Empowering Active Self-management of Arthritis: Raising the Bar With OPERAS (an On-demand Program to EmpoweR Active Self-management)
Phase: NA
Conditions: Joint Disease, Rheumatoid Arthritis
Sponsor: University of British Columbia

 
 
Study protocol and statistical analysis plan 
 
Date: October 22, 2019 
 
Citation:  
Tam J, Lacaille D, Liu-Ambrose T, Shaw C, Xie H, Backman CL, Esdaile J, Miller K, Petrella 
R, Li LC. Empowering active self-management of arthritis: Effectiveness of an online self-
management tool for people with rheumatoid arthritis: a research protocol. BMC Trials. (DOI: 
10.1186/s13063-019-3851-0) 20:712, 2019. 
https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-019-3851-0  
 

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Study protocol and statistical analysis plan - Oct 22, 2019 
 
Background 
 
 
Self-management is a key component of successful chronic disease management [1].  
Broadly, it is a process where patients are actively participating in a variety of activities that 
contribute to lessening of the physical and emotional impact of their illness. Such activities 
include adhering to their treatment plan, being physically active, and seeking medical help when 
the treatment target is not met. It is important to make self-management a priority as patients can 
benefit from learning about how their daily activities and treatments correlate with their 
symptoms and health status on an ongoing basis. This contextualized health data provides a 
baseline for patients to determine if their symptoms warrant seeking medical attention and could 
inform their decisions about daily activities. For example, a patient who is aware of how much 
pain and fatigue they experience following a day of housework will know to pace their activities 
the next day.  
 
Chronic disease self-management is hard work; it can be difficult to follow treatment 
regimes in the context of busy lives and fluctuating symptoms. There is a need to develop a 
multifaceted approach that can promote self-management and adherence to recommended 
treatments. As an example, for people with RA, there is ample evidence to support early and 
persistent use of disease-modifying anti-rheumatic drugs (DMARDs) [2]. However, for people 
who started RA medications, the adherence rates are as low as 30% [3]. Furthermore, despite the 
compelling evidence supporting a physically active lifestyle in reducing symptoms [4-7], the 
majority of patients do not meet the minimum recommended level of moderate-to-vigorous 
physical activity (MVPA) [8].  
 
In general, patients feel a moral obligation to “manage well” [9]. However, some become 
disengaged from self-management activities [10] because of a frustration from managing their 

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Study protocol and statistical analysis plan - Oct 22, 2019 
health on a trial-and-error basis [11]. The current practice relies on patients applying their self-
management knowledge and skills in daily life with little feedback on their performance or how 
their actions affect their health. In light of these findings, we believe that there is a need for a 
multifaceted approach that provides support in terms of knowledge, skill development and timely 
advice from health professionals, as well as motivational support for patients to be engaged in 
their care and to stay physically active. 
 
 
OPERAS web application 
The OPERAS web app was designed to engage patients in monitoring and managing 
their health. Building from two existing software programs, the Arthritis Health Journal (AHJ) 
[12] and FitViz [13], OPERAS allows users to track symptoms, self-management goals, 
medication use, and physical activity. The latter is automatically measured by Fitbit. Using 
OPERAS, a physiotherapist (PT) will provide remote counselling to guide the user in setting 
realistic physical activity goals.  Based on the users’ goals, the PT can adjust parameters of 
physical activity intensity and duration to provide automated personalized feedback on their goal 
attainment.  
The objectives of this study are to: 1) assess the efficacy of OPERAS at improving self-
management ability of people with RA in a 6-month randomized controlled trial (RCT); 2) 
explore the effect of OPERAS on disease status, pain, fatigue, depressive symptoms, and 
characteristics of habitual behavior; and 3) assess barriers to implementation and sustainability of 
OPERAS from the perspectives of participants involved in the study. 
 
