Karin Magnusson

Aims: To describe a novel nationwide research project entitled “Norwegian Register-Based Studies on Sports & Health (NorSport),” focusing on the data available and the theoretical background guiding their use. Methods: NorSport links individual-level data from the Norwegian Sports Membership Database and Population Register for all Norwegians aged 10–70 years (in 2010–2024), combined with socioeconomic, healthcare, prescription, and community-level intervention data. To examine the bidirectional links between sports participation and physical and mental health, we will use prospective cohort designs, matched family designs, and community-level quasi-experiments. The data include exact dates for events related to sports participation, socioeconomic status, employment, prescriptions, and healthcare use for physical and mental conditions across individuals and families. Key biases are reduced by design—through full population inclusion, family-based controls for genetic and socioeconomic confounding, and use of objective records instead of self-report. Analyses will be guided by a biopsychosocial framework within sports and health research. Conclusions: With comprehensive national population data and a systematic approach to study design and bias reduction, NorSport enables more valid and actionable insights to guide public health policy and interventions in the field of sports participation and health.

Aims: This study describes the Norwegian Sports Membership Database (NSMD), assesses its coverage, and develops definitions of active sports memberships. Methods: We validated the coverage of the NSMD, a database capturing individual-level sports memberships among people aged 10–70 years between 2015 and 2024. We compared yearly membership counts in the NSMD with figures from official reports from the Norwegian Olympic and Paralympic Committee and Confederation of Sports (NIF). We defined all memberships as lasting one day or longer, and active memberships as lasting at least one season or with a paid licence. Results: Between 2015 and 2024, the NSMD contained 3,148,213 memberships, of which 2,180,621 (60%) were considered active. In total, there were 1,606,048 unique individuals across 55 sports federations and 279 disciplines. Football accounted for 24% of all memberships, handball for 11% and gymnastics for 9%. Database coverage was low in 2015, accounting for 11%–16% of expected memberships, but it improved substantially over time, exceeding 85% of all memberships and 92% of active memberships in 2023 for those aged 13–19. Conclusions: The coverage of the NSMD is high in recent years, representing a level of completeness that allows meaningful population-level analyses from 2019, although with large variations between sports. Linking sports data with health and socio-demographic outcomes provides a potentially significant opportunity for population-based research on sports participation and health across the lifespan.

Introduction: Spinal disorders are among the biggest contributors to health care utilization (HCU). Objective: To develop and externally validate a prediction model for high all-cause HCU (75th percentile during 1 year after the index date) among patients with spinal disorders visiting multidisciplinary secondary care clinics. Methods: We developed and internally validated the model using the Norwegian Neck and Back Registry, including patients registered between January 1, 2016, and December 31, 2020, linked with national health registries (N = 9092). For external validation, we used data from the Danish SpineData Registry, linked with national registries, for the same period (N = 34,853). We assessed Nagelkerke R ² , discrimination (area under receiver operating characteristics curve [AUC]), and calibration (calibration-in-the-large [CITL], slope, and calibration plot). Results: The final model included sex, nationality, education, physical activity, smoking, prior HCU, work status, disability, health-related quality of life, medicine use, diagnosis, kinesiophobia, and comorbidity. It demonstrated acceptable discrimination (AUC 0.78, 95% confidence interval [CI], 0.77–0.78), an R ² of 0.26, and good calibration after internal validation. Upon external validation, the model demonstrated excellent discrimination (AUC 0.81, 95% CI 0.80–0.81) and an R ² of 0.31. The calibration slope was 1.08 (95% CI 1.06–1.11) and CITL was 0.16 (95% CI 0.12–0.19). Predicted probabilities closely matched observed probabilities across all deciles in internal validation, with slight underestimation of high HCU in the top 3 deciles during external validation. Conclusion: Overall, the model shows promise in predicting high HCU in patients with spinal disorders referred to secondary care but requires further testing and validation in implementation settings before recommendation.

Aims To describe the Norwegian Sports Membership Database (NSMD), assess its coverage and develop definitions of sports participation for research purposes. Methods We validated the coverage of NSMD, a database capturing individual-level sports memberships among persons 10 to 70 years of age between 2015 and 2024, by comparing yearly membership counts with official reports from The Norwegian Olympic and Paralympic Committee and Confederation of Sports (NIF). We defined three operational thresholds for active membership: wide (≥ 1 day), intermediate (≥ 90 days), and strict (≥ 1 season or paid licence). Result Between 2015 and 2024, NSMD contained 2,850,802 memberships representing 1,396,467 unique individuals (49%) across 55 sports federations and 279 disciplines. Football accounted for 24% of memberships, handball for 11%, and skiing for 7%. Database coverage was low in 2015, capturing less than 20% of expected memberships, but improved substantially over time and exceeded 66% in 2019. The coverage further exceeded 89% from 2021. Of all recorded memberships, 99% met the wide definition criteria, 91% met the intermediate definition, and 80% met the strict definition. Conclusion The coverage of the NSMD is high in recent years, representing a level of completeness that permits meaningful population-level analyses from 2019, although with large variations between sports. Linking sports data with health and sociodemographic outcomes provides a potentially large opportunity for population-based research on sports participation and health across the lifespan.

