Browse

The clear public messaging from international health authorities is that individuals should “sit less and move more.” While it is acknowledged that this guidance needs to be tailored to the age of people and also to their health, and abilities, the guidance is not tailored to their current level of physical behaviors. This opinion piece aims to highlight that although people with excessive sitting and insufficient moderate-to-vigorous physical activity should sit more and move less, for other people their health would be promoted by sitting more and moving less. Thus, physical behaviors are not always “poison” or “medicine,” but rather the health impact of changes in physical behaviors depends on people’s initial levels. Policy, research, and practice implications of this realization are presented. Only tailoring messaging to age and health status could be far from optimal for people with very different current levels of physical behaviors. Policy, research, and practice will be enhanced when the potential for physical behaviors to be either health hindering or health promoting is adequately considered.

Over the last 4 decades, physical activity researchers have invested heavily in determining “what works” to promote healthy behaviors in schools. Single and multicomponent school-based interventions that target physical education, active transportation, and/or classroom activity breaks effectively increased physical activity among children and youth. Yet, few of these interventions are ever scaled-up and implemented under real-world conditions and in diverse populations. To achieve population-level health benefits, there is a need to design school-based health-promoting interventions for scalability and to consider key aspects of the scale-up process. In this opinion piece, we aim to identify challenges and advance knowledge and action toward scaling-up school-based physical activity interventions. We highlight the key roles of planning for scale-up at the outset, scale-up pathways, trust among partners and program support, program adaptation, evaluation of scale-up, and barriers and facilitators to scaling-up. We draw upon our experience scaling-up effective school-based interventions and provide a solid foundation from which others can work toward bridging the implementation-to-scale-up gap.

Background: Active transportation (AT), described as self-powered modes of travel (eg, walking and cycling), is an important source of health-promoting physical activity. While AT behaviors have been measured on national health surveys in Canada for over 2 decades, historic prevalence has not been previously reported. We aimed to document the measures of AT on Canada’s various national health surveys, examine AT over time, and interpret them within the context of evolving methods of assessment. Methods: We compiled and summarized the questions used to measure AT among Canadians on 4 national health surveys: National Population Health Survey (1994–1998), Canadian Community Health Survey (2000–2020), Canadian Health Measures Survey (2007–2019), and the Health Behaviour in School-aged Children Study (2010–2018). Among youth and adults (12+ y), we summarized over time: (1) the prevalence of AT participation and (2) time spent in AT (in hours per week) among those who report any AT participation. Where possible, we reported separate estimates of walking and cycling and produced an aggregate estimate of total AT. We stratified results by age group and sex. Results: Changes in AT survey questions over time and between surveys limit the interpretation and comparability of temporal trends. Nevertheless, a consistently higher proportion of females report walking, while a higher proportion of males report cycling. Irrespective of mode, males report spending more total time in AT. Participation in AT tends to decrease with age, with youth reporting the highest rates of AT and young adults often spending the most time in AT. Conclusions: Monitoring trends in AT can help assess patterns of behavior and identify whether promotion strategies are needed or whether population interventions are effective. Our evaluation of AT over time is limited by questions surveyed; however, consistent differences in AT by age and sex are evident over time. Moving forward, ensuring consistency of AT measurement over time is essential to monitoring this important behavior.

Background: There is limited evidence from globally diverse samples on the prevalence and correlates of meeting the global guideline of 180 minutes per day of total physical activity (TPA) among 3- to 4-year-olds. Methods: Cross-sectional study involving 797 (49.2% girls) 3- to 4-year-olds from 17 middle- and high-income countries who participated in the pilot phases 1 and 2 of the SUNRISE International Study of Movement Behaviours in the Early Years. Daily step count was measured using thigh-worn activPAL accelerometers. Children wore the accelerometers for at least one 24-hour period. Children were categorized as meeting the TPA guideline based on achieving ≥11,500 steps per day. Descriptive analyses were conducted to describe the proportion of meeting the TPA guideline for the overall sample and each of the sociodemographic variables, and 95% CIs were calculated. Multivariable logistic regression was used to determine the sociodemographic correlates of meeting the TPA guideline. Results: Mean daily step count was 10,295 steps per day (SD = 4084). Approximately one-third of the sample (30.9%, 95% CI, 27.6–34.2) met the TPA guideline. The proportion meeting the guideline was significantly lower among girls (adjusted OR [aOR] = 0.70, 95% CI, 0.51–0.96) and 4-year-olds (aOR = 0.50, 95% CI, 0.34–0.75) and higher among rural residents (aOR = 1.78, 95% CI, 1.27–2.49) and those from lower middle-income countries (aOR = 1.35, 95% CI, 0.89–2.04). Conclusions: The findings suggest that a minority of children might meet the TPA guideline globally, and the risk of not meeting the guideline differed by sociodemographic indicators. These findings suggest the need for more surveillance of TPA in young children globally and, possibly, interventions to improve childhood health and development.

Objective: Previous studies that looked at comparability of accelerometer data focused on epoch or recording level comparability. Our study aims to provide insight into the comparability at raw data level. Methods: We performed five experiments with accelerometers attached to a mechanical shaker machine applying movement along a single axis in the horizontal plane. In each experiment, a 1-min no-movement condition was followed by nineteen 2-min shaker frequency conditions (30–250 rpm). We analyzed accelerometer data from Axivity, ActiGraph, GENEActiv, MOX, and activPAL devices. Comparability between commonly used brands and dynamic ranges was assessed in the frequency domain with power spectra and in the time domain with maximum lagged cross-correlation analyses. The influence of sampling rate on magnitude of acceleration across brands was explored visually. All data were published open access. Results: Magnitude of noise in rest was highest in MOX and lowest in ActiGraph. The signal mean power spectral density was equal between brands at low shaker frequency conditions (<3.13 Hz) and between dynamic ranges within the Axivity brand at all shaker frequency conditions. In contrast, the cross-correlation coefficients between time series across brands and dynamic ranges were higher at higher shaking frequencies. Sampling rate affected the magnitude of acceleration most in Axivity and least in GENEActiv. Conclusions: The comparability of raw acceleration signals between brands and/or sampling rates depends on the type of movement. These findings aid a more fundamental understanding and anticipation of differences in behavior estimates between different implementations of raw accelerometry.

Background: The associations between subjective (self-reported) and objective (actigraphy) sleep measurements are not well documented among survivors of cancer. The purpose of this study was to examine actigraphy measurements across days and the associations of two self-reported sleep measures with actigraphy-measured sleep measures. Methods: Sleep data were collected using self-reported sleep diary, the Pittsburgh Sleep Quality Index, and hip-worn actigraphy at baseline for a subsample participating in the Lifestyle Intervention for oVarian cancer Enhanced Survival (N = 516) randomized controlled trial. Intraclass correlation coefficients were used to evaluate consistency of actigraphy sleep measures across days of wear and associations of sleep diary with actigraphy for total sleep time (TST), time asleep, and time awake. Bland–Altman plots were used to assess the associations of sleep duration and sleep efficiency derived from Pittsburgh Sleep Quality Index and actigraphy. Results: Participants were aged 60.3 years (SD 9.3 years). For TST, the associations were strongest after 3 weekdays of consecutive actigraphy wear (ICC = .43 95% CI [.35, .51]), and actigraphy-measured daily TST was longest (617, SD 135 min) compared with self-reported measures. Sleep diary versus actigraphy associations for TST, time asleep, and time awake were weak to moderate. Pittsburgh Sleep Quality Index versus actigraphy association was weak for all sleep constructs. Conclusion: The strength of association between self-reported and actigraphy measures of sleep ranged from weak to very strong, depending on the sleep construct. Impact: Results highlight the importance of selecting an appropriate measurement tool for estimating individual sleep constructs among survivors of cancer.

The worldwide increase in childhood overweight and obesity underscores the need to study variables like fundamental movement skill (FMS) levels from early childhood. This study investigated the bidirectional longitudinal relationship between body mass index (BMI) and process-oriented FMSs, including locomotor skills and object control skills in 675 Finnish children, aged 3–8 years at baseline (50.5% female, mean age 5.5 years) over 3 years. Standardized BMI-for-age SD scores (BMI SDS z-scores) followed Finnish national standards. The FMS assessment comprised four subtests from the Test of Gross Motor Development, third edition. Age-adjusted standardized residuals of FMS or skill domains and BMI SDS z-scores were used in a two-level, cross-classified, cross-lagged regression analysis, accounting for gender, and baseline value of the dependent variables. The results showed no statistically significant longitudinal relationship between BMI and FMS or its skill domains for either gender in either direction. This suggests that BMI and process-oriented FMS, encompassing locomotor skill and object control skill, develop independently, possibly influenced by unexplored variables. These findings contradict earlier results based on product-oriented measurements, which may include a physical capacity component. The outcomes further underscore the importance of monitoring weight status from early childhood, given its significant association with later-life weight conditions.

The dynamics of mastering the degrees of freedom in motor learning are still far from being understood. The present work explored coordination dynamics in a redundant task, relating it to performance and adaptation in a serial stimulus tracking task. One hundred and sixty-three children (10–14 years of age) continuously responded to sequential stimuli (containing five stimuli) by pressing the respective sensors before the next stimulus presentation. Participants performed 120 trials with a fixed sequence (4–2–5–3–1) and a fixed interstimuli interval (800 ms) to learn the first pattern (practice phase). Then, a changed sequence (4–2–5–1–3) with a shorter interval (700 ms) was presented for 40 trials (adaptation phase). To measure coordination and its change, we calculated the correlation matrix of the stimulus–touch interval between the five sensors in blocks of 20 trials of the practice phase and classified individuals in terms of clusters. We found associations between coordination dynamics, performance curves, and adaptation in both coordination and performance. Furthermore, using network analyses, we found a tendency for all groups to increase the clustering coefficient. We discuss the possibility of this result representing a process of progressive segregation.