 

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Study protocol and statistical analysis plan - Oct 22, 2019 
Methods/design 
Study Design 
 
The Empowering active self-management of arthritis: Raising the bar with OPERAS (an 
On-demand Program to EmpoweR Active Self-management) project will use a mix of 
quantitative and qualitative research methods. The RCT will employ a delayed control design, 
whereby participants will be randomly assigned to start the 6-month intervention immediately 
(Immediate Intervention [II] group) or 6 months later (Delayed Intervention [DI] group) using a 
1:1 allocation ratio. Participants will be assessed 3 times throughout the study (Fig. 1.). At the 
end of the intervention, we will invite a sample of participants to be interviewed regarding their 
experiences.  
 
Participants 
 
Individuals are eligible for the study if they have: 1) a rheumatologist confirmed 
diagnosis of RA; 2) no joint surgery in the past 6 months; 3) no history of acute injury to any 
joints in the past 6 months; 4) an email address and daily access to a computer or mobile device; 
5) the ability to speak and understand English; and 6) not previously participated in studies 
involving the AHJ or physical activity counselling. We will exclude people who should not be 
physically active without medical supervision, as identified by the Physical Activity Readiness 
Questionnaire (PAR-Q) [14]. If participants fail the PAR-Q, a physician’s note will be required 
to determine if they are eligible.   
 
 Participants will be recruited from rheumatology clinics in Metro Vancouver, and 
through the network of patient groups including Arthritis Consumer Experts, and Arthritis 

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Study protocol and statistical analysis plan - Oct 22, 2019 
Research Canada’s Arthritis Patient Advisory Board. We will also post study information in 
social media (Facebook, Twitter, Kajiji, Craigslist) and Arthritis Research Canada’s website.  
Once participants complete the screening process and are deemed fully eligible for the 
study, the study coordinator will provide detailed information on the study, and a consent form 
will be emailed to the participant. When a signed consent form is obtained (e-signature or 
scanned copy), the participant will be enrolled. All potential and consented participants will be 
assigned a study ID which links to their personal and contact information. This information will 
be stored in a password-protected file on a secure database only accessible to the research team.  

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Study protocol and statistical analysis plan - Oct 22, 2019 
Fig 1 Standard protocol items: recommendation for interventional trials (SPIRIT) figure 
 
The Intervention 
Participants randomized into the II group will receive the 6 month intervention 
immediately. They will attend a 2-hour in-person session where they will: 1) participate in a 
group education session from a study PT about self-management and physical activity for people 
with RA, 2) receive a Fitbit Flex 2 and an orientation to the OPERAS web app, and 3) receive 
                                                    STUDY PERIOD 
 
Enrolment 
Baseline 
Post-allocation 
Weeks 
-1 to 0 
0 (T0) 
1 
27 (T1) 
53 (T2) 
ENROLMENT: 
 
 
 
 
 
Eligibility  
X 
 
 
 
 
Informed consent  
X 
 
 
 
 
Allocation 
 
X 
 
 
 
INTERVENTIONS: 
 
 
 
 
 
Immediate Group (N= 67) 
 
 
 
 
 
Education Session 
 
 
X 
 
 
Access to OPERAS web-app 
and Fitbit (Weeks 1-53)  
 
 
 
 
 
PT Phone Calls (Weeks 2- 26)  
 
 
 
 
 
Delayed Group (N=67) 
 
 
 
 
 
Education Session 
 
 
 
X  
 
Access to OPERAS web app 
and Fitbit (Weeks 27-53) 
 
 
 
 
 
PT Phone Calls (Weeks 29-52) 
 
 
 
 
 
ASSESSMENTS: 
 
 
 
 
 
Patient Activation Measure 
(PAM) 
 
X 
 
X 
X 
Rheumatoid Arthritis Diseases 
Activity Index (RADAI) 
 
X 
 
X 
X 
Fatigue Severity Scale (FSS) 
 
X 
 
X 
X 
McGill Pain Questionnaire 
(MPQ-SF) 
 
X 
 
X 
X 
Patient Health Questoinnaire-9 
(PHQ-9) 
 
X 
 
X 
X 
Self-Reported Habit Index 
(SRHI) 
 
X 
 
X 
X 
SenseWear Mini 
 
X 
 
X 
X 

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Study protocol and statistical analysis plan - Oct 22, 2019 
individual counselling from a PT trained in motivational interviewing [15]. All study PTs will 
follow the Brief Action Planning approach [16], whereby participants will be guided to set their 
own physical activity goals, develop an action plan, and identify barriers and solutions.  
We will ask participants to wear the Fitbit throughout the intervention period, and use the 
OPERAS app to record their RA disease activity, symptoms, treatment use, and self-
management action plans. If participants experience any discomfort wearing the Fitbit, we will 
advise them to stop wearing it but continue using the OPERAS web app. Disease activity will be 
tabulated from participants’ self-reported measures, including the Rheumatoid Arthritis Disease 
Activity Index [RADAI], Visual Analogue Scale [VAS], and the Health Assessment 
Questionnaire [HAQ]. Participants will record their symptoms twice a week during periods of 
more active disease (i.e., a disease activity score above 4), and once every 2 weeks during 
periods of stable disease (i.e. no sudden increases in disease activity score). They will also be 
asked to log on and review their physical activity goal achievements at least once a week. 
Participants who have not recorded their disease activity for more than 2 weeks will receive an 
email reminder from the research team.  
 
II participants will receive a phone call from their PTs at weeks 2, 4, 6, 8, 13 and 26 to 
review their physical activity goals and accomplishments. PTs will coach participants to modify 
their goals if they are ready.  
The DI group will receive the same intervention in week 27. Participants will receive a 
monthly newsletter of arthritis news, which are unrelated to disease management prior to the 
intervention period.  
After the six-month intervention period, both II and DI participants can keep the Fitbit 
and their OPERAS app account, but will not have access to a PT. There is no requirement for 

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Study protocol and statistical analysis plan - Oct 22, 2019 
participants to stop their medical treatments and non-pharmacological care during their 
participation in the study. 
 
Measurements 
All participants will be assessed 3 times throughout the study: at baseline (T0), week 27 
(T1), and week 53 (T2). Baseline measurements will be completed before randomization. 
Research personnel performing data analysis will be blinded to the group assignment. We will 
not be blinding the group assignment to the study coordinator, who will be facilitating delivery 
of the intervention, nor the participants who will be receiving it.  
 
Outcomes 
Primary Outcome 
 
Self-management ability will be assessed through the Patient Activation Measure (PAM), 
a 13-item self-reported measure of individual’s confidence in managing chronic diseases [17,18]. 
Each item has a 4-point response, with 1 being strongly disagree and 4 being strongly agree, and 
the aggregate raw score is converted to 0-100 [15]. Hibbard et al [17] described a 4-stage 
activation model based on the standardized scores of PAM: 1) believing an active role is 
important (PAM score <47); 2) having confidence and knowledge to take action (47.1 - 55.1); 3) 
taking action (55.2 - 67); and 4) maintaining healthy behaviors despite setbacks (> 67.1). PAM 
has demonstrated internal consistency (Chronbach’s α > 0.85) [17], and construct validity with 
health status measures (e.g., SF-36 [24]) and healthy behaviours such as exercise, healthy eating 
and medication adherence [19].  
 