Aims: To describe a novel nationwide research project entitled “Norwegian Register-Based Studies on Sports & Health (NorSport)”, focusing on the data available and the theoretical background guiding their use. Methods: NorSport links individual-level data from the Norwegian Sports Membership Database and Population Register for all Norwegians aged 10–70 (2010–2024), combined with socioeconomic, healthcare, prescription, and community-level intervention data. To examine the bidirectional links between sports participation and physical and mental health, we will use prospective cohort designs, matched family designs and community-level quasi-experiments. Results: The data include exact dates for events related to sports participation, socioeconomic status, employment, prescriptions, and healthcare use for physical and mental conditions across individuals and families. Key biases are reduced by design—through full population inclusion, family-based controls for genetic and socioeconomic confounding, and use of objective records instead of self-report. Analyses will be guided by a biopsychosocial framework within sports and health research. Conclusion: With comprehensive national population data and a systematic approach to study design and bias reduction, NorSport enables more valid and actionable insights to guide public health policy and interventions in the field of sports participation and health.

Background: Cardiovascular, respiratory, and musculoskeletal disease are among the leading causes of disability in middle-aged and older people. Health and lifestyle factors in youth have known associations with cardiovascular or respiratory disease in adulthood, but largely unknown associations with musculoskeletal disease. Methods and findings: We included approximately 40,000 18-year-old Swedish males, who completed their conscription examination in 1969 to 1970, followed up until age of 60 years. Exposures of interest were physical health: body mass and height, blood pressure, pulse at rest, muscle strength, cardiorespiratory fitness, and hematocrit; self-reported lifestyle: smoking, alcohol, and drug use; self-reported health: overall, headache and gastrointestinal. We followed the participants through the Swedish National Patient Register for incidence of common musculoskeletal (osteoarthritis, back pain, shoulder lesions, joint pain, myalgia), cardiovascular (ischemic heart disease, atrial fibrillation), and respiratory diseases (asthma, chronic obstructive pulmonary disease, bronchitis). We analyzed the associations using general estimating equations Poisson regression with all exposures included in one model and adjusted for parental education and occupation. We found that higher body mass was associated with higher risk of musculoskeletal (risk ratio [RR] per 1 standard deviation [SD] 1.12 [95% confidence interval, CI 1.09, 1.16]), cardiovascular (RR 1.22 [95% CI 1.17, 1.27] per 1 SD) and respiratory diseases (RR 1.14 [95% CI 1.05, 1.23] per 1 SD). Notably, higher muscle strength and cardiorespiratory fitness were associated with higher risk of musculoskeletal disease (RRs 1.08 [95% CI 1.05, 1.11] and 1.06 [95% CI 1.01, 1.12] per 1 SD difference in exposure), while higher cardiorespiratory fitness was protective against both cardiovascular and respiratory diseases (RRs 0.91 [95% CI 0.85, 0.98] and 0.85 [95% CI 0.73, 0.97] per 1 SD exposure, respectively). We confirmed the adverse effects of smoking, with risk ratios when comparing 11+ cigarettes per day to non-smoking of 1.14 (95% CI 1.06, 1.22) for musculoskeletal, 1.58 (95% CI 1.44, 1.74) for cardiovascular, and 1.93 (95% CI 1.60, 2.32) for respiratory diseases. Self-reported headache (category “often” compared to “never”) was associated with musculoskeletal diseases (RR 1.38 [95% CI 1.21, 1.58]) and cardiovascular diseases (RR 1.29 [95% CI 1.07, 1.56]), but had an inconclusive association with respiratory diseases (RR 1.13 [95% CI 0.79, 1.60]). No large consistent associations were found for other exposures. The most notable associations with specific musculoskeletal conditions were for cardiorespiratory fitness and osteoarthritis (RR 1.23 [95% CI 1.15, 1.32] per 1 SD) and for muscle strength and back pain (RR 1.18 [95% CI 1.12, 1.24] per 1 SD) or shoulder diseases (RR 1.27 [95% CI 1.19, 1.36] per 1 SD). The main limitations include lack of adjustment for genetic factors and environmental exposures from childhood, and that the register data were available for males only. Conclusions: While high body mass was a risk factor for all 3 studied groups of diseases, high cardiorespiratory fitness and high muscle strength in youth were associated with increased risk of musculoskeletal disease in middle age. We speculate that these associations are mediated by chronic overload or acute trauma.