Assessing free-living physical activity using accelerometry : practical issues for researchers and practitioners

Physical activity is an integral component of a healthy lifestyle, with relationships documented between physical activity, chronic diseases, and disease risk factors. There is increasing concern that many people are not sufficiently active to benefit their health. Consequently, there is a need to determine the prevalence of physical activity engagement, identify active and inactive segments of the population, and evaluate the effectiveness of interventions. The aim of the present study was to identify and explain a number of methodological and decision-making processes associated with accelerometry, which is the most commonly used objective measure of physical activity in child and adult research.Specifically, this review addresses:(a) pre-data collection decisions,(b) data collection procedures,(c) processing of accelerometer data, and(d) outcome variables in relation to the research questions posed.An appraisal of the literature is provided to help researchers and practitioners begin field-based research, with recommendations offered for best practice. In addition, issues that require further investigation are identified and discussed to inform researchers and practitioners of the surrounding debates.Overall, the review is intended as a starting point for field-based physical activity research using accelerometers and as an introduction to key issues that should be considered and are likely to be encountered at this time.<br /

Development and Validation of a New Method to Measure Walking Speed in Free-Living Environments Using the Actibelt® Platform

Walking speed is a fundamental indicator for human well-being. In a clinical setting, walking speed is typically measured by means of walking tests using different protocols. However, walking speed obtained in this way is unlikely to be representative of the conditions in a free-living environment. Recently, mobile accelerometry has opened up the possibility to extract walking speed from long-time observations in free-living individuals, but the validity of these measurements needs to be determined. In this investigation, we have developed algorithms for walking speed prediction based on 3D accelerometry data (actibelt®) and created a framework using a standardized data set with gold standard annotations to facilitate the validation and comparison of these algorithms. For this purpose 17 healthy subjects operated a newly developed mobile gold standard while walking/running on an indoor track. Subsequently, the validity of 12 candidate algorithms for walking speed prediction ranging from well-known simple approaches like combining step length with frequency to more sophisticated algorithms such as linear and non-linear models was assessed using statistical measures. As a result, a novel algorithm employing support vector regression was found to perform best with a concordance correlation coefficient of 0.93 (95%CI 0.92–0.94) and a coverage probability CP1 of 0.46 (95%CI 0.12–0.70) for a deviation of 0.1 m/s (CP2 0.78, CP3 0.94) when compared to the mobile gold standard while walking indoors. A smaller outdoor experiment confirmed those results with even better coverage probability. We conclude that walking speed thus obtained has the potential to help establish walking speed in free-living environments as a patient-oriented outcome measure

Substituting prolonged sedentary time and cardiovascular risk in children and youth: a meta-analysis within the International Children’s Accelerometry database (ICAD)

Abstract: Background: Evidence on the association between sitting for extended periods (i.e. prolonged sedentary time (PST)) and cardio-metabolic health is inconsistent in children. We aimed to estimate the differences in cardio-metabolic health associated with substituting PST with non-prolonged sedentary time (non-PST), light (LIPA) or moderate-to-vigorous physical activity (MVPA) in children. Methods: Cross-sectional data from 14 studies (7 countries) in the International Children’s Accelerometry Database (ICAD, 1998–2009) was included. Accelerometry in 19,502 participants aged 3–18 years, together with covariate and outcome data, was pooled and harmonized. Iso-temporal substitution in linear regression models provided beta coefficients (95%CI) for substitution of 1 h/day PST (sedentary time accumulated in bouts > 15 min) with non-PST, LIPA or MVPA, for each study, which were meta-analysed. Results: Modelling substitution of 1 h/day of PST with non-PST suggested reductions in standardized BMI, but estimates were > 7-fold greater for substitution with MVPA (− 0.44 (− 0.62; − 0.26) SD units). Only reallocation by MVPA was beneficial for waist circumference (− 3.07 (− 4.47; − 1.68) cm), systolic blood pressure (− 1.53 (− 2.42; − 0.65) mmHg) and clustered cardio-metabolic risk (− 0.18 (− 0.3; − 0.1) SD units). For HDL-cholesterol and diastolic blood pressure, substitution with LIPA was beneficial; however, substitution with MVPA showed 5-fold stronger effect estimates (HDL-cholesterol: 0.05 (0.01; 0.10) mmol/l); diastolic blood pressure: − 0.81 (− 1.38; − 0.24) mmHg). Conclusions: Replacement of PST with MVPA may be the preferred scenario for behaviour change, given beneficial associations with a wide range of cardio-metabolic risk factors (including adiposity, HDL-cholesterol, blood pressure and clustered cardio-metabolic risk). Effect estimates are clinically relevant (e.g. an estimated reduction in waist circumference of ≈1.5 cm for 30 min/day replacement). Replacement with LIPA could be beneficial for some of these risk factors, however with substantially lower effect estimates

Weather and children's physical activity; how and why do relationships vary between countries?

