Health effects of holistic housing renovation in a disadvantaged neighbourhood in the Netherlands: a qualitative exploration among residents and professionals.

BACKGROUND: Holistic housing renovations combine physical housing improvements with social and socioeconomic interventions (e.g. referral to social services, debt counselling, involvement in decision-making, promoting social cohesion). In a deprived neighbourhood in Utrecht, the Netherlands, this paper examined residents' and professionals' experiences, ideas, and perceptions regarding holistic housing renovation, its health effects, and underlying mechanisms explaining those effects. METHODS: Semi-structured in-depth interviews were conducted with 21 social housing residents exposed to holistic housing renovation, and 12 professionals involved in either the physical renovation or social interventions implemented. Residents were interviewed in various renovation stages (before, during, after renovation). Transcripts were deductively and inductively coded using qualitative software. RESULTS: Residents experienced and professionals acknowledged renovation stress caused by nuisance from construction work (noise, dust), having to move stuff around, and temporary moving; lack of information and control; and perceived violation of privacy. Involvement in design choices was appreciated, and mental health improvement was expected on the long term due to improved housing quality and visual amenity benefits. Social contact between residents increased as the renovation became topic for small talk. Few comments were made regarding physical health effects. The interviews revealed a certain amount of distrust in and dissatisfaction with the housing corporation, construction company, and other authorities. CONCLUSIONS: Renovation stress, aggravated by lack of information and poor accessibility of housing corporation and construction company, negatively affects mental health and sense of control. Potential stress relievers are practical help with packing and moving furniture, and increased predictability by good and targeted communication. Social interventions can best be offered after renovation, when residents live in their renovated apartment and the nuisance and stress from the renovation is behind them. Social partners can use the period leading up to the renovation to show their faces, offer practical help to reduce renovation stress, and increase residents' trust in their organization and authorities in general. This might also contribute to residents' willingness to accept help with problems in the social domain after renovation.

Cow key point detection in indoor housing conditions with a deep learning model.

Lameness in dairy cattle is a costly and highly prevalent problem that affects all aspects of sustainable dairy production, including animal welfare. Automation of gait assessment would allow monitoring of locomotion in which the cows' walking patterns can be evaluated frequently and with limited labor. With the right interpretation algorithms, this could result in more timely detection of locomotion problems. This in turn would facilitate timely intervention and early treatment, which is crucial to reduce the effect of abnormal behavior and pain on animal welfare. Gait features of dairy cows can potentially be derived from key points that locate crucial anatomical points on a cow's body. The aim of this study is 2-fold: (1) to demonstrate automation of the detection of dairy cows' key points in a practical indoor setting with natural occlusions from gates and races, and (2) to propose the necessary steps to postprocess these key points to make them suitable for subsequent gait feature calculations. Both the automated detection of key points as well as the postprocessing of them are crucial prerequisites for camera-based automated locomotion monitoring in a real farm environment. Side-view video footage of 34 Holstein-Friesian dairy cows, captured when exiting the milking parlor, were used for model development. From these videos, 758 samples of 2 successive frames were extracted. A previously developed deep learning model called T-LEAP was trained to detect 17 key points on cows in our indoor farm environment with natural occlusions. To this end, the dataset of 758 samples was randomly split into a train (n = 22 cows; no. of samples = 388), validation (n = 7 cows; no. of samples = 108), and test dataset (n = 15 cows; no. of samples = 262). The performance of T-LEAP to automatically assign key points in our indoor situation was assessed using the average percentage of correctly detected key points using a threshold of 0.2 of the head length (PCKh0.2). The model's performance on the test set achieved a good result with PCKh0.2: 89% on all 17 key points together. Detecting key points on the back (n = 3 key points) of the cow had the poorest performance PCKh0.2: 59%. In addition to the indoor performance of the model, a more detailed study of the detection performance was conducted to formulate postprocessing steps necessary to use these key points for gait feature calculations and subsequent automated locomotion monitoring. This detailed study included the evaluation of the detection performance in multiple directions. This study revealed that the performance of the key points on a cows' back were the poorest in the horizontal direction. Based on this more in-depth study, we recommend the implementation of the outlined postprocessing techniques to address the following issues: (1) correcting camera distortion, (2) rectifying erroneous key point detection, and (3) establishing the necessary procedures for translating hoof key points into gait features.

