A randomized trial examining differential meal replacement adherence in a weight
loss maintenance program after one-year follow-up
Rachel A. Annunziatoa,b,⁎, C. Alix Timkob, Canice E. Crerandb, Elizabeth R. Didieb, Dara L. Bellaceb,
Suzanne Phelanb, Irina Kerzhnermanb, Michael R. Loweb
aFordham University, Department of Psychology, 441 East Fordham Road, Bronx, NY 10458, USA
bDrexel University, Department of Psychology, 1505 Race Street, Mail Stop 626, Philadelphia, PA 19102, USA
a b s t r a c t a r t i c l ei n f o
Received 24 August 2008
Received in revised form 31 March 2009
Accepted 13 May 2009
Weight loss maintenance
The purpose of the present study was to examine the relationship between patterns of meal replacement
(MR) adherence and changes in outcomes during a behaviorally-oriented weight loss program. Data from the
present study are based on sixty female participants (age: 29–62 years, BMI: 27.99–37.50 kg/m2).
Participants were randomized into either a control or experimental condition, which tested the use of MRs
during weight loss maintenance. Outcome measures included body weight, depression, physical activity,
cognitive restraint, disinhibition, hunger, and binge eating collected at four assessment points. Within the
experimental condition, we further examined adherence to MRs and its relationship with the outcome
measures. We found evidence of differences at baseline on some measures (e.g., weight, physical activity and
depression) while on others (cognitive restraint, disinhibition, and hunger), differences that emerged over
the course of treatment. Further research is necessary to determine if there are measures associated with
successful MR use that can be detected at baseline and if MR adherence itself leads to changes in eating
© 2009 Elsevier Ltd. All rights reserved.
While current weight loss interventions typically result in
medically significant reductions in body mass, nearly all weight lost
is regained within five years (Foster, Wadden, Kendall, Stunkard, &
Vogt, 1996; Jeffery et al., 2000). It is crucial to develop methods for
improving weight loss maintenance. Strategies that appear promising
include participant chosen modest changes in diet and physical
activity (Lutes et al., 2008), ongoing contact with an interventionist
(Svetkeyet al., 2008), weight loss medication use during maintenance
(Franz et al., 2007), and weight loss medications combined with
dietary or behavioral changes (Turk et al., 2009).
Another area ripe for further exploration is the role of structured
eating for long-term weight loss maintenance. There is abundant
evidence that overweight individuals have superior weight loss
outcomes when the method used to control energy intake is highly
structured (Wing & Jeffery, 2001). Highly structured eating has also
shownpromise as a means of substantially improving the durabilityof
weight loss alone or when combinedwith snacks (Postonet al., 2005).
Recently, research has shown this to be the case among overweight
individuals with type 2 diabetes as well (Cheskin et al., 2008). Several
studies of longer-term maintenance suggest that replacing one meal
(or one meal and one snack) per day with meal/snack replacements is
associated with superior weight loss maintenance (Ashley, St. Jeor,
Perumean-Chaney, Schrage, & Bovee, 2001; Ashley, St. Jeor, Schrage et
al., 2001; Ditschuneit, Fletchner-Mors, Johnson, & Adler, 1999;
Rothacker, 2000) and program evaluation indicates that combining
MRs with weight loss medications may also result in weight gain
prevention (Haddock, Poston, Foreyt, DiBartolomeo, & Warner, 2008).
A meta-analysis aiming to determine the effectiveness of meal
replacements (MRs) for long-term weight maintenance showed that
compared to reduced calorie diets, MRs safely lead to greater short
and long-term weight loss (Heymsfield, van Mierlo, van der Knaap,
Heo, & Frier, 2003).
Meal replacements are palatable, require no preparation, are
portion-controlled, eliminate the need to choose and prepare foods,
and eliminate the food variety that can spur over-consumption of
energy. One of the most reliable methods of achieving long-term
maintenance of changes in eating behavior may be to provide
individuals with pre-portioned meal replacements that are satisfying,
convenient, and easy to incorporate into a wide variety of situations.
Overweight people may have difficulty sufficiently curtailing their
intake in an environment where inexpensive, highly palatable foods
are constantly available (Horgen & Brownell, 2002) and some degree
of structured eating may be necessary for successful long-term loss
weight maintenance. We aimed to examine whether long-term MR
Eating Behaviors 10 (2009) 176–183
⁎ Corresponding author. The Mount Sinai School of Medicine, Department of
Psychiatry, One Gustave L. Levy Place Box 1230, New York, NY 10029, USA. Tel.: +1
212 659 8776; fax: +1 212 849 2561.
