Technology Acceptance and Usability of a Mobile App to Support the Workflow of Health Care Aides Who Provide Services to Older Adults: Pilot Mixed Methods Study.

Abstract

BackgroundHealth care aides are unlicensed support personnel who provide direct care, personal assistance, and support to people with health conditions. The shortage of health care aides has been attributed to recruitment challenges, high turnover, an aging population, the COVID-19 pandemic, and low retention rates. Mobile apps are among the many information communication technologies that are paving the way for eHealth solutions to help address this workforce shortage by enhancing the workflow of health care aides. In collaboration with Clinisys EMR Inc, we developed a mobile app (Mobile Smart Care System [mSCS]) to support the workflow of health care aides who provide services to older adult residents of a long-term care facility.ObjectiveThe purpose of this study was to investigate the technology acceptance and usability of a mobile app in a real-world environment, while it is used by health care aides who provide services to older adults.MethodsThis pilot study used a mixed methods design: sequential mixed methods (QUANTITATIVE, qualitative). Our study included a pre– and post–paper-based questionnaire with no control group (QUAN). Toward the end of the study, 2 focus groups were conducted with a subsample of health care aides (qual, qualitative description design). Technology acceptance and usability questionnaires used a 5-point Likert scale ranging from disagree (1) to agree (5). The items included in the questionnaires were validated in earlier research as having high levels of internal consistency for the Unified Theory of Acceptance and Use of Technology constructs. A total of 60 health care aides who provided services to older adults as part of their routine caseloads used the mobile app for 1 month. Comparisons of the Unified Theory of Acceptance and Use of Technology constructs’ summative scores at pretest and posttest were calculated using a paired t test (2-tailed). We used the partial least squares structural regression model to determine the factors influencing mobile app acceptance and usability for health care aides. The α level of significance for all tests was set at P≤.05 (2-tailed).ResultsWe found that acceptance of the mSCS was high among health care aides, performance expectancy construct was the strongest predictor of intention to use the mSCS, intention to use the mSCS predicted usage behavior. The qualitative data support the quantitative findings and showed health care aides’ strong belief that the mSCS was useful, portable, and reliable, although there were still opportunities for improvement, especially with regard to the mSCS user interface.ConclusionsOverall, these results support the assertion that mSCS technology acceptance and usability are high among health care aides. In other words, health care aides perceived that the mSCS assisted them in addressing their workflow issues.