Deep learning of Parkinson's movement from video, without human-defined measures.

BACKGROUND: The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures. OBJECTIVES: (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model. METHODS: 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips. RESULTS: For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group. CONCLUSIONS: A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.

Development and external validation of the eFalls tool: a multivariable prediction model for the risk of ED attendance or hospitalisation with a fall or fracture in older adults.

BACKGROUND: Falls are common in older adults and can devastate personal independence through injury such as fracture and fear of future falls. Methods to identify people for falls prevention interventions are currently limited, with high risks of bias in published prediction models. We have developed and externally validated the eFalls prediction model using routinely collected primary care electronic health records (EHR) to predict risk of emergency department attendance/hospitalisation with fall or fracture within 1 year. METHODS: Data comprised two independent, retrospective cohorts of adults aged ≥65 years: the population of Wales, from the Secure Anonymised Information Linkage Databank (model development); the population of Bradford and Airedale, England, from Connected Bradford (external validation). Predictors included electronic frailty index components, supplemented with variables informed by literature reviews and clinical expertise. Fall/fracture risk was modelled using multivariable logistic regression with a Least Absolute Shrinkage and Selection Operator penalty. Predictive performance was assessed through calibration, discrimination and clinical utility. Apparent, internal-external cross-validation and external validation performance were assessed across general practices and in clinically relevant subgroups. RESULTS: The model's discrimination performance (c-statistic) was 0.72 (95% confidence interval, CI: 0.68 to 0.76) on internal-external cross-validation and 0.82 (95% CI: 0.80 to 0.83) on external validation. Calibration was variable across practices, with some over-prediction in the validation population (calibration-in-the-large, -0.87; 95% CI: -0.96 to -0.78). Clinical utility on external validation was improved after recalibration. CONCLUSION: The eFalls prediction model shows good performance and could support proactive stratification for falls prevention services if appropriately embedded into primary care EHR systems.

Contactless hand tremor amplitude measurement using smartphones: development and pilot evaluation.

Background - Physiological tremor is defined as an involuntary and rhythmic shaking. Tremor of the hand is a key symptom of multiple neurological diseases, and its frequency and amplitude differs according to both disease type and disease progression. In routine clinical practice, tremor frequency and amplitude are assessed by expert rating using a 0 to 4 integer scale. Such ratings are subjective and have poor inter-rater reliability. There is thus a clinical need for a practical and accurate method for objectively assessing hand tremor.Objective - to develop a proof-of-principle method to measure hand tremor amplitude from smartphone videos.Methods - We created a computer vision pipeline that automatically extracts salient points on the hand and produces a 1-D time series of movement due to tremor, in pixels. Using the smartphones' depth measurement, we convert this measure into real distance units. We assessed the accuracy of the method using 60 videos of simulated tremor of different amplitudes from two healthy adults. Videos were taken at distances of 50, 75 and 100 cm between hand and camera. The participants had skin tone II and VI on the Fitzpatrick scale. We compared our method to a gold-standard measurement from a slide rule. Bland-Altman methods agreement analysis indicated a bias of 0.04 cm and 95% limits of agreement from -1.27 to 1.20 cm. Furthermore, we qualitatively observed that the method was robust to limited occlusion.Clinical relevance - We have demonstrated how tremor amplitude can be measured from smartphone videos. In conjunction with tremor frequency, this approach could be used to help diagnose and monitor neurological diseases.

New Horizons in artificial intelligence in the healthcare of older people.

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.

Fixing the Leaky Pipe: How to Improve the Uptake of Patient-Reported Outcomes-Based Prognostic and Predictive Models in Cancer Clinical Practice.

