Determining the Impact of a New Physiotherapist-led Primary Care Model for Low Back Pain - a Cluster Randomized Controlled Trial
Phase: NA
Conditions: Back Pain, Low
Sponsor: Jordan Miller, PT, PhD

     
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Study Title:  Determining the Impact of a Physiotherapist-Led Primary Care Model  
for Low Back Pain: Study Protocol and Analysis Plan for a Cluster Randomized Controlled Trial and 
Embedded Process Evaluation 
 
Authors:  
Jordan Millera,b, jordan.miller@queensu.ca 
Catherine Donnellya,b, catherine.donnelly@queensu.ca  
Chad McClintocka,b, cwm2@queensu.ca 
Kevin Varettea,b, varettek@queensu.ca 
Yeimi Camargo a,b, yeimi.c@queensu.ca 
Jacquelyn Marshc, jmarsh2@uwo.ca  
Monica Taljaardd,e, mtaljaard@ohri.ca  
Mir Sanim Al Mamuna, mirsanimal.mamun@queensu.ca 
Geneviève Bacchusa, 20gkb1@queensu.ca 
David Barberf, barberd@queensu.ca 
Lynn Cooperg, lkcooperbes@rogers.com 
Simon Frencha,h, simon.french@mq.edu.au  
Michael Greenb,f,i, mgreen@nosm.ca  
Jonathan Hillj, j.hill@keele.ac.uk 
Joy MacDermidc, jmacderm@uwo.ca 
Kathleen Normana, kn4@queensu.ca 
Julie Richardsonk, jrichard@mcmaster.ca  
Joan Tranmerb,l, tranmerj@queensu.ca 
Timothy Widemanm, timothy.wideman@mcgill.ca 
 
aSchool of Rehabilitation Therapy, Queen’s University, Louise D Acton Building, 31 George Street, 
Kingston, Ontario, Canada, K7L 3N6 
bHealth Services and Policy Research Institute, Queen’s University, Abramsky Hall, 21 Arch Street, 
K7L 3N6 
cSchool of Physical Therapy, Western University, Elborn College, 1201 Western Rd, London, Ontario, 
Canada, N6G 1H1 
dOttawa Hospital Research Institute, Ottawa Hospital, Civic Campus, 1053 Carling Ave, Ottawa, 
Ontario, Canada, K1Y 4E9 
eEpidemiology and Public Health, Univerisity of Ottawa, 75 Laurier Avenue East, Ottawa, Ontario, 
Canada, K1N 6N5 
fDepartment of Family Medicine, Queen’s University, 220 Bagot Street, Kingston, Ontario, Canada, 
K7L 3G2 
gCanadian Injured Workers Alliance, 127 Ravenwood Avenue, Thunder Bay, Ontario, P7B 4H7  
hDepartment of Chiropractic, Macquarie University, Level 2/75 Talavera Road, Sydney, New South 
Wales 2109, Australia 
iNorthern Ontario School of Medicine, 955 Oliver Road, Thunder Bay, Ontario P7B 5E1 
jSchool of Allied Health Professionals, Keele University, MacKay Building, Newcastle-under-Lyme, 
United Kingdom, ST5 5BG 
kSchool of Rehabilitation Science, McMaster University, Institute for Applied Health Sciences Building, 
1400 Main Street West, Hamilton, Ontario, Canada, L8S 1C7 
lSchool of Nursing, Queen’s University, Cataraqui Building, 92 Barrie Street Kingston, Ontario K7L 
3N6, Canada 
mSchool of Physical and Occupational Therapy, McGill University, Hosmer House, 3630 prom Sir-
William-Osler, Montreal, Quebec, Canada, H3G 1Y5 

     
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Corresponding author:  
Jordan Miller, Associate Professor,  
School of Rehabilitation Therapy, Health Services and Policy Research Institute, Queen’s University, 
Louise D Acton Building, 31 George Street, Kingston, Ontario, Canada, K7L 3N6 
Jordan.miller@queensu.ca 
 
Version Date: November 20, 2025 
 
Trial Status 
This trial was registered prospectively in January, 2020 at ClinicalTrials.gov (NCT04287413). 
Recruitment of primary care sites (clusters) was completed in June 2023 (after being delayed due to the 
COVID-19 Pandemic). Cluster randomization was performed in July 2023. Patient participant 
recruitment was initiated in October, 2023 and completed in November, 2024. Data collection is 
ongoing as of the submission of this protocol with data collection planned for completion by December 
19, 2025. Data analysis will be carried out in accordance with this pre-specified analysis plan and will 
commence following the completion of data collection. No interim analyses have been conducted. 
Results are expected to be published in 2026. 
 
 
 

     
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Introduction 
Low back pain (LBP) is the world’s largest contributor to years lived with disability1, costs the Canadian 
healthcare system between $6 and $12 billion annually2, and is a leading contributor to lost work 
productivity3,4. The burden of LBP on the healthcare system is evidenced by frequent healthcare use, 
including unnecessary specialist consultations, diagnostic procedures4,5, opioid prescriptions6,7, and 
emergency department visits8.  
 
LBP is the fifth most common reason for physician visits9-11 with primary care physicians being the 
most frequent first point of contact within the healthcare system for people with LBP12.   A Lancet13-16 
series on LBP highlighted international expert consensus on the need to evaluate new primary care 
models that better support physicians, who receive limited training in the management of 
musculoskeletal (MSK) conditions17,18 and report low confidence addressing LBP19,20. One of these 
models involves integrating physiotherapists (PTs) within primary care teams at the first point of 
contact, which has the potential to provide patients with a more focused LBP consultation, assist with 
evidence-based treatment delivery, reduce low-value care (such as inappropriate imaging, specialist 
physician referrals, and opioid prescriptions), and reduce primary care physician visits. If integrating 
PTs can reduce primary care physician visits, it could contribute to increasing the capacity of primary 
care teams to help address the growing challenge of providing universal access to primary care21 in the 
context of more complex patient encounters22,23, increasing prevalence of multiple chronic conditions24, 
and an aging population25. 
 
Evidence suggests PTs can provide collaborative care26-30 and implement recommendations from LBP 
primary care guidelines31-34, including: screening for serious pathology, the need for diagnostics35-37 and 
identifying risk factors for poor recovery38-40; providing reassurance, encouragement for early return to 
work, and exercise and physical activity recommendations41; and delivering targeted, psychologically 
informed interventions for those at risk of prolonged recovery40. Evidence from outside of Canada 
suggests that early guideline adherent PT care for LBP improves function and disability42,43 while 
reducing diagnostic imaging, opioid prescriptions, and physician specialist referrals43-45, and reduces 
per-person healthcare costs46,47.  
 
Evidence from observational research suggests that involving PTs in providing first-point-of-contact 
care (i.e., PT-led care) for those with work-related injuries in the US military resulted in workers being 
more satisfied with their care, receiving faster access to treatment, having fewer sickness absences, and 
using PTs and specialist physicians more appropriately36,48-52. Observational studies from the UK 
National Health Service on first-contact PT models of care indicate similar health outcomes, high levels 
of patient and physician satisfaction, shorter physician wait times, fewer work absences and diagnostic 
images, lower prescription medication use, and reduced costs53-59.  
 
