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\"This book presents recent advancements in positive psychology, specifically its application across broad areas of current interest. Chapters include submissions from various international authors in the field and cover discussion and presentation of relevant research, theories, and applications. The volume covers topics such as CBT, Psychotherapy, Coaching, Workplaces, Aging, Education, Leadership, Emotion, Interventions, Measurement, Technology, Design, Health, Relationships, Experiences, Communities. With the growing interest in the applications of positive psychology across diverse fields within psychology and beyond, this book will make a worthwhile contribution to the field. It will also fill the current need for a volume that highlights specifically the various recent advancements in positive psychology into diverse fields and as such will be of benefit to a wide range of professionals, including psychologists, educators, clinicians, therapists, and many others.\" -- Publisher's website.

Background Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies.

Iodine deficiency is the most common cause of preventable mental impairment worldwide. It is defined by WHO as mild if the population median urinary iodine excretion is 50–99 μg/L, moderate if 20–49 μg/L, and severe if less than 20 μg/L. No contemporary data are available for the UK, which has no programme of food or salt iodination. We aimed to assess the current iodine status of the UK population. In this cross-sectional survey, we systematically assessed iodine status in schoolgirls aged 14–15 years attending secondary school in nine UK centres. Urinary iodine concentrations and tap water iodine concentrations were measured in June–July, 2009, and November–December, 2009. Ethnic origin, postcode, and a validated diet questionnaire assessing sources of iodine were recorded. 810 participants provided 737 urine samples. Data for dietary habits and iodine status were available for 664 participants. Median urinary iodine excretion was 80·1 μg/L (IQR 56·9–109·0). Urinary iodine measurements indicative of mild iodine deficiency were present in 51% (n=379) of participants, moderate deficiency in 16% (n=120), and severe deficiency in 1% (n=8). Prevalence of iodine deficiency was highest in Belfast (85%, n=135). Tap water iodine concentrations were low or undetectable and were not positively associated with urinary iodine concentrations. Multivariable general linear model analysis confirmed independent associations between low urinary iodine excretion and sampling in summer (p<0·0001), UK geographical location (p<0·0001), low intake of milk (p=0·03), and high intake of eggs (p=0·02). Our findings suggest that the UK is iodine deficient. Since developing fetuses are the most susceptible to adverse effects of iodine deficiency and even mild perturbations of maternal and fetal thyroid function have an effect on neurodevelopment, these findings are of potential major public health importance. This study has drawn attention to an urgent need for a comprehensive investigation of UK iodine status and implementation of evidence-based recommendations for iodine supplementation. Clinical Endocrinology Trust.

Background There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR.

Background The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model. Methods Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained. Results CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness. Conclusions Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.

Control of blood pressure is a key component of cardiovascular disease prevention, but is difficult to achieve and until recently has been the sole preserve of health professionals. This study assessed whether self-management by people with poorly controlled hypertension resulted in better blood pressure control compared with usual care. This randomised controlled trial was undertaken in 24 general practices in the UK. Patients aged 35–85 years were eligible for enrolment if they had blood pressure more than 140/90 mm Hg despite antihypertensive treatment and were willing to self-manage their hypertension. Participants were randomly assigned in a 1:1 ratio to self-management, consisting of self-monitoring of blood pressure and self-titration of antihypertensive drugs, combined with telemonitoring of home blood pressure measurements or to usual care. Randomisation was done by use of a central web-based system and was stratified by general practice with minimisation for sex, baseline systolic blood pressure, and presence or absence of diabetes or chronic kidney disease. Neither participants nor investigators were masked to group assignment. The primary endpoint was change in mean systolic blood pressure between baseline and each follow-up point (6 months and 12 months). All randomised patients who attended follow-up visits at 6 months and 12 months and had complete data for the primary outcome were included in the analysis, without imputation for missing data. This study is registered as an International Standard Randomised Controlled Trial, number ISRCTN17585681. 527 participants were randomly assigned to self-management (n=263) or control (n=264), of whom 480 (91%; self-management, n=234; control, n=246) were included in the primary analysis. Mean systolic blood pressure decreased by 12·9 mm Hg (95% CI 10·4–15·5) from baseline to 6 months in the self-management group and by 9·2 mm Hg (6·7–11·8) in the control group (difference between groups 3·7 mm Hg, 0·8–6·6; p=0·013). From baseline to 12 months, systolic blood pressure decreased by 17·6 mm Hg (14·9–20·3) in the self-management group and by 12·2 mm Hg (9·5–14·9) in the control group (difference between groups 5·4 mm Hg, 2·4–8·5; p=0·0004). Frequency of most side-effects did not differ between groups, apart from leg swelling (self-management, 74 patients [32%]; control, 55 patients [22%]; p=0·022). Self-management of hypertension in combination with telemonitoring of blood pressure measurements represents an important new addition to control of hypertension in primary care. Department of Health Policy Research Programme, National Coordinating Centre for Research Capacity Development, and Midlands Research Practices Consortium.

Objective To assess the accuracy of general practitioners, practice nurses, and interpretative software in the use of different types of electrocardiogram to diagnose atrial fibrillation. Design Prospective comparison with reference standard of assessment of electrocardiograms by two independent specialists. Setting 49 general practices in central England. Participants 2595 patients aged 65 or over screened for atrial fibrillation as part of the screening for atrial fibrillation in the elderly (SAFE) study; 49 general practitioners and 49 practice nurses. Interventions All electrocardiograms were read with the Biolog interpretative software, and a random sample of 12 lead, limb lead, and single lead thoracic placement electrocardiograms were assessed by general practitioners and practice nurses independently of each other and of the Biolog assessment. Main outcome measures Sensitivity, specificity, and positive and negative predictive values. Results General practitioners detected 79 out of 99 cases of atrial fibrillation on a 12 lead electrocardiogram (sensitivity 80%, 95% confidence interval 71% to 87%) and misinterpreted 114 out of 1355 cases of sinus rhythm as atrial fibrillation (specificity 92%, 90% to 93%). Practice nurses detected a similar proportion of cases of atrial fibrillation (sensitivity 77%, 67% to 85%), but had a lower specificity (85%, 83% to 87%). The interpretative software was significantly more accurate, with a specificity of 99%, but missed 36 of 215 cases of atrial fibrillation (sensitivity 83%). Combining general practitioners' interpretation with the interpretative software led to a sensitivity of 92% and a specificity of 91%. Use of limb lead or single lead thoracic placement electrocardiograms resulted in some loss of specificity. Conclusions Many primary care professionals cannot accurately detect atrial fibrillation on an electrocardiogram, and interpretative software is not sufficiently accurate to circumvent this problem, even when combined with interpretation by a general practitioner. Diagnosis of atrial fibrillation in the community needs to factor in the reading of electrocardiograms by appropriately trained people.

Orangutans are the largest habitually arboreal mammal. For them, as for all arboreal mammals, access to the abundant fruits and narrowest gaps found among the thin peripheral branches of tree crowns poses considerable safety risks and energetic demands. Most arboreal primates use flexed-limb postures to minimize problems caused by branch compliance and instability. Here, we show that Sumatran orangutans employ unique locomotor strategies to control compliance and allow access to the terminal branch niche for feeding and gap crossing. We calculated a \"stiffness score,\" which is a measure of the flexibility of the supports on which orangutans moved. We found that certain locomotor behaviors clearly are associated with the most compliant supports; these behaviors appear to lack regular limb sequences, which serves to avoid the risk of resonance in branch sway caused by high-frequency, patterned gait. Balance and increased stability are achieved through long contact times between multiple limbs and supports and a combination of pronograde (horizontal) and orthograde (vertical) body postures, used both above branches and in suspension underneath them. Overall, adult females seem to be the most conservative in their travel, selecting more solid and secure supports than males and adolescents. These results have implications for understanding locomotor diversity in fossil and extant apes and for orangutan conservation and reintroduction programs.

The frequency distribution of serum thyroid stimulating hormone (TSH) shows a skewed pattern that may change with age. The set point of the hypothalamic-pituitary-thyroid axis for an individual is thought to be genetically determined and has been described as a log-linear relationship of serum TSH to free thyroxine (T4); however, the validity of this hypothesis has yet to be established in older people. The aim of the study was to describe the relationship between serum TSH and free T4 in older people and define factors influencing this relationship. We conducted a cross-sectional, observational study of thyroid function in a community population of older subjects over 65 years of age. The relationship between serum TSH and free T4 was not linear as previously described, but is best described as a fourth-order polynomial. Both gender and smoking status affected the relationship. This suggests that more complex modelling is required when investigating the hypothalamic-pituitary-thyroid axis.

Physical inactivity causes approximately 17% of premature mortality in the UK. Walking offers a promising method for lowering risk of cardiovascular disease at population level, though a recent synthesis of evidence is lacking. This study aimed to conduct a meta-analysis of randomised controlled trials that have assessed the effect of walking on risk factors for cardiovascular disease in previously inactive adults. We searched PubMed, Web of Science, ScienceDirect, and the Cochrane Central Register of Controlled Trials for studies published in English between Jan 1, 1970, and May 31, 2012, using the following search terms: \"walking\", \"exercise\", \"health\", and \"cardiovascular risk\". Two authors identified randomised controlled trials of interventions (>4 weeks' duration) that included at least one group with walking as the only treatment and a comparator no-exercise group. Participants were inactive but otherwise healthy at baseline. Pooled results were reported as weighted mean treatment effects and 95% CIs in a random effects model. 32 articles reported the effects of walking interventions on risk factors for cardiovascular disease in participants aged 30–83 years. Mean length of interventions was 18·7 weeks (range 8–52). Duration of walking was 20–60 min on 2–7 days per week. Walking interventions reduced systolic and diastolic blood pressure (−3·6 mm Hg, 95% CI −5·19 to −1·97; −1·5 mm Hg, −2·83 to −0·26). Interventions also improved waist circumference (−1·5 cm, −2·34 to −0·68), weight (−1·4 kg, −1·75 to −1·00), body fat (−1·2%, −1·70 to −0·73), and body-mass index (BMI) (−0·5 kg/m2, −0·72 to −0·35). Walking improved aerobic fitness (3·2 mL/kg per min, 95% CI 2·57 to 3·80) but did not alter blood lipids. Significant heterogeneity (I2 statistic) was noted for aerobic fitness, BMI, weight, and percentage body fat. Many studies did not provide sufficient information to make firm judgments about risk of bias. These findings support the important role of walking in physical activity for health promotion. Health professionals involved in the primary prevention of cardiovascular disease should prescribe walking confident of the benefits it can provide in fitness, blood pressure, and adiposity. Funding was received from the Mary Immaculate College Research Directorate Seed Funding Scheme for assistance with data extraction. The funder had no role in the study.