1,721,127 research outputs found
Summary statistics relating to "GWAS identifies 14 loci for device-measured physical activity and sleep duration"
No full textPhysical activity and sleep duration are established risk factors for many diseases, but their etiology is poorly understood, partly due to relying on self-reported evidence. Our 2018 Nature Communications article reports a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). This data deposit shares the summary statistics related to this GWAS study
Capture-24: Activity tracker dataset for human activity recognition - Temperature & light sensor data
No full textThis dataset complements a previously released motion sensor dataset to include corresponding temperature and light sensor data. The DOI for the previously released motion sensor dataset is: 10.5287/bodleian:NGx0JOMP
Summarisation & Visualisation of Large Volumes of Time-Series Sensor Data
No full texta number of sensors, including an electricity usage
sensor supplied by Episensor. This poses our second
With the increasing ubiquity of sensor data, challenge, how to summarise an extended period of
presenting this data in a meaningful way to electrictiy usage data for a home user.
users is a challenge that must be addressed
before we can easily deploy real-world sensor
network interfaces in the home or workplace. In
this paper, we will present one solution to the
visualisation of large quantities of sensor data
that is easy to understand and yet provides
meaningful and intuitive information to a user,
even when examining many weeks or months of
historical data. We will illustrate this
visulalisation technique with two real-world
deployments of sensing the person and sensing
the home
IAPMA 2011: 2nd Workshop on information access to personal media archives
Get PDFTowards e-Memories: challenges of capturing, summarising, presenting, understanding, using, and retrieving relevant information from heterogeneous data contained in personal media archives.
Welcome to IAPMA 2011, the second international workshop on "Information Access for Personal Media Archives". It is now possible to archive much of our life experiences in digital form using a variety of sources, e.g. blogs written, tweets made, social network status updates, photographs taken, videos seen, music heard, physiological monitoring, locations visited and environmentally sensed data of those places, details of people met, etc. Information can be captured from a myriad of personal information devices including desktop computers, PDAs, digital cameras, video and audio recorders, and various sensors, including GPS, Bluetooth, and biometric devices
OxWalk: Wrist and hip-based activity tracker dataset for free-living step detection and gait recognition
No full textThis dataset contains Axivity AX3 activity tracker (accelerometer) data collected from 39 participants in 2019-2020 around the Oxfordshire area. Calibrated and resampled traxial acceleration data is included, captured during unscripted, free living in healthy adult volunteers (aged 18 and above) with no lower limb injury within the previous 6 months and who were able to walk without an assistive device. Participants wore four triaxial accelerometers concurrently (AX3, Axivity, Newcastle, UK), two placed side-by-side on the dominant wrist and two clipped to the dominant-side hip at the midsagittal plane. Accelerometers were synchronised using the Open Movement GUI software (v.1.0.0.42), with one recording at 100 Hz and the other at 25 Hz at each body location. Foot-facing video was captured using an action camera (Action Camera CT9500, Crosstour, Shenzhen, China) mounted at the participant’s beltline. From the synchronised camera data, a step is annotated in each CSV file by a single "1" at the approximate time of heel strike.
A full data description is available in README.txt upon download
Device-measured 24-hour movement behaviours and risk of incident cardiovascular disease
Get PDFMore time in moderate-to-vigorous physical activity (MVPA) is associated with lower cardiovascular disease (CVD) risk. However, most of the 24-hour day is spent in other behaviours, including light physical activity, sedentary behaviour, and sleep. Device-based movement measurement in large prospective cohorts now enables detailed investigation of 24-hour movement behaviours. However, traditional approaches to classifying behaviours in device data, based on ‘cut-points’, can be crude and sometimes inaccurate. Furthermore, approaches to associate 24-hour movement behaviours with disease risk do not always account for how behaviours make up the 24-hour day. While biases are known to affect analyses, they are rarely assessed quantitatively. This work therefore aimed to investigate effects of device-measured 24-hour movement behaviours on risk of incident CVD, addressing challenges around behaviour classification, analysis of 24-hour movement behaviours, and interpretation.
This work developed machine-learning-based classification methods to classify free-living wrist-worn accelerometer data as sleep, sedentary behaviour, light physical activity behaviours, or moderate-to-vigorous physical activity behaviours. The classifier used a Random Forest with a Hidden Markov model for time smoothing. It was developed using free-living data from a study of 152 participants who had a camera-and diary-based ground-truth measured alongside accelerometry (the CAPTURE-24 study). In Leave-One-Participant-Out analysis, this method classified free-living movement behaviours with a mean accuracy of 88% (87%, 89%) and a mean Cohen’s kappa of 0.80 (0.79, 0.82). UK Biobank (UKB) is a population-based prospective cohort of middle-to-older aged adults, in which∼100,000 participants wore a wrist-worn Axivity AX3 accelerometer for a 7-day period between 2013 and 2015. The machine-learning model was used to classify movement behaviours of UKB participants, and classification patterns by time of day and by participant characteristics showed good face validity.