Understanding the mechanisms linking holistic housing renovations to health and well-being of adults in disadvantaged neighbourhoods: A realist review.

Holistic housing renovations combine physical housing improvements with social and socioeconomic interventions (e.g. referral to social services, debt counselling, involvement in decision-making, promoting social cohesion). This realist review aimed at understanding underlying mechanisms linking holistic housing renovations to health and well-being of adults in disadvantaged neighbourhoods. Following systematic and iterative searching, and relevance and quality appraisals, 18 scientific articles and reports were analysed. We identified three pathways via which physical housing improvements affect health, four pathways via which social and socioeconomic interventions affect health, and two pathways via which both reinforce each other in their health effects. Our findings are theoretically novel, relevant for those conducting holistic housing renovations, and point towards gaps in the literature.

Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud.

More and more sensor and automation data are available that enable animal breeders to define novel traits. However, sensor and automation data are often frequently measured differently (e.g., milk yield and different milk components are continuously measured during each milking). These differences are challenging animal breeders to define traits and use the most appropriate analytical models for genetic evaluation and breeding values. Traditionally, the process from raw data to breeding value estimations involves several steps: data curation, trait definition, variance component estimation, genetic evaluation, and validation of the estimated breeding values (EBV). All these steps often take many iterations and several research projects to optimize the final genetic evaluations. To make this entire process-from raw data to validated EBV-more efficient, we combined all these steps in a cloud environment that allows for faster processing and a faster data distribution time. We used real data (including 1,782,373,113 daily milk-yield records of 1,120,550 dairy cows) and a real trait (a resilience trait based on the deviations from expected milk yields) to demonstrate the functioning of this cloud environment. The daily milk-yield records were incorporated into our cloud solution, in which we have set up central binary large object storage. Subsequent steps were all performed in the cloud. The data set was preprocessed in approximately 6 h to obtain the resilience indicator for 352,871 cows in the first 3 lactations. Estimation of genetic parameters (heritabilities and genetic correlations) was performed by splitting the data into 5 subsets in ASReml, and prediction of subsequent EBV was performed on the entire data set using MiXBLUP. Together with the validation of breeding values, this process encompassed 16.5 h. By combining the different steps from preprocessing sensor data to genetic evaluation of new traits in one cloud environment, we generated EBV and validation plots in approximately 1 working day. Moreover, our setup is a flexible design and can be adapted easily to test new, longitudinal sensor-driven traits and compare the performance of these new traits to previous ones.

Invited review: Toward a common language in data-driven mastitis detection research.

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.

Positron range-free and multi-isotope tomography of positron emitters.

Despite improvements in small animal PET instruments, many tracers cannot be imaged at sufficiently high resolutions due to positron range, while multi-tracer PET is hampered by the fact that all annihilation photons have equal energies. Here we realize multi-isotope and sub-mm resolution PET of isotopes with several mm positron range by utilizing prompt gamma photons that are commonly neglected. A PET-SPECT-CT scanner (VECTor/CT, MILabs, The Netherlands) equipped with a high-energy cluster-pinhole collimator was used to image 124I and a mix of 124I and 18F in phantoms and mice. In addition to positrons (mean range 3.4 mm) 124I emits large amounts of 603 keV prompt gammas that-aided by excellent energy discrimination of NaI-were selected to reconstruct 124I images that are unaffected by positron range. Photons detected in the 511 keV window were used to reconstruct 18F images. Images were reconstructed iteratively using an energy dependent matrix for each isotope. Correction of 18F images for contamination with 124I annihilation photons was performed by Monte Carlo based range modelling and scaling of the 124I prompt gamma image before subtracting it from the 18F image. Additionally, prompt gamma imaging was tested for 89Zr that emits very high-energy prompts (909 keV). In Derenzo resolution phantoms 0.75 mm rods were clearly discernable for 124I, 89Zr and for simultaneously acquired 124I and 18F imaging. Image quantification in phantoms with reservoirs filled with both 124I and 18F showed excellent separation of isotopes and high quantitative accuracy. Mouse imaging showed uptake of 124I in tiny thyroid parts and simultaneously injected 18F-NaF in bone structures. The ability to obtain PET images at sub-mm resolution both for isotopes with several mm positron range and for multi-isotope PET adds to many other unique capabilities of VECTor's clustered pinhole imaging, including simultaneous sub-mm PET-SPECT and theranostic high energy SPECT.

