1 the Effectiveness of Interventions Delivered Via Online Food Ordering Systems Targeting Dietary Behaviours

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The effectiveness of interventions delivered via online food ordering systems targeting dietary behaviours: a systematic review protocol

Rebecca Wyse 1-3, Jacklyn Jackson 1,2 , Alice Grady 1-3, Tessa Petro 1-3, Fiona Stacey 3, Luke Wolfenden 1- 3, Serene Yoong 1-4

1 School of Medicine and Public Health, Faculty of Health and Medicine, University of Newcastle, Callaghan, New South Wales, Australia.

2 Hunter Medical Research Institute (HMRI), New Lambton Heights, New South Wales, Australia.

3 Hunter New England Population Health Unit, Hunter New England Population Health District, Wallsend, New South Wales, Australia.

4 School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia.

Declarations

Funding

Funding for this review was provided from the Priority Research Centre for Health Behaviours, University of Newcastle, Australia.

R. Wyse receives salary support from the Heart Foundation (G1800676).

Conflict of Interest

Some review authors have conducted trials that may meet the review inclusion criteria. Otherwise, authors declare they have no known conflicts of interest. Authors have not received any benefit, in cash or kind, any hospitality, or any subsidy derived from the food industry or any other source perceived to have any interest in the outcome of the review.

Authors’ contribution statement

All authors contributed to the study conception and design. The first draft of the manuscript was written by Rebecca Wyse and Jacklyn Jackson, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

BACKGROUND

Online food ordering systems include website or mobile based platforms and applications that allow consumers to order food and beverages online (either for pick-up or delivery). Examples of such platforms include online grocery stores (e.g. Woolworths online or Instacart), fast food (e.g. McDonalds in-store self-service kiosks) and fast food delivery platforms (e.g. UberEats, Zomato or Grubhub), home delivered meals and subscription boxes (e.g. HelloFresh, Green Chef or Dinnerly), pre-order mobile applications from cafes (e.g. Starbucks Pickup), and online canteen ordering systems (e.g. Flexischool or MunchMonitor).

Online food ordering and delivery systems represent a dynamic and rapidly growing industry, reaching over 1.2 billion users internationally (1). Increased broadband access and improved safety of electronic payments combined with a rising demand for convenient meal options represent major factors driving this growth (2). It has been estimated approximately 60% of users accessing and ordering food via online systems do so at least once a month (3). Additionally, with recent COVID-19 lockdowns enforced across major metropolitan areas internationally, online food ordering system use has been reported to be at an all-time high (4). Given their wide reach and frequent use, online food ordering systems may represent an ideal way to deliver behavioural strategies to support public health nutrition in a large number of people, on a regular basis, at a key behavioural moment (the point-of- purchase).

Objectives:

The primary objective of this systematic review is:

- To describe the effectiveness of dietary behavioural interventions delivered via online food ordering systems, that seek to encourage the selection or purchase of healthy foods and beverages.

Secondary objectives are:

- To identify any unintended adverse consequences of the included interventions. - To describe the cost-effectiveness of the included interventions.

METHODS AND ANALYSIS

This systematic review will be conducted in line with best practice principles as recommended by the Cochrane Handbook (5). The methods are described according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines (6), and will be prospectively registered with PROSPERO and preregistered on the Open Science Framework.

Criteria for considering studies for this review

Types of studies

We will include randomised controlled trials (RCTs), cluster RCTs and controlled trials (CCTs). Observational studies, or experimental trials without a comparison group will be excluded from review.

Published and grey literature full-text articles, including dissertations and theses will be included for review. Conference abstracts without an associated full-text article will be excluded.

Types of participants

This review will include studies targeting the general population, and may include any age group. Studies that target entire organisations or communities (e.g. Workplaces or Schools) will be included for review.

Studies that target populations with specific health conditions (e.g. hypertension or diabetes), or focus on clinical populations (e.g. hospital inpatients) will be excluded, due to their need for possible dietary restrictions or advice that may influence their food selection or purchase in ways that are different to a generally healthy population.

Types of interventions

This review will include interventions that are i) delivered via online food ordering systems; ii) seek to encourage the purchase of healthy foods and/or beverages, and; iii) involves an actual online transaction where money or equivalent (i.e. credit/voucher) is directly or indirectly (i.e. in the form of free or subsidised meal programs or grants) exchanged for food or beverages.