The absence of high-quality randomized trials of PT-led primary care models leaves important 
unanswered questions about the process and impact of integrating PTs within primary care teams for 
people with LBP60. Specifically, there is a need for higher quality evidence on the impact of PT-led 
primary care on patient-oriented outcomes (e.g., function, pain, quality of life), health system outcomes 
(e.g., healthcare access, physician workload, emergency department visits, specialist physician referrals, 
medication use, diagnostic imaging), and societal outcomes (e.g., missed work, cost-effectiveness). 
Also, it is unclear how PTs will navigate primary care challenges, such as providing care for people 
presenting with multiple health concerns or addressing requests for medications, diagnostic imaging, or 
notes for work absences. This cluster randomized trial and embedded process evaluation will address 
these gaps by assessing the impact and implementation of a PT-led primary care model for LBP. The 

     
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results will inform primary care transformations across multiple health systems and potentially improve 
outcomes for patients with LBP.  
 
The purpose of publishing this protocol and analysis plan is to transparently report our design and 
methods, and to transparently communicate our analytic plan in advance of carrying out our planned 
analysis to reduce the risk of analytic or reporting bias. 
 
Research Questions: 
1) Is a PT-led primary care model for LBP effective at improving disability (primary outcome), pain 
intensity, quality of life, global rating of change, patient satisfaction, and adverse events compared to 
usual physician-led primary care, when evaluated over a one-year period? 
2) What is the impact of a PT-led primary care model for LBP on the health system and society 
(healthcare access, primary care physician workload, healthcare utilization, missed work, cost-
effectiveness), evaluated over a one-year period? 
 
Methods  
Trial Design: 
The trial is a parallel arm cluster randomized controlled trial at 20 primary care sites randomized 1:1 to a 
PT-led or usual physician-led primary care model for LBP. Randomization of practices, rather than 
patient participants, allows evaluation of a model where PTs are able to fully integrate within the 
primary care team and reduces potential contamination between study arms61. This protocol and analysis 
plan includes all of the items included in the Standard Protocol Items: Recommendations for 
Interventional Trial (SPIRIT) reporting guidelines62,63. See Table 1 for a summary of the schedule of 
enrolment, interventions and assessments.  
 
Table 1.  Schedule of enrolment, interventions, and assessments. 
 
STUDY PERIOD 
 
Enrolment 
of clusters 
Allocation of 
clusters 
Enrolment 
of patient 
participants 
Post-allocation 
Close-
out 
TIMEPOINT 
Pre-
intervention 
Pre-
intervention 
0  
(baseline) 
6 wk 
12 wk 
6 mo. 
9 mo. 
12 mo. 
ENROLMENT OF PRIMARY CARE SITES: 
Eligibility screen 
X 
 
 
 
 
 
 
 
Informed consent  
X 
 
 
 
 
 
 
 
Allocation to study 
group 
 
X 
 
 
 
 
 
 
ENROLMENT OF PATIENT PARTICIPANTS: 
Eligibility screen 
 
 
X 
 
 
 
 
 
Informed consent 
 
 
X 
 
 
 
 
 
INTERVENTIONS: 
PT-led primary care 
 
 
 
 
 
 
 
 

     
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Usual care 
 
 
 
 
 
 
 
 
ASSESSMENTS: 
Participant 
characteristics and 
demographic 
information 
 
 
X 
 
 
 
 
 
Participant health 
outcomes (RMDQ, 
pain intensity, EQ-
5D-5L, PSEQ, PCS, 
TSK-11, PHQ-2) 
 
 
X 
X 
X 
X 
X 
X 
Participant health 
and experience 
outcomes 
(Satisfaction, GROC, 
adverse events) 
 
 
 
X 
X 
X 
X 
X 
Health system and 
societal outcomes 
(provider encounters, 
EHR utilization, 
healthcare utilization, 
missed occupational 
activities, assistance 
required) 
 
 
 
X 
X 
X 
X 
X 
Implementation 
measures (timely 
access, PT as first 
point of contact, 
access to PT, LBP 
management) 
 
 
X 
X 
X 
X 
X 
X 
Implementation 
measures (patient 
adherence) 
 
 
 
X 
 
 
 
 
QUALITATIVE INVERVIEWS: 
Patient participants 
 
 
 
 
X 
 
 
 
Primary care 
providers and leads, 
medical office 
assistants*  
 
 
 
 
 
 
X 
X 
* These interviews started 9 months after study launch. 
 
The trial incorporates a multi-methods process evaluation informed by United Kingdom Medical 
Research Council (MRC) guidance for developing and evaluating complex interventions64,65, the 
Consolidated Framework for Implementation Research (CFIR)66-69, and findings from the process 
evaluation from our pilot trial70. Process evaluations assess how interventions were implemented and 
under what conditions. They are important to help interpret trial results (e.g., explaining why an 
intervention fails or has unexpected consequences, or why it works and how it can be refined). Process 
evaluations can be particularly valuable for informing future implementation and sustainability of 
complex healthcare interventions65,71. Our process evaluation is informed by and intended to refine our 
program theory64,72 for the PT-led primary care model program for LBP (Figure 1). The program theory 

     
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describes how the PT-led primary care model for low back pain is intended to lead to improvements in 
trial outcomes.  
 
Allocation of Participating Primary Care Teams to Trial Groups: 
Restricted randomization is recommended for cluster randomized trials to ensure the arms are balanced 
at baseline. We used covariate-constrained randomization73 with 1:1 ratio to the intervention and 
comparison arms to retain the merits of random allocation while ensuring baseline balance across the 
arms. We stratified by location (Southeastern Ontario or Interior British Columbia) and included the 
following covariates: number of active patients and rural vs. urban setting. Our maximum tolerable 
difference in the rurality indicator was 1 and the strata balancing criteria for number of active patients 
was set to 10%. An independent statistician implemented the procedure using a SAS macro74. 
Concealment was maintained by ensuring each practice had an anonymized code and performing 
randomization after all sites were recruited. 
 
Methods for Protecting Against Sources of Bias: 
Due to the trial design and interventions being compared, the PTs, patient participants, other members of 
the primary care team, and research assistants have not been blinded. While limitations due to the 
inability to blind are unavoidable, we have taken additional steps to minimize the risk of bias suggested 
for cluster trials75.  A common challenge in cluster trials is identification and recruitment bias when 
patient recruitment must take place after cluster allocation76. To minimize the risks of these biases, we 
invited consecutive patients to participate, provided the same information about the trial to both groups 
prior to participants consenting (not revealing the cluster allocation until after consent and baseline data 
collection), employed research assistants unfamiliar with the patients to recruit and consent participants, 
and rigorously applied inclusion/exclusion criteria at all sites77.  
 
Inclusion/Exclusion Criteria: 
For primary care sites, inclusion criteria were: ≥2 family physicians and/or nurse practitioners and ≥ 
1,500 active patients. Exclusion criteria were: already having a PT on the team or no space available for 
a PT to practice. Patient participants were screened prior to consent by a trained research assistant at 
both intervention and comparison sites. Inclusion criteria for patients participants were: adults (≥19 
years of age) with LBP of any duration, who were able to read, write and speak English. Exclusion 
criteria were: known cancer that could possibly contribute to LBP or inability to complete the scheduled 
follow-ups over one-year.  
 
