L’adhésion au traitement : Ce que devrait faire un physiothérapeute face à un patient mal-adhérent

Travail d'intégration présenté à la Faculté de Médecine en vue de l’obtention du grade de Maîtrise Science en PhysiothérapieIntroduction : La mal-adhésion au traitement est fréquente chez les patients en physiothérapie, ce qui constitue un impact majeur sur les résultats de traitement et sur les coûts de santé. La définition du concept d’adhésion ne fait pas consensus dans la littérature, mais peut être définie comme le fait de respecter le plan de traitement et les recommandations établis en collaboration avec le patient. Objectifs et Méthodologie : L’objectif de ce travail est de fournir des recommandations aux physiothérapeutes sur la prise en charge d’un patient mal-adhérent. Pour ce faire, une revue de la littérature sur l’adhésion au traitement dans différents domaines de la santé a été effectuée afin d’identifier les facteurs influençant l’adhésion, les outils la mesurant et les stratégies l’optimisant. Résultats : Le physiothérapeute doit détecter les signes de mal-adhésion et identifier les principaux facteurs personnels (éléments psychologiques) et environnementaux (support social et relation patient-thérapeute) pouvant nuire à l’adhésion. Ensuite, le physiothérapeute devrait prendre une mesure de l’adhésion grâce au SIRAS, la seule échelle validée et fidèle disponible actuellement. Le but de ces recommandations est finalement d’appliquer une stratégie éducationnelle, motivationnelle ou de développer une relation thérapeutique adéquate, tout en adaptant ces stratégies en fonction des facteurs responsables de la mal-adhésion spécifiques au patient. Conclusion : La mal-adhésion au traitement est un enjeu important en physiothérapie et pourrait être prise en charge selon des étapes présentées dans l’affiche, de la détection de la mal-adhésion à l’application de stratégies pour l’améliorer

Development and interval testing of a naturalistic driving methodology to evaluate driving behavior in clinical research [version 2; referees: 2 approved]

Background: The number of older adults in the United States will double by 2056. Additionally, the number of licensed drivers will increase along with extended driving-life expectancy. Motor vehicle crashes are a leading cause of injury and death in older adults. Alzheimer’s disease (AD) also negatively impacts driving ability and increases crash risk. Conventional methods to evaluate driving ability are limited in predicting decline among older adults. Innovations in GPS hardware and software can monitor driving behavior in the actual environments people drive in. Commercial off-the-shelf (COTS) devices are affordable, easy to install and capture large volumes of data in real-time. However, adapting these methodologies for research can be challenging. This study sought to adapt a COTS device and determine an interval that produced accurate data on the actual route driven for use in future studies involving older adults with and without AD.  Methods: Three subjects drove a single course in different vehicles at different intervals (30, 60 and 120 seconds), at different times of day, morning (9:00-11:59AM), afternoon (2:00-5:00PM) and night (7:00-10pm). The nine datasets were examined to determine the optimal collection interval. Results: Compared to the 120-second and 60-second intervals, the 30-second interval was optimal in capturing the actual route driven along with the lowest number of incorrect paths and affordability weighing considerations for data storage and curation. Discussion: Use of COTS devices offers minimal installation efforts, unobtrusive monitoring and discreet data extraction.  However, these devices require strict protocols and controlled testing for adoption into research paradigms.  After reliability and validity testing, these devices may provide valuable insight into daily driving behaviors and intraindividual change over time for populations of older adults with and without AD.  Data can be aggregated over time to look at changes or adverse events and ascertain if decline in performance is occurring

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking DeviceTM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.  Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance.  Participants were aged 65+ years and had normal cognition. Measurements:  Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations.  Temporal components included number of trips taken during different times of the day.  Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods:  Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.  Results: Primary locations were confirmed to be 100% accurate when compared to known addresses.  Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns.  Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night.  Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions

Cross-modal functional connectivity supports speech understanding in cochlear implant users

