Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Stereo-electroencephalographic seizure localization in patients with mesial temporal sclerosis: A single center experience.

Objective: Epilepsy patients with mesial temporal sclerosis (MTS) on imaging who are drug-resistant usually undergo epilepsy surgery without previous invasive evaluation. However, up to one-third of patients are not seizure-free after surgery. Prior studies have identified risk factors for surgical failure, but it is unclear if they are associated with bilateral or discordant seizure onset. Methods: In this retrospective case series, we identified 17 epilepsy patients who had MRI-confirmed MTS but received invasive stereo-EEG (SEEG) evaluation before definitive intervention. We analyzed their presurgical risk factors in relation to SEEG seizure onset localization and MRI/SEEG concordance. Results: SEEG ictal onset was concordant with MTS localization (i.e. seizures started only from the hippocampus with MTS) in 5 out of 13 patients with unilateral MTS (UMTS) and in 3 out of 4 patients with bilateral MTS.No statistically significant association regarding concordance of SEEG ictal onset and MTS location was found in patients with such risk factors as a history of non-mesial temporal aura, frequent focal to bilateral tonic-clonic seizures, prior viral brain infection, or family history of epilepsy. Nine out of 13 UMTS patients had resective surgery only, 5 out of 9 (56 %) have Engel class I outcome at most recent follow-up (median 46.5 months, range 22-91 months). In Engel class I cohort, the SEEG ictal onset was concordant with MTS location in 3 out of 5 patients, and 2 patients had ipsilateral temporal neocortical ictal onset. Conclusions: Our findings suggest that patients with MTS might have discordant SEEG ictal onset (in 61.5% patients with UMTS in presented cohort), which may explain poor surgical outcome after destructive surgery in these cases. Significance: Although no statistically significant association was found in this under-powered study, these findings could be potentially valuable for future meta-analyses.

Real-Time Seizure Detection Using Behind-the-Ear Wearable System.

INTRODUCTION: This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes. METHODS: This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD. RESULTS: In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD. CONCLUSIONS: Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.

Digital Phenotyping Models of Symptom Improvement in College Mental Health: Generalizability Across Two Cohorts.

Smartphones can be used to gain insight into mental health conditions through the collection of survey and sensor data. However, the external validity of this digital phenotyping data is still being explored, and there is a need to assess if predictive models derived from this data are generalizable. The first dataset (V1) of 632 college students was collected between December 2020 and May 2021. The second dataset (V2) was collected using the same app between November and December 2021 and included 66 students. Students in V1 could enroll in V2. The main difference between the V1 and V2 studies was that we focused on protocol methods in V2 to ensure digital phenotyping data had a lower degree of missing data than in the V1 dataset. We compared survey response counts and sensor data coverage across the two datasets. Additionally, we explored whether models trained to predict symptom survey improvement could generalize across datasets. Design changes in V2, such as a run-in period and data quality checks, resulted in significantly higher engagement and sensor data coverage. The best-performing model was able to predict a 50% change in mood with 28 days of data, and models were able to generalize across datasets. The similarities between the features in V1 and V2 suggest that our features are valid across time. In addition, models must be able to generalize to new populations to be used in practice, so our experiments provide an encouraging result toward the potential of personalized digital mental health care.

Stereo-EEG Evaluation and Surgical Treatment in Patients With Drug-Resistant Focal Epilepsy Associated With Nodular Heterotopia.

