Journal of activity, sedentary and sleep behaviors最新文献

Background: Excessive sedentary behavior (SB) is highly prevalent among adults in the United States (US). From a socio-ecological perspective, residential segregation may affect SB; however, this remains understudied. Thus, we aimed to examine associations between county-level segregation and sedentary time in a nationwide sample of US adults.

Methods: For this cross-sectional study, we analyzed data from 2,637 US adults aged 20-75 years (mean age [45.1 years], female [50.6%]) from the population-based AmeriSpeak panel in 2019. Participants completed the Activities Completed over Time in 24-hours (ACT24) previous-day recall, which is a validated population-level measure of total daily SB (hours/day). Residential segregation was expressed as the isolation index at the county level for non-Hispanic (NH) Black and Hispanic adults (vs. all other racial and/or ethnic groups). Isolation index ranges from 0 to 1, with a higher value indicating higher segregation. We used survey-weighted linear regression models to examine the relationships of race and/or ethnicity specific isolation index with total daily SB, adjusting for covariates. Models were also stratified by sex.

Results: US adults reported a mean of 9.5 h/day of sedentary time, with NH Black and Hispanic adults reporting 9.8 and 8.9 h/day, respectively. NH Black segregation was not related to sedentary time (β = -0.30 [-2.53, 1.94], p = 0.790). Hispanic segregation also showed no relationship (β = 0.32, [-1.64, 2.28], p = 0.743). Moreover, sex-stratified analyses showed null associations.

Conclusions: We found no association between residential segregation and SB among NH Black and Hispanic adults, and these associations did not vary by sex. Future studies should aim to replicate this study with larger samples of underrepresented minority populations.

Background: To enhance evidence on optimal 24-hour movement behaviors (physical activity, sedentary behavior, and sleep) in early childhood, this study investigated cross-sectional and longitudinal associations of the composition of these behaviors with social-emotional development, gross motor development and growth in 0-4-year-olds.

Methods: Data were collected at two timepoints (baseline and 9 months later) in two sub-cohorts from the My Little Moves study: one examining social-emotional development (sub-cohort-SE) and one gross motor development and growth (sub-cohort-GM). Children's time spent in 24-hour movement behaviors was assessed via parent-report using the My Little Moves app. Isometric log-ratios were calculated to represent 24-hour movement behavior composition. Social-emotional and gross motor development were assessed using the Bayley Scales of Infant and Toddler Development-III, with both total raw and norm-referenced scaled scores. Children's weight and height were measured to calculate BMI z-scores. Linear regression and mixed-model analyses examined cross-sectional and longitudinal associations, with significant results further explored using compositional isotemporal reallocation analysis.

Results: Sub-cohort-SE provided data from 101 children at timepoint 1 (age 20.6 ± 12.5 months) and 62 children at timepoint 2 (age 25.7 ± 9.8 months). Sub-cohort-GM provided data from 60 children at timepoint 1 (age 20.4 ± 10.8 months) and 46 children at timepoint 2 (age 27.6 ± 9.6 months). The composition of 24-hour movement behaviors was significantly associated with raw gross motor development scores in both cross-sectional (p < .001, R²Δ = 0.042) and longitudinal (p < .001, R²Δ = 0.033) analyses. The association with BMI z-scores was significant only in the cross-sectional analysis (p = .015, R²Δ = 0.130). Reallocating 10 min from sedentary behavior to physical activity or sleep increased raw gross motor development scores by 0.22 (95% CI [0.11, 0.33]), and 0.27 (95% CI [0.08, 0.45]). Reallocating 10 min from sedentary behavior to sleep increased BMI z-scores by 0.04 (95% CI [0.01, 0.06]).

Conclusions: The composition of 24-hour movement behaviors was significantly associated with BMI z-scores and gross motor development, but not social-emotional development in children aged 0-4 years. Evidence on the optimal distribution of movement behaviors remains unclear and needs further examination in larger longitudinal studies.

Background: Low levels of physical activity (PA) and high levels of sedentary behavior (SB) are major modifiable risk factors for the prevention of non-communicable diseases such as cardiovascular diseases, cancer, and diabetes. However, there is insufficient information on the barriers and facilitators of PA and SB in Ethiopia, particularly at multiple socioecological levels, to inform behavior change interventions that promote active living among office-based working adults. This qualitative formative study aimed to explore the barriers and facilitators to increasing PA and reducing SB in Ethiopian office employees using the social-ecological model (SEM) as a conceptual framework.

Methods: A total of twenty-six office workers (19 physically inactive/sedentary, 7 physically active) and seven key informants participated in in-depth interviews conducted between April and June 2023 in Hawassa, southern Ethiopia. The interviews were audio-recorded, fully transcribed, translated into English, and iteratively coded. Data were analyzed using a thematic analysis approach, and the barriers and facilitators generated were mapped onto the SEM categories.

