BACKGROUND: Evidence on the prevalence, determinants, and health outcomes of physical activity in disabled people is limited. It is possible that the limited availability of high-quality scientific evidence is due to the extent and nature of disability assessment in physical activity research. This scoping review explores how disability has been measured in epidemiological studies that included accelerometer-based measurement of physical activity. METHODS: Data sources: MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus, and CINAHL. ELIGIBILITY CRITERIA: Prospective and cross-sectional studies that included an accelerometer measurement of physical activity. Survey instruments used in these studies were obtained, and questions relating to the International Classification of Functioning, Disability and Health domains of (1) health conditions, (2) body functions and structures, and (3) activities and participation, were extracted for analysis. RESULTS: Eighty-four studies met the inclusion criteria, from which complete information on the 3 domains was obtained for 68. Seventy-five percent of studies (n = 51) captured whether a person had at least one health condition, 63% (n = 43) had questions related to body functions and structures, and 75% (n = 51) included questions related to activities and participation. CONCLUSION: While most studies asked something about one of the 3 domains, there was substantial diversity in the focus and style of questions. This diversity indicates a lack of consensus on how these concepts should be assessed, with implications for the comparability of evidence across studies and subsequent understanding of the relationships between disability, physical activity, and health.

This systematic review and meta-analysis aimed to examine the association between device-measured physical activity (PA) and performance-based measures of physical function (PF). Databases searched included CINAHL, Embase, MEDLINE/PubMed, SPORTDiscus, and Web of Science (last search conducted on November 11, 2022). Observational studies (cross-sectional or prospective) reporting associations between wearable device-measured PA and PF outcomes in non-clinical adults were eligible. Forty-two studies with a pooled sample of 27 276 participants were eligible, with 34 studies reporting a standardised regression coefficient (β) between at least one of four PA measures and one of six PF outcomes. All measures of PA were positively associated with all measures of PF, except for step count with grip strength. Largest associations were seen with lower-body PF tests; gait speed (βs=0.11–0.26), walk tests (βs=0.18–0.41), chair-rise test (βs=0.10–0.26), balance (βs=0.07–0.24) and Timed Up-and-Go (βs=0.10–0.24) all p<0.01. Small or no association was seen with grip strength (βs=0.02–0.07). In observational studies of general adult populations, there were associations between multiple dimensions of PA and a broad range of PF measures. The findings provide provisional support for the use of device measures of movement to remotely monitor people for risk of low PF. Prospective designs are needed to determine the direction of the relationship. Future studies should also explore a broader range of PA metrics beyond simple aggregate measures of time spent at different acceleration values as there is evidence that the temporal distribution of activity is related to health and functional outcomes.

The aim of this review was to understand the association between habitual physical activity (hPA) and carotid-femoral pulse wave velocity (cfPWV) in an ostensibly healthy adult population. Searches were performed in MEDLINE Web of Science, SPORTDiscus and CINAHL databases published up to 01/01/2022 (PROSPERO, Registration No: CRD42017067159). Observational English-language studies assessing the relationship between cfPWV and hPA (measured via self-report or device-based measures) were considered for inclusion in a narrative synthesis. Studies were excluded if studying specific disease. Studies were further included in pooled analyses where a standardised association statistic for continuous hPA and cfPWV was available. 29 studies were included in narrative synthesis, of which 18 studies provided sufficient data for pooled analyses, totalling 15,573 participants. A weak, significant, negative correlation between hPA and cfPWV was observed; partial r = -0.08 95%CI [-0.15, -0.01]; P = 0.045. Heterogeneity was high (I2 = 94.5% P

Abstract
.
. Background and Aims
. Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers.
.
.
. Methods
. Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours.
.
.
. Results
. The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7â€
h sleeping, 10.4â€
h sedentary, 3.1â€
h standing, 1.5â€
h LIPA, and 1.3â€
h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30â€
min of SB, sleep, standing, or LIPA with MVPA was associated with −0.63 (95% confidence interval −0.48, −0.79), −0.43 (−0.25, −0.59), −0.40 (−0.25, −0.56), and −0.15 (0.05, −0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day.
.
.
. Conclusions
. Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.
.

