Determinants of Menstrual Tracking Mobile Application Acceptance Among Adolescent Girls in Lafto Sub City, Addis Ababa

Abstract

The world has been passing through the fourth industrial revolution and the traditional health system has been evolving to digital health. The objective of this study is to explore determinants of menstrual tracking mobile application acceptance among adolescent girls in Lafto sub-city, Addis Ababa. We employed cross-sectional design, sequential mixed research approach (Quan ̶>qual) with integrated Health Belief and Technological Acceptance Model to investigate the aim of the study. Additionally, the sample size was estimated using the inverse square root method and we adopted a combination of
stratified and simple random sampling techniques. We also used the self-administered questionnaires using Google form and a total of 161 adolescent girls participated voluntarily. We employed Stata version 17 and R-studio version 4.3 for descriptive and PLS-SEM analysis, respectively. The finding of our research showed that the majority of the indicator error values produced by PLS–out of sample were less than those produced by linear regression model, which depicted that the overall model had a moderate predictive power. Moreover, all latent path coefficients were significant predictors of behavioural
intention to use such apps except perceived threat to teenage pregnancy and menstrual irregularity. But both had significant
indirect effects. Based on the above findings, we recommend health software developers to increase the significant predictors (perceived usefulness, perceived ease of use, perceived self-efficacy and cues to action), and decrease perceived barriers which lead to positive attitude to use and self-tracking motivations that ultimately impacts acceptance of such apps among adolescent girls.