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Predictors of users’ adherence to a fully automated digital intervention to manage insomnia complaints

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dc.contributor.authorSánchez Ortuño, María Montserrat
dc.contributor.authorPecune, Florian
dc.contributor.authorCoelho, Julien
dc.contributor.authorMicoulaud Franchi, Jean Arthur
dc.contributor.authorSalles, Nathalie
dc.contributor.authorAuriacombe
dc.contributor.authorSerre, Fuschia
dc.contributor.authorLevavasseur, Yannick
dc.contributor.authorSevin, Etienne de
dc.contributor.authorSagaspe, Patricia
dc.contributor.authorPhilip, Pierre
dc.contributor.departmentEnfermería
dc.date.accessioned2025-01-14T10:07:02Z
dc.date.available2025-01-14T10:07:02Z
dc.date.issued2023-12
dc.description© The Author(s) 2023. Esta es una versión editada previamente y producida por el autor de un artículo aceptado para su publicación en Journal of the American Medical Informatics Association después de la revisión por pares. La versión de registro [Maria Montserrat Sanchez-Ortuno, Florian Pecune, Julien Coelho, Jean Arthur Micoulaud-Franchi, Nathalie Salles, Marc Auriacombe, Fuschia Serre, Yannick Levavasseur, Etienne de Sevin, Patricia Sagaspe, Pierre Philip, Predictors of users’ adherence to a fully automated digital intervention to manage insomnia complaints, Journal of the American Medical Informatics Association, Volume 30, Issue 12, December 2023, Pages 1934–1942] está disponible en línea en: https://doi.org/10.1093/jamia/ocad163es
dc.description.abstractObjective Fully automated digital interventions show promise for disseminating evidence-based strategies to manage insomnia complaints. However, an important concept often overlooked concerns the extent to which users adopt the recommendations provided in these programs into their daily lives. Our objectives were evaluating users’ adherence to the behavioral recommendations provided by an app, and exploring whether users’ perceptions of the app had an impact on their adherence behavior. Material and methods Case series study of individuals completing a fully automated insomnia management program, conducted by a virtual agent, during December 2020 to September 2022. Primary outcome was self-reported adherence to the behavioral recommendations provided. Perceptions of the app and of the virtual agent were measured with the Acceptability E-Scale and ECA-Trust Questionnaire. Insomnia was evaluated with the Insomnia Severity Index at baseline (phase 1), after 7 days of sleep monitoring (phase 2) and post-intervention (phase 3). Results A total of 824 users were included, 62.7% female, mean age 51.85 (±12.55) years. Of them, 32.7% reported having followed at least one recommendation. Users’ trust in the virtual agent and acceptance of the app were related to a pre-intervention effect in insomnia severity (phase 2). In turn, larger pre-intervention improvements predicted better adherence. Mediational analyses showed that higher levels of trust in the virtual agent and better acceptance of the app exerted statistically significant positive effects on adherence (β = 0.007, 95% CI, 0.001-0.017 and β = 0.003, 95% CI 0.0004-0.008, respectively). Discussion Users’ adherence is motivated by positive perceptions of the app’s features and pre-intervention improvements. Conclusions Determinants of adherence should be assessed, and targeted, to increase the impact of fully automated digital interventions.es
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dc.identifier.citationJournal of the American Medical Informatics Association, 2023, Vol. 30, Issue 12, pp. 1934-1942
dc.identifier.doihttps://doi.org/10.1093/jamia/ocad163
dc.identifier.issnElectronic: 1527-974X
dc.identifier.urihttp://hdl.handle.net/10201/148402
dc.languageenges
dc.publisherOxford University Presses
dc.relationThis work was supported by the National Grants GPRBRAIN 2030 and Neurocampus (University of Bordeaux).M.M.S.-O. was supported by an award from the EuropeanUnion, Next Generation EU, for the retraining of universityprofessors. F.P. was supported by the French NationalResearch Agency—ANR (Junior Professor Chair).es
dc.relation.publisherversionhttps://academic.oup.com/jamia/article-abstract/30/12/1934/7261462?redirectedFrom=fulltextes
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectInsomniaes
dc.subjectMobile healthes
dc.subjectTreatment adherencees
dc.titlePredictors of users’ adherence to a fully automated digital intervention to manage insomnia complaintses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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