Digital chameleons of the food environment: an exploratory analysis of the shapeshifting nature of dark kitchens in Australia and their regulatory challenges

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Moore, Amelia A. Lake, Alice A. Gibson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9410382/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background/Aim: Dark kitchens are food service models that do not have a physical space for patrons and mostly sell through online food delivery services. Due to the low overhead costs and lack of customer facing premises, dark kitchens are transient systems with ability to open and close quickly and this is potentially facilitated by use of generative artificial intelligence (AI) for branding. This study aimed to characterise dark kitchens in Australia, assessing longevity of businesses, the nutritional quality of menu items; as well as identify and characterise use of AI in generation of menu content. Methods: Dark kitchens in the Greater Sydney Region of New South Wales, Australia, were classified as outlets that had multiple brand names operating from one address on UberEats. Additional information was web-scraped including the opening status of the outlet, menu item names, descriptions, price and images. If dark kitchens had appeared closed on UberEats, we manually searched their brand names on DoorDash, to obtain additional data. Data on dark kitchens were summarised by descriptive analyses including percentages of cuisines, proportion of dark kitchens open by day and hour, frequency of menu items classified into food group categories. Menu item images were analysed for their potential use of AI by assessing distinct features that appear artificial or altered (e.g. image URL containing “altered” or “enhanced”). Results: Over a third of dark kitchen brands (n = 98, 33.1%) had temporarily or permanently closed since the initial dataset was web-scraped in 2023. We subsequently identified 197 unique dark kitchen brands still in operation. Over 70% of total menu items were comprised of unhealthy foods including fries, fried chicken and desserts. Additionally, over 57% (n = 1939/3376) of menu item images contained “enhanced image” or “image touchup” in their URLs, indicating enhancement or alteration of food images using AI. Conclusion: Dark kitchens present a new challenge to digital food environments. Data from this study suggests these “kitchen chameleons” can quickly change menu offerings and branding, primarily selling unhealthy foods, with minimal resources and assistance of AI. Adaptive monitoring is needed to inform and develop policy to minimise their potential risks to public health. Figures Figure 1 INTRODUCTION Non-communicable diseases (NCDs) such as cardiovascular disease, cancers and diabetes are responsible for 41 million deaths each year worldwide ( 1 ) . Unhealthy diets high in excess salt, saturated fats and sugars are a major contributor to NCDs ( 1 ) . Western diets, specifically, are typically associated with being low in fruits and vegetables, but high in fat, sodium, sugars with large portion sizes and high calories ( 2 ) . These dietary patterns are common in ‘Western countries’, defined as nations primarily of European cultural origin, including Western Europe, United States, Canada, Australia, New Zealand and United Kingdom (UK). In these countries and worldwide, a large proportion of diets are now comprised of out-of-home foods ( 3 ) , which have been well-documented in attributing to poorer diet quality characterised by higher energy, total and saturated fats, sugars and salts ( 4 ) . Research on the commercial determinants of health shows large transnational food companies are accountable for escalating rates of unhealthy diets and associated ill health outcomes, while also exacerbating climate impacts and social inequities ( 5 ) . Online food delivery services such as UberEats or JustEat, are major transnational companies and are a global phenomenon, operating in over 45 countries and more than 6000 cities worldwide. These platforms are further facilitating access to unhealthy out-of-home foods and pose significant risks to healthy and sustainable cities. Online food delivery services rely on third-party couriers to transport ready-to-eat meals, foods and drinks, from restaurants to consumers, and have experienced significant growth, since COVID-19 ( 6 , 7 ) ; outpacing any regulatory efforts to minimise its risks to population health ( 8 , 9 ) . They form new ‘digital food environments’ – defined by the World Health Organisation as ‘the online settings through which flows of services and information that influence people’s food and nutrition choices and behaviour are directed’ ( 10 ) . This digital food space is a new frontier in food environment research as public health professionals work to create healthy environments for their populations ( 11 ) . Online food delivery services may be changing restaurant geography through reducing food retailers’ reliance on physical space and location for business. This is critical in the era of rapid urbanisation, where cities are changing and constantly redeveloping to accommodate larger populations. Real estate, built on valuable land, and physical spaces are becoming increasingly unaffordable in many cities around the world. The combination of expensive physical space and proliferation of online food delivery services have therefore fostered growth of ‘dark kitchens’ in urban areas with high population density ( 12 ) . Dark kitchens are food service models that do not have a physical space for patrons and mostly sell ready-to-eat meals cooked to order through online food delivery services ( 13 , 14 ) . They are considered an attractive economic model for food business owners, taking into account the average cost of opening standard full-service restaurants is nearly triple the amount of a takeout-and-delivery only restaurant ( 15 ) . Food business owners cite affordability due to low overheads, flexibility and convenience as key reasons for operating dark kitchens ( 16 ) . By sacrificing consumer-facing real estate, dark kitchens capitalise on economies of scale and speed in food delivery by potentially running multiple “brands” out of the same kitchen ( 17 ) . Research shows that nearly every large online food delivery platform (e.g., Deliveroo, UberEats, DoorDash) has opened their own dark kitchen, providing restaurant partners with industrial kitchen space ( 17 ) . Dark kitchens threaten to significantly change our physical food environments where it is predicted that by 2030, dark kitchen models will make up 50% of all food services worldwide ( 18 ) . For example, in China, with one of the largest and strongest online food delivery markets globally, the rise of dark kitchens is accelerating, and reports suggest that this has started to change the look of neighbourhoods ( 19 ) . Streets that were formerly filled with ethnic restaurants or grocery stores are now occupied by dark kitchens and delivery-oriented coffee shops ( 19 ) . Proliferation of these dark ‘delivery-only’ kitchens, pose substantial risks to population health and wellbeing. With regards to food safety, these challenges include: resource constraints, lack of dark kitchens’ visibility, multiple trading names, insufficient guidance from regulatory body, communication difficulties between food safety authorities and dark kitchens, difficult working conditions in some dark kitchens and problems identifying where responsibility lies ( 20 ) . The relative transience and short longevity of dark kitchens as businesses, may contribute to monitoring difficulties for local food authorities and warrant further investigation. In addition, exploratory research of dark kitchens were offering fast-food (47%) or dessert (21%) options ( 21 ) . Similarly, evidence from studies in Brazil suggest that most menus from dark kitchens comprise of snacks, desserts and pizza ( 22 ) . As such, dark kitchens are offering an abundance of these foods high in fats, sugars and salts, that are strongly linked to elevating rates of obesity ( 23 ) , heart disease ( 24 ) and some cancers ( 25 ) . An issue that may be specific to dark kitchens due to their relatively lower operational costs, is the use of Artificial Intelligence (AI) generated solutions such as branding, online menu imagery and menu item descriptions. The demand for AI solutions is evidenced by the rise in start-ups such as Swipeby and Lunchbox ( 26 ) that are using AI to generate images for online menus and have targeted restaurants and delivery services. While such solutions help business owners further reduce costs for resources, time or budget, they may have implications