ArticlePDF Available


Two 3-month longitudinal studies examined weight loss following a 1-month behavioral intervention (FIT-DSD) focusing on increasing participants’ behavioral flexibility and breaking daily habits. The goal was to break the distal habits hypothesized as playing a role in unhealthy dietary and activity behaviors. The FIT-DSD intervention required participants to do something different each day and to engage in novel weekly activities to expand their behavioral repertoire. These activities were not food- or exercise-related. In Study 1, the FIT-DSD program was compared with a control condition where participants engaged in daily tasks not expected to influence behavioral flexibility. Study 2 used an active or quasicontrol group in which half the participants were also on food diets. Measures in both studies were taken pre-, post-, and post-postintervention. In Study 1, FIT-DSD participants showed greater weight loss that continued post-postintervention. In Study 2, all participants on the FIT-DSD program lost weight, weight loss continued post-postintervention, and participants who were also dieting lost no additional weight. A dose relationship was observed between increases in behavioral flexibility scores and weight loss, and this relationship was mediated by calorie intake. Corresponding reductions in BMI were also present. Increasing behavioral flexibility may be an effective approach for tackling obesity and also provides affective and potential life-skill benefits. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
B.(C)Fletcher et al.: Enhancing Behavioral Flexibility for Weight LossSwissJ.Psychol. 70 (1) © 2011 by Verlag Hans Huber, HogrefeAG,Bern
Original Communication
FIT – Do Something Different
A New Behavioral Program for Sustained Weight Loss
Ben (C) Fletcher1,JillHanson
2, Nadine Page1, and Karen Pine1
1University of Hertfordshire, Hertfordshire, UK, 2University of Derby, Derby, UK
Swiss Journal of Psychology, 70 (1), 2011, 25–34
DOI 10.1024/1421-0185/a000035
Abstract. Two 3-month longitudinal studies examined weight loss following a 1-month behavioral intervention (FIT-DSD) focusing on
increasing participants’ behavioral flexibility and breaking daily habits. The goal was to break the distal habits hypothesized as playing
a role in unhealthy dietary and activity behaviors. The FIT-DSD intervention required participants to do something different each day
and to engage in novel weekly activities to expand their behavioral repertoire. These activities were not food- or exercise-related. In
Study 1, the FIT-DSD program was compared with a control condition where participants engaged in daily tasks not expected to influence
behavioral flexibility. Study 2 used an active or quasicontrol group in which half the participants were also on food diets. Measures in
both studies were taken pre-, post-, and post-postintervention. In Study 1, FIT-DSD participants showed greater weight loss that continued
post-postintervention. In Study 2, all participants on the FIT-DSD program lost weight, weight loss continued post-postintervention, and
participants who were also dieting lost no additional weight. A dose relationship was observed between increases in behavioral flexibility
scores and weight loss, and this relationship was mediated by calorie intake. Corresponding reductions in BMI were also present. In-
creasing behavioral flexibility may be an effective approach for tackling obesity and also provides affective and potential life-skill
Keywords: weight loss, behavioral change, FIT science
Even as more and more diets become available, obesity
rates continue to rise, which has led some to conclude that
“there is little support for the notion that diets lead to lasting
weight loss” (Mann et al., 2007, p. 220). Repeated dieting
has also been shown to have a negative impact on immu-
nocompetence (Shade et al., 2004) and mortality (Soren-
sen, Rissanen, Korkeila, & Kaprio, 2005) and to produce
cognitive impairments (D’Anci, Watts, Kanarek, & Taylor,
Many researchers have shifted the focus from dieting to
emphasizing lifestyle or behavioral modification as a viable,
more effective, and safer approach to weight loss (Dansinger
& Schaefer, 2006; Wardle, 2005). The challenge, however,
has been to identify which aspects of behavior to target since
current behavioral programs seem to produce modest results
at best, even when well conceived and well resourced. For
example, the large-scale TAAG study targeted schools and
community agencies and 8,727 participating adolescent girls
and was informed by the best available models and previous
practice and social marketing over a 2-year intervention. The
intervention produced no reduction in BMI or associated
markers, with only a 1.6 minute/day difference in activity
levels on one of the cohort-control comparisons (in which it
seemed the control group had an unusually low score com-
pared to the other controls) (Webber et al., 2008).
To be effective in weight reduction, behavioral ap-
proaches must tackle the causal characteristics that are
amenable to change. Willpower or intention is a likely can-
didate, yet people have limited willpower, and this is
known to weaken the more it is called upon (Baumeister,
Heatherton, & Tice, 1994), probably because it relies on
glucose as a limited energy source (Gailliot et al., 2007).
Equally, the link between weight control intentions and
food choice is very weak (Wardle, Griffith, Johnson, &
Rapoport, 2000), and good intentions are not in themselves
sufficient to bring about behavioral change (Webb & Shee-
ran, 2006). Behavioral programs that rely too heavily on
participants’ willpower and active intention are therefore
likely to fail in the longer term. Diet and exercise regimes
are maintained for a short time at best, but people simply
do not have the willpower to make every day a diet day.
For sustained success, it is argued, the answer may lie in a
behavioral approach that avoids overreliance on willpower
(Dansinger & Schaefer, 2006). Recent research highlight-
ing the role of genetic factors in weight regulation and obe-
sity (e.g., Blakemore & Froguel, 2008; Hofker & Wijmen-
ga, 2009) and in food intake (Wardle, Llewellyn, Sander-
son, & Plomin, 2009) further amplify the need to target core
psychological mechanisms to deal with the growing obesi-
ty problem.
A key finding from our previous research is that over-
weight people have low behavioral flexibility (Fletcher,
Hanson, & Jones, 2004; Hanson, 2008). In other words,
their range of behaviors across situations is limited. Behav-
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
ioral flexibility can be reliably measured using FIT tools
(Fletcher & Stead, 2000) and has been found to correlate
negatively with BMI in a sample of over 1,000 people. In
essence, heavier people are more habitual and constrained
in the way they behave. A recent study of obese schoolboys
also reported an association between BMI and clinical tests
of cognitive flexibility (Cserjési, Molnár, Luminet, & Lé-
nárt, 2007): Boys with higher BMI were more likely to
persist with ineffective strategies when solving problems.
If behavioral and cognitive flexibility is negatively re-
lated to weight, then we propose a novel hypothesis: in-
creasing it should lead to a reduction in weight. An inter-
vention designed to increase flexibility is the FIT-Do
Something Different (FIT-DSD) program. By doing some-
thing different on a daily basis individuals are challenged
to break existing habits and increase their behavioral flex-
ibility. This may help to break distal habits that other be-
havioral approachesdo not target,yet thatnevertheless play
a key role in maintaining unhealthy behaviors. It is known,
for example, that changing the context of behavior can help
break habits (Wood, Tam, & Guerrero Witt, 2005). Recent
neuroscience research has also shown that doing something
new over a period of several weeks can result in marked
gray matter growth and white matter architecture changes
in the adult brain (Draganski et al., 2004; Scholz, Klein,
Behrens, & Johansen-Berg, 2009). Doing something differ-
ent may have the potential to lay down new learning path-
ways as well as disrupting negative habitual ones.
This approach also avoids the need for sustained will-
power and may provide the impetusfor positive change by
altering the daily habits that trigger overeating and the
broader changes needed for positive health engagement
generally. There is some evidence that positive, habit-based
approaches can lead to weight loss in the longer term if they
target the proximal habits related to eating and exercise
(Lally, Chipperfield, & Wardle, 2008), although no re-
search has so far looked at the effects of tackling more dis-
tal habits. Therefore, this research evaluates the efficacy of
the FIT-DSD program as a tool for purposeful sustained
weight loss. Study 1 is a pilot study comparing twobehav-
ioral interventions, the FIT-DSD program and a narrative
control group whose daily task did not target behavioral
habits. Based on the hypothesized relationship between be-
havioral flexibility and BMI, participants following the
FIT-DSD program were expected to lose weight during the
intervention period and to continue to lose weight post-
postintervention. No specific predictions were made re-
garding the weight losses of narrative control group partic-
ipants, although all wanted to lose weight. The purpose of
this control was to establish whether the active ingredient
of the FIT-DSD intervention was the hypothesized expan-
sion in behavioral flexibility. This would be supported by
a weight loss advantage in the FIT-DSD group over the
narrative control group. If both groups show weight loss to
the same degree (and both were wanting to lose weight),
this would be more easily attributed to other, more general
demand characteristics present in both conditions. Behav-
ioral flexibility is important in many life contexts, and the
studies may also help to examine whether this novel ap-
proach could have broader applications for well-being gen-
Study 1
This study was a 3-month longitudinal design to compare
the weight loss of two groups following behavioral inter-
vention programs. The first group followed the FIT-DSD
program; the second group was a narrative control group
(NCG) (Pennebaker & Seagal, 1999). Both groups fol-
lowed the programs for 1 month and were followed up at
1 month and 2 months post-postintervention. Weight was
measured at pre- and postintervention, and follow-up
weights were obtained at 1 and 2 months post-postinterven-
Participants in the FIT-DSD group were asked to change
behaviors and do something different daily. Participants in
the NCG recorded three positive aspects of their day, each
day. They were asked to reflect upon their thoughts and
feelings in relation to these positive aspects and to write
their entries into a daily diary. Both groups also completed
daily food and exercise diaries.
