Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial - Terhorst, Yannik; Weilbacher, Nadine; Suda, Carolin; Simon, Laura; Messner, Eva-Maria; Sander, Lasse Bosse; Baumeister, Harald (Frontiers in Digital Health, 2023)
Background: Accurate and timely diagnostics are essential for effective mental health care. Given a resource- and time-limited mental health care system, novel digital and scalable diagnostic approaches like smart sensing, which utilizes digital markers collected via sensors from digital devices, are explored. While the predictive accuracy of smart sensing is promising, its acceptance remains unclear. Based on the Unified Theory of Acceptance and Use of Technology, the present study investigated 1) the effectiveness of an Acceptance Facilitating Intervention (AFI), 2) the determinants of acceptance, and 3) the acceptance of adults towards smart sensing. Methods: Participants (N=202) were randomly assigned to a control (CG) or intervention group (IG). The IG received a video AFI on smart sensing, and the CG a video on mindfulness. A reliable online questionnaire was used to assess acceptance, performance expectancy, effort expectancy, facilitating conditions, social influence, and trust. The self-reported interest in using and the installation of a smart sensing app were assessed as behavioral outcomes. Intervention effects were investigated in acceptance using t-tests for observed data and latent structural equation modeling (SEM) with full information maximum likelihood to handle missing data. Behavioral outcomes were analyzed with logistic regression. Determinants of acceptance were analyzed with SEM. RMSEA and SRMR were used to evaluate model fit. Results: The intervention did not affect the acceptance (p=0.357), the interest (OR=0.75, 95%-CI: 0.42 to 1.32, p=.314), or installation rate (OR=0.29, 95%-CI: 0.01 to 2.35, p=.294). Performance expectancy (γ=0.45, p<.001), trust (γ=0.24, p=.002), and social influence (γ=0.32, p=.008) were identified as core determinants of acceptance explaining 68% of its variance. SEM model fits was excellent (RMSEA=0.06, SRMR= 0.05). The overall acceptance was M=10.9 (SD=3.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance. Discussion: The present AFI was not effective. Low to moderate acceptance of smart sensing poses a major barrier to its implementation. Performance expectancy, social influence, and trust should be targeted as core factors of acceptance. Further studies are needed to identify effective ways to foster the acceptance of smart sensing and to develop successful implementation strategies. Registration: 10.17605/OSF.IO/GJTPH