Envisioning a future for precision health psychology: innovative applied statistical approaches to N-of-1 studies

Health psychology clinicians and patients routinely engage in clinical encounters where they attempt to determine the best behaviour change approach for that particular patient. As is recognised frequently by both participants, this encounter is often unsuccessful. As every evidence-based clinician knows, the provision of group randomised controlled trial data and observational epidemiological study results, while crucial, does not necessarily provide relevant information for how the patient in the office right now should be treated, and how they will respond. The evidence we provide for most health psychology problems is derived from between-person designs, or designs that presume that the set of predictors identified for a group apply to the majority (if not all) persons. We further use between-subject designs to test behaviour change techniques, and correctly assume that by comparing (with randomisation) one groups’ response to a behaviour change technique to another groups’ response to an attention control condition, or to an alternative treatment, we will know how the ‘average’ person will respond to the behaviour change intervention. Even when a treatment is found to be beneficial, on average, when compared to the effect of a control condition, there will be some individual treatment participants who did not benefit, or who even experienced harm. Likewise, there will be some individual control participants who experienced benefit. Only if the distributions of response to the treatment and control conditions are non-overlapping, can one presume that most individual participants will benefit from the treatment. Nonoverlapping response distributions is a rare phenomenon in health psychology. This is in contrast to within-person or N-of-1 designs, in which the set of significant predictors is identified for that one person, the risk/variance associated with that set of predictors is calculated for that one person, and the response to treatment(s) is calculated for that one person. A N-of-1 design can also be used to determine if a personalised model is needed for each person, or if a few models can be used for discrete subgroups of persons, or if only one model is all that is needed for the entire population, thus addressing many different health psychology problems where patient heterogeneity is ubiquitous. This approach then can contribute to the creation of a field of precision health psychology. Precision treatment targets may operate differently from person to person, in terms of the set of targets that operate for a person, the time lag that operates for that person, and the level of the behaviour change treatment (dose–response) needed for each target. Therefore, a main methodological issue for N-of-1 designs is incorporating these factors in the data analytics. In the current article (Vieira, McDonald, Araujo-Soares, Sniehotta, & Henderson, 2017), the authors rightfully tackle one of the first barriers we face in making N-of-1 studies available for health psychology clinicians, patients, theoreticians, and the public. The authors suggest using dynamic modelling framework to account for serial dependencies, time-varying covariates, time lag, and periodicity from data from N-of-1 studies. Since the framework is regression model-based, it has the flexibility to