The Application of Decision Aids in Diabetes Patients A Systematic Review Emily McBride, Ronan O’Carroll, Belinda Hacking & Matthew Young

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 years. 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 complications. 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 self- management 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 profiles

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 making. 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 Randomised 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 Full articles screened = 36 Included in review for data- extraction and synthesis of results: TOTAL = 8 Excluded Papers = 28 Reasons for exclusion included: - 21: Didn't meet DA criteria - 2: Pre-diabetes group - 2: Lack of relevant outcome measures - 3: Study protocols

Results (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 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 (3/3 studies) Adherence to treatment- no difference found (5/6 studies)

HOWEVER 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’ “Have you missed any medication doses in the last week?” E.g. “Have you missed any medication doses in the last week?” YES= ‘NON-ADHERENT’ NO = ‘ADHERENT’ 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 post- intervention. Found that patients who used a decision aid were more “persistent” with their treatment plan.Found that patients who used a decision aid were more “persistent” with their treatment plan.

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 this.

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 adherence.

Practice Implications promote shared decisionDAs can help to promote shared decision making in diabetes care. patient involvement.Increase patient involvement. patient knowledgeIncrease patient knowledge of condition and treatments (large effects with high quality DAs) patient accuracy of treatment risk estimations.Increase patient accuracy of treatment risk estimations. reduce decisional conflict.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)