Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial
Open accessAbstract
Background 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. Methods The 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. Results The 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. Discussion 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. Clinical Trial Registration identifier 10.17605/OSF.IO/GJTPH.
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The public attitude and acceptance of digital health care systems remains a major barrier.
“On a more general note, the public attitude and acceptance of digital health care systems remains a major barrier (53, 92–94).”
From Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial (2024) · cites this paper as [53]
The Conclusions say low-to-moderate acceptance is a major barrier to implementing smart sensing. The claim extends that finding to digital healthcare systems generally, although the Discussion cautions against generalizing the study's acceptance findings to the wider mental healthcare sector. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe baseline acceptance in this diabetes sample (M = 12.64, SD = 4.24) was higher than in a healthy population (M = 10.9, SD = 3.73).
“This study revealed an average baseline acceptance within our somatic sample (M = 12.64, SD = 4.24) which was higher compared to a healthy population (M = 10.9, SD = 3.73) (53).”
The proposed predictors performance expectancy, social influence and trust were tested one-sided based on the previous model on acceptance of smart sensing.
“The proposed predictors performance expectancy, social influence and trust were tested one-sided based on the previous model on acceptance of smart sensing (53).”
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Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
<b>lavaan</b> : An <i>R</i> Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · see citing works
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200 · see citing works
(2014) · Climate Change 2013 – The Physical Science Basis · doi:10.1017/cbo9781107415324.004 · Cited by 23,880 · see citing works
James, S. L., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar, H., Abd-Allah, F., Abdela, J., Abdelalim, A., et al. (2018) · The Lancet · doi:10.1016/s0140-6736(18)32279-7 · Cited by 13,867 · see citing works
Selective Cortical Undercutting As a Means of Modifying and Studying Frontal Lobe Function in Man
Scoville, W. B. (1949) · Journal of neurosurgery · doi:10.3171/jns.1949.6.1.0065 · Cited by 7,373 · see citing works
(2010) · Cited by 7,034 · see citing works
Trust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · see citing works
Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)02143-7 · Cited by 5,444 · cited 5× · see citing works
“The expert talk was structured in the following parts: (1) “What is smart sensing?” (2) “Which data is collected?” and (3) “Aims and application areas.”
BackgroundMethodInfluentialThe PHQ-8 as a measure of current depression in the general population
Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2008.06.026 · Cited by 5,121 · see citing works
Rammstedt, B., & John, O. P. (2007) · Journal of Research in Personality · doi:10.1016/j.jrp.2006.02.001 · Cited by 4,244 · see citing works
Walker, E. R., McGee, R. E., & Druss, B. G. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.2502 · Cited by 2,976 · see citing works
Schwarzer, R. (2008) · Applied Psychology · doi:10.1111/j.1464-0597.2007.00325.x · Cited by 2,569 · see citing works
Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799 · see citing works
The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010)
Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.14m09298 · Cited by 1,779 · see citing works
Irving, G., Neves, A. L., Dambha-Miller, H., Oishi, A., Tagashira, H., Verho, A., & Holden, J. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-017902 · Cited by 1,083 · see citing works
Objective user engagement with mental health apps: systematic search and panel-based usage analysis
Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019) · Journal of Medical Internet Research · doi:10.2196/14567 · Cited by 965 · see citing works
Review: Mental health impacts of the COVID‐19 pandemic on children and youth – a systematic review
Samji, H., Wu, J., Ladak, A., Vossen, C., Stewart, E., Dove, N., Long, D., & Snell, G. (2022) · Child and Adolescent Mental Health · doi:10.1111/camh.12501 · Cited by 953 · see citing works
Gomez M, P. J., & Jacome S, J. E. (2018) · DSPACE System - Metalibrary (University of the Coast) · Cited by 948 · see citing works
The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018)
Greenberg, P. E., Fournier, A.-A., Sisitsky, T., Simes, M., Berman, R., Koenigsberg, S. H., & Kessler, R. C. (2021) · PharmacoEconomics · doi:10.1007/s40273-021-01019-4 · Cited by 838 · see citing works
Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
Onnela, J.-P., & Rauch, S. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.7 · Cited by 726 · see citing works
Cuijpers, P., Noma, H., Karyotaki, E., Vinkers, C. H., Cipriani, A., & Furukawa, T. A. (2020) · World Psychiatry · doi:10.1002/wps.20701 · Cited by 540 · see citing works
Ebert, D. D., Van Daele, T., Nordgreen, T., Karekla, M., Compare, A., Zarbo, C., Brugnera, A., Øverland, S., Trebbi, G., Jensen, K. L., et al. (2018) · European Psychologist · doi:10.1027/1016-9040/a000318 · Cited by 437 · see citing works
Systematic review of smartphone-based passive sensing for health and wellbeing
Cornet, V. P., & Holden, R. J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.12.008 · Cited by 406 · see citing works
Digital interventions for the treatment of depression: a meta-analytic review
Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., Pulkki-Råback, L., Baumeister, H., & Sander, L. B. (2021) · Psychological Bulletin · doi:10.1037/bul0000334 · Cited by 390 · see citing works
Self-citationPsychological treatment of depression: Results of a series of meta-analyses
Cuijpers, P., Andersson, G., Donker, T., & van Straten, A. (2011) · Nordic Journal of Psychiatry · doi:10.3109/08039488.2011.596570 · Cited by 335 · see citing works
Huhn, M., Tardy, M., Spineli, L. M., Kissling, W., Förstl, H., Pitschel-Walz, G., Leucht, C., Samara, M., Dold, M., Davis, J. M., et al. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.112 · Cited by 333 · see citing works
A new strategy for testing structural equation models
Moshagen, M., & Erdfelder, E. (2016) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2014.950896 · Cited by 321 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Internet Banking Adoption in Kuala Lumpur: An Application of UTAUT Model
Foon, Y. S., & Yin-Fah, B. C. (2011) · International Journal of Business and Management · doi:10.5539/ijbm.v6n4p161 · Cited by 307 · see citing works
Kraus, J., Scholz, D., Stiegemeier, D., & Baumann, M. (2020) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819853686 · Cited by 276 · see citing works
Ebert, D. D., Berking, M., Cuijpers, P., Lehr, D., Pörtner, M., & Baumeister, H. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.01.056 · Cited by 268 · see citing works
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.625247 · Cited by 261 · see citing works
Self-citationWang, R., Aung, M. S. H., Abdullah, S., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J., Merrill, M., Scherer, E. A., et al. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971740 · Cited by 257 · see citing works
Kroenke, K., & Unutzer, J. (2017) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3967-9 · Cited by 231 · see citing works
Moshagen, M. (2012) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2012.634724 · Cited by 220 · see citing works
Philippi, P., Baumeister, H., Apolinário-Hagen, J., Ebert, D. D., Hennemann, S., Kott, L., Lin, J., Messner, E.-M., & Terhorst, Y. (2021) · Internet Interventions · doi:10.1016/j.invent.2021.100459 · Cited by 200 · cited 3× · see citing works
“The unified theory of acceptance and use of technology (UTAUT) (36) is a widely applied framework for the use and acceptance of technology (37, 38).”
BackgroundRohani, D. A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E. (2017) · doi:10.2196/preprints.9691 · Cited by 195 · see citing works
Smartphone data as objective measures of bipolar disorder symptoms
Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.03.009 · Cited by 185 · see citing works
Was Eysenck right after all? A reassessment of the effects of psychotherapy for adult depression
Cuijpers, P. J., Karyotaki, E., Reijnders, M., & Ebert, D. D. (2018) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796018000057 · Cited by 174 · see citing works
Molnar, L. J., Ryan, L. H., Pradhan, A. K., Eby, D. W., St. Louis, R. M., & Zakrajsek, J. S. (2018) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.06.004 · Cited by 172 · see citing works
Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works
Baumel, A., Edan, S., & Kane, J. M. (2019) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz147 · Cited by 143 · see citing works
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works
EXAMINING HEALTHCARE PROFESSIONALS' ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS USING UTAUT
(2008) · Issues in Information Systems · doi:10.48009/2_iis_2008_396-401 · Cited by 137 · see citing works
Baumel, A., & Kane, J. M. (2018) · Journal of Medical Internet Research · doi:10.2196/11491 · Cited by 131 · see citing works
Baumel, A., & Yom-Tov, E. (2018) · Translational Behavioral Medicine · doi:10.1093/tbm/ibx037 · Cited by 128 · see citing works
Baumeister, H., Nowoczin, L., Lin, J., Seifferth, H., Seufert, J., Laubner, K., & Ebert, D. D. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.04.031 · Cited by 125 · see citing works
Baumeister, H., Seifferth, H., Lin, J., Nowoczin, L., Lüking, M., & Ebert, D. (2015) · The Clinical Journal of Pain · doi:10.1097/ajp.0000000000000118 · Cited by 124 · see citing works
The accuracy of passive phone sensors in predicting daily mood
Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A. (2019) · Depression and Anxiety · doi:10.1002/da.22822 · Cited by 119 · see citing works
Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses
Benoit, J. R. A., Onyeaka, H., Keshavan, M. S., & Torous, J. B. (2020) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000268 · Cited by 114 · see citing works
Gender Imbalance in Instructional Dynamic Versus Static Visualizations: a Meta-analysis
Castro-Alonso, J. C., Wong, M., Adesope, O., Ayres, P., & PAAS, F. G. W. C. (2019) · Educational Psychology Review · doi:10.1007/s10648-019-09469-1 · Cited by 110 · see citing works
Digital Phenotyping and Mobile Sensing
Baumeister, H., & Montag, C. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4 · Cited by 101 · see citing works
Lin, J., Faust, B., Ebert, D. D., Krämer, L., & Baumeister, H. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9925 · Cited by 97 · see citing works
Making an Effort Versus Experiencing Load
Klepsch, M., & Seufert, T. (2021) · Frontiers in Education · doi:10.3389/feduc.2021.645284 · Cited by 79 · see citing works
Adler, D. A., Wang, F., Mohr, D. C., & Choudhury, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266516 · Cited by 79 · see citing works
Nicholas, J., Shilton, K., Schueller, S. M., Gray, E. L., Kwasny, M. J., & Mohr, D. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/12578 · Cited by 77 · see citing works
The Treatment of Depression in Primary Care
Trautman, S., & Beesdo‐Baum, K. (2017) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2017.0721 · Cited by 74 · see citing works
Wurcel, V., Cicchetti, A., Garrison, L. P., Kip, M. M. A., Koffijberg, H., Kolbe, A., Leeflang, M., Merlin, T., Mestre‐Ferrandiz, J., Oortwijn, W., et al. (2019) · Public Health Genomics · doi:10.1159/000501832 · Cited by 72 · see citing works
Salgado, T., Tavares, J., & Oliveira, T. (2020) · JMIR mhealth and uhealth · doi:10.2196/17588 · Cited by 72 · see citing works
Brodersen, K., Hammami, N., & Katapally, T. R. (2022) · Youth · doi:10.3390/youth2010003 · Cited by 56 · see citing works
Impact of an acceptance facilitating intervention on psychotherapists' acceptance of blended therapy
Baumeister, H., Terhorst, Y., Grässle, C., Freudenstein, M., Nübling, R., & Ebert, D. D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236995 · Cited by 53 · see citing works
Schneider, S., Krieglstein, F., Beege, M., & Rey, G. D. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13229 · Cited by 45 · see citing works
Hennemann, S., Kuhn, S., Witthöft, M., & Jungmann, S. M. (2022) · JMIR Mental Health · doi:10.2196/32832 · Cited by 31 · see citing works
Smart Sensors for Health Research and Improvement
Garatva, P., Terhorst, Y., Meßner, E.-M., Karlen, W., Pryss, R. C., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_23 · Cited by 26 · see citing works