Background: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours. Methods: the review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss. Results: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent. Conclusion: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

OBJECTIVE: This research took a co-design approach to develop a social intervention to support people affected by a cancer diagnosis to be physically active. METHODS: We conducted semi-structured interviews with five key stakeholder groups: (1) adults with a recent breast or prostate cancer diagnosis; (2) family and friends of cancer patients; (3) healthcare professionals; (4) physical activity providers; and (5) cancer charity representatives. Inductive content analysis was used to identify themes in the data. We then worked with a subset of participants to co-develop the intervention. RESULTS: Participants welcomed the idea of a social approach to a physical activity intervention. Input was received on the timing and format of delivery, how to communicate about physical activity to cancer patients and their family and friends and the types of physical activity that would be appropriate. Our findings suggest that interventions need to be flexible in terms of timing and delivery and offer a wide range of physical activity options. These findings directly informed the co-development of 'All Together Active'. CONCLUSION: all Together Active is designed to support cancer patients and their family and friends to be active throughout treatment and beyond, benefiting their physical and mental health.

Objective: Elexacaftor/Tezacaftor/Ivacaftor is a cystic fibrosis transmembrane conductance regulator (CFTR) modulator with the potential to improve exercise capacity. This case series of three adolescents with CF aimed to investigate whether 6 weeks treatment with Elexacaftor/Tezacaftor/Ivacaftor could improve exercise capacity in CFTR modulator naive adolescents with CF. Methods: Three adolescents (14.0 ± 1.4 years) with CF (FEV1% predicted: 62.5 ± 17.1; F508del/F508del genotype) completed an exhaustive maximal cardiopulmonary exercise test on a cycle ergometer to determine peak oxygen uptake ((Formula presented.) O2peak) and measure changes in gas exchange and ventilation during exercise at 6 weeks. We also analyzed wrist-worn device-based physical activity (PA) data in two of the three cases. Validated acceleration thresholds were used to quantify time spent in each PA intensity category. Results: Clinically meaningful improvements in (Formula presented.) O2peak were observed in all three cases (+17.6%, +52.4%, and +32.9%, respectively), with improvements greatest in those with more severe lung disease and lower fitness at baseline. Although lung function increased in all cases, inconsistent changes in markers of ventilatory and peripheral muscle efficiency likely suggest different mechanisms of improvement in this case group of adolescents with CF. Device-based analysis of PA was variable, with one case increasing and one case decreasing. Conclusion: in this case series, we have observed, for the first time, improvements in exercise capacity following 6 weeks of treatment with Elexacaftor/Tezacaftor/Ivacaftor. Improvements were greatest in the presence of more severe CF lung disease and lower aerobic fitness at baseline. The mechanism(s) responsible for these changes warrant further investigation in larger trials.

Background and Aims
When people with Type 1 diabetes (T1D) exercise, we suggest they stay in the range of 7-15 mmol/L and take action if glucose moves outside of this range. In a study of 39 people with T1D who took part in a half marathon, 70% had to stop to deal with a low or high glucose event. If people with T1D were warned pre-exercise of the risk of a glycaemic event, it would allow them to take countermeasures to prevent these events occurring. Using standard demographic data, blood tests and information about the exercise bout, we aimed to examine whether machine learning (ML) could predict low or high glucose events during exercise.

Material and methods
Data came from 2 exercise studies (EXTOD education and EXTOD 101) with each having data on age, sex, length of diabetes, body mass index, HbA1c, C-peptide and time, length, intensity (BORG scale) and type (aerobic, anaerobic and mixed) of exercise sessions. EXTOD education had 2 weeks of Dexcom G6 data from 54 participants and EXTOD 101 8 weeks of Freestyle Libre data from 34 participants. In total there were 976 bouts of exercise.

The thresholds for low glucose and high glucose were set to 7 and 15mmol/L – the point at which intervention is advised. A single reading below or above the threshold was considered as a positive event. 486 bouts contained a glucose reading below 7 and 151 had glucose reading above 15 mmol/L.

