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.

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.

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.

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.

Physical and mental health impact of the COVID-19 pandemic at first year in a Spanish adult cohort.

The COVID-19 pandemic and the political and health measures have profoundly affected the health of our populations. However, very few studies have been published assessing its impact using a prospective cohort. The aim of this study is to describe the impact on physical and mental health due to the COVID-19 pandemic in the general population in Spain, and according to COVID-19 clinical status, during the first year of the pandemic. A longitudinal cohort study with two online surveys were performed on a representative sample of the adult Spanish population before (N = 2005, October/November 2019) and during the pandemic (N = 1357, November/December 2020). We assessed disability using the World Health Organisation Disability Assessment Schedule (WHODAS), major depressive episode (MDE) and suicidal thoughts and behaviours (STB), using an adapted version of the Composite International Diagnostic Interview (CIDI 3.0); generalised anxiety disorder (GAD) using the GAD-7 scale; post-traumatic stress disorder (PTSD) symptoms using the PTSD checklist for DSM-5 (PCL-5). For physical health, there was a statistically significant loss of weight (mean/SD) (T0, 73.22/15.56 vs. T1, 71.21/11.94), less use of tobacco (T0, 11.4% vs. T1, 9.0%) and decreased disability (mean/SD) (T0, 21.52/9.22 vs. T1, 19.03/7.32). For mental health, there was a significant increase in MDE (T0, 6.5% vs. T1, 8.8%) and in the prevalence of GAD (T0, 13.7% vs. T1, 17.7%). The prevalence of STB (T0, 15.1% vs. T1, 7.1%) significantly decreased. Individuals who declared they had been diagnosed with COVID-19 (3.6%) showed a worsening in physical health and an increase in mental health problems and PTSD symptoms. Although suicide risk during the first year of the pandemic was significantly less, many suicide risk factors increased: such as the incidence and persistence of MDE and GAD, the presence of PTSD symptoms in those diagnosed with COVID-19, and a worsening in self-assessed health status. We expect an increase in STB in the population in the long-term. Future research should gather information about the long-term impact of the pandemic.

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.

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.

A Visual Aid and Objective Rule Encompassing the Data Features of Visual Analysis.

Visual analysis of single-case research is commonly described as a gold standard, but it is often unreliable. Thus, an objective tool for applying visual analysis is necessary, as an alternative to the Conservative Dual Criterion, which presents some drawbacks. The proposed free web-based tool enables assessing change in trend and level between two adjacent phases, while taking data variability into account. The application of the tool results in (a) a dichotomous decision regarding the presence or absence of an immediate effect, a progressive or delayed effect, or an overall effect and (b) a quantification of overlap. The proposal is evaluated by applying it to both real and simulated data, obtaining favorable results. The visual aid and the objective rules are expected to make visual analysis more consistent, but they are not intended as a substitute for the analysts' judgment, as a formal test of statistical significance, or as a tool for assessing social validity.

Compassion focused therapy for self-stigma and shame in autism: a single case pre-experimental study.

