Introduction: Suboptimal or partial adherence to antiepileptic drugs (AEDs) is an avoidable cause of seizures and deleterious outcomes in epilepsy. As self-rated adherence may be unreliable, suboptimal adherence may go undetected. This study assessed generalizability of a performance-based measure of medication management to patients with intractable epilepsy.
Materials And Methods: Participants were 50 adults (age = 42 ± 14 years, 60% female, 82% Black, 20% Hispanic/Latino) with ≥2 seizures in the preceding 6 months. Antiepileptic drug adherence was electronically monitored for one month via Medication Event Monitoring Systems (MEMS) and self-rated (1 = very poor to 6 = excellent). The Medication Management Ability Assessment (MMAA) was administered at follow-up and scored by raters blind to adherence results. Spearman correlations and Poisson regressions assessed their associations.
Results: On average, participants self-reported good-to-very good adherence. According to MEMS, participants took AEDs as prescribed 73% of the time; most participants (58%) missed ≥3 doses. The MMAA demonstrated strong internal consistency (Kuder-Richardson 20 = 0.81) and was associated with MEMS: percentage of days doses were taken correctly (r = 0.29, p = 0.04) and frequency of missed doses (r = -0.31, p = 0.03). The MMAA was not associated with self-rated adherence. Poisson regressions showed that self-ratings and MMAA performance accounted for unique variance in frequency of missed AED doses.
Conclusions: These findings provide evidence of the MMAA's criterion validity as a measure of capacity to manage AEDs. It may prove useful in cases where suboptimal adherence is suspected but unreported by patients. Its lack of significant association with self-rated adherence is consistent with prior reports; however, future studies should replicate these findings with larger samples.
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http://dx.doi.org/10.1016/j.yebeh.2018.09.022 | DOI Listing |
Ann Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
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Participatory eHealth and Health Data Research Group, Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.
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View Article and Find Full Text PDFJMIR Form Res
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Department of Design Innovation, College of Design, University of Minnesota, Twin Cities, Minneapolis, MN, United States.
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Department of Computer Science, University of California, Irvine, Irvine, CA, United States.
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View Article and Find Full Text PDFJ Med Internet Res
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Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
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