Personal factors associated with health-related quality of life in persons with morbid obesity on treatment waiting lists in Norway

Purpose To explore relationships of socio-demographic variables, health behaviours, environmental characteristics and personal factors, with physical and mental health variables in persons with morbid obesity, and to compare their health-related quality of life (HRQoL) scores with scores from the general population. Methods A cross-sectional correlation study design was used. Data were collected by self-reported questionnaire from adult patients within the first 2 days of commencement of a mandatory educational course. Of 185 course attendees, 142 (76.8%) volunteered to participate in the study. Valid responses on all items were recorded for 128 participants. HRQoL was measured with the Short Form 12v2 from which physical (PCS) and mental component summary (MCS) scores were computed. Other standardized instruments measured regular physical activity, social support, self-esteem, sense of coherence, self-efficacy and coping style. Results Respondents scored lower on all the HRQoL subdomains compared with norms. Linear regression analyses showed that personal factors that included self-esteem, self-efficacy, sense of coherence and coping style explained 3.6% of the variance in PCS scores and 41.6% in MCS scores. Conclusion Personal factors such as self-esteem, sense of coherence and a high approaching coping style are strongly related to mental health in obese persons

Reciprocal relationships between trajectories of depressive symptoms and screen media use during adolescence

Adolescents are constantly connected with each other and the digital landscape through a myriad of screen media devices. Unprecedented access to the wider world and hence a variety of activities, particularly since the introduction of mobile technology, has given rise to questions regarding the impact of this changing media environment on the mental health of young people. Depressive symptoms are one of the most common disabling health issues in adolescence and although research has examined associations between screen use and symptoms of depression, longitudinal investigations are rare and fewer still consider trajectories of change in symptoms. Given the plethora of devices and normalisation of their use, understanding potential longitudinal associations with mental health is crucial. A sample of 1,749 (47% female) adolescents (10-17 years) participated in six waves of data collection over two years. Symptoms of depression, time spent on screens, and on separate screen activities (social networking, gaming, web browsing, TV/passive) were self-reported. Latent growth curve modelling revealed three trajectories of depressive symptoms (Low-Stable, High-Decreasing, and Low-Increasing) and there were important differences across these groups on screen use. Some small, positive associations were evident between depressive symptoms and later screen use, and between screen use and later depressive symptoms. However, a Random Intercept Cross Lagged Panel Model revealed no consistent support for a longitudinal association. The study highlights the importance of considering differential trajectories of depressive symptoms and specific forms of screen activity to understand these relationships

Polypharmacy among anabolic-androgenic steroid users: A descriptive metasynthesis

Background: As far as we are aware, no previous systematic review and synthesis of the qualitative/descriptive literature on polypharmacy in anabolic-androgenic steroid(s) (AAS) users has been published. Method: We systematically reviewed and synthesized qualitative/descriptive literature gathered from searches in electronic databases and by inspecting reference lists of relevant literature to investigate AAS users' polypharmacy. We adhered to the recommendations of the UK Economic and Social Research Council's qualitative research synthesis manual and the PRISMA guidelines. Results: A total of 50 studies published between 1985 and 2014 were included in the analysis. Studies originated from 10 countries although most originated from United States (n = 22), followed by Sweden (n = 7), England only (n = 5), and the United Kingdom (n = 4). It was evident that prior to their debut, AAS users often used other licit and illicit substances. The main ancillary/supplementary substances used were alcohol, and cannabis/cannabinoids followed by cocaine, growth hormone, and human chorionic gonadotropin (hCG), amphetamine/meth, clenbuterol, ephedra/ephedrine, insulin, and thyroxine. Other popular substance classes were analgesics/opioids, dietary/nutritional supplements, and diuretics. Our classification of the various substances used by AAS users resulted in 13 main groups. These non-AAS substances were used mainly to enhance the effects of AAS, combat the side effects of AAS, and for recreational or relaxation purposes, as well as sexual enhancement. Conclusions: Our findings corroborate previous suggestions of associations between AAS use and the use of other licit and illicit substances. Efforts must be intensified to combat the debilitating effects of AAS-associated polypharmacy

Self-reported screen time and cardiometabolic risk in obese dutch adolescents

BACKGROUND: It is not clear whether the association between sedentary time and cardiometabolic risk exists among obese adolescents. We examined the association between screen time (TV and computer time) and cardiometabolic risk in obese Dutch adolescents. METHODS AND FINDINGS: For the current cross-sectional study, baseline data of 125 Dutch overweight and obese adolescents (12-18 years) participating in the Go4it study were included. Self-reported screen time (Activity Questionnaire for Adolescents and Adults) and clustered and individual cardiometabolic risk (i.e. body composition, systolic and diastolic blood pressure, low-density (LDL-C), high-density (HDL-C) and total cholesterol (TC), triglycerides, glucose and insulin) were assessed in all participants. Multiple linear regression analyses were used to assess the association between screen time and cardiometabolic risk, adjusting for age, gender, pubertal stage, ethnicity and moderate-to-vigorous physical activity. We found no significant relationship between self-reported total screen time and clustered cardiometabolic risk or individual risk factors in overweight and obese adolescents. Unexpectedly, self-reported computer time, but not TV time, was slightly but significantly inversely associated with TC (B = -0.002; CI = [-0.003;-0.000]) and LDL-C (B = -0.002; CI = [-0.001;0.000]). CONCLUSIONS: In obese adolescents we could not confirm the hypothesised positive association between screen time and cardiometabolic risk. Future studies should consider computer use as a separate class of screen behaviour, thereby also discriminating between active video gaming and other computer activities