Repeat Purchasing Behavior of Visitors of a Company Lunch Restaurant

1 1 ABSTRACT 2

3 Objective: To characterize lunch compositions and lunch habits of regular registered visitors of a

4 lunch restaurant.

5 Design: Lunch selections (n=1541) of registered regular visitors of a company lunch restaurant

6 were monitored over a period of 40 consecutive weekdays.

7 Setting: An instrumented company lunch restaurant.

8 Subjects: Regular registered visitors of the company lunch restaurant.

9 Results: The lunch selections were analysed with respect to lunch composition and repeated

10 selections of the same visitors (lunch habits).

11 Lunch composition. Lunch energy averaged 46646 kCals (SD 2541) and was positively correlated

12 with price, weight, number of items, and energy from fat and negatively correlated with energy

13 from fibres, proteins and carbohydrates (including sugars) (p<0.01). Inclusion of drinks in lunches

14 added to weight, variety, sugar content and energy (in the case of non-dairy drinks). In contrast,

15 soup also added to weight but was associated with reduced lunch energy, even though energy from

16 fat increased. but added little to variety, reduced energy from sugars, and was associated with

17 reduced lunch energy content.

18 Lunch habits: Lunch selections were relatively stable with regard to energy from fat and

19 carbohydrates, somewhat less stable with regard to energy from proteins and fibers and relatively

20 unstable with regard to energy from sugars. The stability for the nutrient non-specific parameters

21 fell between the least and most stable nutrient parameters.

22 Conclusions: The results suggest that healthier dietary lunch patterns can be promoted by reducing

23 the variety of lunch items and their fat content. Inclusion of non-dairy beverages or soups may also

24 contribute to a healthier pattern because they add lunch weight but not lunch energy.

1 26 2 INTRODUCTION 27

28 Dutch people, like most people in other parts of the Western world, eat too much and too

29 unhealthy1. More specifically, the consumption of certain nutrients and foods is too high (e.g.,

30 saturated fat, added sugar and salt) whereas the consumption of others (e.g., fruits, fish and

31 vegetables) is too low2. Current international guidelines3 for total daily intake recommend 2500

32 kCals for males and 2000 kCals for females with 20-35 per cent of the energy coming from fat, 40-

33 65% from carbohydrates (of which no more than 25% from added sugars), 10-35% from proteins

34 (or 0.8 grams of proteins per kilogram body weight) and 25-38 grams of fibres. Overconsumption is

35 typically associated with excessive consumption of carbohydrates and fats, whereas consumption of

36 proteins may actually reduce intake due to its satiating or filling properties4, 5. Overconsumption is

37 also associated with meal variation; people tend to consume more from a varied meal than from a

38 monotonous meal. This phenomenon is typically explained by sensory specific satiety or the

39 declining satisfaction generated by the consumption of a certain type of food, and the consequent

40 renewal in appetite resulting from the exposure to a new flavor or food6. The form in which

41 nutrients are consumed may be important as well. Beverages and other calorie-containing liquid

42 foods are associated with low satiation and relatively high energy intake7 with the possible

43 exception of soups which seem to reduce energy intake8.

44 Given these facts it is not surprising that the focus of guidelines towards improved diets has started

45 to shift from individual nutrients to combinations of foods or dietary patterns9 which requires

46 knowledge on what the individual patterns are; i.e., the combinations of foods typically consumed

47 during breakfast, lunch, and dinner and any other meal moments. Dietary patterns should ideally be

48 collected at the level of individual consumers and over multiple meals to assess the stability of

49 individual meal patterns. Insight in intra-and inter individual variation over meals provides

50 information on determining factors behind repeat meal selections. For example, if repeated food

51 choice is primarily driven by meal energy, then successful intervention strategies may focus on

2 52 dietary modifications that leave the meal energy unaltered. If possible, the assessment of dietary

53 patterns should be based on objective measurements rather than self-report, which typically leads to

54 underreporting10. Finally, dietary patterns are probably assessed best in situations where the meals

55 are typically consumed and where people show their natural food-related behaviours, e.g., in one’s

56 home or lunch restaurant11.