PURPOSE: This discussion paper outlines challenges and proposes solutions for successfully implementing prediction models that incorporate patient-reported outcomes (PROs) in cancer practice. METHODS: We organized a full-day multidisciplinary meeting of people with expertise in cancer care delivery, PRO collection, PRO use in prediction modeling, computing, implementation, and decision science. The discussions presented here focused on identifying challenges to the development, implementation and use of prediction models incorporating PROs, and suggesting possible solutions. RESULTS: Specific challenges and solutions were identified across three broad areas. (1) Understanding decision making and implementation: necessitating multidisciplinary collaboration in the early stages and throughout; early stakeholder engagement to define the decision problem and ensure acceptability of PROs in prediction; understanding patient/clinician interpretation of PRO predictions and uncertainty to optimize prediction impact; striving for model integration into existing electronic health records; and early regulatory alignment. (2) Recognizing the limitations to PRO collection and their impact on prediction: incorporating validated, clinically important PROs to maximize model generalizability and clinical engagement; and minimizing missing PRO data (resulting from both structural digital exclusion and time-varying factors) to avoid exacerbating existing inequalities. (3) Statistical and modeling challenges: incorporating statistical methods to address missing data; ensuring predictive modeling recognizes complex causal relationships; and considering temporal and geographic recalibration so that model predictions reflect the relevant population. CONCLUSION: Developing and implementing PRO-based prediction models in cancer care requires extensive multidisciplinary working from the earliest stages, recognition of implementation challenges because of PRO collection and model presentation, and robust statistical methods to manage missing data, causality, and calibration. Prediction models incorporating PROs should be viewed as complex interventions, with their development and impact assessment carried out to reflect this.

Parkinsonian Hand or Clinician's Eye? Finger Tap Bradykinesia Interrater Reliability for 21 Movement Disorder Experts.

BACKGROUND: Bradykinesia is considered the fundamental motor feature of Parkinson's disease (PD). It is central to diagnosis, monitoring, and research outcomes. However, as a clinical sign determined purely by visual judgement, the reliability of humans to detect and measure bradykinesia remains unclear. OBJECTIVE: To establish interrater reliability for expert neurologists assessing bradykinesia during the finger tapping test, without cues from additional examination or history. METHODS: 21 movement disorder neurologists rated finger tapping bradykinesia, by Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS), in 133 videos of hands: 73 from 39 people with idiopathic PD, 60 from 30 healthy controls. Each neurologist rated 30 randomly-selected videos. 19 neurologists were also asked to judge whether the hand was PD or control. We calculated intraclass correlation coefficients (ICC) for absolute agreement and consistency of MDS-UPDRS ratings, using standard linear and cumulative linked mixed models. RESULTS: There was only moderate agreement for finger tapping MDS-UPDRS between neurologists, ICC 0.53 (standard linear model) and 0.65 (cumulative linked mixed model). Among control videos, 53% were rated > 0 by MDS-UPDRS, and 24% were rated as bradykinesia by MBRS subscore combination. Neurologists correctly identified PD/control status in 70% of videos, without strictly following bradykinesia presence/absence. CONCLUSION: Even experts show considerable disagreement about the level of bradykinesia on finger tapping, and frequently see bradykinesia in the hands of those without neurological disease. Bradykinesia is to some extent a phenomenon in the eye of the clinician rather than simply the hand of the person with PD.

Characteristics of good home-based end-of-life care: analysis of 5-year data from a nationwide mortality follow-back survey in England.

BACKGROUND: Recently, there has been an emphasis on providing good-quality end-of-life care; however, little is known about it and its determinants for patients living at home. AIM: To determine what characterises good-quality end-of-life care for patients living at home. DESIGN AND SETTING: An observational study using 5-year data from the National Survey of Bereaved People (Views of Informal Carers - Evaluation of Services [VOICES]) in England. METHOD: Analysis was based on data for 63 598 decedents, who were cared for at home in the last 3 months of life. Data were drawn from 110 311 completed mortality follow-back surveys of a stratified sample of 246 763 deaths registered in England between 2011 and 2015. Logistic regression analyses were used to identify independent variables associated with overall quality of end-of-life care and other indicators of end-of-life care quality. RESULTS: Patients who received good continuity of primary care (adjusted odds ratio [AOR] 2.03; 95% confidence interval [CI] = 2.01 to 2.06) and palliative care support (AOR 1.86; 95% CI = 1.84 to 1.89) experienced better overall quality of end-of-life care than those who did not, as perceived by relatives. Decedents who died from cancer (AOR 1.05; 95% CI = 1.03 to 1.06) or outside of hospital were more likely to receive good end-of-life care, as perceived by relatives. Being older, female (AOR 1.16; 95% CI = 1.15 to 1.17), from areas with least socioeconomic deprivation, and White (AOR 1.09; 95% CI = 1.06 to 1.12) were associated with better overall end-of-life care, as perceived by relatives. CONCLUSION: Better quality of end-of-life care was associated with good continuity of primary care, specialist palliative care support, and death outside of hospital. Disparities still exist for those from minority ethnic groups and those living in areas of socioeconomic deprivation. Future commissioning and initiatives must consider these variables to provide a more-equitable service.

