{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:55:14Z","timestamp":1774367714920,"version":"3.50.1"},"reference-count":73,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:00:00Z","timestamp":1689206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The participants (<jats:italic>N<\/jats:italic>\u2009=\u2009202) 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 <jats:italic>t<\/jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The intervention did not affect the acceptance (<jats:italic>p<\/jats:italic>\u2009=\u20090.357), interest (OR\u2009=\u20090.75, 95% CI: 0.42\u20131.32, <jats:italic>p<\/jats:italic>\u2009=\u20090.314), or installation rate (OR\u2009=\u20090.29, 95% CI: 0.01\u20132.35, <jats:italic>p<\/jats:italic>\u2009=\u20090.294). The performance expectancy (<jats:italic>\u03b3<\/jats:italic>\u2009=\u20090.45, <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.001), trust (<jats:italic>\u03b3<\/jats:italic>\u2009=\u20090.24, <jats:italic>p<\/jats:italic>\u2009=\u20090.002), and social influence (<jats:italic>\u03b3<\/jats:italic>\u2009=\u20090.32, <jats:italic>p<\/jats:italic>\u2009=\u20090.008) were identified as the core determinants of acceptance explaining 68% of its variance. The SEM model fit was excellent (RMSEA\u2009=\u20090.06, SRMR\u2009=\u20090.05). The overall acceptance was <jats:italic>M<\/jats:italic>\u2009=\u200910.9 (SD\u2009=\u20093.73), with 35.41% of the participants showing a low, 47.92% a moderate, and 10.41% a high acceptance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Clinical Trial Registration<\/jats:title><jats:p>identifier 10.17605\/OSF.IO\/GJTPH.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2023.1075266","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T16:52:25Z","timestamp":1689267145000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Acceptance of smart sensing: a barrier to implementation\u2014results from a randomized controlled trial"],"prefix":"10.3389","volume":"5","author":[{"given":"Yannik","family":"Terhorst","sequence":"first","affiliation":[]},{"given":"Nadine","family":"Weilbacher","sequence":"additional","affiliation":[]},{"given":"Carolin","family":"Suda","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Simon","sequence":"additional","affiliation":[]},{"given":"Eva-Maria","family":"Messner","sequence":"additional","affiliation":[]},{"given":"Lasse Bosse","family":"Sander","sequence":"additional","affiliation":[]},{"given":"Harald","family":"Baumeister","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,7,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1001\/jamapsychiatry.2014.2502","article-title":"Mortality in mental disorders and global disease burden implications","volume":"72","author":"Walker","year":"2015","journal-title":"JAMA Psychiatry"},{"key":"B2","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1111\/camh.12501","article-title":"Review: mental health impacts of the COVID-19 pandemic on children and youth\u2014a systematic review","volume":"27","author":"Samji","year":"2022","journal-title":"Child Adolesc Ment Health"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1016\/S0140-6736(21)02143-7","article-title":"Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic","volume":"398","author":"Santomauro","year":"2021","journal-title":"Lancet"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1016\/S0140-6736(18)32279-7","article-title":"Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u20132017: a systematic analysis for the global burden of disease study 2017","volume":"392","author":"James","year":"2018","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"155","DOI":"10.4088\/JCP.14m09298","article-title":"The economic burden of adults with major depressive disorder in the United States (2005 and 2010)","volume":"76","author":"Greenberg","year":"2015","journal-title":"J Clin Psychiatry"},{"key":"B6","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1007\/s40273-021-01019-4","article-title":"The economic burden of adults with major depressive disorder in the United States (2010 and 2018)","volume":"39","author":"Greenberg","year":"2021","journal-title":"Pharmacoeconomics"},{"key":"B7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1017\/S2045796018000057","article-title":"Was Eysenck right after all? A reassessment of the effects of psychotherapy for adult depression","volume":"28","author":"Cuijpers","year":"2019","journal-title":"Epidemiol Psychiatr Sci"},{"key":"B8","doi-asserted-by":"publisher","first-page":"354","DOI":"10.3109\/08039488.2011.596570","article-title":"Psychological treatment of depression: results of a series of meta-analyses","volume":"65","author":"Cuijpers","year":"2011","journal-title":"Nord J Psychiatry"},{"key":"B9","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1001\/jamapsychiatry.2014.112","article-title":"Efficacy of pharmacotherapy and psychotherapy for adult psychiatric disorders","volume":"71","author":"Huhn","year":"2014","journal-title":"JAMA Psychiatry"},{"key":"B10","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1002\/wps.20701","article-title":"A network meta-analysis of the effects of psychotherapies, pharmacotherapies and their combination in the treatment of adult depression","volume":"19","author":"Cuijpers","year":"2020","journal-title":"World Psychiatry"},{"key":"B11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1037\/bul0000334","article-title":"Digital interventions for the treatment of depression: a meta-analytic review","volume":"147","author":"Moshe","year":"2021","journal-title":"Psychol Bull"},{"key":"B12","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1027\/1016-9040\/a000318","article-title":"Internet- and mobile-based psychological interventions: applications, efficacy, and potential for improving mental health","volume":"23","author":"Ebert","year":"2018","journal-title":"Eur Psychol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"h2512","DOI":"10.1136\/BMJ.H2512","article-title":"Challenges to primary care in diagnosing and managing depression in children and young people","volume":"350","author":"Kramer","year":"2015","journal-title":"Br Med J"},{"key":"B14","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1159\/000501832","article-title":"The value of diagnostic information in personalised healthcare: a comprehensive concept to facilitate bringing this technology into healthcare systems","volume":"22","author":"Wurcel","year":"2019","journal-title":"Public Health Genom"},{"key":"B15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/FAMPRA\/CMX129","article-title":"Depression screening and management in primary care","volume":"35","author":"Kroenke","year":"2018","journal-title":"Fam Pract"},{"key":"B16","doi-asserted-by":"publisher","first-page":"721","DOI":"10.3238\/ARZTEBL.2017.0721","article-title":"The treatment of depression in primary care\u2014a cross-sectional epidemiological study","volume":"114","author":"Trautman","year":"2017","journal-title":"Dtsch Arztebl Int"},{"key":"B17","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1007\/S11606-016-3967-9","article-title":"Closing the false divide: sustainable approaches to integrating mental health services into primary care","volume":"32","author":"Kroenke","year":"2017","journal-title":"J Gen Intern Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e017902","DOI":"10.1136\/BMJOPEN-2017-017902","article-title":"International variations in primary care physician consultation time: a systematic review of 67 countries","volume":"7","author":"Irving","year":"2017","journal-title":"BMJ Open"},{"key":"B19","doi-asserted-by":"publisher","first-page":"E32832","DOI":"10.2196\/32832","article-title":"Diagnostic performance of an app-based symptom checker in mental disorders: comparative study in psychotherapy outpatients","volume":"9","author":"Hennemann","year":"2022","journal-title":"JMIR Ment Heal"},{"key":"B20","doi-asserted-by":"publisher","first-page":"625247","DOI":"10.3389\/fpsyt.2021.625247","article-title":"Predicting symptoms of depression and anxiety using smartphone and wearable data","volume":"12","author":"Moshe","year":"2021","journal-title":"Front Psychiatry"},{"key":"B21","doi-asserted-by":"publisher","first-page":"e26540","DOI":"10.2196\/26540","article-title":"Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis: exploratory study","volume":"9","author":"Opoku Asare","year":"2021","journal-title":"JMIR MHealth UHealth"},{"key":"B22","first-page":"2627","article-title":"Journaling data for daily PHQ-2 depression prediction and forecasting","author":"Kathan","year":"2022","journal-title":"In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)"},{"key":"B23","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/978-3-030-98546-2_23","article-title":"Smart sensors for health research and improvement","volume-title":"Digital phenotyping and mobile sensing","author":"Garatva","year":"2023"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1038\/npp.2016.7","article-title":"Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health","volume":"41","author":"Onnela","year":"2016","journal-title":"Neuropsychopharmacology"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e2537","DOI":"10.7717\/peerj.2537","article-title":"The