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Best-Worst Scaling (BWS) has the potential to improve menu design by determining the most and least preferred combat ration pack (CRP) items by military personnel. However, individual differences among large groups means that a ration pack design based on the average of preferences may leave many individuals dissatisfied. This paper extends the existing literature by segmenting Australian military personnel based on the preferences for specific ration pack items. Military personnel (n = 300) provided BWS data via an online survey providing product preferences for 21 items. Participants were randomly allocated to one of four different military scenarios developed and refined by Australian Defence Force (ADF) personnel, ensuring consistency with typical operational and training exercises. The four scenarios were presented with two environment variations (hot/dry or hot/humid) and two resupply variations (daily or resupply on day four and day seven) creating four survey sub-groups. Two-step cluster analysis revealed three military segments (Snackers, Balanced eaters, and Mindful eaters) with distinct CRP item preferences across the larger military group. This study provides insights of the feasibility and usefulness of segmenting military personnel based on ration pack item preferences. This approach could streamline the process for decision makers responsible for designing and developing ration packs, and ultimately increase consumption so that personnel obtain essential nutrition to support their performance. [ABSTRACT FROM AUTHOR]

Eating at a faster speed is positively correlated with having a higher BMI. Modifying eating speed may offer a treatment opportunity for those with overweight and obesity. This review sought to understand the feasibility, acceptability, and benefit to using eating speed interventions in paediatric clinical weight-management settings. The PICO Framework was used. Clinical studies of eating speed interventions as a treatment for paediatric patients with overweight or obesity were included. No limits to search date were implemented. A systematic search of MEDLINE, PsychINFO and EMBASE via OVID, Web of Science and JBI, Database of systematic reviews and Implementation reports, along with trial registers NICE, ClinicalTrials.gov and Cochrane Central Register of Controlled Trials was conducted. Two authors were responsible for screening, extraction, and evaluation of the risk of bias. Fifteen papers reporting twelve interventions addressing eating-speed were identified, involving a total of 486 active participants (range 7-297). Study design was weak with only one full RCT and there were some concerns over quality and risk of bias (Cochrane RoB 2.0). Limited sample sizes and different measured outcomes did not allow powered evaluations of effect for all outcomes. There is some indication, overall, that addressing eating speed has the potential to be a beneficial adjunct to clinical obesity treatment, although the pooled effect estimate did not demonstrate a difference in BMISDS status following eating speed interventions compared to control [pooled mean difference (0.04, 95% CI -0.39 to 0.46, N = 3)]. Developments to improve the engagement to, and acceptability of, interventions are required, alongside rigorous high-quality trials to evaluate effectiveness. [ABSTRACT FROM AUTHOR]

Evidence demonstrates that participation in regular physical activity (PA) reduces the risk of morbidity and mortality. However, current PA guidelines are focused on weekly accumulation of 150 min of moderate intensity PA as a threshold. Although recent developments of this guidance have discussed the merits of short bouts of physical activity, guidance that sets large behavioural goals for PA has not been successful in supporting the public to become sufficiently physically active and a 'one-size fits all' approach to PA guidelines may not be optimal. A complementary 'whole day' approach to PA promotion (i.e. incorporating PA throughout the day) that could motivate the population to be more physically active, is a concept we have called 'Snacktivity™'. The Snacktivity™ approach promotes small or 'bite' size bouts (e.g. 2-5 min) of PA accumulated throughout the whole day. Snacktivity™ is consistent with the small change approach which suggest that behaviour change and habit formation are best achieved through gradual building of task self-efficacy, celebrating small successes. Snacktivity™ also offers opportunities to "piggyback" on to existing behaviours/habits, using them as prompts for Snacktivity™. Moreover, small behaviour changes are easier to initiate and maintain than larger ones. A plethora of evidence supports the hypothesis that Snacktivity may be a more acceptable and effective way to help the public reach, or exceed current PA guidelines. This paper outlines the evidence to support the Snacktivity™ approach and the mechanisms by which it may increase population levels of physical activity. Future research directions for Snacktivity™ are also outlined. [ABSTRACT FROM AUTHOR]

• An efficient and robust fitness action analysis framework to analyze fitness actions with computer vision. • A 2D/3D Fitness-28 dataset with 7530 action videos, which can be used to evaluate action analysis algorithms. • An efficient 2D/3D skeleton feature encoding method for action analysis, which is robust to spatio-temporal variation. • A local-global geometrical registration strategy for action assessment, which can evaluate the action performance effectively. Human action analysis has been an active research area in computer vision, and has many useful applications such as human computer interaction. Most of the state-of-the-art approaches of human action analysis are data-driven and focus on general action recognition. In this paper, we aim to analyze fitness actions with skeleton sequences and propose an efficient and robust fitness action analysis framework. Firstly, fitness actions from 15 subjects are captured and built to a fitness action dataset (Fitness-28). Secondly, skeleton information is extracted and made alignment with a simplified human skeleton model. Thirdly, the aligned skeleton information is transformed to an uniform human center coordinate system with the proposed spatial–temporal skeleton encoding method. Finally, the action classifier and local–global geometrical registration strategy are constructed to analyze the fitness actions. Experimental results demonstrate that our method can effectively assess fitness action, and have a good performance on artificial intelligence fitness system. [ABSTRACT FROM AUTHOR]