A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation

In the current study, we aimed to develop an algorithm based on biomarkers obtained through nonor 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.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Objective monitoring of facioscapulohumeral dystrophy during clinical trials using a smartphone app and wearables: observational study

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

Impacts of changes in alcohol consumption patterns during the first 2020 COVID-19 restrictions for people with and without mental health and neurodevelopmental conditions: A cross sectional study in 13 countries

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/nonbinary; 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

Development and technical validation of a smartphone-based cry detection algorithm

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.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

A multimodal, comprehensive characterization of a cutaneous wound model in healthy volunteers

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 Delivery Technolog

Technical validity and usability of a novel smartphone-connected spirometry device for pediatric patients with asthma and cystic fibrosis

Background: Diagnosis and follow-up of respiratory diseases traditionally rely on pulmonary function tests (PFTs), which are currently performed in hospitals and require trained personnel. Smartphone-

Usefulness of Plasma Amyloid as a Prescreener for the Earliest Alzheimer Pathological Changes Depends on the Study Population

Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Usefulness of Plasma Amyloid as a Prescreener for the Earliest Alzheimer Pathological Changes Depends on the Study Population

Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

GDS special edition on Covid-19 interim report global.

This report is based on data from > 40,000 people who participated in the first 3 weeks of the special Covid-19 global drugs survey. GDS is not a nationally representative sample, but our current project does represent one of the largest studies of drug use conducted during the Covid-19 pandemic. The findings can inform policy, health service development and, most importantly, provide people who use drugs with practical advice on how to keep healthy and minimize the harms associated with the use of psychoactive substances. Findings are preliminary and subject to change on further analyses. Throughout this report we provide some country comparisons on some key areas that may be of interest to our audience. Because the samples we have obtained from different countries vary considerably in size, demographics and drug use, these comparisons have to be treated with caution. The results do not necessarily represent the wider drug using community

Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers

Background: Pediatric 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. Methods: In 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. Results: Median 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. Conclusions: The 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