Associations with Non-Persistence with Intra-Vitreal Therapy for Neovascular Age-Related Macular Degeneration at 24 Months.

AIMS: The aim of the study was to investigate non-persistence with treatment for neovascular age-related macular degeneration (NvAMD) before day 720 (24 months) after initiation, explore associations with baseline characteristics and variation between sites. METHODS: Anonymised demographic and clinical data were extracted from electronic medical records at treating National Health Service (NHS) Trusts for NvAMD eyes starting intra-vitreal therapy from 2017 to 2018. Time to non-persistence with treatment, defined as no recorded attendance for either monitoring or treatment for a period ≥6 months, was visualised with a Kaplan-Meier survival plot. Associations with treatment non-persistence were investigated using a Cox proportional hazards model. RESULTS: Analysis included 7,970 eyes of 7,112 patients treated at 13 NHS trusts. Censoring deaths and those eyes in which treatment was stopped permanently, the Kaplan-Meier analyses demonstrated survival figures of 77.7% for persistence with treatment to day 360 and 71.8% to day 720. Hazard ratios for non-persistence with treatment were reduced at 10 sites, relative to the reference, with first-treated eye status and with baseline acuity worse than or equal to LogMAR 1.0. Hazard ratios increased with younger age, in the presence of other ocular co-morbidities and with baseline acuity better than or equal to LogMAR 0.5. After an episode of non-persistence, visual acuity decreased by at least 0.1 and 0.3 LogMAR in 39% and 18% of eyes, respectively. CONCLUSIONS: Non-persistence with treatment was common, especially in the first year of treatment, and was often associated with a decrease in visual acuity. Treatment site, baseline visual acuity, and age were the strongest predictors of treatment non-persistence before day 720. Understanding and addressing reasons for non-persistence are important to ensure that effective but expensive treatments are used cost-effectively and to maintain acuity. Variation in non-persistence between sites, even after adjustment for other variables, suggests that local factors in treatment provision may be particularly important.

A novel temporal generative adversarial network for electrocardiography anomaly detection.

Cardiac abnormality detection from Electrocardiogram (ECG) signals is a common task for cardiologists. To facilitate efficient and objective detection, automated ECG classification by using deep learning based methods have been developed in recent years. Despite their impressive performance, these methods perform poorly when presented with cardiac abnormalities that are not well represented, or absent, in the training data. To this end, we propose a novel one-class classification based ECG anomaly detection generative adversarial network (GAN). Specifically, we embedded a Bi-directional Long-Short Term Memory (Bi-LSTM) layer into a GAN architecture and used a mini-batch discrimination training strategy in the discriminator to synthesis ECG signals. Our method generates samples to match the data distribution from normal signals of healthy group so that a generalised anomaly detector can be built reliably. The experimental results demonstrate our method outperforms several state-of-the-art semi-supervised learning based ECG anomaly detection algorithms and robustly detects the unknown anomaly class in the MIT-BIH arrhythmia database. Experiments show that our method achieves the accuracy of 95.5% and AUC of 95.9% which outperforms the most competitive baseline by 0.7% and 1.7% respectively. Our method may prove to be a helpful diagnostic method for helping cardiologists identify arrhythmias.

The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity.

