Behavior Modification最新文献

Nonsuicidal self-injury (NSSI) by proxy is the intentional destruction of one's own body tissue through the elicitation of another being's actions. Despite its clinical relevance, research on NSSI by proxy is limited and there are no available measures of this behavior. This research aimed to characterize NSSI by proxy among young adults and provide preliminary data on the validity of a new self-report measure, the NSSI by Proxy Questionnaire (NSSIBPQ). Two nationwide community samples of young adults (one general community sample and one with a history of traditional NSSI and suicidality) completed online studies. NSSI by proxy was reported by 18% of the general community sample and 45% of the self-injuring sample. Findings support the clinical relevance of NSSI by proxy and its potential to meet criteria for an NSSI disorder diagnosis. Results also provide preliminary support for the internal consistency and convergent, discriminant, and concurrent validity of the NSSIBPQ.

It is considered best practice to conduct a functional analysis and visually inspect data collected to determine the function of problem behavior, which then informs the intervention approaches applied. Visual inspection has been described as a "subjective" process that may be affected by factors unrelated to the data. Structured decision-making guidelines have been established to address some of these shortcomings. The current paper is a follow-up to earlier work describing positive outcomes related to the viability of a decision support system based on structured criteria from Roane et al. Here, we demonstrate important improvements in a computer script's interpretation of functional analysis data, including improvement in agreement between the updated computer script version and experienced human raters (89%) compared to our original agreement outcomes (81%). This paper further supports the use of decision support systems for functional analysis interpretation.

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.

Critical appraisal scales play an important role in evaluating methodological rigor (MR) of between-groups and single-case designs (SCDs). For intervention research this forms an essential basis for ascertaining the strength of evidence. Yet, few such scales provide classifications that take into account the differential weighting of items contributing to internal validity. This study aimed to develop an algorithm derived from the Risk of Bias in N-of-1 Trials (RoBiNT) Scale to classify MR and risk of bias magnitude in SCDs. The algorithm was applied to 46 SCD experiments. Two experiments (4%) were classified as Very High MR, 14 (30%) as High, 5 (11%) as Moderate, 2 (4%) as Fair, 2 (4%) as Low, and 21 (46%) as Very Low. These proportions were comparable to the What Works Clearinghouse classifications: 13 (28%) met standards, 8 (17%) met standards with reservations, and 25 (54%) did not meet standards. There was strong association between the two classification systems.

The current article presents a systematic review of consistency in single-case ABAB phase designs. We applied the CONsistency of DAta Patterns (CONDAP) measure to a sample of 460 data sets retrieved from 119 applied studies published over the past 50 years. The main purpose was to (a) identify typical CONDAP values found in published ABAB designs and (b) develop interpretational guidelines for CONDAP to be used for future studies to assess the consistency of data patterns from similar phases. The overall distribution of CONDAP values is right-skewed with several extreme values to the right of the center of the distribution. The B-phase CONDAP values fall within a narrower range than the A-phase CONDAP values. Based on the cumulative distribution of CONDAP values, we offer the following interpretational guidelines in terms of consistency: very high, 0 ≤ CONDAP ≤ 0.5; high, 0.5 < CONDAP ≤ 1; medium, 1 < CONDAP < 1.5; low, 1.5 < CONDAP ≤ 2; very low, CONDAP > 2. We give examples of combining CONDAP benchmarks with visual analysis of single-case ABAB phase designs and conclude that the majority of data patterns (41.2%) in published ABAB phase designs is medium consistent.

Recent work has highlighted that process-outcome relationships are likely to vary depending on the client, yet much work remains to be done in the area of tailoring interventions to a given client. This naturalistic single-case analysis provides an example of augmenting a treatment protocol with "off protocol" relaxation methods, based on routinely collected outcome information to guide shared decision making. Intensive case study analyses were applied to one client with principal generalized anxiety disorder and comorbid major depressive disorder receiving transdiagnostic cognitive-behavioral therapy. The client completed two routine anxiety and depression symptom and functioning scales prior to each session of naturalistic treatment. Time series analyses were applied to the two symptom measures. Among the results, (a) significant linear decreases in anxiety and depression from baseline to posttreatment were observed; and (b) the introduction of relaxation methods had a significant impact on the course of anxiety symptom change. In conclusion, routine outcome assessment can be used to inform intervention augmentation with individual clients. Furthermore, regular assessment is needed to determine if a client may benefit from an alternative set of specific intervention strategies.

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.

The continuation of a baseline pattern of responding into a treatment phase, sometimes referred to as a "transition state," can obscure interpretation of data depicted in single-case experimental designs (SCEDs). For example, when using visual analysis, transition states may lead to the conclusion that the treatment is ineffective. Likewise, the inclusion of overlapping data points in some statistical analyses may lead to conclusions that the treatment had a small effect size and give rise to publication bias. This study reviewed 20 volumes in a journal that publishes primarily SCEDs studies. We defined a transition state as a situation wherein at least the first three consecutive data points of a treatment phase or condition are within the range of the baseline phase or condition. Results indicate that transitions states (a) were present for 7.4% of graphs that met inclusion criteria and (b) occurred for a mean of 4.9 data points before leading to behavior change. We discuss some implications and directions for future research on transition states.

Multilevel modeling (MLM) is an approach for meta-analyzing single-case experimental designs (SCED). In this paper, we provide a step-by-step guideline for using the MLM to meta-analyze SCED time-series data. The MLM approach is first presented using a basic three-level model, then gradually extended to represent more realistic situations of SCED data, such as modeling a time variable, moderators representing different design types and multiple outcomes, and heterogeneous within-case variance. The presented approach is then illustrated using real SCED data. Practical recommendations using the MLM approach are also provided for applied researchers based on the current methodological literature. Available free and commercial software programs to meta-analyze SCED data are also introduced, along with several hands-on software codes for applied researchers to implement their own studies. Potential advantages and limitations of using the MLM approach to meta-analyzing SCED are discussed.