80% of this data was used to train an ML algorithm (XGBoost) which then determined how important each measure was and what was the best combination. This was then tested on the remaining 20% of data using area under the receiver operator curve (ROC AUC) score as the validation metric.

Results
For predicting a glucose reading below 7mmol/L, the addition of the best 6 measures identified by ML resulted in a ROC AUC score 0.902. These, in order of importance, were glucose at start of exercise (starting glucose), duration of exercise, type of exercise, intensity, time of day and C-peptide. For predicting glucose above 15 mmol/L, incorporating the best 7 measures resulted in a ROC score of 0.973. These were start glucose, duration, intensity, sex, years since diagnosis, C-peptide, and time of day.

The two most important measures were start glucose and duration of exercise bout in both contexts. Using only these features, a ROC AUC score of 0.890 was achieved for predicting low glucose and 0.968 for high glucose. The model accuracy and sensitivity-specificity intersect was 0.804 and 0.804 for low glucose and 0.949 and 0.900 for high.

Using logistic analysis, we have taken these two measures to develop a heat map that can help patients predict their risk of going below 7 or above 15mmol/L during an exercise (see figure 1).

Conclusions
ML has produced a simple heat map to predict risk of glucose going below 7 or above 15mmol/L during exercise. We will go on to look at whether it can predict lower glucose levels during exercise and at times further away from the start of exercise.

INTRODUCTION: Throughout the life course, there are major life transitions that are associated with reduced physical activity, which may have further implications for health and well-being. Having a child is one such transition that has been identified as a critical transformative experience and window for intervention. We will conduct a scoping review of available evidence exploring the impact of having a child on physical activity in the UK. METHODS AND ANALYSIS: We will use best-practice methodological frameworks to map key concepts and available evidence, summarise and disseminate findings to stakeholders, and identify knowledge gaps. A three-step search strategy will identify primary research studies, including reviews, from published and grey literature, exploring the impact of having a child on physical activity in the UK, from the preconception period, throughout pregnancy, the postpartum period, and into parenthood. An initial limited search will identify relevant reviews, from which keywords and index terms will be extracted. We will conduct searches of CINAHL, Embase, Medline, PsycINFO and Web of Science to identify relevant articles written in English from inception to February 2022. Two reviewers will independently screen titles and abstracts of identified studies for inclusion and chart data, with a third reviewer resolving any conflicts. Backwards citation tracking will identify any additional studies. We will conduct numerical and thematic analysis to map data in tabular and diagrammatic format and provide a description of findings by theme. ETHICS AND DISSEMINATION: Ethical approval is not required for this scoping review. We will disseminate findings to stakeholders through publications, conferences, social media platforms and in-person communications. Consultations with key stakeholders, with their unique expertise and perspectives, will provide greater insight. We will establish the main priorities for future research to inform the research questions of subsequent studies. SCOPING REVIEW REGISTRATION: Open Science Framework (https://osf.io/gtqa4/) DOI 10.17605/OSF.IO/GTQA4.

INTRODUCTION: Physical activity (PA) interventions have a promising role in the management of gestational diabetes mellitus (GDM). Digital technologies can support PA at scale and remotely. The protocol describes a study designed to determine the feasibility and acceptability of a complex intervention; known as +Stay Active. +Stay Active combines motivational interviewing with a bespoke behaviour change informed smartphone application (Stay-Active) to augment PA levels in women with GDM. METHODS AND ANALYSIS: This is a non-randomised feasibility study using a mixed methods approach. Participants will be recruited from the GDM antenatal clinic at the Women Centre, John Radcliffe Hospital, Oxford. Following baseline assessments (visit 1) including self-reported and device determined PA assessment (wearing a wrist accelerometer), women will be invited to participate in an online motivational interview, then download and use the Stay-Active app (Android or iOS) (visit 2). Women will have access to Stay-Active until 36 weeks gestation, when engagement and PA levels will be reassessed (visit 3). The target sample size is 60 women. Primary outcomes are recruitment and retention rates, compliance and assessment of participant engagement and acceptability with the intervention. Secondary outcomes are assessment of blood glucose control, self-reported and device determined assessment of PA, usage and structured feedback of participant's attitudes to +Stay Active, assessment of health costs and description of maternal and neonatal outcomes. This study will provide key insights into this complex intervention regarding engagement in smartphone technology and the wearing of accelerometers. These data will inform the development of a randomised controlled trial with refinements to intervention implementation. ETHICS AND DISSEMINATION: the study has received a favourable opinion from South Central-Hampshire B Research Ethics Committee; REC reference: 20/SC/0342. Written informed consent will be obtained from all participants. Findings will be disseminated through peer-reviewed journals, conferences and seminar presentations. TRIAL REGISTRATION NUMBER: ISRCTN11366562.

