Effectiveness Of Android-Based Preeclampsia Early Detection And Promply Treatment Education To Improve Ante-Natal Care Adherence And Prevent Ante-Natal Complications

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Nia Desriva, Tukimin Bin Sansuwito, Regidor III Dioso

Abstract

Background


Indonesia is one of the Asian countries in which the maternal mortality rate has tremendously increased. That is why this study deemed to determine the effectiveness of android-based preeclampsia early detection and promptly treatment education to improve ante natal care adherence and prevent ante natal complications.


Methodology


The Solomon Four-Group Design was the research design used in this quasi-experimental study. The pre- and post-tests used a survey questionnaire that underwent pilot study and member checking from experts. The full-scale study used 80 experimental and 80 control having a total of 160 enrolled participants using the quota sampling technique with inclusion and exclusion criteria.


Results


Before implementation of the android-based preeclampsia detector for both experimental and control groups, Complications were found to be 26.33±30.09 significant (p<0.05), Adherence with 40±45.25 were significant (p<0.05), and participants' Knowledge Levels with 23.33±13.05 were significant (p<0.05).  After implementation of the conventional way of detecting preeclampsia among the control group, Complications were found to be 1.94±0.54 with insignificant probabilities classified as “No Risk” (p=0.175), “Moderate Risk” (p=0.7125), and “High Risk” (p=0.1125). The Adherence were found to be 1.0±0.30 with insignificant probabilities classified as “Obedient” (p=0.10), and “Disobedient” (p=0.90). While participants' Knowledge Levels were found to be 1.68±0.71 with insignificant probabilities classified as “Bad” (p=0.46), “Fair” (p=0.3625), and “Good” (p=0.1750).  After implementation of the android-based preeclampsia detector among the experimental group, Complications were found to be 1.14±0.413 significant (p=0.046), Adherence 1.38±0.487 to be significant (p=0.0504), and participants' Knowledge Levels to be 1.61 ±0.665 significant (p<0.05).


Conclusion


Ultimately, the Android-based preeclampsia detector has the potential to significantly improve health outcomes for pregnant women by ensuring that they have the tools and resources necessary to manage their health proactively.


 

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