Patient-specific game-based transfer method for Parkinson's disease severity prediction.

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. The mean values of mean absolute error, root mean square error, and volatility obtained by predicting motor-UPDRS and total-UPDRS for target patients are 1.59, 1.95, 1.56 and 1.98, 2.54, 1.94, respectively. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.

The effect of socioeconomic status and training programmes on burnout in postgraduate trainees in the United Kingdom: a cross-sectional analysis.

PURPOSE: Burnout is described as a state of mental exhaustion caused by one's professional life and is characterised by three domains: emotional exhaustion, depersonalisation, and a reduced sense of accomplishment. The prevalence of stress is high amongst doctors and varies by specialty, gender, trainee level, and socioeconomic status. The authors set out to examine the scale of the problem, as well as to determine the influence of both socioeconomic status and chosen training programme on burnout amongst postgraduate trainees. This would identify at-risk groups and aid in future targeted interventions. METHODS: Cross-sectional data were obtained, following approval from the General Medical Council, from The National Training Survey, completed annually by all trainees in the United Kingdom. Data were then anonymised and analysed. Burnout scores were derived from the Copenhagen Burnout Inventory and are positively framed (higher scores equal lower burnout). RESULTS: The questionnaire was completed by 63 122 participants from 2019 to 2020. Mean burnout amongst all trainees was 52.4 (SD = 19.3). Burnout scores from the most deprived quintile was significantly lower compared with those from the least deprived quintile: 51.0 (SD = 20.6) versus 52.9 (SD = 18.9), respectively (P < 0.001). The highest levels of burnout were reported in Internal Medical Training, Emergency Medicine, Obstetrics and Gynaecology, and Core Surgical Training, respectively. CONCLUSION: Postgraduates from lower socioeconomic backgrounds are more likely to encounter burnout during training. At-risk groups who may also benefit from targeted intervention have been identified, requiring further examination through future studies.

Emergency definitive management of a subtalar dislocation with an associated lateral malleolus fracture.

Lateral subtalar dislocations are rare injuries with the potential for misdiagnosis and for poor patient outcomes if missed. Prompt reduction and surgical intervention is the consensus and is key to improved patient outcomes.We present a case of a male patient in his 70s who was brought in by ambulance to the emergency department late one afternoon with a lateral subtalar dislocation associated with calcaneal and lateral malleolar fractures. He was operated on that evening, involving cannulated screw fixation of the calcaneus and buttress plating of the lateral malleolus. Non-weight-bearing mobilisation was permitted at 2 weeks and weight-bearing was commenced at 6 weeks postoperatively.The authors could not find any precedent in the literature for same-day definitive fixation and early mobilisation of this rare but significant injury, the combination of which resulted in excellent functional outcomes for the patient.

Remote smartphone monitoring of Parkinson's disease and individual response to therapy.

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson's disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity (r = 0.71; P < 1.8 × 10-6) when compared with motor Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments.

Surgical training in the UK: is cost a barrier to entry?

BACKGROUND: There is good quality evidence linking socioeconomic background and the likelihood of a surgical career. Additionally, training in surgery is more expensive than in other specialties. Our aim was to assess the awareness and perceptions of trainees and medical students of the relative costs of surgical training and to determine whether perceptions of cost deter potential surgical trainees. METHODS: Medical students, foundation doctors and core trainees in England were surveyed over a 2-week period. χ2 tests of independence were used to assess statistically significant associations between measured variables MAIN FINDINGS: A total of 284 responses were received. More than half of respondents (54%) were not previously aware of the high costs of surgical training. More than a quarter of respondents (27%) did not take out a student loan. There was a significant association (p=0.003) between familial income and being less likely to consider a surgical career due to the costs. Respondents who reported receipt of a student loan were also significantly less likely to consider a surgical career due to the costs (p=0.033). CONCLUSION: Our study demonstrates an important relationship between perceived costs of surgical training and future career aspirations. This suggests that access to surgical training may still be difficult for many. This study also highlights a general lack of awareness of high surgical training costs. It is important that surgical training is accessible. Financial status should not be a significant disincentive and widening access to surgical training can only serve to enrich and advance the specialty.

Detecting Parkinson Disease Using a Web-Based Speech Task: Observational Study.

BACKGROUND: Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases-fueled mostly by environmental pollution and an aging population-can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. OBJECTIVE: In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. METHODS: We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, "the quick brown fox jumps over the lazy dog." We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning-based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model's output. RESULTS: We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost-a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing "ahh") influence the model's decision the most. CONCLUSIONS: Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Smartphones for Remote Symptom Monitoring of Parkinson's Disease.