relationship between mobile phone location sensor data and depressive symptom severity","volume":"4","author":"Saeb","year":"2016","journal-title":"PeerJ"},{"key":"B26","article-title":"Digital phenotyping and mobile sensing","volume-title":"New developments in psychoinformatics","author":"Baumeister","year":"2023"},{"key":"B27","first-page":"4679","article-title":"Depression diagnosis and forecast based on mobile phone sensor data","author":"He","year":"2022","journal-title":"In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)"},{"key":"B28","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1002\/da.22822","article-title":"The accuracy of passive phone sensors in predicting daily mood","volume":"36","author":"Pratap","year":"2019","journal-title":"Depress Anxiety"},{"key":"B29","doi-asserted-by":"publisher","first-page":"e0266516","DOI":"10.1371\/JOURNAL.PONE.0266516","article-title":"Machine learning for passive mental health symptom prediction: generalization across different longitudinal mobile sensing studies","volume":"17","author":"Adler","year":"2022","journal-title":"PLoS One"},{"key":"B30","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jbi.2017.12.008","article-title":"Systematic review of smartphone-based passive sensing for health and wellbeing","volume":"77","author":"Cornet","year":"2018","journal-title":"J Biomed Inform"},{"key":"B31","doi-asserted-by":"publisher","first-page":"e165","DOI":"10.2196\/mhealth.9691","article-title":"Correlations between objective behavioral features collected from mobile and wearable devices and depressive mood symptoms in patients with affective disorders: systematic review","volume":"6","author":"Rohani","year":"2018","journal-title":"JMIR MHealth UHealth"},{"key":"B32","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1097\/HRP.0000000000000268","article-title":"Systematic review of digital phenotyping and machine learning in psychosis spectrum illnesses","volume":"28","author":"Benoit","year":"2020","journal-title":"Harv Rev Psychiatry"},{"key":"B33","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/J.PSYCHRES.2014.03.009","article-title":"Smartphone data as objective measures of bipolar disorder symptoms","volume":"217","author":"Faurholt-Jepsen","year":"2014","journal-title":"Psychiatry Res"},{"key":"B34","first-page":"1","author":"Gruenerbl","year":"2014"},{"key":"B35","first-page":"886","article-title":"CrossCheck","author":"Wang","year":"2016"},{"key":"B36","doi-asserted-by":"publisher","first-page":"425","DOI":"10.2307\/30036540","article-title":"User acceptance of information technology: toward a unified view","volume":"27","author":"Venkatesh","year":"2003","journal-title":"MIS Q"},{"key":"B37","article-title":"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)","author":"Blut","year":"","journal-title":"J Assoc Inf Syst"},{"key":"B38","doi-asserted-by":"publisher","first-page":"100459","DOI":"10.1016\/j.invent.2021.100459","article-title":"Acceptance towards digital health interventions\u2014model validation and further development of the unified theory of acceptance and use of technology","volume":"26","author":"Philippi","year":"2021","journal-title":"Internet Interv"},{"key":"B39","doi-asserted-by":"publisher","first-page":"161","DOI":"10.5539\/ijbm.v6n4p161","article-title":"Internet banking adoption in Kuala Lumpur: an application of UTAUT model","volume":"6","author":"Foon","year":"2011","journal-title":"Int J Bus Manag"},{"key":"B40","first-page":"396","article-title":"Examining healthcare professionals\u2019. Acceptance of electronic medical records using UTAUT","volume":"9","author":"Wills","year":"2008"},{"key":"B41","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1177\/0018720819853686","article-title":"The more you know: trust dynamics and calibration in highly automated driving and the effects of take-overs. System malfunction, and system transparency","volume":"62","author":"Kraus","year":"2020","journal-title":"Hum Factors"},{"key":"B42","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1518\/HFES.46.1.50_30392","article-title":"Trust in automation: designing for appropriate reliance","volume":"46","author":"Lee","year":"2004","journal-title":"Hum Factors"},{"key":"B43","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/J.TRF.2018.06.004","article-title":"Understanding trust and acceptance of automated vehicles: an exploratory simulator study of transfer of control between automated and manual driving","volume":"58","author":"Molnar","year":"2018","journal-title":"Transp Res Part F Traffic Psychol Behav"},{"key":"B44","doi-asserted-by":"publisher","first-page":"e0236995","DOI":"10.1371\/journal.pone.0236995","article-title":"Impact of an acceptance facilitating intervention on psychotherapists\u2019 acceptance of blended therapy","volume":"15","author":"Baumeister","year":"2020","journal-title":"PLoS One"},{"key":"B45","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1097\/AJP.0000000000000118","article-title":"Impact of an acceptance facilitating intervention on patients\u2019 acceptance of internet-based pain interventions\u2014a randomised controlled trial","volume":"31","author":"Baumeister","year":"2015","journal-title":"Clin J Pain"},{"key":"B46","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.diabres.2014.04.031","article-title":"Impact of an acceptance facilitating intervention on diabetes patients\u2019 acceptance of internet-based interventions for depression: a randomized controlled trial","volume":"105","author":"Baumeister","year":"2014","journal-title":"Diabetes Res Clin Pract"},{"key":"B47","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jad.2015.01.056","article-title":"Increasing the acceptance of internet-based mental health interventions in primary care patients with depressive symptoms. A randomized controlled trial","volume":"176","author":"Ebert","year":"2015","journal-title":"J Affect Disord"},{"key":"B48","doi-asserted-by":"publisher","first-page":"e244","DOI":"10.2196\/jmir.9925","article-title":"A web-based acceptance-facilitating intervention for identifying patients\u2019 acceptance, uptake, and adherence of internet- and mobile-based pain interventions: randomized controlled trial","volume":"20","author":"Lin","year":"2018","journal-title":"J Med Internet Res"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1464-0597.2007.00325.x","article-title":"Modeling health behavior change: how to predict and modify the adoption and maintenance of health behaviors","volume":"57","author":"Schwarzer","year":"2008","journal-title":"Appl Psychol"},{"key":"B50","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.jrp.2006.02.001","article-title":"Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German","volume":"41","author":"Rammstedt","year":"2007","journal-title":"J Res Pers"},{"key":"B51","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.jad.2008.06.026","article-title":"The PHQ-8 as a measure of current depression in the general population","volume":"114","author":"Kroenke","year":"2009","journal-title":"J Affect Disord"},{"key":"B52","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1001\/archinte.166.10.1092","article-title":"A brief measure for assessing generalized anxiety disorder: the GAD-7","volume":"166","author":"Spitzer","year":"2006","journal-title":"Arch Intern Med"},{"key":"B53","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1207\/S15327566IJCE0401_04","article-title":"Foundations for an empirically determined scale of trust in automated systems","volume":"4","author":"Jian","year":"2010","journal-title":"Int J Cogn Ergon Lawrence Erlbaum Associates, Inc"},{"key":"B54","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1177\/0049124192021002005","article-title":"Alternative ways of assessing model fit","volume":"21","author":"Browne","year":"1992","journal-title":"Sociol Methods Res"},{"key":"B55","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1080\/10705511.2012.634724","article-title":"The model size effect in SEM: inflated goodness-of-fit statistics are due to the size of the covariance matrix","volume":"19","author":"Moshagen","year":"2012","journal-title":"Struct Equ Modeling"},{"key":"B56","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1080\/10705511.2014.950896","article-title":"A new strategy for testing structural equation models","volume":"23","author":"Moshagen","year":"2016","journal-title":"Struct Equ Modeling"},{"key":"B57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10705519909540118","article-title":"Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives","volume":"6","author":"Hu","year":"1999","journal-title":"Struct Equ Modeling"},{"key":"B58","volume-title":"Applied missing data analysis","author":"Enders","year":"2010"},{"key":"B59","volume-title":"R: a language and environment for statistical computing. R foundation for statistical