The Application of Decision Aids in Diabetes Patients

The Application of Decision
Aids in Diabetes Patients
A Systematic Review
Emily McBride, Ronan O’Carroll,
Belinda Hacking & Matthew Young
[email protected]
Diabetes Mellitus
• 3.2 million individuals diagnosed with
diabetes in the UK (6%)
• Estimated 360 million worldwide (8.5%)
• Predictions totalling 5 million by 2025 in
the UK
• 552 million worldwide by 2030
– I.e. 53% increase forecast over next 15
Diabetes UK (2013)
Economic Implications
• Increased financial pressure due to both rise in
numbers and the chronic nature of diabetes.
• £10 billion on direct diabetes care (NHS, 2012).
• Accounted for 10% of the overall budget.
• 80% of this figure was spent on avoidable
Diabetes UK (2012)
How do we improve health and
economic outcomes?
In order to control and/or improve clinical
outcomes in diabetes, the condition requires
patients to take an active role in the selfmanagement and maintenance of treatments
as a long term stipulation.
Norris, Lau, Smith, Schmid, and Engelgau (2002)
Chronic nature of diabetes…
• Allows flexibility for development of
patient-focused tailored approaches
• Can promote the control necessary to gain
optimal benefit from treatments.
• However, this flexibility can also permit a large
window, in which regression without
perceived visible loss can occur.
• For a large part, this has been argued to
account for a lack of treatment adherence in
this population.
Estimated that the mean
adherence rate is 67% in
diabetes patients
(DiMatteo, 2004)
Shared Decision Making
• NICE (2009) urged adoption of a patient-centred
approach in diabetes population.
• SDM may improve health outcomes through improved
quality of care and increased treatment adherence.
• Promotion of the active patient role and SDM has
increased treatment adherence in other chronic health
populations, e.g. asthma.
• In diabetes, increased patient involvement predicts
enhanced quality of life.
(Montori et al, 2006; Michie et al, 2003; van Dam et al, 2005; Wilson et al, 2010)
Decision Aids
• Tools aimed at promoting informed care and facilitating
shared decision making, e.g. pamphlet, online
information, card packs.
• Provide balanced information with regards to possible
treatment choices (pros and cons).
• Unique to generic health educational materials in that
they contain personalised and/or detailed options, and
usually contain a breakdown of the costs and benefits
associated various decision options.
‘Diabetes Medication Choice’ Decision Aid
• Shown to enhance patient decision making
in other populations (predominantly cancer):
 Increase knowledge of condition and treatments
 Increase patient involvement in care
 Increase confidence in care and treatment
 Increase patient satisfaction
 Reduce conflict around treatment-related decisions
No adverse effects on clinical outcomes
O’Conner et al (2009); Volk et al (2007); Stacey (2012); Waljee et al (2007)
Diabetes Decision Aids
• 13 decision aids suitable for use in
diabetes patients (Lenz et al, 2006).
• There had been no review to consider
whether the use of decision aids was
feasible in the diabetes population.
Systematic Review Aims
• To consider the quality of evidence which
exists and the efficacy of decision aids for
use in diabetes mellitus patients, with
respect to:
– (i) Decisional outcomes, e.g. knowledge,
decisional conflict
– (ii) Behavioural outcomes, e.g. adherence,
medication starts.
– (iii) Clinical outcomes, e.g. HbA1c, lipid
Selection Criteria
The abstracts of all generated searches were read in order
to select those with the following inclusion criteria:
Article published in a peer-reviewed journal
Sample drawn from an adult population (18+) clinically
diagnosed with any form of diabetes mellitus.
Includes an intervention to aid patient decision
Randomised Controlled Trial design employed.
Article written in English.
Measurement of at least one decisional, clinical,
and/or behavioural outcome.
Total number of papers found with key
term searches:
Pub Med – 5,343;
