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Dobrynya Myshkin
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Categorise


The temptation to categorise those who want to join Islamic State as confused, naïve and marginalised young men conceals the reality that they have chosen to give their life to the most violent, merciless jihad imaginable. (The Herald Sun)




categorise


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Research investigating the influence of the environmental and social factors on eating behaviours in free-living settings is limited. This study investigates the utility of using wearable camera images to assess the context of eating episodes. Adult participants (N = 40) wore a SenseCam wearable camera for 4 days (including 1 familiarisation day) over a 15-day period in free-living conditions, and had their diet assessed using three image-assisted multiple-pass 24-hour dietary recalls. The images of participants' eating episodes were analysed and annotated according to their environmental and social contexts; including eating location, external environment (indoor/outdoor), physical position, social interaction, and viewing media screens. Data for 107 days were used, with a total of 742 eating episodes considered for annotation. Twenty nine per cent (214/742) of the episodes could not be categorised due to absent images (12%, n = 85), dark/blurry images (8%, n = 58), camera not worn (7%, n = 54) and for mixed reasons (2%, n = 17). Most eating episodes were at home (59%) and indoors (91%). Meals at food retailers were 24.8 minutes longer (95% CI: 13.4 to 36.2) and were higher in energy (mean difference = 1196 kJ 95% CI: 242, 2149) than at home. Most episodes were seated at tables (27%) or sofas (26%), but eating standing (19%) or at desks (18%) were common. Social interaction was evident for 45% of episodes and media screens were viewed during 55% of episodes. Meals at home watching television were 3.1 minutes longer (95% CI: -0.6 to 6.7) and higher in energy intake than when no screen was viewed (543 kJ 95% CI: -32 to 1120). The environmental and social context that surrounds eating and dietary behaviours can be assessed using wearable camera images.


Adults were given an Actical hip-mounted accelerometer and a SenseCam wearable camera (worn via lanyard). The onboard clocks on both devices were time-synchronised. Participants engaged in free-living activities for 3 days. Actical data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified. Actical episodes were categorised according to their social and environmental context and Physical Activity (PA) compendium category as identified from time-matched SenseCam images.


Wearable camera images offer an objective method to capture a spectrum of activity behaviour types and context across 81% of accelerometer-identified episodes of activity. Wearable cameras represent the best objective method currently available to categorise the social and environmental context of accelerometer-defined episodes of activity in free-living conditions.


Episode annotation using SenseCam images. Overview of software developed to annotate SenseCam images. The left hand side displays the initial screen which lists all the episodes identified from Actical data. Once the researcher has clicked on an individual episode, a new screen opens up which is displayed on the right hand side of the figure. This screen allows the researcher to view all the images associated with the given episode, and then manually categorise its behavioural type and context. This particular example shows a participant driving a vehicle during the selected episode. The red-bordered images represent photos taken before the selected episode.


Sample of annotated episodes using SenseCam. A selection of episodes of activity that were categorised using SenseCam images. The Physical Compendium category, contextual annotation, and associated Actical counts per minute is displayed with each example episode.


The type and context of behaviour episodes can be identified through manual annotation of wearable camera images. This verifies that machine vision classifiers should be evaluated on whether they can automatically replicate these manual annotations. If this is satisfied future studies using accelerometers could consider the use of wearable cameras to objectively categorise and contextualise accelerometer identified episodes of activity, regardless of choice of episode or bout type identification algorithm. The wearable camera should not be viewed as a replacement device for the hip-mounted accelerometer, but instead as a complementary source of information to provide much needed contextual information. This will enable researchers to better understand human behaviour using an objective free-living behaviour, thus providing better quality information to devise appropriate public health interventions [4]. Future studies using both of these devices together will likely provide better objective measurement of episodes of interest in terms of: type, context; intensity (from accelerometer, and also cross-referencing Physical Activity Compendium); time, and duration (from either device).


In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind.


Previous work has been done on the categorisation of continuous variables. In a review of methods for categorising a predictor, the methods concerned were divided into two groups [6]: a) exploratory plots; and, b) a minimum p-value approach. Insofar as exploratory plots are concerned, Hin et al. proposed a method for dichotomising continuous variables using Generalised Additive Models (GAMs) [8]. The number of categories into which the continuous predictor should be categorised depends partly on the relationship -graphical and numerical- between the predictor and the outcome: hence, the need to create more than two categories should be borne in mind. This happens, for instance, with variables such as blood pressure, where a single cut point cannot be used to divide patients into high- and low-risk groups, since patients with high and those with low values are both associated with higher risk [9]. Furthermore, a very recent work criticised the arbitrary and frequent use made of quantiles in epidemiological research to categorise continuous variables [10].


The relationship shown on the graph between the covariate and the outcome given by the GAM is not linear, which means that there is either a jump or a change in the slope. Firstly, we propose to proceed as described above for the first three categories, labelled "average-risk", "low-risk" and "high-risk". Secondly, the points at which the slope change occurs will be deemed to be extra cut points. Consequently, this will lead to the corresponding low-risk or high-risk categories, or both, being re-categorised as very low- and low- or very high- and high-risk categories respectively. The selection of these extra cut points will be made on the basis of graphical visualisation of the slope and the clinical significance of the cut point in question. This hypothetical situation of one extra cut point is depicted in Figure 1(b)


Our proposal, motivated in part by the work by Hin et al (1999) [8], occupies the middle ground between their approach and the original continuous predictor. This paper shows that our categorisation proposal does not lose critical information from the original predictor, respects the relationship between the original predictor and the outcome, and offers validated results with better predictive ability than does the dichotomous approach. Moreover, our proposal starts by suggesting a minimum number of 3 categories, and offering the necessary cut points to ensure that such a categorisation is a good categorised approximation to the continuous option. We have shown that in general, this approach improves Hin et al proposal (1999) in terms of fitting and prediction. The proposal includes a method to build an interval around the average-risk point using the inverse of the 95% confidence interval for the expected response. Although more complex techniques could provide other alternatives, in our opinion this is a simple and easy to understand way that shows the advantages and usefulness of a three category approach. In any given case, the need for more categories can be evaluated by researchers, depending upon the relationship between the predictor and the outcome, sample size and clinical knowledge of the problem. Moreover, any improvement resulting from the addition of more categories can be statistically tested. Although this is an illustrative example, in the application presented here we selected 4 categories for PCO2 and 3 categories for RR.


Hi, I work for conglomerate that own many smaller companies worldwide. Each company has access to shared Azure resources. Many of our companies have created Azure apps. Now, when conglomerate IT manager goes to "App registrations" page he sees apps of every company on "All applications" tab. Its a mess. We are trying to agree on naming convention to so that we could see which app belong to who. The problem is that users who sign into the app also see the ugly name. We don't want users seeing app names like "MLG-LDN-ComplianceTool" even though it might help us to categorise things.


Driven by how participants across the five countries categorised and made sense of the various nutrition and health claims presented in this study, we propose a typology based on three key dimensions:


A feature of EndNote 20 is the ability to categorise references in the Microsoft word bibliography. This allows for sub-headings to be included in bibliographies, as in the example below. This is useful for AGLC4 citation style, for example. 041b061a72


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