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Should snacks be recommended in obesity treatment? A 1-year randomized clinical trial


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To study the effect to recommend no snacks vs three snacks per day on 1-year weight loss. The hypothesis was that it is easier to control energy intake and lose weight if snacks in between meals are omitted. SUBJECTS/METHOD: In total 140 patients (36 men, 104 women), aged 18-60 years and body mass index>30 kg/m(2) were randomized and 93 patients (27 men, 66 women) completed the study. A 1-year randomized intervention trial was conducted with two treatment arms with different eating frequencies; 3 meals/day (3M) or 3 meals and 3 snacks/day (3+3M). The patients received regular and individualized counseling by dieticians. Information on eating patterns, dietary intake, weight and metabolic variables was collected at baseline and after 1 year. Over 1 year the 3M group reported a decrease in the number of snacks whereas the 3+3M group reported an increase (-1.1 vs +0.4 snacks/day, respectively, P<0.0001). Both groups decreased energy intake and E% (energy percent) fat and increased E% protein and fiber intake but there was no differences between the groups. Both groups lost weight, but there was no significant difference in weight loss after 1 year of treatment (3M vs 3+3M=-4.1+/-6.1 vs -5.9+/-9.4 kg; P=0.31). Changes in metabolic variables did not differ between the groups, except for high-density lipoprotein that increased in the 3M group but not in 3+3M group (P<0.033 for group difference). Recommending snacks or not between meals does not influence 1-year weight loss.
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Should snacks be recommended in obesity
treatment? a 1-year randomized clinical trial
H Berte
us Forslund
, S Klingstro
, H Hagberg
, JS Torgerson
and AK Lindroos
Department of Metabolism and Cardiovascular Research, Sahlgrenska Academy, Go
teborg University, Go
teborg, Sweden;
Helsingborgs Lasarett, Helsingborg, Sweden;
Skaraborgs Sjukhus, Sko
vde, Sweden;
Norra A
lvsborgs La¨nssjukhus, Trollha¨ttan,
Sweden and
MRC Human Nutrition Research, Cambridge, UK
Objective: To study the effect to recommend no snacks vs three snacks per day on 1-year weight loss. The hypothesis was that it
is easier to control energy intake and lose weight if snacks in between meals are omitted.
Subjects/Method: In total 140 patients (36 men, 104 women), aged 18–60 years and body mass index430 kg/m
randomized and 93 patients (27 men, 66 women) completed the study. A 1-year randomized intervention trial was conducted
with two treatment arms with different eating frequencies; 3 meals/day (3M) or 3 meals and 3 snacks/day (3 þ 3M). The patients
received regular and individualized counseling by dieticians. Information on eating patterns, dietary intake, weight and
metabolic variables was collected at baseline and after 1 year.
Results: Over 1 year the 3M group reported a decrease in the number of snacks whereas the 3 þ 3M group reported an increase
(1.1 vs þ 0.4 snacks/day, respectively, Po0.0001). Both groups decreased energy intake and E% (energy percent) fat and
increased E% protein and fiber intake but there was no differences between the groups. Both groups lost weight, but there was
no significant difference in weight loss after 1 year of treatment (3M vs 3 þ 3M ¼4.176.1 vs 5.979.4 kg; P ¼ 0.31). Changes
in metabolic variables did not differ between the groups, except for high-density lipoprotein that increased in the 3M group but
not in 3 þ 3M group (Po0.033 for group difference).
Conclusion: Recommending snacks or not between meals does not influence 1-year weight loss.
European Journal of Clinical Nutrition (2008) 62, 13081317; doi:10.1038/sj.ejcn.1602860; published online 15 August 2007
Keywords: snacking; eating patterns; obesity; recommendations; adherence; weight loss
Although the importance of regular mealtimes is consistently
advocated in obesity treatment (Wing et al., 1996; NIH, 1998;
DPP, 2002; SBU, 2002; Wadden and Stunkard, 2002; Elfhag
and Rossner, 2005), the role of eating frequency in obesity is
unclear (WHO, 2003) and there is no clear evidence of the
impact of in-between meal snacking and frequent eating
patterns on energy balance and weight loss (Drummond et al.,
1996; Kirk, 2000; Booth et al., 2004; Jebb, 2005).
Yet, snacking may play a role in obesity management as
snacking may influence energy intake and thus body weight.
Spreading the energy load over the day by including several
snacks may reduce appetite (Speechly et al., 1999) and as a
consequence, decrease energy intake and body weight.
On the other hand, snacking between meals may not be
satiating (Booth, 1988; Marmonier et al., 2002) and there-
fore, habitual snacking may be a factor driving energy intake
up and instead increase body weight. Although, short
experimental studies in obese subjects have not shown any
effect of eating frequency on weight loss in energy-restricted
diets (Garrow et al., 1981; Verboeket-van de Venne and
Westerterp, 1993), epidemiological studies suggest a link
between snacking and weight gain (Basdevant et al., 1993;
Coakley et al., 1998; Levitsky et al., 2004). Subjects who
regain weight after successful weight reduction also report
more snacks than those who maintain their weight loss
Received 8 December 2006; revised 17 April 2007; accepted 13 June 2007;
published online 15 August 2007
Correspondence: Dr H Berte
us Forslund, Department of Clinical Nutrition,
Sahlgrenska Academy, Go¨teborg University, Medicinaregatan 7a, S-405 30
Go¨teborg, Sweden.
Contributors: HBF initiated, designed and conducted the study, collected the
data, did the statistical analysis and wrote the paper. SK, HH and ML collected
the data, participated in the discussion of results and reviewed the paper. JT
and AKL participated in the study design, the discussion of the results and
reviewed the paper.
European Journal of Clinical Nutrition (2008) 62, 13081317
2008 Macmillan Publishers Limited All rights reserved 0954-3007/08 $
(Kayman et al., 1990). These findings are in line with a
number of studies showing that a high meal frequency and
snacking are related to a high energy intake (Dwyer et al.,
2001; Zizza et al., 2001; Berte
us Forslund et al., 2002, 2005).
The effect of eating frequency is important to understand
and an evidence-based appraisal is needed (Bellisle et al.,
1997; de Graaf, 2000; Bray and Bouchard, 2004; Mattson,
2005; Parks and McCrory, 2005). If snacking increases
the total energy intake the recommendation to eat snacks
in between meals may be questioned in obesity treatment.
For that reason longer, randomized interventions in free-
living obese subjects are needed to elucidate the role of
snacking in obesity treatment. To our knowledge no such
studies exist.
The aim of this study was therefore to study the effect
of two different recommended eating frequencies on 1-year
weight loss in a randomized design. The hypothesis was that
it is easier to control energy intake and lose weight if food
intake is concentrated to three main meals per day compared
to three main meals and three snacks.
