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BackgroundUnrelieved chemotherapy-induced nausea (CIN) occurs 48% of patients undergoing chemotherapy and is one of the most debilitating symptoms that patients report.ObjectiveThe aims of this study were to identify subgroups of patients with distinct CIN profiles and determine how these subgroups differed on demographic and clinical characteristics; severity, frequency, and distress of CIN; and the co-occurrence of common gastrointestinal symptoms.MethodsPatients (n = 1343) completed demographic questionnaire and Memorial Symptom Assessment Scale 6 times over 2 cycles of chemotherapy. Latent class analysis was used to identify subgroups of patients with distinct CIN profiles. Differences among these subgroups were evaluated using parametric and nonparametric statistics.ResultsFour distinct CIN profiles were identified: none (40.8%), increasing-decreasing (21.5%), decreasing (8.9%), and high (28.8%). Compared with the none class, patients in the high class were younger, had a lower annual household income, had child care responsibilities, had a lower Karnofsky Performance Status score and a higher Self-administered Comorbidity Questionnaire score, and were more likely to have received chemotherapy on a 14-day cycle and a highly emetogenic chemotherapy regimen. In addition, patients in the high class reported high occurrence rates for dry mouth, feeling bloated, diarrhea, lack of appetite, abdominal cramps, difficulty swallowing, mouth sores, weight loss, and change in the way food tastes.ConclusionsThat 60% of the patients reported moderate to high CIN occurrence rates confirms that this unrelieved symptom is a significant clinical problem.Implications for practiceNurses need to evaluate patients' level of adherence to their antiemetic regimen and make appropriate referrals for physical therapy, psychological services, and dietary counseling.

When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design with two correlated outcomes, a simulation study was conducted to compare the performance of MVMM to MLMs. The results showed that MVMM and MLM performed similarly when data were complete or missing completely at random. However, when outcome data were missing at random, MVMM continued to provide unbiased estimates, whereas MLM produced grossly biased estimates and severely inflated Type I error rates. As such, this study provides further support for using MVMM rather than univariate analyses, particularly when outcome data are incomplete.

Aims: To examine how perceived balance problems are associated with self‐reported falls in the past month after controlling for known correlates of falls among older adults. Background: Approximately 30% of adults age 65 and older fall each year. Most accidental falls are preventable, and older adults' engagement in fall prevention is imperative. Limited research suggest that older adults do not use the term 'fall risk' to describe their risk for falls. Instead, they commonly use the term 'balance problems'. Yet, commonly used fall risk assessment tools in both primary and acute care do not assess older adults' perceived balance. Design and Method: The Health Belief Model and the concept of perceived susceptibility served as the theoretical framework. A retrospective, cross‐sectional secondary analysis using data from the National Health and Aging Trends Study from year 2015 was conducted. The outcome variable was self‐reported falls in the last month. Results: A subsample of independently living participants (N = 7499) was selected, and 10.3% of the sample reported a fall. Multiple logistic regression analysis revealed that the odds of reporting a fall in the past month was 3.4 times (p <.001) greater for participants who self‐reported having a balance problem compared to those who did not. In contrast, fear of falling and perceived memory problems were not uniquely associated with falls. Using a mobility device, reporting pain, poor self‐rated health status, depression and anxiety scores were also associated with falling. Conclusion and Implications: Older adults' perceived balance problem is strongly associated with their fall risk. Perceived balance may be important to discuss with older adults to increase identification of fall risk. Older adults' perceived balance should be included in nursing fall risk assessments and fall prevention interventions. A focus on balance may increase older adults' engagement in fall prevention. [ABSTRACT FROM AUTHOR]

Women remain underrepresented in physical science, engineering, and computer science college majors and careers. Prior research has suggested that motivational beliefs and experiences in high school play a critical role in girls' persistence in science fields. We hypothesized that compared to male students, female high school students may experience less daily autonomy support from teachers and lower psychological need satisfaction in physics, chemistry, and engineering courses. In turn, we expected that these differences would explain girls' lower daily engagement in these courses compared to boys. In line with current trends indicating gender parity in biology and biomedical fields, we did not expect to find gender differences in biology courses. Results from a six-week intensive longitudinal study in which high school students reported their daily experiences of teacher autonomy support, need satisfaction, and engagement during science class supported our hypotheses. The implications of the results for theory and practice are discussed.

This diary study provided the first classroom-based empirical test of the relations between student perceptions of high school science teachers' various autonomy supporting and thwarting practices and students' motivation and engagement on a daily basis over the course of an instructional unit. Perceived autonomy supporting practices were hypothesized to predict autonomous motivation and engagement outcomes, while perceived autonomy thwarting practices were hypothesized to predict controlled motivation and disaffection outcomes. In line with this prediction, multilevel modeling results based on regular reports of 208 high school students in 41 science classes across 6 weeks suggested that 4 perceived daily supports (choice provision, consideration for student preferences and interests, rationales for importance, and question opportunities) and 1 daily thwart (use of uninteresting activities) predicted changes in daily autonomous motivation and engagement. In contrast, changes in students' daily controlled motivation and disaffection were predicted primarily by 3 perceived daily thwarts (controlling messages, suppression of student perspectives, and use of uninteresting activities). Results also suggested that practices interacted such that the perception of thwarts generally bolstered desirable daily relationships between perceived supports and students' motivation and the perception of supports generally mitigated undesirable daily relationships between thwarts and motivation. Supplemental exploratory results suggested that the effects of choice and suppression of student perspectives may be heterogeneous and depend on the outcome and/or the presence of other practices. Implications of the findings are discussed.

Alzheimer's disease (AD) affects Latinos disproportionately. One of the reasons underlying this disparity may be type 2 diabetes (T2D) that is a risk factor for AD. The purpose of this study was to examine the associations of T2D and AD blood biomarkers and the differences in these associations between Mexican Americans and non-Hispanic Whites. This study was a secondary analysis of baseline data from the observational Health and Aging Brain Study: Health Disparities (HABS-HD) that investigated factors underlying health disparities in AD in Mexican Americans in comparison to non-Hispanic Whites. HABS-HD participants were excluded if they had missing data or were large outliers (z-scores >|4|) on a given AD biomarker. Fasting blood glucose and glycosylated hemoglobin (HbA1c) levels were measured from clinical labs. T2D was diagnosed by licensed clinicians. Plasma amyloid-beta 42 and 40 (Aβ42/42) ratio, total tau (t-tau), and neurofilament light (NfL) were measured via ultra-sensitive Simoa assays. The sample sizes were 1,552 for Aβ42/40 ratio, 1,570 for t-tau, and 1,553 for NfL. Mexican Americans were younger (66.6±8.7 vs. 69.5±8.6) and had more female (64.9% female vs. 55.1%) and fewer years of schooling (9.5±4.6 vs. 15.6±2.5) than non-Hispanic Whites. Mexican Americans differed significantly from non-Hispanic Whites in blood glucose (113.5±36.6 vs. 99.2±17.0) and HbA1c (6.33±1.4 vs. 5.51±0.6) levels, T2D diagnosis (35.3% vs. 11.1%), as well as blood Aβ42/40 ratio (.051±.012 vs..047±.011), t-tau (2.56±.95 vs. 2.33±.90), and NfL levels (16.3±9.5 vs. 20.3±10.3). Blood glucose, blood HbA1c, and T2D diagnosis were not related to Aβ42/40 ratio and t-tau but explained 3.7% of the variation in NfL (p <.001). Blood glucose and T2D diagnosis were not, while HbA1c was positively (b = 2.31, p <.001, β = 0.26), associated with NfL among Mexican Americans. In contrast, blood glucose, HbA1c, and T2D diagnosis were negatively (b = -0.09, p <.01, β = -0.26), not (b = 0.34, p =.71, β = 0.04), and positively (b = 3.32, p <.01, β = 0.33) associated with NfL, respectively in non-Hispanic Whites. To conclude, blood glucose and HbA1c levels and T2D diagnosis are associated with plasma NfL levels, but not plasma Aβ and t-tau levels. These associations differ in an ethnicity-specific manner and need to be further studied as a potential mechanism underlying AD disparities. [ABSTRACT FROM AUTHOR]

This investigation examined relations between adolescent students' daily and cumulative perceptions of teachers' practice and their experience of autonomy. Two-hundred and eighteen high school science students in 43 classes participated in a 6-week diary study. Multilevel modeling results suggested that perceptions of 8 out of 9 practices predicted a change in students' daily autonomy. Results also revealed that autonomy-relevant practices interacted with time, each other, and characteristics of students and classrooms. Implications are discussed.

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Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in intervention studies and analysis models that can simultaneously include such outcomes are available, we found very limited use of these models in intervention research. To encourage greater use of multivariate analysis for mixed outcomes, this article highlights the benefits and describes important features of models that can incorporate a mix of normal and binary outcomes. Models for intervention research are then fit using Mplus and results interpreted using data from an evaluation of the Early Head Start program, a randomized trial designed to improve child outcomes for an at-risk population. The models illustrated estimate treatment effects for mixed responses in standard and multilevel experimental designs.

This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the calculation and interpretation of the effect size measures using a simulated dataset and an empirical dataset from a cluster-randomized trial of peer tutoring. SAS syntax is provided for parametric percentile bootstrapped confidence intervals of the effect sizes. The use of any of the 3 standardized effect size measures depends on the nature of the inference the researcher wishes to make within a single site, across the broad population, or at the site level.

We designed an Internet survey to identify the educational priorities that parents have for their children with autism spectrum disorders and to examine the relation between these priorities and the children's level of adaptive behavior functioning. The survey listed 54 skills/behaviors (e.g., toileting, expressing wants and needs, and tantrums) representing 10 adaptive behavior domains (e.g., self-care, communication, and problem behavior). Parents rated their child's current level of ability/performance with respect to each skill/behavior and indicated the extent to which training/treatment was a priority. Ninety surveys were completed during the 8-month data collection period. The top 10 treatment priorities represented skills/behaviors from six different domains. Results supported the view that parent priorities tended to be higher in areas where children have the greatest deficits or show emerging skills. The results suggest there may be value in targeting the assessment of children's deficits and emergent skills for treatment planning. (Contains 2 tables and 2 figures.)

This article examines college students' self-reported mood states during a suicidal crisis and the relationship between mood and indicators of suicidality. Multilevel modeling demonstrated that the moods of hopelessness and anger predicted stronger intent; anxiety/worry predicted weaker thoughts of suicide; hopelessness increased the odds of receiving professional help, having a specific plan, and having contemplated methods; and guilt decreased the odds of having made preparations for an attempt. The associations between mood and precipitants of suicidal ideation are presented. Implications of these findings for increasing collaboration during the course of risk assessment and treatment are also discussed. (Contains 2 tables.)

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a second model that included first-order autoregressive and moving average autocorrelation parameters. The results indicated that the estimates of the overall trend in the data were accurate regardless of model specification across most conditions. Variance components estimates were biased across many conditions but improved as sample size and series length increased. In general, the two models that incorporated autocorrelation parameters performed well when sample size and series length were large. The COFM had the best overall performance. (Contains 6 tables and 1 footnote.)

Purpose: Cognitive training improves mental abilities in older adults, but the benefit to minority elders is unclear. We conducted a subgroup analysis of subjects in the SeniorWISE (Wisdom Is Simply Exploration) trial to examine this issue. Design and Methods: SeniorWISE was a Phase 3 randomized trial that enrolled 265 nondemented community-dwelling older adults aged 65 years and older between 2001 and 2006. Participants were randomly assigned to 12 hr of either memory or health training. Results: The sample was 79% female, 71% Caucasian, 17% Hispanic, and 12% African American. On the Rivermead Behavioural Memory Test (RBMT), 28% of the sample scored normal, 47% scored poor, and 25% impaired. Memory performance changed differently over time depending on the demographic characteristics of participants. Both Hispanics and Blacks performed better than Whites on visual memory, and Blacks performed better over time on instrumental activities of daily living. On all performance measures, lower pretest scores were associated with relatively greater improvements over time. Implications: Our analyses suggested that minority participants received differential benefits from the memory training; however, this remains speculative because the 3 ethnic groups in the sample were not equivalent in size. The question of why Black and Hispanic participants often made greater improvements needs further exploration.

Using multilevel models of data from the Early Childhood Longitudinal Study-Kindergarten Cohort (N = 20,356), the authors find that parental involvement in education partially mediates the association between family poverty and children's math and reading achievement in kindergarten, but differences exist across race. In Asian families, poor and nonpoor children have similar levels of achievement. Poverty is not related to Black children's participation in organized activities, but these activities are not associated with Black children's achievement. Home-learning activities predict reading achievement in Hispanic families only. The findings provide support for application of the family process model to educational outcomes during the transition to elementary school and underscore the need to examine developmental models across racial subsets of the population. (Contains 5 tables and 3 notes.)

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I error rates for the tests of the fixed effects were generally accurate when the present authors correctly specified or underspecified the model. However, random effects were poorly estimated under many conditions, even under correct model specification. Further, fit criteria performed inconsistently and were especially inaccurate when small sample sizes and short series lengths were combined. With the exception of elevated Type I error rates that occurred under some conditions, the best performance was obtained by use of an unstructured covariance matrix at the first level of the growth curve model. (Contains 4 tables.)

Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class, school) are present in a study setting. This article presents analysis models that can be used to test indirect effects in experimental designs having three levels where random assignment is at the third (school) or second (class) level and where the indirect effect may be random. In the presentation, simulated datasets are used to illustrate model specification and results interpretation for hypothetical three-level educational experiments involving mediation and moderation of treatment effects. (Contains 6 tables and 3 figures.)