Sensory deprivation can lead to cross-modal cortical changes, whereby sensory brain regions deprived of input may be recruited to perform atypical function. Enhanced cross-modal responses to visual stimuli observed in auditory cortex of postlingually deaf cochlear implant (CI) users are hypothesized to reflect increased activation of cortical language regions, but it is unclear if this cross-modal activity is adaptive or mal-adaptive for speech understanding. To determine if increased activation of language regions is correlated with better speech understanding in CI users, we assessed task-related activation and functional connectivity of auditory and visual cortices to auditory and visual speech and non-speech stimuli in CI users (n = 14) and normal-hearing listeners (n = 17) and used functional near-infrared spectroscopy to measure hemodynamic responses. We used visually presented speech and non-speech to investigate neural processes related to linguistic content and observed that CI users show beneficial cross-modal effects. Specifically, an increase in connectivity between the left auditory and visual cortices-presumed primary sites of cortical language processing-was positively correlated with CI users\u27 abilities to understand speech in background noise. Cross-modal activity in auditory cortex of postlingually deaf CI users may reflect adaptive activity of a distributed, multimodal speech network, recruited to enhance speech understanding

Revisiting CFHTLenS cosmic shear: optimal E/B mode decomposition using COSEBIs and compressed COSEBIs

We present a re-analysis of the CFHTLenS weak gravitational lensing survey using Complete Orthogonal Sets of E/B-mode Integrals, known as COSEBIs. COSEBIs provide a complete set of functions to efficiently separate E-modes from B-modes and hence allow for robust and stringent tests for systematic errors in the data. This analysis reveals significant B-modes on large angular scales that were not previously seen using the standard E/B decomposition analyses. We find that the significance of the B-modes is enhanced when the data are split by galaxy type and analysed in tomographic redshift bins. Adding tomographic bins to the analysis increases the number of COSEBIs modes, which results in a less-accurate estimation of the covariance matrix from a set of simulations. We therefore also present the first compressed COSEBIs analysis of survey data, where the COSEBIs modes are optimally combined based on their sensitivity to cosmological parameters. In this tomographic CCOSEBIs analysis, we find the B-modes to be consistent with zero when the full range of angular scales are considered

Modeling and mapping isotopic patterns in the Northwest Atlantic derived from loggerhead sea turtles

Stable isotope analysis can be used to infer geospatial linkages of highly migratory species. Identifying foraging grounds of marine organisms from their isotopic signatures is becoming de rigueur as it has been with terrestrial organisms. Sea turtles are being increasingly studied using a combination of satellite telemetry and stable isotope analysis; these studies along with those from other charismatic, highly vagile, and widely distributed species (e.g., tuna, billfish, sharks, dolphins, whales) have the potential to yield large datasets to develop methodologies to decipher migratory pathways in the marine realm. We collected tissue samples (epidermis and red blood cells) for carbon (delta C-13) and nitrogen (delta N-15) stable isotope analysis from 214 individual loggerheads (Caretta caretta) in the Northwest Atlantic Ocean (NWA). We used discriminant function analysis (DFA) to examine how well delta C-13 and delta N-15 classify loggerhead foraging areas. The DFA model was derived from isotopic signatures of 58 loggerheads equipped with satellite tags to identify foraging locations. We assessed model accuracy with the remaining 156 untracked loggerheads that were captured at their foraging locations. The DFA model correctly identified the foraging ground of 93.0% of individuals with a probability greater than 66.7%. The results of the external validation (1) confirm that assignment models based on tracked loggerheads in the NWA are robust and (2) provide the first independent evidence supporting the use of these models for migratory marine organisms. Additionally, we used these data to generate loggerhead-specific delta C-13 and delta N-15 isoscapes, the first for a predator in the Atlantic Ocean. We found a latitudinal trend of delta C-13 values with higher values in the southern region (20-25 degrees N) and a more complex pattern with delta N-15, with intermediate latitudes (30-35 degrees N) near large coastal estuaries having higher delta N-15-enrichment. These results indicate that this method with further refinement may provide a viable, more spatially-explicit option for identifying loggerhead foraging grounds

Engineered In vitro Models for Pathological Calcification: Routes Toward Mechanistic Understanding

Physiological calcification plays an essential part in the development of the skeleton and teeth; however, the occurrence of calcification in soft tissues such as the brain, heart, and kidneys associates with health impacts, creating a massive social and economic burden. The current paradigm for pathological calcification focuses on the biological factors responsible for bone-like mineralization, including osteoblast-like cells and proteins inducing nucleation and crystal growth. However, the exact mechanism responsible for calcification remains unknown. Toward this goal, this review dissects the current understanding of structure–function relationships and physico-chemical properties of pathologic calcification from a materials science point of view. We will discuss a range of potential mechanisms of pathological calcification, with the purpose of identifying universal mechanistic pathways that occur across multiple organs/tissues at multiple length scales. The possible effect of extracellular components in signaling and templating mineralization, as well as the role of intrinsically disordered proteins in calcification, is reviewed. The state-of-the-art in vitro models and strategies that can recreate the highly dynamic environment of calcification are identified

Complex history of dog (Canis familiaris) origins and translocations in the Pacific revealed by ancient mitogenomes

Archaeological evidence suggests that dogs were introduced to the islands of Oceania via Island Southeast Asia around 3,300 years ago, and reached the eastern islands of Polynesia by the fourteenth century AD. This dispersal is intimately tied to human expansion, but the involvement of dogs in Pacific migrations is not well understood. Our analyses of seven new complete ancient mitogenomes and five partial mtDNA sequences from archaeological dog specimens from Mainland and Island Southeast Asia and the Pacific suggests at least three dog dispersal events into the region, in addition to the introduction of dingoes to Australia. We see an early introduction of dogs to Island Southeast Asia, which does not appear to extend into the islands of Oceania. A shared haplogroup identified between Iron Age Taiwanese dogs, terminal- Lapita and post-Lapita dogs suggests that at least one dog lineage was introduced to Near Oceania by or as the result of interactions with Austronesian language speakers associated with the Lapita Cultural Complex. We did not find any evidence that these dogs were successfully transported beyond New Guinea. Finally, we identify a widespread dog clade found across the Pacific, including the islands of Polynesia, which likely suggests a post-Lapita dog introduction from southern Island Southeast Asia

Diagnostic accuracy of post-mortem MRI for thoracic abnormalities in fetuses and children

OBJECTIVES: To compare the diagnostic accuracy of post-mortem magnetic resonance imaging (PMMR) specifically for non-cardiac thoracic pathology in fetuses and children, compared with conventional autopsy. METHODS: Institutional ethics approval and parental consent was obtained. A total of 400 unselected fetuses and children underwent PMMR before conventional autopsy, reported blinded to the other dataset. RESULTS: Of 400 non-cardiac thoracic abnormalities, 113 (28 %) were found at autopsy. Overall sensitivity and specificity (95 % confidence interval) of PMMR for any thoracic pathology was poor at 39.6 % (31.0, 48.9) and 85.5 % (80.7, 89.2) respectively, with positive predictive value (PPV) 53.7 % (42.9, 64.0) and negative predictive value (NPV) 77.0 % (71.8, 81.4). Overall agreement was 71.8 % (67.1, 76.2). PMMR was most sensitive at detecting anatomical abnormalities, including pleural effusions and lung or thoracic hypoplasia, but particularly poor at detecting infection. CONCLUSIONS: PMMR currently has relatively poor diagnostic detection rates for the commonest intra-thoracic pathologies identified at autopsy in fetuses and children, including respiratory tract infection and diffuse alveolar haemorrhage. The reasonable NPV suggests that normal thoracic appearances at PMMR exclude the majority of important thoracic lesions at autopsy, and so could be useful in the context of minimally invasive autopsy for detecting non-cardiac thoracic abnormalities. KEY POINTS: • PMMR has relatively poor diagnostic detection rates for common intrathoracic pathology • The moderate NPV suggests that normal PMMR appearances exclude most important abnormalities • Lung sampling at autopsy remains the "gold standard" for pulmonary pathology

Communicating complex ecological models to non-scientist end users

Complex computer models are used to predict how ecological systems respond to changing environ- mental conditions or management actions. Communicating these complex models to non-scientists is challenging, but necessary, because decision-makers and other end users need to understand, accept, and use the models and their predictions. Despite the importance of communicating effectively with end users, there is little guidance available as to how this may be achieved. Here, we review the challenges typically encountered by modellers attempting to communicate complex models and their outputs to managers and other non-scientist end users. We discuss the implications of failing to communicate effec- tively in each case. We then suggest a general approach for communicating with non-scientist end users. We detail the specific elements to be communicated using the example of individual-based models, which are widely used in ecology. We demonstrate that despite their complexity, individual-based models have characteristics that can facilitate communication with non-scientists. The approach we propose is based on our experiences and methods used in other fields, but which until now have not been synthesised or made broadly available to ecologists. Our aim is to facilitate the process of communicating with end users of complex models and encourage more modellers to engage in it by providing a structured approach to the communication process. We argue that developing measures of the effectiveness of communication with end users will help increase the impact of complex models in ecology