@article{Smith2019,
title = {Characteristics of the environment and physical activity in midlife: Findings from UK Biobank},
author = {Lindsey Smith and Jenna Panter and David Ogilvie},
url = {https://www.sciencedirect.com/science/article/pii/S0091743518303438},
year = {2019},
date = {2019-01-01},
journal = {Preventitive medicine},
abstract = {Characteristics of the environment influence health and may promote physical activity. We explored the associations between neighborhood environmental characteristics grouped within five facets (spaces for physical activity, walkability, disturbance, natural environment, and the sociodemographic environment) and objective (‘recorded’) and self-reported (‘reported’) physical activity in adults from UK Biobank. Recorded activity was assessed using wrist-worn accelerometers (2013–2015, n = 65,967) and time spent in moderate-to-vigorous physical activity (MVPA), walking, and walking for pleasure was self-reported (2006–2010, n = 337,822). Associations were assessed using linear and multinomial logistic regression models and data were analyzed in 2017. We found participants living in areas with higher concentrations of air pollution recorded and reported lower levels of physical activity and those in rural areas and more walkable areas had higher levels of both recorded and reported activity. Some associations varied according to the specificity of the outcome, for example, those living in the most deprived areas were less likely to record higher levels of MVPA (upper tertile: RRR: 0.80 95% CI: 0.74, 0.86) but were more likely to report higher levels of walking (upper tertile: RRR: 1.09, 95% CI: 1.06, 1.13). Environmental characteristics have the potential to contribute to different physical activities but interventions which focus on a single environmental attribute or physical activity outcome may not have the greatest benefits.},
keywords = {20684, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Cook2018b,
title = {The prevalence of co-morbidities and their impact on physical activity in people with inflammatory rheumatic diseases compared with the general population: results from the UK Biobank},
author = {Michael J Cook and Eftychia Bellou and John Bowes and Jamie C Sergeant and Terence W O’Neill and Anne Barton and Suzanne M M Verstappen},
url = {https://academic.oup.com/rheumatology/advance-article/doi/10.1093/rheumatology/key224/5068968},
year = {2018},
date = {2018-08-09},
journal = {Rheumatology},
abstract = {Objectives

To compare the prevalence and incidence of chronic co-morbidities in people with inflammatory rheumatic and musculoskeletal diseases (iRMDs), and to determine whether the prevalent co-morbidities are associated with physical activity levels in people with iRMDs and in those without iRMDs.

Methods

Participants were recruited to the UK Biobank; a population-based cohort. Data were collected about demographics, physical activity, iRMDs (RA, PsA, AS, SLE) and other chronic conditions, including angina, myocardial infarction, stroke, hypertension, pulmonary disease, diabetes and depression. The standardized prevalence of co-morbidities in people with iRMDs was calculated. Cox regression was used to determine the relationship between the presence of an iRMD and an incident co-morbidity. The relationship between the presence (versus absence) of a (co-)morbidity and physical activity level (low, moderate, high) in people with iRMDs and in those without was assessed using multinomial logistic regression.

Results

A total of 488 991 participants were included. The estimated prevalence of each co-morbidity was increased in participants with an iRMD, compared with in those without, particularly for stroke in participants with SLE (standardized morbidity ratio (95% CI), 4.9 (3.6, 6.6). Compared with people with no iRMD and no morbidity, the odds ratios (95% CI) for moderate physical activity were decreased for: no iRMD and morbidity, 0.87 (0.85, 0.89); iRMD and no co-morbidity, 0.71 (0.64, 0.80); and iRMD and co-morbidity, 0.58 (0.54, 0.63).

