Exploring and developing methods of assessing sedentary behaviour in children

Evidence suggests that sedentary behaviour (SB) is associated with adverse health outcomes. Children’s SB is a complex set of behaviours that includes different types of activities taking place in a variety of settings. Therefore, assessing children’s SB is challenging and currently no single method exists that captures the behaviour as a whole. This thesis aims to explore and develop new and existing methods of assessing children’s SB, by employing a range of quantitative and qualitative methods. Accelerometry has become a widely used method of estimating sedentary time (ST). Study 1 identified raw acceleration thresholds to classify children’s sedentary and stationary behaviours, using two accelerometer brands across three placements. Thresholds however, do not account for the postural element of SB, as per its definition. Study 2 validated the Sedentary Sphere method in children, allowing for the most likely posture classification from wrist-worn accelerometers. Study 3 added contextual information to accelerometer data by using a digitalised data capturing tool, the Digitising Children’s Data Collection (DCDC) for Health application (app). Children used the app to report their SBs daily through photos, drawings, voice recordings as well as answering a multiple-choice questionnaire. Results from the DCDC app identified specific SBs to be targeted in future interventions. Data showed distinct differences between boys and girls’ screen-based behaviours, suggesting gender-specific interventions are needed to reduce screen time. Using the DCDC app in combination with accelerometry often explained patterns of SB and physical activity observed in accelerometer data. Study 4 added information about parents’ perceptions of the factors that influence their children’s SBs. This study identified parents/carers as a target for future interventions in view of perceptions reported about PA and SB and their need for support to help reduce the time children spend using screen-based devices

Rethinking legal skills education in an LLB curriculum

Over the past decade, there have been growing complaints regarding the low levels of literacy, research and numeracy skills demonstrated by law graduates in practice, and a call for universities to more adequately address these skill gaps. The Faculty of Law at the Nelson Mandela Metropolitan University (NMMU) responded to this call by redesigning their first-year Legal Skills course using a stand-alone skills-based model and a context-based teaching approach. The redesign process is outlined and particular themes in each stage of the process are discussed. This includes identifying contextual factors, defining essential skills; course content analysis; course restructuring; teaching reformulation; adaptation of assessment and feedback; implementing a blended learning approach, and collaboration within the Faculty and across faculties and service providers. The article argues that a stand-alone skills-based model can be effective in developing a minimum level of competence, but that a sense of shared responsibility for skills development across the LLB programme is essential for a higher level of skill attainment. Lessons learned during the redesign process are highlighted, and where possible, recommendations for future considerations are explored

The backwards comparability of wrist worn GENEActiv and waist worn ActiGraph accelerometer estimates of sedentary time in children

Objectives: To examine the backward comparability of a range of wrist-worn accelerometer estimates of sedentary time (ST) with ActiGraph 100 count∙min-1 waist ST estimates. Design: Cross-sectional, secondary data analysis Method: One hundred and eight 10-11-year-old children (65 girls) wore an ActiGraph GT3X+ accelerometer (AG) on their waist and a GENEActiv accelerometer (GA) on their non-dominant wrist for seven days. GA ST data were classified using a range of thresholds from 23-56 mg. ST estimates were compared to AG ST 100 count∙min-1 data. Agreement between the AG and GA thresholds was examined using Cronbach’s alpha, intraclass correlation coefficients (ICC), limits of agreement (LOA), Kappa values, percent agreement, mean absolute percent error (MAPE) and equivalency analysis. Results: Mean AG total ST was 492.4 minutes over the measurement period. Kappa values ranged from 0.31-0.39. Percent agreement ranged from 68-69.9%. Cronbach’s alpha values ranged from 0.88-0.93. ICCs ranged from 0.59-0.86. LOA were wide for all comparisons. Only the 34 mg threshold produced estimates that were equivalent at the group level to the AG ST 100 count∙min-1 data though sensitivity and specificity values of ~64% and ~74% respectively were observed. Conclusions: Wrist-based estimates of ST generated using the 34 mg threshold are comparable with those derived from the AG waist mounted 100 count∙min-1 threshold at the group level. The 34 mg threshold could be applied to allow group-level comparisons of ST with evidence generated using the ActiGraph 100 count∙min-1 method though it is important to consider the observed sensitivity and specificity results when interpreting findings

