Jo Michie

Effects of a free school breakfast programme on children's attendance, academic achievement and short-term hunger: results from a stepped-wedge, cluster randomised controlled trial

Background Free school breakfast programmes (SBPs) exist in a number of high-income countries, but their effects on educational outcomes have rarely been evaluated in randomised controlled trials. Methods A 1-year stepped-wedge, cluster randomised controlled trial was undertaken in 14 New Zealand schools in low socioeconomic resource areas. Participants were 424 children, mean age 9±2 years, 53% female. The intervention was a free daily SBP. The primary outcome was childrens school attendance. Secondary outcomes were academic achievement, self-reported grades, sense of belonging at school, behaviour, short-term hunger, breakfast habits and food security. Results There was no statistically significant effect of the breakfast programme on childrens school attendance. The odds of children achieving an attendance rate <95% was 0.76 (95% CI 0.56 to 1.02) during the intervention phase and 0.93 (95% CI 0.67 to 1.31) during the control phase, giving an OR of 0.81 (95% CI 0.59 to 1.11), p=0.19. There was a significant decrease in childrens self-reported short-term hunger during the intervention phase compared with the control phase, demonstrated by an increase of 8.6 units on the Freddy satiety scale (95% CI 3.4 to 13.7, p=0.001). There were no effects of the intervention on any other outcome. Conclusions A free SBP did not have a significant effect on childrens school attendance or academic achievement but had significant positive effects on childrens short-term satiety ratings. More frequent programme attendance may be required to influence school attendance and academic achievement. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR)—ACTRN12609000854235.

Development of an Evidence-Based mHealth Weight Management Program Using a Formative Research Process

Background There is a critical need for weight management programs that are effective, cost efficient, accessible, and acceptable to adults from diverse ethnic and socioeconomic backgrounds. mHealth (delivered via mobile phone and Internet) weight management programs have potential to address this need. To maximize the success and cost-effectiveness of such an mHealth approach it is vital to develop program content based on effective behavior change techniques, proven weight management programs, and closely aligned with participants’ needs. Objective This study aims to develop an evidence-based mHealth weight management program (Horizon) using formative research and a structured content development process. Methods The Horizon mHealth weight management program involved the modification of the group-based UK Weight Action Program (WAP) for delivery via short message service (SMS) and the Internet. We used an iterative development process with mixed methods entailing two phases: (1) expert input on evidence of effective programs and behavior change theory; and (2) target population input via focus group (n=20 participants), one-on-one phone interviews (n=5), and a quantitative online survey (n=120). Results Expert review determined that core components of a successful program should include: (1) self-monitoring of behavior; (2) prompting intention formation; (3) promoting specific goal setting; (4) providing feedback on performance; and (5) promoting review of behavioral goals. Subsequent target group input confirmed that participants liked the concept of an mHealth weight management program and expressed preferences for the program to be personalized, with immediate (prompt) and informative text messages, practical and localized physical activity and dietary information, culturally appropriate language and messages, offer social support (group activities or blogs) and weight tracking functions. Most target users expressed a preference for at least one text message per day. We present the prototype mHealth weight management program (Horizon) that aligns with those inputs. Conclusions The Horizon prototype described in this paper could be used as a basis for other mHealth weight management programs. The next priority will be to further develop the program and conduct a full randomized controlled trial of effectiveness.

Effects of interpretive nutrition labels on consumer food purchases: the Starlight randomized controlled trial

Background: Nutrition labeling is a prominent policy to promote healthy eating.Objective: We aimed to evaluate the effects of 2 interpretive nutrition labels compared with a noninterpretive label on consumer food purchases.Design: In this parallel-group randomized controlled trial, we enrolled household shoppers across New Zealand who owned smartphones and were aged ≥18 y. Eligible participants were randomly assigned (1:1:1) to receive either traffic light labels (TLLs), Health Star Rating labels (HSRs), or a control [nutrition information panel (NIP)]. Smartphone technology allowed participants to scan barcodes of packaged foods and to receive allocated labels on their smartphone screens. The primary outcome was the mean healthiness of all packaged food purchases over the 4-wk intervention period, which was measured by using the Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (NPSC).Results: Between October 2014 and November 2015, 1357 eligible shoppers were randomly assigned to TLL (n = 459), HSR (n = 443), or NIP (n = 455) labels. Overall difference in the mean transformed NPSC score for the TLL group compared with the NIP group was -0.20 (95% CI: -0.94, 0.54; P = 0.60). The corresponding difference for HSR compared with NIP was -0.60 (95% CI: -1.35, 0.15; P = 0.12). In an exploratory per-protocol analysis of participants who used the labeling intervention more often than average (n = 423, 31%), those who were assigned to TLL and HSR had significantly better NPSC scores [TLL compared with NIP: -1.33 (95% CI: -2.63, -0.04; P = 0.04); HSR compared with NIP: -1.70 (95% CI: -2.97, -0.43; P = 0.01)]. Shoppers who were randomly assigned to HSR and TLL also found the labels significantly more useful and easy to understand than the NIP (all P values <0.001).Conclusions: At the relatively low level of use observed in this trial, interpretive nutrition labels had no significant effect on food purchases. However, shoppers who used interpretive labels found them to be significantly more useful and easy to understand, and compared with frequent NIP users, frequent TLL and HSR users had significantly healthier food purchases. This trial was registered at the Australian New Zealand Clinical Trials Registry (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366446&isReview=true) as ACTRN12614000644662.

Effects of interpretive front-of-pack nutrition labels on food purchases: protocol for the Starlight randomised controlled trial

BackgroundInterpretive front-of-pack nutrition labels are better understood than non-interpretive labels. However, robust evidence on the effects of such labels on consumer food purchases in the real-world is lacking. Our aim is to assess the effects of two interpretive front-of-pack nutrition labels, compared with a non-interpretive label, on the healthiness of consumer food purchases.Methods/DesignA five-week (1-week baseline and 4-week intervention) three-arm parallel randomised controlled trial will be conducted using a bespoke smartphone application, which will administer study questionnaires and deliver intervention (Multiple Traffic Light and Health Star Rating) and control (Nutrition Information Panel) labels. To view their allocated nutrition label, participants scan the barcode of packaged food products using their smartphone camera. The assigned label is displayed instantly on the smartphone screen.1500 eligible participants (New Zealand adult smartphone owners who shop in a supermarket at least once a week and are main household shoppers) will be randomised in a 1:1:1 ratio to one of the three nutrition label formats, using computer-generated randomisation sequences. Randomisation will be stratified by ethnicity and interest in healthy eating. Food and beverage purchase data will be collected continuously throughout the study via hard copy till receipts and electronic grocery purchase lists recorded and transmitted using the smartphone application. The primary outcome will be healthiness of food purchases in each trial arm, assessed as mean Food Standards Australia New Zealand nutrient profiling score criterion score for all food and beverages purchased over the intervention period. Secondary outcomes will include saturated fat, sugar, sodium and energy content of food purchases; food expenditure; labelling profile of food purchases (i.e. mean number of Health Star Rating stars and proportion of red, green and amber traffic lights); nutrient profiling score over time and by food categories; purchases of unpackaged foods; self-reported nutrition knowledge and recorded use of assigned labelling system.DiscussionThe Starlight randomised, controlled trial will determine the effects of interpretive front-of-pack nutrition labels on the healthiness of consumer food purchases in the real world.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12614000644662 (registered 18 June 2014).

Smart RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials.

Background There is substantial interest in the effects of nutrition labels on consumer food-purchasing behavior. However, conducting randomized controlled trials on the impact of nutrition labels in the real world presents a significant challenge. Objective The Food Label Trial (FLT) smartphone app was developed to enable conducting fully automated trials, delivering intervention remotely, and collecting individual-level data on food purchases for two nutrition-labeling randomized controlled trials (RCTs) in New Zealand and Australia. Methods Two versions of the smartphone app were developed: one for a 5-arm trial (Australian) and the other for a 3-arm trial (New Zealand). The RCT protocols guided requirements for app functionality, that is, obtaining informed consent, two-stage eligibility check, questionnaire administration, randomization, intervention delivery, and outcome assessment. Intervention delivery (nutrition labels) and outcome data collection (individual shopping data) used the smartphone camera technology, where a barcode scanner was used to identify a packaged food and link it with its corresponding match in a food composition database. Scanned products were either recorded in an electronic list (data collection mode) or allocated a nutrition label on screen if matched successfully with an existing product in the database (intervention delivery mode). All recorded data were transmitted to the RCT database hosted on a server. Results In total approximately 4000 users have downloaded the FLT app to date; 606 (Australia) and 1470 (New Zealand) users met the eligibility criteria and were randomized. Individual shopping data collected by participants currently comprise more than 96,000 (Australia) and 229,000 (New Zealand) packaged food and beverage products. Conclusions The FLT app is one of the first smartphone apps to enable conducting fully automated RCTs. Preliminary app usage statistics demonstrate large potential of such technology, both for intervention delivery and data collection. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12614000964617. New Zealand trial: Australian New Zealand Clinical Trials Registry ACTRN12614000644662.

