Research in Gerontological Nursing

Focus on Methods 

Defining Usual Care in Clinical Trials

Heather M. Young, PhD, RN, FAAN; Sheridan Miyamoto, PhD, RN; Yajarayma Tang-Feldman, MA; Madan Dharmar, PhD, MBBS; Tom Balsbaugh, MD; Deborah Greenwood, PhD, RN, BC-ADM, CDE, FAADE


Pragmatic trials occur within the complexity of real-world care delivery, and when effective, contribute to more rapid translation into practice because of their greater generalizability. Research with older adults is complex when participants have chronic conditions and multiple comorbidities. Often pragmatic trials introduce a novel intervention and try to determine whether it offers a benefit beyond the usual or routine care provided. Researchers commonly focus attention on describing the intervention, yet the comparator condition of usual or routine care can be anything but standard, reducing the effect size of the intervention and introducing threats to the overall validity of the study. The current article describes clinical trial guidelines, then illustrates the complexity of characterizing usual care for interventions addressing type 2 diabetes. The authors provide recommendations for improving description of usual care and discuss implications for gerontological nursing research. [Research in Gerontological Nursing, 13(3), 125–129.]


Pragmatic trials occur within the complexity of real-world care delivery, and when effective, contribute to more rapid translation into practice because of their greater generalizability. Research with older adults is complex when participants have chronic conditions and multiple comorbidities. Often pragmatic trials introduce a novel intervention and try to determine whether it offers a benefit beyond the usual or routine care provided. Researchers commonly focus attention on describing the intervention, yet the comparator condition of usual or routine care can be anything but standard, reducing the effect size of the intervention and introducing threats to the overall validity of the study. The current article describes clinical trial guidelines, then illustrates the complexity of characterizing usual care for interventions addressing type 2 diabetes. The authors provide recommendations for improving description of usual care and discuss implications for gerontological nursing research. [Research in Gerontological Nursing, 13(3), 125–129.]

Research on health care interventions is vital to achieve high value and advance the most appropriate care for each older adult. Increasingly, funders, such as the Patient-Centered Outcomes Research Institute (PCORI), support pragmatic trials or clinical effectiveness trials that embed stakeholder-driven interventions in the community or health care settings (Frank, Basch, & Selby, 2014). These studies occur within all the complexity of real-world care delivery, and when effective, contribute to more rapid translation into practice because of their greater generalizability. Often such studies are introducing a novel intervention and trying to determine whether it offers a benefit beyond the usual or routine care provided. Researchers commonly focus attention on the intervention and the protocol associated with its delivery to assure fidelity and minimize threats to internal validity. Yet, the comparator condition of usual or routine care can be anything but standard, reducing the effect size of the intervention and introducing threats to the overall validity of the study (de Bruin, Viechtbauer, Hospers, Schaalma, & Kok, 2009; Freedland, Mohr, Davidson, & Schwartz, 2011). deBruin et al. (2009) highlight the range of possibilities in usual treatment for a given condition, including pharmacological, nonpharmacological, and clinical monitoring options as well as variability across providers, insurance coverage, quality standards in different settings, clinical guidelines issued by different disciplines, and evolving practice.

Several investigators have examined the rigor of descriptions of usual care across intervention studies. In a review of randomized controlled trials aimed at improving health for individuals with type 1 diabetes, the authors found minimal detail about the control arm of the studies, with 16 (80%) of 20 trials reporting how often participants had a clinic appointment during the trial, 41% identifying which health professionals provided the care, and almost no detail about what constituted standard care (Ayling, Brierley, Johnson, Heller, & Eiser, 2015). This observation was corroborated in a review of pragmatic trials covering a variety of interventions conducted in primary care settings (Smelt, van der Weele, Blom, Gussekloo, & Assendelft, 2010). Among 73 individually randomized (n = 38) and cluster randomized (n = 35) interventions, only two provided any information about usual care.

There are several resources to guide intervention and study design, including CONSORT (Schulz, Altman, & Moher, 2010), PRECIS (Loudon et al., 2015), and the PCORI (2019) Methodology Standards. The CONSORT guidelines specify that studies describe the intervention “with sufficient details to allow replication, including how and when they were actually administered.” (Schulz et al., 2010, p. 3). The PRECIS-2 tool guides researchers to address five questions related to the intervention itself, including the setting, the organization (i.e., resources, provider expertise, care delivery), flexibility in delivery, flexibility in adherence, and follow up of participants in the intervention vs. in usual care (Loudon et al., 2015). The PCORI (2019) methodology standards offer greater specificity aiming to enhance internal and external validity of intervention trials, starting with the selection of the intervention and the comparator. Standard 1-RQ-5 addresses the research question and specifies that the requirement of a description of the comparator including why it was selected. Standard 13-SCI-1 addresses complex interventions, requiring descriptions of the intervention and comparator, including their purpose, method of delivery, who will provide care, materials/tools, dose, and frequency/intensity.

In research among older adults, the term usual care (also referred to as routine care, control case, or standard treatment) describes a wide spectrum of care practices. Organizations vary in the degree to which they use protocols for care in the context of chronic conditions, multiple comorbidities, and recurring clinical presentations including: (a) models that support clinician autonomy; (b) models that specify expected outcomes but allow variable practices; and (c) models that specify care pathways and expected outcomes (Boult et al., 2009). Value-based care has precipitated greater attention to understanding and replicating successful care practices that achieve desired clinical and organizational outcomes resulting in a push toward greater standardization of care (Berwick, Nolan, & Whittington, 2008; Porter, 2009; Porter, Pabo, & Lee, 2013).

In the context of clinical research, usual care practices have an impact on assessments of the value, efficacy, and comparative effectiveness of experimental interventions or trials. Usual care as a comparator to behavior change interventions is even more complicated. As an example of a common condition among older adults, type 2 diabetes encompasses a variable range of care offerings that differ across institutions and clinicians in the same institution. Furthermore, older adults receiving care may elect or decline specific elements offered as part of usual care. The current article discusses the challenge of defining and measuring usual care in the context of clinical trials using type 2 diabetes as an example and offers suggestions to address this threat to validity.

What Is the Standard for Usual Care in Type 2 Diabetes for Older Adults?

The prevalence of diabetes is highest among older adults, affecting >25% of this population (Kirkman et al., 2012). According to the American Diabetes Association (ADA; 2017) Standards of Medical Care in Diabetes, standard care in diabetes must include medical care from a physician-coordinated team that involves physicians, nurses, nurse practitioners, dieticians, pharmacists, mental health professionals, and diabetes educators with expertise and interest in diabetes. The health care team should develop a collaborative management plan that includes diabetes self-management education and support (DSMES) from the team as basic components of the plan.

Self-management and behavior change are essential elements of care as a person with diabetes lives >99% of their life outside of the health care system and is required to make hourly decisions. These choices include a variety of self-care behaviors including healthy eating, being active, monitoring, taking medications, problem solving, healthy coping, and reducing risks as described in the American Association of Diabetes Educators (AADE) AADE7 self-care behaviors (Powers et al., 2017). An effective DSMES plan should result in better clinical outcomes and better quality of life. In addition to these standards, there are consensus guidelines for older adults that consider risk and comorbidities, allowing appropriate individualization of the plan (Kirkman et al., 2012).

Diabetes is a progressive condition that requires ongoing assessment of the unique self-management needs of older adults. An important recommendation from the ADA and AADE is to tailor diabetes management plans to the individual considering age, physical activity, disease burden, and social and cultural factors among others. For older adults, such tailoring is even more pertinent with the heterogeneity of comorbidities and functional ability (Kirkman et al., 2012). The person with diabetes and the DSMES team ideally develop an educational plan together based on needs and priorities identified by the person with the disease in one or more DSMES content areas. The priorities of the person with diabetes must take precedence over programmatic structure and goals (Beck et al., 2017). Beck et al. (2017) recommend tailoring standardized elements of care across the trajectory of managing a chronic condition, rather than standardizing care overall.