for misleading advertising and may influence unhealthy eating behaviours with research showing that AI-generated food images are generally better received by consumers than real photos ( 27 ) . Australia is a high-income country with predominantly Western dietary patterns contributing to rising rates of obesity where food systems are rapidly evolving with the rise in online food delivery platforms and dark kitchen business models. However, to our knowledge, there is a lack of research on dark kitchens specifically in Australia, and their potential risks to population health. The aim of this study is to characterise dark kitchens in New South Wales Australia to-date, including their longevity as a business, nutritional quality of their menu offerings, opening hours, and use of artificial intelligence (AI) in generation of menu content. In addition, this study aimed to explore regulatory challenges of dark kitchens in the Australian context from a consumer law and food safety perspective. METHODS Setting The setting is the Greater Sydney Region of Sydney, Australia, the most populous city in the country. Greater Sydney’s population is over 5.3 million, nearly 20% of the total population in Australia, and is a highly multicultural region, with almost half of residents speaking a language other than English at home ( 28 ) . Data collection To identify dark kitchens, we matched online food outlets to physical food outlet locations from our previous dataset of 16,158 food outlets web-scraped in January 2023 from UberEats, a leading online food delivery app in Australia ( 29 ) . Potential dark kitchens were flagged as food outlets that had a shared address but with differing operating names (e.g. “Oven Bake” and “Lucky Rolls”). Next, we refined the dataset by removing any distinct food outlets from shopping centres, markets, malls, squares, service stations as these also share the same address. We subsequently found 1003 dark kitchen outlets with the same address on Uber Eats and yet no physical store location matched with our Google Maps dataset. From this list of dark kitchens, we further identified 295 unique dark kitchen brands. Brands were identified as dark kitchens that had the same operating name across different locations. If a dark kitchen had a unique name at only one location, this was still identified as a brand. Additional data on the characteristics of dark kitchens was web-scraped using Python scripts in Jupyter Notebook. Data items included cuisine of food outlet, opening hours, rating (out of five) and number of ratings, menu item names, menu item descriptions, menu item price and any available image of menu items. We also recorded any reference to whether AI generated content was used for menus and their descriptions (e.g., terms such as ‘enhanced image’). During this process, we manually cleaned the dataset for any food outlets that were incorrectly identified as a dark kitchen- such as different food outlets inside shopping centres or other multi-level buildings. We also recorded any dark kitchens that were closed, inactive or no longer operating on UberEats but were on an alternate online food delivery platform (i.e., DoorDash). Data items were similarly collected for dark kitchens found on DoorDash. UberEats and DoorDash are considered the two market-leading online food delivery applications in Australia ( 30 ) . Data analyses Outlet and menu item analyses Web-scraped menu item data was exported as CSV files and imported into R Statistical Software for analyses. Descriptive statistics were obtained including percentages of cuisines which were self-labelled by outlets on UberEats, average ratings and rating counts and proportion of dark kitchens open by day and hour. Menu items were classified into food group categories based on the Australian Dietary Guidelines ( 31 ) by university-trained and qualified accredited practising dietitians (SJ, SRP). Image analyses Out of 8122 menu items, 6575 had an image URL. Of these, 3199 were placeholder images and were blank or generic images not representative of the menu item, leaving 3376 images to analyse. To identify the extent of AI use in menu item images, we identified image URLs with the words “enhanced image” or “image touchup”. RESULTS Characteristics We observed that of 295 unique dark kitchens identified in our dataset of outlets on UberEats web-scraped in 2023, 98 were temporarily or permanently closed which represents 33% of our original dataset. While some food outlets were no longer operational on UberEats, we found that some of these were still active on DoorDash. This resulted in 197 unique dark kitchen brands that were still operating on UberEats or DoorDash, as of August 2025 (Table 1 ). The top five cuisines of dark kitchens determined by frequencies of category keywords were: American, Burgers, Comfort Food, Fast Food and Indian Food, comprising 32.9% of all cuisines. Only 16 dark kitchens had a rating, with the median rating being 4.6 out of 5. The median number of rating counts was 66 which was determined from 56 dark kitchens. Table 1 Australian dark kitchen outlet characteristics Characteristics N Total unique dark kitchen brands identified from 2023 dataset 295 Unique dark kitchen brands in operation in 2025 197 (66.9%) Unique dark kitchen brands permanently or temporarily closed 98 (33.1%) Most popular cuisines American (8.6%) Burgers (6.7%) Comfort Food (6.4%) Fast Food (5.9%) Indian (5.0%) Median rating (out of 5) 4.6 (n = 16 distinct) Median number of ratings per dark kitchen 66 (n = 56 distinct) Franchise brands vs Independent dark kitchens We found 78 unique dark kitchen brands that appeared to be a franchise with more than one location in New South Wales. These typically had a name with a characteristic pun in the name and use of alliteration (e.g. “Friend Fries”, “Burgers with Bite”, “Burger Busters”, “Crispy Fried Chicken”, “Moonlight Masala”, “Baby Dahl”). The remaining 119 dark kitchen brands were independent or standalone stores. Most of these independent dark kitchens appeared to be former physical restaurants that have permanently closed as indicated by their Google Business listing and have shifted to being delivery-only. These types of dark kitchens offered variations or a subset of their menu that are more delivery-friendly, such as wings or fries. Opening hours As shown in Figure 1 below, the most common opening hours for dark kitchens appear to be between 6PM to 8PM on Thursdays, Fridays, Saturdays and Sundays. Between 20-30% of dark kitchens operate at midnight to early morning hours. Characteristics of menu items offered A total of 8023 menu items were analysed. As shown in Table 2 , the top ten categories of foods offered in ascending order were: fries, fried chicken, desserts, sugar-sweetened beverages, bread/pastry dishes, salad and vegetables, burgers, rice-based dishes, pizza and ‘combo’ meals. These comprised 71% of the total menu items (n = 5693/8023). Table 2 Top ten food categories of dark kitchen menu items Category N (%) Fries 890 (11.1%) Fried Chicken 663 (8.26%) Dessert 659 (8.21%) Sugar-Sweetened Beverages 652 (8.13%) Bread/Pastry 609 (7.59%) Salad and Vegetables 589 (7.29%) Burger 585 (7.34%) Rice-Based Dishes 361 (4.50%) Pizza 353 (4.40%) Combo Meals 332 (4.14%) AI generated content Menu item descriptions Dark kitchens, particularly on DoorDash, appeared to have AI generated content for menu item descriptions and potentially, menu item images. Of the 288 menu items scraped from DoorDash, 26 had a note that mentioned “Description generated by AI”, representing 9%. While dark kitchens on DoorDash displays the disclosure on use of AI in these menu item descriptions, the same was not found for dark kitchens on UberEats. In addition, over 57% (n = 1939/3376) of menu item images contained “enhanced image” or “image touch-up” in their URLs, indicating enhancement or alteration of food images. Table 3 Selection of AI-generated menu item descriptions from dark kitchens on DoorDash Menu Item AI-generated menu item description Chicken Nuggets Breaded chicken pieces, typically served with a dipping sauce. Fries Crispy golden potatoes served as a delightful accompaniment. Coleslaw Crisp shredded cabbage in a creamy, tangy dressing. Samosa Crispy pastries filled with spiced potatoes and peas, traditionally deep-fried, suitable for gatherings. Peri Peri Sauce A vibrant blend of chillies, garlic, lemon, and herbs, creating a spicy and tangy sauce ideal for dipping. DISCUSSION Globally, dark kitchens are a new challenge to our digital and physical food environments. These ‘kitchen chameleons’ are able to quickly change menu offerings and branding, with minimal resources and the assistance of AI. Over 70% of menu items comprised of unhealthy fried foods with 1 in 10 menu items comprised of fries, exacerbating offerings of unhealthy foods easily accessible via online food delivery services. There is also a lack of transparency around whether a business is a ‘dark kitchen’, which may potentially mislead consumers and leave them vulnerable to making uninformed food choices. Despite the risks which dark kitchens pose to population health and wellbeing, there is almost no existing regulation of these emerging food business models. REGULATORY CHALLENGES AND OPTIONS Kitchen chameleons: Our results have shown that dark kitchens open and close quickly with over 33% of these dark kitchen brands found ‘permanently/temporarily closed’ or ‘inactive’ in less than two years. This presents a challenge for regulators to track and monitor these businesses. As an example, a dark kitchen named ‘Puff Zone’ in Pennant Hills, Sydney is now operating as ‘Sizzling Slice’ with the same menu offerings. It is not known exactly why dark kitchens change branding so quickly, although Laheri and colleagues suggest that these businesses can easily adapt and respond to changes in consumer preferences through the flexibility offered ( 16 ) . Poor nutritional quality and food safety: Dark kitchens predominantly offer fried foods with 11% of menu items comprised of fries. This signifies a further decline in nutritional quality of menu offerings available via online food delivery services, especially in comparison to independent restaurants that are non-franchise, often local businesses that offer a culture-based cuisine. In Wang & Korai’s study of Sydney’s independent food outlets on the market leading online food delivery platform, a majority of menu items (42.3%) comprised of discretionary cereal-based mixed meals such as pizzas, burgers, pasta, wraps, sandwiches and rolls ( 31 ) . Dark kitchens on the other hand are dominated by fries and fried chicken – which are not main meals and provide to an even greater extent - excess ‘empty’ calories with minimal nutrients. High consumption of fried foods have been positively associated with increased risk of hypertension and obesity ( 32 ) . In addition to the poor nutritional quality of menu items offered, the temporal nature of dark kitchens also has serious implications for food safety. The NSW Food Authority is a state government agency that regulates food businesses to provide certainty in the safety and integrity of the NSW food supply chain. In early 2025, it was recently announced that the NSW Food Authority was working with local councils to clamp down on illegally operating dark kitchens in the state ( 33 ) . Despite this, the NSW Food Authority noted that there were challenges ‘reaching’ these businesses as they lack a public physical premise. Use of AI-generated content: It appears that online food delivery platforms themselves are encouraging the rise in dark kitchens. DoorDash specifically, has a support page for merchants wanting to open a dark kitchen ( 34 ) . Some of these terms and conditions include: i) having at least 50% differentiation in main menu items from other restaurant menus on DoorDash at the same address ii) requiring a unique header image and distinctive photos for each menu items iii) contain family-friendly language in store names, menu item names and descriptions ( 34 ) . Under such guidance from online food delivery platforms, dark kitchen business owners or operators, may use AI to assist generation of menu item images, names and descriptions. Our study showed that over 57% of menu item images and nearly 1 in 10 descriptions on DoorDash were potentially AI-generated. It is likely that AI significantly saves business owners time and resources, with research showing it is already being used by brick-and-mortar restaurants for menu personalisation and marketing ( 35 ) . The use of AI by Big Food is even more prevalent and has become increasingly scrutinised, with Coca Cola ( 36 ) and McDonald’s ( 37 ) recently under fire for their creative content generation in advertisements. However, the use of AI-generated content may be a form of misinformation and potentially violate consumer laws. In Australia, the Australian Consumer Law (ACL) prohibits misleading or deceptive conduct and false representations in trade or commerce. While there is no AI-specific provision, any AI use that is misleading would be unlawful under ACL ( 38 ) . Despite this, there is currently no general law forcing disclosure of AI generated content, meaning consumers are potentially unaware of how AI could mislead their food choices as research shows that AI images of food may look more appealing ( 27 ) . Once it was disclosed that AI was used to generate food images, a stark drop in appeal amongst consumers was noted ( 27 ) – thereby signalling the importance of a disclosure statement to ensure consumers are well-informed. Consumer trust: Consumers seem to be willing to buy food from dark kitchens although they may not know the foods are from dark kitchens ( 39 , 40 ) . A study from the UK showed that over half of participants would be willing to purchase from a dark kitchen, provided that they know they were ordering from such a business ( 41 ) . In addition, this study showed that ratings for dark kitchens were rare, with only 16 of 197 dark kitchen brands with a rating with a relatively low number of total ratings, compared to more established food outlets. Dark kitchens in Brazil also observed fewer number of user ratings for dark kitchens compared to standard restaurants ( 22 ) . Ratings can build trust with consumers and can influence how a restaurant is perceived ( 42 ) . As such, it is interesting to observe that despite fewer ratings for dark kitchens, these businesses remain viable with consumers. Limitations and future directions The transient nature of dark kitchens, with businesses frequently opening and closing, was a major limitation of this study, potentially causing the dataset to become quickly outdated. Nonetheless, this study is a useful addition to the growing literature on digital food environments; by highlighting the issue with dark kitchens being a shapeshifting business model predominantly offering unhealthy fried foods and brings attention to a critical need for adaptive monitoring. Further research is needed to identify and understand how dark kitchens operate to inform regulatory bodies such as local councils and authorities. In addition, obtaining web-scraped data from online food delivery services is proving more difficult with anti-bot detection technologies complicating this process. Anti-bot detection technologies prevent bots from accessing a system, including researchers wanting to collect large volumes of data from websites in an efficient manner. It is likely that researchers may need to look for alternative ways to obtain data from online food retailers, in future investigations of the digital food environment. Moreover, there is currently no standardised framework or method to objectively analyse the potential use of generative AI in food images. The methods applied in this study, relied on analysing the URLs of menu images. Nevertheless, identifying the issue of potential AI generated content is an important first step to monitoring and regulating their use by the dark kitchen and restaurant industry. Future studies could employ AI-detection models that can more accurately and objectively predict whether images are ‘real’ or ‘AI generated’. CONCLUSION Dark kitchens may be the future of online food delivery technologies with figures predicting that over 50% of food services will switch to a dark kitchen model by 2030. These business models are highly flexible and adaptable - incentivising owners to create menus that are predominantly fast-foods; and use AI to generate menu item images, and descriptions. This may erode consumer trust, especially if there is no transparency or disclosure of a dark kitchen’s operational status. Dark kitchens therefore present a significant risk to population diets and health, and adaptive smart monitoring is needed. The rising use of AI by dark kitchens, and the restaurant industry more broadly, warrants further attention to minimise potential misinformation. Consumer interactions in digital food environments are rapidly evolving; research and policy must keep pace. Abbreviations ACL - Australian Consumer Law AI – Artificial intelligence NCD – Non-communicable diseases NSW – New South Wales UK – United Kingdom URL – Uniform Resource Locator Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and materials: The data that support the findings of this study are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at The University of Sydney. Competing Interests: The authors declare no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions: S.J. - Conceptualisation, Methodology, Analysis, Writing - Original Draft, Writing - Review & Editing. 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The Guardian. ‘Ruined my Christmas spirit’: McDonald’s removes AI-generated ad after backlash: The Guardian; 2025 [Available from: https://www.theguardian.com/business/2025/dec/11/mcdonalds-removes-ai-generated-christmas-ad-advert-backlash. Australian Government Treasury. Review of AI and the Australian Consumer Law. Canberra, Australia: The Australian Government Treasury; 2025. Hakim MP, Dela Libera VM, Zanetta LDA, Nascimento LGP, da Cunha DT. What is a dark kitchen? A study of consumer's perceptions of deliver-only restaurants using food delivery apps in Brazil. Food Research International. 2022;161:111768. Cai R, Leung XY, Chi CG-Q. Ghost kitchens on the rise: Effects of knowledge and perceived benefit-risk on customers’ behavioral intentions. International Journal of Hospitality Management. 2022;101:103110. Nield L, Martin H, Wall C, Pearce J, Rundle R, Bowles S, et al. Consumer knowledge of and engagement with traditional takeaway and dark kitchen food outlets. NIHR Open Res. 2024;4:64. Forman C, Ghose A, Wiesenfeld B. Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research. 2008;19(3):291-313. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 28 Apr, 2026 Submission checks completed at journal 27 Apr, 2026 First submitted to journal 27 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9410382","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632740975,"identity":"9d399149-bfcb-48dd-a01a-6c5c0cef589d","order_by":0,"name":"Sisi Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACZgaGAww8Egz8QLYERCiBSC2SDURrgQGDA8RqkW9nf3iAQcZC3vj8GcPbBRWHGfjZcwwYfrbhMfwwjwHIYYbbbuQYW884c5hBsueNAWMvPi3MPGC/MG67wWMmzdt2mMHgBtAWXjxa5JvZH4C02G/uPwPU8u8wgz1QC+NfPFoYDjOAHZa4gSEHqKUByJXIMWDGZwvYLwk8EskzbqQVW/McS+eROPOs4LDMOTwO6z/++MPHnjrb/v7DG2/z1FjL8bcnb3z4pgyPw0AgsQdEchgAiWYeEPMAAQ1A8ANEsD8AEnWEFY+CUTAKRsGIAwB9okwbu/VmtwAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Sisi","middleName":"","lastName":"Jia","suffix":""},{"id":632740976,"identity":"ea975777-197b-4fae-bcdc-b5b853515e9b","order_by":1,"name":"Heather Brown","email":"","orcid":"","institution":"Lancaster University","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"Brown","suffix":""},{"id":632740977,"identity":"c823133d-441d-4cc2-93d8-78392d5c0cb2","order_by":2,"name":"Helen J. Moore","email":"","orcid":"","institution":"Teesside University","correspondingAuthor":false,"prefix":"","firstName":"Helen","middleName":"J.","lastName":"Moore","suffix":""},{"id":632740978,"identity":"4a162605-b7de-4d19-9838-d11a54778193","order_by":3,"name":"Amelia A. Lake","email":"","orcid":"","institution":"Fuse – The Centre for Translational Research in Public Health","correspondingAuthor":false,"prefix":"","firstName":"Amelia","middleName":"A.","lastName":"Lake","suffix":""},{"id":632740979,"identity":"d8a89dc7-8168-47f6-9b38-c2f3470113c3","order_by":4,"name":"Alice A. Gibson","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Alice","middleName":"A.","lastName":"Gibson","suffix":""},{"id":632740980,"identity":"b9535d16-8c65-457c-85c5-afa6b6480bb1","order_by":5,"name":"Stephanie R. Partridge","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"R.","lastName":"Partridge","suffix":""}],"badges":[],"createdAt":"2026-04-14 04:24:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9410382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9410382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108969512,"identity":"829f20cf-7ee8-442b-a589-4f5b91d1576f","added_by":"auto","created_at":"2026-05-11 10:11:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63063,"visible":true,"origin":"","legend":"\u003cp\u003eHeat grid of the proportion of dark kitchens in Sydney, Australia open by day and hour. Yellow indicates 10-20%, orange indicates 20% - 40%, light purple indicates 40-60% and dark purple represents over 60% of dark kitchens.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9410382/v1/fd4de8c01a0ad42956d55753.png"},{"id":108970135,"identity":"60c0eda9-821a-403c-a8a2-4cd530157c7c","added_by":"auto","created_at":"2026-05-11 10:14:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":331140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9410382/v1/b0888c2b-4b53-4c1a-ab2b-09b1e3b3c2aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital chameleons of the food environment: an exploratory analysis of the shapeshifting nature of dark kitchens in Australia and their regulatory challenges","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNon-communicable diseases (NCDs) such as cardiovascular disease, cancers and diabetes are responsible for 41\u0026nbsp;million deaths each year worldwide\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. Unhealthy diets high in excess salt, saturated fats and sugars are a major contributor to NCDs\u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e. Western diets, specifically, are typically associated with being low in fruits and vegetables, but high in fat, sodium, sugars with large portion sizes and high calories\u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e. These dietary patterns are common in \u0026lsquo;Western countries\u0026rsquo;, defined as nations primarily of European cultural origin, including Western Europe, United States, Canada, Australia, New Zealand and United Kingdom (UK). In these countries and worldwide, a large proportion of diets are now comprised of out-of-home foods\u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e, which have been well-documented in attributing to poorer diet quality characterised by higher energy, total and saturated fats, sugars and salts\u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch on the commercial determinants of health shows large transnational food companies are accountable for escalating rates of unhealthy diets and associated ill health outcomes, while also exacerbating climate impacts and social inequities\u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e. Online food delivery services such as UberEats or JustEat, are major transnational companies and are a global phenomenon, operating in over 45 countries and more than 6000 cities worldwide. These platforms are further facilitating access to unhealthy out-of-home foods and pose significant risks to healthy and sustainable cities. Online food delivery services rely on third-party couriers to transport ready-to-eat meals, foods and drinks, from restaurants to consumers, and have experienced significant growth, since COVID-19\u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e; outpacing any regulatory efforts to minimise its risks to population health \u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e. They form new \u0026lsquo;digital food environments\u0026rsquo; \u0026ndash; defined by the World Health Organisation as \u0026lsquo;the online settings through which flows of services and information that influence people\u0026rsquo;s food and nutrition choices and behaviour are directed\u0026rsquo;\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. This digital food space is a new frontier in food environment research as public health professionals work to create healthy environments for their populations\u003csup\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOnline food delivery services may be changing restaurant geography through reducing food retailers\u0026rsquo; reliance on physical space and location for business. This is critical in the era of rapid urbanisation, where cities are changing and constantly redeveloping to accommodate larger populations. Real estate, built on valuable land, and physical spaces are becoming increasingly unaffordable in many cities around the world. The combination of expensive physical space and proliferation of online food delivery services have therefore fostered growth of \u0026lsquo;dark kitchens\u0026rsquo; in urban areas with high population density\u003csup\u003e(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDark kitchens are food service models that do not have a physical space for patrons and mostly sell ready-to-eat meals cooked to order through online food delivery services\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e. They are considered an attractive economic model for food business owners, taking into account the average cost of opening standard full-service restaurants is nearly triple the amount of a takeout-and-delivery only restaurant\u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e. Food business owners cite affordability due to low overheads, flexibility and convenience as key reasons for operating dark kitchens\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e. By sacrificing consumer-facing real estate, dark kitchens capitalise on economies of scale and speed in food delivery by potentially running multiple \u0026ldquo;brands\u0026rdquo; out of the same kitchen\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e. Research shows that nearly every large online food delivery platform (e.g., Deliveroo, UberEats, DoorDash) has opened their own dark kitchen, providing restaurant partners with industrial kitchen space\u003csup\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDark kitchens threaten to significantly change our physical food environments where it is predicted that by 2030, dark kitchen models will make up 50% of all food services worldwide\u003csup\u003e(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/sup\u003e. For example, in China, with one of the largest and strongest online food delivery markets globally, the rise of dark kitchens is accelerating, and reports suggest that this has started to change the look of neighbourhoods\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e. Streets that were formerly filled with ethnic restaurants or grocery stores are now occupied by dark kitchens and delivery-oriented coffee shops \u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProliferation of these dark \u0026lsquo;delivery-only\u0026rsquo; kitchens, pose substantial risks to population health and wellbeing. With regards to food safety, these challenges include: resource constraints, lack of dark kitchens\u0026rsquo; visibility, multiple trading names, insufficient guidance from regulatory body, communication difficulties between food safety authorities and dark kitchens, difficult working conditions in some dark kitchens and problems identifying where responsibility lies\u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/sup\u003e. The relative transience and short longevity of dark kitchens as businesses, may contribute to monitoring difficulties for local food authorities and warrant further investigation. In addition, exploratory research of dark kitchens were offering fast-food (47%) or dessert (21%) options\u003csup\u003e(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e. Similarly, evidence from studies in Brazil suggest that most menus from dark kitchens comprise of snacks, desserts and pizza\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. As such, dark kitchens are offering an abundance of these foods high in fats, sugars and salts, that are strongly linked to elevating rates of obesity\u003csup\u003e(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e, heart disease\u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/sup\u003e and some cancers\u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn issue that may be specific to dark kitchens due to their relatively lower operational costs, is the use of Artificial Intelligence (AI) generated solutions such as branding, online menu imagery and menu item descriptions. The demand for AI solutions is evidenced by the rise in start-ups such as Swipeby and Lunchbox\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e that are using AI to generate images for online menus and have targeted restaurants and delivery services. While such solutions help business owners further reduce costs for resources, time or budget, they may have implications for misleading advertising and may influence unhealthy eating behaviours with research showing that AI-generated food images are generally better received by consumers than real photos\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAustralia is a high-income country with predominantly Western dietary patterns contributing to rising rates of obesity where food systems are rapidly evolving with the rise in online food delivery platforms and dark kitchen business models. However, to our knowledge, there is a lack of research on dark kitchens specifically in Australia, and their potential risks to population health. The aim of this study is to characterise dark kitchens in New South Wales Australia to-date, including their longevity as a business, nutritional quality of their menu offerings, opening hours, and use of artificial intelligence (AI) in generation of menu content. In addition, this study aimed to explore regulatory challenges of dark kitchens in the Australian context from a consumer law and food safety perspective.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThe setting is the Greater Sydney Region of Sydney, Australia, the most populous city in the country. Greater Sydney\u0026rsquo;s population is over 5.3\u0026nbsp;million, nearly 20% of the total population in Australia, and is a highly multicultural region, with almost half of residents speaking a language other than English at home\u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eTo identify dark kitchens, we matched online food outlets to physical food outlet locations from our previous dataset of 16,158 food outlets web-scraped in January 2023 from UberEats, a leading online food delivery app in Australia \u003csup\u003e(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e. Potential dark kitchens were flagged as food outlets that had a shared address but with differing operating names (e.g. \u0026ldquo;Oven Bake\u0026rdquo; and \u0026ldquo;Lucky Rolls\u0026rdquo;). Next, we refined the dataset by removing any distinct food outlets from shopping centres, markets, malls, squares, service stations as these also share the same address. We subsequently found 1003 dark kitchen outlets with the same address on Uber Eats and yet no physical store location matched with our Google Maps dataset.\u003c/p\u003e \u003cp\u003eFrom this list of dark kitchens, we further identified 295 unique dark kitchen \u003cb\u003ebrands.\u003c/b\u003e Brands were identified as dark kitchens that had the same operating name across different locations. If a dark kitchen had a unique name at only one location, this was still identified as a brand. Additional data on the characteristics of dark kitchens was web-scraped using Python scripts in Jupyter Notebook. Data items included cuisine of food outlet, opening hours, rating (out of five) and number of ratings, menu item names, menu item descriptions, menu item price and any available image of menu items. We also recorded any reference to whether AI generated content was used for menus and their descriptions (e.g., terms such as \u0026lsquo;enhanced image\u0026rsquo;). During this process, we manually cleaned the dataset for any food outlets that were incorrectly identified as a dark kitchen- such as different food outlets inside shopping centres or other multi-level buildings. We also recorded any dark kitchens that were closed, inactive or no longer operating on UberEats but were on an alternate online food delivery platform (i.e., DoorDash). Data items were similarly collected for dark kitchens found on DoorDash. UberEats and DoorDash are considered the two market-leading online food delivery applications in Australia\u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eData analyses\u003c/h3\u003e\n\u003cp\u003eOutlet and menu item analyses\u003c/p\u003e \u003cp\u003eWeb-scraped menu item data was exported as CSV files and imported into R Statistical Software for analyses. Descriptive statistics were obtained including percentages of cuisines which were self-labelled by outlets on UberEats, average ratings and rating counts and proportion of dark kitchens open by day and hour. Menu items were classified into food group categories based on the Australian Dietary Guidelines\u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e by university-trained and qualified accredited practising dietitians (SJ, SRP).\u003c/p\u003e \u003cp\u003eImage analyses\u003c/p\u003e \u003cp\u003eOut of 8122 menu items, 6575 had an image URL. Of these, 3199 were placeholder images and were blank or generic images not representative of the menu item, leaving 3376 images to analyse. To identify the extent of AI use in menu item images, we identified image URLs with the words \u0026ldquo;enhanced image\u0026rdquo; or \u0026ldquo;image touchup\u0026rdquo;.