Background: Schools do not typically implement food labelling in their canteens, therefore young people may not be given nutrition information on which to make their food choices. One way of expressing the energy/calorie content of foods is to provide this information in the form of physical activity calorie equivalent (PACE) food labelling, which may help to contextualise the energy content of food/drinks to young people in a simple and understandable way. The study aimed to assess the usefulness of implementing PACE labelling in school canteens and to conduct a process evaluation of using this type of food labelling with young people.
Methods: A parallel two-armed cluster RCT to evaluate a PACE labelling intervention in secondary schools (typically, adolescents aged 11 and above) in England was conducted. Schools were randomised on a 2:1 basis to display PACE labelling by cakes/sweet biscuits in canteens or to continue with usual practice (comparator) for up to six weeks. There was a baseline period of no PACE labelling for a minimum of four weeks in all schools. Anonymised purchase data were provided by schools and analysed both descriptively and using analysis of covariance.
Results: Eighteen schools in England were randomised and 11 participated (6 intervention and 5 comparators). Analyses are based on ~ 99,000 purchase transactions of cakes and biscuits from participating schools. There was a reduction in cake/biscuit purchases in intervention schools versus comparators of ~ 11 items per week per 100 students at follow-up (adjusted mean difference = -0.112, 95% CI [-0.179 to -0.045], p = 0.005). Intervention schools did not report major difficulties with the implementation of PACE labelling.
Conclusions: PACE labelling appeared to reduce cakes/biscuit purchases by a small amount and may be a useful approach to reducing the purchase of discretionary foods in young people in the school environment. The implementation of PACE labelling appeared feasible for some schools, but other schools had reservations about the adverse effects this type of labelling may have on the well-being of students.
Trial Registration: Registered on ClinicalTrials.gov on 18th November 2022. NCT05623618, https://clinicaltrials.gov/study/NCT05623618 .
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http://dx.doi.org/10.1186/s12966-025-01710-1 | DOI Listing |
Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extrinsic physical characteristics such as locomotion and pose can be accurately tracked in real-time, reliably capturing mental states is more challenging. Quantitative psychology allows considering more intrinsic features like emotion, attention, or cognitive load.
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Department of Neurological Surgery, Northwestern University, Chicago, IL.
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Centre for Lifestyle Medicine and Behaviour, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, LE11 3TU, UK.
Background: Schools do not typically implement food labelling in their canteens, therefore young people may not be given nutrition information on which to make their food choices. One way of expressing the energy/calorie content of foods is to provide this information in the form of physical activity calorie equivalent (PACE) food labelling, which may help to contextualise the energy content of food/drinks to young people in a simple and understandable way. The study aimed to assess the usefulness of implementing PACE labelling in school canteens and to conduct a process evaluation of using this type of food labelling with young people.
View Article and Find Full Text PDFNeural Netw
June 2025
School of Computer Science, China University of Geosciences, Wuhan, 430078, China; State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430078, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430078, China. Electronic address:
Semi-supervised Relation Extraction methods play an important role in extracting relationships from unstructured text, which can leverage both labeled and unlabeled data to improve extraction accuracy. However, these methods are grounded under the closed-world assumption, in which the relationship types of labeled and unlabeled data belong to the same closed set, that are not applicable to real-world scenarios that involve novel relationships. To address this issue, this paper proposes an open-world semi-supervised relation extraction task and a novel method, Seen relation Identification and Novel relation Discovery (SIND), to extract both seen and novel relations simultaneously.
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Behaviour and Health Research Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
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