Web of Knowledge – 1,722;
Science Direct – 907
TOTAL: 7,972
Filtered for
Controlled Trials:
Abstracts & titles
screened = 620
Excluded Papers = 580
Reasons for exclusion included:
- Duplicates
- Not RCT
- Not relevant/ on topic
- No decision aid
- Not 18 + in age
- Not in English
Excluded Papers = 28
Reasons for exclusion included:
Full articles
screened = 36
Included in review for dataextraction and synthesis of results:
- 21: Didn't meet DA criteria
- 2: Pre-diabetes group
- 2: Lack of relevant outcome
- 3: Study protocols
(i) Decisional Outcomes
Decisional Outcomes
All studies (8) included at least one decisional outcome.
Increased patient
knowledge (5/6 studies)
d= 1.4, d=0.48, d=0.28
Increased patient
involvement in care
(2/2 studies)
Patients more accurately
predicted risk estimation of
health outcomes &
complications associated
with treatment (2/2 studies)
Decisional Conflict
Decision Aid
Study or Subgroup Mean SD Total Mean SD
Branda 2013
17.1 13.2 53 18.8 13.8
Mathers 2013
17.4 12.6 95 25.2 14.9
Mullan 2009
14.1 17.89 48 14.95 12.68
Weymil er 2007
14.9 12.8 52 24.7 16
Total (95% CI)
Heterogeneity: Chi² = 7.30, df = 3 (P = 0.06); I² = 59%
Test for overall effect: Z = 4.23 (P < 0.0001)
Mean Difference
IV, Fixed, 95% CI
-1.70 [-6.92, 3.52]
-7.80 [-11.93, -3.67]
-0.85 [-7.35, 5.65]
-9.80 [-15.59, -4.01]
Mean Difference
IV, Fixed, 95% CI
213 100.0% -5.59 [-8.19, -3.00]
-100 -50
50 100
Favours [experimental] Favours [control]
2 studies found large effects; 4 studies found no significant
difference. Potential moderation effect?
Decisional Outcomes
• No impact on patient trust of healthcare
professional (3/3 studies)
• No impact of decisional regret (3/3 studies)
Decisional conclusions…
It would therefore seem promising that
DAs may assist in meeting the recent
government objectives of utilising a
shared care model in diabetes
treatment (NICE, 2009).
(ii) Behavioural Outcomes
Behavioural Outcomes
6/8 studies considered at least one behavioural measure
No change in the
number of new
medication starts
Adherence to
treatment- no
difference found
(3/3 studies)
(5/6 studies)
• Only one study employed the use of a
validated measure of adherence.
• Also, there was a lack of consistency in
the procedures/tools used to measure
adherence between studies.
Adherence ‘measure’
E.g. “Have you missed any medication
doses in the last week?”
Average: 67% adherence in diabetes!
~75% average across all health domains
“Persistence” to treatment rather
than quantity/frequency
• One study considered “persistence” with
treatment rather than frequency/quantities of
medication use.
• I.e. continuation with treatment at 6 months postintervention.
• Found that patients who used a decision aid
were more “persistent” with their treatment
• This may be indicative that DAs improve
adherence through promoting continuation
with the chosen treatment in the long-term.
• Rather than frequency of immediate
treatment engagement.
• However, further research with valid,
reliable and consistent measures &
methods would be required to ascertain
Clinical Outcomes
• 3/8 studies measured HbA1c
• 1 study measured lipid profiles
• No effect found
• But expected as these clinical markers are
hard to detect without long-term follow-up,
and there was no obvious impact on
Practice Implications
• DAs can help to promote shared decision
making in diabetes care.
• Increase patient involvement.
• Increase patient knowledge of condition and
treatments (large effects with high quality DAs)
• Increase patient accuracy of treatment risk
• May reduce decisional conflict.
• May encourage patients to continue with their
chosen treatment.
Further comments
• Importantly, this review also revealed
the need for the development of DAs in
alignment with international guidelines
(IPDAS, 2006).
– Only one diabetes DA was identified as being
developed in accordance with IPDAS (guidelines) (inventory) (NHS)
Related flashcards


15 cards


15 cards

British nurses

52 cards

Nursing researchers

20 cards

Create Flashcards