Is point of care renal function testing reliable screening pre-IV contrast administration?

PURPOSE: Intravenous iodinated contrast is a commonly used diagnostic aid to improve image quality on computed tomography. There exists a small risk of post-contrast acute kidney injury in patients receiving IV contrast. One of the biggest risk factors for developing PC-AKI is the presence of pre-existing renal dysfunction, making it important to measure the renal function prior to contrast administration. Point of care (POC) devices offer a quick estimation of renal function, potentially improving workflows in radiology departments. METHOD: Two POC devices were evaluated, the Nova StatSensor and Abbott iSTAT. Patients undergoing routine radiological investigations had blood collected and analysed by a POC method and the laboratory method (Beckman AU5800). The two values were analysed and compared. Renal function was calculated using eGFR via the CKD-EPI result. eGFR values were stratified as high risk (eGFR < 30), moderate risk (eGFR 30-59) and low risk (eGFR ≥ 60). RESULTS: One hundred eighty-six patients were included in the study. One hundred one patients underwent the Abbott iSTAT analysis, 139 patients underwent Nova StatSensor analysis, and 53 had both. Statistical analysis revealed that the StatSensor R2 value was 0.77, and coefficient variation was 10.65%. iSTAT had a R2 value of 0.83 and coefficient variation of 7.36%. The POC devices did not miss any high-risk patients but underreported eGFR values in certain patients. CONCLUSION: POC devices are moderately accurate at detecting renal impairment in patients undergoing radiological investigations. They seem to be a good screening tool; however, any low eGFR values should be further examined.

Describing objectively measured intensive care nurses' physical work activity behavioural patterns during a 12-hr shift.

AIM AND OBJECTIVES: To describe intensive care unit (ICU) nurses' physical work activity behavioural patterns over 12 hr using dual accelerometry, following a job demands-recovery framework. BACKGROUND: Limited studies utilised accelerometry to objectively analyse nurses' physical workloads. Little is known about intensive care nurses' physical activity patterns during a 12-hr shift. DESIGN: A cross-sectional study was conducted with intensive care nurses from four units in Auckland, New Zealand. METHODS: Each participant wore two Axivity AX3 accelerometers to measure physical activity during a 12-hr day or night shift. An online survey captured participants' demographic information. R software (version 3.6.1) and SPSS version 26 were utilised for data analysis. The STROBE was followed. RESULTS: A total of 102 nurses were included in this study. A high level of light intensity activity behaviours (standing, dynamic standing, walking) was observed throughout the day shifts, with no higher intensity behaviours identified. Activity levels were highest at the beginning of shifts and followed a consistent pattern, with an additional peak around midday for day shifts and at the end of the shift for night shifts. Observable differences were seen between day and night shifts with a greater prevalence of sitting and lying during night shifts. Standing, dynamic standing, sitting, lying and walking were significant factors in the differences of the physical work behaviours between the day shift nurses and the night shift nurses. Significant differences in dynamic standing and lying were found between ICUs. CONCLUSIONS: Intensive care nurses' physical work activity involved a large amount of standing and dynamic standing during a 12-hr shift. The overall physical workload during a 12-hr day shift was significantly higher than that during a 12-hr night shift. RELEVANCE TO CLINICAL PRACTICE: Results may help managers attain a better understanding of nurses' physical workloads during a 12-hr shift.

Physical activity and personal factors associated with nurse resilience in intensive care units.

