PROTOCOL: Key characteristics of effective preschool-based interventions to promote self-regulation: A systematic review and meta-analysis.

This is the protocol for a Cochrane Review. The objectives are as follows: The aim of this systematic review is to advance our understanding of the key characteristics of effective preschool-based interventions designed to foster self-regulation. To accomplish this, the review addresses the following questions: 1. What types of preschool-based interventions have been developed to promote self-regulation? 2. What is the average effect of these preschool-based interventions on self-regulation, focusing on four key constructs: integrative effortful control, integrative executive function, self-regulation, and self-regulated learning? 3. What characteristics-such as Resource Allocation, Activity Type, and Instruction Method-could potentially contribute to the effects of preschool-based interventions in promoting self-regulation?

The effect of eye movement desensitization and reprocessing on fibromyalgia: A multiple-baseline experimental case study across ten participants.

Fibromyalgia (FM) is a chronic pain disorder characterized by widespread musculoskeletal pain, fatigue, and stiffness in muscles and joints. Traumatic life experiences and post-traumatic stress symptoms play a role in its development and persistence. Although previous research suggests that pain and FM symptoms decrease after eye movement desensitization and reprocessing (EMDR) therapy, its effectiveness has not been investigated in a controlled manner. The present study investigated the effectiveness of a six-session, 90-minute EMDR therapy using a multiple baseline single-case experimental design (SCED) across ten adult females with FM. The SCED involved a baseline, intervention, one- and three-month follow-up phase. The primary outcome was pain. Secondary outcomes included post-traumatic stress symptoms, other FM symptoms (fatigue, stiffness in muscles and joints), and the impact of FM on daily activities and sleep. Data were statistically analyzed by primarily testing means across phases on an individual and group level. Post-traumatic stress symptoms improved significantly in seven participants. Pain severity decreased significantly in six participants, with three of them maintaining significant improvement three months later. One participant showed clinically relevant change one month later. Furthermore, improvements were observed in secondary outcome measures. The findings support the efficacy of EMDR in reducing FM symptoms.

Outcomes of Warfarin Home INR Monitoring vs Office-Based Monitoring: a Retrospective Claims-Based Analysis.

BACKGROUND: Home INR testing (patient self-testing) is feasible and effective for warfarin patients but little is known about real-world differences in outcomes for patients using PST versus laboratory-based INR monitoring. OBJECTIVE: To compare the safety/efficacy of patient self-testing of real-world warfarin therapy versus office/lab-based monitoring of therapy. DESIGN/SETTING/PARTICIPANTS/EXPOSURE: A retrospective claims-based analysis of warfarin patients enrolled in the MarketScan® Commercial Claims and Encounters and Medicare databases between January 1, 2013, and March 30, 2020. Stratification was based on INR testing method: patient self-testing versus testing at physicians' offices/local laboratory. The probability of adverse events in each cohort was determined after adjusting for demographic and baseline clinical characteristics using a repeated measures analysis. MAIN MEASURES: Rates of all adverse events: deep venous thrombosis, pulmonary embolism, bleeding, and stroke. A secondary outcome of interest was emergency department visits. KEY RESULTS: A total of 37,837 patients were included in the analysis: 1592 patients in the patient self-testing group and 36,245 in the office-based therapy group. After adjusting for demographic and baseline clinical characteristics, patients in the office-based group had statistically significantly higher rates of all adverse events (incidence rate ratio [IRR]=2.07, 95% CI [1.82, 2.36]), and specific adverse events including thromboembolism (IRR=4.38, 95% CI [3.29, 5.84]), major bleed (IRR=1.45, 95% CI [1.28, 1.64]), and stroke (IRR=1.30, 95% CI [1.05, 1.61]) than patients in the patient self-testing group. Office-based patients also had a statistically significant higher rate of emergency department visits than patient self-testing patients (IRR = 1.65, 95% CI [1.47, 1.84]). CONCLUSIONS/RELEVANCE: This analysis of real-world claims data shows lower rates of stroke, thromboembolism, and major bleeding, as well as fewer emergency department visits, with patient self-testing compared to office-based/lab INR monitoring. Our finding that PST is safe and effective among current users suggests that more patients may benefit from its use.

