Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial
BackgroundAccurate 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1075266/full |
| _version_ | 1797780874214768640 |
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| author | Yannik Terhorst Nadine Weilbacher Carolin Suda Laura Simon Eva-Maria Messner Lasse Bosse Sander Harald Baumeister |
| author_facet | Yannik Terhorst Nadine Weilbacher Carolin Suda Laura Simon Eva-Maria Messner Lasse Bosse Sander Harald Baumeister |
| author_sort | Yannik Terhorst |
| collection | DOAJ |
| description | BackgroundAccurate 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.MethodsThe participants (N = 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 t-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.ResultsThe intervention did not affect the acceptance (p = 0.357), interest (OR = 0.75, 95% CI: 0.42–1.32, p = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01–2.35, p = 0.294). The performance expectancy (γ = 0.45, p < 0.001), trust (γ = 0.24, p = 0.002), and social influence (γ = 0.32, p = 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 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.DiscussionThe 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.Clinical Trial Registrationidentifier 10.17605/OSF.IO/GJTPH. |
| first_indexed | 2024-03-12T23:49:58Z |
| format | Article |
| id | doaj.art-0d47dd72cf32441da5890bd596c832cb |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-03-12T23:49:58Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-0d47dd72cf32441da5890bd596c832cb2023-07-13T16:52:10ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2023-07-01510.3389/fdgth.2023.10752661075266Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trialYannik Terhorst0Nadine Weilbacher1Carolin Suda2Laura Simon3Eva-Maria Messner4Lasse Bosse Sander5Harald Baumeister6Department of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, GermanyDepartment of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, GermanyDepartment of Rehabilitation Psychology and Psychotherapy, Institute of Psychology, Albert-Ludwigs University Freiburg, Freiburg, GermanyDepartment of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, GermanyDepartment of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, GermanyMedical Psychology and Medical Sociology, Faculty of Medicine, Albert-Ludwigs University Freiburg, Freiburg, GermanyDepartment of Clinical Psychology and Psychotherapy, Institute of Psychology and Education, University Ulm, Ulm, GermanyBackgroundAccurate 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.MethodsThe participants (N = 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 t-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.ResultsThe intervention did not affect the acceptance (p = 0.357), interest (OR = 0.75, 95% CI: 0.42–1.32, p = 0.314), or installation rate (OR = 0.29, 95% CI: 0.01–2.35, p = 0.294). The performance expectancy (γ = 0.45, p < 0.001), trust (γ = 0.24, p = 0.002), and social influence (γ = 0.32, p = 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 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.DiscussionThe 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.Clinical Trial Registrationidentifier 10.17605/OSF.IO/GJTPH.https://www.frontiersin.org/articles/10.3389/fdgth.2023.1075266/fullsmart sensingdigital healthacceptanceimplementationunified theory of acceptance and use of technology acceptance of smart sensing |
| spellingShingle | Yannik Terhorst Nadine Weilbacher Carolin Suda Laura Simon Eva-Maria Messner Lasse Bosse Sander Harald Baumeister Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial Frontiers in Digital Health smart sensing digital health acceptance implementation unified theory of acceptance and use of technology acceptance of smart sensing |
| title | Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial |
| title_full | Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial |
| title_fullStr | Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial |
| title_full_unstemmed | Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial |
| title_short | Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial |
| title_sort | acceptance of smart sensing a barrier to implementation results from a randomized controlled trial |
| topic | smart sensing digital health acceptance implementation unified theory of acceptance and use of technology acceptance of smart sensing |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2023.1075266/full |
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