Kenjin B. Chang
Alyssa Wooden
Lori Rosman3
Daphene Altema-Johnson
Rebecca Ramsing- 1Center for a Livable Future, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
- 2Department of Human Centered Design, College of Human Ecology, Cornell University, Ithaca, NY, United States
- 3Welch Medical Library, Johns Hopkins University, Baltimore, MD, United States
Introduction: Despite the considerable public and planetary health benefits associated with reducing the amount of meat consumed in high-income countries, there is a limited empirical understanding of how these voluntary changes in food choice can be effectively facilitated across different settings. While prior reviews have given us broad insights into the varying capacities of behavior change strategies to promote meaningful reductions in meat consumption, none have compared how they perform relative to each other within a uniform dining context.
Methods: To address this gap in the literature, we synthesized the available research on university-implemented meat reduction interventions and examined the variations in the success rates and effect estimates associated with each of the three approaches identified in our systematic review.
Results: From our analyses of the 31 studies that met our criteria for inclusion (n = 31), we found that most were successful in reducing the amount of meat consumed within university settings. Moreover, independent of the number of individual strategies being used, multimodal interventions were found to be more reliable and effective in facilitating these changes in food choice than interventions targeting the choice architecture of the retail environment or conscious decision-making processes alone.
Discussion: In addition to demonstrating the overall value of behavior change initiatives in advancing more sustainable dining practices on college and university campuses, this study lends further insights into the merits and mechanics underlying strategically integrated approaches to dietary change. Further investigations exploring the persistence and generalizability of these effects and intervention design principles are needed.
Systematic review registration: https://doi.org/10.17605/OSF.IO/DXQ5V, identifier: 10.17605/OSF.IO/DXQ5V.
1. Introduction
Dietary change has been cited as a necessary measure for achieving a variety of international sustainability targets (Springmann et al., 2018; Clark et al., 2020; Chen et al., 2022). In particular, there has been an emphasis on the co-benefits of reducing excess meat consumption among adolescents and adults living in high-income countries (HICs) (Yip et al., 2013; Aleksandrowicz et al., 2016; Springmann et al., 2016), specifically with regard to the advantages these population-level shifts would present for human health, climate change, and the global ecology (Godfray et al., 2018; Willett et al., 2019; Parlasca and Qaim, 2022).
Programs aimed at limiting the sale and consumption of meat have therefore become an important object of interest among food systems researchers (De Boer et al., 2014; Garnett et al., 2015; Wellesley et al., 2015; Jiang et al., 2020; Rust et al., 2020), with evidence to suggest that actualizing these transitions within HICs could reduce agricultural emissions, water use, and land appropriation by more than half (Aleksandrowicz et al., 2016; Sun et al., 2022), all while avoiding millions of diet-related deaths each year (Wang et al., 2016; Willett et al., 2019; Zhang et al., 2021).
Due to pervasive public resistance to programs restricting individual choice (Sievert et al., 2020; Ewald et al., 2021; Pechey et al., 2022), much of this research has focused on the value of behavior change strategies in promoting voluntary shifts in food choice (Bianchi et al., 2018a,b; Graça et al., 2019). Within the context of meat reduction interventions, common strategies include those that (1) modify the presentation and arrangement of items on menus, (2) add to the existing set of meal options, (3) manipulate the layouts of dining areas, (4) utilize promotional messaging, and (5) introduce pricing incentives.
Each of these strategies leverage different behavior change principles to motivate individuals toward more sustainable food choices without actively limiting the options available to them. Dual-process accounts of conditional reasoning (Evans, 2011; Kahneman, 2011) provide us with a useful framework for classifying these differences, with some strategies targeting more implicit decision-making processes and others focusing instead on the more deliberate aspects of cognition.
Using this operating principle, we can group the existing set of meat reduction interventions into three categories (referred to as “approaches” hereafter) based on the cognitive systems being targeted by their individual strategies. More specifically, we specify between (1) interventions that target the choice architecture of the retail environment (i.e., interventions that modify the presentation and arrangement of items on menus, add to the existing set of meal options, and/or manipulate the layouts of dining areas), (2) interventions that target conscious decision-making processes (i.e., interventions that utilize promotional messaging and/or introduce pricing incentives), and (3) interventions that target both systems simultaneously (i.e., interventions that involve at least two strategies corresponding to each of the initial two approaches; referred to as “multimodal interventions” hereafter) (see Figure 1).
Figure 1. Flow diagram outlining the organizing principles of our intervention classification scheme. Interventions are considered “multimodal” if they involved two or more strategies corresponding to each of the remaining two approach styles. Therefore, at minimum, multimodal interventions must include at least one strategy targeting the choice architecture of the retail environment and at least one strategy targeting conscious decision-making processes.
While prior reviews have given us broad insights into the value of dietary change interventions in shifting attitudes (Hartmann and Siegrist, 2017; Sanchez-Sabate and Sabaté, 2019; Valli et al., 2019) and behaviors (Bianchi et al., 2018a,b; Harguess et al., 2020; Kwasny et al., 2022) related to meat consumption, none have compared how these approaches perform relative to each other within a uniform dining context—information that could meaningfully inform the underlying theory and design of setting-specific policies and interventions. In the health promotion literature, settings-based evaluation techniques are frequently used to identify overlapping traits between high-performing interventions, allowing researchers to isolate the strategic components that are most valuably contributing to the behavioral changes being observed with that context (Whitelaw et al., 2001; Dooris, 2009; Bloch et al., 2014).
Due to the high mitigation potential associated with lowering the resource requirements tied to institutional foodservice operations (Jones et al., 2019; Bull et al., 2022; Sherry and Tivona, 2022) and the developing interest in improving the sustainability of college and university environments (Leal Filho et al., 2018; Amaral et al., 2020; Ruiz-Mallén and Heras, 2020), we elected to apply these techniques toward the subset of meat reduction interventions that have been implemented within higher education institutions (HEIs) (referred to as “university settings” hereafter). In particular, we wanted to leverage this existing evidence base to (1) determine whether meat reduction interventions have been successful in reducing the amount of meat consumed within university settings and (2) identify which of the three investigated approaches has generated the most favorable dietary change outcomes.
To accomplish this, we synthesized the existing research on university meat reduction and examined the variations in the success rates and effect estimates between interventions targeting the choice architecture of the retail environment, interventions targeting conscious decision-making processes, and multimodal interventions. Based on these analyses, we found that the majority of included interventions were successful in reducing the amount of meat consumed within university settings, and that multimodal interventions were more reliable and effective in facilitating these changes than either of the remaining two approaches, regardless of the number of individual strategies being used. Further insights regarding the state of the literature and its possible future directions are also provided.
2. Materials and methods
This paper has been presented in accordance with the guidelines stipulated by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (see Supplementary Table 1). For further information on our methodologies, a more detailed version of this previously registered protocol can be viewed alongside a record of its revisions at https://osf.io/zhg5b/.
2.1. Search strategy
To identify the relevant literature published between January 1, 2000, and June 3, 2021, we consulted seven electronic databases: ERIC, PsycINFO, PubAg, PubMed, SCOPUS, Sociological Abstracts, and Web of Science. For each database, we used a combination of keywords and controlled vocabulary operationalizing our three principal search concepts: behavior change, meat consumption, and college and university settings.
To develop these searches, we generated an initial base strategy for the PubMed database using the Yale Medical Subject Heading (MeSH) Analyzer to create a list of terms represented across 11 previously identified seed articles. After filtering these terms based on their relevance to our search concepts, we translated the resulting base strategy across the remaining six databases. These searches are provided in full in Supplementary Table 2.
