Cognitive Orientation to daily Occupational Performance to improve occupational performance goals for children with executive function deficits after acquired brain injury.

AIM: To determine the effectiveness of the Cognitive Orientation to daily Occupational Performance (CO-OP) approach in improving the occupational performance goals of children and young people with executive function deficits after acquired brain injury (ABI) (e.g. etiologies such as stroke, encephalitis, brain tumor, and traumatic brain injury). METHOD: A replicated single-case experimental study using a randomized multiple baseline design across participants and goals was used. Three clusters of four participants (12 participants, nine males and three females, aged 8-16 years) were included. The intervention consisted of 14 individual CO-OP sessions. Each participant chose four goals; three goals were trained during the intervention sessions and a fourth goal served as the control. The Goal Attainment Scale (GAS) was used as a repeated measure to determine goal achievement while the Canadian Occupational Performance Measure (COPM) was used to identify the perceived goal achievement of children, young people, and their parents. RESULTS: For 26 of the 35 trained goals, the intervention led to statistically significant improvements in the GAS. Perceived occupational performance and satisfaction improved significantly for the trained goals (30 out of 35 goals for the COPM performance and satisfaction of participants; 26 out 31 goals for the COPM performance of parents; 24 out of 31 goals for the COPM satisfaction of parents) and were maintained at the follow-up. Almost all COPM control goal results were significant, but these changes were not supported by the GAS measures or the statistical analysis. INTERPRETATION: The generally positive results of this study provide evidence of the benefits of using the CO-OP approach with this population.

How to assess and take into account trend in single-case experimental design data.

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend and especially when dealing with improving baseline trend: (a) What options exist for assessing the presence of trend?; (b) Once assessed, what criterion can be followed for deciding whether it is necessary to control for baseline trend?; (c) What strategy can be followed for controlling for baseline trend?; and (d) How to proceed in case there is baseline trend only in some A-B comparisons? Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.Trial registration: ClinicalTrials.gov identifier: NCT04560777.

A practitioner's guide to conducting and analysing embedded randomized single-case experimental designs.

Single-case experimental designs (SCEDs) are a class of experimental designs suited for answering research questions at an individual level. The main designs available in SCED research are phase designs, multiple baseline designs, alternation designs, and changing criterion designs. Embedded designs, also referred to as combination or hybrid designs, consist of one of these basic designs forms embedded in another design (e.g., a changing criterion design embedded in a multiple baseline design). Systematic reviews of SCEDs have repeatedly indicated that embedded designs are frequently used in applied SCED research. In spite of their popularity, specific recommendations on the conduct and analysis of embedded SCED designs are lacking to date. The purpose of the present article is therefore to provide guidance to applied researchers wishing to conduct embedded SCED designs in terms of design options, design requirements, randomization, and data analysis.

Does the choice of a linear trend-assessment technique matter in the context of single-case data?

Trend is one of the data aspects that is an object of assessment in the context of single-case experimental designs. This assessment can be performed both visually and quantitatively. Given that trend, just like other relevant data features such as level, immediacy, or overlap does not have a single operative definition, a comparison among the existing alternatives is necessary. Previous studies have included illustrations of differences between trend-line fitting techniques using real data. In the current study, I carry out a simulation to study the degree to which different trend-line fitting techniques lead to different degrees of bias, mean square error, and statistical power for a variety of quantifications that entail trend lines. The simulation involves generating both continuous and count data, for several phase lengths, degrees of autocorrelation, and effect sizes (change in level and change in slope). The results suggest that, in general, ordinary least squares estimation performs well in terms of relative bias and mean square error. Especially, a quantification of slope change is associated with better statistical results than quantifying an average difference between conditions on the basis of a projected baseline trend. In contrast, the performance of the split-middle (bisplit) technique is less than optimal.

Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework.

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, researchers are confronted with delayed, gradual, and temporary treatment effects; in general, with "response functions" that are markedly different from single-step functions. We here introduce a general framework that allows specifying a test statistic for a randomization test based on predicted response functions that is sensitive to a wide variety of data patterns beyond immediate and sustained changes in level: different latencies (degrees of delay) of effect, abrupt versus gradual effects, and 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, justifying an exploratory approach studying the effect of specifying different latencies or delayed effects and different durations for temporary effects. We provide illustrations of the proposal with real data, and we present a user-friendly freely available web application implementing it.

Type I error rates and power of two randomization test procedures for the changing criterion design.

Single-case experimental design (SCED) data can be analyzed following different approaches. One of the first historically proposed options is randomizations tests, benefiting from the inclusion of randomization in the design: a desirable methodological feature. Randomization tests have become more feasible with the availability of computational resources, and such tests have been proposed for all major types of SCEDs: multiple-baseline, reversal/withdrawal, alternating treatments, and changing criterion designs. The focus of the current text is on the last of these, given that they have not been the subject of any previous simulation study. Specifically, we estimate type I error rates and statistical power for two different randomization procedures applicable to changing criterion designs: the phase change moment randomization and the blocked alternating criterion randomization. We include different series lengths, number of phases, levels of autocorrelation, and random variability. The results suggest that type I error rates are generally controlled and that sufficient power can be achieved with as few as 28-30 measurements for independent data, although more measurements are needed in case of positive autocorrelation. The presence of a reversal to a previous criterion level is beneficial. R code is provided for carrying out randomization tests following the two randomization procedures.

Tailoring Cognitive Behavioral Therapy for Depression and Anxiety Symptoms in Mexican Terminal Cancer Patients: A Multiple Baseline Study.

The purpose of this study was to assess the effect of tailored cognitive behavioral therapy (CBT) on depression and anxiety symptoms present in Mexican terminal cancer patients. A non-concurrent multiple baseline design was used across individuals. Nine patients participated in the study, each receiving four to six therapy sessions. The effect size of the intervention range (NAP and Tau indexes) in the nine patients indicates that CBT intervention resulted in weak to moderate impact for anxiety and depression symptoms in this population. The overall standardized mean difference is also moderate, with a reduction of 0.54 and 0.76 standard deviations in depression and anxiety symptoms, respectively. This study provides initial evidence to support a positive effect from CBT on patients with terminal cancer and with mood disorders when facing their impending death.

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.

Reducing anger outbursts after a severe TBI: a single-case study.

Anger outbursts constitute a frequent behavioural issue after a traumatic brain injury (TBI) and have a strong negative impact on the social outcomes resulting from the TBI. However, few studies have examined the efficacy of specific intervention strategies to reduce the frequency and intensity of anger outbursts. We therefore performed a single-case study on this topic by administering two successive and complementary psychological interventions with an AB design with maintenance (first intervention) and an AC design with maintenance plus a one-month follow-up (second intervention) to a patient with a severe TBI. Whereas the first intervention focused on improving the recognition and expression of basic emotions, the second consisted of a self-regulation programme, including various features such as psychoeducation about self-control strategies, relaxation and assertiveness training that aimed to establish adequate behaviours, which were further promoted by an implementation intentions strategy in the patient's daily life. The results indicated that all interventions resulted in a reduced frequency and intensity of anger outbursts, and the data upheld the specificity of these effects. In addition, a meta-analytic integration of the effects of both interventions on the outcomes indicated a medium effect size. Further research is needed on other patients who experience long-standing anger outbursts to examine whether the observed gains can be replicated, sustained on a longer-term basis and improved.

The use of a smartwatch as a prompting device for people with acquired brain injury: a single case experimental design study.

Prompting-based memory compensation is a potential application for smartwatches. This study investigated the usability and efficacy of a Moto360 smartwatch as a memory aid. Four community dwelling adults with memory difficulties following acquired brain injury (ABI) were included in an A-B-A single case experimental design study. Performance of everyday memory tasks was tested over six weeks with the smartwatch and software provided during weeks three and four. Participants were asked to use their usual memory aids and strategies during the control phases (weeks 1-2, 5-6). Three participants successfully used the smartwatch throughout the intervention weeks and gave positive usability ratings. A fourth participant experienced a seizure and subsequently left the study before the intervention phase. Three participants showed improved memory performance when using the smartwatch. Nonoverlap of all pairs (NAP) analysis showed a non-significant small increase in memory performance between baseline and intervention phases (mean NAP = 0.1, p = .84). There was a larger, significant decline between the intervention and return to baseline (mean NAP = 0.58, p < .01). The use of an off-the-shelf smartwatch device and software was feasible for people with ABI in the community. It was effective compared to practice as usual, although this was only apparent on withdrawal of the device.

Extrapolating baseline trend in single-case data: Problems and tentative solutions.

Single-case data often contain trends. Accordingly, to account for baseline trend, several data-analytical techniques extrapolate it into the subsequent intervention phase. Such extrapolation led to forecasts that were smaller than the minimal possible value in 40% of the studies published in 2015 that we reviewed. To avoid impossible predicted values, we propose extrapolating a damping trend, when necessary. Furthermore, we propose a criterion for determining whether extrapolation is warranted and, if so, how far out it is justified to extrapolate a baseline trend. This criterion is based on the baseline phase length and the goodness of fit of the trend line to the data. These proposals were implemented in a modified version of an analytical technique called Mean phase difference. We used both real and generated data to illustrate how unjustified extrapolations may lead to inappropriate quantifications of effect, whereas our proposals help avoid these issues. The new techniques are implemented in a user-friendly website via the Shiny application, offering both graphical and numerical information. Finally, we point to an alternative not requiring either trend line fitting or extrapolation.

Can an external device create and trigger intention in a patient with a severe brain injury?

Goal-directed behaviour (GDB), the capacity that allows us to control our environment according to our desires and needs, requires different stages from initial intention to goal achievement. Although GDB is frequently disrupted after acquired brain injury, few studies have addressed the remediation of reduced GDB in patients with severe brain injury. The present study aimed to raise this question in RZ, a patient with severe brain injury who presented a serious reduction in GDB related to difficulties in creating an intention from internal determinants and in selecting an action plan, as well as to memory and attentional impairments. Our objective was to investigate the efficacy of an intervention programme to create and trigger a specific intention in RZ. More specifically, this programme consisted in written prompts, first accompanied by an alarm provided by his mobile phone, but gradually reduced until only the alarm remained, which was implemented in order to improve RZ's attendance at workshop sessions. Results showed that RZ's attendance rate at the workshop sessions increased, indicating that the use of an external device allowed us to create and trigger intention in a patient with severe cognitive impairments.

Efficacy of metacognitive therapy in improving mental health: A meta-analysis of single-case studies.

OBJECTIVE: Metacognitive therapy and one of its treatment components, the attention training technique, are increasingly being delivered to improve mental health. We examined the efficacy of metacognitive therapy and/or attention training technique on mental health outcomes from single-case studies. METHOD: A total of 14 studies (53 patients) were included. We used the d-statistic for multiple baseline data and the percentage change index to compute the effect sizes. RESULTS: Metacognitive therapy has a large effect on depression, anxiety, other psychopathological symptoms, and all outcomes together. Effect sizes were significantly moderated by the number of sessions, the severity and duration of symptoms, and patient gender, but not by study quality or attention training technique when used as a stand-alone treatment. At the follow-up, 77.36% of the individuals were considered recovered or had maintained improvement. CONCLUSION: Metacognitive therapy and attention training technique strongly contribute to improving mental health outcomes. This study effectively informs evidence-based practice in the clinical milieu.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement.

We developed a reporting guideline to provide authors with guidance about what should be reported when writing a paper for publication in a scientific journal using a particular type of research design: the single-case experimental design. This report describes the methods used to develop the Single-Case Reporting guideline In BEhavioural interventions (SCRIBE) 2016. As a result of 2 online surveys and a 2-day meeting of experts, the SCRIBE 2016 checklist was developed, which is a set of 26 items that authors need to address when writing about single-case research. This article complements the more detailed SCRIBE 2016 Explanation and Elaboration article (Tate et al., 2016 ) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated. We recommend that the SCRIBE 2016 is used by authors preparing manuscripts describing single-case research for publication, as well as journal reviewers and editors who are evaluating such manuscripts. SCIENTIFIC ABSTRACT Reporting guidelines, such as the Consolidated Standards of Reporting Trials (CONSORT) Statement, improve the reporting of research in the medical literature (Turner et al., 2012 ). Many such guidelines exist and the CONSORT Extension to Nonpharmacological Trials (Boutron et al., 2008 ) provides suitable guidance for reporting between-groups intervention studies in the behavioural sciences. The CONSORT Extension for N-of-1 Trials (CENT 2015) was developed for multiple crossover trials with single individuals in the medical sciences (Shamseer et al., 2015 ; Vohra et al., 2015 ), but there is no reporting guideline in the CONSORT tradition for single-case research used in the behavioural sciences. We developed the Single-Case Reporting guideline In BEhavioural interventions (SCRIBE) 2016 to meet this need. This Statement article describes the methodology of the development of the SCRIBE 2016, along with the outcome of 2 Delphi surveys and a consensus meeting of experts. We present the resulting 26-item SCRIBE 2016 checklist. The article complements the more detailed SCRIBE 2016 Explanation and Elaboration article (Tate et al., 2016 ) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated.

The Single-Case Reporting Guideline In BEhavioural Interventions (SCRIBE) 2016 Statement.

Reporting guidelines, such as the Consolidated Standards of Reporting Trials (CONSORT) Statement, improve the reporting of research in the medical literature (Turner et al., 2012). Many such guidelines exist, and the CONSORT Extension to Nonpharmacological Trials (Boutron et al., 2008) provides suitable guidance for reporting between-groups intervention studies in the behavioral sciences. The CONSORT Extension for N-of-1 Trials (CENT 2015) was developed for multiple crossover trials with single individuals in the medical sciences (Shamseer et al., 2015; Vohra et al., 2015), but there is no reporting guideline in the CONSORT tradition for single-case research used in the behavioral sciences. We developed the Single-Case Reporting guideline In Behavioral interventions (SCRIBE) 2016 to meet this need. This Statement article describes the methodology of the development of the SCRIBE 2016, along with the outcome of 2 Delphi surveys and a consensus meeting of experts. We present the resulting 26-item SCRIBE 2016 checklist. The article complements the more detailed SCRIBE 2016 Explanation and Elaboration article (Tate et al., 2016) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated.

Further developments in summarising and meta-analysing single-case data: An illustration with neurobehavioural interventions in acquired brain injury.

Data analysis for single-case designs is an issue that has prompted many researchers to propose a variety of alternatives, including use of randomisation tests, regression-based procedures, and standardised mean difference. Another option consists in computing unstandardised or raw differences between conditions: the changes in slope and in level, or the difference between the projected baseline (including trend) and the actual treatment phase measurements. Apart from the strengths of these procedures (potentially easier interpretation clinically, separate estimations and an overall quantification of effects, reasonable performance), they require further development, such as (a) creating extensions for dealing with methodologically strong designs such as multiple baseline, (b) achieving comparability across studies and making possible meta-analytical integrations, and (c) implementing software for the extensions. The proposals are illustrated herein in the context of a meta-analysis of 28 studies on (neuro)behavioural interventions in adults who have challenging behaviours after acquired brain injury.

Single case experimental designs: introduction to a special issue of Neuropsychological Rehabilitation.

This paper introduces the Special Issue of Neuropsychological Rehabilitation on Single Case Experimental Design (SCED) methodology. SCED studies have a long history of use in evaluating behavioural and psychological interventions, but in recent years there has been a resurgence of interest in SCED methodology, driven in part by the development of standards for conducting and reporting SCED studies. Although there is consensus on some aspects of SCED methodology, the question of how SCED data should be analysed remains unresolved. This Special Issues includes two papers discussing aspects of conducting SCED studies, five papers illustrating use of SCED methodology in clinical practice, and nine papers that present different methods of SCED data analysis. A final Discussion paper summarises points of agreement, highlights areas where further clarity is needed, and ends with a set of resources that will assist researchers conduct and analyse SCED studies.

Single-case experimental designs: reflections on conduct and analysis.

In this editorial discussion we reflect on the issues addressed by, and arising from, the papers in this special issue on Single-Case Experimental Design (SCED) study methodology. We identify areas of consensus and disagreement regarding the conduct and analysis of SCED studies. Despite the long history of application of SCEDs in studies of interventions in clinical and educational settings, the field is still developing. There is an emerging consensus on methodological quality criteria for many aspects of SCEDs, but disagreement on what are the most appropriate methods of SCED data analysis. Our aim is to stimulate this ongoing debate and highlight issues requiring further attention from applied researchers and methodologists. In addition we offer tentative criteria to support decision-making in relation to the selection of analytical techniques in SCED studies. Finally, we stress that large-scale interdisciplinary collaborations, such as the current Special Issue, are necessary if SCEDs are going to play a significant role in the development of the evidence base for clinical practice.

Weighting strategies in the meta-analysis of single-case studies.

Establishing the evidence base of interventions taking place in areas such as psychology and special education is one of the research aims of single-case designs, in conjunction with the aim of improving the well-being of participants in the studies. The scientific criteria for solid evidence focus on the internal and external validity of the studies, and for both types of validity, replicating studies and integrating the results of these replications (i.e., meta-analyzing) is crucial. In the present study, we deal with one of the aspects of meta-analysis-namely, the weighting strategy used when computing an average effect size across studies. Several weighting strategies suggested for single-case designs are discussed and compared in the context of both simulated and real-life data. The results indicated that there are no major differences between the strategies, and thus, we consider that it is important to choose weights with a sound statistical and methodological basis, while scientific parsimony is another relevant criterion. More empirical research and conceptual discussion are warranted regarding the optimal weighting strategy in single-case designs, alongside investigation of the optimal effect size measure in these types of designs.

Assigning and combining probabilities in single-case studies: a second study.

The present study builds on a previous proposal for assigning probabilities to the outcomes computed using different primary indicators in single-case studies. These probabilities are obtained by comparing the outcome to previously tabulated reference values, and they reflect the likelihood of the results in the case that no intervention effect is present. In the present study, we explored how well different metrics are translated into p values in the context of simulation data. Furthermore, two published multiple-baseline data sets were used to illustrate how well the probabilities might reflect the intervention effectiveness, as assessed by the original authors. Finally, the importance of which primary indicator would be used in each data set to be integrated was explored; two ways of combining probabilities were used: a weighted average and a binomial test. The results indicated that the translation into p values worked well for the two nonoverlap procedures, with the results for the regression-based procedure diverging due to some undesirable features of its performance. These p values, when either taken individually or combined, were well aligned with effectiveness for the real-life data. These results suggest that assigning probabilities can be useful for translating the primary measure into the same metric, using these probabilities as additional evidence of the importance of behavioral change, complementing visual analysis and professionals' judgments.