ORCID Profile
0000-0003-4406-3970
Current Organisation
Deakin University
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Publisher: Cambridge University Press (CUP)
Date: 30-08-2022
DOI: 10.1017/S0007114522002756
Abstract: The Thumbs food classification system was developed to assist remote Australian communities to identify food healthiness. This study aimed to assess: (1) the Thumbs system’s alignment to two other food classification systems, the Health Star Rating (HSR) and the Northern Territory School Canteens Guidelines (NTSCG) (2) its accuracy in classifying ‘unhealthy’ (contributing to discretionary energy and added sugars) and ‘healthy’ products against HSR and NTSCG (3) areas for optimisation. Food and beverage products sold between 05/2018 and 05/2019 in fifty-one remote stores were classified in each system. System alignment was assessed by cross-tabulating percentages of products, discretionary energy and added sugars sold assigned to the same healthiness levels across the systems. The system/s capturing the highest percentage of discretionary energy and added sugars sold in ‘unhealthy’ products and the lowest levels in ‘healthy’ products were considered the best performing. Cohen’s κ was used to assess agreement between the Thumbs system and the NTSCG for classifying products as healthy. The Thumbs system classified product healthiness in line with the HSR and NTSCG, with Cohen’s κ showing moderate agreement between the Thumbs system and the NTSCG ( κ = 0·60). The Thumbs system captured the most discretionary energy sold (92·2 %) and added sugar sold (90·6 %) in unhealthy products and the least discretionary energy sold (0 %) in healthy products. Modifications to optimise the Thumbs system include aligning several food categories to the NTSCG criteria and addressing core/discretionary classification discrepancies of fruit juice/drinks. The Thumbs system offers a classification algorithm that could strengthen the HSR system.
Publisher: MDPI AG
Date: 12-11-2021
Abstract: Globally, there is increasing interest in monitoring actions to create healthy, equitable and environmentally sustainable food environments. Currently, there is a lack of detailed tools for monitoring and benchmarking university food environments. This study aimed to develop the University Food Environment Assessment (Uni-Food) tool and process to benchmark the healthiness, equity, and environmental sustainability of food environments in tertiary education settings, and pilot test its implementation in three Australian universities in 2021. The Uni-Food tool development was informed by a review of the literature and input from an expert advisory panel. It comprises three components: (1) university systems and governance, (2) c us facilities and environments, and (3) food retail outlets. The process for implementing the tool is designed for universities to self-assess the extent to which they have implemented recommended practice in 68 indicators, across 16 domains, weighted based on their relative importance. The pilot implementation of the tool identified moderate ersity in food environments across universities and highlighted several opportunities for improvements at each institution. The assessment process was found to be reliable, with assessors rating the tool as easy to use, requiring minimal resources. Broad application of the tool has the potential to increase accountability and guide best practice in tertiary education and other complex institutional settings.
Publisher: Wiley
Date: 12-07-2021
DOI: 10.1111/OBR.13311
Abstract: Providing simple information that identifies healthier/less healthy products at the point‐of‐sale has been increasingly recognized as a potential strategy for improving population diet. This review evaluated the effect on healthiness of food purchasing/intake of interventions that identify specific products as healthier/less healthy at the point‐of‐sale in food retail settings. Five databases were searched for peer‐reviewed randomized controlled or quasi‐experimental trials published 2000–2020. Effects on primary outcomes of the 26 eligible studies (322 stores and 19,002 participants) were positive ( n = 14), promising (effective under certain conditions n = 3), mixed (different effect across treatment arms/outcomes n = 4), null ( n = 3), negative ( n = 1), or unclear ( n = 1). Shelf‐label studies (three studies of two rating systems across all products) were positive. Technology‐delivered (mobile applications odcast/kiosk) interventions were positive ( n = 3/5) or promising/mixed ( n = 2/5). In‐store displays ( n = 16) had mixed effectiveness. Interventions provided information on targeted healthier products only ( n = 17), unhealthy products only ( n = 1), both healthy and unhealthy ( n = 2), and across all products ( n = 5). No patterns were found between behavior change technique used and effectiveness. Study quality was mixed. These findings indicate that point‐of‐sale interventions identifying healthy/unhealthy options can lead to healthier customer purchasing behavior, particularly those delivered using shelf‐labels or technology. Further research on discouraging unhealthy foods is needed.
No related grants have been discovered for Jasmine Chan.