57 The present study objectively characterizes lunch selections of individual regular visitors of a

58 company lunch restaurant in the Netherlands, a representative of the out-of-home eating category

59 that increasingly contributes to unhealthy diets12. The study focusses on the nutrient and energy

60 composition of individual lunches and of repeated lunch selections of individual consumers to

61 provide input for dietary interventions. Associations are explored between lunch energy, specific

62 nutrients, and variables such as price, weight and number of lunch items. Specifically, hypothesis

63 are tested regarding positive associations between lunch energy and lunch variation or beverages,

64 and negative associations between lunch energy and protein content or soup.

65 The study also verifies the stability of lunch selections over time made by individual consumers.

66 Actual repeated dietary lunch patterns are characterized for individual consumers in terms of

67 nutritional and energy variables as well as variables such as weight and number of lunch items, and

68 price. Previous studies have demonstrated effects on food choice and food perception of non-

69 nutritional variables such as the number and colour of the food items of which a meal is composed,

70 the weight and size of the containers in which the food is presented, branding, packaging and

71 price13, 14. We hypothesize an inverse relationship between the importance of a variable for repeated

72 lunch choice and its variability over repeat lunches. For example, if lunch selections are primarily

73 driven by economical rather than health motifs we expect that price remains relatively constant over

74 repeated lunch selections while health-related variables, such as energy, are more variable.

3 75 3 Methods 76

77 3.1 Test location 78 79 Repeat lunch selection behavior was measured in an instrumented company lunch restaurant named

80 the “Restaurant of the Future” (RoF) located in Wageningen, The Netherlands. The RoF is set-up as

81 a regular company buffet restaurant in which employees of Wageningen University & Research

82 Centre can select and consume their lunch. The restaurant is open during 48 weeks per year on

83 weekdays between noon and 1.30 p.m. Lunch prices are comparable to those in similar other lunch

84 restaurants. The lay-out of the buffets in the Restaurant follows a free-flowing principle, i, e. the

85 buffets are distributed evenly across the buffet area assuring customers access from all directions to

86 each buffet. Each buffet contains a specific group of products and is labeled accordingly. During

87 their first visit visitors fill in an informed consent, and a questionnaire soliciting demographic

88 information as well as information regarding food habits and preferences. This procedure was

89 developed in collaboration with the Medical Ethical Committee of Wageningen University. Next,

90 they receive an ID card that allows them to use the cash registers, which are used to record

91 personalized sales. Visitors are not informed about individual studies and whether or not their sales

92 data are used. At the time of the study approximately 550 people had registered as RoF visitors, of

93 which approximately 300 visited the RoF on a frequent basis.

94 3.2 Nutritional Information 95

96 During the test period, approximately 330 foods and beverages were offered to the visitors.

97 Nutritional information per serving was compiled from the online NEVO food composition

98 database15 and consumption intake information was converted from foods to nutrients using VBS

99 food conversion software16 and the Portion size table17. The recipes were coded according to

100 standardized guidelines that are used by nutritionists of the Division of Human Nutrition of

4 101 Wageningen University; correspond in general to the EUROFIR guidelines18, 19. Nutritional

102 information was complemented with the lunch item’s weight and price.

103 3.3 Study Set Up. 104 105 Lunch selections were recorded per individual registered consumer over a period of 8 weeks or 40

106 working days. Visitors were not aware of their participation in this particular study. Visitors were

107 included in this study when they 1) had registered, and 2) visited the restaurant at least twice during

108 the test period. Lunches were included that consisted of at least two items and were not

109 complemented by items brought in from outside the restaurant.

110 3.4 Statistical Analysis.

111 3.4.1 Lunch composition.

112 Correlational analysis was carried out to verify associations between lunch parameters. Lunches

113 were categorized whether or not they included soups, dairy drinks and non-dairy drinks. Mixed

114 models ANOVAs were used to test systematic differences between various types of lunches while

115 compensating for differences in visiting frequencies between visitors. Visitors IDs were entered as

116 random factors and lunch type as fixed factor.

117 3.4.2 Stability of lunch selections of individual consumers (lunch habits).

118 The ratio of the within and between subject variance was used to quantify the stability of repeated

119 lunch selections of individual visitors for each lunch parameter. For example, a relatively low ratio

120 for lunch energy indicates that repeated lunch selections of individual visitors in terms of lunch

121 energy are relatively stable whereas the variations between visitors are relatively large. Variance

122 components were estimated per lunch parameter with visitor ID as random factor. All analyses were

123 carried out with IBM SPSS Statistics v.19.

5 124 4 RESULTS 125

126 4.1 General. 127

128 During the test period, 168 visitors, 48% females and 52% males, met all inclusion criteria. One

129 hundred thirty-four of these visitors were native speakers; the others were English-speaking. The

130 age of the average visitors was 39.4 yrs. (SD 10.4) with a BMI of 23.5 kg/m 2 (SD 3.0) Over the 40-

131 day test period, the visitors visited the Restaurant on average 9.3 times (SD 7.6) and selected 1541

132 lunches at an average cost of 4.21 euro (SD 1.4), consisting of 3.6 items (SD 1.1) with a combined

133 weight of 528 grams (SD 157).

134 4.2 Lunch composition. 135

136 The average lunch contained 4646 kCals of energy (SD 2541). Thirty four per cent (SD 14.4) of the

137 lunch energy was derived from fat, 47% (SD 14.4) from carbohydrates (of which 17 % (SD 15.4)

138 from sugars), 18% (SD 7.1) from proteins and 1.1 % (SD 1.0) from dietary fibres.

139 Correlation coefficients between consumer and lunch variables are given in Table 1 and show

140 significant positive correlations between lunch energy and price, weight, lunch variation (number of

141 lunch items), and energy from fat and significant negative correlations with energy from proteins,

142 carbohydrates (including energy from sugars) and fibres. Age and BMI of the visitors did not

143 correlate with lunch energy.

144 TABLE 1 ABOUT HERE

145 The effects of inclusion of soup and beverages on total lunch energy and lunch composition are

146 summarized in Table 2 together with information regarding the visitors who selected these lunches.

147 Inclusion of a beverage (either dairy or non-dairy) add variety (i.e. add items) and weight to the

148 lunch. Inclusion of a dairy beverage is associated with higher lunch energy whereas inclusion of

149 other beverages has no effect on lunch energy, and inclusion of soup is associated with somewhat

6 150 lower lunch energy. Not surprisingly, the nutrient balance of the lunch is affected by the selected

151 beverage. Lunches with milk are relatively high in (energy from) proteins and low in fibres and

152 carbohydrates whereas lunches with other beverages are relatively low in proteins and fat and high

153 in carbohydrates (and especially sugars). The nutrient balance of lunches with soup is relatively

154 similar to that of lunches without soup. Visitors who select milk for their lunches are typically male,

155 somewhat older, and somewhat heavier. Soups are also selected by somewhat older visitors,

156 irrespective of gender.

159 4.3 Stability of lunch selections of individual consumers.

160 Stabilities of lunch selections are shown in Table 3 for selected lunch parameters that are either

161 nutrient specific or non-specific. Lunch selections are relatively stable with regard to energy from

162 fat and carbohydrates, somewhat less stable with regard to energy from proteins and fibers and

163 relatively unstable with regard to energy from sugars. The stability for the nutrient non-specific

164 parameters falls between the least and most stable nutrient parameters with somewhat better

165 stability for weight and energy than for price and number of items.

7 170 5 DISCUSSION 171

172 5.1 Lunch composition 173 174 The lunch that is typically selected in the test restaurant consists of 3 or 4 items with a combined

175 weight of about 530 grams, a cost of about 4.20 euro, and a total energy of about 440 kCals. Lunch

176 energy increases with price, weight, number of lunch items, and relative energy from fat and

177 decreases with relative energy from proteins, carbohydrates (including sugars) and fibres. The

178 relation between energy, price and weight is to be expected: more money buys more food with more

179 energy. The relation between energy and number of items is less obvious and may be caused by

180 sensory specific satiety. Sensory specific satiety is typically used to explain the phenomenon that

181 more food is consumed when a larger variety of food is offered. The present results suggest that

182 sensory specific satiety is also involved when a larger variety of foods is selected. Visitors may

183 already anticipate reduced satiation and compensate by selecting more food.

184 Previous studies suggest that proteins are more filling – or satiating- than carbohydrates or fat. This

185 study supports that notion because low energy lunches are characterized by relatively high protein

186 content. These lunches are also characterized by high fibre content. Fibres contain relatively little

187 energy but add much volume to the food resulting in low energy-dense foods. These foods are

188 typically more satiating than high energy-dense foods20, 21. Lower energy lunches also contain

189 relatively much sugar which is unexpected because sugars are generally seen of one of the

190 contributors to overconsumption, especially if consumed in the form of carbonated beverages. In

191 our study, energy from sugar was also associated with consumption of dairy and non-dairy

192 beverages (table 2). In addition to sugar, inclusion of beverages in lunches also add to the lunch

193 weight and the variety (i.e. the lunch contains more items). Unexpectedly, the increase in sugar,

194 weight and variety is associated with higher energy only when the beverage is a dairy one. In other

195 cases, lunches with non-dairy beverages are not associated with more energy. It has to be noted

8 196 however, that the non-dairy beverages selected in the lunch restaurant were typically non-

197 carbonated juices, whereas overconsumption is typically associated with carbonated beverages.

198 Our results show striking differences between lunches with beverages, either dairy or non-dairy,

199 and lunches with soup. Whereas beverages add to weight, variety, sugar content, and energy (in the

200 case of dairy beverages), soup also adds to weight but adds little to variety. In contrast to beverages,

201 soup reduces sugar content, and is associated with reduced lunch energy content. These results

202 correspond well to results from other studies that demonstrate the satiating properties of soups

203 compared to other liquids8. Obviously, comparisons of lunches with and without beverages have to

204 be regarded with caution because they may be consumed by consumer segments that differ with

205 regard to gender, age and BMI and probably many other variables that were not included in this

206 study (table 2).

207 The average lunch contained approximately 440 kCals of energy which is relatively low compared

208 to averages reported by others. However, these estimations are based on the dietary patterns of other

209 countries which may differ from the Dutch patterns. In the present study, 34 per cent of the lunch

210 energy came from fat and 47% from carbohydrates which is in line with the national guidelines for

211 daily intake. Seventeen per cent of the lunch energy came from proteins, which equates to

212 approximately 0.27 grams of protein per kg of bodyweight, which is in line with the recommended

213 daily consumption.

214 Lunch selections were relatively stable with regard to energy from fat and carbohydrates, somewhat

215 less stable with regard to energy from proteins and fibers and relatively unstable with regard to

216 energy from sugars. The stability for the nutrient non-specific parameters fall between the least and

217 most stable nutrient parameters with somewhat better stability for weight and energy than for price

218 and number of items. (Table 3). The stability of repeated lunch selections in terms of energy and

219 nutritional protein, fat and carbohydrate composition is probably not based on information provided

220 by the restaurant because this information is either absent, as in the case of freshly prepared items,

9 221 or typically ignored by visitors as in the case of packaged items. Hence, this stability probably

222 reflects preferences of visitors for specific lunch categories with specific energies and composition,

223 e.g., visitors who routinely selects snacks or salads. Even though different snacks or salads may be

224 selected and/or offered during repeat visits, variation in energies and composition between items

225 from the same category is probably typically smaller than variation between categories resulting in

226 greater stability. The relative low stability of lunch selections in terms of energy from sugar

227 suggests that intake of sugar is more occasionally than habitually. High-sugar desserts may be

228 selected on “special” occasions (e.g., “it is Friday”) but ignored during the rest of the time, possibly

229 for health reasons.

230 In contrast to energy and nutritional composition, lunch weight can actually be monitored by the

231 visitor via the weight of the tray. Visitors may anticipate that a certain weight of food selected from

232 specific lunch categories is typically satiating enough to get them through the remainder of their

233 working day without feeling hungry. Anticipated satiety was demonstrated by others to be a major

234 determinant of portion size22.

235 The fact that visitors seem to base their lunch selections again and again on specific criteria

236 suggests that these selection criteria satisfy their needs although there is always the possibility that

237 visitors compensate during breakfast, dinner, and/or snacking for the nutritional variations during

238 lunch.

239 Obviously, extrapolation of the current results to healthy dietary patterns in general has to be

240 regarded with caution given the fact that the current study relates to only one meal moment for a

241 relatively homogeneous group of consumers and a restricted lunch assortment. Nevertheless, the

242 results offer some insights in the way dietary patterns may be changed into healthier ones. The

243 results suggest that healthier dietary lunch patterns with less energy can be promoted by reducing

244 the variety of lunch items and their fat content. Inclusion of non-dairy beverages or soups may also

245 contribute to a healthier pattern because they add lunch weight but not lunch energy.

10 247 6 REFERENCES 248

249 1. Kreijl CF van, Knaap A GAC, van Raaij JMA (eds.) (2006). Our Food, Our Health: Healthy diet

250 and safe food in the Netherlands. Bilthoven, the Netherlands : RIVM, Report 270555009.

251 2. Hulshof KFAM, van Rossum C TM, Buurma-Rethans EJM et al (2003). Resultaten van de

252 voedselconsumptiepeiling, in TNO report nr. V6000, TNO, RIVM: Zeist, Utrecht 2003.

253 3. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein,

254 and Amino Acids (Macronutrients) (2005). Food and Nutrition Board (FNB). The national

255 academies press. Washington D.C.

256 4. Westerterp-Plantenga MS, Rolland V, Wilson SAJ, Westerterp KR (1999). Satiety related to 24 h

257 diet-induced thermogenesis during high protein/carbohydrate vs. high fat diets measured in a

258 respiration chamber. Eur J Clin Nutr; 53, 495–502.

259 5. Eisenstein J, Roberts SB, Dallal G, Saltzman E. (2002). High-protein weight-loss diets: are they

260 safe and do they work? A review of experimental and epidemiologic data. Nutr Rev; 60, 189–200.

261 6. Rolls BJ, Rolls ET, Rowe EA, Sweeney K (1981). Sensory specific satiety in man. Physiol

262 Behav.;27(1):137-42

263 7. Mattes R (2006). Fluid calories and energy balance: The good, the bad, and the uncertain 2006

264 Physiol Behav; 89, 66-70.

265 8. Mattes R. Soup and satiety (2005). Physiol Behav; 83, 739-747

266 9. Editorial Dietary Patterns and Dietary Makeovers. Public Health Nutr 2004; 7, 473–474.

267 10.Briefel RR, Sempos CT, McDowell MA et al (1997). Dietary methods research in the Third

268 National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin

269 Nutr; 65, 1203–1209.

270 11.De Castro, JM (2000).Eating behaviour: lessons from the real world of humans. Nutrition; 16, 800-

271 813.

11 272 12.O’Dwyer NA, McCarthy SN, Burke SJ et al. (2005).The temporal pattern of the contribution of fat

273 to energy and of food groups to fat at various eating locations: implications for developing food-

274 based dietary guidelines. Public Health Nutr; 8, 249–257.

275 13.Zampollo F, Wansink B, Kniffin KM et al. (2012). Looks Good Enough to Eat. How Food Plating

276 Preferences Differ Across Cultures and Continents. Cross-Cultural Res; 46, 31-49.

277 14.Piqueras-Fiszman B, Harrar V, Alcaide J et al. (2011). Does the weight of the dish influence our

278 perception of food? Food Qual Pref; 22, 753-756.

279 15.NEVO: Dutch food composition database. Netherlands Nutrition Centre. 2001, The Hague, the

280 Netherlands: Netherlands Nutrition Centre.

281 16.NEVO, VBS Komeet, in hv07. 2008: Arnhem, The Netherlands. VBS Food calculation system.

282 17.Donders-Engelen M, van der Heijden L, Hulshof KFAM (2003). Maten, gewichten en

283 codenummers. Wageningen University, department of Human Nutrition.

284 18.Reinivuo H, Bell S, Ovaskainen ML (2009). Harmonization of recipe calculation procedures in

285 European food composition databases, J Food Comp Anal 22: 410–413.

286 19.Bell S, Vasquez-Caicedo AN, Hartman BM et al (2006) Report on Nutrient Losses and Gains

287 Factors used in European Food composition Databases.

288 20.Rolls BJ, Bell EA, Waugh BA (2000). Increasing the volume of food by incorporating air affects

289 satiety in men. Am J Clin Nutr 72: 361-368.

