Self-management for adults with epilepsy: Aggregate Managing Epilepsy Well Network findings on depressive symptoms

Clinical correlates of perceived stigma among people living with epilepsy enrolled in a self-management clinical trial

BACKGROUND AND PURPOSE

Stigma is a pervasive barrier for people living with epilepsy (PLWE) and can have substantial negative effects. This study evaluated clinical correlates of perceived stigma in a research sample of PLWE considered to be at high risk due to frequent seizures or other negative health events.

METHODS

Analyses were derived from baseline data from an ongoing Centers for Disease Control and Prevention (CDC)-funded randomized controlled trial (RCT) testing an epilepsy self-management approach. Standardized measures assessed socio-demographics, perceived epilepsy stigma, epilepsy-related self-efficacy, epilepsy self-management competency, health literacy, depressive symptom severity, functional status, social support and epilepsy-related quality of life.

RESULTS

There were 160 individuals, mean age of 39.4, (Standard deviation/SD=12.2) enrolled in the RCT, 107 (66.9 %) women, with a mean age of epilepsy onset of 23.9 (SD 14.0) years. The mean seizure frequency in the prior 30 days was 6.4 (SD 21.2). Individual factors correlated with worse perceived stigma were not being married or cohabiting with someone (p = 0.016), lower social support (p < 0.0001), lower self-efficacy (p < 0.0001), and lower functional status for both physical health (p = 0.018) and mental health (p < 0.0001). Perceived stigma was associated with worse depressive symptom severity (p < 0.0001). Multivariable linear regression found significant independent associations between stigma and lower self-efficacy (β -0.05; p = 0.0096), lower social support (β -0.27; p = 2.4x10-5, and greater depression severity (β 0.6; p = 5.8x10-5).

CONCLUSIONS

Perceived epilepsy stigma was positively correlated with depression severity and negatively correlated with social support and self-efficacy. Providers caring for PLWE may help reduce epilepsy stigma by screening for and treating depression, encouraging supportive social relationships, and providing epilepsy self-management support. Awareness of epilepsy stigma and associated factors may help reduce some of the hidden burden borne by PLWE.

An information-motivation-behavioral skills model-based intervention for patients with epilepsy

PURPOSE

Self-management is crucial for patients with epilepsy (PWE) as it can effectively control the condition, improve treatment outcomes, and reduce the risk of seizures. The objective of this study was to evaluate the effectiveness of health education based on the Information-Motivation-Behavioral Skills (IMB) model in improving self-management, quality of life, and medication adherence among PWE.

METHOD

This study was conducted in a district in Shanghai, China, from September 2022 to March 2023. A total of 90 PWE were randomly assigned to an intervention group or a control group. Patients in the intervention group received health education based on the IMB model, whereas those in the control group received routine medical treatment only. The effectiveness of the intervention was evaluated by measuring self-management ability, quality of life, and medication adherence at three months and six months after the intervention.

RESULTS

The study included a total of 90 PWE; among them, 55 patients (61.11%) were male, divided into two groups: 45 participants in the control group and 45 participants in the intervention group. After 3 and 6 months of intervention, the self-management scores of the intervention group were significantly higher than those of the control group (t = 14.274, 12.448; P < 0.05). After 6 months of the intervention, the quality-of-life scores of the intervention group were significantly higher than those of the control group (t = 2.025, P < 0.05), and the medication adherence in the intervention group was significantly higher than that in the control group (X2 = 9.003, P < 0.05).

CONCLUSION

Health education based on the IMB model is a promising approach to improving self-management, quality of life, and medication adherence among PWE. It provides personalized, patient-centered interventions that consider patients' knowledge, motivation, and behavior skills, and could be a useful tool for healthcare providers in developing comprehensive care plans for individuals with epilepsy.

What is the volume, diversity and nature of recent, robust evidence for the use of peer support in health and social care? An evidence and gap map

Background

Peer support interventions involve people drawing on shared personal experience to help one another improve their physical or mental health, or reduce social isolation. If effective, they may also lessen the demand on health and social care services, reducing costs. However, the design and delivery of peer support varies greatly, from the targeted problem or need, the setting and mode of delivery, to the number and content of sessions. Robust evidence is essential for policymakers commissioning peer support and practitioners delivering services in health care and community settings. This map draws together evidence on different types of peer support to support the design and delivery of interventions.

