Journal of activity, sedentary and sleep behaviors最新文献

Background: Recreational sedentary screen time (rSST) is the most prevalent form of discretionary sedentary behavior and is strongly linked to poor health outcomes. However, the relationship between time spent in rSST and other 24-h behaviors is not well understood. The purpose of this study was to examine between- and within-day associations between rSST and other 24-h behaviors that include non-rSST or other sedentary time (other-SED), standing (STAND), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and total sleep (SLEEP).

Methods: Baseline data from participants randomized to the StandUPTV study, an intervention aimed to reduce rSST in adults, were included. All 24-h behaviors were assessed continuously for 7-days. The activPAL device was used to assess rSST, other-SED, STAND, LPA, and MPVA; SLEEP was assessed using a GENEactiv accelerometer. rSST was collected using Wi-Fi plugs to capture TV time and tablet app usage. A multilevel modelling approach was used to assess bidirectional associations between rSST (total, daytime, evening) and 24-h behaviors at the between-person (across persons) and within-person (across days) levels, adjusting for age, sex, chronotype, education level, and week versus weekend day. The results were scaled hourly for interpretation.

Results: On average, 8.0 ± 1.6 days of continuous daily 24-h behavior data were included from 94 participants (age [M ± SD: 42.3 ± 11.5] years; 82% female; 78% White; BMI [M ± SD: 29.8 ± 7.8] kg/m²). Greater total rSST was significantly associated with less other-SED (between-person b =  - 45.0, SE = 4.4, p < 0.01; within-person b =  - 44.5, SE = 2.0, p < 0.01). Similar results were observed when examining both daytime and evening rSST with other-SED. Negative associations were also observed between other-SED, STAND, LPA, and MVPA with rSST variables. No significant associations were observed between rSST variables and SLEEP.

Conclusions: This is the first known analysis of the bidirectional relationship between rSST and 24-h behaviors. The negative association between rSST and other-SED suggests that rSST may displace rather than contribute to more cumulative sedentary time. These findings advocate that contexts of sedentary behavior should be considered as distinct behavioral targets in intervention development. Future interventions targeting rSST reduction should also include strategies to reduce total sedentary time.

Clinical trials registration: NCT04464993.

Background: Although interest in objective screen time measurement is growing, questions remain regarding data processing. The aim of this study was to investigate how different image capture intervals and processing assumptions influence screen time estimates from wearable camera images.

Methods: Screen time was measured using chest-worn Brinno TLC130 cameras which took static images every two seconds over four days in children participating in a crossover trial manipulating sleep. Images were coded for screen usage and only children with data from the same time block (before school, after school, weekends) during both intervention conditions were included. Analyses compared estimates of screen time using different intervals of image capture (2, 4, 6, 8, 10, 20, 30, 60 s) and processing rules (images with screens only, Rules 1 and 2 which allowed for blocked [device being used not visible in photos] images as long as the surrounding images were coded as screen time).

Results: 51 children (51% female, 14% indigenous Māori) had sufficient data (187 observations, 1.8 million images). Intervals of up to 60 s between images did not meaningfully influence total screen time estimates compared to 2 s, at the group level. At the individual level, a 10-s interval provided the optimal balance between reducing the number of images to code and accurate screen time estimates. Allowing blocked images between successive screen images to be coded as screen time increased screen time by a median of 8.8 (25th,75th percentiles: 4.4,14.9, Rule 1) to 59.8 (28.6,87.4, Rule 2) minutes.

Conclusion: Researchers can confidently use up to 60 s intervals between images to measure total screen use in children at the group level, but shorter intervals are required for individual level data. Processing rules which allow blocked images to be coded as screens may influence average screen time estimates by up to 32%.

Trial registration: Australian New Zealand Clinical Trials Registry ANZCTR ACTRN12618001671257, 10th Oct 2018.