A novel mixed-method approach to assess children's sedentary behaviours

Purpose: Accurately measuring sedentary behavior (SB) in children is challenging by virtue of its complex nature. While self-report questionnaires are susceptible to recall errors, accelerometer data lacks contextual information. This study aimed to explore the efficacy of using accelerometry combined with the Digitising Children’s Data Collection (DCDC) for Health application (app), to capture SB comprehensively. Methods: 74 children (9–10 years old) wore ActiGraph GT9X accelerometers for 7 days. Each received a SAMSUNG Galaxy Tab4 (SM-T230) tablet, with the DCDC app installed and a specially designed sedentary behavior study downloaded. The app uses four data collection tools: 1) Questionnaire, 2) Take a photograph, 3) Draw a picture, and 4) Record my voice. Children self-reported their SB daily. Accelerometer data were analyzed using R-package GGIR. App data were downloaded and individual participant profiles created. SBs reported were grouped into categories and reported as frequencies. Results: Participants spent, on average, 629 min (i.e., 73% of their waking time) sedentary. App data revealed most of their out-of-school SB consisted of screen time (112 photos, 114 drawings, and screen time mentioned 135 times during voice recordings). Playing with toys, reading, arts and crafts, and homework were also reported across all four data capturing tools on the app. On an individual level, data from the app often explained irregular patterns in physical activity and SB observed in accelerometer data. Conclusion: This mixed methods approach to assessing SB adds context to accelerometer data, providing researchers with information needed for intervention design

Validating the Sedentary Sphere method in children: does wrist or accelerometer brand matter?

This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9-10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with phone, walking (criterion for all 7: observation) and ten minutes free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Percent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77%-78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC>0.81, lower 95% CI>0.75), between brands worn on the same wrist (±5%, ICC>0.84, lower 95% CI>0.80) and between brands worn on opposing wrists (±6%, ICC>0.78, lower 95% CI>0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results

Establishing Raw Acceleration Thresholds to Classify Sedentary and Stationary Behaviour in Children

This study aimed to: (1) compare acceleration output between ActiGraph (AG) hip and wrist monitors and GENEActiv (GA) wrist monitors; (2) identify raw acceleration sedentary and stationary thresholds for the two brands and placements; and (3) validate the thresholds during a free-living period. Twenty-seven from 9- to 10-year-old children wore AG accelerometers on the right hip, dominant- and non-dominant wrists, GA accelerometers on both wrists, and an activPAL on the thigh, while completing seven sedentary and light-intensity physical activities, followed by 10 minutes of school recess. In a subsequent study, 21 children wore AG and GA wrist monitors and activPAL for two days of free-living. The main effects of activity and brand and a significant activity × brand × placement interaction were observed (all p < 0.0001). Output from the AG hip was lower than the AG wrist monitors (both p < 0.0001). Receiver operating characteristic (ROC) curves established AG sedentary thresholds of 32.6 mg for the hip, 55.6 mg and 48.1 mg for dominant and non-dominant wrists respectively. GA wrist thresholds were 56.5 mg (dominant) and 51.6 mg (non-dominant). Similar thresholds were observed for stationary behaviours. The AG non-dominant threshold came closest to achieving equivalency with activPAL during free-living

Visualization of Frequent Itemsets with Nested Circular Layout and Bundling Algorithm

International audienceFrequent itemset mining is one of the major data mining issues. Once generated by algorithms, the itemsets can be automatically processed, for instance to extract association rules. They can also be explored with visual tools, in order to analyze the emerging patterns. Graphical itemsets representation is a convenient way to obtain an overview of the global interaction structure. However, when the complexity of the database increases, the network may become unreadable. In this paper, we propose to display itemsets on concentric circles, each one being organized to lower the intricacy of the graph through an optimization process. Thanks to a graph bundling algorithm, we finally obtain a compact representation of a large set of itemsets that is easier to exploit. Colors accumulation and interaction operators facilitate the exploration of the new bundle graph and to illustrate how much an itemset is supported by the data

Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents.

Background: Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex. Methods: Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d-1 and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences. Results: The analytical sample included 1250 participants. Physical activity peaked between ages 6.5-10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3rd-50th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001). Conclusions: Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion

ExoClock Project. III. 450 New Exoplanet Ephemerides from Ground and Space Observations

The ExoClock project has been created to increase the efficiency of the Ariel mission. It will achieve this by continuously monitoring and updating the ephemerides of Ariel candidates, in order to produce a consistent catalog of reliable and precise ephemerides. This work presents a homogenous catalog of updated ephemerides for 450 planets, generated by the integration of ∼18,000 data points from multiple sources. These sources include observations from ground-based telescopes (the ExoClock network and the Exoplanet Transit Database), midtime values from the literature, and light curves from space telescopes (Kepler, K2, and TESS). With all the above, we manage to collect observations for half of the postdiscovery years (median), with data that have a median uncertainty less than 1 minute. In comparison with the literature, the ephemerides generated by the project are more precise and less biased. More than 40% of the initial literature ephemerides had to be updated to reach the goals of the project, as they were either of low precision or drifting. Moreover, the integrated approach of the project enables both the monitoring of the majority of the Ariel candidates (95%), and also the identification of missing data. These results highlight the need for continuous monitoring to increase the observing coverage of the candidate planets. Finally, the extended observing coverage of planets allows us to detect trends (transit-timing variations) for a sample of 19 planets. All the products, data, and codes used in this work are open and accessible to the wider scientific community

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations