Introduction: The aim of the present study was to investigate the different roles of m-Health in pandemic management using the
Partial Least Square (PLS) modeling technique. Owing to the limited existing literature regarding theorizing and the lack of the default model in predicting the role of m-Health in pandemic management, this method was used for exploratory modeling.
Material and Methods: The PLS model was performed with smart-PLS software for the following steps: estimating weight ratios, considering weight ratios as input, estimating parameters, model-fitting and testing hypotheses. In addition, Factor scores in regression equations were used to estimate structural parameters. PLS algorithm, Cronbach's alpha, and Composite Reliability were used for the measurement and reliability evaluation model Goodness-of-fit. In addition, the R۲ index was used to evaluate the model adequacy. Bootstrapping was used for significant coefficients. The Goodness-of-fit of the model was examined via the Standardized Root Mean Square Residual (SRMR) criterion.
Results: It is determined the measurement models goodness-of-fit which the alpha values were as follows: diagnosis construct=۰.۷۸۶, follow-up=۰.۷۷۲, treatment=۰.۷۹۶, health care providers=۰.۷۰۴ and education=۰.۸۳۹ with more than ۰.۷ for all measures for Composite Reliability, the structural model measures such as R۲ were higher than ۰.۶ for all areas and the overall model goodness-of-fit was -۰.۰۰۷ for SRMR, the five hypotheses developed in the model were confirmed according standardized coefficients more than ۱.۹۶ for all paths. Furthermore, the proposed model concerning the positive and significant role of m-Health in diagnosis, treatment and follow-up, education and health providers during the pandemic era was approved.
Conclusion: The results of the present study can be used as a theoretical basis in developing models related to the role of m-Health in pandemic management. Also, health policymakers and practitioners could use the results to manage current and post-coronary conditions and to promote services based on various m-Health apps.