Recruitment: 
We recruited 20 sites (14 in Ontario and 6 in British Columbia, Canada). Site recruitment focused 
primarily on the engagement of contacted sites to reduce the risk of site withdrawl78. For patient 
participant recruitment, staff at each primary care site screened patients for their willingness to 
participate when they called to book an appointment. If patients booked online, their reason for visit was 
screened by the clinic staff and staff reached out to potential participants for their willingness to be 
contacted by a research assistant. Trained research assistants screened potential participants using 
inclusion/exclusion criteria and enrolled consenting patients.  Patients who raised LBP with any provider 
during a clinical encounter were also identified as potential participants. In these cases, they were 
directed to the administrative team, who then connected them with a research assistant for screening and 
enrollment. Balance in recruitment across arms and across clusters was monitored throughout the study 
and strategies were implemented to maintain consistent implementation of recruitment processes across 
sites. 
 
Sample Size and Power Calculations: 

     
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We used the methodology of Teerenstra et al.79 to calculate the required number of clusters based on an 
ANCOVA analysis for the primary outcome (Roland-Morris Disability Questionnaire  [RMDQ]) at 12 
months adjusting for baseline. Our target sample was 20 clusters (10 per arm) allowing for 1560 patient 
participants (78 per practice before attrition). Conservatively accounting for attrition of two clusters 
(resulting in 18 clusters) and 20% of patients (resulting in 63 patients/cluster), this target would achieve 
90% power to detect a minimally important mean difference of 2.5 points80 (Cohen's d=0.4) using a two-
sided α=0.05 and assuming a standard deviation (SD) of 5.7 points based on pilot study data, a 
conservative intracluster correlation coefficient of 0.181, a cluster autocorrelation coefficient (correlation 
between cluster means at baseline and follow-up) of 0.5, and an individual autocorrelation coefficient 
(correlation between participant scores at baseline and follow-up) of 0.6 informed by our pilot study82. 
An average rate of 1.5 patient participants per site each week over one year would achieve the 
recruitment target of 1560 participants, which we anticipated based on our pilot study where we 
recruited and retained four sites and achieved a recruitment rate of >1.7 participants/site/week. 
Recruitment of 400 patient participants across 18 clusters (22 participants/cluster) would achieve 80% 
power. 
 
Clinical Partner Sites: 
This study is being coordinated from a primary Research Coordinating Centre at Queen’s University 
with 20 participating primary care sites (clusters) between Southeastern Ontario (14) and Interior British 
Columbia (6), Canada. Sites include representation from urban and rural settings in both regions to 
facilitate generalizability.  
 
Patient and Public Involvement:  
A senior advisor for the Ontario Ministry of Health contributed to the plan for this study. An individual 
with lived experience is a co-author on this protocol (LC) who has been engaged in the design of this 
study, including the identification and decision on the trial outcome measures that are important to 
patients. Their involvement in carrying out the study includes pre-testing of data collection methods, 
interpretation of process evaluation results, and participation in the development of knowledge 
mobilization  products (i.e., designing summaries appropriate for patient organizations). 
 
Trial Interventions: 
1. Index Intervention: PT-Led Primary Care Model for LBP 
The index intervention involves integrating a PT within the primary care team and making them 
available at the first point of contact for people with LBP. Patients with LBP are encouraged to book 
with the PT except when the primary reason for their visit is medication renewal or when additional 
health concerns require another provider’s attention. The intervention has four components: 1) 
assessment and screening; 2) brief individualized intervention; 3) supporting patients to access 
appropriate health services based on assessment findings; and 4) providing additional PT care to people 
with an unmet need. Ten registered PTs, who have completed two days of training on this care model, 
are participating in delivery of the model across the 10 practices randomized to the PT-led primary care 
arm.  
 
Assessment and Screening: The assessment and screening includes: taking a history; screening for 
pathology (e.g., cauda equina syndrome, traumatic fracture, cancer); physical and neurological 
examination; application of evidence-based tools to identify comorbid health conditions (e.g., 2-item 
Patient Health Questionnaire83 for depression) requiring additional care; and using a validated tool 
(Keele STarT Back Tool39,40) to identify risk factors associated with persistent LBP and disability.  
 

     
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Brief Individualized Intervention: The PTs provide a brief individualized intervention at the initial visit. 
This intervention is intended to be based on primary care guidelines for LBP 34 and consists of effective 
communication to validate the patient’s experiences84 and allow the patient to disclose the impact of 
their LBP on their lives85,86, cognitive reassurance87, individually tailored exercises88,89, and 
advice/strategies to stay active90.  
 
Supporting Patients to Access Appropriate Health Services Based on Assessment Findings: It is 
intended that the PT collaborates with the patient to identify appropriate health services based on 
assessment findings, collaborates with relevant primary care team members to provide the needed care, 
and integrates health service providers from outside of the team considering the patient’s needs and 
access to health services. First, the PT identifies potential pathology needing urgent referral (e.g., cauda 
equina, fracture, infection). Next, they identify comorbid conditions that require collaboration with other 
primary care team members (e.g., physicians, nurse practitioners, nurses, social workers, occupational 
therapists, pharmacists, and dieticians). For example, people who screen positive for depression are 
referred to their physician or nurse practitioner or a member of the mental health team. Finally, primary 
care PTs refer to community PT as informed by the patient’s score on the Keele STarT Back tool39,40 (if 
appropriate). The STarT Back tool categorizes patients with LBP into low, medium, or high risk of 
persistent pain and disability based on physical and psychosocial risk factors39. The recommended 
matched treatment for low-risk patients is to provide self-management advice and to avoid referral and 
investigations where possible. This intervention is brief and it is expected that it can take place at the 
first visit. The recommended matched treatment for medium-risk patients is referral for standard 
community PT, and the matched treatment for high-risk patients is PT care from the primary care PT 
who received specific training aimed at reducing physical and psychosocial risk factors for chronic pain 
and disability as part of the two-day training91,92. This stratified approach to care has demonstrated 
improved function, quality of life, and cost-effectiveness in comparison to usual care in the UK40. When 
a need for PT care is identified, the primary care PT helps these patients navigate the available PT 
resources (i.e., private PT clinics when the patient has private health insurance; government funded PT 
for those who meet the criteria; or PT in primary care for those without access to a PT elsewhere).  
 
Providing Additional PT Care to People with an Unmet Need: The PT provides additional care at the 
primary care site for patients who are appropriate for PT (based on STarT Back score) and who have 
barriers to accessing PT care in their community. For example, people who may benefit from 
physiotherapy but do not have access to services—due to a lack of private or government insurance 
coverage or because of geographic or transportation barriers—are offered additional physiotherapy care. 
Care includes evidence-based, guideline consistent management, such as individualized 
education￼93exercise￼88,89behavioural approaches￼91To avoid duplication of available PT services, 
patient participants with private or government funded health insurance for PT are referred to external 
PT services.  
 
2. Comparison: Usual Physician-Led Care  
The usual physician- or nurse practitioner-led primary care intervention has been unstandardized to 
reflect usual primary care clinical practice in Canada. Our pilot study suggested the most common 
management approaches in the usual care model included: diagnostic imaging (12% of patients), 
medication renewal (21%), new medication prescription (14%), notes to employers (12%), and referral 
(PT 23%, chronic pain clinic 5%, massage therapy 5%, physician specialists 5%, and dietician 2%). 
Interventions provided or recommended by the physician have been and will continue to be recorded 
and monitored throughout the full trial. 
 

     
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There are no required or prohibited concomitant treatment options for either treatment arm. Should a 
participant choose to explore other or additional treatment options within their primary care team or 
from other health service providers, they are free to do so. This was made clear in the letter of 
information and consent form provided to the participants upon entry to the study. 
 