Study design
A 1-year, parallel group, randomized clinical trial was
conducted with two treatment groups with different eating
frequencies; three meals per day (3M) or three meals
and three snacks per day (3 þ 3M). The study was conducted
at three medical outpatient clinics in the western and
southern part of Sweden (Sahlgrenska Hospital, Skaraborg
Hospital and Helsingborg Hospital). The recruitment period
was from September 2002 to January 2005 and the
intervention period from September 2002 to January 2006.
The study was coordinated from the obesity unit at
Sahlgrenska University Hospital, Go
teborg and at each study
site a local dietician, physician and nurse were responsible
for the running of the study. All participants received written
and oral information about the study protocol from the
registered dietician at each site and gave written informed
consent. The study was approved by the ethics committees
at the Faculty of Medicine, Go
teborg University (Go
and Sko
vde) and Faculty of Medicine, Lund University
Body weight, height (only at baseline), waist and hip
circumference and blood pressure were measured and
fasting blood samples were collected at baseline and after
1 year. In addition, body weight was measured at every
visit. Self-administered questionnaires including information
on eating frequency, energy intake and physical activity were
also completed at baseline and after 1 year. The
primary outcome was change in weight after 1 year of
treatment. Secondary outcomes included changes in blood
pressure, cardiovascular risk factors, energy intake,
eating frequency and the subjects’ own evaluation of the
Dietary intervention
The study was a 1-year intervention with dietician counsel-
ing at a regular basis. Before study start, all patients met a
dietician at a screen visit and received written and oral
information about the study. From start of the study to the
inclusion visit the patient met the dietician every 2 weeks up
to week 12 and thereafter, every 4 weeks up to week 52. In
total, 17 visits were offered, from inclusion visit to week 52.
Each visit lasted for approximately 45 min.
At the inclusion visit the dietician gave instruction about
the allocated eating frequency. As a guide for portion sizes
and meal/snack composition an individualized energy-
restricted, nutritionally balanced diet plan was prepared
and handed out to the patients at the next visit. The
calculations of prescribed energy level were based on basal
metabolic rate (BMR) estimated according to the formula of
Harris and Benedict (1919). From BMR, total daily energy
expenditure was calculated by multiplying a physical activity
level (PAL) 1.3 for moderate physical activity and PAL 1.5
for heavy physical activity. From the estimated total energy
expenditure 30% was subtracted to get the prescribed energy
intake. The minimum energy level prescribed was 1400 kcal/
day. The prescribed energy level was divided into three meals
or three meals and three snacks depending on which group
the patient was randomized to. Recommended energy intake
in the group of 3M was divided in breakfast, 30% of daily
energy intake (D%), lunch 35D% and dinner 35D% and no
snacks with the exception of limited fruit intake and calorie-
free drinks. For the group of 3 þ 3M the daily energy intake
was divided in breakfast 20D%, lunch 25D%, dinner 25D%
and three snacks, each on 10D%. In all other respects the
prescribed diet followed Swedish Nutrition Recommenda-
tions (SNR) (Livsmedelsverket, 1997). The patients were
encouraged to follow the allocated eating frequency
throughout the study and the individualized diet plan was
used as a guideline to enable changing eating behavior. In
addition, the patients were encouraged to increase their
physical activity, primarily walking on a regular basis.
Thus, the patients received individual counseling in
changes of diet and physical activity behavior. A diet-
counseling plan was followed by the dieticians to ensure a
concordant treatment between the study sites. The diet-
counseling plan included themes for every visit, nutritional
information, fact sheets and self-monitoring exercises. Food
and physical activity records could be used as a pedagogic
tool. Even if each visit had a preplanned topic the counseling
was individualized, focusing on specific individual problems.
However, adherence to the allocated eating pattern was
emphasized at all visits.
Compliance to the recommended eating frequency was
evaluated by repeated telephone interviews at six predefined
time periods during the year of intervention. The interviews
were carried out by the dietician, who coordinated the study
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
at Sahlgrenska University Hospital. ‘The meal pattern
questionnaire’ was used as a basis for the assessment of
intake occasions (Berte
us Forslund et al., 2002). The subjects
were asked about their intake pattern the previous day
specifying time and type of intake occasions. Food choices
at snack meals were registered specifically; other intake
occasions were registered according to the meal types in the
questionnaire. The telephone interviews were conducted on
randomly selected days with emphasis to cover different
days of the week. If it was impossible to get in contact with
the subject in the predefined time period the interview was
omitted in this period.
Anthropometrical measures
Body weight was measured to the nearest 0.05 kg with the
patient wearing underwear and no shoes, using calibrated
scales. Body height was measured without shoes to the
nearest 0.05 cm. Body mass index (BMI) was calculated from
weight (kg) divided in height squared (m
). Waist circum-
ference was measured in a standing position at the midpoint
between lower border of the rib cage and the iliac crest. Hip
was measured at the symphysis major trochanter level.
Blood pressure and biochemical analyses
Blood pressure was measured after 5 min in a sitting position
on the right arm. Blood samples; P-glucose, S-insulin, S-
cholesterol, S-high-density lipoprotein (HDL), S-low-density
lipoprotein (LDL) and S-triglycerides were drawn in a fasting
state, that is no food or drink were allowed from 1200 the
night before measurement day. Blood samples were analyzed
locally at the central clinical laboratories at Sahlgrenska
University Hospital, Skaraborg Hospital and Helsingborg
Hospital. Laboratory analyses were the same as those used in
ordinary patient care according to local practice.
Assessment of eating pattern
A self-administered questionnaire, ‘The meal pattern ques-
tionnaire’, was used to assess habitual daily intake pattern.
The questionnaire was distributed at baseline and at the end
of study. The subjects were asked to describe how they eat ‘an
ordinary’ day, specifying time for each intake occasion and
choose one of four predefined types of intake occasions;
main meal, light meal/breakfast, snacks and drink only.
In the analysis of the eating pattern, main meals and light
meal/breakfast were added together and called principal
meals (one light meal/breakfast and two main meals or two
light meals/breakfast and one main meal). The questionnaire
is described elsewhere (Berte
us Forslund et al., 2002).
Assessment of dietary intake
A self-administered dietary questionnaire to assess habitual
energy and macronutrient intake during the past 3 months
was used. The questionnaire was distributed at baseline and
at the end of the study. The dietary questionnaire is judged
to give valid results in both obese and normal weight
subjects. The questionnaire is described elsewhere (Lindroos
et al., 1993).
Assessment of physical activity
A questionnaire describing physical activity at work and
during leisure time was used (Larsson et al., 2004). Occupa-
tional PAL was categorized in five levels; unemployed,
sedentary work, moderately sedentary work, moderately
heavy work and heavy work. Leisure time physical activity
was categorized in four levels; sedentary leisure, moderately
activity, moderate exercise and heavy exercise. The partici-
pants choose one of the alternatives corresponding to their
usual activity pattern. In our analyses, the leisure time
activity level ‘sedentary leisure’ and occupational PAL
‘sedentary work’ were coded as sedentary in a dichotomous
variable, sedentary yes ¼ 1, no ¼ 0.
