OBJECTIVE: To estimate the prevalence of falls in elderly individuals and to analyze associated factors. METHODS: Cross-sectional study with 420 elderly subjects (aged 60 years or older) living in the city of Juiz de Fora (Southeastern Brazil) in 2010. A household survey was conducted and the occurrence of falls in the 12 previous months was described. For the analysis of factors associated with the outcome, a theoretical determination model with three hierarchical blocks was built. The variables were adjusted among each other within each block; those with level of signifi cance ≤ 0.20 were included in the Poisson regression model and adjusted to the immediately higher level, with 5% signifi cance level. RESULTS: The prevalence of falls among the elderly was 32.1% (95%CI: 27.7; 36.9). Among those who experienced falls, 53% had a single fall and 19% had fractures as a consequence. Most of the falls (59%) occurred at the elderly person's home. The occurrence of falls was associated with old age, female sex, need of help for locomotion and self-reported diagnosis of osteoporosis. CONCLUSIONS: Falls are frequent among the elderly. Knowledge of the factors associated with the occurrence of this event can aid the development of prevention strategies and adequate health services.

Marine protected areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspots"). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core areas") to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas.

ObjectiveTo classify wear and non-wear time of accelerometer data for accurately quantifying physical activity in public health or population level research.DesignA bi-moving-window-based approach was used to combine acceleration and skin temperature data to identify wear and non-wear time events in triaxial accelerometer data that monitor physical activity.SettingLocal residents in Swansea, Wales, UK.Participants50 participants aged under 16 years (n=23) and over 17 years (n=27) were recruited in two phases: phase 1: design of the wear/non-wear algorithm (n=20) and phase 2: validation of the algorithm (n=30).MethodsParticipants wore a triaxial accelerometer (GeneActiv) against the skin surface on the wrist (adults) or ankle (children). Participants kept a diary to record the timings of wear and non-wear and were asked to ensure that events of wear/non-wear last for a minimum of 15 min.ResultsThe overall sensitivity of the proposed method was 0.94 (95% CI 0.90 to 0.98) and specificity 0.91 (95% CI 0.88 to 0.94). It performed equally well for children compared with adults, and females compared with males. Using surface skin temperature data in combination with acceleration data significantly improved the classification of wear/non-wear time when compared with methods that used acceleration data only (p<0.01).ConclusionsUsing either accelerometer seismic information or temperature information alone is prone to considerable error. Combining both sources of data can give accurate estimates of non-wear periods thus giving better classification of sedentary behaviour. This method can be used in population studies of physical activity in free-living environments.

ObjectiveTo estimate the direct healthcare cost of being overweight or obese throughout pregnancy to the National Health Service in Wales.DesignRetrospective prevalence-based study.SettingCombined linked anonymised electronic datasets gathered on a cohort of women enrolled on the Growing Up in Wales: Environments for Healthy Living (EHL) study. Women were categorised into two groups: normal body mass index (BMI; n=260) and overweight/obese (BMI>25; n=224).Participants484 singleton pregnancies with available health service records and an antenatal BMI.Primary outcome measureTotal health service utilisation (comprising all general practitioner visits and prescribed medications, inpatient admissions and outpatient visits) and direct healthcare costs for providing these services in the year 2011–2012. Costs are calculated as cost of mother (no infant costs are included) and are related to health service usage throughout pregnancy and 2 months following delivery.ResultsThere was a strong association between healthcare usage cost and BMI (p<0.001). Adjusting for maternal age, parity, ethnicity and comorbidity, mean total costs were 23% higher among overweight women (rate ratios (RR) 1.23, 95% CI 1.230 to 1.233) and 37% higher among obese women (RR 1.39, 95% CI 1.38 to 1.39) compared with women with normal weight. Adjusting for smoking, consumption of alcohol, or the presence of any comorbidities did not materially affect the results. The total mean cost estimates were £3546.3 for normal weight, £4244.4 for overweight and £4717.64 for obese women.ConclusionsIncreased health service usage and healthcare costs during pregnancy are associated with increasing maternal BMI; this was apparent across all health services considered within this study. Interventions costing less than £1171.34 per person could be cost-effective if they reduce healthcare usage among obese pregnant women to levels equivalent to that of normal weight women.

The relationship between the size of an egg and its energy content was analyzed using published data for 47 species of echinoderms. Scaling relationships were evaluated for all species, as well as for subsets of the species, based on mode of development. Regressions were calculated using linear, power function, full allometric, and second-order polynomial models. The full allometric model is preferred because it is relatively simple and the most general. Among these species of echinoderms, larger eggs contain more energy. Egg energy content scales isometrically across a wide range of egg sizes both among and within different modes of development. The only exception is among species with feeding larval development, where there does not seem to be a clear scaling relationship. In most cases, the regressions were statistically significant and explained a very large proportion of the variance in energy content. However, there were wide confidence intervals around the estimated regression parameters. In all cases, the predictive power of the regression was poor, requiring large differences in egg size to yield significantly different predictions of energy content. Consequently, egg size is of limited value for the quantitative prediction of egg energy content and should be used with caution in life-history studies.

BackgroundThis study examines the effect of low daily physical activity levels and overweight/obesity in pregnancy on delivery and perinatal outcomes.MethodsA prospective cohort study combining manually collected postnatal notes with anonymised data linkage. A total of 466 women sampled from the Growing Up in Wales: Environments for Healthy Living study. Women completed a questionnaire and were included in the study if they had an available Body mass index (BMI) (collected at 12 weeks gestation from antenatal records) and/or a physical activity score during pregnancy (7-day Actigraph reading). The full statistical model included the following potential confounding factors: maternal age, parity and smoking status. Main outcome measures included induction rates, duration of labour, mode of delivery, infant health and duration of hospital stay.FindingsMothers with lower physical activity levels were more likely to have an instrumental delivery (including forceps, ventouse and elective and emergency caesarean) in comparison to mothers with higher activity levels (adjusted OR:1.72(95%CI: 1.05 to 2.9)). Overweight/obese mothers were more likely to require an induction (adjusted OR:1.93 (95%CI 1.14 to 3.26), have a macrosomic baby (adjusted OR:1.96 (95%CI 1.08 to 3.56) and a longer hospital stay after delivery (adjusted OR:2.69 (95%CI 1.11 to 6.47).ConclusionsThe type of delivery was associated with maternal physical activity level and not BMI. Perinatal outcomes (large for gestational age only) were determined by maternal BMI.

Background Digital technologies such as wearables, websites and mobile applications are increasingly used in interventions targeting physical activity (PA). Increasing access to such technologies makes an attractive prospect for helping individuals of low socioeconomic status (SES) in becoming more active and healthier. However, little is known about their effectiveness in such populations. The aim of this systematic review was to explore whether digital interventions were effective in promoting PA in low SES populations, whether interventions are of equal benefit to higher SES individuals and whether the number or type of behaviour change techniques (BCTs) used in digital PA interventions was associated with intervention effects. Methods A systematic search strategy was used to identify eligible studies from MEDLINE, Embase, PsycINFO, Web of Science, Scopus and The Cochrane Library, published between January 1990 and March 2020. Randomised controlled trials, using digital technology as the primary intervention tool, and a control group that did not receive any digital technology-based intervention were included, provided they had a measure of PA as an outcome. Lastly, studies that did not have any measure of SES were excluded from the review. Risk of Bias was assessed using the Cochrane Risk of Bias tool version 2. Results Of the 14,589 records initially identified, 19 studies were included in the final meta-analysis. Using random-effects models, in low SES there was a standardised mean difference (SMD (95%CI)) in PA between intervention and control groups of 0.06 (− 0.08,0.20). In high SES the SMD was 0.34 (0.22,0.45). Heterogeneity was modest in both low (I2 = 0.18) and high (I2 = 0) SES groups. The studies used a range of digital technologies and BCTs in their interventions, but the main findings were consistent across all of the sub-group analyses (digital interventions with a PA only focus, country, chronic disease, and duration of intervention) and there was no association with the number or type of BCTs. Discussion Digital interventions targeting PA do not show equivalent efficacy for people of low and high SES. For people of low SES, there is no evidence that digital PA interventions are effective, irrespective of the behaviour change techniques used. In contrast, the same interventions in high SES participants do indicate effectiveness. To reduce inequalities and improve effectiveness, future development of digital interventions aimed at improving PA must make more effort to meet the needs of low SES people within the target population.

Exercise referral schemes have shown small but positive impacts in randomized controlled trials (RCTs). Less is known about the long-term reach of scaled up schemes following a RCT. A RCT of the National Exercise Referral Scheme (NERS) in Wales was completed in 2010, and the scheme scaled up across Wales. In this study, using a retrospective data linkage design, anonymized NERS data were linked to routine health records for referrals between 2008 and 2017. Rates of referral and uptake were modelled across years and a multilevel logistic regression model examined predictors of uptake. In total, 83,598 patients have been referred to the scheme and 67.31% of eligible patients took up NERS. Older adults and referrals for a musculoskeletal or level four condition were more likely to take up NERS. Males, mental health referrals, non-GP referrals and those in the most deprived groupings were less likely to take up NERS. Trends revealed an overall decrease over time in referrals and uptake rates among the most deprived grouping relative to those in the least deprived group. Findings indicate a widening of inequality in referral and uptake following positive RCT findings, both in terms of patient socioeconomic status and referrals for mental health.