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R E S E A R C H Open Access
Waste the waist: a pilot randomised controlled
trial of a primary care based intervention to
support lifestyle change in people with high
cardiovascular risk
Colin Greaves
1*
, Fiona Gillison
2
, Afroditi Stathi
2
, Paul Bennett
2
, Prasuna Reddy
3
, James Dunbar
4
, Rachel Perry
2
,
Daniel Messom
5
, Roger Chandler
6
, Margaret Francis
6
, Mark Davis
7
, Colin Green
1
, Philip Evans
1
and Gordon Taylor
2
Abstract
Background: In the UK, thousands of people with high cardiovascular risk are being identified by a national
risk-assessment programme (NHS Health Checks). Waste the Waist is an evidence-informed, theory-driven
(modified Health Action Process Approach), group-based intervention designed to promote healthy eating and
physical activity for people with high cardiovascular risk. This pilot randomised controlled trial aimed to assess
the feasibility of delivering the Waste the Waist intervention in UK primary care and of conducting a full-scale
randomised controlled trial. We also conducted exploratory analyses of changes in weight.
Methods: Patients aged 40–74 with a Body Mass Index of 28 or more and high cardiovascular risk were identified
from risk-assessment data or from practice database searches. Participants were randomised, using an online
computerised randomisation algorithm, to receive usual care and standardised information on cardiovascular risk
and lifestyle (Controls) or nine sessions of the Waste the Waist programme (Intervention). Group allocation was
concealed until the point of randomisation. Thereafter, the statistician, but not participants or data collectors were
blinded to group allocation. Weight, physical activity (accelerometry) and cardiovascular risk markers (blood tests)
were measured at 0, 4 and 12 months.
Results: 108 participants (22% of those approached) were recruited (55 intervention, 53 controls) from 6 practices
and 89% provided data at both 4 and 12 months. Participants had a mean age of 65 and 70% were male. Intervention
participants attended 72% of group sessions. Based on last observations carried forward, the intervention group did not
lose significantly more weight than controls at 12 months, although the difference was significant when co-interventions
and co-morbidities that could affect weight were taken into account (Mean Diff 2.6Kg. 95%CI: −4.8 to −0.3, p = 0.025).
No significant differences were found in physical activity.
Conclusions: The Waste the Waist intervention is deliverable in UK primary care, has acceptable recruitment and
retention rates and produces promising preliminary weight loss results. Subject to refinement of the physical activity
component, it is now ready for evaluation in a full-scale trial.
Trial registration: Current Controlled Trials ISRCTN10707899.
Keywords: Weight loss, Behaviour change, Diet, Physical activity, Randomised controlled trial, Pilot trial
* Correspondence: c.j.greaves@exeter.ac.uk
1
University of Exeter Medical School, St Luke’s Campus, Magdalen Road,
Exeter EX1 2LU, UK
Full list of author information is available at the end of the article
© 2015 Greaves et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Greaves et al. International Journal of Behavioral Nutrition
and Physical Activity (2015) 12:1
DOI 10.1186/s12966-014-0159-z
Background
The prevention or delay of cardiovascular disease has
great potential for both patient benefit and for reducing
costs to health services and the wider economy in most
developed countries. Cardiovascular disease is a major
cause of reduced patient quality of life, chronic functional
limitations and mortality [1,2].
Obesity and physical inactivity are strongly associated
with the development of cardiovascular disease [3,4]. A
wealth of evidence from randomised controlled trials
shows that relatively modest changes in weight (2-3Kg)
or physical activity (30–60 mins /week of moderate
intensity) modify key cardiovascular risk factors (e.g.
cholesterol, blood pressure, HbA1c) to a clinically
meaningful extent [5-9]. Type 2 diabetes (a major cause of
cardiovascular illness) is also preventable through moderate
changes in weight and physical activity in people with
Impaired Glucose Regulation (IGR) [10-12]. As well as
increasing risk for cardiovascular disease and type 2
diabetes, obesity is associated with an increased likelihood
of developing kidney disease, fatty liver disease, osteoarth-
ritis, several cancers, hypertension, dementia, depression
and sleep apnoea [13]. In the UK alone, if current trends
continue, the combined cost to the National Health Service
and to UK society of obesity related illness is forecast to
reach £49.9 billion ($82 billion) per year by 2050 [1].
In England, the Department of Health recently began a
national programme (NHS Health Checks) to screen all
adults aged 40 to 74 to identify and treat high cardiovascular
risk [14]. NHS Health Checks should include brief lifestyle
advice and more intensive interventions for people with
IGR and people with a 10-year cardiovascular risk of 20% or
more (calculated using a risk-scoring algorithm and clinical
data on risk factors [15]).
However, few of the services that have been used and
evaluated to date in the UK, such as Exercise on Referral,
Slimming on Referral and group walking schemes have
good evidence of either effectiveness or cost-effectiveness
[16-19]. This may be because, in practice, these interven-
tions often lack a sound theoretical basis, do not apply up
to date evidence on supporting behaviour change
[13,20-24] and lack adequate intensity [25,26]. Hence, more
sophisticated interventions, which draw on the developing
international evidence base for supporting behaviour
change are urgently needed.
In prior work [27], we used Intervention Mapping
techniques [28] to develop the Waste the Waist interven-
tion. This included extensive literature searching to identify
evidence-based practice [25], and assessment of the needs
of multiple stakeholders (service users, potential service
providers and NHS collaborators). The intervention
was designed to have the potential for delivery on a
large scale, a cost which is acceptable to primary care
stakeholders and to include intervention components
recommended in evidence-based guidelines for supporting
behaviour change [13,20,26,29].
This paper presents data from a pilot study designed to
assess the acceptability and feasibility of the Waste the
Waist intervention and of the methods and procedures
needed to conduct a full-scale randomised controlled trial
and to explore the potential effectiveness of the intervention
for supporting weight loss. Further data testing the process
model underpinning the intervention and qualitative
feedback on acceptability, feasibility and intervention
fidelity are reported elsewhere.
Methods
Design
A randomised controlled pilot study with nested quantita-
tive and qualitative process evaluation.
Participants
We recruited people aged 40–74 with a body mass index
(BMI) of 28Kg/m
2
or more and with high cardiovascular
risk. High cardiovascular risk was defined as any combin-
ation of a) a ten-year cardiovascular risk score of 20% or
more using either the Framingham [30] or QRISK2
algorithm [15] (these algorithms calculate the risk of future
cardiovascular events from clinical data on BMI, blood
pressure, cholesterol and other cardiovascular risk factors)
b) Impaired Glucose Regulation, defined as either a 2-hour
glucose of 7.8 to 11.0 mmol/l (Impaired Glucose Tolerance)
or a fasting plasma glucose of 6.1 to 6.9 mmol/l (Impaired
Fasting Glycaemia) c) having hypertension, hypercholester-
olemia, family history of diabetes or heart disease, history of
gestational diabetes, or polycystic ovary syndrome.
We excluded people with existing heart disease, type 2
diabetes or BMI > 45; people who were pregnant or cur-
rently using weight loss drugs; people not fluent in English;
people with terminal illness and anyone who, in their
General Practitioner’s opinion had other co-morbidities
which would prevent engagement with the intervention.
All seven of the local practices that were implementing
NHS Health Checks in Bath and North and East Somerset
at the start of the study were invited to participate and six
agreed. These practices provide a range of socio-economic
status and ethnic mix which is representative of the Bath
and North and East Somerset area. However, this area has
limited ethnic diversity, compared with the UK nationally.
Potentially eligible participants were identified either
from their NHS Health Check test results or by
searching practice databases for field codes representing
the inclusion /exclusion criteria. Participants’GPs checked
their records for exclusion criteria.
Protocol deviation
It is worth noting that our original protocol required a
minimum BMI of 30 (and 27.5 for S Asians), but we
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 2 of 13
reduced this to 28 to simplify database searching (practice
databases do not record ethnicity) and to facilitate recruit-
ment of sufficient numbers to make up an intervention
group in each participating surgery.
Sample size
The sample size was calculated to provide realistic esti-
mates (and confidence intervals (CIs)) for the recruitment
and study completion rates. Based on an expected 30%
recruitment rate, in order to estimate the recruitment rate
with 95% confidence intervals of +/−5%, we needed to
approach 323 people (and recruit around 100). A recruited
sample of 100 participants would enable estimation of
study completion rates with 95% CIs between +/−9.1
and +/−5.9% (for completion rates of 70 to 90%). To
provide reasonable confidence that recruitment would
be achievable in a range of practices, we recruited
from six general practices.
Theprimaryaimofthisstudywasnottodetect
differences between groups. However, with 100 partici-
pants, we would have 80% power to detect a difference of
3Kg or more in weight loss between groups, assuming a
standard deviation of 5.4Kg (which is typical in weight loss
trials [31,32]).
Measures
For the pilot study, the main measures of interest
were the recruitment rate and study completion rate
(the proportion providing data at 12 months). Intervention
attendance (mean number of sessions attended and the
proportion attending five or more of the nine sessions) was
also measured. All outcome measures to be used in the
main trial were also taken, as follows:
Primary outcome (for the main trial)
Change in weight in Kg at 12 months, measured on
calibrated scales.
Secondary outcomes
We measured physical activity (mins /day of at least
moderate intensity, mins /day of sedentary time, steps
per day and total accelerometer counts) using Actigraph
GT3XE accelerometers. We measured resting systolic
and diastolic blood pressure (taking the lowest of three
measurements in the right arm, following at least
3 minutes seated); fasting plasma glucose; fasting LDL,
HDL and total cholesterol; triglycerides; glycosylated
haemoglobin (HbA1c); liver function tests (our clinical
liaison specified alanine transaminase (ALT), which is
used as a marker of inflammation [33] as the main
variable of interest); BMI; waist circumference (mean
of three consecutive measurements); dietary intake
(using the DINE questionnaire) [34]; and the EQ-5D
health-related quality of life measure [35]. Based on clinical
risk markers we calculated QRISK2 ten-year cardiovascular
risk score [15] and the presence of metabolic
syndrome (a composite cardiovascular risk classifica-
tion based on having a combination of high waist
circumference, fasting plasma glucose, blood pressure
and /or lipid abnormalities. We used the WHO definition
of metabolic syndrome to assess this [36]).
All outcomes were measured on entry into the study
and 12 months later. Four months after baseline we
assessed weight, physical activity and questionnaire-based
measures to identify the immediate (short-term) impact of
the intervention on weight and lifestyle behaviours.
Demographic data
Using a questionnaire and practice records, we recorded
age, gender, level of education (primary school, some
secondary school, secondary school up to 16 years,
secondary school up to 18 years, additional training,
undergraduate university, postgraduate university),
smoking status (ever having smoked and current
smoking status), area deprivation (Index of Multiple
Deprivation derived from postcode and national census
data [37]), family history of heart disease or diabetes (in a
parent or sibling prior to age 60), and ethnicity.
Process evaluation
Both qualitative and quantitative process evaluations
were conducted to determine the relative usefulness of
different intervention components and identify ways to
refine/improve the intervention [38]. These will be
reported in detail elsewhere, but briefly incorporated a) a
battery of questionnaires at baseline, four and 12 months
to assess changes in all the variables targeted by the
process model b) semi-structured interviews with a
purposivesampleof18participantsandfocusgroups
with our seven intervention provider staff and c)
audio-recording of all intervention sessions to allow
checking of intervention fidelity.
Co-intervention and co-morbidity
Changes in medications or new diagnoses which might have
affected weight (such as thyroid problems, or a prescription
of metformin) and participation in other lifestyle-related
programmesduringthestudywererecordedbyself-report
and by examination of participants’medical records by the
researcher at the end of the study.
Intervention cost
Intervention delivery costs were estimated from timesheets
kept throughout the project by the lifestyle coaches
combined with data recorded by the researcher (RP)
on administration needs and other resource use (e.g. venue
costs, pedometers). The primary item of resource use was
the lifestyle coaches (contact time and participant-related
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 3 of 13
non-contact time). Other items of resource use were
supervisory time, venue costs for intervention delivery, con-
sumables (e.g. pedometers) and initial set-up /training costs
for the intervention. Costs for delivery of the intervention
are based on unit costs from published sources, and unit
costs collected within the study and are expressed as a
mean cost per participant.
Further information
Practices were asked to provide anonymised data on
patient characteristics for all participants who were
eligible and who were invited to take part in the study. The
data provided were age, gender, blood pressure, smoking
status, cholesterol levels, blood glucose levels, body mass
index. This allowed us to assess whether the sample
recruited was representative of the wider population.
Procedures and data collection
Potentially eligible people were invited using a joint
letter from their family doctor and a researcher to attend a
35–40 minute study recruitment meeting at their GP
surgery. Non-responders and non-attenders were sent a
reminder letter. At the recruitment meeting, a researcher
(RP) explained the study, answered questions, took consent
and measured waist circumference, baseline weight and
height. Waist-worn accelerometers and baseline question-
naires were distributed, along with instructions on their
use. A practice nurse (or phlebotomist) took blood samples
for assessment of baseline clinical measures. Where recent
(within two months) fasting blood test data were already
available, these tests were not repeated. Any people found
to have diabetes at this stage were excluded from further
participation and appropriate clinical care was initiated.
Eligible participants returned two weeks later to return
baseline questionnaires and accelerometers, and were then
randomised to either the intervention or control group
and further study instructions provided.
Four months later, data were collected by the researcher
either at the participant’s home or the University, with
accelerometers and questionnaires posted out 14 days
beforehand. Data collection at 12 months repeated the
baseline data collection procedures although questionnaire
and accelerometer data collection was arranged by post.
Non-responders were re-contacted and offered a home
visit from a research nurse, or phlebotomist.
Randomisation
After baseline measurements, participants were rando-
mised to the intervention or control group. The sample
was stratified by practice and we then used hierarchical
minimisation based on (in order of importance) BMI
(up to 35 and 35-plus); pre-diabetes status; and gender.
This method uses a computerised algorithm to allocate
participants in such a way that a stochastic (random
chance) element is preserved, but which also achieves
balance between groups in relation to key participant
characteristics [39]. An internet-based central allocation ser-
vice, with 24-hour telephone and/or internet access was
developed and implemented by the Peninsula Clinical Trials
Unit to ensure independent randomisation/minimisation of
any possible selection biases. Concealment of group
allocation from both researchers and participants was
maintained until the point of allocation.
Blinding
The researcher and participants were aware of group
allocation at the 4 month and 12 month follow up
points, but not at baseline. The rest of the research
team, including the nurse or phlebotomist taking
blood, the participants’care team and the statistician
were blind to group allocation until the allocation
codes were unlocked following analysis.
Analysis
Recruitment, intervention attendance, measures-completion
rates (for each outcome), resource use and costs and the
variance in continuous outcomes at 4 and 12 months were
reported using descriptive statistics. Accelerometer data
were processed using Actigraph Version 6 software and a
protocol successfully used in previous studies [40]. As data
for moderate-to-vigorous physical activity were highly
skewed, these data were log-transformed. Baseline
characteristics were compared between groups using
independent t-tests for continuous data and Chi-squared
tests for categorical variables. To explore the variance
further, exploratory analyses of differences between groups
were conducted for weight loss and all the secondary
outcomes using ANCOVA analyses. Baseline values for
the outcome were entered as covariates. Analyses were
based on the intention-to-treat principle, with any missing
data imputed used the last observation carried forward
(LOCF) method. To examine the possible sensitivity
of the data to assumptions about missing data, a
complete-case analysis was also conducted. The effects of
a) the presence of co-interventions and co-morbidities and
b) of any patient characteristics that differed significantly
between groups at baseline and c) the minimisation
variables on the findings were examined in further analyses
which entered these variables as covariates.
The study protocol is published on the International
Current Controlled Trials Register (ISRCTN10707899)
and the study procedures were reviewed and approved
by the NHS National Research Ethics Service SW
Research Ethics Committee. The results are reported
according to CONSORT guidance for reporting of
non-pharmacological interventions [41] as outlined
in Additional file 1.