2.2. Selection criteria
Prior to implementing our search strategy, we developed a set of selection criteria based on our research objectives and pre-existing knowledge of the literature. These specifications were designed to parameterize our analyses by qualifying eligible studies based on (1) the populations that were sampled, (2) the settings that were investigated, (3) the outcome variables that were assessed, and (4) the evaluation methods that were used. We elaborate on these criteria in greater detail below.
2.2.1. Population
Studies were required to sample members of the hosting university's student body. To allow exploration into the effects of interventions on the broader university environment, we elected to retain instances where members of the faculty and staff were sampled alongside university students.
2.2.2. Setting
Studies were required to report on interventions that had been conducted naturalistically within physical university dining environments. Records documenting laboratory-based, artificial choice experiments were excluded.
2.2.3. Evaluation method
Studies were required to evaluate the effects of a campaign, program, or initiative in at least one of two ways: (1) by comparing behavioral measures before and after the implementation of an intervention or (2) by making comparisons between treatment and control groups. Studies utilizing cross-sectional data were excluded.
2.2.4. Outcome variables
Studies were required to use at least one of the following three measures to evaluate the effect of the intervention on food choice: (1) intended changes in meat consumption, (2) self-reported changes in meat consumption, and (3) observed changes in meat consumption. Studies using alternate measures of food choice were excluded.
2.3. Screening procedure
After importing the records into a citation management system (Clarivate, 2020), we documented the total volume of search results before removing duplicate entries. Non-duplicate records were then transferred to an online web-based program for screening (Veritas Health Innovation, 2021).
Our screening procedure was conducted successively by two independent reviewers, with a third designated to settle arising conflicts in voting. The initial stage of title-and-abstract screening was used to sort non-duplicate records according to their potential relevance, with all decisions being based around whether the readable contents met any of our four exclusion criteria. By contrast, during the final stage of full-text review, records were only included if the readable contents met all four of our inclusion criteria (see Figure 2).
Figure 2. Adapted PRISMA 2020 flow diagram outlining the stepwise results of our study selection process. There were seven instances where a third independent reviewer was needed to settle arising disputes in voting. Each of these instances emerged during the initial stage of title-and-abstract screening and were ultimately resolved via group consensus.
2.4. Data extraction
For each included record, data on the article's author(s), title, publication year, and implementation year were collected alongside information describing the university and country in which the intervention was implemented. Using the classification scheme described in Figure 1, we typified included interventions based on the strategies they used and the larger approach category they fell under. Information on their evaluation methods, outcome measures, and targeted meat types were also extracted.
To standardize how significance was determined across studies, we also collected the relevant data used to interpret the intervention's effect on meat consumption. This involved extracting study-level information on mean outcomes, group sample sizes, and error. All extracted variables were exported to and compiled in Microsoft Excel (see Table 1) (Microsoft Corporation, 2018).
Table 1. Summary table outlining the study-level characteristics associated with each of the 31 interventions included in our analyses.
2.5. Quality assessment
We used the Evidence Project's Risk of Bias Tool to establish a minimum standard for our analyses (Viswanathan et al., 2018; Kennedy et al., 2019), such that each included study needed to meet at least one of the eight specified criteria for inclusion. This was done to ensure that studies were of sufficient rigor and appropriately accounted for common sources of bias.
Studies were therefore required to satisfy at least one of the following eight criteria: (1) monitor a cohort over time, (2) involve the use of a control or comparison group, (3) use pre-post intervention data, (4) use random assignment, (5) use random selection, (6) report a rate of attrition below 20 percent, (7) demonstrate equivalency across sociodemographic variables, or (8) demonstrate equivalency across outcome variable of interest (Kennedy et al., 2019).
2.6. Main analyses
Our main analyses were divided into three parts, with the first evaluating differences in the success rates across interventions, the second comparing mean differences in their effect estimates, and the third examining changes in performance over time.
2.6.1. Success rates
As a function of both the direction and significance of an intervention's effect on meat consumption, we used success rates to compare the capacities of the three investigated approaches to facilitate changes in food choice. These values were calculated by dividing the number of interventions reporting significant reductions in meat consumption within an approach category by the total number of interventions that used that particular approach.
To standardize how significance was determined across studies, we used a consistent set of methods to internally calculate changes in behavior resulting from the intervention. For studies inferring change based on the differences in the sample means reported between control and treatment conditions, we used two-tailed t-tests, and for those inferring change based on proportional differences in sales, we used chi-square tests. In both cases, we set an alpha of 0.05 and depicted these results visually using Boon and Thomson (2021) revised methods for visualizing patterns, which leverage the “<>,” “∧,” and “∨” symbols to represent neutral, positive, and negative effects, respectively.
2.6.2. Effect estimates
In addition to using success rates to understand the relative frequency with which interventions significantly reduced the amount of meat consumed within university settings, we also calculated the associated odds ratios to evaluate how the magnitudes of these effects varied across approaches. For the studies using mean differences to form these comparisons, we calculated these effect estimates using the means and standard deviations of the control and interventions groups, and for those using differences in sales proportions, effect estimates were calculated using the frequency distributions derived from the sample sizes and proportional shares of control and intervention groups.
To perform these calculations, we used the Practical Meta-Analysis Calculator from Lipsey and Wilson (2001) and later verified these initial results using the “effectsize” (Ben-Shachar et al., 2020) and “metafor” (Schwarzer, 2022) packages within RStudio (RStudio Team, 2021).
2.6.3. Time series
Prior research has indicated that there has been increasing concern and awareness surrounding how diets influence different processes related to global environmental change (Macidarmid et al., 2016; Jürkenbeck et al., 2021). To determine whether this phenomenon has contributed to differences in the performance of interventions over time, we used Sturge's rule to construct equal-sized time intervals and compared the success rates and effect estimates of interventions conducted between 2001 and 2007, 2008 and 2014, and 2015 and 2021.
3. Results
3.1. Study selection
Of the 11,546 unique records screened for their relevance and eligibility, a total of 29 research articles documenting 31 independently eligible studies met the necessary criteria for inclusion (see Figure 2).
3.2. Study characteristics
3.2.1. Time and place
The 31 studies included in our analyses were all conducted and published between 2001 and 2021, with each cumulative frequency distribution increasing exponentially over time (R2 = 0.97, R2 = 0.96) (see Figure 3A). The studies were conducted at a total of 33 different intervention sites spanning 24 colleges and universities, nine countries, and two continents (see Figure 3B). More than half were conducted in the United States (51.5%) while the remaining 16 were implemented at colleges and universities in England (16.1%), Italy (9.1%), Sweden (9.1%), Canada (3.0%), Spain (3.0%), Norway (3.0%), Scotland (3.0%), and Wales (3.0%).
Figure 3. (A) Grouped bar graph plotting the annual increases in the number of meat reduction interventions implemented within university settings between 2000 and 2021. (B) Choropleth map illustrating the countries where, and frequency with which, included interventions were conducted between 2000 and 2021.
3.2.2. Strategies and approaches
In total 51.6% of interventions targeted conscious decision-making processes (n = 16) while the remaining 48.4% targeted either the choice architecture of the retail environment (n = 5) or both drivers simultaneously (n = 10). Of the five strategies identified in our systematic review, promotional messaging was the most prominently used (80.6%), followed by strategies that modified the presentation and arrangement of items on menus (35.5%), manipulated the layouts of dining areas (9.7%), introduced pricing incentives (9.7%), and added to the existing set of meal options (8.3%).
In total, 61.3% of interventions utilized a single strategy in isolation while the remaining 12 used at least two in combination (38.7%). Among this latter group, promotional messaging was the most commonly integrated strategy (91.7%), followed by strategies that modified the presentation and arrangement of items on menus (83.3%), introduced pricing incentives (16.7%), manipulated the layouts of dining areas (8.3%), and added to the existing set of meal options (8.3%).
Interventions that manipulated the layouts of dining areas were the least likely to be implemented alongside at least one other strategy (33.3%). By contrast, interventions that utilized promotional messaging (44.0%), modified the presentation and arrangement of items on menus (90.9%), introduced pricing incentives (66.7%), and added to the existing set of meal options (50.0%) were all more likely to be integrated alongside another strategy.
3.2.3. Outcome variables and evaluation methods
Among the included interventions, differences in meat consumption were most often measured observationally (54.8%), with self-reported measures (32.3%) and measures of intention (12.9%) being used at a lower relative frequency. In tracking these changes, nine studies used between-group comparisons when estimating the effects of the intervention on food choice while the remaining 22 either evaluated within-group differences over time (22.6%) or used both evaluation methods concurrently (48.4%).
Over two-thirds of interventions targeted reductions in all types of meat (67.7%) while four focused on reductions in red and process meats (12.9%), three focused on reductions in ruminant meats (9.7%), two focused on red meat alone (6.5%), and one focused on processed meat alone (3.2%).
3.3. Quality assessment
All of the included studies satisfied at least one of the eight criteria specified by the Risk of Bias Tool, meaning that each study met the minimum standard of rigor for our main analyses (see Table 2). The mean summary score across all included studies was 3.19 (sd = 2.17), indicating a high degree of study-level variation. Among the evaluated criteria, 90.3% used a control or comparison group, 51.6% collected pre- and post-intervention data, 38.7% assessed equivalence between groups at baseline, 32.3% assessed equivalence across potentially relevant sociodemographic characteristics, 29.0% had a follow-up rate of at least 80%, and 25.8% randomly assigned participants for assessment. No study randomly selected participants for assessment.
Table 2. Table summarizing the results of our quality assessment and risk of bias analysis.
The least applicable items were those assessing attrition (51.6%) and equivalence across potentially relevant sociodemographic factors (3.2%). The items that were most relevant to our studies, on the other hand, were those assessing equivalence across potentially relevant sociodemographic factors (61.3%) and equivalence between groups at baseline on disclosure (58.1%).
3.4. Main analyses
3.4.1. Success rate variations
Over two-thirds of the included interventions were associated with significant reductions in meat consumption (67.7%). The remaining interventions yielded no differences in behavior (32.3%), with none of the included studies reporting any increases in meat consumption resulting from negative reactance or rebound effects.
Between the three investigated approaches, multimodal interventions were significantly more likely to be associated with reductions in meat consumption than those targeting conscious decision-making processes or the choice architecture of the retail environment alone (p = 0.029) (see Figure 4). There was no difference in the rate of success across interventions targeting the choice architecture of the retail environment and conscious decision-making process.
Figure 4. Grouped bar graph comparing the proportion of interventions associated with significant reductions in meat consumption across each investigated approach. Relative to other approaches, multimodal interventions were more likely to lead to significant reductions in the amount of meat consumed within university settings (p = 0.029). No increases in meat consumption were reported.
Interventions using at least two strategies concurrently were also more likely to be associated with reductions in meat consumption than interventions using a single strategy in isolation (p = 0.024), though both sets of interventions significantly reduced the amount of meat consumed within university settings on at least half of the evaluated occasions. Interventions that used promotional messaging strategies, in particular, were successful 57.1% of the time when used in isolation and 76.0% of the time when used in combination with other strategies (p = 0.029).
When comparing the performance between multimodal interventions and unimodal interventions leveraging two or more strategies, multimodal interventions were associated with a higher rate of success (100%, compared to 50.0%) and a greater overall effect on food choice (OR = 2.88 [1.95, 4.64]), compared to (OR = 2.13 [1.64, 3.05]).
There were no significant differences in the success rates associated with interventions conducted in Europe and North America (p = 0.28).
3.4.2. Effect size variations
The effect estimates associated with the included studies ranged from 17.97 [9.60, 33.63] to 0.61 [0.23, 1.58], with a mean standardized effect of 2.88 [1.85, 4.77], indicating that the included interventions reduced the overall odds of consuming meat within these settings by an average of 187.5%.
However, due to the heterogeneity in the mean effect estimates associated with studies using self-reported (OR = 4.20 [2.99, 5.88]) and intended (OR = 4.14 [2.23, 7.73]) measures of change, relative to those using observational measures of change (OR = 1.82 [1.37, 2.75]) (see Figure 5), we elected to use a fixed-effect model for our meta-analysis, limiting our comparisons to the 17 studies (n = 17) that used observational methods to evaluate changes in meat consumption (see Figure 6).
Figure 5. Violin plot comparing the effect size distributions for each of the three included evaluation methods. The differences in the magnitude of these effect estimates suggest some level of effect size heterogeneity across the three included methods of evaluation, with observational methods yielding smaller and more precise measurements of dietary behavior.
Figure 6. Forest plot comparing the effect estimates of the 17 studies included in the fixed effect model. Effects sizes are given as odds ratios, with error bars denoting 95% confidence intervals.
From this analysis, we found that interventions targeting the choice architecture of the retail environment had a greater mean effect on meat consumption (OR = 1.82 [1.57, 2.24]) than interventions targeting people's conscious decision-making processes (OR = 1.68 [1.43, 2.05]), though both approaches had a smaller average effect on meat consumption than multimodal interventions (OR = 1.89 [1.21, 3.41]) (see Figure 7). Across all approaches, this narrower set of interventions reduced the overall odds of consuming meat within university settings by 81.8%.
Figure 7. Violin plot comparing the effect size distributions of the three investigated approaches. Compared to the other two approaches, multimodal interventions had a greater overall effect on meat consumption.
3.4.3. Time series variations
No significant differences were found in intervention performance over time.
4. Discussion
Prior research has indicated that organizational change can play a unique role in advancing sustainable development (Krabbe et al., 2015; Hamam et al., 2021; Garnett and Balmford, 2022; Nielsen, 2023). In particular, there is an interest in understanding how HEIs can contribute to this larger effort by reducing the amount of meat consumed within campus environments (Ruiz-Mallén and Heras, 2020; Sherry and Tivona, 2022; Taylor et al., 2023). Despite the empirical progress that has been made over the last two decades, there has been little research to date reflecting on how useful these interventions have been in promoting voluntary changes in food choice within university settings. To address this gap in the literature, we synthesized the evidence on university-implemented meat reduction interventions to determine whether these interventions have been generally effective in promoting behavior change, and identify whether there are any relevant approach-dependent differences in intervention performance within these dining contexts. A summary of our findings and their implications for intervention design and dietary change research are given below.
4.1. Summary of main findings
In this systematic review of the university meat reduction literature, we identified and analyzed 31 dietary change interventions that had been implemented over the course of 21 years across 24 universities, nine countries, and two continents (see Figure 3). Of these interventions, over two-thirds led to significant changes in food choice (see Figure 4), lowering the overall odds of consuming meat within college and university settings by an average of 82% (see Figure 6).
Between the three approaches investigated in this systematic review (see Figure 1), multimodal interventions were found to be more reliable and effective in reducing meat consumption than interventions targeting the choice architecture of the retail environment or conscious decision-making processes alone (see Figure 7). This remained true even after controlling for the the number of individual strategies being used, indicating that these performance-related advantages were a function of more than just strategic volume. As such, from an intervention design standpoint, there may be inherent value in understanding how strategies can be integrated to exert influence on both implicit and deliberate decision-making processes.
The remaining two unimodal approaches were equally successful in reducing the amount of meat consumed within university settings (see Figure 3), though interventions exclusively targeting the choice architecture of the retail environment were found to have a greater mean effect on food choice than interventions targeting conscious decision-making alone (see Figure 7).
Over the evaluated 21-year period, there was an exponential increase in the amount of research that was conducted and published on university-implemented meat reduction interventions (see Figure 3). Despite this proliferation, time series analysis revealed no salient improvements in intervention performance over time, highlighting a possible need for more effective, setting-specific guidelines for reducing meat consumption within university settings.
Our findings on the overall success of university-implemented meat reduction interventions were largely consistent with prior research examining the value of behavior change strategies in promoting reductions in meat consumption (Bianchi et al., 2018a,b; Harguess et al., 2020; Kwasny et al., 2022; Ronto et al., 2022). However, the comparatively higher rate of success observed in this study relative to previously published reviews could be attributed to the differences in the populations being targeted by included interventions, with earlier reviews investigating the broader effects of behavior change strategies on the general population and ours focusing instead on changes in food choice among mostly young adults and adolescents, who have been found to be more receptive to plant-rich diets and dietary change interventions, more broadly (de Villiers and Faber, 2019; Hargreaves et al., 2021; Jürkenbeck et al., 2021).
Our comparisons of the three approaches investigated in this study provide an intuitive, decision-centered framework for implementing meat reduction interventions within university settings. More specifically, our study contrasts earlier work on the subject by using dual-process accounts of conditional reasoning to typify interventions based on the cognitive processes targeted by their strategies (Evans, 2011; Kahneman, 2011), rather than the various sources of influence involved in dietary decision making (i.e., intrapersonal-, interpersonal-, organizational-, community-, and policy-level factors). While socioecological perspectives provide a useful method of conceptualizing how different contextual factors interact to influence food choice at a systems level (Robinson, 2008; Townsend and Foster, 2013), they tend to be less instructive for intervention design when the desired changes in behavior (i.e., reductions in meat consumption) are voluntary and the dining context (i.e., university cafeterias) is fixed (Schölmerich and Kawachi, 2016). The classification scheme used in this study circumvents these limitations by focusing instead on how common meat reduction strategies differentially inform decision-making processes at the individual level.
The results from our joint analysis were mostly consistent with earlier research supporting the value of integrated approaches to meat reduction (Kahn-Marshall and Gallant, 2012; Gittelsohn and Lee, 2013; Vo et al., 2019; Ramsing et al., 2021). In addition to replicating these general findings, we were also able to use our novel classification scheme to distinguish between three different types of meat reduction approaches, with multimodal interventions outperforming interventions targeting the choice architecture of the retail environment and conscious decision-making processes alone, independent of the number of strategies involved. While past research has suggested that the advantages to integrated approaches are a result of probability (Kahn-Marshall and Gallant, 2012) and focused efforts to leverage links between socioecological levels (Schölmerich and Kawachi, 2016), our findings seem to suggest an alternative explanation—that the benefits to performance are a function of both the number of strategies used and the nature of how those strategies coalesce to exert influence on relevant decision-making processes.
With respect to the remaining two approach categories, our findings on interventions targeting the choice architecture of the retail environment were consistent with prior research supporting nudging interventions as an effective way of promoting dietary change (Bucher et al., 2016; Byerly et al., 2018; Vandenbroele et al., 2020; Ensaff, 2021; Mertens et al., 2022). However, for interventions targeting conscious decision-making processes, the research has been more mixed (Worsley, 2002; Bianchi et al., 2018a; Thakur and Mathur, 2021), with some doubts being raised about the sufficiency of education-based strategies in influencing actual behavior within applied contexts (Kaur et al., 2017). Despite the salience of these concerns, we did not find evidence of this in our analyses, (see Figure 4), with interventions targeting conscious decision-making processes yielding significant changes in intention as well as behavior in 56% of cases. However, we did find that interventions targeting conscious decision-making processes did tend to perform better when they were combined with at least one other strategy.
Finally, the exponential increase reported in the number of university-implemented meat reduction interventions mirrors the increase in meat reduction interventions that have been implemented more generally over the last several decades (Kwasny et al., 2022). It is therefore unclear how much of this can be attributed to the establishment of institutional network programs, like the Association for the Advancement of Sustainability in Higher Education (AASHE) and the United Nation's Higher Education Sustainability Initiative (HESI), and how much can be attributed to increasing public concern and awareness surrounding the environmental, climate, and health implications of food (Macidarmid et al., 2016; Jürkenbeck et al., 2021). Despite the increasing awareness of the interactions between agriculture, diet, public health, and the global ecology, we found no evidence pointing to any increases in the relative performance of university-implemented meat reduction interventions over time.
4.2. Strengths and limitations
To the best of our knowledge, our study is the first to critically examine the value of university-implemented meat reduction interventions, and the first to jointly analyze the relative merits between these three approaches to dietary change. We accomplished this using a novel, theory-informed classification scheme that typifies interventions according to the decision-making processes targeted by their individual strategies, allowing us to make nuanced comparisons between interventions that consciously incentivize individuals to make more sustainable food choices, interventions that manipulate the physical environment in ways that make those choices easier, and interventions that do both simultaneously. In doing so, we were able to identify which approach was most effective in promoting dietary change within these settings while also providing supporting, evidence-based explanations highlighting the behavioral mechanisms that could be involved in driving these observed effects. In addition, our evaluations of these meat reduction approaches made use of two distinct performance-related outcomes: one focused on the capacity of interventions to generate significant reductions in meat consumption and one focused on measuring the degree to which change occurred within these university environments. By distinguishing between these outcomes, we were able to leverage the existing evidence to compare the performance of these interventions across multiple dimensions. Finally, by undertaking this exercise, we were also demonstrate how settings-based evaluation techniques can be used to meaningfully inform the design of setting-specific interventions and policies.
However, within the context of this study, we were unable to evaluate the long-term effects of university meat reduction efforts on food choice. Due to the limited duration of the included studies' evaluation periods, we were unable to assess the how long these changes in behavior persisted within these environments, and whether their persistence was at all contingent on the type of approach used. This limitation is especially salient given the existing empirical concerns surrounding the durability of nudging individuals toward healthier food options (Van Rookhuijzen et al., 2021). For the same reasons, we were also unable to evaluate whether these interventions were associated with any rebound effects resulting from psychological reactance (Osman, 2020). Furthermore, because the included studies evaluated behaviors that were specific to university environments, it is also unclear whether the benefits of implementing these types of interventions within university environment led to meaningful instances of contextual spillover (Verfuerth et al., 2021), or if they induced change across other desirable pro-environmental behaviors (Carrico, 2021). Finally, while we identified meat reduction interventions that had been implemented in universities across nine different countries, all of these countries are situated in either Europe or North America. Therefore, it remains unclear whether these findings are generalizable across other cultural contexts.
4.3. Recommendations for future research
To better account for the asymmetries in the effect sizes associated with each of the three included outcome measures, future investigations should prioritize using observational methods to track changes in dietary behaviors where possible. In addition to providing more precise approximations of changes in food choice (Webb and Sheeran, 2006; Loy et al., 2016; Meyer and Simons, 2021) and minimizing the risks of bias that stem from the so-called “intention-behavior gap,” collecting observational data has the additional benefit of equipping institutional policymakers and foodservice providers with a practical means of making complementary supply-side changes based on information that is collected at the point of purchase. Furthermore, to allow for a better sense of whether the investigated approaches can lead to lasting changes in behavior, researchers should endeavor to evaluate the effects of these initiatives more frequently and over longer intervals of time. In addition to allowing investigations to be more sensitive to instances of reactance, higher monitoring frequency may also allow researchers to pick up on uninvestigated patterns in meat consumption, like those resulting from seasonality, that may inform how these types of meat reduction interventions could be more strategically timed. Furthermore, researchers could additionally use survey items to understand whether these approaches are associated with meaningful contextual spillover effects, or if they lead individuals to engage in other pro-environmental behaviors unrelated to meat consumption. Finally, the question of how generalizable these effects are across other cultural contexts and institutional settings remains unanswered. Future research should therefore investigate whether settings-based evaluation techniques may prove useful across similar dining contexts (Moore et al., 2013; Hertwig and Grüne-Yanoff, 2017), such as in hospital and workplace cafeterias, and whether these findings can be replicated in universities that stand to benefit from these interventions that are situated outside of a Western context, such as in China and Brazil.
5. Conclusions
The results from this systematic and meta-analysis provide compelling evidence in favor of university-implemented meat reduction interventions and their value in promoting dietary change within university settings. Through our comparisons of the different approaches that have been used within these environments, we were able to identify a number of strategic advantages associated with using multimodal interventions to facilitate these desired changes in food choice. Institutional stakeholders interested in engaging in these types of sustainable dining initiatives should therefore consider incorporating these design principles into future interventions. Despite the promise of these initial findings, further research is still needed to understand how long these effects endure within university environments, and whether these design principles are generalizable across other settings and cultural contexts.
Data availability statement
The datasets presented in this study can be found in the following online repositories: [link 1] and https://github.com/kenjinc/university-meat-reduction-srma/blob/main/markdowns/visuals.md.
Author contributions
KC initiated and conceptualized the project. LR designed and implemented the search strategy with assistance from KC and AW. KC and AW conducted the relevant screening procedures with assistance from DA-J and RR. KC synthesized the extracted data, conducted the relevant analyses, and drafted the manuscript. RR acquired funding for the project and provided administrative support and supervision throughout. All authors reviewed the final version of the manuscript prior to submission.
Funding
This research was funded by the Johns Hopkins Center for a Livable Future with a gift from the Santa Barbara Foundation. The funders had no role in the design or conduct of the study or the decision to submit this manuscript for publication.
Acknowledgments
We would like to thank Keeve Nachman and Shawn McKenzie for their feedback on this manuscript and Raychel Santo and Roni Neff for lending their support and expertise.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1103060/full#supplementary-material
References
Aleksandrowicz, L., Green, R., Joy, E. J., Smith, P., and Haines, A. (2016). The impacts of dietary change on greenhouse gas emissions, land use, water use, and health: a systematic review. PLoS ONE 11, e0165797. doi: 10.1371/journal.pone.0165797
Amaral, A. R., Rodrigues, E., Gaspar, A. R., and Gomes, Á. (2020). A review of empirical data of sustainability initiatives in university campus operations. J. Clean. Prod. 250, 119558. doi: 10.1016/j.jclepro.2019.119558
Andersson, O., and Nelander, L. (2021). Nudge the lunch: A field experiment testing menu-primacy effects on lunch choices. Games 12, 2. doi: 10.3390/g12010002
Ben-Shachar, M. S., Lüdecke, D., and Makowski, D. (2020). effectsize: Estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815. doi: 10.21105/joss.02815
Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P., and Jebb, S. A. (2018a). Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. Int. J. Behav. Nutr. Phys. Activ. 15, 1–25. doi: 10.1186/s12966-018-0729-6
Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P., and Jebb, S. A. (2018b). Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. The Lancet Planetary Health 2, e384–e397. doi: 10.1016/S2542-5196(18)30188-8
Bloch, P., Toft, U., Reinbach, H. C., Clausen, L. T., Mikkelsen, B. E., Poulsen, K., et al. (2014). Revitalizing the setting approach–supersettings for sustainable impact in community health promotion. Int. J. Behav. Nutr. Phys. Activ. 11, 1–15. doi: 10.1186/s12966-014-0118-8
Boon, M. H., and Thomson, H. (2021). The effect direction plot revisited: Application of the 2019 Cochrane Handbook guidance on alternative synthesis methods. Res. Synth. Methods 12, 29–33. doi: 10.1002/jrsm.1458
Brunner, F., Kurz, V., Bryngelsson, D., and Hedenus, F. (2018). Carbon label at a university restaurant–label implementation and evaluation. Ecol. Econ. 146, 658–667. doi: 10.1016/j.ecolecon.2017.12.012
Bucher, T., Collins, C., Rollo, M. E., McCaffrey, T. A., De Vlieger, N., Van der Bend, D., et al. (2016). Nudging consumers towards healthier choices: a systematic review of positional influences on food choice. Br. J. Nutr. 115, 2252–2263. doi: 10.1017/S0007114516001653
Bull, J. W., Taylor, I., Biggs, E., Grub, H. M., Yearley, T., Waters, H., et al. (2022). Analysis: The biodiversity footprint of the University of Oxford. Nature. 604, 420–424. doi: 10.1038/d41586-022-01034-1
Byerly, H., Balmford, A., Ferraro, P. J., Hammond Wagner, C., Palchak, E., Polasky, S., et al. (2018). Nudging pro-environmental behavior: evidence and opportunities. Front. Ecol. Environ. 16, 159–168. doi: 10.1002/fee.1777
Campbell-Arvai, V. (2011). Incorporating environmental concerns into decisions about food: contributions from research on decision-making. Michigan State University. Community, Agriculture, Recreation and Resource Studies.
Carfora, V., Caso, D., and Conner, M. (2016). Randomized controlled trial of a messaging intervention to increase fruit and vegetable intake in adolescents: Affective versus instrumental messages. Br. J. Health Psychol. 21, 937–955. doi: 10.1111/bjhp.12208
Carfora, V., Caso, D., and Conner, M. (2017). Correlational study and randomised controlled trial for understanding and changing red meat consumption: The role of eating identities. Soc. Sci. Med. 175, 244–252. doi: 10.1016/j.socscimed.2017.01.005
Carfora, V., Catellani, P., Caso, D., and Conner, M. (2019). How to reduce red and processed meat consumption by daily text messages targeting environment or health benefits. J. Environ. Psychol. 65, 101319. doi: 10.1016/j.jenvp.2019.101319
Carrico, A. R. (2021). Climate change, behavior, and the possibility of spillover effects: Recent advances and future directions. Curr. Opin. Behav. Sci. 42, 76–82. doi: 10.1016/j.cobeha.2021.03.025
Cerezo-Prieto, M., and Frutos-Esteban, F. J. (2021). Towards healthy pathways: Effect of nutrition labels on eating behaviours in a university canteen. Atenc. Primaria 53, 102022–102022. doi: 10.1016/j.aprim.2021.102022
Chen, C., Chaudhary, A., and Mathys, A. (2022). Dietary change and global sustainable development goals. Front. Sustain. Food Syst. 6, 71041. doi: 10.3389/fsufs.2022.771041
Clarivate (2020). EndNote (Versionx9.3.3) [Computer Software]. Available online at: https://endnote.com/
Clark, M. A., Domingo, N. G., Colgan, K., Thakrar, S. K., Tilman, D., Lynch, J., et al. (2020). Global food system emissions could preclude achieving the 1.5 and 2 C climate change targets. Science 370, 705–708. doi: 10.1126/science.aba7357
De Boer, J., Schösler, H., and Aiking, H. (2014). “Meatless days” or “less but better”? Exploring strategies to adapt Western meat consumption to health and sustainability challenges. Appetite. 76, 120–128. doi: 10.1016/j.appet.2014.02.002
de Villiers, A., and Faber, M. (2019). Changing young people's food-related behaviour: A socio-ecological perspective. Public Health Nutr. 22, 1917–1919. doi: 10.1017/S136898001900123X
Dissen, A., and Crowell, T. (2020). We are what we eat: Assessing the use of a documentary film as an educational tool to change students' nutritional attitudes and behaviors. Am. J. Lifestyle Med. 16, 241–250. doi: 10.1177/1559827620903701
Dooris, M. (2009). Holistic and sustainable health improvement: the contribution of the settings-based approach to health promotion. Perspect. Public Health 129, 29–36. doi: 10.1177/1757913908098881
Ensaff, H. (2021). A nudge in the right direction: the role of food choice architecture in changing populations' diets. Proc. Nutr. Soc. 80, 195–206. doi: 10.1017/S0029665120007983
Evans, J. S. B. (2011). Dual-process theories of reasoning: Contemporary issues and developmental applications. Develop. Rev. 31, 86–102. doi: 10.1016/j.dr.2011.07.007
Ewald, J., Sterner, T., and Sterner, E. (2021). Understanding the resistance to carbon taxes. Resources for the Future working paper.
Garnett, E. E., and Balmford, A. (2022). The vital role of organizations in protecting climate and nature. Nat. Hum. Behav. 6, 319–321. doi: 10.1038/s41562-021-01260-z
Garnett, E. E., Balmford, A., Marteau, T. M., Pilling, M. A., and Sandbrook, C. (2021). Price of change: Does a small alteration to the price of meat and vegetarian options affect their sales? J. Environ. Psychol. 75, 101589. doi: 10.1016/j.jenvp.2021.101589
Garnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A., and Marteau, T. M. (2019). Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Nat. Acad. Sci. 116, 20923–20929. doi: 10.1073/pnas.1907207116
Garnett, E. E., Marteau, T. M., Sandbrook, C., Pilling, M. A., and Balmford, A. (2020). Order of meals at the counter and distance between options affect student cafeteria vegetarian sales. Nature Food 1, 485–488. doi: 10.1038/s43016-020-0132-8
Garnett, T., Mathewson, S., Angelides, P., and Borthwick, F. (2015). Policies and actions to shift eating patterns: what works. Foresight 515, 518–522.
Gittelsohn, J., and Lee, K. (2013). Integrating educational, environmental, and behavioral economic strategies may improve the effectiveness of obesity interventions. Appl. Econ. Perspect. Policy 35, 52–68. doi: 10.1093/aepp/pps044
Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimer, J., et al. (2018). Meat consumption, health, and the environment. Science 361, eaam5324. doi: 10.1126/science.aam5324
Graça, J., Godinho, C. A., and Truninger, M. (2019). Reducing meat consumption and following plant-based diets: Current evidence and future directions to inform integrated transitions. Trends Food Sci. Technol. 91, 380–390. doi: 10.1016/j.tifs.2019.07.046
Hamam, M., Chinnici, G., De Vita, G., Pappalardo, G., Pecorino, B., Maesano, G., et al. (2021). Circular economy models in agri-food systems: A review. Sustainability 13, 3453. doi: 10.3390/su13063453
Hargreaves, D., Mates, E., Menon, P., Alderman, H., Devakumar, D., Fawzi, W., et al. (2021). Strategies and interventions for healthy adolescent growth, nutrition, and development. The Lancet. 399, 198–210. doi: 10.1016/S0140-6736(21)01593-2
Harguess, J. M., Crespo, N. C., and Hong, M. Y. (2020). Strategies to reduce meat consumption: A systematic literature review of experimental studies. Appetite 144, 104478. doi: 10.1016/j.appet.2019.104478
Hartmann, C., and Siegrist, M. (2017). Consumer perception and behaviour regarding sustainable protein consumption: A systematic review. Trends Food Sci. Technol. 61, 11–25. doi: 10.1016/j.tifs.2016.12.006
Hertwig, R., and Grüne-Yanoff, T. (2017). Nudging and boosting: Steering or empowering good decisions. Perspect. Psychol. Sci. 12, 973–986. doi: 10.1177/1745691617702496
Hormes, J. M., Rozin, P., Green, M. C., and Fincher, K. (2013). Reading a book can change your mind, but only some changes last for a year: food attitude changes in readers of The Omnivore's Dilemma. Front. Psychol. 4, 778. doi: 10.3389/fpsyg.2013.00778
Jalil, A. J., Tasoff, J., and Bustamante, A. V. (2020). Eating to save the planet: Evidence from a randomized controlled trial using individual-level food purchase data. Food Policy 95, 101950. doi: 10.1016/j.foodpol.2020.101950
Jay, J. A., D'Auria, R., Nordby, J. C., Rice, D. A., Cleveland, D. A., Friscia, A., et al. (2019). Reduction of the carbon footprint of college freshman diets after a food-based environmental science course. Clim. Change 154, 547–564. doi: 10.1007/s10584-019-02407-8
Jiang, G., Ameer, K., Kim, H., Lee, E. J., Ramachandraiah, K., and Hong, G. P. (2020). Strategies for sustainable substitution of livestock meat. Foods 9, 1227. doi: 10.3390/foods9091227
Jones, K., Pfeifer, K., and Castillo, G. (2019). “Trends in the global food system and implications for institutional foodservice,” in Institutions as Conscious Food Consumers (Academic Press) 21–46. doi: 10.1016/B978-0-12-813617-1.00002-2
Jürkenbeck, K., Spiller, A., and Schulze, M. (2021). Climate change awareness of the young generation and its impact on their diet. Cleaner and Responsible Consumption 3, 100041. doi: 10.1016/j.clrc.2021.100041
Kahn-Marshall, J. L., and Gallant, M. P. (2012). Making healthy behaviors the easy choice for employees: a review of the literature on environmental and policy changes in worksite health promotion. Health Educ. Behav. 39, 752–776. doi: 10.1177/1090198111434153
Kaur, A., Scarborough, P., and Rayner, M. (2017). A systematic review, and meta-analyses, of the impact of health-related claims on dietary choices. Int. J. Behav. Nutr. Phys. Activ. 14, 1–17. doi: 10.1186/s12966-017-0548-1
Kennedy, C. E., Fonner, V. A., Armstrong, K. A., Denison, J. A., Yeh, P. T., O'Reilly, K. R., et al. (2019). The Evidence Project risk of bias tool: assessing study rigor for both randomized and non-randomized intervention studies. System. Rev. 8, 1–10. doi: 10.1186/s13643-018-0925-0
Krabbe, O., Linthorst, G., Blok, K., Crijins-Graus, W., Van Vuuren, D. P., Höhne, N., et al. (2015). Aligning corporate greenhouse-gas emissions targets with climate coals. Nat. Clim. Change 5, 1057–1060. doi: 10.1038/nclimate2770
Kurz, V. (2018). Nudging to reduce meat consumption: Immediate and persistent effects of an intervention at a university restaurant. J. Environ. Econ. Manag. 90, 317–341. doi: 10.1016/j.jeem.2018.06.005
Kwasny, T., Dobernig, K., and Riefler, P. (2022). Towards reduced meat consumption: A systematic literature review of intervention effectiveness, 2001–2019. Appetite 168, 105739. doi: 10.1016/j.appet.2021.105739
Larner, E., Fish, A. L., Way, C. H., Graham, F., Armstrong, B., Patel, V., et al. (2021). Reaction to a low-carbon footprint food logo and other sustainable diet promotions in a UK University's Student Union ‘Living Lab'. Fut. Food. 9, 12593. doi: 10.17170/kobra-202011192217
Leal Filho, W., Pallant, E., Enete, A., Richter, B., and Brandli, L. L. (2018). Planning and implementing sustainability in higher education institutions: An overview of the difficulties and potentials. Int. J. Sustain. Develop. World Ecol. 25, 713–721. doi: 10.1080/13504509.2018.1461707
Loy, L. S., Wieber, F., Gollwitzer, P. M., and Oettingen, G. (2016). Supporting sustainable food consumption: Mental contrasting with implementation intentions (MCII) aligns intentions and behavior. Front. Psychol. 7, 607. doi: 10.3389/fpsyg.2016.00607
Macidarmid, J. I., Douglas, F., and Campbell, J. (2016). Eating like there's no tomorrow: Public awareness of the environmental impact of food and reluctance to eat less meat as part of a sustainable diet. Appetite 96, 497–493. doi: 10.1016/j.appet.2015.10.011
Malan, H., Amsler Challamel, G., Silverstein, D., Hoffs, C., Spang, E., Pace, S. A., et al. (2020). Impact of a scalable, multi-campus “foodprint” seminar on college students' dietary intake and dietary carbon footprint. Nutrients 12, 2890. doi: 10.3390/nu12092890
Malan, H. J. (2020). Swap the meat, save the planet: A community-based participatory approach to promoting healthy, sustainable food in a university setting (Order No. 27999051). ProQuest Dissertations and Theses Global. (2419336239). Los Angeles: University of California. Available online at: https://www.proquest.com/dissertations-theses/swap-meat-save-planet-community-based/docview/2419336239/se-2 (accessed June 4, 2021).
McClain, A. D., Hekler, E. B., and Gardner, C. D. (2013). Incorporating prototyping and iteration into intervention development: A case study of a dining hall–based intervention. J. Am. College Health 61, 122–131. doi: 10.1080/07448481.2012.755189
McDonough, T. (2012). Modifying Students' Intentions to Eat Sustainably. McGill University. doi: 10.18848/2325-1212/CGP/v09i02/55300
Mertens, S., Herberz, M., Hahnel, U. J., and Brosch, T. (2022). The effectiveness of nudging: A meta-analysis of choice architecture interventions across behavioral domains. Proc. Nat. Acad. Sci. 119, e2107346118. doi: 10.1073/pnas.2107346118
Meyer, K. B., and Simons, J. (2021). Good attitudes are not good enough: an ethnographical approach to investigate attitude-behavior inconsistencies in sustainable choice. Foods 10, 1317. doi: 10.3390/foods10061317
Michels, K. B., Bloom, B. R., Riccardi, P., Rosner, B. A., and Willett, W. C. (2008). A study of the importance of education and cost incentives on individual food choices at the Harvard School of Public Health cafeteria. J. Am. College Nutr. 27, 6–11. doi: 10.1080/07315724.2008.10719669
Microsoft Corporation (2018). Excel (Version 16.1.1) [Computer software]. Available online at: https://www.microsoft.com/en-us/microsoft-365/excel
Moore, L., de Silva-Sanigorski, A., and Moore, S. N. (2013). A socio-ecological perspective on behavioural interventions to influence food choice in schools: alternative, complementary or synergistic?. Public Health Nutr. 16, 1000–1005. doi: 10.1017/S1368980012005605
Nash, J. T. (2014). The Green Eating Project: Web-Based Intervention to Promote Environmentally Conscious Eating Behaviors. University of Rhode Island. doi: 10.23860/diss-nash-jessica-2014
Nielsen, K. S. (2023). Organizational change for biodiversity. Nat. Food 4, 15–16. doi: 10.1038/s43016-022-00635-3
Osman, M. (2020). Backfiring, reactance, boomerang, spillovers, and rebound effects: Can we learn anything from examples where nudges do the opposite of what they intended? PsyArXiv [Preprint]. doi: 10.31234/osf.io/ae756
Papadaki, A., and Scott, J. A. (2005). The Mediterranean eating in Scotland experience project: evaluation of an Internet-based intervention promoting the Mediterranean diet. Br. J. Nutr. 94, 290–298. doi: 10.1079/BJN20051476
Parlasca, M. C., and Qaim, M. (2022). Meat Consumption and Sustainability. Ann. Rev. Resour. Econ. 14, 17–41. doi: 10.1146/annurev-resource-111820-032340
Pechey, R., Reynolds, J. P., Cook, B., Marteau, T. M., and Jebb, S. A. (2022). Acceptability of policies to reduce consumption of red and processed meat: A population-based survey experiment. J. Environ. Psychol. 81, 101817. doi: 10.1016/j.jenvp.2022.101817
Piester, H. E., DeRieux, C. M., Tucker, J., Buttrick, N. R., Galloway, J. N., and Wilson, T. D. (2020). “I'll try the veggie burger”: Increasing purchases of sustainable foods with information about sustainability and taste. Appetite 155, 104842. doi: 10.1016/j.appet.2020.104842
Ramsing, R., Chang, K., Hendrickson, Z., Xu, Z., Friel, M., and Calves, E. (2021). The role of community-based efforts in promoting sustainable diets: Lessons from a grassroots meat-reduction campaign. J. Agric. Food Syst. Commun. Develop. 10, 373–397. doi: 10.5304/jafscd.2021.102.026
Ring, M., Cheung, E., Mahadevan, R., Folkens, S., and Edens, N. (2019). Cooking up health: a novel culinary medicine and service learning elective for health professional students. J. Alter. Complem. Med. 25, 61–72. doi: 10.1089/acm.2018.0313
Robinson, T. (2008). Applying the socio-ecological model to improving fruit and vegetable intake among low-income African Americans. J. Commun. Health 33, 395–406. doi: 10.1007/s10900-008-9109-5
Ronto, R., Saberi, G., Leila Robbers, G. M., Godrich, S., Lawrence, M., Somerset, S., et al. (2022). Identifying effective interventions to promote consumption of protein-rich foods from lower ecological footprint sources: A systematic literature review. PLOS Global Public Health 2, e0000209. doi: 10.1371/journal.pgph.0000209
RStudio Team (2021). RStudio (Version 7.1.544) [Computer software]. Available online at: https://posit.co/download/rstudio-desktop/
Ruiz-Mallén, I., and Heras, M. (2020). What sustainability? Higher education institutions' pathways to reach the Agenda 2030 goals. Sustainability. 12, 1290. doi: 10.3390/su12041290
Rust, N. A., Ridding, L., Ward, C., Clark, B., Kehoe, L., Dora, M., et al. (2020). How to transition to reduced-meat diets that benefit people and the planet. Sci. Total Environ. 718, 137208. doi: 10.1016/j.scitotenv.2020.137208
Sanchez-Sabate, R., and Sabaté, J. (2019). Consumer attitudes towards environmental concerns of meat consumption: A systematic review. Int. J. Environ. Res. Public Health 16, 1220. doi: 10.3390/ijerph16071220
Schölmerich, V. L., and Kawachi, I. (2016). Translating the socio-ecological perspective into multilevel interventions: Gaps between theory and practice. Health Educ. Behav. 43, 17–20. doi: 10.1177/1090198115605309
Schwarzer, G. (2022). Systematic Reviews in Health Research: Meta-Analysis in Context. Oxford, UK: John Wiley & Sons. 510–534. doi: 10.1002/9781119099369.ch26
Schwitzgebel, E., Cokelet, B., and Singer, P. (2020). Do ethics classes influence student behavior? Case study: Teaching the ethics of eating meat. Cognition 203, 104397. doi: 10.1016/j.cognition.2020.104397
Sherry, J., and Tivona, S. (2022). Reducing the environmental impact of food service in universities using life cycle assessment. Int. J. Sustain. Higher Educ. 23, 1469–1481. doi: 10.1108/IJSHE-06-2021-0224
Sievert, K., Lawrence, M., Parker, C., and Baker, P. (2020). Understanding the political challenge of red and processed meat reduction for healthy and sustainable food systems: a narrative review of the literature. Int. J. Health Policy Manag. 10, 1–16. doi: 10.34172/ijhpm.2020.238
Slapø, H. B., and Karevold, K. I. (2019). Simple eco-labels to nudge customers toward the most environmentally friendly warm dishes: An empirical study in a cafeteria setting. Front. Sustain. Food Syst. 3, 40. doi: 10.3389/fsufs.2019.00040
Springmann, M., Clark, M., Mason-D'Croz, D., Wiebe, K., Bodirsky, B. L., Lassaletta, L., et al. (2018). Options for keeping the food system within environmental limits. Nature 562, 519–525. doi: 10.1038/s41586-018-0594-0
Springmann, M., Godfray, H. C. J., Rayner, M., and Scarborough, P. (2016). Analysis and valuation of the health and climate change cobenefits of dietary change. Proc. Nat. Acad. Sci. 113, 4146–4151. doi: 10.1073/pnas.1523119113
Sun, Z., Scherer, L., Tukker, A., Spawn-Lee, S. A., Bruckner, M., Gibbs, H. K., et al. (2022). Dietary change in high-income nations alone can lead to substantial double climate dividend. Nature Food 3, 29–37. doi: 10.1038/s43016-021-00431-5
Taylor, I., Bull, J. W., Ashton, B., Biggs, E., Clark, M., Gray, N., et al. (2023). Nature-positive goals for an organization's food consumption. Nature Food 4, 98–103. doi: 10.1038/s43016-022-00660-2
Thakur, S., and Mathur, P. (2021). Nutrition knowledge and its relation with dietary behaviour in children and adolescents: a systematic review. Int. J. Adolesc. Med. Health. 15, 192. doi: 10.1515/ijamh-2020-0192
Townsend, N., and Foster, C. (2013). Developing and applying a socio-ecological model to the promotion of healthy eating in the school. Public Health Nutr. 16, 1101–1108. doi: 10.1017/S1368980011002655
Turnwald, B. P., and Crum, A. J. (2019). Smart food policy for healthy food labeling: Leading with taste, not healthiness, to shift consumption and enjoyment of healthy foods. Preven. Med. 119, 7–13. doi: 10.1016/j.ypmed.2018.11.021
Valli, C., Rabassa, M., Johnston, B. C., Kuijpers, R., Prokop-Dorner, A., Zajac, J., et al. (2019). Health-related values and preferences regarding meat consumption: a mixed-methods systematic review. Ann. Internal Med. 171, 742–755. doi: 10.7326/M19-1326
Van Rookhuijzen, M., De Vet, E., and Adriaanse, M. A. (2021). The effects of nudges: One-shot only? Exploring the temporal spillover effects of a default nudge. Front. Psychol. 12, 683262. doi: 10.3389/fpsyg.2021.683262
Vandenbroele, J., Vermeir, I., Geuens, M., Slabbinck, H., and Van Kerckhove, A. (2020). Nudging to get our food choices on a sustainable track. Proc. Nutr. Soc. 79, 133–146. doi: 10.1017/S0029665119000971
Verfuerth, C., Gregory-Smith, D., Oates, C. J., Jones, C. R., and Alevizou, P. (2021). Reducing meat consumption at work and at home: facilitators and barriers that influence contextual spillover. J. Market. Manage. 37, 671–702. doi: 10.1080/0267257X.2021.1888773
Veritas Health Innovation (2021). Covidence [Online software]. Available online at: www.covidence.org
Viswanathan, M., Patnode, C. D., Berkman, N. D., Bass, E. B., Chang, S., Hartling, L., et al. (2018). Recommendations for assessing the risk of bias in systematic reviews of health-care interventions. J. Clin. Epidemiol. 97, 26–34. doi: 10.1016/j.jclinepi.2017.12.004
Vo, L., Albrecht, S. S., and Kershaw, K. N. (2019). Multilevel interventions to prevent and reduce obesity. Curr. Opin. Endocr. Metab. Res. 4, 62–69. doi: 10.1016/j.coemr.2018.11.002
Wang, X., Lin, X., Ouyang, Y. Y., Liu, J., Zhao, G., Pan, A., et al. (2016). Red and processed meat consumption and mortality: dose–response meta-analysis of prospective cohort studies. Public Health Nutr. 19, 893–905. doi: 10.1017/S1368980015002062
Webb, T. L., and Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol. Bull. 132, 249. doi: 10.1037/0033-2909.132.2.249
Wellesley, L., Happer, C., and Froggatt, A. (2015). Changing climate, changing diets. Chatham House Report.
Whitelaw, S., Baxendale, A., Bryce, C., Machardy, L., Young, I., and Witney, E. (2001). ‘Settings' based health promotion: a review. Health Prom. Int. 16, 339–353. doi: 10.1093/heapro/16.4.339
Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., et al. (2019). Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492. doi: 10.1016/S0140-6736(18)31788-4
Wolstenholme, E., Poortinga, W., and Whitmarsh, L. (2020). Two birds, one stone: The effectiveness of health and environmental messages to reduce meat consumption and encourage pro-environmental behavioral spillover. Front. Psychol. 11, 577111. doi: 10.3389/fpsyg.2020.577111
Worsley, A. (2002). Nutrition knowledge and food consumption: can nutrition knowledge change food behaviour? Asia Pacific J. Clin. Nutr. 11, S579–S585. doi: 10.1046/j.1440-6047.11.supp3.7.x
Yip, C. S. C., Crane, G., and Karnon, J. (2013). Systematic review of reducing population meat consumption to reduce greenhouse gas emissions and obtain health benefits: effectiveness and models assessments. Int. J. Public Health 58, 683–693. doi: 10.1007/s00038-013-0484-z
Keywords: dietary change, health promotion, systematic review, universities and higher education institutions, behavioral interventions, meat reduction, meta-analysis, sustainable nutrition
Citation: Chang KB, Wooden A, Rosman L, Altema-Johnson D and Ramsing R (2023) Strategies for reducing meat consumption within college and university settings: A systematic review and meta-analysis. Front. Sustain. Food Syst. 7:1103060. doi: 10.3389/fsufs.2023.1103060
Received: 24 November 2022; Accepted: 06 March 2023;
Published: 31 March 2023.
Edited by:
Tuba Esatbeyoglu, Leibniz University Hannover, GermanyReviewed by:
Esther Jolanda Veen, Aeres University of Applied Science, NetherlandsGaetano Chinnici, University of Catania, Italy
Copyright © 2023 Chang, Wooden, Rosman, Altema-Johnson and Ramsing. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kenjin B. Chang, kbc45@cornell.edu