To evaluate the subjects’ own opinion on the allocated
eating pattern (3M or 3 þ 3M) they were asked to answer the
questions on a Visual Analog Scale: ‘How content are you
with eating 3 (3 þ 3) meals per day?’ (not content ¼ 0, very
content ¼ 100). ‘How easy did you find it eating 3 (3 þ 3)
meals per day? (very difficult ¼ 0, very easy ¼ 100). ‘Would
you consider eating 3 (3 þ 3) meals per day from now on?’
(Yes ¼ 1/No ¼ 2).
Patients referred to the obesity unit at Sahlgrenska University
Hospital, Go
teborg, obesity research unit at Helsingborg
Hospital, Helsingborg and at the Medical clinic at Skaraborgs
Hospital, Sko
vde were invited to participate at the first visit
to the clinics. At the latter, clinic participants were also
recruited through local advertisement. The patients were
recruited continuously over time, starting at Sahlgrenska
University Hospital in September 2002. To speed up recruit-
ment Helsingborg Hospital joined in March 2003 and
Skaraborgs Hospital in March 2004.
The selection criteria to enter the study included age 18–60
years and BMI430 kg/m
. Subjects reporting previous obe-
sity surgery, anti-obesity drug treatment the last year, drug-
or insulin-treated diabetes, hypothyroidism, severe psychia-
tric disorder, bulimia, drug or alcohol abuse were not eligible
for the study.
Pre-study power calculations showed that 70 subjects were
needed in each group to obtain a significant (Po0.05)
difference in body weight change of 3.075.2 kg with a power
of 80% and an estimated dropout rate of 35%. Accordingly,
two groups of each 70 patients were randomly allocated to
the two different intervention groups; three meals or three
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
meals and three snacks per day. A block randomization was
used to keep the two groups balanced at all times and evenly
spread throughout the year, according to Altman (1991). The
two groups were in blocks of four at a time. In each block
two subjects got group ‘three meals’ and two subjects got
group ‘three meals þ three snacks per day’ in a random order.
Blinded and sealed envelopes for the randomization were
prepared at the Sahlgrenska site and sent out to the two
other sites. The procedure was supervised from the Sahl-
grenska University Hospital and the sites were in contact
continuously. Randomization was carried out at the inclu-
sion visit and the dietician gave instruction about the
allocated eating frequency.
To analyze differences between groups w
test was used for
proportions, McNemars’ test for paired proportions and t-test
for continuous variables. Survival analysis was used to
compare time for dropout in the two study groups. Weight,
anthropometry and laboratory variables were analyzed in
completers and in all participants using the last observation
carried forward (LOCF). Repeated measures analysis was used
to analysis weight change between study groups over time.
The SAS 8.2 statistical package was used for all analyses (SAS
Institute Inc., Cary, NC, USA).
Participation flow
A total of 140 (36 men and 104 women) patients were
randomized and 93 (27 men and 66 women) patients
completed the entire study. Participation flow is shown in
Figure 1. Dropout rate was 30% in the 3M group and 37.1%
in the 3 þ 3M group, although the difference was not
statistical significant (P ¼ 0.37). There was no difference in
time of attrition between the study groups (P ¼ 0.27).
However, younger patients (P ¼ 0.004) and patients with
lower BMI (P ¼ 0.01) dropped out from the study program
earlier than older patients and those with higher BMI. In
addition more men in the 3m group dropped out compared
to the 3 þ 3M group (7/18 compared to 2/18, respectively;
P ¼ 0.05) whereas in women dropout rate was higher in
the 3 þ 3M group than the 3M group (24/52 and 14/52,
respectively; P ¼ 0.04). Baseline characteristics for all study
participants and for completers in both groups are shown in
Table 1. Baseline characteristics did not differ significantly
between completers and all participants included in the
study. Neither did the participants who completed the study
differ between the three study sites.
Eating frequency
Intake of eating occasions at baseline and after 1 year of
treatment is presented in Table 2. The change in number of
principal meals per day did not differ between the 3M and
3 þ 3M group. However, change in number of snacks differed
significantly between the two groups. The 3M group
decreased the number of snacks whereas the 3 þ 3M group
increased snack frequency (Po0.0001, confidence interval
(CI) 2.18 to 1.06). Figures 2a and b show the percent
completers in each group reporting number of principal
meals and snacks before treatment and after 1 year.
After 1 year of treatment 22 patients (45%) in the 3M
group reported consuming the recommended three principal
meals and no snacks whereas 21 patients (48%) in the 3 þ 3M
group reported having the recommended three principal
meals and three snacks.
Diet and physical activity
Mean energy and macronutrient intake and physical activity
at baseline and after 1 year of treatment is shown in Table 2.
Reported energy intake decreased with 2955 kJ (707 kcal) in
the 3M group compared to 2178 kJ (521 kcal) in the 3 þ 3M
group and the decrease did not differ significantly between
the two groups. The reported change in energy intake was in
men 4140 kJ (991 kcal) and 2021 kJ (484 kcal) in 3 and 3 þ 3M
groups, respectively. Corresponding figures for women were
2584 kJ (618 kcal) and 2274 kJ (544 kcal) in the 3 and 3 þ 3M
groups, respectively. Furthermore, change in energy percent
macronutrient intake did not differ between the two groups.
Although both groups decreased the energy percent fat
intake and increased energy percent protein and fiber intake
expressed as g/1000 kcal from baseline to week 52.
After 1 year of treatment number of patients reporting
sedentary lifestyle decreased significantly in both groups and
there was no significant difference between the groups.
Neither did changes in sedentary work differ between the
groups (Table 2).
n = 170
Randomly allocated to two
treatment groups
n = 140
Allocated to 3 meals regimen
n = 70
Allocated to 3 meals and
3 snacks regimen
n = 70
Completed the intervention
n = 49
Dropped out during intervention
n = 26
Dropped out during intervention
n = 21
Completed the intervention
n = 44
Excluded or
refused to participate
n = 30
Figure 1 Flow chart.
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
Table 1 Baseline characteristics for the two groups; three meals (3M) and three meals þ three snacks (3 þ 3M) in 140 patients included in the study and
in 93 patients who completed the study
Characteristic All 3M (n ¼ 70) All 3 þ 3M (n ¼ 70) Completers 3M (n ¼ 49) Completers 3 þ 3M (n ¼ 44)
Gender (M/F) 18/52 18/52 11/38 16/28
Age (year) 38.7711.6 40.1711.5 40.6711.1 41.8711.0
Weight (kg) 113.0718.6 112.6721.5 113.9719.8 118.2723.0
Height (m) 1.7270.1 1.7170.1 1.7170.1 1.7370.1
BMI (kg/m
) 38.375.3 38.476.0 38.875.8 39.476.5
Circumference measure (cm)
Waist 117.0711.7 115.7712.8 117.5712.0 118.0713.6
Hip 125.2711.6 123.4711.6 125.6712.8 124.9713.0
Blood pressure (mm Hg)
Systolic 127.1715.2 129.7716.5 127.5715.2 131.2716.4
Diastolic 82.879.0 81.9710.5 83.379.3 83.879.8
Blood analysis
P-glucose (mmol/l) 5.470.6 5.470.9 5.470.6 5.470.8
S-insulin (mU/L) 18.5711.6 17.7711.4 18.8713.1 18.6712.5
S-cholesterol (mmol/l) 5.370.9 5.270.9 5.470.9 5.370.9
S-HDL (mmol/l) 1.470.4 1.470.3 1.370.3 1.470.3
S-LDL (mmol/l) 3.370.8 3.270.8 3.470.8 3.370.8
S-triglycerides (mmol/l) 1.871.0 1.670.6 1.871.0 1.670.6
Abbreviations: F, female; HDL, high-density lipoprotein; LDL, low-density lipoprotein; M, male; P, plasma; S, serum.
Mean values7s.d. are presented.
Table 2 Intake of meals and snacks, dietary intake and physical activity in the three-meal (3M, n ¼ 49) and 3 þ 3 meal (3 þ 3M, n ¼ 44) groups of
completers at baseline and after 1 year of treatment
Variable Baseline Week 52 P for difference between changes 95% CI for difference between changes
Principal meals (n)
3 meals (n ¼ 47) 2.970.7 2.970.4
3 þ 3 meals (n ¼ 42) 2.870.7 3.070.3
0.051 0.66 to 0.004
Snacks (n)
3 meals (n ¼ 47) 1.870.9 0.770.7
3 þ 3 meals (n ¼ 42) 1.971.6 2.370.9
o0.0001 2.18 to 1.06
Energy intake, kJ (kcal)
3 meals (n ¼ 46) 11 72575141 877072546
(280571230) (20987609)
3 þ 3 meals (n ¼ 44) 11 08573804 898773666 0.51 3118 to 1568
(26527910) (21507877)
(746 to 375)
Protein (E%)
3 meals (n ¼ 46) 15.872.6 17.072.4
3 þ 3 meals (n ¼ 44) 16.472.2 18.272.7
0.31 2.0 to 0.6
Fat (E%)
3 meals (n ¼ 46) 35.275.0 33.474.2
3 þ 3 meals (n ¼ 44) 34.975.7 32.275.6
0.54 2.1 to 4.0
Carbohydrate (E%)
3 meals (n ¼ 46) 46.975.1 46.975.0
3 þ 3 meals (n ¼ 44) 46.476.1 47.575.7 0.48 4.3 to 2.0
Mono-disaccharides (E%)
3 meals (n ¼ 46) 21.476.6 20.675.1
3 þ 3 meals (n ¼ 44) 21.076.0 21.075.8 0.64 4.4 to 2.7
Fiber (g/1000 kcal)
3 meals (n ¼ 46) 8.972.4 11.273.1
3 þ 3 meals (n ¼ 44) 8.972.2 11.672.7
0.17 2.0 to 1.2
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
Repeated interviews on eating frequencies with emphasis
on snacking were conducted throughout the study. Mean
number of interviews was 4.4 per subject. Reported mean
number of principal meals and snacks is described in Table 3.
The 3M group reported fewer snacks than the 3 þ 3M group.
In the 3 þ 3M group the frequency of snacks was decreasing
in the latter study period. The results of compliance are in
line with the meal frequency reported by the subjects at the
end of study as described in the section ‘Eating frequency’.
Weight loss
Weight loss after 1 year of treatment was in the 3M group
4.176.1 kg (3.674.9%) and in the 3 þ 3M group
5.979.4 kg (4.776.7%) and did not differ significantly
Table 2 Continued
Variable Baseline Week 52 P for difference between changes 95% CI for difference between changes
Sedentary leisure time (%)
3 meals (n ¼ 49) 30.6 14.3
3 þ 3 meals (n ¼ 44) 38.6 22.7
Sedentary at work (%)
3 meals (n ¼ 49) 32.7 30.6
3 þ 3 meals (n ¼ 44) 40.9 38.6 0.63
Abbreviations: CI, confidence interval; E%, energy percent.
The P-value and 95% CIs are difference in change between the two groups from baseline to W52.
P ¼ 0.06,
Po0.01 for difference from baseline.
Baseline One year
% completers
Number of principal meals per day
Baseline One year
% completers
Number of snacks per day
3 M
3+3 M
3+3 M
3 M
Figure 2 (a) Percent completers reporting number of principal meals per day at baseline and after 1 year of treatment in the 3M and 3 þ 3M
groups. (b) Percent completers reporting number of snacks per day at baseline and after 1 year of treatment in the 3M and 3 þ 3M groups.
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
(P ¼ 0.31). When analyzing weight loss over time no
difference was found between the two groups neither in
the completers only (P ¼ 0.34) nor in all participants using
LOCF (P ¼ 0.35) (Figures 3a and b).
Metabolic variables
Changes in blood pressure, blood glucose, insulin, cholesterol,
LDL, HDL and triglycerides did not differ between the
groups. However, HDL increased in the 3M group compared
to the 3 þ 3M group (Po0.033) (Table 4).
The patients’ personal opinion on the meal regimen was
evaluated. When analyzing the question ‘How content are
you with eating 3 (3 þ 3) meals per day?’ no difference was
found between the two groups. The mean score was 55 and
63% in the 3M and 3 þ 3M groups (P ¼ 0.14), respectively.
Neither was a difference found between the groups replying
the question ‘How easy did you find it eating 3 (3 þ 3) meals
per day?’ showing a mean score of 50 and 55% in the 3M and
3 þ 3M group (Po0.30), respectively. Nor was a difference
found when asking ‘Would you consider eating 3 (3 þ 3)
meals per day from now on?’; 51% of the patients in the 3M
group reported ‘yes’ compared to 68% in the 3 þ 3M group
In this 1-year randomized clinical trial subjects in both
groups lost weight and improved their metabolic profile over
1 year. However, weight loss did not differ significantly
between the two intervention arms suggesting that recom-
mending snacks or not between meals is not an important
factor for achieved weight loss after 1 year. As previous cross-
sectional studies have shown that a high eating frequency
and snacking increase total energy intake (Berte
us Forslund
et al., 2002, 2005), we hypothesized that no snacking
between meals would facilitate the restriction of energy
intake and weight loss. Even if omitting snacks may help
cutting down energy intake, our result implies that when
patients attain extensive support and diet counseling they
manage to cut down calories despite a high snacking
frequency. The choice of low-energy dense snacks is crucial
and we can only speculate if the good quality snack choices
Table 3 Compliance to the meal pattern recommendation in the 3M and 3 þ 3M groups of completers at repeated interviews during 1-year dietary
Principal meals 2.8 (1–3) 2.9 (2–4) 2.7 (1–4) 2.8 (2–3) 2.7 (2–3) 2.7 (1–3)
Snacks 0.5 (0–3) 0.4 (0–2) 0.5 (0–2) 0.5 (0–2) 0.8 (0–3) 0.7 (0–2)
(n ¼ 49) (n ¼ 32) (n ¼ 28) (n ¼ 41) (n ¼ 43) (n ¼ 30) (n ¼ 23)
3 þ 3M
Principal meals 2.8 (1–4) 2.8 (1–4) 2.7 (0–3) 2.4 (1–3) 2.9 (2–4) 2.6 (1–3)
snacks 2.2 (0–4) 2.5 (1–5) 2.2 (1–4) 1.4 (0–3) 1.6 (0–3) 1.9 (0–4)
(n ¼ 44) (n ¼ 38) (n ¼ 31) (n ¼ 41) (n ¼ 40) (n ¼ 36) (n ¼ 27)
Reported mean (min–max) number of principal meals and snacks at six interview periods.
w. 0 w. 12 w. 24 w. 36 w. 52
Visit week
w. 0 w. 12 w. 24 w. 36 w. 52
Visit week
3 M
3+3 M
3 M
3+3 M
Figure 3 (a) Mean weight and 95% CI in completers (n ¼ 92). (b)
Mean weight and 95% CI in all subjects (n ¼ 140) using LOCF.
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
will be sustained without extensive support. It may be
suggested that the role of snacking is different in obese ‘real
life’ and during treatment conditions. Not only frequency
but regularity of meal times may also have an impact on
energy intake. In a recent study by Farshchi et al. (2005)
obese women were instructed to maintain their usual intake
on an irregular (‘caotic’ pattern with 3–9 meals/day) vs an
regular (6 meals/day) meal pattern in a 14-day crossover
design. The obese women reported a significantly higher
energy intake during the irregular meal pattern than during
the regular meal pattern. In a similar study in lean women,
energy intake did not differ between the two meal patterns
suggesting that eating patterns may have different implica-
tions in normal weight and obese subjects (Farshchi et al.,
2004). Although we do not know how regular the patients
were eating during the intervention, it is possible that the
extensive support helped the patients to follow a more
regular meal pattern.
A crucial point when evaluating our weight loss results is
the adherence to the allocated intervention. Both groups
changed eating patterns toward the recommended number
of snacks and the reported number of snacks differed
significantly between the groups after 1 year. Adherence
was also similar in the two groups. This suggests that many
subjects in the present study managed to change eating
patterns despite the difficulties in doing so reported by other
investigators (King and Gibney, 1999). It is noteworthy
that the subject’s own opinion on difficulties did not differ
between the groups. However it should be noted that the
discrepancy in snacking between the two groups was not
as large as intended. This suggests that the difference in
snacking patterns might not have been large enough to
attain a difference in weight loss.
The weight loss difference between the treatment arms
was 1.8 kg. It may be argued that we did not have enough
statistical power to find a difference due to too small study
groups. When planning the study we decided that a
difference of 3 kg or more would be considered clinically
relevant in a weight loss trial. This is in line with anticipated
weight loss differences used in power calculations in other
studies (Heshka et al., 2003; Samaha et al., 2003; Brinkworth
et al., 2004).
Previous studies on eating patterns have focused mainly
on the influence on metabolic factors. Spreading the
nutrient load on many small meals may reduce insulin and
glucose response and improve blood lipid profile (Fa
et al., 1964; Jenkins et al., 1989, 1992) although findings are
inconsistent (Beebe et al., 1990; Arnold et al., 1994, 1997;
Thomsen et al., 1997). In this study metabolic variables were
improved in both groups after 1 year but did not differ
except for HDL cholesterol that increased in the 3
M group.
The literature on eating frequency and HDL cholesterol is
inconsistent. In short experimental studies HDL cholesterol
has been positively (McGrath and Gibney, 1994), negatively
(Murphy et al., 1996; Thomsen et al., 1997) or unrelated
(Arnold et al., 1993, 1994) to eating frequency. Therefore, we
cannot role out that the difference in HDL cholesterol is a
chance finding.
Attrition is usually high in obesity treatment studies
(Glenny et al., 1997). The dropout rate in this study was
similar to what we had expected and in line with with-
drawals found in other studies (Clark et al., 1995; Torgerson
et al., 1999). In line with other studies we also found that
younger patients dropped out earlier than older patients
(Andersson and Rossner, 1997; Torgerson et al., 1999; Lantz
et al., 2003a, b). However, patients with lower BMI dropped
out earlier, which is in contrast to others that found no
association between BMI and attrition (Andersson and
Rossner, 1997; Torgerson et al., 1999; Lantz et al., 2003a) or
that those with higher BMI dropped out more frequently
(Clark et al., 1995). One study with a very high dropout rate
(77%) also found that dropouts had a slightly lower BMI
than completers (Inelmen et al., 2005).
The larger withdrawal in men from the 3M group and
women from the 3 þ 3M group indicates that preferred
snacking frequency may differ by gender. We can only
Table 4 Fasting blood samples and blood pressure in the three-meal
(3M, n ¼ 49) and 3 þ 3 meal (3 þ 3M, n ¼ 44) groups of completers at
baseline and after 1 year of treatment
Baseline 1 year Change P-value
P-glucose (mmol/l)
M 5.470.6 5.370.6 0.1670.46* NS
3 þ 3
M 5.470.8 5.070.5 0.3370.78**
S-insulin (mU/l)
M 18.8713.1 14.678.4 4.0711.0* NS
3 þ 3
M 18.6712.5 15.377.9 3.4710.3*
S-cholesterol (mmol/l)
M 5.470.9 5.371.0 0.1170.59 NS
3 þ 3
M 5.370.9 5.170.9 0.1670.64
S-HDL (mmol/l)
M 1.370.3 1.470.3 þ 0.170.21** 0.033
3 þ 3
M 1.470.3 1.470.3 þ 0.0270.15
S-LDL (mmol/l)
M 3.470.8 3.370.8 0.1070.50 NS
3 þ 3
M 3.370.8 3.270.8 0.0870.60
S-TG (mmol/l)
M 1.871.0 1.671.0 0.1770.88 NS
3 þ 3
M 1.670.6 1.470.6 0.2370.58**
Systolic BP (mm Hg)
M 127715 125716 3.3711.3* NS
3 þ 3
M 131716 128716 4.0712.7*
Diastolic BP (mm Hg)
M 837981710 2.4710.3 NS
3 þ 3
M 84710 81710 2.379.9
Abbreviations: BP, blood pressure; HDL, high-density lipoprotein; LDL, low-
density lipoprotein; NS, not significant; P, plasma; S, serum; TG, triglycerides.
Mean values7s.d. are presented.
Significant difference from baseline within group *Po0.05, **Po0.01.
Snacks in obesity treatment
H Berte
us Forslund et al
European Journal of Clinical Nutrition
speculate if men find it easier to adhere to a frequent
snacking pattern than no snacks whereas women do the
opposite. Although, gender differences have also been noted
in a previous intervention study. This study showed that
men who adhered to three principal meals and two or three
snacks per day lost more weight than those who did not,
whereas women who adhered to this eating pattern lost less
weight than those who did not (H Berte
us Forslund, personal
communication). Gender differences have also been noted
in observational studies suggesting a negative association
between meal frequency and BMI or body weight in men and
a positive or no relationship in women (Drummond et al.,
1998; Titan et al., 2001).
Thus, one limitation of the present study is that we lack
power to analyze gender differences. Another limitation is
that the recruitment period was very long. To speed up
recruitment we involved two other study sites. This made the
study more heterogeneous. On the other hand recruiting
subjects from different parts of Sweden strengthens the
generalizablity of the results.
In Sweden, the commonly used dietary recommendation
in obesity treatment is based on the general dietary
recommendations for the whole Swedish population, SNR
(Livsmedelsverket, 1997). The SNR recommendations in-
clude eating frequency as well as temporal distribution of
energy over the day. An eating frequency of 3 main meals
and 2–3 snacks has been recommended, although revised to
1–3 snacks/day recently (Livsmedelsverket, 2005). However,
the evidence that this recommendation facilitates energy
restriction and weight control is not substantiated. This
study showed that approximately half of the patients in each
group managed to adhere to the allocated ‘no snack’ or
‘three-snack’ pattern and also considered to continue this
eating pattern after the study had ended. The findings from
this study also suggest that a recommended energy-restricted
diet results in similar weight loss irrespective high- or low-
eating frequency. Consequently, recommending snacks in
obesity treatment should be based on individual needs rather
than that all patients should eat snacks or not.
We thank Ted Lystig for statistical advice. This study was
supported by a grant from Va
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... Due to the different definitions of snacking in observational studies (23), and confounding of snacking with eating frequency (24,25), laboratory-based crossover studies have been widely used with conflicting findings that range from full compensation of energy consumed as snack preloads (26)(27)(28) to no compensation (29)(30)(31)(32). In most randomized controlled trials where free-living subjects added investigator-provided snacks of known composition and energy content to habitual diets, there were no differences in body weight after 8 to 52 weeks of intervention (33)(34)(35)(36). ...
... Conversely, complete compensation was reported in other laboratory-based crossover studies with lean men (27) or women (28), as the total daily energy intake did not differ on the snack compared with the no-snack days. Longer-term intervention trials of the addition of one or more snacks to self-reported habitual intake of free-living subjects also suggest complete snack-energy compensation, as there was no change in body weight and self-reported energy intakes during the snack intervention (33)(34)(35)(36). ...
... Despite these improvements, dietary recalls and all other assessment methods that rely on self-reports of dietary intake are widely acknowledged to contain both random and systematic measurement errors (especially energy intake underreporting) (54)(55)(56). All randomized controlled trials of snacking interventions also rely on self-reported dietary data to establish compliance (33)(34)(35)(36). Notably, self-reports of dietary intake from intervention trials also contain additional intervention-related bias (57). ...
Full-text available
Background: Most Americans snack and some snack several times a day; however, compensatory dietary and eating behaviors associated with snacking in free-living individuals are poorly understood. Objective: The aim of the study was to examine within-person differences in reported energy intake and eating patterns on a snack day relative to a no-snack day. Methods: We used 2 d of dietary recall data from the NHANES 2007-2014 to replicate the crossover nutrition study paradigm in a natural setting. Respondents reporting a snack episode in only one of two available dietary recalls were eligible for inclusion in the study (n = 1,917 men and 1,860 women). We used multivariable regression methods to compare within-person differences in quantitative, qualitative, and eating pattern outcomes between the snack and no-snack recall days. Results: On the snack day, snack episodes provided (mean difference and 95% CI) 493 (454, 532) kcal of energy in men and 360 (328, 392) kcal in women. The 24-h energy intake on snack day was higher by 239 (140, 337) kcal in men and 219 (164, 273) kcal in women (P < 0.0001). On the snack day, both men and women were more likely to skip main meals and reported lower energy intake from main meals (P < 0.0001); however, the energy density of foods or beverages reported on the snack compared with no-snack days were not different. Fruit servings were higher on the snack day (P ≤ 0.0004), but intakes of vegetables and key micronutrients did not differ. The 24-h ingestive period was longer on the snack day (P < 0.0001). Conclusions: Free-living men and women partially compensated for snack energy by decreasing energy intake from main meals without adverse associations with qualitative dietary characteristics or time of meal consumption. Women compensated to a smaller extent than men. Thus, over the long term, snack episodes may contribute to positive energy balance, and the risk may be higher in women.
... Moreover, a longitudinal study in female adolescents found that a low meal frequency, assessed by two three-day dietary records, predicted a higher BMI and waist circumference 10 years later [31]. However, previous RCTs and cross-sectional studies have been inconsistent as to whether an increase in meal frequency results in increased or decreased energy intake [30,32]. ...
Full-text available
Knowledge on how energy intake and macronutrients are distributed during the day and the role of daily eating patterns in body composition among adults with overweight/obesity and prediabetes is lacking. Therefore, we evaluated the diurnal dietary intake and studied the associations of daily eating patterns with body fat percentage. A total of 119 adults with prediabetes were included (mean (SD) HbA1c 41 (2.3) mmol/mol, BMI 31.5 (5.0) kg/m2, age 57.8 (9.3) years, 44% men). Information on dietary intake was obtained from self-reported food records for three consecutive days. All foods and beverages (except water) were registered with information on time of ingestion. Body fat was measured by dual-energy X-ray absorptiometry. A total of 60.5% of the participants reported a daily eating window of 12 or more hours/day, and almost half of the daily total energy intake was reported in the evening. In analyses adjusted for age, gender, and total daily energy intake, having the first daily energy intake one hour later was associated with slightly higher body fat percentage (0.64% per hour, 95% CI: 0.28; 1.01; p < 0.001), whereas higher meal frequency was associated with slightly lower body fat percentage (0.49% per extra daily meal, 95% CI: −0.81; −0.18; p = 0.002). Prospective studies are warranted to address the clinical implications of daily eating patterns on body fat and cardiometabolic health.
... Experimental studies have shown that eating in the absence of physiological hunger (in a state of post-ingestive hyperglycemia) induces poor compensation for the energy ingested and therefore leads to overeating [27]. When snacks are regular and predictable, they are integrated in the daily pattern of physiological/ behavioral events; they are triggered by a metabolic hunger signal and exert positive effects on satiety mechanisms, postintake insulin profiles and thermogenesis [28]. For snacks as well as meals, the total energy intake and satiety are affected by cognitive factors, such as the attention paid to the act of eating [29]. ...
Full-text available
Over the last decades, eating episodes in addition to the three daily main meals have been observed worldwide; the prevalence of these “snacking” episodes raises health questions that mindful eating may contribute to answering. The goal of the symposium entitled “Mindful eating applied to snacking: a promising behavioral approach supported by research” was to introduce, for the first time in a scientific congress, the emerging science related to mindful eating and to evaluate its application to snacking occasions. It was held at the 21st International Congress of Nutrition (IUNS), which took place in Buenos Aires from October 15-20, 2017. Three primary topics were presented: 1) the definition of snacking and its role in dietary quality in adults; 2) the value of eating mindfully as an emerging concept, in relation to snacking occasions; 3) a detailed approach to mindful eating from theoretical principles to applications. Promoting mindful eating is a relatively new ‘third-wave’ cognitive-behavioral approach that enhances individuals’ awareness of, and attention to, physiological hunger and satiety, eating enjoyment, portion size and nutritional health when eating or when making food choices. Encouraging results have been obtained in obese individuals. Applied to snacking, mindful eating may help individuals’ better control food intake, and help orient their choices without compromising pleasure while eating. This symposium was organized by Mondelez International R&D.
Preventing obesity (OB) among adults is a public health priority. One factor that seems to contribute to OB, due to the extra energy intake it involves, is the greater consumption of snacks. Whether snacking promotes OB in adults is however a source of controversy in the literature at present. The aim of this paper was to evaluate the effects of snacking on body weight status, along with contextual factors such as snacking location, food source, timing, and social context of snacking. To better understand the nature of snacking behavior, seven currently used definitions of snacking were described. Studies published prior to November 2020 were identified by searching the PubMed and Scopus databases, with thirty-three observational studies being identified and included. The consumption of energy-dense snacks may contribute to higher energy intake and weight in adult populations. The context in which adults snacks—such as eating alone, outside home or work, late in the day, in front of a TV or computer—is also important for this behavior. However, the lack of consensus on the definition of snacks in the literature makes these considerations suggestive rather than objective. Better-designed research is needed to determine the prospective association between snacking behavior and weight status in adults. Snacking may be an important behavior that can be modified to prevent obesity on the population level. Social education focusing on promoting morning snacks and replacing energy-dense snacks by more nutritious ones, e.g. fruit and vegetables, may thus be beneficial.
Purpose The consumption of sugar-sweetened beverages (SSBs) is associated with weight gain in both children and adults. In addition to environmental factors, such as food availability, psychological variables, including mood states, also impact intake. In the current study, we focus on momentary associations between feelings of loneliness and craving for SSBs among adolescents and explore the moderating role of family functioning. Loneliness has been associated with a wide range of health outcomes, but to date, few studies have examined its association with cravings for SSBs. Methods Using an ecological-momentary assessment design, data were collected on 158 (males = 68, mean age = 15.13 ± 2.27 years) participants. Multilevel mixed-effects models were used to examine the relations between the main and interactive effects of loneliness and family functioning on cravings for SSBs, independent of other negative emotions. Results Results suggest that loneliness in adolescents was associated with a small increase in craving for SSBs. Importantly, the relationship held after controlling for negative emotions, suggesting the unique role of loneliness. However, positive family functioning did not mitigate the relations between loneliness and craving for SSBs. Conclusions Loneliness uniquely contributes to cravings for SSBs. At the same time, family functioning did not buffer the influence of loneliness on cravings for SSBs among adolescents.
Technical Report
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Sobrepeso e Obesidade em Adultos Portaria SCTIE nº 53 - 11/11/2020
Background Although several randomized controlled trials (RCTs) have supported the beneficial effects of higher meal frequency (MF) on cardiometabolic risk factors, the putative effects of higher MF on health remain inconclusive. This study systematically reviewed the evidence from RCTs of the effect of higher compared with lower MF on the blood lipid profile, glucose homeostasis, and adipokines. Methods PubMed, Scopus, ISI Web of Science, and the Cochrane database were searched up to October 2020 to retrieve relevant RCTs. A DerSimonian and Laird random effects model was used to pool mean differences and 95 % CI for each outcome. The quality of studies and evidence was assessed through standard methods. Results Twenty-one RCTs (686 participants) were included in this meta-analysis. Overall results showed a significant improvement in total cholesterol [weighted mean difference (WMD) = -6.08 mg/dl; 95% CI: -10.68, -1.48; P = 0.01; I2= 88%], and low-density cholesterol (LDL-C) (WMD= -6.82 mg/dl; 95% CI: -10.97, -1.60; P= 0.009; I2= 85.7%), while LDL-C to high-density cholesterol ratio (LDL-C: HDL-C) increased (WMD= 0.22; 95% CI: 0.07, 0.36; P= 0.003; I2= 0.0%) in higher MF vs. lower MF. No significant effects were found on measures of glycemic control, apolipoproteins-A1 and B, or leptin. In subgroup analyses, higher MF significantly reduced serum triglyceride (TG), and increased HDL-C, compared with lower MF in interventions > 12 weeks, and decreased serum TC and LDL-C in healthy participants. A significant reduction in LDL-C also was observed in studies where the same foods given both arms, simply divided into different feeding occasions, and in feeding studies, following higher MF compared to lower MF. Conclusion Our meta-analysis found that higher, compared with lower MF may improve total cholesterol, and LDL-C. The intervention does not affect measures of glycemic control, apolipoproteins-A1 and B, or leptin. However, the GRADE ratings of low credibility of the currently available evidence highlights the need for more high-quality studies in order to reach a firm conclusion.
The relation between meal frequency and measures of obesity is inconclusive. Therefore, this systematic review and network meta-analysis (NMA) set out to compare the isocaloric effects of different meal frequencies on anthropometric outcomes and energy intake (EI). A systematic literature search was conducted in 3 electronic databases (Medline, Cochrane Library, Web of Science; search date, 11 March 2019). Randomized controlled trials (RCTs) were included with ≥2 wk intervention duration comparing any 2 of the eligible isocaloric meal frequencies (i.e., 1 to ≥8 meals/d). Random-effects NMA was performed for 4 outcomes [body weight (BW), waist circumference (WC), fat mass (FM), and EI], and surface under the cumulative ranking curve (SUCRA) was estimated using a frequentist approach (P-score: value is between 0 and 1). Twenty-two RCTs with 647 participants were included. Our results suggest that 2 meals/d probably slightly reduces BW compared with 3 meals/d [mean difference (MD): -1.02 kg; 95% CI: -1.70, -0.35 kg) or 6 meals/d (MD: -1.29 kg; 95% CI: -1.74, -0.84 kg; moderate certainty of evidence). We are uncertain whether 1 or 2 meals/d reduces BW compared with ≥8 meals/d (MD1 meal/d vs. ≥8 meals/d: -2.25 kg; 95% CI: -5.13, 0.63 kg; MD2 meals/d vs. ≥8 meals/d: -1.32 kg; 95% CI: -2.19, -0.45 kg) and whether 1 meal/d probably reduces FM compared with 3 meals/d (MD: -1.84 kg; 95% CI: -3.72, 0.05 kg; very low certainty of evidence). Two meals per day compared with 6 meals/d probably reduce WC (MD: -3.77 cm; 95% CI: -4.68, -2.86 cm; moderate certainty of evidence). One meal per day was ranked as the best frequency for reducing BW (P-score: 0.81), followed by 2 meals/d (P-score: 0.74), whereas 2 meals/d performed best for WC (P-score: 0.96). EI was not affected by meal frequency. In conclusion, our findings indicate that there is little robust evidence that reducing meal frequency is beneficial.