Background: Establishing early physical activity (PA) habits is vital for long-term health, with parents considered as key influencers on children's PA. Yet, most previous parent-offspring dyads examining PA associations were cross-sectional, rarely used device-based measures, and often overlooked movement composition. The aim of this study was to determine whether mother's and father's waking movement composition is cross-sectionally or longitudinally associated with those of their children.

Methods: The SOPHYA cohort recruited families from a nation-wide population-based random sample stratified by child's sex, birth year, and language. All youth aged 6-16 years and their parents officially residing in Switzerland, were eligible. Baseline and follow-up assessment occurred in 2013-2015 and 2019-2020, respectively. Questionnaire information and accelerometer measurements were collected remotely. The main predictor was parental movement composition at baseline. The associations between parental and child movement compositions were examined using Dirichlet regression models, adjusting for child's age and sex, parental education, and language region. The endpoints were children's movement composition at baseline (cross-sectional) and follow-up (longitudinal), respectively.

Results: Baseline assessment provided accelerometer and self-reported covariate data for the same measurement week in 686 mother-child and 373 father-child pairs. Follow-up assessment provided accelerometer data for 263 children with maternal and 149 with paternal baseline data. Cross-sectionally, replacing parental sedentary behaviour (SB) with moderate-to-vigorous activity (MVPA) (mothers: 0.10, p < 0.001; fathers: 0.09, p = 0.002) or replacing SB with light physical activity (LPA) (mothers: 0.13; < 0.001; fathers: 0.09; p < 0.005) was associated with similar, but smaller shifts in children. Longitudinally, replacing parental SB with LPA was associated with similar, but smaller shifts in children five years later (mothers: coefficient: 0.12, p = 0.021; fathers: coefficient: 0.10, p = 0.108). The cross-sectional change in children's LPA/SB ratio predicted from a parent's 20% decrease in SB and corresponding 20% increase in LPA was about 18-fold smaller than the observed maternal shift and about 29-fold smaller than the paternal shift from which it was predicted.

Conclusions: Dirichlet regression results suggest that parental movement composition may predict children's movement composition, highlighting parental movement patterns, particularly SB, as potentially effective targets for short- and long-term interventions to increase PA in both parents and children.

Background: There is increasing interest in the importance of patterns of accumulation and overall daily time-use composition of physical activity (PA) and sedentary time (SED) for children's cardiometabolic health. This study examined cross-sectional associations between the time-use composition of PA and SED patterns with cardiometabolic risk factors in 4-year-olds.

Methods: Data were drawn from the Barwon Infant Study 4-year review (n = 467). Accelerometer data were classified into short (≤ 1-minute) and long (> 1-min) SED, light-, moderate-, and vigorous-intensity PA (LPA, MPA, VPA) bouts. A waking time-use composition of eight distinct components (total volumes plus short and long bouts of SED, LPA MPA, VPA) was constructed using compositional data analysis. Linear mixed models examined associations between composition patterns and body mass index (BMI), percent body fat, triceps and subscapular skinfold thickness, blood pressure, heart rate, carotid-femoral pulse wave velocity, and aortic and carotid intima-media thickness.

Results: Adjusted models indicated a higher ratio of long versus short LPA bouts was associated with higher z-BMI (β = 1.69, SE = 0.83, p = 0.04), percent body fat (β = 10.72, SE = 3.71, p = 0.004), and z-triceps (β = 1.90, SE = 0.93, p = 0.04). A higher ratio of long versus short MPA bouts was associated with lower z-BMI (β = - 0.99, SE = 0.46, p = 0.03) and percent body fat (β = - 4.63, SE = 1.93, p = 0.02). A higher total volume of MPA versus VPA was associated with higher percent body fat (β = 4.07, SE = 1.63, p = 0.01) and z-triceps (β = 1.05, SE = 0.43, p = 0.01). Other outcomes showed no associations (p ≥   0.05).