Background: Globally most children do not engage in enough physical activity. Day length and weather conditions have been identified as determinants of physical activity, although how they may be overcome as barriers is not clear. We aim to examine if and how relationships between children’s physical activity and weather and day length vary between countries and identify settings in which children were better able to maintain activity levels given the weather conditions they experienced. Methods: In this repeated measures study, we used data from 23,451 participants in the International Children’s Accelerometry Database (ICAD). Daily accelerometer-measured physical activity (counts per minute; cpm) was matched to local weather conditions and the relationships assessed using multilevel regression models. Multilevel models accounted for clustering of days within occasions within children within study-cities, and allowed us to explore if and how the relationships between weather variables and physical activity differ by setting. Results: Increased precipitation and wind speed were associated with decreased cpm while better visibility and more hours of daylight were associated with increased cpm. Models indicated that increases in these variables resulted in average changes in mean cpm of 7.6/h of day length, −13.2/cm precipitation, 10.3/10 km visibility and −10.3/10kph wind speed (all p < 0.01). Temperature showed a cubic relationship with cpm, although between 0 and 20 degrees C the relationship was broadly linear. Age showed interactions with temperature and precipitation, with the associations larger among younger children. In terms of geographic trends, participants from Northern European countries and Melbourne, Australia were the most active, and also better maintained their activity levels given the weather conditions they experienced compared to those in the US and Western Europe. Conclusions: We found variation in the relationship between weather conditions and physical activity between ICAD studies and settings. Children in Northern Europe and Melbourne, Australia were not only more active on average, but also more active given the weather conditions they experienced. Future work should consider strategies to mitigate the impacts of weather conditions, especially among young children, and interventions involving changes to the physical environment should consider how they will operate in different weather conditions.The pooling of the data was funded through a grant from the National Prevention Research Initiative (Grant Number: G0701877) (http://www.mrc.ac.uk/research/initiatives/national-prevention-research-initiative-npri/). The funding partners relevant to this award are: British Heart Foundation; Cancer Research UK; Department of Health; Diabetes UK; Economic and Social Research Council; Medical Research Council; Research and Development Office for the Northern Ireland Health and Social Services; Chief Scientist Office; Scottish Executive Health Department; The Stroke Association; Welsh Assembly Government and World Cancer Research Fund. This work was additionally supported by the Medical Research Council [MC_UU_12015/3; MC_UU_12015/7], Bristol University, Loughborough University and Norwegian School of Sport Sciences. We also gratefully acknowledge the contribution of Professor Chris Riddoch, Professor Ken Judge and Dr. Pippa Griew to the development of ICAD. The UK Medical Research Council and the Wellcome Trust (Grant ref.: 102,215/2/13/2) and the University of Bristol provide core support for ALSPAC. The CLAN study was funded by Financial Markets Foundation for Children (baseline); follow-ups were funded by the National Health and Medical Research Council (274309). The HEAPS study was funded by VicHealth (baseline); follow-ups were funded by the Australian Research Council (DP0664206). The work of Flo Harrison and Esther M F van Sluijs was supported, wholly or in part, by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence (RES-590-28-0002). Funding from the British Heart Foundation, Department of Health, Economic and Social Research Council, Medical Research Council, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged. The work of Esther MF van Sluijs was supported by the Medical Research Council (MC_UU_12015/7). Anna Goodman’s contribution was supported by an National Institute for Health Research (NIHR) post-doctoral fellowship (PDF-2010-03-130). Anna Timperio’s contribution was supported by a National Heart Foundation of Australia Future Leader Fellowship (Award 10,046). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of any study funders

Adiposity and grip strength as long-term predictors of objectively measured physical activity in 93 015 adults: the UK Biobank study

BACKGROUND/OBJECTIVES: Fatness and fitness are associated with physical activity (PA) but less is known about the prospective associations of adiposity and muscle strength with PA. This study aimed to determine longitudinal associations of body mass index (BMI), waist circumference (WC) and grip strength (GS) with objectively measured PA. SUBJECTS/METHODS: Data are from the UK Biobank study. At baseline (2006-2010), BMI, WC and GS were objectively measured. At follow-up (2013-2015), a sub-sample of 93 015 participants (52 161 women) wore a tri-axial accelerometer on the dominant wrist for 7 days. Linear regression was performed to investigate longitudinal associations of standardised BMI, WC and GS at baseline with moderate-to-vigorous PA (MVPA) and acceleration after a median 5.7-years follow-up (interquartile range: 4.9-6.5 years). RESULTS: Linear regression revealed strong inverse associations for BMI and WC, and positive associations for GS with follow-up PA; in women, MVPA ranges from lowest to highest quintiles of GS were 42-48 min day(-1) in severely obese (BMI⩾35 kg m(-)(2)), 52-57 min day(-1) in obese (30⩽BMI<35 kg m(-)(2)), 61-65 min day(-1) in overweight (25⩽BMI<30 kg m(-)(2)) and 69-75 min day(-1) in normal weight (18.5⩽BMI<25 kg m(-2)). Follow-up MVPA was also lower in the lowest GS quintile (42-69 min day(-1)) compared with the highest GS quintile (48-75 min day(-1)) across BMI categories in women. The pattern of these associations was generally consistent for men, and in analyses using WC and mean acceleration as exposure and outcome, respectively. CONCLUSIONS: More pronounced obesity and poor strength at baseline independently predict lower activity levels at follow-up. Interventions and policies should aim to improve body composition and muscle strength to promote active living.International Journal of Obesity advance online publication, 6 June 2017; doi:10.1038/ijo.2017.122.This work was supported by the UK Medical Research Council (MC_UU_12015/3), a PhD studentship from MedImmune (to TW), and an Intermediate Basic Science Research Fellowship of British Heart Foundation (FS/12/58/29709 to KW). No financial disclosures were reported by the authors of this paper. This research has been conducted using the UK Biobank Resource under Application Numbers 262 and 12885

