Identification of Behaviour Change Techniques in Deprescribing Interventions: A Systematic Review and Meta-Analysis

Abstract

Aims:
Deprescribing interventions safely and effectively optimise medication use in older people. However, questions remain about which componentsof interventions are key to effectively reduce inappropriate medication use. This systematic review examines the behaviour change techniques (BCTs) of deprescribing interventions and summarises intervention effectiveness on medication use and inappropriate prescribing.

Methods:
MEDLINE, EMBASE, Web of Science and Academic Search Complete and grey literature were searched for relevant literature. Randomised controlled trials (RCTs) were included if they reported on interventions in people aged ≥65 years. The BCT taxonomy was used to identify BCTs frequently observed in deprescribing interventions. Effectiveness of interventions on inappropriate medication use was summarised in meta-analyses. Medication appropriateness was assessed in according to STOPP criteria, Beers' criteria and national or local guidelines Between study heterogeneity was evaluated by I-squared and Chi-squared statistics. Risk of bias was assessed using the Cochrane Collaboration Tool for randomised controlled studies.

Results:
Of the 1561 records identified, 25 studies were included in the review. Deprescribing interventions were effective in reducing number of drugs and inappropriate prescribing, but a large heterogeneity in effects was observed. BCT clusters including goals and planning; social support; shaping knowledge; natural consequences; comparison of behaviour; comparison of outcomes; regulation; antecedents; and identity had a positive effect on the effectiveness of interventions.

Conclusions:
In general, deprescribing interventions effectively reduce medication use and inappropriate prescribing in older people. Successful deprescribing is facilitated by the combination of BCTs involving a range of intervention components.

Full text

SYSTEMATIC REVIEW AND META‐ANALYSIS
Identification of behaviour change techniques
in deprescribing interventions: a systematic
review and meta-analysis
Correspondence Christina R. Hansen, Pharmaceutical Care Research Group, School of Pharmacy, Cavanagh Pharmacy Building,
University College Cork (UCC), College Road, Cork, Ireland. Tel.: +353 21 490 1690; E-mail: christina.raaehansen@ucc.ie
Received 1 February 2018; Revised 31 July 2018; Accepted 12 August 2018
Christina R. Hansen
1,2
,DenisO’Mahony
3,4
, Patricia M. Kearney
5
, Laura J. Sahm
1,6
, Shane Cullinan
7
,
C.J.A. Huibers
8
, Stefanie Thevelin
9
, Anne W.S. Rutjes
10
, Wilma Knol
8
, Sven Streit
11
and Stephen Byrne
1
1
Pharmaceutical Care Research Group, School of Pharmacy, Cavanagh Pharmacy Building, University College Cork, Cork, Ireland,
2
Section for Social
and Clinical Pharmacy, Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen Ø, Denmark,
3
Department of Medicine, University College Cork, Cork, Ireland,
4
Department of Geriatric Medicine, Cork University Hospital, Cork, Ireland,
5
Department of Epidemiology & Public Health, UCC, Cork, Ireland,
6
Pharmacy Department, Mercy University Hospital, Cork, Ireland,
7
School of
Pharmacy,RoyalCollegeofSurgeonsofIreland,Dublin,Ireland,
8
Department of Geriatric Medicine and Expertise Centre Pharmacotherapy in Old
Persons, University Medical Centre Utrecht, Utrecht, The Netherlands,
9
Clinical Pharmacy Research Group, Louvain Drug Research Institute,
Université Catholique de Louvain, Brussels, Belgium,
10
Institute of Social and Preventive Medicine, University of Bern, Switzerland & Institute of
Primary Health Care (BIHAM), University of Bern, Switzerland, and
11
Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
Keywords behaviour change techniques, deprescribing, meta-analysis, systematic review
AIMS
Deprescribing interventions safely and effectively optimize medication use in older people. However, questions remain about
which components of interventions are key to effectively reduce inappropriate medication use. This systematic review examines
the behaviour change techniques (BCTs) of deprescribing interventions and summarizes intervention effectiveness on medication
use and inappropriate prescribing.
METHODS
MEDLINE, EMBASE, Web of Science and Academic Search Complete and grey literature were searched for relevant literature.
Randomized controlled trials (RCTs) were included if they reported on interventions in people aged ≥65 years. The BCT taxonomy
was used to identify BCTs frequently observed in deprescribing interventions. Effectiveness of interventions on inappropriate
medication use was summarized in meta-analyses. Medication appropriateness was assessed in accordance with STOPP criteria,
Beers’criteria and national or local guidelines. Between-study heterogeneity was evaluated by I-squared and Chi-squared statis-
tics. Risk of bias was assessed using the Cochrane Collaboration Tool for randomized controlled studies.
RESULTS
Of the 1561 records identified, 25 studies were included in the review. Deprescribing interventions were effective in reducing
number of drugs and inappropriate prescribing, but a large heterogeneity in effects was observed. BCT clusters including goals
and planning;social support;shaping knowledge;natural consequences;comparison of behaviour;comparison of outcomes;regulation;
antecedents;andidentity had a positive effect on the effectiveness of interventions.
British Journal of Clinical
Pharmacology
Br J Clin Pharmacol (2018) •• ••–•• 1
© 2018 The Authors. British Journal of Clinical Pharmacology
published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.
DOI:10.1111/bcp.13742
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproductioninany
medium, provided the original work is properly cited and is not used for commercial purposes.