A Monte Carlo study compared the statistical performance of standard and robust multilevel mediation analysis methods to test indirect effects for a cluster randomized experimental design under various departures from normality. The performance of these methods was examined for an upper-level mediation process, where the indirect effect is a fixed effect and a group-implemented treatment is hypothesized to impact a person-level outcome via a person-level mediator. Two methods--the bias-corrected parametric percentile bootstrap and the empirical-M test--had the best overall performance. Methods designed for nonnormal score distributions exhibited elevated Type I error rates and poorer confidence interval coverage under some conditions. Although preliminary, the findings suggest that new mediation analysis methods may provide for robust tests of indirect effects. (Contains 5 tables and 1 figure.)

The hierarchical linear model (HLM) is now commonly accepted as a useful modeling approach for multilevel data resulting from randomized field experiments. When multiple outcomes of interest exist, a multivariate extension of the conventional univariate HLM offers advantages over the usual application of separate HLM analyses for each of the outcomes. In this article, the authors review these advantages, discuss the device that allows the univariate HLM procedure to model multiple outcomes, and present a series of multivariate models that would be useful in addressing typical questions in field experiments. In addition to the multivariate multilevel versions of basic analysis of variance (ANOVA) or analysis of covariance (ANCOVA) designs, the authors present more complex models that allow the testing of moderation and mediation of the treatment effect. The various analyses are illustrated with computer generated data for a hypothetical scenario. (Contains 2 tables and 4 figures.)

The Behavior Flexibility Rating Scale (BFRS) is designed to assess insistence on sameness or lack of behavioral flexibility, which is often associated with autism and other developmental disabilities. This study was designed to assess the factor structure of this scale for a sample of 968 individuals with autism, Asperger's syndrome, and Down syndrome. To establish factorial validity, an exploratory factor analysis (EFA) was conducted with half of the sample, with a confirmatory factor analysis (CFA) conducted with the remaining cases to cross-validate the model obtained with the EFA. The factor analyses supported the presence of two factors--Interruption/Disruption and Position/Location. However, further analysis suggested the presence of a third factor, Interpersonal Mishaps, for the Asperger's syndrome group.

The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested alternative models that seek to explain student intention to use an e-learning system when the system is used as a supplementary learning tool within a traditional class or a stand-alone distance education method. The models integrated determinants from the well-established technology acceptance model as well as system and participant characteristics cited in the research literature. Following a demonstration and use phase of the e-learning system, data were collected from 259 college students. Structural equation modeling provided better support for a model that hypothesized stronger effects of system characteristics on e-learning system use. Implications for both researchers and practitioners are discussed.

Researchers associate an insistence on sameness or lack of behavioral flexibility with autism and Asperger syndrome, but few studies have sought to identify specific situations in which individuals insist on sameness. Along these lines, we developed the "Behavioral Flexibility Rating Scale" (BFRS) and conducted an Internet survey of parents of individuals with autism, Asperger syndrome, and Down syndrome. A total of 726 respondents submitted usable returns during the 3-month survey period. Parent report indicated that individuals with Asperger syndrome showed the most problems in relation to the insistence on sameness as measured by the BFRS, followed by individuals with autism and Down syndrome, respectively. When diagnosis was controlled, there were no gender or age differences. This preliminary investigation suggests that the BFRS may be useful in identifying specific situations associated with an insistence on sameness. Identifying specific situations in which individuals insist on sameness or show a lack of behavioral flexibility may be useful in planning interventions focused on promoting greater behavioral flexibility.

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and included a single intervening variable and outcome, but they differed in whether the mediator was measured at the participant or site level. A bias-corrected bootstrap had the best statistical performance for each design and was closely followed by the empirical-Mtest, either of which is recommended for testing and estimating indirect effects in multilevel designs. In addition, consistent with previous research, the commonly used z test had relatively poor performance.

We developed an Internet survey to identify treatments used by parents of children with autism. The survey listed 111 treatments and was distributed via colleagues and through chapters of the Autism Society of America and Autism Organizations Worldwide. A total of 552 parents submitted usable returns during the 3-month survey period. On average the parents reported using seven different treatments. The number of treatments used varied as a function of the child's age and type/severity of disability within the autism spectrum. Speech therapy was the most commonly reported intervention, followed by visual schedules, sensory integration, and applied behavior analysis. In addition, 52% of parents were currently using at least one medication to treat their child, 27% were implementing special diets, and 43% were using vitamin supplements. Because parents were using a large number of treatments, many of which lack empirical support, future research should focus on understanding the decision-making processes that underlie treatment selection by parents of children with autism.