Parkinson's disease is a complex and heterogeneous condition, and there are many gaps in the medical community's scientific and practical understanding of the disease. Closing these gaps relies on objective data about symptoms and signs, collected over long durations. Smartphones contain sensor devices which can be used to remotely capture behavioral signals. From these signals, computational algorithms can distill metrics of symptom severity and progression. This brief review introduces the main concepts of the discipline, addressing the experimental, hardware and software logistics, and computational analysis. The article finishes with an exploration of future prospects for the technology.

Remote Assessment of Parkinson's Disease Symptom Severity Using the Simulated Cellular Mobile Telephone Network.

Telemonitoring of Parkinson's Disease (PD) has attracted considerable research interest because of its potential to make a lasting, positive impact on the life of patients and their carers. Purpose-built devices have been developed that record various signals which can be associated with average PD symptom severity, as quantified on standard clinical metrics such as the Unified Parkinson's Disease Rating Scale (UPDRS). Speech signals are particularly promising in this regard, because they can be easily recorded without the use of expensive, dedicated hardware. Previous studies have demonstrated replication of UPDRS to within less than 2 points of a clinical raters' assessment of symptom severity, using high-quality speech signals collected using dedicated telemonitoring hardware. Here, we investigate the potential of using the standard voice-over-GSM (2G) or UMTS (3G) cellular mobile telephone networks for PD telemonitoring, networks that, together, have greater than 5 billion subscribers worldwide. We test the robustness of this approach using a simulated noisy mobile communication network over which speech signals are transmitted, and approximately 6000 recordings from 42 PD subjects. We show that UPDRS can be estimated to within less than 3.5 points difference from the clinical raters' assessment, which is clinically useful given that the inter-rater variability for UPDRS can be as high as 4-5 UPDRS points. This provides compelling evidence that the existing voice telephone network has potential towards facilitating inexpensive, mass-scale PD symptom telemonitoring applications.

Day case locked anterior plating for distal radial fractures is safe with uninterrupted antithrombotic therapy.

We undertook a matched prospective cohort study over a 4-year period to examine the safety of continuing the administration of regular antithrombotic treatment with warfarin, clopidogrel or aspirin during day case surgical fixation of distal radial fractures. One hundred and one patients were identified and consented to participate in this study. There was only one reported complication: a superficial wound infection treated with antibiotics. No episodes of excessive bleeding were noted intraoperatively. All patients were discharged home on the day of surgery and there were no episodes of readmission, significant bleeding, haematoma requiring intervention, compartment syndrome or wound dehiscence. Complication rates were comparable with those of the matched cohort of patients undergoing the same procedure but who were not taking antithrombotic medications.Level of evidence: IV.

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life.

Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play a key role in enabling such monitoring in a non-obtrusive fashion. However, sensor data collected in daily life reflect multiple health and behavior-related factors together. This creates the need for a structured principled analysis to produce reliable and interpretable predictions that can be used to support clinical diagnosis and treatment. In this work we develop a principled modelling approach for free-living gait (walking) analysis. Gait is a promising target for non-obtrusive monitoring because it is common and indicative of many different movement disorders such as Parkinson's disease (PD), yet its analysis has largely been limited to experimentally controlled lab settings. To locate and characterize stationary gait segments in free-living using accelerometers, we present an unsupervised probabilistic framework designed to segment signals into differing gait and non-gait patterns. We evaluate the approach using a new video-referenced dataset including 25 PD patients with motor fluctuations and 25 age-matched controls, performing unscripted daily living activities in and around their own houses. Using this dataset, we demonstrate the framework's ability to detect gait and predict medication induced fluctuations in PD patients based on free-living gait. We show that our approach is robust to varying sensor locations, including the wrist, ankle, trouser pocket and lower back.

Surgical fixation of fractures of the distal radius within 2 weeks reduces postoperative finger stiffness.

Locked anterior plating remains the most common form of surgical fixation for displaced fractures of the distal radius. We hypothesized that delayed surgical fixation later than 2 weeks after injury contributes to poorer patient outcomes for patients with displaced or intra-articular fractures of the distal radius. We conducted a retrospective cohort study of 158 patients who underwent locked anterior plating for an intra-articular or displaced fracture of the distal radius using multiple regression analysis to identify any relationship between patient, injury or treatment factors with the patient-rated wrist and hand evaluation score or finger and wrist stiffness. There was no significant correlation between timing of surgery and functional scores. A delay in time to surgery of more than 2 weeks did correlate significantly with increased finger and thumb stiffness. However, this relationship was not seen for wrist stiffness. Our study supports the consensus that early recovery and function is facilitated by surgical fixation within 2 weeks and that delayed fixation beyond this may contribute to protracted finger stiffness.Level of evidence: IV.

Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP).

Ongoing biomarker development programs have been designed to identify serologic or imaging signatures of clinico-pathologic entities, assuming distinct biological boundaries between them. Identified putative biomarkers have exhibited large variability and inconsistency between cohorts, and remain inadequate for selecting suitable recipients for potential disease-modifying interventions. We launched the Cincinnati Cohort Biomarker Program (CCBP) as a population-based, phenotype-agnostic longitudinal study. While patients affected by a wide range of neurodegenerative disorders will be deeply phenotyped using clinical, imaging, and mobile health technologies, analyses will not be anchored on phenotypic clusters but on bioassays of to-be-repurposed medications as well as on genomics, transcriptomics, proteomics, metabolomics, epigenomics, microbiomics, and pharmacogenomics analyses blinded to phenotypic data. Unique features of this cohort study include (1) a reverse biology-to-phenotype direction of biomarker development in which clinical, imaging, and mobile health technologies are subordinate to biological signals of interest; (2) hypothesis free, causally- and data driven-based analyses; (3) inclusive recruitment of patients with neurodegenerative disorders beyond clinical criteria-meeting patients with Parkinson's and Alzheimer's diseases, and (4) a large number of longitudinally followed participants. The parallel development of serum bioassays will be aimed at linking biologically suitable subjects to already available drugs with repurposing potential in future proof-of-concept adaptive clinical trials. Although many challenges are anticipated, including the unclear pathogenic relevance of identifiable biological signals and the possibility that some signals of importance may not yet be measurable with current technologies, this cohort study abandons the anchoring role of clinico-pathologic criteria in favor of biomarker-driven disease subtyping to facilitate future biosubtype-specific disease-modifying therapeutic efforts.

Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study.

BACKGROUND: Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. OBJECTIVE: This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. METHODS: The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch's method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. RESULTS: From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. CONCLUSIONS: We present a new video-referenced data set that includes unscripted activities in and around the participants' homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

Surgical intervention for paediatric infusion-related extravasation injury: a systematic review.

OBJECTIVES: This systematic review aims to assess the quality of literature supporting surgical interventions for paediatric extravasation injury and to determine whether there is sufficient evidence to support invasive techniques in children. METHODS: We performed a systematic review by searching Ovid MEDLINE and EMBASE as well as AMED, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and clinicaltrials.gov from inception to February 2019. Studies other than case reports were eligible for inclusion if the population was younger than 18 years old, if there was a surgical intervention aimed at treating extravasation injury and if they reported on outcomes. Study quality was graded according to the National Institutes of Health study quality assessment tools. RESULTS: 26 studies involving 728 children were included-one before-and-after study and 25 case series. Extravasation injuries were mainly confined to skin and subcutaneous tissues but severe complications were also encountered, including amputation (one toe and one below elbow). Of the surgical treatments described, the technique of multiple puncture wounds and instillation of saline and/or hyaluronidase was the most commonly used. However, there were no studies in which its effectiveness was tested against another treatment or a control and details of functional and aesthetic outcomes were generally lacking. CONCLUSION: Surgical management is commonly reported in the literature in cases where there is significant soft tissue injury but as there are no comparative studies, it is unclear whether this is optimal. Further observational and experimental research evaluating extravasation injuries, including a centralised extravasation register using a universal grading scheme and core outcome set with adequate follow-up, are required to provide evidence to guide clinician decision-making.

The virtual fracture clinic improves quality of care for patients with hand and wrist injuries: an assessment of 3709 patients.

Fracture clinic services are under significant pressures to meet patients' expectations of a high-quality service. The virtual fracture clinic has shown early promise in helping to reduce such pressures. We used the virtual fracture clinic for hand and wrist injuries treated in the orthopaedic fracture clinic and used key quality indicators to measure improvement. Over the first 21 months, key patient outcome measures and satisfaction scores for patients discharged from the virtual fracture clinic with education to self-care were excellent. Our results show that a virtual fracture clinic model can be applied to provide high-quality care for hand and wrist injuries. The main advantage of the virtual fracture clinic is its ability to direct patients to the right person for timely treatment. We conclude from our 21-month experience that this model of care allows safe, effective, patient-centred, efficient and equitable care to the patients with hand and wrist fractures.Level of evidence: IV.

