Abstract
Objective
Most pragmatic trials follow the PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) criteria. The criteria specify unobtrusive measurement of participants’ protocol adherence and practitioners’ intervention fidelity but suggest no special monitoring strategies to assure trial integrity. We present experience with adherence/fidelity monitoring in the Pain Management Collaboratory (PMC) and provide recommendations for their monitoring in pragmatic trials to preserve inferences of treatment comparisons.
Methods
In November 2021, we surveyed 10 of 11 originally funded PMC pragmatic trials to determine the extent to which adherence and fidelity data were being monitored.
Results
Of the 10 PMC trials, 8 track adherence/fidelity. The electronic health record is the most frequent source for monitoring adherence (7/10) and fidelity (5/10). Most adherence data are used to monitor participant engagement with the trial intervention (4/10) and are reviewed by study teams (8/10) and often with a data and safety monitoring board (DSMB) (5/10). Half of the trials (5/10) reported using fidelity data for feedback/training; such data are not shared with a DSMB (0/10). Only 2 of 10 trials reported having prespecified guidance or rules around adherence/fidelity (eg, stopping rules or thresholds for corrective action, such as retraining).
Conclusions
As a best practice for pragmatic trials, we recommend early and regular adherence/fidelity monitoring to determine whether intervention delivery is as intended. We propose a 2-stage process with thresholds for intervening and triggers for conducting a formal futility analysis if adherence and fidelity are not maintained. The level of monitoring should be unobtrusive for both participants and those delivering the intervention; resulting data should be reviewed by an independent DSMB.
Keywords: PRECIS-2, adherence, fidelity, data and safety monitoring board, intent-to-treat analysis, per-protocol analysis
Introduction
Pain is a significant public health problem currently affecting as many as 100 million people in the United States. Numerous evidence-based nonpharmacological approaches to treating pain are recommended by professional health organizations.4 However, these recommendations have been rarely adopted.5 Several agencies have encouraged large pragmatic trials to address the gap between the science and the practice and implementation of pain management6–9 therapies. Large pragmatic trials are a staple for assessing the effectiveness of interventions in real-world settings. With the potential to bridge the gap between explanatory trials and clinical practice, the popularity of large pragmatic trials has increased dramatically in the past few decades.1
The PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) criteria, which score trials on a continuum between explanatory and pragmatic, were established to guide trialists in their decisions to assure consistency between the trial design and ultimate study goals.2 Two domains of the PRECIS-2 relate to (1) the flexibility in the study participant’s adherence to the intervention and related protocol procedures and (2) the flexibility in the delivery of the intervention by the research and/or clinical team, which we will refer to as fidelity. PRECIS-2 scoring guidance for adherence states that “having measures in place to monitor patient adherence with the protocol and measures to address poor adherence,” beyond what would be implemented in usual care, would reduce the trial’s pragmatism. Similarly, for fidelity, “having measures in place to monitor compliance of those delivering the intervention (such as physicians or research coordinators) with the protocol and measures to address poor compliance” would similarly result in lower pragmatism.
Modification of design features, especially those influencing adherence and fidelity, to increase trial pragmatism might have unintended consequences.3 Lack of adherence or fidelity might be an unavoidable intrinsic property of the intervention, reflecting ineffectiveness, toxicity/safety, or participant burden. Alternatively, inadequate adherence or fidelity either could represent an opportunity to improve or, in the worst-case scenario, could lead to an avoidable failure in implementation and operationalization of the intervention protocol. Nevertheless, as adherence and fidelity decrease, the impact of the intervention becomes diluted and is often less likely to be distinguished from the comparator. As such, decisions on design features impacting adherence and fidelity require clear specification of the research question; careful consideration of the core functions of the intervention; and a commensurate plan for ongoing monitoring, changes in protocol design, and data analysis.