AIM AND OBJECTIVES: To assess intensive care nurses' resilience and identify associated personal factors and physical activity behaviours using a job demands-recovery framework. BACKGROUND: Currently, there is inconsistent evidence as to whether nurse resilience is associated with personal factors or with physical activity at work or during leisure time. DESIGN: A cross-sectional study was conducted with nurses from four intensive care units in Auckland, New Zealand. METHODS: An online survey was conducted to collect nurses' personal information and assess their resilience levels using the Connor-Davidson Resilience Scale 25. Participants were nurses working at least 32 hr fortnightly and providing direct patient care. Physical activity was objectively measured using a pair of accelerometers worn on the back and thigh over four consecutive days (two workdays followed by two nonworkdays). Bivariable and multivariable regression were used to identify personal factors and physical activity behaviours associated with resilience (followed the STROBE checklist). RESULTS: A total of 93 nurses were included in the study. The participants' average resilience level was low. Resilience was positively associated with the objectively measured physical job demands factors: occupational physical activity, moderate-to-vigorous physical activity at work and dynamic standing at work. Resilience was negatively associated with one objectively measured recovery factor: sleep during leisure time. In multivariable modelling, being married and moderate-to-vigorous physical activity at work were positively associated with resilience, while not having religious beliefs and sleep during leisure time were negatively associated with resilience. CONCLUSIONS: Resilient nurses have a greater tolerance to high physical activity at work and lower sleep duration during leisure time. Strategies are needed to improve intensive care nurses' resilience levels. RELEVANCE TO CLINICAL PRACTICE: Results may help managers gain a better understanding of the ICU nurses' characteristics associated with resilience, leading them to develop strategies for improving ICU nurse resilience.

Physical activity, cognition and academic performance: an analysis of mediating and confounding relationships in primary school children.

BACKGROUND: Exploring the relationship between physical activity, cognition and academic performance in children is an important but developing academic field. One of the key tasks for researchers is explaining how the three factors interact. The aim of this study was to develop and test a conceptual model that explains the associations among physical activity, cognition, academic performance, and potential mediating factors in children. METHODS: Data were sourced from 601 New Zealand children aged 6-11 years. Weekday home, weekday school, and weekend physical activity was measured by multiple pedometer step readings, cognition by four measures from the CNS Vital Signs assessment, and academic performance from the New Zealand Ministry of Education electronic Assessment Tools for Teaching and Learning (e-asTTle) reading and maths scores. A Structured Equation Modelling approach was used to test two models of variable relationships. The first model analysed the physical activity-academic performance relationship, and the second model added cognition to determine the mediating effect of cognition on the physical activity-academic performance association. Multigroup analysis was used to consider confounding effects of gender, ethnicity and school socioeconomic decile status. RESULTS: The initial model identified a significant association between physical activity and academic performance (r = 0.225). This direct association weakened (r = 0.121) when cognition was included in the model, demonstrating a partial mediating effect of cognition. While cognition was strongly associated with academic performance (r = 0.750), physical activity was also associated with cognition (r = 0.138). Subgroups showed similar patterns to the full sample, but the smaller group sizes limited the strength of the conclusions. CONCLUSIONS: This cross-sectional study demonstrates a direct association between physical activity and academic performance. Furthermore, and importantly, this study shows the relationship between physical activity and academic performance is supported by an independent relationship between physical activity and cognition. Larger sample sizes are needed to investigate confounding factors of gender, age, socioeconomic status, and ethnicity. Future longitudinal analyses could investigate whether increases in physical activity can improve both cognition and academic performance.

Associations between lifestyle behaviours and optimal wellbeing in a diverse sample of New Zealand adults.

BACKGROUND: In positive psychology optimal wellbeing is considered a broad, multi-dimensional construct encompassing both feelings and functioning. Yet, this notion of wellbeing has not been translated into public health. The purpose of this study is to integrate public health and positive psychology to determine associations between lifestyle behaviours and optimal wellbeing in a diverse sample of New Zealand adults. METHODS: A web-based survey design was employed to collect data. Participants reported on their wellbeing and lifestyle behaviours including nutrition, exercise, sedentary behaviour, and sleep. Optimal wellbeing was calculated using a multi-dimensional scale designed to mirror the internationally recognised diagnostic criteria for mental disorders. Binary logistic regression was used to calculate associations between 10 lifestyle behaviours and optimal wellbeing. RESULTS: Of the total sample (n = 9514), 24 % met the criteria for optimal wellbeing. Compared to reference groups, the association with optimal wellbeing was greater for those who reported exercising ≥ 7 times/week (odds ratio: 1.61, 95 % confidence interval: 1.22-2.13, p < 0.01) and sitting "almost none of the time" (1.87, 1.01-3.29, p < 0.01). Optimal wellbeing was lower for those reporting restless sleep "almost all of the time" (0.24, 95 % CI: 0.17-0.32 p < 0.01) and consuming sugary drinks 5-6 times/week (0.73, 95 % CI: 0.53-0.95, p < 0.05). CONCLUSIONS: Public health and positive psychology were integrated to provide support for a relationship between lifestyle behaviours and a multi-dimensional measure of optimal wellbeing. It is likely this relationship between lifestyle behaviours and optimal wellbeing is bidirectional giving rise to the debate that holistic approaches are needed to promote positive health.

