## 5 Discussion  
The hybrid workflow described in this paper helped our research team improve the accuracy and efficiency of our study selection by enabling us to identify relevant studies with greater precision. Nonetheless, the LLM application was not free from challenges in terms of efficiency and dependence on effective prompt engineering. Effective prompt design has the potential to speed human decision-making and reduce labor time during processes such as data cleaning.  
How Can Prompt Engineering Be Optimized to Leverage the Capabilities of LLMs in Extracting Relevant Information from Full-Text Articles in SRs?  
Our hybrid workflow, which integrated an LLM into the SR process to validate human-only SR results, demonstrated an increase in efficiency and accuracy by combining the strengths of LLMs in efficient and accurate information extraction with the critical thinking and decision-making capabilities of human reviewers. This integration capitalizes on the computational power of LLMs to swiftly process vast quantities of text, thereby minimizing the extensive reading typically required of human reviewers. Consequently, the human element of the workflow is refined to focus on higher-level evaluative and interpretive tasks.  
For instance, in the case study, we demonstrated some potential for using LLM to extract occupation information about study participants. Tasks such as identifying sensor names and their placement on the body, which once demanded considerable time and meticulous work from human reviewers, were executed with precision by the LLM. This illustrates the LLM's capacity for reducing the manual burden of data extraction, allowing researchers to allocate more time to critical analysis. However, the case study also revealed limitations in the LLM's logical interpretation. When the LLM classified survey-based studies as employing \"objective measurement\" devices, it highlighted the model's difficulty  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _(n = 390)_ & _LLM-Assisted Hybrid Workflow_ & \multicolumn{3}{c}{_Traditional Workflow_} \\ \hline  & _Correct_ & _Incorrect_ & _Correct_ & _Incorrect_ \\ \hline Inclusion & 170 & 0 & 167 & 3 \\ \hline Exclusion & 220 & 0 & 217 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the two methods for inclusion/exclusion classification.  
with some complex reasoning and context-understanding tasks. This example underscores the necessity for human oversight to correct such logical discrepancies and ensure the review's criteria are met accurately. After data cleaning, any records lacking value in objective measurement can be excluded.  
LLMs excel in rapidly sifting through and extracting pertinent details from full-text articles, a process that would otherwise be laborious and time-consuming for human reviewers. By leveraging the advanced natural language processing abilities of LLMs, researchers can swiftly gather data points that are relevant to their specific inclusion and exclusion criteria. Even with a poorly defined prompt, it is effective in saving time, and humans can filter out a large number of irrelevant studies. For instance, in the case study, we used the prompt \"_participantsOccupation_\" to quickly filter records unrelated to desk-based workers, excluding those with occupations such as bus drivers, blue-collar workers, factory workers, etc. This was achieved by quickly sorting and filtering the CSV file using basic sorting tools in Microsoft Excel. This streamlined the process by quickly identifying irrelevant studies, allowing us to focus on our population of interest. This efficiency improvement is not achieved at the expense of accuracy. Human reviewers' oversight ensures that the extracted information is verified and validated, maintaining the integrity of the review process.