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On the road to obesity: Television viewing increases intake of high-density foods


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Television viewing (TVV) has been linked with obesity, possibly through increased sedentary behavior and/or through increased ingestion during TVV. The proposition that TVV causes increased feeding, however, has not been subjected to experimental verification until recently. Our objective was to determine if the amount eaten of two familiar, palatable, high-density foods (pizza and macaroni and cheese) was increased during a 30-min meal when watching TV. In a within-subjects design, one group of undergraduates (n = 10) ate pizza while watching a TV show of their choice for one session and when listening to a symphony during the other session. A second group of undergraduates (n = 10) ate macaroni and cheese (M&C). TVV increased caloric intake by 36% (one slice on average) for pizza and by 71% for M&C. Eating patterns also differed between conditions. Although the length of time to eat a slice of pizza remained stable between viewing conditions, the amount of time before starting another slice was shorter during TVV. In contrast, M&C was eaten at a faster rate and for a longer period of time during TVV. Thus, watching television increases the amount eaten of high-density, palatable, familiar foods and may constitute one vector contributing to the current obesity crisis.
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On the road to obesity: Television viewing increases intake of
high-density foods
Elliott M. Blass , Daniel R. Anderson, Heather L. Kirkorian, Tiffany A. Pempek,
Iris Price, Melanie F. Koleini
Department of Psychology and Program in Neuroscience and Behavior, University of Massachusetts, Amherst, MA 01003, United States
Received 8 November 2005; received in revised form 17 May 2006; accepted 17 May 2006
Television viewing (TVV) has been linked with obesity, possibly through increased sedentary behavior and/or through increased ingestion
during TVV. The proposition that TVV causes increased feeding, however, has not been subjected to experimental verification until recently. Our
objective was to determine if the amount eaten of two familiar, palatable, high-density foods (pizza and macaroni and cheese) was increased
during a 30-min meal when watching TV. In a within-subjects design, one group of undergraduates (n= 10) ate pizza while watching a TV show of
their choice for one session and when listening to a symphony during the other session. A second group of undergraduates (n= 10) ate macaroni
and cheese (M&C). TVV increased caloric intake by 36% (one slice on average) for pizza and by 71% for M&C. Eating patterns also differed
between conditions. Although the length of time to eat a slice of pizza remained stable between viewing conditions, the amount of time before
starting another slice was shorter during TVV. In contrast, M&C was eaten at a faster rate and for a longer period of time during TVV. Thus,
watching television increases the amount eaten of high-density, palatable, familiar foods and may constitute one vector contributing to the current
obesity crisis.
© 2006 Published by Elsevier Inc.
Keywords: Body mass index; Energy-dense food; Macaroni and cheese; Obesity; Overeating; Pizza; Sensory-specific satiety; Television viewing
1. Introduction
Obesity poses a worldwide health problem [1] especially in
the United States [2]. Obesity contributes to over 400,000
deaths annually in the US and scars the morbid in its ranks [3].
Although multiple sources contribute to obesity, this condition
generally reflects behavioral systems that have gone awry as
manifest in decreased exercise [47] and increased caloric
intake [8,9]. Television viewing (TVV) targets both systems,
particularly when one eats while watching TV. Indeed, many
studies have linked TVV with obesity, although not experi-
mentally [2,1012].
The relationship between TVV and obesity may exist
because people eat more while watching TV. Stroebel and
deCastro [13] reported relatively high food intake on days in
which at least one meal was eaten in front of the TV, though this
was not confirmed in other self-reports on TVV and eating
[14,15], thereby leaving the issue unresolved. Until recently, no
studies have directly measured food intake under controlled
experimental conditions during a fixed time period when eating
occurred either in the presence or absence of TVV. The aim of
the current study was to directly determine if more high-density
foods are eaten while watching TV, during a fixed period of
Accordingly we measured food intake and recorded feeding
patterns of participants who ate pizza or macaroni and cheese
(M&C), two high-density, familiar, palatable foods, while
watching or not watching television. After our data collection
had been completed, a study by Bellisle et al. [16] appeared
that also directly measured feeding during TVV. We replicate
the Bellisle et al. [16] findings and extend them by reporting
intake on different high-density foods and providing detailed
Physiology & Behavior 88 (2006) 597 604
Corresponding author. Tel.: +1 314 545 0283, +1 617 308 6121 (cell); fax:
+1 413 545 0996.
E-mail address: (E.M. Blass).
0031-9384/$ - see front matter © 2006 Published by Elsevier Inc.
observations of feeding behavior patterns to reveal how more
food was eaten during TVV.
2. Experimental procedures
2.1. Design
The fundamental issue that we addressed is: Does watching
TV while eating increase food intake? Participants ate either
M&C or pizza, both high-density foods. To help reduce
variability and to focus more closely on individual feeding
characteristics, a within-subjects design was employed with
regard to the TV condition. Participants, therefore, ate two
lunches or dinners, one while watching a TV show of their
choice and the other while listening to Rachmaninoff's Second
Symphony which, like TV shows, starts slowly and builds to a
crescendo. Listening to a symphony was deemed a better
control than sitting in silence to help prevent the stress of ennui.
Order of conditions was counterbalanced with 1 week (± 1 h)
separating tests. The experiments were conducted successively
during the Spring 2004 semester with pizza being served to the
first group and M&C to the second.
2.2. Participants
Twenty undergraduates from the University of Massachu-
setts-Amherst (15 female, 5 male) ate meals of either pizza
(n= 10, 8F, 2M) or M&C (n= 10, 7F, 3M). Descriptive data for
both groups are presented in Table 1.
Participants were recruited from introductory-level Psychol-
ogy courses with the incentive of receiving extra course credit, a
standard procedure for participant recruitment. They were told
that the study focused on whether watching TV affected
memory for everyday events. Written informed consent was
obtained at the start of the session and after disclosure. The
study was approved by the Human Subjects Committee of the
Department of Psychology, a subcommittee of the University
2.3. Setting and apparatus
The study room (2.5 × 3.3 m) was equipped with a
comfortable sofa and a coffee table to accommodate the food.