Background: Structured Medication Reviews (SMRs) are intended to help deliver the NHS Long Term Plan for medicines optimisation in people living with multiple long-term conditions and polypharmacy. It is challenging to gather the information needed for these reviews due to poor integration of health records across providers and there is little guidance on how to identify those patients most urgently requiring review. Objective: To extract information from scattered clinical records on how health and medications change over time, apply interpretable artificial intelligence (AI) approaches to predict risks of poor outcomes and overlay this information on care records to inform SMRs. We will pilot this approach in primary care prescribing audit and feedback systems, and co-design future medicines optimisation decision support systems. Design: DynAIRx will target potentially problematic polypharmacy in three key multimorbidity groups, namely, people with (a) mental and physical health problems, (b) four or more long-term conditions taking ten or more drugs and (c) older age and frailty. Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. AI systems will be trained to identify patterns of conditions, medications, tests, and clinical contacts preceding adverse events in order to identify individuals who might benefit most from an SMR. Discussion: By implementing and evaluating an AI-augmented visualisation of care records in an existing prescribing audit and feedback system we will create a learning system for medicines optimisation, co-designed throughout with end-users and patients.

Mapping and characterising electronic palliative care coordination systems and their intended impact: A national survey of end-of-life care commissioners.

OBJECTIVES: In England, Electronic Palliative Care Coordination Systems (EPaCCS) were introduced in 2008 to support care coordination and delivery in accordance with patient preferences. Despite policy supporting their implementation, there has been a lack of rigorous evaluation of EPaCCS and it is not clear how they have been translated into practice. This study sought to examine the current national implementation of EPaCCS, including their intended impact on patient and service outcomes, and barriers and facilitators for implementation. METHODS: We conducted a national cross-sectional online survey of end-of-life care commissioning leads for Clinical Commissioning Groups (CCGs) in England. We enquired about the current implementation status of EPaCCS, their role in information sharing and intended impact, and requested routine patient-level data relating to EPaCCS. RESULTS: Out of 135 CCGs, 85 (63.0%) responded, with 57 (67.1%) having operational EPaCCS. Use of EPaCCS were confined to healthcare providers with most systems (67%) not supporting information sharing with care homes and social care providers. Most systems (68%) sought to facilitate goal concordant care, although there was inconsonance between intended impacts and monitoring measures used. Common challenges to implementation included healthcare professionals' limited engagement. Only one-third of patients had an EPaCCS record at death with limited recording of patient preferences. CONCLUSIONS: Critical gaps exist in engagement with EPaCCS and their ability to facilitate information sharing across care providers. The limited alignment between stated goals of EPaCCS and their monitoring impedes efforts to understand which characteristics of systems can best support care delivery.

Moving towards intelligent telemedicine: Computer vision measurement of human movement.

BACKGROUND: Telemedicine video consultations are rapidly increasing globally, accelerated by the COVID-19 pandemic. This presents opportunities to use computer vision technologies to augment clinician visual judgement because video cameras are so ubiquitous in personal devices and new techniques, such as DeepLabCut (DLC) can precisely measure human movement from smartphone videos. However, the accuracy of DLC to track human movements in videos obtained from laptop cameras, which have a much lower FPS, has never been investigated; this is a critical gap because patients use laptops for most telemedicine consultations. OBJECTIVES: To determine the validity and reliability of DLC applied to laptop videos to measure finger tapping, a validated test of human movement. METHOD: Sixteen adults completed finger-tapping tests at 0.5 Hz, 1 Hz, 2 Hz, 3 Hz and at maximal speed. Hand movements were recorded simultaneously by a laptop camera at 30 frames per second (FPS) and by Optotrak, a 3D motion analysis system at 250 FPS. Eight DLC neural network architectures (ResNet50, ResNet101, ResNet152, MobileNetV1, MobileNetV2, EfficientNetB0, EfficientNetB3, EfficientNetB6) were applied to the laptop video and extracted movement features were compared to the ground truth Optotrak motion tracking. RESULTS: Over 96% (529/552) of DLC measures were within +/-0.5 Hz of the Optotrak measures. At tapping frequencies >4 Hz, there was progressive decline in accuracy, attributed to motion blur associated with the laptop camera's low FPS. Computer vision methods hold potential for moving us towards intelligent telemedicine by providing human movement analysis during consultations. However, further developments are required to accurately measure the fastest movements.

To the emergency room and back again: Circular healthcare pathways for acute functional neurological disorders.

