1000 Norms Project: protocol of a cross-sectional study cataloging human variation. Issue 1 (March 2016) - McKay, Marnee J.; Baldwin, Jennifer N.; Ferreira, Paulo; Simic, Milena; Vanicek, Natalie; Hiller, Claire E.; Nightingale, Elizabeth J.; Moloney, Niamh A.; Quinlan, Kate G.; Pourkazemi, Fereshteh; Sman, Amy D.; Nicholson, Leslie L.; et al.
Abstract: Background: Clinical decision-making regarding diagnosis and management largely depends on comparison with healthy or 'normal' values. Physiotherapists and researchers therefore need access to robust patient-centred outcome measures and appropriate reference values. However there is a lack of high-quality reference data for many clinical measures. The aim of the 1000 Norms Project is to generate a freely accessible database of musculoskeletal and neurological reference values representative of the healthy population across the lifespan. Methods/design: In 2012 the 1000 Norms Project Consortium defined the concept of 'normal', established a sampling strategy and selected measures based on clinical significance, psychometric properties and the need for reference data. Musculoskeletal and neurological items tapping the constructs of dexterity, balance, ambulation, joint range of motion, strength and power, endurance and motor planning will be collected in this cross-sectional study. Standardised questionnaires will evaluate quality of life, physical activity, and musculoskeletal health. Saliva DNA will be analysed for the ACTN3 genotype ('gene for speed'). A volunteer cohort of 1000 participants aged 3 to 100 years will be recruited according to a set of self-reported health criteria. Descriptive statistics will be generated, creating tables of mean values and standard deviations stratified for age and gender. Quantile regression equations will be used to generate age charts and age-specific centile values. Discussion: This project will be a powerful resource to assist physiotherapists and clinicians across all areas of healthcare to diagnose pathology, track disease progression and evaluate treatment response. This reference dataset will also contribute to the development of robust patient-centred clinical trial outcome measures.