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 4 of 13
Intervention
The development of the “Waste the Waist”intervention
is described elsewhere [27] and the intervention content
is described in detail in Additional file 2. Briefly, the
intervention aimed to encourage weight loss by
increasing physical activity, reducing intake of total
and saturated fat, increasing fibre intake and other
dietary changes (such as reducing portion sizes). Targets
were set by participants, but we presented the health
benefits of 5% weight loss and of 150 mins per week
of moderate activity and suggested that these should
be minimum long-term targets for health gain. The
intervention was based on the Australian “Greater Green
Triangle”(GGT) Programme [42], but we extended the
intervention and its theoretical model (the Health Action
Process Approach [43]) to include a greater emphasis on
social support, self-monitoring and relapse management
and the use of coping plans [27]. The intervention
processes (Figure 1) involved a) increasing motivation
(perceived importance of healthy lifestyle, self-efficacy
for achieving healthy lifestyle, perceived risk and outcome
expectations); b) making a specific action plan (including
plans for social support and for overcoming barriers
(coping plans)) and c) supporting maintenance through
repeated ‘self-regulatory cycles’of feedback/reflection, use
of self-monitoring and relapse prevention techniques and
revision of action plans. There was also a strong emphasis
on empowering participants to develop autonomous
motivation and to practice skills for lifestyle behaviour
change.
To promote sustainability of weight loss we advised
participants to make a series of small, achievable changes,
rather than dramatic, unsustainable changes. We encour-
aged participants to prioritise ideas for change that would
not detract from their enjoyment of food (for dietary
changes) or that would be enjoyable or easy to build into a
routine (for physical activity) [47].
Training and delivery
The style of delivery was considered to be important and
we trained our lifestyle coaches to use person-centred
counselling techniques derived from motivational interview-
ing (open questioning, affirmation, reflective listening,
summaries, use of the elicit-provide-elicit (e-p-e) technique
for information exchange) [48,49] to promote autonomous
motivation and to deliver all of the intervention content.
We recruited seven lifestyle coaches from the local
community with varied backgrounds and experience,
including group-based counselling (n=1), academic
qualifications in nutrition or physical activity (n=2)
and fitness industry /lifestyle coaching (n=4). A 2.5 day
training course was developed and delivered by the
co-authors (primarily CG, FG, AS). Supervision meetings
were held approximately every two months, where barriers
and solutions to delivery were discussed. The lifestyle
coaches were given formative feedback from the interven-
tion trainers based on listening to audio-recordings of 1–2
sessions for each pair of trainers.
The Waste the Waist intervention was delivered in local
community venues (e.g. community halls, meeting rooms
in GP practices after hours). The intervention consisted of
four 120-minute group based sessions in the first month
to support initial behaviour change, then five 90-minute
maintenance support sessions at 1.5, 2, 4, 6 and 9 months
after the first session. The total contact time was therefore
13.5 hours spread over 9 months. Groups consisted of
8–12 participants, facilitated by two lifestyle coaches.
Participants also received usual GP care.
Control group
Participants in the control group were posted a standard
pack of written information on cardiovascular risk and
the effects of diet and physical activity on such risk, in
addition to their usual GP care. In the Bath and North
and East Somerset area where the study took place,
Figure 1 The Process Model of Lifestyle Behaviour Change [44-46].
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 5 of 13
usual care for people with high cardiovascular risk varied
considerably, but a number of exercise-on-referral
and slimming-on-referral schemes were available and
self-referral to commercial weight loss programmes
was also possible. Support and encouragement for
weightbyGPsandpracticenurseswasalsopossible,
but this was unlikely to consist of more than simple,
brief advice. After the collection of 12 month data the
control group were offered a condensed (two session)
version of the intervention.
Results
Recruitment and retention
The flow of participants through the study is shown
in Figure 2. Of 483 people approached, 108 (22.4%,
95%CI: 18.6 to 26.2%) were randomised between Feb
2011 and August 2011. Of those randomised, 96 (88.9%,
95%CI: 83.0 to 94.8%) provided weight data at four months
and 96 (88.9%, 95%CI: 83.0 to 94.8%) provided weight data
at 12 months. The recruitment rate was 15.4 /month and
was achieved with one researcher working 3 days per week.
Sample characteristics
The sample characteristics at baseline are shown in
Table 1. The sample was 69% male and 98% white Brit-
ish with a mean age of 65 years. The mean BMI was 33
Kg/m
2
and 8.5% of the sample had Impaired Fasting
Glucose. At baseline, 58% of participants were classified
as having metabolic syndrome. The mean risk of a
cardiovascular event in the next 10 years (QRISK2
score) was 23%. There were no significant differences
between the intervention and control group regarding
BMI, pre-diabetes status, gender, or most other baseline var-
iables. However, the intervention group were significantly
older than controls, by a mean 2.9 years, had significantly
lower diastolic blood pressure by 4.5 mmHg and spent
significantly more time being sedentary (42 minutes /day).
There were no significant differences between the
recruited sample and the wider sample of eligible
participants in age, gender or cardiovascular risk
score(Table2).However,therecruitedsamplehada
significantly lower BMI (Mean Diff 1.3 Kg/m
2
; 95%
CI 0.6 to 2.0 Kg/m
2
).
Exploratory analysis of outcomes
The following analyses (Tables 3 and 4) used last observa-
tion carried forward (LOCF) to impute missing data and
the means are adjusted for baseline values of the outcome
variable: The intervention group lost significantly more
weight than controls at 4 months (Mean Diff −2.0Kg. 95%
CI: −3.3 to −0.7, p = 0.001), but not at 12 months (Mean
Diff −1.9Kg. 95%CI: −4.1 to 0.4, p = 0.103). No significant
differences between groups were found in physical activity
at either follow-up point. There were significant increases
in self-reported fibre score and fruit and vegetable intake
in the intervention group, compared with controls at both
4 and 12 months and changes in waist circumference
(see Table 4) approached significance. Between group
differences in other dietary intake variables were all
in a direction commensurate with healthier eating.
There were no significant changes in measures of
blood pressure, cholesterol measures, blood glucose or
other biometrics (see Table 4), or in quality of life
(EQ-5D VAS overall health score). However, the prevalence
Assessed for eligibility
(n = 599)
Excluded (n= 116)
Did not meet inclusion
criteria (n = 116)
Eligible population
(n = 483)
Refused to participate or
no response (n = 375)
Randomised
(n = 108)
Allocated to intervention
(n = 55)
Received Intervention
(n = 54)
Too busy (n = 1)
Allocated to TAU control
(n = 53)
Received TAU (n = 53)
Lost to follow up from 0
to 4 months (n = 8)
1 died
1 illness
4 withdrew
2 no response
Lost to follow up from 0
to 4 months (n = 4)
1 illness
1 withdrew
2 no response
Lost to follow from 0 to
12 months (n = 9)
1 died
2 illness
4 withdrew
2 no response
Lost to follow up from 0
to 12 months (n = 3)
1 illness
1 withdrew
1 no response
Analysed (N= 53)
Analysed (N= 55)
Figure 2 Flow of participants through the study.
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 6 of 13
of metabolic syndrome reduced in the intervention
group, such that there was a significant difference in
prevalence at 12 months (see Table 5), with a relative
risk reduction of 28% in the intervention group and
2% in controls.
Sensitivity analyses
The pattern of change scores in each group is shown in
Figure 3. This shows that very few people in the interven-
tion group gained weight over the 12 month study period.
However, in the control group, similar numbers of people
Table 1 Sample characteristics at baseline
Variable Whole sample
a
N Intervention N Control N p
b
Weight (kg) 97.1 (13.4) 108 96.6 (14.0) 55 97.6 (12.8) 53 0.715
BMI (Kg/m
2
) 32.7 (3.1) 108 33.0 (3.2) 55 32.3 (3.0) 53 0.253
Waist (cm) 110 (9.8) 108 110 (10.7) 55 110 (8.8) 53 0.818
Impaired fasting glucose (IFG) 9 (8.5%) 106 3 (5.7%) 53 6 (11.3%) 53 0.488
Male gender 75 (69.4%) 108 36 (65.5%) 55 39 (73.6%) 53 0.407
Age (yrs) 65.1 (7.0)* 108 66.6 (6.4) 55 63.7 (7.4) 53 0.032*
Area deprivation (IMD score) 11.9 (9.5) 108 12.0 (9.2) 55 11.7 (9.8) 53 0.860
Ethnicity
White British 106 (98.1%) 108 54 (98.2%) 55 52 (98.1%) 53
White Irish 1 (0.9%) 108 0 (0%) 55 1 (1.9%) 53
White Other 1 (0.9%) 108 1 (1.8%) 55 0 (0.0%) 53 0.368
Systolic BP (mmHg) 138.6 (16.0) 108 137.7 (15.7) 55 139.5 (16.4) 53 0.577
Diastolic BP (mmHg) 82.0 (11.9) 108 79.8 (13.7) 55 84.3 (9.3) 53 0.046*
Fasting glucose (mmol/l) 5.28 (0.56) 106 5.21 (0.50) 53 5.36 (0.60) 53 0.164
Fasting LDL (mmol/l) 3.19 (0.96) 106 3.19 (0.95) 55 3.20 (0.99) 51 0.930
Fasting HDL (mmol/l) 1.37 (0.37) 107 1.36 (0.34) 55 1.39 (0.39) 52 0.672
Fasting total (mmol/l) 5.36 (1.16) 107 5.29 (1.05) 55 5.44 (1.27) 52 0.519
Triglycerides (mmol/l) 1.72 (0.81) 107 1.70 (0.70) 55 1.74 (0.92) 52 0.810
Hba1c (mmol/l) 38.6 (4.29) 106 38.1 (3.5) 54 39.1 (5.0) 52 0.231
Liver function: ALT (IU/l) 28.9 (11.3) 103 27.6 (11.2) 53 30.3 (11.3) 50 0.235
Metabolic syndrome 61 (57.5%) 106 29 (53.7%) 54 32 (61.5%) 52 0.415
QRISK2 Score: 10 year risk (%) 23.1 (10.1) 106 23.6 (9.8) 55 22.5 (10.4) 51 0.556
Dietary intake (DINE scores)
Fat score 31.1 (10.1) 106 30.0 (9.1) 54 32.2 (10.9) 51 0.252
Unsaturated fat score 9.04 (1.80) 105 9.31 (1.65) 54 8.75 (1.93) 51 0.106
Fibre score 36.8 (10.8) 105 36.7 (11.6) 54 37.0 (10.0) 51 0.903
Fruit and Veg score 21.3 (6.9) 105 21.6 (7.3) 54 21.1 (6.6) 51 0.748
Physical activity
MVPA (min /day) 22.0 (19.5) 106 22.5 (22.3) 53 21.6 (16.5) 53 0.813
Sedentary time (min /day) 567.6 (84.1)) 106 588.8 (74.4) 53 546.4 (88.5) 53 0.009*
Steps /day 6486 (2757) 106 6420 (3016) 53 6551 (2499) 53 0.807
Counts /minute of valid wear-time (Axis 1) 255.6 (113.1) 106 240.6 (118.4) 53 270.5 (106.6) 53 0.175
EQ-5D VAS overall health score 76.7 (15.9) 107 77.0 (14.9) 55 76.4 (17.0) 52 0.843
Education levels
Up to age 16 or less 50 (46.3%) 108 20 (36.4%) 55 30 (56.6%) 53
Up to age 18 8 (7.4%) 108 6 (10.9%) 55 2 (3.8%) 53
Some additional 23 (21.3%) 108 14 (25.5%) 55 9 (17.0%) 53
Undergraduate or higher degree 27 (25.0%) 108 15 (27.3%) 55 12 (22.7%) 53 0.193
a
Figures are mean (SD) for continuous data, or N(%) for categorical data.
b
Analyses are independent group t-tests for continuous data or Chi
2
for categorical data.
*p<0.05.
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 7 of 13
gained and lost weight and the mean weight loss was
dominated by the success of four individuals, who each
lost over 15Kg.
The results were sensitive to co-interventions and
co-morbidities, as follows: More controls (n=7) than
intervention group participants (n=1) developed illnesses
or took medications that could affect weight. More
controls (n=6) than intervention group participants (n=2)
engaged in other weight loss programmes during the
course of the study. When these potential influences
were entered into the analysis as covariates, there was
a significant difference in weight loss between groups
at 12 months, favouring the intervention group (Mean
Diff 2.6Kg. 95%CI: −4.8 to −0.3, p = 0.025).
However, the results were not sensitive to differences
between characteristics at baseline. Analyses including
baseline values for age, diastolic blood pressure and
sedentary time as covariates (either together or separately),
or including the minimisation variables (BMI, pre-diabetes
status and gender) found that none of these variables had a
significant covariate effect (p > 0.1 in all cases).
A complete-case analysis of the data (including only
people who provided data at both time points) showed
that the intervention group lost significantly more
weight then controls at 4 months (Mean Diff 2.5Kg: 95%
CI: −3.9 to −1.1, p = 0.001, N = 96). The difference at
12 months approached, but did not reach significance
(Mean Diff 2.4Kg: 95%CI: −4.8 to 0.1, p = 0.06, N = 96).
Other relevant data
The correlation between baseline weight and weight at
12 months was 0.88 (p < 0.001). The intra-cluster correl-
ation coefficient (ICC) for clustering of weight loss (0–12
months) by GP practice was 0.000 (95% CI 0.000, 0.084)
for the whole sample (and was similar when examined
within each group).
Attendance
Participants attended between zero and nine sessions
(median = 7), with 70% of participants attending 5 or
more of the 9 sessions. Attendance tapered off over
time, particularly after session 6 (the attendance rates
from sessions 1 to 9 respectively were 86%, 80%, 79%,
75%, 73%, 74%, 55%, 59%, 64%). Those attending 5 or
more sessions lost 3.7Kg more at 4 mths than those
attending less than 5 sessions (95%CI: 2.0 to 5.5, p < 0.001)
and 4.1Kg more at 12 mths (95%CI: 1.2 to 7.1, p = 0.007).
Acceptability and feasibility
Feedback from questionnaires and participant interviews
suggested a high level of satisfaction with the intervention.
The fidelity checks showed good overall intervention
fidelity, with some variation between providers. However,
we identified a number of ways that we could improve
intervention delivery and the content of the intervention
and these will be reported in our quantitative and
qualitative process evaluation reports.
Cost
The intervention cost was estimated to be £310 per par-
ticipant. This is based on delivery to groups of 10 people
with 2 lifestyle coaches per group. A detailed breakdown
of costs is provided in Additional file 3.
Discussion
Summary of findings
In this study we established the feasibility of conducting
a full-scale trial of the Waste the Waist intervention. We
successfully recruited a population with high cardiovascular
risk, the intervention sessions were well attended and there
is a strong signal that the intervention has the potential to
be effective. People receiving the intervention lost an aver-
age 3.3Kg at 4 months and 4.3Kg at 12 months, demonstrat-
ing an encouraging weight maintenance profile. The control
group lost more weight than expected, but this was largely
explained by the performance of four individuals. Weight
loss in the control group was mediated by the uptake of
other weight loss programmes and co-morbidities. When
these were taken into account, the difference between
groups was significant at both time-points. Furthermore,
increased exposure to the intervention was associated with
aconsiderableincreaseinweightloss.
Changes in biometric risk markers were not significant,
but the study was not powered to detect such changes.
However, the presence of metabolic syndrome was
reduced significantly at follow up in the intervention
group compared with controls. This may reflect increased
sensitivity to change in weight for this composite outcome
Table 2 Comparison of participants and non-participants
Participants
a
N Eligible non-participants N
b
p-value
c
Age 65.1 (7.0) 108 65.0 (7.3) 276 0.835
BMI 32.7 (3.1) 108 34.0 (3.4) 400 <0.001
QRISK2 23.1 (10.1) 106 22.8 (9.3) 352 0.820
% Male 75 (69.4%) 108 262 (62.5%) 419 0.216
a
Figures are mean (SD) for continuous data, or N(%) for categorical data.
b
Not all surgeries were able to supply full demographic data for non-participants.
c
Analyses are independent group t-tests for continuous data or Chi
2
for categorical data.
