@article{Youngwon Kim2019,
title = {Specific physical activities, sedentary behaviours and sleep as long-term predictors of accelerometer-measured physical activity in 91,648 adults: a prospective cohort study},
author = {Youngwon Kim and Katrien Wijndaele and Stephen J. Sharp and Tessa Strain and Matthew Pearce and Tom White and Nick Wareham and Soren Brage
},
url = {https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-019-0802-9#Sec1},
year = {2019},
date = {2019-05-07},
journal = {International journal of behavioural nutrition and physical activity},
abstract = {Background
The evidence for the prospective relationships between specific physical activities (PA), sedentary behaviours (SB) and sleep on subsequent total PA levels is scarce. The purpose of this study was to examine prospective associations of self-reported PA, SB and sleep, and changes in these with subsequent accelerometer-measured PA.

Methods
A sub-sample of 91,648 UK Biobank participants reported moderate-to-vigorous PA (MVPA), lifestyle activities, TV viewing, computer use and sleep through screen-based questionnaires at baseline (2006–2010), and provided valid accelerometry data (dominant wrist-worn for 7 days between 2013 and 2015). A further sub-sample of 7709 participants repeated the screen-based questionnaires between 2012 and 2013.

Results
In both women (n = 51,545) and men (n = 40,103), positive associations were observed between all self-reported measures of PA at baseline (MVPA, lifestyle/job-related activities, active transporting modes) and accelerometer-measured PA levels at follow-up (median 5.7 years); an exception was ‘walking/standing at work’ in women. Sedentary time at work, TV viewing and computer use were inversely associated with PA at follow-up. Sleeping either more or less than 7 h/day at baseline was associated with lower PA at follow-up (except for ≤6 h/day in men). In the repeat self-report sub-sample (median 4.3 years), relatively higher physical activity at follow-up was observed in those who maintained or achieved favourable levels of MVPA, walking for pleasure, strenuous sports, other exercises, heavy DIY (in women), heavy physical work, and walking/standing at work (in women), sedentary time at work, getting about methods (in women), commuting methods (in women), TV viewing, computer use or sleep.

Conclusions
Initial levels of PA, SB and sleep, and changes in these variables were generally associated with subsequent accelerometer-measured PA in the expected directions, suggesting these specific behaviours all contribute to the total volume of physical activity over time and could thus be targets for intervention.},
keywords = {12885, 262, accelerometer, physical activity, sleep},
pubstate = {published},
tppubtype = {article}
}

@article{Griffin2019,
title = {Driving status, travel modes and accelerometer-assessed physical activity in younger, middle-aged and older adults: a prospective study of 90 810 UK Biobank participants },
author = {Samantha Hajna Tom White Jenna Panter Søren Brage Katrien Wijndaele James Woodcock David Ogilvie Fumiaki Imamura Simon J Griffin},
url = {https://academic.oup.com/ije/advance-article/doi/10.1093/ije/dyz065/5475456},
year = {2019},
date = {2019-04-19},
journal = {International Journal of Epidemiology},
abstract = {Associations between driving and physical-activity (PA) intensities are unclear, particularly among older adults. We estimated prospective associations of travel modes with total PA, sedentary time (ST), light-intensity PA (LPA), and moderate-to-vigorous intensity PA (MVPA) among adults aged 39–70 years.

Methods
We studied 90 810 UK Biobank participants (56.1 ± 7.8 years). Driving status, specific travel modes (non-work travel; commuting to/from work) and covariates were assessed by questionnaire (2006–10). PA was assessed over 7 days by wrist-worn accelerometers (2013–15). We estimated associations using overall and age-stratified multivariable linear-regression models.

Results
Drivers accumulated 1.4% more total PA (95% confidence interval: 0.9, 1.9), 11.2 min/day less ST (–12.9, –9.5), 12.2 min/day more LPA (11.0, 13.3) and 0.9 min/day less MVPA (–1.6, –0.2) than non-drivers. Compared with car/motor-vehicle users, cyclists and walkers had the most optimal activity profiles followed by mixed-mode users (e.g. for non-work travel, cyclists: 10.7% more total PA, 9.0, 12.4; 20.5 min/day less ST, –26.0, –15.0; 14.5 min/day more MVPA, 12.0, 17.2; walkers: 4.2% more total PA, 3.5, 5.0; 7.5 min/day less ST –10.2, –4.9; 10.1 min/day more MVPA, 8.9, 11.3; mixed-mode users: 2.3% more total PA, 1.9, 2.7; 3.4 min/day less ST –4.8, –2.1; 4.9 min/day more MVPA, 4.3, 5.5). Some associations varied by age (p interaction < 0.05), but these differences appeared small.

