## 4 Discussion
### Limitations and Future Research Directions  
The present systematic review reveals several key limitations in the current body of research. The predominance of small sample sizes (\(n<50\)) in half of the analysed studies [7; 17; 18; 19; 22; 23; 24] limits the generalisability of findings and the robustness of machine learning models. Additionally, the heavy reliance on a single accelerometer type (ActiGraph GT3X+) across studies, while providing methodological consistency, restricts our understanding of how different sensor technologies might influence algorithm performance. The variation in data collection protocols, particularly in sampling frequencies and epoch lengths, further complicates the establishment of standardised assessment procedures. These methodological limitations have significant implications for machine learning applications in this field. Small sample sizes (\(n<50\)) not only limit statistical power but also increase the risk of overfitting, particularly when using complex algorithms with numerous \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Features** & **GT3X+** & **Axiivity AX3** & **GENEActiv** & **SenseWear** \\ \hline \multirow{4}{*}{_Measurements_} & Acceleration in three & Acceleration in three & Acceleration in three & Multiple variables \\  & other variables such as energy expenditure, intensity of physical activity & axes, high temporal resolution & axes, high temporal resolution & including physical & including physical \\ \cline{1-1} \cline{2-5} \multirow{2}{*}{_Primary Use_} & Scientific and clinical research & Scientific research & Scientific and clinical research & Clinical applications & excluding and personal wellness \\ \cline{1-1} \cline{2-5} \multirow{2}{*}{_Connectivity_} & Data transfer to mobile devices or computers & Data transfer via USB & Data transfer via USB or Bluetooth & Data transfer via USB or Bluetooth \\ \cline{1-1} \cline{2-5} \multirow{2}{*}{_Applications_} & Scientific and clinical research & Scientific research & Scientific and clinical research & Clinical applications and personal wellness \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of key features among common accelerometer devices used in physical activity research. parameters. This overfitting risk is especially pronounced in models trained on laboratory data that may not generalise well to free-living conditions, as demonstrated by the reduced accuracy (11-21% decrease) observed by [PERSON] et al. [18] when laboratory-trained models were tested in real-world settings. The inconsistency in sensor placement further complicates cross-study comparisons and practical implementation, as the substantial performance variations between hip and wrist placements (10-20% accuracy differences) make it difficult to establish universal recommendations for optimal sensor positioning. Additionally, the heterogeneity in data collection protocols, particularly in sampling frequencies and epoch lengths, introduces methodological inconsistencies that may lead to variable algorithm performance across different studies and settings. These technical variations, combined with the inherent challenges of capturing sporadic movement patterns in preschool children, create significant barriers to widespread implementation of these technologies in educational or clinical environments. Future research should prioritise larger-scale validation studies with diverse populations to enhance the generalizability of machine learning approaches in preschool settings. As highlighted by recent advances [21; 28], there is a need to explore more sophisticated algorithms while maintaining practical applicability. Researchers should prioritise rigorous external validation of machine learning models across diverse preschool populations and settings.