Journal of activity, sedentary and sleep behaviors最新文献

Background: Previous physical activity and sedentary behaviour studies during the pandemic have largely utilized online surveys, with known limitations including recall bias. Employing both device-based and self-reported measurements may provide a more comprehensive picture of both behaviours. Physical activity and sedentary behaviour research in adolescents is still limited in low- and middle-income countries (LMICs), including Indonesia. Male adolescents had been identified as more active than females but have had a greater decrease in physical activity during the pandemic. The present study aimed to investigate the quantity, temporal patterns, contexts, and biopsychosocial factors of physical activity and sedentary behaviour during the COVID-19 pandemic in a small group of male Indonesian adolescents.

Methods: Male adolescents (n = 5; 14-15 years old) from Yogyakarta wore accelerometers and automated wearable cameras for four days, and completed diaries and interviews in November 2020.

Results: Participants' activity was dominated by light intensity (67% of all physical activity). Sedentary behaviour was high; accelerometer, school days: 456 ± 145 min (78 ± 10% of wear time), non-school days: 344 ± 160 min (79 ± 17% of wear time); camera, school days: 176 ± 101 min (81 ± 46% of wear time), non-school days: 210 ± 165 min (86 ± 67% of wear time). Sedentary behaviour was mainly done during school hours on school days and from late afternoon to evening on non-school days. Screen time was largely for leisure purposes and action games were most favoured. Smartphones were the most used device, mainly used in a solitary context in the bedroom. Non-screen-based sedentary behaviour was consistently low. Interviews suggested that during the pandemic, supporting factors for physical activity are: self-determination, enjoyment, parental support, and physical education; meanwhile, factors influencing screen time are: educational demands, device and internet availability, screen time opportunities, parental control, social facilitators, phone notifications, and emotional state.

Conclusions: Most participants were not able to stay active during the pandemic. Using digital platforms may be beneficial to shift some screen-based sedentary behaviour to 'screen-based' or 'screen-prompted' physical activity.

Supplementary information: The online version contains supplementary material available at 10.1186/s44167-022-00014-0.

Background: The World Health Organization (WHO) has developed guidelines for 24-h physical activity (PA), sedentary behaviour and sleep for young children. Lower socioeconomic status (SES) has been linked to a lower likelihood of meeting these guidelines. The outbreak of the novel coronavirus disease (COVID-19) raised concerns about young children's opportunities to meet the guidelines. The study focused on the prevalence of meeting the WHO's 24-h guidelines on screen time (ST), PA and sleep among 2-6-year-old children, in association with family SES, before COVID-19 outbreak in 2019, and during the pandemic in 2020 and 2021 in Finland.

Methods: Data were collected at three timepoints by an online survey through day-care centres. Meeting the WHO 24-h guidelines was defined for each behaviour, from a parent-reported seven-day recall of a typical day on weekdays and weekend days and adapted to the national context. Children were considered to meet the ST guideline if they had maximum of 60 min of ST, the PA guideline if they had minimum of 60 min of outdoor PA, and the sleep guidelines if they had minimum of 11/10/9 h (2/3-5/6 years) of good or very good quality sleep. Binary logistic regression models were used to examine the odds ratios of meeting the guidelines.

Results: The prevalence of meeting the ST guideline was highest before the COVID-19 pandemic in 2019. The PA guideline was most met during the strict pandemic restrictions in 2020. Children from higher SES families were more likely to meet the ST and sleep duration guidelines either on weekdays or weekends. The PA guideline was met more on weekdays by children whose parents had lower education levels at all timepoints. In 2020, sleep quality guideline was less likely met by children with parents with the highest education levels.

Conclusion: Higher SES may increase the odds of young children meeting the ST and sleep duration guidelines, but the results are more complex regarding PA and SES. The impact of the COVID-19 pandemic on ST, outdoor PA, and sleep of young children varied by family SES, and further research is recommended to identify causality of these relationships.

Supplementary information: The online version contains supplementary material available at 10.1186/s44167-022-00010-4.