The Clustering of Lifestyle Behaviours in New Zealand and their Relationship with Optimal Wellbeing.

PURPOSE: The purpose of this research was to determine (1) associations between multiple lifestyle behaviours and optimal wellbeing and (2) the extent to which five lifestyle behaviours-sleep, physical activity, sedentary behaviour, sugary drink consumption, and fruit and vegetable intake-cluster in a national sample. METHOD: A national sample of New Zealand adults participated in a web-based wellbeing survey. Five lifestyle behaviours-sleep, physical activity, sedentary behaviour, sugary drink consumption, and fruit and vegetable intake-were dichotomised into healthy (meets recommendations) and unhealthy (does not meet recommendations) categories. Optimal wellbeing was calculated using a multi-dimensional flourishing scale, and binary logistic regression analysis was used to calculate the relationship between multiple healthy behaviours and optimal wellbeing. Clustering was examined by comparing the observed and expected prevalence rates (O/E) of healthy and unhealthy two-, three-, four-, and five-behaviour combinations. RESULTS: Data from 9425 participants show those engaging in four to five healthy behaviours (23 %) were 4.7 (95 % confidence interval (CI) 3.8-5.7) times more likely to achieve optimal wellbeing compared to those engaging in zero to one healthy behaviour (21 %). Clustering was observed for healthy (5 %, O/E 2.0, 95 % CI 1.8-2.2) and unhealthy (5 %, O/E 2.1, 95 % CI 1.9-2.3) five-behaviour combinations and for four- and three-behaviour combinations. At the two-behaviour level, healthy fruit and vegetable intake clustered with all behaviours, except sleep which did not cluster with any behaviour. CONCLUSION: Multiple lifestyle behaviours were positively associated with optimal wellbeing. The results show lifestyle behaviours cluster, providing support for multiple behaviour lifestyle-based interventions for optimising wellbeing.

Environmental and socio-demographic associates of children's active transport to school: a cross-sectional investigation from the URBAN Study.

BACKGROUND: Active transport (e.g., walking, cycling) to school (ATS) can contribute to children's physical activity and health. The built environment is acknowledged as an important factor in understanding children's ATS, alongside parental factors and seasonality. Inconsistencies in methodological approaches exist, and a clear understanding of factors related to ATS remains equivocal. The purpose of this study was to gain a better understanding of associates of children's ATS, by considering the effects of daily weather patterns and neighbourhood walk ability and neighbourhood preferences (i.e., for living in a high or low walkable neighbourhood) on this behaviour. METHODS: Data were drawn from the Understanding Relationships between Activity and Neighbourhoods study, a cross-sectional study of physical activity and the built environment in adults and children in four New Zealand cities. Parents of participating children completed an interview and daily trip diary that assessed their child's mode of travel to school, household and individual demographic information, and parental neighbourhood preference. Daily weather data were downloaded from New Zealand's national climate database. Geographic information systems-derived variables were calculated for distance to school and neighbourhood walkability. Bivariate analyses were conducted with ATS and potential associates; factors related to ATS at p < 0.20 were considered simultaneously in generalized estimation equation models, and backwards elimination of non-significant factors was conducted; city was treated as a fixed effect in all models. RESULTS: A total of 217 children aged 6.5-15 years participated in this study. Female sex, age, city, household income, limited/no car access, residing in zone of school, shorter distance to school, neighbourhood self selection, rainfall, and sunlight hours were simultaneously considered in multivariate generalised estimation equation modelling (all p < 0.20 in bivariate analyses). After elimination of non-significant factors, age (p = 0.005), shorter distance to school (p < 0.001), city (p = 0.03), and neighbourhood self selection (p = 0.04) remained significantly associated with ATS in the multivariate analysis. CONCLUSION: Distance to school is the prevailing environmental influencing factor on children's ATS. This study, in conjunction with previous research, suggests that school siting is likely an important associate of children's ATS.

