Prostate Cancer (PC) is associated with disease- and treatment-related side effects that can compromise quality of life (QoL). Psychosocial interventions designed to improve adjustment and quality of life (QoL) for post-treatment PC patients have been conducted with mixed results. Intervention effects are typically analyzed using either mean difference scores or a single estimate of growth parameters (e.g., intercept and slope factors) across groups. These methods assume homogeneity within groups. Evidence suggests, however, considerable variability both in the experience of disease-specific outcomes and in the long-term adjustment and emotional well-being of men with PC. The present study used growth mixture modeling (GMM) to explore the effects of a cognitive behavioral stress management (CBSM) intervention on emotional well-being among men recently treated for localized PC. This methodology allowed examination of intervention effects across unobserved subgroups characterized by different trajectories of emotional well-being and identified factors associated with intervention efficacy.

This study integrated variable- and child-centered techniques to investigate trajectories of four learning behaviors (initiative, persistence, planning, and problem-solving flexibility) and their influence on Head Start preschoolers' academic school readiness. Variable-centered findings revealed differential, quadratic growth trajectories for each of the four learning behaviors. However, where children began the year (intercept), how much they changed across the year (slope), and how much their rate of change changed across the year (quadratic) differed depending on the learning behavior. Initiative and problem-solving flexibility emerged as significant predictors of end-of-year academic school readiness skills, controlling for persistence and planning. There was no evidence of moderation of the relations between learning behaviors and academic skills by child demographic characteristics. Child-centered results provided a more nuanced description of the development of these four learning behaviors. Analyses suggested there may be subgroups of children with different developmental trajectories for each of the four learning behaviors and that these subgroups have significantly different school readiness skills at the end of the year. These findings help extend our current understanding of learning behaviors and, if replicated, may inform the content and timing of early childhood teaching practices and interventions.

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This study examined heterogeneity in adolescents experimentation with partnered sexual behaviours. Participants were 88 high school students in Edmonton, Alberta (M age = 16.59, SD = .95). Students completed surveys online once per two months from December, 2008 through December, 2009. Surveys tracked students reports of seven sexual behaviours ranging in intimacy from holding hands to intercourse. Growth mixture models were used to sort students trajectories of sexual behaviours across months into latent classes based on similar profiles. The best-fitting model revealed three distinct classes, labeled inexperienced, experimenting, and experienced. Students classified as inexperienced primarily reported only lower-intimacy, non-genital sexual behaviours across months, and many reported no sexual behaviours. Students classified as experimenting and experienced reported similar levels of higher-intimacy sexual behaviours across months. Most experimenting students behaviours appeared to increase gradually from less to more intimate, whereas experienced students appeared to make abrupt transitions between lower- and higher-intimacy behaviours, month-to-month. Demographic, personal, peer, and family variables provided additional information that increased distinction among classes, and explained residual within-class heterogeneity. The probability of being classified as inexperienced was highest for students who were younger, reported fewer sexually experienced friends, and lower parent behavioural control. Students who reported higher parent behavioural control had the highest probability of being classified as experimenting. Relations between trajectories of sexual behaviour intimacy and risk factors (e.g., later pubertal timing, fewer problem behaviours) and protective-enhancing resources (e.g., higher psychosocial maturity, more intimate friendships) varied across classes. This study shows that there are multiple pathways of experimentation with sexual behaviour in adolescence. Results are consistent both with studies that emphasize the potential for sex in adolescence to be high-risk, and with studies and arguments that emphasize the potential for sex in adolescence to play an important preparatory role toward healthy adult sexual functioning. Theoretical arguments and discussion are guided by a person-centered, developmental systems approach.

