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\begin{document}

\title{Developing and testing complex behaviour change interventions to support
proactive deprescribing}



\author[1]{Sion Scott}%
\author[1]{Jacqueline Martin-Kerry}%
\author[1]{Debi Bhattacharya}%
\affil[1]{University of Leicester}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\textbf{Title:} Developing and testing complex behaviour change
interventions to support proactive deprescribing

\textbf{Running title:} Behaviour change and deprescribing

\textbf{Authors:}

Sion Scott, School of Healthcare, University of Leicester, Leicester,
LE1 8RH

Jacqueline Martin-Kerry, School of Healthcare, University of Leicester,
Leicester, LE1 8RH, jackie.martinkerry@leicester.ac.uk

Debi Bhattacharya, School of Healthcare, University of Leicester,
Leicester, LE1 8RH

\textbf{Key words:} deprescribing, behaviour change, implementation,
logic model, complex intervention

\textbf{The nature of the problem}

A culture of prescribing to treat long-term conditions, coupled with
age-associated changes in the processes of metabolising medicine, have
led to an `overprescribing epidemic' amongst older adults (1). The World
Health Organization recognises overprescribing as a serious problem yet
deprescribing of medicines with more risks than benefits is not routine
practice. Approximately half of older adults admitted to hospital are
prescribed at least one medicine with more risks than benefits (2);
however, only 1\% have a medicine deprescribed during an admission (3).

\textbf{Behaviour}

Older adults and informal caregivers expect healthcare professionals to
deprescribe medicines that are no longer appropriate (4). This requires
behaviour change from healthcare professionals. Deprescribing is a
complex and heterogenous process and the `target behaviour', which is
the \emph{thing} we are asking healthcare professionals to do, varies
depending on several factors. For example, asking healthcare
professionals to `reactively' deprescribe medicines that have already
caused harm is a different behaviour to asking them to `proactively'
deprescribe medicines that may cause harm in the future. The first step
in changing behaviour is to \emph{define the target behaviour} .

\textbf{Intervention components and behaviour change techniques (BCTs)}

Behaviour change interventions should target and address the barriers
and enablers (determinants) to the target behaviour. There are multiple
determinants of behaviour and thus interventions require several
components to address them. Behaviour change interventions are therefore
complex interventions comprising of multiple interacting components (5).
Behaviour change techniques (BCTs) are the irreducible `active
ingredients' of a behaviour change intervention that bring about
behaviour change

Selection and formulation of BCTs into tangible components that can be
combined to form an intervention should be informed by evidence and
behaviour change theory. Evidence provides a rich understanding of the
behavioural determinants. Theory enables the behavioural mechanisms by
which BCTs exert their effects to be defined. There are numerous
behaviour change theories, many with overlapping constructs thus
selecting any one theory to fully represent the potential determinants
of a behaviour is challenging. The theoretical domains framework (TDF)
is an integrative framework of behaviour change theories comprising 14
mechanisms of action domains such as \emph{knowledge} , \emph{beliefs
about capabilities} and \emph{emotion} (6) and has been successfully
applied to define the behavioural mechanisms of action of deprescribing
in hospital (7).

Interventions and how they are hypothesised to change behaviour and
resultant outcomes can be visually depicted in a logic model. Logic
models represent the causal theoretical assumptions of an intervention
and moderating factors. They can also describe the context within which
an intervention is implemented and how the two interact. Below we
outline some of the key components of a logic model that should be
tested and refined using learning from feasibility testing and
evaluation processes.

\textbf{Context}

Understanding the context within which a behaviour change intervention
is implemented helps explain why an intervention did or did not change
behaviour (5). Contextual factors include the setting e.g. the size of a
hospital and characteristics of its wards, and organisational
arrangements of how staff work and interact with the intervention. Other
contextual factors that may influence a deprescribing intervention
include the characteristics of staff delivering or receiving the
intervention such as their level of experience, views on deprescribing
and other initiatives also occurring at the site e.g. other
deprescribing research. An intervention may change behaviour in one
setting but not another owing to contextual differences. Understanding
these contextual factors is key to facilitating the intervention being
adopted into the wider healthcare system after completing a definitive
trial (5). This permits the implementation strategy for an intervention
to be adapted according to local contextual factors.

\textbf{Outputs and moderating factors}

Outputs are the activities undertaken when implementing a behaviour
change intervention (8, 9). Historically, evaluation has focussed on the
intervention's effectiveness without evaluating fidelity (9). Fidelity
is the degree to which an intervention is implemented as intended. It is
essential for interpreting whether the observed outputs are a faithful
measure of intervention effectiveness. Guidance for process evaluations
of complex interventions emphasises the importance of evaluating
fidelity, but does not provide recommendations regarding how to do it.
Several frameworks are available to guide fidelity evaluation; however,
they are frequently not adopted in trials of behaviour change
interventions (9).

Evaluating fidelity has historically focussed on \emph{dose}
and\emph{reach,} whereas fidelity of delivery, receipt and enactment are
now recognised as important (9). The Conceptual Framework for
Implementation Fidelity (8) measures four components of fidelity:
content, coverage, frequency and duration of the intervention. It also
captures four moderators of fidelity: intervention complexity,
facilitation strategies, quality of intervention delivery and
responsiveness of participants (8). By comprehensively capturing
fidelity of each intervention component, it can also be determined which
are essential to achieving the desired change in behaviour. Any
adaptation or flexibility of intervention delivery should also be
captured to understand how sites implement an intervention component(s)
and how this variation impacts the intervention's effectiveness.

\textbf{Evaluation}

Trials commonly include both outcome measures and process measures.
Outcome measures indicate whether or not the intervention has worked.
For example, the intended outcome of a deprescribing trial may be an
improvement in quality of life or reduction in hospital readmissions.
The outcome measure is used to inform the required sample size of a
trial. The larger the difference that is anticipated between
intervention and control, the smaller the required sample size to detect
this difference with adequate precision. Researchers may choose a
non-inferiority study; however, in most cases the required sample size
prohibits such studies. In order to prove that the outcome from the
intervention is `no worse' than the control, the study requires a sample
size that can detect a difference that is deemed negligible in clinical
significance. This means that the study has to be powered to detect a
very small difference unlike superiority studies when the research has
to prove that the intervention is better than the control and choose a
sample size accordingly.

Process measures represent intermediate steps to achieving an outcome.
An example of a process measure to help explain the observed outcome is
the number of medicines stopped. For example, in a trial powered to
detect a difference in quality of life, if intervention participants
have a significantly higher quality of life than control participants
then we might assume that the intervention is effective. If, however, we
find that the average number of medicines stopped is the same in both
intervention and control arms, then we know that something other than
stopping medicines is attributable to the difference.

Given that deprescribing is a complex behaviour with multiple
determinants, effective interventions will also be complex as they need
to comprise multiple components to address the target determinants. For
example, the practitioner behaviour change intervention being tested in
the CompreHensive geriAtRician-led MEdication Review (CHARMER) trial,
aims to address five determinants of proactive deprescribing in hospital
and thus comprises five intervention components. It is essential to
evaluate the intervention's mechanism of action in order to understand
how the intervention leads to any changes in deprescribing activities
and how this then impacts on the primary outcome measure. Understanding
mechanisms of actions can be achieved by developing questionnaires or
surveys (6). To understand how the CHARMER deprescribing intervention
changes the behaviours of pharmacists and geriatricians, we developed a
questionnaire to identify changes in the five target determinants. This
will enable identification of any targeted determinants that the
intervention does not address and help explain how the intervention
works (or does not work) in changing proactive deprescribing.

