Self-questionnaire in Osteoporosis

The main objective of this project is to test the validity of this new self-administered questionnaire, by studying the concordance between its answers and those obtained from a patient's family tree by telephone.

Primary Objective: To test the validity of a self-administered questionnaire to facilitate the identification of rare genetic bone diseases in adults with osteoporosis.

Secondary Objectives: To adapt the self-administered questionnaire to increase the accuracy of responses compared to those obtained using a family tree, with the aim of using it for clinical screening of rare genetic bone diseases in adults.

Data collection:

Sociodemographic data (age, sex, ethnicity, body mass index, menopausal status, smoking, alcohol consumption, physical activity, history of falls in the past year) and clinical data will be collected from participants' electronic medical records at the CHU de Québec-Université Laval (age at osteoporosis diagnosis, history of osteoporotic fractures, osteoporosis risk category based on the most recent bone density scan, calcium and/or vitamin D supplement use, history of anti-osteoporosis medications, presence of comorbidities, use of prednisone or antihormonal medications) to describe the participants and the severity of their osteoporosis. Other data collection will be conducted in two stages. First, recruited patients will be randomized to either begin with the self-administered questionnaire or the family tree. Then, 3 months later, the people who started with the questionnaire will be able to do the interview for the family tree and vice versa.

Randomization: For this collection, a balanced block randomization will be used to randomly assign patients to one of the two assessment administration sequences. This approach, which corresponds to a crossover design with sequence randomization, will balance order effects and ensure a similar distribution in each sequence. In addition, the use of blocks (of size 4) will ensure that the balance between the two conditions is maintained throughout the recruitment period.

Statistical analyzes:

Descriptive statistics will first be performed to characterize the participants (age, sex, number of first- and second-degree relatives, as well as the clinical manifestations detailed in the self-administered questionnaire) and to report, based on the relationship, the frequency of different clinical manifestations in relatives. In this study, the pedigree will serve as the gold standard to describe the presence or absence of familial bone disease, specifying the degree of affected kinship, the type of inheritance, and the most likely diagnosis (e.g., hypophosphatesia, osteogenesis imperfecta, etc.). The concordance between the responses provided by the self-administered questionnaire and the information from the pedigree will be primarily assessed by calculating the kappa coefficient. This analysis will constitute the primary statistical approach of the study. In addition, secondary analyses will be conducted to determine the sensitivity and specificity of the self-administered questionnaire compared to the gold standard. These measurements will make it possible to evaluate the diagnostic performance of the tool developed in the detection of familial bone disease.

Sample size calculation: The agreement between the two assessment tools will be based mainly on questions with three response categories (Yes, No, Don't know) with respective frequencies assumed to be (0.1, 0.3 and 0.6). The objective being to test whether the agreement, measured by the kappa coefficient, exceeds the threshold of 0.7 (H0: Kappa ≤ 0.7 versus H1: Kappa > 0.7), the comparison will be carried out using a one-sided Z-test for kappa, setting the type I error rate (α) at 0.05. To detect a kappa coefficient of 0.9 with a power of 80%, it is necessary to recruit 58 patients. The sample size was calculated using PASS 2024 software, version 24.0.2.

Inclusion criteria

• Adult over 18
• Followed by the rheumatology or endocrinology clinics at the CHUL (CHU de Quebec-Universite Laval)
• Suffer from osteoporosis
• Have internet access

Exclusion criteria

• Unfit, unable to consent, unable to answer a questionnaire, unknown family history (e.g. adopted person)