Impact of an Interprofessional Shared Decision-making and Goal-setting Decision Aid for Patients With Diabetes - A Pilot Cluster Randomized Controlled Trial
Phase: NA
Conditions: Diabetes Mellitus
Sponsor: Unity Health Toronto

Study Protocol and Statistical Analysis Plan 
Official title:  
Impact of an Interprofessional Shared Decision-making and Goal-setting Decision 
Aid for Patients With Diabetes 
NCT number:   
NCT02379078 
Document date:  
May 10, 2020 
 
 
 

Study protocol: 
Study design:  
A two-step clustered RCT will be conducted. The initial aim of this project is to assess intervention fidelity 
and the feasibility of conducting a larger clustered RCT. Due to the complexity of interprofessional team 
dynamics; we will focus on intervention fidelity at the individual level. The secondary aim is to evaluate the 
impact of the intervention on decisional conflict, diabetes distress, chronic care delivery and health-related 
quality of life. The use of a clustered RCT prevents contamination of the control group in trials of 
population-level interventions and thus avoids biased estimates of effect size1. The first step will be a 
provider-directed phase (the intervention will be delivered to the provider only); the second step (which 
will occur 6-9 months later) will be a provider- and patient-directed phase (the intervention will also be 
delivered to the patient). The use of the 2-step approach will allow us to monitor barriers influencing uptake 
by population (provider or patient). We selected a 6-9-month interval for each phase to ensure sufficient 
time period for health care providers and patients to follow-up with each other, to enable adequate 
exposure and use of the decision aid. 
 
Participants:  
Family practice groups will be recruited from the 14 LHINs in Ontario. Groups that do not have a nurse, 
dietician or pharmacist on staff, or do not have an electronic medical record capable of identifying patients 
with diabetes will be excluded. We have the expertise of OntarioMD to develop system-specific algorithms 
to identify patients with diabetes (Letter of Collaboration). We have the support of the Primary Care Lead 
for Toronto Central LHIN and a representative Medical Director of a family practice unit (Letters of 
Collaboration).The research team will contact medical directors of eligible practices to explain the project. 
Groups for whom not all health care providers have consented will be excluded. Patients with diabetes and 
2 other comorbidities will be identified from each consenting practice by the research coordinator through 
the electronic medical record; those who do not speak English, have documented cognitive deficits, are 
unable to give informed consent, have limited life expectancy (<1 year) or are not available for follow-up 
will be excluded. Once patients have been identified, a recruitment letter signed by their family physician 
will be mailed to the patient. The letter will explain the study and ask the patient to contact the research 
coordinator if they are interested in participating. In addition, a study information letter will be handed to 
each identified patient by the family physician when the patient visits the clinic. 
 
Intervention:  
At study start, the intervention consisting of 1-page provider enabler, a point-of-care worksheet including 
a simplified algorithm and a patient workbook, will be distributed to each of the health care providers in 
practices randomized to the intervention by the research coordinator; this constitutes the provider-
directed intervention phase (step 1). After 9 months, copies of the patient decision aid and workbook will 
be mailed to eligible patients; this constitutes the provider- and patient-directed phase (step 2). Based on 
the results of substudies 1 and 2, we will incorporate other components (e.g. training video) to overcome 
barriers to use and maximize implementation. 
 
Control:  
At study start, a hard copy of the executive summary of the CDA CPG will be distributed to each of the 
health care providers in practices randomized to the control by the research coordinator. After 9 months, 
copies of a CDA patient education pamphlet regarding diabetes self-management will be mailed to eligible 
patients. In addition, provider- and patient-directed guideline dissemination tools (not incorporating SDM) 
will also be publicly accessible from the CDA website.  
 

Data collection:  
At trial entry, sociodemographic information will be obtained (providers: age, gender, duration in practice, 
practice load, remuneration plan, academic/community, rural/urban, solo/group; patients: age, gender, 
ethnicity, age at diagnosis, comorbidities, educational attainment, annual income). Data on outcomes will 
be collected by participant-completed questionnaires completed either online or mailed at 6-month 
intervals and at study completion, corresponding to intervention steps 1 and 2 for a total of 3 data points 
over a 12-month period (3.2 Timeline).  
 
Outcome measures: 
Initial aim: In order to assess intervention fidelity and the feasibility of conducting a larger clustered RCT, 
we will use structured exit questionnaires to assess for intervention use, the manner with which it was used 
(e.g. which components, which individuals, frequency), acceptability, reasons for use and non-use as well 
as barriers and facilitators to use. To assess the study’s feasibility, we will keep detailed logs of all study 
processes to assess recruitment and retention, such as number of eligible patients, recruitment response 
rate, duration of recruitment period, questionnaire response rate, willingness of participants to be 
randomized, and willingness of clinicians to recruit patients.  
 
Secondary aim: Primary outcome is decisional conflict; secondary outcomes are diabetes distress, chronic 
illness care, and quality of life. These outcomes will be assessed by well-validated patient-completed 
questionnaires (Appx3). These outcomes were selected because they are direct measures of knowledge 
use by patients that will allow us to better understand mediating variables of knowledge use such as patient 
activation, goal-setting, problem-solving, and decision support. Decisional conflict was chosen to allow us 
to assess the impact of our decision aid on the quality of the decision-making process, an important first 
measure of the effectiveness of a decision aid2and the SDM process3. Diabetes distress was selected as 
more holistic and patient-centred measure of knowledge use that uniquely acknowledges patient 
prioritization of health care goals 4,5. Patients’ and providers’ intention to engage in SDM will be assessed 
with a 11-item questionnaire based on the Theory of Planned Behaviour6. Surrogate clinical outcomes 
(egA1c) were not chosen because of their limited relevance to the individualized nature of our intervention. 
 