The Pain Management Collaboratory (PMC) was established with 11 pragmatic trials focused on developing, implementing, and testing cost-effective, large-scale, real-world research on nonpharmacological approaches to the management of pain and comorbid medical and mental health conditions in the Department of Defense and Department of Veterans Affairs (VA) health care delivery organizations.10 These trials typically evaluate complex, multimodal interventions delivered to a heterogenous group of individuals with chronic pain. Examples of nonpharmacological approaches include psychologically based interventions, such as cognitive behavioral therapy (CBT), and body-based interventions, such as acupuncture, chiropractic care, or physical therapy. In contrast to pharmacological trials, nonpharmacological, multimodal, behavioral interventions are inherently challenging with regard to adherence and fidelity. Furthermore, as opposed to explanatory trials, which follow standardized protocols, in pragmatic trials, where provider discretion is allowed, there could be wide variability in the application of nonpharmacological behavioral interventions. This emphasizes the importance of their monitoring.
In this article, we present the results of a survey of adherence and fidelity monitoring practices in the PMC studies and provide recommendations for their monitoring in pragmatic trials of nonpharmacological, behavioral pain interventions, with attention to maintaining differentiation between interventions for comparisons.
Methods
To understand the practices used to monitor adherence and fidelity, a survey was conducted among 10 of the 11 originally funded pragmatic PMC trials in November 2021. One trial involving percutaneous peripheral nerve stimulation (vs placebo) in surgical patients with chronic pain was excluded from the survey, given that the intervention evaluated was analgesic rather than behavior based. As background, 8 of the 10 trials are being monitored by a data and safety monitoring board (DSMB), and half of the DSMBs have adherence or fidelity monitoring as part of their charters. Four of the trials are blinded but have an unblinded statistician reporting to the DSMB.
The survey (Appendix S1; see Supplementary Material) was developed by the authors, who are members of the Biostatistics and Study Design Work Group for the PMC. The survey defined adherence as “a participant’s compliance with the protocol-specified intervention” and fidelity as “the interventionist’s delivery of the protocolized intervention.” Items on the survey were mostly open-ended and asked whether adherence and fidelity were tracked in the study, what data were collected to monitor adherence and fidelity, what mode of data collection was used (eg, the electronic health record [EHR]), who had access to summarized data, what was the frequency of review, and what were the thresholds for action. The survey was evaluated for face validity with the Work Group members, but because of the small sample size, it was not beta tested; items were selected on the basis of a consensus of the Work Group about what were sensible and reasonable data to collect. The survey was distributed to the study teams via Qualtrics.
We also provide protocol details about adherence and fidelity monitoring in 5 selected PMC trials in Appendix S2 in the Supplementary Material.11–15
Results
Trial characteristics, including data collected and modes of collection for adherence and fidelity from the survey, are listed in Table 1. The study team determined that PRECIS scores ranged from 2 to 5 (mode 4) for both adherence and delivery. Most PMC trials (8 of the 10) track adherence data. Among these, all track protocol visit attendance. Most trials (7 of 8) used 2 methods to track adherence. The EHR is the most frequently reported source for adherence data collection (7 trials), followed by interventionist report (4 trials), and patient report (2 trials). Trials reported using adherence data for multiple purposes, including monitoring (eg, to confirm that participants engaged with the intervention; 4 trials), reporting to the DSMB (5 trials), and inclusion in their secondary analyses (5 trials). All 8 trials tracking adherence reported that the study team reviews the adherence data. Most trials (4 out of 5) reporting adherence data to the DSMB did so in an unblinded manner.
Table 1.
PMC trial characteristics and collection of adherence/fidelity data.
