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Recent RCTs and meta-analyses compare the effectiveness of different types of exercise for chronic whiplash associated disorder (WAD). This study aimed to verify whether the results of these studies translate to statistically significant and clinically meaningful effects in individual participants.A series of replicated randomised single case experimental design studies (SCEDs) with A-B design (A: baseline, B: intervention). Eight participants with chronic WAD (8 female, mean [SD] age 47 [10] years) were randomised into one of four baseline durations (5, 8, 11, and 14 days) and to one of two eight-week exercise interventions (aerobic or strengthening). Daily measures of pain intensity, bothersomeness, and interference were collected during the baseline phase and the intervention phase.Visual analyses indicated that three participants in the aerobic exercise group meaningfully improved. No improvements were found in the strengthening group. Effect sizes favoured the aerobic exercise group, yet randomisation tests of pooled effects did not show a difference in between-intervention effectiveness.Contrary to our expectations, three out of four participants were nearly pain-free at the end of the aerobic exercise intervention, whereas none of the participants in the strengthening group improved meaningfully. This suggests that aerobic exercise may be favourable for WAD.Implications for RehabilitationOur results suggest that aerobic exercises are favourable over strengthening exercises and may be the preferred option for patients with chronic WAD.We found substantial variability in self-reported outcomes within participants, clinicians should be aware of this in the judgement of treatment effectiveness.

BACKGROUND Health anxiety by proxy is described as a parent's obsessive worries that their child is severely sick although not medically confirmed. Research on health anxiety by proxy suggests that it is highly distressing for the parent, and their child may be at risk of developing maladaptive symp-tom coping strategies. Until now no targeted treatment options for this group existed. PROXY is a newly developed 8-week psychological internet treatment for parents with health anxiety by proxy. OBJECTIVE The aim of this article is to describe the protocol for a study investigating potential effect of PROXY on parents' worries about their children's health. METHODS A replicated randomized single-case experimental design with AB-phases will be conducted. Each of the five participants will be randomized to a baseline period between 7-26 days before entering treatment and they will report daily anxiety level by text message throughout the study. Data will be submitted to visual analysis, and randomization tests will be conducted for each participant to test the null hypothesis that PROXY will have no effect on participants' anxiety. RESULTS The recruitment of parents began in June 2022 and is expected to be complete in February 2023, with data collection ceasing in April 2023. CONCLUSIONS This is the first testing of a treatment for health anxiety by proxy. Using single-case experimental design with replicated randomized phases enables a methodologically sound testing with very few participants. Results will inform researchers on individual treatment courses and effect which may direct future research. CLINICALTRIAL Trial registration number is NCT04830605.

ispartof: EARLY CHILDHOOD RESEARCH QUARTERLY vol:61 pages:106-116 status: published

Immediacy is one of six data aspects (alongside level, trend, variability, overlap, and consistency) that has to be accounted for when visually analyzing single-case data. Given that it is one of the aspects that has received considerably less attention than other data aspects, the current text offers a review of the proposed conceptual definitions of immediacy (i.e., what it refers to) and also of the suggested operational definitions (i.e., how exactly is it assessed and/or quantified). Provided that a variety of conceptual and operational definitions is identified, we propose following a sensitivity analysis using a randomization test for assessing immediate effects in single-case experimental designs, by identifying when changes were most clear. In such a sensitivity analysis, the immediate effects are tested for multiple possible intervention points and for different possible operational definitions. Robust immediate effects can be detected if the results for the different operational definitions converge. ispartof: JOURNAL OF THE EXPERIMENTAL ANALYSIS OF BEHAVIOR vol:118 issue:3 pages:462-492 ispartof: location:United States status: published

The many studies with coin-tossing tasks in literature show that the concept of randomness is challenging for adults as well as children. Systematic errors observed in coin-tossing tasks are often related to the representativeness heuristic, which refers to a mental shortcut that is used to judge randomness by evaluating how well a set of random events represents the typical example for random events we hold in our mind. Representative thinking is explained by our tendency to seek for patterns in our surroundings. In the present study, predictions of coin-tosses of 302 third-graders were explored. Findings suggest that in third grade of elementary school, children make correct as well as different types of erroneous predictions and individual differences exist. Moreover, erroneous predictions that were in line with representative thinking were positively associated with an early spontaneous focus on regularities, which was assessed when they were in second year of preschool. We concluded that previous studies might have underestimated children's reasoning about randomness in coin-tossing contexts and that representative thinking is indeed associated with pattern-based thinking tendencies.

Background: Parents with borderline personality disorder (BPD) experience deficiencies in parenting skills leading to increased family burden and stress, mental health problems in children and even family violence. The present study evaluated the effectiveness of a Dutch version of the parenting training based on Dialectical Behaviour Therapy (DBT) for mothers with BPD developed by Renneberg and Rosenbach (2016). Methods: Given the high heterogeneity found in patients with BPD, a multiple baseline single-case experimental design study was conducted with standardised and idiosyncratic assessments of parenting behaviour and parental stress before, biweekly during, and after the training. Participants (N=10; mean age = 35.9, SD = 4.65) were mothers with a full diagnosis of BPD with at least one child under the age of 6 at the start of the training.Results: Four participants discontinued the training. Among the six training completers, clinically reliable improvements in parenting skills between pre- and post-/follow-up training were most pronounced for inconsistent discipline, involvement, positive parenting and corporal punishment. Frequent, idiosyncratic assessments showed that for all six mothers the DBT training had positive effect on parenting skills, while for five mothers this effect was large. Positive changes in parenting stress were found in three (post) to four (follow-up) participants. Positive changes in parenting behaviour were also found for the four training non-completers. Participants were satisfied with the training.Conclusions and implications: DBT parenting training has a great clinical value for mothers with BPD struggling with raising their children. In our effectiveness study, we found empirical evidence to confirm this clinical impression. Through tentative investigation of individual effects among the six training completers, we learned that, in comparison to other mothers, mothers with comorbid PTSD (N = 3) show almost no changes in the levels of parenting stress after the training. The mothers with most positive results received anti-depressants and had clear arrangements about the role of partners in the family. The complexity and heterogeneity of the condition requires a more idiosyncratic approach. Mothers with BPD could profit from more stress management regarding parenting and increasing commitment to participation in DBT parenting training.This study was registered (retrospectively) in ISRCTN (ID ISRCTN74447767).https://www.isrctn.com/ISRCTN74447767, March 2022

Behavioral parent training (BPT) is an evidence-based treatment for children with attention-deficit/hyperactivity disorder (ADHD). Stimulus control techniques (antecedent-based techniques, e.g., clear rules, instructions) and contingency management techniques (consequent-based techniques, e.g., praise, ignore) are the most common ones that are being taught to parents in BPT. However, research into the additive effects of these techniques is scarce. In this replicated single-case experimental ABC phase design, including six children on stable medication for ADHD (8-11 years) and their parents, the added efficacy of consequent-based techniques on top of antecedent-based techniques was evaluated. After a baseline period (phase A), we randomized the commencement time of two sessions parent training in antecedent-based techniques and two sessions parent training in consequent-based techniques for each child. Children's behaviors were assessed by daily parent ratings of selected problem behaviors and an overall behavior rating. Although visual inspection showed that behavior improved for most children in both phases, randomization tests did not demonstrate the added efficacy of the consequent-based techniques on top of the antecedent-based techniques. Limitations of the study and recommendations for future single-case experiments in this population are discussed. ispartof: BEHAVIOR MODIFICATION ispartof: location:United States status: Published online

Randomization tests represent a class of significance tests to assess the statistical significance of treatment effects in randomized single-case experiments. Most applications of single-case randomization tests concern simple treatment effects: immediate, abrupt, and permanent changes in the level of the outcome variable. However, in educational practice, these simple effects are the exception rather than the rule. In many educational applications, researchers are confronted with delayed, gradual, and temporary treatment effects; in general with “response functions” that are markedly different from single-step functions. In the current text we introduce a general framework that allows specifying a test statistic for a randomization test based on predicted response functions that is sensitive for a wide variety of data patterns. For instance, the response functions allow for different latencies (degrees of delay) of effect, for abrupt versus gradual effects, and for different durations of the effect (permanent or temporary). There may be reasonable expectations regarding the kind of effect (abrupt or gradual), entailing a different focal data feature (e.g., level or slope). However, the exact amount of latency and the exact duration of a temporary effect may not be known a priori. Therefore, a multiverse approach can be followed exploring the effect of specifying different latencies or delayed effects and different durations for temporary effects. In the paper, we illustrate the randomization tests analyses, using response functions as a test statistic and following a multiverse approach, with real data. We also provide a user-friendly and freely available web application implementing several response functions from a multiverse perspective.

In this article, we present single-case causal mediation analysis as the application of causal mediation analysis to data collected within a single-case experiment. This method combines the focus on the individual with the focus on mechanisms of change, rendering it a promising approach for both mediation and single-case researchers. For this purpose, we propose a new method based on time-discrete state-space modeling to estimate the direct and indirect treatment effects. We demonstrate how to estimate the model for a single-case experiment on stress and craving in a routine alcohol consumer before and after an imposed period of abstinence. Furthermore, we present a simulation study that examines the estimation and testing of the standardized indirect effect. All parameters used to generate the data were recovered with acceptable precision. We use maximum likelihood and permutation procedures to calculate p-values and standard errors of the parameters estimates. The new method is promising for testing mediated effects in single-case experimental designs. We further discuss limitations of the new method with respect to causal inference, as well as more technical concerns, such as the choice of the time lags between the measurements.

In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as amodel with a randomstudy effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified randomeffects models are not often used although theymight account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel metaanalysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given. ispartof: Behavior Research Methods vol:52 issue:5 pages:2031-2052 ispartof: location:United States status: published

The aim of the present study was to examine the additive effect of elements of cognitive behavioral therapy (CBT) on the video game Mindlight in decreasing anxiety of children with an autism spectrum disorder (ASD). A non-concurrent multiple baseline design with 8 children with ASD in the age of 8-12 was used. CBT did not have the hypothesized additive effect on Mindlight in decreasing anxiety of children with ASD. Instead, multiple participants already experienced a decrease in anxiety during the Mindlight sessions. Yet, several participants did experience a stabilization in anxiety at a low level during the CBT sessions. For now, it can be concluded that CBT does not have an additive effect on Mindlight. ispartof: JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS vol:52 issue:1 pages:150-168 ispartof: location:United States status: published

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.

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. ispartof: Contemporary Clinical Trials Communications vol:23 pages:1-8 ispartof: location:Netherlands status: published

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 farmost 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. ispartof: Behavior Research Methods vol:53 issue:4 pages:1371-1384 ispartof: location:United States status: published

Quality standards for single-case experimental designs (SCEDs) recommend inspecting six data aspects: level, trend, variability, overlap, immediacy, and consistency of data patterns. The data aspect consistency has long been neglected by visual and statistical analysts of SCEDs despite its importance for inferring a causal relationship. However, recently a first quantification has been proposed in the context of A-B-A-B designs, called CONsistency of DAta Patterns (CONDAP). In the current paper, we extend the existing CONDAP measure for assessing consistency in designs with more than two successive A-B elements (e.g., A-B-A-B-A-B), multiple baseline designs, and changing criterion designs. We illustrate each quantification with published research. ispartof: Behavior Modification vol:45 issue:4 pages:560-580 ispartof: location:United States status: published

Comparing probabilities is a useful skill in life. Binary choice tasks are popular means in research on probabilistic reasoning. Falk, Yudilevich-Assouline, and Elstein (Educational Studies in Mathematics, 81 (2), 2012) noted that many of these tasks contain design flaws. We designed and evaluated an extended and improved binary choice item set. In each trial, children were shown two boxes containing desired and undesired elements and had to identify the best box to blindly draw from. We took into account four necessary item set characteristics: (un)desired elements in the correct box, total number of elements in the correct box, and difference between desired and undesired elements in the correct box. Furthermore, some extensions to Falk et al.’s design (2012) were implemented: items in which one box certainly provided a desired element, items with three competing colors of elements, and items with lower “countability” of the elements. Results showed that extensions added to the design did not imperil internal consistency and validity. Younger children were more likely to give correct answers when the correct box contained more desired elements and older children are better at comparing probabilities than younger ones overall. ispartof: Educational Studies In Mathematics vol:103 issue:1 pages:27-42 status: published

Aim: To critically evaluate single-case design (SCD) studies performed within the population of children/adolescents with cerebral palsy (CP).Methods: A scoping review of SCD studies of children/adolescents with CP. Demographic, methodological, and statistical data were extracted. Articles were evaluated using the Risk of Bias in N-of-1 Trials (RoBiNT) Scale and the Consolidated Standards of Reporting Trials (CONSORT) extension for N-of-1 trials (CENT 2015). Comments regarding strengths and limitations were analyzed.Results: Studies investigated the effects of a wide range of interventions on various outcomes. Most SCD types were adopted in multiple studies. All studies used visual inspection rather than visual analysis, often complemented with basic statistical descriptives. Risk of bias was high, particularly concerning internal validity. Many CENT items were insufficiently reported. Several benefits and limitations of SCD were identified.Conclusions: The quality of evidence from results of SCD studies needs to be increased through risk of bias reduction. ispartof: Developmental Neurorehabilitation vol:23 issue:2 pages:73-105 ispartof: location:England status: published

Randomization tests for alternating treatments designs, multiple baseline designs, and withdrawal/reversal designs are well-established. Recent classifications, however, also mention the "changing criterion design" as a fourth important type of single-case experimental design. In this paper, we examine the potential of randomization tests for changing criterion designs. We focus on the rationale of the randomization test, the random assignment procedure, the choice of the test statistic, and the calculation of randomization test p-values. Two examples using empirical data and an R computer program to perform the calculations are provided. We discuss the problems associated with conceptualizing the changing criterion design as a variant of the multiple baseline design, the potential of the range-bound changing criterion design, experimental control as an all-or-none phenomenon, the necessity of random assignment for the statistical-conclusion validity of the randomization test, and the use of randomization tests in nonrandomized designs.

Multiple quantitative methods for single-case experimental design data have been applied to multiple-baseline, withdrawal, and reversal designs. The advanced data analytic techniques historically applied to single-case design data are primarily applicable to designs that involve clear sequential phases such as repeated measurement during baseline and treatment phases, but these techniques may not be valid for alternating treatment design (ATD) data where two or more treatments are rapidly alternated. Some recently proposed data analytic techniques applicable to ATD are reviewed. For ATDs with random assignment of condition ordering, the Edgington's randomization test is one type of inferential statistical technique that can complement descriptive data analytic techniques for comparing data paths and for assessing the consistency of effects across blocks in which different conditions are being compared. In addition, several recently developed graphical representations are presented, alongside the commonly used time series line graph. The quantitative and graphical data analytic techniques are illustrated with two previously published data sets. Apart from discussing the potential advantages provided by each of these data analytic techniques, barriers to applying them are reduced by disseminating open access software to quantify or graph data from ATDs. ispartof: Perspectives on Behavior Science vol:45 issue:1 pages:1-36 ispartof: location:Switzerland status: Published online

Consistency is one of the crucial single-case data aspects that are expected to be assessed visually, when evaluating the presence of an intervention effect. Complementarily to visual inspection, there have been recent proposals for quantifying the consistency of data patterns in similar phases and the consistency of effects for reversal, multiple-baseline, and changing criterion designs. The current text continues this line of research by focusing on alternation designs using block randomization. Specifically, three types of consistency are discussed: consistency of superiority of one condition over another, consistency of the average level across blocks, and consistency in the magnitude of the effect across blocks. The focus is put especially on the latter type of consistency, which is quantified on the basis of partitioning the variance, as attributed to the intervention, to the blocking factor or remaining as residual (including the interaction between the intervention and the blocks). Several illustrations with real and fictitious data are provided in order to make clear the meaning of the quantification proposed. Moreover, specific graphical representations are recommend for complementing the numerical assessment of consistency. A freely available user-friendly webpage is developed for implementing the proposal.

