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Nutrient profiling systems (NPS) are used to classify foods according to their nutritional composition. However, investigating their prospective associations with health is key to their validation. The study investigated the associations of the original Food Standards Agency (FSA)-NPS and three variants (Food Standards Australia New Zealand Nutrient Profiling Scoring Criterion (NPSC), Health Star Rating NPS and the French High Council of Public Health NPS (HCSP-NPS)), with weight status. Individual dietary indices based on each NPS at the food level were computed to characterise the dietary quality of 71Â 403 French individuals from the NutriNet-Santé cohort. Associations of these indices with weight gain were assessed using mixed models and with overweight and obesity risks using Cox models. Participants with a higher dietary index (reflecting lower diet nutritional quality) were more likely to have a significant increase in BMI over time (β-coefficients positive) and an increased risk of overweight (hazard ratio (HR) T3 v. T1 = 1·27 (95 % CI 1·17, 1·37)) for the HCSP-Dietary Index, followed by the original FSA-Dietary Index (HR T3 v. T1 = 1·18 (95 % CI 1·09, 1·28)), the NPSC-Dietary Index (HR T3 v. T1 = 1·14 (95 % CI 1·06, 1·24)) and the Health Star Rating-Dietary Index (HR T3 v. T1 = 1·12 (95 % CI 1·04, 1·21)). Whilst differences were small, the HCSP-Dietary Index appeared to show significantly greater association with overweight risk. Overall, these results show the validity of NPS derived from the FSA-NPS, supporting their use in public policies for chronic disease prevention.

Following publication of the original article [1], the authors identified errors in Table 2 and Table 3. The correct tables are given below. The original article [1] has been corrected.

Background: The Health Star Rating (HSR) is the government-endorsed front-of-pack labeling system in Australia and New Zealand. Objectives: We aimed to examine prospective associations of a dietary index (DI) based on the HSR, as an indicator of overall diet quality, with all-cause and cardiovascular disease (CVD) mortality. Methods: We utilized data from the national population-based Australian Diabetes, Obesity and Lifestyle Study. The HSR-DI at baseline (1999-2000) was constructed by 1) calculation of the HSR points for individual foods in the baseline FFQ, and 2) calculation of the HSR-DI for each participant based on pooled HSR points across foods, weighted by the proportion of energy contributed by each food. Vital status was ascertained by linkage to the Australian National Death Index. Associations of HSR-DI with mortality risk were assessed by Cox proportional hazards regression. Results: Among 10,025 eligible participants [baseline age: 51.6 ± 14.3 y (mean ± standard deviation)] at entry, higher HSR-DI (healthier) was associated with higher consumption of healthy foods such as fruits, vegetables, and nuts, and lower consumption of discretionary foods such as processed meats and confectionery (P-trend < 0.001 for each). During a median follow-up of 16.9 y, 1682 deaths occurred with 507 CVD deaths. In multivariable models adjusted for demographic characteristics, lifestyle factors, and medical conditions, higher HSR-DI was associated with lower risk of all-cause mortality, with a hazard ratio (95% confidence interval) of 0.80 (0.69, 0.94; P-trend < 0.001) comparing the fifth with the first HSR-DI quintile. A corresponding inverse association was observed for CVD mortality (0.71; 0.54, 0.94; P-trend = 0.008). Conclusions: Better diet quality as defined by the HSR-DI was associated with lower risk of all-cause and CVD mortality among Australian adults. Our findings support the use of the HSR nutrient profiling algorithm as a valid tool for guiding consumer foo

Background Front-of-pack nutrition labelling (FoPL) of packaged foods can promote healthier diets. Australia and New Zealand (NZ) adopted the voluntary Health Star Rating (HSR) scheme in 2014. We studied the impact of voluntary adoption of HSR on food reformulation relative to unlabelled foods and examined differential impacts for more-versus-less healthy foods. Methods and findings Annual nutrition information panel data were collected for nonseasonal packaged foods sold in major supermarkets in Auckland from 2013 to 2019 and in Sydney from 2014 to 2018. The analysis sample covered 58,905 unique products over 14 major food groups. We used a difference-in-differences design to estimate reformulation associated with HSR adoption. Healthier products adopted HSR more than unhealthy products: >35% of products that achieved 4 or more stars displayed the label compared to <15% of products that achieved 2 stars or less. Products that adopted HSR were 6.5% and 10.7% more likely to increase their rating by ≥0.5 stars in Australia and NZ, respectively. Labelled products showed a −4.0% [95% confidence interval (CI): −6.4% to −1.7%, p = 0.001] relative decline in sodium content in NZ, and there was a −1.4% [95% CI: −2.7% to −0.0%, p = 0.045] sodium change in Australia. HSR adoption was associated with a −2.3% [−3.7% to −0.9%, p = 0.001] change in sugar content in NZ and a statistically insignificant −1.1% [−2.3% to 0.1%, p = 0.061] difference in Australia. Initially unhealthy products showed larger reformulation effects when adopting HSR than healthier products. No evidence of a change in protein or saturated fat content was observed. A limitation of our study is that results are not sales weighted. Thus, it is not able to assess changes in overall nutrient consumption that occur because of HSR-caused reformulation. Also, participation into labelling and reformulation is jointly determined by producers in this observational study, impacting its generalisability to settings with

Childhood obesity is a major public health issue. The increase in consumption of foods with poor nutritional value, such as processed foods, contributes to this. Breakfast cereals are often advertised to be a healthy way to start the day, but the healthiness of these products varies greatly.

Background: The Australian federal government will soon release voluntary sodium reduction targets for 30 packaged food categories through the Healthy Food Partnership. Previous assessments of voluntary targets show variable industry engagement, and little is known about the extent that major food companies and their products contribute to dietary sodium purchases among Australian households. Methods: The aim of this cross-sectional study was to identify the relative contribution that food companies and their products made to Australian household sodium purchases in 2018, and to examine differences in sodium purchases by household income level. We used 1 year of grocery purchase data from a nationally representative consumer panel of Australian households who reported their grocery purchases (the Nielsen Homescan panel), combined with database that contains product-specific sodium content for packaged foods and beverages (FoodSwitch). The top food companies and food categories were ranked according to their contribution to household sodium purchases. Differences in per capita sodium purchases by income levels were assessed by 1-factor ANOVA. All analyses were modelled to the Australian population in 2018 using sample weights. Results: Sodium data were available from 7188 households who purchased 26,728 unique products and purchased just under 7.5 million food product units. Out of 1329 food companies, the top 10 accounted for 35% of unique products and contributed to 58% of all sodium purchased from packaged foods and beverages. The top three companies were grocery food retailers each contributing 12-15% of sodium purchases from sales of their private label products, particularly processed meat, cheese and bread. Out of the 67 food categories, the top 10 accounted for 73% of sodium purchased, particularly driven by purchases of processed meat (14%), bread (12%) and sauces (11%). Low-income Australian households purchased significantly more sodium from packaged produ

BACKGROUND: The Health Star Rating (HSR) is the government-endorsed front-of-pack labeling system in Australia and New Zealand. OBJECTIVES: We aimed to examine prospective associations of a dietary index (DI) based on the HSR, as an indicator of overall diet quality, with all-cause and cardiovascular disease (CVD) mortality. METHODS: We utilized data from the national population-based Australian Diabetes, Obesity and Lifestyle Study. The HSR-DI at baseline (1999-2000) was constructed by 1) calculation of the HSR points for individual foods in the baseline FFQ, and 2) calculation of the HSR-DI for each participant based on pooled HSR points across foods, weighted by the proportion of energy contributed by each food. Vital status was ascertained by linkage to the Australian National Death Index. Associations of HSR-DI with mortality risk were assessed by Cox proportional hazards regression. RESULTS: Among 10,025 eligible participants [baseline age: 51.6 ± 14.3 y (mean ± standard deviation)] at entry, higher HSR-DI (healthier) was associated with higher consumption of healthy foods such as fruits, vegetables, and nuts, and lower consumption of discretionary foods such as processed meats and confectionery (P-trend < 0.001 for each). During a median follow-up of 16.9 y, 1682 deaths occurred with 507 CVD deaths. In multivariable models adjusted for demographic characteristics, lifestyle factors, and medical conditions, higher HSR-DI was associated with lower risk of all-cause mortality, with a hazard ratio (95% confidence interval) of 0.80 (0.69, 0.94; P-trend < 0.001) comparing the fifth with the first HSR-DI quintile. A corresponding inverse association was observed for CVD mortality (0.71; 0.54, 0.94; P-trend = 0.008). CONCLUSIONS: Better diet quality as defined by the HSR-DI was associated with lower risk of all-cause and CVD mortality among Australian adults. Our findings support the use of the HSR nutrient profiling algorithm as a valid tool for guiding consumer foo

BACKGROUND: Food taxes and subsidies are one intervention to address poor diets. Price elasticity (PE) matrices are commonly used to model the change in food purchasing. Usually a PE matrix is generated in one setting then applied to another setting with differing starting consumptions and prices of foods. This violates econometric assumptions resulting in likely mis-estimation of total food consumption. In this paper we demonstrate this problem, canvass possible options for rescaling all consumption after applying a PE matrix, and illustrate the use of a total food expenditure elasticity (TFEe; the expenditure elasticity for all food combined given the policy-induced change in the total price of food). We use case studies of: NZ$2 per 100g saturated fat (SAFA) tax, NZ$0.4 per 100g sugar tax, and a 20% fruit and vegetable (F&V) subsidy. METHODS: We estimated changes in food purchasing using a NZ PE matrix applied conventionally, and then with TFEe adjustment. Impacts were quantified for pre- to post-policy changes in total food expenditure and health adjusted life years (HALYs) for the total NZ population alive in 2011 over the rest of their lifetime using a multistate lifetable model. RESULTS: Two NZ studies gave TFEe's of 0.68 and 0.83, with international estimates ranging from 0.46 to 0.90 (except a UK outlier of 0.04). Without TFEe adjustment, total food expenditure decreased with the tax policies and increased with the F&V subsidy-implausible directions of shift given economic theory and the external TFEe estimates. After TFEe adjustment, HALY gains reduced by a third to a half for the two taxes and reversed from an apparent health loss to a health gain for the F&V subsidy. With TFEe adjustment, HALY gains (in 1000's) were: 1,805 (95% uncertainty interval 1,337 to 2,340) for the SAFA tax; 1,671 (1,220 to 2,269) for the sugar tax; and 953 (453 to 1,308) for the F&V subsidy. CONCLUSIONS: If PE matrices are applied in settings beyond where they were derived, addit

Sugar-sweetened beverage (SSB) intake is associated with tooth decay, obesity and diabetes. We aimed to model the health and cost impact of reducing the serving size of all single serve SSB to a maximum of 250 ml in New Zealand. A 250 ml serving size cap was modeled for all instances of single serves (<600 ml) of sugar-sweetened carbonated soft drinks, fruit drinks, carbonated energy drinks, and sports drinks in the New Zealand National Nutrition Survey intake data (2008/09). A multi-state life-table model used the change in energy intake and therefore BMI to predict the resulting health gains in quality-adjusted life-years (QALYs) and health system costs over the remaining life course of the New Zealand population alive in 2011 (N = 4.4 million, 3% discounting). The ‘base case’ model (no compensation for reduced energy intake) resulted in an average reduction in SSB and energy intake of 23 ml and 44 kJ (11 kcal) per day or 0.22 kg of weight modeled over two years. The total health gain and cost-savings were 82,100 QALYs (95% UI: 65100 to 101,000) and NZ$1.65 billion [b] (95% UI: 1.19 b to 2.24 b, (US$1.10 b)) over the lifespan of the cohort. QALY gains increased to 116,000 when the SSB definition was widened to include fruit juices and sweetened milks. A cap on single serve SSB could be an effective part of a suite of obesity prevention and sugar reduction interventions in high income countries.

Background: The Health Star Rating (HSR) is an interpretive front-of-pack labelling system that rates the overall nutritional profile of packaged foods. The algorithm underpinning the HSR includes total sugar content as one of the components. This has been criticised because intrinsic sugars naturally present in dairy, fruits, and vegetables are treated the same as sugars added during food processing. We assessed whether the HSR could better discriminate between core and discretionary foods by including added sugar in the underlying algorithm. Methods: Nutrition information was extracted for 34,135 packaged foods available in The George Institute’s Australian FoodSwitch database. Added sugar levels were imputed from food composition databases. Products were classified as ‘core’ or ‘discretionary’ based on the Australian Dietary Guidelines. The ability of each of the nutrients included in the HSR algorithm, as well as added sugar, to discriminate between core and discretionary foods was estimated using the area under the curve (AUC). Results: 15,965 core and 18,350 discretionary foods were included. Of these, 8230 (52%) core foods and 15,947 (87%) discretionary foods contained added sugar. Median (Q1, Q3) HSRs were 4.0 (3.0, 4.5) for core foods and 2.0 (1.0, 3.0) for discretionary foods. Median added sugar contents (g/100 g) were 3.3 (1.5, 5.5) for core foods and 14.6 (1.8, 37.2) for discretionary foods. Of all the nutrients used in the current HSR algorithm, total sugar had the greatest individual capacity to discriminate between core and discretionary foods; AUC 0.692 (0.686; 0.697). Added sugar alone achieved an AUC of 0.777 (0.772; 0.782). A model with all nutrients in the current HSR algorithm had an AUC of 0.817 (0.812; 0.821), which increased to 0.871 (0.867; 0.874) with inclusion of added sugar. Conclusion: The HSR nutrients discriminate well between core and discretionary packaged foods. However, discrimination was improved when added sugar was also included

