Introduction The purpose of this study was to identify unique organized sport trajectories from early childhood to late adolescence in an Australian pregnancy cohort, the Raine Study. Methods Participation in organized sport was assessed at ages 5, 8, 10, 14, and 17 yr. Physical activity, body composition, and self-rated physical and mental health were assessed at the age of 20 yr. Latent class analysis was used to identify patterns of sport participation. To assess the internal validity of the trajectory classes, differences in health characteristics between trajectories were analyzed using generalized linear models. Results For girls, three trajectory classes were identified: consistent sport participators (47.5%), sport dropouts (34.3%), and sport nonparticipators (18.1%). For boys, three trajectory classes were identified: consistent sport participators (55.2%), sport dropouts (36.9%), and sport joiners (8.1%). For girls, there were overall differences across trajectory classes in lean body mass (P = 0.003), lean mass index (P = 0.06), and physical health (P = 0.004). For boys, there were differences across classes in physical activity (P = 0.018), percent body fat (P = 0.002), lean body mass (P < 0.001), lean mass index (P < 0.001), physical health (P = 0.06), and depression scores (P = 0.27). Conclusion This study identified unique, sex-specific trajectories of organized sport participation. The differences in health outcomes between trajectory classes, such as participants with consistent sport participation having more preferable health outcomes at the age of 20 yr, support the internal validity of the trajectories. Strategies are needed to identify and encourage those in the dropout trajectory to maintain their participation and those in the nonparticipator or joiner trajectories to join sport earlier. Specifically, interventions to encourage early sport participation in girls and help nonparticipating boys to join sport during adolescence may help more children receive the benefits of sport participation.

Objectives. To assess habitual physical activity levels in patients with RA compared with healthy control participants and to compare these measures with health-related quality of life and disease activity in the RA patients. Methods. Fifty RA patients [age 48 (13) years] and 22 BMI, sex and geographically matched control participants were recruited. Habitual physical activity was measured using an Actical accelerometer worn on the hip for 2 consecutive weeks. Patients completed the Short Form-36 (SF-36) and modified Health Assessment Questionnaires (HAQ-DI). Disease activity was assessed using the Simplified Disease Activity Index (SDAI). RA patients were further categorized as more physically active (n = 25) and less physically active (n = 25) according to their average activity counts. Results. The RA group spent more time in sedentary activity than the control group (71% vs 62% of the day respectively, P = 0.002) and had bimodal decreases in diurnal physical activity compared with the control group in the morning (P < 0.001) and late afternoon (P < 0.001). HAQ-DI, when adjusted for age and disease duration, was negatively correlated with physical activity in the RA group (r = −0.343, P = 0.026). The more physically active patients scored better than the less physically active patients on every component of the SF-36. Conclusion. Patients with RA lead a significantly more sedentary lifestyle than healthy controls and show diurnal differences in physical activity due to morning stiffness and fatigue. Higher levels of habitual physical activity may be protective of functional capacity and are highly associated with improved health-related quality of life in RA patients.

To provide a detailed description of young adults’ sedentary time and physical activity. 384 young women and 389 young men aged 22.1 ± 0.6 years, all participants in the 22 year old follow-up of the Raine Study pregnancy cohort, wore Actigraph GT3X+ monitors on the hip for 24 h/day over a one-week period for at least one ‘valid’ day (≥10 h of waking wear time). Each minute epoch was classified as sedentary, light, moderate or vigorous intensity using 100 count and Freedson cut-points. Mixed models assessed hourly and daily variation; t-tests assessed gender differences. The average (mean ± SD) waking wear time was 15.0 ± 1.6 h/day, of which 61.4 ± 10.1 % was spent sedentary, 34.6 ± 9.1 % in light-, 3.7 ± 5.3 % in moderate- and, 0.3 ± 0.6 % in vigorous-intensity activity. Average time spent in moderate to vigorous activity (MVPA) was 36.2 ± 27.5 min/day. Relative to men, women had higher sedentary time, but also higher vigorous activity time. The ‘usual’ bout duration of sedentary time was 11.8 ± 4.5 min in women and 11.7 ± 5.2 min in men. By contrast, other activities were accumulated in shorter bout durations. There was large variation by hour of the day and by day of the week in both sedentary time and MVPA. Evenings and Sundays through Wednesdays tended to be particularly sedentary and/or inactive. For these young adults, much of the waking day was spent sedentary and many participants were physically inactive (low levels of MVPA). We provide novel evidence on the time for which activities were performed and on the time periods when young adults were more sedentary and/or less active. With high sedentary time and low MVPA, young adults may be at risk for the life-course sequelae of these behaviours.

