Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth

Environmental, social, and behavioral challenges of the human circadian clock in real-life conditions

Urban environments, in which ambient light has become a less-reliable entrainer, are challenging for the biological clock to maintain performance. As a consequence, human circadian rhythms are less robust and more variable among individuals. Assessing the individual phase of entrainment, as well as its plastic shifts in response to disturbances of the physical and social environment, is a way to measure circadian disruption. However, this is still difficult to address in real-life scenarios in which several factors modulate the circadian phase not always in a concerted manner. In this perspective, we present the contribution of two real-life situations, in which the circadian system is challenged by important alterations in entraining signals: 1) a trip to the Antarctic summer (socio-environmental challenge), and 2) dancers trained in morning/night shifts (socio-behavioral challenge). Both natural chronobiological experiments are helpful in exploring the functioning and plasticity of the circadian clock and allow for considering individual characteristics and history.

Longitudinal effects of dog ownership, dog acquisition, and dog loss on children's movement behaviours: findings from the PLAYCE cohort study

INTRODUCTION

Regular physical activity is important for children's physical and mental health, yet many children do not achieve recommended amounts of physical activity. Dog ownership has been associated with increased physical activity in children, however, there have been no longitudinal studies examining this relationship. This study used data from the Play Spaces and Environments for Children's Physical Activity (PLAYCE) cohort study to examine the longitudinal effects of dog ownership status on children's movement behaviours.

METHODS

Change in dog ownership from preschool (wave 1, age 2-5) to fulltime school (wave 2, age 5-7) was used as a natural experiment with four distinct dog ownership groups: continuing non-dog owners (n = 307), continuing dog owners (n = 204), dog acquired (n = 58), and dog loss (n = 31; total n = 600). Daily movement behaviours, including physical activity, sedentary time, sleep, and screen time, were measured using accelerometry and parent-report surveys. Differences between groups over time and by sex were tested using linear mixed effects regression models.

RESULTS

Girls who acquired a dog increased their light intensity activities and games by 52.0 min/day (95%CI 7.9, 96.0) and girls who lost a dog decreased their light intensity activities and games by 62.1 min/day (95%CI -119.3, -4.9) compared to no change among non-dog owners. Girls and boys who acquired a dog increased their unstructured physical activity by 6.8 (95%CI 3.2, 10.3) and 7.1 (95%CI 3.9, 10.3) occasions/week, compared to no changes among non-dog owners. Girls and boys who lost a dog reduced their unstructured physical activity by 10.2 (95%CI -15.0, -5.3) and 7.7 (95%CI -12.0, -3.5) occasions/week. Girls who lost a dog decreased their total physical activity by 46.3 min/day (95%CI -107.5, 14.8) compared to no change among non-dog owners. Continuing dog ownership was typically not associated with movement behaviours. Dog ownership group was not associated with changes in sleep and had mixed associations with screen time.

CONCLUSION

The positive influence of dog ownership on children's physical activity begins in early childhood and differs by child sex. Further research should examine the specific contributions dog-facilitated physical activity makes to children's overall physical activity, including the intensity and duration of dog walking and play.

Individual light history matters to deal with the Antarctic summer

The effect of light, main zeitgeber of the circadian system, depends on the time of day it is received. A brief trip to the Antarctic summer (ANT) allowed us to explore the impact of a sudden and synchronized increase in light exposure on activity-rest rhythms and sleep patterns of 11 Uruguayan university students, and to assess the significance of light history in determining individual circadian phase shift. Measurements collected in the peri-equinox in Montevideo, Uruguay (baseline situation, MVD) and in ANT, included sleep logs, actigraphy, and salivary melatonin to determine dim-light melatonin onset (DLMO), the most reliable marker of circadian phase. The increase in light exposure in ANT with respect to MVD (affecting both light-sensitive windows with opposite effects on the circadian phase) resulted in no net change in DLMO among participants as some participants advanced their DLMO and some others delayed it. The ultimate cause of each participant's distinctive circadian phase shift relied on the unique change in light exposure each individual was subjected to between their MVD and ANT. This study shows an association between the individual light history and the circadian phase shift.

