Resemblance in physical activity levels: The Portuguese sibling study on growth, fitness, lifestyle, and health.

OBJECTIVES: To investigate the relationships of biological, behavioral, familial, and environmental characteristics with siblings´ physical activity (PA) levels as well as the intrapair resemblance in PA. METHODS: The sample comprises 834 (390 females) biological siblings [brother-brother (BB), sister-sister (SS), brother-sister (BS)] aged 9 to 20 years. Total PA index (TPAI) was estimated by questionnaire. Information on potential behavioral, familial, and environmental correlates was obtained by self-report; body mass index (BMI), biological maturation, and physical fitness were measured. Multilevel models were used to analyze siblings´ clustered data, and sibling resemblance was estimated with the intraclass correlation (ρ). RESULTS: On average, younger sibs, those more physically fit, and those with more parental support had greater TPAI. Further, BB pairs had higher TPAI levels than SS or BS pairs, but also had greater within-pair variance. When adjusted for all covariates, SS pairs demonstrated greater resemblance in TPAI (ρ = 0.53, 95%CI = 0.38-0.68) than BS (ρ = 0.26, 95%CI = 0.14-0.43) or BB pairs (ρ = 0.18, 95%CI = 0.06-0.44). CONCLUSIONS: Age, physical fitness, and parental support were the best predictors of TPAI levels. A moderate level of resemblance in TPAI was observed in SS pairs, while lower resemblance was found for BS and BB pairs. These findings may be due to differences in the roles of shared genetic factors, familial, and environmental characteristics across different sibling types.

Correlates of children's compliance with moderate-to-vigorous physical activity recommendations: a multilevel analysis.

This study aimed to investigate the association between individual and school characteristics associated with the number of school days children comply with moderate-to-vigorous physical activity (MVPA) recommendations. Sample comprises 612 Portuguese children, aged 9-11 years, from 23 schools. Time spent in MVPA was measured by accelerometry, while individual-level correlates were obtained by anthropometry and questionnaires. School-level variables were collected by questionnaire, and accelerometer wear time and season were also considered. Maximum likelihood estimates of model parameters were obtained via a multilevel analysis with children as level-1, and school as level-2. Children who spent more time in sedentary activities and girls were less likely to comply with MVPA/daily. More mature children and those who use active transportation to school were more likely to attain the PA recommendation. Furthermore, greater accelerometer wear time and spring season increased the chance to achieve the recommended MVPA. In terms of school-level correlates, a greater number of available facilities was negatively associated with children MVPA compliance. Given the set of variables, our results showed that individual characteristics seem to be more relevant for children's compliance rates with PA/day than school context variables, which should be taken into account in the implementation of school policies and practices.

Obesity prevention in low socioeconomic status urban African-american adolescents: study design and preliminary findings of the HEALTH-KIDS Study.

OBJECTIVES: Obesity prevention among children and adolescents is a public health priority; however, limited school-based intervention trials targeting obesity have been conducted. This article provides an overview of the study design and baseline preliminary findings of our ongoing school-based intervention study. DESIGN: Randomized intervention trial to test a school-based, environmental obesity prevention program in urban low socioeconomic status (SES) African-American adolescents. The intervention program was developed based on several behavioral theories and was guided by preliminary findings based on focus group discussion and baseline data. SETTING: Four Chicago public schools in the US. SUBJECTS: Over 450 5-7th graders and their families and schools were involved. RESULTS: Our baseline data indicate a high prevalence of overweight (43% in boys and 41% in girls) and a number of problems in these children's physical activity and eating patterns. Only 26% reported spending > or = 20 min engaged in vigorous-moderate exercise in > or = 5 days over the past 7 days; 29% reported spending > or = 5 h each day watching TV, playing video games, or using computer. They also consumed too many fried foods and soft drinks. On average, 55% consumed fried foods > or = 2 times/day over the past 7 days; regarding soft drinks, 70% reported consuming > or = 2 times/day. CONCLUSION: School-based obesity prevention programs are urgently needed in the target US urban, low SES, minority communities. These data can be used to inform intervention activities.

Validity of the Test of Infant Motor Performance for discriminating among infants with varying risk for poor motor outcome.

OBJECTIVE: The objective was to assess the ability of the Test of Infant Motor Performance (TIMP) to discriminate among infants with varying degrees of risk for motor developmental morbidity on the basis of perinatal medical complications. STUDY DESIGN: Ninety-eight infants were tested weekly with the TIMP until 4 months of age. Comparisons were made among 5 groups of infants: (1) term infants without significant medical problems (low risk); (2) infants born prematurely with no significant medical problems (medium risk); (3) infants born at <30 weeks' gestational age or with birth weight <1500 g (high risk); (4) infants with chronic lung disease (high risk); and (5) infants with brain insults (high risk). A random-effects growth curve analysis assessed differences between the groups in slope and level of development across time. RESULTS: Infants in the low- and medium-risk groups did not differ from each other but were significantly better performers than infants in the high-risk groups. Infants with brain insults performed significantly less well than all other infants, both in absolute level of performance and in developmental slope across time. Performance by black infants averaged 2 points higher than that of other infants. CONCLUSIONS: The TIMP can discriminate among infants with differing risks for motor developmental delay.

A comparative evaluation of three self-rating scales for acute mania.

This study compared the performance of three self-rating mania scales, The Internal State Scale (ISS), the Self-Report Manic Inventory (SRMI), and the Altman Self-Rating Mania Scale (ASRM), in a group of patients with acute mania. Forty-four adult inpatients with bipolar disorder, manic or mixed, completed all scales shortly after admission, and 31 patients completed them again after 4-6 weeks of pharmacotherapy. Patients also were rated by clinicians on the Clinician-Administered Rating Scale for Mania (CARS-M). At baseline, scores on the ASRM and the ISS well-being subscale were significantly correlated with CARS-M scores. Posttreatment scores were significantly decreased for the ASRM, SRMI, and the ISS activation subscale. The sensitivities for each scale to correctly identify patients with acute symptoms was 45% for the ISS, 86% for the SRMI, and 93% for the ASRM. Specificities were 73%, 46.6%, and 33%, respectively. The ASRM and SRMI were more sensitive than the ISS in screening patients with acute mania. All three measures were sensitive to treatment effects; however, the item content of the SRMI and the poor sensitivity of the ISS may limit their utility in inpatient settings.

Statistical analysis of randomized trials in tobacco treatment: longitudinal designs with dichotomous outcome.

This article considers two important issues in the statistical treatment of data from tobacco-treatment clinical trials: (1) data analysis strategies for longitudinal studies and (2) treatment of missing data. With respect to data analysis strategies, methods are classified as 'time-naïve' or longitudinal. Time-naïve methods include tests of proportions and logistic regression. Longitudinal methods include Generalized Estimating Equations and Generalized Linear Mixed Models. It is concluded that, despite some advantages accruing to 'time-naïve' methods, in most situations, longitudinal methods are preferable. Longitudinal methods allow direct effects of the tests of time and the interaction of treatment with time, and allow model estimates based on all available data. The discussion of missing data strategies examines problems accruing to complete-case analysis, last observation carried forward, mean substitution approaches, and coding participants with missing data as using tobacco. Distinctions between different cases of missing data are reviewed. It is concluded that optimal missing data analysis strategies include a careful description of reasons for data being missing, along with use of either pattern mixture or selection modeling. A standardized method for reporting missing data is proposed. Reference and software programs for both data analysis strategies and handling of missing data are presented.

An application of the thresholds of change model to the analysis of mental health data.

The threshold of change model (TCM) is a statistical technique for analyzing ordered stages of change variables. TCM focuses on the thresholds that separate the ordered stages, and the effects of explanatory variables are evaluated in terms of raising or lowering the thresholds. TCM also allows the explanatory variables to exert differential influence on each threshold. In this paper, we use TCM to analyze the data from a clinical trial that compared assertive community treatment (ACT) with standard case management (SCM) for patients with co-occurring severe mental illness and substance use disorder. Endpoint data (36-month follow up) were used for this analysis. The response variable is the recoded Substance Abuse Treatment Scale with three ordered levels (engagement/persuasion, active treatment, and recovery/relapse prevention), and hence two thresholds. The explanatory variables are gender and group (ACT vs. SCM). The results indicate that gender exerts constant and significant effects on both thresholds. The group effect is somewhat mixed: ACT lowers the first threshold (active treatment), but raises the second threshold (recovery/relapse prevention).

A random-effects mixture model for classifying treatment response in longitudinal clinical trials.

A random-effects regression model that allows the random coefficients to have a multivariate normal mixture distribution is described for classifying treatment response in longitudinal clinical trials. The proposed model is capable of dealing with longitudinal data from unknown heterogeneous populations. As applied to longitudinal clinical trials, for example, the model can distinguish subgroups of treatment response. Use of the proposed model is illustrated by analyzing data from two psychiatric clinical trials. The first includes depressed patients assigned to drug treatment who are repeatedly measured in terms of their level of depression. The second trial examined schizophrenic patients longitudinally who were assigned to either a drug or placebo condition. For both, the random-effects mixture model allows an assessment of whether patients comprise distinct populations in terms of their treatment response. Based on parameter estimates of the mixture model, ample evidence for a mixture of response to treatment is observed for both datasets.

Random-effects regression analysis of correlated grouped-time survival data.

Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.

Analysis of longitudinal substance use outcomes using ordinal random-effects regression models.

In this paper we describe analysis of longitudinal substance use outcomes using random-effects regression models (RRM). Some of the advantages of this approach is that these models allow for incomplete data across time, time-invariant and time-varying covariates, and can estimate individual change across time. Because substance use outcomes are often measured in terms of dichotomous or ordinal categories, our presentation focuses on categorical versions of RRM. Specifically, we present and describe an ordinal RRM that includes the possibility that covariate effects vary across the cutpoints of the ordinal outcome. This latter feature is particularly useful because a treatment can have varying effects on full versus partial abstinence, for example. Data from a smoking cessation study are used to illustrate application of this model for analysis of longitudinal substance use data.

The thresholds of change model: an approach to analyzing stages of change data.

Stage models are prominent in research describing health behavior change. Since stage models often propose that different factors have varying influences on membership in the different stage, statistical methods that can estimate the thresholds that separate the stages and the relative value of variables in influencing these thresholds are useful. This article describes use of a "thresholds of change" model for analyzing the thresholds separating stages and specifically for examining the effects of explanatory variables on these thresholds using a generalization of an ordinal logistic (or probit) regression model. Data from a skin cancer prevention study (N=3,185) in which participants were grouped into three stages for sunscreen use (precontemplation, contemplation, and action) are used to illustrate the Thresholds of Change Model. For this example, two thresholds exist: a contemplation (between precontemplation and contemplation) and an action threshold (between contemplation and action). Variables examined include gender, skin type, perceived susceptibility to sunburn, worry about skin cancer, and sun protection self-efficacy. We examine models that assume that the effects of these variables are the same across thresholds, and then allow the effects of these variables to vary across thresholds. Results indicate that perceived susceptibility has an equal effect on both thresholds, but that worry and self-efficacy have differential effects: worry exerts a greater influence on the contemplation threshold, whereas self-efficacy has a significantly stronger effect on the action threshold. Gender also has a stronger effect on the action threshold; males were less likely to be classified in the action stage than females. This analytic approach has broad applications to many types of stage data.

Modelling ordinal responses from co-twin control studies.

The co-twin control design has been widely used in studying the effects of environmental factors on the development of diseases. For binary outcomes that arise from co-twin control studies, the conditional likelihood method is commonly used. This approach, however, does not readily extend to ordinal response data because the standard conditional likelihood does not exist for cumulative logit or proportional odds models. In this paper, we investigate the applicability of the random-effects and GEE approaches in analysing ordinal response data from co-twin control studies. Using both approaches, we re-analyse data from a co-twin control study of the impact of military services during the Vietnam era on post-traumatic stress disorders (PTSD). The ordinal models have considerably increased power in detecting the effects of exposure when compared to the analyses using a dichotomized response. We discuss the interpretation of the estimates from GEE and random-effect models in the context of the twin data.

Comparison of population-averaged and subject-specific approaches for analyzing repeated binary outcomes.

Several approaches have been proposed to model binary outcomes that arise from longitudinal studies. Most of the approaches can be grouped into two classes: the population-averaged and subject-specific approaches. The generalized estimating equations (GEE) method is commonly used to estimate population-averaged effects, while random-effects logistic models can be used to estimate subject-specific effects. However, it is not clear to many epidemiologists how these two methods relate to one another or how these methods relate to more traditional stratified analysis and standard logistic models. The authors address these issues in the context of a longitudinal smoking prevention trial, the Midwestern Prevention Project. In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from two and seven repeated measurements, respectively. In the comparison, the authors focus on the interpretation of both time-varying and time-invariant covariates under different models. Implications of these methods for epidemiologic research are discussed.

The Altman Self-Rating Mania Scale.

We report on the development, reliability, and validity of the Altman Self-Rating Mania Scale (ASRM). The ASRM was completed during medication washout and after treatment by 22 schizophrenic, 13 schizoaffective, 36 depressed, and 34 manic patients. The Clinician-Administered Rating Scale for Mania (CARS-M) and Mania Rating Scale (MRS) were completed at the same time to measure concurrent validity. Test-retest reliability was assessed separately on 20 depressed and 10 manic patients who completed the ASRM twice during washout. Principal components analysis of ASRM items revealed three factors: mania, psychotic symptoms, and irritability. Baseline mania subscale scores were significantly higher for manic patients compared to all other diagnostic groups. Manic patients had significantly decreased posttreatment scores for all three subscales. ASRM mania subscale scores were significantly correlated with MRS total scores (r = .718) and CARS-M mania subscale scores (r = .766). Test-retest reliability for the ASRM was significant for all three subscales. Significant differences in severity levels were found for some symptoms between patient ratings on the ASRM and clinician ratings on the CARS-M. Mania subscale scores of greater than 5 on the ASRM resulted in values of 85.5% for sensitivity and 87.3% for specificity. Advantages of the ASRM over other self-rating mania scales are discussed.

