The study of carbon dioxide expiration is called capnometry. The graphical representation of capnometry is called capnography. There is a growing interest in the usage of capnography as the usage has expanded toward the study of metabolism, circulation, lung perfusion and diffusion, quality of spontaneous respiration, and patency of airways outside of its typical usage in the anesthetic and emergency medicine field. The parameters of the capnograph could be classified as carbon dioxide (CO2) concentration and time points and coordinates, slopes angle, volumetric studies, and functional transformation of wave data. Up to date, there is no gold standard device for the calculation of the capnographic parameters. Capnography digitization using the image processing technique could serve as an option. From the algorithm we developed, eight identical breath waves were tested by four investigators. The values of the parameters chosen showed no significant difference between investigators. Although there were no significant differences between any of the parameters tested, there were a few related parameters that were not calculable. Further testing after refinement of the algorithm could be done. As more capnographic parameters are being derived and rediscovered by clinicians and researchers alike for both lung and non-lung-related diseases, there is a dire need for data analysis and interpretation. Although the proposed algorithm still needs minor refinements and further large-scale testing, we proposed that the digitization of the capnograph via image processing technique could serve as an intellectual option as it is fast, convenient, easy to use, and efficient.
Digital healthcare has grown in popularity in recent years as a scalable solution to address increasing rates of mental illness among employees, but its clinical potential is limited by low engagement and adherence, particularly in open access interventions. Personalized guidance, involving structuring an intervention and tailoring it to the user to increase accountability and social support, is one way to increase engagement with digital health programs. This exploratory retrospective study therefore sought to examine the impact of guidance in the form of personalized prompts from a lay-person (i.e., non-health professional) on user's (N = 88) engagement with a 16-week Behavioral Intervention Technology targeting employee mental health and delivered through a mobile application. Chi-squared tests and Mann-Whitney tests were used to examine differences in retention and engagement between individuals who received personalized prompts throughout their 4-month program and individuals for whom personalized prompts were introduced in the seventh week of their program. There were no significant differences between the groups in the number of weeks they remained active in the app (personalized messages group Mdn = 3.5, IQR = 3; control group Mdn = 2.5, IQR = 4.5; p = 0.472). In the first 3 weeks of the intervention program, the proportion of individuals who explored the educational modules feature and the messaging with health coaches feature was also not significantly associated with group (ps = 1.000). The number of modules completed and number of messages sent to health coaches in the first 3 weeks did not differ significantly between the two groups (ps ≥ 0.311). These results suggest that guidance from a non-health professional is limited in its ability to increase engagement with an open access Behavioral Intervention Technology for employees. Moreover, the findings suggest that the formation of a relationship between the individual and the agent providing the guidance may be necessary in order for personalized guidance to increase engagement.