Ahnjili ZhuParris, PhD

This research focuses on creating composite biomarkers that can classify diagnoses, estimate symptom severity, and detect treatment effects using data from wearable sensors and smartphone applications. The thesis consists of an introduction to machine learning techniques and their use in developing biomarkers for the central nervous system; a narrative review of the relevant literature; and detailed studies on the application of these techniques in various health conditions. Specifically, the research includes observational and cross-sectional studies on facioscapulohumeral muscular dystrophy (FSHD) and major depressive disorder (MDD), demonstrating how smartphone and wearable sensor data can be used to monitor disease severity and progression. Additionally, the research identified the use of a tablet-based finger tapping task to monitor the real-time effects of antiparkinson's drugs on Parkinson's symptom severity. Key findings highlight the potential of mHealth biomarkers to provide continuous, real-time monitoring of patients, which can enhance the accuracy of clinical assessments and potentially reduce the burden on patients and healthcare systems. The thesis also addresses the challenges of variability in mHealth device data and emphasizes the need for robust validation and standardization to ensure the reliability of these biomarkers in clinical settings.

Clinical trial costs seem to be inflating excessively, without a clear increase in the value of the information gained. Here, we highlight factors that could be driving this trend and discuss potential solutions.

Introduction: EDP1815 is a single-strain of Prevotella histicola with preclinical immunomodulatory properties. The aim of this study was to evaluate pharmacodynamic effects of EDP1815 in healthy volunteers.

Seborrheic dermatitis (SD) is a chronic inflammatory skin disease characterized by erythematous papulosquamous lesions in sebum rich areas such as the face and scalp. Its pathogenesis appears multifactorial with a disbalanced immune system, Malassezia driven microbial involvement and skin barrier perturbations. Microbial involvement has been well described in SD, but skin barrier involvement remains to be properly elucidated. To determine whether barrier impairment is a critical factor of inflammation in SD alongside microbial dysbiosis, a cross-sectional study was performed in 37 patients with mild-to-moderate facial SD. Their lesional and non-lesional skin was comprehensively and non-invasively assessed with standardized 2D-photography, optical coherence tomography (OCT), microbial profiling including Malassezia species identification, functional skin barrier assessments and ceramide profiling. The presence of inflammation was established through significant increases in erythema, epidermal thickness, vascularization and superficial roughness in lesional skin compared to non-lesional skin. Lesional skin showed a perturbed skin barrier with an underlying skewed ceramide subclass composition, impaired chain elongation and increased chain unsaturation. Changes in ceramide composition correlated with barrier impairment indicating interdependency of the functional barrier and ceramide composition. Lesional skin showed significantly increased Staphylococcus and decreased Cutibacterium abundances but similar Malassezia abundances and mycobial composition compared to non-lesional skin. Principal component analysis highlighted barrier properties as main discriminating features. To conclude, SD is associated with skin barrier dysfunction and changes in the ceramide composition. No significant differences in the abundance of Malassezia were observed. Restoring the cutaneous barrier might be a valid therapeutic approach in the treatment of facial SD.

Development of pharmacological interventions for wound treatment is challenging due to both poorly understood wound healing mechanisms and heterogeneous patient populations. A standardized and well-characterized wound healing model in healthy volunteers is needed to aid in-depth pharmacodynamic and efficacy assessments of novel compounds. The current study aims to objectively and comprehensively characterize skin punch biopsy-induced wounds in healthy volunteers with an integrated, multimodal test battery. Eighteen (18) healthy male and female volunteers received three biopsies on the lower back, which were left to heal without intervention. The wound healing process was characterized using a battery of multimodal, non-invasive methods as well as histology and qPCR analysis in re-excised skin punch biopsies. Biophysical and clinical imaging read-outs returned to baseline values in 28 days. Optical coherence tomography detected cutaneous differences throughout the wound healing progression. qPCR analysis showed involvement of proteins, quantified as mRNA fold increase, in one or more healing phases. All modalities used in the study were able to detect differences over time. Using multidimensional data visualization, we were able to create a distinction between wound healing phases. Clinical and histopathological scoring were concordant with non-invasive imaging read-outs. This well-characterized wound healing model in healthy volunteers will be a valuable tool for the standardized testing of novel wound healing treatments.

Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials. 30 unipolar depressed patients and 29 age- and gender-matched healthy controls were enrolled in this study. Each participant's daily physiological, physical, and social activity were monitored using a smartphone-based application (CHDR MORE™) for 3 weeks continuously. Self-reported depression anxiety stress scale-21 (DASS-21) and positive and negative affect schedule (PANAS) were administered via smartphone weekly and daily respectively. The structured interview guide for the Hamilton depression scale and inventory of depressive symptomatology-clinical rated (SIGHD-IDSC) was administered in-clinic weekly. Nested cross-validated linear mixed-effects models were used to identify the correlation between the CHDR MORE™ features with the weekly in-clinic SIGHD-IDSC scores. The SIGHD-IDSC regression model demonstrated an explained variance (R2) of 0.80, and a Root Mean Square Error (RMSE) of ± 15 points. The SIGHD-IDSC total scores were positively correlated with the DASS and mean steps-per-minute, and negatively correlated with the travel duration. Unobtrusive, remotely monitored behavior and self-reported outcomes are correlated with depression severity. While these features cannot replace the SIGHD-IDSC for estimating depression severity, it can serve as a complementary approach for assessing depression and drug effects outside the clinic.

BackgroundCentral nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.ObjectiveThis narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers.MethodsThis review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed.ResultsThis review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials.ConclusionmHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

BackgroundFacioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions.ObjectiveWe aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data. The adoption of remote monitoring tools, such as smartphones and wearables, would provide a novel opportunity for continuous, passive, and objective monitoring of FSHD symptom severity outside the clinic.MethodsIn total, 38 genetically confirmed patients with FSHD were enrolled. The FSHD Clinical Score and the Timed Up and Go (TUG) test were used to assess FSHD symptom severity at days 0 and 42. Remote sensor data were collected using an Android smartphone, Withings Steel HR+, Body+, and BPM Connect+ for 6 continuous weeks. We created 2 single-task regression models that estimated the FSHD Clinical Score and TUG separately. Further, we built 1 multitask regression model that estimated the 2 clinical assessments simultaneously. Further, we assessed how an increasingly incremental time window affected the model performance. To do so, we trained the models on an incrementally increasing time window (from day 1 until day 14) and evaluated the predictions of the clinical severity on the remaining 4 weeks of data.ResultsThe single-task regression models achieved an R2 of 0.57 and 0.59 and a root-mean-square error (RMSE) of 2.09 and 1.66 when estimating FSHD Clinical Score and TUG, respectively. Time spent at a health-related location (such as a gym or hospital) and call duration were features that were predictive of both clinical assessments. The multitask model achieved an R2 of 0.66 and 0.81 and an RMSE of 1.97 and 1.61 for the FSHD Clinical Score and TUG, respectively, and therefore outperformed the single-task models in estimating clinical severity. The 3 most important features selected by the multitask model were light sleep duration, total steps per day, and mean steps per minute. Using an increasing time window (starting from day 1 to day 14) for the FSHD Clinical Score, TUG, and multitask estimation yielded an average R2 of 0.65, 0.79, and 0.76 and an average RMSE of 3.37, 2.05, and 4.37, respectively.ConclusionsWe demonstrated that smartphone and remote sensor data could be used to estimate FSHD clinical severity and therefore complement the assessment of FSHD outside the clinic. In addition, our results illustrated that training the models on the first week of data allows for consistent and stable prediction of FSHD symptom severity. Longitudinal follow-up studies should be conducted to further validate the reliability and validity of the multitask model as a tool to monitor disease progression over a longer period.Trial registrationClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735.