Results: The findings were organized into thirteen themes that belong to the five levels of the SEM. Six themes were categorized to the intrapersonal level (sociodemographic and economic status; knowledge, experience and misperceptions; attitudes, beliefs and values; motivational challenges; sitting habit; and physical competence/skill); one theme belonged to the interpersonal level (social support); two themes were classified to the organizational level (work-related factors; organizational support); three themes were categorized to the environmental/community level (physical environment; social norm associated with walking; and PA promotional initiatives); and one theme belonged to the policy level (workplace health promotion policies).

Conclusions: A complex interplay of multiple factors contributes to office workers' PA and SB engagement. Interventions should consider multi-component behavior change strategies that target barriers and facilitators at multiple levels, including individually tailored approaches, in order to effectively influence these physical behaviors.

Objectives: Movement behaviors, such as sedentary behavior (SB), light intensity physical activity (LPA), and moderate to vigorous intensity physical activity (MVPA) are mutually exclusive, in that time spent in one behavior inevitably displaces time spent in another. Yet, few studies have simultaneously considered these health behaviors and their association with health-related quality of life (HRQoL), an important indicator of health and well-being in aging populations. This study aimed to explore the role of waking movement behaviors (i.e., SB, LPA, MVPA) in prospectively predicting HRQoL in older adults. The study further aimed to cross-validate associations between movement behaviors and HRQoL using two different device-based measures of behavior placed on unique body positions.

Methods: Older adults wore accelerometers on the thigh (ActivPAL micro) and waist (Actigraph GT3X-BT) for 14 days to assess waking movement behaviors. Participants subsequently reported HRQoL on the 36-Item Short Form Health Survey (SF-36) six months later. Compositional linear regression and isotemporal substitution models were used to explore longitudinal relationships between movement behaviors and HRQoL.

Results: The movement composition for the sample (N = 202; M_age = 70 years, 72.3% female) consisted of 71-87% SB, 10-26% LPA, and about 3% MVPA depending on the device. For both devices, MVPA was positively associated with Physical Functioning, Pain, and General Health subscales. SB was negatively associated with Physical Functioning, Vitality, Emotional Well-being, and Social Functioning subscales across both devices. LPA generally was not associated with HRQoL indicators. Across both devices, reallocation of between five and 15 min of SB or LPA to MVPA was generally associated with better scores for HRQoL indicators.

Conclusions: Movement compositions were for the most part prospectively associated with HRQoL indicators among older adults, which was driven by positive associations for MVPA and negative associations for SB. Findings were generally consistent across devices, however, discrepancies may be attributable to the differences in the data processing algorithms (activity counts vs. cadence based) and device placement locations. Interventions targeting age-related declines in HRQoL should prioritize promoting increased MVPA at the expense of SB to support health and well-being in old age.

Background: Time spent in sleep, sedentary behaviour (SB), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA) all impact child health. The aim of this study was to investigate cross-sectional and longitudinal associations between time use and adiposity and cardio-respiratory fitness (CRF) in school-aged children.

Methods: Cross-sectional (n = 281) and longitudinal (n = 305) data were used from the Life on Holidays study with assessments over three time periods (two consecutive school years, and the interleaving summer holiday period). 24-h time use was measured using wrist-worn accelerometers. Adiposity was assessed as BMI z-score from measured weight and height, using World Health Organization reference data, and from body fat percentage (%BF) measured via bioelectrical impedance. CRF was operationalised as estimated VO₂max from the 20-m shuttle run test. Compositional data analysis in conjunction with linear mixed-effects models was used to investigate the associations between time-use composition and outcomes after controlling for covariates. Cross-sectional models used baseline measures, and longitudinal models used rates of change in each outcome. Sensitivity analyses explored relationships for moderate physical activity (MPA) and vigorous physical activity (VPA) separately.

Results: In cross-sectional analyses, time spent in MVPA was favourably associated with all adiposity and fitness outcomes (all p < 0.01) whereas time in LPA was unfavourably associated with all outcomes (all p < 0.01). Sleep was favourably associated with %BF, whereas SB was unfavourably associated (both p = 0.02). In longitudinal models, only MVPA was significantly associated with any of the three outcomes. 30 min/day more time spent in MVPA was associated with a decrease in %BF rate of change (-0.60 to -0.48) when time was reallocated from LPA or SB, and with an increase in VO₂max (+ 0.89 to + 1.01) when time was reallocated from any other behaviour. Sensitivity analyses showed VPA was significantly associated with changes in %BF and VO₂max.

Conclusions: All behaviours displayed cross-sectional associations with adiposity and CRF. However, only MVPA (and in particular VPA) was significantly associated with changes in %BF or VO₂max in longitudinal models. Future efforts should focus on increasing participation in MVPA with school-aged children to see the most benefit to health.

Background: Parents influence and support children's healthy movement behaviours (physical activity, sedentary time, sleep). During the COVID-19 pandemic, families spent even more time together. Thus, parents likely had greater influence on their child's movement behaviours than usual. This study assessed the associations between parent and child movement behaviours and compliance with movement behaviour guidelines at two time points during the COVID-19 pandemic.

Methods: National samples of parents of children and adolescents (5-17 years) living in Canada completed online surveys about their own and their child's movement behaviours in October 2020 (n = 1,568, 58% women) and April 2021 (n = 1,600, 60% women). Associations between parent and child movement behaviours and compliance with 24-hour movement behaviour guidelines were examined.

Results: We observed mostly positive, significant correlations between parent and child movement behaviours in October 2020 (r = 0.12-0.26, p < 0.05) and April 2021 (r = 0.12-0.20, p < 0.05). A parent meeting an individual movement behaviour guideline (e.g., physical activity) was associated with an overall higher incidence of their child meeting the same guideline in October 2020 (Incidence Rate Ratio (IRR):=1.10-3.06) and April 2021 (IRR: 1.19-2.26). The incidence of children meeting the 24-hour movement guidelines in October 2020 (IRR = 3.06, 95% CI: 1.55, 6.04) and in April 2021 (IRR = 2.26, 95% CI: 1.34, 3.83) was higher when parents met the 24-hour movement guidelines.

Conclusions: Parent and child movement behaviours were associated with one another during the COVID-19 pandemic, particularly for children (compared with adolescents). In times of severe public health restrictions, health promotion efforts should target family units to promote healthy movement of families collectively.

Background: Despite the established evidence that physical activity, sedentary behavior, and sleep affect cognitive function individually, less is known about the combined effects of these movement behaviors. The study aimed to identify movement patterns of physical activity, sitting time, and sleep and to examine the association of movement patterns with cognitive function.

Methods: This cross-sectional study included 1,240 participants aged ≥ 55 years participating in the Cooper Center Longitudinal Study who visited the Cooper Clinic, Dallas (2016-2019) for preventive health care. Four movement behaviors were self-reported, including leisure-time aerobic activity, muscle-strengthening activity, sitting time, sleep, and other characteristics. Cognitive function was assessed by the Montreal Cognitive Assessment (MoCA). Four categorical indicators were created for each movement behavior and used to identify latent classes. Information criterion, scaled relative entropy and model interpretability were used to determine the optimal number of classes. Participants were assigned to the predicted classes based on their highest posterior probabilities. Multinomial regressions examined the association between movement patterns and each covariate. Linear and logistic regression models examined the association of movement patterns and cognitive function. A sensitivity analysis accounted for misclassification errors.

Results: Participants were predominantly White (95%), male (71%), with an average age of 62 years. A 3-class model was selected, comprising class 1: active long sleepers, class 2: very active short sleepers, and class 3: moderately active short sleepers, representing 11%, 62%, and 27% of the sample. Compared to class 2, class 1 was more likely to be older and female, while class 3 was more likely to be female, have less education, be overweight and obese, and have chronic conditions. Compared to class 2, class 3 was associated with a lower MoCA total score, adjusting for sociodemographic factors. There were no differences in MoCA total score between class 2 and class 3 when further controlling for health behaviors and indicators. Sensitivity analysis accounting for misclassification suggested that class 3 had a significantly lower average MoCA total score than class 2.

Conclusions: The current study identified three distinct movement classes that exhibited different sociodemographic, health characteristics and cognitive functions. Findings highlight that less active, more sedentary, and shorter sleep individuals had worse cognitive function.

Objective: Physical inactivity (PI), sedentary behaviour time (SB) and screen time (ST) are related but distinct behaviors for which separate research and environmental intervention frameworks may be warranted. We examined associations between neighbourhood features and PI/SB/ST among boys and girls at risk of obesity at two timepoints, i.e., childhood (8-10 years old) and pre-adolescence (10-12 years old).

Methods: Data were from the QUALITY cohort, an ongoing study of the natural history of obesity in 630 Quebec families. Based on accelerometry, excess PI was defined as accumulating < 60 min/day of moderate to vigorous physical activity and excess SB as recording < 100 counts per minute for > 50% of wear time, and excess ST was based on self report and defined as reporting > 2 h/day of recreational ST. Neighbourhood features including presence of physical activity installations, green space, walkability, traffic indicators, physical disorder and foodscape indicators were measured using direct observation and geographic information systems. Neighbourhood features were measured when children were 8-10 years of age. Separate logistic regression models were estimated at each time point. Models controlled for child's age, parental BMI, parental education, and area-level material deprivation.

Results: The odds of excess ST were lower in neighbourhoods with a higher number of parks, across all age and sex groups [ORs ranging from 0.70 (95% CI: 0.54-0.91) to 0.81(95% CI: 0.65-1.01)]. Among boys, the odds of excess SB were lower in neighbourhoods with more physical activity structures (OR: 0.44; 95% CI: 0.20-0.99); among girls, the odds of excess SB were lower in neighbourhoods with more sidewalks (OR: 0.67, 95% CI 0.47-0.95) and those that were exclusively residential (OR: 0.13, 95% CI: 0.04-0.45). Few neighbourhood features were associated with PI.

Conclusion: Our findings suggest that PI, SB and ST have both shared and distinct environmental determinants among children with parental obesity. While different patterns are likely to emerge across diverse contexts and populations, it remains relevant to consider that transforming specific features of the built environment may be more effective for some outcomes than others, and may not benefit all groups equally.

Background: Childhood is a critical period for the development of movement behaviours such as physical activity, sleep and sedentary behaviour. The PLAYCE Cohort was established to investigate how movement behaviours change over early to middle childhood, across key behaviour settings and relationships with health and development. An overview of the PLAYCE cohort, summary of key findings to date, and future research opportunities are presented.

Methods: Children were recruited at 2-5 years of age (preschool; Wave 1) via early childhood education and care (ECEC) services and were followed up in junior primary school (5-7 years; Wave 2) at 8-10 years (Wave 3) and again at 11-13 years (Wave 4; in progress). Children's movement behaviours were measured via parent-report and accelerometry. Social-emotional development, motor development, weight status, diet, and child and family socio-demographics were parent-reported. Physical environmental features of children's key behaviour settings (home, neighbourhood, ECEC and school) were collected using geo-spatial and audit data.

Results to date: At wave 1 (2-5 years), only 8% of children met all three recommendations of the Australian 24-hour Movement Guidelines for the Early Years. Meeting all recommendations (8%) was positively associated with boys social-emotional development. Physical environment features of the home yard (size, play equipment, natural features) were positively associated with preschool children's physical activity. Tree canopy and more portable play equipment in ECEC outdoor areas was also positively associated with children's outdoor time and physical activity.

Conclusions: Wave 4 (11-13 years) data collection will be completed in early 2026. Traditional longitudinal and compositional data analysis of the PLAYCE cohort will be undertaken. Four waves of data will provide detailed patterns of movement behaviours and their effect on child health and development as well as the environmental influences on children's movement behaviours across early to middle childhood. The findings can be used to inform national and international 24-Hour Movement Guidelines and behaviour setting-specific as well as population-level interventions to benefit child health and wellbeing across early to middle childhood.

Introduction: Movement behaviours (e.g., sleep, sedentary behaviour, light physical activity [LPA], moderate to vigorous physical activity [MVPA]) are associated with numerous health and well-being outcomes. Compositional data analyses (CoDA) accounts for the interdependent nature of movement behaviours. This systematic review and meta-analysis provides a timely synthesis of the first decade of CoDA research examining the association between movement behaviours, health, and well-being in school-aged children.

Methods: Databases were systematically searched for peer-reviewed studies examining CoDA associations between movement behaviours and health or well-being in school-aged children (5.0-17.9 years). All health and well-being outcomes were eligible for inclusion, as were all methods of reporting CoDA results. Where possible meta-analyses were conducted.

Results: Twenty-six studies were included in the review. Sample sizes ranged from 88 - 5,828 (median = 387) participants and the mean ages ranged from 8 to 16 years. Regression parameters (k_studies=16) were the most common method of reporting results, followed by substitution effects (k_studies=12), optimal compositions (k_studies=3), and movement behaviour clusters (k_studies =1). Weighted compositional means of movement behaviours were calculated (e.g., 49.8 min/day of MVPA). For regression analyses, results were generally null, though some favourable trends were observed for MVPA and unfavourable trends for LPA and sedentary behaviour within individual health and well-being outcomes categories. Meta-analyses of substitutions supported the benefits of MVPA, with the risks of reducing MVPA for other movement behaviours being double the magnitude compared to the benefits of adding MVPA.

Discussion: The most consistent conclusions within this review align with previous reviews that support the benefits of MVPA. Further, some evidence supported 24-hour movement behaviour guideline recommendations of increasing sleep and decreasing sedentary behaviour. This review also quantified not only the need to promote MVPA, but perhaps more importantly the urgency needed to preserve the limited MVPA children currently accumulate. Findings reinforce the "more/less is better" messages for movement behaviours, but do not allow us to recommend more specific balances of movement behaviours. As CoDA of movement behaviours progresses and accumulates further research, the methods and discussion points within the current review can aide future meta-analyses aimed at advancing the precision health guidance needed for optimizing children's health and well-being.