AUTHOR=Terhorst Yannik , Weilbacher Nadine , Suda Carolin , Simon Laura , Messner Eva-Maria , Sander Lasse Bosse , Baumeister Harald 

TITLE=Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial

JOURNAL=Frontiers in Digital Health

VOLUME=5

YEAR=2023

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1075266

DOI=10.3389/fdgth.2023.1075266

ISSN=2673-253X

ABSTRACT=<sec><title>Background</title><p>Accurate and timely diagnostics are essential for effective mental healthcare. Given a resource- and time-limited mental healthcare system, novel digital and scalable diagnostic approaches such as 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 toward smart sensing.</p></sec><sec><title>Methods</title><p>The participants (<italic>N</italic> = 202) were randomly assigned to a control group (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. The intervention effects were investigated in acceptance using <italic>t</italic>-tests for observed data and latent structural equation modeling (SEM) with full information maximum likelihood to handle missing data. The behavioral outcomes were analyzed with logistic regression. The determinants of acceptance were analyzed with SEM. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used to evaluate the model fit.</p></sec><sec><title>Results</title><p>The intervention did not affect the acceptance (<italic>p</italic> = 0.357), interest (OR = 0.75, 95% CI: 0.42–1.32, <italic>p</italic> = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01–2.35, <italic>p</italic> = 0.294). The performance expectancy (<italic>γ</italic> = 0.45, <italic>p</italic> &lt; 0.001), trust (<italic>γ</italic> = 0.24, <italic>p</italic> = 0.002), and social influence (<italic>γ</italic> = 0.32, <italic>p</italic> = 0.008) were identified as the core determinants of acceptance explaining 68% of its variance. The SEM model fit was excellent (RMSEA = 0.06, SRMR = 0.05). The overall acceptance was <italic>M</italic> = 10.9 (SD = 3.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance.</p></sec><sec><title>Discussion</title><p>The present AFI was not effective. The low to moderate acceptance of smart sensing poses a major barrier to its implementation. The performance expectancy, social influence, and trust should be targeted as the 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.</p></sec><sec><title>Clinical Trial Registration</title><p>identifier 10.17605/OSF.IO/GJTPH.</p></sec>