Introduction: Exposure to public stigma can lead to the internalization of autism-related stigma (i.e., self-stigma), associated with negative health, occupational and social outcomes. Importantly, self-stigma is linked to shame and social isolation. Although elevated self-stigma has been reported in autistic adults, to the best of our knowledge, interventions designed to target this issue are lacking. Compassion is an effective way to reduce the emotional correlates of self-stigma (i.e., shame) and their impacts on mental health. However, no study has investigated whether compassion focused therapy (CFT) can effectively reduce self-stigma in autistic adults. The present study aims at investigating whether and how self-compassion improvement following CFT may reduce self-stigma and shame in an autistic individual. Methods: A single case pre-experimental design (SCED) was used with weekly repeated measures during four phases: (i) pure baseline without any intervention (A), (ii) case conceptualization (A'), (iii) intervention (B) where CFT was delivered, (iv) follow-up without intervention (FU). The participant is a 46-year-old autistic man with high self-stigma and shame. Self-report measures of self-compassion and self-stigma and a daily idiographic measure of shame were used. Results: There was a large increase in self-compassion between pure baseline (A) and the intervention phase (A'B) (Tau-U = 0.99), maintained at follow-up. Similarly, there was a moderate decrease of self-stigma (Tau-U = 0.32). In contrast, when we compared the whole baseline phase AA' (i.e., considering the conceptualisation phase as baseline) to the intervention (B), there was no change in self-stigma (Tau-U = -0.09). There was no change in self-stigma between the intervention (B) and follow-up (Tau-U = -0.19). There was a moderate decrease in daily shame reports between the baseline (AA') and the intervention (B) (Tau-U = 0.31) and a moderate decrease between the pure baseline (A) and intervention phase (A'B) (Tau-U = 0.51). Conclusion: CFT was feasible for this autistic client and our results show that CFT led to the improvement of self-compassion. Changes on self-stigma measures were moderate. Self-stigma may need more time to change. Because self-stigma is involved in poorer social functioning and mental health in autistic adults, our results are promising and suggesting conducting more large-scale studies on CFT in autistic adults.

Low Resilience Was a Risk Factor of Mental Health Problems during the COVID-19 Pandemic but Not in Individuals Exposed to COVID-19: A Cohort Study in Spanish Adult General Population.

BACKGROUND: The aim is to analyze whether people with low resilience are at higher risk of mental health problems during the COVID-19 pandemic in Spanish adults. METHODS: a longitudinal cohort study was carried out. Resilience was measured with the CD-RISC. Mental health problems that were assessed included: Major Depressive Episode (MDE), Generalized Anxiety Disorder (GAD), Suicidal Thoughts and Behaviors (STB), and Posttraumatic Stress Disorder (PTSD) symptoms. RESULTS: we found statistically significant differences between groups and resilience scores in MDE [F (3; 48.40) = 19.55], GAD [F (3; 19.63) = 6.45] and STB [F (3; 111.74) = 31.94]. Multivariable analyses showed individuals with very low resilience were at a 5-fold risk of Incidence of MDE and a 4-fold risk of STB. Persistent group presented a 21-fold risk of MDE and 54-fold risk of STB. No evidence of higher risk was found for GAD. Individuals with low resilience and exposed to COVID-19 were not at higher risk. Individuals with low resilience were at higher risk of PTSD in general population [ß(95% CI) = -3.25 (-3.969 to -2.54)], but not for individuals with COVID-19. CONCLUSIONS: in the general population, having low or very low resilience increases the risk of suffering MDE, STB, and PTSD, but not GAD during the COVID-19 pandemic, and not in the population with COVID-19.

Defining and assessing immediacy in single-case experimental designs.

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.

A proposal for the assessment of replication of effects in single-case experimental designs.

In science in general and in the context of single-case experimental designs, replication of the effects of the intervention within and/or across participants or experiments is crucial for establishing causality and for assessing the generality of the intervention effect. Specific developments and proposals for assessing whether an effect has been replicated or not (or to what extent) are scarce, in the general context of behavioral sciences, and practically null in the single-case experimental designs context. We propose an extension of the modified Brinley plot for assessing how many of the effects replicate. To make this assessment possible, a definition of replication is suggested, on the basis of expert judgment, rather than on statistical criteria. The definition of replication and its graphical representation are justified, presenting their strengths and limitations, and illustrated with real data. A user-friendly software is made available for obtaining automatically the graphical representation.

A Priori Justification for Effect Measures in Single-Case Experimental Designs.

Due to the complex nature of single-case experimental design data, numerous effect measures are available to quantify and evaluate the effectiveness of an intervention. An inappropriate choice of the effect measure can result in a misrepresentation of the intervention effectiveness and this can have far-reaching implications for theory, practice, and policymaking. As guidelines for reporting appropriate justification for selecting an effect measure are missing, the first aim is to identify the relevant dimensions for effect measure selection and justification prior to data gathering. The second aim is to use these dimensions to construct a user-friendly flowchart or decision tree guiding applied researchers in this process. The use of the flowchart is illustrated in the context of a preregistered protocol. This is the first study that attempts to propose reporting guidelines to justify the effect measure choice, before collecting the data, to avoid selective reporting of the largest quantifications of an effect. A proper justification, less prone to confirmation bias, and transparent and explicit reporting can enhance the credibility of the single-case design study findings.

Quantitative Techniques and Graphical Representations for Interpreting Results from Alternating Treatment Design.

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.

Socio-economic and psychological impact of COVID-19 pandemic in a Spanish cohort BIOVAL-D-COVID-19 study protocol / Impacto socioeconómico y psicológico de la pandemia de COVID-19 en una cohorte española protocolo del estudio BIOVAL-D-COVID-19

Objective: SARS-CoV-2 outbreak has a negative psychological impact among general population. Data comparing mental health status before and during the outbreak is needed. The BIOVAL-D-COVID-19 study assess the socio-economic and psychological impact of the COVID-19 pandemic and lockdown in a representative sample of non-institutionalized Spanish adult population, and estimate the incidence of mental health disorders, including suicidal behaviours, and possible related factors. Method: Observational longitudinal study including two online surveys: baseline survey (T0) performed during 2019 and follow-up survey (T1) conducted 12-month later. The latter included nine sections: socio-demographic, health status, mental health, employment conditions and status, material deprivation, use of healthcare services, intimate partner violence and resilience. Four of the nine sections are administered in T0 and T1 assessments. Longitudinal data analyses will estimate adjusted incidence rates of mental health disorders using Poisson regression models. Risk and protective factors will be analysed through multiple logistic regression models. (AU)

Objetivo: el estudio BIOVAL-D-COVID-19 evaluará el impacto socioeconómico y psicológico de la pandemia de COVID-19 y del confinamiento en una muestra representativa de población española adulta no institucionalizada, y estimará la incidencia de trastornos de salud mental, incluida la conducta suicida, y los posibles factores relacionados. Métodos: estudio longitudinal que incluye dos encuestas online: la encuesta basal (T0) realizada durante 2019 y la encuesta de seguimiento (T1) realizada 12 meses después. Esta última tiene nueve secciones: variables sociodemográficas, estado de salud general, salud mental, condiciones laborales y estatus laboral, privación material, uso de servicios de salud, violencia de pareja y resiliencia. Cuatro de las nueve secciones se administran en ambas encuestas (T0 y T1). Se utilizarán modelos de regresión de Poisson para el análisis longitudinal de las tasas de incidencia ajustadas de trastornos de salud mental. Los factores de riesgo y de protección se analizarán mediante modelos de regresión logística múltiple. (AU)

Socio-economic and psychological impact of COVID-19 pandemic in a Spanish cohort BIOVAL-D-COVID-19 study protocol.

OBJECTIVE: SARS-CoV-2 outbreak has a negative psychological impact among general population. Data comparing mental health status before and during the outbreak is needed. The BIOVAL-D-COVID-19 study assess the socio-economic and psychological impact of the COVID-19 pandemic and lockdown in a representative sample of non-institutionalized Spanish adult population, and estimate the incidence of mental health disorders, including suicidal behaviours, and possible related factors. METHOD: Observational longitudinal study including two online surveys: baseline survey (T0) performed during 2019 and follow-up survey (T1) conducted 12-month later. The latter included nine sections: socio-demographic, health status, mental health, employment conditions and status, material deprivation, use of healthcare services, intimate partner violence and resilience. Four of the nine sections are administered in T0 and T1 assessments. Longitudinal data analyses will estimate adjusted incidence rates of mental health disorders using Poisson regression models. Risk and protective factors will be analysed through multiple logistic regression models.

Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots.

The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications.

The Assessment of Consistency in Single-Case Experiments: Beyond A-B-A-B Designs.

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.

Assessing Consistency in Single-Case Alternation Designs.

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.