Duration of Treatment Period: 
The PT-led primary care model is being implemented over a one-year period from the time of consent. 
All participants were offered an initial assessment with the PT and have access to the PT for follow-up 
needs for a one-year period after their initial assessment. The majority of participants classified as low-
risk using the STarT Back tool and those with private or government-funded health insurance for PT are 
intended to only see the PT in primary care at their initial visit, but will have access to the PT as a 
member of their primary care team throughout the one-year follow-up period if they need or want a 
follow-up visit. Participants identified as medium or high risk without access to PT elsewhere are 
offered additional PT care from the PT in primary care. The frequency and duration of the treatment 
plan is determined by the PT and patient participant. In our pilot study, participants who took part in 
ongoing care received an average of four visits over eight weeks. 
 
Intervention Modifications: 
We do not anticipate encountering a situation that would require the withdrawal of a participant for 
safety concerns related to the PT-led primary care or usual physician-led primary care. As per normal 
primary care and PT practice for LBP, the intervention will be modified by the primary care team to 
maintain participant safety as part of the model of care (e.g., modification of exercise in response to 
increases in pain that may be experienced with an exercise intervention, discontinuation of medications 
if adverse effects are experienced). Due to the low-risk nature of the study and the fact that patient 
participants maintained access to their usual primary care providers, a Data Monitoring Committee was 
not established. Ongoing oversight and patient participant safety monitoring was provided by the study 
investigators, including monitoring all adverse events reported at all time points.  
 
Data Collection and Management: 
All patient participant-reported outcome measures are being collected from participants using REDCap 
(Research Electronic Data Capture), a secure online survey and data capture tool hosted at Queen’s 
University94,95. A distinct link for the surveys is being sent to each participant at each assessment time-
point. We are providing the option of completing the questionnaires in-person or by phone if participants 
have barriers to completing them online. Data are also being extracted from participants’ electronic 
health records (EHR) related to care provided for LBP.  Data are being extracted using pre-piloted EHR 
extraction forms.  
 
At study completion, responses to the surveys will be exported directly from REDCap to encrypted and 
password protected datasets and stored securely in Microsoft OneDrive at Queen’s University. All data 
collected from the EHR, along with a master linking log that links 
study identification numbers with participants, will be stored in password-protected and encrypted files 
in OneDrive. The linking log will be permanently destroyed at the end of the data analysis 
period. Qualitative interview recordings will be transcribed, deidentified, and stored securely 
in OneDrive.  
 
We implemented multiple strategies to promote participant retention across all time points. Research 
assistants sent reminders every 2-3 days through personalized emails, phone calls, and text messages to 
encourage timely survey completion.  When requested, surveys were completed in-person or by phone 
to strengthen engagement and minimize loss to follow-up. 

     
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Frequency and Duration of Follow-Up: 
Patient characteristics and demographic information were collected at baseline. All patient-reported 
outcome measures are being collected at baseline, 6 weeks, and 3, 6, 9, and 12 months from the initial 
visit, with the primary comparison at 12 months. Patient satisfaction, global rating of change, adverse 
effects, and healthcare utilization are being collected at follow-up time points only. Patient adherence to 
PT recommendations is being collected at the 6-week follow-up.  
 
Participant Characteristics: 
The following characteristics and demographic information were collected from patients at baseline: 
age, first three digits of their postal code (to determine rural/urban status), biological sex, gender, 
identification as a member of a racialized group, duration of the current episode of LBP, previous 
history of LBP, number of other pain locations, highest level of education achieved, household income, 
and work status. In addition, the following questionnaires were administered at baseline as potential 
covariates and to inform subgroup analyses as part of our process evaluation. 
 
Functional Comorbidity Index: an 18-item list of comorbidities that are associated with physical 
functioning. Each comorbidity is assigned a score of 1 and the total score is the sum of the comorbidity 
elements96-98.  
 
Keele STarT Back Tool: categorizes patients with LBP into low, medium, or high risk of persistent pain 
and disability based on physical and psychosocial risk factors39,40,91.  
 
Patient Health and Experience Outcomes: 
Self-Reported Disability (Primary Outcome): using the RMDQ, which demonstrates reliability, validity, 
and responsiveness in people with acute and chronic LBP99,100.  
 
Pain Intensity: using a Numeric Pain Rating Scale (0-10)101 for pain at rest, pain when walking, and pain 
when lifting a bag of groceries from the floor. Each will be reported on a scale of 0 (no pain at all) to 10 
(worst imaginable pain). 
 
Health-Related Quality of Life: using the EuroQOL-5D (EQ-5D-5L)102, which demonstrates good 
reliability and validity, and is suitable for economic evaluation in LBP103. The EQ-5D-5L VAS score (0-
100) will be reported as a patient health outcome. The EQ5D responses will be converted to an EQ5D 
index value using the value set calculated for the Canadian context104. The index value will be reported 
as a patient-level health outcome and will also be used to calculate quality-adjusted life years (QALY) 
for our outcome in the economic evaluation.   
 
Global Rating of Change: using an 11-point global rating of change scale, from a great deal better (+5) 
to a great deal worse (-5), as has been recommended for self-reported rating of change105,106. 
Participant satisfaction: using an 11-point scale for satisfaction with care, extremely dissatisfied (-5) to 
extremely satisfied (+5). 
 
Adverse events: using an adverse events questionnaire consistent with reporting guidelines107,108 that 
asks: 1) if the patient participant has experienced any adverse event(s) as a result of any of the 
treatments received; 2) what adverse event(s) were experienced; 3) how long the event(s) lasted; and 4) 
how severe each adverse event was. For analysis, adverse events will be identified as serious or non-
serious. An adverse event will be identified as serious if any of the following criteria are met: the 
participant requires in-patient hospitalization or an emergency department visit due to the adverse event, 

     
11 
the adverse event results in significant and persistent disability (beyond 72 hours), or the adverse event 
is life-threatening or results in death. These responses are being monitored as they are completed in 
order to provide ongoing oversight of patient participant safety.  
 
The following measures will be assessed as secondary outcomes, reported in the trial results, and 
included in the process evaluation as potential mechanisms through which the PT-led primary care 
model influences LBP-related disability. 
 
Self-efficacy: confidence in abilities to participate in usual activities using the Pain Self-Efficacy 
Questionnaire (PSEQ)109,110 
 
Psychosocial risk factors for persistent pain and disability: The Pain Catastrophizing Scale (PCS) 111,112, 
Tampa Scale of Kinesiophobia 11 (TSK-11) 113,114, and 2-item Patient Health Questionnaire (PHQ-2) 
83,115 will measure psychosocial factors associated with pain-related disability. 
 
Health System and Societal Outcomes:  
Primary care physician or nurse practitioner encounters: the number of new and repeat primary care 
physician or nurse practitioner visits for LBP per patient. This measure will be considered a proxy for a 
potential increase in primary care team capacity achieved if the LBP-related workload of primary care 
physicians or nurse practitioners is reduced, thus increasing their availability to provide care to other 
patients. 
 
Healthcare utilization within the primary care team: using data being collected from the EHR: 
consultations with other primary care team members (e.g., physicians, nurse practitioners, nurses, social 
workers, and occupational therapists) and group programming accessed within the primary care 
organization.  
 