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics\u003c/h2\u003e \u003cp\u003eWe observed that of 295 unique dark kitchens identified in our dataset of outlets on UberEats web-scraped in 2023, 98 were temporarily or permanently closed which represents 33% of our original dataset. While some food outlets were no longer operational on UberEats, we found that some of these were still active on DoorDash. This resulted in 197 unique dark kitchen brands that were still operating on UberEats or DoorDash, as of August 2025 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe top five cuisines of dark kitchens determined by frequencies of category keywords were: American, Burgers, Comfort Food, Fast Food and Indian Food, comprising 32.9% of all cuisines. Only 16 dark kitchens had a rating, with the median rating being 4.6 out of 5. The median number of rating counts was 66 which was determined from 56 dark kitchens.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAustralian dark kitchen outlet characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal unique dark kitchen brands identified from 2023 dataset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUnique dark kitchen brands in operation in 2025\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (66.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUnique dark kitchen brands permanently or temporarily closed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMost popular cuisines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmerican (8.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurgers (6.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComfort Food (6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFast Food (5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndian (5.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian rating (out of 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (n\u0026thinsp;=\u0026thinsp;16 distinct)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian number of ratings per dark kitchen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (n\u0026thinsp;=\u0026thinsp;56 distinct)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFranchise brands vs Independent dark kitchens\u003c/h2\u003e \u003cp\u003eWe found 78 unique dark kitchen brands that appeared to be a franchise with more than one location in New South Wales. These typically had a name with a characteristic pun in the name and use of alliteration (e.g. \u0026ldquo;Friend Fries\u0026rdquo;, \u0026ldquo;Burgers with Bite\u0026rdquo;, \u0026ldquo;Burger Busters\u0026rdquo;, \u0026ldquo;Crispy Fried Chicken\u0026rdquo;, \u0026ldquo;Moonlight Masala\u0026rdquo;, \u0026ldquo;Baby Dahl\u0026rdquo;).\u003c/p\u003e \u003cp\u003eThe remaining 119 dark kitchen brands were independent or standalone stores.\u003c/p\u003e \u003cp\u003eMost of these independent dark kitchens appeared to be former physical restaurants that have permanently closed as indicated by their Google Business listing and have shifted to being delivery-only. These types of dark kitchens offered variations or a subset of their menu that are more delivery-friendly, such as wings or fries.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOpening hours\u003c/h3\u003e\n\u003cp\u003eAs shown in Figure 1 below, the most common opening hours for dark kitchens appear to be between 6PM to 8PM on Thursdays, Fridays, Saturdays and Sundays. Between 20-30% of dark kitchens operate at midnight to early morning hours.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCharacteristics of menu items offered\u003c/h3\u003e\n\u003cp\u003eA total of 8023 menu items were analysed. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the top ten categories of foods offered in ascending order were: fries, fried chicken, desserts, sugar-sweetened beverages, bread/pastry dishes, salad and vegetables, burgers, rice-based dishes, pizza and \u0026lsquo;combo\u0026rsquo; meals. These comprised 71% of the total menu items (n\u0026thinsp;=\u0026thinsp;5693/8023).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop ten food categories of dark kitchen menu items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e890 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFried Chicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e663 (8.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDessert\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e659 (8.21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugar-Sweetened Beverages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e652 (8.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBread/Pastry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e609 (7.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalad and Vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e589 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e585 (7.34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice-Based Dishes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e361 (4.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePizza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353 (4.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombo Meals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e332 (4.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAI generated content\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eMenu item descriptions\u003c/h2\u003e \u003cp\u003eDark kitchens, particularly on DoorDash, appeared to have AI generated content for menu item descriptions and potentially, menu item images. Of the 288 menu items scraped from DoorDash, 26 had a note that mentioned \u0026ldquo;Description generated by AI\u0026rdquo;, representing 9%. While dark kitchens on DoorDash displays the disclosure on use of AI in these menu item descriptions, the same was not found for dark kitchens on UberEats. In addition, over 57% (n\u0026thinsp;=\u0026thinsp;1939/3376) of menu item images contained \u0026ldquo;enhanced image\u0026rdquo; or \u0026ldquo;image touch-up\u0026rdquo; in their URLs, indicating enhancement or alteration of food images.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection of AI-generated menu item descriptions from dark kitchens on DoorDash\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenu Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-generated menu item description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken Nuggets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreaded chicken pieces, typically served with a dipping sauce.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrispy golden potatoes served as a delightful accompaniment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColeslaw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrisp shredded cabbage in a creamy, tangy dressing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSamosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrispy pastries filled with spiced potatoes and peas, traditionally deep-fried, suitable for gatherings.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri Peri Sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA vibrant blend of chillies, garlic, lemon, and herbs, creating a spicy and tangy sauce ideal for dipping.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGlobally, dark kitchens are a new challenge to our digital and physical food environments. These \u0026lsquo;kitchen chameleons\u0026rsquo; are able to quickly change menu offerings and branding, with minimal resources and the assistance of AI. Over 70% of menu items comprised of unhealthy fried foods with 1 in 10 menu items comprised of fries, exacerbating offerings of unhealthy foods easily accessible via online food delivery services. There is also a lack of transparency around whether a business is a \u0026lsquo;dark kitchen\u0026rsquo;, which may potentially mislead consumers and leave them vulnerable to making uninformed food choices. Despite the risks which dark kitchens pose to population health and wellbeing, there is almost no existing regulation of these emerging food business models.