Dr Suzanne McDonald

In this article we briefly examine the unique features of Single-Case Designs (SCDs) (studies in a single participant), their history and current trends, and real-world clinical applications. The International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN) is a formal collaborative network for individuals with an interest in SCDs. The ICN was established in 2017 to support the SCD scientific community and provide opportunities for collaboration, a global communication channel, resource sharing and knowledge exchange. In May 2021, there were more than 420 members in 31 countries. A member survey was undertaken in 2019 to identify priorities for the ICN for the following few years. This article outlines the key priorities identified and the ICN's progress to date in these key areas including network activities (developing a communications strategy to increase awareness, collecting/sharing a comprehensive set of resources, guidelines and tips, and incorporating the consumer perspective) and scientific activities (writing position papers and guest editing special journal issues, exploring key stakeholder perspectives about SCDs, and working to streamline ethical approval processes for SCDs). The ICN provides a practical means to engage with this methodology through membership. We encourage clinicians, researchers, industry, and healthcare consumers to learn more about and conduct SCDs, and to join us in our mission of using SCDs to improve health outcomes for individuals and populations.

Interest in N-of-1 trials and single-case designs is increasing worldwide, particularly
due to the movement towards personalised medicine and patient-centred healthcare.
For decades, group-based designs such as the randomised controlled trial have been
understood as the “gold standard” for testing treatments, however these designs have
provided us with little information about the individual-level improvements in health
and well-being outcomes that are of vital importance to healthcare. There is growing
recognition of the wide applicability of N-of-1 trials and single-case designs to a number
of diverse health disciplines and the value they can bring to clinical research and practice
through the focus on understanding individuals.
This Special Issue aimed to showcase novel applications of N-of-1 trials and singlecase
designs in any health-related discipline, with a specific focus on applications in new
health conditions, interventions and contexts, as well as developments in data analysis.
This Special Issue presents a collection of thirteen articles that highlight the importance of
these methods in both clinical research and practice. Together, the articles report findings
from research studies, describe protocols for future studies, and outline key discussion
points and opinions for advancing the field. The articles represent a variety of singlecase
designs, including experimental and observational designs, and demonstrate the
substantial flexibility and versatility of N-of-1 trials and single-case studies and their value
in healthcare.

Background: N-of-1 observational studies can be used to describe natural intra-individual changes in health-related behaviours or symptoms over time, to test behavioural theories and to develop highly personalised health interventions. To date, N-of-1 observational methods have been under-used in health psychology and behavioural medicine. One reason for this may be the perceived complexity of statistical analysis of N-of-1 data.

Objective: This tutorial paper describes a 10-step procedure for the analysis of N-of-1 observational data using dynamic regression modelling in SPSS for researchers, students and clinicians who are new to this area. The 10-step procedure is illustrated using real data from an N-of-1 observational study exploring the relationship between pain and physical activity.

Conclusion: The availability of a user-friendly and robust statistical technique for the analysis of N-of-1 data using SPSS may foster increased awareness, knowledge and skills and establish N-of-1 designs as a useful methodological tool in health psychology and behavioural medicine.

N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

To the Editor: We read with interest the recent article by Kravitz and colleagues1 describing a randomized clinical trial comparing n-of-1 trials with standard care for treatment of chronic musculoskeletal pain.

The goal of the study was to establish the “benefits of participating in an n-of-1 trial, not to assess the superiority or inferiority of any particular treatment.”1(1369) However, there appears to be a disconnect between the study goal and the choice of outcomes, which were focused on pain interference scores across different treatment regimens. Therefore, the null results should be interpreted with respect to treatment efficacy, not design. The n-of-1 participants who demonstrated a better response to 1 of 2 treatments were likely to experience improved pain outcomes as a result of continuing to receive the superior treatment. However, there was a high proportion (>75%) of n-of-1 participants who had no treatment superiority, and this may explain the trial’s findings.

Background
Existing evidence about the impact of retirement on physical activity (PA) has primarily focused on the average change in PA level after retirement in group-based studies. It is unclear whether findings regarding the direction of PA change after retirement from group-based studies apply to individuals. This study aimed to explore changes in PA, PA determinants and their inter-relationships during the retirement transition at the individual level.

Methods
A series of n-of-1 natural experiments were conducted with seven individuals who were aged 55–76 years and approaching retirement. PA was measured by tri-axial accelerometry. Twice-daily self-report and ecological momentary assessments of evidence- and theory-based determinants of PA (e.g. sleep length/quality, happiness, tiredness, stress, time pressure, pain, intention, perceived behavioural control, priority, goal conflict and goal facilitation) were collected via a questionnaire for a period of between 3 and 7 months, which included time before and after the participant’s retirement date. A personalised PA determinant was also identified by each participant and measured daily for the duration of the study. Dynamic regression models for discrete time binary data were used to analyse data for each individual participant.

Results
Two participants showed a statistically significant increase in the probability of engaging in PA bouts after retirement and two participants showed a significant time trend for a decrease and increase in PA bouts over time during the pre- to post-retirement period, respectively. There was no statistically significant change in PA after retirement for the remaining participants. Most of the daily questionnaire variables were significantly associated with PA for one or more participants but there were no consistent pattern of PA predictors across participants. For some participants, the relationship between questionnaire variables and PA changed from pre- to post-retirement.

Conclusions
The findings from this study demonstrate the impact of retirement on individual PA trajectories. Using n-of-1 methods can provide information about unique patterns and determinants of individual behaviour over time, which has been obscured in previous research. N-of-1 methods can be used as a tool to inform personalised PA interventions for individuals within the retirement transition.

N-of-1 studies are based on repeated observations within an individual or unit over time and are acknowledged as an important research method for generating scientific evidence about the health or behaviour of an individual. Statistical analyses of n-of-1 data require accurate modelling of the outcome while accounting for its distribution, time-related trend and error structures (e.g., autocorrelation) as well as reporting readily usable contextualised effect sizes for decision-making. A number of statistical approaches have been documented but no consensus exists on which method is most appropriate for which type of n-of-1 design. We discuss the statistical considerations for analysing n-of-1 studies and briefly review some currently used methodologies. We describe dynamic regression modelling as a flexible and powerful approach, adaptable to different types of outcomes and capable of dealing with the different challenges inherent to n-of-1 statistical modelling. Dynamic modelling borrows ideas from longitudinal and event history methodologies which explicitly incorporate the role of time and the influence of past on future. We also present an illustrative example of the use of dynamic regression on monitoring physical activity during the retirement transition. Dynamic modelling has the potential to expand researchers' access to robust and user-friendly statistical methods for individualised studies.

n-of-1 studies test hypotheses within individuals based on repeated measurement of variables within the individual over time. Intra-individual effects may differ from those found in between-participant studies. Using examples from a systematic review of n-of-1 studies in health behaviour research, this article provides a state of the art overview of the use of n-of-1 methods, organised according to key methodological considerations related to n-of-1 design and analysis, and describes future challenges and opportunities. A comprehensive search strategy (PROSPERO:CRD42014007258) was used to identify articles published between 2000 and 2016, reporting observational or interventional n-of-1 studies with health behaviour outcomes. Thirty-nine articles were identified which reported on n-of-1 observational designs and a range of n-of-1 interventional designs, including AB, ABA, ABABA, alternating treatments, n-of-1 randomised controlled trial, multiple baseline and changing criterion designs. Behaviours measured included treatment adherence, physical activity, drug/alcohol use, sleep, smoking and eating behaviour. Descriptive, visual or statistical analyses were used. We identify scope and opportunities for using n-of-1 methods to answer key questions in health behaviour research. n-of-1 methods provide the tools needed to help advance theoretical knowledge and personalise/tailor health behaviour interventions to individuals.