The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity. Data were collected during a placebo-controlled, crossover study with 20 PD patients. The alternate index and middle finger tapping (IMFT), alternative index finger tapping (IFT), and thumb-index finger tapping (TIFT) tasks and the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III were performed during treatment. We trained classification algorithms to select features consisting of the MDS-UPDRS III item scores; the individual IMFT, IFT, and TIFT; and all three tapping tasks collectively to classify treatment effects. Furthermore, we trained regression algorithms to estimate the MDS-UPDRS III total score using the tapping task features individually and collectively. The IFT composite biomarker had the best classification performance (83.50% accuracy, 93.95% precision) and outperformed the MDS-UPDRS III composite biomarker (75.75% accuracy, 73.93% precision). It also achieved the best performance when the MDS-UPDRS III total score was estimated (mean absolute error: 7.87, Pearson's correlation: 0.69). We demonstrated that the IFT composite biomarker outperformed the combined tapping tasks and the MDS-UPDRS III composite biomarkers in detecting treatment effects. This provides evidence for adopting the IFT composite biomarker for detecting antiparkinsonian treatment effect in clinical trials. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

GDS2022, our current survey, is our 10th. To help promote the survey we wanted to produce some data stories that get people thinking about drugs and the people who use them.

BackgroundDigital biomarkers are a promising novel method to capture clinical data in a home setting. However, clinical validation prior to implementation is of vital importance. The aim of this study was to clinically validate physical activity, heart rate, sleep and forced expiratory volume in 1 s (FEV1) as digital biomarkers measured by a smartwatch and portable spirometer in children with asthma and cystic fibrosis (CF).MethodsThis was a prospective cohort study including 60 children with asthma and 30 children with CF (aged 6-16 years). Participants wore a smartwatch, performed daily spirometry at home and completed a daily symptom questionnaire for 28 days. Physical activity, heart rate, sleep and FEV1 were considered candidate digital end-points. Data from 128 healthy children were used for comparison. Reported outcomes were compliance, difference between patients and controls, correlation with disease activity, and potential to detect clinical events. Analysis was performed with linear mixed effects models.ResultsMedian compliance was 88%. On average, patients exhibited lower physical activity and FEV1 compared with healthy children, whereas the heart rate of children with asthma was higher compared with healthy children. Days with a higher symptom score were associated with lower physical activity for children with uncontrolled asthma and CF. Furthermore, FEV1 was lower and (nocturnal) heart rate was higher for both patient groups on days with more symptoms. Candidate biomarkers appeared able to describe a pulmonary exacerbation.ConclusionsPortable spirometer- and smartwatch-derived digital biomarkers show promise as candidate end-points for use in clinical trials or clinical care in paediatric lung disease.

IntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.MethodsThe training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions.ResultsThe final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p ConclusionThis novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

Background The initial period of COVID-19-related restrictions affected substance use in some population groups. We explored how changes in alcohol use at the beginning of the pandemic impacted the health and wellbeing of people with and without mental health and neurodevelopmental conditions (MHDCs). Methods Data came from the Global Drug Survey Special Edition on COVID-19 conducted in May-June 2020. Measured were; changes in drinking compared to February 2020 (pre-COVID-19 restrictions), reasons for changes, and impact on physical health, mental health, relationships, finances, work/study, and enjoyment. This study included 38,141 respondents (median age = 32 IQR 25-45; 51.9% cis man; 47.8% cis woman; 1.2% trans/non-binary; 30.2% with MHDCs e.g. depression 20.0%, anxiety 16.3%, ADHD 3.8%, PTSD 3.3%). Results A third (35.3%) of respondents with MHDCs and 17.8% without MHDCs indicated that increased drinking affected their mental health negatively (p<.001); 44.2% of respondents with MHDCS compared to 32.6% without MHDCs said it affected their physical health negatively (p<.001). Reduced drinking was associated with better mental health among a fifth (21.1%) of respondents with MHDCS and 14.4% without MHDCs (p<.001). Age, relationship status, living arrangements, employment, coping and distress were significant predictors of increases in drinking. Conclusion Among people with MHDCS, reduced alcohol consumption was associated with better mental health, while the negative effects of increased drinking were more pronounced when compared to people without MHDCS. When supporting people in reducing alcohol consumption during uncertain times, people with MHDCS may need additional support, alongside those experiencing greater levels of distress.