Researchers are increasingly using 24 h accelerometer wear protocols. No automated method has been published that accurately distinguishes ‘waking’ wear time from other data (‘in-bed’, non-wear, invalid days) in young adults. This study examined the validity of an automated algorithm developed to achieve this for hip-worn Actigraph GT3X + 60 s epoch data. We compared the algorithm against a referent method (‘into-bed’ and ‘out-of-bed’ times visually identified by two independent raters) and benchmarked against two published algorithms. All methods used the same non-wear rules. The development sample ( n = 11) and validation sample ( n = 95) were Australian young adults from the Raine pregnancy cohort (54% female), all aged approximately 22 years. The agreement with Rater 1 in each minute’s classification (yes/no) of waking wear time was examined as kappa ( κ ), limited to valid days (⩾10 h waking wear time per day) according to the algorithm and Rater 1. Bland–Altman methods assessed agreement in daily totals of waking wear and in-bed wear time. Excellent agreement ( κ > 0.75) was obtained between the raters for 80% of participants (median κ = 0.94). The algorithm showed excellent agreement with Rater 1 ( κ > 0.75) for 89% of participants and poor agreement ( κ < 0.40) for 1%. In this sample, the algorithm (median κ = 0.86) performed better than algorithms validated in children (median κ = 0.77) and adolescents (median κ = 0.66). The mean difference (95% limits of agreement) between Rater 1 and the algorithm was 7 (−220, 234) min d ⁻¹ for waking wear time on valid days and −41 (−309, 228) min d ⁻¹ for in-bed wear time. In this population, the automated algorithm’s validity for identifying waking wear time was mostly good, not worse than inter-rater agreement, and better than the evaluated published alternatives. However, the algorithm requires improvement to better identify in-bed wear time.

Background: In the cancer survivorship context, physical activity and sedentary behavior have been measured using different methods. Purpose: To conduct a narrative review of published research in cancer survivor populations to summarize the quality and identify gaps in reporting on accelerometer data collection, data processing, and outcome measures in cancer survivors. Methods: An initial PubMed® search of articles published in English was conducted in January 2017, and a final search was conducted in May 2017. Variables extracted included study characteristics, methods for accelerometry data collection (e.g., device used), data processing (e.g., cut points used), and data reporting (e.g., time spent in different activity intensities). Results: A total of 46 articles were eligible for inclusion in the review. The majority of studies (34 of 46) targeted a single cancer group and 18 of these 34 studies were in survivors of breast cancer. Half (54%) of the studies used an ActiGraph® accelerometer. Methods of accelerometer data processing varied across studies. Definitions of non–wear time, vectors used during processing, and filters applied during processing were reported by 51%, 60%, and 8% of studies, respectively. Most studies reported moderate and vigorous physical activity (78%), 50% reported sedentary time, and 43% reported light-intensity activity. Cut points to categorize these activities varied between studies. Conclusions: This narrative review highlights inconsistency in the methods used to collect, process, and report accelerometry data across cancer survivor studies. Accelerometry has potential to add detailed knowledge of the levels and patterns of physical activities and sedentary behaviors across the cancer spectrum. Recommendations are made to improve data processing and reporting methods to maximize the scientific validity of future accelerometer research in this field.

Prior studies examining longitudinal patterns of television (TV) watching have tended to use analytical approaches which do not allow for heterogeneity in the variation of TV watching over time. In the current study, we used latent class analysis (LCA) to examine the relationships between television watching (from childhood to early adulthood) and body fat percentage (%) and mental health. Data were collected from 2411 participants (50% female) from the Raine Study, a prospective birth cohort study in Australia. Participants were followed up over 15 years and answered questions about hours of TV watching per week at six time-points (5, 8, 10, 14, 17 and 20yrs). Trajectories of television watching were estimated using LCA and appropriate regression models used to test the association of television watching class with percentage body fat (measured by DXA) and mental health (DASS-21) at age 20. Physical activity was used as a covariate. Three distinct trajectories of TV watching were identified. Class 1 (47.4%) had consistently high (>14 hrs/wk) levels of TV watching, Class 2 (37.9%) was characterised by an increase in TV watching over adolescence and Class 3 (14.7%) had consistently lower (<14 hrs/wk) TV watching over 15 years. Sex was used as an active covariate in the latent class model and was significantly associated with class membership (p<0.001), with females comprising 45%, 47% and 59% of Class 1, 2 and 3 respectively. In females, membership in Class 2 or 3 was associated with lower body fat % at age 20, compared to Class 1 (p0.05). TV watching from childhood to young adulthood appears to be a relatively stable behavior for around two thirds of participants, but not everyone tracks consistently. This study identified a subset of participants with low levels of TV watching in childhood and also that this group, despite an increase in TV watching over adolescence, maintained a lower level of body fat in young adulthood.