Background: Time-use estimates are typically used to describe 24-hour movement behaviours. However, these behaviours can additionally be characterised by other easily measured metrics. These include sleep quality (e.g., sleep efficiency), 24-hour rest-activity rhythmicity (e.g., between-day rhythm variability), and directly measured acceleration metrics (e.g., intensity gradient). Associations between these characteristics and youth mental health are unclear. This study aimed to [1] compare 24-hour movement behaviour characteristics by sex and age groups, [2] determine which movement behaviour characteristics were most strongly associated with mental health outcomes, and [3] investigate the optimal time-use behaviour compositions for different mental health outcomes.

Methods: Three-hundred-and-one children and adolescents (age 9-13 y; 60% girls) wore accelerometers for 24-hours/day over 7-days. Overall mental health, externalising, and internalising problems were self-reported using the Strengths and Difficulties Questionnaire. 24-hour movement behaviour characteristics were categorised as time-use estimates, sleep quality, 24-hour activity rhythmicity, and directly measured acceleration. Linear mixed models and compositional data analysis were used to analyse the data in alignment with the study aims.

Results: Time-use estimates, directly measured accelerations, and 24-hour rest-activity rhythm metrics indicated that children were significantly more physically active (p = .01-<0.001) than adolescents. Children were also less sedentary (p < .01), slept longer (p = .02-0.01), and had lower sleep efficiency. Boys were significantly more active than girls (p < .001) who in turn accrued more time in sleep (p = .02). The timing of peak activity was significantly later among adolescents (p = .047). Overall mental health and externalising problems were significantly associated with sleep, sedentary time, sleep efficiency, amplitude, and inter-daily stability (p = .04-0.01). The optimal time-use compositions were specific to overall mental health and externalising problems and were characterised by more sleep, light and vigorous physical activity, and less sedentary time and moderate physical activity than the sample's mean time-use composition.

Conclusions: Extracting and examining multiple movement behaviour characteristics from 24-hour accelerometer data can provide a more rounded picture of the interplay between different elements of movement behaviours and their relationships with mental health than single characteristics alone, such as time-use estimates. Applying multiple movement behaviour characteristics to the translation of research findings may enhance the impact of the data for research users.

Supplementary information: The online version contains supplementary material available at 10.1186/s44167-023-00021-9.

Background: Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days.

Methods: MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application.

Results: MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes.

Conclusions: Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.

Background: Early childhood is important for cognitive and social-emotional development, and a time in which to promote healthy movement behaviors (sedentary behavior, physical activity, and sleep). Movement behaviors may have interactive influences on cognition and social-emotional factors in young children, but most previous research has explored them independently. The purpose of this study was to determine if movement behaviors are associated with measures of cognitive and social-emotional health in young children and if so, to describe optimal compositions of movement behaviors of a daily cycle for such outcomes.

Methods: Children (n = 388, 33 to 70 months, 44.6% female) from a clinical trial (ClinicalTrials.gov ID: NCT03285880, first posted September 18, 2017) wore accelerometers on their wrists for 24-h for 9.56 ± 3.3 days. Movement behavior compositions consisted of time spent in sedentary behaviors, light intensity physical activity, moderate to vigorous intensity physical activity (MVPA), and sleep. Outcomes were cognitive (receptive vocabulary, declarative and procedural memory, and executive attention) and social-emotional measures (temperament and behavioral problems). Compositional linear regression models with isometric log ratios were used to investigate the relations between the movement behavior composition and the cognitive and social-emotional health measures. If a significant association was found between the composition and an outcome, we further explored the "optimal" 24-h time-use for said outcome.

Results: Movement behavior compositions were associated with receptive vocabulary. The composition associated with the predicted top five percent of vocabulary scores consisted of 12.1 h of sleep, 4.7 h of sedentary time, 5.6 h of light physical activity, and 1.7 h of MVPA.

Conclusions: While behavior compositions are related to vocabulary ability in early childhood, our findings align with the inconclusiveness of the current evidence regarding other developmental outcomes. Future research exploring activities within these four movement behaviors, that are meaningful to cognitive and social-emotional development, may be warranted.

Supplementary information: The online version contains supplementary material available at 10.1186/s44167-023-00016-6.