Dataset Information

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

ABSTRACT: 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.

SUBMITTER: Clark S

PROVIDER: S-EPMC8709415 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Clark Stephen S Lomax Nik N Morris Michelle M Pontin Francesca F Birkin Mark M

Sensors (Basel, Switzerland) 20211209 24

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, ...[more]

PMID: 34960314

Similar Datasets

Project description:BackgroundHigher levels of physical activity (PA) are associated with a lower risk of cardiovascular disease (CVD). However, uncertainty exists on whether the inverse relationship between PA and incidence of CVD is greater at the highest levels of PA. Past studies have mostly relied on self-reported evidence from questionnaire-based PA, which is crude and cannot capture all PA undertaken. We investigated the association between accelerometer-measured moderate, vigorous, and total PA and incident CVD.Methods and findingsWe obtained accelerometer-measured moderate-intensity and vigorous-intensity physical activities and total volume of PA, over a 7-day period in 2013-2015, for 90,211 participants without prior or concurrent CVD in the UK Biobank cohort. Participants in the lowest category of total PA smoked more, had higher body mass index and C-reactive protein, and were diagnosed with hypertension. PA was associated with 3,617 incident CVD cases during 440,004 person-years of follow-up (median (interquartile range [IQR]): 5.2 (1.2) years) using Cox regression models. We found a linear dose-response relationship for PA, whether measured as moderate-intensity, vigorous-intensity, or as total volume, with risk of incident of CVD. Hazard ratios (HRs) and 95% confidence intervals for increasing quarters of the PA distribution relative to the lowest fourth were for moderate-intensity PA: 0.71 (0.65, 0.77), 0.59 (0.54, 0.65), and 0.46 (0.41, 0.51); for vigorous-intensity PA: 0.70 (0.64, 0.77), 0.54 (0.49,0.59), and 0.41 (0.37,0.46); and for total volume of PA: 0.73 (0.67, 0.79), 0.63 (0.57, 0.69), and 0.47 (0.43, 0.52). We took account of potential confounders but unmeasured confounding remains a possibility, and while removal of early deaths did not affect the estimated HRs, we cannot completely dismiss the likelihood that reverse causality has contributed to the findings. Another possible limitation of this work is the quantification of PA intensity-levels based on methods validated in relatively small studies.ConclusionsIn this study, we found no evidence of a threshold for the inverse association between objectively measured moderate, vigorous, and total PA with CVD. Our findings suggest that PA is not only associated with lower risk for of CVD, but the greatest benefit is seen for those who are active at the highest level.

Project description:BackgroundPrevious studies on physical activity (PA) and pelvic organ prolapse (POP) were largely limited to self-reported PA in athletes, soldiers, and women in postpartum. We aimed to investigate the association of accelerometer-measured PA and sedentary behavior with the risk of POP in middle-aged and elderly women.MethodsIn this prospective cohort derived from the UK Biobank, the intensity and duration of PA and sedentary behavior were measured with wrist-worn accelerometers over a 7-day period in 2013-2015 for 47,674 participants (aged 42.8-77.9 years) without pre-existing POP. Participants were followed up until the end of 2022, during which incident POP was ascertained mainly by the electronic health records. Multivariable-adjusted Cox proportional hazards models and restricted cubic splines were used to assess the associations of interest. Isotemporal substitution models were applied to test the effects of substituting a type of activity with equivalent duration of others.ResultsDuring a median follow-up of 8.0 years, 779 cases of POP were recorded. The duration of light-intensity PA (LPA) was positively whereas sedentary time was negatively associated with the risk of POP. Every additional 1 h/day of LPA elevated the risk of POP by 18% (95% confidence interval [CI], 10%-26%). In contrast, the risk decreased by 5% (95% CI, 0-8%) per 1 h/day increment in sedentary behavior. No associations were found between moderate-intensity PA (MPA) or vigorous-intensity PA (VPA) and POP, except that women who had a history of hysterectomy were more likely to develop POP when performing more VPA (53% higher risk for every additional 15 min/day). Substituting 1 h/day of LPA with equivalent sedentary time was associated with a 18% (95% CI, 11%-24%) lower risk of POP. The risk can also be reduced by 17% (95% CI, 7%-25%) through substituting 30 min/day of LPA with MPA.ConclusionsMore time spent in LPA or less sedentary time was linked to an elevated risk of POP in middle-aged and elderly women, while MPA or VPA was not. Substituting LPA with equivalent duration of sedentary behavior or MPA may lower the risk of POP.

