Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Sanchez-Ortuno MM

PROVIDER: S-EPMC10654843 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Sanchez-Ortuno Maria Montserrat MM Pecune Florian F Coelho Julien J Micoulaud-Franchi Jean Arthur JA Salles Nathalie N Auriacombe Marc M Serre Fuschia F Levavasseur Yannick Y de Sevin Etienne E Sagaspe Patricia P Philip Pierre P

Journal of the American Medical Informatics Association : JAMIA 20231101 12

<h4>Objective</h4>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.< ...[more]

PMID: 37672004

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Project description:ObjectiveTreatment adherence is suggested to be associated with greater improvement in patient outcomes. Despite the growing use of digital therapeutics in osteoarthritis management, there is limited evidence of person-level factors influencing adherence to these interventions in real-world settings. We aimed to determine the relative importance of factors influencing adherence to a digital self-management intervention for hip/knee osteoarthritis.MethodsWe obtained data from people participating in a digital OA treatment, known as Joint Academy, between January 2019 and September 2021. We collected data on the participants' adherence, defined as the percentage of completed activities (exercises, lessons, and quizzes), at 3 (n = 14,610)- and 12-month (n = 2682) follow-up. We used dominance and relative weight analyses to assess the relative importance of sociodemographic (age, sex, place of residence, education, year of enrolment), lifestyle (body mass index, physical activity), general health (comorbidity, overall health, activity impairment, anxiety/depression), and osteoarthritis-related (index joint, fear of moving, walking difficulties, pain, physical function, wish for surgery, Patient Acceptable Symptom State) factors, measured at baseline, in explaining variations in adherence. We used bootstrap (1000 replications) to compute 95% confidence intervals.ResultsMean (SD) adherences at 3 and 12 months were 86.3% (16.1) and 84.1% (16.7), with 75.1% and 70.4% of participants reporting an adherence ≥ 80%, respectively. The predictors included in the study explained only 5.6% (95% CI 5.1, 6.6) and 8.1% (7.3, 11.6) of variations in 3- and 12-month adherences, respectively. Sociodemographic factors were the most important predictors explaining more variations than other factors altogether. Among single factors, age with a nonlinear relationship with adherence, was the most important predictor explaining 2.3% (95% CI 1.9, 2.8) and 3.7% (2.4, 5.3) of variations in 3- and 12-month adherences, respectively.ConclusionPerson-level factors could only modestly explain the variations in adherence with sociodemographic characteristics, mainly age, accounting for the greatest portion of this explained variance.

Project description:Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

Dataset Information

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

Objective

Material and methods

Results

Discussion

Conclusions

Publications

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

Similar Datasets

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