Effects of green space on physical activity and body weight status among Chinese adults: a systematic review

Background

Green space may provide many benefits to residents' health behaviors and body weight status, but the evidence is still relatively scattered among Chinese adults. The purpose of this study was to review the scientific evidence on the effects of green space on physical activity (PA) and body weight status among Chinese adults.

Methods

A keyword and reference search was conducted in Pubmed, Web of Science, MEDLINE, and PsycINFO. Studies examining the associations between green space and PA, body mass index (BMI) among Chinese adults were included. The quality of the included literature was evaluated using the National Institutes of Health's Observational Cohort and Cross-Sectional Study Quality Assessment Tool.

Results

A total of 31 studies were included that met the inclusion criteria, including 25 studies with a cross-sectional design, 3 studies with a longitudinal design, and 3 studies with an experimental design. Street-level green view index and green space accessibility were found to be positively associated with PA, but negatively associated with BMI. In most studies, there was a correlation between green space ratio in local areas and BMI. In addition, green space interventions were effective in increasing PA and decreasing BMI among Chinese adults. In contrast, further evidence is needed to support the association between the design characteristics of green space and PA and BMI.

Conclusion

Preliminary evidence suggests that green space has a positive effect on PA and BMI among Chinese adults. However, there are contradictory findings, and future studies adopting longitudinal and quasi-experimental studies are needed to further explore the causal relationship between green space and PA and BMI to provide a relevant theoretical basis for policymakers.

Web-based physical activity promotion in young people with CF: a randomised controlled trial

BACKGROUND

Physical activity levels are known to decline following hospitalisation for people with cystic fibrosis (pwCF). However, optimal physical activity promotion strategies are unclear. This study investigated the effect of a web-based application (ActivOnline) in promoting physical activity in young pwCF.

METHODS

Multicentre randomised controlled trial with assessor blinding and qualitative evaluation. People with CF (12-35 years) admitted to hospital for a respiratory cause were eligible and randomised to the 12-week ActivOnline intervention (AO) or usual care (UC). The primary outcome was change in device-based time spent in moderate-to-vigorous physical activity (MVPA) from baseline to post-intervention. Follow-up was at 6 months from hospital discharge when qualitative evaluation was undertaken.

RESULTS

107 participants were randomised to AO (n=52) or UC (n=55). Sixty-three participants (59%) contributed to the intention-to-treat analysis. Mean (SD) age was 21 (6) years (n=46, <18 years). At baseline, physical activity levels were high in both groups (AO 102 (52) vs UC 127 (73) min/day). There was no statistically significant difference in MVPA between groups at either timepoint (post-intervention mean difference (95% CI) -14 mins (-45 to 16)). Uptake of the intervention was low with only 40% (n=21) of participants accessing the web application.

CONCLUSION

A web-based application, including individualised goal setting, real-time feedback and motivation for behavioural change, was no better than usual care at promoting physical activity in young pwCF following hospital discharge. High levels of baseline physical activity levels in both groups, and limited engagement with the intervention, suggest alternative strategies may be necessary to identify and support young pwCF who would benefit from enhanced physical activity.

TRIAL REGISTRATION NUMBER

ACTRN12617001009303, 13 July 13 2017.

Validation of an automated sleep detection algorithm using data from multiple accelerometer brands

