Effects of a remotely delivered group-format epilepsy self-management program on adverse health outcomes in vulnerable people with epilepsy: A causal mediation analysis

Informing Digital Programs for Lupus Self-Management Education: A Systematic Scoping Review

OBJECTIVE

We describe the characteristics, content, and effectiveness of digital self-management (SM) education programs for lupus and other chronic conditions to identify gaps and inform the improvement of future programs in lupus.

METHODS

Three bibliographic databases were searched for articles published between May 2012 and April 2022. The search was cast to capture the breadth of digital SM education programs in the following conditions: lupus, epilepsy, fibromyalgia, multiple sclerosis, sickle cell anemia, Sjögren syndrome, psoriatic arthritis, and rheumatoid arthritis. Title and abstract screening, as well as full-text review, was conducted by two independent reviewers. Data extraction was first completed by one author charting all studies and then, a second time, by four members of the research team charting collaboratively.

RESULTS

Of the 1,969 articles identified through the search, 14 met inclusion criteria. Two additional articles were included following bibliography review. The 16 articles represented 12 unique digital SM education programs. Programs covered five conditions: epilepsy (n = 3), fibromyalgia (n = 2), multiple sclerosis (n = 4), lupus (n = 1), and rheumatoid arthritis (n = 2). Most programs were asynchronous and internet-based (n = 9) with a prescribed sequence of content (n = 8). Peer, technical, or specialist support was offered in seven programs. Most programs demonstrated statistically significant improvement of symptoms in the intervention group (n = 8).

CONCLUSION

This scoping review summarizes the current landscape for digital SM education programs in lupus and similar conditions. In lupus, further investigation will fill in the gaps around digital SM education needs, user experience, and evaluation of outcomes.

Latent class analysis of eHealth behaviors among adults with epilepsy

OBJECTIVE

The objective of this study was to determine the proportions of uptake and factors associated with electronic health (eHealth) behaviors among adults with epilepsy.

METHODS

The 2013, 2015, and 2017 National Health Interview Surveys were analyzed. We assessed the proportions of use of five domains of eHealth in those with epilepsy: looked up health information on the internet, filled a prescription on the internet, scheduled a medical appointment on the internet, communicated with a health care provider via email, and used chat groups to learn about health topics. Multivariate logistic regressions were conducted to identify factors associated with any eHealth behaviors among those with active epilepsy. Latent class analysis was performed to identify underlying patterns of eHealth activity. Survey participants were classified into three discrete classes: (1) frequent, (2) infrequent, and (3) nonusers of eHealth. Multinomial logistic regression was performed to identify factors associated with frequency of eHealth use.

RESULTS

There were 1770 adults with epilepsy, of whom 65.87% had at least one eHealth behavior in the prior year. By domain, 62.61% looked up health information on the internet, 15.81% filled a prescription on the internet, 14.95% scheduled a medical appointment on the internet, 17.20% communicated with a health care provider via email, and 8.27% used chat groups to learn about health topics. Among those with active epilepsy, female sex, more frequent computer usage, and internet usage were associated with any eHealth behavior. Female sex and frequent computer use were associated with frequent eHealth use as compared to nonusers.

SIGNIFICANCE

A majority of persons with epilepsy were found to use at least one form of eHealth. Various technological and demographic factors were associated with eHealth behaviors. Individuals with lower eHealth behaviors should be provided with targeted interventions that address barriers to the adoption of these technologies.

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.

Factors associated with self-efficacy among patients with epilepsy in Turkey

PURPOSE

This study aimed to determine the self-efficacy levels of individuals with epilepsy and also compare individuals' self-efficacy with sociodemographic and epilepsy-related characteristics.

MATERIAL AND METHODS

The cross-sectional study was carried out with 200 individuals with epilepsy who were suitable for the inclusion criterias. Data were collected using patient information form which includes sociodemographic and clinical characteristics and Epilepsy Self-Efficacy Scale for evaluating epilepsy-related self-efficacy levels of patients.

RESULTS

The self-efficacy levels of the patients were moderate. Education, gender, occupational status, income, seizure frequency, regular use of medication, the number of drugs used, patient behavior following a drug-related problem, family support, the ability to obtain information about the illness, and hiding the illness from other people were significant factors determining scores on the epilepsy self-efficacy scale.

CONCLUSION

This study confirms the findings of previous studies that were conducted to determine factors affecting self-efficacy. In order to increase self-efficacy, the findings show that there is a need for social support, education of epilepsy individuals, and information and awareness in society.

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.

Best care for older people with epilepsy: A scoping review

There are two peaks of diagnosis of epilepsy: in childhood and in people over 65. Older people may have complex needs like co-morbidity, polypharmacy, frailty, and social isolation. This scoping review focusses on the care of older people with epilepsy beyond diagnosis and medical treatment. We sought to identify areas within the UK health service needing development either in clinical practice or through further research. The search returned 4864 papers with 33 papers included in the review. The papers were grouped into psychosocial, self-management and services themes. Only one randomised controlled trial was found. Research was mainly based on cohort and case-control studies. Older people require more information to self-manage epilepsy and more psychological support to help with symptoms of anxiety and depression. People reported experiencing stigma and a reluctance to disclose their condition. This may increase the risk of isolation and difficulties in managing epilepsy. Studies reported that older people are referred less to neurologists, suggesting there may be a gap in care provision compared to younger people. Generalist health professionals may be better placed to provide holistic care, but they may need additional training to alleviate uncertainties in managing epilepsy. Care plans could help provide information, particularly for co-morbidity, but few had one. Our findings highlight psychological and self-management needs for managing epilepsy in older people. Health service staff may require upskilling to shift epilepsy management from neurologists to generalists. More research is needed regarding psychological and self-management interventions, particularly in the form of randomised controlled trials.

A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.