository for Handling Models Behaviors

If PMMS solve the two drawbacks of the loose coupling of a MMS with a database, they currently do not support the same flexibility concerning the definition of behavioral semantics (procedural aspects) of models and metamodels. Indeed PMMS focus mainly on the definition of the structure of metamodels and models but provide a limited support for the definition of operations on models and metamodels. For example, some PMMS provide hard-encoded operators for model management (e.g., Match, Merge, Union [8], [1]), or only give access to the database procedural languages (e.g., PL/SQL) that do not support the manipulation of models and metamodels (they only manipulate relational tables). The most advanced PMMS concerning the definition of metamodels and models behaviors is ConceptBase [2]. Using this PMMS user-defined operations can be defined on models and metamodels as deductive rules implemented with a specific language (PROLOG). However this PMMS lacks the possibility to integrate operations that have already been implemented using a given programming language or provided as an external web service.
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Investigating Non-Verbal Behaviors Conveying Interpersonal Stances

Investigating Non-Verbal Behaviors Conveying Interpersonal Stances

Other works have gone further by also consid- ering global behavior tendencies and reactions to the interactants’ behaviors: the Laura agent (Bick- more and Picard, 2005) was used to develop long term relationships with users, and would adapt the frequency of gestures and facial signals as the re- lationship with the user grew. However, domi- nance was not investigated, and the users’ behav- iors were not taken into account as they used a menu-based interface. Prepin et al. (Prepin et al., 2013) have investigated how smile alignment and synchronisation can contribute to stance building in a dyad of agents. Although not directly related to dominance or friendliness, Sensitive Artificial Listeners designed in the Semaine project (Bevac- qua et al., 2012) produce feedback and backchan- nels depending of the personality of an agent, de- fined by extraversion and emotional stability.
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GroddDroid: a Gorilla for Triggering Malicious Behaviors

GroddDroid: a Gorilla for Triggering Malicious Behaviors

To prevent the distribution of malware, Google has de- veloped a service called Bouncer that analyzes statically and dynamically applications submitted on Google Play. Static analysis has strong limitations since malware resorts to a lot of techniques to hide the malicious behavior within legitimate applications. They can obfuscate their code, use reflection or dynamic libraries. Additionally, a lot of in- teresting information are only available at runtime, for ex- ample the content and the recipient of a SMS, the content of a message received by a remote server, etc. Dynamic analysis can bring more information on malware behaviors. Research efforts have to be done on the setup of efficient dynamic analysis platforms as it is not reliable to deploy large scale analysis tools on user’s devices.
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BeMoRe: a Repository for Handling Models Behaviors

BeMoRe: a Repository for Handling Models Behaviors

If PMMS solve the two drawbacks of the loose coupling of a MMS with a database, they currently do not support the same flexibility concerning the definition of behavioral semantics (procedural aspects) of models and metamodels. Indeed PMMS focus mainly on the definition of the structure of metamodels and models but provide a limited support for the definition of operations on models and metamodels. For example, some PMMS provide hard-encoded operators for model management (e.g., Match, Merge, Union [8], [1]), or only give access to the database procedural languages (e.g., PL/SQL) that do not support the manipulation of models and metamodels (they only manipulate relational tables). The most advanced PMMS concerning the definition of metamodels and models behaviors is ConceptBase [2]. Using this PMMS user-defined operations can be defined on models and metamodels as deductive rules implemented with a specific language (PROLOG). However this PMMS lacks the possibility to integrate operations that have already been implemented using a given programming language or provided as an external web service.
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Persistence of Problematic Sexual Behaviors in Children

Persistence of Problematic Sexual Behaviors in Children

Research has shown that some adolescent and adult sexual abusers exhibited problematic sexual behaviors (PSB) at an early age (Burton, 2000; Vizard, Hickey, & McCrory, 2007). These are childhood sexual behaviors that involve body parts (i.e., genitals, anus, buttocks, or breasts) and that are developmentally inappropriate or potentially harmful to the child or others (Association for the Treatment of Sexual Abusers [ATSA], 2006). There is extant concern that if children with PSB are left untreated, their behaviors may persist over time (Burton, 2000; Manocha & Mezey, 1998). Child Protective Services (CPS) have become particularly concerned that the PSB manifested by some children might compromise the safe living environment in a residential facility (Baker, Schneiderman, & Parker, 2001; Farmer & Pollock, 2003). The high prevalence of PSB found in residential facilities, up to one third of the children in their charge, implies that many children are directly affected, as are the many work- ers who must deal with these behaviors (Baker et al.,
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«Friendly» complaining behaviors: toward a relational approach

«Friendly» complaining behaviors: toward a relational approach

Despite this growing interest in consumer complaining behaviors (with a few exceptions -e.g., Fornell & Wernerfelt, 1988; Dacin & Davidow, 1997-), very few researchers argue that it is important to encourage consumers to complain. This same pattern is true with the investigation of appropriate organizational responses to various complaint behaviors. As already said, using information contained in complaints and giving right answers to such complaints is important. How can a company give the right answers and stimulate a « positive » complaining behavior from the consumer. The objective is to stimulate complaining behavior leading to useful information. A «friendly» complaint provides signals about the need to reconsider attributes important to the company and on which the company thinks it is able to respond quickly?
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Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study

Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study

important [44, 45] for understanding the role played by social and physical environmental factors in determining this behaviour. The evidence currently available might be limited but seems trustworthy as it generally comes from high qual- ity studies. The median quality score was 82 % with an inter-quantile range of 46 to 96 %. A main common feature of the lowest quality studies was that they tended to be secondary data analyses of studies not specifically designed to investigate determinants of sedentary behav- iour. Consequently, these often rated low in terms of analysis and in particular regarding the way they con- trolled for potential confounders. In contrast, studies rated highest were either specifically designed to investi- gate the determinants of sedentary behaviour or focused their analysis on the association between a single factor and a specific domain of sedentary behaviour. Sample size did not appear to be a determining feature of quality as a number of smaller size studies which were well designed and provided more focused analysis rated higher than some large scale epidemiological studies.
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Emphasising nonlinear behaviors for cubic coupled Schrödinger systems

Emphasising nonlinear behaviors for cubic coupled Schrödinger systems

Sur ce type de comportement, en temps fini, on peut citer l’article de Grébert et Villegas-Blas ([ 23 ]) dans le cas d’une non-linéarité cubique perturbée par un cosinus ; l’article de G[r]

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Prosapip1-a novel target of alcohol drinking behaviors

Prosapip1-a novel target of alcohol drinking behaviors

Prosapip1 Knock-down Reduces Self-Administration of Alcohol Ltv-shSCR-GFP Ltv-shProsapip1-GFP Lt v- sh SC R -G FP Lt v- sh Pr os ap ip 1- G FP Prosapip1 GAPDH GFP GFP NeuN Merge 100 mm [r]

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