Personalizing Behavioral Interventions Through Single‐Patient (N‐of‐1) Trials

Abstract

Behavioral interventions are typically studied with the use of a conventional between-subject randomized controlled trial (RCT) design. In this design, the effect of an intervention on one group of patients is compared with the effect of a control condition on another group of patients, such that a between-subject change is tested. A between-subject design has an underlying assumption that there is a homogenous treatment effect for a behavioral intervention, drug or psychotherapy, and that the way the intervention operates in the study that will tend to operate in the same way in many other patients. We review some of the philosophical and practical problems with the use of this design when a clinician is attempting to decide on a course of behavioral treatment aimed at within-subject change in patients who are likely to have heterogeneous or unique responses to behavioral treatment. We also review the biases inherent in our current clinical practice model, which does not use any empirical data collection or design for testing if a treatment is useful, and also in the conventional between-subject personalized medicine RCT designs. We propose increased use of single-patient (also known as N-of-1) trials that employ within-subject designs, in cases where treatment response is heterogeneous—as is the case for most psychological and behavioral treatments. Limitations of such designs include that they can only be used when the treatment is potentially reversible, the patient can act as their own control, and the outcome can be measured repeatedly. Increased use of within-subject trials may address in many more instances the more clinically relevant question of how a specific patient will respond to a specific treatment, and could introduce a more harmonious scientific approach into the way we treat our patients. We have incorporated a case presentation that illustrates the complexities of applying evidence drawn from these different designs to selecting and evaluating treatments for the behavioral issues commonly faced by clinicians and patients.

Breast cancer survivors’ preferences for technology-supported exercise interventions

“…However, they should be examined in future research before deciding not only what features to implement, but how to implement them. Finally, future research should systematically and efficiently optimize interventions by testing potential intervention components and decision rules using A-B quasi experimental [41], multiphase optimization strategy (MOST) factorial experiments [42], sequential multiple assignment (SMART) designs [42], and microrandomized trials (MRT) [43]. These rigorous trial designs allow for rapid studies to identify and adapt the most effective technology-supported exercise intervention components, or component sequence, to answer the questions of what works for whom, in what contexts, and for what outcomes [44].…”

The Essential Guide to N-of-1 Trials in Health

“…Davidson et al propose increased use of N-of-1 trials in situations where treatment response is heterogeneous -as is the case for most psychological and behavioral treatments ( Davidson et al 2014 ). Davidson and team have recently been funded for a project called Engaging Stakeholders in Building Patient-Centered, N-of-1 Randomized and Other Controlled Trial Methods.…”

N-of-1 Randomized Intervention Trials in Health Psychology: A Systematic Review and Methodology Critique

“…Specifically, the treatment target in N-of-1 trials must exhibit some variation and must be measurable over time; thus, rapidly progressive conditions are not well suited to N-of-1 trials. The intervention to be tested must be able to be withdrawn, and its effects must be reversible-something that limits the behavior change techniques (BCTs) [9,10] that can be tested within this design; the washout period between exposures to the intervention must be able to be estimated; and, at least in early comparative tests of an intervention, a credible placebo with similar expectancy effects or comparator intervention should ideally exist [11]. There should be a priori decisions about when and how outcomes will be measured; preferably, outcomes are measured objectively, and at minimum the assessors of the outcome are kept masked to the intervention assignment for that particular period.…”