Objectives

The aim of this map was to provide an overview of the volume, diversity and nature of recent, high quality evidence on the effectiveness and cost-effectiveness of the use of peer support in health and social care.

Search Methods

We searched MEDLINE, seven further bibliographic databases, and Epistemonikos for systematic reviews (in October 2020), randomised controlled trials (in March 2021) and economic evaluations (in May 2021) on the effectiveness of peer support interventions in health and social care. We also conducted searches of Google Scholar, two trial registers, PROSPERO, and completed citation chasing on included studies.

Selection Criteria

Systematic reviews, randomised controlled trials and economic evaluations were included in the map. Included studies focused on adult populations with a defined health or social care need, were conducted in high-income countries, and published since 2015. Any measure of effectiveness was included, as was any form of peer support providing the peer had shared experience with the participant and a formalised role.

Data Collection and Analysis

Data were extracted on the type of peer support intervention and outcomes assessed in included studies. Standardised tools were used to assess study quality for all studies: assessing the methodological quality of systematic reviews 2 for systematic reviews; Cochrane risk of bias tool for randomised controlled trials; and consensus health economic criteria list for economic evaluations.

Main Results

We included 91 studies: 32 systematic reviews; 52 randomised controlled trials; and 7 economic evaluations. Whilst most included systematic reviews and economic evaluations were assessed to be of low or medium quality, the majority of randomised controlled trials were of higher quality. There were concentrations of evidence relating to different types of peer support, including education, psychological support, self-care/self-management and social support. Populations with long-term health conditions were most commonly studied. The majority of studies measured health-related indicators as outcomes; few studies assessed cost-effectiveness. Studies were unevenly distributed geographically, with most being conducted in the USA. Several gaps were evident regarding the delivery of peer support, particularly the integration of peers and professionals in delivering support and interventions of longer duration.

Authors' Conclusions

Although there is evidence available to inform the commissioning and delivery of peer support in health and social care, there are also clear gaps that need to be addressed to further support provision, particularly regarding cost-effectiveness. The effectiveness of peer support in different countries, with varying health and social care systems, is a priority for future research, as is the integration of peers with professionals in delivering peer support.

Reduction of the Adult Epilepsy Self-Management Measure Instrument (AESMMI)

Managing one's own symptoms, medications, treatments, lifestyle, and psychological and social aspects of chronic disease is known as self-management. The Institute of Medicine has identified three categories of epilepsy self-management, including medication management, behavior management, and emotional support. Overall, there has been limited research of interventions measuring epilepsy self-management behaviors. The present study aimed to develop an abbreviated version of the full, previously published, Adult Epilepsy Self-Management Measurement Instrument (AESMMI) using confirmatory factor analysis. Data come from a cross-sectional survey of people with epilepsy. The sample included adults with epilepsy (n = 422), who reported that a clinician diagnosed them with epilepsy or a seizure disorder. We ran confirmatory factor analyses in testing the abbreviated scale. The scale was reduced using a theory-driven data-informed approach. The full AESMMI length was reduced by 40% (from 65 to 38 items) with an overall internal consistency of 0.912. The abbreviated AESMMI retained the 11 subdomains, with Cronbach's alphas from 0.535 to 0.878. This reduced item scale can be useful for assessing self-management behaviors for people with epilepsy or measuring outcomes in self-management research.

Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention

OBJECTIVE

To develop a classifier that predicts reductions in depression severity in people with epilepsy after participation in an epilepsy self-management intervention.

METHODS

Ninety-three people with epilepsy from three epilepsy self-management randomized controlled trials from the Managing Epilepsy Well (MWE) Network integrated research database met the inclusion criteria. Supervised machine learning algorithms were utilized to develop prediction models for changes in self-reported depression symptom severity. Features considered by the machine learning classifiers include age, gender, race, ethnicity, education, study type, baseline quality of life, and baseline depression symptom severity. The models were trained and evaluated on their ability to predict clinically meaningful improvement (i.e., a reduction of greater than three points on the nine-item Patient Health Questionnaire (PHQ-9)) between baseline and follow-up (<=12 weeks) depression scores. Models tested were a Multilayer Perceptron (ML), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression with Stochastic Gradient Descent (SGD), K-nearest Neighbors (KNN), and Gradient Boosting (GB). A separate, outside dataset of 41 people with epilepsy was used in a validation exercise to examine the top-performing model's generalizability and performance with external data.