Randomization:  
A biostatistician will simultaneously randomize and allocate practices to either intervention or control using 
computer-generated randomization in a 1:1 ratio. Investigators and research coordinators will be blinded 
to group allocation. Each practice will be given a code; the biostatistician will analyse the data blindly. Codes 
will be accessed after analysis is completed. 
 
Analysis:  
Initial aim: We will deem the study feasible if we are able to complete recruitment within a 6- to 9-month 
period and attain a response rate of ≥75% for questionnaire completion. This will inform whether we will 
proceed with a larger trial or if we need modify study processes prior to proceeding. In addition, standard 
deviations of our outcome measures will be used to estimate sample size of the larger trial.  
 
Secondary aim: We hypothesize that patients in the intervention arm will have reduced decisional conflict 
and diabetes distress, and improved decision-making satisfaction, chronic care delivery and quality of life. 
Analysis will be done by intention-to-treat. The analysis to test this hypothesis will be carried out using 
multilevel hierarchical models (logistic regression for binary outcome and linear regression for continuous 
outcomes) to account for the clustered nature of the data. The group assignment will be a physician-level 
variable. We will also assess the impact of sociodemographic variables on these outcomes, as literature 
demonstrates that not all patients and providers want shared decision-7making7-9. 

 
References 
1. 
Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating 
complex interventions: the new Medical Research Council guidance. BMJ. 2008. 
2. 
Elwyn G, Edwards A, Hood K, et al. Achieving involvement: process outcomes from a cluster 
randomized trial of shared decision making skill development and use of risk communication aids 
in general practice. Fam Pract. 2004;21:337 - 346. 
3. 
Entwistle V, Prior M, Skea ZC, Francis JJ. Involvement in treatment decision making: its meaning 
to people with diabetes and implications for conceptualization. Social Science and Medicine 
2008;66(2):362-375. 
4. 
Murad MH, Shah ND, Van Houten HK, et al. Individuals with diabetes preferred that future trials 
use patient-important outcomes and provide pragmatic inferences. Journal of Clinical 
Epidemiology. 2011;64(7):743-748. 
5. 
Gandhi GY, Murad MH, Fujiyoshi A, Mullan RJ, Flynn DN, et al. Patient-important outcomes in 
registered diabetes trials. JAMA. 2008;299:2543–2549. 
6. 
Francis JJ, Eccles MP, Johnston M, Walker A, Grimshaw J, al. e. Constructing Questionnaires based 
on the theory of planned behavior – A manual for health services researchers. United Kingdom: 
Centre for Health Services Research;2004. 
7. 
Deber RB, Kraetschmer N, Urowitz S, Sharpe N. Do people want to be autonomous patients? 
Preferred roles in treatment decision-making in several patient populations. Health Expectations. 
2007;10(3):248-258. 
8. 
Levinson W, Kao A, Kuby A, Thisted RA. Not All Patients Want to Participate in Decision Making. 
Journal of General Internal Medicine. 2005;20(6):531–535. 
9. 
Arora NK, McHorney CA. Patient Preferences for Medical Decision Making: Who Really Wants to 
Participate? Medical Care. 2000;38(3):335-341. 
 
 
 

Statistical analysis plan: 
Analysis will be done by intention-to-treat.  For the primary and secondary outcomes, a linear mixed 
effect model will be used to analyze the total score for each scale where site was the random effect with 
adjustment for baseline value. The impact of sociodemographic variables on these outcomes (decisional 
conflict, diabetes distress, health-related quality of life, chronic illness care) will be assessed.  Specifically, 
we will fit a main effects model that adjusted for age, sex, ethnicity, education, employment status and 
living arrangements as well as a fully-adjusted model that included all interactions between treatment 
and the preceding variables. P-values for the treatment effect in the baseline adjusted models 
use Satterthwaite's approximation for the denominator degrees of freedom while the tests of 
interactions (sub-group effects) employed likelihood ratio tests from a full maximum likelihood estimation 
1. Irrespective of the test result on sub-groups, the treatment effects will be then shown by subgroup, 
estimated from the second model specified above along with 95% confidence intervals and a p-value that 
tested each interaction in this fully-adjusted model.  Descriptive statistics will be used to describe 
intervention effect by MyDiabetesPlan use. ANOVA will be used to assess difference in clinician’s 
intention to practice IPSDM scores between intervention and control groups at baseline, 6 months and 12 
months. Analysis will be performed in R version 3.5.2 2 and used the packages lme4 (version 1.1-21) and 
lmerTest (version 3.1-0) 1 to fit and report the mixed-effect models.  
 
References 
1. 
A K, PB B, RHB C. lmerTest Package: Tests in Linear Mixed Effects Models. In. Vol 82: Journal of 
Statistical Software; 2017:1-26. 
2. 
Team RC. R: A language and environment for statistical computing. In. Vienna, Austria. : R 
Foundation for Statistical Computing, ; 2016.