INTRODUCTION: Exercise and physical activity (PA) are fundamental to the treatment of type 2 diabetes. Current exercise and PA strategies for newly diagnosed individuals with type 2 diabetes are either clinically effective but unsuitable in routine practice (supervised exercise) or suitable in routine practice but clinically ineffective (PA advice). Mobile health (mHealth) technologies, offering biometric data to patients and healthcare professionals, may bridge the gap between supervised exercise and PA advice, enabling patients to engage in regular long-term physically active lifestyles. This feasibility randomised controlled trial (RCT) will evaluate the use of mHealth technology when incorporated into a structured home-based exercise and PA intervention, in those recently diagnosed with type 2 diabetes. METHODS AND ANALYSIS: This feasibility multicentre, parallel group RCT will recruit 120 individuals with type 2 diabetes (diagnosis within 5-24 months, aged 40-75 years) in the UK (n=60) and Canada (n=60). Participants will undertake a 6-month structured exercise and PA intervention and be supported by an exercise specialist (active control). The intervention group will receive additional support from a smartwatch and phone app, providing real-time feedback and enabling improved communication between the exercise specialist and participant. Primary outcomes are recruitment rate, adherence to exercise and loss to follow-up. Secondary outcomes include a qualitative process evaluation and piloting of potential clinical outcome measures for a future RCT. ETHICS AND DISSEMINATION: the trial was approved in the UK by the South East Scotland Research Ethics Committee 01 (20/SS/0101) and in Canada by the Clinical Research Ethics Board of the University of British Columbia (H20-01936), and is being conducted in accordance with the Declaration of Helsinki and Good Clinical Practice. Results will be published in peer-reviewed journals and presented at national and international scientific meetings. TRIAL REGISTRATION NUMBERS: ISRCTN14335124; ClinicalTrials.gov: NCT04653532.

Information received by women regarding physical activity during and after pregnancy often lacks clarity and may be conflicting and confusing. Without clear, engaging, accessible guidance centred on the experiences of pregnancy and parenting, the benefits of physical activity can be lost. We describe a collaborative process to inform the design of evidence-based, user-centred physical activity resources which reflect diverse experiences of pregnancy and early parenthood. Two iterative, collaborative phases involving patient and public involvement (PPI) workshops, a scoping survey (n = 553) and stakeholder events engaged women and maternity, policy and physical activity stakeholders to inform pilot resource development. These activities shaped understanding of challenges experienced by maternity and physical activity service providers, pregnant women and new mothers in relation to supporting physical activity. Working collaboratively with women and stakeholders, we co-designed pilot resources and identified important considerations for future resource development. Outcomes and lessons learned from this process will inform further work to support physical activity during pregnancy and beyond, but also wider health research where such collaborative approaches are important. We hope that drawing on our experiences and sharing outcomes from this work provide useful information for researchers, healthcare professionals, policy makers and those involved in supporting physical activity behaviour.