UK Biobank participants’ 24-hour movement behaviours were associated with risk of incident cardiovascular disease (ICD-10 codes I20-25, I60-69) using a multivariable-adjusted Cox regression model. To account for the compositional nature of 24-hour movement behaviour data, it was modelled using a Compositional Data Analysis approach. Over a median of 6.2 years’ follow-up, there were 4,105 incident CVD events among 87,498 participants. Reallocating time from other behaviours to MVPA was associated with a lower risk of incident CVD. Reallocating time from sedentary behaviour to other behaviours was also associated with lower risk. For example, compared to the average movement behaviour composition in this cohort, reallocating 20 minutes/day to MVPA from all other behaviours proportionally was associated with a Hazard Ratio (HR) of 0.91 (0.90, 0.93), while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with a HR of 1.05 (1.03, 1.07).
A range of approaches were used to quantitatively assess the impact of information bias, selection bias, reverse causality, and residual/unmeasured confounding on results. Adjusting for within-person variation in measurements modestly de-attenuated some results and little affected others. Although there were clear selection effects from UK Biobank into the accelerometry sub-study, this did not appear to substantially affect analytic results. Findings on reverse causality were mixed, with little difference by time period but different findings on sedentary behaviour by (cardiovascular) health status. There was some evidence residual/unmeasured confounding affected MVPA results, but it did not entirely explain findings.
This work contributes to emerging evidence on device-measured 24-hour movement behaviours and risk of incident cardiovascular disease. It both supports existing guidelines encouraging adults to engage in regular moderate-to-vigorous physical activity and limit sedentary time, replacing it with physical activity of any intensity, and supports the development of future guidelines and interventions targeting behaviours throughout the 24-hour day. This work also supports future research, particularly by developing and making available a machine-learning-based classifier with good performance, and by using emerging methods for associating movement behaviours with incident disease and for assessing bias
An ethical framework for automated, wearable cameras in health behavior research.
Get PDFTechnologic advances mean automated, wearable cameras are now feasible for investigating health behaviors in a public health context. This paper attempts to identify and discuss the ethical implications of such research, in relation to existing guidelines for ethical research in traditional visual methodologies. Research using automated, wearable cameras can be very intrusive, generating unprecedented levels of image data, some of it potentially unflattering or unwanted. Participants and third parties they encounter may feel uncomfortable or that their privacy has been affected negatively. This paper attempts to formalize the protection of all according to best ethical principles through the development of an ethical framework. Respect for autonomy, through appropriate approaches to informed consent and adequate privacy and confidentiality controls, allows for ethical research, which has the potential to confer substantial benefits on the field of health behavior research
The smartphone as a platform for wearable cameras in health research
Get PDFBackground: The Microsoft SenseCam, a small camera that is worn on the chest via a lanyard, increasingly is being deployed in health research. However, the SenseCam and other wearable cameras are not yet in widespread use because of a variety of factors. It is proposed that the ubiquitous smartphones can provide a more accessible alternative to SenseCam and similar devices.Purpose: To perform an initial evaluation of the potential of smartphones to become an alternative to a wearable camera such as the SenseCam.Methods: In 2012, adults were supplied with a smartphone, which they wore on a lanyard, that ran life-logging software. Participants wore the smartphone for up to 1 day and the resulting life-log data were both manually annotated and automatically analyzed for the presence of visual concepts. The results were compared to prior work using the SenseCam.Results: In total, 166,000 smartphone photos were gathered from 47 individuals, along with associated sensor readings. The average time spent wearing the device across all users was 5 hours 39 minutes (SD =4 hours 11 minutes). A subset of 36,698 photos was selected for manual annotation by five researchers. Software analysis of these photos supports the automatic identification of activities to a similar level of accuracy as for SenseCam images in a previous study.Conclusions: Many aspects of the functionality of a SenseCam largely can be replicated, and in some cases enhanced, by the ubiquitous smartphone platform. This makes smartphones good candidates for a new generation of wearable sensing devices in health research, because of their widespread use across many populations. It is envisioned that smartphones will provide a compelling alternative to the dedicated SenseCam hardware for a number of users and application areas. This will be achieved by integrating new types of sensor data, leveraging the smartphone's real-time connectivity and rich user interface, and providing support for a range of relatively sophisticated applications
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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