CONCLUSIONS
In general, deprescribing interventions effectively reduce medication use and inappropriate prescribing in older people.
Successful deprescribing is facilitated by the combination of BCTs involving a range of intervention components.
Introduction
Older people (aged ≥65years)aremorevulnerableto
medication-related harm and inappropriate prescribing
than younger chronically medicated people [1, 2]. Age-
related physiological changes contribute to iatrogenic
vulnerability in older people, but it is equally a conse-
quence of their multimorbidity and frequent use of multi-
ple medications [1, 3–7]. Vulnerability, polypharmacy and
multimorbidity represent complex challenges in the care
of older people and often exclude them from clinical trials
[6, 8–10]. Therefore, some prescriptions in multimorbid
older people are without clear-cut evidence to support
them and inappropriate prescribing is highly prevalent
[11–13]. Excessive inappropriate prescribing in older people
has turned the focus of current research towards
deprescribing –the systematic process of identifying and
discontinuing drugs in patients for which existing and
potential harms outweigh the benefits [14]. Making
informed decisions to deprescribe with the goal of reducing
inappropriate prescribing and improving patient outcome
is hampered by a lack of evidence of withdrawal effects in
older people and is further challenged by prescriber- and
patient-related factors [15, 16].
Research has demonstrated safety and effectiveness of
deprescribing in older people (aged ≥65 years) [17] whilst
reluctance of prescribers to deprescribe a medication
commenced by another prescriber is described as well [18].
Although evidence suggests that pharmacist involvement
and patient-centred interventions are effective, the best ways
to engage and support prescribers in deprescribing remain
unclear [16, 19–23]. Previous reviews examining the effects
of deprescribing interventions on clinical outcomes call for
a better understanding of successful implementation of
deprescribing [6, 17–19].
Within the clinical context of patient care, there is a need
to ensure that behaviour change is a part of any intervention
design in order to maximize the chance that prescribers are
enacting on recommendations [24, 25]. Recent advances in
behavioural science provide insight into the components of
complex interventions aiming at behaviour change. The
Behaviour Change Techniques (BCTs) taxonomy version 1
(BCTTv1) [26] is designed to assist in the identification of
BCTs of interventions. A BCT is defined as ‘an observable,
replicable, and irreducible component of an intervention
designed to alter or redirect causal processes that regulate
behaviour’[27]. A clear description of BCTs will clarify the
essential content of these complex interventions in a
consistent way to assist in future replication of effective
interventions [28]. The application of the BCT taxonomy to
deprescribing is novel. This review was designed to comple-
ment previous reviews [6, 17, 19] on deprescribing by offering
a broader analysis of behaviour change techniques in
deprescribing interventions.
The aims of this review are (i) to identify behaviour
change techniques used more frequently in interventions
effective in reducing number of drugs and inappropriate pre-
scribing, (ii) to describe other characteristics of deprescribing
interventions and (iii) to determine intervention effective-
ness on drug use, prescribing appropriateness and Medica-
tion Appropriateness Index (MAI) score in meta-analyses.
Methods
A systematic search of the primary, secondary and grey
literature to identify randomized controlled trials (RCTs)
on deprescribing was undertaken on December 14, 2016.
This systematic review was reported according to the
PRISMA guidelines for systematic reviews and meta-
analyses [29], and was registered in Prospero (record no.
CRD42016037730).
Search strategy
The search strategy was designed in conjunction with an
experienced medical librarian (JM) who was trained in
systematic review methodology. A combination of text words
and subject headings (such as MeSH terms) related to the
intervention was used, without restricting publication date
or language (Table S1).
The following electronic bibliographic databases were
searched: MEDLINE, EMBASE, The Cochrane Central Register
of Controlled Trials, Web of Science and Academic Search
Complete. Grey literature was searched via the Google
Scholar
®
search engine and from screening reference lists of
included studies as well as relevant systematic reviews.
Additional searches were done in the System for Information
on Grey Literature in Europe (OpenSIGLE) and the clinical
trial registries, namely ClinicalTrials.gov, International Stan-
dard Registered Clinical/soCial sTudy Number (ISRCTN),
WHO International Clinical Trials Registry Platform (ICTRP)
and the Australian New Zealand Clinical Trials Register
(ANZCTR).
Study selection
One reviewer (C.H.) screened titles of all retrieved citations.
Two reviewers (C.H. and S.C.) independently screened
abstracts and full-texts for eligibility according to protocol-
defined inclusion and exclusion criteria. Any disagreements
between reviewers were resolved by consensus and both
reviewers agreed on the final inclusion of studies.
Inclusion and exclusion criteria
Inclusion was restricted to randomized controlled study
design, including randomized controlled trials (RCTs) and
cluster RCTs. The control group could involve either active
interventions or inactivity, e.g. sham or no intervention. This
C. R. Hansen et al.
2 Br J Clin Pharmacol (2018) •• ••–••