Conclusion

Having a (co-)morbidity is associated with reduced physical activity in the general population, and to a greater extent in participants with an iRMD. Optimal management of both iRMDs and co-morbidities may help to reduce their impact on physical activity.},
keywords = {7996, inflammatory disease, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Klimentidis2018,
title = {Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE.},
author = {Yann C Klimentidis and David A Raichlen and Jennifer Bea and David O Garcia and Nathan E Wineinger and Lawrence J Mandarino and Gene E Alexander and Zhao Chen and Scott B Going},
url = {https://www.nature.com/articles/s41366-018-0120-3},
year = {2018},
date = {2018-06-13},
journal = {International Journal of Obesity},
abstract = {Background/objectives

Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual.
Subjects/methods
We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA).
Results

We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10−9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer’s risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10−5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions.
Conclusions

These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.},
keywords = {15678, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Celis-Morales2018b,
title = {Associations of discretionary screen time with mortality, cardiovascular disease and cancer are attenuated by strength, fitness and physical activity: findings from the UK Biobank study},
author = {CA Celis-Morales and DM Lyall and L Steell and SR Gray and S Iliodromiti and J Anderson and DF Mackay and P Welsh and T Yates and JP Pell and N Sattar and JM Gill},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29792209},
year = {2018},
date = {2018-05-24},
journal = {BMC Medicine},
abstract = {BACKGROUND:

Discretionary screen time (time spent viewing a television or computer screen during leisure time) is an important contributor to total sedentary behaviour, which is associated with increased risk of mortality and cardiovascular disease (CVD). The aim of this study was to determine whether the associations of screen time with cardiovascular disease and all-cause mortality were modified by levels of cardiorespiratory fitness, grip strength or physical activity.

METHODS:

In total, 390,089 participants (54% women) from the UK Biobank were included in this study. All-cause mortality, CVD and cancer incidence and mortality were the main outcomes. Discretionary television (TV) viewing, personal computer (PC) screen time and overall screen time (TV + PC time) were the exposure variables. Grip strength, fitness and physical activity were treated as potential effect modifiers.

RESULTS:
Altogether, 7420 participants died, and there were 22,210 CVD events, over a median of 5.0 years follow-up (interquartile range 4.3 to 5.7; after exclusion of the first 2 years from baseline in the landmark analysis). All discretionary screen-time exposures were significantly associated with all health outcomes. The associations of overall discretionary screen time with all-cause mortality and incidence of CVD and cancer were strongest amongst participants in the lowest tertile for grip strength (all-cause mortality hazard ratio per 2-h increase in screen time (1.31 [95% confidence interval: 1.22-1.43], p < 0.0001; CVD 1.21 [1.13-1.30], p = 0.0001; cancer incidence 1.14 [1.10-1.19], p < 0.0001) and weakest amongst those in the highest grip-strength tertile (all-cause mortality 1.04 [0.95-1.14], p = 0.198; CVD 1.05 [0.99-1.11], p = 0.070; cancer 0.98 [0.93-1.05], p = 0.771). Similar trends were found for fitness (lowest fitness tertile: all-cause mortality 1.23 [1.13-1.34], p = 0.002 and CVD 1.10 [1.02-1.22], p = 0.010; highest fitness tertile: all-cause mortality 1.12 [0.96-1.28], p = 0.848 and CVD 1.01 [0.96-1.07], p = 0.570). Similar findings were found for physical activity for all-cause mortality and cancer incidence.

CONCLUSIONS:

The associations between discretionary screen time and adverse health outcomes were strongest in those with low grip strength, fitness and physical activity and markedly attenuated in those with the highest levels of grip strength, fitness and physical activity. Thus, if these associations are causal, the greatest benefits from health promotion interventions to reduce discretionary screen time may be seen in those with low levels of strength, fitness and physical activity.},
keywords = {7155, featured, physical activity, screen time},
pubstate = {published},
tppubtype = {article}
}