Both groups of participants were drawn from large organi-
zations. They responded to a recruitment call in an e-health
bulletin that was distributed to all employees or via a
health-awareness-raising exhibition run by one of the or-
ganizations. All participants wanted to lose weight, and
none were referred to the study for medical reasons because
of their weight. The 15 participants in the FIT-DSD group
were predominantly female (87%). Mean age was 45.4
years (SD = 9.14). Preintervention mean BMI was 31.50
(SD = 6.53); 8 (53%) participants were obese (BMI > 30)
and 5 (33%) were overweight (BMI = 25–30). All except
one participant had dieted previously, and the mean age
when dieting started was 29 years (SD = 8.90); 6 (43%) had
started dieting during their teenage years, 4 (28%) had tried
5 or more diets previously, 3 (21%) had tried three or four
diets, and 7 (50%) had tried one or two diets. All partici-
pants completed the program.
There were 16 participants in the NCG condition; 6 left
the program before completion, but they did not differ sig-
nificantly from the completers on any of the measures tak-
en. There were 70% females, aged 29–58 years (M= 43.00,
SD = 8.12). Participants’ preintervention mean BMI was
27.71 (SD = 3.03): 2 (20%) participants were obese and 6
26 B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
(60%) were overweight. All had tried dieting in the past,
and mean age when dieting started was 35 years (SD =
11.82). One participant had started dieting as a teenager,
and two started during their 20s; 20% had tried three diets
previously and 4 had tried one or two previous diets.
The FIT-DSD group and the NCG did not differ statis-
tically in terms of any of the preintervention measures in-
cluding weight and behavioral flexibility, t(23) = 1.58, p=
.13, and t(23) = 1.32, p= .20, two-tailed, respectively.
Materials and Apparatus
Participants in both groups completed a preintervention
questionnaire that gathered demographic and lifestyle char-
acteristics, including diet history information. Food and ex-
ercise diaries were used by both groups to record daily food
consumption and exercise taken. In addition, both groups
were issued diaries for recording their task completion; par-
ticipants in the NCG also used these for reflection. The FIT
Profiler (Fletcher & Stead, 1999, 2000) was used to mea-
sure aspects of participants’ thinking and behavior tenden-
cies, including behavioral flexibility. The measurement of
behavioral flexibility is derived from 15 items, each mea-
sured on an 11-point Likert scale bounded by differentbe-
haviors (e.g., very reactive very proactive). Participants
indicated their behavioral flexibility on each item by select-
ing either a single response or span of responses. The in-
ternal reliability of the scale is very good (α= .91) and
parallel analysis shows a single factor structure accounting
for 46% of the variance. The FIT Profiler also includes
measures of depression and free-floating anxiety which
both have good internal reliability (α= .78 and .80) and
construct validity with a range of instruments.
The FIT-DSD Program
The FIT-DSD program encourages people to try simple
new or different behaviors or activities every day. It focuses
on changing habits in relation to interactions with other
people and other everyday behaviors. There are daily tasks
that involve trying new behaviors in relation to different
people and situations each day as well as weekly tasks that
are selected from a list of 50 different activities. Partici-
pants were encouraged to select tasks that were new to
them from many options including buying a different news-
paper, contacting an out-of-touch friend, attending a local
government meeting, going to a live music event or trying
a new sport.
The FIT-DSD Program
Week 1: Preparation focused on preparation for change
and participants completed one new task everyday as well
as an additional two tasks throughout the week. Week 2:
Adding to repertoire focused on expanding behaviors and
involved completing a new behavior every day. The behav-
ioral dimensions included were unassertive–assertive,
calm/relaxed–energetic/driven, definite–flexible, sponta-
neous–systematic, introverted–extroverted, convention-
al–unconventional, and individually centered–group cen-
tered. Week 3: Changing habits focused on changing habits
toward people and more general activities, depending upon
the areas that were more habitual. Week 4: Transformation.
Each day involved completing two tasks. One task in-
volved behaving differently toward another individual and
the other involved behaving differently in a situation. Par-
ticipants reflected on any benefits they noticed as a result
of their behavioral changes.
The Narrative Control Program
Participants in the NCG used a narrative method (Penne-
baker & Seagal, 1999) to record up to three positive aspects
of their day, each day, for the 1-month intervention. This
method encourages people to think about their actions and
experiences every day.At the end of each day, participants
reflected upon the experiences, interactions and happen-
ings that occurred during the day and recorded up to three
things they thought were the most positive aspects of their
day. Participants wereencouraged to think about why they
thought these things were positive and explore their
thoughts and feelings in relation to these. They were not
advised about what they should reflect upon and had full
discretion concerning what they recorded in their diaries.
Seca Omega 873 digital weighing scales and a height chart
were used.
Both groups attended separate 2-h induction sessions to
learn about the study. Informed consent was obtained fol-
lowing the briefing, and participants completed the pretrial
questionnaires. The researcher took weight and height
measurements. Study materials were then issued to partic-
ipants; these included daily diaries for the 1-month inter-
vention period – either FIT-DSD task booklets or narrative
thought diaries. One month later, participants in both
groups separately attended a postintervention group fol-
low-up, where they returned their completed diaries or task
booklets and were weighed by the researcher. Follow-up
weight measurements were collected from participants in-
dividually one and 2 months later and participants were
debriefed at the final session.
Statistical Analyses
The changes in weight from pre- to postintervention, and
from postintervention to 1 month post-postintervention are
B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss 27
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
analyzed separately for the FIT-DSD group and the NCG
using repeated measures t-tests. The magnitude of weight
loss for the FIT-DSD group and the NCG are compared
using independent samples t-tests. All analyses are con-
ducted two-tailed, with p= .05. The results are based on
the complete sample for each stage of the study.
Group Weight Changes Pre- to Postintervention
The FIT-DSD group lost a mean of 2.6 kg (SD = 1.98) dur-
ing the month-long intervention. This was a significant re-
duction in weight, t(14) = 5.10, p< .01. The largest weight
loss for a FIT-DSD participant was 7.4 kg, and the only
participant not to lose weight had the lowest BMI preinter-
In contrast, the NCG lost 0.88 kg (SD = 1.49) over the
month, a small but nonsignificant reduction in weight, t(9)
= 1.86, p= .09. The difference in weight loss between the
FIT-DSD group and NCG was statistically significant,
t(23) = 2.34, p= .02, d= 0.98. Not only did FIT-DSD par-
ticipants lose a significant amount of weight during the in-
tervention, they also lost significantly more weight than the
NCG group.
Group Weight Changes Postintervention to
1-Month Post-Postintervention
Participants in the FIT-DSD group continued to lose weight
postintervention and, by 1 month post-postintervention,
they had lost an additional 1.91 kg (SD = 2.66), t(11) = 2.49,
p= .03. From preintervention to 1 month post-postinter-
vention, the mean weight loss was 4.45 kg (SD = 3.05) for
the FIT-DSD group. The largest individual weight loss dur-
ing this follow-up period was 7.81 kg. The weight change
for the NCG was a nonsignificant mean weight gain of
0.52 kg (SD = 1.66), t(9) = 0.99, p= .34. During this fol-
low-up period, 6 NCG participants gained weight.
Participants in the FIT-DSD group lost significantly more
weight from postintervention to 1 month post-postinterven-
tion compared to the NCG, t(20) = 2.50, p=.04,d=1.65.
Group Weight Changes One Month
Post-Postintervention to Two Months
During the second follow-up month, FIT-DSD participants
continued to lose weight and had a further mean weight loss
of 0.57 kg (SD = 1.83). From preintervention to 2 months
post-postintervention, their total mean weight loss was
5.18 kg (SD = 3.85). For the NCG, the mean weight loss
from 1 month to 2 months post-postintervention was a non-
significant 0.43 kg (SD = 1.94).
The results of the pilot study suggested that the FIT-DSD
intervention helped people to lose weight, and weight loss
was not attributable to the demand characteristics of taking
part in an intervention. Participants in the FIT-DSD group
lost weight at a healthy rate, and theintervention worked for
most participants. Furthermore, weight loss continued after
the intervention, which suggests the participants had made
long-lasting changes to habitual patterns of behavior. How-
ever, there is a need to show the positive effects of the FIT-
DSD intervention with a larger group, with a broader set of
pre- and postintervention measures, and to confirm that
weight loss is related to the changes in behavioral flexibility.
There is also the need to consider food and activity levels
moreformallyina studyandto examinetheeffectsofdietary
restriction in combination with the FIT-DSD approach.
The question arises as to what might constitute a suitable
control group against which to compare the FIT-DSD inter-
vention. First, it is not ethical to run other behavioral control
groups(e.g.,theNCG) andmisleadparticipantsintothinking
theywillloseweight.The NCGgroupinthe pilotdidnotlose
weight despite their expressed intention to do so. Indeed, the
failure of the intervention to influence weight may have ac-
counted for the high participant dropout rate in this group.