Project description:BackgroundBetween 2013 and 2015, the UK Biobank collected accelerometer traces from 103,712 volunteers aged between 40 and 69 years using wrist-worn triaxial accelerometers for 1 week. This data set has been used in the past to verify that individuals with chronic diseases exhibit reduced activity levels compared with healthy populations. However, the data set is likely to be noisy, as the devices were allocated to participants without a set of inclusion criteria, and the traces reflect free-living conditions.ObjectiveThis study aims to determine the extent to which accelerometer traces can be used to distinguish individuals with type 2 diabetes (T2D) from normoglycemic controls and to quantify their limitations.MethodsMachine learning classifiers were trained using different feature sets to segregate individuals with T2D from normoglycemic individuals. Multiple criteria, based on a combination of self-assessment UK Biobank variables and primary care health records linked to UK Biobank participants, were used to identify 3103 individuals with T2D in this population. The remaining nondiabetic 19,852 participants were further scored on their physical activity impairment severity based on other conditions found in their primary care data, and those deemed likely physically impaired at the time were excluded. Physical activity features were first extracted from the raw accelerometer traces data set for each participant using an algorithm that extends the previously developed Biobank Accelerometry Analysis toolkit from Oxford University. These features were complemented by a selected collection of sociodemographic and lifestyle features available from UK Biobank.ResultsWe tested 3 types of classifiers, with an area under the receiver operating characteristic curve (AUC) close to 0.86 (95% CI 0.85-0.87) for all 3 classifiers and F1 scores in the range of 0.80-0.82 for T2D-positive individuals and 0.73-0.74 for T2D-negative controls. Results obtained using nonphysically impaired controls were compared with highly physically impaired controls to test the hypothesis that nondiabetic conditions reduce classifier performance. Models built using a training set that included highly impaired controls with other conditions had worse performance (AUC 0.75-0.77; 95% CI 0.74-0.78; F1 scores in the range of 0.76-0.77 for T2D positives and 0.63-0.65 for controls).ConclusionsGranular measures of free-living physical activity can be used to successfully train machine learning models that are able to discriminate between individuals with T2D and normoglycemic controls, although with limitations because of the intrinsic noise in the data sets. From a broader clinical perspective, these findings motivate further research into the use of physical activity traces as a means of screening individuals at risk of diabetes and for early detection, in conjunction with routinely used risk scores, provided that appropriate quality control is enforced on the data collection protocol.

Project description:ObjectiveLittle is known about the role of timing of physical activity in female reproductive disorders. These disorders include polycystic ovary syndrome (PCOS), heavy menstrual bleeding (HMB), endometriosis, infertility, and pregnancy-related disorders. This study aims to investigate the associations of activity patterns with female reproductive diseases.MethodsA total of 49,540 female participants from the UK Biobank with valid accelerometer data were enrolled at baseline. Activity patterns were defined based on the timing of moderate-to-vigorous intensity physical activity (MVPA) throughout the day. Participants were categorized into four groups according to the timing of their MVPA: "morning, evening, mixed, midday-afternoon", with the midday-afternoon group serving as the reference. Cox proportional hazards models were utilized to evaluate the association between activity patterns and female reproductive diseases.ResultsDuring a median follow-up of 12.6 years, a total of 1044 cases of female reproductive diseases were documented. After adjustment for potential confounders, compared to women with midday-afternoon exercise, women with morning exercise and mixed-timing exercise were associated with lower risks for female reproductive diseases (HRmorning=0.81, 95% CI: 0.67-0.98; HRmixed=0.79, 95% CI: 0.69-0.91, P-trend < 0.05). Moreover, morning exercise and mixed-timing exercise had lower risks of PCOS (HRmorning=0.38, 95% CI: 0.15-0.97; HRmixed=0.27, 95% CI: 0.13-0.57, P-trend<0.001), and mixed-timing exercise was associated with a lower risk for HMB (HRmixed=0.81, 95% CI: 0.70-0.95, P-trend < 0.05), compared with the reference group.ConclusionsCompared with midday-afternoon group, morning and mixed MVPA timing groups, but not evening group, were associated with decreased risks for female reproductive diseases and PCOS. In addition, we found that women with mixed MVPA timing exercise had a lower risk of HMB, compared with the reference group.

Dataset Information

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Publications

Clustering Accelerometer Activity Patterns from the UK Biobank Cohort.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets