Nutritional intervention program in elite athletes: single case design research with one professional basketball player

The American College of Sports Medicine argues that sports performance is improved with optimal nutrition (ACSM position stand, MSSE 2009, Mar; 41(3):709-31). However, scientific evidence shows that professional athletes do not achieve nutritional recommendations (Farajian et al, IJSNEM 2004 Oct; 14(5):574-85; Shroeder et al EJSS 2004 Jun 4 (2):1; Nogueira & Da Costa, IJSNEM 2004; 14:684-697) Objectives: a) describe the nutritional practices of one professional basketball player b) carry out a long term nutritional intervention to adapt his diet to current recommendations.

Random assignment of intervention points in two phase single-case designs: data-division-specific distributions

The present study explores the statistical properties of a randomization test based on the random assignment of the intervention point in a two-phase (AB) single-case design. The focus is on randomization distributions constructed with the values of the test statistic for all possible random assignments and used to obtain p-values. The shape of those distributions is investigated for each specific data division defined by the moment in which the intervention is introduced. Another aim of the study consisted in testing the detection of inexistent effects (i.e., production of false alarms) in autocorrelated data series, in which the assumption of exchangeability between observations may be untenable. In this way, it was possible to compare nominal and empirical Type I error rates in order to obtain evidence on the statistical validity of the randomization test for each individual data division. The results suggest that when either of the two phases has considerably less measurement times, Type I errors may be too probable and, hence, the decision making process to be carried out by applied researchers may be jeopardized.

Therapeutic Assessment for an individual with Binge Eating Disorder: A single-case time-series design .

Therapeutic Assessment (TA) is a treatment approach that combines psychological assessment and psychotherapy. The study examines the efficacy of this approach with an individual with Binge Eating Disorder. A replicated single-case time-series design with daily measures is used to assess the effects of TA and to track the process of change during the TA. The individual experienced inconclusive benefits after participation in TA. Significant change occurred in all variables measured, though none of the changes occurred in the hypothesized direction. Further research is needed to determine if TA is an effective treatment for individuals diagnosed with Binge Eating Disorder.

A metacognitive contextual intervention to enhance error awareness and functional outcome following traumatic brain injury: A single-case experimental design

Very few empirically validated interventions for improving metacognitive skills (i.e., self-awareness and self-regulation) and functional outcomes have been reported. This single-case experimental study presents JM, a 36-year-old man with a very severe traumatic brain injury (TBI) who demonstrated long-term awareness deficits. Treatment at four years post-injury involved a metacognitive contextual intervention based on a conceptualization of neuro-cognitive, psychological, and socio-environmental factors contributing to his awareness deficits. The 16-week intervention targeted error awareness and self-correction in two real life settings: (a) cooking at home: and (b) volunteer work. Outcome measures included behavioral observation of error behavior and standardized awareness measures. Relative to baseline performance in the cooking setting, JM demonstrated a 44% reduction in error frequency and increased self-correction. Although no spontaneous generalization was evident in the volunteer work setting, specific training in this environment led to a 39% decrease in errors. JM later gained paid employment and received brief metacognitive training in his work environment. JM's global self-knowledge of deficits assessed by self-report was unchanged after the program. Overall, the study provides preliminary support for a metacognitive contextual approach to improve error awareness and functional Outcome in real life settings.

Humming as a mean of communicating during meal time situations : A Single Case study involving a women with severe dementia and her caregiver

Objective: ‘Music Therapeutic Caregiving’, when caregivers sing for or together with persons with dementia during morning care situations, has been shown to increase verbal and nonverbal communication between persons with dementia and their caregivers, as well as enhance positive and decrease negative emotions in persons with dementia. No studies about singing during mealtimes have been conducted, and this pilot project was designed to elucidate this. However, since previous studies have shown that there is a risk that persons with dementia will start to sing along with the caregiver, the caregiver in this study hummed such that the person with dementia did not sing instead of eat. The aim of this pilot project was threefold: to describe expressed emotions in a woman with severe dementia, and describe communication between her and her caregivers without and with the caregiver humming. The aim was also to measure food and liquid intake without and with humming. Method: The study was constructed as a Single Case ABA design in which the ordinary mealtime constituted a baseline which comprised a woman with severe dementia being fed by her caregivers in the usual way. The intervention included the same woman being fed by the same caregiver who hummed while feeding her. Data comprised video observations that were collected once per week over 5 consecutive weeks. The Verbal and Nonverbal Interaction Scale and Observed Emotion Rating Scale were used to analyze the recorded interactions. Results: A slightly positive influence of communication was shown for the woman with dementia, as well as for the caregiver. Further, the women with dementia showed a slight increase in expressions of positive emotions, and she ate more during the intervention. Conclusion: Based on this pilot study no general conclusions can be drawn. It can be concluded, however, that humming while feeding persons with dementia might slightly enhance communication, and positive expressed emotions in persons with dementia. To confirm this, more studies on group levels are needed. Because previous studies have found that caregiver singing during caring situations influences persons with dementia positively it might be desirable to test the same during mealtime.

WHAT IS THE USE OF A SINGLE-CASE STUDY IN MANAGEMENT RESEARCH?

The purpose of this article is to present a defense of the use of single case studies in management research. The defense is necessary because this type of research has been relegated to a secondary role, or even rejected, by many researchers, who consider it unscientific. Evidence of this low status is the fact that most reputable academic journals in management publish few articles based on single-case studies. In this paper, we examine in detail the objections to the use of such cases in management research. We show the efforts made by some researchers to answer these objections and we show quality criteria for research that are alternatives to the criteria used in the so-called "scientific method." Our analysis suggests that a better understanding - by researchers with different methodological preferences - of the arguments for each particular use of the single-case study as a research method would allow a better dialogue between researchers and benefit management research as a whole.

Positive effects of an angiotensin II type 1 receptor antagonist in Camurati-Engelmann disease: a single case observation.

Camurati-Engelmann disease is characterized by hyperostosis of the long bones and the skull, muscle atrophy, severe limb pain, and progressive joint contractures in some patients. It is caused by heterozygous mutations in the transforming growth factor β1 (TGFβ1) believed to result in improper folding of the latency-associated peptide domain of TGFβ1 and thus in increased or deregulated bioactivity. Losartan, an angiotensin II type 1 receptor antagonist, has been found to downregulate the expression of TGFβ type 1 and 2 receptors. Clinical trials with losartan have shown a benefit in Marfan syndrome, while trials are underway for Duchenne muscular dystrophy and other myopathies associated with TGFβ1 signaling. We hypothesized that due to its anti-TGFβ1 activity, losartan might be beneficial in Camurati-Engelmann disease. This report concerns a boy who presented at age 13 years with severe limb pain and difficulty in walking. Clinical and radiographic evaluation results were compatible with Camurati-Engelmann disease and the diagnosis was confirmed by mutation analysis (c.652C > T [p.Arg218Cys]). The boy underwent an experimental treatment with losartan at a dosage of 50 mg/day, orally. During the treatment period of 18 months, the intensity and frequency of limb pain decreased significantly (as shown by a pain diary), and muscle strength improved, allowing the boy to resume walking and climbing stairs. No obvious side effects were observed. We cautiously conclude that TGFβ1 inhibition with losartan deserves further evaluation in the clinical management of Camurati-Engelmann disease.

Comparing 'visual' effect size indices for single-case designs

Effect size indices are indispensable for carrying out meta-analyses and can also be seen as an alternative for making decisions about the effectiveness of a treatment in an individual applied study. The desirable features of the procedures for quantifying the magnitude of intervention effect include educational/clinical meaningfulness, calculus easiness, insensitivity to autocorrelation, low false alarm and low miss rates. Three effect size indices related to visual analysis are compared according to the aforementioned criteria. The comparison is made by means of data sets with known parameters: degree of serial dependence, presence or absence of general trend, changes in level and/or in slope. The percent of nonoverlapping data showed the highest discrimination between data sets with and without intervention effect. In cases when autocorrelation or trend is present, the percentage of data points exceeding the median may be a better option to quantify the effectiveness of a psychological treatment.

Factors affecting visual analysis in single-case designs

Visual inspection remains the most frequently applied method for detecting treatment effects in single-case designs. The advantages and limitations of visual inference are here discussed in relation to other procedures for assessing intervention effectiveness. The first part of the paper reviews previous research on visual analysis, paying special attention to the validation of visual analysts" decisions, inter-judge agreement, and false alarm and omission rates. The most relevant factors affecting visual inspection (i.e., effect size, autocorrelation, data variability, and analysts" expertise) are highlighted and incorporated into an empirical simulation study with the aim of providing further evidence about the reliability of visual analysis. Our results concur with previous studies that have reported the relationship between serial dependence and increased Type I rates. Participants with greater experience appeared to be more conservative and used more consistent criteria when assessing graphed data. Nonetheless, the decisions made by both professionals and students did not match sufficiently the simulated data features, and we also found low intra-judge agreement, thus suggesting that visual inspection should be complemented by other methods when assessing treatment effectiveness.

Choosing among techniques for quantifying single-case intervention effectiveness

If single case experimental designs are to be used to establish guidelines for evidence-based interventions in clinical and educational settings, numerical values that reflect treatment effect sizes are required. The present study compares four recently developed procedures for quantifying the magnitude of intervention effect using data with known characteristics. Monte Carlo methods were used to generate AB designs data with potential confounding variables (serial dependence, linear and curvilinear trend, and heteroscedasticity between phases) and two types of treatment effect (level and slope change). The results suggest that data features are important for choosing the appropriate procedure and, thus, inspecting the graphed data visually is a necessary initial stage. In the presence of serial dependence or a change in data variability, the Nonoverlap of All Pairs (NAP) and the Slope and Level Change (SLC) were the only techniques of the four examined that performed adequately. Introducing a data correction step in NAP renders it unaffected by linear trend, as is also the case for the Percentage of Nonoverlapping Corrected Data and SLC. The performance of these techniques indicates that professionals" judgments concerning treatment effectiveness can be readily complemented by both visual and statistical analyses. A flowchart to guide selection of techniques according to the data characteristics identified by visual inspection is provided.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

Assigning and combining probabilities in single-case studies

There is currently a considerable diversity of quantitative measures available for summarizing the results in single-case studies. Given that the interpretation of some of them is difficult due to the lack of established benchmarks, the current paper proposes an approach for obtaining further numerical evidence on the importance of the results, complementing the substantive criteria, visual analysis, and primary summary measures. This additional evidence consists of obtaining the statistical significance of the outcome when referred to the corresponding sampling distribution. This sampling distribution is formed by the values of the outcomes (expressed as data nonoverlap, R-squared, etc.) in case the intervention is ineffective. The approach proposed here is intended to offer the outcome"s probability of being as extreme when there is no treatment effect without the need for some assumptions that cannot be checked with guarantees. Following this approach, researchers would compare their outcomes to reference values rather than constructing the sampling distributions themselves. The integration of single-case studies is problematic, when different metrics are used across primary studies and not all raw data are available. Via the approach for assigning p values it is possible to combine the results of similar studies regardless of the primary effect size indicator. The alternatives for combining probabilities are discussed in the context of single-case studies pointing out two potentially useful methods one based on a weighted average and the other on the binomial test.

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.