Conclusions: In preschoolers, accumulating LPA in shorter bursts, MPA in longer bursts, and maintaining a higher proportion of VPA may support healthier adiposity profiles. These findings underscore the importance of minimizing prolonged sedentary time and encouraging sustained, high-intensity PA from early childhood.

Background: Average acceleration (AvAcc) and intensity gradient (IG) are accelerometer metrics which when combined describe the volume and intensity distribution of physical activity, sedentary behaviour, and sleep across the 24-h cycle. Little is known about trajectories of children's AvAcc and IG over time on weekdays and weekends. This study describes school year trajectories of children's weekday and weekend AvAcc and IG.

Methods: During 2023-24 249 children (8-9 years old; 51.4% girls) wore accelerometers for 24 h⋅day^-1 over 7-days at three time points (Autumn, Winter/Spring, Summer). AvAcc and IG were calculated for weekdays and weekends. K-means cluster analyses were performed on Autumn data to group participants according to their combined AvAcc and IG profiles. Linear mixed models examined school year weekday and weekend AvAcc and IG trajectories for the whole sample (Aim 1) and for the clusters (Aim 2).

Results: Aim 1: There were significant increases in weekday AvAcc in Summer compared to Winter/Spring (β = 3.94, 95% CI = 1.20, 6.68) and Autumn (β = 4.43, 95% CI = 2.47, 6.40), but not IG. Weekend AvAcc and IG were relatively stable. Aim 2: Three cluster groupings of children were identified (Most Active, Somewhat Active (weekdays) / Active (weekends), and Least Active). Weekday AvAcc increased significantly from Winter/Spring to Summer in all groups (+ 3.6-4.6 mg, 95% CIs > 0) and from Autumn to Summer in the less active groups only (+ 5.2-5.8 mg, 95% CIs > 0). IG remained stable for the Most and Somewhat Active groups, with a significant increase from Autumn to Summer observed in the Least Active group (+ 0.05, 95% CI = 0.01-0.09). There were no significant within-cluster group changes in weekend AvAcc or IG, although the Least Active children had the most positive AvAcc and IG trajectories.

Conclusions: Weekday physical activity volume but not intensity increased over the school year, while both dimensions of weekend activity had stable trajectories. Weekday and weekend cluster groups had distinct physical activity profiles which followed subtly different AvAcc and IG trajectories. The results reinforce the complementary insights provided by studying AvAcc and IG together and have implications for children's physical activity intervention programming.

Background: Regular Physical Activity (PA) is important for disease prevention and health promotion. PA has been assessed through surveys, questionnaires, and devices such as accelerometers. Alongside PA, Sedentary Behaviour (SB) and sleep are the main components of 24/7 movement behaviours, and their adequate measurement is important for assessing health outcomes. Many different metrics to summarise 24/7 movement behaviours are used; however, little attention has been paid to visualising these metrics. Data visualisation is likely to impact the way results are communicated and understood by different audiences. This study systematically reviews 24/7 movement behaviour metrics, presents an overview of their visualisations, and develops a framework to guide context-specific visualisation choices.

Methods: An umbrella review was conducted in February 2025 in Scopus and Web of Science. Included papers were reviews of any type, with any human population and study design, having at least one of the three 24/7 movement behaviours as exposure or outcome measured through accelerometers, and clearly reporting the outcome metrics. Data extraction and an adapted thematic data analysis were performed in April 2025. The overview of the visualisations used for the metrics identified in the review and thematic analysis was created through non-systematic web searches and use of Microsoft Copilot. Finally, a framework was created based on the sender-receiver model for effective communication.

Results: In total, 93 reviews were included, with a total of 5667 articles reporting on 134 unique output metrics based on accelerometer data. The most common metrics were step counts and time spent in Moderate-to-Vigorous Physical Activity (MVPA). The non-systematic web searches showed that most researchers use bar charts, line graphs, or pie graphs to visualise 24/7 movement behaviour data, while Copilot input provided more options of visualisations. The resulting framework was the product of an iterative process aggregating the previous results, providing clear guidance for organising metrics and their corresponding visualisations.