Background: Front-of-pack nutrition labelling may support healthier packaged food purchases. Australia has adopted a novel Health Star Rating (HSR) system, but the legitimacy of this choice is unknown. Objective: To define the effects of different formats of front-of-pack labelling on the healthiness of food purchases and consumer perceptions. Design: Individuals were assigned at random to access one of four different formats of nutrition labelling—HSR, multiple traffic light labels (MTL), daily intake guides (DIG), recommendations/warnings (WARN)—or control (the nutrition information panel, NIP). Participants accessed nutrition information by using a smartphone application to scan the bar-codes of packaged foods, while shopping. The primary outcome was healthiness defined by the mean transformed nutrient profile score of packaged foods that were purchased over four weeks. Results: The 1578 participants, mean age 38 years, 84% female recorded purchases of 148,727 evaluable food items. The mean healthiness of the purchases in the HSR group was non-inferior to MTL, DIG, or WARN (all p < 0.001 at 2% non-inferiority margin). When compared to the NIP control, there was no difference in the mean healthiness of purchases for HSR, MTL, or DIG (all p > 0.07), but WARN resulted in healthier packaged food purchases (mean difference 0.87; 95% confidence interval 0.03 to 1.72; p = 0.04). HSR was perceived by participants as more useful than DIG, and easier to understand than MTL or DIG (all p < 0.05). Participants also reported the HSR to be easier to understand, and the HSR and MTL to be more useful, than NIP (all p < 0.03). Conclusions: These real-world data align with experimental findings and provide support for the policy choice of HSR. Recommendation/warning labels warrant further exploration, as they may be a stronger driver of healthy food purchases.

BACKGROUND: Dietary salt reduction is included in the top five priority actions for non-communicable disease control internationally. We therefore aimed to identify health gain and cost impacts of achieving a national target for sodium reduction, along with component targets in different food groups. METHODS: We used an established dietary sodium intervention model to study 10 interventions to achieve sodium reduction targets. The 2011 New Zealand (NZ) adult population (2.3 million aged 35+ years) was simulated over the remainder of their lifetime in a Markov model with a 3 % discount rate. RESULTS: Achieving an overall 35 % reduction in dietary salt intake via implementation of mandatory maximum levels of sodium in packaged foods along with reduced sodium from fast foods/restaurant food and discretionary intake (the "full target"), was estimated to gain 235,000 QALYs over the lifetime of the cohort (95 % uncertainty interval [UI]: 176,000 to 298,000). For specific target components the range was from 122,000 QALYs gained (for the packaged foods target) down to the snack foods target (6100 QALYs; and representing a 34-48 % sodium reduction in such products). All ten target interventions studied were cost-saving, with the greatest costs saved for the mandatory "full target" at NZ$1260 million (US$820 million). There were relatively greater health gains per adult for men and for Māori (indigenous population). CONCLUSIONS: This work provides modeling-level evidence that achieving dietary sodium reduction targets (including specific food category targets) could generate large health gains and cost savings for a national health sector. Demographic groups with the highest cardiovascular disease rates stand to gain most, assisting in reducing health inequalities between sex and ethnic groups.