PURPOSE: Nodular heterotopia (NH) is a common cause of drug-resistant epilepsy. Only limited studies detail the treatment of NH with laser interstitial thermal therapy and none analyze the relation between epileptogenicity and NH location. METHODS: We retrospectively studied nine patients with drug-resistant epilepsy and NH who underwent stereoelectroencephalography and subsequent epilepsy surgery. Nodular heterotopia in the frontal lobes or along the bodies of the lateral ventricles was classified as anterior NH. Nodular heterotopia in the trigones, temporal or occipital horns, or temporal lobes was classified as posterior NH. Nodular heterotopia in both anterior and posterior locations was classified as diffuse NH. Interictal and ictal stereoelectroencephalography were analyzed, and patients were followed postoperatively to assess outcomes. RESULTS: Of the six patients who underwent nine laser interstitial thermal therapy procedures either in isolation or in combination with other surgical therapies, four patients were Engel Ia, one was Engel IIb, and one was Engel IIIa, with an average follow-up of 22.8 months. All patients with posterior NH had interictal epileptiform abnormalities and seizures originating from the posterior NH. None of the patients with anterior NH had epileptiform activity recorded from their NH. CONCLUSION: Laser interstitial thermal therapy alone or in combination with other surgical therapies is an effective treatment in those with drug-resistant epilepsy because of NH, even in those with extensive NH and broad seizure onset. We observed a trend suggesting that posterior NH are more likely to be epileptogenic compared with anterior NH and recommend that in patients with anterior NH, alternative epilepsy etiologies and stereoelectroencephalography implantation strategies be considered.

Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students.

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Epilepsy surgery in patient with monogenic epilepsy related to SCN8A mutation.

Epilepsy surgery is superior to prolonged medical therapy in patients with drug-resistant focal epilepsy, but reports on epilepsy surgery outcomes for patients with a genetic etiology are limited, especially in adults. This is the first documented report of a stereoelectroencephalography (SEEG) evaluation and resective surgery outcome in an adult patient with epilepsy related to SCN8A mutation. We describe a patient with epilepsy related to SCN8A mutation which was reported as a variant of uncertain significance at time of his pre-surgical evaluation and reclassified as likely pathogenic about 3 years after resective epilepsy surgery. Most of his pre-surgical evaluation results suggested right temporal lobe epilepsy, but few reported semiological symptoms, ictal SPECT, and neuropsychology results were discordant, and brain MRI was non-lesional. Therefore, SEEG was recommended; ultimately, seizures were localized to the right hippocampus. He was seizure-free for 1.5 years after right anterior temporal lobectomy, then reported three focal to bilateral tonic-clonic (FBTC) seizures in the subsequent 12 months (preoperatively, 6 focal impaired awareness seizures and 4-6 FBTC per year). This case demonstrates that epilepsy surgery reduced seizure burden in a patient with SCN8A-related epilepsy granting him short-term seizure freedom after resection, and then decreased seizure frequency after relapse compared to the preoperative baseline.

Applying machine learning to smartphone based cognitive and sleep assessments in schizophrenia.

BACKGROUND: Cognitive impairment in schizophrenia remains a chief source of functional disability and impairment, despite the potential for effective interventions. This is in part related to a lack of practical and easy to administer screening strategies that can identify and help triage cognitive impairment. This study explores how smartphone-based assessments may help address this need. METHODS: In this study, data was analyzed from 25 subjects with schizophrenia and 30 controls who engaged with a gamified mobile phone version of the Trails-B cognitive assessment in their everyday life over 90 days and complete a clinical neurocognitive testing battery at the beginning and end of the study. Machine learning was applied to the resulting dataset to predict disease status and neurocognitive function and understand which features were most important for accurate prediction. RESULTS: The generated models predicted disease status with high accuracy using static features alone (AUC = 0.94), with the total number of items collected and the total duration of interaction with the application most predictive. The addition of temporal data statistically significantly improved performance (AUC = 0.95), with the amount of idle time a significant new predictor. Correlates of sleep dysfunction were also predicted (AUC = 0.80), with similar feature importance. DISCUSSION: Machine learning enabled the highly accurate identification of subjects with schizophrenia versus healthy controls, and the accurate prediction of neurocognitive function. The addition of temporal data significantly improved the performance of these models, underscoring the value of smartphone-based assessments of cognition as a practical tool for assessing cognition.

Emergency department visits and readmissions in patients with psychogenic nonepileptic seizures (PNES) at a safety net hospital.