A Dual-Accelerometer System for Detecting Human Movement in a Free-living Environment.

PURPOSE: Accurate measurement of various human movement behaviors is essential in developing 24-h movement profiles. A dual-accelerometer system recently showed promising results for accurately classifying a broad range of behaviors in a controlled laboratory environment. As a progressive step, the aim of this study is to validate the same dual-accelerometer system in semi free-living conditions in children and adults. The efficacy of several placement sites (e.g., wrist, thigh, back) was evaluated for comparison. METHODS: Thirty participants (15 children) wore three Axivity AX3 accelerometers alongside an automated clip camera (clipped to the lapel) that recorded video of their free-living environment (ground truth criterion measure of physical activity). Participants were encouraged to complete a range of daily-living activities within a 2-h timeframe. A random forest machine-learning classifier was trained using features generated from the raw accelerometer data. Three different placement combinations were examined (thigh-back, thigh-wrist, back-wrist), and their performance was evaluated using leave-one-out cross-validation for the child and adult samples separately. RESULTS: Machine learning models developed using the thigh-back accelerometer combination performed the best in distinguishing seven distinct activity classes with an overall accuracy of 95.6% in the adult sample, and eight activity classes with an overall accuracy of 92.0% in the child sample. There was a drop in accuracy (at least 11.0%) when other placement combinations were evaluated. CONCLUSIONS: This validation study demonstrated that a dual-accelerometer system previously validated in a laboratory setting also performs well in semi free-living conditions. Although these results are promising and progressive, further work is needed to expand the scope of this measurement system to detect other components of behavior (e.g., activity intensity and sleep) that are related to health.

Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review.

BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. METHODS: Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. RESULTS: Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. CONCLUSIONS: Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.

Deprivation matters: understanding associations between neighbourhood deprivation, unhealthy food outlets, unhealthy dietary behaviours and child body size using structural equation modelling.

BACKGROUND: Children residing in neighbourhoods of high deprivation are more likely to have poorer health, including excess body size. While the availability of unhealthy food outlets are increasingly considered important for excess child body size, less is known about how neighbourhood deprivation, unhealthy food outlets and unhealthy dietary behaviours are interlinked. METHODS: This study involves children aged 8-13 years (n=1029) and resided in Auckland, New Zealand. Unhealthy dietary behaviours (frequency of consumption of unhealthy snacks and drinks) and food purchasing behaviour on the route to and from school were self-reported. Height and waist circumference were measured to calculate waist-to-height ratio (WtHR). Geographic Information Systems mapped neighbourhood deprivation and unhealthy food outlets within individual, child-specific neighbourhood buffer boundaries (800 m around the home and school). Associations between neighbourhood deprivation (calculated using the New Zealand Index of Deprivation 2013), unhealthy food outlets, unhealthy dietary behaviours and WtHR were investigated using structural equation modelling in Mplus V.8.0. Age, sex and ethnicity were included as covariates, and clustering was accounted for at the school level. RESULTS: Structural equation models showed that unhealthy food outlets were unrelated to unhealthy dietary behaviours (estimate 0.029, p=0.416) and excess body size (estimate -0.038, p=0.400). However, greater neighbourhood deprivation and poorer dietary behaviours (estimate -0.134, p=0.001) were associated with greater WtHR (estimate 0.169, p<0.001). CONCLUSION: Excess child body size is associated with neighbourhood deprivation and unhealthy dietary behaviours but not unhealthy outlet density or location of these outlets near home and school.

Personal and work-related factors associated with nurse resilience: A systematic review.