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eREGULATORY CHALLENGES AND OPTIONS\u003c/h2\u003e \u003cp\u003eKitchen chameleons:\u003c/p\u003e \u003cp\u003eOur results have shown that dark kitchens open and close quickly with over 33% of these dark kitchen brands found \u0026lsquo;permanently/temporarily closed\u0026rsquo; or \u0026lsquo;inactive\u0026rsquo; in less than two years. This presents a challenge for regulators to track and monitor these businesses. As an example, a dark kitchen named \u0026lsquo;Puff Zone\u0026rsquo; in Pennant Hills, Sydney is now operating as \u0026lsquo;Sizzling Slice\u0026rsquo; with the same menu offerings. It is not known exactly why dark kitchens change branding so quickly, although Laheri and colleagues suggest that these businesses can easily adapt and respond to changes in consumer preferences through the flexibility offered\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePoor nutritional quality and food safety:\u003c/p\u003e \u003cp\u003eDark kitchens predominantly offer fried foods with 11% of menu items comprised of fries. This signifies a further decline in nutritional quality of menu offerings available via online food delivery services, especially in comparison to independent restaurants that are non-franchise, often local businesses that offer a culture-based cuisine. In Wang \u0026amp; Korai\u0026rsquo;s study of Sydney\u0026rsquo;s independent food outlets on the market leading online food delivery platform, a majority of menu items (42.3%) comprised of discretionary cereal-based mixed meals such as pizzas, burgers, pasta, wraps, sandwiches and rolls \u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e. Dark kitchens on the other hand are dominated by fries and fried chicken \u0026ndash; which are not main meals and provide to an even greater extent - excess \u0026lsquo;empty\u0026rsquo; calories with minimal nutrients. High consumption of fried foods have been positively associated with increased risk of hypertension and obesity\u003csup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the poor nutritional quality of menu items offered, the temporal nature of dark kitchens also has serious implications for food safety. The NSW Food Authority is a state government agency that regulates food businesses to provide certainty in the safety and integrity of the NSW food supply chain. In early 2025, it was recently announced that the NSW Food Authority was working with local councils to clamp down on illegally operating dark kitchens in the state\u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e. Despite this, the NSW Food Authority noted that there were challenges \u0026lsquo;reaching\u0026rsquo; these businesses as they lack a public physical premise.\u003c/p\u003e \u003cp\u003eUse of AI-generated content:\u003c/p\u003e \u003cp\u003eIt appears that online food delivery platforms themselves are encouraging the rise in dark kitchens. DoorDash specifically, has a support page for merchants wanting to open a dark kitchen \u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e. Some of these terms and conditions include: i) having at least 50% differentiation in main menu items from other restaurant menus on DoorDash at the same address ii) requiring a unique header image and distinctive photos for each menu items iii) contain family-friendly language in store names, menu item names and descriptions\u003csup\u003e(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnder such guidance from online food delivery platforms, dark kitchen business owners or operators, may use AI to assist generation of menu item images, names and descriptions. Our study showed that over 57% of menu item images and nearly 1 in 10 descriptions on DoorDash were potentially AI-generated. It is likely that AI significantly saves business owners time and resources, with research showing it is already being used by brick-and-mortar restaurants for menu personalisation and marketing\u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e. The use of AI by Big Food is even more prevalent and has become increasingly scrutinised, with Coca Cola \u003csup\u003e(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e and McDonald\u0026rsquo;s \u003csup\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/sup\u003e recently under fire for their creative content generation in advertisements.\u003c/p\u003e \u003cp\u003eHowever, the use of AI-generated content may be a form of misinformation and potentially violate consumer laws. In Australia, the Australian Consumer Law (ACL) prohibits misleading or deceptive conduct and false representations in trade or commerce. While there is no AI-specific provision, any AI use that is misleading would be unlawful under ACL\u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e. Despite this, there is currently no general law forcing disclosure of AI generated content, meaning consumers are potentially unaware of how AI could mislead their food choices as research shows that AI images of food may look more appealing\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e. Once it was disclosed that AI was used to generate food images, a stark drop in appeal amongst consumers was noted\u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e \u0026ndash; thereby signalling the importance of a disclosure statement to ensure consumers are well-informed.\u003c/p\u003e \u003cp\u003eConsumer trust:\u003c/p\u003e \u003cp\u003eConsumers seem to be willing to buy food from dark kitchens although they may not know the foods are from dark kitchens \u003csup\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/sup\u003e. A study from the UK showed that over half of participants would be willing to purchase from a dark kitchen, provided that they know they were ordering from such a business \u003csup\u003e(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, this study showed that ratings for dark kitchens were rare, with only 16 of 197 dark kitchen brands with a rating with a relatively low number of total ratings, compared to more established food outlets. Dark kitchens in Brazil also observed fewer number of user ratings for dark kitchens compared to standard restaurants\u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/sup\u003e. Ratings can build trust with consumers and can influence how a restaurant is perceived\u003csup\u003e(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/sup\u003e. As such, it is interesting to observe that despite fewer ratings for dark kitchens, these businesses remain viable with consumers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future directions\u003c/h2\u003e \u003cp\u003eThe transient nature of dark kitchens, with businesses frequently opening and closing, was a major limitation of this study, potentially causing the dataset to become quickly outdated. Nonetheless, this study is a useful addition to the growing literature on digital food environments; by highlighting the issue with dark kitchens being a shapeshifting business model predominantly offering unhealthy fried foods and brings attention to a critical need for adaptive monitoring. Further research is needed to identify and understand how dark kitchens operate to inform regulatory bodies such as local councils and authorities.\u003c/p\u003e \u003cp\u003eIn addition, obtaining web-scraped data from online food delivery services is proving more difficult with anti-bot detection technologies complicating this process. Anti-bot detection technologies prevent bots from accessing a system, including researchers wanting to collect large volumes of data from websites in an efficient manner. It is likely that researchers may need to look for alternative ways to obtain data from online food retailers, in future investigations of the digital food environment.\u003c/p\u003e \u003cp\u003eMoreover, there is currently no standardised framework or method to objectively analyse the potential use of generative AI in food images. The methods applied in this study, relied on analysing the URLs of menu images. Nevertheless, identifying the issue of potential AI generated content is an important first step to monitoring and regulating their use by the dark kitchen and restaurant industry. Future studies could employ AI-detection models that can more accurately and objectively predict whether images are \u0026lsquo;real\u0026rsquo; or \u0026lsquo;AI generated\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eDark kitchens may be the future of online food delivery technologies with figures predicting that over 50% of food services will switch to a dark kitchen model by 2030. These business models are highly flexible and adaptable - incentivising owners to create menus that are predominantly fast-foods; and use AI to generate menu item images, and descriptions. This may erode consumer trust, especially if there is no transparency or disclosure of a dark kitchen\u0026rsquo;s operational status. Dark kitchens therefore present a significant risk to population diets and health, and adaptive smart monitoring is needed. The rising use of AI by dark kitchens, and the restaurant industry more broadly, warrants further attention to minimise potential misinformation. Consumer interactions in digital food environments are rapidly evolving; research and policy must keep pace.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACL - Australian Consumer Law\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAI \u0026ndash; Artificial intelligence\u003c/p\u003e\n\u003cp\u003eNCD \u0026ndash; Non-communicable diseases\u003c/p\u003e\n\u003cp\u003eNSW \u0026ndash; New South Wales\u003c/p\u003e\n\u003cp\u003eUK \u0026ndash; United Kingdom\u003c/p\u003e\n\u003cp\u003eURL \u0026ndash; Uniform Resource Locator\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. Data are located in controlled access data storage at The University of Sydney.