computing","year":"2022"},{"key":"B60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v048.i02","article-title":"Lavaan: an R package for structural equation modeling","volume":"48","author":"Rosseel","year":"2012","journal-title":"J Stat Softw"},{"key":"B61","doi-asserted-by":"publisher","first-page":"e13229","DOI":"10.1016\/j.heliyon.2023.e13229","article-title":"Successful learning with whiteboard animations\u2014a question of their procedural character or narrative embedding?","volume":"9","author":"Schneider","year":"2023","journal-title":"Heliyon"},{"key":"B62","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0079-7421(02)80005-6","article-title":"Multimedia learning","volume":"41","author":"Mayer","year":"2002","journal-title":"Multimed Learn"},{"key":"B63","doi-asserted-by":"publisher","first-page":"645284","DOI":"10.3389\/feduc.2021.645284","article-title":"Making an effort versus experiencing load","volume":"6","author":"Klepsch","year":"2021","journal-title":"Front Educ"},{"key":"B64","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s10648-019-09469-1","article-title":"Gender imbalance in instructional dynamic versus static visualizations: a meta-analysis","volume":"31","author":"Castro-Alonso","year":"2019","journal-title":"Educ Psychol Rev"},{"key":"B65","doi-asserted-by":"publisher","first-page":"e17588","DOI":"10.2196\/17588","article-title":"Drivers of mobile health acceptance and use from the patient perspective: survey study and quantitative model development","volume":"8","author":"Salgado","year":"2020","journal-title":"JMIR MHealth UHealth"},{"key":"B66","doi-asserted-by":"publisher","first-page":"e14567","DOI":"10.2196\/14567","article-title":"Objective user engagement with mental health apps: systematic search and panel-based usage analysis","volume":"21","author":"Baumel","year":"2019","journal-title":"J Med Internet Res"},{"key":"B67","doi-asserted-by":"publisher","first-page":"e11491","DOI":"10.2196\/11491","article-title":"Examining predictors of real-world user engagement with self-guided eHealth interventions: analysis of mobile apps and websites using a novel dataset","volume":"20","author":"Baumel","year":"2018","journal-title":"J Med Internet Res"},{"key":"B68","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1093\/tbm\/ibx037","article-title":"Predicting user adherence to behavioral eHealth interventions in the real world: examining which aspects of intervention design matter most","volume":"8","author":"Baumel","year":"2018","journal-title":"Transl Behav Med"},{"key":"B69","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1093\/tbm\/ibz147","article-title":"Is there a trial bias impacting user engagement with unguided e-mental health interventions? A systematic comparison of published reports and real-world usage of the same programs","volume":"9","author":"Baumel","year":"2019","journal-title":"Transl Behav Med"},{"key":"B70","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/978-3-030-98546-2_20","article-title":"Persuasive e-health design for behavior change","volume-title":"Digital phenotyping and mobile sensing","author":"Baumeister","year":"2023"},{"key":"B71","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3390\/youth2010003","article-title":"Smartphone use and mental health among youth: it is time to develop smartphone-specific screen time guidelines","volume":"2","author":"Brodersen","year":"2022","journal-title":"Youth"},{"key":"B72","doi-asserted-by":"publisher","first-page":"e12578","DOI":"10.2196\/12578","article-title":"The role of data type and recipient in individuals\u2019 perspectives on sharing passively collected smartphone data for mental health: cross-sectional questionnaire study","volume":"7","author":"Nicholas","year":"2019","journal-title":"JMIR MHealth UHealth"},{"key":"B73","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/978-3-030-98546-2_24","article-title":"Smart sensing enhanced diagnostic expert systems","volume-title":"Digital phenotyping and mobile sensing","author":"Terhorst","year":"2023"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2023.1075266\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T16:52:35Z","timestamp":1689267155000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2023.1075266\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,13]]},"references-count":73,"alternative-id":["10.3389\/fdgth.2023.1075266"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2023.1075266","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,13]]},"article-number":"1075266"}}