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Study protocol and statistical analysis plan - Oct 22, 2019 
Secondary Outcomes 
We will assess disease status through the RADAI which categorizes disease activity into 
remission, low, moderate, and high states [20]. RADAI has 5 components, including 1) global 
disease activity; 2) joint tenderness/swelling; 3) pain; 4) morning stiffness; and 5) 
number/severity of painful joints. Its validity was supported by moderate correlations with 
physicians’ assessment of disease activity (r = 0.54), swollen joint count (r = 0.54), and C-
reactive protein value (r = 0.43) [18].  
Pain will be measured with the McGill Pain Questionnaire (MPQ-SF), which consists of 
15-pain related words that can be rated from 0 to 3, with the higher number being more severe 
[21].  We will measure fatigue using the Fatigue Severity Scale (FSS), a 9-item questionnaire 
that has demonstrated internal consistency (Cronbach’s α=0.89) [22]. The FSS is also moderately 
correlated with pain (r=0.68) and depression (r=0.46) [23]. We will also assess participants’ 
mood using the Patient Health Questionnaire-9 (PHQ-9) [24] which consists of 9 questions that 
correspond to the diagnostic criteria for major depressive disorder. A total score of greater than 
11 indicates the presence of major depressive disorder [25].  
The Self-Reported Habit Index (SRHI) will be used to measure characteristics of habitual 
behavior. It is a 12-item scale, rated on a 7-point Likert scale, with higher scores indicating a 
stronger habit or behavior that is done frequently, automatically, and done without thinking 
about it [26, 27]. Participants will rate their strength of habit for 3 specific behaviors, including 
sitting during leisure time at home, sitting during usual occupational activities, and walking 
outside for 10 minutes or more.  
We will measure participants’ time spent in physical activity and sedentary activity with 
SenseWear Mini. It integrates tri-axial accelerometer data, physiological sensor data and 

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Study protocol and statistical analysis plan - Oct 22, 2019 
personal demographic information to estimate steps, energy expenditure and Metabolic 
Equivalent of Tasks (METs). Tierney et al. [28] has showed that the SenseWear is an appropriate 
tool in estimating energy expenditure during activities of daily living in people with arthritis 
(intraclass correlation coefficient (ICC) = 0.72). Participants will wear the SenseWear monitor 
over their triceps on the non-dominant arm for 7 days. Almeida et al [29] recommended that a 
minimum of 4 days is required to reliably assess energy expenditure from different levels of 
physical activity in people with RA (ICC > 0.80).  
We will calculate: 1) the mean time spent in bouted MVPA per day. A bout is defined as 
> 10 consecutive minutes at the level of > 3 METs (i.e., the lower bound of MVPA), with 
allowance for interruption of up to one minute below the threshold [30]. 2) Mean daily time 
spent in sedentary behaviours, with an energy expenditure of < 1.5 METs, occurring in bouts of 
> 20 minutes during waking hours [31, 32]. 
 
Implementation Assessment  
At the end of the 6 month intervention, we will conduct one-hour interviews with a 
purposive sample of participants to include both men and women and have different duration of 
experience in using health-related apps (Novice: <2 years; Avid >  2 years). The interview guide 
will explore three topics: 1) the participants’ experiences with the intervention, probing to 
understand their views of each aspect of the program; 2) barriers and facilitators to using 
OPERAS; and 3) the nature of activities they engaged in with the program.  
The OPERAS app will collect program usage data of all participants and these data will 
be securely stored on the Arthritis Research Canada server. Information collected include: 1) 
participants’ frequency of using OPERAS app; 2) duration of each use; 3) adherence to using the 

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Study protocol and statistical analysis plan - Oct 22, 2019 
Fitbit; and 4) adherence to the PT counselling session. The app is designed to allow researchers 
to obtain usage data without access to the personal health information, protecting particpants’ 
health information privacy. 
 
Adverse Events and Data Monitoring 
 
In the follow-up questionnaires at Month 6 and Month 9, participants will be asked to 
note any serious adverse events, including falls and cardiovascular/ musculoskeletal events [33]. 
Adverse events will be reviewed by the Data and Safety Monitoring Committee, which will 
recommend terminating the study, if warranted. Additionally, at any time during the study 
period, participants will report any serious adverse events directly to the study coordinator. In the 
event of a severe adverse event due to participation in the study, participants will be 
recommended to follow-up with their family doctor as well as have the option to meet with a 
rheumatologist (DL), or a physiotherapist (LCL).  
 
