Recommendations for Choosing Single-Case Data Analytical Techniques

The influence of procedural characteristics on within-case effect sizes for academic outcomes

Single-case experimental designs (SCEDs) have been used with increasing frequency to identify evidence-based interventions in education. The purpose of this study was to explore how several procedural characteristics, including within-phase variability (i.e., measurement error), number of baseline observations, and number of intervention observations influenced the magnitude of four SCED effect sizes, including (a) non-overlap of all pairs (NAP), (b) baseline corrected tau (BC-Tau), (c) mean-phase difference (MPD), and (d) generalized least squares (GLS) when applied to hypothetical academic intervention SCED data. Higher levels of measurement error decreased the average magnitude of effect sizes, particularly NAP and BC-Tau. However, the number of intervention observations had minimal impact on the average magnitude of NAP and BC-Tau. Increasing the number of intervention observations dramatically increased the magnitude of GLS and MPD. Increasing the number of baseline observations also tended to increase the average magnitude of MPD. The ratio of baseline to intervention observations had a statistically but not practically significant influence on the average magnitude of NAP, BC-Tau, and GLS. Careful consideration is required when determining the length of time academic SCEDs are conducted and what effect sizes are used to summarize treatment outcomes. This article also highlights the value of using meaningful simulation conditions to understand the performance of SCED effect sizes.

Improving Movement Behavior in People after Stroke with the RISE Intervention: A Randomized Multiple Baseline Study

Objective: High amounts of sedentary behavior increase the risk of cardiovascular disease. This study aimed to determine the preliminary effectiveness and feasibility of the RISE intervention to support community-dwelling people with stroke, who are highly sedentary, to reduce and interrupt sedentary time. Additionally, the added value of including participatory support was determined. Methods: A randomized, multiple-baseline study was conducted including 14 participants. All received the RISE intervention, a 15-week blended behavioral intervention in which a primary care physiotherapist provided personalized coaching in the home setting by using behavior-change techniques and the RISE eCoaching system, including an activity monitor and app to provide real time feedback. Half of the participants (randomly allocated) received participatory support from someone from their social network (e.g., partner or close friend) who joined them in the intervention. Preliminary effectiveness was determined with significant changes in total sedentary time and fragmentation (interruption) of sedentary time using a randomization test. Feasibility was assessed by adherence with the intervention protocol, safety, and satisfaction with the intervention. Results: Participants significantly reduced total sedentary time (p = 0.01) by 1.3 h on average and increased their fragmentation (p < 0.01). Subgroup analyses showed significant improvements in both outcomes only in the group with participatory support. Thirteen (92.9%) participants completed the intervention, no related adverse events occurred, and the reported participant satisfaction was sufficient. Conclusions: The RISE intervention appears promising to support people with stroke who are highly sedentary to reduce and interrupt their sedentary time. Participatory support appears to contribute to greater results. Trial registration: ISRCTN international trial registry, 10694741.

An exploration of anomia rehabilitation in drug-resistant temporal lobe epilepsy

Around 40% of patients who undergo a left temporal lobe epilepsy (LTLE) surgery suffer from anomia (word-finding difficulties), a condition that negatively impacts quality of life. Despite these observations, language rehabilitation is still understudied in LTLE. We assessed the effect of a four-week rehabilitation on four drug-resistant LTLE patients after their surgery. The anomia rehabilitation was based on cognitive descriptions of word finding deficits in LTLE. Its primary ingredients were psycholinguistic tasks and a psychoeducation approach to help patients cope with daily communication issues. We repeatedly assessed naming skills for trained and untrained words, before and during the therapy using an A-B design with follow-up and replication. Subjective anomia complaint and standardized language assessments were also collected. We demonstrated the effectiveness of the rehabilitation program for trained words despite the persistence of seizures. Furthermore, encouraging results were observed for untrained items. Variable changes in anomia complaint were observed. One patient who conducted the protocol as self-rehabilitation responded similarly to the others, despite the different manner of intervention. These results open promising avenues for helping epileptic patients suffering from anomia. For example, this post-operative program could easily be adapted to be conducted preoperatively.

A Description of Missing Data in Single-Case Experimental Designs Studies and an Evaluation of Single Imputation Methods

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies.

Rise of single-case experimental designs: A historical overview of the necessity of single-case methodology

ABSTRACTWindelband ([1894]1980) advocated that two approaches are used for accumulating scientific knowledge. The first is the idiographic approach that derives knowledge from a single unit, and the second is the nomothetic approach that accumulates knowledge of a group. Given these two approaches, the former matches case studies while the latter is more appropriate with experimental group studies. Scientists have criticized both methodologies for their various limitations. Later, the single-case methodology emerged as an alternative that potentially allays these limitations. In this context, this narrative review aims to describe the historical roots of single-case experimental designs (SCEDs) that have emerged to eliminate the tension of nomothetic and idiographic approaches over time. First, the review focuses on the emergence of SCEDs. Second, the strengths and challenges of SCEDs are reviewed, including those to address the limitations of group experimental and case studies. Third, the use and analyses of SCEDs are outlined, considering their current status. Fourth, this narrative review continues to delineate the dissemination of SCEDs in the modern scientific world. As a result, SCEDs can be evaluated as a method that has the potential to overcome the issues encountered in case description and group experimental research. Thus, that helps accumulate nomothetic and idiographic knowledge in determining evidence-based practices.

Characteristics of Missing Data in Single-Case Experimental Designs: An Investigation of Published Data

Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results.

