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.
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.
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, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small.
It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.
ABSTRACT Introduction Tailoring physical activity interventions to individual chronotypes and preferences by time of day could promote more effective and sustainable behavior change; however, our ...understanding of circadian physical behavior patterns is very limited. Objective To characterize and compare 24‐h physical behavior patterns expressed relative to clock time (the standard measurement of time‐based on a 24‐h day) versus wake‐up time in a large British cohort age 46. Methods Data were analyzed from 4979 participants in the age 46 sweep of the 1970 British Cohort Study who had valid activPAL accelerometer data across ≥4 days. Average steps and upright time (time standing plus time stepping) per 30‐min interval were determined for weekdays and weekends, both in clock time and synchronized to individual wake‐up times. Results The mean weekday steps were 9588, and the mean weekend steps were 9354. The mean weekday upright time was 6.6 h, and the mean weekend upright time was 6.4 h. When synchronized to wake‐up time, steps peaked 1 h after waking on weekdays and 2.5 h after waking on weekends. Upright time peaked immediately, in the first 30‐min window, after waking on both weekdays and weekends. Conclusions Aligning accelerometer data to wake‐up times revealed distinct peaks in stepping and upright times shortly after waking. Activity built up more gradually across clock time in the mornings, especially on weekends. Synchronizing against wake‐up times highlighted the importance of circadian rhythms and personal schedules in understanding population 24‐h physical behavior patterns, and this may have important implications for promoting more effective and sustainable behavior change.
There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical ...behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.
The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical ...activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and messages ....
The recommended treatment for idiopathic congenital clubfoot deformity involves a series of weekly castings, surgery, and a period of bracing using a foot abduction brace (FAB). Depending on the age ...of the child, the orthotic should be worn for periods that reduce in duration as the child develops. Compliance is vital to achieve optimal functional outcomes and reduce the likelihood of reoccurrence, deformity, or the need for future surgery. However, compliance is typically monitored by self-reporting, which is time-consuming to implement and lacks accuracy. This study presents a novel method for objectively monitoring FAB wear using a single 3-axis accelerometer. Eleven families mounted an accelerometer on their infant's FAB for up to seven days. Parents were also given a physical diary that was used to record the daily application and removal of the orthotic in line with their treatment. Both methods produced very similar measurements of wear that visually aligned with the movement measured by the accelerometer. Bland Altman plots showed a -0.55-h bias in the diary measurements and the limits of agreement ranging from -2.96 h to 1.96 h. Furthermore, the Cohens Kappa coefficient for the entire dataset was 0.88, showing a very high level of agreement. The method provides an advantage over existing objective monitoring solutions as it can be easily applied to existing FABs, preventing the need for bespoke monitoring devices. The novel method can facilitate increased research into FAB compliance and help enable FAB monitoring in clinical practice.
The quantification of free-living physical activities is important in understanding how physical activity and sedentary behaviour impact on health and also on how interventions might modify ...free-living behaviour to enhance health. Quantification, and the terminology used, has in many ways been determined by the choice of measurement technique. The inter-related issues around measurement devices and terminology used are explored. This paper proposes a terminology and a systematic approach for the analysis of free-living activity information using event-based activity data. The event-based approach uses a flexible hierarchical classification of events and, dependent on the research question, analysis can then be undertaken on a selection of these events. The quantification of free-living behaviour is therefore the result of the analysis on the patterns of these chosen events. The application of this approach is illustrated with results from a range of published studies by our group showing how event-based analysis provides a flexible yet robust method of addressing the research question(s) and provides a deeper insight into free-living behaviour. It is proposed that it is through event-based analysis we can more clearly understand how behaviour is related to health and also how we can produce more relevant outcome measures.
Long uninterrupted sedentary periods, independent of total sedentary time, are risk factors for poor health. There is little objective data relating to workplace sedentary behaviour and adherence to ...current recommendations. The sitting behaviour of office workers (n = 83) was quantified objectively using body-worn accelerometers (activPAL™) over a working week. Adherence to three different recommendations (maximum length of a sitting event of: 20 min; 30 min; 55 min) were assessed. Participants were seated at work for 5.3 ± 1.0 h/d (mean ± 1 SD), equivalent to 66 ± 12% of the working day, accrued in 27 ± 7events/d individual sitting events. Dependent on the recommendation applied, 5-20% of sitting events and 25-67% of time was accumulated in sitting events longer than current guidelines. No participants met the 20 or 30 min recommendations on every working day but seven (8%) participants met the 55 min recommendation. In conclusion, office workers spend a considerable period of their day sitting, accumulated in uninterrupted sitting events longer than current recommendations.
Statement of Relevance: Emerging evidence suggests prolonged sitting has negative health effects. In this study of office workers, 25-67% of time sitting was accumulated in events longer than minimum recommended durations. Adverse sitting behaviour is prevalent in the office, making it an appropriate setting to target the reduction of this behaviour.
Prolonged standing at work is required by an estimated 60% of the employed population and is associated with a high prevalence of musculoskeletal disorders. 'Standing' is expected to encompass a ...range of activities of varying intensity. This study aimed to define a range of 'standing' work-based activities; and objectively explore differences between 'standing' occupations. The following movements were defined using a triaxial accelerometer (ActivPAL) through recordings of known movements (n = 11): static standing, weight-shifting, shuffling, walking and sitting. Movements over a working day were defined for chefs (n = 10), veterinary surgeons (n = 7) and office workers (n = 9). Despite veterinary surgeons and chefs spending a similar time in an upright posture, veterinary surgeons spent 62% of this time standing statically whereas chefs split their time between all the movements. Overall, this study provides the first attempt to define 'standing' activities, allowing the differentiation of activities between occupations spending similar periods of time upright.
Practitioner Summary: This study identified a range of work-based 'standing' activities of varying intensity. Differences in activity were recorded between two occupations spending a similar time in an upright posture (veterinary surgeons and chefs). A broader definition of standing activities could be important when considering factors related to musculoskeletal disorders at work.
This study aimed to describe the scope of accelerometry data collected internationally in adults and to obtain a consensus from measurement experts regarding the optimal strategies to harmonize ...international accelerometry data.
In March 2014, a comprehensive review was undertaken to identify studies that collected accelerometry data in adults (sample size, n ≥ 400). In addition, 20 physical activity experts were invited to participate in a two-phase Delphi process to obtain consensus on the following: unique research opportunities available with such data, additional data required to address these opportunities, strategies for enabling comparisons between studies/countries, requirements for implementing/progressing such strategies, and value of a global repository of accelerometry data.
The review identified accelerometry data from more than 275,000 adults from 76 studies across 36 countries. Consensus was achieved after two rounds of the Delphi process; 18 experts participated in one or both rounds. The key opportunities highlighted were the ability for cross-country/cross-population comparisons and the analytic options available with the larger heterogeneity and greater statistical power. Basic sociodemographic and anthropometric data were considered a prerequisite for this. Disclosure of monitor specifications and protocols for data collection and processing were deemed essential to enable comparison and data harmonization. There was strong consensus that standardization of data collection, processing, and analytical procedures was needed. To implement these strategies, communication and consensus among researchers, development of an online infrastructure, and methodological comparison work were required. There was consensus that a global accelerometry data repository would be beneficial and worthwhile.
This foundational resource can lead to implementation of key priority areas and identification of future directions in physical activity epidemiology, population monitoring, and burden of disease estimates.
Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used ...laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.
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