The Data and Safety Monitoring Committee will also be in charge of auditing trial 
conduct, reviewing trial processes and documents to ensure that research activities comply with 
the requirements of the protocol. It will review screening and consent documentation to ensure 
ethical trial conduct. Committee members will meet, in person or via teleconference, quarterly or 
when 25%, 50% and 75% of the participants are enrolled, whichever is sooner. 
  
Sample Size Calculation 
 
Our primary outcome measure is self-management ability as measured by the (PAM) 
[17,18]. In a study by Turner et al., it was reported that patients with chronic diseases who 
completed a 7-week self-management program would have their mean PAM score improve from 

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Study protocol and statistical analysis plan - Oct 22, 2019 
52.2 to 60.2 at 6 months (standardized effect size = 0.65) [34]. Based on a difference of 8 points, 
an estimated SD of 12.4, and a 2-tailed analysis of covariance (ANOVA), a total of 102 
participants (51 per group) would be needed (90% power; α-level 0.05). We plan to recruit a 
total sample size of 134, with 67 participants in each group, to allow for a ~24% attrition rate. 
 
For the implementation assessment, we will conduct qualitative interviews with 7-10 
participants in each of the 4 purposive sampling strata, including gender (2) and experience in 
using health-related apps (2). Hence, 28 – 40 people will be interviewed. 
 
Data Analysis 
 
The data collected will be de-identified and password protected. All research personnel 
involved with data analysis will have access to the data files. The study coordinator will oversee 
the data sharing process.  
 
Efficacy Analyses 
 
The intervention efficacy will follow an intention-to-treat approach. We will analyze the 
outcomes measured at weeks 26 and 52 in the II and DI groups. We will employ Generalized 
Linear Mixed-effect Models (GLMM), adjusting for baseline value on the outcome variable and 
sex as covariates. These models are the most efficient and recommended statistical methods for 
analyzing longitudinal clinical trial data with missing data [35, 36] and can account for data 
missing at random without the need to perform explicit imputations of the missing values [37]. 
Binary indicators of group and months since baseline, will allow us to test their interaction (p < 
0.05 for 2-tailed Wald test), which addresses if the outcome post-intervention is significantly 
better in the II than DI group. Data transformation, if needed, will be applied to continuous 

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Study protocol and statistical analysis plan - Oct 22, 2019 
outcomes to satisfy the assumption of normality. The sandwich estimators for GLMMs [38] will 
be used to compute empirical standard errors that are robust to model specifications and 
distributional assumptions. An unstructured variance–covariance matrix will be used to model 
the within-subject error variance-covariance for continuous outcomes. The quality of statistical 
inferences will be substantiated through rigorous model checking and validation techniques.  
 
Implementation Analysis 
 
For the qualitative interviews, we will conduct an iterative content analysis, whereby 
codes will be identified and revised as interviews are analyzed. Initial open coding will be 
followed by clustering the labels into thematic categories [39].  Quotes representative of the 
thematic categories will be identified to illustrate experiences of participants with the 
intervention, as well as barriers and facilitators in meeting their physical activity goals. If distinct 
themes based on gender or age are apparent, data will be reanalyzed within subgroups. 
Descriptive analysis will be conducted for all participants’ usage data from the OPERAS app. 
An iterative content analysis will be used to identify and revise codes in the qualitative 
interviews. We will start with an initial open coding and then cluster the labels into thematic 
categories [40]. Quotes representative of the thematic categories will be identified to illustrate 
experiences of participants in using OPERAS, as well as barriers and facilitators in using the 
program. We will reanalyze the data within subgroups if there are distinct themes based on 
gender or age. We will also conduct a descriptive analysis for all participants’ usage data. 
 
 
 
 
 
 

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Study protocol and statistical analysis plan - Oct 22, 2019 
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