BACKGROUND: Health-related food taxes and subsidies may promote healthier diets and reduce mortality. Our aim was to estimate the effects of health-related food taxes and subsidies on deaths prevented or postponed (DPP) in New Zealand. METHODS: A macrosimulation model based on household expenditure data, demand elasticities and population impact fractions for 18 diet-related diseases was used to estimate effects of five tax and subsidy regimens. We used price elasticity values for 24 major commonly consumed food groups in New Zealand, and food expenditure data from national Household Economic Surveys. Changes in mortality from cardiovascular disease, cancer, diabetes and other diet-related diseases were estimated. FINDINGS: A 20% subsidy on fruit and vegetables would result in 560 (95% uncertainty interval, 400 to 700) DPP each year (1.9% annual all-cause mortality). A 20% tax on major dietary sources of saturated fat would result in 1,500 (950 to 2,100) DPP (5.0%), and a 20% tax on major dietary sources of sodium would result in 2,000 (1300 to 2,700) DPP (6.8%). Combining taxes on saturated fat and sodium with a fruit and vegetable subsidy would result in 2,400 (1,800 to 3,000) DPP (8.1% mortality annually). A tax on major dietary sources of greenhouse gas emissions would generate 1,200 (750 to 1,700) DPP annually (4.0%). Effects were similar or greater for Maori and low-income households in relative terms. CONCLUSIONS: Health-related food taxes and subsidies could improve diets and reduce mortality from diet-related disease in New Zealand. Our study adds to the growing evidence base suggesting food pricing policies should improve population health and reduce inequalities, but there is still much work to be done to improve estimation of health impacts.

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 children's 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 children's 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 children's 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 children's school attendance or academic achievement but had significant positive effects on children's 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.

OBJECTIVE: Global health challenges include non-communicable disease burdens, ensuring food security in the context of rising food prices, and environmental constraints around food production, e.g., greenhouse gas [GHG] emissions. We therefore aimed to consider optimized solutions to the mix of food items in daily diets for a developed country population: New Zealand (NZ). METHODS: We conducted scenario development and linear programming to model 16 diets (some with uncertainty). Data inputs included nutrients in foods, food prices, food wastage and food-specific GHG emissions. FINDINGS: This study identified daily dietary patterns that met key nutrient requirements for as little as a median of NZ$ 3.17 per day (US$ 2.41/d) (95% simulation interval [SI] = NZ$ 2.86 to 3.50/d). Diets that included "more familiar meals" for New Zealanders, increased the cost. The optimized diets also had low GHG emission profiles compared with the estimate for the 'typical NZ diet' e.g., 1.62 kg CO2e/d for one scenario (95%SI = 1.39 to 1.85 kg CO2e) compared with 10.1 kg CO2e/d, respectively. All of the optimized low-cost and low-GHG dietary patterns had likely health advantages over the current NZ dietary pattern, i.e., lower cardiovascular disease and cancer risk. CONCLUSIONS: We identified optimal foods and dietary patterns that would lower the risk of non-communicable diseases at low cost and with low greenhouse gas emission profiles. These results could help guide central and local government decisions around which foods to focus policies on. That is which foods are most suitable for: food taxes (additions and exemptions); healthy food vouchers and subsidies; and for increased use by public institutions involved in food preparation.

BACKGROUND: Targeted food pricing policies may improve population diets. To assess their effects on inequalities, it is important to determine responsiveness to price changes across income levels and ethnic groups. OBJECTIVE: Our goal was to estimate price elasticity (PE) values for major commonly consumed food groups in New Zealand, by income and ethnicity. PE values represent percentage change in demand associated with 1% change in price of that good (own-PE) or another good (cross-PE). DESIGN: We used food expenditure data from national household economic surveys in 2007/08 and 2009/10 and Food Price Index data from 2007 and 2010. Adopting an Almost Ideal Demand System approach, own-PE and cross-PE estimates were derived for 24 food categories, household income quintiles, and two ethnic groups (Māori and non-Māori). RESULTS: Own-PE estimates (with two exceptions) ranged from -0.44 to -1.78. Cross-PE estimates were generally small; only 31% of absolute values were greater than 0.10. Excluding the outlier 'energy drinks', nine of 23 food groups had significantly stronger own-PEs for the lowest versus highest income quintiles (average regression-based difference across food groups -0.30 (95% CI -0.62 to 0.02)). Six own-PEs were significantly stronger among Māori; the average difference for Māori: non-Māori across food groups was -0.26 (95% CI -0.52 to 0.00). CONCLUSIONS: Food pricing policies have potential to improve population diets. The greater sensitivity of low-income households and Māori to price changes suggests the beneficial effects of such policies on health would be greatest for these groups.

