Wendy Miller
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Utilizing Innovative Methods to inform Patient-Centered Interventions in People with Epilepsy
All chronic diseases, including epilepsy, require self-management on the part of the patient. Self-management in epilepsy is particularly complex, and influences important outcomes such as social functioning and quality of life. Dr. Wendy Miller’s research focuses on finding new ways to improve the daily self-management and quality of life for people living with epilepsy. For example, Dr. Miller created the Life Changes in Epilepsy Scale that is being used locally, nationally, and internationally by healthcare providers to determine areas in the lives of their patients that need intervention. Also, through her collaboration with other researchers, the American Epilepsy Society updated its guidelines for discussing Sudden Unexpected Death in Epilepsy (SUDEP) with patients with epilepsy and/or their parents/caregivers. Prior to this achievement, accurate information about SUDEP, which is a leading cause of death for people with epilepsy, was not discussed with them.
More recently, Dr. Miller worked with other researchers to capture patient concerns using Big Data machine learning methods. This technology provided a plethora of data that was used to find quality of life issues that had not previously been addressed. Consequently, Dr. Miller created myAURA, a web-based intervention that uses machine learning and artificial intelligence to provide users with individualized, theory-based self-management enhancing content that does not require a human interventionist. This platform has been used during the COVID-19 pandemic to help patients self-manage their epilepsy and to also inform researchers about the ways in which the pandemic has affected self-management of chronic diseases.
Dr. Miller’s work to generate new knowledge and create better interventions for people with epilepsy is another great example of how IUPUI’s faculty members are TRANSLATING their RESEARCH INTO PRACTICE.
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Recent Submissions
Item The Impact of COVID-19 on HIV Self-Management, Affective Symptoms, and Stress in People Living with HIV in the United States(Springer, 2021-09) Wion, Rachel K.; Miller, Wendy R.; School of NursingCOVID-19 has the potential to detrimentally impact HIV self-management in people living with HIV (PLHIV). Effective HIV-self management is critically important in managing symptoms as well as viral suppression. We examined the impact of the COVID-19 pandemic on HIV self-management, social support, social isolation, depressive symptoms, anxiety, and stress in PLHIV. 85 PLHIV were recruited from social media sites and completed an online survey. Data were collected between April 23 and 30, 2020. Participants reported increases in social isolation, depressive symptoms, anxiety, and stress and decreases in social support and overall HIV self-management from pre- to during the pandemic. Additionally, the Social Support domain and Chronic Nature of HIV domain of the HIV Self-Management Scale were also decreased from pre- to during the pandemic. The ability for PLHIV to maintain HIV self-management during this time is essential and HIV care providers should have plans in place to provide support.Item Hybrid Concept Analysis of Self-Management Support: School Nurses Supporting Students with Psychogenic Nonepileptic Seizures(SAGE, 2021) Tanner, Andrea; von Gaudecker, Jane; Buelow, Janice; Miller, WendySelf-management support has been identified as an effective nursing intervention for improving outcomes for people with chronic conditions, yet this concept lacks a clear definition. Furthermore, the concept has not been used in school nursing literature despite the clear connection between school nursing practice and tenets of self-management support. Additionally, the concept has not been explored in the context of difficult-to-manage mental health concerns, such as psychogenic nonepileptic seizures. A conversion disorder in which seizure events in the absence of abnormal brainwave activity result from stress, psychogenic nonepileptic seizures affect the quality of life and school experience for students experiencing them and could be addressed through self-management support. This hybrid concept analysis included a review of extant literature and semi-structured interviews with school nurses to ascertain a definition of self-management support in the context of school nursing using care of students with psychogenic nonepileptic seizures as an exemplar.Item An Integrative Review of School-Based Mental Health Interventions and Implications for Psychogenic Nonepileptic Seizures(Sage, 2020-02) Tanner, Andrea; Miller, Wendy R.; von Gaudecker, Jane; Buelow, Janice M.; School of NursingMillions of students with mental health concerns attend school each day. It is unknown how many of those students experience psychogenic nonepileptic seizures (PNES); however, quality of life, academic, and mental health outcomes for students experiencing PNES can be bleak. Currently, no authors have addressed potential school nurse interventions for students with PNES. Because PNES is a mental health condition and is often influenced by underlying anxiety and/or depression, an integrative review of school nurse interventions and outcomes for students with general mental health concerns was conducted. An integrative review resulted in the identification of 13 quantitative and 2 qualitative studies that met inclusion criteria. The findings from this review suggest school nurses, following principles from the Framework for 21st Century School Nursing Practice, play an active role in mental health interventions and should be involved in replicating and testing known mental health interventions to investigate their effectiveness for students with PNES.Item Epilepsy self-management during a pandemic: Experiences of people with epilepsy(Elsevier, 2020-06-25) Miller, Wendy R.; Von Gaudecker, Jane; Tanner, Andrea; Buelow, Janice M.; School of NursingThe purpose of this descriptive study was to, from the perspective of adult people with epilepsy (PWE) and caregivers of PWE, explore the effects of the current pandemic and resulting societal changes on epilepsy self-management. Ninety-four respondents completed a mixed-methods quantitative and qualitative survey focused on their epilepsy self-management experiences during the coronavirus disease-19 (COVID-19) pandemic. Respondents noted significant disruption in epilepsy self-management. Lack of ability to obtain medications or see epilepsy providers, as well as increased stress, social isolation, and changes in routine were all reported as troublesome, and more than one-third of the sample reported an increase in seizure frequency since the onset of the pandemic. Suggestions are given regarding how to support PWE during future COVID-19 outbreaks and to better prepare PWE and their caregivers for any life-altering events, such as a pandemic, with robust self-management skills that will allow them to maintain the highest level of function possible.Item HIV pre‐exposure prophylaxis uptake by advanced practice nurses: Interplay of agency, community and attitudinal factors(Wiley, 2019-11) Jayawardene, Wasantha; Carter, Gregory; Agley, Jon; Meyerson, Beth; Garcia, Justin R.; Miller, Wendy; School of NursingAims To identify associations among agency, community, personal and attitudinal factors that affect advanced practice nurses’ uptake of HIV pre‐exposure prophylaxis, an intervention consists of emtricitabine/tenofovir once‐daily pill, along with sexual risk reduction education. Design Cross‐sectional. Methods During March‐May 2017, randomly selected Indiana advanced practice nurses were invited to complete an online survey, consisted of several validated self‐rating measures (N = 1,358; response = 32.3%). Final sample (N = 369) was predominantly White, non‐Hispanic, female advanced practice nurses in urban practices (mean age = 46). Conceptual model for structural equation model included 29 original/composite variables and five latent factors. Results Final model consisted of 11 variables and four factors: agency, community, HIV prevention practices (including screening) and motivation to adopt evidence‐based practices overall. Community had direct effects on HIV prevention practices (estimate = 0.28) and agency (estimate = 0.29). Agency had direct effects on HIV prevention practices (estimate = 0.74) and motivation to adopt evidence‐based practices (estimate = 0.24). Community had indirect effects, through agency, on the two remaining factors. Conclusion Barriers exist against pre‐exposure prophylaxis implementation, although practice guidelines are available. HIV prevention practices must be integrated across organizational structures, especially in high‐risk communities, whereas practice change is more effective when focused on changing providers’ attitudes towards intervention. When planning a pre‐exposure prophylaxis intervention, advancing inputs from healthcare professionals, organizational leadership and community members, is crucial to success. Impact In settings where advanced practice nurses are primary contact points for health care, they may be best positioned to have an impact on implementation of HIV risk reduction strategies. Further research is needed to optimize their contributions to pre‐exposure prophylaxis implementation.Item Big Data and Dysmenorrhea: What Questions Do Women and Men Ask About Menstrual Pain?(Liebert, 2018-10) Chen, Chen X.; Groves, Doyle; Miller, Wendy R.; Carpenter, Janet S.; School of NursingBackground: Menstrual pain is highly prevalent among women of reproductive age. As the general public increasingly obtains health information online, Big Data from online platforms provide novel sources to understand the public's perspectives and information needs about menstrual pain. The study's purpose was to describe salient queries about dysmenorrhea using Big Data from a question and answer platform. Materials and Methods: We performed text-mining of 1.9 billion queries from ChaCha, a United States-based question and answer platform. Dysmenorrhea-related queries were identified by using keyword searching. Each relevant query was split into token words (i.e., meaningful words or phrases) and stop words (i.e., not meaningful functional words). Word Adjacency