Second, the literature shows that many dietary and activity
programs have limited success in achieving true weight loss.
The degree to which they lead to success is complex, al-
thoughsustained changes tothecaloric balance overtimeare
fundamental. This is just as true for the FIT-DSD interven-
tion. So the key issue is not whether weight loss with FIT-
DSD is larger or smaller compared to diets or activity-based
interventions, but whether or not the approach results in sus-
tained weight loss, and whether the reasons for the change
can be observed. Therefore, Study 2 does not include artifi-
cial control groups nor does it compare weight loss on the
FIT-DSDprogramwithanydiet-onlyor activity-basedinter-
vention. Our overriding aim was to confirm the effects of
FIT-DSD with a larger sample, to establish that the weight
loss continues postintervention and, importantly, to demon-
strate a dose response between behavioral flexibility changes
and weightloss. Therefore, to see whether the FIT-DSD in-
tervention acts symbiotically with other weight-loss ap-
intervention for those who are on a food diet oftheir choice
and for nondieters.
Study 2
The study used a 3-month between-groups longitudinal de-
sign to evaluate the effectiveness of the FIT-DSD program
28 B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
for facilitating sustained weight loss. Using the American
Psychological Society recommendations (2002), we used a
quasi or active control group design. Both groups followed
the FIT-DSD program for 1 month: One group didnot diet
while they were on the program (n= 31); the other group
followed a diet of their choosing (n= 24). Biometric vari-
ables, a range of psychological variables, and behavioral
flexibility were measured pre-, post- and 2 months post-
postintervention. An additional weight measurement was
also taken at 1 month post-postintervention. Both groups
were expected to lose weight as a result of following the
FIT-DSD program, but there were no specific predictions
regarding the amount of weight loss in each group. A dose
relationship between behavioral flexibility (using the FIT
Profiler) and weight loss was predicted.
Initially, 65 participants were recruited to participate in the
program via radio advertisements and media advertising.
The study was advertised as an initial evaluation of a new
behavioral approach for weight loss. Inclusion criteria re-
quired participants to have no medical contraindications
and be overweight or in the upper end of the healthy BMI
range and therefore safely able to lose a few pounds.
Ten people dropped out before starting because they
were asked to wait 1 month. Of the 55 participants, 24 were
in the FIT-DSD-plus-food diet group and 31 in the FIT-
DSD-only group. Participants in the FIT-DSD-plus-food
diet group were predominantly female (88%). The mean
age was 45.90 years (SD = 9.17, range 23–61 years). Pre-
intervention mean BMI was 31.19 (SD = 5.97); 11 partici-
pants were obese (BMI > 30), 9 were overweight (BMI =
25–30), and 4 were marginally healthy (BMI = 20–25). Di-
ets currently followed were: Weight Watchers (n= 3), ca-
lorie controlled (n= 12), high protein (n= 5), and other (n
= 4).
For the FIT-DSD-only group, ages ranged from 28–59
years (M= 41.54, SD = 9.81). There were 25 (81%) fe-
males. Preintervention mean BMI was 31.21 (SD = 7.04);
16 participants were obese (BMI > 30), 12 were overweight
(BMI = 25–30), and 3 were marginally healthy (BMI =
There were no differences between groups in terms of
preintervention weight or behavioral flexibility, t(53) =
0.28, p= .78, two-tailed and t(53) = 1.14, p= .27, two-
tailed, respectively.
Participants completed a demographic and diet history
questionnaire that assessed whether they were currently
following a food diet and received the FIT-DSD booklet
and the FIT Profiler (Fletcher & Stead, 1999, 2000). In
addition, a range of other psychological variables were
measured, including expectations and self-efficacy, both of
which can shape outcomes in health interventions (Oettin-
gen & Mayer, 2002). These expectations were measured by
asking participants how much weight they wanted and how
much weight they expected to lose, and how likely they felt
they were to lose the weight by following the program (re-
sponses were scored on a 7-point Likert scale, following
Oettingen & Mayer, 2002). Self-efficacy was measured in
two ways: General self-efficacy for losing weight was mea-
sured by asking participants if they felt they would be suc-
cessful in losing the weight they specified and how confi-
dent they felt about losing the weight. More specific mea-
sures of self-efficacy related to diet, exercise, willpower,
preplanning weight-loss behaviors, adhering to the pro-
gram, and increasing their behavioral flexibility levels
were also taken using a 7-point Likert scale.
In the daily food and exercise diaries, participants re-
corded their daily calorie intake and indicated the number
of times they ate each of the main food groups throughout
the day. Participants also recorded any exercise undertaken
that day, indicating type, duration and intensity. Partici-
pants also recorded their task completion in daily and
weekly FIT-DSD booklets.
Participants attended a 2-h group induction session at
which they gave their consent for participation and provid-
ed demographic information, were weighed, completed the
FIT Profiler, and were provided with FIT-DSD task book-
lets. Measurements of self-efficacy, expectations, and other
psychological variables were also taken at this session.
A month later, the participants were weighed, completed
another FIT Profiler, and provided information about their
self-efficacy, expectations, and other psychological vari-
ables. They also returned their completed FIT-DSD book-
lets. The participants’ weights were recorded again 1
month and finally 2 months post-postintervention. FIT Pro-
filers and measures of self-efficacy, expectations and psy-
chological variables were also completed 2 months post-
postintervention, when participants were also individually
Statistical Analyses
Pearson’s correlations were conducted to investigate the
relationships between BMI and behavioral flexibility, BMI
change and change in daily calorie intake and exercise
time, and change in behavioral flexibility and eating and
exercise habits.
The weight loss and changes in behavioral flexibility of
the FIT groups from preintervention to 1 month post-post-
intervention were compared using a 2-way mixed factorial
ANOVA. Subsequently, a one-way repeated measures
ANOVA was conducted to investigate the significance of
B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss 29
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
the weight loss and changes in behavioral flexibility for
both FIT groups combined over the course of the study, and
a hierarchical regression analysis investigated the signifi-
cant predictors of weight change. A frequency analysis of
the BMI groups was conducted. One-way repeated mea-
sures ANOVAs were conducted to monitor the change in
calorie intake, consumption of different food groups,
amount of exercise, and changes in anxiety and depression.
Simple mediation analyses were conducted to investigate
the nature of the relationship between change in behavioral
flexibility and weight loss.
All analyses were conducted two-tailed, p= .05. The
results are based on the 55 participants who completed the
Preintervention BMI and Behavioral Flexibility
For the 55 participants, preintervention BMI was negative-
ly correlated with behavioral flexibility, r(55) = –.40, p=
.002. Thus, as expected, before the intervention, heavier
participants had lower behavioral flexibility.
Group Weight Losses and BMI Changes Pre- to
Postintervention and Post-Postintervention
Between pre- and postintervention, 48 participants lost
weight, 6 stayed the same (4 FIT-DSD-only diet, 2 FIT-
DSD-plus-food diet), and 1 participant in the FIT-DSD-
plus-food diet group gained 0.45 kg. The maximum weight
loss was 3.63 kg. Over the trial period from preintervention
to 2 months post-postintervention all participants lost
weight: Mean weight loss was 5.03 kg (SD = 2.66) for the
FIT-DSD-only diet group and 4.71 kg (SD = 2.36) for the
FIT-DSD-plus-food diet group. The highest amount of
weight loss was 10.43 kg; a participant in the FIT-DSD-on-
ly group achieved this. There was no significant difference
in the weight loss achieved between groups over the trial
period, F(1, 53) = 0.22, p= .64. Thus, being on a food diet
did not bring about any additional weight loss to that asso-
ciated with the FIT-DSD program (see Table 1).
The mean weight losses suggest that the FIT-DSD pro-
gram is effective whether followed independently or in
conjunction with a food diet. The sample’s overall weight
loss from preintervention to 2 months post-postinterven-
tion was significant, F(2, 106) = 4.13, p= .02, partial η2=
.77. The pattern of weight loss followed a quadratic trend,
F(1, 53) = 4.71, p= .03, partial η2= .08: Participants lost
the most weight during the first follow-up month, and this
was significantly more than during the intervention month
(mean difference = 0.44 kg, p= .04), but not more than
during the second follow-up month. Table 1 shows mean
weight losses for participants from preintervention to 2
months post-postintervention.
The weight losses corresponded to changes in BMI
groupings (see Table 2). The number of participants with a
“healthy” BMI had increased by 228% from preinterven-
tion to 2 months post-postintervention. Seven (28%) obese
participants moved into the “overweight” BMI group.
Overall, 19 participants moved to a lower BMI group.
Behavioral Flexibility Pre-, Post- and
Post-Postintervention and Relation to Weight Loss
Participants’ behavioral flexibility increased signifi-
cantly from pre- to postintervention and post-postinter-
vention, F(1.05, 55.80) = 140.72, p< .02, partial η2=
0.73. There was no difference in increase between the
Table 1
Participants’ mean (SD) weight losses (kg) during and post-postintervention in Study 2
Group Pre-postintervention Postintervention – 1 month post-post 1 month post-post – 2 months
post-post Total weight loss
FIT-DSD only 1.49 (1.07) 1.80 (1.11) 1.74 (1.13) 5.03 (2.66)
FIT-DSD plus food diet 1.23 (1.18) 1.83 (1.18) 1.64 (0.85) 4.71 (2.36)
Table 2
Participants BMI grouping pre-, post-, and post-postintervention in Study 2
Group BMI Preintervention Postintervention 1 month post-postintervention 2 months post-postintervention
FIT-DSD only Healthy 3 2 6 9
Overweight 12 12 13 10
Obese 16 15 12 12
FIT-DSD plus
food diet Healthy 4 5 6 7
Overweight 9 10 9 9
Obese 11 9 9 8
30 B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
FIT-DSD-only and the FIT-DSD-plus-food diet groups,
F(1, 53) = 1.38, p= .25. At preintervention, the partici-
pants’ mean behavioral flexibility was 32.37 (SD =
18.49). After the FIT-DSD intervention, at postinterven-
tion, participants’ behavioral flexibility had significantly
increased to 46.76 (SD = 24.75) (mean difference = 14.39,
p< .02). Participants continued to behave more flexibly
post-postintervention (M= 47.37, SD = 24.08) and in-
creased their behavioral flexibility further. Participants
who achieved greater increases in behavioral flexibility
also showed greater weight loss. The degree of change in
behavioral flexibility from pre- to postintervention was
significantly related to weight loss over the same time pe-
riod, r(55) = .29, p= .03. Participants with higher behav-
ioral flexibility post-postintervention also achieved great-
er weight loss throughout the study, r(55) = .41, p< .01.
This supports the hypothesis that behavioral flexibility is
the driver of weight loss. This relationship was examined
further in a multiple regression model. A hierarchical mul-
tiple regression analysis showed that change in behavioral
flexibility from pre- to post-postintervention was the only
significant predictor of weight loss over the same time pe-
riod, b= 0.57, t(52) = 4.30, p< .01, and uniquely account-
ed for 24% of the variance when entered after preinter-
vention behavioral flexibility. This suggests that the abil-
ity to change behavior and become more behaviorally
flexible is more important than the initial level of behav-
ioral flexibility. Simple mediation analysis also supported
this: Controlling preintervention behavioral flexibility did
not affect the relationship between change in behavioral
flexibility and weight loss (β= 0.57, p< .01).
A range of other analyses were performed to explore
the role of the other psychological variables (self-efficacy,
expectations) measured in this study. None of these explo-
rations between groups, across time, or in relation to
weight loss produced a consistent pattern of results or had
a significant effect.
Changes in Food Consumption and Exercise Time
If the FIT-DSD program is an effective weight-loss pro-
gram, then changes in food consumption and exercise
time (resulting from the FIT-DSD program) should be
related to BMI changes. As expected, participants’
change in BMI from preintervention to 2 months post-
postintervention was significantly related to their change
in daily calorie intake and total exercise time over the
course of the study, r(38) = –0.50, p<.01andr(55) =
0.47, p< .01, respectively.
Participants’ daily consumption of different food groups
and their total daily calorie intake changed over the
course of the study. Participants’ total calorie consump-
tion decreased as did their white carbohydrate, fat, pro-
tein and alcohol intake. Participants’ daily fat consump-
tion decreased significantly from pre- to 2 months post-
postintervention, F(1.49, 19.32) = 16.34, p< .01, partial
η2= 0.56 and the pattern of means followed a linear
trend, F(1, 13) = 21.32, p< .01, partial η2= 0.61. The
pairwise comparisons for all time periods (with Bonfer-
roni adjustment) were all significant to at least a p<.02
level. Participants increased their daily consumption of
brown carbohydrates and fruit and vegetables throughout
the study, although the trend was not statistically signif-
The total number of minutes (sum of average cardiovas-
cular and resistance exercise minutes) participants spent
exercising each day changed significantly, F(2, 108) =
25.41, p< .01, partial η2= 0.32. The pattern of means
followed a quadratic trend, F(1, 54) = 38.89, p<.01,par-
tial η2= 0.42, and participants increased their total exer-
cise time during the first two phases of the study (FIT-
DSD intervention and first follow-up month), with the
increase after the FIT-DSD intervention (during the first
follow-up month) being significant, mean difference =
3.27, p< .01. The exercise time of the participants de-
clined during the second follow-up month. However, at
the end of the study, the participants spent longer exer-
cising than at the outset and their mean enjoyment of ex-
ercise was higher at preintervention, M=3.27(SD =
1.70), and at 2 months post-postintervention, M= 4.70
(SD = 1.72). From pre- to 2 months post-postinterven-
tion, participants’ enjoyment of exercise increased sig-
nificantly by paired samples t-test, mean difference =
1.42, t(54) = 7.02, p<.01.
The data suggest that the participants’ reduced fat intake
from the FIT-DSD intervention period to the first follow-
up period was related to their change in behavioral flex-
ibility, r(55) = –0.33, p= .01. In a simple linear regres-
sion model, the change in behavioral flexibility during
the intervention month significantly predicted change in
fat intake (preintervention to 1 month post-postinterven-
tion), F(1, 53) = 6.95, p= .01, and accounted for 12% of
the variance (adjusted = 10%).
Changes in behavioral flexibility from pre- to 2 months
post-postintervention correlated significantly with
changes in exercise time over the same time period, r(55)
=0.70,p< .01, and predicted change in exercise time in
a simple linear regression model, F(1, 53) = 44.07, p<
.01, b= 0.24, t(53) = 6.64, p< .01. Change in behavioral
flexibility from pre- to 2 months post-postintervention
accounted for 45% of the variance (adjusted = 44%).
The nature of the relationship between change in behav-
ioral flexibility and weight loss was investigated further
using mediation analyses. Tests of simple mediation were
conducted separately with change in calorie intake, change
in exercise time and change in fat intake as the mediators.
The effect of change in behavioral flexibility on weight loss
was reduced and became statistically nonsignificant when
change in calorie intake was controlled (β= 0.41, p= .73),
but remained significant when change in exercise time and
change in fat intake were controlled (β= 0.29, p= .05; β
=0.54, p< .01, respectively). Thus, change in calorie intake
overall was identified as a simple mediator.
B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss 31
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Anxiety and Depression
Participants’ mean anxiety and depression scores de-
creased significantly over the course of the study. Anxiety
decreased from M= 10.00 (SD = 2.3) to M= 8.8 (SD = 2.3)
to M= 8.6 (SD = 2.5) from pre- to 2 months post-postin-
tervention with corresponding changes in depression, M=
8.3 (SD = 2.6), M= 6.8 (SD = 2.6) and M= 6.4 (SD = 2.7).
The decrease in anxiety scores was significant, F(2, 108) =
57.96, p< .01, partial η2= 0.52, as was the decrease in
depression scores, F(2, 108) = 33.86, p< .01, partial η2=
0.39. Thus, the participants became less depressed and less
anxious than they were at the beginning of the study. The
pattern of means followed a linear trend for anxiety,
F(1, 54) = 71.98, p< .01, and for depression, F(1, 54) =
42.86, p< .01. The pairwise comparisons suggest that the
FIT-DSD intervention contributed to these decreases as
significant contrasts were found between for pre- to post-
intervention and pre- to 2 months post-postintervention, but
not between postintervention and 2 months post-postinter-
This research evaluates the efficacy of the FIT-DSD pro-
gram as a tool for weight loss. The general nature of this
program potentially has broad applications in various
health and well-being contexts (Fletcher & Pine, 2011).
Based on our previous research (e.g., Hanson, 2008), we
predicted that participants following the FIT-DSD program
would lose weight by breaking existing habits and by be-
coming more behaviorally flexible. This prediction was
tested in two studies and was confirmed in both. In both
studies, the majority of participants on the FIT-DSD pro-
gram lost weight and significant weight loss continued
We hypothesized that weight loss is a result of the effects
of increasing behavioral flexibility and the consequential
benefits of this for breaking habits that prevent positive
caloric balance changes. The FIT-DSD program does not
focus on food, but we hypothesized that it serves to break
down the distal habits that prevent participants from suc-
cessfully changing their caloric balance, which is necessary
for weight loss. We proposed that increasing behavioral
flexibility may lead to consequential disruption of the
chains of habits that maintain unhealthy living. Study 1
included a control group engaged in daily tasks that were
not expected to increase behavioral flexibility and these
participants did not show significant weight loss. These re-
sults thus support the notion that FIT-DSD is an effective
behavioral tool for facilitating sustained weight loss. In
Study 2, we also found that being on a food diet did not
bring about any additional weight loss to that associated
with the FIT-DSD program. The weight losses were attrib-
utable to increased behavioral flexibility, which in turn
proved to be mediated by decreases in calorie consumption.
There was a dose relationship demonstrating that the more
participants increased their behavioral flexibility, the more
weight they lost. Study 2 also showed that there were re-
ductions in anxiety and depression scores of participants
on the FIT-DSD program.
Why should changing comparatively small behaviors
help people lose weight when other multimodal programs
fail? We suggest that the FIT-DSD program acts on mech-
anisms different than those that are usually the focus of
weight loss and weight maintenance programs. First, by
doing something different on a daily basis, the participants
expanded their behavioral repertoire and modified some of
their usual habits. Habits do not exist in isolation; they form
an interconnected web (Neal, Wood, & Quinn, 2006). The
FIT-DSD program may have weakened the web of habits
so that participants’ daily pattern of behavior was less like-
ly to contain triggers that led them to overeat. The behav-
iors that participants modified on a daily basis (e.g., inter-
acting differentlywith aperson, having ano-TV day,taking
an alternative route to work) required little sustained will-
power and led to a measurably greater sense of well-being.
Because their attention was directed toward their behavior
rather than toward their food intake, they did not fall victim
to the food cravings previous work has shown dieters ex-
perience (Fletcher, Pine, Woodbridge, & Nash, 2007). Nor
are they likely to suffer from the behavioral rebound that
can occur as a result of trying to suppress thoughts about
food (Erskine, 2008).
At the same time, changing one’s behavior affords great-
er awareness of one’s actions and choices. This is particu-
larly beneficial for dieters, since it is known that they think
about and respond to food differently than nondieters do
(Baumeister et al., 1994). Dieters tend to ignore their inter-
nal cues, that is, listening to their body and being aware of
hunger and instead are guided by external cues, such as
images, aromas, or time (Herman & Polivy, 1975; Rogers
& Hill, 1989). We suggest that using the FIT-DSD program
to focus on distal habits in order to increase behavioral flex-
ibility takes the emphasis away from food and adds a sense
of agency in that individuals became more aware of them-
selves and of being in control of their actions (Hassin, Ule-
man, & Bargh, 2005).
The participants in Study 2 naturally fell into two
groups: dieters and nondieters. While lack of random allo-
cation and the diversity of diets followedmay be problem-
atic in some respects for comparison of these groups, the
findings that participants on the FIT-DSD program lost as
much weight as those on food diets stands as the notable
outcome. There were no differences between the two
groups in terms of biometric measures, self-efficacy, ex-
pectations, or initial behavioral flexibility, which would
help to rule out other differences being responsible for the
observed weight loss. The nonrandom allocation to the
groups remains an issue, although random allocation would
also have resulted in the problems associated with people
being required to do things against their natural tendencies.
The design used is consistent with the principles of an “ef-
32 B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
fectiveness research paradigm” (see Cavanagh et al., 2006,
p. 500) and avoids potential threats to validity associated
with randomized control trials. Randomly allocated control
groups are not always appropriate or recommended in eval-
uating the effectiveness of treatments, especially those of
a longer term (American Psychological Association, 2002),
as seen in our first study. Here, participants in the control
group were more likely to gain weight than lose it. For
people trying to lose weight, this is contradictory to their
goal, and in Study 2 it was deemed unethical to ask people
to participate in a condition we had no reason to believe
would help them. The same could be said for many types
of “control” groups in this area.
Nonetheless, there are clear limitations to both our stud-
ies resulting from the small sample sizes. This is particu-
larly true with respect to diet and exercise variables, which
are notoriously difficult to measure using self-report instru-
ments (e.g., Lichtman et al., 1992). The small samples also
limit the kinds of analyses that could be done to explore
underlying mechanisms. Larger samples would also allow
a fuller evaluation of the FIT-DSD program against differ-
ent diet controls. It may be, for example, that a do-some-
thing-different structure could be focused on the prepara-
tion and types of food consumed, independent of the purely
behavioral aspects of this program. In these studies, the
behavioral changes were not in any way centered on food
or activity. We predict that the do-something-different
changes need to target distal behaviors outside the dietary
context to be effective.
In Study 2, we were able to show that the changes in
behavioral flexibility we observed directly affected eating
behaviors. All participants, in both studies, completed daily
food and exercise diaries, which are known to influence
behavior and weight loss themselves (Hollis et al., 2008).
We accept that the daily food and exercise diaries we used
were somewhat limited and were unlikely to capture all
elements of behavior that affect caloric balance. The diaries
were designed to make broad estimates and to rule out ma-
jor differences between groups – not as accurate calorie
monitors – and this makes the observed relationships we
reported between behavioral flexibility and food and activ-
ity levels more notable. Keeping a diary is an additional
general demand characteristic that can be ruled out as an
explanation of the weight loss reported here: The FIT-DSD
program produced weight loss over and above the possible
benefits of such demand variables.
These studies provide evidence of how the FIT-DSD
program brings about weight loss without compromising
health, but they are not without limitations. Nevertheless,
we suggest that their potential importance should not be
underestimated and be considered against the failure of
standard dietary and exercise programs to abate the rise in
obesity. Lally et al. (2008) showed that addressing the au-
tomaticity of eating and exercise habits is important for
weight loss. We propose here that there may also be of
benefit in tackling a broader range of everyday habits, and
that increasing behavioral flexibility offers an alternative
safe approach. Importantly, the general nature of the pro-
gram means it can be applied to a range of issues. A recent
random controlled study confirmed that it can successfully
be applied to reduce anxiety and depression – and improve
overall family functioning (Sharma, 2010). This kind of
approach may lead us to consider the wider behavioral and
lifestyle aspects implicated in obesity and may offer some
unique tools not otherwise available.
Authors’ Note
B(C)F developed the theoretical framework and back-
ground for the interventions, led the research team, took an
overview on all aspects of both studies and drafted the
manuscript. JH designed and carried out the second study,
including data analysis. NP designed and carried out the
first study and was responsible for the overall statistical
analyses. KP participated in the overall framework and ap-
proach and helped to draft the manuscript.
American Psychological Association. (2002). Criteria for evalu-
ating treatment guidelines. American Psychologist, 57,
Baumeister, R.F., Heatherton, T.F., & Tice, D. M. (1994). Losing
control: How and why people fail at self-regulation. San Die-
go, CA: Academic Press.
Blakemore, A.I.F., & Froguel, P. (2008). Is obesity our genetic
legacy? Journal of Clinical Endocrinology and Metabolism,
93, S51–S56.
Cavanagh, K., Shapiro, D.A., Van den Berg, S., Swain, S., Bark-
ham, M., & Proudfoot, J. (2006). The effectiveness of com-
puterized cognitive behavioral therapy in routine care. British
Journal of Clinical Psychology, 45, 499–514.
Cserjési, R., Molnár, D., Luminet, O., & Lénárt,L. (2007). Is there
any relationship between obesity and mental flexibility in chil-
dren? Appetite, 49, 675–678.
D’Anci, K. W., Watts, K.L., Kanarek, R.B., & Taylor H.A.
(2009). Low-carbohydrate weight-loss diets: Effects on cog-
nition and mood. Appetite, 52, 96–103.
Dansinger, M.L., & Schaefer, E.J. (2006). Low-fat diets and
weight change. Journal of the American Medical Association,
295, 94–95.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U.,
& May, A. (2004) Changes in gray matter induced by training.
Nature, 427, 311–312.
Erskine, J.A. (2008). Resistance can be futile: Investigating be-
havioral rebound. Appetite, 50, 415–421.
Fletcher, B.(C), & Pine, K. J. (2011). Flex: The key to behavioral
optimization. Hatfield, UK: UH Press.
Fletcher, B.(C), Hanson, J., & Jones, F. (2004, September). A
psychological weight loss intervention program based on the
FIT framework. Paper presented at the8th Health Psychology
Conference of British Psychological Society, Edinburgh.
Fletcher, B.(C), Pine, K.J., Woodbridge, Z., & Nash, A. (2007).
B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss 33
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
How visual images of chocolate affect guilt and craving of
female dieters. Appetite, 48, 211–218.
Fletcher, B.(C), & Stead, R. (1999). The FIT Profiler. Hatfield,
UK: The FIT Corporation.
Fletcher, B.(C), & Stead, R. (2000). (Inner) FITness & The FIT
Corporation. London, UK: International Thomson Press.
Gailliot, M.T., Baumeister, R.F., DeWall, C.N., Maner, J.K.,
Plant, E.A., Tice, D.M., .. . Schmeichel, B.J. (2007). Self-
control relies on glucose as a limited energy source: Willpower
is more than a metaphor. Journal of Personality and Social
Psychology, 92, 325–336.
Hanson, J. (2008). FIT Science in psychological and physical
well-being. Unpublished doctoral thesis, University of Hert-
fordshire, UK.
Hassin, R.R., Uleman, J.S., & Bargh, J.A. (2005). The new un-
conscious. Oxford, UK: Oxford University Press.
Herman, P.C., & Polivy, J. (1975). Anxiety, restraint and eating
behavior. Journal of Abnormal Psychology, 84, 666–672.
Hofker, M., & Wijmenga, C. (2009). A supersized list of obesity
genes. Nature Genetics, 41, 139–140.
Hollis, J.F., Gullion, C. M., Stevens, V. J., Brantley, P.J., Appel,
L.J., Ard, J.D., . . . Funk, K. (2008). Weight loss during the
intensive intervention phase of the weight-loss maintenance
trial. American Journal of Preventative Medicine, 35,
Lally, P., Chipperfield, A., & Wardle, J. (2008). Healthy habits:
Efficacy of simple advice on weight control based on a habit-
formation model. International Journal of Obesity, 32,
Lichtman, S.W., Pisarska, K., Berman, E. R., Pestone, M., Dowl-
ing, H., Offenbacher, E., . . . Heymsfield, S. B. (1992). Dis-
crepancy between self-reported and actual caloric intake and
exercise in obese subjects. New England Journal of Medicine,
327, 1893–1898.
Mann, T., Tomiyama, A.J., Westling, E., Lew, A., Samuels, B.,
& Chatman, J. (2007). Medicare’s search for effective obesity
treatments: Diets are not the answer. American Psychologist,
62, 220–233.
Neal, D.T., Wood, W., & Quinn, J.M. (2006). Habits: A repeat
performance.Current Directions in Psychological Science,15,
Oettingen, G., & Mayer, D. (2002). The motivating function of
thinking about the future: Expectations versus fantasies. Jour-
nal of Personality and Social Psychology, 83, 198–212.
Pennebaker, J.W., & Seagal, J.D. (1999). Forming a story: The
health benefits of narrative. Journal of Clinical Psychology,
55, 1243–1254.
Rogers, P.J., & Hill, A.J. (1989). Breakdown of dietary restraint
following mere exposure to food stimuli: Interrelationships be-
tween restraint, hunger and food intake. Addictive Behaviors,
14, 387–397.
Scholz, J., Klein, M.C., Behrens, T.E. J., & Johansen-Berg, H.
(2009). Training induces changes in white-matter architecture.
Nature Neuroscience, 12, 1370–1371.
Shade, E.D., Ulrich, C.M., Wener, M.H., Wood, B., Yasui, Y.,
LaCroix, K., . . . McTiernan, A. (2004). Frequent intentional
weight loss is associated with lower natural killer cellcytotox-
icity in postmenopausal women: Possible long-term immune
effects. Journal of the American Dietetic Association, 104,
Sharma, S. (2010). FIT family functioning. Unpublished doctoral
thesis, University of Hertfordshire, UK.
Sorensen, T.I.A., Rissanen, A., Korkeila, M., & Kaprio, J.
(2005). Intention to lose weight, weight changes, and 18-y
mortality in overweight individuals without co-morbidities.
PLoS Medicine, 2, 510–520.
Wardle, J. (2005). The triple whammy. The Psychologist, 18,
Wardle, J., Griffith, J., Johnson, F.,& Rapoport, L. (2000). Inten-
tional weight control and food choice habits in a national rep-
resentative sample of adults in the UK. International Journal
of Obesity, 24, 534–540.
Wardle, J., Llewellyn, C., Sanderson, S., & Plomin, R. (2009).
The FTO gene and measured food intake inchildren. Interna-
tional Journal of Obesity, 33, 42–45.
Webb, T.L., & Sheeran, P. (2006). Does changing behavioral in-
tentions engender behavior change? A meta-analysis of the ex-
perimental evidence. Psychological Bulletin, 132, 249–268.
Webber, L.S., Catellier, D.J., Lytle, L.A., Murray, D.M., Pratt,
C. A., Young, D. R., . . . Pate, R. R. (2008). Promoting physical
activity in middle school girls: Trial of Activity for Adolescent
Girls. American Journal of Preventative Medicine, 34,
Wood, W., Tam, L., & Guerrero Witt, M. (2005). Changing cir-
cumstances, disrupting habits. Journal of Personality and So-
cial Psychology, 88, 918–933.
Ben (C) Fletcher
School of Psychology
University of Hertfordshire
College Lane, Hatfield
Hertfordshire, AL10 9AB
34 B.(C) Fletcher et al.: Enhancing Behavioral Flexibility for Weight Loss
Swiss J. Psychol. 70 (1) © 2011 by Verlag Hans Huber, Hogrefe AG, Bern
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
... The current study was conducted based on data from the Do Cardiac Health: Advanced New Generation Ecosystem (Do CHANGE) project, which consisted of two randomized controlled trials that was carried out in The Netherlands, Spain, and Taiwan (NCT02946281; NCT03178305). The intervention was developed to enhance lifestyle change in patients CVD using behavior change theory [18] and innovative technology. [19] In the Do CHANGE trials, two groups were compared (intervention vs. control) and assessed at baseline, 3, and 6 months. ...
... The Do Something Different (DSD) program, [18] which was the active component of the intervention designed to initiate behavior change (through increase of behavioral flexibility), ended 3 months after baseline. After termination of the DSD program, intervention group patients continued using the equipment and monitoring devices that they received at baseline for another 3 months. ...
... The DSD that has been applied in the Do CHANGE project is an intervention that aims to increase behavioral flexibility by giving behavioral prompts to participants. [18] It triggers participants to do something different every day (e.g. take a different route to work today). ...
Objective: Being able to adapt to a changing environment has been associated with better mental and physical health. This adaptivity can be measured by behavioral flexibility assessment tools. However, the mental health consequences of behavioral flexibility have not been examined in patients with cardiovascular disease (CVD). The current study aims to examine if behavioral flexibility is associated with depression and anxiety in patients with CVD. Methods: A total of n = 387 patients with stable CVD were recruited as a part of the Do CHANGE study. At baseline, 3, and 6 months, data were collected. Depression (Patient Health Questionnaire-9) and anxiety (Generalized Anxiety Disorder Scale-7) were assessed at all time points. Results: The mean age of the sample was 61.9 ± 10.23 years, with 274 (71%) being male. An inverse association between behavioral flexibility and depression at baseline, 3, and 6 months was observed. The associations remained significant after adjusting for relevant demographic and medical variables and baseline depression. No longitudinal association between behavioral flexibility and anxiety was found in the multivariate models. Conclusions: Behavioral flexibility is associated with depression in cardiac patients. Future studies should focus on examining the pathways of this association and offering patients with low flexibility levels additional care if needed.
... The ToDo program aims to improve an individual's behavioral flexibility, learning new behaviors so they have more choice over how they react to different situations [32]. The program suggests microbehavioral alternatives (Do's) that gradually change people's habits, with some evidence that these small behavioral changes, which may not directly target the habit of interest, effect health outcomes such as decreases in weight [28,32]. ...
... The ToDo program aims to improve an individual's behavioral flexibility, learning new behaviors so they have more choice over how they react to different situations [32]. The program suggests microbehavioral alternatives (Do's) that gradually change people's habits, with some evidence that these small behavioral changes, which may not directly target the habit of interest, effect health outcomes such as decreases in weight [28,32]. The original program has been adapted by the research team to target sedentary behavior, based on Australian physical activity and cardiac rehabilitation guidelines to create ToDo-CR, a 6-week behavior change program ( Figure 1) [5,33]. ...
... The ToDo-CR program uses a number of behavior change techniques, including action planning (Do's), prompting via advice on ways to achieve small actionable goals, opportunities to practice new behaviors, encouraging participants to change their physical and social environments (variety and social opportunity scores), providing feedback on their behavior for self-monitoring over the course of a day or 14 days, and providing rewards with smiley faces if their behavior positively changes from their baseline assessment. By sending behavioral prompts (Do's), the ToDo-CR program aims to change behavioral habits by disrupting the habits that are common in our daily lives, potentially increasing behavioral or cognitive flexibility, and subsequently changing habits associated with an unhealthy lifestyle [28,32]. It has been suggested that cognitive flexibility is a key mechanism in the reduction of unwanted habits, such as sedentary behavior, and cognitive flexibility can be improved with suitable interventions, resulting in a reduction of habitual sedentary behavior [54]. ...
Full-text available
Background Cardiac rehabilitation participants are encouraged to meet physical activity guidelines to reduce the risk of repeat cardiac events. However, previous studies have found that physical activity levels are low and sedentary behavior is high, both during and after cardiac rehabilitation. There is potential for smartphone apps to be effective in reducing sedentary behavior, although among the few studies that have investigated smartphone apps in cardiac rehabilitation, none targeted sedentary behavior. Objective This study aims to evaluate the feasibility of a behavioral smartphone app (Vire) and a web-based behavior change program (ToDo-CR) to decrease sedentary behavior in cardiac rehabilitation participants. Methods Using a single-center, pre-post design, participants were recruited by nursing staff on admission to cardiac rehabilitation. All eligible participants installed the Vire app, were given a Fitbit Flex, and received the 6-week ToDo-CR program while attending cardiac rehabilitation. The ToDo-CR program uses personalized analytics to interpret important behavioral aspects (physical activity, variety, and social opportunity) and real-time information for generating and suggesting context-specific actionable microbehavioral alternatives (Do’s). Do’s were delivered via the app, with participants receiving 14 to 19 Do’s during the 6-week intervention period. Outcome measures were collected at 0, 6, and 16 weeks. The assessors were not blinded. Feasibility outcomes included recruitment and follow-up rates, resource requirements, app usability (Unified Theory of Acceptance and Use of Technology 2 [UTAUT2] questionnaire), and objectively measured daily minutes of sedentary behavior (ActiGraph) for sample size estimation. Secondary outcomes included functional aerobic capacity (6-min walk test), quality of life (MacNew Heart Disease Health-Related Quality of Life Questionnaire), anxiety and depression (Hospital Anxiety and Depression Scale questionnaire), BMI, waist circumference, waist-to-hip ratio, and blood pressure. Results Between January and May 2019, 20 participants were recruited consecutively. One-third of people who commenced cardiac rehabilitation were eligible to participate. Other than declining to take part in the study (15/40, 38%), not having a smartphone was a major reason for exclusion (11/40, 28%). Those excluded without a smartphone were significantly older than participants with a smartphone (mean difference 20 [SD 5] years; P<.001). Participants were, on average, aged 54 (SD 13) years, mostly male (17/20, 85%), and working (12/20, 67%). At 6 weeks, 95% (19/20) of participants were assessed, and 60% (12/20) of participants were assessed at 16 weeks. Participants were relatively satisfied with the usability of the app (UTAUT2 questionnaire). Overall, participants spent 11 to 12 hours per day sitting. There was a medium effect size (Cohen d=0.54) for the reduction in sedentary behavior (minutes per day) over 16 weeks. Conclusions The use of a behavioral smartphone app to decrease sitting time appears to be feasible in cardiac rehabilitation. A larger randomized controlled trial is warranted to determine the effectiveness of the app.