Digital Phenotyping and Mobile Sensing
(2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2 · Cited by 25 · see citing works
Smart Sensing Enhanced Diagnostic Expert Systems
Terhorst, Y., Knauer, J., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_24 · Cited by 22 · see citing works
Depression screening and management in primary care
Kroenke, K. (2017) · Family Practice · doi:10.1093/fampra/cmx129 · Cited by 19 · see citing works
Persuasive e-Health Design for Behavior Change
Baumeister, H., Kraft, R., Baumel, A., Pryss, R. C., & Meßner, E.-M. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_20 · Cited by 15 · see citing works
Depression Diagnosis and Forecast based on Mobile Phone Sensor Data
He, X., Triantafyllopoulos, A., Kathan, A., Milling, M., Yan, T., Rajamani, S. T., Küster, L., Harrer, M., Heber, E., Großmann, I., et al. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871255 · Cited by 14 · see citing works
Challenges to primary care in diagnosing and managing depression in children and young people
Kramer, T., Als, L. C., & Garralda, M. E. (2015) · BMJ · doi:10.1136/bmj.h2512 · Cited by 9 · see citing works
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Kathan, A., Triantafyllopoulos, A., He, X., Milling, M., Yan, T., Rajamani, S. T., Küster, L., Harrer, M., Heber, E., Großmann, I., et al. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871015 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [37]Blut M Chong A Tsiga Z Venkatesh V. Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): challenging its validity and charting a research agenda in the red ocean (March 16, 2021). J Assoc Inf Syst. (Forthcoming). Available at SSRN: https://ssrn.com/abstract=383487234321961
- [62]Mayer RE. Multimedia learning. Multimed Learn. (2002) 41: 85– 139. 10.1016/S0079-7421(02)80005-6 · DOI · Paper page
Cited by
Sorted by citations
Terhorst, Y., Knauer, J., Philippi, P., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/51875 · Cited by 22 · see citing works
Rottstädt, F., Becker, E., Wilz, G., Croy, I., Baumeister, H., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1335776 · Cited by 11 · see citing works
Self-citationTerhorst, Y., Meßner, E.-M., Asare, K. O., Montag, C., Kannen, C., & Baumeister, H. (2024) · Journal of Medical Internet Research · doi:10.2196/55308 · Cited by 9 · see citing works
The relation between passively collected data and PTSD: a systematic review and meta-analysis
Zhu, N., Sarawgi, A., Bühner, M., Baumeister, H., Garatva, P., Ehring, T. W., & Terhorst, Y. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01825-6 · Cited by 10 · see citing works
Simon, L., Terhorst, Y., Cohrdes, C., Pryss, R. C., Steinmetz, L., Elhai, J. D., & Baumeister, H. (2024) · Sleep Medicine X · doi:10.1016/j.sleepx.2024.100114 · Cited by 9 · see citing works
Knauer, J., Baumeister, H., Schmitt, A., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1352762 · Cited by 2 · see citing works
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Simon, L., Steubl, L., Terhorst, Y., Cohrdes, C., Pryss, R., & Baumeister, H. (2026) · Diagnostica · doi:10.1026/0012-1924/a000363 · Cited by 1 · see citing works
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Knauer, J., Terhorst, Y., & Baumeister, H. (2025) · CBT: science into practice · doi:10.1007/978-3-031-90428-8_10
Judijanto, L., & Nurhayati, S. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438762
Schwientek, A.-K., Drüge, M., Terhorst, Y., Kabrel, M., Sahan, F., & Apolinário-Hagen, J. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-981-95-6872-7_29
- Authors
- Terhorst, Y., Weilbacher, N., Suda, C., Simon, L., Messner, E.-M., Sander, L. B., & Baumeister, H.
- Affiliations
- Universität Ulm (DE)
- University of Freiburg (DE)
- Corresponding author
- Terhorst, Yannik
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2023-07-13
- Volume
- 5
- Pages
- 1075266-1075266
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC10373890
- OpenAlex
- W4384197557
- Cited by
- 10
- References
- 72
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5
- FWCI
- 2.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Albert-Ludwigs-Universität Freiburg
- Universität Ulm
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- smart sensing, technology acceptance, UTAUT, psychiatric diagnosis, digital mental health, performance expectancy, effort expectancy, trust, social influence, facilitating conditions, smartphone sensors, digital markers, adults, video-based intervention, implementation barriers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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