Between-herd variation in resilience and relations to herd performance.

Resilient cows are minimally affected in their functioning by infections and other disturbances, and recover quickly. Herd management is expected to have an effect on disturbances and the resilience of cows, and this effect was investigated in this study. Two resilience indicators were first recorded on individual cows. The effect of herd-year on these resilience indicators was then estimated and corrected for genetic and year-season effects. The 2 resilience indicators were the variance and the lag-1 autocorrelation of daily milk yield deviations from an expected lactation curve. Low variance and autocorrelation indicate that a cow does not fluctuate much around her expected milk yield and is, thus, subject to few disturbances, or little affected by disturbances (resilient). The herd-year estimates of the resilience indicators were estimated for 9,917 herd-year classes based on records of 227,655 primiparous cows from 2,644 herds. The herd-year estimates of the resilience indicators were then related to herd performance variables. Large differences in the herd-year estimates of the 2 resilience indicators (variance and autocorrelation) were observed between herd-years, indicating an effect of management on these traits. Furthermore, herd-year classes with a high variance tended to have a high proportion of cows with a rumen acidosis indication (r = 0.31), high SCS (r = 0.19), low fat content (r = -0.18), long calving interval (r = 0.14), low survival to second lactation (r = -0.13), large herd size (r = 0.12), low lactose content (r = -0.12), and high production (r = 0.10). These correlations support that herds with high variance are not resilient. The correlation between the variance and the proportion of cows with a rumen acidosis indication suggests that feed management may have an important effect on the variance. Herd-year classes with a high autocorrelation tended to have a high proportion of cows with a ketosis indication (r = 0.14) and a high production (r = 0.13), but a low somatic cell score (r = -0.17) and a low proportion of cows with a rumen acidosis indication (r = -0.12). These correlations suggest that high autocorrelation at herd level indicates either good or poor resilience, and is thus a poor resilience indicator. However, the combination of a high variance and a high autocorrelation is expected to indicate many fluctuations with slow recovery. In conclusion, herd management, in particular feed management, seems to affect herd resilience.

Pathogen effects on milk yield and composition in chronic subclinical mastitis in dairy cows.

This study aimed to evaluate the effects of chronic subclinical mastitis (CSM) on milk production and component yields in dairy cows. A total of six herds located in the Midwest area of São Paulo State, Brazil were selected. Herds were visited once every 2 weeks to measure milk yield and to collect milk samples from lactating Holstein cows. Milk samples were collected at two stages (1 and 2), and each stage comprised three milk samplings. In stage 1, a total of 117 of 647 cows were diagnosed with CSM based on at least two of three repeated somatic cell counts (SCC) > 2000,000 cells/mL and positive bacterial milk culture results (BC). Cows with CSM were selected for the second stage. In stage 2, selected cows had quarter sampling aseptically collected for BC analyses prior to milking, and quarter milk yield was measured. Milk components (total protein, fat, lactose, and total solids) were measured using mid-infrared spectroscopy. Mammary quarters were considered healthy if all three repeated SCC results were ≤ 200,000 cells/mL and no bacterial growth was detected on BC. All quarters with positive bacterial growth were classified as having (non-chronic) subclinical mastitis when only one of three SCC results were > 200,000 cells/mL, and CSM when at least two of three SCC results were > 200,000 cells/mL. The effects of CSM by type of pathogen on milk and components yield were assessed using a linear mixed model. Mammary quarters with CSM caused by major pathogens had milk loss of 1.1 kg/quarter milking in comparison to healthy quarters. Milk losses were 0.8 and 1.3 kg/quarter milking when CSM was caused by Staphylococcus aureus or environmental streptococci, respectively. In addition, healthy quarters produced more milk components than quarters with CSM caused by major pathogens. Minor pathogens causing CSM (non-aureus staphylococci and Corynebacterium spp.) had no effect on milk yield. Quarters with CSM had lower milk and component yields when compared with healthy quarters. Milk losses varied according to the type of pathogen and were higher when associated with major pathogens such as S. aureus and environmental streptococci compared with healthy quarters.

