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A review of the effects of lunch on adults' short-term cognitive functioning.


Lunch provides a substantial portion of daily energy (1-4) and nutrients (2-4). For example, data from the 2004 Canadian Community Health Survey showed a 24% contribution of lunch to daily energy intake (1). However, irregular lunch consumption seems to be common in childhood and adulthood.

Although all-day schools are well established in many countries, in only some (e.g., the United States) is the provision of school lunch well organized (5). In other nations (e.g., Canada, European nations), school lunches lack uniformity (6,7). Furthermore, a relatively high percentage of children and adolescents skip school lunch when it is offered (8-10). Ten percent of fourth grade students from Maryland were reported to omit lunch at least three times a week (11).

In a review, Sparks et al. (12) concluded that working time has increased in various countries in recent decades. They found that employees who work for long hours are more prone to poor lifestyle habits such as an inadequate diet (12). In Germany, an association between daily working hours and irregular meal consumption has been observed in employees (13). Similarly, in a US sample of adults, the most frequently missed meal was lunch, with 28% of participants skipping lunch on at least one of two 24-hour recall days (14). Similarly, 22% of a Swedish adult sample reported no usual lunch consumption (15).


One argument often used against meal skipping is the impairment of short-term cognitive functioning. To date, research on the impact of meal consumption on short-term cognitive functioning has focused primarily on breakfast; findings suggest positive cognitive effects associated with having breakfast versus skipping it in children (16) and adults (17). However, the effects of lunch on early afternoon cognitive functioning in the classroom and at work are not yet well understood. This review is a summary of the literature on short-term effects of lunch on cognitive performance in children and adults. Underlying metabolic mechanisms and the role of potential effect modifiers are considered, and implications for future research are discussed.


A MEDLINE search was done in September 2012. The search strings listed in Table 1 were used, and were complemented with cross-referencing. Studies were included or excluded according to various criteria.

Participants and interventions

Studies on healthy participants of all ages and both sexes were included. All types of lunch studies were examined, including studies in which lunch was compared with no lunch, studies of different lunch sizes, and studies of different lunch compositions. Studies on the effects of longer-term school health/lunch programs were excluded, as their focus was on cognitive effects of modifying lunch provision and the lunch environment (e.g., cafeteria layout) (18-20). Hence, observed effects might not be due to lunch alone. Studies on the effects of fortified lunches were also excluded.


Included were studies in which objective measures of cognitive functioning were determined; excluded were those in which only subjective ratings of performance and fatigue were considered. Short-term performance was assessed--not longer-term cognitive effects of lunch modifications.

Figures 1 and 2 show the study selection process and the number of studies accessed and excluded at each stage. The process yielded 139 published studies with children and 170 published studies with adults. All of the studies with children and a Defined as identical reports accessed with several search strings 159 of the studies with adults were excluded because the inclusion criteria were not fulfilled. Therefore, 11 studies (12 papers) found through the MEDLINE search and cross-referencing were included; all were studies of adults.


No studies were found in which authors examined the short-term effects of lunch versus no lunch and of lunch character (size, composition) on children's cognitive functioning. In adults, 11 experimental studies published from 1981 to 1996 included examinations of the effects of lunch on short-term cognitive performance. Three were comparisons of the effects of having lunch with skipping lunch, two were evaluations of the effects of lunch size, five were determinations of the effects of lunch composition, and one was a determination of the effects of lunch size and composition (Table 2).

Lunch versus no lunch

Studies in which having lunch was compared with skipping lunch had inconsistent results. Two showed impaired cognitive performance in some but not all tasks. For example, Craig et al. (21) observed that the ability to discriminate was impaired by the "lunch condition" in comparison with the "pre-lunch condition," but was not altered by the "no lunch condition." With a sustained attention task, Smith and Miles (22) reported a slower response from the lunch group, but not from the no-lunch group in comparison with the pre-lunch condition; however, with a selective attention task they found no differences between the groups. In contrast, Kanarek and Swinney (23) found no lunch effects in experiment 1; in experiment 2, they found positive effects on a reading task after the "lunch condition" in comparison with the "no lunch condition," and no effects for other tasks.

Lunch size

Craig and Richardson (24) observed an increased number of errors after a large lunch and a trend toward a decreased number of errors after a small lunch, when each condition was compared with the pre-lunch condition. Similarly, Smith et al. (25) observed more errors with a selective attention task after a large lunch than after a "normal" lunch and a small lunch. Effects were modified by the size of the test lunch and the regular lunch (25,26).