BACKGROUND AND OBJECTIVES: Studies of Functional Neurological Disorders (FND) are usually outpatient-based. To inform service development, we aimed to describe patient pathways through healthcare events, and factors affecting risk of emergency department (ED) reattendance, for people presenting acutely with FND. METHODS: Acute neurology/stroke teams at a UK city hospital were contacted regularly over 8 months to log FND referrals. Electronic documentation was then reviewed for hospital healthcare events over the preceding 8 years. Patient pathways through healthcare events over time were mapped, and mixed effects logistic regression was performed for risk of ED reattendance within 1 year. RESULTS: In 8 months, 212 patients presented acutely with an initial referral suggesting FND. 20% had subsequent alternative diagnoses, but 162 patients were classified from documentation review as possible (17%), probable (28%) or definite (55%) FND. In the preceding 8 years, these 162 patients had 563 ED attendances and 1693 inpatient nights with functional symptoms, but only 26% were referred for psychological therapy, only 66% had a documented diagnosis, and care pathways looped around ED. Three better practice pathway steps were each associated with lower risk of subsequent ED reattendance: documented FND diagnosis (OR = 0.32, p = 0.004), referral to clinical psychology (OR = 0.35, p = 0.04) and outpatient neurology follow-up (OR = 0.25, p < 0.001). CONCLUSION: People that present acutely to a UK city hospital with FND tend to follow looping pathways through hospital healthcare events, centred around ED, with low rates of documented diagnosis and referral for psychological therapy. When better practice occurs, it is associated with lower risk of ED reattendance.

The Symptom Burden and Quality of Life in Cancer Patients in the Gaza Strip, Palestine: A Cross-Sectional Study.

OBJECTIVES: Cancer is the second leading cause of death in the Gaza Strip, Palestine, but there is an absence of evidence systematically assessing symptom burden and quality of life (QoL) using validated tools. Our objective was to assess associations between socio-demographic and disease-related characteristics, symptom burden and QoL in a sample of cancer patients accessing outpatient services in the Gaza Strip. DESIGN: A cross-sectional, descriptive survey using interviews and medical record review involving patients with cancer accessing oncology outpatient services at Al Rantisi Hospital and European Gaza Hospital (EGH) in the Gaza Strip was employed. Socio-demographic and disease-related data, the Lebanese version of the Memorial Symptom Assessment Scale (MSAS-Leb), and the Arabic version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) were collected. Multiple linear regression was used to judge the relative influence of determinants of QoL. RESULTS: Of 414 cancer patients approached, 385 patients consented to participation. The majority were women (64.7%) with a mean age of 52 years (SD = 16.7). Common cancer diagnoses were breast (32.2%), haematological (17.9%) and colorectal (9.1%). The median number of symptoms was 10 (IQR 1.5-18.5). Mean overall QoL was 70.5 (SD 19.9) with common physical and psychological symptoms identified. A higher burden of symptoms was associated with marital status, education and income. Limited access to both opioids and psychological support were reported. CONCLUSIONS: A high symptom burden was identified in outpatients with cancer. Increasing provision and access to supportive care for physical and psychological symptoms should be prioritised alongside exploring routine assessment of symptom burden and QoL.

Risk scores in anaesthesia: the future is hard to predict.

External validation helps to assess whether a given risk prediction model will perform well in a target population. Validation is an important step in maintaining the utility of risk prediction models, as their ability to provide reliable risk estimates will deteriorate over time (calibration drift).

Suboptimal Dosing of ß-Blockers in Chronic Heart Failure: A Missed Opportunity?

BACKGROUND: The evidence base for the benefits of ß-blockers in heart failure with reduced ejection fraction (HFrEF) suggests that higher doses are associated with better outcomes. OBJECTIVES: The aim of this study was to report the proportion of patients receiving optimized doses of ß-blockers, outcomes, and factors associated with suboptimal dosing. METHODS: This was a prospective cohort study of 390 patients with HFrEF undergoing clinical and echocardiography assessment at baseline and at 1 year. RESULTS: Two hundred thirty-seven patients (61%) were receiving optimized doses (≥5-mg/d bisoprolol equivalent), 72 (18%) could not be up-titrated (because of heart rate < 60 beats/min or systolic blood pressure <100 mm Hg), and the remaining 81 (21%) should have been. Survival was similarly reduced in those who could not and should have been receiving 5 mg/d or greater, and patient factors did not explain the failure to attain optimized dosing. CONCLUSIONS: Many patients with HFrEF are not receiving optimal dosing of ß-blockers, and in around half, there was no clear contraindication in terms of heart rate or blood pressure.

Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency.

BACKGROUND: Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the requirement to hold or strap a smartphone accelerometer. OBJECTIVE: To compare computer vision measurement of hand tremor frequency from smartphone video with a gold standard measure accelerometer. METHODS: A total of 37 smartphone videos of hands, at rest and in posture, were recorded from 15 participants with tremor diagnoses (9 Parkinson's disease, 5 essential tremor, 1 functional tremor). Video pixel movement was measured using the computing technique of optical flow, with contemporaneous accelerometer recording. Fast Fourier transform and Bland-Altman analysis were applied. Tremor amplitude was scored by 2 clinicians. RESULTS: Bland-Altman analysis of dominant tremor frequency from smartphone video compared with accelerometer showed excellent agreement: 95% limits of agreement -0.38 Hz to +0.35 Hz. In 36 of 37 videos (97%), there was <0.5 Hz difference between computer vision and accelerometer measurement. There was no significant correlation between the level of agreement and tremor amplitude. CONCLUSION: The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine.

Experiences of people seen in an acute hospital setting by a liaison mental health service: responses from an online survey.

BACKGROUND: In recent years the UK has expanded the provision of liaison mental health services (LMHS). Little work has been undertaken to explore first-hand experiences of them. AIMS: The aim of this study was to gain insights into the experiences of users of LMHS in both emergency departments and acute inpatient wards in the UK. METHODS: This cross-sectional internet survey was initially advertised from May-July 2017 using the social media platform Facebook. Due to a paucity of male respondents, it was re-run from November 2017-February 2018, specifically targeting male respondents. The survey featured a structured questionnaire divided into three categories: the profile of the respondent, perceived professionalism of LMHS and overall opinion of the service. ANALYSIS: Responses to the structured questionnaire were analysed using descriptive statistics and latent class analysis. Free-text responses were transcribed verbatim and interpreted using thematic analysis. RESULTS: 184 people responded to the survey. 147 were service users and 37 were partners, friends or family members of service users. Only 31% of service users and 27% of close others found their overall contact helpful. Latent class analysis identified three clusters - 46% of service users generally disliked their contact, 36% had an overall positive experience, and 18% did not answer most questions about helpfulness or usefulness. Features most frequently identified as important were the provision of a 24/7 service, assessment by a variety of healthcare professionals and national standardisation of services. Respondents indicated that the least important feature was the provision of a separate service for older people. They desired faster assessments following referral from the parent team, clearer communication about next steps and greater knowledge of local services and third sector organisations. CONCLUSIONS: This survey identified mixed responses, but overall experiences were more negative than indicated in the limited previous research. The evaluation and adaptation of LMHS along the lines suggested in our survey should be prioritised to enhance their inherent therapeutic value and to improve engagement with treatment and future psychiatric care.

Evidence of objective sleep impairment in nonepileptic attack disorder: A naturalistic prospective controlled study using actigraphy and daily sleep diaries over six nights.

Poor sleep is reported by many with nonepileptic attack disorder (NEAD) with correlations evident between self-reported sleep quality and mood and functional impairment. However, it is contended that self-reported sleep impairment in NEAD is a subjective phenomenon, which represents a general tendency to over-report symptoms or misinterpret bodily states in those with NEAD. The present study was therefore designed to investigate the extent of subjective and objective sleep impairments in those with NEAD. Over six nights we prospectively recorded comparable nightly objective (actigraphy) and subjective (consensus sleep diary) sleep parameters in a sample of 17 people with NEAD, and an age- and gender-matched normative control group (N = 20). Participants recorded daily measures of attacks, dissociation, and mood. Alongside higher subjective sleep impairment, the NEAD group had significantly worse objective sleep on several metrics compared to the normative controls, characterized by disrupted sleep (frequent awakenings and wake after sleep onset, low efficiency). Exploratory analyses using mixed effects models showed that attacks were more likely to occur on days preceded by longer, more restful sleep. This study, which had good ecological validity, evidences the presence of objective sleep impairment in NEAD, suggesting that in patient reports of problems with sleep should be given careful consideration in clinical practice.