INTRODUCTION: Readmissions and emergency department (ED) visits after an index admission have been become a quality measure due to associations with poor outcomes and increased healthcare costs. Readmissions and ED encounters have been studied in a variety of conditions including epilepsy but have not been examined exclusively in psychogenic nonepileptic seizures (PNES). In this study we examined the rate of readmissions and ED visits after a discharge from an Epilepsy Monitoring Unit (EMU) in a safety net hospital. We also determined patient phenotypes that are associated with readmissions. MATERIAL AND METHODS: This was a retrospective chart review study with index admission being a discharge from an EMU between January 1 and December 31 2016 with follow-up until August 31 2020. We obtained data regarding demographics, medical and psychiatric history, and social history and treatment interventions. Our outcome variables were both all-cause and seizure-related hospital readmissions and ED visits 30 days following the index discharge and readmissions and ED visits 30 days thereafter. RESULTS: Eleven of 122 patients (9%) had a non-seizure-related ED visit and/or hospitalization within 30 days of index discharge while 45 (37%) had re-contact with the health system thereafter for non-seizure-related issues. Seven of 122 patients (6%) had a seizure-related ED visit or hospital readmission within 30 days of discharge. Twenty-eight (23%) had a seizure-related readmission or ED visit after 30 days. Of these 28, 4 patients had been to an ER within 7 days of EMU discharge. The majority of subsequent encounters with the healthcare system were through the ED (n = 38) as compared to hospital (n = 10) and EMU readmissions (n = 9). On bivariate statistical analysis, charity or self-pay insurance status (p < 0.01), homelessness (p < 0.01), emergent EMU admission on index admission (p < 0.01), history of a psychiatric diagnosis (p < 0.02), and ED encounters 12 months prior to admission (p < 0.01) were significantly associated with readmission; however, on multivariate analysis only charity insurance status was a significant predictor. CONCLUSIONS: In this study of readmissions and ED visits after discharge with a diagnosis of PNES at a safety net hospital, we found a seizure-related readmission rate of approximately 6% in 30 days and 23% thereafter with the majority of re-contact with the hospital being in the ED. On multi-variate analysis insurance status was a significant factor associated with readmission and ED visits. Our future research directions include examining referrals and treatment completion at the hospital's PNES clinic as well as creating a risk score to better identify patients with PNES at risk of readmission.

Emergent Admissions to the Epilepsy Monitoring Unit in the Setting of COVID-19 Pandemic-related, State-mandated Restrictions: Clinical Decision Making and Outcomes.

Due to the coronavirus disease 2019 (COVID-19) pandemic, the state of Texas-limited elective procedures to conserve beds and personal protective equipment (PPE); therefore, between March 22 and May 18, 2020, admission to the epilepsy monitoring unit (EMU) was limited only to urgent and emergent cases. We evaluated clinical characteristics and outcomes of these patients who were admitted to the EMU. Nineteen patients were admitted (one patient twice) with average age of 36.26 years (11 female) and average length of stay 3 days (range: 2-9 days). At least one event was captured on continuous EEG (cEEG) and video monitoring in all 20 admissions (atypical in one). One patient had both epileptic (ES) and psychogenic non-epileptic seizures (PNES) while 10 had PNES and 9 had ES. In 8 of 9 patients with ES, medications were changed, while in 5 patients with PNES, anti-epileptic drugs (AED) were stopped; the remaining 5 were not on medications. Of the 14 patients who had seen an epileptologist pre-admission, 13 (or 93%) had their diagnosis confirmed by EMU stay; a statistically significant finding. While typically an elective admission, in the setting of the COVID-19 pandemic, urgent and emergent EMU admissions were required for increased seizure or event frequency. In the vast majority of patients (13 of 19), admission lead to medication changes to either better control seizures or to change therapeutics as appropriate when PNES was identified.

Non-lesional mesial temporal lobe epilepsy requires bilateral invasive evaluation.