BACKGROUND: Oxidative stress and inflammation may contribute to anabolic resistance in response to protein and exercise in older adults. We investigated whether consumption of montmorency cherry concentrate (MCC) increased anabolic sensitivity to protein ingestion and resistance exercise in healthy older men. METHODS: Sixteen healthy older men were randomized to receive MCC (60 mL·d-1) or placebo (PLA) for two weeks, after baseline measures in week 1. During week 3, participants consumed 10 g whey protein·d-1 and completed three bouts of unilateral leg resistance exercise (4 × 8-10 repetitions at 80% 1RM). Participants consumed a bolus (150 mL) and weekly (50 mL) doses of deuterated water. Body water 2H enrichment was measured in saliva and vastus lateralis biopsies were taken from the non-exercised leg after weeks 1, 2 and 3, and the exercised leg after week 3, to measure tracer incorporation at rest, in response to protein and protein + exercise. RESULTS: Myofibrillar protein synthesis increased in response to exercise + protein compared to rest (p 

Acute exercise transiently suppresses the orexigenic gut hormone acylated ghrelin, but the extent to which exercise intensity and duration determine this response is not fully understood. The effects of manipulating exercise intensity and duration on acylated ghrelin concentrations and hunger were examined in two experiments. In experiment one, nine healthy males completed three, 4-h conditions (control, moderate-intensity running (MOD) and vigorous-intensity running (VIG)), with an energy expenditure of ~2.5 MJ induced in both MOD (55-min running at 52% peak oxygen uptake (V.O2peak)) and VIG (36-min running at 75% V.O2peak). In experiment two, nine healthy males completed three, 9-h conditions (control, 45-min running (EX45) and 90-min running (EX90)). Exercise was performed at 70% V.O2peak in both experiments, participants consumed standardised meals, and acylated ghrelin concentrations and hunger were quantified at predetermined intervals. In experiment one, delta acylated ghrelin concentrations were lower than control in MOD (ES = 0.44, P = 0.01) and VIG (ES = 0.98, P 

Background: Ecological models are currently the most used approaches to classify and conceptualise determinants of sedentary behaviour, but these approaches are limited in their ability to capture the complexity of and interplay between determinants. The aim of the project described here was to develop a transdisciplinary dynamic framework, grounded in a system-based approach, for research on determinants of sedentary behaviour across the life span and intervention and policy planning and evaluation. Methods: a comprehensive concept mapping approach was used to develop the Systems of Sedentary behaviours (SOS) framework, involving four main phases: (1) preparation, (2) generation of statements, (3) structuring (sorting and ranking), and (4) analysis and interpretation. The first two phases were undertaken between December 2013 and February 2015 by the DEDIPAC KH team (DEterminants of DIet and Physical Activity Knowledge Hub). The last two phases were completed during a two-day consensus meeting in June 2015. Results: During the first phase, 550 factors regarding sedentary behaviour were listed across three age groups (i.e. youths, adults and older adults), which were reduced to a final list of 190 life course factors in phase 2 used during the consensus meeting. In total, 69 international delegates, seven invited experts and one concept mapping consultant attended the consensus meeting. The final framework obtained during that meeting consisted of six clusters of determinants: Physical Health and Wellbeing (71 % consensus), Social and Cultural Context (59 % consensus), Built and Natural Environment (65 % consensus), Psychology and Behaviour (80 % consensus), Politics and Economics (78 % consensus), and Institutional and Home Settings (78 % consensus). Conducting studies on Institutional Settings was ranked as the first research priority. The view that this framework captures a system-based map of determinants of sedentary behaviour was expressed by 89 % of the participants. Conclusion: Through an international transdisciplinary consensus process, the SOS framework was developed for the determinants of sedentary behaviour through the life course. Investigating the influence of Institutional and Home Settings was deemed to be the most important area of research to focus on at present and potentially the most modifiable. The SOS framework can be used as an important tool to prioritise future research and to develop policies to reduce sedentary time.

