Systematic review of interventions designed to reduce screen time in children and adolescents

This descriptive systematic review describes intervention trials for children and youth that targeted screen time (ST) as a way to prevent or control obesity and measured ST, and at least one of the following: physical activity, dietary intake, and adiposity. Both “hands-on” (e.g., video games) and “hands free” (e.g., television viewing) ST were included. Published, completed intervention trials (k=12), not-yet-published, completed trials (k=6), and in-progress trials (k=11) were identified through searches of electronic databases, including trial registries and bibliographies of eligible study reports. Study characteristics of the 29 identified trials were coded and presented in evidence tables. Considerable attention was paid to the type of ST addressed, measures used, and the type of interventions. Based on the number of in-progress and not-yet-published trials, the number of completed, published reports will double in the next three years. Most of the studies were funded by federal sources. General populations, not restricted by race, gender, or weight status, were targets of most interventions with children ages 9-12 yeas as the modal age group. Most trials used randomized control trials in which the majority of control or comparison group received an intervention. The mean number of participants was 242.8 (SD=314.7) and interventions were delivered over an average of 10.5 months and consisted of approximately 16 sessions, with a total time of about eight hours. The majority of completed trials evaluate each of the four constructs, however, most studies have more than one measure to assess each construct (e.g., BMI and tricep skinfold thickness to evaluate adiposity) and rarely did studies use the same measures. This is likely why the majority of studies produced at least one significant intervention effect on each outcome that was assessed. The four major outcomes should be evaluated in all interventions attempting to reduce screen time in order to determine the mechanisms involved that may contribute to obesity. More importantly researchers should work together to determine the best measures to evaluate the four main constructs to allow studies to be compared. Another area for consensus is the definition of ST. ^

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli.

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.

Immunology and HIV expression in skin, in vitro, and in response to ultraviolet light

HIV can enter the body through Langerhans cells, dendritic cells, and macrophages in skin mucosa, and spreads by lysis or by syncytia. Since UVL induces of HIV-LTR in transgenic mice mid in cell lines in vitro, we hypothesized that UVB may affect HIV in people and may affect HIV in T cells in relation to dose, apoptosis, and cytokine expression. To determine whether HIV is induced by UVL in humans, a clinical study of HIV+ patients with psoriasis or pruritus was conducted during six weeks of UVB phototherapy, Controls were HIV-psoriasis patients receiving UVB and HIV+ KS subjects without UVB.Blood and skin biopsy specimens were collected at baseline, weeks 2 and 6, and 4 weeks after UVL. AIDS-related skin diseases showed unique cytokine profiles in skin and serum at baseline. In patients and controls on phototherapy, we observed the following: (1) CD4+ and CD8+ T cell numbers are not significantly altered during phototherapy, (2) p24 antigen levels, and also HIV plasma levels increase in patients not on antiviral therapy, (3) HIV-RNA levels in serum or plasma. (viral load) can either increase or decrease depending on the patient's initial viral load, presence of antivirals, and skin type, (4) HIV-RNA levels in the periphery are inversely correlated to serum IL-10 and (5) HIV+ cell in skin increase after UVL at 2 weeks by RT-PCR in situ hybridization mid we negatively correlated with peripheral load. To understand the mechanisms of UVB mediated HIV transcription, we treated Jurkat T cell lines stably transfected with an HIV-LTR-luciferase plasmid only or additionally with tat-SV-40 early promoter with UVB (2 J/m2 to 200 J/m2), 50 to 200 ng/ml rhIL-10, and 10 μg/ml PHA as control. HIV promoter activity was measured by luciferase normalized to protein. Time points up to 72 hours were analyzed for HIV-LTR activation. HIV-LTR activation had the following properties: (1) requires the presence of Tat, (2) occurs at 24 hours, and (3) is UVB dose dependent. Changes in viability by MTS (3-(4,5-dimethyhhiazol-2-y1)-5-(3-carboxymethoxyphonyl)-2-(4-sulfophenyl)-2H-tetrazolium) mixed with PMS (phenazine methosulfate) solution and apoptosis by propidium iodide and annexin V using flow cytometry (FC) were seen in irradiated Jurkat cells. We determined that (1) rhIL-10 moderately decreased HIV-LTR activation if given before radiation and greatly decreases it when given after UVB, (2) HIV-LTR activation was low at doses of greater than 70 J/m2, compared to activation at 50 J/m2. (Abstract shortened by UMI.)^

An ecologic comparison study of the impact of economic disadvantage on Texas Assessment of Knowledge and Skills performance, graduation rates, and readiness for higher education for students attending public and charter schools in Texas from 2004 to 2006

Purpose. No Child Left Behind aimed to "improve the academic achievement of the disadvantaged." The primary research question considered how academic achievement of those from economic disadvantage compared to those not from disadvantage? ^ Economically disadvantaged students can potentially have added academic disadvantage. Research shows low academic achievement can potentially result in drug abuse, youth violence, and teen pregnancy. ^ Methods. To compare the student populations, measures included TAKS results and academic indicator data collected by the Texas Education Agency. ^ Results. T-test analyses showed a significant difference between the economically and non-economically disadvantaged student populations in meeting the TAKS passing standard, graduation, and preparation for higher education.^ Conclusions. The achievement gap between students remained as indicated by the Texas testing program. More research and time are needed to observe if the desired impact on those from economic disadvantage will be reflected by academic achievement data.^

The coordinated approach to child health in underserved population (CATCH UP) curriculum: A quantitative evaluation of an innovative preschool obesity prevention program

Obesity has been on the rise in the United States over the last 30 years for all populations, including preschoolers. The purpose of the project was to develop an observation tool to measure physical activity levels in preschool children and use the tool in a pilot test of the CATCH UP curriculum at two Head Start Centers in Houston. Pretest and posttest interobserver agreements were all above 0.60 for physical activity level and physical activity type. Preschoolers spent the majority of their time in light physical activity (75.33% pretest, 87.77% posttest), and spent little time in moderate to vigorous physical activity (MVPA) (24.67% pretest, 12.23% posttest). Percent time spent in MVPA decreased significantly pretest to posttest from (F=5.738, p=0.043). While the pilot testing of the CATCH UP curriculum did not show an increase in MVPA, the SOFIT-P tool did show promising results as being a new method for collecting physical activity level data for preschoolers. Once the new tool has undergone more reliability and validity testing, it could allow for a more convenient method of collecting physical activity levels for preschoolers. ^

Use of light intensity, temperature, and humidity to verify exposure location

Research studies on the association between exposures to air contaminants and disease frequently use worn dosimeters to measure the concentration of the contaminant of interest. But investigation of exposure determinants requires additional knowledge beyond concentration, i.e., knowledge about personal activity such as whether the exposure occurred in a building or outdoors. Current studies frequently depend upon manual activity logging to record location. This study's purpose was to evaluate the use of a worn data logger recording three environmental parameters—temperature, humidity, and light intensity—as well as time of day, to determine indoor or outdoor location, with an ultimate aim of eliminating the need to manually log location or at least providing a method to verify such logs. For this study, data collection was limited to a single geographical area (Houston, Texas metropolitan area) during a single season (winter) using a HOBO H8 four-channel data logger. Data for development of a Location Model were collected using the logger for deliberate sampling of programmed activities in outdoor, building, and vehicle locations at various times of day. The Model was developed by analyzing the distributions of environmental parameters by location and time to establish a prioritized set of cut points for assessing locations. The final Model consisted of four "processors" that varied these priorities and cut points. Data to evaluate the Model were collected by wearing the logger during "typical days" while maintaining a location log. The Model was tested by feeding the typical day data into each processor and generating assessed locations for each record. These assessed locations were then compared with true locations recorded in the manual log to determine accurate versus erroneous assessments. The utility of each processor was evaluated by calculating overall error rates across all times of day, and calculating individual error rates by time of day. Unfortunately, the error rates were large, such that there would be no benefit in using the Model. Another analysis in which assessed locations were classified as either indoor (including both building and vehicle) or outdoor yielded slightly lower error rates that still precluded any benefit of the Model's use.^

Effects of congressional modernization laws on public access to innovative medical device technologies

The Federal Food and Drug Administration (FDA) and the Centers for Medicare and Medicaid (CMS) play key roles in making Class III, medical devices available to the public, and they are required by law to meet statutory deadlines for applications under review. Historically, both agencies have failed to meet their respective statutory requirements. Since these failures affect patient access and may adversely impact public health, Congress has enacted several “modernization” laws. However, the effectiveness of these modernization laws has not been adequately studied or established for Class III medical devices. ^ The aim of this research study was, therefore, to analyze how these modernization laws may have affected public access to medical devices. Two questions were addressed: (1) How have the FDA modernization laws affected the time to approval for medical device premarket approval applications (PMAs)? (2) How has the CMS modernization law affected the time to approval for national coverage decisions (NCDs)? The data for this research study were collected from publicly available databases for the period January 1, 1995, through December 31, 2008. These dates were selected to ensure that a sufficient period of time was captured to measure pre- and post-modernization effects on time to approval. All records containing original PMAs were obtained from the FDA database, and all records containing NCDs were obtained from the CMS database. Source documents, including FDA premarket approval letters and CMS national coverage decision memoranda, were reviewed to obtain additional data not found in the search results. Analyses were conducted to determine the effects of the pre- and post-modernization laws on time to approval. Secondary analyses of FDA subcategories were conducted to uncover any causal factors that might explain differences in time to approval and to compare with the primary trends. The primary analysis showed that the FDA modernization laws of 1997 and 2002 initially reduced PMA time to approval; after the 2002 modernization law, the time to approval began increasing and continued to increase through December 2008. The non-combined, subcategory approval trends were similar to the primary analysis trends. The combined, subcategory analysis showed no clear trends with the exception of non-implantable devices, for which time to approval trended down after 1997. The CMS modernization law of 2003 reduced NCD time to approval, a trend that continued through December 2008. This study also showed that approximately 86% of PMA devices do not receive NCDs. ^ As a result of this research study, recommendations are offered to help resolve statutory non-compliance and access issues, as follows: (1) Authorities should examine underlying causal factors for the observed trends; (2) Process improvements should be made to better coordinate FDA and CMS activities to include sharing data, reducing duplication, and establishing clear criteria for “safe and effective” and “reasonable and necessary”; (3) A common identifier should be established to allow tracking and trending of applications between FDA and CMS databases; (4) Statutory requirements may need to be revised; and (5) An investigation should be undertaken to determine why NCDs are not issued for the majority of PMAs. Any process improvements should be made without creating additional safety risks and adversely impacting public health. Finally, additional studies are needed to fully characterize and better understand the trends identified in this research study.^

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

AN ILLNESS-DEATH PROCESS WITH TIME-DEPENDENT COVARIATES

A general model for the illness-death stochastic process with covariates has been developed for the analysis of survival data. This model incorporates important baseline and time-dependent covariates to make proper adjustment for the transition probabilities and survival probabilities. The follow-up period is subdivided into small intervals and a constant hazard is assumed for each interval. An approximation formula is derived to estimate the transition parameters when the exact transition time is unknown.^ The method developed is illustrated by using data from a study on the prevention of the recurrence of a myocardial infarction and subsequent mortality, the Beta-Blocker Heart Attack Trial (BHAT). This method provides an analytical approach which simultaneously includes provision for both fatal and nonfatal events in the model. According to this analysis, the effectiveness of the treatment can be compared between the Placebo and Propranolol treatment groups with respect to fatal and nonfatal events. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.