Musculoskeletal pain in 13-year-old children: the generation R study.
Guido J van LeeuwenMarleen M van den HeuvelPatrick J E BindelsSita M A Bierma-ZeinstraMarienke van MiddelkoopPublished in: Pain (2024)
Musculoskeletal (MSK) pain is a common reason for consultation in general practice and frequently reported in children and adolescents. This study examined the prevalence of MSK pain in 13-year-old children and assessed associations with physical and psychosocial factors. Data from the Generation R Study, a population-based birth cohort, was used. Prevalence and characteristics of MSK pain were assessed, using a pain mannequin, at 13 years of age (N = 3062). Demographics and data on physical activity, sedentary behaviors, previous reported MSK pain, and behavioral problems were extracted from questionnaires. The body mass index (BMI) SD-score was calculated from objectively measured weight and height. A prevalence of 23.3% was found for MSK pain in children of which 87.2% persisted for more than 3 months (ie, chronic), 45.5% experienced pain daily. More physically active children and children with a higher BMI reported MSK pain more frequently compared with non-MSK pain and no pain. The knee was the most often reported location. Children with MSK pain were more likely to have reported MSK pain at 6 years. Multivariable analyses showed significant associations for male sex (OR 0.74, 95% CI 0.56-0.98), high maternal educational (OR 0.69, 95% CI 0.49-0.96), higher BMI (OR 1.19, 95% CI 1.05-1.35), being physically active (OR 1.41, 95% CI 1.03-1.91), and behavioral problems (OR 1.85, 95% CI 1.33-2.59) with the presence of MSK pain. The chronic nature of MSK pain in combination with the relatively high prevalence of MSK pain in this study shows that MSK pain is already an important problem at a young age.
Keyphrases
- chronic pain
- pain management
- neuropathic pain
- body mass index
- physical activity
- mental health
- spinal cord
- total knee arthroplasty
- weight gain
- machine learning
- palliative care
- general practice
- pregnant women
- primary care
- electronic health record
- big data
- artificial intelligence
- depressive symptoms
- gestational age
- middle aged
- deep learning
- drug induced
- birth weight
- pregnancy outcomes
- psychometric properties