PURPOSE: Mesial temporal lobe epilepsy (MTLE) usually responds well to surgical treatment, although in non-lesional cases up to 50% of patients experience seizure relapse. The possibility of bilateral independent seizure onset should be considered as a reason for epilepsy surgery failure. METHODS: In a cohort of 177 patients who underwent invasive presurgical evaluation with stereo-tactically placed electrodes in two level four epilepsy centers, 29 had non-lesional MTLE. Invasive evaluation results are described. RESULTS: Among 29 patients with non-lesional MRI and mesial temporal lobe seizure onset recorded during stereo-EEG (SEEG) evaluation, four patients with unilateral preimplantation hypothesis had independent bilateral mesial temporal seizures on SEEG despite of unilateral non-invasive evaluation data. Three of these patients were treated with bitemporal responsive neurostimulator system (RNS). Independent bilateral mesial temporal seizures have been confirmed on RNS ECoG (electrocorticography). The fourth patient underwent right anterior temporal lobectomy. CONCLUSION: We propose that patients with non-lesional mesial temporal lobe epilepsy would benefit from bilateral invasive evaluation of mesial temporal structures to predict those patients who would be at most risk for surgical failure. Neurostimulaiton could be an initial treatment option for patients with independent bitemporal seizure onset.

Physical activity status and quality of life in patients with epilepsy - Survey from level four epilepsy monitoring units.

PURPOSE: People with epilepsy (PWE) tend to have sedentary lifestyles which may predispose them to a lower perceived quality of life (QOL). Moreover, the relationship between physical activity (PA) and QOL in populations of PWE with high disease burden has been under-studied. The goal of this study was to evaluate PA level and its impact on health-related QOL in PWE who were admitted to Level-4 epilepsy monitoring units (EMU). METHODS: In this prospective observational study, 200 patients from two EMUs in Dallas, Texas completed the following standard surveys: Rapid Assessment of Physical Activity (RAPA), the Quality of Life in Epilepsy (QOLIE-31), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder 7-item (GAD-7) questionnaire. Information on self-reported epilepsy history, severity of disease, and socioeconomic status were also collected. The diagnosis of epilepsy was confirmed by video-EEG monitoring. RESULTS: Among the 200 who completed the survey, 113 had a diagnosis of epilepsy and 109 of them completed the RAPA. Ninety-two (84 %) of these PWE reported a sedentary level of physical activity (RAPA < 6) and 16 % reported an active level (RAPA ≥ 6). Self-reported QOL was slightly higher in PWE with an active level of PA compared to PWE with a sedentary level of PA (63.8 ± 15.0 vs 53.7 ± 17.9, p = 0.07), even though there was no difference in the severity of self-reported mood symptoms. After controlling for employment and seizure frequency, physical activity level measured by RAPA score was also positively related to QOL (r = 0.39, p = 0.01) and negatively correlated with anxiety symptoms (r = -0.28, p = 0.02) and depression symptoms (r = -0.25, p = 0.04). CONCLUSION: The majority of PWE in this survey reported sedentary lifestyles despite most of them being young to middle-aged adults. Higher PA level was associated with fewer self-reported mood symptoms and higher QOL. In conjunction with the literature, these results suggest that PWE with a wide range of disease burden should be encouraged to participate in regular exercise to potentially improve QOL.

The high-value pharmacy enterprise framework: Advancing pharmacy practice in health systems through a consensus-based, strategic approach.

PURPOSE: The high-value pharmacy enterprise (HVPE) framework and constituent best practice consensus statements are presented, and the methods used to develop the framework's 8 domains are described. SUMMARY: A panel of pharmacy leaders used an evidence- and expert opinion-based approach to define core and aspirational elements of practice that should be established within contemporary health-system pharmacy enterprises by calendar year 2025. Eight domains of an HVPE were identified: Patient Care Services; Business Services; Ambulatory and Specialty Pharmacy Services; Inpatient Operations; Safety and Quality; Pharmacy Workforce; Information Technology, Data, and Information Management; and Leadership. Phase 1 of the project consisted of the development of draft practice statements, performance elements, and supporting evidence for each domain by panelists, followed by a phase 2 in-person meeting for review and development of consensus for statements and performance elements in each domain. During phase 3, the project cochairs and panelists finalized the domain drafts and incorporated them into a full technical report and this summary report. CONCLUSION: The HVPE framework is a strategic roadmap to advance pharmacy practice by ensuring safe, effective, and patient-centered medication management and business practices throughout the health-system pharmacy enterprise. Grounded in evidence and expert recommendations, the statements and associated performance elements can be used to identify strategic priorities to improve patient outcomes and add value within health systems.

