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Objectives: This study aims to investigate the cost-effectiveness of individually tailored self-management support, delivered via the artificial intelligence-based selfBACK app, as an add-on to usual care for people with low back pain (LBP)., Design: Secondary health-economic analysis of the selfBACK randomised controlled trial (RCT) with a 9-month follow-up conducted from a Danish national healthcare perspective (primary scenario) and a societal perspective limited to long-term productivity in the form of long-term absenteeism (secondary scenario)., Setting: Primary care and an outpatient spine clinic in Denmark., Participants: A subset of Danish participants in the selfBACK RCT, including 297 adults with LBP randomised to the intervention (n=148) or the control group (n=149)., Interventions: App-delivered evidence-based, individually tailored self-management support as an add-on to usual care compared with usual care alone among people with LBP., Outcome Measures: Costs of healthcare usage and productivity loss, quality-adjusted life-years (QALYs) based on the EuroQol-5L Dimension Questionnaire, meaningful changes in LBP-related disability measured by the Roland-Morris Disability Questionnaire (RMDQ) and the Pain Self-Efficacy Questionnaire (PSEQ), costs (healthcare and productivity loss measured in Euro) and incremental cost-effectiveness ratios (ICERs)., Results: The incremental costs were higher for the selfBACK intervention (mean difference €230 (95% CI -136 to 595)), where ICERs showed an increase in costs of €7336 per QALY gained in the intervention group, and €1302 and €1634 for an additional person with minimal important change on the PSEQ and RMDQ score, respectively. At a cost-effectiveness threshold value of €23250, the selfBACK intervention has a 98% probability of being cost-effective. Analysis of productivity loss was very sensitive, which creates uncertainty about the results from a societal perspective limited to long-term productivity., Conclusions: From a healthcare perspective, the selfBACK intervention is likely to represent a cost-effective treatment for people with LBP. However, including productivity loss introduces uncertainty to the results., Trial Registration Number: NCT03798288., Competing Interests: Competing interests: All authors of this paper, except the health economists LPK and CVS, have been involved in the EU project selfBACK. The company selfBack Aps is introducing the selfBACK system to the commercial market via a licensing agreement with the NTNU Technology Transfer Office. Authors affiliated with NTNU may in the future receive personal compensation according to the licensing agreement with the NTNU Technology Transfer Office. Furthermore, Center for Muscle and Joint Health at the University of Southern Denmark may in the future receive research funding according to the licensing agreement with the NTNU Technology Transfer Office., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Point-of-care testing (POCT) of HbA1c may result in improved diabetic control, better patient outcomes, and enhanced clinical efficiency with fewer patient visits and subsequent reductions in costs. In 2008, the Danish regulators created a framework agreement regarding a new fee-for-service fee for the remuneration of POCT of HbA1c in general practice. According to secondary research, only the Capital Region of Denmark has allowed GPs to use this new incentive for POCT. The aim of this study is to use patient data to characterize patients with diabetes who have received POCT of HbA1c and analyze the variation in the use of POCT of HbA1c among patients with diabetes in Danish general practice. Methods: We use register data from the Danish Drug Register, the Danish Health Service Register and the National Patient Register from the year 2011 to define a population of 44,981 patients with diabetes (type 1 and type 2 but not patients with gestational diabetes) from the Capital Region. The POCT fee is used to measure the amount of POCT of HbA1c among patients with diabetes. Next, we apply descriptive statistics and multilevel logistic regression to analyze variation in the prevalence of POCT at the patient and clinic level. We include patient characteristics such as gender, age, socioeconomic markers, health care utilization, case mix markers, and municipality classifications. Results: The proportion of patients who received POCT was 14.1% and the proportion of clinics which were "POCT clinics" was 26.9%. There were variations in the use of POCT across clinics and patients. A part of the described variation can be explained by patient characteristics. Male gender, age differences (older age), short education, and other ethnicity imply significantly higher odds for POCT. High patient costs in general practice and other parts of primary care also imply higher odds for POCT. In contrast, high patient costs for drugs and/or morbidity in terms of the Charlson Comorbidity index mean lower odds for POCT. The frequency of patients with diabetes per 1000 patients was larger in POCT clinics than Non-POCT clinics. A total of 22.5% of the unexplained variability was related to GP clinics. Conclusions: This study demonstrates variation in the use of POCT which can be explained by patient characteristics such as demographic, socioeconomic, and case mix markers. However, it appears relevant to reassess the system for POCT. Further studies are warranted in order to assess the impacts of POCT of HbA1c on health care outcomes., Competing Interests: The authors declare no conflict of interest.