This will include interventions implemented via online food ordering platforms that are available to the general community (e.g. supermarkets, restaurants, online delivery services), or available in specific food service environments within the community including schools, workplaces or hospital cafeterias.

Online food ordering interventions that include non-online components (i.e. providing implementation support to food service staff) will be considered for inclusion provided that at least one core intervention strategy is delivered via an online food ordering system.

Included interventions must seek to encourage healthy food or beverage purchases for consumers/users and must be delivered directly to consumers/users through an online food ordering system. Online food ordering platforms may include website or mobile based applications that allow consumers to order foods and beverages online from supermarkets and grocery stores, or food service

3 providers including canteens, cafeterias, restaurants, cafes or takeaway restaurants, and may include online meal delivery services such as UberEATs and HelloFresh.

The strategies utilised by included intervention will be classified (where possible) according to the following choice architecture techniques as defined by Mȕnscher et al (7):

- Translating information: includes reframing or simplifying information. - Making information visible: includes providing feedback on behaviour, or making external information visible. - Providing a social reference point: includes referring to the behaviour of peer groups, or role models. - Changing choice defaults: includes setting no-action defaults, or the use of prompted choice. - Changing option-related effort: includes increasing or decreasing physical or financial effort to encourage choice - Changing the range or composition of options: includes changing categories or changing the grouping of options. - Changing option consequences: includes connecting decisions to benefits or costs, or changing the social consequences of the decision. - Providing decision assistance: includes providing reminders, or facilitating commitment (e.g. self-commitment or public commitment).

Control

We will include studies that report the outcomes of an intervention compared with no intervention (control), delayed intervention (wait-list-control), usual care or an alternate intervention that does not seek to influence food purchasing behaviour via an online ordering system.

Types of outcomes

Primary outcomes

We will include studies that seek to encourage the purchase of healthy foods and beverages. Primary outcomes of interest may include, but are not limited to:

- The contents of food purchases/selections according to food groups or categories or target items (e.g. servings of fruit and vegetables, the proportion of ‘healthy items’ and ‘less healthy’ items or core and non-core food groups). - Macro and micro-nutrient content of food purchases (e.g. mean energy, saturated fat, total sugar, sodium, % energy from saturated fat, % energy from total sugar, energy density)

Secondary outcomes

Secondary outcomes of interest include:

- Any unintended adverse consequences: e.g. adverse consequences to service operations or revenue, increased cost to the consumer. - Cost and cost-effectiveness of included interventions: e.g. crude or total cost of the intervention; and cost-effectiveness ratio.

Search methods for identification of studies

Electronic searches

The systematic search will be undertaken from database inception until October 2020 using the following electronic databases:

- Medline - EMBASE - PsycINFO - ERIC - CINAHL - Scopus - Business Source Complete - Informit Business Collection

We will not impose any language restriction on the search.

Grey literature search

Google and Google-scholar search (first 100 results).

Searching other sources

We will also undertake hand searches of included study reference lists identified from the electronic and grey literature searches.

Search terms

Our search will combine terms describing:

1. Intervention modality (i.e. Online), AND 2. Intervention target (i.e. Food/Nutrition), AND 3. Outcome (i.e. Selection/Purchases), AND 4. Study Design (i.e. Randomised controlled trials OR control clinical trials)

Data collection and analysis

Selection of studies

Pairs of review authors will independently screen titles and abstracts of all included studies using Covidence software (8). If discrepancies occur between reviewers that cannot be resolved by consensus, a third reviewer will be consulted to inform study progression to full text review.

Review authors will obtain the full text of relevant studies, and those that could not be excluded on the basis of study title and abstract. Pairs of review authors will review full-text articles for their eligibility for inclusion. If discrepancies occur that cannot be resolved by consensus, a third reviewer will be consulted to inform final study inclusion. If information to inform study inclusion is unavailable or unclear, the corresponding author will be contacted.

Reasons for excluding full-text articles will be documented and reported in a PRISMA flow diagram.

Data extraction

Pairs of independent, un-blinded reviewers will extract data for included studies. Any discrepancies between reviewers will be resolved by consensus or a third reviewer.

