Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

Functional proportional hazards mixture cure model with applications in cancer mortality in NHANES and post ICU recovery

We develop a functional proportional hazards mixture cure model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying populations with a cure fraction of a particular event of interest is extended to functional data. We employ the expectation-maximization algorithm and develop a semiparametric penalized spline-based approach to estimate the dynamic functional coefficients of the incidence and the latency part. The proposed method is computationally efficient and simultaneously incorporates smoothness in the estimated functional coefficients via roughness penalty. Simulation studies illustrate a satisfactory performance of the proposed method in accurately estimating the model parameters and the baseline survival function. Finally, the clinical potential of the model is demonstrated in two real data examples that incorporate rich high-dimensional biomedical signals as functional covariates measured at the baseline and constitute novel domains to apply cure survival models in contemporary medical situations. In particular, we analyze (i) minute-by-minute physical activity data from the National Health And Nutrition Examination Survey 2003-2006 to study the association between diurnal patterns of physical activity at baseline and all cancer mortality through 2019 while adjusting for other biological factors; (ii) the impact of daily functional measures of disease severity collected in the intensive care unit on post intensive care unit recovery and mortality event. Our findings provide novel epidemiological insights into the association between daily patterns of physical activity and cancer mortality. Software implementation and illustration of the proposed estimation method are provided in R.

"Couch-to-5k or Couch to Ouch to Couch!?" Who Takes Part in Beginner Runner Programmes in the UK and Is Non-Completion Linked to Musculoskeletal Injury?

Physical activity has mental and physical health benefits; however, globally, three-quarters of the population do not meet physical activity guidelines. The Couch-to-5k is a beginner runner programme aimed at increasing physical activity. However, this programme lacks an evidence base, and it is unclear who is attracted to the programme; running also has a high rate of musculoskeletal (MSK) injuries. The aims of this study were to identify the characteristics of people taking part and the incidence of MSK injuries as well as exploring the experiences of people who dropped out of a modified 9-week Couch-to-5k programme. A total of 110 runners (average age was 47.1 ± 13.7 years) participated in the study, which involved completion of questionnaires (running experience and footwear information, quality of life (EQ-5D-5L), physical activity level (IPAQ-short form), MSK injury history and knee condition (SNAPPS and KOOS-PS)) at the start, middle and end of the programme and collecting sociodemographic information (age, gender, social economic status, relationship status, education level), as well as body mass index, running experience, footwear information, quality of life, physical activity levels, MSK injuries and knee condition. Fifteen drop-outs were interviewed to explore experiences of the programme. Runners were mainly females (81.8%) with an average age 47.1 years, average body mass index of 28.1 kg.m2, mainly from high socio-economic levels, married and educated to degree level. In total, 64% of the sample had previous running experience and were classified as active. Half the sample self-reported pain/discomfort and 37.2% reported anxiety/depression at the start of the programme via the EQ-5D-5L scale. Self-reported health scores increased (p = 0.047) between baseline (73.1 ± 18.8 out of 100) and at the midpoint (81.2 ± 11.6), but there were no significant differences between any other time points (end point 79.7 ± 17.5, p > 0.05). Twenty-one injuries were reported during the programme (19%). Previous injury increased the risk of new injury (OR 7.56 95% CI from 2.06 to 27.75). Only 27.3% completed the programme. Three themes emerged from interviews; MSK injury, negative emotions linked to non-completion and design of the programme. The Couch-to-5k may not attract diverse inactive populations, but future work with larger sample sizes is needed to substantiate this finding. Dropping out was linked to MSK injury and progressive design, so future programmes should consider including injury prevention advice and more flexible designs.

Setting your clock: associations between timing of objective physical activity and cardiovascular disease risk in the general population

AIMS

Little is known about the impact of daily physical activity timing (here referred to as 'chronoactivity') on cardiovascular disease (CVD) risk. We aimed to examined the associations between chronoactivity and multiple CVD outcomes in the UK Biobank.

METHODS AND RESULTS

physical activity data were collected in the UK-Biobank through triaxial accelerometer over a 7-day measurement period. We used K-means clustering to create clusters of participants with similar chronoactivity irrespective of the mean daily intensity of the physical activity. Multivariable-adjusted Cox-proportional hazard models were used to estimate hazard ratios (HRs) comparing the different clusters adjusted for age and sex (model 1), and baseline cardiovascular risk factors (model 2). Additional stratified analyses were done by sex, mean activity level, and self-reported sleep chronotype. We included 86 657 individuals (58% female, mean age: 61.6 [SD: 7.8] years, mean BMI: 26.6 [4.5] kg/m2). Over a follow-up period of 6 years, 3707 incident CVD events were reported. Overall, participants with a tendency of late morning physical activity had a lower risk of incident coronary artery disease (HR: 0.84, 95%CI: 0.77, 0.92) and stroke (HR: 0.83, 95%CI: 0.70, 0.98) compared to participants with a midday pattern of physical activity. These effects were more pronounced in women (P-value for interaction = 0.001). We did not find evidence favouring effect modification by total activity level and sleep chronotype.

CONCLUSION

Irrespective of total physical activity, morning physical activity was associated with lower risks of incident cardiovascular diseases, highlighting the potential importance of chronoactivity in CVD prevention.

Can genetics guide exercise prescriptions in osteoarthritis?

Osteoarthritis (OA) is the most common form of arthritis and has a multifactorial etiology. Current management for OA focuses on minimizing pain and functional loss, typically involving pharmacological, physical, psychosocial, and mind-body interventions. However, there remain challenges in determining which patients will benefit most from which interventions. Although exercise-based interventions are recommended as first-line treatments and are known to be beneficial for managing both the disease and illness of OA, the optimal exercise “prescription” is unknown, due in part to our limited understanding of the precise mechanisms underlying its action. Here we present our perspective on the potential role of genetics in guiding exercise prescription for persons with OA. We describe key publications in the areas of exercise and OA, genetics and OA, and exercise and genetics, and point to a paucity of knowledge at the intersection of exercise, genetics, and OA. We suggest there is emerging evidence to support the use of genetics and epigenetics to explain the beneficial effects of exercise for OA. We identify missing links in the existing research relating to exercise, genetics, and OA, and highlight epigenetics as a promising mechanism through which environmental exposures such as exercise may impact OA outcomes. We anticipate future studies will improve our understanding of how genetic and epigenetic factors mediate exercise-based interventions to support implementation and ultimately improve OA patient care.

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: "Active 9 to 5", "Active", "Morning Movers", "Get up and Active", "Live for the Weekend", "Moderates", "Leisurely 9 to 5", "Sedate" and "Inactive". These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.