Background Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients’ quality of life. Real-world data related to physical activity, sleep, and social behavior could potentially provide additional insight into the impact of the disease and might be useful in assessing treatment effects on aspects that are important contributors to the functioning and well-being of patients with FSHD. Objective This study investigated the feasibility of using smartphones and wearables to capture symptoms related to FSHD based on a continuous collection of multiple features, such as the number of steps, sleep, and app use. We also identified features that can be used to differentiate between patients with FSHD and non-FSHD controls. Methods In this exploratory noninterventional study, 58 participants (n=38, 66%, patients with FSHD and n=20, 34%, non-FSHD controls) were monitored using a smartphone monitoring app for 6 weeks. On the first and last day of the study period, clinicians assessed the participants’ FSHD clinical score and Timed Up-and-Go test time. Participants installed the app on their Android smartphones, were given a smartwatch, and were instructed to measure their weight and blood pressure on a weekly basis using a scale and blood pressure monitor. The user experience and perceived burden of the app on participants’ smartphones were assessed at 6 weeks using a questionnaire. With the data collected, we sought to identify the behavioral features that were most salient in distinguishing the 2 groups (patients with FSHD and non-FSHD controls) and the optimal time window to perform the classification. Results Overall, the participants stated that the app was well tolerated, but 67% (39/58) noticed a difference in battery life using all 6 weeks of data, we classified patients with FSHD and non-FSHD controls with 93% accuracy, 100% sensitivity, and 80% specificity. We found that the optimal time window for the classification is the first day of data collection and the first week of data collection, which yielded an accuracy, sensitivity, and specificity of 95.8%, 100%, and 94.4%, respectively. Features relating to smartphone acceleration, app use, location, physical activity, sleep, and call behavior were the most salient features for the classification. Conclusions Remotely monitored data collection allowed for the collection of daily activity data in patients with FSHD and non-FSHD controls for 6 weeks. We demonstrated the initial ability to detect differences in features in patients with FSHD and non-FSHD controls using smartphones and wearables, mainly based on data related to physical and social activity. Trial Registration ClinicalTrials.gov NCT04999735; https://www.clinicaltrials.gov/ct2/show/NCT04999735

BackgroundIn the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer's disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs.MethodsHealthy elderly subjects (n = 200; age 65-70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier.ResultsTwo hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65-86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65-83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10).ConclusionThis algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs.Trial registrationISRCTN.org identifier: ISRCTN79036545 (retrospectively registered).

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology - to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying. Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm. Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone. Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.

Key findings from the 2020 Global Drug Survey on psychedelic substance use patterns, motivations, and outcomes from a large international sample.

BackgroundPediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device.MethodsIn this study, patients admitted due to pneumonia (n = 30), preschool wheezing (n = 30), and asthma exacerbation (AE; n = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day "healthy" period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores.ResultsMedian compliance was 47% (interquartile range [IQR] 33-81%) during the entire study period, 68% (IQR 54-91%) during the recovery period, and 28% (IQR 0-74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia.ConclusionsThe digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.

The Global Drug Survey (GDS) runs the world’s largest anonymous annual web survey of drug use. This chapter provides an overview of GDS history and methods before presenting alcohol findings from 2015 to 2020, starting with drinking prevalence in respondents from different countries. Then, we explore intoxication, regrets, and pre-loading. Many GDS respondents consume in excess of weekly guidelines in order to feel their desired level of intoxication. Next, we discuss harms from drinking, including seeking emergency treatment and harms from others’ drinking. We then examine GDS data about interventions. While digital tools are popular, heavier drinkers in the sample preferred face to face specialist support. Our findings on alcohol labeling are stark; two-thirds of respondents were unaware about links between alcohol and cancer. Finally, we reflect on what we need to do better in order to improve diversity of the GDS sample. Our research with trans participants is helping us to understand and advocate for trans people who use alcohol. However, there is work to do to include and advocate for more diverse groups of people. Throughout, we discuss practical implications and further research that is needed to help reduce harms associated with the world’s favorite drug.

