The SELF Trial: A self-efficacy based behavioral intervention trial for weight loss maintenance.

The SELF Trial examined the effect of adding individual self‐efficacy (SE) enhancement sessions to standard behavioral weight loss treatment (SBT). Participants were randomly assigned to SBT or SBT plus SE sessions (SBT+SE). Outcome measures were weight loss maintenance, quality of life, intervention adherence, and self‐efficacy at 12 and 18 months. The sample (N = 130) was female (83.08%) with a mean (SD) body mass index of 33.15 (4.11) kg m2. There was a significant time effect for percent weight change (P = 0.002) yet no significant group or group‐by‐time effects. The weight loss for the SBT+SE group was 8.38% (7.48) at 12 months and 8.00% (7.87) at 18 months, with no significant difference between the two time points (P = 0.06). However, weight loss for the SBT group was 6.95% (6.67) at 12 months and 5.96% (7.35) at 18 months, which was significantly different between the two time points (P = 0.005), indicating that the SBT group had significant weight regain. Both groups achieved clinically significant weight loss. The group receiving an intervention targeting enhanced self‐efficacy had greater weight loss maintenance whereas the SBT group demonstrated significant weight regain possibly related to the greater attention provided to the SBT+SE group

The Use of mHealth to Deliver Tailored Messages Reduces Reported Energy and Fat Intake.

Evidence supports the role of feedback in reinforcing motivation for behavior change. Feedback that provides reinforcement has the potential to increase dietary self-monitoring and enhance attainment of recommended dietary intake. The aim of this study was to examine the impact of daily feedback (DFB) messages, delivered remotely, on changes in dietary intake. This was a secondary analysis of the Self- Monitoring And Recording using Technology (SMART) Trial, a single-center, 24-month randomized clinical trial of behavioral treatment for weight loss. Participants included 210 obese adults (mean body mass index, 34.0 kg/m2) who were randomized to either a paper diary (PD), personal digital assistant (PDA), or PDA plus daily tailored feedback messages (PDA + FB). To determine the role of daily tailored feedback in dietary intake, we compared the self-monitoring with DFB group (DFB group; n = 70) with the self-monitoring without DFB group (no-DFB group, n = 140). All participants received a standard behavioral intervention for weight loss. Self-reported changes in dietary intake were compared between the DFB and no-DFB groups and were measured at baseline and at 6, 12, 18, and 24 months. Linear mixed modeling was used to examine percentage changes in dietary intake from baseline. Compared with the no-DFB group, the DFB group achieved a larger reduction in energy (−22.8% vs −14.0%; P = .02) and saturated fat (−11.3% vs −0.5%; P = .03) intake and a trend toward a greater decrease in total fat intake (−10.4% vs −4.7%; P = .09). There were significant improvements over time in carbohydrate intake and total fat intake for both groups (P values < .05). Daily tailored feedback messages designed to target energy and fat intake and delivered remotely in real time using mobile devices may play an important role in the reduction of energy and fat intake

Sociodemographic, Anthropometric, and Psychosocial Predictors of Attrition across Behavioral Weight-Loss Trials.

Preventing attrition is a major concern in behavioral weight loss intervention studies. The purpose of this analysis was to identify baseline and six-month predictors associated with participant attrition across three independent clinical trials of behavioral weight loss interventions (PREFER, SELF, and SMART) that were conducted over 10 years. Baseline measures included body mass index, Barriers to Healthy Eating, Beck Depression Inventory-II (BDI), Hunger Satiety Scale (HSS), Binge Eating Scale (BES), Medical Outcome Study Short Form (MOS SF-36 v2) and Weight Efficacy Lifestyle Questionnaire (WEL). We also examined early weight loss and attendance at group sessions during the first 6 months. Attrition was recorded at the end of the trials. Participants included 504 overweight and obese adults seeking weight loss treatment. The sample was 84.92% female and 73.61% white, with a mean (± SD) age of 47.35 ± 9.75 years. After controlling for the specific trial, for every one unit increase in BMI, the odds of attrition increased by 11%. For every year increase in education, the odds of attrition decreased by 10%. Additional predictors of attrition included previous attempts to lose 50–79 lbs, age, not possessing health insurance, and BES, BDI, and HSS scores. At 6 months, the odds of attrition increased by 10% with reduced group session attendance. There was also an interaction between percent weight change and trial (p < .001). Multivariate analysis of the three trials showed education, age, BMI, and BES scores were independently associated with attrition (ps ≤ .01). These findings may inform the development of more robust strategies for reducing attrition

Population Monte Carlo algorithms

We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms. In these algorithms, a set of ``walkers'' or ``particles'' is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms -- ``quantum Monte Carlo'', ``transfer-matrix Monte Carlo'', ``Monte Carlo filter (particle filter)'',``sequential Monte Carlo'' and ``PERM'' etc. Here we discuss them in a coherent framework. We also touch on related algorithms -- genetic algorithms and annealed importance sampling.Comment: Title is changed (Population-based Monte Carlo -> Population Monte Carlo). A number of small but important corrections and additions. References are also added. Original Version is read at 2000 Workshop on Information-Based Induction Sciences (July 17-18, 2000, Syuzenji, Shizuoka, Japan). No figure

Feasibility study of large-scale deployment of colour-ringing on black-legged kittiwake populations to improve the realism of demographic models assessing the population impacts of offshore wind farms

• Renewable energy developments, including offshore wind farms have been identified as a key component in international efforts to mitigate climate change and its impact on biodiversity. This has led to an increasing number of offshore wind farms around the UK, however, these can have negative impacts on seabird populations. • Population Viability Analysis (PVA) is frequently used to quantify these potential negative effects on seabird populations and is a vital part of the consenting process. However, a lack of empirical data on many aspects of seabird demography means that there can be considerable uncertainty in these assessments. • Black-legged Kittiwake Rissa tridactyla populations are thought to be particularly sensitive to additional mortality caused by collision with offshore wind turbines and are often highlighted as a feature of Special Protection Areas (SPAs). Offshore wind farms, therefore, have been identified as potentially causing an adverse effect on site integrity at some SPAs. • Despite being a relatively well-studied species, there is still much uncertainty in our knowledge of Kittiwake demographic rates and meta-population dynamics, which impedes our ability to accurately assess the way populations might respond to additional wind farm-induced mortality. • The Offshore Wind Strategic Monitoring and Research Forum (OWSMRF) identified a large-scale colour-ringing programme of Kittiwake colonies across the UK as one potential approach for improving empirical estimates of Kittiwake demographic rates. • Therefore, the main aim of this project was to determine the extent to which colour-ringing can be used to obtain reliable baseline estimates of key demographic rates in Kittiwake populations to improve the realism of demographic models assessing the population impacts of offshore wind farms, and thereby reduce uncertainty around these predicted impacts

Treatment of vulvar intraepithelial neoplasia with topical imiquimod: seven years median follow-up of a randomized controlled trial

Objective Recently we reported on the efficacy of imiquimod for treating vulvar intraepithelial neoplasia (VIN) in a placebo-controlled, double-blinded randomized clinical trial (RCT). Four weeks after treatment, a complete response was observed in 35% of patients and a partial response in 46%. All complete responders remained disease-free at 12 months follow-up. In the current investigations, we assessed long-term follow-up at least 5 years after the initial RCT. Methods Twenty-four of 26 imiquimod-treated patients who had participated in the initial RCT were seen for follow-up. Primary endpoint was durability of clinical response to imiquimod assessed by naked eye vulvar examination and histology. Long-term clinical response was correlated to lesion size before start of the initial RCT. Secondary endpoints were mental health, global quality of life, body image and sexual function in relation with long-term clinical response. Results Median follow-up period was 7.2 years (range 5.6-8.3 years). VIN recurred in one of nine complete responders. Of the initial partial responders, two became disease-free after additional imiquimod treatment. In the other partial responders, VIN recurred at least once after the initial RCT. In long-term complete responders, lesion size at study entry was smaller and these patients had a significantly better global quality of life at follow-up than patients with residual disease and/or recurrence after imiquimod treatment. Conclusions In case of a complete response, imiquimod is effective in the long-term. Furthermore, patients with a long-term complete response had a significantly better global quality of life than patients who recurred after imiquimod treatment