E-mail addresses: firstname.lastname@example.org, email@example.com
(R.A. Annunziato), firstname.lastname@example.org (M.R. Lowe).
1471-0153/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.
Contents lists available at ScienceDirect
use not only improves weight loss maintenance but contributes to
improvements in mood, eating behaviors, and healthy lifestyle
engagement (e.g., physical activity).
1.1. Differential meal replacement adherence
The present study is based on data from an intervention designed
to improve weight maintenance that combined two promising
approaches, long-term usage of MRs and instruction in reduced
energy density eating (REDE) (Annunziato et al., 2001). We
hypothesized that long-term MR use combined with REDE would be
associated with improved weight loss maintenance, mood, and eating
behavior (e.g., greater cognition restraint and less disinhibited eating,
hunger, and binge eating) and increased physical activity. However,
due to an unexpected change in personnel, adherence to the REDE
component of the experimental condition was poor. Instead, we were
able to conduct exploratory analyses examining factors associated
with adherence to the meal replacement prescription and whether
differential MR adherence was related to changes in weight, mood,
physical activity and eating behavior. Previous research on MR use has
not examined whether differential adherence is associated with
differential outcomes across psychosocial and behavioral domains.
In order tounderstand betterhow MR adherence was differentially
related to outcome, we explored two possible relationships between
MR adherence level and performance on the outcome measures. The
first relationship investigated was that adherence to any behavioral
techniques may be a proxy for overall motivation level. A second
possibility for explaining the relation of adherence to outcome is that
there aresome consequences of consistentlyconsumingMRsthatonly
become evident after participants have consistently used them. The
purposeof thepresent studywastoexaminetherelationshipbetween
each of these patterns of MR adherence and changes in weight, mood,
physical activity and eating behavior during a behaviorally-oriented
weight loss program.
Participants were 60 obese women who were recruited through a
column about weight control in a Philadelphia newspaper. Participants
had a mean (±SD) age of 46.71±7.92 years, a baseline mean weight of
Approximately 16.9% of the participants finished high school only,
49.2% had at least some college education and 33.9% of participants
completed somepost-college graduate education.Most (83.3%) were
Caucasian, 15.0% were African-American and 1.7% were Native-
2.2.1. Initial assessment
Inventory (WALI; Wadden & Foster, 2001) a questionnaire designed to
assess weight and dieting history, as well as current psychosocial
functioning. Responses were then reviewed with the applicant by a
member of the investigative team. The purpose of this screening
interview was to determine whether prospective participants met the
following inclusion criteria: a body mass index of 27 or higher; no
history in the past 10 years of eating disorders (including Binge Eating
Disorder); no history of Bipolar Disorder or a major depressive
episode; not currently taking any psychotropic medications that
impact weight; no history of a substance-abuse or dependence
disorder; and absence of other major psychiatric disorders. In order
to evaluate medical stability to begin treatment, applicants were also
referred to their general practitioner to obtain permission to
participate. Medical exclusion criteria were any disease, condition,
or use of medication that could be expected to impact weight or near-
term life expectancy. The physician completed a physical examination
form provided by, and returned to, the investigators.
2.2.2. Study conditions
Those applicants who qualified for participation were randomly
assigned to one of two treatment conditions, both of which involved
23 weekly sessions. Each session was 90 min long. All participants
spent the first week of treatment preparing to adopt the controlled
diet. Both groups then lost weight during an 8-week modified fast
comprised of approximately 1100 kcal/day that consisted of four
servings of Optifast (at 160 kcal/packet), combined with an evening
meal of a frozen-food entree and two cups of salad and a fruit.
Throughout treatment, both groups received standard cognitive-
behavioral therapy (CBT) treatment in the form of the Optitrim
manual (Sandoz Nutrition, 1987). The manual covered major CBT
topics including environmental control of food cues, changing eating
behaviors, modification of problematic beliefs and thought patterns,
increasing exercise, nutritional guidelines for weight control, and
social support. Most of the group sessions from weeks 1 to 9 involved
suggestions for incorporating the controlled diet into participants'
lives, avoiding “off the diet” eating, and preventing any such eating
fromworsening.Bothgroupmembersand group leaders wereblind to
participants' treatment assignment until week 10, when the con-
trolled diet ended and the maintenance period began.
