Single-case experimental designs to evaluate novel technology-based health interventions

Abstract

Technology-based interventions to promote health are expanding rapidly. Assessing the preliminary efficacy of these interventions can be achieved by employing single-case experiments (sometimes referred to as n-of-1 studies). Although single-case experiments are often misunderstood, they offer excellent solutions to address the challenges associated with testing new technology-based interventions. This paper provides an introduction to single-case techniques and highlights advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable. These advances include quality control standards, heuristics to guide visual analysis of time-series data, effect size calculations, and statistical analyses. They also include experimental designs to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. The paper concludes with a discussion of issues related to the generality of findings derived from single-case research and how generality can be established through replication and through analysis of behavioral mechanisms.

Figure 1

Example of a reversal design showing experimental control and replications within and between subjects (each panel represents a different participant, each of whom experienced two baseline and two treatment conditions).

Figure 2

Example of a multiple baseline design showing experimental control and replications between subjects (each row represents a different participant, each of whom experienced a baseline and treatment; the baseline durations differed across participants).

Figure 3

A visual example of how to calculate the percentage of nonoverlapping data (see text for calculations).

Figure 4

Two examples of possible results from a component analysis (BSL=baseline, X=first component, Y=second component).