BACKGROUND: Nursing shortages have profoundly impacted hospitals and consequently increased financial expenditure, resulting in work overload, thus augmenting nurses' stress and burnout levels. Studies have found that resilience helps nurses reduce the effects of stress and burnout. However, the factors associated with nurse resilience are yet to be determined. OBJECTIVES: This systematic review aims to identify the associated personal and work-related factors of nurse resilience. DESIGN: This systematic review has been registered in the international prospective register of systematic reviews (Registered Number: CRD 42018094080). Results are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. DATA SOURCES: The systematic search was undertaken between March and April 2018 in five databases: CINAHL Plus, MEDLINE (Ovid), PsycINFO, EMBASE, and Scopus. The searched terms combined in each database were: resilience, hardiness, work, employ, occupation, job, and nursing. REVIEW METHODS: Full-text English articles published between 2000 and 2018 were included. Studies were also included if they involved: (1) nurses who provided direct patient care, (2) resilience and its associated factors, (3) an empirical quantitative study, and (4) a quality assessment grade of 'good' or 'fair'. Two authors carried out the study eligibility and quality assessment independently. A narrative synthesis was utilised following the Job Demands-Resources model to identify the factors of job demands and resources, which were associated with nurse resilience. RESULTS: A total of 38 articles met the criteria and were systematically reviewed and narratively synthesised. Various resilience scales utilised in these studies made it unfeasible to synthesise the evidence using a meta-analysis. Inconsistencies exist when examining personal and work-related factors. Job demands (stress, burnout, posttraumatic stress disorder, and workplace bullying) were negatively associated with resilience, while job resources (coping skills, self-efficacy, social support, job satisfaction, job retention, and general wellbeing) were positively related to resilience. Using a quality assessment tool, 23 studies were rated as 'Good', 15 were assessed as 'Fair', and 20 were found to have a risk of bias. CONCLUSIONS: Understanding nurse resilience can proactively help nurses identify or prevent potential problems, thus fostering job resources and ultimately achieving personal and professional growth. Increased nurse resilience can help nurses reduce emotional exhaustion, increase work engagement, and enhance function when facing workplace challenges. This can assist nurses to establish strategies to deal with adversity and attenuate the effects of job demands. Further research is needed to explore nurse resilience and develop a consistent instrument for measuring resilience.

Visualising Combined Time Use Patterns of Children's Activities and Their Association with Weight Status and Neighbourhood Context.

Compositional data techniques are an emerging method in physical activity research. These techniques account for the complexities of, and interrelationships between, behaviours that occur throughout a day (e.g., physical activity, sitting, and sleep). The field of health geography research is also developing rapidly. Novel spatial techniques and data visualisation approaches are increasingly being recognised for their utility in understanding health from a socio-ecological perspective. Linking compositional data approaches with geospatial datasets can yield insights into the role of environments in promoting or hindering the health implications of the daily time-use composition of behaviours. The 7-day behaviour data used in this study were derived from accelerometer data for 882 Auckland school children and linked to weight status and neighbourhood deprivation. We developed novel geospatial visualisation techniques to explore activity composition over a day and generated new insights into links between environments and child health behaviours and outcomes. Visualisation strategies that integrate compositional activities, time of day, weight status, and neighbourhood deprivation information were devised. They include a ringmap overview, small-multiple ringmaps, and individual and aggregated time⁻activity diagrams. Simultaneous visualisation of geospatial and compositional behaviour data can be useful for triangulating data from diverse disciplines, making sense of complex issues, and for effective knowledge translation.

Children's Transport Built Environments: A Mixed Methods Study of Associations between Perceived and Objective Measures and Relationships with Parent Licence for Independent Mobility in Auckland, New Zealand.

Children's independent mobility is declining internationally. Parents are the gatekeepers of children's independent mobility. This mixed methods study investigates whether parent perceptions of the neighbourhood environment align with objective measures of the neighbourhood built environment, and how perceived and objective measures relate to parental licence for children's independent mobility. Parents participating in the Neighbourhood for Active Kids study (n = 940) answered an open-ended question about what would make their neighbourhoods better for their child's independent mobility, and reported household and child demographics. Objective measures of the neighbourhood built environment were generated using geographic information systems. Content analysis was used to classify and group parent-reported changes required to improve their neigbourhood. Parent-reported needs were then compared with objective neighbourhood built environment measures. Linear mixed modelling examined associations between parental licence for independent mobility and (1) parent neighbourhood perceptions; and (2) objectively assessed neighbourhood built environment features. Parents identified the need for safer traffic environments. No significant differences in parent reported needs were found by objectively assessed characteristics. Differences in odds of reporting needs were observed for a range of socio-demographic characteristics. Parental licence for independent mobility was only associated with a need for safer places to cycle (positive) and objectively assessed cycling infrastructure (negative) in adjusted models. Overall, the study findings indicate the importance of safer traffic environments for children's independent mobility.

Being barefoot. Prevalence at home, in school and during sport: a cross-sectional survey of 714 New Zealand secondary school boys.

BACKGROUND: It may be assumed that a combination of culture, climate and economic resource are the major reasons that non-industrialised countries have a higher prevalence of barefoot activity. New Zealand is an industrialised country with comparable resources to that of many European countries; however, it seems to remain socially acceptable to carry out barefoot activities. A chance observation of students competing barefoot on a tartan track, prompted us to determine the prevalence of barefoot activity in an all-boys secondary school in Auckland New Zealand. METHOD: An 11-question survey was administered at an Auckland boys secondary school, of high socioeconomic status, to determine the footwear habits of students (n = 714) during: a) daily life b) school life (c) physical education class and (d) sport. To classify students as habitually barefoot or shod, students were asked to select whether they were barefoot most of the time (2-points), half of the time (1-point) or none of the time (0-points) in three settings: around the house, during sport and during school. A score of ≥3 was required to be considered habitually barefoot. Participants were also asked to specify, when running at their most recent athletics event (100 m - 3000 m) on a track, whether they ran barefoot, in shoes, in spikes or another type of footwear. Finally, participants were asked to indicate if leg pain had interrupted running during the previous 12-months. Analysis was conducted using IBM SPSS. RESULTS: 45% (95% CI: 41.5-49.5%) of the participants in our sample were classified as habitually barefoot. More than half of the sample reported being barefoot most of the time at home (n = 404, 56.6%) and during PE class (n = 420, 58.8%). Over 50% of the sample reported being barefoot half of the time or more during sport (n = 380, 53.2%). A smaller amount went to the supermarket (n = 140, 19.6%) or took the bus (n = 59, 8.3%) whilst barefoot around half of the time or more. The percentage of barefoot competitors declined with increasing distance: 100 m (46.5%), 200 m (41.8%), 400 m (38%), 800 m (31%), 1500 m (31%) and 3000 m (20%). The prevalence of leg pain interfering with running was 23.5%. There was no difference in the prevalence of leg pain between those classified as habitually barefoot and shod (Χ2(1, N = 603) = 0.005, p = 0.946). CONCLUSION: The results of this survey demonstrate that over 50% of students at an all-boys secondary school in Auckland, of high socioeconomic status, are barefoot at home, during physical education and sport half of the time or more. These results may point towards a cultural difference between New Zealand and other modern industrialised countries.

A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults.

INTRODUCTION: Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults. METHODS: Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually. RESULTS: Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. CONCLUSIONS: When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.

Wear-Time Compliance with a Dual-Accelerometer System for Capturing 24-h Behavioural Profiles in Children and Adults.

To advance the field of time-use epidemiology, a tool capable of monitoring 24 h movement behaviours including sleep, physical activity, and sedentary behaviour is needed. This study explores compliance with a novel dual-accelerometer system for capturing 24 h movement patterns in two free-living samples of children and adults. A total of 103 children aged 8 years and 83 adults aged 20-60 years were recruited. Using a combination of medical dressing and purpose-built foam pouches, participants were fitted with two Axivity AX3 accelerometers—one to the thigh and the other to the lower back—for seven 24 h periods. AX3 accelerometers contain an inbuilt skin temperature sensor that facilitates wear time estimation. The median (IQR) wear time in children was 160 (67) h and 165 (79) h (out of a maximum of 168 h) for back and thigh placement, respectively. Wear time was significantly higher and less variable in adults, with a median (IQR) for back and thigh placement of 168 (1) and 168 (0) h. A greater proportion of adults (71.6%) achieved the maximum number of complete days when compared to children (41.7%). We conclude that a dual-accelerometer protocol using skin attachment methods holds considerable promise for monitoring 24-h movement behaviours in both children and adults.

Novel Nutrition Profiling of New Zealanders' Varied Eating Patterns.

