Factors Affecting Acceptance of Smartphone Application for Management of Obesity
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Objectives: The factors affecting the acceptance of mobile obesity-management applications (apps) by the public were analyzed using a mobile healthcare system (MHS) technology acceptance model (TAM). Methods: The subjects who participated in this study were Android smartphone users who had an intent to manage their weight. They used the obesity-management app for two weeks, and then completed an 18-item survey designed to determine the factors influencing the acceptance of the app. Three questions were asked pertaining to each of the following six factors: compatibility, self-efficacy, technical support and training, perceived usefulness, perceived ease of use, and behavior regarding intention to use. Cronbach’s alpha was used to assess the reliability of the scales. Pathway analysis was also performed to evaluate the MHS acceptance model. Results: A total of 94 subjects participated in this study. The results indicate that compatibility, perceived usefulness, and perceived ease of use significantly affected the behavioral intention to use the mobile obesity-management app. Technical support and training also significantly affected the perceived ease of use; however, the hypotheses that self-efficacy affects perceived usefulness and perceived ease of use were not supported in this study. Conclusions: This is the first attempt to analyze the factors influencing mobile obesity-management app acceptance using a TAM. Further studies should cover not only obesity but also other chronic diseases and should analyze the factors affecting the acceptance of apps among healthcare consumers in general.
Abstract
Objectives: The factors affecting the acceptance of mobile obesity-management applications (apps) by the public were analyzed using a mobile healthcare system (MHS) technology acceptance model (TAM). Methods: The subjects who participated in this study were Android smartphone users who had an intent to manage their weight. They used the obesity-management app for two weeks, and then completed an 18-item survey designed to determine the factors influencing the acceptance of the app. Three questions were asked pertaining to each of the following six factors: compatibility, self-efficacy, technical support and training, perceived usefulness, perceived ease of use, and behavior regarding intention to use. Cronbach’s alpha was used to assess the reliability of the scales. Pathway analysis was also performed to evaluate the MHS acceptance model. Results: A total of 94 subjects participated in this study. The results indicate that compatibility, perceived usefulness, and perceived ease of use significantly affected the behavioral intention to use the mobile obesity-management app. Technical support and training also significantly affected the perceived ease of use; however, the hypotheses that self-efficacy affects perceived usefulness and perceived ease of use were not supported in this study. Conclusions: This is the first attempt to analyze the factors influencing mobile obesity-management app acceptance using a TAM. Further studies should cover not only obesity but also other chronic diseases and should analyze the factors affecting the acceptance of apps among healthcare consumers in general.
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