Heibatollah Baghi

Childhood Obesity Study: A Pilot Study of the Effect of the Nutrition Education Program Color My Pyramid

The need for successful nutrition interventions is critical as the prevalence of childhood obesity increases. Thus, this pilot project examines the effect of a nutrition education program, Color My Pyramid, on children’s nutrition knowledge, self-care practices, activity levels, and nutrition status. Using a pretest–posttest, quasiexperimental design, 126 fourth- and fifth-grade students from experimental and control schools are compared. The intervention program incorporates an online component www.MyPyramid.gov, Orem’s Self-Care Deficit Nursing Theory, and consists of six classes taught over a 3-month period. Results indicated that the program increased nutrition knowledge in the control group. Furthermore, it increased activity time from pretest to posttest and decreased systolic blood pressure for children in both groups; however, there were no significant differences in BMI percentiles. The findings indicate that Color My Pyramid can be successfully employed in school settings and thus support school nursing practice.

Impact of online counseling on drug use: a pilot study.

Purpose To examine the effect of online counseling abuse counseling on drug use among underserved patients. Methods Subjects were recruited from an Indian Reservation in Eagle Butte, South Dakota; a family court in Newark, New Jersey; a probation office in Alexandria, Virginia; and a co-occurring disorders treatment clinic in Washington, District of Columbia. Subjects were predominantly poor, undereducated, unemployed, court involved, or diagnosed with co-occurring psychiatric disorders. A total of 79 subjects volunteered to participate in the project. Subjects were randomly assigned to either a control group or an experimental group. The control and experimental groups were both issued an Internet-ready computer and 1 year of Internet service. Only the experimental group had access to online counseling intervention. Drug use was measured using a combination of self-usage reporting and supervised urine tests. Results Urine tests were available for 37% of subjects. Exit surveys containing self-reported usage were obtained from 54% of the subjects. Self-usage reports or urine test results were available from 70% of subjects. The difference of the rates of drug use in the control and experimental groups (as calculated from urine tests or through self-report) was not significantly different from zero, suggesting that online counseling had not led to a reduction in substance use. It is possible that the study lacked sufficient power to detect small differences in the rate of drug use in the experimental and control groups. Conclusions Additional research is needed to establish the efficacy of online counseling in hard-to-reach populations.

Using the Internet to Facilitate Positive Attitudes of College Students Toward Aging and Working with Older Adults

ABSTRACT Published data suggest that a preponderance of negative attitudes toward the elderly and insufficient knowledge of aging may be the primary reasons that geriatrics is not the primary choice of employment for nurses. This study measured attitude changes toward the elderly as a result of participation by nursing, gerontology and health science students in an intergenerational reciprocal service-learning program. Using a pre and posttest design, results revealed a significant improvement (p < .001) for the total group, with undergraduates showing a significantly greater mean increase in positive attitudes toward the elderly (p < .001). This study suggests that pairing students with well elderly and engaging in on-going exposure, meaningful intergenerational exchanges and using Internet-based activities to communicate are effective strategies to improve attitudes of students toward the elderly.

Statistical and nonstatistical significance: implications for health care researchers.

Quality improvement professionals have to decide whether a change has led to improvement. This is typically done through testing the statistical significance of the findings. In this article, we explore controversies surrounding statistical significance testing with attention to contemporary criticism of bad practice resulting from the misuse of statistical significance testing. Most statistical significance tests use tests (eg, F, χ2) with known distributions with the P values used as the main evidence to evaluate whether tests are statistically significant. The primary conclusion of this article is that the P value alone as a measure of statistical significance does not give sufficient information about testing of hypotheses. When it is coupled with other measures, however, such as the point estimation of the effect size and the use of a confidence interval around it, the combination of these statistics can provide a more thorough explanation of statistical testing. This article offers recommendations for process improvement investigators as to when to appropriately apply and not to apply statistical significance testing.

Self-experiments and analytical relapse prevention.

Patients give many reasons for why they have not kept up with their resolutions; research shows that many of these causal attributions are wrong. This article provides a tool to help patients sort out causes of and constraints on their behavior, in general, and exercise, in particular. Patients diary data can be analyzed to flag erroneous causal attributions, and thus assist patients to understand their behavior. To start the diary, the clinician works with the patient to assemble a list of possible causes. Using the list, a diary is organized that tracks the occurrences of various causes and the target behavior. At the end of 2 to 3 weeks, the diary data is analyzed using conditional probability models, causal Bayesian networks or logistic regression. A key issue in the analysis of diary data is to separate out the effect of various causes. Typically, causes co-occur, making it difficult to understand their independent effects. Another problem with analysis of diary data is the small size of the data. This article shows how small longitudinal data from patient diaries can be analyzed. The analysis may refute or support causes hypothesized by the client. The patient uses the insights gained from the diary analysis to prevent relapse to unhealthy behaviors. The process is continued for several cycles of organizing, keeping, and analyzing the diary data. In each cycle, the patient gains new insights and makes additional attempts to create a positive environment that allows him or her to succeed even if his or her motivation waivers. This article provides details of how diary data can be analyzed to help patients make correct causal attributions.

Two decades of nurse-led research on smoking during pregnancy and postpartum: concept development to intervention trials.

Tobacco use during pregnancy and postpartum is a leading cause of preventable morbidities for women and their infants. Over the past two decades, nursing research has addressed this recalcitrant clinical problem from a variety of conceptual and methodological perspectives. The 64 published studies (1988–2009) that met inclusion criteria for this systematic review represent the full research trajectory from concept development to intervention testing. Meta-analysis demonstrated an overall significant trend in nursing intervention efficacy (OR = 1.14, 95% CI = 1.08–1.2) for studies that examined comparable prenatal and postpartum smoking cessation outcomes. Implications for future nursing research and evidence-based policy are presented.

Demonstration of two types of fatigue in subjects with chronic liver disease using factor analysis

PurposeThe purpose of this investigation was to determine if it was possible to separate fatigue self-reports into two distinct types of fatigue symptom clusters in research subjects with chronic liver disease (CLD). It was hypothesized that when items from the Medical Outcomes Study Short-Form (SF-36v2) are combined with items from the Fatigue Severity Scale (FSS), these distinct factors will emerge.MethodsConfirmatory and exploratory factor analyses from data collected in a prospective, natural history study of CLD patients were conducted. Items were selected from the SF-36v2 and the FSS for entry into the factor analyses. In order to establish convergent and discriminant validity, derived factor scores were correlated with subscale scores of the Human Activity Profile (HAP), Mental Component Score (MCS) from the SF-36v2, and the Emotional Functioning Subscale of the Chronic Liver Disease Questionnaire (CLDQ-EF).Results106 participants with CLD were included (50% female; age: 51 ± 10). Two factors were identified. The factors included one that clustered around questions addressing fatigue related to physical activity (peripheral fatigue) and the other to the questions addressing generalized fatigue that did not require physical tasks to produce the fatigue (central fatigue). The standardized factor loadings of all items were greater than 0.6 on their underlying constructs. Moreover, all factor loadings are significant at p < 0.01. Peripheral fatigue was related to HAP (r = 0.26, r = 0.24, p < 0.01), as was central fatigue (r = −0.34, r = −0.33, p < 0.01). Central fatigue was related to MCS and CLDQ-EF (r = −0.60; r = −0.63, p < 0.01), whereas peripheral fatigue was not (r = 0.07, p > 0.40). We then tested the original scales to determine if the newly created factors correlated better with the validity measures. The full FSS did not correlate as well as the newly created central fatigue scale, while the original peripheral fatigue scale (the SF-36v2 physical functioning) was more related to HAP than the newly created scale.ConclusionsIn individuals with CLD, two separate factors pertaining to fatigue were identified. This recognition of the multifaceted nature of fatigue may help increase the specificity of self-reports of fatigue and lead to treatments that can specifically address the underlying factors contributing to fatigue.

Measurement of substance abuse treatment outcomes over time.

There are many different ways of calculating the impact of treatment on drug use; percentage of positive drug tests, probability of drug use, percentage of patients abstaining from any use, total number of days of use, daily probability of use and average days till next use, are some examples reported in the literature. We prefer average days till next use because (1) it allows intermittent drug use and relapse; (2) it fits the clients count of drug-free days, and (3) it simultaneously accounts for both tests results and time between tests. We show by way of an example, how conclusions arrived at using average days till next use are likely to be different from other measures in analysis of recent data from impact of online treatment on drug use.

Health care research: alternative approaches to study design and data analysis.

In determining intervention effects, quality improvement researchers typically use statistical testing—Fishers “significance testing” and/or Neyman and Pearsons “hypothesis testing.” Such tests are employed in an effort to demonstrate whether or not a statistically and practically significant difference exists when comparing experimental and comparison group(s). Although power analysis is often not considered when these tests are applied, this article postulates potential benefits of including power analysis in the early stages of a studys design. Two procedures developed by Fisher and Neyman and Pearson are reviewed. Important background statistical concepts including α values, β values, P values, effect sizes, and statistical power analysis are defined and discussed. A proposed statistical approach combining both Fisher and Neyman-Pearson procedures along with power analysis for sample size determination and the effect sizes is described and illustrated in a hypothetical research context. The benefits of this combination are discussed within a framework of adding value to a study design and data analysis.

Simulated environment is not appropriate.

In the Spring 2005 issue of Quality Management in Health Care, Borckand et al examined the performance of Tukeys chart in a simulated environment. Unfortunately, the simulated environment does not reflect the type of settings where Tukeys chart has been proposed to be most effective. Tukeys charts are ideally used on relatively small data sets. In these data sets, we hypothesize that it is unlikely to have the high autocorrelations simulated in the Borckand et al study. Furthermore, Tukeys chart will perform well in data coming from non-Normal or non-Uniform distributions. The simulation study was based on random numbers generated with Uniform or Normal distributions. We encourage Borckand et al to examine the performance of Tukeys chart in the modified circumstances.