Growth mixture modeling (GMM) is a useful tool for examining both between- and within-persons change over time and uncovering unobserved heterogeneity in growth trajectories. Importantly, the correct extraction of latent classes and parameter recovery can be dependent upon the type of covariates used. Time-varying covariates (TVCs) can influence class membership but are scarcely included in GMMs as predictors. Other times, TVCs are incorrectly modeled as time-invariant covariates (TICs). Additionally, problematic results can occur with the use of maximum likelihood (ML) estimation in GMMs, including convergence issues and sub-optimal maxima. In such cases, Bayesian estimation may prove to be a useful solution. The present Monte Carlo simulation study aimed to assess class enumeration accuracy and parameter recovery of GMMs with a TVC, particularly when a TVC has been incorrectly specified as a TIC. Both ML estimation and Bayesian estimation were examined. Results indicated that class enumeration indices perform less favorably in the case of TVC misspecification, particularly absolute class enumeration indices. Additionally, in the case of TVC misspecification, parameter bias was found to be greater than the generally accepted cutoff of 10%, particularly for variance estimates. It is recommended that researchers continue to use a variety of class enumeration indices during class enumeration, particularly relative indices. Additionally, researchers should take caution when interpreting variance parameter estimates when the GMM contains a misspecified TVC.

Research has suggested that depression symptoms generally decrease after late adolescence; however, there is increasing attention paid to depression symptoms among college students given the stressors unique to this time period and negative outcomes associated with depression. This study examined latent trajectories of depression symptom severity among college students. Participants were 9,889 college students who participated in the Spit for Science project (Dick et al., 2011). Growth Mixture Modeling was used to identify the presence of four subgroups of individuals with similar patterns of initial level and change in depression severity over four years of college, including Low/Minimal (55.9%), Decreasing (2.8%), Increasing (11.6%), and Chronically Elevated (29.7%) groups. Risk factors of belonging to a depressed mood trajectory include female gender; lesbian, gay, or bisexual orientation; and experiencing a greater number of stressful life events. Higher social support and self-reported resilience were associated with decreased likelihood of belonging to any of the depressed mood trajectories. Overall, it appears that most college students in this sample experience only mild depression symptoms; however, it is important to recognize and intervene early with individuals who report elevated depression symptoms as some are at risk for persistent and increasing depression across college.

The purpose of this study was to assess fear of cancer recurrence (FCR) in breast cancer survivors returning for regularly scheduled follow-up mammograms. FCR was hypothesized to increase prior to the mammogram, decrease from immediately pre- to immediately post-mammogram, and then increase following the mammogram. Based on the cognitive-behavioral model (CBM) of health anxiety, greater perceived risk of recurrence, worse perceived consequences of a recurrence, lower coping self-efficacy, and more engagement in reassurance-seeking behaviors were hypothesized to be associated with greater FCR in each time segment. Finally, exploratory analyses evaluated the various trajectories in FCR over time using growth mixture modeling and the CBM to predict class membership. The sample comprised 161 women who completed treatment for stage 0-IIIA breast cancer between 6 and 36 months previously. Participants completed the following measures at least 31 days prior to the scheduled mammogram: perceived risk and perceived consequences of breast cancer recurrence, treatment efficacy beliefs, coping self-efficacy, and reassurance seeking behaviors. Participants reported FCR at one month, one week, and immediately prior to the mammogram as well as one month, one week, and immediately after the mammogram using visual analogue scales (VAS) to rate anxiety and worry about cancer recurrence, the Cancer Worry Scale (CWS), and the Fear of Cancer Recurrence Inventory (FCRI). State anxiety and reassurance post-mammogram were also assessed. FCR significantly changed over time with increases in CWS scores prior to the mammogram, a significant decline on the VAS observed immediately following receipt of results, and a significant increase on the VAS, and decrease in reassurance during the month following the mammogram. The CBM did not significantly predict change in FCR over time, but certain variables did predict fluctuations including coping-self efficacy and perceived risk in the expected directions. Finally, growth mixture models revealed two classes, high-FCR and low-FCR, which were predicted by the CBM. These study findings support the use of the CBM in predicting which cancer survivors experience greater FCR and indicates that CBM-driven interventions may prove beneficial for reducing distressing FCR.