Among the six studies in which lunch composition was examined, four studies focused on cognitive effects of low-fat versus high-fat lunches and two studies focused on high-protein versus high-carbohydrate lunches. For example, Smith et al. (26) observed that subjects responded more accurately but slowly after a high-fat lunch than after a low-fat lunch. In older subjects, Spring et al. (27) reported lower accuracy and more omission errors with a sustained attention task after a high-carbohydrate lunch than after a high-protein lunch. Smith et al. (28) also found more negative effects of a high-carbohydrate lunch than of a high-protein lunch, with slower reactions to peripheral targets. In general, cognitive effects of lunch composition were observed for some but not all tasks (Table 2).


Lunch versus no lunch

Only a few investigators have examined the effects on cognitive performance of having lunch versus skipping lunch; most found impairments in some but not all tasks after lunch. In adults, lunch therefore seems to have adverse effects on certain types of tasks. However, differences and limitations in the methodology of existing studies make a comparison of data difficult. For example, the cognitive aspects assessed (e.g., perceptual discrimination, attention, memory) differ between studies. Only Kanarek and Swinney (23) used a crossover design; in the remaining studies, investigators used randomized, controlled designs and standardization was sometimes inadequate. For instance, unlimited tea and coffee intake was allowed in two of three studies. Psychoactive substances such as caffeine may produce larger cognitive effects, which may mask smaller "true" lunch effects on cognitive functioning (26). Furthermore, the two lunch conditions possibly resulted in different caffeine absorption rates because of different gastric emptying rates; in turn, this may have influenced cognitive functioning.

Although only weak evidence exists for the detrimental effects of lunch on cognitive functioning, several explanations have been proposed. First, the so-called post-lunch dip has often been discussed. It may not be caused only by endogenous factors; it may also be influenced by exogenous factors (e.g., lunch size) (29). In a review, Kanarek (30) concluded that the suggestion the post-lunch dip may reflect an endogenous alertness rhythm was supported by observed declines in some afternoon tasks by both subjects who had eaten lunch and subjects who had skipped lunch. For other tasks, afternoon performance was more impaired in subjects who had eaten lunch than in those who had not, indicating that food intake may be partly responsible for the dip in the early afternoon (30).

The post-lunch dip is assumed to begin approximately one hour after lunch is started, with data suggesting that performance begins to recover approximately two hours after lunch (31). In most of the studies in which lunch was compared with no lunch, the authors examined post-lunch effects 60 to 75 minutes after a test meal (21,22,32). Only Kanarek and Swinney (23) administered cognitive assessments 210 minutes after lunch. Thus, little information is available on the effects that may appear later in the afternoon.

Another explanation for unfavourable short-term effects of lunch is a change in stress hormone status (e.g., glucocorticoid level). Cortisol is the major natural glucocorticoid in humans (33), and it rapidly increases to a lunch-related plasma peak about one hour after lunch (at approximately 1:00 p.m.). This is also the time when only a slow increase in plasma cortisol is observable in fasting subjects (34). An enhancement in plasma cortisol seems to be associated with a decrease in cognitive functioning (35). In their review, Lupien et al. (36) concluded that an endogenous increase of glucocorticoids is as efficient as an exogenous administration at inducing cognitive impairments. The effects of exogenous glucocorticoid administration on cognitive functioning were supported by the hippocampus and the frontal lobes, the brain regions with the largest concentrations of glucocorticoid receptors, and modulated according to an inverted U-shaped function (36).

Lunch size

The lunch effect on short-term cognitive functioning in adults may also depend on lunch size, in terms of energy content (24,25) or weight (26). For example, Craig and Richardson (24) observed an increased number of errors after an energy intake of approximately 5.8 MJ and a trend toward a decreased number of errors after an energy intake of approximately 1.1 MJ, when each condition was compared with the pre-lunch condition. Smith et al. (25) report similar findings, with more errors on a search task after a large lunch (2.8 to 4.6 MJ) than after smaller lunches (1.4 to 3.5 MJ). Smith et al. (26) had inconsistent results.

In only three studies did the authors examine the effects of lunch size on cognitive functioning in the early afternoon; these studies differed in methodology (cognitive aspects assessed, definition of meal size). Furthermore, the studies are characterized by an inadequate description of methodology. For instance, Craig and Richardson (24) gave information only on meal type (three-course meal versus sandwich) and energy content. Smith et al. (25) provided more details of the test meal, but no information on participants' assignment to the intervention group or control group. Furthermore, although energy intake seems to be oriented toward participants' habitual energy intakes in Craig and Richardson's study (24), a lunchtime energy intake of 5.8 MJ appears to be relatively high. In general, for a detailed evaluation of the effects of lunch size on cognitive performance to be possible, test meal compositions should be almost identical. This is because the relative carbohydrate content also plays a role in postprandial glycemic response. Circulating blood glucose, in turn, seems to modify cognitive functioning (17). The current results could lead one to hypothesize that a large lunch would be accompanied by decreased cognitive performance.

