Dataset Information

Automaticity facets applied to screen-time sedentary behaviours and active commuting measured by accelerometers.

ABSTRACT: Background: The time adults spend sitting in front of screens is a health risk factor. In contrast, walking and cycling to and from work, also known as active commuting, could promote physical activity and improve population health. Objective: This study investigated automatic properties role in explaining active commuting and screen-based sedentary behaviours. The stable, daily conditions for carrying out active commuting and screen-based sedentary behaviour are most likely to develop automatic properties. These characteristics mean performing behaviours via external cues (i.e. lack of intentionality), with an unpleasant emotional experience of not carrying out a set routine (i.e. lack of controllability), and without paying much attention (i.e. efficiency). Method: This article describes findings of a prospective and correlational study in which 128 people participated. First, participants responded to questions assessed using the Generic Multifaceted Automaticity Scale (GMAS), which measured the automatic properties of screen-based sedentary behaviour and active commuting. The following week, both behaviours were assessed by daily logs to document active commuting and screen-based sedentary behaviour events, and by an accelerometer, worn for seven days, as an objective criterion. Confirmatory factor analyses, bivariate correlations, and multiple linear regressions were computed for the associations between the GMAS scores and objective criterion measures of screen-based sedentary behaviours and active commuting. Results: Automaticity facets displayed different relationships with screen-based sedentary behaviours and active commuting - people with higher lack of intentionality and lack of controllability for active commuting present higher levels of moderate physical activity. In contrast, the lack of controllability of screen-based sedentary behaviours was a significant predictor of sedentary screen time. Conclusions: The multidimensional approach to automaticity could be useful in determining more precisely the features that need to be addressed to promote the adoption of active commuting and limit the time spent sitting in front of screens.

SUBMITTER: Marchant G

PROVIDER: S-EPMC8130716 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background:Canadian youth are among the top users of cannabis globally. The Canadian federal government identified protecting youth from cannabis-related harms as a key public health objective aligned with the legalization and strict regulation of cannabis. While there are well-established associations between screen time sedentary behaviour (STSB) and alcohol and tobacco use, the association with cannabis use is understudied. The purpose of this study is to examine the association between various types of STSBs and cannabis use in a large sample of Canadian youth. Methods:Using cross-sectional data from 46,957 grade 9 to 12 students participating in year 5 of the COMPASS host study (2016-2017), four gender-stratified ordinal logistic regression models were used to examine how total STSB and four different types of STSBs (watching/streaming TV shows/movies, playing video games, Internet use, emailing/messaging/texting) are associated with frequency of cannabis use. Results:One-quarter of participants (24.9%) reported using cannabis in past 12 months; the largest proportion of this group (37.9%) reported rare/sporadic use. Overall, participants spent an average 7.45 ( ±5.26) hours/day on STSBs. Total STSB was positively associated with more frequent cannabis use, and when separated by type, internet use and messaging were significant. Playing video games and watching TV/movies were also significantly associated with more frequent cannabis use, but only for females. Conclusions:The associations between frequency of cannabis use and various measures of STSBs suggest that screen time may be a risk factor for cannabis use among youth. This association may be mediated by youths' mental wellbeing, given emerging evidence that STSB is a risk factor for poor mental health, and the tendency for individuals to use substances as a coping mechanism. Further, the ubiquity of pro-substance use content on the internet may also contribute to increased exposure to and normalization of cannabis, further promoting its use.

Project description:BackgroundLittle is known regarding the patterning and socio-demographic distribution of multiple sedentary behaviours in children. The aims of this study were to: 1) describe the leisure-time sedentary behaviour of 9-10 year old British children, and 2) establish associations with objectively-measured sedentary time.MethodsCross-sectional analysis in the SPEEDY study (Sport, Physical activity and Eating behaviour: Environmental Determinants in Young people) (N=1513, 44.3% boys). Twelve leisure-time sedentary behaviours were assessed by questionnaire. Objectively-measured leisure-time sedentary time (Actigraph GT1M, <100 counts/minute) was assessed over 7 days. Differences by sex and socioeconomic status (SES) in self-reported sedentary behaviours were tested using Kruskal-Wallis tests. The association between objectively-measured sedentary time and the separate sedentary behaviours (continuous (minutes) and categorised into 'none' 'low' or 'high' participation) was assessed using multi-level linear regression.ResultsSex differences were observed for time spent in most sedentary behaviours (all p ≤ 0.02), except computer use. Girls spent more time in combined non-screen sedentary behaviour (median, interquartile range: girls: 770.0 minutes, 390.0-1230.0; boys: 725.0, 365.0 - 1182.5; p = 0.003), whereas boys spent more time in screen-based behaviours (girls: 540.0, 273.0 - 1050.0; boys: 885.0, 502.5 - 1665.0; p < 0.001). Time spent in five non-screen behaviours differed by SES, with higher values in those of higher SES (all p ≤ 0.001). Regression analyses with continuous exposures indicated that reading (β = 0.1, p < 0.001) and watching television (β = 0.04, p < 0.01) were positively associated with objectively-measured sedentary time, whilst playing board games (β = -0.12, p < 0.05) was negatively associated. Analysed in categorical form, sitting and talking (vs. none: 'low' β = 26.1,ns; 'high' 30.9, p < 0.05), playing video games (vs. none: 'low' β = 49.1, p < 0.01; 'high' 60.2, p < 0.01) and watching television (vs. lowest tertile: middle β = 22.2,ns; highest β = 31.9, p < 0.05) were positively associated with objectively-measured sedentary time whereas talking on the phone (vs. none: 'low' β = -38.5, p < 0.01; 'high' -60.2, p < 0.01) and using a computer/internet (vs. none: 'low' β = -30.7, p < 0.05; 'high' -4.2,ns) were negatively associated.ConclusionsBoys and girls and children of different socioeconomic backgrounds engage in different leisure-time sedentary behaviours. Whilst a number of behaviours may be predictive of total sedentary time, collectively they explain little overall variance. Future studies should consider a wide range of sedentary behaviours and incorporate objective measures to quantify sedentary time where possible.

Dataset Information

Automaticity facets applied to screen-time sedentary behaviours and active commuting measured by accelerometers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets