Not all sedentary behaviour is equal: Children's adiposity and sedentary behaviour volumes, patterns and types

OBJECTIVE: The importance of different constructs of sedentary behaviours in relation to childhood obesity is uncertain. Thus, this study aimed to investigate relationships between volume, patterns and types of sedentary behaviour and adiposity in children. METHODS: A case-control study was undertaken involving 234 children aged 10-13 years who were either of a healthy-weight (74 boys, 56 girls) or classified as obese (56 boys, 48 girls). Percent body fat (by dual-energy X-ray absorptiometry) and waist-to-height ratio were assessed. Time, type (television, videogame, computer, eating, passive transport) and bout length of sedentary behaviours were measured using accelerometry and the Multimedia Activity Recall for Children and Adolescents. Time use (total daily energy expenditure, sleep, physical activity), age, household income and Tanner stage were covariates in sex-stratified partial least squares analyses. RESULTS: Daily energy expenditure and income were negatively associated with adiposity for both sexes. Television time was consistently positively associated with adiposity. In boys only, prolonged bouts of sedentary behaviour and time spent playing video games/computer were positively linked with adiposity. Non-screen sedentary behaviour was negatively associated with adiposity in girls. Independent of total energy expenditure, total sedentary time was only inconsistently associated with fatness. CONCLUSIONS: These data suggest that (1) characteristics of sedentary time other than duration are associated with adiposity in children, and (2) associations may be sex-specific

Average acceleration and intensity gradient of primary school children and associations with indicators of health and wellbeing

Average acceleration (AvAcc) and intensity gradient (IG) have been proposed as standardised metrics describing physical activity (PA) volume and intensity, respectively. We examined hypothesised between-group PA differences in AvAcc and IG, and their associations with health and wellbeing indicators in children. ActiGraph GT9X wrist accelerometers were worn 24-h·d−1 56 over seven days by 145 children aged 9-10. Raw accelerations were averaged per 5-s epoch to represent AvAcc over 24-h. IG represented the relationship between log values for intensity and time. Moderate-to-vigorous PA (MVPA) was estimated using youth cutpoints. BMI z-scores, waist-to-height ratio (WHtR), peak oxygen uptake (VO2peak), Metabolic Syndrome risk (MetS score), and wellbeing were assessed cross-sectionally, and 8-weeks later. Hypothesised between-group differences were consistently observed for IG only (p<.001). AvAcc was strongly correlated with MVPA (r=0.96), while moderate correlations were observed between IG and MVPA (r=0.50) and AvAcc (r=0.54). IG was significantly associated with health indicators, independent of AvAcc (p<.001). AvAcc was associated with wellbeing, independent of IG (p<.05). IG was significantly associated with WHtR (p<.01) and MetS score (p<.05) at 8-weeks follow-up. IG is sensitive as a gauge of PA intensity that is independent of total PA volume, and which relates to important health indicators in children

GGIR: A research community-driven open-source R-package for generating physical activity and sleep outcomes from multi-day raw accelerometer data

GGIR: A research community-driven open-source R-package for generating physical activity and sleep outcomes from multi-day raw accelerometer dat

Beyond cut-points: Introducing a novel accelerometer metric that captures the physical activity intensity distribution

Overall activity level, defined as average acceleration over a 24 h period, is directly measured and comparable across studies. However, it tells us little about the intensity distribution. It is important to capture both overall activity and the intensity distribution as, for some health markers, it appears the volume of activity is more important than the intensity, but for others the converse appears to be true. Herein we introduce a new metric, the intensity gradient, that: captures the entire intensity distribution; does not rely on calibration protocols (that, by nature, are population- and protocolspecific); and is independent of overall activity level, thus can be used alongside average acceleration. The intensity gradient is taken from the log-log regression line of the negative curvilinear relationship between intensity and time accumulated at that intensity. To demonstrate its potential we applied it to two datasets: 1669 adolescent girls, and 295 adults with type 2 diabetes. The intensity gradient was negatively associated with body fatness in the girls and positively associated with physical function in the adults; associations were independent of average acceleration and co-variates. In contrast, moderate-to-vigorous physical activity was not independently associated with body fatness or physical function. In summary, collectively the average acceleration and the intensity gradient provide a complementary description of a person’s entire activity profile, facilitating investigation of the relative importance of intensity and volume of activity for a given outcome. Crucially, the metrics are not subject to the error and population-specificity associated with converting acceleration into physical activity outcomes

Innovations in the use of raw accelerometry in epidemiology: A basis for harmonisation of physical activity outcomes across international datasets

Background: To capitalise on the increasing availability of accelerometry data for epidemiological research it is desirable to pool data from multiple surveys worldwide. This study aimed to establish which physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Methods: Eleven adult participants wore a GENEActiv, Axivity and ActiGraph on both wrists for up to7-days. Accelerometer data were processed using open-source software (GGIR) to generate mean daily activity outcomes (including average dynamic acceleration (ACC), intensity gradient, time inactive(100 mg)). Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC, 95% confidence interval (CI)). Results: ACC and time active were higher (p0.88, lower 95%CI>0.61).The intensity gradient(ICC>0.88,lower 95%CI>0.55),time inactive (ICC>0.69, lower 95%CI>-0.06)and the number of valid days (ICC>0.95,lower 95%CI>0.81), could be considered equivalent between all monitor/wrist pairings. Conclusion: Free-living measures of average dynamic acceleration, and outputs that depend on acceleration magnitude, are higher at the dominant relative to the non-dominant wrist. Outputs that take into account the distribution of data, e.g. the intensity gradient and wear-time, are more consistent across wrist and monitor brand. These results will provide an evidence base for researchers wishing to harmonise data from surveys using different protocols and/or monitor brands.</p