Data will be extracted using an adapted version of the Cochrane Public Health data extraction template, which will be piloted prior to use. Included studies will have the following data extracted:

- Study characteristics: first author, publication year, country, study design, study aim, funding source - Participant characteristics: age, gender, ethnicity, sample size - Intervention characteristics: provider of the online food ordering platform, food ordering environment (e.g. school canteen, restaurant or supermarket), intervention description, intervention strategies, duration and intensity of the intervention. - Outcome characteristics: definitions, method of outcome assessment, and time points of outcome measurements. - Study results relevant to the primary outcome (e.g. food and beverage purchases and selection) - Study results relevant to the secondary outcomes (e.g. unintended adverse events, economic data/evaluation). - Conflict of interest, using the Tools for Addressing Conflicts of Interest in Trials: https://tacit.one/

Risk of bias assessment

Study risk of bias for included studies will be independently assessed by two review authors using the Cochrane Collaboration’s Risk of Bias (RoB) tool (9), as described in the Cochrane Handbook for Systematic Reviews of Interventions (5). Any discrepancies between review authors will be resolved by consensus or a third reviewer.

RoB domains of interest will relate to the intention-to-treat-effect (i.e. whether the intervention was effective regardless of whether the intervention was received as intended). The specific domains of RoB assessment will relate to: selection bias; performance bias; detection bias; attrition bias; reporting bias, and other bias. Additional RoB domains will be assessed for cluster-RCTs relating to biases due to the timing of identification and recruitment of participants. An overall RoB for the study and individual study results will be judged as ‘low’, ‘high’ or ‘unclear’.

Strategy for data synthesis

If there is adequate data available, and outcomes are comparable and sufficiently homogenous, we will pool measures of the same quantitative primary outcomes using a random effects model. Study heterogeneity will be evaluated using forest plots and assessing for asymmetry, and sources of heterogeneity will be informed by narrative description of study characteristics.

If we cannot combine data in meta-analysis, we will conduct a narrative summary of trial findings consistent with methods outlined in the Cochrane Handbook (e.g. summarise effect estimates, combining p-values, vote counting based on direction of effect) (5).

Analysis of subgroups or subsets

Subgroup analyses may be used to explore sources of heterogeneity. Is sufficient data is available, this will be explored across subgroups related to population (e.g. Adults vs Children), intervention (e.g. Choice architecture strategies vs Multi-component intervention), comparison (e.g. no active intervention vs alternate intervention), and outcome (e.g. Target items vs overall selection) (PICO) characteristics. The impact of study RoB will be explored in a sensitivity analysis.

References

1. statista. Online Food Delivery worldwide 2020 [cited 2020 21 August 2020]. Available from: https://www.statista.com/outlook/374/100/online-food-delivery/worldwide . 2. Li C, Mirosa M, Bremer P. Review of Online Food Delivery Platforms and their Impacts on Sustainability. Sustainability. 2020;12(14):5528. 3. Resendes S. 31 Online Ordering Statistics Every Restauranteur Should Know in 2020: Upserve; 2020 [Available from: https://upserve.com/restaurant-insider/online-ordering-statistics/ . 4. Research and markets. Food Delivery: COVID-19. Food Delivery On the Rise Due to COVID-19 2020 [Available from: https://www.researchandmarkets.com/issues/food-delivery-on-the- rise?utm_source=dynamic&utm_medium=BW&utm_code=pwtnvq&utm_campaign=1383474+- +Food+Delivery+on+the+Rise+Due+to+COVID-19+Lockdown&utm_exec=joca220bwd . 5. Deeks JJ, Higgins J, Altman DG, Green S. Cochrane handbook for systematic reviews of interventions version 5.1. 0 (updated March 2011). The Cochrane Collaboration. 2011:2. 6. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic reviews. 2015;4(1):1. 7. Münscher R, Vetter M, Scheuerle T. A review and taxonomy of choice architecture techniques. Journal of Behavioral Decision Making. 2016;29(5):511-24. 8. Covidence. World-class systematic review management [Available from: https://www.covidence.org/home . 9. Higgins JP, Savovi ć J, Page MJ, Elbers RG, Sterne JA. Assessing risk of bias in a randomized trial. Cochrane Handbook for Systematic Reviews of Interventions. 2019:205-28.