Tracking of total sedentary time and sedentary patterns in youth: a pooled analysis using the International Children’s Accelerometry Database (ICAD)

Abstract: Background: To gain more understanding of the potential health effects of sedentary time, knowledge is required about the accumulation and longitudinal development of young people’s sedentary time. This study examined tracking of young peoples’ total and prolonged sedentary time as well as their day-to-day variation using the International Children’s Accelerometry Database. Methods: Longitudinal accelerometer data of 5991 children (aged 4-17y) was used from eight studies in five countries. Children were included if they provided valid (≥8 h/day) accelerometer data on ≥4 days, including ≥1 weekend day, at both baseline and follow-up (average follow-up: 2.7y; range 0.7–8.2). Tracking of total and prolonged (i.e. ≥10-min bouts) sedentary time was examined using multilevel modelling to adjust for clustering of observations, with baseline levels of sedentary time as predictor and follow-up levels as outcome. Standardized regression coefficients were interpreted as tracking coefficients (low: 0.6). Results: Average total sedentary time at study level ranged from 246 to 387 min/day at baseline and increased annually by 21.4 min/day (95% confidence interval [19.6–23.0]) on average. This increase consisted almost entirely of prolonged sedentary time (20.9 min/day [19.2–22.7]). Total (standardized regression coefficient (B) = 0.48 [0.45–0.50]) and prolonged sedentary time (B = 0.43 [0.41–0.45]) tracked moderately. Tracking of day-to-day variation in total (B = 0.04 [0.02–0.07]) and prolonged (B = 0.07 [0.04–0.09]) sedentary time was low. Conclusion: Young people with high levels of sedentary time are likely to remain among the people with highest sedentary time as they grow older. Day-to-day variation in total and prolonged sedentary time, however, was rather variable over time

Variations in accelerometry measured physical activity and sedentary time across Europe – harmonized analyses of 47,497 children and adolescents

Funder: Ministero delle Politiche Agricole Alimentari e Forestali; doi: http://dx.doi.org/10.13039/501100005401Funder: ZonMw; doi: http://dx.doi.org/10.13039/501100001826Funder: The Research Council of Norway, Division for Society and Health.Abstract: Background: Levels of physical activity and variation in physical activity and sedentary time by place and person in European children and adolescents are largely unknown. The objective of the study was to assess the variations in objectively measured physical activity and sedentary time in children and adolescents across Europe. Methods: Six databases were systematically searched to identify pan-European and national data sets on physical activity and sedentary time assessed by the same accelerometer in children (2 to 9.9 years) and adolescents (≥10 to 18 years). We harmonized individual-level data by reprocessing hip-worn raw accelerometer data files from 30 different studies conducted between 1997 and 2014, representing 47,497 individuals (2–18 years) from 18 different European countries. Results: Overall, a maximum of 29% (95% CI: 25, 33) of children and 29% (95% CI: 25, 32) of adolescents were categorized as sufficiently physically active. We observed substantial country- and region-specific differences in physical activity and sedentary time, with lower physical activity levels and prevalence estimates in Southern European countries. Boys were more active and less sedentary in all age-categories. The onset of age-related lowering or leveling-off of physical activity and increase in sedentary time seems to become apparent at around 6 to 7 years of age. Conclusions: Two third of European children and adolescents are not sufficiently active. Our findings suggest substantial gender-, country- and region-specific differences in physical activity. These results should encourage policymakers, governments, and local and national stakeholders to take action to facilitate an increase in the physical activity levels of young people across Europe

Physical Activity and Respiratory Health (PhARaoH): Data from a Cross-Sectional Study

The dataset consists of a densely phenotyped sample of adults collected from March to August 2014. The dataset captures behavioural, physical, physiological and psychosocial characteristics of individuals with and without a General Practitioner diagnosis of chronic obstructive pulmonary disease (COPD). Data were collected at Glenfield Hospital on 436 individuals (139 COPD patients and 297 apparently healthy adults) aged 40–75 years, residing in Leicestershire and Rutland, United Kingdom. The dataset includes seven days of raw wrist-worn accelerometry, venous blood biomarkers, non-invasive point-of-care cardio-metabolic risk profiles, physical measures and questionnaire data