4.1 The extent to which early detection of CVD can be applied starting from productive age to the elderly in the COVID-19 pandemic situation
To date, the NCD control program at the health center is a public service aimed at the elderly and has not been able to reach the productive age group, considering that this group is still active and has high mobility (8, 12). Research conducted by Subhah et al. (2019) showed that the participation of the productive age population (15–45 years) in NCD prevention was only 45.38%. However, prevention at an early age, namely in young adults, will be more efficient than the cost of treatment for the elderly (13).
The use of the mHealth application, which is supported by community-based participatory programs, is proven to be able to reach a high proportion (87.1%) of the productive age population. Through previous research, this study can be further developed because of the support system that has been established in the form of a community-based health information system, which is supported by community empowerment and the use of mHealth (14). Community empowerment appears to be an essential factor in the successful use of mHealth for early detection (15–18).
The COVID-19 pandemic condition, which limits mobility, has motivated this research to build online communication via WhatsApp, starting from researchers, cadres, to community members, who are then encouraged to carry out self-assessments using mHealth. Henry et al.’s (19) research stated that WhatsApp is a form of innovation that can support community-based communication during an emergency outbreak.
4.2. The extent to which the features in the mHealth application can perform early detection of CVD
The risk index analysis in the mHealth application refers to the EZ-CVD risk score, which consists of six risk predictor items. It has several advantages compared to the recommended guidelines for the risk score for Atherosclerotic Cardiovascular Disease (ASCVD) (20). By referring to the EZ-CVD risk score, this mHealth application develops a self-assessment and does not require laboratory testing as early detection to predict CVD risk. Furthermore, Mansoor et al. (2019) argued that the inability to access comprehensive examinations is a major limitation that has the potential to result in many patients being missed for CVD risk assessment and receiving recommendations for preventive therapy. A similar study conducted at LMIC showed a slightly higher risk prediction value using a Community Health Workers-based (CHW-based) model for CVD screening (see Fig. 2) (21). The results of the analysis of the risk score on the mHealth application meanwhile show a proportion of CVD risk that is more or less comparable to the results of EZ-CVD and ASCVD (see Fig. 2) (10, 21). Even the mHealth figures for which no laboratory tests were done had a proportional level close to that of ASCVD, which has a high sensitivity for identifying future CVD occurrence of around 80%, with a fairly high specificity (69%) and a positive predictive value (17%) (23). These findings indicate that at least the mHealth application can be used easily by community members to provide CVD risk predictions. In the future, of course, it is necessary to undertake further research to show the extent of the sensitivity and specificity of this application as a tool for the early detection of CVD.
4.3 The extent to which the mHealth application can serve as a source for health promotion
This study obtained feedback from 82 respondents. It was found that 70% of them acknowledged that the mHealth application was useful in providing health promotion suitable for their individual needs. The mHealth applications can be classified into two categories: applications designed for disease management and applications that can support changes in the user’s health behavior (24). Currently, the mHealth application is increasingly being used as a tool to promote changes in user behavior to prevent NCDs (25). Besides being designed to detect CVD risk, the mHealth application is also designed to provide information on health conditions and follow-up recommendations for CVD prevention and control for its users. Research conducted by Handayani et al. (26) showed the factors that determine the successful use of the mHealth application in Indonesia, one of which is the availability of relevant information according to user needs. It seems that the features in the mHealth application are proven to be acceptable to users, especially its ability to provide personal information that is given directly to each user.
The study population was the community of the Babakan Madang sub-district, who were selected purposively by cadres. Hence, the results of this study could not be generalized to other community groups. Furthermore, several potential CVD risk factors requiring physical and laboratory examinations were not included in this study. We rely on self-assessment results for behavioral risk factors reported by respondents via the mHealth application. This can lead to misclassification of the diagnosis in some individuals who may have an undiagnosed condition. However, this mHealth application is used as an early detection method to identify individuals who require preventive therapy and follow-up recommendations according to the CVD risk calculation in the next 10 years.