Storing, combining and analysing turkey experimental data in the Big Data era.

With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an 'extract, transform and load' (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the 'wall time' (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.

Predicting survival in dairy cattle by combining genomic breeding values and phenotypic information.

Advances in technology and improved data collection have increased the availability of genomic estimated breeding values (gEBV) and phenotypic information on dairy farms. This information could be used for the prediction of complex traits such as survival, which can in turn be used in replacement heifer management. In this study, we investigated which gEBV and phenotypic variables are of use in the prediction of survival. Survival was defined as survival to second lactation, plus 2 wk, a binary trait. A data set was obtained of 6,847 heifers that were all genotyped at birth. Each heifer had 50 gEBV and up to 62 phenotypic variables that became gradually available over time. Stepwise variable selection on 70% of the data was used to create multiple regression models to predict survival with data available at 5 decision moments: distinct points in the life of a heifer at which new phenotypic information becomes available. The remaining 30% of the data were kept apart to investigate predictive performance of the models on independent data. A combination of gEBV and phenotypic variables always resulted in the model with the highest Akaike information criterion value. The gEBV selected were longevity, feet and leg score, exterior score, udder score, and udder health score. Phenotypic variables on fertility, age at first calving, and milk quantity were important once available. It was impossible to predict individual survival accurately, but the mean predicted probability of survival of the surviving heifers was always higher than the mean predicted probability of the nonsurviving group (difference ranged from 0.014 to 0.028). The model obtained 2.0 to 3.0% more surviving heifers when the highest scoring 50% of heifers were selected compared with randomly selected heifers. Combining phenotypic information and gEBV always resulted in the highest scoring models for the prediction of survival, and especially improved early predictive performance. By selecting the heifers with the highest predicted probability of survival, increased survival could be realized at the population level in practice.

The effects of small-scale physical and social environmental interventions on walking behaviour among Dutch older adults living in deprived neighbourhoods: results from the quasi-experimental NEW.ROADS study.

PURPOSE: Improving the physical and social conditions of residential neighbourhoods may increase walking, especially among older people. Evidence on the effects of physical and social environmental interventions, and particularly the combination of both, on walking behaviour is scarce. We evaluated the effects of a small-scale physical environmental intervention (designated walking route), a social environmental intervention (neighbourhood walking group) and the combination of both on walking behaviour of older adults living in deprived neighbourhoods. METHODS: Survey data of 644 older adults residing in four deprived neighbourhoods of Rotterdam, the Netherlands, were used to compare changes in walking behaviour over time (weekly minutes spent recreational walking, utilitarian walking and total walking) of those exposed to 1) a designated walking route (physical condition), 2) walking groups (social condition), 3) walking routes and walking groups (combined condition), and 4) no intervention (control condition). Measurements took place at baseline (T0), and 3 months (T1) and 9 months (T2) after the intervention. Data were analysed on a multiple imputed dataset, using multi-level negative binomial regression models, adjusting for clustering of observations within individuals. All models were adjusted for demographic covariates. RESULTS: Total time spent walking per week increased between T0 and T1 for all conditions. The Incidence Rate Ratio (IRR) for the physical condition was 1.46 (95% CI:1.06;2.05) and for the social intervention 1.52 (95%CI:1.07;2.16). At T2, these differences remained significant for the physical condition, but not for the social condition and the combined condition. These findings were mirrored for utilitarian walking. No evidence was found for an effect on recreational walking. CONCLUSION: Implementing small scale, feasible, interventions in a residential neighbourhood may increase total and utilitarian walking behaviour among older adults.

Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle.

