## 4 Discussion
Although this review found that planned _'Session duration'_, _'Frequency'_, and _'Intervention length'_ were well reported (100%, 89%, and 93%, respectively) in robotic interventions, this may be attributed to our study's inclusion criteria, which are based on the earlier systematic review and meta-analysis [11] and required adequate reporting to determine if the interventions were dose-matched. In contrast, the plan for how much time and/or repetitions would be spent actively engaging with core components of the robotic intervention (_'How much--Essential elements amount'_, 32%) was largely absent. Robotic systems are capable of monitoring and quantifying the amount of practice within a session, offering a unique opportunity to measure the actual time spent engaging in the intervention, detect differences between planned and actual dose, and compare between robotic and conventional interventions [67]. Yet, across both interventions, the plan to capture this detail (_'How well--Plan'_, 27%) and the actual amount of rehabilitation undertaken (_'How well--Actual'_, 22%) were also poorly documented. Given that the efficacy of these interventions is likely contingent on maximizing active practice time, the ability to accurately quantify the dose and separate this from inactive time (e.g., setting up the device, giving instructions) is critical to investigating and optimizing rehabilitation dose-response relationships [11; 14; 67]. This underscores the importance of comprehensive documentation and reporting on both the planned and actual dose, including active and inactive time, to enhance intervention replication, evaluation, and outcomes. Another critical consideration in upper-limb stroke rehabilitation is the ability to determine and systematically progress challenge for the individual [63; 65]. Maintaining an optimal level of challenge over the course of rehabilitation is vital to ensuring engaging, rewarding, and effective rehabilitation [63; 67]. Yet, the methods for setting and monitoring challenge (_'How challenging'_) was 51% incomplete, and the progression of dosage (_'Regression/Progression'_) was 62% absent across robotic and conventional interventions. This could indicate either a limited understanding of the use of challenge in rehabilitation or an assumption that it is self-evident within practice [62]. In conventional rehabilitation, task challenge was often described as being set and progressed based on 'therapist judgement' [38; 48; 50; 51; 52; 54; 55]. While this may be an attempt to simplify the multi-factorial complexities of optimizing challenge to the individual [14; 62], such descriptions offer little insight into how this critical parameter is operationalized. In robotic rehabilitation, systemssuch as adaptive assist-as-needed technologies can monitor, respond to, and provide real-time feedback on performance, enabling the manipulation of challenge in a structured and measurable way [63; 67]. But, without precise reporting of how challenge is monitored, set, progressed, and implemented, robotic interventions cannot be replicated, nor can research data be meta-analyzed to investigate the effect of different challenge levels [11; 68]. This confusion is exacerbated by inconsistent terminology, where terms like 'active', 'active-assisted', and 'assistive' are frequently used interchangeably to describe the level of robotic assistance, without any further definition [11]. Transparent, detailed reporting on challenge and progression is, therefore, essential to advancing the field and fully leveraging the therapeutic potential of upper-limb robotics in stroke rehabilitation. Despite being central to intervention acceptability, aspects related to '_Personalisation_', '_Harms_', and '_Protocol deviations_' were the most poorly and sporadically reported. Personalization, a cornerstone of person-centered rehabilitation, is critical when introducing novel health technologies, as it shapes both clinicians' and patients' perceptions of an intervention's legitimacy [59; 60; 69].