Abstract: Axial spondyloarthritis (axSpA) is characterised by inflammation of the sacroiliac joints and spine. Sleep disturbance, pain and fatigue are reported in the literature to be key symptoms and impacts of axSpA. Three customised Patient-Reported Outcomes Measurement Information System (PROMIS) Short Forms (Sleep Disturbance, Pain Interference and Fatigue), previously developed for use in rheumatoid arthritis, have been proposed for use in patients with axSpA to assess the key concepts. Objectives To conduct in-depth qualitative interviews to further understand the patient experience of axSpA and evaluate the content validity of the three PROMIS Short Forms to support their use as endpoints in axSpA clinical trials. Methods A non-interventional, cross-sectional qualitative (concept elicitation [CE] and cognitive debriefing [CD] ) study was conducted in 28 adult patients with diagnosed axSpA, including radiographic axSpA (r-axSpA), also known as ankylosing spondylitis (AS, n=12), and non-radiographic axSpA (nr-axSpA, n=16). Patients took part in 90-minute telephone interviews. A semi-structured interview guide was used to assist discussions. The CE section used broad, open-ended questions to elicit information about symptoms and impact experienced by patients. The CD section involved a ‘think-aloud’ exercise in which patients read out each instruction, item and response option for the three PROMIS Short Forms and shared their feedback. Patients were also asked detailed questions about the relevance of the items, response options and recall period. Verbatim interview transcripts were subject to thematic and content analysis. Results Patients were from the United States (n=20) and Germany (n=8), mean age was 52.8 years, and 57% (n=16) were male; mean time since diagnosis of axSpA was 9.5 years (range 0.3–31.3 years). The CE section identified 12 distinct signs and symptoms that characterised patients’ experience of axSpA: pain, sleep problems, fatigue/tiredness, stiffness, swelling, vision/eye issues, restricted body movements, headache/migraine, spasms, change in posture/stature, balance/coordination problems and numbness. Pain, sleep problems and fatigue/tiredness were all reported to be experienced by ≥90% of patients, occurring simultaneously and exacerbating one another. 78% (n=21/27) of patients reported pain to be the most bothersome symptom, and 88% (n=23/26) described it as the symptom they would most like treatment to improve. Patients reported axSpA to impact their lives across six health-related quality of life (HRQoL) domains: physical functioning (100%), emotional wellbeing (89%), work/volunteering (79%), social functioning (75%), activities of daily living (61%) and cognitive functioning (54%). Impacts were most frequently described as being associated with pain, stiffness and fatigue. The experiences of symptoms and impacts were consistent between the AS and nr-axSpA patients. CD showed all three PROMIS instruments are conceptually comprehensive and well understood by patients with axSpA. No patients reported misunderstanding of instructions and/or items of the sleep disturbance instrument, and only one and four items had a small number of instances of misunderstanding for the fatigue and pain interference instruments, respectively. Across each instrument, all items were relevant to at least half of patients, and almost all patients reported the instruments to be appropriate for measuring their experience of sleep problems, pain and fatigue due to axSpA. Both AS and nr-axSpA patients confirmed the three PROMIS Short Forms to be relevant and appropriate for assessing their disease experience. Conclusion Pain, sleep problems and fatigue are pivotal symptoms of axSpA and associated with HRQoL impacts. Interpretability and content validity of the PROMIS customised Short Forms have been confirmed, with each deemed to adequately assess key impacts associated with axSpA, making them suitable for use in clinical trials of patients with axSpA. Funding: GSK [209770] Acknowledgements Medical writing support was provided by Tony Reardon, of of Aura, a division of Spirit Medical Communications Group Limited (Manchester, UK), and was funded by GlaxoSmithKline. Disclosure of Interests Amy Findley Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, Jessica Middlehurst Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, Chloe Howse Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, Molly Clifford Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, William Neill Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, Sophi Tatlock Grant/research support from: employee of Adelphi Values, who received fees from GlaxoSmithKline for the conduct of this study, Wen-Hung Chen Shareholder of: GlaxoSmithKline, Employee of: GlaxoSmithKline , Marguerite Bracher Shareholder of: GlaxoSmithKline, Employee of: GlaxoSmithKline, Dharm Patel Shareholder of: GlaxoSmithKline, Employee of: GlaxoSmithKline

Abstract: This study deals with the sensitivity of the NCHRP 1-37A Pavement Design Guide predictions to traffic data input. A number of traffic data collection scenarios are simulated with the use of extended coverage (more than 299 days per year) weigh-in-motion (WIM) data from the Long-Term Pavement Performance (LTPP) database. These scenarios consist of combinations of site-specific, regional, and national data, including total truck counts, truck counts by class, and axle-load distributions by axle type. For each simulated scenario, traffic input to the NCHRP 1-37 Pavement Design Guide is estimated with the methodologies prescribed in the Traffic Monitoring Guide. For discontinuous time coverage scenarios, all possible time coverage combinations are considered, and the range in the estimated traffic input parameters is computed. Pavement life predictions are obtained under mean traffic input for all traffic data collection scenarios and under low percentile input for the discontinuous traffic data collection scenarios. For each scenario and confidence level, pavement life errors are predicted for the life estimated by the complete extended coverage WIM data set. Two sources of error are identified: one from specifying the mean value for all traffic input and one from specifying the lowest percentile for all traffic input simultaneously (the latter applies to discontinuous coverage scenarios only). Overall error is computed by the addition of the range in the mean of the second error component to the range of the first error component. The results are in the form of plots of pavement life prediction error, versus confidence level, versus traffic data collection scenario.