Psychosocial interventions had medium-size effects on both pain severity and interference. These robust findings support the systematic implementation of quality-controlled psychosocial interventions as part of a multimodal approach to the management of pain in patients with cancer.

Our findings suggest that psychological and pharmacologic approaches can be targeted productively toward cancer patients with elevated depressive symptoms. Research is needed to maximize effectiveness, accessibility, and integration into clinical care of interventions for depressed cancer patients.

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

Control conditions are the primary methodology used to reduce threats to internal validity in randomized controlled trials (RCTs). This meta-analysis examined the effects of control arm design and implementation on outcomes in RCTs examining psychological treatments for depression. A search of MEDLINE, PsycINFO, and EMBASE identified all RCTs evaluating psychological treatments for depression published through June 2009. Data were analyzed using mixedeffects models. One hundred twenty-five trials were identified yielding 188 comparisons. Outcomes varied significantly depending control condition design (p<0.0001). Significantly smaller effect sizes were seen when control arms used manualization (p=0.006), therapist training (p=0.002), therapist supervision (p=0.009), and treatment fidelity monitoring (p=0.003). There were no significant effects for differences in therapist experience, level of expertise in the treatment delivered, or nesting vs. crossing therapists in treatment arms. These findings demonstrate the substantial effect that decisions regarding control arm definition and implementation can have on RCT outcomes. KeywordsMeta-analysis, Depression, Control conditions, Randomized controlled trial design, Methodology Over the past half century, evidence has accumulated to support a number of psychological and behavioral interventions for mental health and medical conditions [6]. The backbone of treatment outcome research is the randomized controlled trial (RCT), a planned experiment designed to test the efficacy or effectiveness of an intervention. Although many aspects of RCT methodology have received considerable attention [2], until recently, surprisingly little attention has been paid to how to select and implement control conditions. The aim of this paper is to examine the effects of the design and implementation of control conditions on RCT outcomes for the treatment of depression using meta-analysis. These results will be interpreted in light of recent efforts to formulate a framework to support decisions regarding the selection, design, and implementation of control conditions [20].RCTs can vary in their aim, from explanatory trials evaluating efficacy or effectiveness under ideal conditions, to more pragmatic trials that evaluate the intervention under conditions found in clinical settings [13,28]. In either case, the experimental treatment is always determined relative to a control condition. Consequently, what an RCT reveals about the effectiveness of the experimental treatment inherently depends as much on the control condition as on the experimental treatment. One of the principal reasons for using a control condition is to eliminate alternative causal explanations. In statistical terms, the purpose of a control condition is to filter out the variance due to factors that are not specific to the experimental intervention, leaving only the variance due specifically

There is uncertainty whether current strategies for providing CVD risk scores affect CVD events. Providing CVD risk scores may slightly reduce CVD risk factor levels and may increase preventive medication prescribing in higher-risk people without evidence of harm. There were multiple study limitations in the identified studies and substantial heterogeneity in the interventions, outcomes, and analyses, so readers should interpret results with caution. New models for implementing and evaluating CVD risk scores in adequately powered studies are needed to define the role of applying CVD risk scores in primary CVD prevention.

The objectives of this review were to evaluate the use of consumer-targeted wearable and mobile sleep monitoring technology, identify gaps in the literature and determine the potential for use in behavioral interventions. We undertook a scoping review of studies conducted in adult populations using consumer-targeted wearable technology or mobile devices designed to measure and/or improve sleep. After screening for inclusion/exclusion criteria, data were extracted from the articles by two co-authors. Articles included in the search were using wearable or mobile technology to estimate or evaluate sleep, published in English and conducted in adult populations. Our search returned 3897 articles and 43 met our inclusion criteria. Results indicated that the majority of studies focused on validating technology to measure sleep (n = 23) or were observational studies (n = 10). Few studies were used to identify sleep disorders (n = 2), evaluate response to interventions (n = 3) or deliver interventions (n = 5). In conclusion, the use of consumer-targeted wearable and mobile sleep monitoring technology has largely focused on validation of devices and applications compared with polysomnography (PSG) but opportunities exist for observational research and for delivery of behavioral interventions. Multidisciplinary research is needed to determine the uses of these technologies in interventions as well as the use in more diverse populations including sleep disorders and other patient populations.

Aims-The prospect of weight gain discourages many cigarette smokers from quitting. Practice guidelines offer varied advice about managing weight gain after quitting smoking, but no systematic review and meta-analysis have been available. We reviewed evidence to determine whether behavioral weight control intervention compromises smoking cessation attempts, and if it offers an effective way to reduce post-cessation weight gain.Methods-We identified randomized controlled trials that compared combined smoking treatment and behavioral weight control to smoking treatment alone for adult smokers. Englishlanguage studies were identified through searches of PubMed, Ovid MEDLINE, CINAHL, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials. Of 779 articles identified and 35 potentially relevant RCTs screened, 10 met criteria and were included in the meta-analysis.Results-Patients who received both smoking treatment and weight treatment showed increased abstinence (OR=1.29, 95% CI=1.01,1.64) and reduced weight gain (g = -0.30, 95% CI=-0.63, -0.04) in the short term (<3 months) compared with patients who received smoking treatment alone. Differences in abstinence (OR=1.23, 95% CI=0.85, 1.79) and weight control (g= -0.17, 95% CI=-0.42, 0.07) were no longer significant in the long term (>6 months).Conclusions-Findings provide no evidence that combining smoking treatment and behavioral weight control produces any harm and significant evidence of short-term benefit for both abstinence and weight control. However, the absence of long-term enhancement of either smoking cessation or weight control by the time-limited interventions studied to date provides insufficient basis to recommend societal expenditures on weight gain prevention treatment for patients who are quitting smoking.

Objective: Self-efficacy expectations are associated with improvements in problematic outcomes widely considered clinically significant (ie, emotional distress, fatigue, and pain), related to positive health behaviors, and as a type of personal agency, inherently valuable. Self-efficacy expectancies, estimates of confidence to execute behaviors, are important in that changes in self-efficacy expectations are positively related to future behaviors that promote health and well-being. The current meta-analysis investigated the impact of psychological interventions on self-efficacy expectations for a variety of health behaviors among cancer patients. Methods: Ovid Medline, PsycINFO, CINAHL, EMBASE, Cochrane Library, and Web of Science were searched with specific search terms for identifying randomized controlled trials (RCTs) that focused on psychologically based interventions. Included studies had (a) an adult cancer sample, (b) a self-efficacy expectation measure of specific behaviors, and (c) an RCT design. Standard screening and reliability procedures were used for selecting and coding studies. Coding included theoretically informed moderator variables.Results: Across 79 RCTs, 223 effect sizes, and 8678 participants, the weighted average effect of self-efficacy expectations was estimated as g = 0.274 (P < .001).Consistent with the self-efficacy theory, the average effect for in-person intervention delivery (g = 0.329) was significantly greater than for all other formats (g = 0.154, P = .023; eg, audiovisual, print, telephone, and Web/internet). Conclusions: The results establish the impact of psychological interventions on self-efficacy expectations as comparable in effect size with commonly reported outcomes (distress, fatigue, pain). Additionally, the result that in-person interventions achieved the largest effect is supported by the social learning theory and could inform research related to the development and evaluation of interventions.