| Reference | PMC trial title | Design | Intervention | Unit of randomization | Primary endpoint(s) | n | PRECIS score | Adherence Data collected (mode) | fidelity/delivery collection (mode) | |
|---|---|---|---|---|---|---|---|---|---|---|
| George et al.14 | Improving Veteran Access to Integrated Management of Low Back Pain (AIM Back) | Cluster randomized controlled trial (RCT) | Integrated, sequenced care pathway vs coordinated, pain navigator pathway | Cluster | Pain interference (PROMIS) Physical function (PROMIS) | n = 1680 | Flexibility adherence | 3 |
|
Observed fidelity to protocol (direct observation of random subset) |
| Flexibility delivery | 3 | |||||||||
| Seal et al.19 | Whole Health Team vs Primary Care Group Education to Promote Nonpharmacological Strategies to Improve Pain, Functioning, and Quality of Life in Veterans |
|
Whole Health Team (WHT) vs primary care group education and both vs usual care | Individual | Pain interference (BPI) | n = 765 | Flexibility adherence | 4 |
|
|
| Flexibility delivery | 4 | |||||||||
| Long et al.12 | Chiropractic Care for Veterans: A Pragmatic Randomized Trial Addressing Dose Effects for cLBP (VERDICT)12 |
|
|
Individual | Change in disability (RMDQ) | n = 766 | Flexibility adherence | 4 | No. of visits to chiropractor (EHR) |
|
| Flexibility delivery | 3 | |||||||||
| Fritz et al.20 | SMART Stepped Care Management for Low Back Pain in the Military Health System | Sequential multiple randomization trial (SMART) |
|
Individual | Pain interference (PROMIS) | n = 1200 | Flexibility adherence | 3 |
|
|
| Flexibility delivery | 4 | |||||||||
| Heapy et al.11 |
|
Parallel group RCT | IVR-based CBT-CP (asynchronous) vs VA CBT-CP (synchronous, eg, in-person, video/telephone) | Individual | Pain interference (BPI) | n = 764 | Flexibility adherence | 4 |
|
|
| Flexibility delivery | 4 | |||||||||
| Martino et al.13 | Engaging Veterans Seeking Service-Connection Payments in Pain Treatment (SBIRT) | Parallel group RCT | SBIRT-PM vs usual care | Individual |
|
n = 1100 | Flexibility adherence | 2 | No. and length of sessions (interventionist report) | Observed fidelity to protocol (recordings, interventionist report) |
| Flexibility delivery | 3 | |||||||||
| Zeliadt et al.15 | The APPROACH Trial: Assessing Pain, Patient Reported Outcomes and Complementary and Integrative Health (A VA National Demonstration Project) | Pragmatic quasi-experimental study | “Nudges” to combined self-care CIH (eg, yoga, meditation, tai chi) and practitioner CIH (eg, chiropractic, acupuncture) vs self-care CIH vs practitioner CIH | N/A | Brief Pain Inventory (BPI) | n = 18 000 | Flexibility adherence | 5 |
|
None |
| Flexibility delivery | 5 | |||||||||
| Goodie et al.21 | Targeting Chronic Pain in Primary Care Settings Using Behavioral Health Consultants | Parallel group RCT | Brief Cognitive Behavioral Therapy for Chronic Pain (BCBT-CP) vs usual care | Cluster |
|
n = 800 | Flexibility adherence | 4 | None | None |
| Flexibility delivery | 4 | |||||||||
| Burgess et al.22 | Learning to Apply Mindfulness to Pain (LAMP) | Parallel group RCT | Mobile mindfulness-based intervention (MBI) plus (Zoom virtual group delivery with facilitator) vs mobile MBI vs usual care | Individual | Pain interference (BPI) | n = 811 | Flexibility adherence | 3 | No. and length of sessions (EHR, patient self-report) | Observed fidelity to protocol (interventionist report, direct observation) |
| Flexibility delivery | 2 | |||||||||
| Farrokhi et al.23 | Resolving the Burden of Low Back Pain in Military Service Members and Veterans: A Multi-Site Pragmatic Clinical Trial (RESOLVE Trial) | Stepped-wedge cluster RCT | CPG adherence (intervention arm) vs usual care (comparator arm) | Cluster |
|
n = 4672 | Flexibility adherence | 4 | None | Compliance with guidelines (EHR) |
| Flexibility delivery | 4 | |||||||||
| Ilfeld et al.24 | Ultrasound Guided Percutaneous Peripheral Nerve Stimulation: A Nonpharmacological Alternative for the Treatment of Postoperative Pain | Parallel group RCT | Percutaneous peripheral nerve stimulation (PNS) vs placebo | Individual |
|
n = 250 | Flexibility adherence | 5 | Nonresponse | Nonresponse |
| Flexibility delivery | 5 | |||||||||
Abbreviations: ASSIST = Alcohol, Smoking and Substance Involvement Screening Test; BCBT-CP = Brief Cognitive Behavioral Therapy for Chronic Pain; BPI = Brief Pain Inventory; CBT-CP = Cognitive Behavioral Therapy for Chronic Pain; CIH = complementary and integrative health; CPG = Clinical Practice Guidelines; EHR = Electronic Health Record; IVR = Interactive Voice Response; MBI = Mindfulness Based Intervention; NRS = Numeric Pain Rating Scale; PNS = Percutaneous Peripheral Nerve Stimulation; PROMIS = Patient-reported Outcomes Measurement Information Systems; PT = Physical Therapy; RCT = Randomized Controlled Trial; RMDQ = Roland Morris Disability Questionnaire; SBIRT-PM = Screening Brief Intervention and Referral to Treatment for Pain Management; SMART = Sequential Multiple Assignment Randomized Trial; WHT = Whole Health Team.
Most trials (8 of the 10) also track fidelity data. Only one trial reported tracking neither adherence nor fidelity data. Treatments delivered, session details, and core intervention components were typically tracked; 2 trials reported that fidelity was examined in a subset of patients. Most trials (5 of 8) used 2 or more methods to track fidelity. The EHR was the most frequently reported source for fidelity data collection (5 trials), followed by interventionist report (4 trials); 4 trials reported recording (audio/video) or direct observation of intervention sessions. The majority (5 trials) reported using the fidelity data for feedback or training, and 3 trials reported that the data will be used to quantify intervention quality in reports or will be incorporated as part of the analysis. The majority (6 trials) reported that the study team reviews the fidelity data (5 in an unblinded fashion), and 3 trials indicated that a small subset of the study team reviews the data. None reported sharing fidelity data with the DSMB.
Of the 8 trials that tracked adherence and fidelity data, 2 reported having prespecified guidance or rules around adherence and fidelity (eg, stopping rules or thresholds for corrective action, such as retraining research personnel delivering the intervention). In 1 trial, the threshold parameters were set as at least 70% of participants receiving counseling and 50% receiving at least 2 sessions, with inadequate performance on 2 consecutive sessions triggering a review by the principal investigator. In another trial comparing the delivery medium of CBT (synchronous vs asynchronous),11 data on adherence are used to monitor divergence between groups, and if insufficient separation is observed between groups, a futility analysis might be required by the DSMB. A threshold was not set for the futility analysis.
Discussion
The PRECIS-2 criteria assess the pragmatism of a trial on a continuum and state that both “having measures in place to monitor patient adherence with the protocol and measures to address poor adherence” and “having measures in place to monitor compliance of those delivering the intervention (such as physicians or research coordinators) with the protocol and measures to address poor compliance” would reduce pragmatism. However, failure to maintain separation of treatment arms or prevent drift in the delivery of interventions as intended in the protocol could affect inferences drawn from the trial findings, because the more the interventions look similar, the harder it is to detect differences. Notably, monitoring of adherence and fidelity itself is not necessarily nonpragmatic if measures and corrective actions do not extend beyond what would be implemented in usual care. For example, automated tracking of adherence to CBT delivered through an interactive voice response system is a pragmatic approach, whereas self-report of sessions attended would be less pragmatic. Similarly, evaluation of fidelity via structured fields or provider notes within the EHR puts little burden on the intervention team, whereas detailed interventionist reports or checklists not typically captured as part of routine care might be less pragmatic. Nevertheless, a challenge in pragmatic clinical trials is how to achieve a balance between the extent of fidelity/adherence monitoring and trial pragmatism, acknowledging that more monitoring is viewed as less pragmatic.
We found that the majority of trials in the PMC are tracking adherence and fidelity, with the EHR being the most frequently reported source for monitoring. Most adherence data are used to monitor participant engagement with the intervention and are reviewed by study teams and often with a DSMB. Most fidelity data are used to track treatment delivery and session attendance and are reviewed by study teams for feedback or retraining; fidelity data are not typically shared with a DSMB. Only 2 trials reported having prespecified guidance or rules with regard to adherence and fidelity (eg, stopping rules or thresholds for corrective action, such as retraining). As evidenced by our survey, the majority of PMC trial researchers recognized the importance of monitoring adherence and fidelity even for a pragmatic trial—ie, without knowing what is being given, it is more difficult to assess and understand intervention comparisons. Thus, most instituted trial-specific measures, like use of the EHR, to reduce participant and investigator burden. Our survey focused on the data collected, mode of data collection, and purpose of the monitoring of adherence and fidelity rather than the corrective actions taken. Details of adherence/fidelity monitoring for 5 selected PMC trials are provided in Appendix S2 as examples of the types of adherence/fidelity monitoring and corrective actions being done in the PMC, but they are not discussed.
Despite the potential impact on pragmatism, on the basis of our PMC experience, we recommend that some level of regular adherence/fidelity monitoring of interventions by the study team be instituted early in all nonpharmacological pain pragmatic trials to assess whether the interventions are being delivered as intended. This concept of early monitoring of adherence and fidelity was proposed in Kerns et al.16 by “developing thresholds to trigger action by a research team to improve fidelity of intervention delivery.” This type of monitoring is designed to make early course corrections, such as retraining, to maintain separation of interventions so that proper inferences can be made. One note of caution is that such monitoring might not be part of regular clinical practice and could change treatment, introducing a Hawthorne effect.17 Furthermore, special attention is required with regard to strategies to enhance adherence in a pragmatic trial, particularly when adherence is included as part of the outcome measures.
Issues with monitoring adherence and fidelity during a trial include establishing measures for assessing adherence and fidelity, developing thresholds for when to intervene if adherence or fidelity is lacking, defining who reviews the adherence/fidelity data, and determining whether the inability to maintain separation of interventions should trigger a formal interim look for futility. All of these issues should be addressed in relation to the research question and goals of the study and would need to be prespecified in the study protocol’s monitoring plan. In trials with a DSMB, the DSMB would review and approve the monitoring plan, including thresholds for when to intervene and triggers for an interim look for futility.
Blinding is an important consideration to avoid the possibility of decisions about corrective actions to improve adherence and fidelity introducing bias in intervention comparisons. Ideally, when intervention protocols are of a similar nature, review could be conducted in aggregate across intervention groups. Alternatively, in cases where there are disparities in the components of the intervention, appointing an intervention expert (ie, an individual with a thorough understanding of the delivery of the key components of the intervention) to review and implement corrective actions to improve adherence and fidelity independently from the study team would be recommended. If a DSMB is involved, the intervention expert could present data aggregated by or across intervention groups with or without masking depending on the nature of the comparison interventions, the likelihood of introducing bias, and DSMB preference. At the discretion of the intervention expert or in collaboration with the DSMB, study-wide or intervention-specific corrective actions could then be taken to improve adherence and fidelity, with the study team and site investigators blinded to intervention-specific data. On the basis of the data, the DSMB could recommend a futility analysis once sufficient information on outcomes has been accrued (usually, at least 50%). It is important to note that regardless of whether adherence and fidelity monitoring is done, a futility analysis could be specified as part of the study’s interim monitoring plan. The principal investigator (and research team) should be unaware of any futility analyses being conducted, as it could compromise the integrity of a trial; only the unblinded statistician reporting to the DSMB should have this knowledge. Early stopping could be recommended by the DSMB to the sponsors if there is strong evidence of futility. The issue is less clear in trials without a DSMB because there is no independent monitoring, which creates a potential conflict of interest for a principal investigator to consider stopping his/her trial for futility. Nevertheless, in this situation, consideration should be given to having an assessment of futility by a party not involved in the design or day-to-day operation of the trial.
Another benefit of monitoring adherence and fidelity is that it provides data for per-protocol analyses. Although the traditional analysis of pragmatic trials is according to intent-to-treat, Hernan and Robbins18 argue that per-protocol analyses complement these analyses in the presence of nonadherence. They state that “the validity of per-protocol analyses depends not only on the choice of the appropriate method but also on an explicit definition of the per-protocol effect, an a priori specification of the statistical plan for the per-protocol analysis, and the collection of high-quality data on adherence and prognostic factors.” The latter can be achieved only by monitoring adherence and fidelity. Because per-protocol analyses turn a randomized trial into an observational study, appropriate methods of analyses should be considered, such as the instrumental variable and g-methods recommended by Hernan and Robbins.
Lastly, comparator arms often encompass giving standard of care or usual care, which can be highly variable in pragmatic trials across many diverse sites. Unlike interventions, usual care is often not protocolized (standardized) because it would be different from what is given in local clinical practice. Although our survey did not specifically address monitoring of usual care, we believe some tracking of what constitutes usual care is prudent if done in an unobtrusive manner. Such tracking becomes important when there are trial interruptions, such as the COVID-19 pandemic, in which delivery of care was altered or modified (eg, the shift from in-person care to telehealth). These changes created modifications in the delivery of care that affected inferences, and tracking these changes can help inform analytic plans. In addition, journal reviewers often request information about usual care, and having some data will help to address this issue. Because usual care is naturally heterogenous, we do not recommend standardizing usual care in a pragmatic trial, as it would make a trial less pragmatic and more like an efficacy trial, nor do we recommend monitoring the usual care arm for adherence and fidelity like a protocolized intervention with the intent to intervene. However, we do recommend that some tracking of what treatment study participants are receiving as part of usual care be recorded, as it provides information about generalizability.
In summary (see Table 2), as a best practice, we recommend that some regular level of adherence/fidelity monitoring of interventions be done to determine whether an intervention is being delivered as intended. The monitoring should be initiated early in a trial to identify problems and deploy corrective actions, and as needed, subsequent preventive actions. We propose a 2-stage process that has thresholds for intervening and triggers for conducting a formal futility analysis if adherence or fidelity cannot be maintained at satisfactory levels. Ideally, consistent with a pragmatic trial approach, the monitoring of fidelity and adherence should be unobtrusive and not introduce any undue burden for either participants or those delivering the intervention, and the resulting data should be reviewed by an intervention expert, as well as an independent group such as a DSMB.
Table 2.
Summary recommendations for monitoring adherence and fidelity in pragmatic trials.
| Recommendation 1: Where possible, pilot intervention procedures before trial initiation. |
Recommendation 2: Develop a prespecified adherence/fidelity monitoring plan before initiation of enrollment. Details of the plan should be included as part of the clinical trial protocol. If the study has a data and safety monitoring board, the board should review and approve the plan. The plan should describe:
|
| Recommendation 3: Implement adherence/fidelity monitoring early in a trial (ie, ideally beginning assessment at trial initiation) to identify problems and take corrective, and as appropriate, subsequent preventive actions. |
|
|
Supplementary Material
Acknowledgments
Disclaimer: The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions, or policies of the National Center for Complementary and Integrative Health, the Office of Behavioral and Social Sciences Research, the National Institutes of Health, the Uniformed Services University, or the US Department of Veterans Affairs or the United States Government.
Contributor Information
James Dziura, Pain Management Collaboratory Coordinating Center, Yale University, New Haven, CT 06519, United States; Department of Emergency Medicine, School of Medicine, Yale University, New Haven, CT 06519, United States; Department of Biostatistics and Yale Center for Analytical Sciences, School of Public Health, Yale University, New Haven, CT 06519, United States.
Kathryn Gilstad-Hayden, Department of Psychiatry, Yale School of Medicine, New Haven, CT 06519, United States.
Cynthia J Coffman, ADAPT Center of Innovation, Durham VA Health Care System, Durham, NC 27705, United States; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27705, United States.
Cynthia R Long, Palmer Center for Chiropractic Research, Palmer College of Chiropractic, Davenport, IA 52803, United States.
Qilu Yu, Office of Clinical & Regulatory Affairs, National Center for Complementary and Integrative Health (NCCIH), Bethesda, MD 20892-5475, United States.
Eugenia Buta, Department of Biostatistics and Yale Center for Analytical Sciences, School of Public Health, Yale University, New Haven, CT 06519, United States.
Scott Coggeshall, VA Center of Innovation (COIN) for Veteran-Centered and Value-Driven Care, VA Puget Sound Healthcare System, Seattle, WA 98108, United States.
Mary Geda, Pain Management Collaboratory Coordinating Center, Yale University, New Haven, CT 06519, United States; Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT 06519, United States.