To evaluate the criterion validity of an automated sleep detection algorithm applied to data from three research-grade accelerometers worn on each wrist with concurrent laboratory-based polysomnography (PSG). A total of 30 healthy volunteers (mean [SD] age 31.5 [7.2] years, body mass index 25.5 [3.7] kg/m² ) wore an Axivity, GENEActiv and ActiGraph accelerometer on each wrist during a 1-night PSG assessment. Sleep estimates (sleep period time window [SPT-window], sleep duration, sleep onset and waking time, sleep efficiency, and wake after sleep onset [WASO]) were generated using the automated sleep detection algorithm within the open-source GGIR package. Agreement of sleep estimates from accelerometer data with PSG was determined using pairwise 95% equivalence tests (±10% equivalence zone), intraclass correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA). Accelerometer-derived sleep estimates except for WASO were within the 10% equivalence zone of the PSG. Reliability between data from the accelerometers worn on either wrist and PSG was moderate for SPT-window duration (ICCs ≥ 0.65), sleep duration (ICCs ≥ 0.54), and sleep onset (ICCs ≥ 0.61), mostly good for waking time (ICCs ≥ 0.80), but poor for sleep efficiency (ICCs ≥ 0.08) and WASO (ICCs ≥ 0.08). The mean bias between all accelerometer-derived sleep estimates worn on either wrist and PSG were low; however, wide 95% LoA were observed for all sleep estimates, apart from waking time. The automated sleep detection algorithm applied to data from Axivity, GENEActiv and ActiGraph accelerometers, worn on either wrist, provides comparable measures to PSG for SPT-window and sleep duration, sleep onset and waking time, but a poor measure of wake during the sleep period.

A healthy lifestyle text message intervention for adolescents: protocol for the Health4Me randomized controlled trial

BACKGROUND

Adolescence presents a window of opportunity to establish good nutrition and physical activity behaviours to carry throughout the life course. Adolescents are at risk of developing cardiovascular and other chronic diseases due to poor the complex interplay of physical and mental health lifestyle risk factors. Text messaging is adolescents main form of everyday communication and text message programs offer a potential solution for support and improvement of lifestyle health behaviours. The primary aim of this study is to determine effectiveness of the Health4Me text message program to improve adolescent's physical activity or nutrition behaviours among adolescents over 6-months, compared to usual care.

METHODS

Health4Me is a virtual, two-arm, single-blind randomised controlled trial, delivering a 6-month healthy lifestyle text message program with optional health counselling. Recruitment will be through digital advertising and primary care services. In total, 330 adolescents will be randomised 1:1 to intervention or control (usual care) groups. The intervention group will receive 4-5 text messages per week for 6-months. All text messages have been co-designed with adolescents. Messages promote a healthy lifestyle by providing practical information, health tips, motivation and support for behaviour change for physical activity, nutrition, mental health, body image, popular digital media and climate and planetary health. Virtual assessments will occur at baseline and 6-months assessing physical health (physical activity, nutrition, body mass index, sleep), mental health (quality of life, self-efficacy, psychological distress, anxiety, depression, eating disorder risk) and lifestyle outcomes (food insecurity and eHealth literacy).

DISCUSSION

This study will determine the effectiveness of a 6-month healthy lifestyle text message intervention to improve physical activity and nutrition outcomes in adolescents.

TRIAL REGISTRATION

Australia New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000949785 , Date registered: 05/07/2022.

Physical activity and quality of life in children with idiopathic toe walking: a cross sectional study

Objectives

To determine if children with idiopathic toe walking (ITW) reach Australian 24-hour movement guidelines. Additional objectives were to identify any factors associated with moderate to vigorous physical activity time of children with ITW.

Design

Cross sectional.

Setting

Private practice, public health outpatient, community clinics.

Participants

Children between 4 and 14 years, who toe walked and had no medical conditions known to cause ITW.

Outcome measures

Physical activity intensity, sedentary behaviour and sleep data were collected via an ActiGraph. Physical activity level intensity data were triangulated with the Child Leisure Activities Study Survey (CLASS) to highlight the subjective nature of parent-reported measures. Health related quality of life information was collected using the Parent-Proxy and Child-Self Report Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scale. Regression analyses were used to explore individual factors associated with moderate to vigorous physical activity.

