Despite the widely known benefits of exercise and physical activity, adherence rates to these activities are poor. Understanding exercise facilitators, barriers, and preferences may provide an opportunity to personalize exercise prescription and improve adherence. The purpose of this study was to develop the Personalized Exercise Questionnaire (PEQ) to identify these facilitators, barriers, and preferences to exercise in people with osteoporosis. This study comprises two phases, instrument design and judgmental evidence. A panel of 42 experts was used to validate the instrument through quantitative (content validity) and qualitative (cognitive interviewing) methods. Content Validity Index (CVI) is the most commonly used method to calculate content validity quantitatively. There are two kinds of CVI: Item-CVI (I-CVI) and Scale-level CVI (S-CVI). Preliminary versions of this tool showed high content validity of individual items (I-CVI range: 0.50 to 1.00) and moderate to high overall content validity of the PEQ (S-CVI/UA = 0.63; S-CVI/Ave = 0.91). Through qualitative methods, items were improved until saturation was achieved. The tool consists of 6 domains and 38 questions. The 6 domains are: 1) support network; 2) access; 3) goals; 4) preferences; 5) feedback and tracking; and 6) barriers. There are 35 categorical questions and 3 open-ended items. Using an iterative approach, the development and evaluation of the PEQ demonstrated high item-content validity for assessing the facilitators, barriers, and preferences to exercise in people with osteoporosis. Upon further validation it is expected that this measure might be used to develop more client-centered exercise programs, and potentially improve adherence.

Osteoporosis and osteoporotic fractures remain significant public health challenges worldwide. Recently the concept of frailty in relation to osteoporosis in the elderly has been increasingly accepted, with emerging studies measuring frailty as a predictor of osteoporotic fractures. In this overview, we reviewed the relationship between frailty and osteoporosis, described the approaches to measuring the grades of frailty, and presented current studies and future research directions investigating osteoporosis and frailty in the elderly. It is concluded that measuring the grades of frailty in the elderly could assist in the assessment, management and decision-making for osteoporosis and osteoporotic fractures at a clinical research level and at a health care policy level.

The British STRATIFY tool was previously developed to predict falls in hospital. Although the tool has several strengths, certain limitations exist which may not allow generalizability to a Canadian setting. Thus, we tested the STRATIFY tool with some modification and re-weighting of items in Canadian hospitals. This was a prospective validation cohort study in four acute care medical units of two teaching hospitals in Hamilton, Ontario. In total, 620 patients over the age of 65 years admitted during a 6-month period. Five patient characteristics found to be risk factors for falls in the British STRATIFY study were tested for predictive validity. The characteristics included history of falls, mental impairment, visual impairment, toileting, and dependency in transfers and mobility. Multivariate logistic regression was used to obtain optimal weights for the construction of a risk score. A receiver-operating characteristic curve was generated to show sensitivities and specificities for predicting falls based on different threshold scores for considering patients at high risk. Inter-rater reliability for the weighted risk score indicated very good agreement (inter-class correlation coefficient = 0.78). History of falls, mental impairment, toileting difficulties, and dependency in transfer / mobility significantly predicted fallers. In the multivariate model, mental status was a significant predictor (P < 0.001) while history of falls and transfer / mobility difficulties approached significance (P = 0.089 and P = 0.077 respectively). The logistic regression model led to weights for a risk score on a 30-point scale. A risk score of 9 or more gave a sensitivity of 91% and specificity of 60% for predicting who would fall. Good predictive validity for identifying fallers was achieved in a Canadian setting using a simple-to-obtain risk score that can easily be incorporated into practice.