The maintenance phase of treatment was delivered between
weeks 10 and 24. During weeks 10–12, the control group gradually
replaced their Optifast intake with normal foods. The experimental
group reduced their Optifast intake by half, but was encouraged to
continue replacing one meal per day with an Optifast shake. They
were told that ongoing adherence to one meal replacement could save
several hundred caloriesperdayand therebyfacilitateongoing weight
maintenance. Participants in both conditions were given individua-
lized estimates of their caloric requirements for maintenance and
were encouraged to increase their caloric intake until they reached
their caloric goal for weight maintenance.
The remainder of the Optitrim manual was administered during
the 14-week weight maintenance period and was modified such that
it addressed weight maintenance rather than weight loss. Because of
the additional nutrition-related interventions, the Optitrim lessons,
and discussion about them, had to be condensed in the experimental
The experimental condition also received a multi-component
nutritional intervention (CBT-N) which utilized two additional
components. One was an enhanced nutritional modification program
that emphasized the adoption of a diet lower in energy density. The
goal of this approach was to introduce participants to the concept of
energy density (Rolls & Barnett, 2000) and its advantages as a method
of weight control (relative to other approaches they might be familiar
with — e.g. the Food Guide Pyramid or Exchange System). The intent
of this interventionwas topresent and problem solve avarietyof ways
to amend dietary intake to replace foods, ingredients, and cooking
methods that increased energy density with alternatives to reduce
energy density. The second dietary change emphasized was the
ongoing use of one meal replacement per day during the three-month
maintenance phase and throughout the follow-up period. Experi-
mental participants were provided with Optifast regularly during the
14-week maintenance period and for one year following treatment.
After treatment, participants were asked to attend follow-up
sessions three months and one-year later. Whenparticipants returned
to three-month follow-up, we observed large differences within the
experimental group in their reported ongoing use of MRs. Because of
this variability, we decided to conduct post-hoc analyses of MR
adherence and its relation to post-intervention weight maintenance.
We continued to collect data on MR adherence at one-year follow-up.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
Given the wide variability in adherence to MRs at both the three-
month and one-year follow-ups, we are focusing on how MR
adherence was related to outcomes at the two follow-up assessments.
2.3.1. Weight and Lifestyle Inventory (WALI)
The WALI (Wadden & Foster, 2001) is a semi-structured interview
that assesses readiness to begin weight control and its accompanying
2.3.2. Body weight
Participants' weight was measured on a digital scale without shoes
to the nearest 0.1 lb and converted to kilograms for analysis. Height
was determined (at baseline) to the nearest half-inch using a
2.3.3. The Beck Depression Inventory (BDI; Beck & Steer, 1987)
The BDI is a widely used 21-item scale assessing severity of
depressive symptoms. It has demonstrated good reliability and
validity (Beck, Steer, & Garbin, 1988). Based on meta-analysis, Beck
et al. (1988) report overall coefficient alpha values of .86 (psychiatric
patients) and .81 (nonpsychiatric samples). The BDI's ability to
discriminate between psychiatric and nonpsychiatric samples has
been established and significant correlations are reported between
the BDI and clinical measures of depression (e.g., the Hamilton
Psychiatric Rating Scale for Depression) (Beck et al., 1988).
2.3.4. The Three-Factor Eating Questionnaire (TFEQ; Stunkard &
The TFEQ is a 51-item self-report questionnaire that measures three
dimensions of eating including cognitive restraint, disinhibition, and
hunger. Adequate internal consistency for all three subscales, TFEQ-
Cognitive Restraint (α=.95), TFEQ-Disinhibition (α=.91), and TFEQ-
scores on the TFEQ-Disinhibition subscale are associatedwithincreased
Pudel,1994; Williamson, Barker, Bertman, & Gleaves,1995). The TFEQ-
Disinhibition subscale has also been found to prospectively predict
weight gain (McGuire, Wing, Klem, Lang, & Hill, 1999). Wing, Marcus,
Epstein, and Kupfer (1983) demonstrated construct validity in that the
TFEQ-Disinhibition and TFEQ-Hunger subscales correlated with binge
severity while the TFEQ-Cognitive Restraint subscale did not.
