From FITT to FISTT: The task-skills fit before the introduction of assistive, digital health technologies()

OBJECTIVE: To enhance the predictive power of the Fit between Individuals, Task and Technology (FITT) framework in mobile, individual consumer settings by restructuring the individual-task fit to prominently emphasise the task-skills fit. DESIGN: A mixed study involving a quantitative survey of 679 potential patients (adopters) and a qualitative content analysis of ten semi-structured interviews with clinic assistants. SETTING: For the survey, three combined random samples of potential patients from Atteridgeville, Bapong and Garankuwa (South Africa). Ten Unjani clinic assistants were also interviewed about their tasks, skills, and related properties and attributes using a semi-structured interview guide. PARTICIPANTS: Participants in the survey were potential patients over 18 years of age in the three sampled locations. In the qualitative study, interviewed participants were employed as clinic assistants in ten clinics within the Unjani Clinic Network. MAIN OUTCOME MEASURES: in the quantitative study, the statistical significance of the relationships between smartphone experience and health motivation on the one hand and the adopter's perceived self-efficacy on the other. In the qualitative study, the extent to which task properties, context, and the adopters' levels of education and training affect their perceived self-efficacy. FINDINGS: There is a significant relationship between smartphone experience and perceived self-efficacy and a moderately significant relationship between health motivation and perceived self-efficacy. Furthermore, task properties, task context, and an adopter's level of education and training considerably influence their perceived self-efficacy on a given future assistive digital health technology (ADHT). CONCLUSION: Extending the FITT to the FISTT framework to explicitly include the task-skills fit may improve the explanatory and predictive power of the traditional FITT framework in mobile individual consumer settings.