BACKGROUND: Sitting behaviours have been linked with increased risk of all-cause mortality independent of moderate to vigorous physical activity (MVPA). Previous studies have tended to examine single indicators of sitting or all sitting behaviours combined. This study aims to enhance the evidence base by examining the type-specific prospective associations of four different sitting behaviours as well as total sitting with the risk of all-cause mortality. METHODS: Participants (3720 men and 1412 women) from the Whitehall II cohort study who were free from cardiovascular disease provided information on weekly sitting time (at work, during leisure time, while watching TV, during leisure time excluding TV, and at work and during leisure time combined) and covariates in 1997-99. Cox proportional hazards models were used to investigate prospective associations between sitting time (h/week) and mortality risk. Follow-up was from date of measurement until (the earliest of) death, date of censor or July 31 2014. RESULTS: over 81 373 person-years of follow-up (mean follow-up time 15.7 ± 2.2 years) a total of 450 deaths were recorded. No associations were observed between any of the five sitting indicators and mortality risk, either in unadjusted models or models adjusted for covariates including MVPA. CONCLUSIONS: Sitting time was not associated with all-cause mortality risk. The results of this study suggest that policy makers and clinicians should be cautious about placing emphasis on sitting behaviour as a risk factor for mortality that is distinct from the effect of physical activity.

BACKGROUND: the Sedentary Sphere is a method for the analysis, identification, and visual presentation of sedentary behaviors from a wrist-worn triaxial accelerometer. PURPOSE: This study aimed to introduce the concept of the Sedentary Sphere and to determine the accuracy of posture classification from wrist accelerometer data. METHODS: Three samples were used: 1) free living (n = 13, ages 20-60 yr); 2) laboratory based (n = 25, ages 30-65 yr); and 3) hospital inpatients (n = 10, ages 60-90 yr). All participants wore a GENEActiv on their wrist and activPAL on their thigh. The free-living sample wore an additional GENEActiv on the thigh and completed the Multimedia Activity Recall for Children and Adults. The laboratory-based sample wore the monitors while seated at a desk for 7 h, punctuated by 2 min of walking every 20 min. The free-living and inpatient samples wore the monitors for 24 h. Posture was classified from wrist-worn accelerometry using the Sedentary Sphere concept. RESULTS: Sitting time did not differ between the wrist GENEActiv and the activPAL in the free-living sample and was correlated in the three samples combined (rho = 0.9, P < 0.001), free-living and inpatient samples (r ≃ 0.8, P < 0.01). Mean intraindividual agreement was 85% ± 7%. In the laboratory-based and inpatient samples, sitting time was underestimated by the wrist GENEActiv by 30 min and 2 h relative to the activPAL, respectively (P < 0.05). Posture classification disagreed during reading while standing, cooking while standing, and brief periods during driving. Posture allocation validity was excellent when the GENEActiv was worn on the thigh, evidenced by the near-perfect agreement with the activPAL (96% ± 3%). CONCLUSIONS: the Sedentary Sphere enables determination of the most likely posture from the wrist-worn GENEActiv. Visualizing behaviors on the sphere displays the pattern of wrist movement and positions within that behavior.

BACKGROUND: the Sedentary Sphere is a method for the analysis, identification, and visual presentation of sedentary behaviors from a wrist-worn triaxial accelerometer. PURPOSE: This study aimed to introduce the concept of the Sedentary Sphere and to determine the accuracy of posture classification from wrist accelerometer data. METHODS: Three samples were used: 1) free living (n = 13, ages 20-60 yr); 2) laboratory based (n = 25, ages 30-65 yr); and 3) hospital inpatients (n = 10, ages 60-90 yr). All participants wore a GENEActiv on their wrist and activPAL on their thigh. The free-living sample wore an additional GENEActiv on the thigh and completed the Multimedia Activity Recall for Children and Adults. The laboratory-based sample wore the monitors while seated at a desk for 7 h, punctuated by 2 min of walking every 20 min. The free-living and inpatient samples wore the monitors for 24 h. Posture was classified from wrist-worn accelerometry using the Sedentary Sphere concept. RESULTS: Sitting time did not differ between the wrist GENEActiv and the activPAL in the free-living sample and was correlated in the three samples combined (rho = 0.9, P < 0.001), free-living and inpatient samples (r ? 0.8, P < 0.01). Mean intraindividual agreement was 85% ± 7%. In the laboratory-based and inpatient samples, sitting time was underestimated by the wrist GENEActiv by 30 min and 2 h relative to the activPAL, respectively (P < 0.05). Posture classification disagreed during reading while standing, cooking while standing, and brief periods during driving. Posture allocation validity was excellent when the GENEActiv was worn on the thigh, evidenced by the near-perfect agreement with the activPAL (96% ± 3%). CONCLUSIONS: the Sedentary Sphere enables determination of the most likely posture from the wrist-worn GENEActiv. Visualizing behaviors on the sphere displays the pattern of wrist movement and positions within that behavior. © 2014 by the American College of Sports Medicine.

