## 1 Introduction  
Low back pain (LBP) is a significant global public health challenge, affecting individuals across all age groups and socioeconomic strata. It is a leading cause of disability worldwide, with an estimated lifetime prevalence of 84%, with non-specific low back pain (NSLBP) constituting approximately 90% of all cases [1; 2; 3]. In 2020, the number of individuals living with LBP reached 619 million globally, with forecasts suggesting that this figure will rise to 843 million by 2050 [4]. This substantial prevalence is further reflected in a survey of 54 countries, where the mean point prevalence of LBP was 18.3%, and the 1-year prevalence reached 38.0% [5]. LBP not only impacts individuals' physical functioning but also imposes heavy economic burdens. For example, in the United Kingdom, the National Health Service (NHS) spends nearly 65 billion annually on LBP management and associated costs [6]. Among the spectrum of LBP conditions, NSLBP has garnered increasing attention due to its high prevalence and the complexity of its management. NSLBP, unlike specific LBP, cannot be attributed to an identifiable pathological cause discernible through imaging or diagnostic testing [7]. Specific LBP, often resulting from conditions such as herniated discs or spinal fractures, can typically be addressed through targeted treatments, including surgery or pharmacological interventions. Conversely, NSLBP lacks definitive treatment pathways, necessitating a multidisciplinary approach involving physiotherapy, exercise programs, and behavioral interventions. Its prevalence is substantial across various demographics, including adolescents and older adults [7]. In Canada, a 1-year prevalence study among adolescents aged 13.8 years revealed a prevalence rate of 17.2% [8]. Similarly, in Portugal, the prevalence among adolescents was 15.7% [9]. The burden of NSLBP extends beyond physical symptoms, as it is often accompanied by psychological issues, such as anxiety and depression, which exacerbate its impact on daily life and work productivity [10; 11]. Traditional approaches to managing NSLBP, including supervised physiotherapy and self-management strategies, have shown varying degrees of success. According to the National Institute for Health and Care Excellence (NICE) guidelines, exercise therapy, education, and behavioral interventions are central to NSLBP management [12]. While exercise physiotherapy has demonstrated benefits in improving pain and physical function [13], issues such as patient adherence and accessibility have hindered its effectiveness. For example, patients often lack adequate supervision during home-based rehabilitation, which can compromise the quality and consistency of exercise routines [12; 14]. Studies have also shown that patients in self-management programs frequently report low motivation and limited adherence to prescribed physiotherapy regimens, further limiting the efficacy of these interventions [15]. Given these challenges, integrating artificial intelligence (AI) into physiotherapy presents a promising solution. AI-assisted physiotherapy employs machine learning (ML) algorithms and data-driven analytics to provide personalized rehabilitation plans, real-time feedback, and enhanced patient monitoring [16; 17]. Mobile health (mHealth) platforms, including smartphone applications and wearable devices, are at the forefront of AI-assisted physiotherapy. These platforms enable patients to engage in home-based rehabilitation while receiving immediate feedback on their performance [18]. For example, AI systems can dynamically adapt exercise regimens based on real-time monitoring data, ensuring precision and continuity in treatment [19]. Moreover, studies indicate that AI-assisted physiotherapy can significantly enhance patient adherence. Features such as automated reminders and interactive feedback mechanisms increase compliance, leading to improved outcomes compared to traditional physiotherapy methods [20; 21].