Single-case design meta-analyses in education and psychology: a systematic review of methodology.

Meta-analysis is of increasing importance as this quantitative synthesis technique has the potential to summarize a tremendous amount of research evidence, which can help making evidence-based decisions in policy, practice, and theory. This paper examines the single-case meta-analyses within the Education and Psychology fields. The amount of methodological studies related to the meta-analysis of Single-Case Experimental Designs (SCEDs) is increasing rapidly, especially in these fields. This underscores the necessity of a succinct summary to help methodologists identify areas for further development in Education and Psychology research. It also aids applied researchers and research synthesists in discerning when to use meta-analytic techniques for SCED studies based on criteria such as bias, mean squared error, 95% confidence intervals, Type I error rates, and statistical power. Based on the summary of empirical evidence from 18 reports identified through a systematic search procedure, information related to meta-analytic techniques, data generation and analysis models, design conditions, statistical properties, conditions under which the meta-analytic technique is appropriate, and the study purpose(s) were extracted. The results indicate that three-level hierarchical linear modeling is the most empirically validated SCED meta-analytic technique, and parameter bias is the most prominent statistical property investigated. A large number of primary studies (more than 30) and at least 20 measurement occasions per participant are recommended for usage of SCED meta-analysis in Education and Psychology fields.

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.

Reducing face touching through haptic feedback: A treatment evaluation against fomite-mediated self-infection.

Fomite-mediated self-infection via face touching is an understudied transmission pathway for infectious diseases. We evaluated the effect of computer-mediated vibrotactile cues (presented through experimental bracelets located on one or both hands of the participant) on the frequency of face touching among eight healthy adults in the community. We conducted a treatment evaluation totaling over 25,000 min of video observation. The treatment was evaluated through a multiple-treatment design and hierarchical linear modeling. The one-bracelet intervention did not produce significantly lower levels of face touching across both hands, whereas the two-bracelet intervention did result in significantly lower face touching. The effect increased over repeated presentations of the two-bracelet intervention, with the second implementation producing, on average, 31 fewer face-touching percentual points relative to baseline levels. Dependent on the dynamics of fomite-mediated self-infection via face touching, treatment effects could be of public health significance. The implications for research and practice are discussed.

Characteristics of Moderators in Meta-Analyses of Single-Case Experimental Design Studies.

Hierarchical linear modeling (HLM) has been recommended as a meta-analytic technique for the quantitative synthesis of single-case experimental design (SCED) studies. The HLM approach is flexible and can model a variety of different SCED data complexities, such as intervention heterogeneity. A major advantage of using HLM is that participant and-or study characteristics can be incorporated in the model in an attempt to explain intervention heterogeneity. The inclusion of moderators in the context of meta-analysis of SCED studies did not yet receive attention and is in need of methodological research. Prior to extending methodological work validating the hierarchical linear model including moderators at the different levels, an overview of characteristics of moderators typically encountered in the field is needed. This will inform design conditions to be embedded in future methodological studies and ensure that these conditions are realistic and representative for the field of SCED meta-analyses. This study presents the results of systematic review of SCED meta-analyses, with the particular focus on moderator characteristic. The initial search yielded a total of 910 articles and book chapters. After excluding duplicate studies and non peer-reviewed studies, 658 unique peer-reviewed studies were maintained and screened by two independent researchers. Sixty articles met the inclusion criteria and were eligible for data retrieval. The results of the analysis of moderator characteristics retrieved from these 60 meta-analyses are presented. The first part of the results section contains an overview of moderator characteristics per moderator level (within-participant level, participant level, and study level), including the types of moderators, the ratio of the number of moderators relative to the number of units at that level, the measurement scale, and the degree of missing data. The second part of the results section focuses on the metric used to quantify moderator effectiveness and the analysis approach. Based on the results of the systematic review, recommendations are given for conditions to be included in future methodological work.

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.

The Power to Explain Variability in Intervention Effectiveness in Single-Case Research Using Hierarchical Linear Modeling.

This study investigated the power of two-level hierarchical linear modeling (HLM) to explain variability in intervention effectiveness between participants in context of single-case experimental design (SCED) research. HLM is a flexible technique that allows the inclusion of participant characteristics (e.g., age, gender, and disability types) as moderators, and as such supplements visual analysis findings. First, this study empirically investigated the power to estimate intervention and moderator effects using Monte Carlo simulation techniques. The results indicate that larger values for the true effects and the number of participants resulted in a higher power. The more moderators added to the model, the more participants needed to detect the effects with sufficient power (i.e., power ≥.80). When a model includes three moderators, at least 20 participants are required to capture the intervention effect and moderator effects with sufficient power. For that same condition, but only including one moderator, seven participants are sufficient. Specific recommendations for designing a SCED study with sufficient power to estimate intervention and moderator effects were provided. Second, this study introduced a newly developed user-friendly point and click Shiny tool, PowerSCED. This tool assists applied SCED researchers in designing a SCED study that has sufficient power to detect intervention and moderator effects. To end, the use of HLM with the inclusion of moderators was demonstrated using two previously published SCED studies in the journal School Psychology Quarterly.

Causal Mediation Analysis in Single Case Experimental Designs: Introduction to the Special Issue.

This special issue of Evaluation and the Health Professions is dedicated to methods for causal mediation analysis in Single Case Experimental Designs (SCEDs). Mediation analysis is used to identify intermediate variables that transmit the effect of the independent variable on the outcome. Until recently, mediation analysis was mostly confined to between-subjects designs and panel studies with few exceptions. Consequently, most of the developments in causal mediation analysis have also been restricted to such designs. In applied health research, SCEDs have been used to evaluate total effects of treatments on outcomes of interest. Providing researchers with the methods for evaluating causal indirect effects for individual participants can lead to important improvements in diagnosis, treatment, and prevention. This special issue includes articles that describe advanced quantitative methods for testing mediators in SCEDs, propose and test approaches that allow for relaxing statistical assumptions that may not hold in real data, and illustrate mediation analysis for a single participant in real and simulated SCEDs data.

Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data.

We have entered an era in which scientific knowledge and evidence increasingly inform research practice and policy. As there is an exponential increase in the use of personalized trials, there is a remarkable growing interest in the quantitative synthesis of personalized trials. One technique that is developed and can be applied for this purpose is meta-analysis. Meta-analysis involves the quantitative integration of effect sizes from several personalized trials. In this study, aggregated data (AD) and individual patient data (IPD) methods for meta-analysis of personalized trials are discussed, together with an empirical demonstration using a subset of a real meta-analytic data set. For the empirical demonstration, 26 personalized trials received usual care and yoga intervention in a randomized sequence. Results show a general consensus between the AD and IPD approach in terms of conclusions-that both usual care and the yoga intervention are effective in reducing pain. However, the IPD approach provides more information about the intervention effectiveness and intervention heterogeneity. IPD is a more flexible modeling approach, allowing for a variety of modeling options.

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.

Single case designs for early phase behavioral translational research in health psychology.

OBJECTIVE: The biomedical research community has long recognized that much of the basic research being conducted, whether in the biological, behavioral or social sciences, is not readily translated into clinical and public health applications. This translational gap is due in part to challenges inherent in moving research findings from basic or discovery research to applied research that addresses clinical or public health problems. In the behavioral and social sciences, research designs typically used in the early phases of translational research are small, underpowered "pilot" studies that may lack sufficient statistical power to test the research question of interest. While this approach is discouraged, these studies are often employed to estimate effect sizes before embarking on a larger trial with adequate statistical power to test the research hypothesis. The goal of this paper is to provide an alternative approach to early phase studies using single case designs (SCDs). METHOD: Review basic principles of SCDs; provide a series of hypothetical SCD replication experiments to illustrate (1) how data from SCDs can be analyzed to test the effects of an intervention on behavioral and biological outcomes and (2) how sample sizes can be derived for larger randomized controlled trials (RCTs) based on clinically meaningful effects from SCDs; and review feedback between SCDs and RCTs. RESULTS: The paper illustrates the use of SCD reversal and multiple baseline designs for early phase translational research. CONCLUSION: SCDs provide a flexible and efficient platform for the use of experimental methods in early phase translational research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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).

MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling.

The MultiSCED web application has been developed to assist applied researchers in behavioral sciences to apply multilevel modeling to quantitatively summarize single-case experimental design (SCED) studies through a user-friendly point-and-click interface embedded within R. In this paper, we offer a brief introduction to the application, explaining how to define and estimate the relevant multilevel models and how to interpret the results numerically and graphically. The use of the application is illustrated through a re-analysis of an existing meta-analytic dataset. By guiding applied researchers through MultiSCED, we aim to make use of the multilevel modeling technique for combining SCED data across cases and across studies more comprehensible and accessible.

Evaluating "mentalizing positive affect" as an intervention for enhancing positive affectivity in borderline personality disorder using a single-case multiple-baseline design.

Evidence-based treatments for borderline personality disorder (BPD), including mentalization-based treatment (MBT), have not adequately examined changes in positive affect (PA) in the treatment of BPD. Therefore, we developed a new intervention, "mentalizing positive affect," and evaluated its effect on PA, negative affect, BPD severity, ego-resiliency, and quality of life during MBT treatment for BPD. In a single-case multiple-baseline design, 4 female BPD patients received 6 months of individual MBT, after which they were followed up for 2 months. Intensive repeated measurements data were subjected to hierarchical linear modeling to analyze whether the positive intervention was related to changes in self-reported outcome measures. Our results failed to support a co-occurring increase in the reporting of PA related to the "mentalizing positive affect" intervention. However, the slope of PA increased at a quicker rate after the end of treatment, perhaps indicating a delayed treatment effect. "Mentalizing positive affect" was related to a marginally significant decrease in the mean level of BPD severity compared with standard MBT. Moreover, focusing on PA in MBT seemed feasible for maintaining a good working alliance. Our findings call for more research to test interventions aimed at enhancing PA in the treatment of BPD. Such efforts might well involve treatment of longer duration and higher intensity to increase the number of sessions, as well as longer follow-up periods, than we used. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Meta-Analysis of Single-Case Research via Multilevel Models: Fundamental Concepts and Methodological Considerations.

Multilevel modeling is an approach that can be used to summarize single-case experimental design (SCED) data. Multilevel models were developed to analyze hierarchical structured data with units at a lower level nested within higher level units. SCEDs use time series data collected from multiple cases (or subjects) within a study that allow researchers to investigate intervention effectiveness at the individual level and also to investigate how these individual intervention effects change over time. There is an increased interest in the field regarding how SCEDs can be used to establish an evidence base for interventions by synthesizing data from a series of intervention studies. Although using multilevel models to meta-analyze SCED studies is promising, application is often hampered by being potentially excessively technical. First, this article provides an accessible description and overview of the potential of multilevel meta-analysis to combine SCED data. Second, a summary of the methodological evidence on the performance of multilevel models for meta-analysis is provided, which is useful given that such evidence is currently scattered over multiple technical articles in the literature. Third, the actual steps to perform a multilevel meta-analysis are outlined in a brief practical guide. Fourth, a suggestion for integrating the quantitative results with a visual representation is provided.

Feasibility and Effectiveness of Intervention With the Playskin Lift Exoskeletal Garment for Infants at Risk.