Results

Twenty-seven participants, 17(63%) male, age mean = 6.62 (SD = 2.29) years, provided information on physical activity (CLASS n = 18, ActiGraph n = 22), physical functioning and psychosocial functioning domains on the PedsQL (Parent-Proxy n = 25, Child n = 22). All participants exceeded Australian recommendations for physical activity, 44% (8/18) met recommended screen time amounts, and two (9%) met recommended sleep times. The Child-Self Report PedsQL scale score of social functioning was the only factor associated with an increase in physical activity (Coef = 0.48, 95%CI = 0.09 to 0.87, p = 0.019).

Conclusion

Participants achieved high levels of daily moderate to vigorous physical activity, and this was associated with social functioning. Given current uncertainty regarding benefits and effectiveness of treatment choices for children who have ITW, these findings should encourage clinicians to consider how their treatment recommendations interact with the PA level and sleep of children with ITW. Any treatment choice should also be implemented with consideration of how it may impact social functioning. This study had a small sample size therefore results should be cautiously interpreted and not generalised to all children with ITW.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03583-w.

Intensity and Duration of Physical Activity and Cardiorespiratory Fitness

OBJECTIVES

There is no clear guidance on the intensity and duration of physical activity (PA) that adolescents require to maximise cardiorespiratory fitness (CRF). We aimed to determine the strength of associations between each PA intensity and CRF, independently of other intensities, and the PA duration at each intensity associated with maximal CRF.

METHODS

PA and CRF were assessed in 339 adolescents aged 13 to 14 years by wrist-worn accelerometers and 20-m shuttle runs, respectively. Partial regression modeling was used to construct residualized PA variables at each PA intensity that were uncorrelated with each other. Moving average models were optimally fitted to determine relationships between residualized PA variables and CRF. Threshold regression models determined the duration of PA above which CRF improvement was minimal.

RESULTS

Greater vigorous PA (VPA) was associated with better CRF until about 20 minutes of daily VPA, when the relationship plateaued. Moderate and light PA, and sedentary time were not associated with CRF in partial models. Adolescents performing 14 (range 12-17) minutes of daily VPA had median CRF. Participants in the upper quartile of VPA had 1.03 z-scores higher CRF than those in the lowest quartile (95% confidence interval: 0.75 to 1.30).

CONCLUSIONS

Our data suggest that 20 minutes of daily VPA may be best for maximizing CRF in adolescence. As moderate-to-vigorous PA guidelines can be satisfied by only undertaking moderate PA, with no apparent independent benefit, we suggest that future guidelines focus on VPA alone, simplifying public health messaging.

Evaluation of environmental, social, and behavioral modulations of the circadian phase of dancers trained in shifts

The interplay of environmental, social, and behavioral factors influencing human circadian phase in ecological conditions remains elusive. The Uruguayan national dance school END-SODRE operating in two shifts (morning: 8:30–12:30 and night: 20:00–24:00) allowed us to evaluate how social demands, chronotype, environmental light, physical activity, and sleep patterns affected individual circadian phase measured by the onset of the nocturnal increase of melatonin (DLMO) in a single study. The DLMO was 1.5 h earlier in morning-shift dancers (n = 7) compared to night-shift dancers (n = 11). Sleep time and chronotype (only in night-shift dancers) were associated with the circadian phase. In training days, during each participant’s phase-advance and phase-delay time windows, light exposure was similar between morning and night-shift dancers and did not correlate with DLMO. In contrast, the time spent in moderate-vigorous physical activity during each participant’s phase-lag time window was higher in night-shift dancers than in morning-shift dancers and positively correlated with DLMO.

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Circadian phase was earlier in morning than in night dancers in real-life conditions

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Sleep time (and night-shift chronotypes) correlated with the circadian phase

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In the phase-advance and phase-lag windows, light was not associated with DLMO

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In the phase-lag time window, the longer the intense exercise the later the phase