The role of condition-orientated measures in evaluating trials is
unclear. For some research funders, condition-orientated measures such
as blood pressure or lipid profile are acceptable trial outcome measures
whilst for other research funders, these are deemed process outcomes.
This disparity in expectations generates variation in practice and
therefore reduces opportunity for the results from deprescribing trials
to be compared, and trial data to be aggregated to increase the power
and thus precision of the estimated effect of deprescribing. A Core
Outcome Set (COS) is an agreed, standardised set of outcomes to be
measured and reported in all clinical trials of a particular health
condition or specific area of healthcare. Research teams can
measure\emph{additional} outcomes in their deprescribing trials, in
addition to the COS, to suit their context and particular focus. A COS
for hospital deprescribing trials for older people under the care of a
geriatrician was recently reported (10). Consistent use of this COS by
deprescribing trials will enhance uniformity of reporting; there is also
a need for funders to align expectations on acceptable outcome measures.



\textbf{Conclusion}

There is a clearly defined pathway to developing and evaluating complex
interventions; however, significant variation exists in the extent to
which the pathway is followed. Within the pathway, there are
inconsistencies of interpretation, for example the definition of an
acceptable primary outcome. There is a need for the deprescribing
community to address the inconsistencies and then follow the agreed
pathway from defining the target deprescribing behaviour through to
evaluating intervention fidelity alongside effectiveness.

\textbf{References}

1. Scott S. Deprescribing: a call for research that supports
implementation in practice. \emph{Int J Pharm Pract} . 2021;29(6):525-6.

2. Gallagher P, Lang PO, Cherubini A, Topinkov\selectlanguage{ngerman}á E, Cruz-Jentoft A,
Montero Errasquín B, et al. Prevalence of potentially inappropriate
prescribing in an acutely ill population of older patients admitted to
six European hospitals. \emph{Eur J Clin Pharmacol} .
2011;67(11):1175-88.

3. Scott S, Clark A, Farrow C, May H, Patel M, Twigg MJ, et al.
Deprescribing admission medication at a UK teaching hospital; a report
on quantity and nature of activity. \emph{Int J Clin Pharm} .
2018;40(5):991-6.

4. Scott S, Clark A, Farrow C, May H, Patel M, Twigg MJ, et al.
Attitudinal predictors of older peoples' and caregivers' desire to
deprescribe in hospital. \emph{BMC Geriatr} . 2019;19(1):108.

5. Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM,
et al. A new framework for developing and evaluating complex
interventions: update of Medical Research Council guidance. \emph{Bmj} .
2021;374:n2061.

6. Atkins L, Francis J, Islam R, O'Connor D, Patey A, Ivers N, et al. A
guide to using the Theoretical Domains Framework of behaviour change to
investigate implementation problems. \emph{Implement Sci} .
2017;12(1):77.

7. Scott S, Twigg MJ, Clark A, Farrow C, May H, Patel M, et al.
Development of a hospital deprescribing implementation framework: A
focus group study with geriatricians and pharmacists. \emph{Age Ageing}
. 2019;49(1):102-10.

8. Carroll C, Patterson M, Wood S, Booth A, Rick J, Balain S. A
conceptual framework for implementation fidelity. \emph{Implement Sci} .
2007;2:40.

9. Toomey E, Hardeman W, Hankonen N, Byrne M, McSharry J,
Matvienko-Sikar K, et al. Focusing on fidelity: narrative review and
recommendations for improving intervention fidelity within trials of
health behaviour change interventions. \emph{Health Psychol Behav Med} .
2020;8(1):132-51.

10. Martin-Kerry J, Taylor J, Scott S, Patel M, Wright D, Clark A, et
al. Developing a core outcome set for hospital deprescribing trials for
older people under the care of a geriatrician. \emph{Age and Ageing} .
2022;51(11).



\textbf{Funding}

This research is funded by the National Institute for Health and Care
Research Programme Grants for Applied Research stream (award ID PGfAR
200874). This study is supported by the National Institute for Health
and Care Research (NIHR) Applied Research Collaboration East of England
(NIHR EoE ARC) at Cambridge and Peterborough NHS Foundation Trust. The
views expressed in this publication are those of the authors and not
necessarily those of the NHS, the NIHR, or the Department of Health and
Social Care.

\textbf{Conflict of interest statement}

The authors declare that they do not have any conflict of interest.

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