study design was chosen to allow for between-study
comparison of intervention effectiveness in meta-analyses.
Studies were included if they reported on interventions
encouraging the deprescribing of existing drugs or the
reduction of existing inappropriate prescribing. Only those
interventions involving older patients (aged ≥65 years) or a
healthcare professional with prescribing, dispensing or
administration authority were included. No restrictions were
applied to language, clinical setting of the intervention,
sample size, blinding procedures or other design characteris-
tics. We excluded interventions specifically focusing on the
clinical effects of drug withdrawal processes, e.g. opioid
withdrawal effects.
Risk of bias assessment
Risk of bias was assessed separately by two reviewers (C.H.
and A.R.) using the Cochrane Collaboration Tool for random-
ized controlled studies [30] with a descriptive purpose of
summarizing the quality of the studies that met inclusion
criteria. Studies were not excluded from data analysis because
of methodological flaws if they otherwise met inclusion
criteria. Incomplete outcome data was in general rated as
high risk of bias if the loss of patients to follow-up was 20%
or higher and rated as low risk of bias if the loss was 10% or
less. Imbalance in the numbers lost to follow-up between in-
tervention and control groups was also considered to intro-
duce bias. The risk of bias assessment is described in detail
in Table S2.
Data extraction strategy
Data were collected using a pre-agreed data extraction form
(see Table S3). Two reviewers (C.H. and L.S.) independently
pilot tested the form on two randomly chosen studies both
included in the review. Thereafter data extraction on all
studies was completed independently by L.S. and C.H.
Disagreements on study inclusion/exclusion were resolved
by discussion leading to consensus; where consensus could
not be achieved, the study was excluded. Primary outcomes
were: (i) number of total and inappropriate prescriptions
and/or drugs as defined in the individual studies according
to prescribing appropriateness criteria, e.g. STOPP criteria,
Beers’criteria and local or national prescribing guidelines;
(ii) proportion of participants with a reduction in number of
total and inappropriate prescriptions and/or drugs; and (iii)
implementation of recommendations. Secondary outcome
was change in MAI score.
Behaviour change techniques coding
Coding of BCTs was performed independently by two
reviewers (C.H. for all interventions and C.J.A., S.T. and L.S.
for a subset of interventions each) by identifying BCTs for
each intervention using the BCTTv1 [26]. C.H. had
completed online training in BCTTv1. A coding manual and
instructions made by C.H. were given to the other reviewers
and, exercises from the online training were made available
to them. Any questions about the coding were solved by
discussion and consensus between the reviewers. The target
behaviour was the decision making to discontinue a drug or
an inappropriate prescription. Findings were tabulated across
studies by computing frequencies. The information was used
to determine the BCTs used more frequently in studies that
reported effectiveness of interventions to reduce number of
drugs and/or improve prescribing appropriateness.
Statistical analysis
We calculated odds ratios (OR) with standard deviations (SD)
for each of the reported outcomes and used RevMan v5.3 to
statistically combine the outcome data [31]. Continuous out-
comes were expressed as difference in means between groups
with a 95% confidence interval (95% CI). The level of
between-study heterogeneity was evaluated by calculation
of the I
2
and Chi-squared statistics. Where possible, stratified
random effects meta-analyses was used to identify factors
affecting intervention effectiveness. Subgroup analyses were
performed by risk of bias assessment, intervention setting
and intervention target. If the level of reporting did not allow
for inclusion of a study in one or more meta-analyses,
additional information was sought from the study authors.
If the information was not made available, the study was
excluded from the meta-analysis.
Results
Literature search and review process
The database search identified 1444 records, and grey liter-
ature yielded 117 records. After removal of duplicates and
title screening, 178 abstracts were screened for eligibility
and 58 of these met the inclusion criteria. Assessment of
full texts resulted in 25 studies included in this review
[32–56]. Study selection and reasons for exclusion are illus-
trated in Figure 1.
Study characteristics
Included studies were RCTs (n= 22) [32–41, 43–45, 47,
49–56] and cluster RCTs (n= 3) [42, 46, 48] with a
follow-up period from 6 weeks [45] to 13 months [42]. A
total of 20 812 patients were enrolled in the studies
ranging from 95 [41] to 1188 per study [55]. Detailed study
characteristics are provided in Table 1. Three studies aimed
primarily to reduce the number of drugs taken by patients
[41, 44, 46]. Other objectives included reduced prevalence
of inappropriate medications [32, 33, 38, 39, 42, 49],
improved prescribing appropriateness [34–36, 47, 50–55],
or better patient health outcomes and medicines manage-
ment [35, 40, 48, 56]. Ten out of the 25 studies included
in this review showed evidence to support intervention
effectiveness [34, 35, 37, 40, 41, 45, 46, 50, 53, 56]. Most
of the studies reporting intervention effectiveness of the
key outcomes of this review delivered recommendations
or feedback to the prescriber orally, often face-to-face, and
many of them followed up on the recommendations/
feedback given. Recommendations and feedback were
given immediately after identification of a problem or at
the time of prescribing using an on-demand service. For
studies reporting no intervention effectiveness on the key
outcomes, some delivered recommendations using written
communication and many of the interventions did not fol-
low up on the recommendations with the prescriber. None
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 3