@article{Willetts2018,
title = {Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants},
author = {M Willetts and S Hollowell and L Aslett and C Holmes and A Doherty},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29784928},
year = {2018},
date = {2018-05-21},
journal = {Scientific Reports},
abstract = {Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanced random forests with Hidden Markov Models, to reliably detect a number of activity modes. We show that physical activity and sleep behaviours can be classified with 87% accuracy in 159,504 minutes of recorded free-living behaviours from 132 adults. These trained models can be used to infer fine resolution activity patterns at the population scale in 96,220 participants. For example, we find that men spend more time in both low- and high- intensity behaviours, while women spend more time in mixed behaviours. Walking time is highest in spring and sleep time lowest during the summer. This work opens the possibility of future public health guidelines informed by the health consequences associated with specific, objectively measured, physical activity and sleep behaviours.},
keywords = {9126, physical activity, sleep},
pubstate = {published},
tppubtype = {article}
}

@article{Tikkanen2018b,
title = {Associations of Fitness, Physical Activity, Strength, and Genetic Risk With Cardiovascular Disease: Longitudinal Analyses in the UK Biobank Study},
author = {E Tikkanen and S Gustafsson and E Ingelsson},
url = {https://www.ncbi.nlm.nih.gov/pubmed/29632216},
year = {2018},
date = {2018-04-09},
journal = {Circulation},
abstract = {Background -Observational studies have shown inverse associations among fitness, physical activity, and cardiovascular disease. However, little is known about these associations in individuals with elevated genetic susceptibility for these diseases. Methods -We estimated associations of grip strength, objective and subjective physical activity, and cardiorespiratory fitness with cardiovascular events and all-cause death in a large cohort of 502635 individuals from the UK Biobank (median follow-up, 6.1 years; interquartile range, 5.4-6.8 years). Then we further examined these associations in individuals with different genetic burden by stratifying individuals based on their genetic risk scores for coronary heart disease and atrial fibrillation. We compared disease risk among individuals in different tertiles of fitness, physical activity, and genetic risk using lowest tertiles as reference. Results -Grip strength, physical activity, and cardiorespiratory fitness showed inverse associations with incident cardiovascular events (coronary heart disease: hazard ratio [HR], 0.79; 95% confidence interval [CI], 0.77- 0.81; HR, 0.95; 95% CI, 0.93-0.97; and HR, 0.68; 95% CI, 0.63-0.74, per SD change, respectively; atrial fibrillation: HR, 0.75; 95% CI, 0.73- 0.76; HR, 0.93; 95% CI, 0.91-0.95; and HR, 0.60; 95% CI, 0.56-0.65, per SD change, respectively). Higher grip strength and cardiorespiratory fitness were associated with lower risk of incident coronary heart disease and atrial fibrillation in each genetic risk score group (Ptrend <0.001 in each genetic risk category). In particular, high levels of cardiorespiratory fitness were associated with 49% lower risk for coronary heart disease (HR, 0.51; 95% CI, 0.38-0.69) and 60% lower risk for atrial fibrillation (HR, 0.40; 95%, CI 0.30-0.55) among individuals at high genetic risk for these diseases. Conclusions - Fitness and physical activity demonstrated inverse associations with incident cardiovascular disease in the general population, as well as in individuals with elevated genetic risk for these diseases.},
keywords = {13721, cardiovascular disease, genetic risk, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Rask-Andersen2017,
title = {Gene-environment interaction study for BMI reveals interactions between genetic factors and physical activity, alcohol consumption and socioeconomic status.},
author = {M Rask-Andersen and T Karlsson and WE Ek and A Johansson},
url = {https://www.ncbi.nlm.nih.gov/pubmed/28873402},
year = {2017},
date = {2017-09-05},
journal = {PLOS Genetics},
abstract = {Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.45*10-29},
keywords = {15152, alcohol, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{WIJNDAELE2017b,
title = {Mortality Risk Reductions from Substituting Screen Time by Discretionary Activities},
author = {Katrien WIJNDAELE and Stephen J SHARP and Nicholas J WAREHAM and Soren Brage},
url = {http://journals.lww.com/acsm-msse/fulltext/2017/06000/Mortality_Risk_Reductions_from_Substituting_Screen.7.aspx},
year = {2017},
date = {2017-06-30},
journal = {Medicine &amp; Science in Sports &amp; Exercise},
abstract = {Purpose: Leisure screen time, including TV viewing, is associated with increased mortality risk. We estimated the all-cause mortality risk reductions associated with substituting leisure screen time with different discretionary physical activity types, and the change in mortality incidence associated with different substitution scenarios.

