Physical Activity and Adiposity Markers at Older Ages: Accelerometer Vs Questionnaire Data

Physical activity is critically important for successful aging, but its effect on adiposity markers at older ages is unclear as much of the evidence comes from self-reported data on physical activity. We assessed the associations of questionnaire-assessed and accelerometer-assessed physical activity with adiposity markers in older adults

Accelerometer assessed moderate-to-vigorous physical activity and successful ageing: results from the Whitehall II study.

Physical activity is key for successful ageing, but questions remain regarding the optimal physical activity pattern. We examined the cross-sectional association between physical activity and successful ageing using data on 3,749 participants (age range = 60-83years) of the Whitehall II study. The participants underwent a clinical assessment, completed a 20-item physical activity questionnaire, and wore a wrist-mounted accelerometer for 9 days. Successful ageing was defined as good cognitive, motor, and respiratory functioning, along with absence of disability, mental health problems, and major chronic diseases. Time spent in moderate-to-vigorous physical activity (MVPA) episodes assessed by accelerometer was classified as "short" (1-9.59 minutes) and "long" (≥10 minutes) bouts. Linear multivariate regression showed that successful agers (N = 789) reported 3.79 (95% confidence interval (CI): 1.39-6.19) minutes more daily MVPA than other participants. Accelerometer data showed this difference to be 3.40 (95% CI:2.44-4.35) minutes for MVPA undertaken in short bouts, 4.16 (95% CI:3.11-5.20) minutes for long bouts, and 7.55 (95% CI:5.86-9.24) minutes for all MVPA bouts lasting 1 minute or more. Multivariate logistic regressions showed that participants undertaking ≥150 minutes of MVPA per week were more likely to be successful agers with both self-reported (Odd Ratio (OR) = 1.29,95% (CI):1.09-1.53) and accelerometer data (length bout ≥1 minute:OR = 1.92, 95%CI:1.60-2.30). Successful agers practice more MVPA, having both more short and long bouts, than non-successful agers

Association of daily composition of physical activity and sedentary behaviour with incidence of cardiovascular disease in older adults.

BACKGROUND: Moderate-to-vigorous physical activity (MVPA) is proposed as key for cardiovascular diseases (CVD) prevention. At older ages, the role of sedentary behaviour (SB) and light intensity physical activity (LIPA) remains unclear. Evidence so far is based on studies examining movement behaviours as independent entities ignoring their co-dependency. This study examines the association between daily composition of objectively-assessed movement behaviours (MVPA, LIPA, SB) and incident CVD in older adults. METHODS: Whitehall II accelerometer sub-study participants free of CVD at baseline (N = 3319, 26.7% women, mean age = 68.9 years in 2012-2013) wore a wrist-accelerometer from which times in SB, LIPA, and MVPA during waking period were extracted over 7 days. Compositional Cox regression was used to estimate the hazard ratio (HR) for incident CVD for daily compositions of movement behaviours characterized by 10 (20 or 30) minutes greater duration in one movement behaviour accompanied by decrease in another behaviour, while keeping the third behaviour constant, compared to reference composition. Analyses were adjusted for sociodemographic, lifestyle, cardiometabolic risk factors and multimorbidity index. RESULTS: Of the 3319 participants, 299 had an incident CVD over a mean (SD) follow-up of 6.2 (1.3) years. Compared to daily movement behaviour composition with MVPA at recommended 21 min per day (150 min/week), composition with additional 10 min of MVPA and 10 min less SB was associated with smaller risk reduction - 8% (HR, 0.92; 95% CI, 0.87-0.99) - than the 14% increase in risk associated with a composition of similarly reduced time in MVPA and more time in SB (HR, 1.14; 95% CI, 1.02-1.27). For a given MVPA duration, the CVD risk did not differ as a function of LIPA and SB durations. CONCLUSIONS: Among older adults, an increase in MVPA duration at the expense of time in either SB or LIPA was found associated with lower incidence of CVD. This study lends support to public health guidelines encouraging increase in MVPA or at least maintain MVPA at current duration

Estimating sleep parameters using an accelerometer without sleep diary

This is the final version. Available from the publisher via the DOI in this record.Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60–82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.Medical Research Council (MRC)National Institute of Health (NIH

GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies.

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers' decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines

A formative study exploring perceptions of physical activity and physical activity monitoring among children and young people with cystic fibrosis and health care professionals

Background: Physical activity (PA) is associated with reduced hospitalisations and maintenance of lung function in patients with Cystic Fibrosis (CF). PA is therefore recommended as part of standard care. Despite this, there is no consensus for monitoring of PA and little is known about perceptions of PA monitoring among children and young people with CF. Therefore, the research aimed to explore patients’ perceptions of PA and the acceptability of using PA monitoring devices with children and young people with CF. Methods: An action research approach was utilised, whereby findings from earlier research phases informed subsequent phases. Four phases were utilised, including patient interviews, PA monitoring, follow-up patient interviews and health care professional (HCP) interviews. Subsequently, an expert panel discussed the study to develop recommendations for practice and future research. Results: Findings suggest that experiences of PA in children and young people with CF are largely comparable to their non-CF peers, with individuals engaging in a variety of activities. CF was not perceived as a barrier per se, although participants acknowledged that they could be limited by their symptoms. Maintenance of health emerged as a key facilitator, in some cases PA offered patients the opportunity to ‘normalise’ their condition. Participants reported enjoying wearing the monitoring devices and had good compliance. Wrist-worn devices and devices providing feedback were preferred. HCPs recognised the potential benefits of the devices in clinical practice. Recommendations based on these findings are that interventions to promote PA in children and young people with CF should be individualised and involve families to promote PA as part of an active lifestyle. Patients should receive support alongside the PA data obtained from monitoring devices. Conclusions: PA monitoring devices appear to be an acceptable method for objective assessment of PA among children and young people with CF and their clinicians. Wrist-worn devices, which are unobtrusive and can display feedback, were perceived as most acceptable. By understanding the factors impacting PA, CF health professionals will be better placed to support patients and improve health outcomes

Step detection and activity recognition accuracy of seven physical activity monitors

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications

Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study

BACKGROUND: Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. METHODS: Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. RESULTS: 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5-7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen's d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. CONCLUSIONS: It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.The UK Biobank Activity Project and the collection of activity data from participants was funded by the Wellcome Trust (https://wellcome.ac.uk/) and the Medical Research Council (http://www.mrc.ac.uk/). The analysis was supported by the British Heart Foundation Centre of Research Excellence at Oxford (http://www.cardioscience.ox.ac.uk/bhf-centre-of-research-excellence) [grant number RE/13/1/30181 to AD], the Li Ka Shing Foundation (http://www.lksf.org/) [to AD], the UK Medical Research Council (http://www.mrc.ac.uk/) [grant numbers MC_UU_12015/1 and MC_UU_12015/3 to NW and SB], the RCUK Digital Economy Research Hub on Social Inclusion through the Digital Economy (SiDE) (http://www.rcuk.ac.uk/) [EP/G066019/1 to NH], the EPSRC Centre for Doctoral Training in Digital Civics (https://www.epsrc.ac.uk/)[EP/L016176/1 to DJ], and the National Institute for Health Research (http://www.nihr.ac.uk/) [SRF-2011-04-017 to MIT]. The MRC and Wellcome Trust played a key role in the decision to establish UK Biobank, and the accelerometer data collection. No funding bodies had any role in the analysis, decision to publish, or preparation of the manuscript

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