of the included studies reported the use of explicit theories
of behaviour change as part of the interventions and no
study reported the use of a systematic and theoretical ap-
proach, such as the UK Medical Research Council’scom-
plex intervention framework [57], in the intervention
design. Reported educational interventions were based on
the principles of constructive learning theory in one study
[39] and social constructivist learning and self-efficacy the-
ory in another study [46].
Risk of bias
Risk of bias assessment is illustrated in Figure 2. Risk of bias
not pertaining to any of the defined categories were catego-
rized as ‘others’and these are described in Table S2.
Behaviour change techniques
All but one study [48] reported the behaviour change compo-
nents underpinning the intervention. The BCT coding is
Figure 1
PRISMA flow chart of study selection
C. R. Hansen et al.
4 Br J Clin Pharmacol (2018) •• ••–••

Table 1
Characteristics of included studies (n=25)
Author
(year) Country Setting
No. of patients
%Female
Mean age of
patients
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
Allard et al.
(2001) [32]
Canada
Community
266
67.7%
80.6 (4.5) (I) Medication review and
suggestions made and
mailed to GPs
(D) Multidisciplinary team
of physicians, pharmacists
and nurses
Reducing the number
of potentially inappropriate
prescriptions given.
(P) GPs.
Bregnhøj et al.
(2009) [47]
Denmark
Primary care
physician practice
212
66.1%
76.5 (7.2) (I) Interactive educational
meeting (single intervention)
and combined with individualized
feedback on prescribed medication
(combined intervention)
(D) Clinical pharmacologist
and pharmacists
Improving prescribing
appropriateness.
(P) GPs.
Crotty et al.
(2004) [48]
Australia
Nursing home
154
59.6%
84.5 (5.0) (I) Medication review and case
conferences
(D) Multidisciplinary team of
geriatrician, pharmacist,
representative of the Alzheimer’s
Association of South Australia
Improving medication
appropriateness.
(P) Residential care
staff and residents’GPs.
Dalleur et al.
(2014) [33]
Belgium
Teaching hospital
146
63.0%
85.0 (5.2) (I) Medication review and
recommendations provided
to discontinue medications
based on the STOPP criteria
(D) Multidisciplinary team of
nurses, geriatricians, dietician,
occupational therapist,
physiotherapist, speech
therapist and psychologist
Disconti nuation of PIMs
(P) Hospital physicians
Fick et al.
(2004) [49]
USA
Primary care physician
practice
Not specified Not specified (I) Decision support service
comprising educational
brochure, list of suggested
inappropriate medications
based on the STOPP criteria,
and list of patients with
STOPP criteria identified
(D) Research team and
expert panel of physicians
and pharmacists
Changing prescribing
behaviour and decreasing
PIM use.
(P) GPs
Frankenthal et al.
(2014) [56]
IsraelChronic care
geriatric facility
239
66.6%
82.7 (8.7) (I) Medication review and
recommendations provided
based on the STOPP/
START criteria
(D) Study pharmacist
Improving clinical and
economic outcomes by
giving STOPP/START
recommendations. (P)
Chief physicians.
Gallagher et al.
(2011) [34]
Ireland
Teaching hospital
382
53.1%
75.6 (7.3) (I) Medication review and
recommendations provided
to change medications based
on the STOPP/START criteria
(D) Research physician
Improving prescribing
appropriateness
(P) Hospital physician
and medical care team
García-Gollarte
et al. (2014) [35]
Spain
Nursing home
1018
73.0%
84.4 (12.7) (I) Educational workshops,
material and on-demand
advice on prescriptions
(D) Nursing home physician
with geriatric expertise
Improving the quality
of prescriptions
(P) Nursing home physicians
Hanlon et al. (1996)
[36]
USA
Ambulatory clinic
172
1.0%
a
69.8 (3.8) (I) Medication review
and prescribing
recommendations
provided
Improving prescribing
appropriateness
(P) GPs and patients
(continues)
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 5