Methods: A total of 423,659 UK Biobank participants, without stroke, myocardial infarction, or cancer history, were followed for 7.6 (1.4) yr, median (interquartile range [IQR]). They reported leisure screen time (TV watching and home computer use) and leisure/home activities, categorized as daily life activities (walking for pleasure, light do-it-yourself [DIY], and heavy DIY) and structured exercise (strenuous sports and other exercises). Isotemporal substitution modeling in Cox regression provided hazard ratios (95% confidence intervals) for all-cause mortality when substituting screen time (30 min·d−1) with different discretionary activity types of the same duration. Potential impact fractions estimated the proportional change in mortality incidence associated with different substitution scenarios.

Results: During 3,202,105 person-years of follow-up, 8928 participants died. Each 30-min·d−1 difference in screen time was associated with lower mortality hazard when modeling substitution of screen time by an equal amount of daily life activities (0.95, 0.94–0.97), as well as structured exercise (0.87, 0.84–0.90). Reallocations from screen time into specific activity subtypes suggested different reductions in mortality hazard: walking for pleasure (0.95, 0.92–0.98), light DIY (0.97, 0.94–1.00), heavy DIY (0.93, 0.90–0.96), strenuous sports (0.87, 0.79–0.95), and other exercises (0.88, 0.84–0.91). The lowest hazard estimates were found when modeling replacement of TV viewing. Potential impact fractions ranged from 4.3% (30-min·d−1 substitution of screen time into light DIY) to 14.9% (TV viewing into strenuous sports).

Conclusion: Substantial public health benefits could be gained by replacing small amounts of screen time with daily life activities and structured exercise. Daily life activities may provide feasible screen time alternatives, if structured exercise is initially too ambitious.},
keywords = {262, mortality, obesity, physical activity, screen time},
pubstate = {published},
tppubtype = {article}
}

@article{Stiles2017,
title = {A small amount of precisely measured high-intensity habitual physical activity predicts bone health in pre- and post-menopausal women in UK Biobank},
author = {Victoria H Stiles and Brad S Metcalf and Karen M Knapp and Alex V Rowlands},
url = {https://academic.oup.com/ije/article-abstract/doi/10.1093/ije/dyx080/3902973/A-small-amount-of-precisely-measured-high?redirectedFrom=fulltext#89693296},
year = {2017},
date = {2017-06-29},
journal = {International Journal of Epidemiology},
abstract = {Background: Physical inactivity is a highly modifiable risk factor for the development of osteoporosis but, due to a lack of research that has precisely and objectively meaured physical activity (PA) relevant to bone, the specific contribution that PA can make to bone health is poorly understood. This study examined whether a more precise measure of PA relelvant to bone was associated with meaures of bone health in pre- and post-menopausal women in UK Biobank.

Methods: Time spent at intensities specific to bone health [≥750 milli-gravitational units (mg) and ≥1000 mg] were analysed from raw tri-axial acceleration data averaged over 1-second epochs from 7-day monitoring of habitual PA using accelerometry-based activity monitors (100 Hz; AX3, Axivity, UK) of 1218 pre- and 1316 post-menopausal healthy women. In a cross-sectional analysis, associations between categories of time (<1, 1–2 and ≥2 minutes) spent above the intensity thresholds and calcaneal quantitative ultrasound measures of bone health (bone mineral density T-score, BMDT-score; speed of sound, SOS; and broadband ultrasound attenuation, BUA) were examined.

Results: Compared with <1 minute, spending 1–2 or ≥2 minutes/day at intensities ≥1000 mg in pre-menopausal and ≥750 mg in post-menopausal women was positively associated with BMDT-score, SOS and BUA.