A Transparent Method for Step Detection using an Acceleration Threshold

Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply do not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and non-ambulatory activities. Seventy-five adults (21-39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22-2.2ms^-1. Directly observed steps were hand-tallied. Participants wore GENEActiv and ActiGraph accelerometers, one of each on their waist and on their non-dominant wrist. Raw acceleration (g) signals from the anterior-posterior, medial-lateral, vertical, and vector magnitude (VM) directions were assessed separately for each device. Signals were demeaned across all activities and bandpass filtered [0.25, 2.5Hz]. Steps were detected via peak picking, with optimal thresholds (i.e., minimized absolute error from accumulated hand counted) determined by iterating minimum acceleration values to detect steps. Step counts were converted into cadence (steps/minute), and k-fold cross-validation quantified error (root mean squared error [RMSE]). We report optimal thresholds for use of either device on the waist (threshold=0.0267g) and wrist (threshold=0.0359g) using the VM signal. These thresholds yielded low error for the waist (RMSE<173 steps, ≤2.28 steps/minute) and wrist (RMSE<481 steps, ≤6.47 steps/minute) across all activities, and outperformed ActiLife's proprietary algorithm (RMSE=1312 and 2913 steps, 17.29 and 38.06 steps/minute for the waist and wrist, respectively). The thresholds reported herein provide a simple, transparent framework for step detection using accelerometers during treadmill ambulation and activities of daily living for waist- and wrist-worn locations.

Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach

It remains unclear whether the time-use composition of 24-h movement behaviours (sleep, sedentary time (ST), physical activity (PA)) and recreational screen use are independently associated with psychosocial health. This study examined the cross-sectional and longitudinal associations between 24-h movement behaviour composition, recreational screen use and psychosocial health outcomes in children. Measures completed at baseline (n = 127; 11.7 years) and follow-up (n = 88; 12.8 years) included accelerometer-based 24-h movement behaviours, self-reported recreational screen use and psychosocial health (Strengths and Difficulties Questionnaire, Kessler’s Psychological Distress Scale). Linear mixed models were used to examine the cross-sectional and longitudinal associations between the 24-h movement behaviour composition and recreational screen use levels with psychosocial health outcomes. Overall, the movement behaviour composition (p < 0.05) and recreational screen use levels (p < 0.01) were both cross-sectionally but not longitudinally associated with psychosocial health outcomes. Relative to other behaviours, sleep was negatively associated, while light-intensity PA was positively associated with internalising problems and total difficulties scores. ST was positively associated with internalising problems. High levels of recreational screen use (>2 h/day) were associated with greater externalising problems, total difficulties scores and psychological distress. These findings reinforce the importance of achieving a balance between different types of movement behaviours over a 24-h period for psychosocial health.

Cross-sectional associations between 24-hour activity behaviours and mental health indicators in children and adolescents: A compositional data analysis

We examined associations between youth 24-hour activity behaviour compositions and mental health. Data were collected from 359 participants (aged 9-13 years). Activity behaviours (sleep, sedentary time (ST), light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA)) were assessed using wrist-worn accelerometers. Questionnaires and a computerized cognitive test battery assessed mental health outcomes. Linear mixed models examined associations between activity behaviour compositions and mental health. Post-hoc analyses modelled the influence of reallocating fixed durations of time between activity behaviours on mental health. ST was associated with worse internalizing problems (all participants; p< 0.05) and poorer prosocial behaviour (primary school participants only; p< 0.05), relative to the other activity behaviours. LPA was associated with worse cognitive test scores among primary school participants; p< 0.05). For all participants, reallocating time to ST from sleep and MVPA was associated with higher internalizing problems. Among primary school participants, reallocating time to ST from any other behaviour was associated with poorer prosocial behaviour, and reallocating time to LPA from any other behaviour was associated with lower executive function. Children's mental health may be promoted by schools integrating opportunities for MVPA throughout the day. Our results provide further evidence for the influence of daily activity behaviours on youth mental health.

Free-living Evaluation of Laboratory-based Activity Classifiers in Preschoolers

Machine learning classification models for accelerometer data are potentially more accurate methods to measure physical activity in young children than traditional cut point methods. However, existing algorithms have been trained on laboratory-based activity trials, and their performance has not been investigated under free-living conditions.

PURPOSE

This study aimed to evaluate the accuracy of laboratory-trained hip and wrist random forest and support vector machine classifiers for the automatic recognition of five activity classes: sedentary (SED), light-intensity activities and games (LIGHT_AG), walking (WALK), running (RUN), and moderate to vigorous activities and games (MV_AG) in preschool-age children under free-living conditions.

METHODS

Thirty-one children (4.0 ± 0.9 yr) were video recorded during a 20-min free-living play session while wearing an ActiGraph GT3X+ on their right hip and nondominant wrist. Direct observation was used to continuously code ground truth activity class and specific activity types occurring within each class using a bespoke two-stage coding scheme. Performance was assessed by calculating overall classification accuracy and extended confusion matrices summarizing class-level accuracy and the frequency of specific activities observed within each class.

RESULTS

Accuracy values for the hip and wrist random forest algorithms were 69.4% and 59.1%, respectively. Accuracy values for hip and wrist support vector machine algorithms were 66.4% and 59.3%, respectively. Compared with the laboratory cross validation, accuracy decreased by 11%-15% for the hip classifiers and 19%-21% for the wrist classifiers. Classification accuracy values were 72%-78% for SED, 58%-79% for LIGHT_AG, 71%-84% for MV_AG, 9%-15% for WALK, and 66%-75% for RUN.

CONCLUSION

The accuracy of laboratory-based activity classifiers for preschool-age children was attenuated when tested on new data collected under free-living conditions. Future studies should train and test machine learning activity recognition algorithms using accelerometer data collected under free-living conditions.

Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children

Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children’s movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%–86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children’s movement behaviors under real-world conditions.

Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5-7-year-old children

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5-7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.

Decline of physical activity in early adolescence: A 3-year cohort study

This study analyses the changes of moderate-to-vigorous physical activity (MVPA) in a cohort of boys and girls aged 11 (n = 50) and 14 (n = 50). Physical activity was assessed with Bodymedia SenseWear Pro Armband monitor for 6 days in October 2013 and October 2016, considering 90% of daily wear time (21h and 40min). The initial sample (n = 160) included the children who wore the monitors at age 11 but the final analyzed sample included only those children from the initial sample (n = 50), whose data fulfilled the inclusion criteria at age 11 and 14. Physical fitness and somatic characteristics of the final sample (n = 50) were compared to a representative sample of Slovenian schoolchildren at ages 11 (n = 385) and 14 (n = 236) to detect possible bias. Changes in MVPA were controlled for maturity using the timing of adolescent growth spurt as its indicator. The average MVPA decreased more than one quarter (34.96 min) from age 11 to age 14. Children were significantly more active at age 11 than at age 14 (p < 0.01, d = 0.39). The timing of puberty onset in girls was significantly earlier (12.01 ± 1.0 years) (p < 0.01) than in boys (13.2 ± 0.75 years) (p < 0.01, d = 1.35). There was a significant gender difference in moderate-to vigorous physical activity at age 14 (p < 0.05, η2 = 0.12) and between moderate-to vigorous physical activity at age 11 and 14 (η2 = 0.11). After controlling for the timing of adolescent growth spurt the girls at age 11 showed significantly higher level of physical activity than at age 14 (p < 0.01, η2 = 0.17). Early adolescence is crucial for the development of physical activity behaviours, which is especially pronounced in girls. The significant decline of MVPA between ages 11 and 14 in Slovenia are likely influenced by environmental changes since the timing of adolescent growth spurt did not prove as a factor underlying the decline of MVPA.

Associations between early-life screen viewing and 24 hour movement behaviours: findings from a longitudinal birth cohort study

BACKGROUND

Screen viewing is a sedentary behaviour reported to interfere with sleep and physical activity. However, few longitudinal studies have assessed such associations in children of preschool age (0-6 years) and none have accounted for the compositional nature of these behaviours. We aimed to investigate the associations between total and device-specific screen viewing time at age 2-3 years and accelerometer-measured 24 h movement behaviours, including sleep, sedentary behaviour, light physical activity, and moderate-to-vigorous physical activity (MVPA) at age 5·5 years.