Table 1
(Continued)
Author
(year) Country Setting
No. of patients
%Female
Mean age of
patients
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
(D) Pharmacists
Lenaghan et al.
(2007) [37]
UK
Primary care physician
practice
136
65.6%
84.3
b
(I) Medication review and
development of action
plan of agreed amendments
(D) Pharmacists
Reducing hospital
admissions and number
of drug items prescribed
(P) GPs and patients
Meredith et al.
(2002) [50]
USA
Home health setting
317
74.9%
80.0 (8.0) (I) Medication review and
development of action plan
to address identified problem
(D) Multidisciplinary team of
physicians, nurses and pharmacists
Improving medication use
(P) Nurses and patients
Milos et al.
(2013) [38]
Sweden
Nursing home and
community
374
74.9%
87.4 (5.7) (I) Medication review and
feedback given to physician
on drug-related problems
(D) Pharmacists
Reducing the number of
patients using PIMs
(P) GPs
Pitkälä et al.
(2014) [39]
Finland
Nursing home
227
71.0%
83.0 (7.2) (I) Staff training and list of
harmful medications provided
to encourage nurses to bring
this to the physician’sattention
(D) Research team
Improving the use of
potentially harmful
medications
(P) Nurses
Pope et al.
(2011) [40]
Ireland
Hospital
225
62.9%
82.9
b
(I) Clinical assessment by a
senior doctor and multidisciplinary
medication review using Beer’s
criteria. Recommendations
given to GP
(D)Consultantorsenior
specialist registrar and a
multidisciplinary panel of
consultant geriatricians,
specialist registrars, hospital
pharmacists and senior
nurse practitioners
Reducing the number
of drugs prescribed
(P) GPs
Potter et al.
(2016) [41]
Australia
Nursing home
95
52.0%
84.0 (7.0) (I) Medication review and
cessation plan of non-
beneficial medications
(D) Research team of GP
and geriatrician
Reducing the total
number of medicines
taken
(P) GPs and patients
Richmond et al.
(2010) [51]
UK
Primary care trusts
760
43.2%
80.4 (4.1) (I) Pharmaceutical care
including medication reviews
(D) Research team
Improving prescribing
appropriateness
(P) GPs
Saltvedt et al.
(2005) [52]
Norway
Teaching hospital
254
65.0%
82.1 (5.0) (I) Comprehensive geriatric
assessment and treatment
of all illnesses
(D) Multidisciplinary team
of geriatrician, nurses,
residents, occupational
therapists and physiotherapists
Increasing the number
of drugs withdrawn
(P) Medical care team
Schmader et al.
(2004) [53]
USA
Hospital
864
2.5%
a
46% aged
65–73
54% aged
≥74 years
(I) Treatment in a geriatric
evaluation and management
unit (GEMU) in either
inpatient or outpatient
care or both
(D) Pharmacists and a
multi-disciplinary team
of geriatrician, social
worker and nurse
Improving prescribing
(P) Medical care team
(continues)
C. R. Hansen et al.
6 Br J Clin Pharmacol (2018) •• ••–••

presented in Table S4. Based on the reported results, 10 of
the 25 studies showed an effect on the key outcomes (i)
or (ii) of this review when comparing the intervention
group to the control group [34, 35, 37, 40, 41, 45, 46, 50,
53, 56]. No direct pattern was seen between the number
of individual BCTs used and reported intervention effec-
tiveness. The median number of BCTs used were similar
for studies reporting effective and non-effective interven-
tions (6 BCTs, IQR 3–8and5BCTs,IQR4–7, respectively).
BCT clusters coded more frequently in studies reporting
effectiveness [34, 35, 37, 40, 41, 45, 46, 50, 53, 56] com-
pared to studies reporting no effectiveness were: goals and
planning;social support;shaping knowledge;natural conse-
quences;comparison of behaviour;comparison of outcomes;reg -
ulation;antecedents;andidentity (see Figure 3).
Intervention effectiveness
(a) Drug use
Overall, the mean number of drugs post-intervention was sig-
nificantly lower among intervention participants compared
to the control participants in the presence of moderate
between-study heterogeneity (mean difference 0.96, 95%
Table 1
(Continued)
Author
(year) Country Setting
No. of patients
%Female
Mean age of
patients
(±SD), years
Intervention (I)
Delivered by (D)
Target behaviour
Target person(s) (P)
Spinewine et al.
(2007) [54]
Belgium
Hospital
203
69.4%
82.2 (6.6) (I) Pharmaceutical care
including medication
review and development
of a therapeutic care plan
with prescribing
recommendations
(D) Pharmacists
Improving prescribing
appropriateness
(P) Medical care team
and patients
Tamblyn et al.
(2003) [42]
Canada
Primary care physician
practice
12 560
62.7%
75.4 (6.3) (I) Electronic alerts instituted
in the electronic patient
prescription record to
identify prescribing problems
(D) Research team
Reducing inappropriate
prescribing
(P) GPs
Tannenbaum et al.
(2014) [46]
Canada
Community pharmacy
303
69.0%
75.0 (6.3) (I) Educational booklet
to empower and encourage
patients to discontinue
benzodiazepines
(D) Research team
Disconti nuation of
benzodiazepines
(P) Patients
Vinks et al.
(2009) [43]
The Netherlands
Community pharmacy
196
74.7%
76.6 (6.5) (I) Medication review
and prescribing
recommendations
provided
(D) Pharmacists
Reducing the number
of potential DRPs and the
number of drugs prescribed
(P) GPs
Weber et al.
(2008) [44]
USA
Ambulatory clinic
620
79.3%
76.9
b
(I) Electronic messages
sent to physician via
electronic medication
record to give
prescribing
recommendations
(D) Pharmacist
and geriatrician
Reducing medication
use
(P) GPs
Williams et al.
(2004) [45]
USA
Ambulatory clinic
140
57.1%
73.7 (5.9) (I) Medication review
based on MAI and
prescribing recommendations
provided and action plan made
(D) Pharmacists
Simplifying medication
regimens
(P) Patients
Zermansky et al.
(2001) [55]
UK
Primary care physician
practice
1188
56.0%
73.5 (6.5) (I) Prescription review
and treatment recommendations
given to patients
(D) Pharmacist and physician
Making changes to repeat
prescriptions and reducing
the number of medicines
taken
(P) Patients
a
The low percentages of females reported was explained by the nature of male patients in Veterans Affairs (VA) clinics
b
The SDs were not reported and could not be retrieved from the authors
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 7

CI 1.53, 0.38, heterogeneity I
2
= 70% and P= 0.002,
Figure S1). Regarding the difference in change in the number
of drugs taken per patient, deprescribing interventions
lowered the number (0.74, 95% CI 1.26, 0.22), but
effects varied greatly across studies (I
2
=92%,P<0.001)
(Figure 4). Stratified analyses by: (i) whether the intervention
was patient-centred or targeting solely healthcare profes-
sionals (Figure S2), (ii) intervention setting (Figure 4) and
(iii) study quality (Figure S3) showed no effect of these
factors on summary estimates. In addition, the unexplained
variation within subgroups remained large.
(b) Prescribing appropriateness
Deprescribing interventions demonstrated a relatively
small effect and a high level of heterogeneity on the number
Figure 3
Frequency of behaviour change techniques (BCTs) coded for studies reporting intervention effectiveness on the key outcomes of this review
compared to studies reporting no effectiveness of interventions. The frequencies are weighed values based on the number of studies in each
group, i.e. effectiveness versus no effectiveness
Figure 2
Results of risk of bias assessment
C. R. Hansen et al.
8 Br J Clin Pharmacol (2018) •• ••–••