Background: Prospective studies report associations between indicators of time spent sitting and obesity risk. Most studies use a single indicator of sedentary behavior and are unable to clearly identify whether sedentary behavior is a cause or a consequence of obesity. Purpose: to investigate cross-sectional and prospective associations between multiple sitting time indicators and obesity and examine the possibility of reverse causality. Methods: Using data from the Whitehall II cohort, multiple logistic models were fitted to examine associations between prevalent obesity (BMI ≥30) at Phase 5 (1997-1999), and incident obesity between Phases 5 and 7 (2003-2004) across four levels of five sitting exposures (work sitting, TV viewing, non-TV leisure-time sitting, leisure-time sitting, and total sitting). Using obesity data from three prior phases (1985-1988, 1991-1993; and recalled weight at age 25 years), linear regression models were fitted to examine the association between prior obesity and sitting time at Phase 5. Analyses were conducted in 2012. Results: None of the sitting exposures were associated with obesity either cross-sectionally or prospectively. Obesity at one previous measurement phase was associated with a 2.43-hour/week (95% CI=0.07, 4.78) increase in TV viewing; obesity at three previous phases was associated with a 7.42-hour/week (95% CI=2.7, 12.46) increase in TV-viewing hours/week at Phase 5. Conclusions: Sitting time was not associated with obesity cross-sectionally or prospectively. Prior obesity was prospectively associated with time spent watching TV per week but not other types of sitting. © 2013 American Journal of Preventive Medicine.

BACKGROUND: Associations between socioeconomic position (SEP) and sedentary behaviour in children are unclear. Existing studies have used aggregate measures of weekly sedentary time that could mask important differences in the relationship between SEP and sedentary time at different times of the day or between weekdays and weekend days. These studies have also employed a variety of measures of SEP which may be differentially associated with sedentary time. This paper examines associations of multiple indicators of SEP and accelerometer-measured, temporally specific, sedentary time in school children. METHODS: Between 2006 and 2007 sedentary time data (minutes spent below 100 accelerometer counts per minute) for weekdays before-school (7.00-8.59AM), during school-time (9.00AM-2.59PM) and after-school (3.00PM-11.00PM), and weekend days were recorded for 629 10-11 year old children using accelerometers. Ordinary least squares regression was used to examine associations with 5 indicators of SEP (area deprivation, annual household income, car ownership, parental education and access to a private garden). Covariates were; gender, BMI, minutes of daylight, accelerometer wear time and school travel method. Analyses were conducted in 2012. RESULTS: Following adjustments for covariates, having a parent educated to university degree level was associated with more minutes of school (5.87 [95% CI 1.72, 10.04]) and after-school (6.04 [95% CI 0.08, 12.16]) sedentary time. Quartiles of area deprivation (most to least deprived) were positively associated with after-school (Q2: 4.30 [95% CI -6.09, 14.70]; Q3: 10.77 [95% CI 0.47, 21.06]; Q4: 12.74 [95% CI 2.65, 22.84]; P(trend) = 0.04) and weekend (Q2: 26.34 [95% CI 10.16, 42.53]; Q3: 33.28 [95% CI 16.92, 49.65]; Q4: 29.90 [95% CI 14.20, 45.60]; P(trend) = 0.002) sedentary time. Having a garden was associated with less sedentary time after-school (-14.39 [95% CI -25.14, -3.64]) and at weekends (-27.44 [95% CI -43.11, -11.78]). CONCLUSIONS: Associations between SEP and children's sedentary-time varied by SEP indicator and time of day. This highlights the importance of measuring multiple indicators of SEP and examining context specific sedentary time in children in order to fully understand how SEP influences this behaviour. Further research should combine self-report and objective data to examine associations with specific sedentary behaviours in the contexts within which they occur, as well as total sedentary time.

Background: UK breastfeeding rates are low and socially distributed. Childcare provides a potential setting for breastfeeding promotion. However, little is known about the association between childcare and breastfeeding in different socio-economic groups. Methods: Using data from a contemporary UK cohort of infants (n=18 050) the authors calculated RR for breastfeeding for at least 4 months according to informal childcare (care by friends, grandparents, other relatives, etc) and formal childcare (eg, nurseries, crèches), both lasting at least 10 h a week and commencing before the age of 4 months, compared to being cared for 'only by a parent' (this includes childcare for less than 10 h a week), overall and by socio-economic group. Results: Compared to being looked after only by a parent, informal (RR 0.51 (95% CI 0.43 to 0.59)) and formal (0.84 (0.72 to 0.99)) childcare was associated with a reduced likelihood of breastfeeding. For informal childcare, both part-time and full-time care was associated with a reduced risk of breastfeeding, whereas for formal care, only full-time formal childcare was associated with a reduced likelihood of breastfeeding. The reduced likelihood of breastfeeding in informal childcare was similar across all socio-economic groups, whereas for formal childcare the reduced likelihood was only seen for mothers from managerial and professional backgrounds (0.76 (0.62 to 0.94)), those who had a degree (0.71 (0.58 to 0.86)) and couple families (0.79 (0.66 to 0.94)). In contrast, lone mothers were more likely to breastfeed if their infant was cared for in formal childcare (1.65 (1.04 to 2.63)). Conclusions: Informal childcare was associated with a reduced likelihood of breastfeeding for all groups of mothers. Formal childcare arrangements were only associated with a reduced likelihood of breastfeeding if used full-time by more advantaged families.

BACKGROUND: Accurate objective assessment of sedentary and physical activity behaviours during childhood is integral to the understanding of their relation to later health outcomes, as well as to documenting the frequency and distribution of physical activity within a population. PURPOSE: to calibrate the Actigraph GT1M accelerometer, using energy expenditure (EE) as the criterion measure, to define thresholds for sedentary behaviour and physical activity categories suitable for use in a large scale epidemiological study in young children. METHODS: Accelerometer-based assessments of physical activity (counts per minute) were calibrated against EE measures (kcal x kg(-1) x hr(-1)) obtained over a range of exercise intensities using a COSMED K4b(2) portable metabolic unit in 53 seven-year-old children. Children performed seven activities: lying down viewing television, sitting upright playing a computer game, slow walking, brisk walking, jogging, hopscotch and basketball. Threshold count values were established to identify sedentary behaviour and light, moderate and vigorous physical activity using linear discriminant analysis (LDA) and evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: EE was significantly associated with counts for all non-sedentary activities with the exception of jogging. Threshold values for accelerometer counts (counts x minute(-1)) were

Background: in recent decades the proportion of infants and young children being cared for in childcare has increased. Little is known about the impact that nonparental care has on childhood unintentional injury and whether this varies by socioeconomic group. Methods: Using data from a contemporary UK cohort of children at age 9 months (N = 18114) and 3 years (N = 13718), Poisson regression was used to explore the association between childcare type (parental, informal, formal) and the risk of unintentional injury, overall and by socioeconomic group. Results: at age 9 months there was no overall association between childcare and injury. However, when stratifying the analyses, infants from higher socioeconomic groups were less likely to be injured If they were cared for in formal childcare (compared with being cared for only by a parent), whereas those from lower social groups were more likely to be injured. At age 3 years informal childcare was associated with an increased risk of injury overall; in the stratified analyses this increased risk occurred only in less affluent groups. Formal childcare was no longer associated with injury at age 3 in any strata. Conclusions Previous findings have shown that childcare can have a positive influence on childhood injury; however, a recent Unicef report highlighted that a lack of access to high-quality chlldcare could lead to a widening of inequalities. Our analyses indicate that childcare does have the potential to widen inequalities in injury; further research is required to understand why childcare has a differential impact on unintentional injury and how this might be prevented.