Graph (WAG) modeling was used to detect clusters of queries and visualize the range of dysmenorrhea-related topics. We constructed two WAG models respectively from queries by women of reproductive age and bymen. Salient themes were identified through inspecting clusters of WAG models. Results: We identified two subsets of queries: Subset 1 contained 507,327 queries from women aged 13–50 years. Subset 2 contained 113,888 queries from men aged 13 or above. WAG modeling revealed topic clusters for each subset. Between female and male subsets, topic clusters overlapped on dysmenorrhea symptoms and management. Among female queries, there were distinctive topics on approaching menstrual pain at school and menstrual pain-related conditions; while among male queries, there was a distinctive cluster of queries on menstrual pain from male's perspectives. Conclusions: Big Data mining of the ChaCha® question and answer service revealed a series of information needs among women and men on menstrual pain. Findings may be useful in structuring the content and informing the delivery platform for educational interventions.Item Big Data and Dysmenorrhea: What Questions Do Women and Men Ask About Menstrual Pain?(Mary Ann Liebert, 2018-10) Chen, Chen X.; Groves, Doyle; Miller, Wendy R.; Carpenter, Janet S.; School of NursingBACKGROUND: Menstrual pain is highly prevalent among women of reproductive age. As the general public increasingly obtains health information online, Big Data from online platforms provide novel sources to understand the public's perspectives and information needs about menstrual pain. The study's purpose was to describe salient queries about dysmenorrhea using Big Data from a question and answer platform. MATERIALS AND METHODS: We performed text-mining of 1.9 billion queries from ChaCha, a United States-based question and answer platform. Dysmenorrhea-related queries were identified by using keyword searching. Each relevant query was split into token words (i.e., meaningful words or phrases) and stop words (i.e., not meaningful functional words). Word Adjacency Graph (WAG) modeling was used to detect clusters of queries and visualize the range of dysmenorrhea-related topics. We constructed two WAG models respectively from queries by women of reproductive age and bymen. Salient themes were identified through inspecting clusters of WAG models. RESULTS: We identified two subsets of queries: Subset 1 contained 507,327 queries from women aged 13-50 years. Subset 2 contained 113,888 queries from men aged 13 or above. WAG modeling revealed topic clusters for each subset. Between female and male subsets, topic clusters overlapped on dysmenorrhea symptoms and management. Among female queries, there were distinctive topics on approaching menstrual pain at school and menstrual pain-related conditions; while among male queries, there was a distinctive cluster of queries on menstrual pain from male's perspectives. CONCLUSIONS: Big Data mining of the ChaCha® question and answer service revealed a series of information needs among women and men on menstrual pain. Findings may be useful in structuring the content and informing the delivery platform for educational interventions.Item Chronic Disease Self-Management: A Hybrid Concept Analysis(Elsevier, 2015-03) Miller, Wendy R.; Lasiter, Sue; Bartlett Ellis, Rebecca J.; Buelow, Janice M.; School of NursingBACKGROUND: Chronic diseases require chronic disease self-management (CDSM). Existing CDSM interventions, while improving outcomes, often do not lead to long-lasting effects. To render existing and new CDSM interventions more effective, an exploration of the concept of CDSM from both the literature and patient perspectives is needed. The purpose of this study was to describe the current conceptualization of CDSM in the literature, identify potential inadequacies in this conceptualization based on a comparison of literature- and patient-based CDSM descriptions, and to offer a more comprehensive definition of CDSM. METHODS: A hybrid concept analysis was completed. DISCUSSION: In the literature, CDSM is defined as behaviors influenced by individual characteristics. Patients in the fieldwork phase discussed aspects of CDSM not well represented in the literature. CONCLUSIONS: CDSM is a complex process involving behaviors at multiple levels of a person's environment. Pilot work to develop and test CDSM interventions based on both individual and external characteristics is needed.Item Menopause and Big Data: Word Adjacency Graph Modeling of Menopause-Related ChaCha® Data(Wolters Kluwer, 2017-07) Carpenter, Janet S.; Groves, Doyle; Chen, Chen X.; Otte, Julie L.; Miller, Wendy; School of NursingOBJECTIVE: To detect and visualize salient queries about menopause using Big Data from ChaCha. METHODS: We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. RESULTS: We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. CONCLUSIONS: We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.Item Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data(Sage, 2017-01) Miller, Wendy R.; Groves, Doyle; Knopf, Amelia; Otte, Julie L.; Silverman, Ross D.; School of NursingThere is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions.