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Biomedical subjects

Linda M Collins

Publications and source records attributed to Linda M Collins.

14 recordsLinked to original sources
PubMed2026

Optimizing smoking cessation pharmacotherapy and counseling for adult primary care patients: a factorial randomized controlled trial.

BACKGROUND: Even with the most effective smoking cessation pharmacotherapies (i.e., varenicline or combination nicotine replacement [C-NRT]), the majority of people ultimately return to smoking. This research explored how to optimize the use of varenicline and C-NRT to promote smoking cessation. METHODS: Primary care patients participated in a 2x2x2x2 factorial experiment that evaluated 4 factors: 1) Medication Type (Varenicline vs. C-NRT [patch + mini-lozenge]), 2) Preparation (pre-quit) Medication (4 Weeks vs. Standard); 3) Medication Duration (Extended [24 weeks] vs. Standard [12 weeks]); and 4) Counseling Type (Cessation Counseling [4 sessions] vs. Referral Support [2 sessions focused on use of referral resources]). This study was discontinued prior to reaching the proposed sample size (N = 608) due to pandemic-related budgetary constraints. RESULTS: Participants (N = 496) were 55% women and 45.6% Black individuals. There were no statistically significant main effects of the 4 factors on abstinence at 12, 26 or 52 weeks. There was a 3-way interaction between Medication Type, Preparation Medication, and Counseling Type (p = 0.04) predicting the primary outcome of biochemically confirmed abstinence at 52 weeks; cessation counseling vs. referral support improved varenicline quit rates when 4 weeks versus 1 week of pre-quit medication was offered. For C-NRT, counseling type did not significantly improve quit rates regardless of the use of preparation medication. CONCLUSIONS: There was no robust evidence that enhanced pre-quit or extended duration of varenicline or C-NRT increased abstinence rates. More intensive counseling may support cessation for different pharmacotherapy regimens. Given the lack of consistent findings, this research should be viewed as exploratory to guide future research.

Humans↗
PubMed2006

Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction.

The goal of this paper is to describe the role that control engineering principles can play in developing and improving the efficacy of adaptive, time-varying interventions. It is demonstrated that adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. This article illustrates how to express an adaptive intervention in control engineering terms, and how to use this framework in a computer simulation to investigate the anticipated impact of intervention design choices on efficacy. The potential benefits of operationalizing decision rules based on control engineering principles are particularly significant for adaptive interventions that involve multiple components or address co-morbidities, situations that pose significant challenges to conventional clinical practice.

Drug Administration Schedule↗
PubMed2006

Analysis of longitudinal data: the integration of theoretical model, temporal design, and statistical model.

This article argues that ideal longitudinal research is characterized by the seamless integration of three elements: (a) a well-articulated theoretical model of change observed using (b) a temporal design that affords a clear and detailed view of the process, with the resulting data analyzed by means of (c) a statistical model that is an operationalization of the theoretical model. Two general varieties of theoretical models are considered: models in which the time-related change of primary interest is continuous, and those in which it is characterized by movement between discrete states. In addition, two general types of temporal designs are considered: the longitudinal panel design and the intensive longitudinal design. For each general category of theoretical models, some of the analytic possibilities available for longitudinal panel designs and for intensive longitudinal designs are discussed. The article concludes with brief discussions of two issues particularly relevant to longitudinal research--missing data and measurement--and a few words about exploratory research.

Humans↗
PubMed2006

A mixture model of discontinuous development in heavy drinking from ages 18 to 30: the role of college enrollment.

OBJECTIVE: The purpose of this study was to illustrate the use of latent class analysis to examine change in behavior over time. Patterns of heavy drinking from ages 18 to 30 were explored in a national sample; the relationship between college enrollment and pathways of heavy drinking, particularly those leading to adult heavy drinking, was explored. METHOD: Latent class analysis for repeated measures is used to estimate common pathways through a stage-sequential process. Common patterns of development in a categorical variable (presence or absence of heavy drinking) are estimated and college enrollment is a grouping variable. Data were from the National Longitudinal Survey of Youth (N=1,265). RESULTS: Eight patterns of heavy drinking were identified: no heavy drinking (53.7%); young adulthood only (3.7%); young adulthood and adulthood (3.7%); college age only (2.6%); college age, young adulthood, and adulthood (8.7%); high school and college age (4.4%); high school, college age, and young adulthood (6.3%); and persistent heavy drinking (16.9%). CONCLUSIONS: We found no evidence that prevalence of heavy drinking for those enrolled in college exceeds the prevalence for those not enrolled at any of the four developmental periods studied. In fact, there is some evidence that being enrolled in college appears to be a protective factor for young adult and adult heavy drinking. College-enrolled individuals more often show a pattern characterized by heavy drinking during college ages only, with no heavy drinking prior to and after the college years, whereas nonenrolled individuals not drinking heavily during high school or college ages are at increased risk for adult heavy drinking.

Adolescent↗
PubMed2006

A multidimensional developmental model of alcohol use during emerging adulthood.

OBJECTIVE: Longitudinal analyses identified unique multidimensional classes of alcohol use and examined individuals' movement among these classes during emerging adulthood. METHOD: Latent transition analysis was used to identify a developmental model of alcohol use incorporating four aspects of use: use in the past year, frequency of use, quantity of use, and heavy episodic drinking. Participants were drawn from the Reducing Risk in Young Adult Transitions study (N = 1,143). Participants' alcohol use was assessed at mean ages of 18.5, 20.5, and 22.5 years. RESULTS: Through exploratory analysis, a five-class developmental model was identified as the best description of participants' alcohol use between ages 18.5 and 22.5 years. This model consisted of five multidimensional alcohol-use latent variables: no use, occasional low use, occasional high use, frequent high use, and frequent high use with heavy episodic drinking. Analyses provided information regarding the proportion of participants in each latent class in the model at each measurement occasion and patterns of participants' movement among latent classes during the observed age period. CONCLUSIONS: Although alcohol use increased overall for study participants between ages 18.5 and 22.5, participants in lower-level alcohol-use latent classes were more likely to remain in low-level latent classes over time, and participants in moderate- and high-level latent classes were more likely to be in the frequent high use with heavy episodic drinking latent class over time. Implications for the prevention of heavy episodic drinking are discussed.

Adolescent↗
PubMed2005

Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis.

Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concerning stage-sequential change over time in longitudinal data. Both approaches have been used with increasing frequency in the social sciences. The objective of this article is to illustrate data augmentation (DA), a Markov chain Monte Carlo procedure that can be used to obtain parameter estimates and standard errors for LCA and LTA models. By use of DA it is possible to construct hypothesis tests concerning not only standard model parameters but also combinations of parameters, affording tremendous flexibility. DA is demonstrated with an example involving tests of ethnic differences, gender differences, and an Ethnicity x Gender interaction in the development of adolescent problem behavior.

Humans↗
PubMed2005

A strategy for optimizing and evaluating behavioral interventions.

BACKGROUND: Although the optimization of behavioral interventions offers the potential of both public health and research benefits, currently there is no widely agreed-upon principled procedure for accomplishing this. PURPOSE: This article suggests a multiphase optimization strategy (MOST) for achieving the dual goals of program optimization and program evaluation in the behavioral intervention field. METHODS: MOST consists of the following three phases: (a) screening, in which randomized experimentation closely guided by theory is used to assess an array of program and/or delivery components and select the components that merit further investigation; (b) refining, in which interactions among the identified set of components and their interrelationships with covariates are investigated in detail, again via randomized experiments, and optimal dosage levels and combinations of components are identified; and (c) confirming, in which the resulting optimized intervention is evaluated by means of a standard randomized intervention trial. To make the best use of available resources, MOST relies on design and analysis tools that help maximize efficiency, such as fractional factorials. RESULTS: A slightly modified version of an actual application of MOST to develop a smoking cessation intervention is used to develop and present the ideas. CONCLUSIONS: MOST has the potential to husband program development resources while increasing our understanding of the individual program and delivery components that make up interventions. Considerations, challenges, open questions, and other potential benefits are discussed.

Behavior Therapy↗
PubMed2004

Using growth models to relate acquisition of nicotine self-administration to break point and nicotinic receptor binding.

Growth modeling can be used to characterize individual and mean acquisition trajectories for drug self-administration. Individual characteristics can also be incorporated into the growth model, providing a powerful tool for investigating the relationship between acquisition and other behavioral and biological measures. We illustrate the utility of this method by examining the relationship between acquisition of nicotine self-administration and (1) break point on a progressive ratio schedule of reinforcement, and (2) the density of brain nicotinic receptors (B(max)). Daily infusion rates from male and female Sprague-Dawley rats were modeled with break point or B(max) as time-invariant covariates. Use of this model led to two novel findings regarding individual differences in acquisition. First, greater rates of change in infusions early in acquisition were related to higher break points; this relationship was mediated by a similar effect of increasing the number of responses required to obtain nicotine. Second, animals displaying more resistance to increases in the response requirement during acquisition, as indicated by a smaller drop in the rate of nicotine self-administration, generally had fewer nicotinic receptors at the end of the experiment. The relationships revealed demonstrate the usefulness of growth models in the quantitative analysis of individual differences in drug self-administration behavior.

Animals↗
PubMed2004

Analyzing the acquisition of drug self-administration using growth curve models.