of inappropriate drugs per participant comparing interven-
tion and control groups post-intervention (0.19, 95% CI
0.40, 0.02, heterogeneity I
2
= 90% and P=0.07,FigureS4).
The proportion of participants with at least one inappropriate
drug, as defined in the individual studies, were reduced when
a deprescribing intervention was applied, but confidence in-
tervals were wide, and a high level of heterogeneity was
present (Figure 5).
(c) Implementation of recommendations
Only four studies reported implementation rates of rec-
ommendations to discontinue a medication or change a med-
ication [36, 38, 43, 49]. Action was taken in 55.1% of
recommendations given by a pharmacist compared to only
19.8% of the nurse recommendations as part of usual phar-
maceutical care [36]. In the study by Vinks et al.[43],27.7%
of pharmacists’recommendations were implemented, and
action was taken in 56% of drug-related problems identified
by a pharmacist in Milos et al. [38]. A lower recommendation
implementation rate of 15.4% was shown in Fick et al.[49].
This result was based on self-reported action taken by the
physicians; only 71% of physicians reported this, which
may explain the lower frequency of action observed.
(d) MAI score
Seven studies reported changes in MAI scores for participants
pre- and post-interventions [34, 36, 47, 48, 51, 53, 54]. Across
studies, deprescribing interventions demonstrated a significant
effect on reducing the MAI score comparing intervention and
control groups post-intervention (5.04, 95% CI 7.40,
2.68, heterogeneity I
2
= 88% and P<0.0001, Figure S5).
Discussion
Effectiveness of deprescribing interventions is determined by
a combination of factors. Consistent with the findings of re-
cent reviews [6, 17], our meta-analysis showed that
deprescribing interventions are effective in reducing the
number of drugs and inappropriate prescribing (reduced
MAI scores) in older people, although the evidence is
heterogeneous.
Based on the findings of the BCT coding exercise, effec-
tive deprescribing interventions included: (i) a goal and an
action plan to solve prescribing problems, (ii) monitoring
of behaviour, (iii) social support and the use of a credible
source, and (iv) clear instructions and guidance on imple-
mentation to the prescriber and information about health
consequences of doing/not doing the behaviour. Support
from colleagues and information about potential risks and
benefits to the patients in the presence/absence of a
behaviour change may also be effective techniques of
deprescribing.
Differences in the delivery of prescribing recommenda-
tions were seen in the studies reporting intervention
effectiveness compared to studies reporting no effect on
keyoutcomesofthisreview.Studies reporting effectiveness
[34, 35, 37, 40, 41, 45, 46, 50, 53, 56] used oral and face-
to-face communication to discuss and implement
deprescribing recommendations consistent with the princi-
ples of educational outreach to inform clinical decision mak-
ing as described by Soumerai and Avorn [58]. Investigation
of the delivery of recommendations to deprescribe may pro-
vide useful information on the delivery of a successful
deprescribing intervention in addition to the use of BCTs.
Figure 4
Mean difference in the change in number of drugs comparing experimental (intervention) group and control group. Subgroup analysis on inter-
vention setting (outpatient setting versus hospital setting)
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 9

Pharmacist recommendations to reduce drug intake and
inappropriate prescribing were frequently enacted on in
some studies [36, 38], consistent with previous literature
reporting benefits of pharmacist-led interventions to
optimize medication use in older people [21, 59]. Other
studies [43, 49] reported a lower acceptance rate of phar-
macist recommendations, between 15% and 28% of recom-
mendations enacted on. Recent research has demonstrated
a high level of agreement between prescribers and pharma-
cists in the assessment of potential target medications for
deprescribing [60, 61]. In contrast, other research studies
indicate that acceptance rates for recommendations made
by pharmacists are lower than those made by their physi-
cian colleagues [62]. The lower uptake of pharmacist
recommendations despite a high level of agreement
about deprescribing is noteworthy. It may indicate that
challenges to deprescribing are in fact dependent on the
particular ways deprescribing interventions are delivered,
particularly when there is a question of behaviour change.
Based on the findings of this review, we suggest that future
research should investigate the behaviours associated with
theacceptanceandrejectionof deprescribing recommenda-
tions to gain a better understanding of a successful delivery
of deprescribing interventions.
This is the first review to identify BCTs in deprescri-
bing interventions necessary to achieve a change in
behaviours towards deprescribing. Our findings comple-
ment previous reviews on deprescribing [17, 19] by offer-
ing a broader analysis of BCTs that are effective for
deprescribing.
Limitations and strengths
The review findings are based on a comprehensive search
of the literature. The novel aspect of this review is in the
use of a validated taxonomy to describe intervention con-
tent that facilitates behaviour change. Limitations of this
review reside mostly in the limited data available. RCTs to
date are of a relatively small size (often ≤100 participants)
and usually with short follow-up periods. Other limitations
relate to the high-risk blinding procedures; these were
needed because the interventions in question required
blinding of the personnel whose behaviour was targeted,
and this was logistically difficult. Absence of blinding
procedures for outcome assessors were not considered to
introduce important bias because the study outcomes,
e.g. number of drugs taken, was not a particularly subjec-
tive measure. Random sequence generation and allocation
concealment were considered high importance biases in
this review because participant characteristics such as
multimorbidity, age and polypharmacy could have an
impact on the number of drugs taken and risk of inappro-
priate prescribing [1, 5–8].
The meta-analysis was reliant on published or reported
data and, while some reported outcomes were adjusted for
baseline patient characteristics, others were not, which
makes the direct comparison of intervention effect on
specific outcomes open to question. Similarly, and as
described in a previous review [27], the BCT coding was lim-
ited to the intervention descriptions reported in the studies.
Limited reporting on interventions used to encourage
deprescribing could have resulted in BCTs being undercoded
Figure 5
Number of participants with inappropriate drugs comparing experimental (intervention) group and control group. Subgroup analysis on risk of
bias assessment (allocation concealment)
C. R. Hansen et al.
10 Br J Clin Pharmacol (2018) •• ••–••