Conclusion: Brief bursts of high-intensity PA relevant to bone health can be captured by applying bone-specific thresholds of intensity to raw tri-axial accelerations averaged over 1-second epochs. Accumulating 1–2 minutes/day of high-intensity PA, equivalent to running in pre-menopausal women and slow jogging in post-menopausal women, is associated with better bone health.},
keywords = {10995, bone health, featured, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Hagenaars2017bb,
title = {Cognitive ability and physical health: a Mendelian randomization study},
author = {Ian Deary & Sarah Harris J E Saskia P. Hagenaars Catharine R. Gale},
url = {https://www.nature.com/articles/s41598-017-02837-3},
year = {2017},
date = {2017-06-01},
journal = {Scientific Reports},
abstract = {Causes of the association between cognitive ability and health remain unknown, but may reflect a shared genetic aetiology. This study examines the causal genetic associations between cognitive ability and physical health. We carried out two-sample Mendelian randomization analyses using the inverse-variance weighted method to test for causality between later life cognitive ability, educational attainment (as a proxy for cognitive ability in youth), BMI, height, systolic blood pressure, coronary artery disease, and type 2 diabetes using data from six independent GWAS consortia and the UK Biobank sample (N = 112 151). BMI, systolic blood pressure, coronary artery disease and type 2 diabetes showed negative associations with cognitive ability; height was positively associated with cognitive ability. The analyses provided no evidence for casual associations from health to cognitive ability. In the other direction, higher educational attainment predicted lower BMI, systolic blood pressure, coronary artery disease, type 2 diabetes, and taller stature. The analyses indicated no causal association from educational attainment to physical health. The lack of evidence for causal associations between cognitive ability, educational attainment, and physical health could be explained by weak instrumental variables, poorly measured outcomes, or the small number of disease cases.},
keywords = {10279, cognition, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Bradbury2017b,
title = {Association between physical activity and body fat percentage, with adjustment for BMI: a large cross-sectional analysis of UK Biobank},
author = {BJ Cairns ME Armstrong TJ Key KE Bradbury W Guo},
url = {https://www.ncbi.nlm.nih.gov/pubmed/28341684},
year = {2017},
date = {2017-03-24},
journal = {BMJ Open},
abstract = {OBJECTIVES:

The objective of this study was to examine if, in the general population, physically active adults have less body fat after taking body mass index (BMI) into account.
DESIGN:

A cross-sectional analysis of participants recruited into UK Biobank in 2006-2010.
SETTING:

UK Biobank assessment centres throughout the UK.
PARTICIPANTS:

119 230 men and 140 578 women aged 40-69 years, with complete physical activity information, and without a self-reported long-term illness, disability or infirmity.
EXPOSURES:

Physical activity measured as excess metabolic equivalent (MET)-hours per week, estimated from a combination of walking, and moderate and vigorous physical activity. BMI from measured height and weight.
MAIN OUTCOME MEASURE:

Body fat percentage estimated from bioimpedance.
RESULTS:
BMI and body fat percentage were highly correlated (r=0.85 in women; r=0.79 in men), and both were inversely associated with physical activity. Compared with <5 excess MET-hours/week at baseline, ≥100 excess MET-hours/week were associated with a 1.1 kg/m2 lower BMI (27.1 vs 28.2 kg/m2) and 2.8 percentage points lower body fat (23.4% vs 26.3%) in men, and 2.2 kg/m2 lower BMI (25.6 vs 27.7 kg/m2) and 4.0 percentage points lower body fat (33.9% vs 37.9%) in women. For a given BMI, greater physical activity was associated with lower average body fat percentage (for a BMI of 22.5-24.99 kg/m2: 2.0 (95% CI 1.8 to 2.2), percentage points lower body fat in men and 1.8 (95% CI 1.6 to 2.0) percentage points lower body fat in women, comparing ≥100 excess MET-hours per week with <5 excess MET-hours/week).
CONCLUSIONS:

In this sample of middle-aged adults, drawn from the general population, physical activity was inversely associated with BMI and body fat percentage. For people with the same BMI, those who were more active had a lower body fat percentage.},
keywords = {3248, BMI, featured, 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}
}

@article{Cooper2016,
title = {Bidirectional association between physical activity and muscular strength in older adults: Results from the UK Biobank study},
author = {Sharp Simmons S J R K A.J.M. Cooper M.J.E. Lamb and S J Griffin},
url = {http://ije.oxfordjournals.org/content/early/2016/05/20/ije.dyw054.full},
year = {2016},
date = {2016-05-21},
journal = {International Journal of Epidemiology},
abstract = {Background: The relationship between physical activity and muscular strength has not been examined in detail among older adults. The objective of this study was to examine the associations between physical activity and hand grip strength among adults aged ≥ 60 years.

Methods: Using data from the UK Biobank study, we included 66 582 men and women with complete baseline data and 6599 with 4.5 years of follow-up data. We used multiple linear regression models to examine the cross-sectional, longitudinal and bidirectional associations between moderate-to-vigorous physical activity (MVPA) and grip strength, adjusting for potential confounding by age, sex, height, weight, health status, education level, smoking status, Townsend deprivation index and retirement status.
Results: In cross-sectional analyses, grip strength and MVPA were linearly and positively associated with each other. Longitudinally, baseline MVPA was not associated with grip strength at follow-up difference between quintile [Q] 5 and Q1 = 0.40 [95% confidence interval (CI): -0.14, 0.94]kg, whereas baseline grip strength was associated with MVPA at follow-up [Q5 vs Q1 = 7.15 (1.18, 13.12) min/day]. People who maintained/increased time spent in MVPA did not experience any benefit in grip strength [0.08 (−0.20, 0.37) kg] whereas those who increased their grip strength spent 3.69 (0.20, 7.17) min/day extra in MVPA.

Conclusion: Promotion of strength-training activities may enable and maintain participation in regular physical activity among older adults.},
keywords = {5180, grip strength, physical activity},
pubstate = {published},
tppubtype = {article}
}

@misc{Guo2015b,
title = {Body size and composition, physical activity and breast cancer risk: results from the UK biobank prospective cohort},
author = {TJ Key W Guo GK Reeves},
url = {http://jech.bmj.com/content/69/Suppl_1/A52.2.abstract},
year = {2015},
date = {2015-09-09},
journal = {Journal of epidemiology and community health},
abstract = {Background Body size and physical activity are important modifiable risk factors for breast cancer. However, many previous studies are limited by the use of body mass index (BMI), which is unable to distinguish between fat and lean mass. Questions also remain over the role of vigorous compared to lower intensity physical activity in relation to breast cancer risk. We investigate the associations between BMI, body fat percentage, waist-to-hip ratio, waist-to-height ratio, total and vigorous physical activity and both premenopausal and postmenopausal breast cancer.

Methods We analysed data from 48,713 premenopausal and 127,850 postmenopausal women in UK Biobank followed prospectively from 2006 through 2012. We observed 443 premenopausal and 1,422 postmenopausal incident invasive breast cancers during a mean 3.84 years of follow-up. Body size was measured by trained technicians. Self-reported frequency and duration of walking, moderate, and vigorous physical activity was collected by touchscreen questionnaire and calculated as metabolic equivalent task hours per week (MET-hrs/wk). Multivariable-adjusted Cox regression was used. Current users of hormone replacement therapy (HRT) were excluded. All analyses were stratified by age, region, and socioeconomic status and adjusted for family history of breast cancer, previous HRT use, height, parity, age at first birth, smoking, age at menarche, alcohol intake frequency, and age at menopause (for postmenopausal women only). Stata 13 (StataCorp LP, Texas, USA) was used for all statistical analyses.
Results All measures of adiposity were positively associated with increased breast cancer risk in postmenopausal but not premenopausal women. Compared with postmenopausal women in the lowest quartile of body fat percentage (16.8–32.9%; median, 29.6%), women in the highest quartile (41.9–54.3%; 44.8%) had an increased risk of breast cancer (HR, 1.47; 95% confidence interval, 1.24–1.74; ptrend < 0.001). Vigorous physical activity was associated with lower breast cancer risk in postmenopausal but not premenopausal women. Postmenopausal women who participated in an average of 17.5 MET-h/week of vigorous physical activity had a 15% lower risk of breast cancer (HR 0.85; 0.74–0.98; ptrend = 0.025) which was attenuated after adjusting for body fat percentage (HR 0.88, 0.77–1.02},
keywords = {3248, breast cancer, physical activity},
pubstate = {published},
tppubtype = {presentation}
}

@misc{Tyrrell2015b,
title = {‘Genetic variants associated with Body Mass Index “interact” with multiple aspects of the obesogenic environment’},
author = {Samuel Jones Hanieh Yaghootkar Andrew Wood Marcus Tuke Katherine Ruth Rachel Freathy Anna Murray Michael Weedon Timothy Frayling. Jessica Tyrrell Robin Beaumont},
year = {2015},
date = {2015-09-04},
abstract = {The UK BIOBANK study was designed to understand the role of genes, the environment and their interaction in disease. Previous studies have concluded that genetic variants associated with obesity interact with specific aspects of the obesogenic environment, for example sugary drink intake and physical activity. These interactions suggest the need for public health interventions targeted at the specific environmental factors.

The UK Biobank provides a unique opportunity to test the hypothesis that genetic variants associated with BMI interact with the environment. We selected 11 measures of the obesogenic environment that were associated with BMI in the expected directions in up to 119,688 British ancestry individuals. We dichotomised these environmental measures into high and low exposure groups. We generated a 71 SNP genetic risk score (GRS) for obesity and tested its association with BMI (corrected for age, sex and principle components) in high and low obesogenic environments and tested for interactions. We performed these analyses in BMI on its natural (kgm-2) scale and an inverse normalised (mean 0, SD 1) scale to assess if differences were driven by higher variance in BMI in heavier groups.

Higher BMI was associated with self-reported measures of poor diet, sedentary behaviour, less physical activity and living in a more urbanised environment (all P<0.001). The variation in BMI was higher in the obesogenic categories. The association between the 71 SNP BMI genetic risk score and BMI was accentuated in individuals watching more TV (Pinteraction 1x10-7) reporting more sedentary time (Pinteraction 1x10-5), in less active individuals (Pinteraction 3x10-8), performing less vigorous exercise (Pinteraction 8x10-6) and those living in urban environments (Pinteraction 0.025). For the measures of activity, TV watching and sedentary behaviour, the evidence for these interactions was still present, but much weaker, when BMI in the different environments was analysed on the same (inverse normalised) scale (all P<0.05).

Our UK Biobank study shows that BMI-gene environment interactions may not be specific to environmental exposures, but due to individuals exposed to more obesogenic environments having more variation in their BMI. Therefore care should be taken when interpreting gene-environment interactions. However, there is evidence of genuine interaction between genetic variation and self-reported measures of physical activity in the UK Biobank – individuals with a strong genetic predisposition to high BMI},
keywords = {9072, obesity, physical activity},
pubstate = {published},
tppubtype = {presentation}
}

@article{Guo2015b,
title = {Physical activity in relation to body size and composition in women in UK Biobank},
author = {Wenji Guo and Kathryn E Bradbury and Gillian K Reeves and Timothy J Key},
url = {http://www.annalsofepidemiology.org/article/S1047-2797%2815%2900043-5/abstract?rss=yes},
year = {2015},
date = {2015-02-04},
journal = {Annals of Epidemiology},
keywords = {3248, BMI, physical activity, Women},
pubstate = {published},
tppubtype = {article}
}