Conclusions: This study structures and summarises types of visualisations of accelerometer-derived metrics to describe 24/7 human movement behaviour data. Future research is needed to apply the framework in practical contexts and investigate how the visualisations are perceived by different audiences.

Background: Children's decreasing outdoor time and increasing screen time (ST) raise concern, as both can impact children's health unfavourably. However, little is known about the association between young children's context-specific outdoor time and ST. This study investigated the associations between parent-child outdoor visit frequency in nature, own yard, playground, and in total and preschoolers' ST on weekdays and weekends.

Methods: Data were collected via a survey assessing parent-child outdoor visits and a 7-day ST diary completed by parents of children (n = 673, 3-6 years) participating in the DAGIS intervention study in Finland. The cross-sectional associations were examined by linear regression analyses adjusted for child age, sex, socioeconomic factors, and season.

Results: More frequent parent-child visits to own yard (B -1.98, 95%CI -3.43, -0.52) and nature (B -2.31, 95%CI -4.01, -0.60) were associated with less weekday ST among children, whereas more frequent visits to playground (B -3.68, 95%CI -7.18, -0.18) were associated with less weekend ST. Also, more frequent visits to nature (B -2.28, 95%CI -4.20, -0.38), own yard (B -2.38, 95%CI -4.03, -0.73), and playground (B -2.48, 95%CI -4.71, -0.25) were associated with less whole-week ST. More frequent total outdoor visits were associated with less weekday (B -1.547, 95%CI -2.38, -0.71), weekend (B -1.91, 95%CI -3.38, -0.45), and whole-week ST (B -1.77, 95%CI -2.71, -0.84).

Conclusion: In conclusion, visiting different outdoor spaces was associated differentially with children's ST on weekdays and weekend days, whereas total outdoor visit frequency was associated with less ST throughout the week. Hence, municipalities should ensure the availability of different types of safe outdoor spaces in neighbourhoods to provide alternatives for young children's screen use.

Trial registration number: ISRCTN57165350, Registration date 08/01/2015.

Background: The retirement transition provides a window of opportunity for the optimization of activity, sedentary and sleep behaviors. Identifying groups at risk for non-favorable changes is important in this matter. There are indications that lower socio-economic position (SEP) adults might be more prone to non-favorable changes. However, previous studies mainly used self-reported behaviors and only one indicator of SEP. The purpose of the present study was therefore to examine the association of SEP with changes in device-measured physical activity, SB and sleep during the retirement transition in adults in Flanders (Belgium) in a longitudinal study.

Methods: The behaviors were measured pre-retirement and at three, six and twelve months post-retirement with a wrist-worn accelerometer (n = 96). The raw acceleration data were processed using the R package GGIR and analysed using compositional data analysis in linear mixed models including the SEP indicators education, occupation and income. Additionally, changes in intensity gradient and average acceleration were examined.

Results: The results showed that on average, physical activity was stable, sleep increased (+ 18 min) and SB decreased (- 15 min). The intensity gradient and average acceleration did not change significantly. The higher income group had significantly more favorable changes in movement behaviors compared to the lower income group. More specifically, they increased physical activity and shifted towards more intense physical activity, while the lower income group did not. The higher education and occupation groups showed a non-significant trend towards more favorable changes. The changes occurred mainly between pre-retirement and three months post-retirement and were relatively stable afterwards.

Conclusion: The behaviors shifted towards more healthy behaviors in general, with stable physical activity, a decrease in SB and an increase in sleep. The changes in the behaviors were more favorable for retirees with higher SEP compared to retirees with lower lower socio-economic position. There seems to be a need for strategies to improve 24-h movement behaviors of lower SEP adults during the retirement transition. However, our results are based on a small sample and should be validated in larger studies.