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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. The field of technology-based behavioral health interventions is expanding rapidly. New technologies are enabling access to, and assessment of, individuals and their health-related behavior [ 1 - 3 ]. The fields of eHealth, mHealth, and the promise of emerging technologies have the potential to transform many systems of health care and improve public health by increasing access to cost-effective interventions.
With these opportunities comes the need to evaluate rigorously the potential efficacy of new treatments. In this paper, we describe some challenges and methodological solutions associated with testing preliminary efficacy.
In particular, we focus on the solutions offered by single-case experiments, which fill a unique and vital niche in the ecology of research designs. We also highlight advances in developing and evaluating single-case experiments, which help ensure that treatment outcomes are reliable, replicable, and generalizable.
Finally, we describe experimental designs that allow researchers to isolate the active elements in a treatment package and to assess the mechanisms of behavior change. Our goal is to introduce a range of techniques that will be relevant to behavioral scientists that are unfamiliar with single-case research and that are particularly well suited for the research and development of new technology-based interventions.
We hope to supply enough detail to achieve a basic understanding of the mechanics, utility, and versatility of single-case research and enough resources to propel further inquiry. Broadly, single-case designs include a family of methods in which each participant serves as his or her own control.
In a typical study, some behavior or self-reported symptom is measured repeatedly during all conditions for all participants. The experimenter systematically introduces and withdraws control and intervention conditions and then assesses effects of the intervention on behavior across replications of these conditions within and across participants.
Thus, the telltale traits of these studies include repeated and frequent assessment of behavior, experimental manipulation of the independent variable, and replication of effects within and across participants. Although some forms of replication are readily apparent, such as replications of effects within and between subjects, other forms may be more subtle.
For example, replication within subjects also occurs by simply measuring behavior repeatedly within a condition. Assuming some degree of stability of the dependent variable within a condition, there will be many replications of the effects of a treatment on behavior. A recent study illustrates the efficiency and rigor of a single-case design to assess a novel technology-based treatment [ 8 ].
Raiff and Dallery assessed whether an Internet-based incentive program could increase adherence to blood glucose testing for 4 teenagers diagnosed with Type 1 diabetes. Teens monitored glucose levels with a glucose meter during a 5-day baseline control condition. During a 5-day treatment condition, participants earned vouchers statements of earnings exchangeable for goods and services for adhering to blood glucose testing recommendations ie, 4 tests per day.
After the treatment condition, participants monitored blood glucose just as they did during the first baseline condition for 5 days, without the possibility of earning incentives. Participants submitted a mean of 1. Because adherence increased only when the treatment was implemented for all 4 participants and because behavior within each condition was stable ie, five replications of treatment effects per participant and ten replications of control levels per participant , this experiment suggested that an Internet-based incentive program can reliably increase adherence to self-monitoring of blood glucose.
We believe that a symbiosis exists between single-case experiments and technology-based interventions. Single-case designs can capitalize on the ability of technology to easily, unobtrusively, and repeatedly assess health-related behavior [ 7 , 9 ]. For example, researchers have used technology-based measures of activity in the form of daily step counts [ 10 ], twice-daily measurements of exhaled carbon monoxide as an indicator of smoking status [ 11 ], and medication adherence on a daily basis [ 12 ].
Such repeated assessment, whether through existing or new technology, provides excellent opportunities to analyze the effects of treatment variables using single-case experiments. In addition, many technology-delivered behavioral health interventions permit automated treatment delivery [ 15 ]. This means that treatment can be delivered with high fidelity, which can minimize between-subject variability in treatment dose and quality.
Because detecting treatment effects in single-case designs requires replications across subjects, ensuring equivalent treatment fidelity and quality across participants enhances the internal validity of the study. There are two additional advantages of single-case research, and these advantages exist whether patient improvement is measured with technology-based or alternative methods.
Single-case research requires a fine-grained view of health-related behavior over time, and technology-based data capture can enable this view. Patient improvement can be revealed by changes in health-related behavior from baseline to treatment, and the cause of these changes can be verified via replications within and across participants. Experimental designs, such as group designs cf. In addition to the fit between the logic of single-case designs and the data capture capabilities of technology, single-case designs may obviate some logistical issues in using between group designs to conduct initial efficacy testing.
For example, prototypes of a new technology may be expensive and time consuming to produce [ 1 ]. Similarly, troubleshooting and refining the hardware and software may entail long delays. For these reasons, enrolling a large sample for a group design may be prohibitive. Also, during development of a new technology-based treatment, a researcher may be interested in which components of treatment are necessary.
For example, a mobile-phone based treatment may involve self-monitoring, prompts, and feedback. Assessing these components using a group design may be cumbersome.
Single-case designs can be used to perform efficient, systematic component analyses [ 19 ]. Although some logistical issues may be mitigated by using single-case designs, they do not represent easy alternatives to traditional group designs. They require a considerable amount of data per participant as opposed to a large number of individuals in a group , enough participants to reliably demonstrate experimental effects, and systematic manipulation of variables over a long duration.
Nevertheless, in many cases, single-case designs can reduce the resource and time burdens associated with between group designs. There are several common misconceptions about single-case designs [ 20 , 21 ]. The number of participants in a typical study is always more than 1, usually around 6 but sometimes as many as 20, 40, or more participants [ 11 , 22 ].
Given that the unit of analysis is each case, a single study could be conceptualized as a series of single-case experiments. Second, single-case designs are not limited to interventions that produce large immediate changes in behavior.
They can be used to detect small but meaningful changes in behavior and to assess behaviors that may change slowly over time eg, learning a new skill [ 23 ]. Third, findings from single-case research do not inherently lack external validity or generality. This misconception is perhaps the most prejudicial, and addressing it requires some background in the logic and mechanics of single-case design.
Thus, we shall save our discussion of this misconception to the end of this paper. The most common single-case designs—and those that are most relevant to technology-based interventions—are presented in Table 1. The table also presents some procedural information, as well as advantages and disadvantages for each design.
All of these designs permit inferences about causal relations between independent and dependent variables observations of behavior, self-reports of symptoms, etc. Procedural controls must be in place to make these inferences such as clear, operational definitions of the dependent variables, and reliable and valid techniques to assess the behavior. The experimental design must be sufficient to rule out alternative hypotheses for the behavior change. Table 2 presents a summary of the main methodological and assessment elements that must be present to permit conclusions about treatment effects [ 24 ].
The majority of the criteria in Table 2 have been validated to evaluate the quality of single-case research [ 25 ]. As such, the items listed in the table represent quality control standards for single-case research.
Common single-case designs, including general procedures, advantages, and disadvantages. We have added one criterion to Table 2 , that is, researchers should authenticate the participant who generated the dependent variable or use validation methods to assess whether the participant and not some other person was the source of the data. Authentication or validation is important when data capture occurs remotely with technology.
To solve this problem, for example, a web-based video [ 7 ] or new methods in biometric fingerprinting could authenticate the end-user [ 26 , 27 ]. As an alternative, or as a complement, validation measures can be collected.
For example, in-person viral load assessments could be measured at various points during a study to increase antiretroviral medication adherence [ 12 ], or body mass and physiological measures could be measured during an exercise or activity-based intervention. There are two additional assessment-related items in Table 2 that warrant discussion in the context of novel technology-based interventions.
The first is assessing the fidelity of technology-based treatments [ 28 ]. This definition entails measurement of the delivery and receipt of the intervention, which are related but not necessarily synonymous. What is delivered via technology may not be what is received by the end-user. Dabbs and associates [ 28 ] provide a list of questionnaire items that could be easily adapted to assess the fidelity of technology-based interventions. These items are based on the Technology Acceptance Model [ 30 ].
The second is assessing whether the methods and results are socially valid [ 31 , 32 ]; see Foster and Mash [ 33 ] for methods to assess social validity. Social validity refers to the extent to which the goals, procedures, and results of an intervention are socially acceptable to the client, the clinician or health care practitioner, and society [ 33 - 37 ]. During initial efficacy testing, social validity from the perspective of the client should be assessed. Indeed, technology may engender risks to privacy and confidentiality, and even an effective intervention may be perceived as too intrusive.
Of the designs listed in Table 1 , the reversal, multiple-baseline, and changing criterion designs may be most applicable for initial efficacy testing of technology-based interventions. All of these designs entail a baseline period of observation.
During this period, the dependent variable is measured repeatedly under control conditions, for example for several days. Ideally, the control conditions should include all treatment elements eg, access to the Internet, the use of a mobile phone, or technology-based self-monitoring except for the active treatment ingredients [ 38 ].
For instance, Dallery and colleagues used a reversal design to assess effects of Internet-based incentive program to promote smoking cessation, and the baseline phase included self-monitoring, video-based carbon monoxide confirmation via a web camera, and monetary incentives [ 11 ].
The active ingredient in the intervention, incentives contingent on objectively verified smoking abstinence via video , was not introduced until the treatment phase. An additional consideration in the context of technology is the time needed to simply learn how to operate the device, website, or software.
Baseline control conditions may need to take this learning into account before the active ingredients of the intervention are introduced. The baseline condition in the study by Dallery et al, for example, provided ample time for the participants to learn how to upload videos and navigate the study website.
The duration of the baseline should be sufficient to predict future behavior. That is, the level of the dependent variable should be stable enough to predict its direction if the treatment were not introduced. If there is a trend in the direction of the anticipated treatment effect during baseline, the ability to detect a treatment effect will be limited.
Thus, stability, or trend in the direction opposite the predicted treatment effect, is desirable. The decision to change conditions is an experimenter decision, which can be supplemented with a priori stability criteria [ 39 - 41 ].
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