@article{Folley2019,
title = {Physical Activity, APOE Genotype, and Cognitive Decline: Exploring Gene-Environment Interactions in the UK Biobank},
author = {S Folley and et al},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31450492},
year = {2019},
date = {2019-08-20},
journal = {Journal of Alzheimer's Disease},
abstract = {BACKGROUND:
Apolipoprotein E (APOE) genotype affects the risk of Alzheimer's disease (AD) with inconclusive evidence on the opportunity to mitigate related adverse effects by lifestyle changes.

OBJECTIVE:
To examine the individual and interactive associations of APOE and objectively-measured physical activity (PA) and sedentary activity with cognitive decline.

METHODS:
We used data from middle-aged and older UK Biobank participants with repeat tests on visuospatial memory (n = 52,767) and fluid intelligence (n = 19,713), and who also took part in the accelerometer sub-study. PA and sedentary activity were assessed by a wrist-worn accelerometer over a seven-day period. Cognitive decline was defined as >1 standard deviation (SD) reduction in memory or fluid intelligence score, and the mean follow up from baseline was 5.8 years.

RESULTS:
There was an age dependent association between APOEɛ4 genotype and memory decline (page-interaction = 0.01), with the association only seen in participants who were >65 years (OR 1.33, 95% CI 1.11 to 1.24; for <65 years OR 1.06, 95% CI 0.99 to 1.14). The OR for the APOEɛ4 association with fluid intelligence decline was 1.11 (95% CI 0.99 to 1.24), and there was no evidence for age interaction (page-interaction = 0.99). High PA and low sedentary activity were associated with reduced mean memory decline (p < 0.02 for both). There was no interaction between PA or sedentary activity with APOEɛ4 regarding either of the cognitive decline measures (p > 0.63 for all).

CONCLUSION:
This large-scale study using objectively measured PA did not find differential effects of PA on cognitive decline based on APOE genotype.},
keywords = {10171, cognitive decline, genetics, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Celis-Morales2019b,
title = {Do physical activity commuting mode cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study},
author = {CA Celis-Morales},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31168053},
year = {2019},
date = {2019-08-01},
journal = {International journal of obesity},
abstract = {OBJECTIVE:
To investigate whether the association between a genetic profile risk score for obesity (GPRS-obesity) (based on 93 SNPs) and body mass index (BMI) was modified by physical activity (PA), cardiorespiratory fitness, commuting mode, walking pace and sedentary behaviours.

METHODS:
For the analyses we used cross-sectional baseline data from 310,652 participants in the UK Biobank study. We investigated interaction effects of GPRS-obesity with objectively measured and self-reported PA, cardiorespiratory fitness, commuting mode, walking pace, TV viewing, playing computer games, PC-screen time and total sedentary behaviour on BMI. Body mass index (BMI) was the main outcome measure.

RESULTS:
GPRS-obesity was associated with BMI (β:0.54 kg.m-2 per standard deviation (SD) increase in GPRS, [95% CI: 0.53; 0.56]; P = 2.1 × 10-241). There was a significant interaction between GPRS-obesity and objectively measured PA (P[interaction] = 3.3 × 10-11): among inactive individuals, BMI was higher by 0.58 kg.m-2 per SD increase in GPRS-obesity (p = 1.3 × 10-70) whereas among active individuals the relevant BMI difference was less (β:0.33 kg.m-2, p = 6.4 × 10-41). We observed similar patterns for fitness (Unfit β:0.72 versus Fit β:0.36 kg.m-2, P[interaction] = 1.4 × 10-11), walking pace (Slow β:0.91 versus Brisk β:0.38 kg.m-2, P[interaction] = 8.1 × 10-27), discretionary sedentary behaviour (High β:0.64 versus Low β:0.48 kg.m-2, P[interaction] = 9.1 × 10-12), TV viewing (High β:0.62 versus Low β:0.47 kg.m-2, P[interaction] = 1.7 × 10-11), PC-screen time (High β:0.82 versus Low β:0.54 kg.m-2, P[interaction] = 0.0004) and playing computer games (Often β:0.69 versus Low β:0.52 kg.m-2, P[interaction] = 8.9 × 10-10). No significant interactions were found for commuting mode (car, public transport, active commuters).

CONCLUSIONS:
Physical activity, sedentary behaviours and fitness modify the extent to which a set of the most important known adiposity variants affect BMI. This suggests that the adiposity benefits of high PA and low sedentary behaviour may be particularly important in individuals with high genetic risk for obesity.},
keywords = {physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Raichlen2019,
title = {Differential associations of engagement in physical activity and estimated cardiorespiratory fitness with brain volume in middle-aged to older adults},
author = {DA Raichlen and YC Klimentidis and PK Bharadwaj and GE Alexander},
url = {https://www.ncbi.nlm.nih.gov/pubmed/31209836},
year = {2019},
date = {2019-06-17},
journal = {Brain imaging behaviour},
abstract = {Previous work has confirmed the benefits of aerobic exercise for brain aging, however mechanisms underlying these effects remain unclear. Two measures of exercise, time spent in moderate-to-vigorous physical activity (MVPA) and cardiorespiratory fitness (CRF), may reflect different pathways linking activity to brain health. Using data from the UK Biobank, the largest sample combining neuroimaging and objectively measured MVPA available to date (n = 7148, nmale = 3062, nfemale = 4086; age = 62.14 ± 7.40 years), we found that, when adjusted for covariates including MVPA, CRF was positively associated with overall gray matter volume (FDR p = 1.28E-05). In contrast, when adjusted for covariates including CRF, MVPA was positively associated with left and right hippocampal (FDR pleft = 0.01; FDR pright = 0.02) volumes, but not overall gray matter volume. Both CRF and MVPA were inversely associated with white matter hyperintensity lesion loads (FDR pCRF = 0.002; pMVPA = 0.02). Our results suggest separable effects of engagement in exercise behaviors (MVPA) and the physiological effects of exercise (CRF) on structural brain volumes, which may have implications for differential pathways linking exercise and brain benefits.},
keywords = {21259, brain volume, cardiorespiratory fitness, imaging, physical activity},
pubstate = {published},
tppubtype = {article}
}

@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{Yogini V. Chudasama2019,
title = {Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study},
author = {Yogini V. Chudasama and Kamlesh K. Khunti and Francesco Zaccardi and Alex V. Rowlands and Thomas Yates and Clare L. Gillies and Melanie J. Davies and Nafeesa N. Dhalwani},
url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1339-0},
year = {2019},
date = {2019-05-01},
journal = {BMC Medicine},
abstract = {Multimorbidity is an emerging public health priority. Physical activity (PA) is recommended as one of the main lifestyle behaviours, yet the benefits of PA for people with multimorbidity are unclear. We assessed the benefits of PA on mortality and life expectancy in people with and without multimorbidity.
Methods
Using the UK Biobank dataset, we extracted data on 36 chronic conditions and defined multimorbidity as (a) 2 or more conditions, (b) 2 or more conditions combined with self-reported overall health, and (c) 2 or more top-10 most common comorbidities. Leisure-time PA (LTPA) and total PA (TPA) were measured by questionnaire and categorised as low (< 600 metabolic equivalent (MET)-min/week), moderate (600 to < 3000 MET-min/week), and high (≥ 3000 MET-min/week), while objectively assessed PA was assessed by wrist-worn accelerometer and categorised as low (4 min/day), moderate (10 min/day), and high (22 min/day) walking at brisk pace. Survival models were applied to calculate adjusted hazard ratios (HRs) and predict life expectancy differences.
Results
491,939 individuals (96,622 with 2 or more conditions) had a median follow-up of 7.0 (IQR 6.3–7.6) years. Compared to low LTPA, for participants with multimorbidity, HR for mortality was 0.75 (95% CI 0.70–0.80) and 0.65 (0.56–0.75) in moderate and high LTPA groups, respectively. This finding was consistent when using TPA measures. Using objective PA, HRs were 0.49 (0.29–0.80) and 0.29 (0.13–0.61) in the moderate and high PA groups, respectively. These findings were similar for participants without multimorbidity. In participants with multimorbidity, at the age of 45 years, moderate and high LTPA were associated with an average of 3.12 (95% CI 2.53, 3.71) and 3.55 (2.34, 4.77) additional life years, respectively, compared to low LTPA; in participants without multimorbidity, corresponding figures were 1.95 (1.59, 2.31) and 1.85 (1.19, 2.50). Similar results were found with TPA. For objective PA, moderate and high levels were associated with 3.60 (− 0.60, 7.79) and 5.32 (− 0.47, 11.11) life years gained compared to low PA for those with multimorbidity and 3.88 (1.79, 6.00) and 4.51 (2.15, 6.88) life years gained in those without. Results were consistent when using other definitions of multimorbidity.},
keywords = {14614, life expectancy, physical activity},
pubstate = {published},
tppubtype = {article}
}

@article{Woodbridge2019,
title = {Physical activity and left ventricular trabeculation in the UK Biobank community-based cohort study},
author = {SP Woodbridge and N Aung and JM Paiva and MM Sanghvi and F Zemrak and K Fung SE Petersen },
url = {https://www.ncbi.nlm.nih.gov/pubmed/30723101},
year = {2019},
date = {2019-02-05},
journal = {Heart},
abstract = {OBJECTIVE:
Vigorous physical activity (PA) in highly trained athletes has been associated with heightened left ventricular (LV) trabeculation extent. It has therefore been hypothesised that LV trabeculation extent may participate in exercise-induced physiological cardiac remodelling. Our cross-sectional observational study aimed to ascertain whether there is a 'dose-response' relationship between PA and LV trabeculation extent and whether this could be identified at opposite PA extremes.

METHODS:
In a cohort of 1030 individuals from the community-based UK Biobank study (male/female ratio: 0.84, mean age: 61 years), PA was measured via total metabolic equivalent of task (MET) min/week and 7-day average acceleration, and trabeculation extent via maximal non-compaction/compaction ratio (NC/C) in long-axis images of cardiovascular magnetic resonance studies. The relationship between PA and NC/C was assessed by multivariate regression (adjusting for potential confounders) as well as between demographic, anthropometric and LV phenotypic parameters and NC/C.

RESULTS:
There was no significant linear relationship between PA and NC/C (full adjustment, total MET-min/week: ß=-0.0008, 95% CI -0.039 to -0.037, p=0.97; 7-day average acceleration: ß=-0.047, 95% CI -0.110 to -0.115, p=0.13, per IQR increment in PA), or between extreme PA quintiles (full adjustment, total MET-min/week: ß=-0.026, 95% CI -0.146 to -0.094, p=0.67; 7-day average acceleration: ß=-0.129, 95% CI -0.299 to -0.040, p=0.49), across all adjustment levels. A negative relationship was identified between left ventricular ejection fraction and NC/C, significantly modified by PA (ß difference=-0.006, p=0.03).

CONCLUSIONS:
In a community-based general population cohort, there was no relationship at, or between, extremes, between PA and NC/C, suggesting that at typical general population PA levels, trabeculation extent is not influenced by PA changes.},
keywords = {2964, imaging, physical activity},
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{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{Cassidy2018,
title = {Accelerometer-derived physical activity in those with cardio-metabolic disease compared to healthy adults: a UK Biobank study of 52,556 participants},
author = {S Cassidy and H Fuller and J Chau and M Catt and A Bauman and M Trenell },
url = {https://www.ncbi.nlm.nih.gov/pubmed/29808390},
year = {2018},
date = {2018-09-05},
journal = {Acta Diabetelogica},
abstract = {AIM:
Cardio-metabolic disease and physical activity are closely related but large-scale objective studies which measure physical activity are lacking. Using the largest accelerometer cohort to date, we aimed to investigate whether there is an association between disease status and accelerometer variables after a 5-year follow-up.

METHODS:
106,053 UK Biobank participants wore a wrist-worn GENEactiv monitor. Those with acceptable wear time (> 3 days) were split into 4 cardio-metabolic disease groups based on self-report disease status which was collected 5 ± 1 years prior. Multiple linear regression models were used to investigate associations, controlling for confounders and stratified for gender.

RESULTS:
Average daily acceleration was lower in men ('healthy'-42 ± 15 mg v 'Type 2 diabetes + cardiovascular disease (CVD)'-31 ± 12 mg) and women ('healthy'-44 ± 13 mg v 'Type 2 diabetes + CVD'-31 ± 11 mg) with cardio-metabolic disease and this was consistent across both week and weekend days. Men and women with the worst cardio-metabolic disease perform around half of moderate to vigorous physical activity on a daily basis compared to healthy individuals, and spend almost 7 h per day in 30 min inactivity bouts. Significant associations were seen between cardio-metabolic disease and accelerometer variables 5 years on when controlling for confounders.

CONCLUSION:
In the largest accelerometer cohort to date, there are significant associations between cardio-metabolic disease and physical activity variables after 5 years of follow-up. Triaxial accelerometers provide enhanced measurement opportunities for measuring lifestyle behaviours in chronic disease.},
keywords = {cardio-metabolic, 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}
}

@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}
}