Lunch composition

Lunch composition may affect cognitive performance. Smith et al. (26) observed that subjects who had eaten a high-fat lunch responded more accurately but more slowly than did subjects who had eaten a low-fat lunch. This finding is consistent with that of Wells et al. (37), who showed that speed of correct responses was significantly slower after a high-fat lunch than after a low-fat lunch. Lloyd et al. (38) also reported a significantly longer reaction time after a high-fat/low-carbohydrate lunch than at baseline. However, different lunch conditions and cognitive tasks were used. Thus, we conclude only cautiously that a highfat lunch results in slower but more accurate responses.

Changes in the fat content of meals also accompany changes in carbohydrate and protein content. However, Smith et al. (26) have suggested that the effects mentioned above cannot be due to the changes in other macronutrients because their findings were very different from those of an earlier study, which included modified proportions of carbohydrates and protein (27). Further studies are needed to determine whether an isolated fat effect on cognition exists, and whether macronutrient substitution has no effect on cognition.

Some evidence indicates that the potential effects of lunch composition on short-term cognition may vary between sub groups. Spring et al. (27) classified subjects into younger (18 to 39 years) and older (40 to 65 years) age groups and gave both groups isocaloric high-protein and high-carbohydrate meals. The older subjects showed significantly impaired cognitive performance after consuming a high-carbohydrate lunch, whereas the younger subjects did not.

Another study showed that subjects who had eaten medium-fat/medium-carbohydrate lunches had improved simple reaction-time tasks when they were compared with subjects who had eaten low-fat/high-carbohydrate and high-fat/low-carbohydrate lunches, and when their reaction time was compared with baseline times (38). The medium-fat/medium-carbohydrate lunch was most similar to the subjects' usual eating habits in terms of meal size and macronutrient composition. One might suppose that this nutrient profile would be preferred intuitively because it benefits subjects' performance after lunch. Of note is the fact that the effects were mostly very small, inconsistent, and restricted to certain tasks (e.g., attention and search tasks) and subgroups (e.g., older subjects).

Effect modifiers

The studies evaluated had some inconsistent results. These differences between studies may be partly because of effect modifiers.

Female or male sex: Regardless of the nutritional intervention, men achieved more favourable results on visuospatial memory tests, whereas women tended to do better on verbal memory tests (39). Furthermore, sex seems to influence the way a meal affects cognitive functioning (e.g., male students had improved visuospatial memory after eating breakfast, but female students did not) (40). In contrast, Mahoney et al. (41) observed that, in comparison with boys, girls suffer more when they skip breakfast and benefit more when they eat a low glycemic index breakfast. Mahoney et al. (41) suggested that mood and usual breakfast habits may play a role in these differences. In a review, Blaak (42) concluded that impaired glucose tolerance in general and higher glycemic responses to glucose-containing meals are more common in women than in men. This may be due to men's higher muscle mass, which enables faster glucose uptake (42).

Habitual lunch size: Craig and Richardson (24) categorized subjects as light lunchers or heavy lunchers, depending on their normal eating habits. All of them performed a sustained attention task, both before and after a light lunch and a heavy lunch. The largest drop in performance was observed in subjects who usually ate light lunches but in the test ate a heavy lunch, whereas the greatest improvement was observed in subjects who normally ate heavy lunches but in the test ate a light lunch. These results reflect the so-called conditioning hypothesis, which suggests that a physiological under- or over-reaction occurs with an unusual meal (24).

Habitual lunch consumption was considered in seven of the 11 studies. In two studies (23,43), only usual breakfast and lunch eaters were included, probably because habitual meal skipping may mask short-term lunch effects. In one study (22,32), most participants reported that the test meal was larger than their habitual lunch. The authors suggested that the observed faster completion of an afternoon task when lunch was skipped than when lunch was eaten might not have been due to the test lunch, but to a deviation from the usual lunch.

Biorhythm: Biorhythm seems to vary from individual to individual, with observable differences between so-called "morning people" and "evening people" (29). Morning people have better performance efficiency in the morning than in the evening. In contrast to morning people, who show a general decline throughout the day, evening people show a steady improvement throughout the day (44). May et al. (44) have suggested that the synchrony between optimal cognitive performances and testing times may be a critical variable in the determination of group differences in cognitive functioning. Furthermore, the likelihood of exhibiting a post-lunch dip may be greater in morning people. Horne et al. (45) found an obvious post-lunch dip in morning people, who showed a notable decline in performance between noon and 2:00 p.m., which was after lunch for most subjects. In contrast, evening people showed a slight improvement between noon and 2:00 p.m.

Implications for future research

To date, only a few studies with adults and no studies with children are available to permit conclusions about the effect on cognitive functioning of lunch character (size, composition) or lunch versus no lunch. Because of age- and growth-related issues affecting children (e.g., rapid growth, high metabolic rate), findings from adult studies are not necessarily transferable to children. Therefore, well-designed studies in children and adults are needed to permit scientifically well-founded conclusions. Crossover studies are well suited to this purpose because they can eliminate between-patient variations. Factors such as female or male sex, age, habitual lunch consumption, and biorhythm may modify the lunch effect and therefore also need to be considered.

Test lunches should be optimally adjusted to the subject's habitual lunch. Dye and Blundell (46) speculated that a meal's palatability could increase the subject's positive mood, which, in turn, could enhance cognitive performance. Therefore, providing a well-liked test meal in a familiar environment would be the most appropriate method for identifying the lunch effect. The establishment of standardized procedures (e.g., standardized test meals) is another condition for minimizing bias.


Only limited information from adult studies is available on the effects of lunch on short-term cognitive functioning, and all but one study suggest that eating lunch potentially impairs some aspects of cognitive functioning. Factors such as lunch size and lunch composition may be involved. However, we entertain these suggestions only cautiously because findings are based on a few studies with some design limitations, which further restrict the validity of the evidence. If nutritional recommendations are to be provided, additional well-designed studies in children and adults are needed. In the meantime, lunch should be part of an overall balanced diet in Canada and other coun tries, as it provides a considerable percentage of daily energy and nutrients and may positively affect subjects' longer-term general and cognitive health.


(1.) Garriguet D. Canadians' eating habits. Health Rep. 2007;18(2):17-32.

(2.) Nelson M, Lowes K, Hwang V. The contribution of school meals to food consumption and nutrient intakes of young people aged 4-18 years in England. Public Health Nutr. 2007;10:652-62.

(3.) Hoppu U, Lehtisalo J, Tapanainen H, Pietinen P. Dietary habits and nutrient intake of Finnish adolescents. Public Health Nutr. 2010;13:965-72.

(4.) Alexy U, Freese J, Kersting M, Clausen K. Lunch habits of German children and adolescents: composition and dietary quality. Ann Nutr Metab. 2013;62:75-9.

(5.) Ralston K, Newman C, Clauson A, Guthrie J, Buzby JC. The National School Lunch Program: background, trends, and issues. Washington: United States Department of Agriculture; 2008. Economic Research Report No. (ERR-61).

(6.) World Health Organization. Food and nutrition policy for schools--a tool for the development of school nutrition programmes in the European region. Copenhagen: World Health Organization Regional Office for Europe; 2006.

(7.) Harper C, Wood L, Mitchell C. The provision of school food in 18 countries. School Food Trust; 2008 [cited 2013 Feb 6]. Available from:

(8.) Hoglund D, Samuelson G, Mark A. Food habits in Swedish adolescents in relation to socioeconomic conditions. Eur J Clin Nutr. 1998;52:784-9.

(9.) Dubuisson C, Lioret S, Dufour A, Calamassi-Tran G, Volatier JL, Lafay L, et al. Socio-economic and demographic variations in school lunch participation of French children aged 3-17 years. Public Health Nutr. 2011;14:227-38.

(10.) Wurbach A, Zellner K, Kromeyer-Hauschild K. Meal patterns among children and adolescents and their associations with weight status and parental characteristics. Public Health Nutr. 2009;12:1115-21.

(11.) Gross SM, Bronner Y, Welch C, Dewberry-Moore N, Paige DM. Breakfast and lunch meal skipping patterns among fourth-grade children from selected public schools in urban, suburban, and rural Maryland. J Am Diet Assoc. 2004;104:420-3.

(12.) Sparks K, Faragher B, Cooper C. Well-being and occupational health in the 21st century workplace. J Occup Organ Psychol. 2001;74:489-509.

(13.) Muff C, Reinhardt JD, Erbel R, Dragano N, Moebus S, Mohlenkamp S, et al. Who is at risk of irregular meal intake? Results from a population-based study. J Public Health. 2011;19:453-62.

(14.) Howarth NC, Huang TT, Roberts SB, Lin BH, McCrory MA. Eating patterns and dietary composition in relation to BMI in younger and older adults. Int J Obes (Lond). 2007;31:675-84.

(15.) Berg C, Lappas G, Wolk A, Strandhagen E, Toren K, Rosengren A, et al. Eating patterns and portion size associated with obesity in a Swedish population. Appetite. 2009;52:21-6.

(16.) Hoyland A, Dye L, Lawton CL. A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. Nutr Res Rev. 2009;22:220-43.

(17.) Mahoney CR, Taylor HA, Kanarek RB. The acute effects of meals on cognitive performance. In: Lieberman HR, Kanarek RB, Prasad C, editors. Nutritional neuroscience. New York: Taylor & Francis Group; 2005. p. 73-92.

(18.) Parcel GS, Simons-Morton B, O'Hara NM, Baranowski T, Wilson B. School promotion of healthful diet and physical activity: impact on learning outcomes and self-reported behavior. Health Educ Q. 1989;16:181-99.

(19.) Golley R, Baines E, Bassett P, Wood L, Pearce J, Nelson M. School lunch and learning behaviour in primary schools: an intervention study. Eur J Clin Nutr. 2010;64:1280-8.

(20.) Storey HC, Pearce J, Ashfield-Watt PA, Wood L, Baines E, Nelson M. A randomized controlled trial of the effect of school food and dining room modifications on classroom behaviour in secondary school children. Eur J Clin Nutr. 2011;65:32-8.

(21.) Craig A, Baer K, Diekmann A. The effects of lunch on sensory-perceptual functioning in man. Int Arch Occup Environ Health. 1981;49:105-14.

(22.) Smith AP, Miles C. Effects of lunch on selective and sustained attention. Neuropsychobiology. 1986;16:117-20.

(23.) Kanarek RB, Swinney D. Effects of food snacks on cognitive performance in male college students. Appetite. 1990;14:15-27.

(24.) Craig A, Richardson E. Effects of experimental and habitual lunch-size on performance, arousal, hunger and mood. Int Arch Occup Environ Health. 1989;61:313-9.

(25.) Smith AP, Ralph A, McNeill G. Influences of meal size on post-lunch changes in performance efficiency, mood, and cardiovascular function. Appetite. 1991;16:85-91.

(26.) Smith A, Kendrick A, Maben A, Salmon J. Effects of fat content, weight, and acceptability of the meal on postlunch changes in mood, performance, and cardiovascular function. Physiol Behav. 1994;55:417-22.

(27.) Spring B, Maller O, Wurtman J, Digman L, Cozolino L. Effects of protein and carbohydrate meals on mood and performance: interactions with sex and age. J Psychiatr Res. 1982;17:155-67.

(28.) Smith AP, Leekam S, Ralph A, McNeill G. The influence of meal composition on post-lunch changes in performance efficiency and mood. Appetite. 1988;10:195-203.

(29.) Monk TH. The post-lunch dip in performance. Clin Sports Med. 2005;24:e1523, xi-xii.

(30.) Kanarek R. Psychological effects of snacks and altered meal frequency. Br J Nutr. 1997;77(Suppl 1):105-18.

(31.) Craig A. Acute effects of meals on perceptual and cognitive efficiency. Nutr Rev. 1986;44(Suppl):163-71.

(32.) Smith AP, Miles C. The effects of lunch on cognitive vigilance tasks. Ergonomics. 1986;29:1251-61.

(33.) Fietta P, Delsante G. Central nervous system effects of natural and synthetic glucocorticoids. Psychiatry Clin Neurosci. 2009;63:613-22.

(34.) Follenius M, Brandenberger G, Hietter B. Diurnal cortisol peaks and their relationships to meals. J Clin Endocrinol Metab. 1982;55:757-61.

(35.) Kirschbaum C, Wolf OT, May M, Wippich W, Hellhammer DH. Stress- and treatment-induced elevations of cortisol levels associated with impaired declarative memory in healthy adults. Life Sci. 1996;58:1475-83.

(36.) Lupien SJ, Maheu F, Tu M, Fiocco A, Schramek TE. The effects of stress and stress hormones on human cognition: implications for the field of brain and cognition. Brain Cogn. 2007;65:209-37.

(37.) Wells AS, Read NW, Craig A. Influences of dietary and intraduodenal lipid on alertness, mood, and sustained concentration. Br J Nutr. 1995;74:115-23.

(38.) Lloyd HM, Green MW, Rogers PJ. Mood and cognitive performance effects of isocaloric lunches differing in fat and carbohydrate content. Physiol Behav. 1994;56:51-7.

(39.) Weiss EM, Kemmler G, Deisenhammer EA, Fleischhacker WW, Delazer M. Sex differences in cognitive functions. Pers Individual Differences. 2003;35:863-75.

(40.) Widenhorn-Muller K, Hille K, Klenk J, Weiland U. Influence of having breakfast on cognitive performance and mood in 13- to 20-year-old high school students: results of a crossover trial. Pediatrics. 2008;122:279-84.

(41.) Mahoney CR, Taylor HA, Kanarek RB, Samuel P. Effect of breakfast composition on cognitive processes in elementary school children. Physiol Behav. 2005;85:635-45.

(42.) Blaak E. Sex differences in the control of glucose homeostasis. Curr Opin Clin Nutr Metab Care. 2008;11:500-4.

(43.) Wells AS, Read NW. Influences of fat, energy, and time of day on mood and performance. Physiol Behav. 1996;59:1069-76.

(44.) May CP, Hasher L, Stoltzfus ER. Optimal time of day and the magnitude of age differences in memory. Psychol Sci. 1993;4:326-30.

(45.) Horne JA, Brass CG, Pettitt AN. Circadian performance differences between morning and evening "types." Ergonomics. 1980;23:29-36.

(46.) Dye L, Blundell J. Functional foods: psychological and behavioural functions. Br J Nutr. 2002;88(Suppl 2):S187-211.

KATRIN MULLER, MSc, LARS LIBUDA, PhD, ANNA MARIA TERSCHLUSEN, Dipl-Oecotroph, MATHILDE KERSTING, PhD, Research Institute of Child Nutrition (FKE), Dortmund, Germany

Table 1

Search strings used in the literature review

1 Lunch AND children/adults AND cogniti*

2 "Lunch composition" AND children/adults AND cogniti *

3 Lunch AND children/adults AND memory

4 Lunch AND children/adults AND attention

5 Lunch AND children/adults AND vigilance

6 Lunch AND children/adults AND "reaction time"

7 Lunch AND children/adults AND spatial

8 Lunch AND children/adults AND visuo-spatial

9 Lunch AND children/adults AND "problem solving"

* Denotes word truncation

Table 2
Eleven experimental studies of lunch and cognitive
functioning in adults

Study           Sample

Lunch versus no lunch

Craig et al..   40 adults
1981, UK        63% male
(21)            Median age
                23 years

Smith and       Publ. 1
Miles, 1986,    48 adults
UKa (22,32)     38% male
                Age not

                Publ. 2
                see publ. 1

Kanarek and     Exp. 1
Swinney,        10 men
1990, USA       Mean age 21
(23)            years

                Exp. 2
                8 men

Lunch size

Craig and       24 men
Richardson,     (12 LL, 12
1989, UK        HL)
(24)            Median age
                23 years

Smith et al.,   35 women
1991, UK        Mean age
(25)            26 years

Lunch composition

Spring          184 adults
et al.,         (lunch n=92)
1982,           70% male
USA             18-39 years
(27)            (n=81)
                40-65 years
Smith           11 adults
et al.,         46% male
1988,           Mean age
UK              27 years

Lloyd           18 adults
et al.,         17% male
1994,           Mean age
UK              27 years

Wells           16 men
et al.,         (lunch n=8)
1995,           Mean age
UK              23 years

Wells           18 men
and             (lunch n=9)
Read,           Mean age
1996,           27 years

Lunch size and composition

Smith           46 adults
et al.,         43% male
1994,           Age not
UK              provided
(26)            (university

                Design and lunch
Study           intervention

Lunch versus no lunch

Craig et al..   Randomized controlled
1981, UK        intervention study with 2
(21)            conditions:
                a. Lunch: standard 3-course
                b. No lunch: tea or coffee,
                Intervention from 12:00-
Smith and
Miles, 1986,    Randomized controlled
UKa (22,32)     intervention study with 2
                a. Lunch: standard 3-course
                b. No lunch

                Intervention from 12:00-
                13:15 (early) and 13:15-
                14:30 (late)
                See publ. 1

Kanarek and     Counterbalanced crossover
Swinney,        with 4 lunch conditions:
1990, USA       a. Lunch/caloric snack
(23)            b. Lunch/non-caloric snack
                c. No lunch/caloric snack
                d. No lunch/non-caloric
                Lunch at 12:00 (early) or
                12:30 (late)
                See exp. 1
                Different caloric snack (fruit-
                flavoured yogurt instead of
                confectionery product)

Lunch size

Craig and       Counterbalanced crossover
Richardson,     with 2 conditions:
1989, UK        a. Large: 3-course meal
(24)            (5.8 MJ)
                b. Small: sandwich (1.1 MJ)
                Lunch from 12:00-13:00

Smith et al.,   Intervention study with 3
1991, UK        conditions:
(25)            a. Normal (2.5-3.5 MJ)
                b. Large (2.8-4.6 MJ): 40%
                more energy than EIR
                c. Small (1.4-3.1 MJ): 40%
                less energy than EIR
                Lunch at 12:30, 13:00, and
                13:30 (staggered starting)

Lunch composition

Spring          Randomized intervention study with
et al.,         2 lunch conditions:
1982,           a. High-protein: ~57 g P, 4 g F, 1 g
USA             CH, 30-90 mg choline
(27)            b. High-carbohydrate: 0 g P,
                ~4 g F, 57 g CH
                Lunch between 11:00 and 13:00

Smith           Counterbalanced crossover with 3
et al.,         conditions:
1988,           a. High-protein: 55% of energy as P,
UK              30% as F, 14% as starch, and 1%
(28)            as sugar
                b. High-starch: 15% of energy as P,
                30% as F, 40% as starch, and 15%
                as sugar
                c. High-sugar: 15% of energy as P,
                30% as F, 15% as starch, and 40%
                as sugar
                Lunch at 13:00
Lloyd           Counterbalanced crossover with 3
et al.,         conditions:
1994,           a. LFHO: 54% of energy as CH, 29%
UK              as F, and 15% as P
(39)            b. MFMC: 42% of energy as CH, 45%
                as F, and 12% as P
                c. HFLC: 24% of energy as CH, 62%
                as F, and 13% as P
                Lunch from 12:30-12:45

Wells           Counterbalanced crossover with 2
et al.,         lunch conditions:
1995,           a. HFLC: 18% of energy as CH, 64%
UK              as F, and 18% as P
(38)            b. LFHC: 75% of energy as CH, 7% as
                F, and 18% as P
                30-min lunch at 12:45
Wells           Counterbalanced crossover with 2
and             lunch conditions:
Read,           a. HFLC: 3181 kJ, F:CH energy ratio
1996,           54:41
UK              b. LFHC: 3599 kJ, F:CH energy ratio
(43)            7:88
                Lunch at 12:30

Lunch size and composition

Smith           Randomized intervention study with 4
et al.,         conditions:
1994,           a. Low-fat, small: 29 g P, 18 g F, 148 g
UK              CH, 840 kcal, meal wt: 600 g
(26)            b. Low-fat, large: 34 g P, 23 g F, 144 g
                CH, 880 kcal, meal wt: 860 g
                c. High-fat, small: 47 g P, 79 g F, 103 g
                CH, 1290 kcal, meal wt: 530 g
                d. High-fat, large: 28 g P, 84 g F, 118 g
                CH, 1300 kcal, meal wt: 840 g
                Lunch at 12:30 (early) and 13:15 (late)

Study           assessment

Lunch versus no lunch

Craig et al..   Perceptual discrimination
1981, UK        1 h CA at 11:00 and 13:00
(21)            Subjects tested individually

Smith and
Miles, 1986,    Sustained attention
UKa (22,32)     (5CSRTT)
                Selective attention
                (Stroop effect)
                45 min CA at 10:45/13:15
                (early) and 12:00/14:30

                Subjects tested in pairs

                Sustained attention
                (DORN, PROP)
                See publ. 1

Kanarek and     Working memory (DST)
Swinney,        Arithmetic reasoning
1990, USA       Reading
(23)            Sustained attention
                CA at 15:30 (early) or
                16:00 (late)
                Participants tested

                See exp. 1

Lunch size

Craig and       Sustained attention (LCT)
Richardson,     30 min CA at 11:15 and
1989, UK        13:15
(24)            Participants tested

Smith et al.,   Selective attention
1991, UK        (CRTT, ST)
(25)            CA at 11:50, 12:20, 12:50,
                and 13:50, 14:20, 14:50
                Subjects tested in small

Lunch composition

Spring          Alertness (SRTT)
et al.,         Sustained attention
1982,           (DS)
USA             CA from 13:00-15:00
(27)            Participants tested

Smith           CRTT
et al.,         ST
1988,           CA at 12:15 and
UK              14:15

Lloyd           Working memory,
et al.,         sustained attention
1994,           (BT)
UK              Motor speed (TFTT)
(39)            Short-term memory
                CA 30 min before and
                30, 90, and 150 min
                after lunch

Wells           Sustained-attention
et al.,         task
1995,           CA from 9:00-17:00
UK              (at hourly intervals)
(38)            Participants tested

Wells           BT
and             SRTT
Read,           CRTT
1996,           CA at 12:00 and at
UK              13:30, 14:30, 15:30
(43)            Subjects tested

Lunch size and composition

Smith           Logical reasoning
et al.,         Selective attention
1994,           (FAT, ST)
UK              BT
(26)            CA at 11:30 and
                14:00 (early) and
                12:15 and 14:45

Study           Reported findings

Lunch versus no lunch

Craig et al..   Ability to discriminate
1981, UK        impaired by condition a.
(21)            when compared with pre-
                lunch condition, but not
                altered by condition b.

Smith and
Miles, 1986,    5CSRTT: response times
UKa (22,32)     longer after condition a.
                when compared with pre-
                lunch condition, but not
                after condition b.
                Stroop effect: no lunch


                DORN: detection of fewer
                targets after condition a.
                when compared with pre-
                lunch condition, but not after
                condition b.
                PROP: no lunch effects
Kanarek and     DST, arithmetic reasoning,
Swinney,        reading, sustained attention:
1990, USA       no lunch effects

                Reading: faster after
                conditions a. and b. when
                compared with conditions c.
                and d.
                DST, arithmetic reasoning,
                sustained attention: no lunch

Lunch size

Craig and       Omission errors increased
Richardson,     after condition a. when
1989, UK        compared with pre-lunch
(24)            condition and tended to
                decrease after condition
                b.; effects modified by
                habitual lunch size

Smith et al.,   CRTT: no lunch effects
1991, UK        ST: more errors after condition
(25)            b. when compared with
                conditions a. and c.

Lunch composition

Spring          SRTT: no lunch effects
et al.,         DS: in older subjects accuracy lower
1982,           and omission errors higher after
USA             condition b. when compared with
(27)            condition a.

Smith           CRTT: no lunch effects
et al.,         ST: slower reactions to peripheral
1988,           targets after condition b. and c.
UK              when compared with condition a.

Lloyd           SRTT: shorter reaction time 90
et al.,         and 150 min after condition b.
1994,           when compared with condition
UK              a., condition c. and pre-lunch
(39)            condition; longer reaction time
                90 min after condition c. when
                compared with pre-lunch condition
                BT, TFTT, FRT: no lunch effects

Wells           Correct responses slower after
et al.,         condition a. than condition b.
1995,           Accuracy declined after condition
UK              a. when compared with pre-lunch
(38)            condition

Wells           BT, SRTT, CRTT: no lunch effects

Lunch size and composition

Smith           Selective attention:
et al.,         Slower but more accurate
1994,           responses after conditions c. and
UK              d. when compared with conditions
(26)            a. and b. (P value not provided)
                Degree of distraction from near
                and far distractors and accuracy
                of responses to central and
                peripheral targets influenced by
                meal weight
                Logical reasoning, BT: no lunch

Study           Comments

Lunch versus no lunch

Craig et al..   No crossover design
1981, UK        Unlimited intake of tea or
(21)            coffee
                No information on the
                consumption of other foods
                Groups well matched in
                terms of age and male-to-
                female ratio

Smith and
Miles, 1986,    No crossover design
UKa (22,32)     No standardization of test
                Coffee, tea, and smoking
                Test meal larger than

                participants' habitual lunch

                See publ. 1

Kanarek and     Standardization of breakfast
Swinney,        Determination of dietary
1990, USA       habits
(23)            Irregular meal consumption
                as exclusion criterion
                Participants asked not to
                eat or drink except for test
                Small sample size
                See exp. 1

Lunch size

Craig and       Crossover design
Richardson,     Protein-to-carbohydrate ratio
1989, UK        of both lunch conditions
(24)            almost identical
                No detailed information on
                meal composition
                Determination of habitual
                lunch size

Smith et al.,   No crossover design
1991, UK        No information on
(25)            assignment to groups
                Standardization of breakfast
                Nutrient composition of
                lunch conditions identical
                Determination of habitual
                lunch size

Lunch composition

Spring          No crossover design
et al.,         No baseline data
1982,           Standardization of
USA             breakfast and abstaining
(27)            from food until test meal

Smith           Crossover design
et al.,         Standardization of
1988,           breakfast
UK              No tea or coffee permitted
(28)            until completion of test
                No information on testing
                Small sample size

Lloyd           Crossover design
et al.,         Participants asked to eat a
1994,           similar breakfast on each
UK              test day and not to eat or
(39)            drink except for test lunch
                Determination of habitual
                lunch intake
                Lunch conditions similar
                in energy and protein

Wells           No information on
et al.,         assignment to testing order
1995,           Conditions similar in energy
UK              and protein content
(38)            Standardization of breakfast
                Small sample size

Wells           Crossover design
and             Determination of quantity
Read,           and quality of sleep the night
1996,           before
UK              Smoking, illness, and irregular
(43)            meal consumption as
                exclusion criteria
                Determination of usual eating
                Small sample size

Lunch size and composition

Smith           No crossover design
et al.,         Determination of usual
1994,           eating habits

(a) Results from a single study presented in two publications
(publ. 1, publ. 2) 5CSRTT = five-choice serial reaction time task;
BT = Bakan task; CA = cognitive assessment; CH = carbohydrates;
CRTT = choice reaction time task; DORN = detection of repeated
numbers; DS = dichotic shadowing; DST = digit span task; EIR =
estimated individual requirement; exp. = experiment; F = fat; FAT =
focused attention task; FRT = free recall task; HFLC =
high-fat/low-carbohydrate; HL = heavy lunche LCT = letter
cancellation task; LFHC = low-fat/high-carbohydrate; LL = light
luncher; MFMC = medium fat/medium carbohydrate; P = protein; PROP =
estimation of the proportions of two classes of events in a signal
stream; publ. = publication; SRTT = simple reaction time task; ST =
search task; TFTT = two-finger tapping task
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Article Details
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Author:Muller, Katrin; Libuda, Lars; Terschlusen, Anna Maria; Kersting, Mathilde
Publication:Canadian Journal of Dietetic Practice and Research
Article Type:Report
Geographic Code:1CANA
Date:Jan 1, 2013
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