Patient Interpersonal Factors and the Therapeutic Alliance in Two Treatments for Bulimia Nervosa

Although the therapeutic alliance is a robust predictor of psychotherapy outcomes, less is known about specific factors that influence its development. The present study examined the association between patient-rated alliance and several patient interpersonal factors (distress, rigidity, & style) in the context of a randomized clinical trial comparing cognitive-behavioral therapy (CBT) and interpersonal therapy (IPT) for bulimia nervosa. Using hierarchical linear modeling, the study found that early and middle alliance quality were both negatively associated with patients’ baseline interpersonal distress and positively associated with baseline interpersonal affiliation. Middle alliance quality was also predicted by interactions between treatment group and rigidity, treatment group and affiliation, and treatment group and control. Overall, the rate of alliance growth was higher in IPT than in CBT. Using group-based trajectory analysis, the study found three divergent patterns of alliance development in the sample (high & improving, low & improving, and low & stable) and detected group mean differences between two of the trajectory groups in terms of patient interpersonal distress and hostile-submissiveness

An Analysis Of Patterns And Predictors Associated With Patient Compliance Using Group-Based Trajectory Modeling

The purpose of the study was to identify differential trajectories of patient compliance in a clinical trial and to determine demographic and health risk factors associated with compliance trajectory membership. The data was obtained from an 18 month, double-blinded, placebo-controlled trial looking at the long-term impact of increased dietary protein on bone mass in older men and women. Two hundred and eight subjects were randomized to either a protein treatment or carbohydrate placebo group. Statistical analysis utilized a group-based trajectory modeling framework to identify distinct clusters of individuals who follow similar compliance trajectories over time. Post hoc analysis using multinomial and standard logistic regression models were conducted to incorporate risks factors associated with compliance group membership. A four-group trajectory model was selected and determined that reported adverse event was a significant risk factor. This analysis will provide supplementation to the standard intention-to-treat analysis to understand how efficacy is driven by compliance and will pave the way to improve compliance in subsequent protein-supplemented trials

The longitudinal interplay between negative and positive symptom trajectories in patients under antipsychotic treatment: a post hoc analysis of data from a randomized, 1-year pragmatic trial

BACKGROUND: Schizophrenia is a highly heterogeneous disorder with positive and negative symptoms being characteristic manifestations of the disease. While these two symptom domains are usually construed as distinct and orthogonal, little is known about the longitudinal pattern of negative symptoms and their linkage with the positive symptoms. This study assessed the temporal interplay between these two symptom domains and evaluated whether the improvements in these symptoms were inversely correlated or independent with each other. METHODS: This post hoc analysis used data from a multicenter, randomized, open-label, 1-year pragmatic trial of patients with schizophrenia spectrum disorder who were treated with first- and second-generation antipsychotics in the usual clinical settings. Data from all treatment groups were pooled resulting in 399 patients with complete data on both the negative and positive subscale scores from the Positive and Negative Syndrome Scale (PANSS). Individual-based growth mixture modeling combined with interplay matrix was used to identify the latent trajectory patterns in terms of both the negative and positive symptoms. Pearson correlation coefficients were calculated to examine the relationship between the changes of these two symptom domains within each combined trajectory pattern. RESULTS: We identified four distinct negative symptom trajectories and three positive symptom trajectories. The trajectory matrix formed 11 combined trajectory patterns, which evidenced that negative and positive symptom trajectories moved generally in parallel. Correlation coefficients for changes in negative and positive symptom subscale scores were positive and statistically significant (P < 0.05). Overall, the combined trajectories indicated three major distinct patterns: (1) dramatic and sustained early improvement in both negative and positive symptoms (n = 70, 18%), (2) mild and sustained improvement in negative and positive symptoms (n = 237, 59%), and (3) no improvement in either negative or positive symptoms (n = 82, 21%). CONCLUSIONS: This study of symptom trajectories over 1 year shows that changes in negative and positive symptoms were neither inversely nor independently related with each other. The positive association between these two symptom domains supports the notion that different symptom domains in schizophrenia may depend on each other through a unified upstream pathological disease process

Nonlinear modeling of FES-supported standing-up in paraplegia for selection of feedback sensors

This paper presents analysis of the standing-up manoeuvre in paraplegia considering the body supportive forces as a potential feedback source in functional electrical stimulation (FES)-assisted standing-up. The analysis investigates the significance of arm, feet, and seat reaction signals to the human body center-of-mass (COM) trajectory reconstruction. The standing-up behavior of eight paraplegic subjects was analyzed, measuring the motion kinematics and reaction forces to provide the data for modeling. Two nonlinear empirical modeling methods are implemented-Gaussian process (GP) priors and multilayer perceptron artificial neural networks (ANN)-and their performance in vertical and horizontal COM component reconstruction is compared. As the input, ten sensory configurations that incorporated different number of sensors were evaluated trading off the modeling performance for variables chosen and ease-of-use in everyday application. For the purpose of evaluation, the root-mean-square difference was calculated between the model output and the kinematics-based COM trajectory. Results show that the force feedback in COM assessment in FES assisted standing-up is comparable alternative to the kinematics measurement systems. It was demonstrated that the GP provided better modeling performance, at higher computational cost. Moreover, on the basis of averaged results, the use of a sensory system incorporating a six-dimensional handle force sensor and an instrumented foot insole is recommended. The configuration is practical for realization and with the GP model achieves an average accuracy of COM estimation 16 /spl plusmn/ 1.8 mm in horizontal and 39 /spl plusmn/ 3.7 mm in vertical direction. Some other configurations analyzed in the study exhibit better modeling accuracy, but are less practical for everyday usage

Using Growth Mixture Modeling to Identify Classes of Sodium Adherence in Adults with Heart Failure

BACKGROUND: The prevention of fluid retention is important to reduce hospitalizations in patients with heart failure (HF). Following a low-sodium diet helps to reduce fluid retention. OBJECTIVE: The primary objective of this study was to use growth mixture modeling to identify distinct classes of sodium adherence-characterized by shared growth trajectories of objectively measured dietary sodium. The secondary objective was to identify patient-level determinants of the nonadherent trajectory. METHODS: This was a secondary analysis of data collected from a prospective longitudinal study of 279 community-dwelling adults with previously or currently symptomatic HF. Growth mixture modeling was used to identify distinct trajectories of change in 24-hour urinary sodium excretion measured at 3 time points over 6 months. Logistic modeling was used to predict membership in observed trajectories. RESULTS: The sample was predominantly male (64%), had a mean age of 62 years, was functionally compromised (59% New York Heart Association class III), and had nonischemic HF etiology. Two distinct trajectories of sodium intake were identified and labeled adherent (66%) and nonadherent (34%) to low-sodium diet recommendations. Three predictors of the nonadherent trajectory were identified, confirming our previous mixed-effect analysis. Compared with being normal weight (body mass index/m2), being overweight and obese was associated with a 4-fold incremental increase in the likelihood of being in the nonadherent trajectory (odds ratio [OR], 4.63; 95% confidence interval [CI], 1.66-12.91; P \u3c .002). Being younger than 65 years (OR, 4.66; 95% CI, 1.04-20.81; P = .044) or having diabetes (OR, 4.15; 95% CI, 1.29-13.40; P = .016) were both associated with more than 4 times the odds of being in the nonadherent urine sodium trajectory compared with being older than 65 years or not having diabetes, respectively. CONCLUSIONS: Two distinct trajectories of sodium intake were identified in patients with HF. The nonadherent trajectory was characterized by an elevated pattern of dietary sodium intake shown by others to be associated with adverse outcomes in HF. Predictors of the nonadherent trajectory included higher body mass index, younger age, and diabetes