Typicality of Level Change (TLC) as an Additional Effect Measure to NAP and Tau-U in Single Case Research

Single case research is a viable way to obtain evidence for social and psychological interventions on an individual level. Across single case research studies various analysis strategies are employed, varying from visual analysis to the calculation of effect sizes. To calculate effect sizes in studies with few measurements per time period (<40 data points with a minimum of five data points in each phase), non-parametric indices such as Nonoverlap of All Pairs (NAP) and Tau-U are recommended. However, both indices have restrictions. This article discusses the restrictions of NAP and Tau-U and presents the description, calculation, and benefits of an additional effect size, called the Typicality of Level Change (TLC) index. In comparison to NAP and Tau-U, the TLC index is more aligned to visual analysis, not restricted by a ceiling effect, and does not overcompensate for problematic trends in data. The TLC index is also sensitive to the typicality of an effect. TLC is an important addition to ease the restrictions of current nonoverlap methods when comparing effect sizes between cases and studies.

Personalized pain management: Is it time for process-based therapy for particular people with chronic pain?

BACKGROUND AND OBJECTIVE

Psychological treatments for chronic pain have helped many people around the world. They are among the most researched and best evidenced treatments a person can receive when they have persistent, disabling and distressing pain. At the same time, improvements in the effectiveness of these treatments appear to be at a standstill. This may be due to an inherent lack of generalizability from aggregated group data to the individual, limited utility of our current schemes for categorizing people with pain conditions, faced with their inherent heterogeneity, our relatively coarse categories of treatment types and focus on treatment packages rather than individual methods, and our current failures to find adequate predictors of outcome, or to assign people their best-suited treatment methods, based on group data. In this review, it is argued that the development and examination of truly personalized treatment is a next logical step to create progress and improve the results people achieve.

METHODS

Key research studies pertaining to psychological treatments, treatment outcome, heterogeneity in chronic pain, prediction of treatment outcome, subtyping and treatment tailoring are reviewed.

RESULTS AND CONCLUSION

It is suggested that development of future treatments for chronic pain ought to incorporate an idiographic, process-based approach, focused on evidence-based mechanisms of change, individually and dynamically addressed, based on contextually sensitive ongoing assessment. Knowledge and practical solutions needed to make process-based therapy for chronic pain happen are discussed.

SIGNIFICANCE

Psychological approaches to chronic pain have been highly successful in the past but improvement in the effectiveness of these over time is slow to nonexistent. It is argued here that this has happened due to a failure to adequately consider the individual. Future psychological treatments for chronic pain ought to incorporate an idiographic, process-based approach, focused on evidence-based mechanisms of change, individually and dynamically addressed, grounded in ongoing contextually sensitive assessment.

Using Single-Case Experimental Design and Patient-Reported Outcome Measures to Evaluate the Treatment of Cancer-Related Cognitive Impairment in Clinical Practice

Cancer-related cognitive impairment (CRCI) affects a large proportion of cancer survivors and has significant negative effects on survivor function and quality of life (QOL). Treatments for CRCI are being developed and evaluated. Memory and attention adaptation training (MAAT) is a cognitive-behavioral therapy (CBT) demonstrated to improve CRCI symptoms and QOL in previous research. The aim of this article is to describe a single-case experimental design (SCED) approach to evaluate interventions for CRCI in clinical practice with patient-reported outcome measures (PROs). We illustrate the use of contemporary SCED methods as a means of evaluating MAAT, or any CRCI treatment, once clinically deployed. With the anticipated growth of cancer survivorship and concurrent growth in the number of survivors with CRCI, the treatment implementation and evaluation methods described here can be one way to assess and continually improve CRCI rehabilitative services.

Systematic Review of Descriptions and Justifications Provided for Single-Case Quantification Techniques

There are currently a multitude of quantification techniques that have been developed for use with single-case designs. As a result, choosing an appropriate quantification technique can be overwhelming and it can be difficult for researchers to properly describe and justify their use of quantification techniques. However, providing clear descriptions and justifications is important for enhancing the credibility of single-case research, and allowing others to evaluate the appropriateness of the quantification technique used. The aim of this systematic literature review is to provide an overview of the quantification techniques that are used to analyze single-case designs, with a focus on the descriptions and justifications that are provided. A total of 290 quantifications occurred across 218 articles, and the descriptions and justifications that were provided for the quantification techniques that were used are systematically examined. Results show that certain quantification techniques, such as the non-overlap indices, are more commonly used. Descriptions and justifications provided for using the quantification techniques are sometimes vague or subjective. Single-case researchers are encouraged to complement visual analysis with the use of quantification techniques for which they can provide objective and appropriate descriptions and justifications, and are encouraged to use tools to guide their choice of quantification techniques.

Single-case intervention research design standards: Additional proposed upgrades and future directions

Single-case intervention research design standards have evolved considerably over the past decade. These standards serve the dual role of assisting in single-case design (SCD) intervention research methodology and as guidelines for literature syntheses within a particular research domain. In a recent article (Kratochwill et al., 2021), we argued for a need to clarify key features of these standards. In this article we offer additional recommendations for SCD research and synthesis standards that have been either underdeveloped or missing in the conduct of research and in literature syntheses. Our recommendations are organized into three categories: expanding design standards, expanding evidence standards, and expanding the applications and consistency of SCDs. The recommendations we advance are for consideration for future standards, research design training, and they are especially important to guide the reporting of SCD intervention investigations as they enter the literature-synthesis phase of evidence-based practice initiatives.

Single-case experimental designs for child neurological rehabilitation and developmental disability research

Single-case experimental designs (SCEDs) are a group of methodologies of growing interest, aiming to test the effectiveness of an intervention at the single-participant level, using a rigorous and prospective methodology. SCEDs may promote flexibility on how we design research protocols and inform clinical decision-making, especially for personalized outcome measures, inclusion of families with challenging needs, measurement of children's progress in relation to parental implementation of interventions, and focus on personal goals. Design options for SCEDs are discussed in relation to an expected on/off effect of the intervention (e.g. school/environmental adaptation, assistive technology devices) or, alternatively, on an expected carry-on/maintenance of effects (interventions aiming to develop or restore a function). Randomization in multiple-baseline designs and 'power' calculations are explained. The most frequent reasons for not detecting an intervention effect in SCEDs are also presented, especially in relation to baseline length, trend, and instability. The use of SCEDs on the front and back ends of randomized controlled trials is discussed.

The randomized marker method for single-case randomization tests: Handling data missing at random and data missing not at random

Single-case experiments are frequently plagued by missing data problems. In a recent study, the randomized marker method was found to be valid and powerful for single-case randomization tests when the missing data were missing completely at random. However, in real-life experiments, it is difficult for researchers to ascertain the missing data mechanism. For analyzing such experiments, it is essential that the missing data handling method is valid and powerful for various missing data mechanisms. Hence, we examined the performance of the randomized marker method for data that are missing at random and data that are missing not at random. In addition, we compared the randomized marker method with multiple imputation, because the latter is often considered the gold standard among imputation techniques. To compare and evaluate these two methods under various simulation conditions, we calculated the type I error rate and statistical power in single-case randomization tests using these two methods of handling missing data and compared them to the type I error rate and statistical power using complete datasets. The results indicate that while multiple imputation presents an advantage in the presence of strongly correlated covariate data, the randomized marker method remains valid and results in sufficient statistical power for most of the missing data conditions simulated in this study.

Integrative couple treatment for pathological gamblers with an emphasis on forgiveness processes: A case study with three couples

Gambling's impact on a couple's relationship is an essential element in the gambling disorder (GD). Gamblers tend to lie to their partner to conceal the extent of their gambling problems and debts, which can lead to a serious relational transgression for the couple. One promising avenue is a couple treatment focusing on forgiveness processes. The objective of this study was to determine whether the Integrative Couple Treatment for Pathological Gamblers (ICT-PG) with an emphasis on forgiveness processes helped couples to enhance these processes. A Single-Case Research Design (SCRD) was used with three couples in which one of the members had a GD. The results highlight the importance of jointly analyzing the forgiveness processes between the gamblers and their partner, which constantly influenced each other. These promising results illustrate the relevance of integrating forgiveness processes in treatment for couples where one of the members has a GD.

Violin plots as visual tools in the meta-analysis of Single-Case Experimental Designs

Despite the existence of sophisticated statistical methods, systematic reviews regularly indicate that single-case experimental designs (SCEDs) are predominantly analyzed through visual tools. For the quantitative aggregation of results, different meta-analytical techniques are available, but specific visual tools for the meta-analysis of SCEDs are lacking. The present article therefore describes the use of violin plots as visual tools to represent the raw data. We first describe the underlying rationale of violin plots and their main characteristics. We then show how the violin plots can complement the statistics obtained in a quantitative meta-analysis. The main advantages of violin plots as visual tools in meta-analysis are (a) that they preserve information about the raw data from each study, (b) that they have the ability to visually represent data from different designs in one graph, and (c) that they enable the comparison of score distributions from different experimental phases from different studies.

Wisdom enhancement and life skills to augment CBT outcomes for depression in later life: a series of N-of-1 trials

BACKGROUND

It has been suggested that cognitive behavioural therapy for older adults be augmented with age-appropriate methods to enhance outcomes for depression treatment.

AIMS

This study investigated whether a CBT wisdom enhancement timeline technique for older adults reduced depression, as well as increase self-compassion and self-assessed wisdom.

METHOD

An N-of-1 series trial with non-concurrent multiple-baseline AB design was conducted. Older adults experiencing depression, recruited from mental health service waiting lists, were randomly assigned to baseline conditions. Participants received five individual sessions of the examined intervention, offering a structured way of utilising one's life experiences to evolve the psychological resource of wisdom within a cognitive behavioural framework, in order to improve mood. Participants completed idiographic daily measures and self-report standardised measures of depression, anxiety, self-compassion and wisdom during baseline and intervention phases, and at 1 month follow-up.

RESULTS

Six participants competed the study and were subject to standardised and single-case data analyses. Four participants were deemed responders with reliable changes in depression post-intervention with idiographic changes coinciding with intervention onset. Two participants saw clinically significant changes in depression scores at follow-up. One responder saw significant changes in measures of self-compassion and self-assessed wisdom.

CONCLUSIONS

The examined technique shows promise as an effective technique for reducing depression in older adults. There is insufficient evidence to implicate wisdom and/or self-compassion as significant mechanisms of change. Clinical and theoretical implications are discussed.

Performance criteria-based effect size (PCES) measurement of single-case experimental designs: A real-world data study

Visual analysis and nonoverlap-based effect sizes are predominantly used in analyzing single case experimental designs (SCEDs). Although they are popular analytical methods for SCEDs, they have certain limitations. In this study, a new effect size calculation model for SCEDs, named performance criteria-based effect size (PCES), is proposed considering the limitations of 4 nonoverlap-based effect size measures, widely accepted in the literature and that blend well with visual analysis. In the field test of PCES, actual data from published studies were utilized, and the relations between PCES, visual analysis, and the 4 nonoverlap-based methods were examined. In determining the data to be used in the field test, 1,052 tiers (AB phases) were identified from 6 journals. The results revealed a weak or moderate relation between PCES and nonoverlap-based methods due to its focus on performance criteria. Although PCES has some weaknesses, it promises to eliminate the causes that may create issues in nonoverlap-based methods, using quantitative data to determine socially important changes in behavior and to complement visual analysis.

Applied hybrid single-case experiments published between 2016 and 2020: A systematic review

Single-case experimental designs (SCEDs) are frequently used research designs in psychology, (special) education, and related fields. Hybrid designs are formed by combining two or more of the basic SCED forms (i.e. phase designs, alternation designs, multiple baseline designs, and changing criterion designs). Hybrid designs have the potential to tackle complex research questions and increase internal validity, but relatively little is known about their use in actual research practice. Therefore, we systematically reviewed SCED hybrid designs published between 2016 and 2020. The systematic review of 67 studies indicates that a hybrid of phase designs and multiple baseline designs is most popular. Hybrid designs are most frequently analyzed by means of visual analysis paired with descriptive statistics. Randomization in the study design is common only for one particular kind of hybrid design. Examples of hybrid studies reveal that these designs are particularly popular in educational research. We compare some of the results of the systematic review to those obtained by Hammond and Gast, Shadish and Sullivan, and Tanious and Onghena. Finally, we discuss the results of the present systematic review in light of the need for specific guidelines for hybrid designs, including analytical methods, design specific randomization and reporting, and the need for terminological clarification.

Multilevel Meta-Analysis of Individual Participant Data of Single-Case Experimental Designs: One-Stage versus Two-Stage Methods

To conduct a multilevel meta-analysis of multiple single-case experimental design (SCED) studies, the individual participant data (IPD) can be analyzed in one or two stages. In the one-stage approach, a multilevel model is estimated based on the raw data. In the two-stage approach, an effect size is calculated for each participant and these effect sizes and their sampling variances are subsequently combined to estimate a meta-analytic multilevel model. The multilevel model in the two-stage approach has fewer parameters to estimate, in exchange for the reduction of information of the raw data to effect sizes. In this paper we explore how the one-stage and two-stage IPD approaches can be applied in the context of meta-analysis of single-case designs. Both approaches are compared for several single-case designs of increasing complexity. Through a simulation study we show that the two-stage approach obtains better convergence rates for more complex models, but that model estimation does not necessarily converge at a faster speed. The point estimates of the fixed effects are unbiased for both approaches across all models, as such confirming results from methodological research on IPD meta-analysis of group-comparison designs. In light of these results, we discuss the implementation of both methods in R.

Advancing the Application and Use of Single-Case Research Designs: Reflections on Articles from the Special Issue

This special issue of Perspective on Behavior Science is a productive contribution to current advances in the use and documentation of single-case research designs. We focus in this article on major themes emphasized by the articles in this issue and suggest directions for improving professional standards focused on the design, analysis, and dissemination of single-case research.

Methods for Modeling Autocorrelation and Handling Missing Data in Mediation Analysis in Single Case Experimental Designs (SCEDs)

Single-Case Experimental Designs (SCEDs) are increasingly recognized as a valuable alternative to group designs. Mediation analysis is useful in SCEDs contexts because it informs researchers about the underlying mechanism through which an intervention influences the outcome. However, methods for conducting mediation analysis in SCEDs have only recently been proposed. Furthermore, repeated measures of a target behavior present the challenges of autocorrelation and missing data. This paper aims to extend methods for estimating indirect effects in piecewise regression analysis in SCEDs by (1) evaluating three methods for modeling autocorrelation, namely, Newey-West (NW) estimation, feasible generalized least squares (FGLS) estimation, and explicit modeling of an autoregressive structure of order one (AR(1)) in the error terms and (2) evaluating multiple imputation in the presence of data that are missing completely at random. FGLS and AR(1) outperformed NW and OLS estimation in terms of efficiency, Type I error rates, and coverage, while OLS was superior to the methods in terms of power for larger samples. The performance of all methods is consistent across 0% and 20% missing data conditions. 50% missing data led to unsatisfactory power and biased estimates. In light of these findings, we provide recommendations for applied researchers.

Behavioral Medicine Physiotherapy in the Context of Return to Work for Chronic Pain: A Single-Case Experimental Design Study

Effective interventions are needed for return-to-work (RTW) for individuals with chronic pain on long-term sick leave. In this study, a behavioral medicine physiotherapy protocol was systematically replicated and added to workplace components. The intervention was evaluated for fidelity and effects on target activities and work ability. A single-case experimental design was used with five participants. Daily and weekly ratings of personalized target activities at work as well as work ability were carried out throughout the study period of 26-28 weeks. Effects of the behavioral medicine physiotherapy intervention were evaluated for each individual using visual analysis of displayed graphs and quantitative non-overlap methods. Goal achievement for target activities was reviewed. Three participants completed the intervention. The results indicated an effect from the behavioral medicine physiotherapy intervention on task-specific self-efficacy for target activities, but no consistent effect on experience of target activities or work ability. All three participants had increased function in target activities in line with pre-defined goals. Fidelity to the intervention manual was good. Behavioral medicine physiotherapy can be successfully adapted to work disability and was here replicated in an RTW context for individuals with chronic pain. The intervention protocol should be further evaluated in large-scale studies.

Estimation and statistical inferences of variance components in the analysis of single-case experimental design using multilevel modeling

Multilevel models (MLMs) can be used to examine treatment heterogeneity in single-case experimental designs (SCEDs). With small sample sizes, common issues for estimating between-case variance components in MLMs include nonpositive definite matrix, biased estimates, misspecification of covariance structures, and invalid Wald tests for variance components with bounded distributions. To address these issues, unconstrained optimization, model selection procedure based on parametric bootstrap, and restricted likelihood ratio test (RLRT)-based procedure are introduced. Using simulation studies, we compared the performance of two types of optimization methods (constrained vs. unconstrained) when the covariance structures are correctly specified or misspecified. We also examined the performance of a model selection procedure to obtain the optimal covariance structure. The results showed that the unconstrained optimization can avoid nonpositive definite issues to a great extent without a compromise in model convergence. The misspecification of covariance structures would cause biased estimates, especially with small between case variance components. However, the model selection procedure was found to attenuate the magnitude of bias. A practical guideline was generated for empirical researchers in SCEDs, providing conditions under which trustworthy point and interval estimates can be obtained for between-case variance components in MLMs, as well as the conditions under which the RLRT-based procedure can produce acceptable empirical type I error rate and power.

Machine Learning to Support Visual Inspection of Data: A Clinical Application

Practitioners in pediatric feeding programs often rely on single-case experimental designs and visual inspection to make treatment decisions (e.g., whether to change or keep a treatment in place). However, researchers have shown that this practice remains subjective, and there is no consensus yet on the best approach to support visual inspection results. To address this issue, we present the first application of a pediatric feeding treatment evaluation using machine learning to analyze treatment effects. A 5-year-old male with autism spectrum disorder participated in a 2-week home-based, behavior-analytic treatment program. We compared interrater agreement between machine learning and expert visual analysts on the effects of a pediatric feeding treatment within a modified reversal design. Both the visual analyst and the machine learning model generally agreed about the effectiveness of the treatment while overall agreement remained high. Overall, the results suggest that machine learning may provide additional support for the analysis of single-case experimental designs implemented in pediatric feeding treatment evaluations.

Avoid or engage? Outcomes of graded exposure in youth with chronic pain using a sequential replicated single-case randomized design

Pain-related fear is typically associated with avoidance behavior and pain-related disability in youth with chronic pain. Youth with elevated pain-related fear have attenuated treatment responses; thus, targeted treatment is highly warranted. Evidence supporting graded in vivo exposure treatment (GET) for adults with chronic pain is considerable, but just emerging for youth. The current investigation represents the first sequential replicated and randomized single-case experimental phase design with multiple measures evaluating GET for youth with chronic pain, entitled GET Living. A cohort of 27 youth (81% female) with mixed chronic pain completed GET Living. For each participant, a no-treatment randomized baseline period was compared with GET Living and 3- and 6-month follow-ups. Daily changes in primary outcomes fear and avoidance and secondary outcomes pain catastrophizing, pain intensity, and pain acceptance were assessed using electronic diaries and subjected to descriptive and model-based inference analyses. Based on individual effect size calculations, a third of participants significantly improved by the end of treatment on fear, avoidance, and pain acceptance. By follow-up, over 80% of participants had improved across all primary and secondary outcomes. Model-based inference analysis results to examine the series of replicated cases were generally consistent. Improvements during GET Living was superior to the no-treatment randomized baseline period for avoidance, pain acceptance, and pain intensity, whereas fear and pain catastrophizing did not improve. All 5 outcomes emerged as significantly improved at 3- and 6-month follow-ups. The results of this replicated single-case experimental phase design support the effectiveness of graded exposure for youth with chronic pain and elevated pain-related fear avoidance.

Commercial head-mounted display virtual reality for upper extremity rehabilitation in chronic stroke: a single-case design study

Background

Rehabilitation is crucial for maximizing recovery after stroke. Rehabilitation activities that are fun and rewarding by themselves can be more effective than those who are not. Gamification with virtual reality (VR) exploits this principle. This single-case design study probes the potential for using commercial off-the-shelf, room-scale head-mounted virtual reality for upper extremity rehabilitation in individuals with chronic stroke, the insights of which can inform further research.

Methods

A heterogeneous volunteer sample of seven participants living with stroke were recruited through advertisement. A single-case design was employed with a 5-week baseline (A), followed by a 10-week intervention (B) and a 6-month follow-up. Upper extremity motor function was assessed with validated kinematic analysis of drinking task. Activity capacity was assessed with Action Research Arm Test, Box and Block Test and ABILHAND questionnaire. Assessments were done weekly and at follow-up. Playing games on a VR-system with head-mounted display (HTC Vive) was used as rehabilitation intervention. Approximately 300 games were screened and 6 tested. Visual analysis and Tau-U statistics were used to interpret the results.

Results

Visual analysis of trend, level shift and overlap as well as Tau-U statistics indicated improvement of Action Research Arm Test in six participants. Four of these had at least a moderate Tau-U score (0.50–0.92), in at least half of the assessed outcomes. These four participants trained a total of 361 to 935 min. Two out of four participants who were able to perform the drinking task, had the highest training dose (> 900 min) and showed also improvements in kinematics. The predominant game played was Beat Saber. No serious adverse effects related to the study were observed, one participant interrupted the intervention phase due to a fall at home.

Conclusions

This first study of combining commercial games, a commercial head-mounted VR, and commercial haptic hand controls, showed promising results for upper extremity rehabilitation in individuals with chronic stroke. By being affordable yet having high production values, as well as being an easily accessible off-the-shelf product, this variant of VR technology might facilitate widespread adaption. Insights garnered in this study can facilitate the execution of future studies.

Trial registration The study was registered at researchweb.org (project number 262331, registered 2019-01-30, https://www.researchweb.org/is/vgr/project/262331) prior to participant enrolment.

A systematic review of applied single-case research published between 2016 and 2018: Study designs, randomization, data aspects, and data analysis

Single-case experimental designs (SCEDs) have become a popular research methodology in educational science, psychology, and beyond. The growing popularity has been accompanied by the development of specific guidelines for the conduct and analysis of SCEDs. In this paper, we examine recent practices in the conduct and analysis of SCEDs by systematically reviewing applied SCEDs published over a period of three years (2016-2018). Specifically, we were interested in which designs are most frequently used and how common randomization in the study design is, which data aspects applied single-case researchers analyze, and which analytical methods are used. The systematic review of 423 studies suggests that the multiple baseline design continues to be the most widely used design and that the difference in central tendency level is by far most popular in SCED effect evaluation. Visual analysis paired with descriptive statistics is the most frequently used method of data analysis. However, inferential statistical methods and the inclusion of randomization in the study design are not uncommon. We discuss these results in light of the findings of earlier systematic reviews and suggest future directions for the development of SCED methodology.