METHODS

The Growing Up in Singapore Towards healthy Outcomes (GUSTO) study is an ongoing longitudinal birth cohort study in Singapore, which began in June 2009. We recruited pregnant women during their first ultrasound scan visit at two major public maternity units in Singapore. At clinic visits done at age 2-3 years, we collected parent-reported information about children's daily total and device-specific screen viewing time (television, handheld devices, and computers). At 5·5 years, children's movement behaviours for 7 consecutive days were measured using wrist-worn accelerometers. We assessed the associations between screen viewing time and movement behaviours (sedentary behaviour, light physical activity, MVPA, and sleep) using Dirichlet regression, which accounts for the compositional nature of such behaviours. This study is active but not recruiting and is registered with ClinicalTrials.gov, NCT01174875.

FINDINGS

Between June 1, 2009, and Oct 12, 2010, 1247 pregnant women enrolled and 1171 singleton births were enrolled. 987 children had parent-reported screen data at either 2 or 3 years, of whom 840 attended the clinic visit at age 5·5 years, and 577 wore an accelerometer. 552 children had at least 3 days of accelerometer data and were included in the analysis. Total screen viewing time at age 2-3 years had a significant negative association with sleep (p=0·008), light physical activity (p<0·0001), and MVPA (p<0·0001) in relation to sedentary behaviour at age 5·5 years. Compared with children who spent 1 h or less per day screen viewing at age 2-3 years, children who screen viewed for 3 h or more per day at 2-3 years engaged in more sedentary behaviour (439·8 mins per day [≤1 h screen viewing time] vs 480·0 mins per day [≥3 h screen viewing time]), and less light physical activity (384·6 vs 356·2 mins per day), and MVPA (76·2 vs 63·4 mins per day) at age 5·5 years. No significant differences in time spent sleeping were observed between the groups (539·5 vs 540·4 mins per day). Similar trends were observed for television viewing and handheld device viewing.

INTERPRETATION

Longer screen viewing time in children aged 2-3 years was associated with more time spent engaged in sedentary behaviour and shorter time engaged in light physical activity and MVPA in later childhood. Our findings indicate that screen viewing might displace physical activity during early childhood, and suggest that reducing screen viewing time in early childhood might promote healthier behaviours and associated outcomes later in life.

FUNDING

Singapore National Research Foundation, and Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR).

An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing

Background

Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep.

Purpose

Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices.

Methods

We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X.

Results

During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors.

Conclusions

Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

A monitoring system to provide feedback on student physical activity during physical education lessons

School-based physical education (PE) provides opportunities to accumulate moderate-to-vigorous physical activity (MVPA), but many students are insufficiently active during PE lessons. Providing teachers with feedback regarding their students' physical activity may increase the effectiveness of PE for achieving MVPA goals, but existing physical activity monitoring technologies have limitations in class environments. Therefore, the purpose of this study was to develop and validate a system capable of providing feedback on PE lesson MVPA. Equations for translating step counts to %MVPA were derived from measures in 492 students who concurrently wore an ActiGraph GT3X+ (ActiGraph) and Yamax pedometer (Yamax) during a PE lesson. To enhance feedback availability during PE lessons, we then developed a bespoke monitoring system using wireless tri-axial pedometers (HMM) and a smart device app. After developing and testing the monitoring system, we assessed its validity and reliability in 100 students during a PE lesson. There was a strong correlation of 0.896 between step counts and accelerometer-determined %MVPA and quantile regression equations showed good validity for translating step counts to %MVPA with a mean absolute difference of 5.3 (95% CI, 4.4-6.2). The physical activity monitoring system was effective at providing %MVPA during PE lessons with a mean difference of 1.6 ± 7.1 compared with accelerometer-determined %MVPA (7% difference between the two measurement methods). Teachers and students can use a smart device app and wireless pedometers to conveniently obtain feedback during PE lessons. Future studies should determine whether such technologies help teachers to increase physical activity during PE lessons.