The sofa faced a CD player and a 27 in. Panasonic TV
connected to a VHS player. The observation room was
separated from the study room by a 2.5 × 1.3 m, one-way
window which occupied most of the wall.
2.4. Procedure
Students chose a tape from a library of 6 pre-recorded TV
shows popular among college undergraduates, including
Friends,Seinfeld,My Wife and Kids,MTV Real World/Road
Rules Challenge,The Simpsons, and Everybody Loves Ray-
mond. Programs were 30 min in length with original
commercials. Bottles of condiments (salt, pepper, basil,
oregano, red pepper flakes) were on the coffee table with lids
removed. The food and designated cold beverage (water for
pizza meals and cola for M&C) were also placed on the table.
Cola (38.5 kcal/100 ml) was chosen for the second group to
determine whether the phenomenon of TVV enhancement for
calorically dense food also extended to fluid intake. The
experimenter then turned on the TV or the music and left the
room for the remainder of the session, at which time the 30-
min meal commenced. Subjects reported subjective ratings of
hunger, satiety (prior to entering the test room, but after
selecting a tape) and food palatability at the beginning and
end of each session on separate visual analog scales (see
below). After the first session, a number of questions were
posed concerning recent events (including the time of last
meal) to facilitate the alleged purpose of the study (i.e., to
test influences on memory). At the end of the second session
each participant was debriefed and given a post-disclosure
consent form. The participant then completed the Three
Factor Eating Questionnaire (TFEQ; 17), an instrument that
has reliably revealed individual differences in eating style.
Lastly, body weight (±0.1 kg) and height (cm) were recorded
by direct measurement. Food and fluid intake were
determined by weighing (± 0.1 g) each before and after the
2.5. Food
Each food was served in portions that were larger than could
reasonably be eaten in a single meal. Pizza (12 in. pie,
DiGiornos, 4 Cheese, 233 kcal/100 g) was cut into 8 slices.
Macaroni and cheese (Family Size, Stouffers, 148 kcal/100 g)
was presented in a large bowl that accommodated the entire
contents of the package (circa 900 g). Both foods were prepared
per manufacturer's directions. Subjects ate the same meal in
both sessions (n= 10 for each food).
2.6. Visual analogue scales
Visual Analogue Scales (VAS) assessed hunger, satiety, and
food palatability both before and after the meal [18]. Initial
hunger and satiety ratings were obtained in the interview room
before either watching TV or listening to the music. The scales
were anchored on the left by either not at all hungry,not at
all full,ortastes badfor hunger, satiety, and palatability
scales, respectively. The right edge of the 200 mm scales was
designated extremely hungry,very full, and delicious.
Subjects completed the first palatability VAS after taking their
first bite of food and the second at the end of the 30-min session
after taking a final bite of the food at the request of the
Table 1
Descriptive statistics for individual difference measures as a function of food
type (n=10 per group)
Factor Macaroni and cheese Pizza
Mean S.D. Min. Max. Mean S.D. Min. Max.
Height 168.35 7.20 158.00 178.00 166.40 8.07 154.00 181.00
Weight 64.86 15.73 54.40 105.60 72.85 18.35 44.78 105.08
BMI 22.71 4.02 19.36 33.33 26.35 6.66 18.80 36.79
TFEQ 20.40 6.74 12.00 30.00 21.70 8.41 11.00 37.00
598 E.M. Blass et al. / Physiology & Behavior 88 (2006) 597604
experimenter and subsequently rated hunger and satiety. This
was deemed appropriate because it avoided interrupting feeding
and calling attention to the act rather than to the study's focus
on memory.
2.7. Behavioral observations
A large-faced digital stopwatch was placed between two
scorers. The scorers sat in a darkened observation room
adjacent to the test room and independently recorded
ingestive behavior within 30-s blocks through the one-way
window. For pizza meals, the interval during which a slice
was first picked up and when finished were recorded. For
M&C, the incidence of each ingested bite was recorded
within 30-s intervals. The relatively leisurely rate at which
eating occurred rendered this a simple observation task with
9597% agreement between the two observers. Fluid intake
was recorded in like manner. Participants did not hold the
bottle when not drinking. These indices were chosen because
individual bites of pizza were highly variable. Eating
duration/slice reliably marks progress through the pizza
meal. Bite size accurately captures M&C consumption,
which was constrained by the size of the plastic forks used
in this study and, therefore provided a stable and reliable
index of M&C consumption pattern.
2.8. Statistical evaluation
A 2 (condition: TV, music) × 2 (food: pizza, M&C) × 2
(order: TV/music first) mixed design analysis of variance
(ANOVA) was employed, with TV/music as the within-subject
factor and order and food type as between-subject factors.
Separate analyses were conducted for weight (grams) and
energy (kcal) consumed. Finally, the influence of TVV on affect
was gauged through reports of satiety and food acceptability in
separate 2 (condition) × 2 (food) × 2 (order)× 2 (time: before,
after) mixed design ANOVAs. Scores (measured in mm) at the
beginning and end of the meal were compared in TVV and
music conditions and between the two food types. There was no
indication that program choice or meal time systematically
influenced amount eaten, or any of the other indices reported
herein. Accordingly, the data were pooled for statistical
Bivariate correlations assessed the relationship between
intake and BMI, TFEQ factors, time of privation, and hunger
ratings. In addition, to determine the relationship of intake
between the two viewing conditions, the amount eaten during
TVV was plotted against intake during the music condition for
the 20 relational data points, one generated by each subject in
this study, and the regression calculated.
Lastly, feeding patterns were evaluated through Hierarchical
Linear Modeling (HLM). HLM lends itself well to assessing the
contribution of covariates to patterns that are differentially
expressed over time, as in the patterns of individual meal taking
with and without TV [19,20]. Included in the HLM analysis
were the covariates of condition, linear and quadratic growth,
and the associated interactions.
3. Results
The major findings are as follows: (1) caloric intake while
watching TV was markedly increased for pizza (36%) and
M&C (71%); (2) the relationship between intake when
viewing and not viewing TV for pizza and M&C was linear
with a slope of 0.95 and an added constant of 288 kcal /meal
during TVV; (3) feeding pattern and rate depended on
viewing condition; (4) intake was correlated with VAS hunger
ratings but not with deprivation length; and (5) food intake
was not reliably linked to either BMI or eating restraint
3.1. TVV influences on feeding
Fig. 1 presents intake in calories and grams of both food
types with and without TV. Intake (grams) was not system-
atically different for the two food types, although because
pizza was more calorically dense, more calories were ingested
overall from pizza (862.0 kcal) than from M&C (469.9 kcal),
F(1, 16) = 17.03, p= 0.0 01. There was also a reliable main
effect of viewing condition such that more calories were
ingested overall with TV on (793.7 kcal) than with TV off
(538.2 kcal), F(1, 16) = 18.41, p< 0.001. For pizza, this
translates to approximately one additional slice per subject
during TVV. The increase for the M&C group reflects an
increase in M&C intake to 408.5 g during TVV from the
baseline of 239.1 g and an increase in cola intake during TVV
(330.1 vs. 253.7 ml), F(1, 7) = 7.33, p= 0.03. Water intake was
stable between the two viewing conditions (280.7 ml with TV
and 274.1 ml with music) despite the increase in consumption
of the osmotically and calorically dense pizza. This argues
against the increased cola consumption being secondary to
increased intake of M&C. The interaction between food type
and condition was not statistically reliable (p's = 0.47 for grams
and 0.88 for calories). Order of conditions did not influence
the outcome of any measure (all pvalues > 0.20). Our data
replicate the major findings of Bellisle et al. [16] that TVV
enhances food intake and extends them to more calorie-dense
foods and to a sweetened beverage.
Fig. 1. Mean (+S.E.) caloric intake (kcal; left panel) and volume intake (grams;
right panel) as a function of food type and viewing condition. The effect of
viewing conditions is statistically reliable (p< 0.001).
599E.M. Blass et al. / Physiology & Behavior 88 (2006) 597604
Although there was substantial feeding variability among the
participants in both amount eaten and feeding style, individual
eating patterns were remarkably stable. Fig. 2 presents the
relationship between the amount of pizza or M&C during TVV
and that when not watching TV for each of the 20 participants.
Pizza and M&C data were combined because there were no
significant differences between regression slopes. Only 3 of the
20 subjects ate more when not watching TV than during TVV
(i.e., three points fell in the shaded area below unity). The
equation for the fitted line is Y= 0.95X+ 288. The regression
slope approached a value of one, indicating individual
consistency in amount eaten. The intercept of 288 kcal suggests
that, in addition to the mechanisms that governed intake while
not watching TV, some other unspecified factor(s) increased
intake by an average 288 kcal during TVV.
3.2. Meal pattern analyses
Eating patterns (in forkfuls) for M&C as a function of
viewing condition are presented in Fig. 3. Intake was vigorous,
although not equivalent, during the initial 5-min segment when
gastric clearance could not have been a factor (right panel).
Cumulative intake (left panel) diverged markedly after the
initial segment. Feeding essentially came to a halt during the
second 5-min epoch when the TV was off, having reached an
asymptote of 19 forkfuls. During TVV, however, M&C intake
continued for an additional 15 min, reaching asymptote circa
20 min, at which point a mean of 38 forkfuls had been taken.
Accordingly, TVV reliably increased both M&C ingestion rate
and, especially, meal length.
The Hierarchical Linear Modeling (HLM) analyses quanti-
fying M&C eating patterns are presented in the upper portion of
Table 2. The intercept in each equation represents the fitted
models 15 min into the session after centering the time variable
at the midpoint as is customary when conducting HLM
analyses. All other factors indicate the influence of condition,
change over time (linear and quadratic), and, where appropriate,
interactions between the two. For the number of bites per 5-min
interval (Fig. 3, left panel), significant Level-1 predictors were
condition and linear and quadratic change over time, but there
were no significant interactions among these variables.
Although the rate of ingestion generally slowed over time,
intake was consistently greater during TVV until the last 10 min
when intake essentially ceased for both viewing conditions. For
cumulative units of M&C consumed across the session, the
HLM analyses revealed a significant Level-1 (within-subject)
effect of condition, time (linear and quadratic change), and the
interactions. In short, the interaction between TV and time
reflects the significantly more dramatic curve in the function for
M&C intake during TVV.
Fig. 4 presents the amount of time taken to eat each of the
first three slices of pizza in each condition (left panel) and
interval between slices, with the first interval reflecting the time
from when the experimenter left the room and the participant
Fig. 2. Regression of caloric intake (kcal) while watching TV against intake
while listening to music. The free slope of 0.95, calculated from the data, does
not differ from unity, which is presented as the diagonal separating the gray from
the clear portions of the figure. Points in the clear space represent subjects who
ate more during TVV than in the music condition. Points in the gray space
represent the three subjects who ate less when watching TV. The constant of the
fitted line was 288 kcal indicating the average enhancement during TVV.
Fig. 3. Number of bites of M&C during each 5-min interval during TVVor while listening to music (right panel; note the differences in intake even during the initial 5-
min segment of the meal) and cumulative intake of M&C as a function of condition (left panel, note differences in ordinate values). For the HLM analyses, time was
measured in minutes and centered at the midpoint such that the intercept for those equations (i.e., time zero) falls midway between intervals 3 and 4 above. Therefore,
intervals 1 through 6 above are represented by 12.5, 7.5, 2.5, 2.5, 7.5, and 12.5 min, respectively, in the HLM equations presented in Table 2.
600 E.M. Blass et al. / Physiology & Behavior 88 (2006) 597604
took the first bite (right panel). Results from statistical analyses
are presented in the bottom portion of Table 2. Although the
overall amount of pizza eaten differed between conditions, the
rate at which a slice was eaten was not affected either by how
deep into the meal the slice occurred or by condition. In
contrast, the interval between slices (Fig. 4, right panel) was
influenced by both time into the meal and condition. The initial
slice was taken immediately when the meal was presented, the
second slice within half a minute of finishing the first (range 0
1 min), and the third slice within approximately 4 min of when
the second slice was finished. Statistical analyses supported the
general increase in latency over time as well as an interaction
between condition and slice number. Although inter-slice
interval increased progressively into the meal, the increase
was reliably greater when listening to music.
Taken together Table 2 and Figs. 3 and 4 reveal that even
though M&C and pizza intake was considerably enhanced
during TVV, the pathways differed. M&C was eaten essentially
continuously during both viewing conditions until the end of the
meal, albeit at a faster rate and for longer during TVV. Absent
were the lengthy pauses that characterized pizza intake. As
indicated, the latencies between slices lengthened over time,
particularly during the music condition, but the rate of ingestion
of each slice did not differ over time or between viewing
3.3. The relation between affect and intake
VAS measures as a function of food type and condition are
reported in Fig. 5 which presents mean difference scores in VAS
ratings for hunger, satiety, and palatability for pizza and M&C
in each condition. The differences between hunger ratings at the
beginning and end of a meal, not surprisingly, were substantial,
F(1, 15)= 56.75, p< 0.001, and were greater with TV than with
music, F(1, 15) = 9.55 p= 0.007, presumably reflecting the
greater amount eaten during TVV. The interaction between food
type and media was not reliable for hunger differences between
meal beginning and end. Changes in hunger ratings, therefore,
reflected ingested volume of calorically dense foods indepen-
dent of the particular food, at least in the current study.
Appropriate directionality and proportionality of change in
hunger ratings do not support a loss of sensitivity to hunger
status during TVV. Furthermore, intake of food was generally
related to hunger ratings at the start of the session (r= 0.52,
p= 0.02, and r=0.51, p= 0.02, for TV and music, respectively)
but not to time since last reported meal (all p's > 0.30).
As with hunger ratings, satiety perception changed con-
siderably from meal beginning to end for both foods, F(1, 15) =
93.98, p< 0.001. Additionally, pizza ratings changed more than
M&C, F(1, 15) = 7.42, p= 0.016, particularly when comparing
the music conditions, F(1, 15) = 4.63, p= 0.048. Differences in
fullness change between TV and music conditions were not
reliable (p's > 0.10), however. This is of interest given ho w
much more was consumed during TVV and may help shed light
on the etiology of TVV-enhanced intake. Finally, the change in
taste ratings between meal beginning and end was reliable, F(1,
16) = 21.61 p< 0.001.
3.4. Individual differences and intake
There was no obvious relationship between BMI and amount
eaten for pizza or M&C. This was not expected given individual
differences in intake, in feeding style, and in differential food
enhancement during TVV. Moreover, the relationship between
food intake and TFEQ composite (and individual factor) scores
did not reach statistical reliability. Thus, feeding style, which is
Table 2
Final HLM models for M&C (cumulative bites and bites per interval) and pizza (slice duration and inter-slice interval)
Fixed effect Macaroni: bites per 5-min interval Macaroni: cumulative bites
Coefficient (S.E.) Significance test Coefficient (S.E.) Significance test
Intercept 3.82 (0.72) t(9) = 5.33, p< 0.000 24.39 (3.53) t(9) = 6.90, p< 0.000
Condition 2.90 (1.09) t(9) = 2.67, p= 0.026 4.43 (2.22) t(9) = 2.00, p= 0.076
Time 1.22 (0.20) t(9)= 6.06, p< 0.000 1.08 (0.32) t(9) = 3.39, p=0.009
Time squared 0.03 (0.01) t(9) = 4.27, p=0.002 0.03 (0.01) t(9) = 3.04, p= 0.003
Condition× time 1.21 (0.42) t(9) = 2.92, p= 0.018
Condition× time squared 0.03 (0.01) t(9) = 2.67, p= 0.026
Fixed effect Pizza: slice duration Pizza: inter-slice interval
Coefficient (S.E.) Significance test Coefficient (S.E.) Significance test
Intercept 3.58 (0.69) t(9) = 5.22, p< 0.000 3.32 (1.30) t(9) = 2.56, p= 0.031
Condition 0.81 (0.57) t(9) = 1.42, p= 0.189 1.38 (0.85) t(9) = 1.63, p= 0.138
Slice 0.22 (0.20) t(9) = 1.10, p= 0.300 4.42 (1.37) t(9) = 3.22, p= 0.011
Condition× slice 0.41 (0.27) t(9) = 1.53, p= 0.161 1.98 (0.83) t(9) = 2.39, p= 0.040
Coefficients for Interceptare the intercepts of the equations; coefficients for all other factors are the slopes associated with those predictors. Condition is a
dichotomous variable for which music is represented by zero and TV by one. For M&C, time represents the minutes since the midpoint of the session such that the
intercept reflects the estimated value 15 min from commencement. Because time was centered at the midpoint, the intercept indicates the estimated value 15 min into
the session in the music condition (i.e., when condition equals a value of zero). For pizza, slice represents place in the meal (first, second, third). Because sliceis not
centered, the intercepts for pizza equations represent the value for the hypothetical zero-ithslice in the music condition, thus the negative intercept for inter-slice
interval. Other variables are linear and quadric growth (Time and Time Squared) and interactions with condition. Because only Level-1 (within subjects) variables are
included in the analysis, the equations can be interpreted in much the same way as regression analyses. For example, the final equation for bites per interval is 3.82+
2.90(Condition) 1.22(Time)+ 0.03(Time Time).
601E.M. Blass et al. / Physiology & Behavior 88 (2006) 597604
known to influence eating in other situations [17,21], did not
influence the amount of food consumed under the present
conditions. This may reflect the social environment of eating
alone rather than with friends or family [22].
3.5. Summary
Considerably more energy-dense food was eaten when
watching television than when listening to classical music.
Indeed, only 3 of 20 possible reversals occurred. Both eating
rate and amount eaten were enhanced for M&C during TVV
before post-absorptive factors could have come into play. For
pizza, one additional slice was eaten on average during TVV.
Moreover, the interval between slices increased over time,
particularly for the music condition. Although protracted meals
could reflect inattention to the internal signals that normally
bring a meal to conclusion, this possibility is diminished by the
marked and appropriate changes in hunger and satiety
perceptions and the stability of eating patterns. On the other
hand, the differences in satiety ratings between TV and music
were not reliable for pizza intake despite the substantial
difference in intake. No factors (including food restriction
attitude, BMI, and time since last meal) reliably predicted the
amount of either food consumed. However, intake was
correlated with hunger perception at meal initiation.
4. General discussion
This study has shown that college undergraduates ate more
pizza and M&C during a 30-min meal when watching TV. This
addresses the focal issue of when and how one particular facet
of the eating environment [21] influences feeding. These
findings, coupled with Bellisle et al. [16], may also provide
insights into determinants and mechanisms underlying obesity.
Both studies have revealed disconnections between need and
4.1. Enhanced intake with TVV
In both viewing conditions, each pizza slice was eaten at the
individual's idiosyncratic rate and style, regardless of ordinal
place in the meal. This suggests that once a decision has been
Fig. 4. Mean (+S.E.) duration of time to eat each of the first three slices of pizza during a meal as a function of viewing condition (left panel). Position of slice during
the meal is indicated on abscissa. Right panel indicates the mean (+ S.E.) latency between successive slices. Eating time/slice did not differ either between viewing
conditions, or by ordinal position in a meal. In contrast, inter-slice intervals differed between conditions and by position of the slice in a meal. The interaction was also
statistically reliable (p< 0.01).
Fig. 5. Mean (+S.E.) difference in affective qualities of hunger, satiety, and taste derived from VAS as a function of food type and condition. Scores were derived by
[VAS post-meal VAS pre-meal].
602 E.M. Blass et al. / Physiology & Behavior 88 (2006) 597604
made to eat of a food unit, ingestive determinants reflect
individual differences and the completion of the unit. Within
limits, this may be independent of gastric and post-ingestive
factors, an assertion that is supported by the pizza data of the
current studies and the literature on super-sized portions in
which the determinant is often completing the unit [9,23].
Intervals between slices increased as the meal progressed but
less so in the presence of television. For pizza, an additional
slice was eaten during TVV. Together with the lack of difference
in satiety ratings between viewing conditions, these data
suggest that internal signals of satiety may not have registered
as strongly during TVV, a suggestion that is consonant with the
distraction hypothesis of Bellisle et al. [16].
Increased caloric intake during M&C meals reflects
increased cola consumption as well as increased M&C intake.
This extends the TVV phenomenon into the domain of sweet-
tasting fluids because water intake was not enhanced during
TVV. Because sweet liquids are routinely consumed while
watching TV and also accompany super-sized meals in like-
sized portions [9], increased intake of sweet fluids during TVV
merits further experimental attention.
The pathways controlling M&C intake may differ from those
controlling pizza ingestion. Intake rate of the former was
enhanced during TVV, even during the initial 5-min segment
before post-ingestive signals could have come into play. This
suggests that watching TV may have caused a state of excitation
that enhances food intake, a suggestion that is consistent with
the findings of Cools et al. [24] and Patel and Schlundt [25],
who reported increased excitation during film viewing, even of
material that was not frightening.
On the other hand, meal duration was enhanced, suggesting
that the gastric, intestinal, and behavioral [26] signals that
terminated intake in the music condition were ineffectual during
TVV because twice the number of forkfuls were taken. In short,
intake parameters in the present study appear to have been set
by, among other things, portion size, food characteristics (unit
vs. continuous), density, and degree of hunger, although not
privation, BMI, or TFEQ scores, at least in this study.
The 71% increase in M&C and 36% increase in pizza caloric
intake are substantially greater than the 15% reported by
Bellisle et al. [16] in participants who ate hamburger/potato
Parmantier with a caloric value circa 100 kcal/100 g. Macaroni
and cheese was about 50% more dense, at 148 kcal/100 g, and
pizza was more than twice as dense at 233 kcal/100 g. Bellisle et
al. [16] and we agree that TVV increases food intake of energy-
dense foods. Moreover, neither BMI nor restrictive feeding style
in either study was related to baseline intake or to the increase
that could be attributed to TVV. Together these findings raise
the possibility that overeating dense, tasty, familiar foods while
watching TV may characterize adult ingestive behavior and not
be particular to specific classes of eaters. This possibility is
supported by overeating occurrence across a range of individual
feeding styles.
We believe that the HLM analyses [19,20] may prove useful
in determining the contribution of particular factors to ingestive
behavior. Although the sample size in the current study was
modest, the phenomena documented with HLM support were
extremely robust, including how place in a meal influenced
future intake pattern. Moreover, with a sufficiently large N,
HLM has the potential to simultaneously weigh both within-
subjects (e.g., time, condition) and between-subjects (e.g., BMI,
feeding style, deprivation) factors on feeding. Finally, the HLM
format is sensitive to linear and quadratic trends in behavior
and, in this regard, proved to be more informative than ANOVA
in revealing relationships rather than mean differences.
The current findings bear directly on the contribution to
obesity of eating while watching TV. Based on the mean
increase of 288 kcal per energy-dense meal during TVV, eating
two such meals weekly in front of the TV would cause an
annual weight gain of 3.6 kg (8.2 lb). This assumes that there
would not be an accurate compensatory change in exercise
habits, in the size of subsequent meals [27], or in basal
metabolic rate [28].
In summary, we have demonstrated that intake of high-
density foods increases during TVV and have identified changes
in feeding patterns that resulted in these increases. Future
research will be needed to test the alternative mechanisms
underlying differences in intake pattern proposed above.
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... 1.6 Fernsehen geht mit Steigerung der Kalorienzufuhr einher. (Adaptiert nachBlass et al. 2006) Davon abzugrenzen sind die monogenetischen Formen der Adipositas mit Mutationen hauptsächlich im Leptin-Melanocortin-Signalweg. Diese Adipositasformen stellen eine Rarität dar und treten bereits im Kindesalter auf. ...
Obesity has become a pandemic. More than 670 million people worldwide are obese. In Germany 24% of women and 23% of men have a BMI > 30 kg/m2. More than 2% of the people in Germany have a BMI > 40 kg/m2. Especially children and adolescents are increasingly affected by this metabolic disease. The increase in energy intake due to changes in food composition and eating habits and the reduction of energy consumption due to reduced physical activity are at the forefront. In addition, genetic, psychological, socioeconomic and endocrine causes must be taken into account.
... Time displacement hypothesized when adolescents spent more time on video/computer games, they may have less time available for PA (50), which is supported by Sandercock (49), and Biddle (51). Moreover, longer durations of ST are associated with decreased physical fitness (52), and decreased muscle strength (39), which may also affects the level of PA. Furthermore, the high prevalence of insufficient physical activity might influence this association. ...
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Background In adolescents, excessive screen time leads to many adverse health outcomes and is associated with a variety of lifestyle behaviors. This study was conducted to investigate the associations between the two types of screen time and a variety of lifestyle behaviors in American adolescents. Methods Based on the Youth Risk Behavior Surveillance System, this cross-sectional study was conducted. With the help of data collectors, participants self-reported screening time, lifestyle behaviors, and demographic data via well-validated tools. Results 19% and 43.4% of the participants spent more than two hours a day watching television and using the computer, respectively, while the prevalence of physical inactivity and insufficient sleep was 75.1 and 74.4% respectively. Furthermore, 11.9, 7.3, 14.3, and 21.3% of the participants reported skipping fruits, vegetables, breakfast and milk, respectively. Moreover, the prevalence of alcohol and tobacco consumption and sexual activity was 26.8, 5.3, and 23.5% respectively. More than two hours of television time was significantly associated with high risks of fewer frequency for eating fruit (OR = 1.605, 95%CI: 1.308–1.970), vegetables (OR = 1.389, 95%CI: 1.029–1.873), and smoking (OR = 1.465, 95%CI: 1.088–1.972). Computer/video time for more than two hours was significantly associated with high risks of physical inactivity (OR = 1.724, 95%CI: 1.531–1.941), insufficient sleep (OR = 1.354, 95%CI: 1.151–1.592), and not eating fruit (OR = 1.434, 95%CI: 1.179–1.745). Conclusion Increased screen time may be associated with specific unhealthy lifestyle behaviors in adolescents. Furthermore, the associations between different types of screen time and various lifestyle behaviors varied.
... While watching TV programs of their choice, readings for energy intake, hunger, satiety, and palatability were taken. Results revealed 71% higher energy consumption from macaroni and cheese and 36% from pizza (60). Similarly, another study concluded that consuming a meal while watching TV not only enhanced energy intake at mealtime but also affected the normal mealtime satiation or satiety followed by reduced satiety signals from previously consumed foods (61). ...
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Food intake and body weight regulation are of special interest for meeting today’s lifestyle essential requirements. Since balanced energy intake and expenditure are crucial for healthy living, high levels of energy intake are associated with obesity. Hence, regulation of energy intake occurs through shortand longterm signals as complex central and peripheral physiological signals control food intake. This work aims to explore and compile the main factors influencing satiating eciency of foods by updating recent knowledge to point out new perspectives on the potential drivers of satiety interfering with food intake regulation. Human internal factors such as genetics, gender, age, nutritional status, gastrointestinal satiety signals, gut enzymes, gastric emptying rate, gut microbiota, individual behavioral response to foods, sleep andcircadian rhythmsarelikely tobeimportantindeterminingsatiety. Besides, theexternal factors (environmentalandbehavioral)impactingsatietyeciency are highlighted. Based on mechanisms related to food consumption and dietary patternsseveralphysical,physiological,andpsychologicalfactorsaect satiety or satiation. A complex network of endocrine and neuroendocrine mechanisms controls the satiety pathways. In response to food intake and other behavioral cues, gut signals enable endocrine systems to target the brain. Intestinal and gastric signals interact with neural pathways in the central nervous system to halt eating or induce satiety. Moreover, complex food composition and structures result in considerable variation in satiety responses for dierent food groups. A better understanding of foods and factors impacting the eciency of satiety could be helpful in making smart food choices and dietary recommendations for a healthy lifestyle based on updated scientific evidence.
... Our findings are in line with previous studies showing that TV viewing presents a stronger association with negative health outcomes compared to other types of sedentary behaviors, such as sitting time 7 and PC use 6 . Possible explanations are related to enhances in food intake during TV viewing 21,22 and poor dietary habits 21 , such as snack consumption, which may elevate the probability for these health outcomes 23 . However, similar to the previous findings 7,9,24,25 , our findings were adjusted for a nutritional indicator (soft drinks). ...
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We analyzed the associations of screen-based behaviors with obesity, hypertension, and diabetes, and the moderation of different physical activity (PA) domains in these associations. We used data from the 2019 Brazilian National Health Survey, including data from 80,940 adults (mean age of 32.6 years). TV viewing, other screens (PC, tablet, and cell phone), PA domains (leisure-time, occupational, and transport) were collected via interview. Logistic regression models were used. There was a dose–response association of higher TV viewing with diabetes. Within the groups with medium and higher time spent on other screens, those with < 150 min/week in leisure-time PA increased the odds for obesity [1–2.9 h/day: OR = 1.18 (1.01, 1.39)] and hypertension [1–2.9 h/day: OR = 1.29 (1.08, 1.53); ≥ 6 h/day: OR = 1.47 (1.03, 2.09)]. Likewise, among the participants who spent ≥ 6 h/day of TV viewing, those with < 150 min/week of occupational PA presented higher odds for hypertension [OR = 1.61 (1.03, 2.53)]. In the group with higher use of other screens, < 150 min per week of occupational PA was associated with lower odds for obesity [1–2.9 h/day: OR = 0.81 (0.68, 0.97)] and hypertension [≥ 6 h/day: OR = 0.65 (0.44, 0.98)]. In conclusion, the associations of other screens with obesity and hypertension were strongest among those without leisure-time PA, while the moderator role of occupational PA was not clear.
... Similar findings were reported for depression [3]; thus, people in a relationship could be more likely to have support during confinement and report fewer consequences. Concerning the role of television/computer, eating while watching television can lead to increased food intake among youths, because television watching might be associated with mindless eating [43,44]. Possibly, a similar relationship exists among adults and should be further investigated. ...
Objective : This study aimed to explore the consequences of the lockdown among the Italian general population by focusing on depression and emotional overeating (EO). Research Methods & Procedures: QuarantEat was an Italian nationwide cross-sectional study conducted using a Computer-Assisted Web Interview method (6th-31st May 2020). The 40-item questionnaire included the 5-item World Health Organization Well-Being Index and the Emotional Overeating Questionnaire-5. Multivariable logistic regressions were performed. Results : A total of 1865 adults participated. Depression and EO were reported by 57.6% and 49.3% of the sample, respectively. Considering multivariable models, females, students, participants who smoked more/equal during lockdown and individuals with EO were more likely to report depression. Following a healthier diet or exercising during lockdown reduced the probability of depression. The likelihood of EO was higher for participants who were female, consumed more food, had a less healthy diet, were overweight and obese, consumed more chocolate, consumed more snacks between meals or before going to sleep/during the night, and were at risk for depression. Increasing age, having a relationship, and not having increased TV/computer watching time while eating reduced the odds of EO. Conclusions : QuarantEat highlighted high levels of depression and EO right after the end of lockdown measures. It also outlined the importance of the relationships between mental health and health risk behaviors, such as smoking, exercise, diet, and changes in eating behaviors due to lockdown. It is essential to plan interventions using a holistic approach and reach every individual overcoming the limits caused by the restrictive measures.
... It is known that distracting people while they are eating can divert their attention away from monitoring their intake and/or interfere with processing satiation cues (Stroebele & de Castro, 2006). It is therefore unsurprising that distraction during the meal, for instance watching TV, listening to music or listening to audiobooks, can result in an immediate increase in energy intake (Bellisle et al., 2004;Blass et al., 2006;Long et al., 2011;Stroebele & de Castro, 2006). In a meta-analysis of studies pertaining to the effects of attentive eating on intake, noted that while distraction increased immediate intake of food, it had a significantly greater effect on food consumed a few hours after the distracted meal. ...
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The present thesis investigated the meal-recall effect, wherein remembering a recent meal reduces subsequent snack intake. A review of the literature suggested that the meal-recall effect might be driven by a temporary increase in interoceptive ability, which could then help individuals to perceive lingering satiety signals more strongly and to resolve ambiguous gastrointestinal signals (Chapter 2). A laboratory-based replication of the meal-recall effect was attempted, however, due to testing restrictions, data collection was prematurely ceased (Chapter 3). Instead, the effect was replicated online, with food photographs used as a proxy for intake (Chapter 4). The effect was not elicited in Experiment 1, potentially due to methodological issues, but changes to the design in Experiment 2 resulted in the meal-recall effect being successfully replicated. There was no evidence to support the idea that improved interoception was the mechanism underlying the meal-recall effect. Imagining a recent meal as bigger than in reality was shown to be an effective method of reducing biscuit intake, but visualising details of a previous meal disrupted the manifestation of the meal-recall effect (Chapter 5). Two weight loss interventions based on the meal-recall effect were tested for usability, by asking users for feedback (questionnaires and interviews) after using the interventions for a week (Chapter 6). Finally, the feasibility of a memory-based weight loss intervention was tested over a six-week period, and a number of potential improvements were identified (Chapter 7). The difference in weight loss between the intervention (1.81kg) and the control group (1.07kg) was not significant. The results suggest that a weight loss intervention based on the meal-recall effect has the potential to be feasible and acceptable to users, however more research is required to understand why the effect occurs and why it seems easily disrupted by contextual factors.
... Eating in front of a screen is not considered healthy eating behavior as this is associated with increased dietary intake and the inability to notice when you are full (41)(42)(43). For households of more than one person, screen use during mealtimes may be considered harmful as it creates a barrier to connect with others (44). ...
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COVID-19 lockdown meant disruptions to daily routines for households in Aotearoa New Zealand. The research presented here investigates how mealtimes changed for people living in New Zealand during the first COVID-19 lockdown in mid-2020 and sought to determine if household composition type and psychological distress impacted the frequency of engaging in several mealtime behaviors. The COVID Kai Survey collected data using an anonymous, online survey and asked questions on sociodemographic characteristics including household composition, frequency of engaging in different mealtime behaviors before and during lockdown, and psychological distress, which was measured using the Kessler 6 screening tool. The findings of this study shows an increase in the perceived importance of mealtimes (n = 807, 26.9% before lockdown, n = 1,154, 38.5% during lockdown) and an increase in the proportion of the survey respondents who stated that they frequently ate meals at the dinner table (n = 1,343, 44.8% before lockdown, n = 1,481, 49.4% during lockdown). There was a decrease, across all household composition types, in the proportion of respondents who ate out frequently at a restaurant or café (n = 878, 29.3% before lockdown, n = 5, 0.2% during lockdown, P < 0.001). The use of meal kits, e-dining, and eating meals in front of screens is also presented and discussed. All results are discussed with reference to Aotearoa New Zealand’s stringent lockdown restrictions. Respondents who experienced psychological distress during lockdown were 1.47 times more likely to consider mealtimes an important part of their day and respondents living in households with one adult and at least one child who also experienced psychological distress were 5.95 times more likely to eat dinner at the dinner table than those who did not report psychological distress. Findings of this study further the understanding of the wider societal impact of COVID-19 lockdown on everyday life.
The rapidly increasing prevalence of overweight and obesity has heightened the need for a better understanding of obesity-related eating patterns and dietary behaviours. Recent work suggests that distracted eating is causally related to increased immediate and later food, pushing the need for a better understanding of the prevalence of distracted consumption and how this relates to body weight. To extract insights in the relationship between demographics, daily consumption settings, and BMI, we performed secondary data analyses on data from 1011 individuals representative of the Dutch population (adults, 507F, BMI 17–50kg/m²). The most commonly reported distractions were talking to others (32.7%) and watching television (21.7%). Only 18.4% of respondents reported no distractions during meals. To examine how different distractions related to BMI, we performed OLS regression which showed, among other things, that watching tv while eating lunch (η² = 0.37) and working during dinner were associated with a higher BMI (η² = 1.63). To examine the robustness of these findings, machine learning techniques were used. A random forest analysis (RMSE = 4.09) showed that next to age and education level, distraction during lunch and snack was amongst the largest predictors of BMI. Multiple linear regression with lasso penalty (RMSE = 4.13) showed that specifically watching tv while eating lunch or snacks was associated with a higher BMI. In conclusion, our analyses confirmed the assumption that people are regularly distracted during their daily meals, with distinct distractors relating to BMI. These findings provide a starting point for evidence-based recommendations on which consumption settings are associated with healthier eating patterns and body weight.
