Objective: This study aimed to conduct a systematic review and meta-analysis of the effects of technology-based decision aids on contraceptive use, continuation, and patient-reported and decision-making outcomes.
Data Sources: A systematic search was conducted in OVID MEDLINE, Cochrane Database of Systematic Reviews, CENTRAL, CINAHL, Embase, PsycINFO, and SocINDEX databases from January 2005 to April 2022. Eligible references from a concurrent systematic review evaluating contraceptive care were also included for review.
Study Eligibility Criteria: Studies were included if a contraceptive decision aid was technology-based (ie, mobile/tablet application, web, or computer-based) and assessed contraceptive use and/or continuation or patient-reported outcomes (knowledge, self-efficacy, feasibility/acceptability/usability, decisional conflict). The protocol was registered under the International Prospective Register of Systematic Reviews (CRD42021240755).
Methods: Three reviewers independently performed data abstraction and quality appraisal. Dichotomous outcomes (use and continuation) were evaluated with an odds ratio, whereas continuous outcomes (knowledge and self-efficacy) were evaluated with the mean difference. Subgroup analyses were performed for the mode of delivery (mobile and tablet applications vs web and computer-based) and follow-up time (immediate vs >1 month).
Results: This review included 18 studies evaluating 21 decision aids. Overall, there were higher odds of contraceptive use and/or continuation among decision aid users compared with controls (odds ratio, 1.27; 95% confidence interval, [1.05-1.55]). Use of computer and web-based decision aids was associated with higher odds of contraceptive use and/or continuation (odds ratio, 1.36; 95% confidence interval, [1.08-1.72]) than mobile and tablet decision aids (odds ratio, 1.27; 95% confidence interval, [0.83-1.94]). Decision aid users also had statistically significant higher self-efficacy scores (mean difference, 0.09; 95% confidence interval, [0.05-0.13]), and knowledge scores (mean difference, 0.04; 95% confidence interval, [0.01-0.07]), with immediate measurement of knowledge having higher retention than measurement after 1 month. Other outcomes were evaluated descriptively (eg, feasibility, applicability, decisional conflict) but had little evidence to support a definite conclusion. Overall, the review provided moderate-level evidence for contraceptive use and continuation, knowledge, and self-efficacy.
Conclusion: The use of technology-based contraceptive decision aids to support contraceptive decision-making has positive effects on contraceptive use and continuation, knowledge, and self-efficacy. There was insufficient evidence to support a conclusion about effects on other decision-making outcomes.
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http://dx.doi.org/10.1016/j.ajog.2022.06.050 | DOI Listing |
Appl Environ Microbiol
January 2025
Department of Forest Mycology and Plant Pathology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden.
In Sweden, reforestation of managed forests relies predominantly on planting nursery-produced tree seedlings. However, the intense production using containerized cultivation systems (e.g.
View Article and Find Full Text PDFTransl Behav Med
January 2025
Department of Health Outcomes and Behavior, H. Lee Moffitt Cancer Center, Tampa, FL, 33162, USA.
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View Article and Find Full Text PDFAnal Methods
January 2025
Jiangsu Beier Machinery Co. Ltd, Jiangsu, 215600, China.
Plastic waste management is one of the key issues in global environmental protection. Integrating spectroscopy acquisition devices with deep learning algorithms has emerged as an effective method for rapid plastic classification. However, the challenges in collecting plastic samples and spectroscopy data have resulted in a limited number of data samples and an incomplete comparison of relevant classification algorithms.
View Article and Find Full Text PDFInt J Technol Assess Health Care
January 2025
Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA.
Objectives: Advances in mobile apps, remote sensing, and big data have enabled remote monitoring of mental health conditions, but the cost-effectiveness is unknown. This study proposed a systematic framework integrating computational tools and decision-analytic modeling to assess cost-effectiveness and guide emerging monitoring technologies development.
Methods: Using a novel decision-analytic Markov-cohort model, we simulated chronic depression patients' disease progression over 2 years, allowing treatment modifications at follow-up visits.
Biometrics
January 2025
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada.
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterogeneous effect of a time-varying exposure on the mean of an outcome in the presence of time-varying confounding.
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