Deep Phenotyping of Parkinson's Disease.

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Probabilistic Modelling for Unsupervised Analysis of Human Behaviour in Smart Cities.

The growth of urban areas in recent years has motivated a large amount of new sensor applications in smart cities. At the centre of many new applications stands the goal of gaining insights into human activity. Scalable monitoring of urban environments can facilitate better informed city planning, efficient security, regular transport and commerce. A large part of monitoring capabilities have already been deployed; however, most rely on expensive motion imagery and privacy invading video cameras. It is possible to use a low-cost sensor alternative, which enables deep understanding of population behaviour such as the Global Positioning System (GPS) data. However, the automated analysis of such low dimensional sensor data, requires new flexible and structured techniques that can describe the generative distribution and time dynamics of the observation data, while accounting for external contextual influences such as time of day or the difference between weekend/weekday trends. In this paper, we propose a novel time series analysis technique that allows for multiple different transition matrices depending on the data's contextual realisations all following shared adaptive observational models that govern the global distribution of the data given a latent sequence. The proposed approach, which we name Adaptive Input Hidden Markov model (AI-HMM) is tested on two datasets from different sensor types: GPS trajectories of taxis and derived vehicle counts in populated areas. We demonstrate that our model can group different categories of behavioural trends and identify time specific anomalies.

A review of accelerometer-derived physical activity in the idiopathic inflammatory myopathies.

BACKGROUND: The idiopathic inflammatory myopathies (IIMs) are a group of rare conditions characterised by muscle inflammation (myositis). Accurate disease activity assessment is vital in both clinical and research settings, however, current available methods lack ability to quantify associated variation of physical activity, an important consequence of myositis.This study aims to review studies that have collected accelerometer-derived physical activity data in IIM populations, and to investigate if these studies identified associations between physical and myositis disease activity. METHODS: A narrative review was conducted to identify original articles that have collected accelerometer-derived physical activity data in IIM populations. The following databases were searched from February 2000 until February 2019: Medline via PubMed, Embase via OVID and Scopus. RESULTS: Of the 297 publications screened, eight studies describing accelerometer use in 181 IIM cases were identified. Seven out of the eight studies investigated juvenile dermatomyositis (JDM) populations and only one reported on an adult-onset population. Population sizes, disease duration, accelerometer devices used, body placement sites, and study duration varied between each study.Accelerometer-derived physical activity levels were reduced in IIM cohorts, compared to healthy controls, and studies reported improvement of physical activity levels following exercise programme interventions, thus demonstrating efficacy.Higher levels of accelerometer-derived physical activity measurements were associated with shorter JDM disease duration, current glucocorticoid use and lower serum creatine kinase. However, no clear association between muscle strength and accelerometer-derived physical activity measures was identified. CONCLUSIONS: The use of accelerometer-derived physical activity in IIM research is in its infancy. Whilst knowledge is currently limited to small studies, the opportunities are promising and future research in this area has the potential to improve disease activity assessment for clinical and research applications.

Complications of Cosmetic Surgery Abroad - Cost Analysis and Patient Perception.

BACKGROUND: Cosmetic surgery tourism is rapidly becoming more prevalent in the United Kingdom. We aim to identify the motivational factors underlying patients' decisions to go abroad for their treatment and gather information about the ensuing complications. METHODS: A retrospective review (January 2013-August 2017) was conducted of patients seen at a single major trauma center for complications from cosmetic surgery performed overseas. Cost analysis was performed based on national tariffs. Complications were grouped based on Clavien-Dindo classification and the Clinical Commissioning Group cost. A telephone survey was conducted to evaluate reasons for travel, details of complications, and impression of healthcare at home and abroad. RESULTS: A total of 20 patients (one male, 19 females) with a mean age 36 years (23-59 years) were included. Lower cost was the most popular reason for travel, followed by lack of expertise and friend's recommendation. Abdominoplasty (n = 9) had the highest number of complications followed by gluteal augmentation (n = 7). All major complications were due to gluteal augmentation (n = 4). The cost was for minor (n = 8, £3,448), intermediate (n = 8, £18,271), and major (n = 4, £42,083.59) complications. CONCLUSIONS: We raise serious concerns about the lack of regulation in cosmetic tourism and the absence of patient follow-up abroad. A particular concern was all gluteal augmentation cases had major complications. An international consensus to regulate surgical practice abroad is crucial to protect patients' interests and promote safe cosmetic surgery.

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes.

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes - sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing.