Sunday, October 25, 2020

Co-designing "healthy eating" interventions with supermarket retailers: Consumers did not fall in the trap & altered shelf placement alone did not improve the (official) healthiness of food purchases

The effect of a shelf placement intervention on sales of healthier and less healthy breakfast cereals in supermarkets: A co-designed pilot study. Leanne Young et al. Social Science & Medicine, September 1 2020, 113337.


• Co-designing healthy eating interventions with supermarket retailers is feasible.

• Altered shelf placement alone did not improve the healthiness of food purchases.

• Customers noted brand preferences and price as key determinants of purchases.

• In-store promotions present opportunities to improve healthiness of food purchases.

• Product promotional strategies should align with healthy eating interventions.

Abstract: Supermarkets are the principal source of grocery food in many high-income countries. Choice architecture strategies show promise to improve the healthiness of food choices. A retailer-academic collaboration was formed to co-design and pilot selected commercially sustainable strategies to increase sales of healthier foods relative to less healthy foods in supermarkets. Two co-design workshops, involving supermarket corporate staff and public health nutrition academics, identified potential interventions. One intervention, more prominent shelf placement of healthier products within one category (breakfast cereals), was selected for testing. A pilot study (baseline, intervention and follow-up, 12-weeks each) was undertaken in six supermarkets (three intervention and three control) in Auckland, New Zealand. Products were ranked by nutrient levels and profile, and after accounting for the supermarkets’ space management principles, healthier products were placed at adult eye level. The primary outcome was change in sales of healthier products relative to total category sales. Secondary outcomes were nutrient profile of category sales, in-store product promotions, customer perceptions, and retailer feedback. There was no difference in proportional sales of more prominently positioned healthier products between intervention (56%) and control (56%) stores during the intervention. There were no differences in the nutrient profile of category sales. A higher proportion of less healthy breakfast cereals were displayed in intervention versus control stores (57% vs 43%). Most customers surveyed supported shelf placement as a strategy (265, 88%) but noted brand preferences and price were more salient determinants of purchases. Retailers were similarly supportive but balancing profit, health/nutrition and customer satisfaction was challenging. Shelf placement alone was not an effective strategy to increase purchases of healthier breakfast cereals. This study showed co-design of a healthy eating intervention with a commercial retailer is feasible, but concurrent retail environment factors likely limited the public health impact of the intervention.

Keywords: SupermarketsDietsShelf placementCo-designNutritionChoice architecture

4. Discussion

In this pilot study, the co-designed intervention, more prominent shelf placement of healthier products, had no effect on healthier breakfast cereal sales. Whilst small increases in sales were shown in two cereal segments and for two of the three intervention stores these were not statistically significant. Altering the shelf placement of products was the sole change made to the food category; therefore, this study was useful to test the effectiveness of this strategy in isolation. This single strategy study was unique compared to many supermarket interventions (Adam and Jensen, 2016Hartmann-Boyce et al., 2018), which commonly test multiple strategies (signage, placement, education, price) and therefore the effect of individual strategies within a multi-faceted intervention is usually less able to be determined (Cameron et al., 2016). Despite this, a systematic review found that single and multi-strategy interventions share the same high success rate (70%) (Cameron et al., 2016). Inclusion of a whole category rather than individual products within a category was also a distinctive feature of this study. However, the findings suggest that shelf placement alone (in the absence of other strategies) is a weak lever for influencing the healthiness of shopper purchases in the breakfast cereal category.

Secondly, there was no effect of the shelf placement intervention on the nutritional composition of sales within the breakfast cereal category. This intervention was implemented in a ‘real world’ supermarket setting. Therefore, the nutritional ranking of breakfast cereals by cereal category segment (by researchers) was subject to the usual supermarket space management criteria for shelf placement. These included segmentation (e.g. all oats grouped together), brand blocking (brands located together), pack size blocking (similar sized packages located together) and visual appeal of products on shelves that aim to make product selection easy for shoppers. These requirements and that just over half (56%) of the products did not change position resulted in relatively small differences in the nutritional composition of products located in prominent versus non-prominent shelf locations, which are likely reasons for the lack of effect on nutrient sales. Interviews with store staff supported the notion that space management criteria compromised the ideal placement of products. Furthermore, the range of nutrient composition values (e.g. energy) was narrow for some smaller segments, e.g. biscuits (n = 12).

Thirdly, in-store product display promotions appear to have interacted with the shelf placement intervention. There were multiple breakfast cereals on in-store displays across all six stores (n = 1268), with a slightly higher proportion in intervention stores (54%) and a higher proportion of less healthy, less prominent products compared to control stores. Per store, there were 19 breakfast cereals (includes flavour variants of products) each week in aggregated displays (4–6 actual display areas per store featuring multiple products and product variants) (data was collected at one time point each week). Store managers are provided with guidance from a national display matrix, which provides product promotional options within a category/segment for each designated display space. When products are on promotion the entire brand range may be included (healthy and less healthy). It is possible that at the time of the audit the healthier choices had already sold out on displays and gaps were filled by other, less healthy products in the range. It is also plausible that the higher number of promotions for less healthy products in intervention stores may have been orchestrated by store personnel (consciously or unconsciously) to feature higher selling, more profitable products, that had been moved to less prominent shelf positions, and thus counteracted shelf prominence of healthier products. Data on in-store promotions were not collected in the pre- or post-intervention periods therefore change in the type of promotions over time could not be determined.

The lack of effect of prominent placement on product sales shown in our study generally aligns with findings from Foster et al. (2014) who suggested that brand loyalty and product preferences may be dominant in this particular category. Brand loyalty in the breakfast cereal category also emerged as a strong theme from our shopper survey, with shoppers commenting that they tended to purchase the same brand repeatedly. Similarly, strong shopper preferences and habitual purchase behaviours were found in an experiment examining the effects of a change of placement for types of bread (de Wijk et al., 2016). The bread category was described as less able to be ‘nudged’ because a nudge needs to be of sufficient strength to overrule usual purchase habits. Other mechanisms in the environment can also influence habitual health behaviours and consumer choices (Wilson et al., 2016), for example, product price, nutrition labelling/information and availability (Arno and Thomas, 2016). Price was another key factor that shoppers highlighted in our current study as influencing product choice, although brands with high loyalty tend to use price less to generate sales compared to minor brands (Empen et al., 2011).

This study had several strengths. It utilised co-design to enhance the likely fiscal sustainability of the intervention. This process allowed a strong working relationship to be built with the retail partner, which facilitated intervention delivery, access to sales and promotional data, and possible future research opportunities. Intervention selection was informed by commercial knowledge and not preconceived by researchers. A single strategy, more prominent shelf placement of healthier products within an entire food category, was piloted in a real-world environment using a controlled study design to determine potential effectiveness. Inclusion of pre-intervention and follow-up periods allowed measurement of change over time. Supermarket sales data was used as a direct measure of change in shopper purchases to determine the effect of the intervention rather than reliance on self-reported purchases (Bandy et al., 2019). Weekly product auditing and retailer follow-up of anomalies resulted in high intervention compliance.

The study was however, limited by its small sample size (6 stores) and lack of randomisation. Although, it has been acknowledged that randomisation in supermarket intervention design is difficult due to the innate nature of real-life implementation (Escaron et al., 2013). The original aim was to pilot the intervention in a limited number of stores with the intention that if findings were promising, a larger sufficiently powered randomised controlled trial would be conducted. However, our experience working alongside a major retailer suggests that successful interventions would likely be rolled out to a larger number of stores very quickly, with little time for a larger randomised controlled study to be organised. Other limitations to note briefly include lack of alignment of the intervention with other concomitant breakfast cereal promotions (price reductions, mailers, and in-store displays), lower compliance with product planograms in control stores, selection of a category where customer brand loyalty and purchase habits are strong which likely minimised potential impact, and relatively small difference in the healthiness of prominent and less prominent products. More research is needed to understand the effects of the range of in-store promotions, including price, on sales within the supermarket environment. Other categories where shoppers do not purchase the same products habitually may also have been more suited to testing shelf placement, for example, convenience foods, ready meals or soups.

Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data

Improving Prediction of Real-Time Loneliness and Companionship Type Using Geosocial Features of Personal Smartphone Data. Congyu Wu et al. arXiv October 2020,

Abstract: Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Androidusing college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and companionship type and (2) propose and validate novel geosocial features of smartphone-based Bluetooth and GPS data for predicting loneliness and companionship type in real time. We base our features on intuitions characterizing the quantity and spatiotemporal predictability of an individual’s Bluetooth encounters and GPS location clusters to capture personal significance of social exposure scenarios conditional on their temporal distribution and geographic patterns. We examine our features’ statistical correlation with momentary loneliness through regression analyses and evaluate their predictive power using a sliding window prediction procedure. Our features achieved significant performance improvement compared to baseline for predicting both momentary loneliness and companionship type, with the effect stronger for the loneliness prediction task. As such we recommend incorporation and further evaluation of our geosocial features proposed in this study in future mental health sensing and context-aware computing applications.

