A Community-Level, Mesoscale Analysis of Fish Assemblage Structure in Shoreline Habitats of a Large River Using Multivariate Regression Trees.

The mesoscale (100–102 m) of river habitats has been identified as the scale that simultaneously offers insights into ecological structure and falls within the practical bounds of river management. Mesoscale habitat (mesohabitat) classifications for relatively large rivers, however, are underdeveloped compared with those produced for smaller streams. Approaches to habitat modelling have traditionally focused on individual species or proceeded on a species-by-species basis. This is particularly problematic in larger rivers where the effects of biological interactions are more complex and intense. Community-level approaches can rapidly model many species simultaneously, thereby integrating the effects of biological interactions while providing information on the relative importance of environmental variables in structuring the community. One such community-level approach, multivariate regression trees, was applied in order to determine the relative influences of abiotic factors on fish assemblages within shoreline mesohabitats of San Pedro River, Chile, and to define reference communities prior to the planned construction of a hydroelectric power plant. Flow depth, bank materials and the availability of riparian and instream cover, including woody debris, were the main variables driving differences between the assemblages. Species strongly indicative of distinctive mesohabitat types included the endemic Galaxias platei. Among other outcomes, the results provide information on the impact of non-native salmonids on river-dwelling Galaxias platei, suggesting a degree of habitat segregation between these taxa based on flow depth. The results support the use of the mesohabitat concept in large, relatively pristine river systems, and they represent a basis for assessing the impact of any future hydroelectric power plant construction and operation. By combing community classifications with simple sets of environmental rules, the multivariate regression trees produced can be used to predict the community structure of any mesohabitat along the reach.

Evaluating a School Community Linked Physical Activity Intervention Programme: a Multi-level Analysis.

Previous research has highlighted the importance of positive physical activity (PA) behaviors during childhood to promote sustained active lifestyles throughout the lifespan (Telama et al. 2005; 2014). It is in this context that the role of schools and teachers in facilitating PA education is promoted. Research suggests that teachers play an important role in the attitudes of children towards PA (Figley 1985) and schools may be an efficient vehicle for PA provision and promotion (McGinnis, Kanner and DeGraw, 1991; Wechsler, Deveraux, Davis and Collins, 2000). Yet despite consensus that schools represent an ideal setting from which to ‘reach’ young people (Department of Health and Human Services, UK, 2012) there remains conceptual (e.g. multi-component intervention) and methodological (e.g. duration, intensity, family involvement) ambiguity regarding the mechanisms of change claimed by PA intervention programmes. This may, in part, contribute to research findings that suggest that PA interventions have had limited impact on children’s overall activity levels and thereby limited impact in reducing children’s metabolic health (Metcalf, Henley & Wilkin, 2012). A marked criticism of the health promotion field has been the focus on behavioural change while failing to acknowledge the impact of context in influencing health outcomes (Golden & Earp, 2011). For years, the trans-theoretical model of behaviour change has been ‘the dominant model for health behaviour change’ (Armitage, 2009); this model focusses primarily on the individual and the psychology of the change process. Arguably, this model is limited by the individual’s decision-making ability and degree of self-efficacy in order to achieve sustained behavioural change and does not take account of external factors that may hinder their ability to realise change. Similar to the trans-theoretical model, socio-ecological models identify the individual at the focal point of change but also emphasises the importance of connecting multiple impacting variables, in particular, the connections between the social environment, the physical environment and public policy in facilitating behavioural change (REF). In this research, a social-ecological framework was used to connect the ways a PA intervention programme had an impact (or not) on participants, and to make explicit the foundational features of the programme that facilitated positive change. In this study, we examined the evaluation of a multi-agency approach to a PA intervention programme which aimed to increase physical activity, and awareness of the importance of physical activity to key stage 2 (age 7-12) pupils in three UK primary schools. The agencies involved were the local health authority, a community based charitable organisation, a local health administrative agency, and the city school district. In examining the impact of the intervention, we adopted a process evaluation model in order to better understand the mechanisms and context that facilitated change. Therefore, the aim of this evaluation was to describe the provision, process and impact of the intervention by 1) assessing changes in physical activity levels 2) assessing changes in the student’s attitudes towards physical activity, 3) examining student’s perceptions of the child size fitness equipment in school and their likelihood of using the equipment outside of school and 4) exploring staff perceptions, specifically the challenges and benefits, of facilitating equipment based exercise sessions in the school environment. Methodology, Methods, Research Instruments or Sources Used Evaluation of the intervention was designed as a matched-control study and was undertaken over a seven-month period. The school-based intervention involved 3 intervention schools (n =436; 224 boys) and one control school (n=123; 70 boys) in a low socioeconomic and multicultural urban setting. The PA intervention was separated into two phases: a motivation DVD and 10 days of circuit based exercise sessions (Phase 1) followed by a maintenance phase (Phase 2) that incorporated a PA reward program and the use of specialist kid’s gym equipment located at each school for a period of 4 wk. Outcome measures were measured at baseline (January) and endpoint (July; end of academic school year) using reliable and valid self-report measures. The children’s attitudes towards PA were assessed using the Children’s Attitudes towards Physical Activity (CATPA) questionnaire. The Physical Activity Questionnaire for Children (PAQ-C), a 7-day recall questionnaire, was used to assess PA levels over a school week. A standardised test battery (Fitnessgram®) was used to assess cardiovascular fitness, body composition, muscular strength and endurance, and flexibility. After the 4 wk period, similar kid’s equipment was available for general access at local community facilities. The control school did not receive any of the interventions. All physical fitness tests and PA questionnaires were administered and collected prior to the start of the intervention (January) and following the intervention period (July) by an independent evaluation team. Evaluation testing took place at the individual schools over 2-3 consecutive days (depending on the number of children to be tested at the school). Staff (n=19) and student perceptions (n = 436) of the child sized fitness equipment were assessed via questionnaires post-intervention. Students completed a questionnaire to assess enjoyment, usage, ease of use and equipment assess and usage in the community. A questionnaire assessed staff perceptions on the delivery of the exercise sessions, classroom engagement and student perceptions. Conclusions, Expected Outcomes or Findings Findings showed that both the intervention (16.4%) and control groups increased their PAQ-C score by post-intervention (p < 0.05); with the intervention (17.8%) and control (21.3%) boys showing the greatest increase in physical activity levels. At post-intervention, there was a 5.5% decline in the intervention girls’ attitudes toward PA in the aesthetic subdomains (p = 0.009); whereas the control boys had an increase in positive attitudes in the health domain (p = 0.003). No significant differences in attitudes towards physical activity were observed in any other domain for either group at post-intervention (p > 0.05). The results of the equipment questionnaire, 96% of the children stated they enjoyed using the equipment and would like to use the equipment again in the future; however at post-intervention only 27% reported using the equipment outside of school in the last 7 days. Students identified the ski walker (34%) and cycle (32%) as their favorite pieces of equipment; with the single joint exercises such as leg extension and bicep/tricep machine (<3%) as their least favorite. Key themes from staff were that the equipment sessions were enjoyable, a novel activity, children felt very grown-up, and the activity was linked to a real fitness experience. They also expressed the need for more support to deliver the sessions and more time required for each session. Findings from this study suggest that a more integrated approach within the various agencies is required, particularly more support to increase teachers pedagogical content knowledge in physical activity instruction which is age appropriate. Future recommendations for successful implementation include sufficient time period for all students to access and engage with the equipment; increased access and marketing of facilities to parents within the local community, and professional teacher support strategies to facilitate the exercise sessions.

Mania Triggered by Sleep Loss: Implications for Postpartum Psychosis

Background and Aims To examine whether a history of mood episodes triggered by sleep loss is associated with (1) postpartum psychosis (PP) and (2) more broadly-defined postpartum mood episodes that included postnatal depression (PND), in women with bipolar disorder (BD). Methods Participants were 622 parous women with a diagnosis of bipolar-I disorder recruited in the UK to the Bipolar Disorder Research Network. Diagnosis and perinatal episodes were assessed via interview and case note data. Women were also asked during the interview whether episodes of mania and/or depression were triggered by sleep loss. We compared the rates of PP and PND within women who did and did not endorse sleep loss as a trigger of mood episodes. Results Women who reported that their episodes of mania were usually triggered by sleep loss were twice as likely to have experienced an episode of PP (OR = 2.00, 95% CI = 1.20–3.36) than women who did not report this. This effect remained significant when controlling for clinical and demographic factors. We found no significant associations between depression triggered by sleep loss and PP. Analyses in which we defined postpartum episodes at a broader level to include both PP and PND were not significant. Conclusions In pregnant women with BD, a history of mania following sleep loss could be a potential marker of vulnerability to severe postpartum episodes. Further study in prospective samples is required in order to confirm these findings, which may have important implications for understanding the aetiology of PP and of mood disorders more generally.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.