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Article Info ABSTRACT 10.30699/jambs.28.126.41 Background & Objective: Recent studies have shown the effect of meal timing on weight-loss diet success with controversial results. The current study evaluated the effect of evening meal timing on weight and body mass index (BMI) in overweight/obese subjects who were on a standard weight-loss diet. Materials & Methods: A total of 70 obese/overweight employees of Ahvaz Jundishapur University of Medical Sciences took part in this randomized clinical trial (RCT). Participants were randomly assigned into a limited meal timing weight-loss (LMTWL) group with the last meal before 06:00 PM and a non-limited meal timing weight-loss (NLMTWL) group with the last meal before 12:00 AM. All participants followed a standard weight-loss diet as follows: 53% carbohydrate 30% fat and 17% protein. Weight and body mass index was measured at the run-in-period (2 weeks), baseline and the end of four weeks. Independent sample T-test was used to compare parametric continuous variables between the two groups. Results: Of participants, 60% were female. However, there was no significant difference between the two groups based on sex. Also, age, height, physical activity level, BMI and energy intake was similar between the two groups. There were no differences in LMTWL and NLMTWL groups based on weight (P=0.89) and BMI (P=0.91) before and after four weeks of the intervention. Conclusion: Meal timing did not influence the amount of weight lost by overweight/obese subjects on a weight-loss diet. However, more RCTs with larger samples and longer follow-up durations (with a focus on nutrient intake, circadian clock patterns, and the interaction between genotype and chronotype) are needed to confirm this finding.
Background Obesity has emerged as an important risk factor for cardiovascular disease and other chronic diseases. However, dietary treatment of obesity is far from being a closed issue. Therefore, it is critical to identify the most appropriate obesity management approaches. Objective This review summarizes the effects, potentialities, and limitations of nutritional interventions aimed at managing obesity in primary and secondary health care settings, highlighting the most effective strategies and theories. Methods This systematic review of randomized controlled trials evaluated nutritional interventions aimed at achieving weight loss in primary and secondary health care patients. All screening and extraction processes were conducted according to PRISMA. Results From an initial 7,816 studies that were identified, 28 met the criteria and were included in the review. Most studies were conducted in a developed country in primary care, with a higher proportion of women. Most of the nutrition interventions maintained continuous contacts during follow-up, and telephone calls were the most commonly used technology. A physical activity component was included in most studies, and the most common dietary approaches used were energy restrictions, changes in macronutrient distribution, and diet self-monitoring. Regarding theories, interventions mainly incorporated Social Cognitive Theory and Motivational Interviewing. Most trials presented significant and moderate weight loss (≅ 5.0%), in which the key contributors were behavioral theories, the dietary approach ‘calorie restriction’, and interventions delivered by dietitians and psychologists. Conclusions We found that most trials presented better weight loss results with the association of caloric restrictions and interventions theory-based delivered by dietitians or psychologists. We identified the need to develop interventions in other contexts, such as low and middle-income countries; and the need for further trials comparing a theory- vs. not-theory-driven intervention; group-based vs. individually-based intervention; and intervention using or not technology. Systematic review registration n° CRD42018103691
1. The continuing rise in the prevalence of Type 2 diabetes and the burden this places on individuals and society has brought a new impetus to develop strategies for primary prevention. Diet is an important risk factor and dietary change is likely to be critical to primary prevention. 2. Observational studies have suggested that a number of dietary components may modulate risk. The WHO Technical Report 916 (1) in 2003, based predominately though not exclusively, on observational data, pointed to excess weight as a "convincing" factor in the aetiology of diabetes, with saturated fat and fibre as "probably" increasing or decreasing risk respectively. Evidence since then has added support to these risk factors. See for example the review by Steyn et al. (2004) on the role of diet in type 2 diabetes (2, . 3. Other risk factors were identified as "possibly" linked to the risk of diabetes. Here the protective role of low GI foods has strengthened, though evidence is still mixed (perhaps in part because of poor dietary characterisation) (3,4, . The protective role of long chain n-3 fatty acids (or fish) remains plausible but inconclusive. Recent analyses from two large cohorts, in USA (5) and EPIC Norfolk (6) , have found no evidence of an association after adjustment for other dietary factors. Evidence for a beneficial effect of wholegrain foods is limited to some large observational studies of which many are confounded by poor segregation of wholegrain from fibre in the dietary assessment (7 .
About 97 million adults in the United States are overweight or obese. Obesity and overweight substantially increase the risk of morbidity from hypertension; dyslipidemia; type 2 diabetes; coronary heart disease; stroke; gallbladder disease; osteoarthritis; sleep apnea and respiratory problems; and endometrial, breast, prostate, and colon cancers. Higher body weights are also associated with increases in all-cause mortality. The aim of this guideline is to provide useful advice on how to achieve weight reduction and maintenance of a lower body weight. It is also important to note that prevention of further weight gain can be a goal for some patients. Obesity is a chronic disease, and both the patient and the practitioner need to understand that successful treatment requires a life-long effort. Assessment of Weight and Body Fat Two measures important for assessing overweight and total body fat content are; determining body mass index (BMI) and measuring waist circumference. 1. Body Mass Index: The BMI, which describes relative weight for height, is significantly correlated with total body fat content. The BMI should be used to assess overweight and obesity and to monitor changes in body weight. Measurements of body weight alone can be used to determine efficacy of weight loss therapy. BMI is calculated as weight (kg)/height squared (m 2). To estimate BMI using pounds and inches, use: [weight (pounds)/height (inches) 2 ] x 703. Weight classifications by BMI, selected for use in this report, are shown in the table below. • Pregnant women who, on the basis of their pre-pregnant weight, would be classified as obese may encounter certain obstetrical risks. However, the inappropriateness of weight reduction during pregnancy is well recognized (Thomas, 1995). Hence, this guideline specifically excludes pregnant women. Source (adapted from): Preventing and Managing the Global Epidemic of Obesity. Report of the World Health Organization Consultation of Obesity. WHO, Geneva, June 1997.