Psychotic exacerbations and enhanced vasopressin secretion in schizophrenic patients with hyponatremia and polydipsia.

BACKGROUND: For unclear reasons, life-threatening water intoxication often coincides with acute psychosis in polydipsic schizophrenic patients with chronic hyponatremia. In contrast, most polydipsic schizophrenic patients are normonatremic and never manifest hyponatremia. To explore whether the effect of acute psychosis on water balance differs in these 2 schizophrenic subgroups, we compared their responses to drug-induced psychotic exacerbations. METHODS: Matched polydipsic schizophrenic patients with (n = 6) and without (n = 8) hyponatremia were identified based on past and current indexes of fluid intake and hydration. A transient psychotic exacerbation was induced with an infusion of the psychotomimetic methylphenidate hydrochloride (0.5 mg/kg of body weight over a 60-second period). Antidiuretic hormone levels, subjective desire for water, and factors known to influence water balance were measured at 15-minute intervals for 2 hours. RESULTS: Except for the expected differences in plasma osmolality and sodium, basal measures were similar in the 2 groups. Following methylphenidate administration, antidiuretic hormone levels increased more in the hyponatremic patients (P < .02), despite their consistently lower plasma osmolality (P < .007). No known or putative antidiuretic hormone stimulus could account for this finding. Only basal positive psychotic symptoms (P < .09) and plasma sodium (P < .18) were even marginally associated with the peak antidiuretic hormone responses, but neither factor could explain the difference in the response by the 2 groups. CONCLUSION: Psychotic exacerbations are associated with enhanced antidiuretic hormone secretion, for unknown reasons, in schizophrenic patients with hyponatremia and polydipsia, thereby placing them at increased risk of life-threatening water intoxication.

Patterns of use of smokeless tobacco and the unidimensional model of drug involvement.

The present study tested whether smokeless tobacco (ST) fits a unidimensional model of drug involvement and tried to locate ST along the dimension that underlies drug use. The latent trait analysis was employed to quantify drug involvement in a sample of high school students. Analyses showed that although ST use fits the unidimensional model of drug involvement, the place of ST use along the continuum of drug involvement is not stable and differs by gender and ethnicity. Particularly for males, ST use is likely to be preceded by soft drug use and followed by hard drug use; for females, however, ST use is closely associated with hard drug use. The analyses also revealed that the fit of the unidimensional model and the location of ST use along the dimension vary with different ethnic groups.

A worksite smoking intervention: a 2 year assessment of groups, incentives and self-help.

Sixty-three companies in the Chicago area were recruited to participate in a worksite smoking cessation program. Participants in each worksite received a television program and newspaper supplement (part of a community-wide media campaign), and one of three conditions: (1) self-help manuals alone (M), (2) self-help manuals and incentives for 6 months (IM) or (3) maintenance manuals, incentives and cognitive-behavioral support groups for 6 months (GIM). Results at the 2 year assessment are examined using a random-effects regression model. In addition, various definitions of quit-rate commonly used in smoking cessation research are explored and the advantages of using a public health approach in the worksite are examined.

Random effects probit and logistic regression models for three-level data.

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.

Application of random-effects regression models in relapse research.

This article describes and illustrates use of random-effects regression models (RRM) in relapse research. RRM are useful in longitudinal analysis of relapse data since they allow for the presence of missing data, time-varying or invariant covariates, and subjects measured at different timepoints. Thus, RRM can deal with "unbalanced" longitudinal relapse data, where a sample of subjects are not all measured at each and every timepoint. Also, recent work has extended RRM to handle dichotomous and ordinal outcomes, which are common in relapse research. Two examples are presented from a smoking cessation study to illustrate analysis using RRM. The first illustrates use of a random-effects ordinal logistic regression model, examining longitudinal changes in smoking status, treating status as an ordinal outcome. The second example focuses on changes in motivation scores prior to and following a first relapse to smoking. This latter example illustrates how RRM can be used to examine predictors and consequences of relapse, where relapse can occur at any study timepoint.