290 21.Rolls BJ, Bell EA (1999). Intake of fat and carbohydrate: role of energy density. Eur J Clin Nutr

291 53, 166-173.

292 22.Brunstrom JM, Rogers PJ (2009). How many calories are on our plate? Expected fullness, not

293 liking, determines meal-size selection. Obesity 17: 1884–1890.

12 296 Table 1: correlation coefficients between lunch parameters related to the visitors (BMI, age), lunch

297 energy (total energy and energy from nutrients or % Ener), and weight, price and number of lunch

298 items. (n=1541)

299 n i r s e r a b t e r g t o b a r u a i P F S F C )

l a m m m m m s c o o o o o

e r r r r r l K ( F F F F F c

i . . . . . t d t y r r r r r j r i h g e e e e e t r A f g I s n n n n n i . j e e i e E E E E E M o r n e L B N P W E % % % % % Leeftijd 1 .307** .071** -.077** .106** -.042 .062* -.039 .013 -.073** .119** BMI .307** 1 .045 -.005 -.007 -.003 .046 .016 -.032 -.096** .053* No. Articles .071** .045 1 .374** .300** .466** -.124** .128** -.058* -.134** -.180** Prijs -.077** -.005 .374** 1 .698** .636** -.039 .154** -.133** -.025 -.165** Weight .106** -.007 .300** .698** 1 .556** .042 .057* -.068** -.140** .024 Energy (Kcal) -.042 -.003 .466** .636** .556** 1 -.161** .324** -.220** -.345** -.352** %Ener. From Protein .062* .046 -.124** -.039 .042 -.161** 1 -.185** -.309** .072** -.078** %Ener. From Fat -.039 .016 .128** .154** .057* .324** -.185** 1 -.875** -.436** -.462** %Ener. From Carbs .013 -.032 -.058* -.133** -.068** -.220** -.309** -.875** 1 .331** .489** %Ener. From Fiber -.073** -.096** -.134** -.025 -.140** -.345** .072** -.436** .331** 1 .128** %Ener. From Sugar .119** .053* -.180** -.165** .024 -.352** -.078** -.462** .489** .128** 1 **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). 300 301

m m m m m s o o o o o e r r r r r l F F F F F c

i . . . . . t d t y n r r r r r j i r ) i r h s g l e e e e e t e r r a A f g b a t I s n n n n n i e . j r e g e t i c o e E E E E E b M o a r n r u e a i K L B N P W E ( % P % F % C % S % F Leeftijd 1 ,308** ,073** -,082** ,102** -.050 .008 -.023 .024 ,136** -,087** BMI ,308** 1 .046 -.003 -.004 -.005 .004 .031 -.027 ,053* -,101** No. Articles ,073** .046 1 ,370** ,297** ,467** -,170** ,149** -.047 -,201** -,064* Prijs -,082** -.003 ,370** 1 ,700** ,679** ,091** ,138** -,186** -,196** -,054* Weight ,102** -.004 ,297** ,700** 1 ,586** ,115** .042 -,096** .028 -,194** Energy (Kcal) -.050 -.005 ,467** ,679** ,586** 1 -,075** ,322** -,257** -,378** -,324** %Ener. From Protein .008 .004 -,170** ,091** ,115** -,075** 1 -,239** -,337** -,080** -.006 %Ener. From Fat -.023 .031 ,149** ,138** .042 ,322** -,239** 1 -,831** -,441** -,434** %Ener. From Carbs .024 -.027 -.047 -,186** -,096** -,257** -,337** -,831** 1 ,482** ,371** %Ener. From Sugar ,136** ,053* -,201** -,196** .028 -,378** -,080** -,441** ,482** 1 ,077** %Ener. From Fiber -,087** -,101** -,064* -,054* -,194** -,324** -.006 -,434** ,371** ,077** 1 **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). 302

