## 4 Discussion
### Methodological Considerations  
Sample size emerged as a critical methodological consideration across the reviewed studies. The prevalence of small sample sizes (\(n<50\)) in seven studies [7; 17; 18; 19; 22; 23; 24] raises important questions about statistical power and generalizability. This limitation is particularly significant in machine learning applications, where larger datasets typically yield more robust and generalizable models [6]. While these studies provided valuable proof-of-concept results, their limited participant numbers may restrict the broader applicability of their findings, especially given the heterogeneous nature of preschool movement patterns [21].  
The variation in data collection protocols, particularly regarding sampling frequencies (0.25-100 Hz) and epoch lengths (1-60 s), presents significant challenges in result comparison and protocol standardization [11]. This methodological heterogeneity, while allowing exploration of optimal measurement parameters, complicates the establishment of standardized protocols for preschool activity monitoring. Recent studies suggest that these variations in data collection parameters can significantly impact algorithm performance and reliability [9]. Figure 2 indicates the methodological heterogeneity in epoch length, sampling frequency and placement site of the devices between studies.  
The consistent use of AcriGraph GT3X+ across 13 studies presents both advantages and limitations. While this uniformity enables direct comparison of results and methodological approaches [8], it potentially limits our understanding of how different accelerometer types might influence machine learning performance. This standardization, though beneficial for consistency, may not capture the full range of measurement possibilities available with other accelerometer technologies [24; 26].  
Several factors support the predominant use of the GT3X+ in research settings. First, its reliability and validation across multiple studies ensure measurement consistency [31; 32]. Second, its three-axis measurement capability provides comprehensive movement data essential for accurate activity classification. Third, its versatility in measuring multiple variables (acceleration, energy expenditure, steps, intensity) makes it suitable for diverse research applications. Additionally, its substantial memory capacity and extended battery life (approximately 30 days) facilitate long-term data collection without active supervision.  
Alternative devices like Axivity AX3, GENEActiv, and SenseWear offer different measurement capabilities and might provide complementary insights (Table 4). For instance, while GT3X+ focuses on acceleration-based measurements, SenseWear incorporates multiple physiological variables that could enhance activity classification accuracy. However, the standardization around GT3X+ has facilitated result reproducibility and enabled more  
Figure 2: Methodological heterogeneity in accelerometer-based data collection: distribution of epoch lengths, sampling frequencies, and placement sites across studies.  
direct comparisons across studies, contributing to a more coherent body of evidence in preschool activity monitoring.