Current approaches to studying acquisition of drug self-administration have modest power to detect individual differences in the pattern of acquisition or to efficiently and accurately describe trajectories of behavior change. Methodological advances in human research have elucidated approaches to describing repeated measure data that focus on modeling the behavior of individual subjects. In this article, we re-analyzed data published in using growth curve modeling to characterize the acquisition of nicotine-taking in rats. Change over time in the infusion rate was examined, revealing that the acquisition process could be described with a quadratic equation represented by intercept, slope, and acceleration parameters. Unit dose of nicotine, sex and fixed ratio (FR) schedule of reinforcement had significant effects on the acquisition curves. Dose altered the absolute rate of infusions, but not the slope or acceleration, indicating that, when an effective dose was available, the shape of acquisition trajectories was not affected by dose. In addition, dose impacted acquisition by moderating the disruption in infusion rates after an increase in the response requirement. Thus, the role of a higher dose may not be to accelerate the acquisition process but to lead to behavior that is more resistant to change. Trajectories differed between males and females at the smallest dose, but these differences dissipated by the end of acquisition. Growth curve modeling captures the process of acquisition of drug self-administration and facilitates a greater understanding of the individual differences in change in drug-taking behavior over time.

Animals↗
PubMed2004

A conceptual framework for adaptive preventive interventions.

Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant's values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed.

Child↗
PubMed2002

Adaptive sampling in research on risk-related behaviors.

This article introduces adaptive sampling designs to substance use researchers. Adaptive sampling is particularly useful when the population of interest is rare, unevenly distributed, hidden, or hard to reach. Examples of such populations are injection drug users, individuals at high risk for HIV/AIDS, and young adolescents who are nicotine dependent. In conventional sampling, the sampling design is based entirely on a priori information, and is fixed before the study begins. By contrast, in adaptive sampling, the sampling design adapts based on observations made during the survey; for example, drug users may be asked to refer other drug users to the researcher. In the present article several adaptive sampling designs are discussed. Link-tracing designs such as snowball sampling, random walk methods, and network sampling are described, along with adaptive allocation and adaptive cluster sampling. It is stressed that special estimation procedures taking the sampling design into account are needed when adaptive sampling has been used. These procedures yield estimates that are considerably better than conventional estimates. For rare and clustered populations adaptive designs can give substantial gains in efficiency over conventional designs, and for hidden populations link-tracing and other adaptive procedures may provide the only practical way to obtain a sample large enough for the study objectives.

Adolescent↗
PubMed2002

The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: temporal design considerations.

This article explores the impact of the temporal design, i.e. the sampling of times of measurement, on the statistical and substantive conclusions drawn from longitudinal biomedical and social science research. It is shown that for a study of a given duration, if observations are spaced too far apart the resulting data can support misleading conclusions, whereas if observations are spaced relatively close together, a much more veridical picture of the process of interest is provided. The application of these ideas in several areas is discussed, including correlation and regression analysis where a variable measured at one time is used to predict a variable measured at a later time; growth curve analyses; and analyses involving stage-sequential processes. We argue that longitudinal designs should relate the choice of timing and spacing of observations in longitudinal studies to characteristics of the processes being measured. In addition, consideration of the possible effects of measurement design on results of statistical analyses may aid in their interpretation. New approaches involving intensive data collection with much shorter measurement intervals, such as Ecological Momentary Assessment, are promising, but are costly and are not suitable for every research question. More information is needed to help guide researchers in their choice of temporal design.

Follow-Up Studies↗
PubMed2002

Pubertal timing and the onset of substance use in females during early adolescence.

The goal of this study is to examine in detail the relationship between pubertal timing and substance use onset using a sample of females from The National Longitudinal Study of Adolescent Health. The sample includes 966 females who were in 7th grade at Wave 1 and 8th grade at Wave 2. Participants in the sample are approximately 69% White, 20% African American, 4% Asian or Pacific Islander, 2% American Indian, 4% other, of Hispanic origin, and 1% other, not of Hispanic origin. Twenty percent of the females were identified as early maturers based on self-reports of body changes (increased breast size and body curviness) measured in 7th grade. These participants are hypothesized to be at increased risk for substance use onset. Important differences in substance use onset were found between early maturers and their on-time and late-maturing counterparts. During 7th grade, females in the early-maturing group are three times more likely to be in the most advanced stage of substance use (involving alcohol use, drunkenness, cigarette use, and marijuana use) than are those in the on-time/late group. Prevalence rates indicate that early maturers are more likely to have tried alcohol, tried cigarettes, been drunk, and tried marijuana. Prospective findings show that early developers are significantly more likely to transition out of the "No Substance Use" stage between 7th and 8th grade (47% for early developers vs. 22% for on-time and late developers). In addition, early developers are more likely to advance in substance use in general, regardless of their level of use at Grade 7.

Adolescent↗