and others overcoded due to assumptions made about the
strategies used based on the information available. For
example, we assumed that the reporting of prescribing
recommendations given to the prescriber would involve
BCT codes: instructions on how to perform a behaviour and
feedback on behaviour. Prescribing recommendations were
a commonly used intervention in the studies and this
may have resulted in these two BCTs being overcoded.
One study was also excluded from the BCT coding due
to lack of information which could have potentially
impacted the true findings of this review. Furthermore,
we were unable to code BCTs in the control groups due
to limited reporting of the control conditions. The con-
trol conditions such as usual care in hospital settings or
in outpatient settings could include BCTs with potential
implications on the interpretation of the review findings.
Reporting of future behaviour change interventions and
control conditions will benefit from the use of compre-
hensive checklists, such as the TIDieR [63], and give
reviewers the ability to adequately code BCTs and exten-
sively appraise the reporting quality of such interven-
tions. This will improve the identification of
relationships between BCTs used and intervention
effectiveness.
The main limitation of our pooled estimates is the
presence of typically large between-study variation and,
for some of the analyses, the wide confidence intervals
including trivial effects. Some may argue that a meta-
analysis should not be done in the presence of impor-
tant heterogeneity. Meta-analytical methods, however,
allow for the exploration of sources of heterogeneity
and we fully acknowledge that the magnitude of the
summary estimates should be interpreted with care. To
minimize the level of heterogeneity due to different
study designs, we also decided to limit the inclusion
criteria to randomized controlled studies and cluster ran-
domized controlled studies only. Although the direction
of effect was favouring deprescribing, the magnitude of
effect was very variable. This inconsistency, together
with the imprecision and risk of bias issues lower our
confidence in the estimates of effect so that the magni-
tude of effect is very low.
Conclusion
Deprescribing interventions are effective in reducing the
number of drugs taken by patients and improving pre-
scribing inappropriateness. Their success may be
explained by a combination of BCTs spanning a range
of different intervention functions, although we could
not empirically show this. The use of BCTs and delivery
of such behaviour change interventions should be consid-
ered of importance to facilitate successful implementation
of deprescribing. This review contributes to the existing
evidence by critically analysing the content of depres-
cribing interventions in terms of behaviour change,
clearly demonstrating that the current evidence base is
too small to derive strong conclusions on determinants
of success.
Contributors
C.H., S.C., L.S. and S.B. conducted the study selection for this
review, performed data extraction and evaluated study
quality. A.R. verified quality assessments. C.H., P.K., L.S.,
S.T. and C.J.A. performed the quantitative meta-analyses
and the behaviour change analysis. C.H. drafted the
manuscript with contributions from D.O.M., P.K., S.B., L.S.,
S.C., W.K., S.T., C.J.A. and S.S. A.R. helped in the
interpretation of results. All authors read and approved the
final manuscript. S.B. was the senior author.
Competing Interests
In cases where a co-author of this review was also a co-author
of an included study, the author in question was not involved
in the study selection, quality evaluation or data analysis.
The authors wish to thank Prof. Anne Spinewine and Prof.
Nicolas Rondondi for their expert opinions and advice on the
protocol and the manuscript. The authors would also like to thank
JoeMurphy(MercyUniversityHospital,Cork,Ireland)forhis
expertise and help with developing the literature search strategy.
This work is part of the project ‘OPERAM: OPtimising thERapy
to prevent Avoidable hospital admissions in the Multimorbid
elderly’supported by the European Commission (EC) HORIZON
2020, grant agreement number 634238, and by the Swiss State
Secretariat for Education, Research and Innovation (SERI) under
contract number 15.0137. The opinions expressed, and arguments
employed herein are those of the authors and do not necessarily
reflect the official views of the EC and the Swiss government.
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Supporting Information
Additional supporting information may be found online in
the Supporting Information section at the end of the article.
http:/ /onlineli brar y.w iley.com/doi /10.1111/bcp .13742/suppinfo
Table S1 Search strategy
Table S2 Risk of bias assessment
Table S3 Data extraction form
Table S4 Behaviour change techniques taxonomy version 1
(BCTTv1) applied to the included studies and the prevalence
of each BCT and BCT cluster
Figure S1 Mean number of drugs per patient post-interven-
tion comparing experimental (intervention) group and con-
trol group
Figure S2 Subgroup analysis on target person (patient or
healthcare professional) for mean difference in the change
in number of drugs per patient
Figure S3 Subgroup analysis on risk of bias assessment (ran-
dom sequence generation) for mean difference in the change
in number of drugs per patient
Figure S4 Mean difference in the number of inappropriate
drugs per participant comparing experimental (intervention)
group and control group
Figure S5 Mean difference in the change in MAI score per
participant comparing experimental (intervention) group
and control group
Behaviour change techniques in deprescribing interventions
Br J Clin Pharmacol (2018) •• ••–•• 13