Utilizing Structural Equation Modeling–Artificial Neural Network Hybrid Approach in Determining Factors Affecting Perceived Usability of Mobile Mental Health Application in the Philippines

Abstract

Mental health problems have emerged as one of the biggest problems in the world and one of the countries that has been seen to be highly impacted is the Philippines. Despite the increasing number of mentally ill Filipinos, it is one of the most neglected problems in the country. The purpose of this study was to determine the factors affecting the perceived usability of mobile mental health applications. A total of 251 respondents voluntarily participated in the online survey we conducted. A structural equation modeling and artificial neural network hybrid was applied to determine the perceived usability (PRU) such as the social influence (SI), service awareness (SA), technology self-efficacy (SE), perceived usefulness (PU), perceived ease of use (PEOU), convenience (CO), voluntariness (VO), user resistance (UR), intention to use (IU), and actual use (AU). Results indicate that VO had the highest score of importance, followed by CO, PEOU, SA, SE, SI, IU, PU, and ASU. Having the mobile application available and accessible made the users perceive it as highly beneficial and advantageous. This would lead to the continuous usage and patronage of the application. This result highlights the insignificance of UR. This study was the first study that considered the evaluation of mobile mental health applications. This study can be beneficial to people who have mental health disorders and symptoms, even to health government agencies. Finally, the results of this study could be applied and extended among other health-related mobile applications worldwide.

Socio-Economic Factors Affecting Member’s Satisfaction towards National Health Insurance: An Evidence from the Philippines

“…This process implicated how the responses would be linked to members’ satisfaction with the national health insurance. Similar to the study of Yuduang et al [ 72 ], SEM and neural network analysis were conducted to verify the findings and close the limitations brought by SEM [ 49 ].…”

“…The only response from the Likert Scale survey was considered in this study following the study of Ong et al [ 71 ]. In accordance, Yuduang et al [ 72 ] utilized a similar methodology following a data pre-processing stage. The collected data show that most females (52.8%) and males (46.8%) ranging from 18 to 75 years old are all collected around NCR.…”

“…Data preparation was performed before running the MLA, adopted from different studies [ 71 , 72 ]. This study utilized SPSS 25 to review the data and found that there were no missing data, and the data were pre-processed.…”

“…Deep Learning Neural Network (DLNN) is a type of Neural Network (NN) used due to its great ability to generalize and classify data [ 71 , 72 ]. DLNN can mimic the structures of the human brain and central nervous system to recognize hidden patterns among datasets [ 72 ]. It has at least two hidden layers of patterns, containing an input layer and an output layer responsible for prediction [ 71 ].…”

Factors Affecting Willingness to Receive Online Counseling: The Mediating Role of Ethical Concerns

“…Many mental health mobile applications that can help users relieve psychological stress, maintain mental health, and compensate for face-to-face counseling when necessary have also emerged. A recent study by Yuduang et al [ 7 ] found that people have little knowledge of the mental health services available online and that increasing awareness of online mental health services and understanding their benefits and accessibility would increase their willingness to use them. Therefore, studying the factors that influence people’s willingness to use online mental health services can help to better utilize online service platforms to promote mental health.…”