Peter Peduzzi, Pain Management Collaboratory Coordinating Center, Yale University, New Haven, CT 06519, United States; Department of Biostatistics and Yale Center for Analytical Sciences, School of Public Health, Yale University, New Haven, CT 06519, United States.
Tassos C Kyriakides, Pain Management Collaboratory Coordinating Center, Yale University, New Haven, CT 06519, United States; Department of Biostatistics and Yale Center for Analytical Sciences, School of Public Health, Yale University, New Haven, CT 06519, United States; VA Cooperative Studies Program Coordinating Center, West Haven, CT 06516, United States.
Supplementary material
Supplementary material is available at Pain Medicine online.
Funding
Research reported in this publication was made possible by Grant Number U24 AT009769 from the National Center for Complementary and Integrative Health (NCCIH) and the Office of Behavioral and Social Sciences Research (OBSSR). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCCIH, OBSSR, and the National Institutes of Health.
Research reported in this publication was supported by the National Institutes of Health under Award Numbers UG3/UH3-AT009758 (Rosen/Martino SBIRT-PM trial), UG3/UH3 AT009790 (Hastings/George AIM-Back trial), UG3/UH3AT009761 (Long/Goertz VERDICT trial), and UG3/UH3-AT009767 (Heapy/Higgins COPES ExTRA trial).
This work was supported (or supported in part) by VA Health Services Research & Development Service (HSR&D) Award # SDR-17–306 from the United States (US) Department of Veterans Affairs Health Services Research and Development Service.
Conflicts of interest: The authors have no disclosures to report.
Supplement statement
This article appears as part of the supplement titled “Pain Management Collaboratory: Updates, Lessons Learned, and Future Directions.”
This article is a product of the Pain Management Collaboratory. For more information about the Collaboratory, visit https://painmanagementcollaboratory.org/.
References
- 1. Patsopoulos NA. A pragmatic view on pragmatic trials. Dialogues Clin Neurosci. 2011;13(2):217-224. 10.31887/DCNS.2011.13.2/npatsopoulos [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M.. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015;350:h2147. 10.1136/bmj.h2147 [DOI] [PubMed] [Google Scholar]
- 3. Palin V, Van Staa TP, Steels S, et al. ; GetReal Initiative. A first step towards best practice recommendations for the design and statistical analyses of pragmatic clinical trials: a modified Delphi approach. Br J Clin Pharmacol. 2022;88(12):5183-5201. 10.1111/bcp.15441 [DOI] [PubMed] [Google Scholar]
- 4. Qaseem A, Wilt TJ, McLean RM, et al. ; Clinical Guidelines Committee of the American College of Physicians Noninvasive treatments for acute, subacute, and chronic low back pain: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2017;166(7):514-530. [DOI] [PubMed] [Google Scholar]
- 5. Heyward J, Jones CM, Compton WM, et al. Coverage of nonpharmacological treatments for low back pain by US public and private insurers. JAMA Netw Open. 2018;1(6):e183044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Department of Health and Human Services. National Pain Strategy: A Comprehensive Population Health-Level Strategy for Pain. Washington, DC: Department of Health and Human Services; 2016.
- 7. Department of Veterans Affairs. VHA National Pain Management Strategy. Washington, DC: Department of Veterans Affairs; 1998.
- 8. Office of the Army Surgeon General. Pain Management Task Force: Providing a Standardized DoD and VHA Vision and Approach to Pain Management to Optimize the Care for Warriors and their Families. Washington, DC: Office of the Army Surgeon General; 2010.
- 9. National Center for Complementary and Integrative Health. NCCIH Council Working Group Report on Strengthening Collaborations with the U.S. Department of Defense and U.S. Department of Veterans Affairs: Effectiveness Research on Mind Body Interventions. Bethesda, MD: National Center for Complementary and Integrative Health; 2015.
