Predictors of users’ adherence to a fully automated digital intervention to manage insomnia complaints Article Swipe
M. Montserrat Sánchez-Ortuño
,
Florian Pécune
,
Julien Coelho
,
Jean‐Arthur Micoulaud‐Franchi
,
Nathalie Salles
,
Marc Auriacombe
,
Fuschia Serre
,
Yannick Levavasseur
,
Étienne de Sevin
,
Patricia Sagaspe
,
Pierre Philip
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocad163
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocad163
Objective 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.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocad163
- OA Status
- green
- Cited By
- 6
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386467364
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386467364Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/jamia/ocad163Digital Object Identifier
- Title
-
Predictors of users’ adherence to a fully automated digital intervention to manage insomnia complaintsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-06Full publication date if available
- Authors
-
M. Montserrat Sánchez-Ortuño, Florian Pécune, Julien Coelho, Jean‐Arthur Micoulaud‐Franchi, Nathalie Salles, Marc Auriacombe, Fuschia Serre, Yannick Levavasseur, Étienne de Sevin, Patricia Sagaspe, Pierre PhilipList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocad163Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pmc.ncbi.nlm.nih.gov/articles/PMC10654843/pdf/ocad163.pdfDirect OA link when available
- Concepts
-
Insomnia, Intervention (counseling), Psychological intervention, Medicine, Virtual agent, Smartphone app, Clinical psychology, Physical therapy, Psychiatry, World Wide Web, Computer science, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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