With the objective of increasing the magnitude of treatment effects in behavioral health, there is steadily growing interest in tailoring assessments and interventions to better match individual needs. This aligns with the central idea that behavior can be adequately understood by considering the unique characteristics of the individual and context. Thus, data collected at an individual level provides critical evidence that can be used to inform health care decisions, improve treatment, or refine theories. Yet, the majority of research in behavioral health is based on group-level analyses. Recent developments in the field of single-case experimental design (SCED) has provided new opportunities to utilize individual data. The present article provides a state-of-the art overview regarding key aspects of SCED, including a historical background to why and how SCED emerged, declined, and recently reemerged as well as methodological aspects such as design issues, challenges related to reliability and validity of repeated observations, innovations in visual and statistical analyses of individual data, strategies to deal with missing values, methodology to examine effect size, and approaches to summarize data from a large number of SCEDs using multilevel models and meta-analyses of replication data. Finally, the article discusses key concerns and actions needed to move the field forward.

IntroductionChronic pain affects about 20%–40% of the population and is linked to mental health outcomes and impaired daily functioning. Pharmacological interventions are commonly insufficient for producing relief and recovery of functioning. Behavioural health treatment is key to generate lasting benefits across outcome domains. However, most people with chronic pain cannot easily access evidence-based behavioural interventions. The overall aim of the DAHLIA project is to develop, evaluate and implement a widely accessible digital behavioural health treatment to improve well-being in individuals with chronic pain.Methods and analysisThe project follows the four phases of the mHealth Agile Development and Evaluation Lifecycle: (1) development and pre-implementation surveillance using focus groups, stakeholder interviews and a business model; (2) iterative optimisation studies applying single case experimental design (SCED) method in 4–6 iterations with n=10 patients and their healthcare professionals per iteration; (3) a two-armed clinical randomised controlled trial enhanced with SCED (n=180 patients per arm) and (4) interview-based post-market surveillance. Data analyses include multilevel modelling, cost-utility and indicative analyses.In October 2021, inter-sectorial partners are engaged and funding is secured for four years. The treatment content is compiled and the first treatment prototype is in preparation. Clinical sites in three Swedish regions are informed and recruitment for phase 1 will start in autumn 2021. To facilitate long-term impact and accessibility, the treatment will be integrated into a Swedish health platform (www.1177.se), which is used on a national level as a hub for advice, information, guidance and e-services for health and healthcare.Ethics and disseminationThe study plan has been reviewed and approved by Swedish ethical review authorities. Findings will be actively disseminated through peer-reviewed journals, conference presentations, social media and outreach activities for the wider public.Trial registration numberNCT05066087.

In meta-analysis, primary studies often include multiple, dependent effect sizes. Several methods address this dependency, such as the multivariate approach, three-level models, and the robust variance estimation (RVE) method. As for today, most simulation studies that explore the performance of these methods have focused on the estimation of the overall effect size. However, researchers are sometimes interested in obtaining separate effect size estimates for different types of outcomes. A recent simulation study (Park & Beretvas, 2019) has compared the performance of the three-level approach and the RVE method in estimating outcome-specific effects when several effect sizes are reported for different types of outcomes within studies. The goal of this paper is to extend that study by incorporating additional simulation conditions and by exploring the performance of additional models, such as the multivariate model, a three-level model that specifies different study-effects for each type of outcome, a three-level model that specifies a common study-effect for all outcomes, and separate three-level models for each type of outcome. Additionally, we also tested whether the a posteriori application of the RV correction improves the standard error estimates and the 95% confidence intervals. Results show that the application of separate three-level models for each type of outcome is the only approach that consistently gives adequate standard error estimates. Also, the a posteriori application of the RV correction results in correct 95% confidence intervals in all models, even if they are misspecified, meaning that Type I error rate is adequate when the RV correction is implemented. ispartof: BEHAVIOR RESEARCH METHODS vol:53 issue:2 pages:702-717 ispartof: location:United States status: published

Previous research has shown that early numerical abilities are predictive of later mathematical achievement. In line with these previous studies, we investigated whether early numerical abilities a...