In this study, we compared multiple logistic regression, a linear method, to naive Bayes and random forest, 2 nonlinear machine-learning methods. We used all 3 methods to predict individual survival to second lactation in dairy heifers. The data set used for prediction contained 6,847 heifers born between January 2012 and June 2013, and had known survival outcomes. Each animal had 50 genomic estimated breeding values available at birth and up to 65 phenotypic variables that accumulated over time. Survival was predicted at 5 moments in life: at birth, at 18 mo, at first calving, at 6 wk after first calving, and at 200 d after first calving. The data sets were randomly split into 70% training and 30% testing sets to evaluate model performance for 20-fold validation. The methods were compared for accuracy, sensitivity, specificity, area under the curve (AUC) value, contrasts between groups for the prediction outcomes, and increase in surviving animals in a practical scenario. At birth and 18 mo, all methods had overlapping performance; no method significantly outperformed the other. At first calving, 6 wk after first calving, and 200 d after first calving, random forest and naive Bayes had overlapping performance, and both machine-learning methods outperformed multiple logistic regression. Overall, naive Bayes has the highest average AUC at all decision points up to 200 d after first calving. Random forest had the highest AUC at 200 d after first calving. All methods obtained similar increases in survival in the practical scenario. Despite this, the methods appeared to predict the survival of individual heifers differently. All methods improved over time, but the changes in mean model outcomes for surviving and non-surviving animals differed by method. Furthermore, the correlations of individual predictions between methods ranged from r = 0.417 to r = 0.700; the lowest correlations were at first calving for all methods. In short, all 3 methods were able to predict survival at a population level, because all methods improved survival in a practical scenario. However, depending on the method used, predictions for individual animals were quite different between methods.

Invited review: Big Data in precision dairy farming.

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V's) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V's) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.

Hour-by-hour physical activity patterns of adults aged 45-65 years: a cross-sectional study.

Background: Limited information exists on hour-by-hour physical activity (PA) patterns among adults aged 45-65 years. Therefore, this study aimed to distinguish typical hour-by-hour PA patterns, and examined which individuals typically adopt certain PA patterns. Methods: Accelerometers measured light and moderate-vigorous PA. GIS-data provided proportions of land use within an 800 and 1600 m buffer around participant's homes. Latent class analyses were performed to distinguish PA patterns and groups of individuals with similar PA patterns. Results: Four PA patterns were identified: a morning light PA pattern, a mid-day moderate-vigorous PA pattern, an overall inactive pattern and an overall active pattern. Groups of individuals with similar PA patterns differed in ethnicity, dog ownership, and the proportion of roads, sports terrain, larger green and blue space within their residential areas. Conclusions: Four typical hour-by-hour PA patterns, and three groups of individuals with similar patterns were distinguished. It is this combination that can substantially contribute to the development of more tailored policies and interventions. PA patterns were only to a limited extent associated with personal and residential characteristics, suggesting that other factors such as work time regimes, family life and leisure may also have considerable impact on the distribution of PA throughout the day.

How do type and size of natural environments relate to physical activity behavior?

Natural environments (NE) are promoted as places that support physical activity (PA), but evidence on PA distribution across various types and sizes of NE is lacking. Accelerometers and GPS-devices measured PA of Dutch general population adults aged 45-65 years (N=279). Five NE types were distinguished: 'parks', 'recreational area', 'agricultural green', 'forest & moorland', and 'blue space', and four categories of size: 0-3, 3-7, 7-27, and ≥27 ha. Modality (i.e. spatially concentrated PA, walking, jogging, and cycling) and intensity (i.e. sedentary behavior, LPA, and MVPA) of PA varied significantly between NE types. Compared to parks, less sedentary behavior and walking but more spatially concentrated PA was observed in recreational areas and green space. Cycling levels were found to be significantly lower in recreational areas and forest & moorland, but higher in blue space as compared to parks. Larger sized NE (≥7 ha) were associated with higher levels of MVPA, walking, jogging and cycling. Insight in which environments (according to type and size) facilitate PA, contributes to the development of tailored PA promoting interventions with ensuing implications for public health.

Physical and social environmental changes to promote walking among Dutch older adults in deprived neighbourhoods: the NEW.ROADS study.

