Best Practices for Conducting Observational Research to Assess the Relation between Nutrition and Bone: An International Working Group Summary

Diet is a modifiable factor that can affect bone strength and integrity, and the risk of fractures. Currently, a hierarchy of scientific evidence contributes to our understanding of the role of diet on bone health and fracture risk. The strength of evidence is generally based on the type of study conducted, the quality of the methodology employed, the rigor and integrity of the data collected and analysis plan, and the transparency and completeness of the results. Randomized controlled trials (RCTs) are considered to be the gold standard from a clinical research paradigm, but there is a dearth of high-quality diet-related intervention trials with bone as the primary outcome, forcing the use of observational research to inform research and clinical practices. However, for observational research to be of the most utility, standardization and optimization of the study design, accurate and reliable measurement of key variables, and appropriate data analysis and data reporting are paramount. Although there have been recommendations made in relation to RCTs in the field of nutrition, no clear rubric exists for best practices in conducting observational research with regard to nutrition and bone health. Therefore, the purpose of this paper is to describe the best practices and considerations for designing, conducting, analyzing, interpreting, and reporting observational research specifically for understanding the role of nutrition in bone health, amassed by a global panel of scientific experts with strengths in bone, nutrition epidemiology, physical activity, public health, clinical and translational trials, and observational study methods. The global panel of scientific experts represents the leadership and selected participants from the 10th annual International Symposium for the Nutritional Aspects of Osteoporosis. The topics selected and best practices presented reflect expert opinion and areas of scientific expertise of the authors rather than a systematic or comprehensive literature review or professional reporting guidelines

Use of eHealth technologies to enable the implementation of musculoskeletal Models of Care: Evidence and practice

Musculoskeletal (MSK) conditions are the second leading cause of morbidity-related burden of disease globally. EHealth is a potentially critical factor that enables the implementation of accessible, sustainable and more integrated MSK models of care (MoCs). MoCs serve as a vehicle to drive evidence into policy and practice through changes at a health system, clinician and patient level. The use of eHealth to implement MoCs is intuitive, given the capacity to scale technologies to deliver system and economic efficiencies, to contribute to sustainability, to adapt to low-resource settings and to mitigate access and care disparities. We follow a practice-oriented approach to describing the ‘what’ and ‘how’ to harness eHealth in the implementation of MSK MoCs. We focus on the practical application of eHealth technologies across care settings to those MSK conditions contributing most substantially to the burden of disease, including osteoarthritis and inflammatory arthritis, skeletal fragility-associated conditions and persistent MSK pain

Measuring muscle and fat with peripheral quantitative computed tomography : precision, annual changes, monitoring intervals, and associations with fall status in older adults

Objectives: The overall aim of this thesis was to investigate the precision error, annual changes, and monitoring time intervals of muscle and fat outcomes measured by peripheral quantitative computed tomography (pQCT), as well as explore the strength of their associations with fall status in older adults. Methods: Participants aged >60 years old (N=190) were recruited from the Saskatoon Cohort of the Canadian Multicentre Osteoporosis Study (CaMOs). The precision error (Root Mean Squared Co-efficient of Variation, CV%RMS) of soft-tissue outcomes from previously reported pQCT image analysis protocols (n=6) were calculated and compared using repeat forearm and lower leg scans collected from a random sub-sample of women (n=35). Prospective scans were collected with 1 and/or 2 years of follow-up (n=97) to estimate annual changes and monitoring time intervals for pQCT-derived muscle and fat outcomes in women. Imaging data and responses from a retrospective fall status questionnaire were analyzed to investigate the associations of muscle density, functional mobility, and health- related factors to fall status for both men and women (n=183). Results: Precision errors of muscle and fat outcomes ranged from 0.7 to 6.4% in older women, however not all protocols were equally precise. Muscle cross-sectional area decreased by 0.8 to 1.2% per year, with greater losses in the lower limb. Biological changes in muscle area and density may be detected with 80 and 95% certainty within monitoring time intervals of 4 to 9 years. The odds of having reported a fall increased by 17% for every unit decrease in muscle density (mean 70.2, SD 2.6mg/cm3) after adjusting for age, sex, body mass index, general health status, diabetes, the number of comorbidities, and functional mobility. Discussion: This dissertation demonstrated the potential for pQCT to study changes in muscle and fat outcomes in older adults. Both muscle area and density can be precisely measured. Observed annual changes in soft-tissue outcomes were small in older adults; highlighting the importance of precise measurements to detect changes beyond measurement error. Together with the estimated monitoring time intervals, these findings can assist the planning of prospective investigations of musculoskeletal health in aging. Furthermore, based on the observed independent association between muscle density and fall status, monitoring muscle density may further complement the study of musculoskeletal health and fall risk in community-dwelling older adults