Equivalency of sleep estimates: comparison of three research-grade accelerometers

Introduction:This study examined equivalency of sleep estimates from Axivity, GENEActiv and ActiGraph accelerometers worn on non-dominant and dominant wrist, and with and without using a sleep log to guide the algorithm.Methods:Forty-seven young adults wore an Axivity, GENEActiv and ActiGraph accelerometer continuously on both wrists for 4-7 seven days. Sleep time, sleep window, sleep efficiency, sleep onset and wake time were produced using the open-source GGIR package. For each outcome, agreement between accelerometer brands, dominant and non-dominant wrists, and with and without a sleep log, was examined using pairwise 95% equivalence tests (±10% equivalence zone), intra-class correlation coefficients (ICCs) with 95% confidence intervals and limits of agreement (LoA).Results:All sleep outcomes were within a 10% equivalence zoneirrespective of brand, wrist, or use of a sleep log. ICCs were poor-to-good for sleep time (ICCs>0.66) and sleep window (ICCs>0.56). Most ICCs were good-to-excellent for sleepefficiency (ICCs>0.73), sleep onset (ICCs>0.88) and wake time (ICCs>0.87). There werelow levels of mean bias, however wide 95% LoA for sleep time, sleep window, sleep onsetand wake time outcomes. Sleep time (up to 25 min) and sleep window (up to 29 min) werehigher when sleep log was not used. Conclusion: The present findings suggest that sleepoutcomes from the Axivity, GENEActiv and ActiGraph, when analysed identically, arecomparable across studies with different accelerometer brands and wear protocols at a grouplevel. However, caution is advised when comparing studies that differ on sleep logavailability.</p

Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study

Background Multimorbidity is an emerging public health priority. Physical activity (PA) is recommended as one of the main lifestyle behaviours, yet the benefits of PA for people with multimorbidity is unclear. We assessed the benefits of PA on mortality and life expectancy in people with and without multimorbidity. Methods Using the UK Biobank dataset, we extracted data on 36 chronic conditions and defined multimorbidity as: a) 2 or more conditions; b) 2 or more conditions combined with self-reported overall health; c) 2 or more top-10 most common comorbidities. Leisure-time PA (LTPA) and total PA (TPA) were measured by questionnaire and categorised as low (<600 MET-mins/week), moderate (600 to <3000 MET-mins/week), and high (≥3000 MET-mins/week); while objectively-assessed PA was assessed by wrist-worn accelerometer and categorised as low (4 mins/day), moderate (10 mins/day), and high (22 mins/day) walking at brisk pace. Survival models were applied to calculate adjusted hazard ratios (HRs) and predict life expectancy differences. Results 491,939 individuals (96,622 with 2 or more conditions) had a median follow-up of 7.0 (IQR 6.3-7.6) years. Compared to low LTPA, for participants with multimorbidity HR for mortality was 0.75 (95% CI: 0.70-0.80) and 0.65 (0.56-0.75) in moderate and high LTPA groups, respectively. This finding was consistent when using TPA measures. Using objective PA, HRs were 0.49 (0.29-0.80) and 0.29 (0.13-0.61) in the moderate and high PA groups, respectively. These findings were similar for participants without multimorbidity. In participants with multimorbidity, at the age of 45 years moderate and high LTPA were associated with an average of 3.12 (95% CI: 2.53, 3.71) and 3.55 (2.34, 4.77) additional life years, respectively, compared to low LTPA; in participants without multimorbidity, corresponding figures were 1.95 (1.59, 2.31) and 1.85 (1.19, 2.50). Similar results were found with TPA. For objective PA, moderate and high levels were associated with 3.60 (-0.60, 7.79) and 5.32 (-0.47, 11.11) life years gained compared to low PA for those with multimorbidity, and 3.88 (1.79, 6.00) and 4.51 (2.15, 6.88) life years gained in those without. Results were consistent when using other definitions of multimorbidity. Conclusions There was an inverse dose-response association between PA and mortality. A moderate exercise is associated with a longer life expectancy, also in individuals with multimorbidity

Activity intensity, volume & norms: Utility & interpretation of accelerometer metrics

Purpose: The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sexspecific percentile norms. Methods: Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) út (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D). Results: Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/út in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile ‘norms’ showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC. Conclusion: The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sexspecific norms that would be beneficial to researchers

Towards a portable model to discriminate activity clusters from accelerometer data

Few methods for classifying physical activity from accelerometer data have been tested using an independent dataset for cross-validation, and even fewer using multiple independent datasets. The aim of this study was to evaluate whether unsupervised machine learning was a viable approach for the development of a reusable clustering model that was generalisable to independent datasets. We used two labelled adult laboratory datasets to generate a k-means clustering model. To assess its generalised application, we applied the stored clustering model to three independent labelled datasets: two laboratory and one free-living. Based on the development labelled data, the ten clusters were collapsed into four activity categories: sedentary, standing/mixed/slow ambulatory, brisk ambulatory, and running. The percentages of each activity type contained in these categories were 89%, 83%, 78%, and 96%, respectively. In the laboratory independent datasets, the consistency of activity types within the clusters dropped, but remained above 70% for the sedentary clusters, and 85% for the running and ambulatory clusters. Acceleration features were similar within each cluster across samples. The clusters created reflected activity types known to be associated with health and were reasonably robust when applied to diverse independent datasets. This suggests that an unsupervised approach is potentially useful for analysing free-living accelerometer data