2.3.5. The Paffenbarger Physical Activity Recall (Paffenbarger, Wing, &
This 15-item interview-based measure is used for assessing
physical activity during the past month. By converting these activities
into metabolic equivalents based on body mass, total expenditure
from physical activity can be calculated. The validity of this method
has been demonstrated in that cardiovascular disease mortality was
directly associated with physical activity level as assessed by this
method (Paffenbarger et al., 1978). Paffenbarger and colleagues have
reported good test–retest reliability (e.g., Pearson correlations of .75
for moderate and .83 for vigorous activities) for self-report of recent
physical activity (Sallis et al., 1985).
2.3.6. Eating Habits Checklist (Gormally, Black, Daston, & Rardin, 1982)
The Eating Habits Checklist is a 16-item self-report instrument that
assesses respondents' habitual eating behaviors as well as possible
problematic areas controlling eating. This measure has been found to
have acceptable reliability and validity (Gormally et al., 1982).
Gormally et al. (1982) report testing internal consistency by
comparing total scores with scores for each item, with overall
significant results. Factor analysis was used to establish construct
validity (Gormally et al., 1982).
2.3.7. MR adherence
At each of the two follow-up appointments, participants in the
experimental conditionwere askedtoestimatehow manydays during
the follow-up period had they used MRs. At three-month follow-up,
participants were asked specifically how many days they used MRs
during the past 90 days. At one-year follow-up, participants were
asked how many days they used MRs during the past 270 days (e.g.,
the equivalent of nine months since the last follow-up). We then
totaled the number of days for the three-month and nine-month
period. At each follow-up we asked experimental participants to
indicate on a 1–5 Likert Scale, “How helpful has Optifast been to
weight maintenance?” (1=“Not at all helpful” to “5”=“Very help-
ful”; there was also an option “I have not taken it frequently enough to
make a judgment”). Participants were also asked “How much harder
has weight maintenance been than weight loss” with options of
1="Much more difficult" to 5=“Much easier”.
2.4. Statistical analyses
Analyses used the SPSS©10.0 statistical package. Statistical tests
are two-tailed whenever applicable. A p value of 0.05 or less was
chosen as the level of statistical significance. Chi-square analyses were
used to examine differences between conditions on demographic,
dichotomous variables. Using One-way ANOVAs, demographic vari-
ables and baseline scores on the outcome measures were compared
between the two conditions in order to check for any differences.
For our original study aim, Repeated Measures Analyses of
Covariance (ANCOVAs) were used to compare outcomes during the
maintenance phase of the program between the experimental and
the diet phase. A 2×4 analysis was used to examine main effects and
interaction effects for weight change and change on measures of mood,
physical activity and eating behavior over the four data points.
A median split was then done on the number of days participants
reported using MRs from the end of treatment to one-year follow-up.
Out of a possible 360 days of MR use, the median split was 90 days.
Participants who reported using MRs more than 90 days were
considered high MR adherers (the lowest number of days in this
group was 130 days). For these analyses, a 3×4 design, reflecting two
adherence levels and the control group at the four assessment points,
was used to examine main effects and interaction effects for the same
outcome measures. For all effects involving the within-subjects factor,
the F statistic test was based on the Huynh–Feldt adjustment for
degrees of freedom. Similarly, Tukey's adjustments were used for all
tests involving multiple comparisons.
3.1. Preliminary analyses (all participants)
Chi-square analyses showed that there were no baseline differ-
ences between the two conditions in ethnic composition or education
level. One-way ANOVAs similarly showed that there were no
Time point in the study when drop outs occurred by experimental condition (N=60).
Study phase Experimental conditionControl condition
aOne participant completed all exit measures but did not attend enough sessions of
maintenance to be considered “treated.”
bOne participant became pregnant prior to data point.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
age or BMI. We examined whether there were differences in group
composition based on the intake measures. One-way ANOVAs between
the experimental and control groups were conducted on all measures
administered at intake. There were no significant differences between
the two groups. Table 1 shows when in the study dropouts occurred by
condition. A Chi-square analysis showed that there were no differences
between the conditions in either the proportion of participants who
dropped before one-year follow-up or in the time point in the study at
which dropouts occurred. At one-year follow-up, 67% of the experi-
mental group and 75% of the control group remained in the study.
3.2. Main outcome analyses (all participants)
The following statistical analyses are based on data from the 42
Repeated measures ANOVAs were conducted on measures of depres-
sion, physical activity, cognitive restraint, disinhibition, hunger, and
binge eating, and weight by experimental condition for all four data
points. There were no significant interactions detected between change
in scores on any measures over time between the experimental and
control groups. We conducted lastobservation carried forward analyses
on all outcome measures for the 60 participants who began treatment.
These analyses did not change the initial pattern of findings.
3.2.1. Weight change
For weight change, change over time by condition was also
examined controlling for weight lost during the diet phase. This
interaction was not significant. Participants in the experimental
condition lost 11.3% of their body weight during the program
maintaining 5.4% of this improvement at one-year follow-up. In the
control condition, participants lost 10.0% of their body weight during
the program maintaining 4.6% of this loss at one-year.
3.3. Post hoc analyses (experimental condition only)
When participants were divided into three groups, there were 21
participants in the control condition, 11 in the low adherence group
and 10 participants the high adherence group. High adherers
reported consuming MRs 80% of the 90 days during three-month
follow-up, versus 55% for low adherers. High adherers further
reported consuming MRs 74% of the 270 days between three-
month and one-year follow-up, versus 9% for low adherers. The
correlation between days consuming MRs during three-month
follow-up and one-year follow-up was significant, r=.43, p=.05.
Not surprisingly, high adherers indicated that ongoing use of MRs
was significantly more helpful than low adherers did, t(18)=−3.99,
Table 2 depicts means and standard deviations between the three
groups on the outcome measures. Figs.1–7 depict change scores on all
the outcome measures when divided across these three groups. We
found evidence of both the trait and state patterns described earlier.
We refer to“trait markers” as those variables that reflect differences at
baseline that persist over time and “state markers” as those variables
that reflect differential outcome during and after treatment began (but
not at baseline).
3.3.1. Trait markers
Weight change, physical activity and depression level exhibited
trait-like effects. Significant main effects over time were detected on
Mean scores and standard deviations on all psychosocial outcome measures at the four data points (N=42).
IntakeExitThree-month follow-upOne-year follow-up
HA LAC HALAC HALAC HA LAC
Weight (BMI) 31.58
Note. HA = high adherer; LA = low adherer; C = control; BDI = Beck Depression Inventory; PA = physical activity; TFEQ = Three-Factor Eating Questionnaire—Cognitive Restraint,
Disinhibition, and Hunger subscales.
Fig. 1. Change in weight (BMI) over time.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
these measures using all four data points. However, there were no
significant interactions between change over time and adherence
status. Figs. 1–3 depict this pattern. There appear to be differences
between high and low adherers at baseline that persisted throughout
between the two adherence groups on these measures. The difference
between high and low adherers on weight, t(20)=1.72, p=.10, and
depression, t(20)=1.96, p=.06, was marginally significant. For
physical activity the differencewas significant, t(20)=−2.44, p=.02.
3.3.2. State markers
There were significant main effects and interactions detected for
adherence level over time on cognitive restraint, disinhibition, and
hunger. Figs. 4–6 illustrate these patterns. Significant interactions were
detected for cognitive restraint, F(3,35)=3.66, p=.00, ηp
hibition, F(3,35)=2.67, p=.02, partial η2=.14, and hunger, F(3,35)=
2.25, p=.04, ηp
repeated measures ANOVAs were conducted from post-treatment
onward just for high versus low adherers. For cognitive restraint, this
interaction was again significant, F(2,18)=6.19, p=.01, ηp
adherers had higher cognitive restraint scores from this point forward.
Change in hunger score from post through follow-up also showed a
For disinhibition, the interaction was not significant.
2=.15. In order to further test for state effects, 2×3
3.3.3. Relapse prevention
Finally, we found two instances where a clear trend toward
behavioral relapse during maintenance was reversed apparently
because of continued (or increased) adherence to MRs. This occurred
for disinhibition and binge eating (as shown in Figs. 5 and 7). In order
todemonstrate this, 2×2 repeated measures ANOVAs wereconducted
from three-month to one-year follow-up just for high versus low
adherers. On the disinhibition scale, therewas a significant interaction
between change in score and adherence level, F(1,19)=5.36, p=.03,
eating measure, F(1,17)=4.00, p=.06. During this time period, there
was a marginally significant difference in weight change between
these two groups, F(1,19)=4.05, p=.06, with high adherers regaining
2=.22. This interaction was marginally significant for the binge
Previous studies have found that using MRs to facilitate long-term
weight loss maintenance may be quite promising (Ashley, St. Jeor,
Perumean-Chaney et al., 2001; Ashley, St. Jeor, Schrage et al., 2001;
Ditschuneit et al., 1999; Heymsfield et al., 2003). We aimed to study
whether MRs use in conjunction with REDE promotes successful
weight gain prevention as well as psychosocial outcomes such as
improved mood, eating behaviors and increased physical activity.
Though our REDE intervention was not adequately implemented, we
were able to examine whether differential MR use during weight
maintenance is associated with changes in weight and psychosocial
variables. The present results are based on exploratory analyses
conducted on a small sample. Therefore, the results must be
interpreted cautiously. Future research will be needed to evaluate
the replicability of the observed results. We believe though that the
combination of outcomes we obtained allows us to potentially learn
more about the significance of MRs for weight control.
The first relationship investigated was that adherence to any
behavioral techniques may be a proxy for overall motivation level.
That is, MR adherence could reflect characteristics of participants that
might be referred to as “trait-like” (i.e., they are evident from baseline
and presumably reflected general level of motivation to engage in
various weight control behaviors). Adherence to other behavioral
Fig. 2. Change in physical activity level over time.
Fig. 3. Change in depression score over time.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
Fig. 4. Change in cognitive restraint score over time.
Fig. 5. Change in disinhibition score over time.
Fig. 6. Change in hunger level over time.
Fig. 7. Change in binge eating level over time.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
techniques such as self-monitoring of food intake may also reflect
For example, participants' consistency in self-monitoring during
treatment has been shown to be one of the most reliable predictors
of favorable weight loss outcome (Butryn, Phelan, Hill, & Wing, 2008).
A second possibility for explaining the relation of adherence to
outcome is that there are some consequences of consistently
consuming MRs that only become evident after participants have
consistently used them. We refer to these effects as “state-like” (i.e.,
evident in outcomes that were not different between groups at
baseline but that emerged over the course of maintenance).
We found marked individual differences in MR use during weight
loss maintenance, though greateradherence toMRs at the first follow-
period. However, participants who reported high adherence did not
evidence better weight maintenance or eating behaviors relative to a
control group. The absence of benefit for weight control from ongoing
MR use is inconsistent with previous findings for MRs (Heymsfield
et al., 2003). We believe this could be due to major differences in how
the MR prescription was followed but due to limited power we could
not detect differences in weight by adherence level.
In studying the relationship between MR use and outcome, we
found evidence of differences at baseline on some measures; on
others, differences that emerged over the course of treatment.
Although no casual inferences can be drawn, this pattern of results
can be taken to suggest that MR adherence status has implications for
dispositional characteristics (e.g., “trait” markers evident pre-treat-
ment) and differences that only seem activated during and after
treatment (e.g., “state” markers). The potential implications of these
differences are described below.
We first detected possible trait markers evident at study onset. A
tentative hypothesis that can be drawn from our results is that higher
BMI and depression levels as well as lower levels of physical activity
may be negative prognostic factors for long-term MR use. Perhaps it is
now worth studying whether there are patterns that emerge when
tested prospectively. Packianathan, Sheikh, Boniface, & Finer (2005)
examined physiological predictors of adherence in programs using
MRs. None of the variables tested were associated with adherence in
the long-term. Examining whether there are psychosocial variables at
baseline that are associated with long-term adherence might have
implications for treatment matching. Schwartz and Brownell (1995)
have surveyed obesity experts to identify possible indications and
contraindications for 11 approaches to weight loss including MRs.
Moderate tomorbid obesity was oneof the contraindications listedfor
commercial programs that provide food. Presence of a mood disorder
was listed as a possible contraindication for very-low calorie diets.
Further investigation of MR use could continue to elucidate possible
situations where they are more or less effective.
State markers were also explored in our study. Differences that
emerged during treatment (e.g., increased cognitive restraint coupled
with decreased disinhibition and hunger levels) could have stemmed
from adherence per se. For example, one possibility is that ongoing MR
intake. Or, perhaps the caloric savings afforded by consistent MR use
alone leads to improvements in eating control. Eating in this controlled
to regularly consume structured meals during maintenance, it appears
the benefit rapidlysubsides. In two instances, it appears that relapses in
specific eating behaviors (disinhibited eating and binge eating) were
reversed after the three-month follow-up for adherent participants.
order to provide clinical recommendations for their use. For example,
did participants start dieting again after three-month follow-up and
then resume their assigned maintenance strategy again? Or did they
slow down their use or run out of MRs well before three-month follow-
up and then started using them regularly afterwards?