8 Discussion

In this section we reflect on our outcome variables and approach in the grander context of understanding human behavior and enhancing human well-being through mobile sensing and data analytics. 

Temporal resolution The two related outcomes examined in this paper, loneliness and companionship type, fall in two overlapping yet distinguishable areas in ubiquitous computing research, namely mental health sensing and context-aware computing, respectively. Context-aware computing emphasizes a computer’s inference of its user’s activity and surroundings in real-time, thus naturally having a moment-to-moment granularity. However, mental health sensing tasks span a wider range of temporal resolutions. On the low end, we see condition diagnosis tasks observe participants for as long as two months consecutively and then offer a judgment about whether a participant is with a clinical condition such as depression. On the high end reside real-time tracking tasks like the one presented in this paper, which do not aim at a medical diagnosis but focus on raising timely warnings. In the middle of the scale, a number of studies have adopted temporal resolutions ranging from daily and every few days to weekly and bi-weekly. The differences in temporal resolution points to different types, formats, and content of intervention: following a diagnosis, traditional intervention programs may be applied as treatment, whereas predictions of higher temporal resolutions will enable just-in-time adaptive intervention via mobile platforms. Question as to what sensing-intervention scheme will be most efficacious for what cohorts and conditions remains open, challenging, and critical for successful future applications of smart mental health.

Social context Companionship type is a key aspect of an individual’s social context, but far from the entire picture. The extent to which companionship type was captured in this paper covers the existence of a companion and (if true) the nature of a companion but does not consider the number of people surrounding a participant, differences in distance, and the interaction behavior, which altogether constitute a holistic social context in which one is situated. To combat the arbitrariness in defining social context seen in extant literature and to systematically delineate the various aspects of social context sensing, we argue that a formalized response variable definition for future social context inference tasks is needed. We propose that four components, quantity, quality, distance, and interaction, be specified in a definition of social context in future context-aware computing work. Quantity refers to the number of individuals and quality refers to their social significance. The distance element, can be categorized into groups such as “within personal space”, “within social space”, and “beyond social space” based on Edward Hall’s proxemics theory [10]. The interaction element defines the type of in-person verbal interaction taking place, 18 which may include absence of interaction, interaction among others only, interaction involving self. Such a 4-pronged taxonomy will also help phrasing EMA questions to acquire ground truth in future sensing studies: as opposed to only asking “who are you with”, more detailed and rigorous questions may be administered.

Sensing hardware In this paper our core approach is feature engineering, utilizing Bluetooth and GPS data from Android smartphones. The capability of feature engineering in human-centric sensing and inference is inevitably bounded by both (a) the availability and degree of integration of a sensor and (b) the absolute content a sensor captures. In our large participant cohort, 88% were iPhone users, from whom Bluetooth data were unavailable; therefore to further utilize the predictive power of Bluetooth data in mental health sensing and context-aware computing practice, other wearable devices such as smart watches may provide a better habitat for relevant data processing and analytics. In existing literature on social behavior inference, Bluetooth data is the most utilized smartphone sensor but it is not nearly sufficient to distinguish finer grained scenarios such as the social contexts defined with the four components proposed in the previous paragraph. Introduction and fusion of novel or previously overlooked mobile sensors may offer new and more effective solutions to social context detection. Magnetometer and audio sensing are candidate options, as we are observing recent studies using phone-embedded magnetometer to detect coexistence for epidemiology applications [13] as well as ongoing work on wearable voice sensors [14], which have the potential to support emotional state prediction in daily life.

Professional translators with a dominant neurotic personality trait are the most creative; those with a dominant conscientious personality trait prefer literal translation choices (experience & age also have this last preference)

Different strokes for different folks -Exploring personality in professional translation. Ella Wehrmeyer, Ella Wehrmeyer, Sarita Antunes. Translation Cognition & Behavior 3(2):187. Oct 2020. DOI: 10.1075/tcb.00040.weh

Abstract: Until recently, the translator’s personality was a relatively unexplored area of research, but growing evidence points to the influence of personality on the translator’s decisions. Although findings are not always statistically significant, empirical research indicates that professional translators profiles differ from that of the local population, and that certain personality types are more likely to make creative translation choices in translation. This article explores the relationship between personality traits as defined by the Big Five Inventory (McCrae and Costa 1989), and translation choices as defined by Baker (2018) and Molina & Hurtado Albir (2016). The findings indicate that professional translators with a dominant neurotic personality trait are the most creative, whereas those with a dominant conscientious personality trait prefer literal translation choices. However, the findings also indicate that age and experience are competing variables, both indicating a preference for literal translation.