Conclusions
Assessing specific travel modes rather than driving status alone may better capture variations in activity. Walking, cycling and, to a lesser degree, mixed-mode use are associated with more optimal activity profiles in adults of all ages.},
keywords = {4483, accelerometer, commuting, cycling, featured, obesity},
pubstate = {published},
tppubtype = {article}
}

@article{Jones2019c,
title = {Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour},
author = {Samuel E. Jones and Vincent T. van Hees and Diego R. Mazzotti and Pedro Marques-Vidal and Séverine Sabia and Ashley van der Spek and Hassan S. Dashti and Jorgen Engmann and Desana Kocevska and Jessica Tyrrell and Robin N. Beaumont and Melvyn Hillsdon and Katherine S. Ruth and Marcus A. Tuke and Hanieh Yaghootkar and Seth A. Sharp and Yingjie Ji and Jamie W. Harrison and Rachel M. Freathy and Anna Murray and Annemarie I. Luik and Najaf Amin and Jacqueline M. Lane and Richa Saxena and Martin K. Rutter and Henning Tiemeier and Zoltán Kutalik and Meena Kumari and Timothy M. Frayling and Michael N. Weedon and Philip R. Gehrman and Andrew R. Wood},
url = {https://www.nature.com/articles/s41467-019-09576-1},
year = {2019},
date = {2019-04-05},
journal = {Nature Communications},
abstract = {Sleep is an essential human function but its regulation is poorly understood. Using accelerometer data from 85,670 UK Biobank participants, we perform a genome-wide association study of 8 derived sleep traits representing sleep quality, quantity and timing, and validate our findings in 5,819 individuals. We identify 47 genetic associations at P < 5 × 10−8, of which 20 reach a stricter threshold of P < 8 × 10−10. These include 26 novel associations with measures of sleep quality and 10 with nocturnal sleep duration. The majority of identified variants associate with a single sleep trait, except for variants previously associated with restless legs syndrome. For sleep duration we identify a missense variant (p.Tyr727Cys) in PDE11A as the likely causal variant. As a group, sleep quality loci are enriched for serotonin processing genes. Although accelerometer-derived measures of sleep are imperfect and may be affected by restless legs syndrome, these findings provide new biological insights into sleep compared to previous efforts based on self-report sleep measures.},
keywords = {16434, 9072, accelerometer, Genetic, sleep},
pubstate = {published},
tppubtype = {article}
}

@article{Guo2019,
title = {Accelerometer compared with questionnaire measures of physical activity in relation to body size and composition: a large cross-sectional analysis of UK Biobank.},
author = {W Guo and TJ Key and GK Reeves },
url = {https://www.ncbi.nlm.nih.gov/pubmed/30700478},
year = {2019},
date = {2019-01-29},
journal = {BMJ},
abstract = {OBJECTIVES:
Previous studies of the association between physical activity and adiposity are largely based on physical activity and body mass index (BMI) from questionnaires, which are prone to inaccurate and biased reporting. We assessed the associations of accelerometer-measured and questionnaire-measured physical activity with BMI, waist circumference and body fat per cent measured by bioelectrical impedance and dual-energy X-ray absorptiometry (DXA).

DESIGN:
Cross-sectional analysis of UK Biobank participants.

SETTING:
UK Biobank assessment centres.

PARTICIPANTS:
78 947 UK Biobank participants (35 955 men and 42 992 women) aged 40-70 at recruitment, who had physical activity measured by both questionnaire and accelerometer.

MAIN OUTCOME MEASURES:
BMI, waist circumference and body fat per cent measured by bioelectrical impedance.

RESULTS:
Greater physical activity was associated with lower adiposity. Women in the top 10th of accelerometer-measured physical activity had a 4.8 (95% CI 4.6 to 5.0) kg/m2 lower BMI, 8.1% (95% CI 7.8% to 8.3%) lower body fat per cent and 11.9 (95% CI 11.4 to 12.4) cm lower waist circumference. Women in the top 10th of questionnaire-measured physical activity had a 2.5 (95% CI 2.3 to 2.7) kg/m2 lower BMI, 4.3% (95% CI 4.0% to 4.5%) lower body fat per cent and 6.4 (95% CI 5.9 to 6.9) cm lower waist circumference, compared with women in the bottom 10th. The patterns were similar in men and also similar to body fat per cent measured by DXA compared with impedance.

CONCLUSION:
Our findings of approximately twofold stronger associations between physical activity and adiposity with objectively measured than with self-reported physical activity emphasise the need to incorporate objective measures in future studies.},
keywords = {3248, accelerometer, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{ADoherty2017,
title = {Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study},
author = {Nicholas Wareham J Aiden Doherty},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169649},
year = {2017},
date = {2017-02-01},
journal = {PLOS ONE},
abstract = {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},
note = {Aiden Doherty , Dan Jackson, Nils Hammerla, Thomas Plötz, Patrick Olivier, Malcolm H. Granat, Tom White, Vincent T. van Hees, Michael I. Trenell, Christoper G. Owen, Stephen J. Preece, Rob Gillions, Simon Sheard, Nicholas J. Wareham},
keywords = {9126, accelerometer, featured, methodology, physical activity},
pubstate = {published},
tppubtype = {article}
}