BACKGROUND: Physical activity is important for healthy ageing, and daily walking is seen as a feasible way to be active at older ages. Yet, many older persons, particularly in lower socioeconomic groups and residing in deprived neighbourhoods, are insufficiently active. Creating a physical and social neighbourhood environment that is more supportive for walking has the potential to improve walking behaviour. Current evidence of the impact of changes to the physical and/or social environmental on walking behaviour is scarce. The aim of the NEW.ROADS study is to design, implement and evaluate changes to the physical and social environment for the purpose of increasing walking behaviour among older residents of deprived neighbourhoods. METHODS: Physical and social environmental interventions were developed by matching scientific evidence on environmental determinants of walking, with input from the target population and stakeholders, and ongoing neighbourhood activities. Specifically, a neighbourhood walking route was designed and marked, and neighbourhood walking groups were organised. These environmental interventions were evaluated in a four-armed experimental study. In addition, the design of the study to evaluate the effect of these environmental changes on walking behaviour is described. DISCUSSION: Designing and implementing environmental interventions is a complex endeavour, challenged by limited available theory and evidence. Input from the target population and professional stakeholders is essential, but may also put constraints on the evaluation. TRIAL REGISTRATION: NTR3800 (registered 9/1/2013).

Field validation of protocols developed to evaluate in-line mastitis detection systems.

This paper reports on a field validation of previously developed protocols for evaluating the performance of in-line mastitis-detection systems. The protocols outlined 2 requirements of these systems: (1) to detect cows with clinical mastitis (CM) promptly and accurately to enable timely and appropriate treatment and (2) to identify cows with high somatic cell count (SCC) to manage bulk milk SCC levels. Gold standard measures, evaluation tests, performance measures, and performance targets were proposed. The current study validated the protocols on commercial dairy farms with automated in-line mastitis-detection systems using both electrical conductivity (EC) and SCC sensor systems that both monitor at whole-udder level. The protocol for requirement 1 was applied on 3 commercial farms. For requirement 2, the protocol was applied on 6 farms; 3 of them had low bulk milk SCC (128×10(3) cells/mL) and were the same farms as used for field evaluation of requirement 1. Three farms with high bulk milk SCC (270×10(3) cells/mL) were additionally enrolled. The field evaluation methodology and results were presented at a workshop including representation from 7 international suppliers of in-line mastitis-detection systems. Feedback was sought on the acceptance of standardized performance evaluation protocols and recommended refinements to the protocols. Although the methodology for requirement 1 was relatively labor intensive and required organizational skills over an extended period, no major issues were encountered during the field validation of both protocols. The validation, thus, proved the protocols to be practical. Also, no changes to the data collection process were recommended by the technology supplier representatives. However, 4 recommendations were made to refine the protocols: inclusion of an additional analysis that ignores small (low-density) clot observations in the definition of CM, extension of the time window from 4 to 5 milkings for timely alerts for CM, setting a maximum number of 10 milkings for the time window to detect a CM episode, and presentation of sensitivity for a larger range of false alerts per 1,000 milkings replacing minimum performance targets. The recommended refinements are discussed with suggested changes to the original protocols. The information presented is intended to inform further debate toward achieving international agreement on standard protocols to evaluate performance of in-line mastitis-detection systems.

Devices used by automated milking systems are similarly accurate in estimating milk yield and in collecting a representative milk sample compared with devices used by farms with conventional milk recording.

Information on accuracy of milk-sampling devices used on farms with automated milking systems (AMS) is essential for development of milk recording protocols. The hypotheses of this study were (1) devices used by AMS units are similarly accurate in estimating milk yield and in collecting representative milk samples compared with devices used by certified milk recording providers on farms with conventional milking systems (CMS) and (2) devices used on both AMS and CMS comply with accuracy criteria described by the New Zealand Standard and by the International Committee of Animal Recording (ICAR). Milk recording data from 5 AMS farms were collected during 13 milk recording test days between December 2011 and February 2013. Milk yield was estimated by ICAR-approved milk meters on AMS units. Milk samples were collected over a 48-h period and submitted to an off-site certified laboratory for milk composition analysis. Data were also collected manually from 5 to 10 cows per AMS unit; a complete milking of a cow was weighed to serve as gold standard for milk yield, and 3 milk samples per cow milking were collected and analyzed in the laboratory to serve as gold standards for milk composition. A similar procedure was used during 6 milk recording occasions with devices used during conventional milk recording at a CMS research farm. Farm type, breed, season, and region did not appear to affect accuracy of devices used on AMS units. Milk meters used by AMS units complied with ICAR limits in 12.5 and 25% of the milk recording test days for test bucket weights between 2 and 10kg and for test bucket weights >10kg, respectively. These percentages were 52 and 42%, respectively, for devices used on CMS. Analyzing all samples as one milk recording test day, 1.4% fell outside the 20% difference band for AMS compared with 1.1% of the milk samples for CMS. Devices used by AMS complied with ICAR in 73% of the milk recording test days for fat percentage, compared with 42% of the milk recording test days by devices used at the CMS farm. When analyzing all milk samples as one milk recording test day, 3.5% of the milk samples fell outside the 99% ICAR limit for AMS compared with 17.2% of the milk samples for CMS. Applying the ICAR standards for fat percentage to crude protein percentage and SCC, devices used on AMS were accurate in estimating crude protein percentage but not in estimating SCC. Thus, devices on AMS units did not comply with national nor ICAR standards with regard to milk yield and fat percentage. However, devices used on AMS were similarly or more accurate compared with devices used during conventional milk recording. It is proposed that devices used on AMS units, when calibrated regularly and when set up according to the manufacturer's instruction, have similar or improved accuracy compared with CMS devices. Because the New Zealand industry accepts data from devices currently used by certified providers for milk recording on CMS farms, results imply that the AMS devices should also be permitted to be used for milk recording.