Deriving symptom networks from digital phenotyping data in serious mental illness.

BACKGROUND: Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms. AIMS: In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis. METHOD: Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains. RESULTS: The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups. CONCLUSIONS: Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Digital phenotyping for mental health of college students: a clinical review.

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help-the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.

Digital Opportunities for Outcomes in Recovery Services (DOORS): A Pragmatic Hands-On Group Approach Toward Increasing Digital Health and Smartphone Competencies, Autonomy, Relatedness, and Alliance for Those With Serious Mental Illness.

Digital health technologies such as smartphones present the potential for increased access to care and on-demand services. However, many patients with serious mental illnesses (eg, schizophrenia) have not been offered the digital health training necessary to fully utilize these innovative approaches. To bridge this digital divide in knowledge and skills, we created a hands-on and interactive training program grounded in self-determination theory, technology use cases, and the therapeutic alliance. This article introduces the need and theoretical foundation for and the experience of running the resulting Digital Opportunities for Outcomes in Recovery Services (DOORS) group in the setting of 2 programs: a first-episode psychosis program and a clubhouse for individuals with serious mental illness. The experience of running these 2 DOORS groups resulted in 2 publicly available, free training manuals to empower others to run such groups and adapt them for local needs. Future work on DOORS will expand the curriculum to best support digital health needs and increase equity of access to and knowledge and skills related to technology use in serious mental illness.

Comparison of psychiatric comorbidities and impact on quality of life in patients with epilepsy or psychogenic nonepileptic spells.

OBJECTIVES: Psychiatric comorbidity is common in people with epilepsy (PWE) and psychogenic nonepileptic spells (PNES). These comorbidities can be detrimental to quality of life (QOL) and are often underdiagnosed and undertreated. Some types of epilepsy, such as focal temporal lobe epilepsy (TLE), have been associated with higher rates of psychiatric comorbidity. This study examined the impact of psychiatric comorbidity on QOL in patients admitted to two level 4 epilepsy monitoring units (EMUs). METHODS: In this prospective observational study, 200 patients admitted to two level 4 EMUs completed standardized surveys including the Quality of Life in Epilepsy (QOLIE-31-P), Generalized Anxiety Disorder 7-item (GAD-7), Patient Health Questionnaire (PHQ-9), and Beck Depression Inventory-II (BDI-II). Hierarchal multiple regression was performed to assess impact on QOL. RESULTS: Of the 200 participants, 113 had a diagnosis of epilepsy, 36 had a diagnosis of PNES, and 51 were excluded for nondiagnostic evaluation or dual diagnosis. Of those with epilepsy, 65 had TLE, 28 had focal extratemporal lobe epilepsy (ETLE), and 20 had nonfocal epilepsy. Patients with PNES had higher self-reported anxiety and depression levels (GAD-7: p = 0.04, PHQ-9: p < 0.01; BDI-II: p < 0.01) but similar QOL to PWE (p = 0.78). Using hierarchal multiple regression, symptoms of anxiety and depression were significant predictors of lower QOL in PWE but not in patients with PNES. There was no difference in QOL in those with ETLE and TLE. CONCLUSIONS: Our findings suggest that self-reported anxiety and depression symptoms are common in patients admitted to level 4 EMUs regardless of diagnosis and play an important role in predicting QOL in PWE. Our findings emphasize the importance of routinely screening all EMU patients for psychiatric comorbidity.

Assessing Cognition Outside of the Clinic: Smartphones and Sensors for Cognitive Assessment Across Diverse Psychiatric Disorders.

Traditionally, the assessment of cognition and the administration of cognitive therapies has been performed in the clinic, but with modern technology, this clinic-centric view is changing. This article explores the landscape of digital devices used to measure cognition in settings outside the clinic. These devices range in mobility from user-friendly mobile devices to setting-specific devices able to provide powerful, robust cognitive therapy and living assistance in the comfort of a patient's home. Although these methods remain in early stages of developmental, initial studies suggest they may prove useful in treating patients with serious mental illnesses in a widespread clinical setting.