Analyses proceeded in three stages. First, at baseline and 3, 6, and 12 months follow-up, the percent of participants at risk, and the identified preaction stage for those at risk, were determined for each HRB. Second, LCA was employed to discern the common patterns of HRBs at each time point. Third, LTA was used to examine the most likely patterns of HRBs over time. LCA was employed first at baseline, then each follow-up time point for evaluation of structure stability. LCA is a latent variable modeling technique that characterizes homogeneous populations within a larger sample who share common response patterns to categorical indicators . Models of 1–6 classes were fit and standard criteria were used to compare the models. Model selection was based on goodness of model fit, parsimony, and adequacy of the model with respect to the research questions being posed. Four sets of criteria were used for selecting the optimal number of latent classes in factor mixture models as recommended by Muthén and Muthén. First, the Bootstrapped Parametric Likelihood Ratio Test tested for model improvement in each successive model over a model with one fewer class. Second, the Sample Size Adjusted Bayesian Information Criterion and Aikaike’s information criterion were examined, with lower values indicating better model fit. Third, the entropy value, ranging from 0 to 1, measured the clarity of classification. Entropy values that are close to 1 indicate that a model has clearly identified individuals of different types, and it can be a useful summary measure. Finally, the usefulness of latent classes in practice was evaluated by substantive interpretation of the classes in a given model, as well as the parameter estimates including class membership or posterior probabilities and class-specific conditional response probabilities . With LCA,seedling grow rack observations are classified into their most likely latent classes on the basis of the estimated posterior probabilities for the observations. High diagonal and low off-diagonal values in the class classification table indicate good classification. CRPs reflect the probability that an individual within a particular class has a high-risk health behavior.
Based on the patterns of the estimated conditional probabilities, meaningful labels or definitions of the latent classes were made. LTA was then used to examine the extent to which patterns of HRBs at baseline were stable over time, using three analyses . Detailed statistical presentations of the general LTA framework are available in Humphreys and Janson and Reboussin et al.. LTA is a longitudinal strategy that assesses the probabilistic change in class membership over time with categorical latent variables. This analysis extends LCA by assigning transition probabilities, which are conditional probabilities describing the probability of being in a given state at time = t, conditional on the state at time = t − 1. We used an LTA to model the stability of HRBs over the course of 12 months; latent transition probabilities were then used to evaluate how individuals either exhibited the same HRB pattern or changed patterns over 12 months. We hypothesized a priori that the intervention may affect multiple HRBs, because multiple HRBs can occur following interventions aimed at changing a single behavior. Moreover, we found a significant intervention effect on smoking outcomes at 3 months in the RCT. Therefore, the models initially included treatment condition as a covariate. However, because very few participants transitioned between classes over time, adding treatment condition to the model resulted in several empty cells. For example, no participants in the control condition transitioned from substance use risk to low risk between baseline and 3 months. Because adding another parameter resulted in several empty cells, the estimates were unstable and could not be reliably interpreted. Moreover, treatment condition was not a significant covariate in the LCA models . Therefore, all models reported here are without the inclusion of treatment condition. LCA and LTA were conducted with Mplus version 7.4 due to the availability of multiple model fit indices not available in other statistics platforms and the ease of employing randomized starting values. Other analyses were conducted with IBM SPSS Statistics. All available cases were used at each time point.In our sample of young adult smokers, nearly all reported engaging in at least one other HRB at baseline and follow-up, the most prevalent at each time point being diet related. The most prominent patterns of HRBs at four time points highlighted that the more prevalent targets, in addition to tobacco, for behavioral change in young adult smokers are diet-inactivity, sleep habits, and cannabis use.
The 3-class solution that fit the data best at all time points was similar to that found in a sample of adult smokers in a mental health treatment setting, with profiles for a global high-risk group, consisting of substance use and metabolic risks, and one in which risks were primarily metabolic . Three very similar profiles emerged in two very different samples of smokers , suggesting that metabolic and substance use patterns ought to be assessed and ideally addressed through direct treatment or referral, in the context of smoking cessation interventions. HRB patterns in young adult smokers may differ from those of the general young adult population. Prior research in the general young adult population has found that smoking generally clusters with substance use and sexual risk behavior. In the present study, wherein all participants were smokers, likelihood of engaging in condomless sex did not systematically vary with other HRBs. Employing broader measures of sexual risk behavior may have yielded an association between sexual risk behavior and substance use. However, given the similarities in HRB profiles between the present sample and adult smokers with serious mental illness, it is also plausible that the HRB profiles of young adults who smoke differ from those of the general young adult population.There were notable differences in stages of change for different HRBs that could be informative when adding health-related content to smoking cessation interventions for young adults. Given that overall, participants were most ready to change their diet, stress management, sleep, or physical activity, an intervention targeting the metabolic risk group would likely be well-received. Membership in the metabolic risk group at baseline was associated with a greater likelihood of smoking daily and with smoking more cigarettes per day. Young adult smokers with metabolic risk factors may be a group that would particularly benefit from cessation medications. In contrast, motivation to change alcohol and drug use was generally low, suggesting that an intervention targeting the substance use group may need to especially focus on motivational enhancement. Cannabis use was common among participants in the substance use group, and those at risk for cannabis use were least ready to change this behavior compared to all other behaviors. Heavy alcohol use declined from baseline to follow-ups, while cannabis use remained elevated.
Given previous reported differences in stages of change for tobacco and other substance use among young adults, smokers of all types may be less receptive to interventions targeting other substance use than they are tobacco or other HRBs. The substance use group had a higher proportion of males,greenhouse growing racks suggesting that interventions with young adult male smokers may benefit from a focus on enhancing motivation to change substance use. Although class membership was mostly stable over the course of one year, transitions from low risk to metabolic risk were somewhat more frequent than transitions to substance use risk. This finding may reflect a general decline in substance use throughout one’s 20s and underscores the need for intervention on metabolic risk behaviors among the general population of young adult smokers. Given a significant difference in smoking abstinence between treatment and control groups at the 3 month follow-up in the clinical trial, we hypothesized that participants would be more likely to transition to classes characterized by lower risk if they had participated in the Facebook smoking cessation intervention compared to the control condition at each time point. Results showed that classes were very stable over time, with few participants transitioning between them. As such, the model could not be reliably fit when treatment condition was included. This reflects the notable stability of young adults’ patterns of HRBs over 12 months, which may be due to the demanding nature of multiple HRB change and limits of cognitive capacity and self-control, coupled with relatively low readiness to change. Results suggest extended intervention content enhancing motivation and supporting behavior change for a few HRBs is likely needed to create meaningful change in multiple HRBs among young adult smokers participating in any form of smoking cessation intervention. This study recruited a relatively diverse sample of young adult smokers in the USA. Notably, more than one in four participants identified as a sexual or gender minority . This may have been due to the high prevalence of both smoking and social media use among SGM individuals. Moreover, 8.2% of millennials identify as SGM compared to 3.5% of those in Generation X. In this sample, SGM and non-SGM young adults did not significantly differ in smoking cessation rates or other health behaviors, with the exception of physical activity, over time.
Nonetheless, clustering of HRBs may vary by other individual differences , and future research could examine differences in the clustering of HRBs by individual characteristics. Notably, we identified few differences in latent class membership by individual characteristics, suggesting that the HRB profiles in this study have broad applicability to young adult smokers.Study limitations include that the data were self-reported and subject to recall bias. Due to empty cells when additional parameters were included, we were unable to include treatment condition as a covariate or incorporate stage of change into LCA and LTA models. Future research should incorporate these additional characteristics. The sample was neither randomly sampled nor representative, thereby limiting generalization of study findings; however, as an initial investigation, the volume of HRBs among young adult smokers appears high and the patterns stable over 12 months.Over the past century, California has built an extraordinarily complex water management system with hundreds of dams and a vast distribution network that spans the state. This system generates electricity, provides flood protection, delivers reliable water supplies to 40 million people and supports one of the most productive agricultural regions in the world. Yet development of the state’s water management system has come at a price. Damming waterways, diverting water from rivers and streams and altering natural flow patterns have transformed the state’s freshwater ecosystems, leading to habitat degradation, declines of freshwater species and loss of services that river ecosystems provide, including high-quality drinking water, fishing and recreational opportunities, and cultural and aesthetic values. The state aims to accommodate human water needs while maintaining sufficient stream flow for the environment. To support this mission, scientists from the U.S. Geological Survey , The Nature Conservancy and UC have developed new techniques and tools that are advancing sustainable water management in California. At the center of these new advances is the need to understand the natural ebbs and flows in the state’s rivers and streams. Natural patterns in stream flow are characterized by seasonal and annual variation in timing , magnitude , duration and frequency . California’s native freshwater species are highly adapted to these seasonally dynamic changes in stream flows. For example, salmon migration is triggered by pulses of stream flow that follow winter’s first storms, reproduction of foothill yellow-legged frogs is synchronized with the predictable spring snow melt in the Sierra Nevada, and many native fish breed on seasonally inundated floodplains, where juveniles take advantage of productive, slow-moving waters to feed and grow. When rivers are modified by dams, diversions and other activities, flows no longer behave in ways that support native species, contributing to population declines and ultimate extinction. Thus, understanding natural stream flow patterns and the role they play in supporting ecosystem health is an essential first step for developing management strategies that balance human and ecosystem needs. Unfortunately, our ability to assess alteration of natural stream flow patterns, and the ecosystem consequences, is hindered by the absence of stream flow data. California’s stream flow gauging network offers only a limited perspective on how much water is moving through our state’s rivers. In fact, it’s been estimated that 86% of California’s significant rivers and streams are poorly gauged and nearly half of the state’s historic gauges have been taken offline due to lack of funding .