RESULTS

All six classifiers performed better than our baseline mode classifier. Support Vector Machine had the best overall performance (average area under the curve [AUC] = 0.754, highest subpopulation AUC = 0.963). Our analysis of the SVM features revealed that higher baseline depression symptom severity, study type (i.e., intervention program goals), higher baseline quality of life, and race had the strongest influence on increasing the likelihood that a subject would experience a clinically meaningful improvement in depression scores. From the validation exercise, our top-performing SVM model performed similarly or better than the average SVM model with the outside dataset (average AUC = 0.887).

SIGNIFICANCE

We trained an SVM classifier that offers novel insight into subject-specific features that are important for predicting a clinically meaningful improvement in subjective depression scores after enrollment in a self-management program. We provide evidence for machine learning to select subjects that may benefit most from a self-management program and indicate important factors that self-management programs should collect to develop improved digital tools.

Depression and suicidality among Hispanics with epilepsy: Findings from the Managing Epilepsy Well (MEW) Network integrated database

OBJECTIVE

Although psychiatric disorders are more common among people with epilepsy,² depression and suicidal ideation among Hispanics with epilepsy remain understudied. We examined the prevalence and correlates of depression and suicidal ideation among Hispanic adults with epilepsy who participated in self-management studies in the Managing Epilepsy Well³ Network.

METHODS

This cross-sectional analysis of pooled data from ten studies used the Patient Health Questionnaire-9⁴ or Neurological Disease Depression Inventory-Epilepsy⁵ to examine the prevalence of elevated depressive symptoms (PHQ ≥ 10, NDDI-E ≥ 15) and suicidal ideation (PHQ-9 item 9 ≥ 1, NDDI-E item 4 ≥ 2). Multilevel mixed-effects logistic regression models examined associations between ethnicity, elevated depressive symptoms, and suicidal ideation among PWE. Secondary analyses examined correlates of elevated depressive symptoms and suicidal ideation among Hispanic PWE.

RESULTS

Of 559 participants, 49.6% (n = 277) were Hispanic. Elevated depressive symptoms were endorsed by 38.1% (n = 213) of all participants (32.5% of Hispanics); suicidal ideation was endorsed by 18.4% (n = 103) of all participants (16.3% of Hispanics). After adjustment for sociodemographic and health attributes, Hispanic PWE had a 44% lower prevalence of elevated depressive symptoms (OR = 0.56, CI 0.37-0.84, p = 0.0056) compared to non-Hispanics but similar rates of suicidal ideation (OR = 0.84, CI 0.45-1.58, p = 0.59). Acculturation measures were available for 256 (92.4%) of Hispanic PWE: language preference was Spanish for 62.9%, 46.1% were foreign-born. Spanish-speaking Hispanics were less likely than English-speaking Hispanics to report elevated depressive symptoms (OR = 0.43, CI 0.19-0.97, p = 0.041); however, Hispanics who reported fair or poor health status had a four-fold higher depression prevalence compared to those who reported excellent or very good health status [reference group] (OR = 4.44, CI 1.50-13.18, p = 0.0071). Of the Hispanics who provided prior 30-day seizure data, ≥1 monthly seizure was independently associated with higher depression prevalence (OR = 3.11, CI 1.29-7.45, p = 0.01). Being foreign-born was not associated with elevated depressive symptoms or suicidal ideation prevalence.

CONCLUSIONS

In a large, geographically diverse sample of PWE, elevated depressive symptoms were significantly lower in Hispanics compared to non-Hispanics. Spanish language preference was associated with a lower prevalence of elevated depressive symptoms among Hispanic PWE. Future studies should include acculturation data to better screen for depression and suicidal ideation risk and optimize interventions for Hispanic PWE.

Development and validation of a new short form of the self-management ability questionnaire for patients with chronic periodontitis

OBJECTIVES

To establish and validate a short form of the self-management ability questionnaire (SMAQ) for chronic periodontitis patients.

METHODS

A total of 480 chronic periodontitis patients were recruited and divided randomly in into two groups: development group and validation group. The item reduction process of the SMAQ was based on item response theory (IRT) and classical test theory (CTT). The resulting short form of the SMAQ was then validated using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), convergent validity, internal consistency and test-retest reliability.

RESULTS

The item reduction process produced a 12-item short-form SMAQ (SMAQ-12). EFA results on the twelve items extracted three factors consistent with the original SMAQ and CFA results demonstrated acceptable goodness-of-fit indices of this three-factor structure. Moreover, the SMAQ-12 scores had high correlations with the original measure (r_s ≥0.904), good internal consistency and test-retest reliability.

CONCLUSIONS

The SMAQ-12 is an easy-to-use, reliable and valid measure for assessing self-management ability in patients with chronic periodontitis.

The Managing Epilepsy Well (MEW) network database: Lessons learned in refining and implementing an integrated data tool in service of a national U.S. Research Collaborative

Epilepsy self-management (ESM) is the summative set of behaviors that people with epilepsy use to cope with seizures and optimize health. This report describes the implementation and evolution of the Managing Epilepsy Well Network Database (MEW DB), an integrated data resource intended to advance knowledge on ESM. The MEW DB utilizes a three-tiered (Tier 1-3) system of data organization, with tiers of data generally increasing in ascending complexity or collection burden. A MEW DB Steering Committee (SC) establishes consensus on planned analyses using a standardized new analysis request template. The data management structure facilitates harmonization and integration of additional data, or to update the database as new data become available. The current MEW DB comprises 1,563 people with epilepsy. Mean age was 39.9 years, 64.9% women (N = 1006), 12.8% African American (N = 170), 22.2% Hispanic (N = 306). On average, individuals have lived with epilepsy since their early 20s and are prescribed between 1 and 2 antiepileptic drugs. The MEW DB spans multiple socio-ecological levels to provide a robust multi-tiered framework for studying ESM. A total of 41 common data elements have been identified through iterative consensus. This integrated database takes advantage of an extensive collective background of archival evidence in ESM and brings together engaged investigators to build a dataset that represents diverse types of individuals with epilepsy, targets health domains important to ESM, and facilitates analyses that would not be possible with sites operating independently. Overall, the MEW DB serves the greater mission of this research collaborative and has potential to advance ESM research.

Enhancing epilepsy self-management and quality of life for adults with epilepsy with varying social and educational backgrounds using PAUSE to Learn Your Epilepsy

PURPOSE

People with epilepsy (PWE) come from a wide variety of social backgrounds and educational skillsets, making self-management (SM) education for improving their condition challenging. Here, we evaluated whether a mobile technology-based personalized epilepsy SM education intervention, PAUSE to Learn Your Epilepsy (PAUSE), improves SM measures such as self-efficacy, epilepsy SM behaviors, epilepsy outcome expectations, quality of life (QOL), and personal impact of epilepsy in adults with epilepsy.

METHODS

Recruitment for the PAUSE study occurred from October 2015 to March 2019. Ninety-one PWE were educated using an Internet-enabled computer tablet application that downloads custom, patient-specific educational programs from Epilepsy.com. Validated self-reported questionnaires were used for outcome measures. Participants were assessed at baseline (T0), the first follow-up at completion of the PWE-paced 8-12-week SM education intervention (T1), and the second follow-up at least 3 months after the first follow-up (T2). Multiple linear regression was used to assess within-subject significant changes in outcome measures between these time points.

RESULTS

The study population was diverse and included individuals with a wide variety of SM educational needs and abilities. The median time for the first follow-up assessment (T1) was approximately 4 months following the baseline (T0) and 8 months following baseline for the second follow-up assessment (T2). Participants showed significant improvement in all SM behaviors, self-efficacy, outcome expectancy, QOL, and personal impact of epilepsy measures from T0 to T1. Participants who scored lower at baseline tended to show greater improvement at T1. Similarly, results showed that participant improvement was sustained in the majority of SM measures from T1 to T2.

CONCLUSION

This study demonstrated that a mobile technology-based personalized SM intervention is feasible to implement. The results provide evidence that epilepsy SM behavior and practices, QOL, outcome expectation for epilepsy treatment and management, self-efficacy, and outcome expectation and impact of epilepsy significantly improve following a personalized SM education intervention. This underscores a greater need for a pragmatic trial to test the effectiveness of personalized SM education, such as PAUSE to Learn Your Epilepsy, in broader settings specifically for the unique needs of the hard-to-reach and hard-to-treat population of PWE.