BACKGROUND: Food pricing strategies have been proposed to encourage healthy eating habits, which may in turn help stem global increases in non-communicable diseases. This systematic review of simulation studies investigates the estimated association between food pricing strategies and changes in food purchases or intakes (consumption) (objective 1); Health and disease outcomes (objective 2), and whether there are any differences in these outcomes by socio-economic group (objective 3). METHODS AND FINDINGS: Electronic databases, Internet search engines, and bibliographies of included studies were searched for articles published in English between 1 January 1990 and 24 October 2011 for countries in the Organisation for Economic Co-operation and Development. Where ≥ 3 studies examined the same pricing strategy and consumption (purchases or intake) or health outcome, results were pooled, and a mean own-price elasticity (own-PE) estimated (the own-PE represents the change in demand with a 1% change in price of that good). Objective 1: pooled estimates were possible for the following: (1) taxes on carbonated soft drinks: own-PE (n = 4 studies), -0.93 (range, -0.06, -2.43), and a modelled -0.02% (-0.01%, -0.04%) reduction in energy (calorie) intake for each 1% price increase (n = 3 studies); (2) taxes on saturated fat: -0.02% (-0.01%, -0.04%) reduction in energy intake from saturated fat per 1% price increase (n = 5 studies); and (3) subsidies on fruits and vegetables: own-PE (n = 3 studies), -0.35 (-0.21, -0.77). Objectives 2 and 3: variability of food pricing strategies and outcomes prevented pooled analyses, although higher quality studies suggested unintended compensatory purchasing that could result in overall effects being counter to health. Eleven of 14 studies evaluating lower socio-economic groups estimated that food pricing strategies would be associated with pro-health outcomes. Food pricing strategies also have the potential to reduce disparities. CONCLUS

BACKGROUND: Childhood obesity has reached epidemic proportions in developed countries. Sedentary screen-based activities such as video gaming are thought to displace active behaviors and are independently associated with obesity. Active video games, where players physically interact with images onscreen, may have utility as a novel intervention to increase physical activity and improve body composition in children. The aim of the Electronic Games to Aid Motivation to Exercise (eGAME) study is to determine the effects of an active video game intervention over 6 months on: body mass index (BMI), percent body fat, waist circumference, cardio-respiratory fitness, and physical activity levels in overweight children. METHODS/DESIGN: Three hundred and thirty participants aged 10-14 years will be randomized to receive either an active video game upgrade package or to a control group (no intervention). DISCUSSION: An overview of the eGAME study is presented, providing an example of a large, pragmatic randomized controlled trial in a community setting. Reflection is offered on key issues encountered during the course of the study. In particular, investigation into the feasibility of the proposed intervention, as well as robust testing of proposed study procedures is a critical step prior to implementation of a large-scale trial.

BACKGROUND: Accurate measurement of physical activity is a pre-requisite for monitoring population health and for evaluating effective interventions. The International Physical Activity Questionnaire (IPAQ) is used as a comparable and standardised self-report measure of habitual physical activity of populations from different countries and socio-cultural contexts. The IPAQ has been modified to produce a New Zealand physical activity questionnaire (NZPAQ). The aim of this study was to validate the IPAQ and NZPAQ against doubly labelled water (DLW). METHOD: Total energy expenditure (TEE) was measured over a 15-day period using DLW. Activity-related energy expenditure (AEE) was estimated by subtracting the energy expenditure from resting metabolic rate and thermic effect of feeding from TEE. The IPAQ (long form) and NZPAQ (short form) were completed at the end of each 7-day period. Activity-related energy expenditure (IPAQAEE and NZPAQAEE) was calculated from each questionnaire and compared to DLWAEE. RESULTS: Thirty six adults aged 18 to 56 years (56% female) completed all measurements. Compared to DLWAEE, IPAQAEE and NZPAQAEE on average underestimated energy expenditure by 27% and 59%, respectively. There was good agreement between DLWAEE and both IPAQAEE and NZPAQAEE at lower levels of physical activity. However there was marked underestimation of questionnaire-derived energy expenditure at higher levels of activity. CONCLUSION: Both the IPAQ and NZPAQ instruments have a demonstrated systematic bias toward underestimation of physical activity-related energy expenditure at higher levels of physical activity compared to DLW. Appropriate calibration factors could be used to correct for measurement error in physical activity questionnaires and hence improve estimation of AEE.