In addition to the small sample size available for these exploratory
analyses, our study had other limitations. Our measure of adherence
was based on participants' recall of their MR use during the follow-up
period. We did ask participants prior to their follow-up visits to keep
track of how many days they used MRs as suggested, but certainly
recall could have been inaccurate. However, a relative strength of our
study was our high retention rates at both follow-ups. At one-year
follow-up, 75% of the control and 66% of the experimental condition
returned for an assessment. Other studies have reported rates of
approximately 50% at the end of treatment (Haddock et al., 2008;
Poston et al., 2005) and 25–50% at one-year follow-up (Haddock et al.,
2008). We took many steps to minimize attrition including assuring
continuity of staff (e.g., the same study coordinator contacted
participants throughout the duration of the study and attended all
follow-up sessions), sending greetings via mail to participants during
follow-up, and offering flexible scheduling for appointments includ-
ing early morning prior to the workday.
In conclusion, our findings suggest that MR use for weight
maintenance widely varies. Consistent MR use as a weight main-
tenance tool may promote healthier eating behavior but in our study
this did not translate into improved prevention of weight regain.
Unlike previous research, the long-term benefit of MR use was not
shown since even those who adhered to MRs best did not manifest
better weight maintenance than the control group. Identifying
psychosocial predictors of poor MR adherence is an important next
step in order to better inform clinical recommendations. Future
research may be helpful towards determining whether MRs are a
useful tool for relapse prevention among dieters striving for weight
loss maintenance and whether long-term adherence is associated
with robust changes in eating behaviors.
Role of funding source
Funding for this study was supported in part by a grant from Novartis
Pharmaceuticals Corporation to Dr. Lowe. The sponsor had no involvement in the
study design, data collection and analysis, interpretation of results, manuscript
preparation and submission.
All authors contributed to the study described. Dr. Annunziato and Dr. Lowe
conceived the idea. All of the authors contributed tothe study design and implementing
the study protocol. Dr. Annunziato analyzed the data and wrote the manuscript. Drs.
Annunziato, Timko, Crerand, Didie and Lowe contributed to the manuscript develop-
ment. All authors contributed to, and have appoval the final manuscript.
Conflict of interest
All of the authors declare no actual or potential conflicts of interest.
We would like to thank Meghan L. Butryn, Rachel Calagero and
Christopher N. Ochner for their technical assistance. We would like to
thank the reviewers of this manuscript for their thoughtful insights,
which resulted in substantial improvements.
Annunziato, R. A., Crerand, C. E., Didie, E. R., Kerzhnerman, I., Lucks, D. L., Phelan, S., et al.
(2001, November). Effects of standard CBT versus CBT plus an enhanced nutritional
intervention for weight loss maintenance. Poster presented at the Thirty-fifth Annual
Meeting of the Association for the Advancement of Behavior Therapy, Philadelphia, PA.
Ashley, J. M., St. Jeor, S. T., Perumean-Chaney, S., Schrage, J., & Bovee, V. (2001). Meal
replacements in weight intervention. Obesity Research, 9(S4), 312−320.
Ashley, J. M., St. Jeor, S. T., Schrage, J. P., Perumean-Chaney, S. E., Gilbertson, M. C., McCall,
N. L., et al. (2001). Weight control in the physician's office. Archives of Internal
Medicine, 161, 1599−1604.
Beck, A., & Steer, R. (1987). Manual for the Beck Depression Inventory. San Antonio:
Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77−100.
A key component of successful weight loss maintenance. Obesity, 15(12), 3091−3096.
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183
Cheskin, L. J., Mitchell, A. M., Jhaveri, A. H., Mitola, L. M., Davis, R. A., et al. (2008).
Efficacy of meal placements versus a standard food-based diet for weight loss in
type 2 diabetes. The Diabetes Educator, 34, 118−127.
Ditschuneit, H. H., Fletchner-Mors, M., Johnson, T. D., & Adler, G. (1999). Metabolic and
weight-loss effects of a long-term dietary intervention in obese patients. American
Journal of Clinical Nutrition, 69, 198−204.
Foster, G. D., Wadden,T. A., Kendall, P. C., Stunkard, A. J., & Vogt, R. A. (1996). Psychological
effects of weight loss and regain: A prospective evaluation. Journal of Consulting and
Psychology, 64, 587−592.
Franz, M. J., VanWormer, J. J., Crain, L., Boucher, J. L., Histon, T., Caplan, W., et al. (2007).
Weight-loss outcomes: A systematic review and meta-analysis of weight-loss clinical
Gormally, J., Black, S., Daston, S., & Rardin, D. (1982). The assessment of binge eating
severity among obese persons. Addictive Behaviors, 7(1), 47−55.
Haddock, C. K., Poston, W. S. C., Foreyt, J. P., DiBartolomeo, J. J., & Warner, P. O. (2008).
Effectiveness of Medifast supplements combined with obesity pharmacotherapy: A
clinical program evaluation. Eating and Weight Disorders, 13(2), 95−101.
Heymsfield, S. B., van Mierlo,C. A.J., vander Knaap, H. C. M., Heo, M.,& Frier, H. I.(2003).
Weight management using a meal replacement strategy: Meta and pooling analysis
from six studies. International Journal of Obesity, 27, 537−549.
Horgen, K. B., & Brownell, K. D. (2002). Comparison of price change and health message
interventions in promoting healthy food choices. Health Psychology, 21(5),
Jeffery, R. W., Epstein, L. H., Wilson, T. G., Drewnowski, A., Stunkard, A. J., & Wing, R. R.
(2000). Long-term maintenance of weight loss: Current status. Health Psychology,
maintenance: 3-month evidence from the ASPIRE randomized trial. Annals of
Behavioral Medicine, 35(3), 351−357.
McGuire, M. T., Wing, R. R., Klem, M. L., Lang, W., & Hill, J. O. (1999). What predicts
weight regain among a group of successful weight losers? Journal of Consulting &
Clinical Psychology, 67, 177−185.
Packianathan, I., Sheikh, M., Boniface, D., & Finer, N. (2005). Predictors of programme
adherence and weight loss in women in an obesity programme using meal
replacements. Diabetes, Obesity, and Metabolism, 7, 439−447.
attack risk in college alumni. American Journal of Epidemiology, 108(3),161−175.
Poston,W.S. C., Haddock, C. K.,Pinkston,M. M.,Pace,P., Karakoc,N. D.,Reeves,R.S., et al.
(2005). Weight loss with meal replacement and meal replacement plus snacks: A
randomized trial. International Journal of Obesity, 29,1107−1114.
Rolls, B. J., & Barnett, R. (2000). Volumetrics: Feel full on fewer calories. New York:
Rothacker, D. Q. (2000). Five-year self managementof weight using meal replacements:
Comparison with matched controls in rural Wisconsin. Nutrition, 16, 344−348.
Sallis, J. F., Haskell, W. L., Wood, P. D., Fortmann, S. P., Rogers, T., Blair, S. N., et al. (1985).
Physical activity assessment methodology in the five-city project. American Journal
of Epidemiology, 121, 91−106.
Sandoz Nutrition Company (1987). Optitrim manual. Minneapolis: Sandoz Nutrition.
Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure
dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29,
Svetkey, L. P., Stevens, V. J., Brantley, P. J., Appel, L. J., Hollis, J. F., Loria, C. M., et al. (2008).
Comparison of strategies for sustaining weight loss: The weight loss maintenance
randomized controlled trial. Journal of the American Medical Association, 299(10),
Turk, M. W., Yang, K., Hravnak, M., Sereika, S. M., Ewing, L. J., & Burke, L. E. (2009).
Randomized clinical trials of weight loss maintenance: A review. The Journal of
Cardiovascular Nursing, 24(1), 58−80.
Wadden, T. A., & Foster, G. D. (2001). Weight and lifestyle inventory. Philadelphia, PA:
University of Pennsylvania.
Westenhoefer, J., Broeckmann, P., Munch, A. -K., & Pudel, V. (1994). Cognitive control of
eating behaviour and the disinhibition effect. Appetite, 23, 27−41.
Williamson, D. A., Barker, S. E., Bertman, L. J., & Gleaves, D. H. (1995). Body image, body
dysphoria, and dietary restraint: Factor structure in nonclinical subjects. Behaviour
Research and Therapy, 33(1), 85−93.
Wing, R. R., & Jeffery, R. W. (2001). Food provision as a strategy to promote weight loss.
Obesity Research, 9(S4), 271−275.
Wing, R. R., Marcus, M. D., Epstein, L. H., & Kupfer, D. (1983). Mood and weight loss in a
behavioral treatment program. Journal of Clinical and Consulting Psychology, 51,
R.A. Annunziato et al. / Eating Behaviors 10 (2009) 176–183