The timing of elements of care in relation to readiness and motivation can contribute to further variation in usual care. Researchers of behavior change have demonstrated the importance of assessing readiness to change when tailoring interventions (O'Connor et al., 2004; Peterson & Hughes, 2002). In usual care, the needs of an older adult newly diagnosed with diabetes will differ from another who has lived with diabetes for an extensive period and is having trouble achieving adequate blood glucose regulation. In the first instance, education and skill building might dominate a clinical encounter, whereas in the second, exploring current behaviors, developing problem solving skills, and supporting motivation to change might be more effective in achieving optimal clinical outcomes.

Despite guidelines, actual delivery of care is highly variable as illustrated in a systematic review that demonstrated a statistically significant and clinically meaningful improvement in hemoglobin A1C after completing DSMES programs; however, mode of delivery, number of hours of engagement in DSMES, and baseline A1C levels affected outcomes (Chrvala, Sherr, & Lipman, 2016). An additional review of DSMES incorporating telehealth modalities also demonstrated inconsistencies in integration of glucose data and feedback to change treatment and support behavior change (Greenwood, Young, & Quinn, 2014).

Even when organizations adopt clinical protocols, there might be differences in the consistency of implementation and uptake. Factors, such as provider preferences and practice habits, staff changes, competing priorities, and accountability for adherence to protocols might contribute to variability within an organization. Although recommended by the ADA, there is a surprisingly low rate of referrals to DSMES programs, even though services are a covered benefit, at approximately 6.8% for those with private insurance (Li et al., 2014), and even lower for older adults at 5% of Medicare beneficiaries (Strawbridge, Lloyd, Meadow, Riley, & Howell, 2015). This issue resulted in a joint position paper published in 2015 by the AADE, ADA, and Academy of Nutrition and Dietetics establishing an algorithm for referral to DSMES programs and identifying the four key times to refer: a new diagnosis of diabetes, annually, when complicating factors develop, and during transitions of care. Each of these key times will result in different individual needs and DSMES content (Powers et al., 2017). Practice also evolves in response to new evidence and providers may modify care delivery over time.

Organizations also vary in resources available for individuals with diabetes. In addition to clinic visits with providers, these resources can include care management/care coordination with either a social worker, nurse, or health educator depending on need; diabetes education classes; interactive videos; and access to a social worker. Individuals may not take advantage of these offerings, either because of lack of knowledge of their availability, lack of referral by their primary care provider, or due to personal reasons. Even if an individual chooses to use these services, the frequency of contact with a social worker, diabetes educator, or care coordinator may vary, resulting in inequalities in the dose of usual care they receive. Further barriers to full deployment of DSMES include the requirement for physician referral for DSMES and the program accreditation requirements to enable billing for DSMES services. These factors highlight the heterogeneity and potential sources of variation in usual care. Taken together, the complexity of diabetes care delivery at multiple levels (e.g., the older adult, clinician, organization) makes clear identification of usual care or standardized practice a challenging task.

Implications of Variability in Usual Care

Lack of uniformity of usual care practices for type 2 diabetes may have an impact on the assessment of comparative effectiveness of clinical trials. Typically, researchers attend to the delivery and dose of the intervention and apply protocols and documentation of delivery for audit purposes and to establish fidelity. Much less attention is given to the comparator, usual care. If an intervention is compared to an enhanced or higher standard of usual care (e.g., care that includes regular visits with providers, diabetes education, nutritional education, visits with a social worker, use of behavioral techniques), the intervention may show less efficacy than when compared to usual care consisting of only clinic visits with providers. In a review of the literature on randomized controlled trials in type 1 diabetes, Ayling et al. (2015) suggest that, based on subgroup analysis of control groups, higher standards of care may result in larger improvements in clinical and behavioral outcomes. Similarly, de Bruin et al. (2009) studied adherence to HIV medications and reported that high-quality usual care was more effective than low-quality care. When clinical trials were compared to high-quality usual care, the effect size was smaller than when compared to low-quality usual care.

The sources of variation point to the importance of identifying and comparing standardized care practices and methodologies to measure the deployment and dose of usual care when evaluating randomized trials with usual care as a comparator. Typically, clinical trials use protocols and precise description of the intervention, but do not use the same level of precision in describing usual care. This omission may significantly influence the interpretation and reproducibility of results.

Implications and Recommendations for Gerontological Nurse Researchers

Given the importance of conducting pragmatic and clinical trials in gerontological nursing research, the issue of usual care warrants thoughtful attention. The challenge is even greater when the target population has multiple comorbidities and might have diverse health care providers contributing to the plan of care. Several potential strategies could assist in the characterizing of usual care to build this variability into the design of the study. These strategies allow for examining how such variations moderate or mediate study outcomes and could enhance the ability to conduct sub-group analyses to further elucidate the contributions of various elements of the intervention to outcomes.

Authors of systematic reviews focusing on characterization of usual care recognize the limitations of current guidelines. Smelt et al. (2010) suggest expansion of the CONSORT guidelines to provide a more systematic approach to describing the control group, including care provided, details about instructions, and blinding to allocation status. Ayling et al. (2015) developed a checklist to audit the studies and propose its utility in coding behavior change approaches embedded in standard care. They call out the fact that standard care is more than service utilization (e.g., frequency of clinic visits) and should include more detail about the active components of care, such as the approach to behavior change.

In a recent randomized clinical trial (Miyamoto, Dharmar, Fazio, Tang-Feldman, & Young, 2018), the current authors identified an array of elements of usual care that they believed would be important to document when comparing a behavioral health intervention. These elements included service metrics, such as number and length of visits, referrals for resources, use of online resources, provision of educational materials, and completion of education programs. Additional program elements in chronic disease management might include identifying all health care professionals, such as dietitians, RNs, certified diabetes educators, physical therapists, or health coaches, who see older adults as part of usual care. Because the authors' institution did not have standard protocols for diabetes management, they developed a flow diagram of common paths and variations in usual care. In designing data collection, the authors considered what elements of usual care could be captured from the electronic health record to facilitate documentation at the participant level.

For analysis purposes, the authors developed a categorical variable to capture elements and dose (0 = no contact during experimental period, 1 = low intensity; 2 = medium intensity; and 3 = high intensity). The authors prioritized elements that are most like the intervention and could weaken the effect of the intervention, such as online or in-person education. The variation in usual care could then be considered in the statistical analysis. The standard use of common data elements in self-management will support clinical trials and future systematic reviews (Moore et al., 2016).


Pragmatic and clinical trials with older adults are complex and multifaceted. Many chronic diseases, such as diabetes, require self-management, behavior change, monitoring, medication, treatment adherence, and have psychological and social implications. Older adults with diabetes commonly have comorbidities with other chronic illnesses and might be experiencing functional limitations that require specific tailoring in care. Standard or usual care in type 2 diabetes is highly variable, particularly among older adults, and is often the comparator to innovative approaches. This issue also pertains to other common conditions in late life, including hypertension, cardiac disease, chronic obstructive pulmonary disease, and chronic renal failure, among others. In all these cases, the comparator is not a static construct. The authors argue that it is as important to document usual care as it is to assure fidelity of the intervention protocol to maximize the power of the comparison.


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Dr. Young is Professor, Dean Emerita, Ms. Tang-Feldman is Co-Investigator, Betty Irene Moore School of Nursing; Dr. Dharmar is Associate Professor in Residence, Department of Pediatrics, University of California Davis; Dr. Balsbaugh is Sacramento Valley Family Medicine Program Director, Kaiser Permanente—Family Medicine, Sacramento, California; Dr. Miyamoto is Assistant Professor, College of Nursing, Pennsylvania State University, University Park, Pennsylvania; and Dr. Greenwood is President, Deborah Greenwood Consulting, Granite Bay, California.

The authors have disclosed no potential conflicts of interest, financial or otherwise. The current research was funded by Patient Centered Outcomes Research Institute (IHS-1310-07894).

The authors acknowledge the contributions of Sarina Fazio, Jennifer Edwards, Rupinder Colby, Bridget Levich, and their Patient, Provider, and Technology Stakeholder Advisory Committees to the current study.

Address correspondence to Heather M. Young, PhD, RN, FAAN, Professor, Dean Emerita, Betty Irene Moore School of Nursing, University of California Davis, 2570 48th Street, Sacramento, CA 95817; e-mail:

Received: March 07, 2019
Accepted: September 06, 2019
Posted Online: December 13, 2019


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