- 10. Kerns RD, Brandt CA, Peduzzi P.. NIH-DoD-VA Pain Management Collaboratory. Pain Med. 2019;20(12):2336-2345. 10.1093/pm/pnz186 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Heapy AA, Driscoll MA, Higgins DM, et al. Co-Operative Pain Education and Self-management (COPES) Expanding Treatment for Real-World Access (ExTRA): pragmatic trial protocol. Pain Med. 2020;21(12 suppl 2):S21-S28. 10.1093/pm/pnaa365 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Long CR, Lisi AJ, Goertz CM, et al. Veteran Response to Dosage in Chiropractic Therapy (VERDICT): study protocol of a pragmatic randomized trial for chronic low back pain. Pain Med. 2020;21(suppl 2):S37-S44. 10.1093/pm/pnaa289 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Martino S, Lazar C, Rosen MI, et al. Screening, Brief Intervention, and Referral to Treatment for Pain Management for veterans seeking service-connection payments for musculoskeletal disorders: SBIRT-PM Study Protocol. Pain Med. 2020;21(12 suppl 2):S110-S117. 10.1093/pm/pnaa334 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. George SZ, Coffman CJ, Hastings SN, et al. Improving Veteran Access to Integrated Management of Back Pain (AIM-Back): protocol for an embedded pragmatic cluster-randomized trial. Pain Med. 2020;21(suppl 2):S62-S72. 10.1093/pm/pnaa348 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Zeliadt SB, Coggeshall S, Taylor SL, et al. Assessing the relative effectiveness of combining self-care with practitioner-delivered complementary and integrative health therapies to improve pain in a pragmatic trial. Pain Med. 2020;21(suppl 2):S100-S109. 10.1093/pm/pnaa349 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Kerns RD, Davis AF, Fritz J, et al. Intervention fidelity in pain pragmatic trials for nonpharmacologic pain management: nuanced considerations for determining PRECIS-2 flexibility in delivery and adherence. J Pain. 2022;24(4):568-574. 10.1016/j.jpain.2022.12.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Landsberger HA. Hawthorne Revisited: Management and the Worker: Its Critics, and Developments in Human Relations in Industry. Ithaca, NY: Cornell University; 1958. [Google Scholar]
- 18. Hernán MA, Robins JM.. Per-protocol analyses of pragmatic trials. N Engl J Med. 2017;377(14):1391-1398. [DOI] [PubMed] [Google Scholar]
- 19. Seal KH, Becker WC, Murphy JL, et al. Whole Health Options and Pain Education (wHOPE): a pragmatic trial comparing whole health team vs primary care group education to promote nonpharmacological strategies to improve pain, functioning, and quality of life in veterans—rationale, methods, and implementation. Pain Med. 2020;21(suppl 2):S91-S99. 10.1093/pm/pnaa366 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Fritz JM, Rhon DI, Teyhen DS, et al. A Sequential Multiple-Assignment Randomized Trial (SMART) for stepped care management of low back pain in the military health system: a trial protocol. Pain Med. 2020;21(suppl 2):S73-S82. 10.1093/pm/pnaa338 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Goodie JL, Kanzler KE, McGeary CA, et al. Targeting chronic pain in primary care settings by using behavioral health consultants: methods of a randomized pragmatic trial. Pain Med. 2020;21(suppl 2):S83-S90. 10.1093/pm/pnaa346 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Burgess DJ, Evans R, Allen KD, et al. Learning to Apply Mindfulness to Pain (LAMP): design for a pragmatic clinical trial of two mindfulness-based interventions for chronic pain. Pain Med. 2020;21(suppl 2):S29-S36. 10.1093/pm/pnaa337 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Farrokhi S, Russell Esposito E, McPherson D, et al. Resolving the Burden of Low Back Pain in Military Service Members and Veterans (RESOLVE): protocol for a multisite pragmatic clinical trial. Pain Med. 2020;21(suppl 2):S45-S52. 10.1093/pm/pnaa367. Erratum in: Pain Med. 2021;22(8):1887. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Ilfeld BM, Gelfand H, Dhanjal S, et al. Ultrasound-guided percutaneous peripheral nerve stimulation: a pragmatic effectiveness trial of a nonpharmacologic alternative for the treatment of postoperative pain. Pain Med. 2020;21(suppl 2):S53-S61. 10.1093/pm/pnaa332 [DOI] [PMC free article] [PubMed] [Google Scholar]
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