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eS.J. - Conceptualisation, Methodology, Analysis, Writing - Original Draft, Writing - Review \u0026amp; Editing. H.B. - Methodology, Writing - Review \u0026amp; Editing. H.J.M, A.A.L and A.A.G. - Writing - Review \u0026amp; Editing. S.R.P - Supervision, Writing - Review \u0026amp; Editing. \u0026nbsp;All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePan American Health Organization. Noncommunicable Diseases. PAHO; 2025.\u003c/li\u003e\n\u003cli\u003eCordain L, Eaton SB, Sebastian A, Mann N, Lindeberg S, Watkins BA, et al. Origins and evolution of the Western diet: health implications for the 21st century\u0026lt;sup\u0026gt;1,2\u0026lt;/sup\u0026gt;. The American Journal of Clinical Nutrition. 2005;81(2):341-54.\u003c/li\u003e\n\u003cli\u003eLandais E, Miotto-Plessis M, Bene C, Maitre d\u0026rsquo;Hotel E, Truong MT, Som\u0026eacute; JW, et al. Consumption of food away from home in low- and middle-income countries: a systematic scoping review. 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Food Quality and Preference. 2024;116:105149.\u003c/li\u003e\n\u003cli\u003eJia SS, Gibson AA, Ding D, Allman-Farinelli M, Phongsavan P, Redfern J, et al. Perspective: Are Online Food Delivery Services Emerging as Another Obstacle to Achieving the 2030 United Nations Sustainable Development Goals? Frontiers in Nutrition. 2022;Volume 9 - 2022.\u003c/li\u003e\n\u003cli\u003eJia SS, Luo X, Gibson AA, Partridge SR. Developing the DIGIFOOD Dashboard to Monitor the Digitalization of Local Food Environments: Interdisciplinary Approach. JMIR Public Health Surveill. 2024;10:e59924.\u003c/li\u003e\n\u003cli\u003eAustralian Government. Australia Moves to Establish Minimum Pay and Protections for Food Delivery Drivers Canberra, ACT: Australian Government Anti-Slavery Commissioner; 2025 [Available from: https://www.antislaverycommissioner.gov.au/news/australia-moves-establish-minimum-pay-and-protections-food-delivery-drivers.\u003c/li\u003e\n\u003cli\u003eWang C, Korai A, Jia SS, Allman-Farinelli M, Chan V, Roy R, et al. Hunger for Home Delivery: Cross-Sectional Analysis of the Nutritional Quality of Complete Menus on an Online Food Delivery Platform in Australia. Nutrients [Internet]. 2021; 13(3):[905 p.].\u003c/li\u003e\n\u003cli\u003eQin P, Liu D, Wu X, Zeng Y, Sun X, Zhang Y, et al. Fried-food consumption and risk of overweight/obesity, type 2 diabetes mellitus, and hypertension in adults: a meta-analysis of observational studies. Crit Rev Food Sci Nutr. 2022;62(24):6809-20.\u003c/li\u003e\n\u003cli\u003eNSW Food Authority. Shining a light on dark kitchens in NSW 2025 [Available from: https://www.foodauthority.nsw.gov.au/news/departmental-media-releases/shining-light-dark-kitchens-nsw.\u003c/li\u003e\n\u003cli\u003eDoordash. Virtual Brand Quality Requirements Overview 2026 [Available from: https://help.doordash.com/merchants/s/article/Official-DoorDash-Virtual-Brand-Quality-Requirements?language=en_AU\u0026amp;ctry=AU\u0026amp;divcode=NSW.\u003c/li\u003e\n\u003cli\u003eAli L, Ali F, Abdalla MdJ, Alotaibi S. Beyond the hype: Evaluating the impact of generative AI on brand authenticity, image, and consumer behavior in the restaurant industry. International Journal of Hospitality Management. 2025;131:104318.\u003c/li\u003e\n\u003cli\u003eThe Wall Street Journal. Coca-Colar Injects \u0026lsquo;Holidays Are Coming\u0026rsquo; Ads With an Upgraded Dose of AI 2025 [Available from: https://www.wsj.com/articles/coca-cola-injects-holidays-are-coming-ads-with-an-upgraded-dose-of-ai-bc8921e2.\u003c/li\u003e\n\u003cli\u003eThe Guardian. \u0026lsquo;Ruined my Christmas spirit\u0026rsquo;: McDonald\u0026rsquo;s removes AI-generated ad after backlash: The Guardian; 2025 [Available from: https://www.theguardian.com/business/2025/dec/11/mcdonalds-removes-ai-generated-christmas-ad-advert-backlash.\u003c/li\u003e\n\u003cli\u003eAustralian Government Treasury. Review of AI and the Australian Consumer Law. Canberra, Australia: The Australian Government Treasury; 2025.\u003c/li\u003e\n\u003cli\u003eHakim MP, Dela Libera VM, Zanetta LDA, Nascimento LGP, da Cunha DT. What is a dark kitchen? A study of consumer\u0026apos;s perceptions of deliver-only restaurants using food delivery apps in Brazil. Food Research International. 2022;161:111768.\u003c/li\u003e\n\u003cli\u003eCai R, Leung XY, Chi CG-Q. Ghost kitchens on the rise: Effects of knowledge and perceived benefit-risk on customers\u0026rsquo; behavioral intentions. International Journal of Hospitality Management. 2022;101:103110.\u003c/li\u003e\n\u003cli\u003eNield L, Martin H, Wall C, Pearce J, Rundle R, Bowles S, et al. Consumer knowledge of and engagement with traditional takeaway and dark kitchen food outlets. NIHR Open Res. 2024;4:64.\u003c/li\u003e\n\u003cli\u003eForman C, Ghose A, Wiesenfeld B. Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research. 2008;19(3):291-313.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9410382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9410382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Aim:\u003c/strong\u003e Dark kitchens are food service models that do not have a physical space for patrons and mostly sell through online food delivery services. Due to the low overhead costs and lack of customer facing premises, dark kitchens are transient systems with ability to open and close quickly and this is potentially facilitated by use of generative artificial intelligence (AI) for branding. This study aimed to characterise dark kitchens in Australia, assessing longevity of businesses, the nutritional quality of menu items; as well as identify and characterise use of AI in generation of menu content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eDark kitchens in the Greater Sydney Region of New South Wales, Australia, were classified as outlets that had multiple brand names operating from one address on UberEats. Additional information was web-scraped including the opening status of the outlet, menu item names, descriptions, price and images. If dark kitchens had appeared closed on UberEats, we manually searched their brand names on DoorDash, to obtain additional data. Data on dark kitchens were summarised by descriptive analyses including percentages of cuisines, proportion of dark kitchens open by day and hour, frequency of menu items classified into food group categories. Menu item images were analysed for their potential use of AI by assessing distinct features that appear artificial or altered (e.g. image URL containing “altered” or “enhanced”).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOver a third of dark kitchen brands (n = 98, 33.1%) had temporarily or permanently closed since the initial dataset was web-scraped in 2023. We subsequently identified 197 unique dark kitchen brands still in operation. Over 70% of total menu items were comprised of unhealthy foods including fries, fried chicken and desserts. Additionally, over 57% (n = 1939/3376) of menu item images contained “enhanced image” or “image touchup” in their URLs, indicating enhancement or alteration of food images using AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Dark kitchens present a new challenge to digital food environments. Data from this study suggests these “kitchen chameleons” can quickly change menu offerings and branding, primarily selling unhealthy foods, with minimal resources and assistance of AI. Adaptive monitoring is needed to inform and develop policy to minimise their potential risks to public health.\u003c/p\u003e","manuscriptTitle":"Digital chameleons of the food environment: an exploratory analysis of the shapeshifting nature of dark kitchens in Australia and their regulatory challenges","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:08:22","doi":"10.21203/rs.3.rs-9410382/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-30T13:53:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-28T08:30:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-27T23:55:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-04-27T23:51:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31b75cd0-394c-408c-8997-d2ff52261ccc","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"15","date":"2026-04-30T13:53:58+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:08:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9410382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9410382","identity":"rs-9410382","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}