Obesity, as defined as BMI at or above the 95th percentile on the Centers for Disease Control and Prevention’s growth charts, has increased almost 3-fold among children in the United States since 1980. Overweight in adolescence has been associated with increased fat retention and high blood pressure in adulthood, among other symptoms of metabolic syndrome. However, normative patterns of weight change in childhood have not been developed. Groups of children may follow different trajectory patterns of BMI change over time. If common trajectory patterns could be identified, and their risk factors and outcomes understood, more nuanced intervention with families and children at risk for obesity could be developed. This study used a national dataset of 1,364 children whose weight and length was measured 12 times from birth through 15 ½ years. Testing both latent class growth analysis and growth mixture modeling identified four distinct subgroups, or classes, of BMI growth trajectory from 24 months – 8th grade. These classes were compared on numerous demographic, biological, and psychosocial risk factors identified in previous research as related to obesity. Classes were differentiated primarily on the child’s BMI at 15 months, the mother’s BMI at 15 months, birth weight for age, and percent increase in birth weight. Being male, Black, and lower SES were also related to membership in the higher-BMI trajectory classes. Of the psychosocial factors, maternal sensitivity, maternal depression, and attachment classification were also related to BMI class. Membership in these trajectories strongly predicted weight-related and blood-pressure outcomes at 15 ½ years over and above individual risk factors, demonstrating that patterns of change themselves are highly influential. The best-fitting models of weight-related outcomes at 15 ½ years included change trajectory in combination with biological, psychosocial, and SES risk factors from 0-24 months, with R² ranging from .31 = .50. Characteristics predicting adolescent overweight can be identified in the first years of life and should trigger the development and implementation of early intervention protocols in obstetrics and pediatrics. / text

Background: Many patients evaluated for acute coronary syndrome (ACS) in emergency departments (EDs) experience ongoing or recurrent symptoms after discharge, regardless of their ultimate medical diagnosis. A comprehensive understanding of post-ED symptom trajectories is lacking. Aims: Aim 1 was to determine trajectories of severity of common symptoms (chest pressure, chest discomfort, unusual fatigue, chest pain, shortness of breath, lightheadedness, upper back pain and shoulder pain) in the six months following an ED visit for potential ACS. Aim 2 was to identify relationships between symptom trajectories and baseline physiologic factors (age, gender, diabetes status, diagnosis, comorbidities, functional status) and situational factors (marital status, insurance status, education level). Aim 3 was to identify relationships between symptom trajectories and health service use (outpatient visits and calls, ED visits, 911 calls, hospitalization) in the six months after the ED visit. Methods: This was a secondary data analysis from a study conducted in five U.S. EDs. Patients (n=1002) who had abnormal electrocardiogram or biomarker testing and were identified by the triage nurse as potentially having ACS were enrolled. Symptom severity was assessed in the hospital and 30 days and six months post-discharge using the 13-item ACS Symptom Checklist. Symptom severity was modeled across the three study time points using growth mixture modeling. Model selection was based on interpretability, theoretical justification, and statistical fit indices. Patient characteristics were used to predict trajectories using logistic regression and differences in health service use were tested using chi-square analysis. Results: Between two and four distinct trajectory classes were identified for each symptom. Identified trajectories were labeled "tapering off," "mild/persistent," "moderate/persistent," "moderate/worsening," "moderate/improving," "late onset," and "severe/improving." Age, sex, diabetes, BMI, functional status, insurance status, and diagnosis significantly predicted symptom trajectories. Clinic visits and phone calls, 911 calls, ED visits, and probability of hospitalization varied significantly among trajectories. Conclusions: Research on the individual nature of symptom trajectories can support patient-centered care. Patients at risk for ongoing symptoms and increased health service use can be targeted for education and follow-up based on clinically observable characteristics. Further research is needed to verify the existence of multiple symptoms trajectories in diverse populations.

No description available.