How many walking and cycling trips made by elderly are beyond commonly used buffer sizes: results from a GPS study.

In choosing appropriate buffer sizes to study environmental influences on physical activity, studies are hampered by insufficient insight into the distance elderly travel actively. This study aims at getting insight into the number of trips walked and cycled within various buffer sizes using GPS measures. Data were obtained from the Elderly And their Neighborhood study (Spijkenisse, the Netherlands (2011-2012)). Trip length and mode of transport were derived from the GPS data (N=120; total number of trips=337). Distance decay functions were fitted to estimate the percentage of trips to grocery stores within commonly used buffer sizes. Fifty percent of the trips walked had a distance of at least 729m; for trips cycled this was 1665m. Elderly aged under 75 years and those with functional limitations walked and cycled shorter distances than those over 75 years and those without functional limitations. Males cycled shorter distances than females. Distance decay functions may aid the selection of appropriate buffer sizes, which may be tailored to individual characteristics.

Using activity-based monitoring systems to detect dairy cows in oestrus: a field evaluation.

AIM: To assess the use and performance of activity-based oestrus detection systems (ODS) on two commercial dairy farms using a gold standard based on profiles of concentrations of progesterone in milk, artificial insemination (AI) records and pregnancy diagnosis results. METHODS: Two activity-based ODS were evaluated in mature cows on two large pasture-grazed dairy farms (>500 cows) over the first 3 weeks of AI. Farm 1 (n=286 cows) used a leg-mounted device and cows were drafted automatically based on activity alerts. Decisions regarding AI were then made based on tail-paint and cow history for these cows. Farm 2 (n=345 cows) used a collar-mounted device and activity alerts were used in conjunction with other information, before the farmer manually selected cows for AI. The gold standard to define the timing of oestrus was based on profiles of concentrations of progesterone in milk measured twice-weekly, used in conjunction with AI records and pregnancy diagnosis results. Sensitivity and positive predictive value (PPV) were calculated for the activity-based ODS data only, and then for AI decisions, against the gold standard. RESULTS: Farm 1 had 195 confirmed oestrus events and 209 activity alerts were generated. The sensitivity of the activity-based ODS was 89.2% with a PPV of 83.3%. Using tail-paint and cow history to confirm activity-based alerts 175 cows were inseminated, resulting in a sensitivity of 89.2% and an improved PPV of 99.4%. Farm 2 had 343 confirmed oestrus events, and 726 alerts were generated by the activity-based ODS, giving a sensitivity of 69.7% with a PPV of 32.9%. A total of 386 cows had AI records, giving a sensitivity of 81.3% and PPV of 72.3%. CONCLUSIONS: The two activity-based ODS were used differently on-farm; one automatically selecting cows and the other supporting the manual selection of cows in oestrus. Only one achieved a performance level suggested to be acceptable as a stand-alone ODS. Use of additional tools, such as observation of tail paint to confirm activity-based oestrus alerts before AI, substantially improved the PPV. CLINICAL RELEVANCE: A well performing activity-based ODS can be a valuable tool in identifying cows in oestrus prior to visual confirmation of oestrus status. However the performance of these ODS technologies varies considerably.