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

The Empirical Foundations of Teleradiology and Related Applications: A Review of the Evidence

Introduction: Radiology was founded on a technological discovery by Wilhelm Roentgen in 1895. Teleradiology also had its roots in technology dating back to 1947 with the successful transmission of radiographic images through telephone lines. Diagnostic radiology has become the eye of medicine in terms of diagnosing and treating injury and disease. This article documents the empirical foundations of teleradiology. Methods: A selective review of the credible literature during the past decade (2005?2015) was conducted, using robust research design and adequate sample size as criteria for inclusion. Findings: The evidence regarding feasibility of teleradiology and related information technology applications has been well documented for several decades. The majority of studies focused on intermediate outcomes, as indicated by comparability between teleradiology and conventional radiology. A consistent trend of concordance between the two modalities was observed in terms of diagnostic accuracy and reliability. Additional benefits include reductions in patient transfer, rehospitalization, and length of stay.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140295/1/tmj.2016.0149.pd

Femoral Strength Prediction using Finite Element Models : Validation of models based on CT and reconstructed DXA images against full-field strain measurements

Osteoporosis is defined as low bone density, and results in a markedly increased risk of skeletal fractures. It has been estimated that about 40% of all women above 50 years old will suffer from an osteoporotic fracture leading to hospitalization. Current osteoporosis diagnostics is largely based on statistical tools, using epidemiological parameters and bone mineral density (BMD) measured with dual energy X-ray absorptiometry (DXA). However, DXA-based BMD proved to be only a moderate predictor of bone strength. Therefore, novel methods that take into account all mechanical characteristics of the bone and their influence on bone resistance to fracture are advocated. Finite element (FE) models may improve the bone strength prediction accuracy, since they can account for the structural determinants of bone strength, and the variety of external loads acting on the bones during daily life. Several studies have proved that FE models can perform better than BMD as a bone strength predictor. However, these FE models are built from Computed Tomography (CT) datasets, as the 3D bone geometry is required, and take several hours of work by an experienced engineer. Moreover, the radiation dose for the patient is higher for CT than for DXA scan. All these factors contributed to the low impact that FE-based methods have had on the current clinical practice so far. This thesis work aimed at developing accurate and thoroughly validated FE models to enable a more accurate prediction of femoral strength. An accurate estimation of femoral strength could be used as one of the main determinant of a patient’s fracture risk during population screening. In the first part of the thesis, the ex vivo mechanical tests performed on cadaver human femurs are presented. Digital image correlation (DIC), an optical method that allows for a full-field measurement of the displacements over the femur surface, was used to retrieve strains during the test. Then, a subject-specific FE modelling technique able to predict the deformation state and the overall strength of human femurs is presented. The FE models were based on clinical images from 3D CT datasets, and were validated against the measurements collected during the ex vivo mechanical tests. Both the experimental setup with DIC and the FE modelling procedure have been initially tested using composite bones (only the FE part of the composite bone study is presented in this thesis). After that, the method was extended to human cadaver bones. Once validated against experimental strain measurements, the FE modelling procedure could be used to predict bone strength. In the last part of the thesis, the predictive ability of FE models based on the shape and BMD distribution reconstructed from a single DXA image using a statistical shape and appearance model (SSAM, developed outside this thesis) was assessed. The predictions were compared to the experimental measurements, and the obtained accuracy compared to that of CT-based FE models. The results obtained were encouraging. The CT-based FE models were able to predict the deformation state with very good accuracy when compared to thousands of full-field measurements from DIC (normalized root mean square error, NRMSE, below 11%), and, most importantly, could predict the femoral strength with an error below 2%. The performances of SSAM-based FE models were also promising, showing only a slight reduction of the performances when compared to the CT-based approach (NRMSE below 20% for the strain prediction, average strength prediction error of 12%), but with the significant advantage of the models being built from one single conventional DXA image. In conclusion, the concept of a new, accurate and semi-automatic FE modelling procedure aimed at predicting fracture risk on individuals was developed. The performances of CT-based and SSAM-based models were thoroughly compared, and the results support the future translation of SSAM-based FE model built from a single DXA image into the clinics. The developed tool could therefore allow to include a mechanistic information into the fracture risk screening, which may ultimately lead to an increased accuracy in the identification of the subjects at risk