Study protocol: combining experimental methods, econometrics and simulation modelling to determine price elasticities for studying food taxes and subsidies (The Price ExaM Study)

BackgroundThere is a need for accurate and precise food price elasticities (PE, change in consumer demand in response to change in price) to better inform policy on health-related food taxes and subsidies.Methods/DesignThe Price Experiment and Modelling (Price ExaM) study aims to: I) derive accurate and precise food PE values; II) quantify the impact of price changes on quantity and quality of discrete food group purchases and; III) model the potential health and disease impacts of a range of food taxes and subsidies. To achieve this, we will use a novel method that includes a randomised Virtual Supermarket experiment and econometric methods. Findings will be applied in simulation models to estimate population health impact (quality-adjusted life-years [QALYs]) using a multi-state life-table model. The study will consist of four sequential steps:1.We generate 5000 price sets with random price variation for all 1412 Virtual Supermarket food and beverage products. Then we add systematic price variation for foods to simulate five taxes and subsidies: a fruit and vegetable subsidy and taxes on sugar, saturated fat, salt, and sugar-sweetened beverages.2.Using an experimental design, 1000 adult New Zealand shoppers complete five household grocery shops in the Virtual Supermarket where they are randomly assigned to one of the 5000 price sets each time.3.Output data (i.e., multiple observations of price configurations and purchased amounts) are used as inputs to econometric models (using Bayesian methods) to estimate accurate PE values.4.A disease simulation model will be run with the new PE values as inputs to estimate QALYs gained and health costs saved for the five policy interventions.DiscussionThe Price ExaM study has the potential to enhance public health and economic disciplines by introducing internationally novel scientific methods to estimate accurate and precise food PE values. These values will be used to model the potential health and disease impacts of various food pricing policy options. Findings will inform policy on health-related food taxes and subsidies.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12616000122459 (registered 3 February 2016).

Effectiveness of recruitment to a smartphone-delivered nutrition intervention in New Zealand: analysis of a randomised controlled trial

Objectives Delivery of interventions via smartphone is a relatively new initiative in public health, and limited evidence exists regarding optimal strategies for recruitment. We describe the effectiveness of approaches used to recruit participants to a smartphone-enabled nutrition intervention trial. Methods Internet and social media advertising, mainstream media advertising and research team networks were used to recruit New Zealand adults to a fully automated smartphone-delivered nutrition labelling trial (no face-to-face visits were required). Recruitment of Māori and Pacific participants was a key focus and ethically relevant recruitment materials and approaches were used where possible. The effectiveness of recruitment strategies was evaluated using Google Analytics, monitoring of study website registrations and randomisations, and self-reported participant data. The cost of the various strategies and associations with participant demographics were assessed. Results Over a period of 13 months, there were 2448 registrations on the study website, and 1357 eligible individuals were randomised into the study (55%). Facebook campaigns were the most successful recruitment strategy overall (43% of all randomised participants) and for all ethnic groups (Māori 44%, Pacific 44% and other 43%). Significant associations were observed between recruitment strategy and age (p<0.001), household size (p<0.001), ethnicity (p<0.001), gender (p=0.005) and interest in healthy eating (p=0.022). Facebook campaigns resulted in the highest absolute numbers of study registrations and randomisations (966 and 584, respectively). Network strategies and Facebook campaigns cost least per randomised participant (NZ

Effects of a free school breakfast programme on school attendance, achievement, psychosocial function, and nutrition: a stepped wedge cluster randomised trial

4 and NZ

Feasibility, acceptability and potential effectiveness of a mobile health (mHealth) weight management programme for New Zealand adults

5, respectively), whereas radio advertising costs most (NZ

Effects of interpretive front-of-pack nutrition labels on food purchases: Starlight randomised controlled trial

179 per participant). Conclusion Internet and social media advertising were the most effective and least costly approaches to recruiting participants to a smartphone-delivered trial. These approaches also reached diverse ethnic groups. However, more culturally appropriate recruitment strategies are likely to be necessary in studies where large numbers of participants from specific ethnic groups are sought. Trial registration ACTRN12614000644662; Post-results.