Brief Acceptance and Commitment Therapy for Fibromyalgia: Feasibility and Effectiveness of a Replicated Single-Case Design

Objective

Overall, the literature on the effectiveness of psychological treatments in general and those for fibromyalgia in particular has been dominated by research designs that focus on large groups and explore changes on average, so the treatment impact at the individual level remains unclear. In this quasi-experimental, replicated single-case design, we will test the feasibility and effectiveness of a brief acceptance and committed therapy intervention using ecological momentary assessment supported by technology.

Methods

The sample comprised 7 patients (3 in the individual condition and 4 in the group condition) who received a brief, 5-week psychological treatment. Patient evolution was assessed one week prior to treatment onset and during the whole study with a smartphone app. Because ecological momentary assessment and the use of an app are not frequent practices in routine care, we also evaluated the feasibility of this assessment methodology (i.e., compliance with the app). Change was investigated with a nonoverlap of all pairs index. Outcomes were pain interference with sleep and social activities, fatigue, sadness, and pain intensity.

Results

Patient change was not uniform across outcomes. Four patients (two in each condition) showed relatively moderate levels of change (approximately 60% nonoverlap in several outcomes). The remaining patients showed more modest improvements which affected a reduced number of outcomes. Based on nonoverlapping indices, there was no clear evidence in favor of any treatment format.

Conclusions

An alternative design to large-scale trials, one that focuses on the individual change, exists and it can be implemented in pain research. The use of technology (e.g., smartphones) simplifies such designs by facilitating ecological momentary assessment. Based on our findings showing that changes were not homogeneous across patients or outcomes, more single-case designs and patient-centered analyses (e.g., responder and moderation analyses) are required.

The Potential of a Relational Training Intervention to Improve Older Adults' Cognition

Behavioral gerontology rarely focuses on improving older adults' cognitive function. This gap in the literature should be addressed, as our aging population means that greater numbers of older adults are experiencing cognitive decline and reduced functional independence. If cognitive training interventions are to be socially significant, they should target improvements in core executive functions (EFs) that are critical for everyday cognition and functioning independence. Evidence from the cognitive sciences suggests that a cognitive training intervention targeting "relational knowledge" and "cognitive flexibility," which are core EFs, could translate to improvements in cognition and functioning for older adults. Behavioral researchers, interested in the effects of relational training on cognition, have shown a relationship between complex and flexible arbitrarily applicable relational responding (AARRing) and improved performance on measures of intelligence in children and young adults. However, data examining the impact of AARRing on the cognition of older adults are lacking. This article suggests that complex and flexible AARRing may be synonymous with the aforementioned EFs of relational knowledge and cognitive flexibility, and that a behaviorally oriented relational training intervention might improve cognition and functioning for healthy older adults or those experiencing cognitive decline. The article initially presents a brief overview of research in behavioral gerontology and older adult cognition, followed by a detailed explanation of how training complexity and flexibility in AARRing could result in improvements in core EFs. Specific suggestions for designing a relational training intervention and assessing relevant outcomes are provided.

Assessing consistency of effects when applying multilevel models to single-case data

In the context of single-case experimental designs, replication is crucial. On the one hand, the replication of the basic effect within a study is necessary for demonstrating experimental control. On the other hand, replication across studies is required for establishing the generality of the intervention effect. Moreover, the "replicability crisis" presents a more general context further emphasizing the need for assessing consistency in replications. In the current text, we focus on replication of effects within a study, and we specifically discuss the consistency of effects. Our proposal for assessing the consistency of effects refers to one of the promising data analytical techniques, multilevel models, also known as hierarchical linear models or mixed effects models. One option is to check, for each case in a multiple-baseline design, whether the confidence interval for the individual treatment effect excludes zero. This is relevant for assessing whether the effect is replicated as being non-null. However, we consider that it is more relevant and informative to assess, for each case, whether the confidence interval for the random effects includes zero (i.e., whether the fixed effect estimate is a plausible value for each individual effect). This is relevant for assessing whether the effect is consistent in size, with the additional requirement that the fixed effect itself is different from zero. The proposal for assessing consistency is illustrated with real data and is implemented in free user-friendly software.

Efficacy of a Virtual Reality Biofeedback Game (DEEP) to Reduce Anxiety and Disruptive Classroom Behavior: Single-Case Study

BACKGROUND

Many adolescents in special education are affected by anxiety in addition to their behavioral problems. Anxiety leads to substantial long-term problems and may underlie disruptive behaviors in the classroom as a result of the individual's inability to tolerate anxiety-provoking situations. Thus, interventions in special needs schools that help adolescents cope with anxiety and, in turn, diminish disruptive classroom behaviors are needed.

OBJECTIVE

This study aimed to evaluate the effect of a virtual reality biofeedback game, DEEP, on daily levels of state-anxiety and disruptive classroom behavior in a clinical sample. In addition, the study also aimed to examine the duration of the calm or relaxed state after playing DEEP.

METHODS

A total of 8 adolescents attending a special secondary school for students with behavioral and psychiatric problems participated in a single-case experimental ABAB study. Over a 4-week period, participants completed 6 DEEP sessions. In addition, momentary assessments (ie, 3 times a day) of self-reported state-anxiety and teacher-reported classroom behavior were collected throughout all A and B phases.

RESULTS

From analyzing the individual profiles, it was found that 6 participants showed reductions in anxiety, and 5 participants showed reductions in disruptive classroom behaviors after the introduction of DEEP. On a group level, results showed a small but significant reduction of anxiety (d=-0.29) and a small, nonsignificant reduction of disruptive classroom behavior (d=-0.16) on days when participants played DEEP. Moreover, it was found that the calm or relaxed state of participants after playing DEEP lasted for about 2 hours on average.

CONCLUSIONS

This study demonstrates the potential of the game, DEEP, as an intervention for anxiety and disruptive classroom behavior in a special school setting. Future research is needed to fully optimize and personalize DEEP as an intervention for the heterogeneous special school population.

Multilevel meta-analysis of multiple regression coefficients from single-case experimental studies

The focus of the current study is on handling the dependence among multiple regression coefficients representing the treatment effects when meta-analyzing data from single-case experimental studies. We compare the results when applying three different multilevel meta-analytic models (i.e., a univariate multilevel model avoiding the dependence, a multivariate multilevel model ignoring covariance at higher levels, and a multivariate multilevel model modeling the existing covariance) to deal with the dependent effect sizes. The results indicate better estimates of the overall treatment effects and variance components when a multivariate multilevel model is applied, independent of modeling or ignoring the existing covariance. These findings confirm the robustness of multilevel modeling to misspecifying the existing covariance at the case and study level in terms of estimating the overall treatment effects and variance components. The results also show that the overall treatment effect estimates are unbiased regardless of the underlying model, but the between-case and between-study variance components are biased in certain conditions. In addition, the between-study variance estimates are particularly biased when the number of studies is smaller than 40 (i.e., 10 or 20) and the true value of the between-case variance is relatively large (i.e., 8). The observed bias is larger for the between-case variance estimates compared to the between-study variance estimates when the true between-case variance is relatively small (i.e., 0.5).

Behavioural activation therapy to improve participation in adults with depression following brain injury: A single-case experimental design study

Following brain injury, the risk of depression increases. There are few studies of non-pharmacological interventions for this problem. Behavioural Activation (BA) could help because it has been demonstrated to be as effective as cognitive-behaviour therapy but is less cognitively demanding and more suitable for people with brain impairment. The current study evaluated BA using a multiple-baseline design across behaviours with replication. Three male participants with clinically significant depressive symptoms (two with traumatic brain injury aged 26 and 46, one who experienced strokes in infancy, aged 26) engaged in a 10-14-week trial of BA focusing on three activity domains: physical, social and functional activities. Participants completed an online form three times a day which recorded activity participation and responses to a single-item mood scale. There was little evidence in support of BA for increasing participation. There was also a lack of change in average mood, but some positive effects were found on measures of depression symptoms and quality of life in these participants. Various factors affected participation which might have been mitigated by extended treatment contact, greater use of prompts or electronic aids or the addition of other therapy modes.

Effectiveness of Noise-Attenuating Headphones on Physiological Responses for Children With Autism Spectrum Disorders

Objective: The purpose of this study was to evaluate the proof of concept of an intervention to decrease sympathetic activation as measured by skin conductivity (electrodermal activity, EDA) in children with an autism spectrum disorder (ASD) and auditory hypersensitivity (hyperacusis). In addition, researchers examined if the intervention provided protection against the negative effects of decibel level of environmental noises on electrodermal measures between interventions. The feasibility of implementation and outcome measures within natural environments were evaluated.

Method: A single-subject multi-treatment design was used with six children, aged 8–16 years, with a form of Autism (i.e., Autism, PDD-NOS). Participants used in-ear (IE) and over-ear (OE) headphones for two randomly sequenced treatment phases. Each child completed four phases: (1) a week of baseline data collection; (2) a week of an intervention; (3) a week of no intervention; and (4) a week of the other intervention. Empatica E4 wristbands collected EDA data. Data was collected on 16–20 occasions per participant, with five measurements per phase.

Results: Separated tests for paired study phases suggested that regardless of intervention type, noise attenuating headphones led to a significance difference in both skin conductance levels (SCL) and frequency of non-specific conductance responses (NS-SCRs) between the baseline measurement and subsequent phases. Overall, SCL and NS-SCR frequency significantly decreased between baseline and the first intervention phase. A protective effect of the intervention was tested by collapsing intervention results into three phases. Slope correlation suggested constant SCL and NS-SCR frequency after initial use of the headphones regardless of the increase in environmental noises. A subsequent analysis of the quality of EDA data identified that later phases of data collection were associated with better data quality.

Conclusion: Many children with ASD have hypersensitivities to sound resulting in high levels of sympathetic nervous system reactivity, which is associated with problematic behaviors and distress. The findings of this study suggest that the use of noise attenuating headphones for individuals with ASD and hyperacusis may reduce sympathetic activation. Additionally, results suggest that the use of wearable sensors to collect physiological data in natural environments is feasible with established protocols and training procedures.

Evaluating the effectiveness of therapeutic horse riding for children and young people experiencing disability: a single-case experimental design study

Purpose: Therapeutic horse riding aims to improve the health of children and young people experiencing disability; however, its benefits across a range of health domains, particularly the impact on participation outcomes, are not well known. This research evaluated to what extent there was a change in riders balance, functional performance, social responsiveness, quality of life and participation outcomes as a result of therapeutic horse riding.Methods: A multiple-baseline across participants (n = 12) single-case experimental design, with randomly allocated baseline phase lengths, quantitatively evaluated how riders responded to a 20-week intervention.Results: Social participation outcomes measured using the Canadian Occupational Performance Measure demonstrated the most consistent positive between-phase differences (performance ES = 1.20, 95% CI [0.82, 1.63]; satisfaction ES = 1.11, 95% CI [0.73, 1.55]). A causal relationship was seen in three riders, but improvements only reached clinical significance for two riders when accounting for phase data trends. No significant outcome patterns were found comparing riders with principally physical impairments to those with principally psychosocial impairments.Conclusions: Being involved in therapeutic horse riding may improve rider's social participation in home, school and community settings. We postulate that rider self-concept development may be a mechanism of treatment effect leading to participation-level changes.Implications for rehabilitationSocial participation was the health outcome demonstrating the most consistent change following therapeutic horse riding, regardless of rider impairment.Therapeutic horse riding can improve social participation in settings beyond the riding arena.Greater intervention tailoring based on rider responses may enhance therapeutic horse riding intervention effects.

Tailored Text Message Prompts to Increase Therapy Homework Adherence: A Single-Case Randomised Controlled Study

Background:Psychotherapy homework completion is associated with positive treatment outcomes, but many patients show low adherence to prescribed assignments. Whether text-message prompts are effective in increasing adherence to assignments is unknown.Aims:To evaluate whether tailored daily text-message prompts can increase homework adherence in a stress/anxiety treatment.Method:This study used a randomised controlled single-case alternating treatment design with parallel replication in seven participants. Participants received a five-week relaxation program for stress and anxiety with daily exercises. The intervention consisted of daily text messages tailored for each participant. Phases with or without text messages were randomly alternated over the study course. Randomisation tests were used to statistically analyse differences in mean number of completed relaxation exercises between phases.Results:There was a significant (combinedp= .018) effect of daily text messages on homework adherence across participants with weak to medium effect size improvements. No negative effects of daily text messages were identified.Conclusions:Tailored text messages can marginally improve adherence to assignments for patients in CBT. Further studies may investigate how text messages can be made relevant for more patients and whether text messages can be used to increase homework quality rather than quantity.

Assessing Consistency in Single-Case A-B-A-B Phase Designs

Previous research has introduced several effect size measures (ESMs) to quantify data aspects of single-case experimental designs (SCEDs): level, trend, variability, overlap, and immediacy. In the current article, we extend the existing literature by introducing two methods for quantifying consistency in single-case A-B-A-B phase designs. The first method assesses the consistency of data patterns across phases implementing the same condition, called CONsistency of DAta Patterns (CONDAP). The second measure assesses the consistency of the five other data aspects when changing from baseline to experimental phase, called CONsistency of the EFFects (CONEFF). We illustrate the calculation of both measures for four A-B-A-B phase designs from published literature and demonstrate how CONDAP and CONEFF can supplement visual analysis of SCED data. Finally, we discuss directions for future research.

Single-Case Design, Analysis, and Quality Assessment for Intervention Research

BACKGROUND AND PURPOSE

The purpose of this article is to describe single-case studies and contrast them with case studies and randomized clinical trials. We highlight current research designs, analysis techniques, and quality appraisal tools relevant for single-case rehabilitation research.

SUMMARY OF KEY POINTS

Single-case studies can provide a viable alternative to large group studies such as randomized clinical trials. Single-case studies involve repeated measures and manipulation of an independent variable. They can be designed to have strong internal validity for assessing causal relationships between interventions and outcomes, as well as external validity for generalizability of results, particularly when the study designs incorporate replication, randomization, and multiple participants. Single-case studies should not be confused with case studies/series (ie, case reports), which are reports of clinical management of a patient or a small series of patients.

RECOMMENDATIONS FOR CLINICAL PRACTICE

When rigorously designed, single-case studies can be particularly useful experimental designs in a variety of situations, such as when research resources are limited, studied conditions have low incidences, or when examining effects of novel or expensive interventions. Readers will be directed to examples from the published literature in which these techniques have been discussed, evaluated for quality, and implemented.

Applying the Generalized Logistic Model in Single Case Designs: Modeling Treatment-Induced Shifts

Many analytical approaches to single-case data assume either linear effects (regression-based methods) or instant effects (mean-based methods). Neither assumption is realistic; therefore, these approaches’ assumptions are often violated. In this article, we propose modeling curvilinear effects to appropriately parametrize the characteristics of singe-case data. Specifically, we introduce the generalized logistic function as adequate function for this situation. The merits of the proposed procedure are demonstrated using data previously used in single case research that represent typical single case data. We provide the function with auxiliary graphical options to demonstrate the model parameters. The function is freely available in the R package “userfriendlyscience.” The proposed procedure is a new way to analyze single case data, which may provide applied single case researchers with a new tool to better understand their data and avoid applying methods with violated assumptions.

Between-case standardized effect size analysis of single case designs: Examination of the two methods

An increasing movement in single case research is to employ statistical analyses as one form of data analysis. Researchers have proposed different statistical approaches. The purpose of this paper is to examine the utility and discriminant validity of two novel types of between-case standardized effect size analyses with two existing systematic reviews. The between-case analyses found greater effect sizes for the studies in the object play review and smaller effect sizes for studies of sensory intervention, which were consistent with the overall conclusions reached in the original systematic reviews. These findings provide evidence of discriminant validity, although concerns remain around the methods' utility across different single case research designs. Future directions for research and development also are provided.

Evaluating an intervention to increase meaningful activity after severe traumatic brain injury: A single-case experimental design with direct inter-subject and systematic replications

Severe traumatic brain injury (sTBI) often results in significant morbidity, with fewer than 50% returning to work and only a minority resuming leisure and social activity. Yet few effective interventions are available for non-vocational activity. The aim of the study was to develop a new goal-directed intervention, the Programme for Engagement, Participation and Activities (PEPA), and evaluate its effect. The research design was a multiple-baseline design across behaviours, with direct inter-subject and systematic replications. Seven participants with sTBI, neurobehavioural impairment including apathy, inability to work, and limited leisure/social activities were categorised into two groups. Group 1 (n = 4) had cognitive impairments but were functionally independent. Systematic replication was conducted in a further three participants (group 2) with major neurobehavioural impairments and functional disability. Generalisation measures evaluated other life domains in group 1 participants (e.g., mood, community participation). Results of the weighted average Tau-U across the tiers was significant for six out of seven participants, with large effect sizes (≥.64) for five participants. Generalisation effects extended to other domains of life. The PEPA thus shows promise as an effective intervention to increase non-vocational activity and improve mental health outcomes in people with neurobehavioural disability after sTBI. These results add to the evidence for the effectiveness of goal-directed interventions.

A Gradual Effects Model for Single-Case Designs

Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data.