There is increasing recognition that the relationship between nutrition and health is influenced by complex eating behaviors. The aims of this study were to develop novel nutrition profiles of New Zealanders and to describe the prevalence of these profiles. Observational, cross-sectional data from the Sovereign Wellbeing Index, 2014 was used to develop the profiles in an a-priori process. Descriptive prevalence for the total data (N = 10,012; 4797 males; 18+ years) and profiles were reported. Nutrition question responses were presented as: Includers (consumed few time a week or more), Avoiders (few time a month) and Limiters (not eaten). Fruit or non-starchy vegetables were Included (fruit: 83.4%, 95% confidence interval (CI: 82.7, 84.1); vegetables: 82.6% (81.8, 83.4)) by the majority of the sample. Also Included were confectionary (48.6% 95% CI (47.6, 49.6)) and full sugar drinks (34.3% (33.4, 35.2)). The derived nutrition profiles were: Junk Food (22.4% 95% CI (21.6, 23.3)), Moderator (43.0% (42.1, 44.0)), High-Carbohydrate (23.0% (22.2, 23.8)), Mediterranean (11.1% (10.5, 11.8)), Flexitarian (8.8% (8.2, 9.4)), and Low-Carbohydrate (5.4% (4.9, 5.8)). This study suggests that New Zealanders follow a number of different healthful eating patterns. Future work should consider how these alternate eating patterns impact on public health.

Neighbourhoods for Active Kids: study protocol for a cross-sectional examination of neighbourhood features and children's physical activity, active travel, independent mobility and body size.

INTRODUCTION: New Zealand children's physical activity, including independent mobility and active travel, has declined markedly over recent decades. The Neighbourhoods for Active Kids (NfAK) study examines how neighbourhood built environments are associated with the independent mobility, active travel, physical activity and neighbourhood experiences of children aged 9-12 years in primary and intermediate schools across Auckland, New Zealand's largest city. METHODS AND ANALYSIS: Child-specific indices of walkability, destination accessibility and traffic exposure will be constructed to measure the built environment in 8 neighbourhoods in Auckland. Interactive online-mapping software will be used to measure children's independent mobility and transport mode to destinations and to derive measures of neighbourhood use and perceptions. Physical activity will be measured using 7-day accelerometry. Height, weight and waist circumference will be objectively measured. Parent telephone interviews will collect sociodemographic information and parent neighbourhood perceptions. Interviews with school representative will capture supports and barriers for healthy activity and nutrition behaviours at the school level. Multilevel modelling approaches will be used to understand how differing built environment variables are associated with activity, neighbourhood experiences and health outcomes. DISCUSSION: We anticipate that children who reside in neighbourhoods considered highly walkable will be more physically active, accumulate more independent mobility and active travel, and be more likely to have a healthy body size. This research is timely as cities throughout New Zealand develop and implement plans to improve the liveability of intensifying urban neighbourhoods. Results will be disseminated to participants, local government agencies and through conventional academic avenues.

Comparison of Actical and activPAL measures of sedentary behaviour in preschool children.

OBJECTIVES: The purpose of this study was to examine the convergent validity of the Actical and activPAL to measure sedentary behaviour (SB) and non-SB in preschoolers in a free-living environment. DESIGN: A convenience sample of 49 preschoolers (22 boys; 4.0 ± 0.5 years) from six early childhood centres in Auckland, New Zealand were included in data analysis. METHODS: Participants wore a hip-mounted Actical and a thigh-mounted activPAL accelerometer simultaneously during centre attendance for one day and data were collected in 15s epochs. Bland-Altman tests were used to assess differences in group mean minutes and percentage of time in (non-)SB between both monitors. Agreement between binary coded (SB vs. non-SB) 15s-by-15s Actical and activPAL data was evaluated by calculating percentage agreement and κ statistic. RESULTS: The monitors were worn on average for 294.8 ± 46.3 min resulting in a total of 57,780 15s epochs. Bland-Altman tests suggested a small group mean difference in (non-)SB (1.3 min; 0.1%) and a wide prediction interval (121.3 min; 39.2%). No obvious systematic bias was observed in the Bland-Altman plot. Percentage agreement between the 15s-by-15s Actical and activPAL data of all participants was 73.0% (inter-child range: 36.8-93.8%). The κ statistic showed moderate agreement with a value of 0.46 (95% CI: 0.45-0.47). CONCLUSIONS: Although the group mean estimate of (non-)SB was similar between the Actical and activPAL, the output of both monitors cannot be considered convergent as meaningful random disagreement was found between both monitors.