... Behavioral flexibility is associated with a broad range of the behavioral repertoire, making people more open to experience and the adoption of new behaviors [12]. DSD has been evaluated in other patient samples and has shown promising results by producing health behavior change [13]. For this study, the program was adapted to meet the needs of patients with cardiovascular disease (coronary artery disease, CAD; heart failure, HF; and hypertension, HT). ...
... Patients received a total of 32 Do's during the 3-month intervention period (2-3 Do's every week). DSD has been evaluated in other patient samples and has shown promising results with respect to behavior change [13]. For this trial, the program was adapted to the cardiac population with slight differences in the program depending on patients' primary diagnosis (eg, CAD, HF, HT), as the preferred health behaviors may vary depending on the diagnosis. ...
... The findings of the study are not completely in line with previous studies in other patient populations [13]. An explanation for this discrepancy could be the fact that this was the first study implementing the concept of behavioral flexibility and thus the core Do's of the DSD program in the cardiac population. ...
Full-text available
Background: Behavior change methods involving new ambulatory technologies may improve lifestyle and cardiovascular disease outcomes. Objective: This study aimed to provide proof-of-concept analyses of an intervention aiming to increase (1) behavioral flexibility, (2) lifestyle change, and (3) quality of life. The feasibility and patient acceptance of the intervention were also evaluated. Methods: Patients with cardiovascular disease (N=149; mean age 63.57, SD 8.30 years; 50/149, 33.5% women) were recruited in the Do Cardiac Health Advanced New Generation Ecosystem (Do CHANGE) trial and randomized to the Do CHANGE intervention or care as usual (CAU). The intervention involved a 3-month behavioral program in combination with ecological momentary assessment and intervention technologies. Results: The intervention was perceived to be feasible and useful. A significant increase in lifestyle scores over time was found for both groups (F2,146.6=9.99; P<.001), which was similar for CAU and the intervention group (F1,149.9=0.09; P=.77). Quality of life improved more in the intervention group (mean 1.11, SD 0.11) than CAU (mean -1.47, SD 0.11) immediately following the intervention (3 months), but this benefit was not sustained at the 6-month follow-up (interaction: P=.02). No significant treatment effects were observed for behavioral flexibility (F1,149.0=0.48; P=.07). Conclusions: The Do CHANGE 1 intervention was perceived as useful and easy to use. However, no long-term treatment effects were found on the outcome measures. More research is warranted to examine which components of behavioral interventions are effective in producing long-term behavior change. Trial registration: NCT02946281;
... Interventions which offer advice on lifestyle change whilst engaging automatic behaviors (including efficiency, lack of awareness, unintentionality and uncontrollability 12 ) may offer more benefit 4 because automatic behaviors do not require selfcontrol or willpower and strengthen with repetition 11 . Furthermore, breaking old habits by re-structuring daily routines and engaging in novel behaviors, increases an individual's mindful behaviors through conscious, deliberative thought 13 . This increase in conscious thought is proposed to draw attention to the behavior, making it easier to recognize compliance with behavioral goals 14 . ...
... We also expected any weight lost during the intervention to be maintained at the 12-month post-intervention time-point. Therefore, sample size calculations were based on the mean weigh loss from previous DSD and TTT studies at the last available time-point (4 weeks for DSD and 8 weeks for TTT) 13,15 . Our power calculation suggested 19 participants were required in each of the 3 arms to achieve a 90% power and 5% significance criterion to detect a 2.4kg (SD 2.2) weight loss difference between either intervention group and control. ...
... DSD, focuses on increasing participants' behavioral flexibility by breaking daily habits 13 purported to play a role in unhealthy dietary and exercise behaviors 20 . DSD requires participants to do something different each day and to engage in novel, weekly activities to expand their behavioral repertoire 21 . ...
... Some psychologists emphasize the importance of creating new habits, changing people in their daily context, influencing their habits and their basic psychology, rather than following traditional approaches based on simply providing information and education, to achieve a sustained change in behavior that has a beneficial impact on health [69,70]. Related with this issue, the MED4Youth project targets adolescence (13-17 y) as a key and sensitive development stage of life, where healthy lifestyles and behavioral flexibility need to be reinforced. ...
Full-text available
Youth obesity is a strong predictor of adult obesity, which has well-known negative health consequences. Thus, addressing adult obesity requires tackling youth obesity. MED4Youth's main objective is to strengthen the link between the Mediterranean Diet (MD) and the health benefits against youth obesity and associated cardiovascular disease (CVD) risk factors, identifying positive effects exerted by an MD including sourdough bread and healthy products from the Mediterranean basis (chickpeas/hummus, nuts, and pomegranate juice). For this purpose, a multicenter randomized controlled trial in which an MD-based intervention will be compared to a traditional low-fat diet intervention will be carried out with 240 overweight and obese adolescents (13-17 years) from Spain, Portugal, and Italy. Both interventions will be combined with an educational web-application addressed to engage the adolescents through a learning-through-playing approach, using both educational materials and games. To assess the interventions, adherence to the MD, dietary records, physical activity, food frequency, sociodemographic, and quality of life questionnaires as well as classical anthropometric and biochemical parameters will be evaluated. Furthermore, an omics approach will be performed to elucidate whether the interventions can shape the gut microbiota and gut-derived metabolites to gain knowledge on the mechanisms through which the MD can exert its beneficial effects.
... The Do CHANGE program was developed as an ICT-based alternative for providing health education, which leads to behavioral changes in care recipients [16,17]. The Do CHANGE program consists of a 6-month intervention with a set of devices that include self-monitoring tools and the Do Something Different (DSD) behavior change program (only available during the first 3 months of the intervention), which has been shown to be effective in changing health behaviors in previous studies targeting different populations [18]. The Do CHANGE program included the following two phases: Do CHANGE 1 and Do CHANGE 2, which were assessed in two consecutive RCTs ( Figure 1). ...
Full-text available
Background: During the last few decades, preventing the development of cardiovascular disease has become a mainstay for reducing cardiovascular morbidity and mortality. It has been suggested that interventions should focus more on committed approaches of self-care, such as electronic health techniques. Objective: This study aimed to provide evidence to understand the financial consequences of implementing the "Do Cardiac Health: Advanced New Generation Ecosystem" (Do CHANGE 2) intervention, which was evaluated in a multisite randomized controlled trial to change the health behavior of patients with cardiovascular disease. Methods: The cost-effectiveness analysis of the Do CHANGE 2 intervention was performed with the Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing tool, based on a Markov model of five health states. The following two types of costs were considered for both study groups: (1) health care costs (ie, costs associated with the time spent by health care professionals on service provision, including consultations, and associated unplanned hospitalizations, etc) and (2) societal costs (ie, costs attributed to the time spent by patients and informal caregivers on care activities). Results: The Do CHANGE 2 intervention was less costly in Spain (incremental cost was -€2514.90) and more costly in the Netherlands and Taiwan (incremental costs were €1373.59 and €1062.54, respectively). Compared with treatment as usual, the effectiveness of the Do CHANGE 2 program in terms of an increase in quality-adjusted life-year gains was slightly higher in the Netherlands and lower in Spain and Taiwan. Conclusions: In general, we found that the incremental cost-effectiveness ratio strongly varied depending on the country where the intervention was applied. The Do CHANGE 2 intervention showed a positive cost-effectiveness ratio only when implemented in Spain, indicating that it saved financial costs in relation to the effect of the intervention. Trial registration: NCT03178305;
... As per protocol, the behavioral intervention was stopped at the 3-month follow-up. The program showed promising results regarding behavior change in previous samples (19). For a more detailed description of the intervention, we would like to refer the reader to the previously published protocol (18). ...
Full-text available
Objective: Unhealthy lifestyle factors have adverse outcomes in cardiac patients. However, only a minority of patients succeed to change unhealthy habits. Personalization of interventions may result in critical improvements. The current randomized controlled trial provides a proof of concept of the personalized Do CHANGE 2 intervention and evaluates effects on: 1) lifestyle, and 2) quality of life over time. Methods: Cardiac patients (N=150; mean age=61.97±11.61 years; 28.7% women; heart failure N=33, coronary artery disease N=50, hypertension N=67) recruited from Spain and The Netherlands were randomized to either the 'Do CHANGE 2' or 'Care as Usual' group. The Do CHANGE 2 group received ambulatory health-behaviour assessment technologies for six months combined with a 3-month behavioural intervention program. Linear Mixed Models (LMM) analysis wERE used to evaluate the intervention effects and latent class analysis (LCA) was used for secondary subgroup analysis. Results: LMM analysis showed significant intervention effects for lifestyle behaviour (Finteraction(2,138.5)=5.97, p =.003), with improvement of lifestyle behaviour in the intervention group. For quality of life, no significant main effect (F(1,138.18)=.58, p=.447) or interaction effect (F(2,133.1)=0.41, p=.67) were found. Secondary LCA revealed different subgroups of patients per outcome measure. The intervention was experienced as useful and feasible. Conclusion: The personalized eHealth intervention resulted in significant improvements in lifestyle. Cardiac patients and health care providers were also willing to engage in this personalized digital behavioural intervention program. Incorporating eHealth lifestyle programs as part of secondary prevention would be particularly useful when taking into account which patients are most likely to benefit. Trial registration:
... Current findings show an increase of activity levels at the start of the intervention, and a slow decrease over time. An explanation for this finding could be the discontinuation of the DSD behavioral intervention, 19 and thus the lack of encouraging behavioral prompts between three and six months after baseline. Furthermore, only the first three months of the intervention patients were contacted by phone once a week in order to shortly discuss their health status and last achievements. ...
Full-text available
The importance of modifying lifestyle factors in order to improve prognosis in cardiac patients is well-known. Current study aims to evaluate the effects of a lifestyle intervention on changes in lifestyle- and health data derived from wearable devices. Cardiac patients from Spain (n = 34) and The Netherlands (n = 36) were included in the current analysis. Data were collected for 210 days, using the Fitbit activity tracker, Beddit sleep tracker, Moves app (GPS tracker), and the Careportal home monitoring system. Locally Weighted Error Sum of Squares regression assessed trajectories of outcome variables. Linear Mixed Effects regression analysis was used to find relevant predictors of improvement deterioration of outcome measures. Analysis showed that Number of Steps and Activity Level significantly changed over time (F = 58.21, p < 0.001; F = 6.33, p = 0.01). No significant changes were observed on blood pressure, weight, and sleep efficiency. Secondary analysis revealed that being male was associated with higher activity levels (F = 12.53, p < 0.001) and higher number of steps (F = 8.44, p < 0.01). Secondary analysis revealed demographic (gender, nationality, marital status), clinical (co-morbidities, heart failure), and psychological (anxiety, depression) profiles that were associated with lifestyle measures. In conclusion results showed that physical activity increased over time and that certain subgroups of patients were more likely to have a better lifestyle behaviors based on their demographic, clinical, and psychological profile. This advocates a personalized approach in future studies in order to change lifestyle in cardiac patients.
Full-text available
Introduction Cardiac rehabilitation (CR) is recommended for secondary prevention of cardiovascular disease and reducing the risk of repeat cardiac events. Physical activity is a core component of CR; however, studies show that participants remain largely sedentary. Sedentary behaviour is an independent risk factor for all-cause mortality. Strategies to encourage sedentary behaviour change are needed. This study will explore the effectiveness and costs of a smartphone application (Vire) and an individualised online behaviour change program (ToDo-CR) in reducing sedentary behaviour, all-cause hospital admissions and emergency department visits over 12 months after commencing CR. Methods and analysis A multicentre, assessor-blind parallel randomised controlled trial will be conducted with 144 participants (18+ years). Participants will be recruited from three phase-II CR centres. They will be assessed on admission to CR and randomly assigned (1:1) to one of two groups: CR plus the ToDo-CR 6-month programme or usual care CR. Both groups will be re-assessed at 6 months and 12 months for the primary outcome of all-cause hospital admissions and presentations to the emergency department. Accelerometer-measured changes in sedentary behaviour and physical activity will also be assessed. Logistic regression models will be used for the primary outcome of hospital admissions and emergency department visits. Methods for repeated measures analysis will be used for all other outcomes. A cost-effectiveness analysis will be conducted to evaluate the effects of the intervention on the rates of hospital admissions and emergency department visits within the 12 months post commencing CR. Ethics and dissemination This study received ethical approval from the Australian Capital Territory Health (2019.ETH.00162), Calvary Public Hospital Bruce (20–2019) and the University of Canberra (HREC-2325) Human Research Ethics Committees (HREC). Results will be disseminated through peer-reviewed academic journals. Results will be made available to participants on request. Trial registration number ACTRN12619001223123.
Diary studies of personality have shown that personality is variable, and can help the person deal appropriately with the different interpersonal demands they encounter. This study aims to demonstrate how interpersonal context predicts personality states. Thirty-six participants (9 male, 27 female, Mage = 24.72, SD = 7.11) kept an online diary for one month. The diary recorded measurements of HEXACO personality states, momentary interpersonal factors including current interpersonal role (with friend, family member, partner, as employee/student, alone), and social goal orientation (socializing with others, avoidance of others, asserting yourself, personal/work achievement), and dispositional anxiety and depression. Individuals' personality states were found to vary considerably across measurements in a normal distribution. Multilevel modelling analyses showed that interpersonal factors did predict within subject personality variation. Social goal orientations had a greater relative impact than interpersonal roles. Depression had a significant effect on between subject variance in state emotionality. These findings highlight the importance of interpersonal context in predicting stable personality variation.
Full-text available
Does the structure of an adult human brain alter in response to environmental demands? Here we use whole-brain magnetic-resonance imaging to visualize learning-induced plasticity in the brains of volunteers who have learned to juggle. We find that these individuals show a transient and selective structural change in brain areas that are associated with the processing and storage of complex visual motion. This discovery of a stimulus-dependent alteration in the brain's macroscopic structure contradicts the traditionally held view that cortical plasticity is associated with functional rather than anatomical changes.
Full-text available
Over the past two decades, a new picture of the cognitive unconscious has emerged from a variety of disciplines that are broadly part of cognitive science. According to this picture, unconscious processes seem to be capable of doing many things that were thought to require intention, deliberation, and conscious awareness. Moreover, they accomplish these things without the conflict and drama of the psychoanalytic unconscious. These processes range from complex information processing, through goal pursuit and emotions, to cognitive control and self-regulation. This collection of twenty original chapters examines the cognitive unconscious from social, cognitive, and neuroscientific viewpoints, presenting some of the most important developments at the heart of this new picture of the unconscious.
Two forms of thinking about the future are distinguished: expectations versus fantasies. Positive expectations (judging a desired future as likely) predicted high effort and successful performance, but the reverse was true for positive fantasies (experiencing one's thoughts and mental images about a desired future positively). Participants were graduates looking for a job (Study 1), students with a crush on a peer of the opposite sex (Study 2), undergraduates anticipating an exam (Study 3), and patients undergoing hip-replacement surgery (Study 4). Effort and performance were measured weeks or months (up to 2 years) after expectations and fantasies had been assessed. Implications for the self-regulation of effort and performance are discussed.
OBESITY is associated with serious health risks. The odds of contracting diabetes, coronary heart disease, cancer and arthritis are markedly higher in obese than in normalweight adults (Bray, 2004). Life expectancy may be reduced by as much as nine years at the highest levels of obesity. Recent estimates suggest that obesity could soon overtake smoking as the primary cause of chronic disease and premature death (Mokdad et al., 2004). The processes linking adiposity (fatness) with disease are only beginning to be understood. In the past it was assumed that adipose tissue itself was inert and that obesity-related pathology was determined largely by the underlying eating and activity habits. Current thinking is that the adipose cell mass – particularly the abdominal fat depots – is an endocrine organ, secreting a wide range of hormones that affect many bodily systems (Trayhurn & Wood, 2004). With this shift towards a more biological understanding of obesity, does psychology still have a role to play?
Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experience-sampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation.
In "Losing Control," the authors provide a single reference source with comprehensive information on general patterns of self-regulation failure across contexts, research findings on specific self-control disorders, and commentary on the clinical and social aspects of self-regulation failure. Self-control is discussed in relation to what the "self" is, and the cognitive, motivational, and emotional factors that impinge on one's ability to control one's "self." (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This document presents a set of criteria to be used in evaluating treatment guidelines that have been promulgated by health care organizations, government agencies, professional associations, or other entities. Although originally developed for mental health interventions, the criteria presented are equally applicable in other health service areas. This document is organized on the basis of two related dimensions for the evaluation of guidelines. The first dimension is treatment efficacy, the systematic and scientific evaluation of whether a treatment works. The second dimension is clinical utility, the applicability, feasibility, and usefulness of the intervention in the local or specific setting where it is to be offered. This dimension also includes determination of the generalizability of an intervention whose efficacy has been established. To encourage accountability, criteria for evaluating the process of guideline production are also provided. (PsycINFO Database Record (c) 2012 APA, all rights reserved)