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 8 of 13
Table 3 Changes in weight and lifestyle behaviours
Variable Group Change 0–4 mth N Adjusted mean
difference
a
Change 0–12 mth N Adjusted mean
difference
a
Weight
Weight (Kg) Intervention −3.33 (3.48) 47 −2.46 −4.25 (5.49) 46 −2.38
−3.86 to −1.06 −4.84 to 0.08
p = 0.001** p = 0.058Control −0.99 (3.57) 49 −2.04 (6.87) 50
Weight LOCF Intervention −2.85 (3.43) 55 −1.98 −3.65 (5.22) 55 −1.85
−3.27 to −0.69 −4.08 to 0.38
p = 0.103p = 0.003**Control −0.92 (3.44) 53 −1.90 (6.69) 53
Physical Activity (LOCF)
MVPA (min/day) Intervention 1.79 (14.10) 53 −1.49 −0.51 (14.88) 53 −3.45
−6.89 to 3.92 −9.38 to 2.48
p = 0.251p = 0.587Control 3.43 (14.60) 53 3.27 (18.82) 53
Log MVPA (min/day)
b
Intervention 0.019 (0.388) 53 −0.061 −0.021 (0.393) 53 −0.091
−0.195 to 0.072 −0.262 to 0.080Control 0.065 (0.331) 53 0.049 (0.538) 53
p = 0.291p = 0.364
Steps/day Intervention 126.4 (1400) 53 −200 −141.0 (1903) 53 −345
−1100 to 410
p = 0.367−838 to 438
p = 0.535Control 309.1 (1932) 53 166.1 (2291) 53
Sedentary time (min/day) Intervention −11.9 (62.0) 53 13.3 −4.52 (56.05) 53 16.2
−6.3 to 38.6
p = 0.156−9.7 to 36.4
p = 0.255Control −16.0 (58.7) 53 −11.6 (61.4) 53
Counts/minute of valid wear-time (Axis 1) Intervention 15.0 (74.0) 53 −10.6 1.36 (74.52) 53 −25.3
−57.0 to 6.3
p = 0.116−38.8 to 17.6
p = 0.457Control 22.5 (72.3) 53 19.0 (96.2) 53
Dietary intake (LOCF)
Fat score Intervention −5.76 (8.84) 54 −2.29 −5.00 (7.97) 54 −2.22
−5.12 to 0.68
p = 0.132−5.31 to 0.72
p = 0.134Control −4.50 (9.19) 52 −4.00 (10.34) 52
Unsaturated fat score Intervention 0.57 (1.80) 54 0.309 0.43 (1.81) 54 0.334
−0.235 to 0.903−0.314 to 0.933
p = 0.247Control 0.63 (2.10) 51 0.51 (2.09) 51p = 0.328
Fibre score Intervention 3.13 (8.86) 54 5.72 2.94 (10.87) 54 5.33
1.83 to 8.82
p = 0.003**2.80 to 8.65
p = 0.000**Control −2.69 (8.04) 51 −2.51 (10.02) 51
Fruit & Vegetable score Intervention 2.00 (5.62) 54 3.08 2.39 (7.08) 54 2.91
0.65 to 5.16
p = 0.012*1.10 to 5.07
p = 0.003**Control −0.98 (5.03) 51 −0.35 (5.48) 51
a
ANCOVA analysis with baseline value entered into the model.
b
MVPA data were highly skewed, so analyses were repeated using log-transformed data. *p<0.05.
**p < 0.01
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 9 of 13
and differences in the pattern of change in individuals
(most intervention group participants lost some weight,
but changes in the control group were concentrated in
only a few individuals).
The level of weight loss achieved here (between 1.9
and 2.6 Kg more than controls at 12 months, depending
which covariates are taken into account) compares well
with established weight loss programmes used in primary
care, including commercial weight loss programmes. These
typically achieve 2 to 3 Kg of weight loss compared with
usual care at 12 months of follow-up based on similar
intention-to-treat analyses [19,31]. However, a full-scale
trial in a larger sample is needed to produce an accurate
estimate of effectiveness and cost-effectiveness.
Our intervention also seems to have a good weight
maintenance profile compared with other weight loss
interventions, which typically show a tendency towards
weight regain following the initial intervention period
[50]. This may reflect the fact that we offered (tapered)
support for up to 9 months, or it may reflect our specific
efforts to teach participants skills for sustainable
management of their weight. The longer term maintenance
profile (beyond 12 months) remains to be established.
The lack of any discernible impact on physical activity is
aconcern,giventhatthiswasoneofthekeybehaviour
change targets of the intervention. This may be due to a
lack of attention to the process of translating motivation
into practice by building a sense of competence to perform
the target activities and to address concerns about safety of
exercising, or about likely enjoyment. To do this we could
incorporate more techniques such as setting up prompts/
cues, prompting practice, or setting up rewards contingent
on progress, which have recently been associated with
increased physical activity in obese populations [51].
Modelling or demonstrating the intended exercise
may also help to promote the take up of physical activity
in older people [23,52]. These ideas are reinforced by data
from our qualitative and quantitative process evaluation
(to be reported elsewhere), suggesting that incorporation
of practical demonstration/practice sessions, providing
links to local activity-provision service and encouraging
co-attendance of local activities by two or more group
members might help to increase engagement with the
physical activity component of the intervention.
Strengths and limitations
The main strength of this study is the use of rigorous
methods to assess the feasibility of conducting a full-scale
randomised controlled trial and the conduct of exploratory
analyses of the potential efficacy of the intervention. We
used objective methods where possible to assess outcomes,
including weight and physical activity. However, the study
has some limitations: The randomisation resulted in a
good balance between groups in terms of initial BMI,
cardiovascular risk and gender. However, there were
significant differences in age (as well as diastolic
blood pressure and sedentary time, which may be related to
age). We would therefore propose to add age as a mini-
misation variable in the subsequent full-scale trial. Methods
for imputing missing data are widely debated [53] and we
may consider using alternative imputation methods for the
main trial. However, given the overall weight loss profile
(increasing weight loss over time), LOCF seems a reason-
able imputation method. Due to the small sample size, the
large number of analyses conducted and the exploratory
nature of this study, no definitive conclusions can be drawn
about differences between groups for any of the outcomes
measured. Although the care team was blinded to group
allocation, Controls may have discussed this with their GPs
and may have been more likely to seek out or take up offers
of alternative interventions once they knew they were in
the control group. Hence, given the increasing availability
of community based weight loss interventions (both
commercial and via referral from general practice), future
studies should plan to monitor this and include it as
a covariate in the primary analysis. Being from a single,
relatively affluent locality, the sample was not representative
Table 4 Changes in biometric variables and quality of life
at 12 months
Variable Adjusted mean difference
between groups (95%CI)
a
12 mths N
QRISK2 (10 year risk of CV event, %) −0.76 (−2.19 to 0.66) 106
Systolic BP (mmHG) 1.09 (−3.67 to 5.85) 108
Diastolic BP (mmHG) 0.30 (−3.50 to 4.09) 108
Fasting glucose (mmol/l) −0.13 (−0.29 to 0.04) 106
Fasting LDL (mmol/l) 0.15 (−0.08 to 0.37) 106
Fasting HDL (mmol/l) −0.01 (−0.08 to 0.06) 107
Total cholesterol (mmol/l) 0.09 (−0.17 to 0.35) 107
Triglycerides (mmol/l) −0.19 (−0.39 to 0.02) 107
Hba1c (mmol/l) −0.84 (−1.89 to 0.21) 106
Waist (cm) −2.18 (−4.43 to 0.06)
b
108
BMI (Kg/m
2
)−0.51 (−1.28 to 0.26) 108
ALT (IU/l) 0.92 (−2.43 to 4.27) 103
EQ5D 1.36 (−3.37 to 6.04) 107
a
Based on ANCOVA analysis of LOCF data with baseline value entered into the
model.
b
p = 0.06
Table 5 Changes in the prevalence of metabolic
syndrome (using LOCF)
N % with metabolic syndrome Pearson
Chi
2
p
value
Intervention Control
Baseline 106 53.7 61.5 0.67 0.438
12 mth 107 38.9 60.4 4.94 0.034
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 10 of 13
of the wider UK population, particularly in terms of
ethnicity and socio-economic status (i.e. deprivation
index). Replication of the study in a larger and more
diverse sample is therefore needed to establish the
effectiveness of the intervention in a wider range of
populations.
It is worth noting the high correlation between baseline
and follow up weight. This may reflect the large proportion
of relatively invariant mass in the body (from bone and
organs, as opposed to fat) and is an important consider-
ation for sample size calculationinweightlossstudies.
Because of the high degree of invariant mass, sample size
should be based on the standard deviation for change in
weight or on ANCOVA models with the correlation
between baseline and follow-up taken into account.
Implications for practice/future research
The proposed intervention has the potential to impact
on the health of those at high risk of cardiovascular
disease, and on NHS resource use. There is a high
level of interest currently amongst primary care clinicians
and commissioners about how to effectively support
lifestyle change for cardiovascular risk management.
If the generalisability and effectiveness are established
Weight change 0-12 months
Control
Intervention
Figure 3 Pattern of weight loss for individual participants.
Greaves et al. International Journal of Behavioral Nutrition and Physical Activity (2015) 12:1 Page 11 of 13
in a full-scale trial, the intervention could also be
adapted for people with diabetes and/or cardiovascular
disease.
Conclusions
The Waste the Waist intervention is deliverable in UK
primary care, has acceptable recruitment and retention
rates and, although this was only its first use, it seems to
have a good potential for delivering clinically meaningful
levels of weight loss. Subject to changes needed to
the physical activity component and other changes
suggested by our process evaluation (reported else-
where), the intervention is now ready for evaluation
in a full-scale trial.
Additional files
Additional file 1: CONSORT statement for this article.
Additional file 2: Intervention description. The Waste the Waist
Intervention.
Additional file 3: Intervention costs for the Waste the Waist
intervention.
Competing interests
Colin Greaves has received payment to prepare an evidence summary for a
commercial weight loss organisation (Weight Watchers). The remaining
authors declare that they have no competing interests.
Authors’contributions
PB, FG, AS, GT and CG conceived of the study, and participated in its design
and coordination and helped to draft the manuscript. FG, AS, GT, RP, MD,
DM participated in its design and coordination and helped to draft the
manuscript. PR, JD, RC, MF, CGreen, PE participated in its design and helped
to draft the manuscript. GT and CGreaves performed the statistical analyses.
All authors read and approved the final manuscript.
Acknowledgements
This paper presents independent research funded by the National Institute
for Health Research (NIHR) under its Research for Patient Benefit Research
Programme (Grant Reference Number PB-PG-0609-19144). The views
expressed are those of the author(s) and not necessarily those of the NHS,
the NIHR or the Department of Health. We thank all the research practices
and participants who participated in the study and would like to thank the
following for additional advice and help during the course of the study: Dr
Paul Scott (Bath and North & East Somerset County Council), Prof Rod Taylor
and Prof John Campbell (University of Exeter Medical School).
Funding source
National Institute of Health Research.
Author details
1
University of Exeter Medical School, St Luke’s Campus, Magdalen Road,
Exeter EX1 2LU, UK.
2
University of Bath, Claverton Down, Bath BA2 7AY, UK.
3
School of Medicine and Public Health, University of Newcastle, University
Drive, Callaghan, NSW 2308, Australia.
4
Greater Green Triangle University
Department of Rural Health, Flinders and Deakin Universities, PO Box 423,
Warrnambool, VIC 3280, Australia.
5
Bath, Gloucester, Swindon, Wiltshire Area
Public Health Team, Public Health England, 1st Floor Bewley House,
Marshfield Road, Chippenham, Wiltshire SN15 1JW, UK.
6
Waste the Waist
Service User Advisory Group, c/o Colin Greaves, University of Exeter Medical
School, St Luke’s Campus, Magdalen Road, Exeter EX1 2PU, UK.
7
Centre for
Exercise, Nutrition and Health Sciences, University of Bristol, 8 Priory Road,
Bristol BS8 1TZ, UK.
Received: 18 March 2014 Accepted: 19 December 2014
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