Healthcare utilization outside of the primary care team: using self-report data from follow-up surveys 
cross checked with reports the electronic health record when possible:  medications used; walk-in clinic 
visits; ED visits; inpatient hospital stays; surgeries, injections, and other interventional procedures; visits 
to specialist physicians; diagnostic imaging; and visits to other health professionals outside the primary 
care team (e.g., chiropractors, massage therapists, occupational therapists, physiotherapists, chronic pain 
clinics). 
 
Missed Occupational Activities: using self-report data from follow-up surveys: time (days) lost from 
paid employment, volunteer, homemaking, or educational activities related to LBP.  
Assistance required: using self-report data from follow-up surveys: paid and unpaid assistance required. 
For example, self-care (e.g., taking medications, dressing/undressing, going to the bathroom, 
bathing/showering, grooming), shopping/groceries, meal preparation, housework, managing finances, or 
transportation (e.g., to a medical appointment). 
 
Costs: Total costs per person will be calculated by summing direct healthcare costs and indirect costs 
using a human capital approach for missed occupational activities. Sources of direct healthcare cost 
data: Intervention costs will include the PT salary and training needed to carry out the intervention. 
Costs for publicly funded healthcare services will be obtained from the Ontario Ministry of Health 
Schedule of Benefits116. Medication costs will be obtained from the Ontario Drug Benefit formulary. 
Expenses related to health services funded by private insurance or out of pocket will be collected 
through self-report at all follow-up assessments. Other costs incurred by the participant related to their 
LBP are also being collected by self-report, including support or assistance for self-care, housework, 

     
12 
shopping, or transportation (e.g., to healthcare appointments). The total direct costs will be determined 
by multiplying the quantity of resource use by the corresponding unit cost, summing the total cost over 
each follow-up interval, and then calculating the mean cost at each follow-up time point, as well as an 
overall mean cost for the entire study period. Indirect costs: Non-healthcare costs will be limited to loss 
of productivity using a human capital approach117. The mean provincial wage reported by Statistics 
Canada will be used to assign a monetary value to time lost from paid employment. The minimum wage 
value in Ontario and BC will be used to place a value on time lost by those who were retired and time 
lost from volunteer, homemaking, caregiving, or educational activities.   
Cost effectiveness: We will conduct a cost-utility analysis from both societal (primary) and health payer 
(secondary) perspectives to meet the needs of all knowledge users. For both societal and health payer 
perspectives, we will estimate the incremental cost-per-QALY gained118-120.  
  
 
Implementation Measures: 
The following measures will be used to assess how the PT-led primary care model and usual physician- 
or nurse practitioner-led primary care model were implemented.  
Timely access to LBP care: using the percentage of patient participants with LBP who are assessed 
within 48 hours of calling for an appointment. Participants who learned about the study and were invited 
to participate during an appointment for their LBP will not be included in this analysis.   
PT as the first point of contact: using the percentage of patient participants with LBP in the PT-led 
primary care arm who visited a PT as their first point of contact for the current episode of LBP.  
Access to PT services: using the percentage of patients who are classified as medium or high risk on the 
STarT Back screening tool who access PT (as endorsed by guidelines121). 
 
LBP management: using the following data being collected from the EHR to describe the LBP 
management provided to patient participants in each arm: education; exercise; psychological 
approaches; referrals to other primary care team members (e.g., primary care physicians, nurse 
practitioners, nurses, social workers, occupational therapists, group programming); referrals made to 
health professionals outside of the primary care team; medications prescribed, deprescribed, and 
suggested; diagnostic imaging ordered; lab work ordered and received; notes to employers or insurers; 
interprofessional communications with the primary care team; and other interventions provided.  
Patient adherence to recommendations: adherence to recommendations from the primary care PT is 
being collected at the 6-week follow-up.  For patient participants identified as medium- or high-risk 
using the STarT Back classification, whether or not they accessed recommended PT (either through a 
referral to a community PT or through the PT in primary care) is being collected as part of our health 
utilization survey questions.   
 
Qualitative Interviews: 
Semi-structured qualitative interviews are being conducted with patients, PTs, other health professional 
primary care team members, medical office assistants, and primary care organizational leaders. 
Interviews with patient participants, PTs, and other health professionals have two goals: 1) to explore 
the experiences and perspectives with the PT-led primary care model for LBP; and 2) to understand how 
the model of care was implemented, how the intervention interacted with its context, and 
barriers/facilitators to implementation. Interview guides for interviews with patient participant s, PTs, 
and other health professionals start by exploring experiences with the PT-led primary care model for 
LBP using open-ended questions and probing. The interview guides then focus on asking participants 
about how the model of care was implemented, how the intervention interacted with its context, and 
barriers/facilitators to implementation using questions constructed to align with the CFIR  
domains67,68,122. The interview guides for medical office assistants and primary care organizational 

     
13 
leaders will focus on how the model was implemented and contextual factors influencing 
implementation using the CFIR. Interview guides were pre-piloted with persons with lived experience as 
patients and primary care team members prior to conducting the interviews.  
We are using purposive sampling to recruit 8-12 patient participants with diversity in terms of age, 
gender, race, household income, LBP-related disability, LBP duration, STarT Back risk categories, and 
primary care site. We are inviting all PTs who are involved in implementing the PT-led primary care 
model for LBP to participate in an interview. We are purposively sampling 10-15 primary care health 
professionals who work with a PT in the PT-led primary care model for LBP, ensuring variation in terms 
of professional background, gender, and primary care site. We are using purposive sampling to identify 
four to eight medical office assistants and four to eight primary care organizational leaders who have 
experienced implementation of the PT-led primary care model. The concept of information power123 
related to our study objectives is being used to determine when to stop interviewing within each 
informant group based.  
 
Patient participants were asked during their initial consent process for the main trial whether they were 
willing to be contacted for a follow-up qualitative interview about their experiences with the PT-led 
model of care. Willing patient participants were contacted within 12 weeks of enrollment. Based on our 
purposive sampling criteria, research assistants contacted potential participants who had agreed to be 
contacted to provide additional details about the interview purpose, review the consent process, and 
arrange a convenient time for the interview. Participants who agreed to take part were then sent a letter 
of information and consent form in advance. At the time of the interview, the research assistant confirms 
that the participant has reviewed the consent form, responds to any questions, and obtains verbal consent 
before proceeding. Recruitment of healthcare providers, medical office assistants, and organizational 
leaders is being carried out by a study coordinator and research assistant who are familiar with the 
participating clinical teams. Potential non-patient participants are being invited via email or in-person. 
Those who indicate interest are scheduled for an interview. As with patient participants, they will 
receive the letter of information and consent form ahead of the interview, and verbal consent is obtained 
at the start of the session after confirming their understanding and answering any questions.  
 
Protocol and Analysis Plan Amendments: 
Changes to the protocol and analysis plan will be communicated by amending the trial registry at 
ClinicalTrials.gov and reported in the full trial publication. Investigators and participants will be 
communicated with as appropriate based on the changes.   
 
Analysis 
 
Effectiveness analysis  
All quantitative analyses will be conducted as per the Intention-To-Treat principle. Descriptive statistics 
for baseline characteristics and primary and secondary outcomes will be reported by arm using means 
(SD) or medians (interquartile range) for continuous variables and count (percent) for categorical 
variables. All analyses will be performed in SAS, version 9.4 (SAS Institute Inc; Cary, NC). Differences 
between arms will be compared, accounting for site clustering using linear mixed models and 
generalized estimating equations (GEE), and significance will be reported with p-values.  
 
Patient health and experience outcomes 
For our patient health outcomes, we will use linear mixed models to estimate individual patient 
participant outcomes adjusting for clustering by primary care centre. Our primary outcome (RMDQ) 
with repeated measures at baseline and the 6-week, 3-, 6-, 9-, and 12-month follow-up time points will 
be analyzed using linear mixed regression (using PROC MIXED in SAS), estimated using restricted 

     
14 
maximum likelihood (REML) estimation and a Kenward-Rogers degrees of freedom correction124 to 
account for a small number of clusters. The model will include fixed effects for time, intervention group 
by time interaction, (omitting the group main effect to ensure baseline differences are constrained to 
zero125), factors used in the covariate-constrained allocation procedure126 (rurality of the cluster, number 
of active patients) and other pre-specified covariates associated with LBP-related disability (patient 
participant age127, sex128, income129, highest level of education achieved, duration of current episode of 
LBP130, Functional Comorbidity Index score127). The correlation in repeated measures on the same 
participant will be modeled using a suitable covariance structure, identified using information criteria 
(AIC/BIC). To account for clustering within practices, site will be modeled as a random effect. The 
intervention effect will be obtained as the adjusted least square mean difference between arms at 12 
months, with 95% confidence intervals. Secondary comparisons will be obtained using least square 
mean differences at intermediate time points.  
 
The use of REML estimation under an assumption of Missing At Random (MAR) allows the use of all 
available data without the need for multiple imputation. To examine the risk of bias due to missing data, 
we will compare the characteristics of those remaining and those lost to follow-up to identify factors 
associated with attrition. We will perform a sensitivity analysis for a missing not at random (MNAR) 
departure from our MAR assumption using a delta-adjusted imputation pattern mixture model131,132 
approach to investigate the robustness of our trial outcomes with regard to the missing values of the 
RMDQ. Within this sensitivity analysis approach, we will start with the posterior distributions suggested 
by an imputation model using multiple imputation by chained equation. Our imputation model will 
incorporate all variables in our primary analysis model, along with the last-observed-before-time 
covariates, and additional covariates needed for the pattern mixture. The sensitivity parameter (delta) 
will be introduced to explore how a departure from MAR affects results by specifying a maximum delta 
for each pattern of missing. We will set the maximum delta to be twice the residual sample standard 
deviation from the observed data fit of the primary linear mixed model. Our sensitivity analysis will use 
multiple imputation 9 times to generate estimated treatment effects for a range of sensitivity133.  
 
Pain intensity, health-related quality of life (EQ-5D-5L), PSEQ, PCS, TSK-11, and PHQ-2 outcomes 
will be analyzed as described for the primary outcome, adjusted for the same covariates. Patient 
satisfaction and global rating of change outcomes have no baseline measures, and will be analyzed using 
ordinal logistic regression with clusters as random effects, adjusting for the same covariates as described 
above. When individual items are missing from within any of the questionnaires, we will use simple 
mean imputation as suggested by Chavance133. Serious adverse events will be presented descriptively by 
arm due to low expected counts. Any adverse events (yes/no) will be presented as incidence rates with 
confidence intervals and compared by calculating relative risks with confidence intervals from robust 
Poisson regression using GEE-type robust variance estimators (using PROC GLIMMIX with 
EMPIRICAL option in SAS) to account for clustering134 and using an exchangeable working correlation 
matrix. We will use empirical covariance (“sandwich”) bias-adjusted (residual-based) estimators and the 
Fay and Graubard correction to account for small number of clusters in all models comparing incidence 
rates135. In the case of non-convergence or unstable estimates due to the small number of clusters, we 
would attempt to fit the model using an independent working correlation matrix. Other information 
related to non-serious adverse events (i.e., severity and duration) will be reported descriptively.  
We have planned a secondary analysis, a responder analysis136, to compare the proportion of participants 
who experience a meaningful improvement in disability (RMDQ) in the PT-led primary care model arm 
versus the usual care arm. We will define a meaningful improvement as an improvement of greater than 
or equal to 30% improvement on the RMDQ, corresponding to an established minimally important 
difference among people with LBP137,138. We will calculate the proportion of participants who 

     
15 
experience a meaningful improvement in each arm and compare between groups using robust Poisson 
regression, with GEE-type robust variance estimators to account for clustering130. We will use empirical 
covariance (“sandwich”) bias-adjusted (residual-based) estimators and the Fay and Graubard correction 
to account for small number of clusters135. 
Healthcare utilization and missed occupational activity outcomes 
For healthcare utilization and missed occupational activities outcomes, we will estimate the average 
effect at the patient level across the population (marginal models), accounting for clustering using GEE-
type robust variance estimators and robust Poisson regression with an exchangeable working correlation 
matrix. All of the models generated for our healthcare utilization and missed occupational activities 
outcomes will include the same covariates as our analysis for patient health outcomes: age, sex, highest 
level of education achieved, income, duration of current episode of LBP, and Functional Comorbidity 
Index score (individual level), and primary care site rurality and number of active patients (cluster 
level). In the case of non-convergence or model instability, a possibility for any binary outcomes with 
very low or high event rates given our small number of clusters139, we will attempt to model outcomes 
using an independence working correlation matrix.  If we continue to experience issues with non-
convergence or instability with alternate covariance structures, we will remove covariates, starting with 
duration of pain and income based on theoretical grounds and existing evidence on the strength of 
relationships between our covariates and our outcomes. 
 
Primary care physician or nurse practitioner visits will be presented as rates and compared using rate 
ratios with adjusted Poisson or negative binomial regression. Other healthcare utilization within the 
primary care team (whether there were any other consultations with interprofessional team members and 
whether or not there was group programming accessed), and healthcare utilization outside of the primary 
care team (medications, diagnostic imaging, walk-in clinic visits, ED visits, specialist physician visits, 
emergency department visits, hospital admissions, interventional procedures, surgeries, other health 
provider visits) will be presented as incidence rates and compared by calculating relative risks with 
confidence intervals using robust Poisson regression, accounting for clustering134. These models will use 
time as an offset to account for variable follow-up times and will incorporate empirical covariance 
(“sandwich”) bias-adjusted (residual-based) estimators and the Fay and Graubard correction to account 
for small number of clusters135,140,141.  Time (days) lost from occupational activities (paid employment, 
volunteer, homemaking, or educational activities) and assistance required (hours of paid assistance, 
hours of unpaid assistance) due to LBP will be presented as rates and compared using rate ratios with 
negative binomial regression.   
 
Economic evaluation 
 We will estimate the cost-effectiveness of the PT-led care model from both a societal (primary) and 
health system payer (secondary) perspective to meet the needs of all knowledge users. The total costs 
will be determined by multiplying the quantity of resource use (or lost days) by the corresponding unit 
cost (or hourly wage), summing the total cost over each follow-up interval, and then calculating the 
mean cost at each follow-up time point, as well as an overall mean cost for the entire one-year study 
period. Results will be presented as aggregated and disaggregated costs. Utility data will be generated 
using EQ-5D-5L index values (ie. utility scores) from all follow-up assessment time points. We will 
estimate QALYs for every participant, using the area under the curve approach, assuming linear 
interpolation between the measurements. To accommodate the hierarchical structure of the data, we will 
use bivariate multilevel modeling to estimate the incremental cost-effectiveness ratio using a calculation 
of cost-per-QALY gained for PT-led primary care versus usual care118-120 and to estimate the 
incremental net benefit at various willingness to pay values. To account for clustering of study sites, as 
well as heterogeneity in costs and treatment effect across jurisdictions, we will model treatment group as 

     
16 
a fixed effect and the study site a random effect; adjusting for the same covariates as the primary 
analyses as fixed effects in our models. We will conduct a probabilistic sensitivity analysis using 10,000 
Monte Carlo simulations to present the uncertainty in our cost-effectiveness estimates. Simulation 
results will be plotted on cost-effectiveness planes and we will generate cost-effectiveness acceptability 
curves to display the probability that the PT-led care model is cost-effective across a range of 
willingness to pay thresholds.  
 
Subgroup analyses 
In alignment with sex and gender equity in research (SAGER) guidelines142, we plan to conduct 
exploratory analyses for each of our effectiveness outcomes for males and females to explore potential 
sex differences in each of these outcomes. We will include sex and its interaction with time and group 
by time in the models. We will report the interaction p-value along with forest plots to visualize the 
subgroup treatment effects, along with 95% confidence intervals. 
 
Process evaluation analysis 
The multi-methods process evaluation analysis will assess how the PT-led primary care model for LBP 
was implemented, the potential mechanisms of the model, the experiences and perspectives of patients 
and primary care team members toward the model, and how the context influenced implementation and 
outcomes. The analysis is guided by and intended to inform refinements to our program theory64,72 for 
the PT-led primary care model program for LBP (Figure 1).   
 
We will assess how the PT-led primary care model and usual care model were implemented and how the 
model influenced healthcare for people with LBP by:  
1) Describing and comparing the proportion of patient participants who received timely access to LBP 
care between trial arms. The intended implementation outcome is that patient participants seeking 
primary care for LBP receive timely access (within 48 hours) to a LBP assessment from a PT in the PT-
led primary care model for LBP. Access within 48 hours will be presented as incidence rates with 
confidence intervals and compared by calculating relative risks with confidence intervals using GEE-
type variance estimators to account for clustering134, using an exchangeable working correlation matrix. 
We will use empirical covariance (“sandwich”) bias-adjusted (residual-based) estimators and the Fay 
and Graubard correction to account for small number of clusters131-133. We will incorporate the same 
covariates as with our effectiveness analysis.  
2) Describing the proportion of patient participants in the PT-led primary care arm who saw the PT as 
the first point of contact for the current episode of LBP. 
3) Describing and comparing the proportion of patient participants who are categorized as medium or 
high risk on the STarT Back tool who access PT services between trial arms. The intended 
implementation outcome is that patient participants with LBP at medium or high risk of ongoing pain 
and disability access PT services, either through a referral (for participants who have access to PT 
services) or through additional PT care from the primary care PT (for participants who would not 
otherwise have access to PT services). We will report the proportion of participants at medium or high 
risk receiving PT care as incidence rates with confidence intervals and compared by calculating relative 
risks with confidence intervals using GEE-type variance estimators, accounting for clustering134, using 
an exchangeable working correlation matrix. We will use empirical covariance (“sandwich”) bias-
adjusted (residual-based) estimators and the Fay and Graubard correction to account for small number of 
clusters131-133. We will incorporate the same covariates as with our effectiveness analysis.   
4) Describing and comparing the LBP management provided between trial arms and comparing the care 
provided to practice guidelines using recent World Health Organization (WHO) LBP guidelines143 to 
assess the alignment of the care provided with practice guideline recommendations. The 10 
recommended interventions in the WHO guidelines are: structured education, exercise, needling 

     
17 
therapies, spinal manipulation, massage, operant therapy, cognitive behavioural therapy, non-steroidal 
anti-inflammatory drugs, topical cayenne pepper, and multicomponent biopsychosocial care. 
Interventions that are recommended against include: traction, ultrasound, transcutaneous electrical nerve 
stimulation, and lumbar supports. Additionally, referrals for diagnostic imaging or physician specialist 
visits for spinal injections or surgery consultations are rarely needed for LBP and thus will be compared 
between arms for a potential reduction in low-value care144,145. The intended implementation outcome is 
that patient participants receive care recommended in practice guidelines and do not receive care 
recommended against in practice guidelines, and that a low proportion of people receive referrals or 
prescriptions for diagnostic imaging, physician specialist, or medications outside of NSAIDs. Receipt of 
each intervention recommended by the guidelines, recommended against by the guidelines, and referrals 
(for imaging, spinal injections, or surgical consults) will be presented as incidence rates with confidence 
intervals and compared by calculating relative risks with confidence intervals using GEE-type robust 
variance estimates, accounting for clustering134, using an exchangeable working correlation matrix. We 
will use empirical covariance (“sandwich”) bias-adjusted (residual-based) estimators and the Fay and 
Graubard correction to account for small number of clusters135. We will incorporate the same covariates 
as with our effectiveness analysis.  
5) Describing adherence to PT primary care recommendations. The intended implementation outcome is 
that patient participants report actioning the initial recommendations for physical activity and exercise 
and that those who were recommended to access PT services report doing so.  
 
To assess potential mechanisms of the PT-led primary care model, if there is a significant treatment 
effect, we will conduct a series of mediation analyses146 to assess whether changes in LBP-related 
disability is explained by changes in self-efficacy (PSEQ) or changes in psychosocial risk factors (PHQ-
2, PCS, TSK-11) for persistent LBP-related disability. We will carry out this mediation analysis for each 
of the self-efficacy and psychosocial risk factor variables in our entire sample and in the subgroup of 
patient participants identified as high-risk using the STarT Back classification. We hypothesize that 
change in self-efficacy will mediate the relationship between the intervention arm and RMDQ score in 
the full patient participant sample and that change in psychosocial risk factors will mediate the 
relationship between treatment arm and RMDQ score in the high-risk subgroup. We will use a stepwise 
approach to exploring the temporal trends and dynamics of the treatment effect across repeated 
measures as proposed by Beril and colleagues147, along with our theoretical insights regarding the 
mediation effect, when considering the appropriate mediation model. The indirect effect, or the 
intervention effect that can be explained by the mediator, will be determined as the difference between 
the total effect of the intervention and the direct effect of the intervention148,149. The significance of this 
effect can be used to statistically evaluate the possibility of mediation150,151. In this context, the total 
effect is the intervention effect obtained in the full model described in the trial. To be consistent with 
our primary outcome measure in the trial, we will be concerned with the time-specific mediation on the 
RMDQ outcome at the 12-month follow-up152,153. These effect measures will allow us to present the 
proportion of the total effect that is mediated through the respective measures of interest154. 
Development of the causal/associated conceptual model has allowed us to consider, and control for 
where needed, mediation analysis assumptions; that is, there is no intervention-outcome, mediator-
outcome, or intervention-mediator confounding or mediator-outcome confounding that is influenced by 
the intervention itself149,155,156. Additionally, as part of exploring potential mechanisms, we will describe 
the proportion of any differences in total costs that are due to differences in healthcare utilization costs 
(and if so what healthcare services) and what proportion of any differences in total costs are due to 
differences in costs due to missed occupational activities.   
 
We will further explore potential mechanisms for intervention effects by conducting a planned subgroup 
analysis for all process outcomes, RMDQ, and costs based on STarT Back risk classification (low, 

     
18 
medium, or high), recognizing that this analysis is exploratory and likely to be underpowered.  We 
hypothesize that the participants in the PT-led group classified as low risk will: be less likely to receive 
requisitions for diagnostic imaging, referrals to specialist physicians, and prescriptions for medications; 
demonstrate reduced costs; and show similar disability outcomes in comparison to patient participants in 
the usual care group. We hypothesize that participants in the PT-led group classified as medium risk 
will: be more likely to access PT services; receive LBP management closer aligned with guidelines; 
receive fewer requisitions for diagnostic imaging, prescriptions for medications, and referrals to 
specialist physicians; demonstrate reduced costs; and have reduced LBP-related disability. Finally, we 
hypothesize that participants in the PT-led group classified as high risk will: be more likely to access PT 
services; receive LBP management closer aligned with guidelines and more targeted care for 
psychosocial risk factors of persistent LBP-related disability; show greater reductions in the 
psychosocial risk factors; demonstrate reduced costs; and have reduced LBP-related disability.  
 
To explore the experiences and perspectives of patients, PTs, and other primary care health 
professionals who have participated in the PT-led primary care model for LBP, qualitative interviews 
with patient participants, the PTs, and other health professional primary care team members will be 
recorded, transcribed, and coded independently by two investigators. An inductive qualitative analysis 
will be completed in an interpretive description tradition157,158. Interpretive description was chosen 
because of its emphasis on adding an interpretive lens during the analysis process in order identify 
meaningful themes from the data that can be applied in practice. tice. This qualitative approach is 
therefore well aligned with our process evaluation goals to understand experiences and perspectives 
with the PT-led primary care model, potentially leading to refinements in the model of care, the program 
theory, or plans for scaling the intervention if effective. To promote rigour, we will use: two 
independent coders for the first two to three transcripts for each group (patient participants, PTs, other 
health professionals) and meet to reach agreement on the initial coding structure; reflexivity journaling 
and reflexive dialogue amongst team members throughout the analytic process; field notes and written 
memos; prolonged engagement within the data; and an audit trail of the research process and analytic 
decisions159-163. 
 
To explore how the context influenced the implementation of the PT-led primary care model for LBP, a 
concurrent mixed methods analysis will be conducted using the quantitative data collected to describe 
how the intervention is being implemented (analysis described above) and qualitative interview data 
from patient participants, PTs, other primary care health professionals, medical office assistants, and 
primary care organizational leaders. Qualitative analysis will begin by having multiple research team 
members immerse themselves in the data. Data will be coded by two independent research team 
members using an in-depth deductive (codes derived from CFIR constructs164) and inductive (codes 
derived from the data) coding process in alignment with the CFIR User Guide164 and previous 
research165-167￼. Further, we will code relationships between constructs to capture how constructs 
interact, and how implementation determinants relate to how the model was implemented. Integration of 
qualitative and quantitative will be achieved through the design (convergent mixed methods), methods 
(merging), and interpretation (narrative, and joint displays)168. Merging of qualitative and quantitative 
data will be achieved by linking participant characteristics, implementation data, and qualitative 
interview data. By bringing together the quantitative and qualitative data for analysis and linking this 
data at the level of the patient participant and cluster, we will be able to carry out an in-depth analysis of 
how context influenced implementation. Integration at the interpretation phase will be achieved using a 
weaving approach to interpret and report qualitative and quantitative findings together168. Joint displays 
will be used to bring data together in a visual display if appropriate.   
 
Ethics and Dissemination: 

     
19 
Ethics approval for this study has been obtained from the Queen’s University Health Science and 
Affiliated Teaching Hospitals Research Ethics Board (HSREB #6027847). Written consent was obtained 
from all participants willing to participate.  
 
We plan to mobilize the knowledge generated through this cluster randomized trial and process 
evaluation through a series of peer-reviewed manuscripts, with the following foci: i) effectiveness of the 
PT-led primary care model for people with LBP (including patient health, and health system and societal 
outcomes); ii) how the PT-led primary care model for LBP was implemented; iii) potential mechanisms 
through which the PT-led primary care model for LBP influences patient health and health system 
outcomes (if the intervention is effective); iv) experiences and perspectives of patients with LBP who 
participated in the PT-led primary care model; (v) experiences and perspectives of PTs who participated 
in implementing the PT-led primary care model for people with LBP; vi) experiences and perspectives 
of other primary care team members who participated in implementing the PT-led primary care model 
for people with LBP; and (vii) how the context influenced implementation of the PT-led primary care 
model for people with LBP. We plan to present these results at national and international conferences on 
primary care, health services, physiotherapy, and back pain. We will create tailored summary reports for 
each manuscript for each of the following knowledge user groups: patients, health professionals, 
primary care team leaders, and health system decision makers. 
 
Discussion 
The results of this trial will inform new models of primary care in Canada and will be applicable to 
health systems around the world. Our detailed analysis plan has integrated the perspectives of 
knowledge users, including people living with LBP, primary care providers, PTs, researchers, and health 
system decision makers. These knowledge users have helped define study outcomes (i.e., disability, 
quality of life, cost-effectiveness) that will meet the needs of key knowledge users and decision makers. 
This protocol and analysis plan builds on our trial registration by articulating our analytic decisions in 
advance of analyzing our data to reduce risk of analytic or reporting bias.  
 
Our knowledge user team has also contributed to our detailed process evaluation, where we aim to 
explore how the PT-led primary care model for LBP was implemented, potential mechanisms of the 
model of care, experiences of people involved in implementation, and how context influences 
implementation. The updated MRC guidance for the development and evaluation of complex 
interventions emphasizes the importance of moving beyond only questions of effectiveness to also 
explore how complex interventions, like this model of care, will be accepted, adopted, implemented, 
scaled and transferred across contexts64. Our analysis plan for an in-depth process evaluation aims to 
support health system decision makers by providing clear and transparent analytic processes to answer 
the key questions needed to inform scale and spread of this model of care if it is effective.  
 
Acknowledgements: Not applicable. 
Funding  
This study is funded by a Canadian Institutes of Health Research (CIHR) project grant (reference 
number 166177). CIHR played no part in the writing of this protocol and analysis plan. Jordan Miller is 
also supported by an Arthritis Society of Canada and CIHR STARS award which provides financial 
support for him to focus more of his time on research. 
 
Competing Interest  
The authors declare that they have no competing interests.   
 

     
20 
Data Availability   
For access to the data, please contact the corresponding author. 
  
Abbreviations 
BC: British Columbia 
CFIR: Consolidated Framework for Implementation Research 
ED: Emergency Department 
EHR: Electronic health record 
GEE: Generalized estimating equation 
GROC: global rating of change 
LBP: Low back pain 
MAR: Missing at random 
MNAR: Missing not at random 
MRC: Medical Research Council (UK) 
MSK: Musculoskeletal 
PHQ-2: 2-item Patient Health Questionnaire 
PSEQ : Pain Self-Efficacy Questionnaire 
PT: Physiotherapist 
QALY: Quality-adjusted life years 
REDCap: Research Electronic Data Capture 
REML: Restricted maximum likelihood 
RMDQ: Roland-Morris Disability Questionnaire 
TSK-11: Tampa Scale of Kinesiophobia 11 
WHO: World Health Organization 

 
21 
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