BACKGROUND: Infants born preterm and/or with brain injury often exhibit delays in the development of reaching and object exploration, increasing their risk of associated delays in cognitive development. OBJECTIVE: The objective of this study was to longitudinally evaluate feasibility of use of the novel Playskin Lift exoskeletal garment (Playskin; developed and trademarked by Dr. Lobo's Super Suits FUNctional Fashion and Wearable Technology Program at the University of Delaware, Newark, DE, USA), the assistive and rehabilitative effects of intervention with the garment on reaching and object exploration ability, and to relate changes in reaching and object exploration to changes in cognition during intervention for infants at risk for developmental delays. DESIGN: A multiple baseline single-case design with 1- to 2-month Baseline, 4-month Intervention, and 1-month Postintervention phases was implemented. METHODS: Ten infants born preterm and/or with brain injury, mean [SD] age 2.2 [1.3] months at the beginning of the study, were assessed biweekly throughout the study both with and without the Playskin Lift. Assessments included a Reaching Assessment and the cognitive subscale of the Bayley Scales of Infant and Toddler Development. Reaching and object exploration behaviors were coded from videos of the Reaching Assessment. Results were analyzed using multilevel modeling in SAS. RESULTS: The Playskin Lift improved infants' reaching ability, hand orientation for grasp and object exploration, and multimodal object exploration when worn within sessions, especially during the Intervention phase. The garment also improved independent reaching and object exploration across time during the Intervention phase, with retention of gains in the Postintervention phase. Improvement in reaching ability was positively related to changes in cognitive outcomes during the Intervention phase. LIMITATIONS: Limitations included modest sample size and the potential confounds of development and experience with time. CONCLUSIONS: The Playskin Lift can serve as a feasible, effective, and accessible assistive and rehabilitative device to advance reaching, object exploration, and cognition for infants at risk for future delays.

The Effect of Clear Speech on Temporal Metrics of Rhythm in Spanish-Accented Speakers of English.

This paper presents a comparative analysis of temporal rhythm in native American English talkers and Spanish-accented English talkers producing clear (hyperarticulated) speech and typical, conversational-style speech. Five acoustic measures of comparative vocalic and consonantal interval duration ("temporal metrics") were obtained from speech samples of 40 adult men and women (half native and half Spanish-accented talkers). In conversational-style speech, vocalic-based metrics differed significantly between native and Spanish-accented talkers, consistent with phonotactic differences between the two native languages. In clear speech, however, all metric values from the Spanish-accented talkers became more English-like and no longer differed significantly from those observed in the native English talkers. Post-hoc analysis revealed that native English talkers increased the duration of both weak and strong vowels in clear speech, whereas the Spanish-accented talkers increased the duration of strong vowels without changing the duration of weak vowels. Listener ease of understanding, as perceived by monolingual English speakers, was significantly improved in clear- compared with conversational-style speech for all talkers. The acoustic data help to explain the changes that result from use of clear speech in nonnative speakers. Together with the improved listener ease of understanding, these data strongly support the further exploration of clear speech as a clinical tool to improve prosody and hence, interpersonal communication, in nonnative speakers.

Analysis of single-case experimental count data using the linear mixed effects model: A simulation study.

When (meta-)analyzing single-case experimental design (SCED) studies by means of hierarchical or multilevel modeling, applied researchers almost exclusively rely on the linear mixed model (LMM). This type of model assumes that the residuals are normally distributed. However, very often SCED studies consider outcomes of a discrete rather than a continuous nature, like counts, percentages or rates. In those cases the normality assumption does not hold. The LMM can be extended into a generalized linear mixed model (GLMM), which can account for the discrete nature of SCED count data. In this simulation study, we look at the effects of misspecifying an LMM for SCED count data simulated according to a GLMM. We compare the performance of a misspecified LMM and of a GLMM in terms of goodness of fit, fixed effect parameter recovery, type I error rate, and power. Because the LMM and the GLMM do not estimate identical fixed effects, we provide a transformation to compare the fixed effect parameter recovery. The results show that, compared to the GLMM, the LMM has worse performance in terms of goodness of fit and power. Performance in terms of fixed effect parameter recovery is equally good for both models, and in terms of type I error rate the LMM performs better than the GLMM. Finally, we provide some guidelines for applied researchers about aspects to consider when using an LMM for analyzing SCED count data.