13 303 Table 2: Parameters of lunches that contain dairy (milk) or non-dairy beverages or soup with 304 respect to visitors (gender, BMI, age), lunch energy (total energy and energy from nutrients or % 305 Ener), and lunch weight and price. Visitors values are actual means, lunch values are estimated 306 means for the mixed model ANOVAs. Estimated values in bold and italic differ significantly from 307 the control in the row below (p<0.01) except for those indicated with an * (p<0.05). 308 Actual means Estimated means from mixed model analysis used for paper s ) e n s r s i r l s b e ) a i a t b o g r t F ) C o r

u r a a k ) g y u s ( ( S s P F C s s

g E r t e y y ( r r m r r r l y h a e g e e e e e ( a e r t

r g I n n n n n I i T c e . e M i e E E E E E o M g r o n N % B A P N W E % % % % % Lunch with dairy beverage 464 64 24.2 44.4 4.2 3.9 620.0 456* 22.1 32.2 44.9 21.2 0.9 Lunch without dairy beverage 1077 55 23.0 38.1 4.2 3.4 476.0 417.0 16.1 33.7 48.9 17.3 1.2

Lunch with non-dairy beverage 329 54 23.8 39.1 4.6 3.9 570.0 433.0 15.3 27.3 56.2 29.0 1.3 Lunch without non-dairy beverage 1212 59 23.3 40.2 4.1 3.5 514.0 430.0 19.1 34.9 45.0 15.5 1.1

Lunch with soup 848 58 23.3 41.9 4.2 3.7 587.0 417* 17.7 33.4 47.9 18.1 1.0 Lunch without soup 693 58 23.5 37.6 4.3 3.4 453.0 448.0 18.9 32.8 47.1 19.3 1.2 309 Numbers in bold and italic: p<0.01, except * (p<0.05) 310 Actual means Estimated means from mixed model analysis used for paper s ) e n s r s i r l s b e ) a a i t b o g r t F o ) C r

r a u a k ) g u y s ( ( P C s F S s s

g E t r e y y ( r r r m r r l y h a e e e e g e e ( a r e t r g I n n n n n I i T c e . e M i e E E E E E o r M g o n N % B A P N W E % % % % % Lunch with milk 461 64 24.2 44.4 4.2 3.9 620.0 451.9 22.5 31.1 45.6 21.0 0.8 Lunch without milk 1075 55 23.0 38.1 4.2 3.4 476.0 471.7 17.3 33.7 47.7 15.2 1.3

Lunch with drink (no milk) 325 54 23.8 39.1 4.6 3.9 570.0 504.5 15.9 28.4 54.5 27.1 1.2 Lunch without drink (no milk) 1211 59 23.3 40.2 4.1 3.5 514.0 455.4 19.6 34.1 45.1 14.2 1.1

Lunch with soup 846 58 23.3 41.9 4.2 3.7 587.0 452.3 18.0 34.1 46.9 16.8 1.0 Lunch without soup 690 58 23.5 37.6 4.3 3.4 453.0 482.2 19.9 31.5 47.4 17.0 1.2 311 Numbers in bold and italic: p<0.01, except * (p<0.05) 312 313

14 314 Table 3: Ratios between estimated within and between visitor variances for nutrient specific and

315 unspecific lunch parameters.

Nutrient non-specific Nutrient specific s e t a r d s y n h s i s r o e e a b t r r g s t o b a r u a i t y m P F F C S h g e e r g t n n n n n i I c e i E E E E E e o n r p w N E % % % % % Within participant 1.530 19047.000 .920 25056.000 18.490 52.350 50.820 127.400 .320 Between participant 1.750 27552.000 1.180 37968.000 35.310 155.980 159.530 129.370 .650 316 Ratio 0.87 0.69 0.78 0.66 0.52 0.34 0.32 0.98 0.49

Nutrient non-specific Nutrient specific s e t a r d s y n h s i s r o e e a b t r r g s t o b a r u a i t y m P F F C S h g e e r g t n n n n n i I c e i E E E E E e o n r p w N E % % % % % Within participant 1.7 19907.0 0.9 28781.9 20.8 56.7 53.8 130.7 0.3 Between participant 1.8 27410.6 1.2 38519.3 52.4 153.5 158.8 122.6 0.6 318 Ratio 0.94 0.73 0.78 0.75 0.40 0.37 0.34 1.07 0.51

320 321