Photobiomodulation therapy combined with a static magnetic field for chronic non-specific knee pain: protocol for a multicentre, randomised, triple-blind, placebo-controlled trial.

Pediatric obesity is a major global public health concern, with prevalence rates continuing to rise worldwide and substantial implications for cardiometabolic health across the life course []. Children with obesity are at increased risk of developing insulin resistance, dyslipidemia, hypertension, and type 2 diabetes, often already detectable at a young age and likely to track into adulthood [–]. Identifying modifiable behavioral factors that contribute to metabolic risk in this population is therefore a critical research priority. Physical activity (PA) is widely recognized as a cornerstone in the prevention and management of pediatric obesity and its metabolic complications. Current international guidelines recommend that children and adolescents accumulate at least 60 min per day of moderate-to-vigorous physical activity (MVPA) [–]. However, despite these recommendations, the vast majority of children with obesity fail to meet MVPA targets [, , ]. Indeed, an emerging body of evidence suggests that the health benefits of PA may not be fully captured by MVPA duration alone [], specifically in populations characterized by overall low activity levels. Accelerometer-based studies have shown that sedentary behavior, light-intensity PA, and the temporal organization of movement throughout the day are independently associated with cardiometabolic health markers in children [–]. For example, prolonged uninterrupted sedentary time has been linked to insulin resistance and adverse lipid profiles, even after adjustment for MVPA [, ]. Similarly, higher fragmentation of sedentary time and more frequent transitions between activity states appear to be metabolically favorable [, ]. These findings challenge the traditional “minutes-based” paradigm and suggest that how PA is accumulated may be as important as how much is accumulated. Advances in wearable technology and signal processing now allow the extraction of high-resolution accelerometry features that describe not only activity volume and intensity, but also movement variability, bout structure, inequality of activity distribution, and behavioral transitions across the day [, ]. However, integrating this multidimensional information into clinically meaningful constructs remains challenging.