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Incl. bibl., index.
The association of television viewing and obesity in data collected during cycles II and III of the National Health Examination Survey was examined. Cycle II examined 6,965 children aged 6 to 11 years and cycle III examined 6,671 children aged 12 to 17 years. Included in the cycle III sample were 2,153 subjects previously studied during cycle II. These surveys, therefore, provided two cross-sectional samples and one prospective sample. In all three samples, significant associations of the time spent watching television and the prevalence of obesity were observed. In 12- to 17-year-old adolescents, the prevalence of obesity increased by 2% for each additional hour of television viewed. The associations persisted when controlled for prior obesity, region, season, population density, race, socioeconomic class, and a variety of other family variables. The consistency, temporal sequence, strength, and specificity of the associations suggest that television viewing may cause obesity in at least some children and adolescents. The potential effects of obesity on activity and the consumption of calorically dense foods are consistent with this hypothesis.
Objective: To examine prevalence of overweight and trends in overweight for children and adolescents in the US population.Design: Nationally representative cross-sectional surveys with an in-person interview and a medical examination, including measurement of height and weight.Participants: Between 3000 and 14000 youths aged 6 through 17 years examined in each of five separate national surveys during 1963 to 1965, 1966 to 1970, 1971 to 1974, 1976 to 1980, and 1988 to 1991 (Cycles II and III of the National Health Examination Survey, and the first, second, and third National Health and Nutrition Examination Surveys, respectively).Main Outcome Measures: Prevalence of overweight based on body mass index and 85th or 95th percentile cutoff points from Cycles II and III of the National Health Examination Survey.Results: From 1988 to 1991, the prevalence of over-weight was 10.9% based on the 95th percentile and 22% based on the 85th percentile. Overweight prevalence increased during the period examined among all sex and age groups. The increase was greatest since 1976 to 1980, similar to findings previously reported for adults in the United States.Conclusions: Increasing overweight among youths implies a need to focus on primary prevention. Attempts to increase physical activity may provide a means to address this important public health problem.(Arch Pediatr Adolesc Med. 1995;149:1085-1091)
Although we are just beginning to understand how environmental factors such as portion size affect eating behavior, the available data suggest that large portions of energy-dense foods are contributing to the obesity epidemic. Several possible strategies for adjusting portions to bring intake back in line with energy requirements are discussed. The continuing rise in the rates of obesity calls for urgent action.
Objective. —To examine trends in overweight prevalence and body mass index of the US adult population.Design. —Nationally representative cross-sectional surveys with an in-person interview and a medical examination, including measurement of height and weight.Setting/Participants. —Between 6000 and 13000 adults aged 20 through 74 years examined in each of four separate national surveys during 1960 to 1962 (the first National Health Examination Survey [NHES I]), 1971 to 1974 (the first National Health and Nutrition Examination Survey [NHANES I]), 1976 to 1980 (NHANESII), and 1988 to 1991 (NHANES III phase 1).Results. —In the period 1988 to 1991,33.4% of US adults 20 years of age or older were estimated to be overweight. Comparisons of the 1988 to 1991 overweight prevalence estimates with data from earlier surveys indicate dramatic increases in all race/sex groups. Overweight prevalence increased 8% between the 1976 to 1980 and 1988 to 1991 surveys. During this period, for adult men and women aged 20 through 74 years, mean body mass index increased from 25.3 to 26.3; mean body weight increased 3.6 kg.Conclusions. —These nationally representative data document a substantial increase in overweight among US adults and support the findings of other investigations that show notable increases in overweight during the past decade. These observations suggest that the Healthy People 2000 objective of reducing the prevalence of overweight US adults to no more than 20% may not be met by the year 2000. Understanding the reasons underlying the increase in the prevalence of overweight in the United States and elucidating the potential consequences in terms of morbidity and mortality present a challenge to our understanding of the etiology, treatment, and prevention of overweight.(JAMA. 1994;272:205-211)
Objective: This study examined the effects of physical activity, television viewing, video game play, socioeconomic status (SES), and ethnicity on body mass index (BMI). Research Methods and Procedures: The sample was 2389 adolescents, 10 to 16 years of age (12.7 ± 1.0 years); 1240 (52%) females and 1149 (48%) males; 77% white and 23% African American; from rural (77%) and urban (23%) settings. BMI and skinfolds were directly assessed. All other data were obtained from questionnaires. Results: Watching television on non-school days was related to being overweight (p < 0.005). However, when BMI analyses were adjusted for ethnicity and SES, there were no significant effects of television viewing on BMI (p > 0.061). Increased hours of video game play enhanced the risk of being overweight for both genders when analyses were adjusted for ethnicity and SES (p < 0.019). In males, participation in as little as one high-intensity physical activity 3 to 5 days a week decreased the ethnic- and SES-adjusted relative risk of being overweight (RR = 0.646; CI: 0.427 to 0.977). For females, the ethnic- and SES-adjusted relative risk for being overweight was not significantly altered by physical activity. The logistic analyses further indicated the influence of low SES and African American ethnicity overshadowed any direct effect of television or videos. Discussion: Because weight status of male adolescents appears to be more related to exercise habits than to television or video game habits, increased participation in high-intensity exercise appears to be important. For females, neither videos nor exercise habits appear to be related to risk of being overweight. However, ethnicity and SES may be important factors that can influence body weight status, while television viewing may be of some importance. Thus, programs to reduce obesity in female adolescent should focus their efforts in lower SES communities.
We tested the effects of 3 mood inductions (neutral, positive, and negative) on food intake in 91 women of varying degrees of dietary restraint. Mood induction was accomplished by exposure to 1 of 3 film segments: a travelogue (neutral affect), a comedy film (positive affect), and a horror film (negative affect). In subjects exposed to the neutral film, food intake decreased with increasing levels of dietary restraint. Among subjects who viewed either the comedy film or the horror film, however, food intake increased with increasing restraint. Although the horror film appeared to be more disinhibiting than the comedy film, this effect may have resulted from a difference in the intensity of the emotions induced rather than from their valence. These results suggest that emotional arousal, regardless of valence, may trigger overeating among restrained eaters.