BackgroundDigital devices and wearables allow for the measurement of a wide range of health-related parameters in a non-invasive manner, which may be particularly valuable in pediatrics. Incorporation of such parameters in clinical trials or care as digital endpoint could reduce the burden for children and their parents but requires clinical validation in the target population. This study aims to determine the tolerability, repeatability, and reference values of novel digital endpoints in healthy children.MethodsApparently healthy children (n = 175, 46% male) aged 2-16 were included. Subjects were monitored for 21 days using a home-monitoring platform with several devices (smartwatch, spirometer, thermometer, blood pressure monitor, scales). Endpoints were analyzed with a mixed effects model, assessing variables that explained within- and between-subject variability. Endpoints based on physical activity, heart rate, and sleep-related parameters were included in the analysis. For physical-activity-related endpoints, a sample size needed to detect a 15% increase was calculated.FindingsMedian compliance was 94%. Variability in each physical activity-related candidate endpoint was explained by age, sex, watch wear time, rain duration per day, average ambient temperature, and population density of the city of residence. Estimated sample sizes for candidate endpoints ranged from 33-110 per group. Daytime heart rate, nocturnal heart rate and sleep duration decreased as a function of age and were comparable to reference values published in the literature.ConclusionsWearable- and portable devices are tolerable for pediatric subjects. The raw data, models and reference values presented here can be used to guide further validation and, in the future, clinical trial designs involving the included measures.

Background: The Global Drug Survey (GDS) Special Edition on COVID-19 was developed as part of a global effort to better understand the impact of the pandemic on people's substance use and mental health.

Special edition of the Global Drug Survey examining the impact of COVID-19 restrictions on substance use patterns and mental health.

GDS draws on a diverse network of experts from the fields of medicine, toxicology, public health, psychology, chemistry, policy, criminology, sociology, harm reduction, and addiction.

BackgroundDiagnosis and follow-up of respiratory diseases traditionally rely on pulmonary function tests (PFTs), which are currently performed in hospitals and require trained personnel. Smartphone-connected spirometers, like the Air Next spirometer, have been developed to aid in the home monitoring of patients with pulmonary disease. The aim of this study was to investigate the technical validity and usability of the Air Next spirometer in pediatric patients.MethodsDevice variability was tested with a calibrated syringe. About 90 subjects, aged 6 to 16, were included in a prospective cohort study. Fifty-eight subjects performed conventional spirometry and subsequent Air Next spirometry. The bias and the limits of agreement between the measurements were calculated. Furthermore, subjects used the device for 28 days at home and completed a subject-satisfaction questionnaire at the end of the study period.ResultsInterdevice variability was 2.8% and intradevice variability was 0.9%. The average difference between the Air Next and conventional spirometry was 40 mL for forced expiratory volume in 1 second (FEV1) and 3 mL for forced vital capacity (FVC). The limits of agreement were -270 mL and +352 mL for FEV1 and -403 mL and +397 mL for FVC. About 45% of FEV1 measurements and 41% of FVC measurements at home were acceptable and reproducible according to American Thoracic Society/European Respiratory Society criteria. Parents scored difficulty, usefulness, and reliability of the device 1.9, 3.5, and 3.8 out of 5, respectively.ConclusionThe Air Next device shows validity for the measurement of FEV1 and FVC in a pediatric patient population.

Recently, Verberk et al showed that plasma amyloid-beta 42/40 ratio has potential to identify Alzheimer pathological changes in subjects with subjective memory decline.

Background: Despite increasing clinical and neuroscientific research, pharmacological neuroenhancement literature rarely discusses psychedelic drugs. However, psychedelic microdosing, the ingestion of sub-perceptual doses of psychedelics like psilocybin, has gained increasing public and scientific attention. Published research on the topic is scarce and systematic studies of the digital milieus surrounding psychedelic microdosing are currently non-existent. Methods: In this netnographic study, we explore psychedelic microdosing by focusing on Youtube and listing current research projects as a descriptive assemblage. We used the Youtube Data Tool (YDT) for data extraction from the YouTube platform. We selected videos that specifically focused on microdosing with a psychoactive substance and descriptively analysed the ecology of practices of the six most viewed videos focusing on definitions, dosages per substance and claimed effects. Results: Our initial data extraction, completed in 2016, resulted in total of 115 Youtube videos. Additional data extractions done in 2017 and 2018 showed a 290% increase of microdosing videos between 2016 and 2018, indicating that the phenomenon is growing, at least online. The digital milieu of microdosing in 2016 included 48 videos (41,7%) which mentioned a psychoactive substance. The six most viewed videos comprised 92% (N = 934,819) of the total view count and the ecology of practices depicted psychedelic microdosing as beneficial, but the claimed effects and dosing require critical evaluation. Contrary to how typical users of illicit drugs are often portrayed in the media and science, these videos revolved around themes like research, experiments, self-monitoring and the imperative of sharing results. As our descriptive assemblage demonstrates several psychedelic microdosing research projects are under way, potentially influencing user practices and knowledge. Conclusion: This type of online drug research can be used to gather knowledge of under-researched topics, like psychedelic microdosing. However, further digital and non-digital drug research is needed to investigate this potentially rising phenomenon.

A panel discussion exploring the intersection of artificial intelligence, nature, and perception.

Ahnjili is a machine learning engineer, Ph.D. candidate, artist, and science communicator currently working at an AI startup that specializes in developing deep learning models for facial augmentation. Ahnjili's academic research centers around the development of biomarkers for monitoring mental and physical well-being using smartphones and wearables, with a particular focus on their application in clinical trials. Ahnjili's artistic research and science communication efforts are dedicated to raising awareness about A.I. and algorithmic violence, which encompasses the violence that may arise from or be justified by automated decision-making systems.

BARTALK is a lecture, performance, and storytelling series that usually takes place in different bars in the Hague. Each of our podcast seasons has a different theme featuring one guest per episode offering their unique perspective.

A bilingual (English/Polish) monograph on mindful design practice published by the Eugeniusz Geppert Academy of Art and Design in Wroclaw. The chapter on technocontrol discusses Ahnjili ZhuParris's Fashion Police Drones as a satirical critique of algorithmic surveillance and the policing of dress, situating the project alongside real-world examples of drone-enforced dress codes and works by Liam Young. ISBN 978-83-67584-69-2.

This chapter discusses the artistic project Future Wake (2021) by Ahnjili Zhuparris and Tim van Ommeren that examines predictive policing. By shifting the focus from possible future crime offenders to possible future victims of fatal police encounters, using visual and affective means rather than expert knowledge and statistics, the artwork activates critical reflection on the politics and logics of predictive policing systems. The chapter first situates predictive policing in a context of securitization, and discusses how it enhances structures of discrimination. In the second part, Wevers interviews artist Zhuparris about the aims of Future Wake, discussing the artistic and technical process of creating the project, the politics of data, and the role of art in critical discussion on surveillance and AI.

What are the issues with using AI for predictive policing? Could it reduce crime or does it reinforce racial biases in the criminal justice system? The co-founder of art project Future Wake examines unjust policing using AI and storytelling.

Future Wake tells the stories of potential victims.

Winner of the Mozilla Creative Media award for 2021, an interactive website calculates when and where fatal encounters with law enforcement will occur — and tells the stories of the victims.

De cyclus van vrouwen meten in relatie tot Tinder.

A searing critique of predictive policing, Future Wake uses past data on police violence to predict where it might occur in the future—and who will be targeted.