Associations of Comorbid Anxiety With Medication Adherence and Psychiatric Symptomatology in a Population of Nonadherent Bipolar Disorder Subjects.
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ABSTRACT:
SUBMITTER: Aftab A
PROVIDER: S-EPMC5876117 | biostudies-literature | 2018 Apr
REPOSITORIES: biostudies-literature
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Associations of Comorbid Anxiety With Medication Adherence and Psychiatric Symptomatology in a Population of Nonadherent Bipolar Disorder Subjects.
Aftab Awais A Levin Jennifer J Aebi Michelle M Bhat Chetan C Sajatovic Martha M
The Journal of nervous and mental disease 20180401 4
This analysis was conducted on baseline data from 178 nonadherent bipolar disorder subjects in a randomized controlled trial. Medication adherence was measured with Tablets Routine Questionnaire as percentage of days with missed doses. Inclusion criteria required at least 20% nonadherence. Medication adherence, symptomatology, and functioning in individuals with and without a comorbid anxiety disorder were compared. There were 78.9% of subjects who had at least one or more current anxiety disord ...[more]
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Project description:Objective Serious mental illness and type II diabetes mellitus have a high comorbidity, and both have a higher prevalence of anxiety disorders compared to the general population. Targeted Training in Illness Management is a group-based self-management training approach which targets serious mental illness and type II diabetes mellitus concurrently. This analysis examines data from a randomized controlled trial of Targeted Training in Illness Management intervention to examine the impact of comorbid anxiety on baseline psychiatric symptomatology and diabetic control, and on longitudinal treatment outcomes. Methods We conducted secondary analyses on data from a prospective, 60-week, randomized controlled trial testing Targeted Training in Illness Management versus treatment as usual in 200 individuals with serious mental illness and diabetes. Primary outcomes included measures related to serious mental illness symptoms, functional status, general health status, and diabetes control. Measures were compared between those participants with anxiety disorders versus those without anxiety at baseline as well as over time using linear mixed effects analyses. Results Forty seven percent of the participants had one or more anxiety disorders. At baseline, those with an anxiety diagnosis had higher illness severity, depressive, and other psychiatric symptomatology and disability. Diabetic control (HbA1c) was not significantly different at baseline. In the longitudinal analyses, no significant mean slope differences over time (group-by-time interaction effect) between those with anxiety diagnoses and those without in treatment as usual group were found for primary outcomes. Within the Targeted Training in Illness Management arm, those with anxiety disorders had significantly greater improvement in mental health functioning. Those with anxiety comorbidity in the Targeted Training in Illness Management group demonstrated significantly lower HbA1c levels compared to no anxiety comorbidity and also demonstrated a greater improvement in HbA1c over the first 30 weeks compared to those without anxiety comorbidity. Conclusion Comorbid anxiety in serious mental illness and type II diabetes mellitus population is associated with increased psychiatric symptomatology and greater disability. Individuals from this population appear to experience greater improvement in functioning from baseline with the Targeted Training in Illness Management intervention. Anxiety comorbidity in the serious mental illness and type II diabetes mellitus population does not appear to have a negative impact on diabetic control. These complex relationships need further study. Clinical Trials Registration ClinicalTrials.gov: Improving outcomes for individuals with serious mental illness and diabetes (NCT01410357).
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Project description:OBJECTIVE:To explore the effect of patient characteristics and health beliefs on their medication adherence. METHODS:Patients (n=167) with chronic conditions (mean age 58.9; SD=13.54, 53% males) were recruited from March 2009- to March 2010 using a cross sectional study design. Data collected included patients' demographics, medical conditions, medications therapeutic regimen, frequency of physician visits and health beliefs. Patient self-reported adherence to medications was assessed by the researcher using a validated and published scale. Treatment related problems (TRPs) were evaluated for each patient by competent clinical pharmacists. Associations between patient characteristics/health beliefs with adherence were explored. RESULTS:About half of the patients (46.1%) were non-adherent. A significant association was found between lower adherence and higher number of disease states (p<0.001), higher number of medications (p=0.001), and higher number of identified TRPs (p = 0.003). Patient adherence was positively affected by older age, higher educational level, and higher number of physician visits per month, while it was negatively affected by reporting difficulties with getting prescription refills on time. CONCLUSION:This study identified different factors that may negatively affect adherence, including higher number of medications and disease states, higher number of identified TRPs and inability to getting prescription refills on time. Hence, more care needs to be provided to patients with complex therapeutic regimens in order to enhance adherence.
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Project description:ObjectivesAnxiety disorder is present in approximately half of all bipolar disorder (BD) patients. There are neurologic bases for the comorbidity of balance (vestibular) disorders and anxiety. Our objective is to use electrovestibulography (EVestG), which is predominantly a measure of vestibular neural activity to not only quantitatively detect and measure comorbid anxiety disorder but also to quantitatively measure the impacts of anti-depressant, anti-psychotic, and mood stabilizer medication groups on anxiety measures in BD patients.MethodsIn a population of 50 (24 with anxiety disorder) depressive phase BD patients, EVestG signals were measured. Participants were labeled depression-wise as anxious or non-anxious using standard questionnaires. Analyses were conducted on the whole dataset as well as on matched (age/gender/MADRS) and "modeled medication-free" subsets. Modulations of the low-frequency EVestG firing pattern data were measured.FindingsFor BD, the main anxious minus non-anxious difference was the presence of an increase in spectral power proximal to 8-9 Hz, which was best attenuated by mood stabilizers.NoveltyThis is the first study to use an oto-acoustic physiological measure to quantify anxiety disorder in BD wherein it appears to manifest as a peak proximal to 8-9 Hz which we hypothesize as likely linked to hippocampal theta.
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Project description:BackgroundThis study assesses the feasibility of using machine learning methods such as Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Self-Organizing Feature Maps (SOM) to identify and determine factors associated with hypertensive patients' adherence levels. Hypertension is the medical term for systolic and diastolic blood pressure higher than 140/90 mmHg. A conventional medication adherence scale was used to identify patients' adherence to their prescribed medication. Using machine learning applications to predict precise numeric adherence scores in hypertensive patients has not yet been reported in the literature.MethodsData from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.ResultMachine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.ConclusionThis study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.
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